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Research Article
Originally Published 17 December 2014
Free Access

Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association

Table of Contents

Summary e30
1. About These Statistics e37
2. Cardiovascular Health e40
Health Behaviors
3. Smoking/Tobacco Use e61
4. Physical Inactivity e67
5. Nutrition e76
6. Overweight and Obesity e91
Health Factors and Other Risk Factors
7. Family History and Genetics e101
8. High Blood Cholesterol and Other Lipids e106
9. High Blood Pressure e114
10. Diabetes Mellitus e125
11. Metabolic Syndrome e139
12. Chronic Kidney Disease e151
Cardiovascular Conditions/Diseases
13. Total Cardiovascular Diseases e156
14. Stroke (Cerebrovascular Disease) e179
15. Congenital Cardiovascular Defects and Kawasaki Disease e206
16. Disorders of Heart Rhythm e215
17. Sudden Cardiac Arrest e234
18. Subclinical Atherosclerosis e243
19. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectorise254
20. Cardiomyopathy and Heart Failure e269
21. Valvular, Venous, and Aortic Diseases e277
22. Peripheral Artery Disease e285
Outcomes
23. Quality of Care e291
24. Medical Procedures e305
25. Economic Cost of Cardiovascular Disease e310
Supplemental Materials
26. At-a-Glance Summary Tables e315
27. Glossary e320

Summary

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics related to heart disease, stroke, and other cardiovascular and metabolic diseases and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, and others seeking the best available data on these conditions. Together, cardiovascular disease (CVD) and stroke produce immense health and economic burdens in the United States and globally. The Statistical Update brings together in a single document up-to-date information on the core health behaviors and health factors that define cardiovascular health; a range of major clinical disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, and peripheral arterial disease); and the associated outcomes (including quality of care, procedures, and economic costs). Since 2009, the annual versions of the Statistical Update have been cited >20 000 times in the literature. In 2014 alone, the various Statistical Updates were cited >5700 times.
Each annual version of the Statistical Update undergoes major revisions to include the newest nationally representative data, add additional relevant published scientific findings, remove older information, add new sections or chapters, and increase the number of ways to access and use the assembled information. This year-long process, which begins as soon as the previous Statistical Update is published, is performed by the AHA Statistics Committee faculty volunteers and staff. For example, this year’s edition includes a new chapter on cardiac arrest, new data on the monitoring and benefits of cardiovascular health in the population, additional information in many chapters on the global CVD and stroke burden, and further new focus on evidence-based approaches to changing behaviors, implementation strategies, and implications of the AHA’s 2020 Impact Goals. Below are a few highlights from this year’s Update.

Current Status of Cardiovascular Health in the United States (Chapter 2)

The concept of cardiovascular health represents a heightened focus for the AHA, with 3 central and novel emphases:
—An expanded focus on not only CVD prevention but also promotion of positive cardiovascular health, in addition to the treatment of established CVD.
—The prioritization of both health behaviors (healthy diet pattern, appropriate energy intake, physical activity [PA], and nonsmoking) and health factors (optimal blood lipids, blood pressure, glucose levels) throughout the lifespan as primary goals unto themselves.
—Population-level health promotion strategies to shift the majority of the public toward greater cardiovascular health, in addition to targeting those individuals at greatest CVD risk, because CVD occurs at all risk levels across the population and because healthy lifestyles are uncommon throughout the US population.
The prevalence of ideal cardiovascular health is higher in US children and young adults than in US middle-aged and older adults, largely because of the higher prevalence of ideal levels of health factors in US children and young adults. However, with regard to health behaviors, children and young adults were similar to (PA) or worse than (diet) middle-aged and older adults. Poor diet and physical inactivity in childhood and younger age are strong predictors of suboptimal health factors later in life.
Approximately 50% of US children 12 to 19 years of age have ≥5 metrics at ideal levels, with lower prevalence in girls (47%) than in boys (52%).
Only 18% of US adults have ≥5 metrics with ideal levels, with lower prevalence in men (11%) than in women (25%).
Among children, the prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from <1% for the healthy diet pattern (ie, <1 in 100 US children meets at least 4 of the 5 dietary components) to >80% for the smoking, blood pressure, and fasting glucose metrics.
Among US adults, the prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from 0.5% for the healthy diet pattern to up to 78% for the smoking metric (never having smoked or being a former smoker who has quit for >12 months).

Effective Approaches to Improve Cardiovascular Health (Chapter 2)

The current evidence supports a range of complementary strategies to improve cardiovascular health, including:
—Individual-focused approaches, which target lifestyle and treatments at the individual level
—Healthcare systems approaches, which encourage, facilitate, and reward efforts by providers to improve health behaviors and health factors
—Population approaches, which target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation
Such approaches can focus on both (1) improving cardiovascular health among those who currently have less than optimal levels and (2) preserving cardiovascular health among those who currently have ideal levels (in particular, children, adolescents, and young adults) as they age.
The metrics with the greatest potential for improvement are health behaviors, including diet quality, PA, and body weight. However, each of the cardiovascular health metrics can be improved and deserves major focus.
The AHA has a broad range of policy initiatives to improve cardiovascular health among all Americans and meet the 2020 Strategic Impact Goals.

Health Behaviors (Chapters 3 to 6)

Based on comparable risk assessment methods, poor lifestyle behaviors and lifestyle-related risk factors are the foremost causes of death and disability in the United States and in the world.

Smoking/Tobacco Use (Chapter 3)

Although tobacco use has declined substantially in the United States, it remains the second-leading cause of total deaths and disability. The percentage of adults who reported current cigarette use declined from 24.1% in 1998 to 17.9% in 2013; among high school students, the decline was from 36.4% in 1997 to 15.7% in 2013. Still, almost one third of coronary heart disease deaths are attributable to smoking and exposure to secondhand smoke.
Declines in tobacco usage in the United States may be threatened by the >250 e-cigarette products that were available in 2014. To date, the risks and benefits of e-tobacco products remain controversial but are an area of intense investigation by scientists, as well as scrutiny by the US Food and Drug Administration. Public health experts are concerned that e-cigarettes may be a gateway to smoking traditional cigarettes and may be eroding gains in the public’s awareness of the harms of tobacco products.
Annual smoking-attributable economic costs in the United States, including direct medical costs and lost productivity, are estimated to exceed $289 billion.

Physical Inactivity (Chapter 4)

In 2013, 15.2% of adolescents reported being inactive during the prior week, and inactivity was more likely to be reported by girls (19.2%) than boys (11.2%). Inactivity was more commonly reported by black (27.3%) and Hispanic (20.3%) girls than their white counterparts (16.1%); similarly, black (15.2%) and Hispanic (12.1%) boys reported more inactivity than white boys (9.2%).
According to 2013 National Health Interview Survey data, only half of American adults met the current aerobic PA guidelines (≥150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination each week). Women (46.1%) were less likely to meet the guidelines than men (54.2%), and non-Hispanic blacks (41.4%) and Hispanics (42.9%), were less likely to meet them than non-Hispanic whites (53.4%).
Unfortunately, the proportion of individuals meeting PA recommendations is likely to be lower than indicated by self-report data. Studies examining actual (with accelerometers, pedometers, etc) versus self-reported PA indicate that both men and women overestimate their PA substantially (by 44% and 138% for men and women, respectively).

Nutrition (Chapter 5)

The leading risk factor for death and disability in the United States is suboptimal diet quality, which in 2010 led to 678 000 annual deaths of all causes. Major contributors were insufficient intakes of fruits, nuts/seeds, whole grains, vegetables, and seafood, as well as excess intakes of sodium. In the United States, an estimated 58 000 annual CVD deaths (95% confidence interval, 37 000–80 000) in 2010 were attributable to sodium intake >2.0 g/d, representing 1 in 16 (6.3%) of all CVD deaths and 1 in 8 (13.1%) CVD deaths before age 70 years. Globally, an estimated 1.65 million annual CVD deaths (95% confidence interval, 1.10–2.22) were attributable to sodium intake >2.0 g/d, representing nearly 1 in 10 (9.5%) of all CVD deaths.
Although healthier diets cost modestly more than unhealthful diets, comparing extremes of unhealthful versus healthful food-based diet patterns, the more healthful patterns cost on average ≈$1.50 per day more. Similarly priced options are also common; in a comparison of 20 fruits and vegetables versus 20 common snack foods such as cookies, chips, pastries, and crackers, the average price per portion of fruits and vegetables was 31 cents, with an average of 57 calories per portion, versus 33 cents and 183 calories per portion for snack foods.

Obesity (Chapter 6)

Although the overall prevalence of obesity in US youth did not change between 2003 to 2004 and 2011 to 2012, the prevalence decreased among those aged 2 to 5 years. Obesity decreased among those of higher socioeconomic status but increased among those of lower socioeconomic status. In addition, the overall prevalence of severe obesity in US youth continued to increase, especially among adolescent boys.
Overweight and obesity predispose individuals to most major risk factors, including physical inactivity, hypertension, hyperlipidemia, and diabetes mellitus.
Excess body weight is among the leading causes of death and disability in the United States and globally, with burdens expected to increase in coming years.
Among overweight and obese individuals, existing cardiometabolic risk factors should be monitored and treated intensively with diet quality, PA, and pharmacological or other treatments as necessary. Each of these interventions provides benefits independent of weight loss and maintenance.

Health Factors and Other Risk Factors (Chapters 7 to 12)

The prevalence and control of cardiovascular health factors and risks remains a major issue for many Americans.

Family History and Genetics (Chapter 7)

Familial aggregation of CVD is related to the clustering of specific lifestyle factors and risk factors, both of which have environmental and genetic contributors. Patients with a family history of coronary artery disease have a higher prevalence of traditional CVD risk factors, underscoring opportunities for prevention.
The risk of most CVD conditions is higher in the presence of a family history, including CVD (45% higher odds with sibling history), stroke (50% higher odds with history in a first-degree relative), atrial fibrillation (AF, 80% higher odds with parental history), heart failure (70% higher odds with parental history), and peripheral arterial disease (80% higher odds with family history).

High Blood Cholesterol and Other Lipids (Chapter 8)

75.7% of children and 46.6% of adults have ideal cholesterol levels (untreated total cholesterol <170 mg/dL for children and <200 mg/dL for adults). Prevalence of ideal levels has improved over the past decade in children but remained the same in adults.
According to 2009 to 2012 data, >100 million US adults ≥20 years of age have total cholesterol levels ≥200 mg/dL; almost 31 million have levels ≥240 mg/dL.

High Blood Pressure (Chapter 9)

Based on 2009 to 2012 data, 32.6% of US adults ≥20 years of age have hypertension, which represents ≈80.0 million US adults. African American adults have among the highest prevalence of hypertension in the world. Among non-Hispanic black men and women, the age-adjusted prevalence of hypertension was 44.9% and 46.1%, respectively.
National Health and Nutrition Examination Survey (NHANES) data from 2009 to 2012 revealed that among US adults with hypertension, 54.1% were controlled, 76.5% were currently treated, 82.7% were aware they had hypertension, and 17.3% were undiagnosed.

Diabetes Mellitus (Chapter 10)

Diabetes mellitus affects 1 in 10 US adults, with 90% to 95% of cases being type 2 diabetes mellitus. Diabetes mellitus disproportionately affects racial/ethnic minorities. Type 2 diabetes mellitus is increasingly common in children and adolescents; the disease historically was diagnosed primarily in adults ≥40 years of age. The prevalence of type 2 diabetes mellitus in children/adolescents has increased by 30.5% between 2001 and 2009, and it now constitutes ≈50% of all childhood diabetes mellitus.
Diabetes mellitus is associated with reduced longevity, with men with diabetes mellitus living an average of 7.5 years and women with diabetes mellitus living an average of 8.2 years less than their counterparts without diabetes mellitus.

Metabolic Syndrome (Chapter 11)

From 1999 to 2010, the age-adjusted national prevalence of metabolic syndrome in the United States peaked (in the 2001–2002 cycle) and began to fall. This is attributable to decreases in the age-adjusted prevalence among women and no change in men. In addition, there has been variation in the trends over time for each individual component of the metabolic syndrome. Generally, the national prevalences of hypertriglyceridemia and elevated blood pressure have decreased, whereas hyperglycemia and elevated waist circumference have increased. However, these trends also vary significantly by sex and race/ethnicity.

Cardiovascular Conditions/Diseases (Chapters 13 to 22)

Rates of death attributable to CVD have declined in the United States, but the burden remains high.

Total Cardiovascular Diseases (Chapter 13)

The 2011 overall rate of death attributable to CVD was 229.6 per 100 000 Americans. The death rates were 275.7 for males and 192.3 for females. The rates were 271.9 for white males, 352.4 for black males, 188.1 for white females, and 248.6 for black females.
From 2001 to 2011, death rates attributable to CVD declined 30.8%. In the same 10-year period, the actual number of CVD deaths per year declined by 15.5%. Yet in 2011, CVD still accounted for 31.3% (786 641) of all 2 515 458 deaths, or ≈1 of every 3 deaths in the United States.
On the basis of 2011 death rate data, >2150 Americans die of CVD each day, an average of 1 death every 40 seconds. Approximately 155 000 Americans who died of CVD in 2011 were <65 years of age. In 2011, 34% of deaths attributable to CVD occurred before the age of 75 years, which is younger than the current average life expectancy of 78.7 years.

Stroke (Chapter 14)

From 2001 to 2011, the relative rate of stroke death fell by 35.1% and the actual number of stroke deaths declined by 21.2%. Yet each year, ≈795 000 people continue to experience a new or recurrent stroke (ischemic or hemorrhagic). Approximately 610 000 of these are first events and 185 000 are recurrent stroke events. In 2011, stroke caused ≈1 of every 20 deaths in the United States. On average, every 40 seconds, someone in the United States has a stroke, and someone dies of one approximately every 4 minutes.
The decline in stroke mortality over the past decades, a major improvement in population health observed for both sexes and all race and age groups, has resulted from reduced stroke incidence and lower case fatality rates. The significant improvements in stroke outcomes are concurrent with cardiovascular risk factor control interventions. The hypertension control efforts initiated in the 1970s appear to have had the most substantial influence on the accelerated decline in stroke mortality, with lower blood pressure distributions in the population. Control of diabetes mellitus and high cholesterol and smoking cessation programs, particularly in combination with hypertension treatment, also appear to have contributed to the decline in stroke mortality.

Atrial Fibrillation (Chapter 16)

Multiple lines of evidence have increased awareness of the burden of unrecognized AF. In individuals without a history of AF with recent pacemaker or defibrillator implantation, subclinical atrial tachyarrhythmias were detected in 10.1% of patients. Subclinical atrial tachyarrhythmias were associated with a 5.6-fold higher risk of clinical AF and ≈13% of ischemic strokes or embolism. A recent systematic review suggested that one needs to screen 170 community-based individuals at least 65 years of age to detect 1 case of AF.

Sudden Cardiac Arrest (Chapter 17)

In 2011, ≈326 200 people experienced emergency medical services–assessed out-of-hospital cardiac arrests in the United States. Survival to hospital discharge after nontraumatic EMS-treated cardiac arrest with any first recorded rhythm was 10.6% for patients of any age. Of the 19 300 bystander-witnessed out-of-hospital cardiac arrests in 2011, 31.4% of victims survived.
Each year, ≈209 000 people are treated for in-hospital cardiac arrest.

Coronary Heart Disease (Chapter 19)

Coronary heart disease alone caused ≈1 of every 7 deaths in the United States in 2011. In 2011, 375 295 Americans died of coronary heart disease. Each year, an estimated ≈635 000 Americans have a new coronary attack (defined as first hospitalized myocardial infarction or coronary heart disease death) and ≈300 000 have a recurrent attack. It is estimated that an additional 155 000 silent first myocardial infarctions occur each year. Approximately every 34 seconds, 1 American has a coronary event, and approximately every 1 minute 24 seconds, an American will die of one.

Heart Failure (Chapter 20)

In 2011, 1 in 9 death certificates (284 388 deaths) in the United States mentioned heart failure. Heart failure was the underlying cause in 58 309 of those deaths. The number of any-mention deaths attributable to heart failure was approximately as high in 1995 (287 000) as it was in 2011 (284 000). Additionally, hospital discharges for heart failure remained stable from 2000 to 2010, with first-listed discharges of 1 008 000 and 1 023 000, respectively.

Cardiovascular Quality of Care, Procedure Utilization, and Costs (Chapters 23 to 25)

The Statistical Update provides critical data in several sections on the magnitude of healthcare delivery and costs, as well as the quality of healthcare delivery, related to CVD risk factors and conditions.

Quality-of-Care Metrics for CVD (Chapter 23)

The Institute of Medicine has identified 6 domains of quality of care, including safety, effectiveness, patient-centered care, timely care, efficiency, and equitable care.
According to the Medicare Patient Safety Monitoring System, between 2005 and 2011, adverse event rates in hospitalized patients declined for both myocardial infarction (from 5.0% to 3.7%) and congestive heart failure (from 3.7% to 2.7%)
However, in the American College of Cardiology’s Practice Innovation and Clinical Excellence (PINNACLE) outpatient registry, only 66.5% of eligible patients with coronary artery disease received the optimal evidenced-based combination of medications.
A randomized trial of post–acute coronary care syndrome that used multiple modalities to enhance adherence to 4 indicated medications (clopidogrel, statins, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and β-blockers) demonstrated better adherence in the intervention group (89.3% versus 73.9%) at 1 year.
Similarly, challenges persist in the outpatient setting, in discussion and counseling for PA and dietary habits.

Cardiovascular Procedure Use and Costs (Chapters 24 and 25)

The total number of inpatient cardiovascular operations and procedures increased 28% between 2000 and 2010, from 5 939 000 to 7 588 000.
According to the 2012 National Healthcare Cost and Utilization Project statistics, the mean hospital charge for a vascular or cardiac surgery or procedure in 2012 was $78 897: cardiac revascularization cost $149 480, and percutaneous interventions cost ≈$70 027.
For 2011, the estimated annual costs for CVD and stroke were $320.1 billion, including $195.6 billion in direct costs (hospital services, physicians and other professionals, prescribed medications, home health care, and other medical durables) and $124.5 billion in indirect costs from lost future productivity (cardiovascular and stroke premature deaths). CVD costs more than any other diagnostic group.
By comparison, in 2009, the estimated cost of all cancer and benign neoplasms was $216.6 billion ($86.6 billion in direct costs and $130 billion in mortality indirect costs).

Conclusions

The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the Statistical Update. This annual Statistical Update is the product of a full year’s worth of effort by dedicated volunteer physicians and scientists, committed government professionals, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.
Dariush Mozaffarian, MD, DrPH, FAHA
Emelia J. Benjamin, MD, ScM, FAHA
Melanie B. Turner, MPH
On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Note: Population data used in the compilation of NHANES prevalence estimates are for the latest year of the NHANES survey being used. Extrapolations for NHANES prevalence estimates are based on the census resident population for 2012 because this is the most recent year of NHANES data used in the Statistical Update.

1. About These Statistics

The AHA works with the CDC’s NCHS, the NHLBI, the NINDS, and other government agencies to derive the annual statistics in this Heart Disease and Stroke Statistical Update. This chapter describes the most important sources and the types of data we use from them. For more details, see Chapter 27 of this document, the Glossary.
The surveys used are the following:
BRFSS—ongoing telephone health survey system
GCNKSS—stroke incidence rates and outcomes within a biracial population
MEPS—data on specific health services that Americans use, how frequently they use them, the cost of these services, and how the costs are paid
NHANES—disease and risk factor prevalence and nutrition statistics
NHIS—disease and risk factor prevalence
NHDS—hospital inpatient discharges and procedures (discharged alive, dead, or status unknown)
NAMCS—physician office visits
NHHCS—staff, services, and patients of home health and hospice agencies
NHAMCS—hospital outpatient and ED visits
Nationwide Inpatient Sample of the AHRQ—hospital inpatient discharges, procedures, and charges
NNHS—nursing home residents
National Vital Statistics System—national and state mortality data
WHO—mortality rates by country
YRBSS—health-risk behaviors in youth and young adults

Disease Prevalence

Prevalence is an estimate of how many people have a disease at a given point or period in time. The NCHS conducts health examination and health interview surveys that provide estimates of the prevalence of diseases and risk factors. In this Update, the health interview part of the NHANES is used for the prevalence of CVDs. NHANES is used more than the NHIS because in NHANES, AP is based on the Rose Questionnaire; estimates are made regularly for HF; hypertension is based on BP measurements and interviews; and an estimate can be made for total CVD, including MI, AP, HF, stroke, and hypertension.
A major emphasis of this Statistical Update is to present the latest estimates of the number of people in the United States who have specific conditions to provide a realistic estimate of burden. Most estimates based on NHANES prevalence rates are based on data collected from 2009 to 2012 (in most cases, these are the latest published figures). These are applied to census population estimates for 2012. Differences in population estimates cannot be used to evaluate possible trends in prevalence because these estimates are based on extrapolations of rates beyond the data collection period by use of more recent census population estimates. Trends can only be evaluated by comparing prevalence rates estimated from surveys conducted in different years.

Risk Factor Prevalence

The NHANES 2009 to 2012 data are used in this Update to present estimates of the percentage of people with high lipid values, DM, overweight, and obesity. The NHIS is used for the prevalence of cigarette smoking and physical inactivity. Data for students in grades 9 through 12 are obtained from the YRBSS.

Incidence and Recurrent Attacks

An incidence rate refers to the number of new cases of a disease that develop in a population per unit of time. The unit of time for incidence is not necessarily 1 year, although we often discuss incidence in terms of 1 year. For some statistics, new and recurrent attacks or cases are combined. Our national incidence estimates for the various types of CVD are extrapolations to the US population from the FHS, the ARIC study, and the CHS, all conducted by the NHLBI, as well as the GCNKSS, which is funded by the NINDS. The rates change only when new data are available; they are not computed annually. Do not compare the incidence or the rates with those in past editions of the Heart Disease and Stroke Statistics Update (also known as the Heart and Stroke Statistical Update for editions before 2005). Doing so can lead to serious misinterpretation of time trends.

Mortality

Mortality data are generally presented according to the underlying cause of death. “Any-mention” mortality means that the condition was nominally selected as the underlying cause or was otherwise mentioned on the death certificate. For many deaths classified as attributable to CVD, selection of the single most likely underlying cause can be difficult when several major comorbidities are present, as is often the case in the elderly population. It is useful, therefore, to know the extent of mortality attributable to a given cause regardless of whether it is the underlying cause or a contributing cause (ie, its “any-mention” status). The number of deaths in 2011 with any mention of specific causes of death was tabulated by the NHLBI from the NCHS public-use electronic files on mortality.
The first set of statistics for each disease in this Update includes the number of deaths for which the disease is the underlying cause. Two exceptions are Chapter 9 (High Blood Pressure) and Chapter 20 (Cardiomyopathy and Heart Failure). High BP, or hypertension, increases the mortality risks of CVD and other diseases, and HF should be selected as an underlying cause only when the true underlying cause is not known. In this Update, hypertension and HF death rates are presented in 2 ways: (1) As nominally classified as the underlying cause and (2) as any-mention mortality.
National and state mortality data presented according to the underlying cause of death were computed from the mortality tables of the NCHS World Wide Web site or the CDC compressed mortality file. Any-mention numbers of deaths were tabulated from the electronic mortality files of the NCHS World Wide Web site.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 2012 in the computation of morbidity data. NCHS population estimates for 2011 were used in the computation of death rate data. The Census Bureau World Wide Web site1 contains these data, as well as information on the file layout.

Hospital Discharges and Ambulatory Care Visits

Estimates of the numbers of hospital discharges and numbers of procedures performed are for inpatients discharged from short-stay hospitals. Discharges include those discharged alive, dead, or with unknown status. Unless otherwise specified, discharges are listed according to the first-listed (primary) diagnosis, and procedures are listed according to all listed procedures (primary plus secondary). These estimates are from the NHDS of the NCHS unless otherwise noted. Ambulatory care visit data include patient visits to physician offices and hospital outpatient departments and EDs. Ambulatory care visit data reflect the first-listed (primary) diagnosis. These estimates are from the NAMCS and NHAMCS of the NCHS.

International Classification of Diseases

Morbidity (illness) and mortality (death) data in the United States have a standard classification system: the ICD. Approximately every 10 to 20 years, the ICD codes are revised to reflect changes over time in medical technology, diagnosis, or terminology. Where necessary for comparability of mortality trends across the 9th and 10th ICD revisions, comparability ratios computed by the NCHS are applied as noted.2 Effective with mortality data for 1999, we are using the 10th revision (ICD-10). It will be a few more years before the 10th revision is systematically used for hospital discharge data and ambulatory care visit data, which are based on ICD-9-CM.3

Age Adjustment

Prevalence and mortality estimates for the United States or individual states comparing demographic groups or estimates over time either are age specific or are age adjusted to the 2000 standard population by the direct method.4 International mortality data are age adjusted to the European standard.5 Unless otherwise stated, all death rates in this publication are age adjusted and are deaths per 100 000 population.

Data Years for National Estimates

In this Update, we estimate the annual number of new (incidence) and recurrent cases of a disease in the United States by extrapolating to the US population in 2011 from rates reported in a community- or hospital-based study or multiple studies. Age-adjusted incidence rates by sex and race are also given in this report as observed in the study or studies. For US mortality, most numbers and rates are for 2011. For disease and risk factor prevalence, most rates in this report are calculated from the 2009 to 2012 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US population in 2012, recognizing that this probably underestimates the total prevalence, given the relatively high prevalence in the institutionalized population. The numbers and rates of hospital inpatient discharges for the United States are for 2010. Numbers of visits to physician offices, hospital EDs, and hospital outpatient departments are for 2010. Except as noted, economic cost estimates are for 2011.

Cardiovascular Disease

For data on hospitalizations, physician office visits, and mortality, CVD is defined according to ICD codes given in Chapter 27 of the present document. This definition includes all diseases of the circulatory system, as well as congenital CVD. Unless so specified, an estimate for total CVD does not include congenital CVD. Prevalence of CVD includes people with hypertension, HD, stroke, PAD, and diseases of the veins.

Race

Data published by governmental agencies for some racial groups are considered unreliable because of the small sample size in the studies. Because we try to provide data for as many racial groups as possible, we show these data for informational and comparative purposes.

Contacts

If you have questions about statistics or any points made in this Update, please contact the AHA National Center, Office of Science & Medicine at [email protected]. Direct all media inquiries to News Media Relations at [email protected] or 214-706-1173.
We do our utmost to ensure that this Update is error free. If we discover errors after publication, we will provide corrections at our World Wide Web site, http://www.heart.org/statistics, and in the journal Circulation.
Abbreviations Used in Chapter 1
AHAAmerican Heart Association
AHRQAgency for Healthcare Research and Quality
APangina pectoris
ARICAtherosclerosis Risk in Communities Study
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHSCardiovascular Health Study
CVDcardiovascular disease
DMdiabetes mellitus
EDemergency department
FHSFramingham Heart Study
GCNKSSGreater Cincinnati/Northern Kentucky Stroke Study
HDheart disease
HFheart failure
ICDInternational Classification of Diseases
ICD-9-CMInternational Classification of Diseases, Clinical Modification, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
MEPSMedical Expenditure Panel Survey
MImyocardial infarction
NAMCSNational Ambulatory Medical Care Survey
NCHSNational Center for Health Statistics
NHAMCSNational Hospital Ambulatory Medical Care Survey
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHHCSNational Home and Hospice Care Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NINDSNational Institute of Neurological Disorders and Stroke
NNHSNational Nursing Home Survey
PADperipheral artery disease
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System
See Glossary (Chapter 27) for explanation of terms.

References

1.
US Census Bureau population estimates. Historical data: 2000s. US Census Bureau Web site. http://www.census.gov/popest/data/historical/2000s/index.html. Accessed October 29, 2012.
2.
National Center for Health Statistics. Health, United States, 2009, With Special Feature on Medical Technology. Hyattsville, MD: National Center for Health Statistics; 2010. http://www.cdc.gov/nchs/data/hus/hus09.pdf. Accessed October 29, 2012.
3.
National Center for Health Statistics, Centers for Medicare and Medicaid Services. ICD-9-CM Official Guidelines for Coding and Reporting, 2011. http://www.cdc.gov/nchs/data/icd/icd9cm_guidelines_2011.pdf. Accessed October 29, 2012.
4.
Anderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard. Natl Vital Stat Rep. 1998;47:1–16, 20.
5.
World Health Organization. World Health Statistics Annual. Geneva, Switzerland: World Health Organization; 1998.

2. Cardiovascular Health

Chart 2-1. Prevalence (unadjusted) estimates for poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, US children aged 12 to 19 years, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012. *Healthy diet score data reflects 2009 to 2010 NHANES data.
Chart 2-2. Prevalence (unadjusted) estimates of poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US adults aged 20 to 49 years and ≥50 years, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012. *Healthy diet score data reflects 2009 to 2010 NHANES data.
Chart 2-3. Proportion (unadjusted) of US children aged 12 to 19 years meeting different numbers of criteria for ideal cardiovascular health, overall and by sex, National Health and Nutrition Examination Survey 2009 to 2010.
Chart 2-4. Age-standardized prevalence estimates of US adults aged =20 years meeting different numbers of criteria for ideal cardiovascular health, overall and by age and sex subgroups, National Health and Nutrition Examination Survey 2009 to 2010.
Chart 2-5. Age-standardized prevalence estimates of US adults aged ≥20 years meeting different numbers of criteria for ideal cardiovascular health, overall and in selected race subgroups, National Health and Nutrition Examination Survey 2009 to 2010.
Chart 2-6. Prevalence for meeting ≥5 criteria for ideal cardiovascular health among US adults aged ≥20 years (age standardized) and US children aged 12 to 19 years, overall and by sex, National Health and Nutrition Examination Survey 2005 to 2006 and 2009 to 2010.
Chart 2-7. Age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal and poor cardiovascular health, for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US adults aged ≥20 years, National Health and Nutrition Examination Survey 2009 to 2010.
Chart 2-8. Age-standardized cardiovascular health status by US states, Behavioral Risk Factor Surveillance System, 2009. A, Age-standardized prevalence of population with ideal cardiovascular health by states. B, Age-standardized percentage of population with 0 to 2 cardiovascular health metrics by states. C, Age-standardized mean score of cardiovascular health metrics by states. Reprinted from Fang et al.3
Chart 2-9. Trends in prevalence (unadjusted) of meeting criteria for ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US children aged 12 to 19 years, National Health and Nutrition Examination Survey (NHANES) 1999 to 2000 through 2011 to 2012. *Because of changes in the physical activity questionnaire between different cycles of the NHANES, trends over time for this indicator should be interpreted with caution, and statistical comparisons should not be attempted. †Data for the Healthy Diet Score, based on a 2-day average intake, were only available for the 2005 to 2006, 2007 to 2008, and 2009 to 2010 NHANES cycles at the time of this analysis.
Chart 2-10. Age-standardized trends in prevalence of meeting criteria for ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals among US adults aged =20 years, National Health and Nutrition Examination Survey (NHANES) 1999 to 2000 through 2011 to 2012. *Because of changes in the physical activity questionnaire between different cycles of the NHANES, trends over time for this indicator should be interpreted with caution, and statistical comparisons should not be attempted. †Data for the Healthy Diet Score, based on a 2-day average intake, were only available for the 2005 to 2006, 2007 to 2008, and 2009 to 2010 NHANES cycles at the time of this analysis.
Chart 2-11. Prevalence of ideal, intermediate, and poor cardiovascular health metrics in 2006 (American Heart Association 2020 Impact Goals baseline year) and 2020 projections assuming current trends continue. The 2020 targets for each cardiovascular health metric assume a 20% relative increase in ideal cardiovascular health prevalence metrics and a 20% relative decrease in poor cardiovascular health prevalence metrics for men and women. Reprinted from Huffman et al4 with permission. Copyright © 2012, American Heart Association.
Chart 2-12. US age-standardized death rates* from cardiovascular diseases, 2000 to 2012. CHD indicates coronary heart disease; and CVD, cardiovascular disease. *Directly standardized to the age distribution of the 2000 US standard population. †Total CVD: International Classification of Diseases, 10th Revision (ICD-10) I00 to I99, Q20 to Q28. §Stroke (all cerebrovascular disease): ICD-10 I60 to I69. ¶CHD: ICD-10 I20 to I25. **Other CVD: ICD-10 I00 to I15, I26 to I51, I70 to I78, I80 to I89, I95 to I99. Source: Centers for Disease Control and Prevention, National Center for Health Statistics.24
Chart 2-13. Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors. Reprinted from Folsom et al.11 Copyright © 2011, with permission from the American College of Cardiology Foundation.
Table 2-1. Definitions of Poor, Intermediate, and Ideal Cardiovascular Health for Each Metric in the AHA 2020 Goals
 Level of Cardiovascular Health for Each Metric
PoorIntermediateIdeal
Current smoking
 Adults ≥20 y of ageYesFormer ≥12 moNever or quit >12 mo
 Children 12–19 y of ageTried during the prior 30 dNever tried; never smoked whole cigarette
BMI*
 Adults ≥20 y of age≥30 kg/m225–29.9 kg/m2<25 kg/m2
 Children 2–19 y of age>95th percentile85th–95th percentile<85th percentile
PA
 Adults ≥20 y of ageNone1–149 min/wk moderate or1–74 min/wk vigorous or1–149 min/wk moderate + 2×vigorous≥150 min/wk moderate or ≥75 min/wk vigorous or ≥150 min/wk moderate + 2×vigorous
 Children 12–19 y of ageNone>0 and <60 min of moderate or vigorous every day≥60 min of moderate or vigorous every day
Healthy diet pattern, No. of components
 Adults ≥20 y of age0–12–34–5
 Children 5–19 y of age0–12–34–5
Total cholesterol, mg/dL
 Adults ≥20 y of age≥240200–239 or treated to goal<200
 Children 6–19 y of age≥200170–199<170
Blood pressure
 Adults ≥20 y of ageSBP ≥140 mm Hg or DBP ≥90 mm HgSBP 120–139 mm Hg or DBP 80–89 mm Hg or treated to goal<120 mm Hg/<80 mm Hg
 Children 8–19 y of age>95th percentile90th–95th percentile or SBP ≥120 mm Hg or DBP ≥80 mm Hg<90th percentile
Fasting plasma glucose, mg/dL
 Adults ≥20 y of age≥126100–125 or treated to goal<100
 Children 12–19 y of age≥126100–125<100
AHA indicates American Heart Association; BMI, body mass index; DBP, diastolic blood pressure; ellipses (. . .), data not available; PA, physical activity; and SBP, systolic blood pressure.
*
Represents appropriate energy balance, that is, appropriate dietary quantity and PA to maintain normal body weight.
In the context of a healthy dietary pattern that is consistent with a Dietary Approaches to Stop Hypertension [DASH]–type eating pattern, to consume ≥4.5 cups/d of fruits and vegetables, ≥2 servings/wk of fish, and ≥3 servings/d of whole grains and no more than 36 oz/wk of sugar-sweetened beverages and 1500 mg/d of sodium.
Reprinted from Lloyd-Jones et al1 with permission. Copyright © 2010, American Heart Association, Inc.
Table 2-2. Prevalence of Ideal Cardiovascular Health and Its Components in the US Population in Selected Age Strata, From NHANES 2011 to 2012
 NHANES CycleAge 12–19 yAge ≥20 y*Age 20–39 yAge 40–59 yAge ≥60 y
Ideal cardiovascular health profile (7/7)2009–20100.0 (0.0)0.1 (0.1)0.3 (0.3)0.0 (0.0)0.0 (0.0)
 ≥6 Ideal2009–201019.3 (2.6)4.6 (0.6)8.2 (1.5)2.9 (0.9)0.8 (0.5)
 ≥5 Ideal2009–201049.5 (2.5)17.6 (1.3)30.3 (2.6)11.1 (1.6)5.8 (1.4)
Ideal health factors (4/4)2011–201247.8 (2.1)16.7 (1.1)32.1 (2.3)8.8 (1.1)2.5 (1.2)
 Total cholesterol <200 mg/dL2011–201275.7 (1.9)46.6 (0.7)70.4 (1.7)34.2 (1.7)23.9 (1.1)
 SBP <120 and DBP <80 mm Hg2011–201282.3 (1.6)42.2 (1.3)64.4 (2.1)34.4 (1.5)15.7 (1.6)
 Not current smoker2011–201287.1 (1.1)77.8 (1.3)75.2 (2.1)74.3 (1.7)87.1 (1.3)
 Fasting blood glucose <100 mg/dL2011–201285.3 (2.8)56.5 (1.4)74.7 (1.7)52.4 (2.7)31.3 (2.3)
Ideal health behaviors (4/4)2009–20100.0 (0.0)0.1 (0.1)0.4 (0.2)0.0 (0.0)0.0 (0.0)
 PA at goal2011–201236.5 (2.6)44.0 (1.8)53.0 (2.2)41.0 (2.4)35.2 (2.3)
 Not current smoker2011–201287.1 (1.1)77.8 (1.3)75.2 (2.1)74.3 (1.7)87.1 (1.3)
 BMI <25 kg/m22011–201264.7 (2.1)31.3 (1.4)39.7 (2.8)24.7 (1.4)28.4 (2.1)
 4–5 Diet goals met2009–20100.1 (0.1)0.5 (0.2)0.7 (0.4)0.5 (0.3)0.3 (0.1)
  Fruits and vegetables ≥4.5 cups/d2009–20107.5 (1.7)13.7 (0.8)11.5 (1.4)13.8 (1.4)17.0 (1.0)
  Fish ≥2 servings/wk2009–20108.5 (1.2)23.6 (1.6)21.8 (2.1)24.3 (2.4)26.0 (1.8)
  Sodium <1500 mg/d2009–20100.2 (0.1)0.2 (0.1)0.2 (0.1)0.3 (0.2)0.3 (0.2)
  Sugar-sweetened beverages <36 oz/wk2009–201029.5 (2.4)55.1 (1.0)41.9 (2.5)58.1 (1.0)73.5 (1.2)
  Whole grains ≥3 1-oz equivalents/d2009–20105.7 (1.0)11.0 (0.7)10.8 (1.2)10.2 (1.1)11.9 (1.3)
Secondary diet metrics2009–2010 
 Nuts/legumes/seeds ≥4 servings/wk2009–201012.2 (1.5)23.6 (1.1)21.3 (1.2)25.3 (1.9)24.8 (1.4)
 Processed meats <2 servings/wk2009–201053.3 (2.5)57.7 (1.5)53.9 (2.0)58.6 (2.2)62.3 (1.9)
 Saturated fat <7% total kcal2009–20108.2 (1.8)11.8 (0.7)13.8 (1.3)10.2 (0.9)11.3 (0.9)
Values are mean percentage (SE).
BMI indicates body mass index; DBP, diastolic blood pressure; NHANES, National Health and Nutrition Examination Survey; PA, physical activity; and SBP, systolic blood pressure.
*
Standardized to the age distribution of the 2000 US Standard population.
Scaled to 2000 kcal/d and in the context of appropriate energy balance and a DASH (Dietary Approaches to Stop Hypertension)–type eating pattern.
Table 2-3. Evidence-Based Individual Approaches for Improving Health Behaviors and Health Factors in the Clinic Setting
• Set specific goals (Class I; Level of Evidence A). Set specific, proximal goals with the patient, including a personalized plan to achieve the goals (eg, over the next 3 mo, increase fish by 1 serving/wk, reduce smoking by half a pack per day, or walk 30 min 3 times/wk).• Establish self-monitoring (Class I; Level of Evidence A). Develop a strategy for self-monitoring, such as a dietary or physical activity diary or Web-based or mobile applications.• Schedule follow-up (Class I; Level of Evidence A). Schedule regular follow-up (in-person, telephone, written, and/or electronic), with clear frequency and duration of contacts, to assess success, reinforce progress, and set new goals as necessary.• Provide feedback (Class I; Level of Evidence A). Provide feedback on progress toward goals, including using in-person, telephone, and/or electronic feedback.• Increase self-efficacy (Class I; Level of Evidence A). Increase the patient’s perception that they can successfully change their behavior.*• Use motivational interviewing (Class I; Level of Evidence A). Use motivational interviewing when patients are resistant or ambivalent about behavior change.• Provide long-term support (Class I; Level of Evidence B). Arrange long-term support from family, friends, or peers for behavior change, such as in other workplace, school, or community-based programs.• Use a multicomponent approach (Class I; Level of Evidence A). Combine 2 or more of the above strategies into the behavior change efforts.
*
Examples of approaches include mastery experiences (set a reasonable, proximal goal that the person can successfully achieve); vicarious experiences (have the person see someone with similar capabilities performing the behavior, such as walking on a treadmill or preparing a healthy meal); physiological feedback (explain to the patient when a change in their symptoms is related to worse or improved behaviors); and verbal persuasion (persuade the person that you believe in their capability to perform the behavior).
Motivational interviewing represents use of individual counseling to explore and resolve ambivalence toward changing behavior. Major principles include fostering the person’s own awareness and resolution of their ambivalence, as well as their own self-motivation to change, in a partnership with the counselor or provider.
Modified from Artinian et al18 with permission. Copyright © 2010, American Heart Association, Inc.
Table 2-4. Evidence-Based Healthcare Systems Approaches to Support and Facilitate Improvements in Health Behaviors and Health Factors19–23
• Electronic systems for scheduling and tracking initial visits and regular follow-up contacts for behavior change and treatments.• Electronic medical records systems to help assess, track, and report on specific health behaviors (diet, PA, tobacco, body weight) and health factors (BP, cholesterol, glucose), as well as to provide feedback and the latest guidelines to providers.• Practical paper or electronic toolkits for assessment of key health behaviors and health factors, including during, before, and after provider visits.• Electronic systems to facilitate provision of feedback to patients on their progress during behavior change and other treatment efforts.• Education and ongoing training for providers on evidence-based behavior change strategies, as well as the most relevant behavioral targets, including training on relevant ethnic and cultural issues.• Integrated systems to provide coordinated care by multidisciplinary teams of providers, including physicians, nurse practitioners, dietitians, PA specialists, and social workers.• Reimbursement guidelines and incentives that reward efforts to change health behaviors and health factors. Restructuring of practice goals and quality benchmarks to incorporate health behavior (diet, PA, tobacco, body weight) and health factor (BP, cholesterol, glucose) interventions and targets for both primary and secondary prevention.
BP indicates blood pressure; and PA, physical activity.
Table 2-5. Summary of Evidence-Based Population Approaches for Improving Diet, Increasing Physical Activity, and Reducing Tobacco Use*
Diet
 Media and educationSustained, focused media and educational campaigns, using multiple modes, for increasing consumption of specific healthful foods or reducing consumption of specific less healthful foods or beverages, either alone (Class IIa; Level of Evidence B) or as part of multicomponent strategies (Class I; Level of Evidence B)§
On-site supermarket and grocery store educational programs to support the purchase of healthier foods (Class IIa; Level of Evidence B)
 Labeling and informationMandated nutrition facts panels or front-of-pack labels/icons as a means to influence industry behavior and product formulations (Class IIa; Level of Evidence B)
Economic incentivesSubsidy strategies to lower prices of more healthful foods and beverages (Class I; Level of Evidence A)
Tax strategies to increase prices of less healthful foods and beverages (Class IIa; Level of Evidence B)
Changes in both agricultural subsidies and other related policies to create an infrastructure that facilitates production, transportation, and marketing of healthier foods, sustained over several decades (Class IIa; Level of Evidence B)
 SchoolsMulticomponent interventions focused on improving both diet and physical activity, including specialized educational curricula, trained teachers, supportive school policies, a formal PE program, healthy food and beverage options, and a parental/family component (Class I; Level of Evidence A)
School garden programs, including nutrition and gardening education and hands-on gardening experiences (Class IIa; Level of Evidence A)Fresh fruit and vegetable programs that provide free fruits and vegetables to students during the school day (Class IIa; Level of Evidence A)
 WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (Class IIa; Level of Evidence A)
Increased availability of healthier food/beverage options and/or strong nutrition standards for foods and beverages served, in combination with vending machine prompts, labels, or icons to make healthier choices (Class IIa; Level of Evidence B)
 Local environmentIncreased availability of supermarkets near homes (Class IIa; Level of Evidence B)
 Restrictions and mandatesRestrictions on television advertisements for less healthful foods or beverages advertised to children (Class I; Level of Evidence B)
Restrictions on advertising and marketing of less healthful foods or beverages near schools and public places frequented by youths (Class IIa; Level of Evidence B)
General nutrition standards for foods and beverages marketed and advertised to children in any fashion, including on-package promotion (Class IIa; Level of Evidence B)
Regulatory policies to reduce specific nutrients in foods (eg, trans fats, salt, certain fats) (Class I; Level of Evidence B)§
Physical activity
 Labeling and informationPoint-of-decision prompts to encourage use of stairs (Class IIa; Level of Evidence A)
 Economic incentivesIncreased gasoline taxes to increase active transport/commuting (Class IIa; Level of Evidence B)
 SchoolsMulticomponent interventions focused on improving both diet and physical activity, including specialized educational curricula, trained teachers, supportive school policies, a formal PE program, serving of healthy food and beverage options, and a parental/family component (Class IIa; Level of Evidence A)
Increased availability and types of school playground spaces and equipment (Class I; Level of Evidence B)
Increased number of PE classes, revised PE curricula to increase time in at least moderate activity, and trained PE teachers at schools (Class IIa; Level of Evidence A/Class IIb; Level of Evidence A)
Regular classroom physical activity breaks during academic lessons (Class IIa; Level of Evidence A)§
 WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (Class IIa; Level of Evidence A)
Structured worksite programs that encourage activity and also provide a set time for physical activity during work hours (Class IIa; Level of Evidence B)
Improving stairway access and appeal, potentially in combination with “skip-stop” elevators that skip some floors (Class IIa; Level of Evidence B)
Adding new or updating worksite fitness centers (Class IIa; Level of Evidence B)
 Local environmentImproved accessibility of recreation and exercise spaces and facilities (eg, building of parks and playgrounds, increasing operating hours, use of school facilities during nonschool hours) (Class IIa; Level of Evidence B)
Improved land-use design (eg, integration and interrelationships of residential, school, work, retail, and public spaces) (Class IIa; Level of Evidence B)
Improved sidewalk and street design to increase active commuting (walking or bicycling) to school by children (Class IIa; Level of Evidence B)
Improved traffic safety (Class IIa; Level of Evidence B)
Improved neighborhood aesthetics (to increase activity in adults) (Class IIa; Level of Evidence B)
Improved walkability, a composite indicator that incorporates aspects of land-use mix, street connectivity, pedestrian infrastructure, aesthetics, traffic safety, and/or crime safety (Class IIa; Level of Evidence B)
Smoking, continued
 Media and educationSustained, focused media and educational campaigns to reduce smoking, either alone (IIa B) or as part of larger multicomponent population-level strategies (Class I; Level of Evidence A)
 Labeling and informationCigarette package warnings, especially those that are graphic and health related (Class I; Level of Evidence B)§
 Economic incentivesHigher taxes on tobacco products to reduce use and fund tobacco control programs (Class I; Level of Evidence A)§
 Schools and workplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (Class IIa; Level of Evidence A)
 Local environmentReduced density of retail tobacco outlets around homes and schools (Class I; Level of Evidence B)
Development of community telephone lines for cessation counseling and support services (Class I; Level of Evidence A)
 Restrictions and mandatesCommunity (city, state, or federal) restrictions on smoking in public places (Class I; Level of Evidence A)
Local workplace-specific restrictions on smoking (Class I; Level of Evidence A)§
Stronger enforcement of local school-specific restrictions on smoking (Class IIa; Level of Evidence B)
Local residence-specific restrictions on smoking (Class IIa; Level of Evidence B)§
Partial or complete restrictions on advertising and promotion of tobacco products (Class I; Level of Evidence B)
PE indicates physical education.
*
The specific population interventions listed here are either a Class I or IIa recommendation with a Level of Evidence grade of either A or B.
At least some evidence from studies conducted in high-income Western regions and countries (eg, North America, Europe, Australia, New Zealand).
At least some evidence from studies conducted in high-income non-Western regions and countries (eg, Japan, Hong Kong, South Korea, Singapore).
§
At least some evidence from studies conducted in low- or middle-income regions and countries (eg, Africa, China, Pakistan, India).
Based on cross-sectional studies only; only 2 longitudinal studies have been performed, with no significant relations seen.
Class IIa; Level of Evidence A for improving physical activity; Class IIb; Level of Evidence B for reducing adiposity.
Reprinted from Mozaffarian et al19 with permission. Copyright © 2012, American Heart Association, Inc.
Table 2-6. Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 Years, NHANES 2011 to 2012
Percent BP ideal among adults, 2011–201242.17
20% Relative increase50.60
Percent whose BP would be ideal if population mean BP were lowered by* 
 2 mm Hg53.62
 3 mm Hg57.42
 4 mm Hg59.39
 5 mm Hg63.40
Values are percentages. Data are standardized to the age distribution of the 2000 US standard population.
BP indicates blood pressure; and NHANES, National Health and Nutrition Examination Survey.
*
Reduction in BP=(observed average systolic−X mm Hg) AND (observed average diastolic−X mm Hg).
Table 2-7. AHA Advocacy and Policy Strategies Related to the 2020 Impact Goals for Ideal Cardiovascular Health
Measure of Cardiovascular HealthAdvocacy/Policy Solutions
Smoking status Children  Ideal: Never tried or never smoked a whole cigarette  Intermediate: Quit <12 mo  Poor: Current smoker Adults  Ideal: Never smoked or quit >1 y ago  Intermediate: Quit <12 mo  Poor: Current smokerIncrease excise taxes, including federal tobacco tax parity (state and federal)Support comprehensive clean indoor air laws/regulations (state/community level/multiunit housing/hospitals/college campuses, and federal office buildings)Ensure comprehensive tobacco cessation benefits in private and public insurance plans with minimal copay (federal level with Center for Medicare and Medicaid Services and implementation of the ACA and state level with Medicaid and state healthcare plans)Increase funding for tobacco cessation and prevention programs that meet or exceed the CDC recommended levels (state)Ensure FDA regulation of tobacco including cigars and e-cigarettes (federal)Monitor the role of mobile technologies to improve cessation therapy(federal/state)Improve surveillance for the increasing use of e-cigarettes and smokeless tobacco products (federal/state/local)Eliminate the sale of tobacco in pharmacies and other health-related institutions (state/local)
Physical activity Children  Ideal: ≥60 min of moderate to   vigorous physical activity per day  Intermediate: 1–59 min of moderate to vigorous physical activity per day  Poor: No physical activity Adults  Ideal: At least 150 min of moderate or 75 min of vigorous physical activity each week  Intermediate: 1–149 min/wk moderate or 1–74 min/wk vigorous activity  Poor: No physical activityIncrease quality and quantity of physical education in schools (federal/state/local)Increase the quantity of other physical activity opportunities during the school day, such as• Recess• Classroom breaks/activity between classes• Physical activity integrated into the curriculum (state/local)Support the creation and implementation, through legislation and regulation (including licensing), of physical activity standards for preschool, day care, and other out-of-school care programs (state/local)Increase funding for and implementation of Safe Routes to School (federal/state/local)Improve implementation of local wellness policies in schools (federal/state/local)Promote robust physical activity policies in early childcare (state/local)Promote physical activity standards in before-school and after-school programs (federal/state/local)Support and promote healthy meeting/conference guidelines (state/local)Support physical activity opportunities and screening within comprehensive worksite wellness programs, workplace design, and ensure consumer protections with financial incentives tied to healthcare plans (federal/state/local)Change zoning laws to favor/require mixed-use development that places destinations within walking distance of homes (state/local)Promote street-level design and connectivity that facilitate active transport within communities (including Complete Streets policies) (federal/state/local)Change transportation goals away from moving cars to a balanced approach to moving people (federal/state/local)Promote shared use of school facilities (state/local)Provide tax incentives and reimbursement policies that cover health/fitness counseling (federal/state)
Physical activity, continuedEnsure regular revision and update of the Physical Activity Guidelines for Americans (federal)Target funding for physical activity environments, policy implementation, and program to communities with high need; high need could be defined as low-income, high rates of CVD, shorter lifespans (federal/state/local)Target technical assistance and support for grant preparation for physical activity–related grants to low-resource communities (federal/state/local)Screen for physical activity in the clinical environment as a vital sign and incorporate quality measure into electronic health records (federal/state)
BMI Adults  Ideal: BMI between 18.5 and 25 kg/m2  Intermediate: 25–29.9 kg/m2  Poor: >30 kg/m2 Children  Ideal: BMI between the 15th and 85th percentile  Intermediate: BMI between 85th and 95th percentile  Poor: >95th percentileProvide comprehensive coverage for guidelines-based prevention, diagnosis, and treatment of overweight and obesity in the healthcare environment (federal/state)Provide robust surveillance and monitoring of obesity(federal/state)
Healthy diet Adults and Children Ideal for cardiovascular health: In the context of a DASH-type dietary pattern, adults and children should achieve at least 4 of the 5 following key components of a healthy diet:  Fruits and vegetables: >4.5 cups/d  Fish: More than two, 3.5-oz servings/wk (preferably oily fish)  Fiber-rich whole grains (>1.1 g of fiber per 10 g of carbohydrates): three 1-oz-equivalent servings/d  Sodium: <1500 mg/d  Sugar-sweetened beverages: <450 kcal (36 oz)/wk Children/adults  Ideal: Diet Score 4–5  Intermediate: Diet Score 2–3  Poor: Diet Score 0–1Reduce sodium in the food supply:Finalize voluntary FDA standards for reduction of sodium across all food and beverage categories (federal)Improve food labeling• Update of the Nutrition Facts Panel• On-package symbols• Health claims• Structure/function claims(federal)Help shape the Dietary Guidelines for Americans through regulatory means (federal)Robust implementation of school nutrition standards for meals and competitive foods (federal/state)Implement procurement standards for food service and purchasing across federal and state agencies (federal/state)Increase fruit and vegetable consumption:Promote and protect the implementation of robust nutrition standards for school meals and competitive foods (federal/state/local)Promote robust nutrition policies in early childcare (state/local)Promote nutrition standards, nutrition education, and physical activity standards in before-school and after-school programs (federal/state/local)Eliminate unhealthy food marketing and advertising to children Increase healthy food marketing and advertising(state/local)Promote procurement, meeting, and vending standards for foods purchased by governments and employers(federal/state/local)Improve access to healthy affordable foods in the community:• Healthy food financing• Farmers’ markets• School/community gardens• SNAP education• Fresh fruit and vegetable program(federal/state/local)
Healthy diet, continuedContinue to improve nutrition standards and nutrition education in government feeding programs such as:• WIC• SNAP• CACFP(federal)Reduce sugar-sweetened beverage consumption:Support sugar-sweetened beverage taxes(state/local)Increase water subsidies(state/local)Provide funding for placement and maintenance of water fountains or dispensers in public places (federal/state/local)Other disincentives/incentives for healthy beverages within government feeding programs, healthy vending, restaurants, hospital systems, schools, healthy food financing initiatives, and procurement standards(federal/state/local)
Blood pressure Adults  Ideal: BP <120/<80 mm Hg  Intermediate: SBP 120–139 mm Hg or DBP 80–89 mm Hg, or treated <140/<90 mm Hg  Poor: Treated BP >140/>90 mm Hg or untreated >140/>90 mm Hg Children  Ideal: <90th percentile  Intermediate: 90th-95th percentile or SBP ≥120 or DBP ≥80 mm Hg  Poor: >95th percentileBlood glucose Children and adults  Ideal: <100 mg/dL  Intermediate: 100–125 mg/dL or treated to goal  Poor: ≥126 mg/dL Cholesterol Adults  Ideal: <200 mg/dL  Intermediate: 200–239 mg/dL or treated to goal  Poor: ≥240 mg/dL Children  Ideal: <170 mg/dL  Intermediate: 170–199 mg/dL  Poor: ≥200 mg/dLExpand and protect access to affordable, adequate, transparent insurance coverage for all; support implementation of the ACA(federal/state)Expand coverage for groups not adequately covered by the ACA (eg, those in states that do not expand Medicaid; mixed-status families, tobacco users); work to eliminate health insurance disparities that minorities face(federal/state)Support reimbursement and minimal copays for preventive services(federal/state)Help inform/guide Medicare national coverage determinations to ensure that Medicare beneficiaries have access to appropriate treatments and services.(federal)Partner with the Department of Health and Human Services to promote the Million Hearts campaign(federal/state)Ensure continued funding and implementation of the Million Hearts campaign(federal)Promote public funding for heart disease and stroke prevention programs(state)Monitor issues around drugs and devices; address drug formularies(federal/state)Ensure adherence to clinical guidelines and treatment protocols(federal/state/local)Monitor mobile technologies to improve health(federal)
Reduce CVD mortality by 20% by 2020 Acute event: Improve systems of care (acute response and acute care)Support comprehensive, coordinated systems of care• EMS—Support strengthening 9-1-1 systems—Emergency medical dispatch—Support the establishment of quality community CPR/AED programs—Support the establishment of quality school-based programs to promote CPR, AED, and first aid—Promote credentialing for professionals to support strong EMS systems—Support and protect funding for NEMSIS• STEMI• Stroke• Out-of-hospital cardiac arrest• Telehealth (reimbursement/ease licensing/credentialing)(federal/state/local)
Reduce CVD mortality by 20% by 2020, continuedImprove care coordination models:• Medical homes• Other delivery systems reforms(federal/state/local)Ensure optimal use of health information technology(federal)Explore evidence-based opportunities for integration with mobile health technologies in delivery systems of care (federal)Improve the quality and comprehensiveness of healthcare data reporting(federal/state/local)Increase the use of clinical registries(federal/state)Ensure implementation of pulse oximetry screening for newborns(state/local)Integrate the AHA’s principles for palliative care within delivery of care(federal)
Postevent rehabilitation: Increase referral for, use of, adequate reimbursement for, and completion of cardiac rehabilitation and stroke rehabilitationEnsure adequate insurance coverage for cardiac rehabilitation and establish a national coverage determination for cardiac rehabilitation for heart failure patients (federal)Support funding for demonstration projects that expand access to and increase use of cardiac rehabilitation in different settings(federal/state)Ensure adequate coverage/reimbursement for comprehensive stroke rehabilitation(federal/state)Broadly implement automatic and coordinated referral strategies(federal/state/local)
For AHA advocacy resources, including fact sheets, policy briefs, published papers, and position statements, go to http://www.heart.org/HEARTORG/Advocate/PolicyResources/Policy-Resources_UCM_001135_SubHomePage.jsp.
ACA indicates Affordable Care Act; AED, automated external defibrillator; AHA, American Heart Association; BMI, body mass index; BP, blood pressure; CACFP, Child and Adult Care Food Program; CDC, Centers for Disease Control and Prevention; CPR, cardiopulmonary resuscitation; CVD, cardiovascular disease; DASH, Dietary Approaches to Stop Hypertension; DBP, diastolic blood pressure; EMS, emergency medical services; FDA, US Food and Drug Administration; NEMSIS, National Emergency Medical Services Information System; PA, physical activity; SBP, systolic blood pressure; SNAP, Supplemental Nutrition Assistance Program; STEMI, ST-segment–elevation myocardial infarction; and WIC, Women, Infants, and Children program.
After achieving its major Impact Goals for 2010, the AHA created a new set of central organizational Impact Goals for the current decade1:
By 2020, to improve the cardiovascular health of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.1
These goals introduce a new concept, cardiovascular health, which is characterized by 7 health metrics. Ideal cardiovascular health is defined by the absence of clinically manifest CVD together with the simultaneous presence of optimal levels of all 7 metrics, including 4 health behaviors (not smoking and having sufficient PA, a healthy diet pattern, and appropriate energy balance as represented by normal body weight) and 3 health factors (optimal total cholesterol, BP, and fasting blood glucose, in the absence of drug treatment; Table 2-1). Because a spectrum of cardiovascular health can also be envisioned and the ideal cardiovascular health profile is known to be rare in the US population, a broader spectrum of cardiovascular health can also be represented as being “ideal,” “intermediate,” or “poor” for each of the health behaviors and health factors.1 Table 2-1 provides the specific definitions for ideal, intermediate, and poor cardiovascular health for each of the 7 metrics, both for adults (≥20 years of age) and children (age ranges for each metric depending on data availability).
Abbreviations Used in Chapter 2
ACAAffordable Care Act
AEDautomated external defibrillator
AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CACFPChild and Adult Care Food Program
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CPRcardiopulmonary resuscitation
CVDcardiovascular disease
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
EMSemergency medical services
FDAUS Food and Drug Administration
HbA1chemoglobin A1c (glycosylated hemoglobin)
HBPhigh blood pressure
HDheart disease
HRhazard ratio
ICD-10International Classification of Diseases, 10th Revision
IMTintima-media thickness
NEMSISNational Emergency Medical Services Information System
NHANESNational Health and Nutrition Examination Survey
ORodds ratio
PAphysical activity
PEphysical education
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEstandard error
SNAPSupplemental Nutrition Assistance Program
STEMIST-segment–elevation myocardial infarction
WICWomen, Infants, and Children program
This concept of cardiovascular health represents a new focus for the AHA, with 3 central and novel emphases:
An expanded focus on CVD prevention and promotion of positive “cardiovascular health,” in addition to the treatment of established CVD.
Efforts to promote both healthy behaviors (healthy diet pattern, appropriate energy intake, PA, and nonsmoking) and healthy biomarker levels (optimal blood lipids, BP, glucose levels) throughout the lifespan.
Population-level health promotion strategies to shift the majority of the public toward greater cardiovascular health, in addition to targeting those individuals at greatest CVD risk, since healthy lifestyles in all domains are uncommon throughout the US population.
Beginning in 2011, and recognizing the time lag in the nationally representative US data sets, this chapter in the annual Statistical Update evaluates and publishes metrics and information to provide insights into both progress toward meeting the 2020 AHA goals and areas that require greater attention to meet these goals. The AHA has advocated for raising the visibility of patient-reported cardiovascular health status, which includes symptom burden, functional status, and health-related quality of life, as an indicator of cardiovascular health in future organizational goal setting.2

Cardiovascular Health: Current Prevalence

The most up-to-date data on national prevalence of ideal, intermediate, and poor levels of each of the 7 cardiovascular health metrics are shown for adolescents and teens 12 to 19 years of age (Chart 2-1) and for adults ≥20 years of age (Chart 2-2).
For most metrics, the prevalence of ideal levels of health behaviors and health factors is higher in US children than in US adults. Major exceptions are diet and PA, for which prevalence of ideal levels in children is similar to (for PA) or worse (for diet) than in adults.
Among children (Chart 2-1), the prevalence (unadjusted) of ideal levels of cardiovascular health behaviors and factors currently varies from <1% for the healthy diet pattern (ie, <1 in 100 US children meets at least 4 of the 5 dietary components) to >80% for the smoking, BP, and fasting glucose metrics.
Among US adults (Chart 2-2), the age-standardized prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from 0.5% for having at least 4 of 5 components of the healthy diet pattern to up to 78% for never having smoked or being a former smoker who has quit for >12 months.
Age-standardized and age-specific prevalence estimates for ideal cardiovascular health and for ideal levels of each of its components are shown for 2011 to 2012 (excluding diet metrics, which are 2009 to 2010) in Table 2-2.
—In 2011 to 2012, the prevalence of ideal levels across 7 health factors and health behaviors decreased dramatically from younger to older age groups. The same trend was seen in 2007 to 2010.
—The prevalence of both children and adults meeting the dietary goals appeared to improve between 2007 to 2008 and 2009 to 2010, although this improvement should be viewed with caution given the challenges of accurately determining time trends across only 2 cycles of NHANES data collection. The improvement was attributable to the greater numbers of children and adults who met the whole grains goal, greater numbers of middle-aged and older adults who met the fruits and vegetables goal, and greater numbers of adults who met the fish goal.
Chart 2-3 displays the prevalence estimates for the population of US children (12–19 years of age) meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2009 to 2010.
—Few US children (≈5%) meet only 0, 1, or 2 criteria for ideal cardiovascular health.
—Nearly half of US children (45%) meet 3 or 4 criteria for ideal cardiovascular health, and about half meet 5 or 6 criteria (mostly 5 criteria).
—Virtually no children meet all 7 criteria for ideal cardiovascular health.
—Overall distributions are similar in boys and girls.
Charts 2-4 and 2-5 display the age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2009 to 2010, overall and stratified by age, sex, and race.
—Approximately 2% of US adults have 0 of the 7 criteria at ideal levels, and another 12% meet only 1 of 7 criteria. This is much worse than among children.
—Most US adults (≈68%) have 2, 3, or 4 criteria at ideal cardiovascular health, with ≈1 in 4 adults within each of these categories.
—Approximately 13% of US adults meet 5 criteria, 5% meet 6 criteria, and 0.1% meet 7 criteria at ideal levels.
—Presence of ideal cardiovascular health is both age and sex related (Chart 2-4). Younger adults are more likely to meet greater numbers of ideal metrics than are older adults. More than 60% of Americans >60 years of age have ≤2 metrics at ideal levels. At any age, women tend to have more metrics at ideal levels than do men.
—Race is also related to presence of ideal cardiovascular health (Chart 2-5). Blacks and Mexican Americans tend to have fewer metrics at ideal levels than whites or other races. Approximately 6 in 10 white adults and 7 in 10 black or Mexican American adults have no more than 3 of 7 metrics at ideal levels.
Chart 2-6 displays the age-standardized percentages of US adults and percentages of children who have ≥5 of the metrics (out of 7 possible) at ideal levels.
—Approximately 50% of US children 12 to 19 years of age have ≥5 metrics at ideal levels, with lower prevalence in girls (47%) than in boys (52%).
—In comparison, only 18% of US adults have ≥5 metrics with ideal levels, with lower prevalence in men (11%) than in women (25%).
—All populations have improved since baseline year 2007 to 2008 except for men.
Chart 2-7 displays the age-standardized percentages of US adults meeting different numbers of criteria for both poor and ideal cardiovascular health. Meeting the AHA 2020 Strategic Impact Goals is predicated on reducing the relative percentage of those with poor levels while increasing the relative percentage of those with ideal levels for each of the 7 metrics.
—Approximately 92% of US adults have ≥1 metric at poor levels.
—Approximately 34% of US adults have ≥3 metrics at poor levels.
—Few US adults (<3%) have ≥5 metrics at poor levels.
—More US adults have 4 to 6 ideal metrics than 4 to 6 poor metrics.
Using data from the BRFSS, Fang and colleagues3 estimated the prevalence of ideal cardiovascular health by state, which ranged from 1.2% (Oklahoma) to 6.9% (District of Columbia). Southern states tended to have higher rates of poor cardiovascular health, lower rates of ideal cardiovascular health, and lower mean cardiovascular health scores than New England and Western states (Chart 2-8).

Cardiovascular Health: Trends Over Time

The trends over the past decade in each of the 7 cardiovascular health metrics (for diet, trends from 2005–2006 to 2009–2010) are shown in Chart 2-9 (for children 12–19 years of age) and Chart 2-10 (for adults ≥20 years of age).
—Fewer children over time are meeting the ideal BMI metric, whereas more are meeting the ideal smoking and total cholesterol metrics. Other metrics do not show consistent trends over time in children.
—More adults over time are meeting the smoking metric, whereas fewer are meeting the BMI and glucose metrics. Trends for other metrics are not evident over time in adults.
On the basis of NHANES data from 1988 to 2008, if current trends continue, estimated cardiovascular health is projected to improve by 6% between 2010 and 2020, short of the AHA’s goal of 20% improvement (Chart 2-11).4 On the basis of current trends among individual metrics, anticipated declines in prevalence of smoking, high cholesterol, and high BP (in men) would be offset by substantial increases in the prevalence of obesity and DM and small changes in ideal dietary patterns or PA.4
On the basis of these projections in cardiovascular health factors and behaviors, CHD deaths are projected to decrease by 30% between 2010 and 2020 because of projected improvements in total cholesterol, SBP, smoking, and PA (≈167 000 fewer deaths), offset by increases in DM and BMI (≈24 000 more deaths).5

CVD Mortality

In 2011, the age-standardized death rate attributable to all CVD was 229.6 per 100 000 (includes congenital CVD [ICD-10 I00–I99, Q20–Q28]; Chart 2-12), down 11.5% from 259.4 per 100 000 in 2007 (baseline data for the 2020 Impact Goals on CVD and stroke mortality).6
—Death rates in 2011 attributable to stroke, CHD, and other CVDs were 37.9, 109.2, and 81.5 per 100 000, respectively.6

Relevance of Ideal Cardiovascular Health

Since the AHA announced its 2020 Impact Goals, multiple investigations have confirmed the importance of these metrics of cardiovascular health. Overall, these data demonstrate the relevance of the concept of cardiovascular health to the risk of future risk factors, disease, and mortality, including a strong inverse, stepwise association with all-cause, CVD, and ischemic HD mortality, as well as preclinical measures of atherosclerosis, including carotid IMT, arterial stiffness, and coronary artery calcium prevalence and progression.
A stepwise association was present between the number of ideal cardiovascular health metrics and risk of all-cause mortality, CVD mortality, and ischemic HD mortality after 14.5 years of follow-up based on NHANES 1988 to 2006 data.7 The HRs for individuals with 6 or 7 ideal health metrics compared with individuals with 0 ideal health metrics were 0.49 (95% CI, 0.33–0.74) for all-cause mortality, 0.24 (95% CI, 0.13–0.47) for CVD mortality, and 0.30 (95% CI, 0.13–0.68) for ischemic HD mortality.7 Ford et al8 demonstrated similar relationships.
The adjusted population attributable fractions for CVD mortality were as follows7:
—40.6% (95% CI, 24.5%–54.6%) for HBP
—13.7% (95% CI, 4.8%–22.3%) for smoking
—13.2% (95% CI, 3.5%–29.2%) for poor diet
—11.9% (95% CI, 1.3%–22.3%) for insufficient PA
—8.8% (95% CI, 2.1%–15.4%) for abnormal glucose levels
The adjusted population attributable fractions for ischemic HD mortality were as follows7:
—34.7% (95% CI, 6.6%–57.7%) for HBP
—16.7% (95% CI, 6.4%–26.6%) for smoking
—20.6% (95% CI, 1.2%–38.6%) for poor diet
—7.8% (95% CI, 0%–22.2%) for insufficient PA
—7.5% (95% CI, 3.0%–14.7%) for abnormal glucose levels
Data from the REGARDS cohort also demonstrate a stepwise association between cardiovascular health metrics and incident stroke among 22 914 participants free from baseline CVD with a mean follow-up of 4.9 years. Using a cardiovascular health score scale ranging from 0 to 14, every unit increase in cardiovascular health was associated with 8% lower risk of incident stroke (HR, 0.92; 95% CI, 0.88–0.95), with a similar effect size for white (HR, 0.91; 95% CI, 0.86–0.96) and black (HR, 0.93; 95% CI, 0.87–0.98) participants.9
Data from the Cardiovascular Lifetime Risk Pooling Project indicate that adults with all-optimal risk factor levels (similar to having ideal cardiovascular health factor levels of cholesterol, blood sugar, and BP, as well as nonsmoking status) have substantially longer overall and CVD-free survival than those who have poor levels of ≥1 of these cardiovascular health factor metrics. For example, at an index age of 45 years, men with optimal risk factor profiles lived on average 14 years longer free of all CVD events, and ≈12 years longer overall, than individuals with ≥2 risk factors.10
Importantly, in many of these analyses, ideal health behaviors and ideal health factors were each independently associated with lower CVD risk in a stepwise fashion (Chart 2-13). Thus, across any levels of health behaviors, health factors were still associated with incident CVD, and across any levels of health factors, health behaviors were still associated with incident CVD.11
Interestingly, based on NHANES 1999 to 2002, only modest intercorrelations are present between different cardiovascular health metrics. For example, these ranged from a correlation of −0.12 between PA and HbA1c to a correlation of 0.29 between BMI and HbA1c. Thus, although the 7 AHA cardiovascular health metrics appear modestly interrelated, substantial independent variation in each exists, and each is independently related to cardiovascular outcomes.8
Cardiovascular health has been associated with prevalent cognitive function across the domains of visual-spatial memory, working memory, scanning and tracking, executive function, and the global composite score (P<0.05 for all) in the Maine-Syracuse Longitudinal Study.12 Ideal cardiovascular health is also directly associated with global cognitive performance.
In REGARDS, black and white adults aged ≥45 years, free of stroke and baseline cognitive impairment, with mid (OR, 0.65; 95% CI, 0.52–0.81) to high (OR, 0.63; 95% CI, 0.51–0.79) cardiovascular health scores at baseline were found to have a lower associated incidence of clinically relevant cognitive impairment (verbal learning, memory, and fluency) than those with low cardiovascular health. No significant difference was seen between the mid and high ranges, which indicates that even when high levels of cardiovascular health are not achieved, intermediate levels are preferable to low levels.13
The AHA cardiovascular health metrics have also been associated with a lower prevalence of incident depressive symptoms in the REGARDS14 and Aerobics Center Longitudinal Study15 cohorts, respectively.
Recent analyses from the US Burden of Disease Collaborators demonstrated that each of the 7 health factors and behaviors causes substantial mortality and morbidity in the United States. The top risk factor related to overall disease burden was suboptimal diet, followed by tobacco smoking, high BMI, raised BP, high fasting plasma glucose, and physical inactivity.16

Achieving the 2020 Impact Goals

Taken together, these data continue to demonstrate both the tremendous relevance of the AHA 2020 Impact Goals for cardiovascular health and the substantial progress that will be needed to achieve these goals over the next decade.
A range of complementary strategies and approaches can lead to improvements in cardiovascular health. These include each of the following:
—Individual-focused approaches, which target lifestyle and treatments at the individual level (Table 2-3)
—Healthcare systems approaches, which encourage, facilitate, and reward efforts by providers to improve health behaviors and health factors (Table 2-4)
—Population approaches, which target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation (Table 2-5)
Such approaches can focus on both (1) improving cardiovascular health among those who currently have less than optimal levels and (2) preserving cardiovascular health among those who currently have ideal levels (in particular, children, adolescents, and young adults) as they age.
The metrics with the greatest potential for improvement are health behaviors, including diet quality, PA, and body weight. However, each of the cardiovascular health metrics can be improved and deserves major focus.
Continued emphasis is also needed on the treatment of acute CVD events and secondary prevention through treatment and control of health behaviors and risk factors.
For each cardiovascular health metric, modest shifts in the population distribution toward improved health would produce relatively large increases in the proportion of Americans in both ideal and intermediate categories. For example, on the basis of NHANES 2009 to 2010, the current prevalence of ideal levels of BP among US adults is 44.3%. To achieve the 2020 goals, a 20% relative improvement would require an increase in this proportion to 53.1% by 2020 (44.3% × 1.20). On the basis of NHANES data, a reduction in population mean BP of just 2 mm Hg would result in 56.1% of US adults having ideal levels of BP, which represents a 26.8% relative improvement in this metric (Table 2-6). Larger population reductions in BP would lead to even larger numbers of people with ideal levels. Such small reductions in population BP could result from small health behavior changes at a population level, such as increased PA, increased fruit and vegetable consumption, decreased sodium intake, decreased adiposity, or some combination of these and other lifestyle changes, with resulting substantial projected decreases in CVD rates in US adults.17
The AHA has a broad range of policy initiatives to improve cardiovascular health and meet the 2020 Strategic Impact Goals (Table 2-7). Future Statistical Updates will update these initiatives and track progress toward the 2020 Impact Goals.

References

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Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613.
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Rumsfeld JS, Alexander KP, Goff DC, Graham MM, Ho PM, Masoudi FA, Moser DK, Roger VL, Slaughter MS, Smolderen KG, Spertus JA, Sullivan MD, Treat-Jacobson D, Zerwic JJ; on behalf of the American Heart Association Council on Quality of Care and Outcomes Research, Council on Cardiovascular and Stroke Nursing Council on Epidemiology and Prevention, Council on Peripheral Vascular Disease, and Stroke Council. Cardiovascular health: the importance of measuring patient-reported health status: a scientific statement from the American Heart Association. Circulation. 2013;127:2233–2249.
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Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 States and the District of Columbia, 2009. J Am Heart Assoc. 2012;1:e005371.
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Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM. Cardiovascular health behavior and health factor changes (1988–2008) and projections to 2020: results from the National Health and Nutrition Examination Surveys. Circulation. 2012;125:2595–2602.
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Huffman MD, Lloyd-Jones DM, Ning H, Labarthe DR, Guzman Castillo M, O’Flaherty M, Ford ES, Capewell S. Quantifying options for reducing coronary heart disease mortality by 2020. Circulation. 2013;127:2477–2484.
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National Center for Health Statistics. Mortality Multiple Cause Micro-data Files, 2011. Public-use data file and documentation. NHLBI tabulations. http://www.cdc.gov/nchs/products/nvsr.htm. Accessed August 4, 2014.
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Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307:1273–1283.
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Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation. 2012;125:987–995.
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Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, Hong Y, Safford MM, Goyal A, Cushman M. Life’s Simple 7 and risk of incident stroke: the Reasons for Geographic and Racial Differences in Stroke study. Stroke. 2013;44:1909–1914.
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Wilkins JT, Ning H, Berry J, Zhao L, Dyer AR, Lloyd-Jones DM. Lifetime risk and years lived free of total cardiovascular disease. JAMA. 2012;308:1795–1801.
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Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD; ARIC Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol. 2011;57:1690–1696.
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Crichton GE, Elias MF, Davey A, Alkerwi A. Cardiovascular health and cognitive function: the Maine-Syracuse Longitudinal Study. PLoS One. 2014;9:e89317.
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Thacker EL, Gillett SR, Wadley VG, Unverzagt FW, Judd SE, McClure LA, Howard VJ, Cushman M. The American Heart Association Life’s Simple 7 and incident cognitive impairment: the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. J Am Heart Assoc. 2014;3:e000635.
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Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association’s definition in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PLoS One. 2012;7:e52771.
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España-Romero V, Artero EG, Lee DC, Sui X, Baruth M, Ruiz JR, Pate RR, Blair SN. A prospective study of ideal cardiovascular health and depressive symptoms. Psychosomatics. 2013;54:525–535.
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US Burden of Disease Collaborators. The state of US Health, 1990–2010: burden of diseases, injuries, and risk factors. JAMA. 2013;310:591–608.
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Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease. N Engl J Med. 2010;362:590–599.
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Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE; on behalf of the American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation. 2010;122:406–441.
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Mozaffarian D, Afshin A, Benowitz N, Bittner V, Daniels S, Franch H, Jacobs D, Kraus W, Kris-Etherton P, Krummel D, Popkin B, Whitsel L, Zakai N; on behalf of the American Heart Association Council on Epidemiology and Prevention, Council on Nutrition, Physical Activity and Metabolism, Council on Clinical Cardiology, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Peripheral Vascular Disease, and the Advocacy Coordinating Committee. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation. 2012;126:1514–1563.
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Bodenheimer T. Helping patients improve their health-related behaviors: what system changes do we need? Dis Manag. 2005;8:319–330.
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Simpson LA, Cooper J. Paying for obesity: a changing landscape. Pediatrics. 2009;123(suppl 5):S301–S307.
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Quist-Paulsen P. Cessation in the use of tobacco: pharmacologic and non-pharmacologic routines in patients. Clin Respir J. 2008;2:4–10.
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Davis D, Galbraith R. Continuing medical education effect on practice performance: effectiveness of continuing medical education: American College of Chest Physicians Evidence-Based Educational Guidelines. Chest. 2009;135(suppl):42S–48S.
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3. Smoking/Tobacco Use

Chart 3-1. Prevalence (%) of students in grades 9 to 12 reporting current cigarette use by sex and race/ethnicity (Youth Risk Behavior Surveillance System, 2013). NH indicates non-Hispanic. Data derived from MMWR: Morbidity and Mortality Weekly Report.3
Chart 3-2. Prevalence (%) of current smoking for adolescents and adults, by sex and age (National Health Interview Survey, 2003–2012; National Survey on Drug Use and Health, 2003–2012). Data derived from the Centers for Disease Control and Prevention/National Center for Health Statistics and Substance Abuse and Mental Health Services Administration.5
Chart 3-3. Prevalence (%) of current smoking for adults =18 years of age by race/ethnicity and sex (National Health Interview Survey: 2010–2012). All percentages are age adjusted. AIAN indicates American Indian or Alaska Native; NH, non-Hispanic. *Includes both Hispanics and non-Hispanics. Data derived from the Centers for Disease Control and Prevention/National Center for Health Statistics, Health Data Interactive.10
Table 3-1. Cigarette Smoking
Population GroupPrevalence, 2013 Age ≥18 y*6Cost16
Both sexes43 415 000 (17.9%)$289 Billion per year
Males24 080 000 (20.4%)
Females19 298 000 (15.5%)
NH white males21.7%
NH white females18.7%
NH black males21.1%
NH black females15.0%
Hispanic or Latino males16.6%
Hispanic or Latino females6.7%
Asian males14.7%
Asian females4.8%
American Indian/Alaska Native males25.7%
American Indian/Alaska Native females16.7%
Percentages are age adjusted. Estimates for Asian only and American Indian/Alaska Native only include non-Hispanic and Hispanic people.
Ellipses (. . .) indicate data not available; and NH, non-Hispanic.
*
Rounded to the nearest thousand; based on total resident population.
Smoking is a major risk factor for CVD and stroke.1 The AHA has identified never tried or never smoked a whole cigarette (for children) and never smoking or quitting >12 months ago (for adults) as 1 of the 7 components of ideal cardiovascular health.2 According to NHANES 2011 to 2012 data, 87.1% of adolescents and 77.8% of adults met these criteria.

Prevalence

Youth

(See Charts 3-1 and 3-2.)
In 2013, in grades 9 through 123:
—15.7% of students reported current cigarette use (on ≥1 day during the 30 days before the survey), 12.6% of students reported current cigar use, and 8.8% of students reported current smokeless tobacco use. Overall, 22.4% of students reported any current tobacco use (YRBS).
—Male students were more likely than female students to report current cigarette use (16.4% compared with 15.0%). Male students were also more likely than female students to report current cigar use (16.5% compared with 8.7%) and current smokeless tobacco use (14.7% compared with 2.9%; YRBS).4
—Non-Hispanic white students were more likely than Hispanic or non-Hispanic black students to report any current tobacco use, which includes cigarettes, cigars, or smokeless tobacco (26.9% compared with 18.0% for Hispanic students and 14.3% for non-Hispanic black students; YRBS).
Among youths 12 to 17 years of age in 2012, 2.2 million (8.6%) used a tobacco product (cigarettes, cigars, or smokeless tobacco) in the past month, down from 10.0% in 2011. Cigarette use in the past month in this age group declined significantly from 12.2% in 2003 to 6.6% (1.6 million smokers) in 2012 (NSDUH).5
Smoking among adolescent males declined from 12.5% in 2003 to 6.8% in 2012 and for females from 11.9% to 6.3% over the same time period (NSDUH).5
Data from the YRBS in 2013 for students in grades 9 to 12 indicated the following3:
—The percentage of students who reported ever smoking cigarettes remained stable from 1991 to 1999 and then declined from 70.4% in 1999 to 41.1% in 2013.
—The percentage who reported current cigarette use (on at least 1 day in the 30 days before the survey) increased between 1991 and 1997 and then declined from 36.4% in 1997 to 15.7% in 2013.
—The percentage who reported current frequent cigarette use (smoked on ≥20 of the 30 days before the survey) increased from 1991 to 1997 and then declined from 16.7% in 1997 to 5.6% in 2013.
—48.0% of students in grades 9 to 12 who currently smoked cigarettes had tried to quit smoking cigarettes during the previous 12 months. The prevalence of trying to quit smoking was higher among female student smokers (51.0%) than among male student smokers (45.4%). Six of 10 black student smokers (61.0%) tried to quit compared with 48.0% of white students and 42.4% of Hispanic students (YRBS).
Abbreviations Used in Chapter 3
AHAAmerican Heart Association
AIANAmerican Indian or Alaska Native
AMIacute myocardial infarction
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DMdiabetes mellitus
FDAUS Food and Drug Administration
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
NSDUHNational Survey on Drug Use and Health
RRrelative risk
WHOWorld Health Organization
YRBSYouth Risk Behavior Survey

Adults

(See Table 3-1 and Charts 3-2 and 3-3.)
In 2013, among adults ≥18 years of age:
—20.4% of men and 15.5% of women were current cigarette smokers (NHIS).6
—The percentage of current cigarette smokers (17.9%) declined 26% since 1998 (24.1%).6,7
In 2012, the states with the highest percentage of current cigarette smokers were Kentucky (28.3%), West Virginia (28.2%), and Arkansas (25.0%). Utah had the lowest percentage of smokers (10.6%) (BRFSS).8
In 2012, an estimated 69.5 million Americans ≥12 years of age were current (past month) users of a tobacco product (cigarettes, cigars, smokeless tobacco, or tobacco in pipes). The rate of current use of any tobacco product in this age range declined from 2007 to 2012 (from 28.6% to 26.7%; NSDUH).5
From 1998 to 2007, cigarette smoking prevalence among adults ≥18 years of age decreased in 44 states and the District of Columbia. Six states had no substantial changes in prevalence after controlling for age, sex, and race/ethnicity (BRFSS).9
On the basis of age-adjusted estimates in 2010 to 2012, among people ≥65 years of age, 9.2% of men and 8.0% of women were current smokers. In this age group, men were more likely than women to be former smokers (52.4% compared with 31.5%) (NHIS).10
In 2013, among adults ≥18 years of age, Asian men (14.7%) and Hispanic men (16.6%) were less likely to be current cigarette smokers than non-Hispanic white men (21.7%), non-Hispanic black men (21.1%), and American Indian or Alaska Native men (25.7%) on the basis of age-adjusted estimates (NHIS). Similarly, in 2013, Asian women (4.8%) and Hispanic women (6.7%) were less likely to be current cigarette smokers than non-Hispanic black women (15.0%), non-Hispanic white women (18.7%), and American Indian or Alaska Native women (16.7%; NHIS).6
Smoking among 18- to 44-year-old males declined from 27.9% in 2003 to 22.9% in 2013, and for 18- to 44-year-old females, smoking declined from 22.5% to 16.6% over the same time period (NHIS).6
In 2011 to 2012, among women 15 to 44 years of age, past-month cigarette use was lower among those who were pregnant (15.9%) than among those who were not pregnant (24.6%). Rates were higher among women 18 to 25 years of age (20.9% versus 28.2% for pregnant and nonpregnant women, respectively) than among women 26 to 44 years of age (12.5% versus 25.2%, respectively; NSDUH).5

Incidence

In 20125:
—Approximately 2.3 million people ≥12 years of age smoked cigarettes for the first time within the past 12 months, which was similar to the estimate in 2011. The 2012 estimate averages out to ≈6300 new cigarette smokers every day. Half of new smokers (51.4%) in 2012 were <18 years of age when they first smoked cigarettes (NSDUH).
—The number of new smokers <18 years of age (1.2 million) was similar to that in 2002 (1.3 million); however, new smokers ≥18 years of age increased from ≈600 000 in 2002 to 1.1 million in 2012 (NSDUH).
—Among people 12 to 49 years of age who had started smoking within the past 12 months, the average age of first cigarette use was 17.8 years, similar to the average in 2011 (17.2 years).

Morbidity

A 2010 report of the US Surgeon General on how tobacco causes disease summarized an extensive body of literature on smoking and CVD and the mechanisms through which smoking is thought to cause CVD.11 There is a sharp increase in CVD risk with low levels of exposure to cigarette smoke, including secondhand smoke, and a less rapid further increase in risk as the number of cigarettes per day increases.
Smoking is an independent risk factor for CHD and appears to have a multiplicative effect with the other major risk factors for CHD: high serum levels of lipids, untreated hypertension, and DM.11
A meta-analysis comparing pooled data of ≈2.4 million smokers and nonsmokers found the RR ratio of smokers to nonsmokers for developing CHD was 25% higher in women than in men (95% CI, 1.12–1.39).12
Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.13,14
Tobacco exposure is a top risk factor for disability in the United States, second only to dietary risks.15

Mortality

Annually from 2005 to 2009, smoking was responsible for >480 000 premature deaths in the United States among those ≥35 years of age. Furthermore, almost one third of deaths of CHD are attributable to smoking and secondhand smoke exposure.16
Each year from 2005 to 2009, an estimated 41 000 US deaths were attributable to exposure to secondhand smoke among those ≥35 years of age.16
In 2009, smoking was estimated to cause 3.3 million years of potential life lost for males and 2.2 million years for females, excluding deaths attributable to smoking-attributable residential fires and adult deaths attributable to secondhand smoke.16
From 2005 to 2009, smoking during pregnancy resulted in an estimated 970 infant deaths annually.16
On average, male smokers die 13.2 years earlier than male nonsmokers, and female smokers die 14.5 years earlier than female nonsmokers.1
In 2010, tobacco smoking was the second-leading risk factor for death in the United States, after dietary risks.15
Overall mortality among US smokers is 3 times higher than that for never-smokers.17
If current smoking trends continue, 5.6 million U.S. children will die prematurely during adulthood of smoking.16

Smoking Cessation

Smoking cessation reduces the risk of cardiovascular morbidity and mortality for smokers with and without CHD.
—There is no convincing evidence to date that reducing the amount smoked by smoking fewer cigarettes per day reduces the risk of CVD, although in several studies a dose-response relationship has been seen among current smokers between the number of cigarettes smoked per day and CVD incidence.11
Quitting smoking at any age significantly lowers mortality from smoking-related diseases, and the risk declines more the longer the time since quitting smoking.18 Cessation appears to have both short-term (weeks to months) and long-term (years) benefits for lowering CVD risk. Overall, risk appears to approach that of nonsmokers after ≈10 years of cessation.
Smokers who quit smoking at 25 to 34 years of age gained 10 years of life compared with those who continued to smoke. Those aged 35 to 44 years gained 9 years and those aged 45 to 54 years gained 6 years of life, on average, compared with those who continued to smoke.17
In 2010, 48.3% of adult current smokers ≥18 years of age who had a health checkup during the preceding year reported that they had been advised to quit. Smokers between 18 and 24 (31%) and 24 to 44 (44%) years of age were less likely to be advised to quit than those at older ages (57%; NHIS).19
Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.20
In addition to medications, smoke-free policies, increases in tobacco prices, cessation advice from healthcare professionals, and quitlines and other counseling have contributed to smoking cessation.19
In 2010, 52.4% of adult smokers reported trying to quit smoking in the past year; 6.2% reported they recently quit smoking. Of those who tried to quit smoking, 30.0% used cessation medications.19

Electronic Cigarettes

Electronic nicotine delivery systems, more commonly called electronic cigarettes or e-cigarettes, are battery-operated devices that deliver nicotine, flavors, and other chemicals to the user in an aerosol. Although e-cigarettes were introduced less than a decade ago, there are currently >250 e-cigarette brands on the market, and sales in the United States were projected to be $1.7 billion in 2013.21,22
Because these products have not been well studied, their risks and benefits are not fully understood. Specifically, the health risks from the inhaled nicotine and other chemicals in e-cigarettes are not entirely known. E-cigarettes may play a beneficial role in helping smokers reduce or eliminate their conventional cigarette habit. However, there are concerns that e-cigarettes may be a gateway to tobacco use by nonsmokers, especially teenagers. Furthermore, many public health advocates are worried that e-cigarettes will reverse decades of efforts to denormalize smoking, which contributed to the decline in smoking.16,21,22
The answers to some of these questions may become clearer as the regulatory oversight of e-cigarettes becomes more defined.16 Currently, only e-cigarettes that are marketed for therapeutic purposes are regulated by the FDA, but in April 2014, the FDA proposed extending its tobacco product authorities to include e-cigarettes.23

Secondhand Smoke

Data from the US Surgeon General on the consequences of secondhand smoke indicate the following:
—Nonsmokers who are exposed to secondhand smoke at home or at work increase their risk of developing CHD by 25% to 30%.11
—Short exposures to secondhand smoke can cause blood platelets to become stickier, damage the lining of blood vessels, and decrease coronary flow velocity reserves, potentially increasing the risk of an AMI.11
—Exposure to secondhand smoke increases the risk of stroke by 20% to 30%.16
—Nearly 34 000 premature deaths of heart disease occur each year in the United States among nonsmokers.16
In 2008, data from 11 states showed that the majority of people surveyed in each state reported having smoke-free home rules, ranging from 68.8% in West Virginia to 85.6% in Arizona (BRFSS).24
As of January 2, 2014, 25 states and the District of Columbia had laws that prohibited smoking in indoor areas of worksites, restaurants, and bars; no states had such laws in 2000. As of January 2, 2014, an additional 12 states had laws that prohibited smoking in 1 or 2 but not all 3 venues.25
In 2012, 30 of the 50 largest US cities prohibited indoor smoking in private workplaces, either through state or local ordinances.26
Pooled data from 17 studies in North America, Europe, and Australasia suggest that smoke-free legislation can reduce the incidence of acute coronary events by 10%.27
The percentage of the US nonsmoking population with serum cotinine ≥0.05 ng/mL declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008, with declines occurring for both children and adults. During 2007 to 2008, the percentage of nonsmokers with detectable serum cotinine was 53.6% for those 3 to 11 years of age, 46.5% for those 12 to 19 years of age, and 36.7% for those ≥20 years of age. The percentage was also higher for non-Hispanic blacks (55.9%) than for non-Hispanic whites (40.1%) and Mexican Americans (28.5%; NHANES).28

Cost

Each year from 2005 to 2009, US smoking-attributable economic costs were between $289 billion and $333 billion, including $133 billion to $176 billion for direct medical care of adults and $151 billion for lost productivity related to premature death.16
In 2008, $9.94 billion was spent on marketing cigarettes in the United States.29
Cigarette prices in the United States have increased 283% between the early 1980s and 2011, in large part because of excise taxes on tobacco products. Higher taxes have decreased cigarette consumption, which fell from ≈30 million packs sold in 1982 to ≈14 million packs sold in 2011.29

Global Burden of Smoking

Worldwide, tobacco smoking (including secondhand smoke) was 1 of the top 3 leading risk factors for disease and contributed to an estimated 6.2 million deaths in 2010.30
To help combat the global problem of tobacco exposure, in 2003 the WHO adopted the Framework Convention on Tobacco Control treaty. The WHO Framework Convention on Tobacco Control contains a set of universal standards to limit tobacco supply and demand worldwide. These standards include the use of tax policies to reduce tobacco consumption, a ban on the indoor use of tobacco products, implementation of educational programs about the dangers of tobacco use, and restrictions of the sale of tobacco products to international travelers. Since it came into force in 2005, >175 countries have ratified the WHO Framework Convention on Tobacco Control.31

References

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Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613.
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Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, Olsen EO, McManus T, Chyen D, Whittle L, Taylor E, Demissie Z, Brener ND, Thornton J, Moore J, Zaza S. Youth risk behavior surveillance: United States, 2013 [published correction appears in MMWR Morb Wkly Rep. 2014;63:576]. MMWR Surveill Summ. 2014;63:1–168.
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Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H. Youth risk behavior surveillance: United States, 2011. MMWR Surveill Summ. 2012;61:1–162.
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Centers for Disease Control and Prevention (CDC). State-specific prevalence and trends in adult cigarette smoking: United States, 1998–2007. MMWR Morb Mortal Wkly Rep. 2009;58:221–226.
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Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, Creager MA, Culebras A, Eckel RH, Hart RG, Hinchey JA, Howard VJ, Jauch EC, Levine SR, Meschia JF, Moore WS, Nixon JV, Pearson TA; on behalf of the American Heart Association Stroke Council, Council on Cardiovascular Nursing, Council on Epidemiology and Prevention, Council for High Blood Pressure Research, Council on Peripheral Vascular Disease, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published correction appears in Stroke. 2011;42:e26]. Stroke. 2011;42:517–584.
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Shah RS, Cole JW. Smoking and stroke: the more you smoke the more you stroke. Expert Rev Cardiovasc Ther. 2010;8:917–932.
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Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, McAfee T, Peto R. 21st-Century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368:341–350.
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Thun MJ, Carter BD, Feskanich D, Freedman ND, Prentice R, Lopez AD, Hartge P, Gapstur SM. 50-Year trends in smoking-related mortality in the United States. N Engl J Med. 2013;368:351–364.
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Centers for Disease Control and Prevention. Quitting smoking among adults: United States, 2001–2010. MMWR Morb Mortal Wkly Rep. 2011;60:1513–1519.
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Benowitz NL, Goniewicz ML. The regulatory challenge of electronic cigarettes. JAMA. 2013;310:685–686.
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Fairchild AL, Bayer R, Colgrove J. The renormalization of smoking? E-cigarettes and the tobacco “endgame” [published correction appears in N Engl J Med. 2014;370:2354]. N Engl J Med. 2014;370:293–295.
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Centers for Disease Control and Prevention (CDC). State-specific secondhand smoke exposure and current cigarette smoking among adults: United States, 2008. MMWR Morb Mortal Wkly Rep. 2009;58:1232–1235.
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Centers for Disease Control and Prevention (CDC). State smoke-free laws for worksites, restaurants, and bars: United States, 2000–2010. MMWR Morb Mortal Wkly Rep. 2011;60:472–475.
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Centers for Disease Control and Prevention. Comprehensive smoke-free laws: 50 largest U.S. cities, 2000 and 2012. MMWR Morb Mortal Wkly Rep. 2012;61:914–917.
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Mackay DF, Irfan MO, Haw S, Pell JP. Meta-analysis of the effect of comprehensive smoke-free legislation on acute coronary events. Heart. 2010;96:1525–1530.
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Centers for Disease Control and Prevention (CDC). Vital signs: nonsmokers’ exposure to secondhand smoke: United States, 1999–2008. MMWR Morb Mortal Wkly Rep. 2010;59:1141–1146.
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US Department of Health and Human Services. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012. http://www.surgeongeneral.gov/library/reports/preventing-youth-tobacco-use/prevent_youth_by_section.html. Accessed May 30, 2012.
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Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A, Bell ML, Blore JD, Blyth F, Bonner C, Borges G, Bourne R, Boussinesq M, Brauer M, Brooks P, Bruce NG, Brunekreef B, Bryan-Hancock C, Bucello C, Buchbinder R, Bull F, Burnett RT, Byers TE, Calabria B, Carapetis J, Carnahan E, Chafe Z, Charlson F, Chen H, Chen JS, Cheng AT, Child JC, Cohen A, Colson KE, Cowie BC, Darby S, Darling S, Davis A, Degenhardt L, Dentener F, Des Jarlais DC, Devries K, Dherani M, Ding EL, Dorsey ER, Driscoll T, Edmond K, Ali SE, Engell RE, Erwin PJ, Fahimi S, Falder G, Farzadfar F, Ferrari A, Finucane MM, Flaxman S, Fowkes FG, Freedman G, Freeman MK, Gakidou E, Ghosh S, Giovannucci E, Gmel G, Graham K, Grainger R, Grant B, Gunnell D, Gutierrez HR, Hall W, Hoek HW, Hogan A, Hosgood HD, Hoy D, Hu H, Hubbell BJ, Hutchings SJ, Ibeanusi SE, Jacklyn GL, Jasrasaria R, Jonas JB, Kan H, Kanis JA, Kassebaum N, Kawakami N, Khang YH, Khatibzadeh S, Khoo JP, Kok C, Laden F, Lalloo R, Lan Q, Lathlean T, Leasher JL, Leigh J, Li Y, Lin JK, Lipshultz SE, London S, Lozano R, Lu Y, Mak J, Malekzadeh R, Mallinger L, Marcenes W, March L, Marks R, Martin R, McGale P, McGrath J, Mehta S, Mensah GA, Merriman TR, Micha R, Michaud C, Mishra V, Mohd Hanafiah K, Mokdad AA, Morawska L, Mozaffarian D, Murphy T, Naghavi M, Neal B, Nelson PK, Nolla JM, Norman R, Olives C, Omer SB, Orchard J, Osborne R, Ostro B, Page A, Pandey KD, Parry CD, Passmore E, Patra J, Pearce N, Pelizzari PM, Petzold M, Phillips MR, Pope D, Pope CA, Powles J, Rao M, Razavi H, Rehfuess EA, Rehm JT, Ritz B, Rivara FP, Roberts T, Robinson C, Rodriguez-Portales JA, Romieu I, Room R, Rosenfeld LC, Roy A, Rushton L, Salomon JA, Sampson U, Sanchez-Riera L, Sanman E, Sapkota A, Seedat S, Shi P, Shield K, Shivakoti R, Singh GM, Sleet DA, Smith E, Smith KR, Stapelberg NJ, Steenland K, Stockl H, Stovner LJ, Straif K, Straney L, Thurston GD, Tran JH, Van Dingenen R, van Donkelaar A, Veerman JL, Vijayakumar L, Weintraub R, Weissman MM, White RA, Whiteford H, Wiersma ST, Wilkinson JD, Williams HC, Williams W, Wilson N, Woolf AD, Yip P, Zielinski JM, Lopez AD, Murray CJ, Ezzati M, AlMazroa MA, Memish ZA. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 [published corrections appear in Lancet. 2013;381:1276 and Lancet. 2013;381:628]. Lancet. 2012;380:2224–2260.
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World Health Organization. About the WHO Framework Convention on Tobacco Control. World Health Organization Web site. http://www.who.int/fctc/about/en/index.html. Accessed July 18, 2013.

4. Physical Inactivity

Chart 4-1. Prevalence of students in grades 9 to 12 who did not participate in ≥60 minutes of physical activity on any day in the past 7 days by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2013). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.7
Chart 4-2. Percentage of students in grades 9 to 12 who used a computer for ≥3 hours on an average school day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2013). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.7
Chart 4-3. Prevalence of students in grades 9 to 12 who met currently recommended levels of physical activity during the past 7 days by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2013). “Currently recommended levels” was defined as activity that increased their heart rate and made them breathe hard some of the time for a total of ≥60 minutes per day on 5 of the 7 days preceding the survey. NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.7
Chart 4-4. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines among adults ≥18 years of age by race/ethnicity and sex (National Health Interview Survey: 2013). NH indicates non-Hispanic. Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines recommend engaging in moderate leisure-time physical activity for ≥150 minutes per week or vigorous activity ≥75 minutes per week or an equivalent combination. Source: National Health Interview Survey 2013 (National Center for Health Statistics).9
Chart 4-5. Prevalence of children 12 to 15 years of age who had adequate levels of cardiorespiratory fitness, by sex and age (National Health and Nutrition Examination Survey, National Youth Fitness Survey: 2012). Source: Gahche et al.15
Table 4-1. Met 2008 Federal Aerobic and Strengthening PA Guidelines for Adults
Population GroupPrevalence, 2013 (Age ≥18 y), %
Both sexes20.9
Males24.9
Females17.0
NH white only22.7
NH black only17.7
Hispanic or Latino16.6
Asian only18.2
American Indian/Alaska Native only16.6
“Met 2008 federal PA guidelines for adults” is defined as engaging in ≥150 minutes of moderate or 75 minutes of vigorous aerobic leisure-time physical activity per week (or an equivalent combination) and engaging in leisure-time strengthening physical activity at least twice a week.
Data are age adjusted for adults ≥18 years of age.
NH indicates non-Hispanic; and PA, physical activity.
Source: National Health Interview Survey 2013 (National Center for Health Statistics).9
Physical inactivity is a major risk factor for CVD and stroke.1 PA is 1 of the AHA’s 7 components of ideal cardiovascular health for both children and adults.2 The AHA and 2008 federal guidelines on PA recommend that children get at least 60 minutes of PA daily (including aerobic and muscle- and bone-strengthening activity). The guidelines recommend that adults get at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic activity (or an equivalent combination) per week and perform muscle-strengthening activities at least 2 days per week (US Department of Health and Human Services). In 2011 to 2012, on the basis of survey interviews, 36.5% of children and 44.0% of adults met these criteria.
Not only does being physically active improve health, but being inactive is unhealthy.3 PA reduces premature mortality. In addition, PA improves risk factors for CVD (such as HBP and high cholesterol) and reduces the likelihood of diseases related to CVD, including CHD, stroke, type 2 DM, and sudden heart attacks (US Department of Health and Human Services). Benefits from PA are seen for all ages and groups, including older adults, pregnant women, and people with disabilities and chronic conditions. Therefore, the federal guidelines recommend being as physically active as abilities and conditions allow and increasing PA gradually.
Abbreviations Used in Chapter 4
AHAAmerican Heart Association
BMIbody mass index
BNPB-type natriuretic peptide
CARDIACoronary Artery Risk Development in Young Adults
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EFejection fraction
EPIC-NorfolkEuropean Prospective Investigation Into Cancer and Nutrition—Norfolk Cohort
FMDflow-mediated dilation
HbA1chemoglobin A1c
HBPhigh blood pressure
HDLhigh-density lipoprotein
HFheart failure
HRhazard ratio
LVleft ventricular
MImyocardial infarction
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
PAphysical activity
PADperipheral artery disease
PARpopulation attributable risk
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
WHIWomen’s Health Initiative
WHOWorld Health Organization
There are 4 dimensions of PA (mode or type, frequency, duration, and intensity) and 4 common domains (occupational, domestic, transportation, and leisure time). Historically, recommendations on PA for health purposes have focused on leisure-time activity. However, because all domains of PA could have an impact on health, and because an increase in 1 domain may sometimes be compensated for by a decrease in another domain, it is important to generate data on all dimensions and domains of PA.
There are 2 broad categories of methods to assess PA: (1) subjective methods that use questionnaires and diaries/logs and (2) objective methods that use wearable monitors (pedometers, accelerometers, etc). It is very important to keep in mind that the bulk of the data available linking inactivity/PA to cardiovascular outcomes has been obtained with the use of questionnaires. Thus, prevalence data on inactivity/PA must be interpreted with an understanding of the limitations of the tools that have been used to generate such data. Although any activity is better than none, the federal guidelines specify the suggested frequency, duration, and intensity of activity.
Studies that used both subjective and objective methods (such as wearable monitors, like pedometers or accelerometers) have found that there is marked discordance between reported and measured PA.4,5 Therefore, PA estimates based on participant report may overstate the level of PA. Furthermore, surveys often ask only about leisure-time PA; however, PA also may come from occupational, domestic, and transportation responsibilities. People who get a lot of PA from these other responsibilities may be less like to engage in leisure-time PA, and yet they may meet the federal PA guidelines.
Chronic physical inactivity contributes to a poor level of cardiorespiratory (or aerobic) fitness, which is a stronger predictor of adverse cardiometabolic and cardiovascular outcomes than traditional risk factors. Although both PA and cardiorespiratory fitness are inversely related to the risk of CVD and other clinical outcomes, they are in part distinct measures in the assessment of CVD risk.6 PA is a behavior that can potentially improve cardiorespiratory fitness. Although many studies have shown that increasing the amount and quality of PA can improve cardiorespiratory fitness, other factors can contribute, such as a genetic predisposition to perform aerobic exercise. Because cardiorespiratory fitness is directly measured and reflects both participation in PA and the state of physiological systems affecting performance, the relationship between cardiorespiratory fitness and clinical outcomes is stronger than the relationship of PA to a series of clinical outcomes.6 Unlike health behaviors such as PA and risk factors that are tracked by federally funded programs, there are no national data on cardiorespiratory fitness, and the development of a national cardiorespiratory fitness registry has been proposed.6 Such additional data on the cardiorespiratory fitness levels of Americans may give a fuller and more accurate picture of physical fitness levels.6

Prevalence

Youth

Inactivity
(See Chart 4-1.)
In 20137:
Nationwide, 15.2% of adolescents reported that they were inactive during the previous 7 days, as indicated by their response that they did not participate in ≥60 minutes of any kind of PA that increased their heart rate and made them breathe hard on any 1 of the previous 7 days.
Girls were more likely than boys to report inactivity (19.2% versus 11.2%).
The prevalence of inactivity was highest among black (27.3%) and Hispanic (20.3%) girls, followed by white girls (16.1%), black boys (15.2%), Hispanic boys (12.1%), and white boys (9.2%).
Television/Video/Computers
(See Chart 4-2.)
In 20137:
Nationwide, 41.3% of adolescents used a computer for activities other than school work (eg, videogames or other computer games) for ≥3 hours per day on an average school day.
The prevalence of using computers ≥3 hours per day was highest among black boys (51.9%) and black girls (46.6%), followed by Hispanic girls (44.8%), Hispanic boys (42.0%), white boys (39.1%), and white girls (35.6%).
32.5% of adolescents watched television for ≥3 hours per day.
The prevalence of watching television ≥3 hours per day was highest among black boys (55.3%) and girls (52.2%), followed by Hispanic girls (39.0%) and boys (36.5%) and white boys (25.7%) and girls (24.3%).
Increased television time has significant nutritional associations with weight gain (refer to Chapter 5, Nutrition).
Activity Recommendations
(See Chart 4-3.)
In 20137:
—The proportion of students who met activity recommendations of ≥60 minutes of PA on 7 days of the week was 27.1% nationwide and declined from 9th (30.4%) to 12th (24.3%) grades. At each grade level, the proportion was higher in boys than in girls.
—More high school boys (36.6%) than girls (17.7%) self-reported having been physically active ≥60 minutes per day on all 7 days; self-reported rates of activity were higher in white (28.2%) than in black (26.3%) or Hispanic (25.5%) adolescents.
—The proportion of students who participated in muscle-strengthening activities on ≥3 days of the week was 51.7% nationwide and declined from 9th (54.8%) to 12th (47.7%) grades. At each grade level, the proportion was higher in boys than in girls.
—More high school boys (61.8%) than girls (41.6%) self-reported having participated in muscle-strengthening activities on ≥3 days of the week; self-reported rates were higher in Hispanic (53.3%) than in white (52.4%) or black (48.8%) adolescents.
There was a marked discrepancy between the proportion of youth (ages 6–11 years) who reported engaging in ≥60 minutes of moderate to vigorous PA on most days of the week and those who actually engaged in moderate to vigorous PA for ≥60 minutes when activity was measured objectively with accelerometers (ie, portable motion sensors that record and quantify the duration and intensity of movements) in the NHANES 2003 to 2004 survey.4
On the basis of accelerometer counts per minute >2020, 42% of 6- to 11-year-olds accumulated ≥260 minutes of moderate to vigorous PA on ≥5 days per week, whereas only 8% of 12- to 15-year-olds and 7.6% of 16- to 19-year-olds achieved similar counts.4
More boys than girls met PA recommendations (≥60 minutes of moderate to vigorous activity on most days of the week) as measured by accelerometry.4
Structured Activity Participation
Despite recommendations from the National Association for Sport and Physical Education that schools should require daily physical education for students in kindergarten through 12th grade,8 only 29.4% of students attended physical education classes in school daily (34.9% of boys and 24.0% of girls).7
Physical education class participation declined from the 9th grade (47.8% for boys, 36.5% for girls) through the 12th grade (24.4% for boys, 16.1% for girls).7
Little more than half (54.0%) of high school students played on at least 1 school or community sports team in the previous year: 48.5% of girls and 59.6% of boys.7

Adults

Inactivity
According to 2013 data from the NHIS, in adults ≥18 years of age9:
30.5% do not engage in leisure-time PA (“no leisure-time PA/inactivity” refers to no sessions of light/moderate or vigorous PA of ≥10 minutes’ duration).
Inactivity was higher among women than men (32.3% versus 28.6%, age adjusted) and increased with age from 25.1% to 32.8%, 35.7%, and 51.9% among adults 18 to 44, 45 to 64, 65 to 74, and ≥75 years of age, respectively.
Non-Hispanic black and Hispanic adults were more likely to be inactive (38.8% and 39.7%, respectively) than were non-Hispanic white adults (27.0%) on the basis of age-adjusted estimates.
Activity Recommendations
(See Table 4-1 and Chart 4-4.)
According to 2013 data from the NHIS, in adults ≥18 years of age9:
—20.9% met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness.
—The age-adjusted proportion who reported engaging in moderate or vigorous PA that met the 2008 aerobic PA guidelines for Americans (≥150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination each week) was 50.0%; 54.2% of men and 46.1% of women met the recommendations. Age-adjusted prevalence was 53.4% for non-Hispanic whites, 41.4% for non-Hispanic blacks, and 42.9% for Hispanics.7
—The proportion of respondents who did not meet the federal aerobic PA guidelines increased with age from 43.1% of 18- to 44-year-olds to 72.4% of adults ≥75 years of age.
—Non-Hispanic black adults (58.6%) and Hispanic/Latino adults (57.1%) were more likely not to meet the federal aerobic PA guidelines than non-Hispanic white (46.6%) adults, according to age-adjusted estimates.
—The percentage of adults ≥25 years of age not meeting the full (aerobic and muscle-strengthening) federal PA guidelines was inversely associated with education; 66.6% of participants with no high school diploma, 57.2% of those with a high school diploma or a high school equivalency credential, 46.8% of those with some college, and 35.1% of those with a bachelor’s degree or higher did not meet the full federal PA guidelines.
—The proportion of adults ≥25 years of age who met the 2008 federal PA guidelines for aerobic activity was positively associated with education level: 61.2% of those with a college degree or higher met the PA guidelines compared with 30.7% of adults with less than a high school diploma.
The proportion of adults reporting levels of PA consistent with the 2008 Physical Activity Guidelines for Americans remains low and decreases with age.10,11 Thirty-three percent of respondents in a study examining awareness of current US PA guidelines had direct knowledge of the recommended dosage of PA (ie, frequency/duration).12
The percentage of adults reporting ≥150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination weekly decreased with age from 55.8% for adults 18 to 44 years of age to 27.4% for those ≥75 years of age, on the basis of the 2011 NHIS.11
The percentage of men who engaged in both leisure-time aerobic and strengthening activities decreased with age, from 39.8% at age 18 to 24 years to 11.1% at ≥75 years of age. The percentage of women who engaged in both leisure-time aerobic and strengthening activities also decreased with age, from 20.7% at age 18 to 24 years to 5.3% at ≥75 years of age, on the basis of the 2011 NHIS.11
Using PA recommendations that existed at the time of the survey, adherence to PA recommendations was much lower when based on PA measured by accelerometer in NHANES 2003 to 20044:
—Among adults 20 to 59 years of age, 3.8% of men and 3.2% of women met recommendations to engage in moderate to vigorous PA (accelerometer counts >2020/min) for 30 minutes (in sessions of ≥10 minutes) on ≥5 of 7 days.
—Among those ≥60 years of age, adherence was 2.5% in men and 2.3% in women.
Accelerometry data from NHANES 2003 to 2006 showed that men engaged in 35 minutes of moderate activity per day, whereas for women, it was 21 minutes. More than 75% of moderate activity was accumulated in 1-minute bouts. Levels of activity declined sharply after the age of 50 years in all groups.13
In a review examining self-reported versus actual measured PA (eg, accelerometers, pedometers, indirect calorimetry, doubly labeled water, heart rate monitor), 60% of respondents self-reported higher values of activity than what was measured by use of direct methods.14
Among men, self-reported PA was 44% greater than actual measured values; among women, self-reported activity was 138% greater than actual measured PA.14
The discrepancy between reported versus measured PA activity clearly indicates that the proportion of sufficiently active individuals is overestimated and that there is a need to monitor nationwide levels of measured PA.

Trends

Youth

(See Chart 4-5.)
In 20137:
—Among adolescents, a significant decrease occurred overall in the prevalence of having watched television for ≥3 hours per day compared with 1999 (42.8% versus 32.5%); however, the prevalence did not change from 2011 (32.4%) to 2013 (32.5%).
—Among students nationwide, there was a significant increase in the prevalence of having participated in muscle-strengthening activities on ≥3 days per week, from 47.8% in 1991 to 51.7%; however, the prevalence decreased from 2011 (55.6%) to 2013 (51.7%).
—A significant increase occurred in the prevalence of having used computers for ≥3 hours per day compared with 2003 (22.1% versus 41.3%). The prevalence increased from 2003 to 2009 (22.1% versus 24.9%) and then increased more rapidly from 2009 to 2013 (24.9% versus 41.3%). Even more recently, the prevalence increased to 31.1% in 2011.
—Among adolescents nationwide, the prevalence of attending physical education classes at least once per week did not increase significantly, from 25.4% in 1995 to 29.4%.
—The prevalence of adolescents playing ≥1 team sport in the past year decreased from 58.4% in 2011 to 54.0%.
In 2012, the prevalence of adolescents aged 12 to 15 years with adequate levels of cardiorespiratory fitness (based on age- and sex-specific standards) was 42.2% in 2012, down from 52.4% in 1999 to 2000.15

Adults

Between NHANES III (1988–1994) and NHANES 2001 to 2006, the non–age-adjusted proportion of adults who reported engaging in >12 bouts of PA per month declined from 57.0% to 43.3% in men and from 49.0% to 43.3% in women.16
The proportion of US adults who meet criteria for muscle strength has improved between 1998 and 2011. Annual estimates of the percentage of US adults who met the muscle-strengthening criteria increased from 17.7% in 1998 to 24.5% in 2011, and estimates of the percentage who met both the muscle-strengthening and aerobic criteria increased from 14.4% in 1998 to 21.0% in 2011.10,17
A 2.3% decline in physical inactivity between 1980 and 2000 was estimated to have prevented or postponed ≈17 445 deaths (≈5%) attributable to CHD in the United States.18

CVD and Metabolic Risk Factors

Youth

In 2011, more girls (67.9%) than boys (55.7%) reported having exercised to lose weight or to keep from gaining weight. White girls (72.2%) were more likely than black (54.2%) and Hispanic (66.3%) girls to report exercising to lose weight or to keep from gaining weight.19
Total and vigorous PA are inversely correlated with body fat and the prevalence of obesity.20
Among children 4 to 18 years of age, increased time in moderate to vigorous PA was associated with improvements in waist circumference, SBP, fasting triglycerides, HDL cholesterol, and insulin. These findings were significant regardless of the amount of the children’s sedentary time.21
Among children aged 4 to 18 years, both higher activity levels and lower sedentary time measured by accelerometry were associated with more favorable metabolic risk factor profiles.21

Adults

Participants in the Diabetes Prevention Program randomized trial who met the PA goal of 150 minutes of PA per week were 44% less likely to develop DM after 3.2 years of follow-up, even if they did not meet the weight-loss target.22
Exercise for weight loss, without dietary interventions, was associated with significant reductions in DBP (–2 mm Hg; 95% CI, –4 to –1 mm Hg), triglycerides (–0.2 mmol/L; 95% CI, –0.3 to –0.1 mmol/L), and fasting glucose (–0.2 mmol/L; 95% CI, –0.3 to –0.1 mmol/L).23
A total of 120 to 150 minutes per week of moderate-intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.24
In CARDIA, women who maintained high activity through young adulthood gained 6.1 fewer kilograms of weight and 3.8 fewer centimeters in waist circumference in middle age than those with lower activity. Highly active men gained 2.6 fewer kilograms and 3.1 fewer centimeters than their lower-activity counterparts.25
Self-reported low lifetime recreational activity has been associated with increased PAD.26
In 3 US cohort studies, men and women who increased their PA over time gained less weight in the long term, whereas those who decreased their PA over time gained more weight and those who maintained their current PA had intermediate weight gain.27
Among US men and women, every hour per day of increased television watching was associated with 0.3 lb of greater weight gain every 4 years, whereas every hour per day of decreased television watching was associated with a similar amount of relative weight loss.27
In a sample of 466 605 participants in the China Kadoorie Biobank study, a 1-SD (1.5 h/d) increase in sedentary time was associated with a 0.19-unit higher BMI, a 0.57-cm larger waist circumference, and 0.44% more body fat. Both sedentary leisure time and lower PA were independently associated with an increased BMI.28

Morbidity and Mortality

Physical inactivity is responsible for 12.2% of the global burden of MI after accounting for other CVD risk factors such as cigarette smoking, DM, hypertension, abdominal obesity, lipid profile, no alcohol intake, and psychosocial factors.29
In a meta-analysis of longitudinal studies among women, RRs of incident CHD were 0.83 (95% CI, 0.69–0.99), 0.77 (95% CI, 0.64–0.92), 0.72 (95% CI, 0.59–0.87), and 0.57 (95% CI, 0.41–0.79) across increasing quintiles of PA compared with the lowest quintile.30
A 2003 meta-analysis of 23 studies on the association of PA with stroke indicated that compared with low levels of activity, high (RR, 0.79; 95% CI, 0.69–0.91) and moderate (RR, 0.91; 95% CI, 0.80–1.05) levels of activity were inversely associated with the likelihood of developing total stroke (ischemic and hemorrhagic).31
With television watching as a sedentary activity, 2 hours of television per day is associated with an RR for type 2 DM of 1.20 (95% CI, 1.14–1.27), an RR for fatal or nonfatal CVD of 1.15 (95% CI, 1.06–1.23), and an RR for all-cause mortality of 1.13 (95% CI, 1.07–1.18). The risk for all-cause mortality further increases with >3 hours of television daily.32
Longitudinal studies commonly report a graded, inverse association of PA amount and duration (ie, dose) with incident CHD and stroke.33
The PA guidelines for adults cite evidence that ≈150 minutes per week of moderate-intensity aerobic activity, compared with none, can reduce the risk of CVD.34
Adherence to PA guidelines for both aerobic and muscle-strengthening activities is associated with 27% lower all-cause mortality among adults without existing chronic conditions such as DM, cancer, MI, angina, CVD, stroke, or respiratory diseases and with 46% lower mortality among people with chronic comorbidities.34
In the Health Professionals Follow-Up Study, for every 3-hour-per-week increase in vigorous-intensity activity, the multivariate RR of MI was 0.78 (95% CI, 0.61–0.98) for men. This 22% reduction of risk can be explained in part by beneficial effects of PA on HDL cholesterol, vitamin D, apolipoprotein B, and HbA1c.35
In a 20-year study of older male veterans, an inverse, graded, and independent association between impaired exercise capacity and all-cause mortality risk was found. For each increase of 1 metabolic equivalent tasks in exercise capacity, mortality risk was 12% lower (HR, 0.88; 95% CI, 0.86–0.90). Unfit individuals who improved their fitness status had a 35% lower mortality risk (HR, 0.65; 95% CI, 0.46–0.93) than those who remained unfit.36
In the EPIC-Norfolk study, men and women with abdominal obesity with features of the metabolic syndrome who reported themselves to be physically very active were characterized by a lower (≈50%) risk of CHD than sedentary abdominally obese subjects with the metabolic syndrome.37
In the WHI observational study (n=71 018), sitting for ≥10 h/d compared with ≤5 h/d was associated with increased CVD risk (HR, 1.18) in multivariable models that included PA. Low PA was also associated with higher CVD risk. It was concluded that both low PA and prolonged sitting augment CVD risk.38
In a study that prospectively assessed the association of continuous inactivity and of changes in sitting time for 2 years with subsequent long-term all-cause mortality, it was found that compared with people who remained consistently sedentary, the HRs for mortality were 0.91 in those who were newly sedentary, 0.86 in formerly sedentary individuals, and 0.75 in those who remained consistently nonsedentary. Thus, subjects who reduced their sitting time over 2 years experienced an immediate reduction in mortality.39
A meta-analysis of 17 eligible studies on PA in patients with DM revealed that the highest PA category in each study was associated with a lower RR (0.61) for all-cause mortality and CVD (0.71) than the lowest PA category. Although more PA was associated with larger reductions in future all-cause mortality and CVD, in patients with DM, any amount of habitual PA was better than inactivity.40
In a special issue of The Lancet on PA, it was reported that the prevalence of physical inactivity (35%) worldwide is now greater than the prevalence of smoking (26%). On the basis of the HRs associated with these 2 behaviors (1.57 for smoking and 1.28 for inactivity), it was concluded that the PAR was greater for inactivity (9%) than for smoking (8.7%). Thus, inactivity was estimated to be responsible for 5.3 million deaths compared with 5.1 million deaths for smoking.41

Secondary Prevention

PA improves inflammatory markers in people with existing stable CHD. After a 6-week training session, CRP levels declined by 23.7% (P<0.001), and plasma vascular cell adhesion molecule-1 levels declined by 10.23% (P<0.05); there was no difference in leukocyte count or levels of intercellular adhesion molecule-1.42
In a randomized trial of patients with PAD, supervised treadmill exercise training and lower-extremity resistance training were each associated with significant improvements in functional performance and quality of life compared with a usual-care control group. Exercise training was additionally associated with improved brachial artery FMD, whereas resistance training was associated with better stair-climbing ability versus control.43
On the basis of a meta-analysis of 34 randomized controlled trials, exercise-based cardiac rehabilitation after MI was associated with lower rates of reinfarction, cardiac mortality, and overall mortality.44
The benefit of intense exercise training for cardiac rehabilitation in people with HF was tested in a trial of 27 patients with stable, medically treated HF. Intense activity (an aerobic interval-training program 3 times per week for 12 weeks) was associated with a significant 35% improvement in LV EF and decreases in pro-BNP (40%), LV end-diastolic volume (18%), and LV end-systolic volume (25%) compared with control and endurance-training groups.45
Exercise training in patients with HF with preserved EF was associated with improved exercise capacity and favorable changes in diastolic function.46

Costs

The economic consequences of physical inactivity are substantial. In a summary of WHO data sources, the economic costs of physical inactivity were estimated to account for 1.5% to 3.0% of total direct healthcare expenditures in developed countries such as the United States.47
Interventions and community strategies to increase PA have been shown to be cost-effective in terms of reducing medical costs48:
—Nearly $3 in medical cost savings is realized for every $1 invested in building bike and walking trails.
—Incremental cost and incremental effectiveness ratios range from $14 000 to $69 000 per quality-adjusted life-year gained from interventions such as pedometer or walking programs compared with no intervention, especially in high-risk groups.

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Weintraub WS, Daniels SR, Burke LE, Franklin BA, Goff DC, Hayman LL, Lloyd-Jones D, Pandey DK, Sanchez EJ, Schram AP, Whitsel LP; on behalf of the American Heart Association Advocacy Coordinating Committee, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Arteriosclerosis, Thrombosis, and Vascular Biology, Council on Clinical Cardiology, and Stroke Council. Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association. Circulation. 2011;124:967–990.

5. Nutrition

Chart 5-1. Percentage of sodium from dietary sources in the United States, 2005 to 2006. Source: Applied Research Program, National Cancer Institute.125
Chart 5-2. Per capita calories consumed from different beverages by US adults (.19 years of age), 1965 to 2010. Source: Nationwide Food Consumption Surveys (1965, 1977.1978) and National Health and Nutrition Examination Survey (1988.2010), based on data from Duffey and Popkin18 and Kit et al.61 Data from 2010 were only analyzed for soda/cola and sweetened fruit drinks.
Chart 5-3. Total US food expenditures away from home and at home, 1977 and 2007. Data derived from Davis et al.58
Table 5-1. Dietary Consumption in 2009 to 2010 Among US Adults ≥20 Years of Age of Selected Foods and Nutrients Related to Cardiometabolic Health97–100
 NH White MenNH White WomenNH Black MenNH Black WomenMexican American MenMexican American Women
Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*
Foods
 Whole grains, servings/d0.9±0.75.81.0±0.75.00.8±1.03.70.8±0.84.20.6±0.22.90.5±0.51.4
 Total fruit, servings/d1.5±1.69.71.7±1.411.51.1±1.46.21.2±1.15.11.6±1.311.31.8±2.010.4
 Total fruits including 100% juices, servings/d2.0±1.916.12.2±1.618.32.1±1.717.42.1±1.518.72.4±1.722.62.6±2.223.0
 Total vegetables (starchy up to 3 cups/wk), servings/d2.4±1.68.52.7±1.611.41.7±0.92.11.9±1.05.82.0±1.04.12.4±1.48.5
 Total vegetables (starchy up to 3 cups/wk), including vegetable juices/sauces, servings/d2.6±1.69.92.9±1.712.41.7±1.02.42.0±1.16.72.3±1.04.72.6±1.49.8
 Fish and shellfish, servings/wk1.3±0.519.61.2±1.319.91.4±1.523.51.6±1.324.41.7±1.323.81.0±1.318.9
 Nuts, seeds, and beans, servings/wk4.1±4.333.74.0±3.836.12.8±4.023.53.2±3.326.85.9±4.144.54.4±2.341.2
 Processed meats, servings/wk2.6±1.153.81.7±1.368.42.4±0.659.61.7±1.169.61.5±1.271.00.9±1.282.5
 Sugar-sweetened beverages, servings/wk9.3±11.752.86.5±10.465.013.5±9.733.713.0±9.433.713.7±8.827.711.3±10.639.6
 Sweets and bakery desserts, servings/wk5.9±3.940.36.7±4.334.96.1±3.643.05.9±3.942.04.1±1.150.74.5±3.249.1
Nutrients
 Total calories, kcal/d2532±705NA1766±414NA2365±699NA1785±476NA2367±664NA1690±502NA
 EPA/DHA, g/d0.101±0.05210.10.095±0.0528.80.116±0.0699.70.110±0.06412.30.136±0.06413.40.083±0.0647.2
 ALA, g/d1.44±0.3129.31.60±0.3775.21.43±0.2530.61.49±0.1773.61.19±0.3317.81.41±0.3366.9
 n-6 PUFA, % energy7.4±1.5NA7.6±1.4NA7.4±1.2NA7.6±1.0NA6.3±1.4NA7,1±1.5NA
 Saturated fat, % energy11.1±2.335.410.9±2.141.910.2±2.244.910.5±1.845.59.7±1.854.49.7±1.657.9
 Dietary cholesterol, mg/d263±10671.5260±10471.1311±8356.2306±8357.8293±7565.6300±6063.0
 Total fat, % energy33.9±5.352.233.3±4.457.332.5±4.559.333.1±3.456.829.8±5.466.630.6±4.074.4
 Carbohydrate, % energy47.2±7.3NA50.1±6.6NA48.8±6.2NA51.1±5.4NA51.9±3.9NA54.3±5.6NA
 Dietary fiber, g/d16.3±6.16.418.3±6.311.713.6±4.32.215.0±5.24.819.2±5.913.219.6±5.313.0
 Sodium, g/d3.4±0.66.53.6±0.54.63.3±0.611.53.5±0.46.03.2±0.510.33.4±0.59.5
Data from the National Health and Nutrition Examination Survey 2009 to 2010, derived from two 24-hour dietary recalls per person, with population SDs adjusted for within-person vs between-person variation. All values are energy adjusted by individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d. Compared to prior AHA Statistical Updates, the calculations for foods now utilize the USDA Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats, etc) in mixed dishes; in addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of carbohydrate to fiber (analyses courtesy of Dr. Colin Rehm, Tufts University).
ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; NH, non-Hispanic; and n-6-PUFA, ω-6-polyunsaturated fatty acid.
*
All intakes and guidelines adjusted to 2000 kcal/d diet. Servings defined as follows: whole grains (1-oz equivalents), fruits and vegetables (1/2 cup equivalents), fish/shellfish (3.5 oz or 100 g), nuts/seeds/beans (50 g), processed meat (3.5 oz or 100 g), sugar-sweetened beverages (8 fl oz), sweets and bakery desserts (50 g). Foods and guidelines defined as follows: whole grains, 3 or more 1-oz equivalent (eg, 21 g whole wheat bread, 82 g cooked brown rice, 31 g Cheerios) servings per day (Dietary Guidelines for Americans123; fish or shellfish, 2 or more 100-g (3.5-oz) servings/wk123; fruits, 2 or more cups/d119; vegetables, 2 1/2 or more cups/d, including up to 3 cups/wk of starchy vegetables119; nuts, seeds, and beans, 4 or more 50-g servings/wk123 processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings/wk (1/4 of discretionary calories)119; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding whole 100% fruit juices), ≤36 oz/wk (≈1/4 of discretionary calories),119,123; sweets and bakery desserts, 2.5 or fewer 50-g servings/wk (≈1/4 of discretionary calories),119,123; EPA/DHA, ≥0.250 g/d124; ALA, ≥1.6/1.1 g/d (men/women)120; saturated fat, <10% energy; dietary cholesterol, <300 mg/d119; total fat, 20% to 35% energy119; dietary fiber, ≥28/d119; and sodium, <2.3 g/d.119
Table 5-2. Dietary Consumption in 2009 to 2010 Among US Children and Teenagers of Selected Foods and Nutrients Related to Cardiometabolic Health
 Boys (5–9 y)Girls (5–9 y)Boys (10–14 y)Girls (10–14 y)Boys (15–19 y)Girls (15–19 y)
Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*
Foods
 Whole grains, servings/d0.8±0.51.70.6±0.30.50.7±0.32.40.6±0.30.90.7±0.63.00.6±0.23.6
 Total Fruits, servings/d1.5±1.28.61.7±0.98.51.2±1.76.61.2±0.75.11.2±1.05.90.9±1.04.6
 Total fruit including 100% fruit juice, servings/d2.4±1.518.02.5±1.118.62.0±1.913.82.0±1.812.42.1±0.915.71.6±1.08.9
 Total vegetables (starchy up to 3 cups/wk), servings/d1.1±0.70.41.2±0.50.91.0±0.30.31.7±0.33.41.6±0.34.11.5±0.31.2
 Total vegetables (starchy up to 3 cups/wk), including vegetable juices/sauces servings/d1.2±0.70.81.3±0.41.01.2±0.40.31.8±0.43.61.7±0.24.11.7±0.22.0
 Fish and shellfish, servings/wk0.3±0.88.50.5±0.88.50.4±0.87.70.2±0.84.40.8±0.811.20.4±0.77.4
 Nuts, seeds, and beans, servings/wk2.4±2.123.22.5±1.121.32.3±1.722.02.4±1.721.22.6±0.425.23.0±2.025.8
 Processed meats, servings/wk1.9±1.163.71.4±0.271.12.4±1.457.72.0±1.458.42.2±0.661.81.4±0.675.1
 Sugar-sweetened beverages, servings/wk8.0±4.345.07.5±5.240.411.0±6.127.89.2±3.430.617.3±12.420.113.6±10.927.0
 Sweets and bakery desserts, servings/wk8.5±4.424.59.4±4.422.57.2±2.525.47.7±2.626.85.2±3.038.97.5±3.938.8
Nutrients
 Total calories, kcal/d1828±276NA1757±312NA2163±560NA1865±377NA2532±500NA1836±308NA
 EPA/DHA, g/d0.045±0.0493.20.051±0.0484.60.048±0.0492.50.039±0.0480.90.063±0.0526.20.058±0.0673.7
 ALA, g/d1.18±0.1610.71.24±0.1757.11.17±0.2412.91.33±0.2163.01.28±0.1622.31.37±0.3263.0
 n-6 PUFA, % energy6.6±1.2NA6.8±1.2NA6.7±0.9NA7.1±0.8NA6.9±0.5NA7.6±1.8NA
 Saturated fat, % energy11.3±1.731.011.2±1.133.211.3±0.937.811.4±1.633.511.0±1.434.710.7±1.540.2
 Dietary cholesterol, mg/d225±4680.6234±6475.3234±9082.9250±4875.2230±6882.2240±6775.8
 Total fat, % energy32.1±2.469.632.0±1.872.632.4±1.862.332.9±1.764.532.2±3.462.632.4±3.065.4
 Carbohydrate, % energy54.4±2.4NA54.8±2.2NA53.1±3.3NA53.1±3.3NA52.5±4.8NA53.2±3.8NA
 Dietary fiber, g/d14.7±3.51.915.4±3.51.513.9±2.60.514.7±3.20.713.9±2.80.714.1±4.72.2
 Sodium, g/d3.3±0.45.53.3±0.44.83.4±0.32.73.5±0.22.33.4±0.48.73.5±0.45.0
Data from the National Health and Nutrition Examination Survey 2009 to 2010, derived from two 24-hour dietary recalls per person, with population SDs adjusted for within-person vs between-person variation. All values are energy adjusted by individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d. Compared to prior AHA Statistical Updates, the calculations for foods now utilize the USDA Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats, etc) in mixed dishes; in addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of carbohydrate to fiber (analyses courtesy of Dr. Colin Rehm, Tufts University).
ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; and n-6-PUFA, ω-6-polyunsaturatedfatty acid.
*
All intakes and guidelines adjusted to 2000 kcal/d diet. Servings defined as follows: whole grains (1-oz equivalents), fruits and vegetables (1/2 cup equivalents), fish/shellfish (3.5 oz or 100 g), nuts/seeds/beans (50 g), processed meat (3.5 oz or 100 g), sugar-sweetened beverages (8 fl oz), sweets and bakery desserts (50 g). Foods and guidelines defined as follows: whole grains, 3 or more 1-oz equivalent (eg, 21 g whole wheat bread, 82 g cooked brown rice, 31 g Cheerios) servings per day (Dietary Guidelines for Americans123; fruits, 2 or more cups/d119; vegetables, 2 1/2 or more cups/d, including up to 3 cups/wk of starchy vegetables119; nuts, seeds, and beans, 4 or more 50-g servings/wk123; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings/wk (1/4 of discretionary calories)119; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding whole 100% fruit juices), ≤36 oz/wk (≈1/4 of discretionary calories)119,123; sweets and bakery desserts, 2.5 or fewer 50-g servings/wk (≈1/4 of discretionary calories)119,123; EPA/DHA, ≥0.250 g/d124; ALA, ≥1.6/1.1 g/d (men/women)120; saturated fat, <10% energy; dietary cholesterol, <300 mg/d119; total fat, 20% to 35% energy119; dietary fiber, ≥28/d119; and sodium, <2.3 g/d.119
This chapter of the Update highlights national dietary consumption data, focusing on key foods, nutrients, dietary patterns, and other dietary factors related to cardiometabolic health. It is intended to examine current intakes, trends and changes in intakes, and estimated effects on disease to support and further stimulate efforts to monitor and improve dietary habits in relation to cardiovascular health.

Prevalence of Specific Dietary Habits

Abbreviations Used in Chapter 5
ALAα-linoleic acid
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DALYdisability-adjusted life-year
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DHAdocosahexaenoic acid
DMdiabetes mellitus
EPAeicosapentaenoic acid
GFRglomerular filtration rate
GISSIGruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico
HDheart disease
HDLhigh-density lipoprotein
HEIHealthy Eating Index
HFheart failure
LDLlow-density lipoprotein
MImyocardial infarction
n-6-PUFAω-6-polyunsaturated fatty acid
NAnot available
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
ORodds ratio
PAphysical activity
PREDIMEDPrevención con Dieta Mediterránea
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
WHIWomen’s Health Initiative

Foods and Nutrients: Adults

(See Table 5-1 and Chart 5-1; NHANES 2009–2010.)
The dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-1 according to sex and race or ethnic subgroups. Compared to prior AHA Statistical Updates, the calculations for foods now utilize the USDA Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats, etc) in mixed dishes. In addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of carbohydrate to fiber.
Average consumption of whole grains was 0.9 to 1.0 servings per day by white men and women and 0.8 servings per day by black men and women, and 0.5 to 0.6 servings by Mexican American men and women. For each of these groups, less than 6% of adults meet guidelines of ≥3 servings per day.
Average fruit consumption ranged from 1.1 to 1.8 servings per day in these sex and race or ethnic subgroups: 10% to 12% of whites, 5% to 6% of blacks, and 10% to 11% of Mexican Americans met guidelines of ≥2 cups per day. When 100% fruit juices were included, the number of servings increased, and the proportions of adults consuming ≥2 cups per day approximately doubled in whites and Mexican Americans and tripled in blacks.
Average vegetable consumption ranged from 1.7 to 2.7 servings per day; 9% to 11% of whites, 2% to 6% of blacks, and 4 to 9% of Mexican Americans consumed ≥2.5 cups per day; with intakes higher in women than in men in each race/ethnicity subgroup. The inclusion of vegetable juices and sauces produced only modest increases in these consumption patterns.
Average consumption of fish and shellfish was lowest among Mexican American and white women (1.0 and 1.2 servings per week, respectively) and highest among black women and Mexican American men (1.6 and 1.7 servings per week, respectively); less than 1 in 4 of all adults in each sex and race or ethnic subgroup consumed at least 2 servings per week. Approximately 9% to 10% of whites, 10% to 12% of blacks, and 7% to 13% of Mexican Americans consumed ≥250 mg of eicosapentaenoic acid and docosahexaenoic acid per day.
Average consumption of nuts, seeds, and beans was ≈4.0 servings per week among whites, 3.0 servings per week among blacks, and 4 to 6 servings per week among Mexican Americans. Approximately 1 in 3 whites, 1 in 4 blacks, and 2 in 5 Mexican Americans met guidelines of ≥4 servings per week.
Average consumption of processed meats was lowest among Mexican American women (0.9 servings per week) and highest among white men (2.6 servings per week). Between 54% (white men) and 82% (Mexican American women) of adults consumed 2 or fewer servings per week.
Average consumption of sugar-sweetened beverages ranged from ≈6.5 servings per week among white women to nearly 14 servings per week among Mexican American men. Women generally consumed less than men. From 28% (Mexican American men) to 65% (white women) of adults consumed no more than 36 oz per week.
Average consumption of sweets and bakery desserts ranged from ≈4.5 servings per day (Mexican Americans) to 7 servings per day (white women). Approximately one third of white women and up to half of all other sex and race groups consumed no more than 2.5 servings per week.
Between 35% and 58% of adults in each sex and race or ethnic subgroup consumed <10% of total calories from saturated fat, and between 56% and 72% consumed <300 mg of dietary cholesterol per day.
Only 6% to 12% of whites, 2% to 5% of blacks, and 13% of Mexican Americans consumed ≥28 g of dietary fiber per day.
Only 5% to 7% of whites, 6% to 12% of blacks, and 10% of Mexican Americans consumed <2.3 g of sodium per day.
Average daily caloric intake in the United States was ≈2500 calories in adult men and 1800 calories in adult women.
Sodium is widespread in the US food supply, with diverse sources (Chart 5-1).

Foods and Nutrients: Children and Teenagers

(See Table 5-2; NHANES 2009–2010.)
The dietary consumption by US children and teenagers of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-2:
Average whole grain consumption was low, between 0.6 to 0.8 servings per day in all age and sex groups, with <4% of all children in different age and sex subgroups meeting guidelines of ≥3 servings per day.
Average fruit consumption was low and decreased with age: 1.5 to 1.7 servings per day in younger boys and girls (5–9 years of age), 1.2 servings per day in adolescent boys and girls (10–14 years of age), and 0.9 to 1.2 servings per day in teenage boys and girls (15–19 years of age). The proportion meeting guidelines of ≥2 cups per day was also low and decreased with age: about 8% to 9% in those 5 to 9 years of age, 5% to 7% in those 10 to 14 years of age, and 5% in those 15 to 19 years of age. When 100% fruit juices were included, the number of servings consumed approximately doubled, and proportions consuming ≥2 cups per day increased to nearly 1 in 5 of those 5 to 9 years of age and 1 in 7 of those 10 to 14 years and 15 to 19 years of age.
Average vegetable consumption was low, ranging from 1.1 to 1.7 servings per day, with <5% (and often <1%) of children in different age and sex subgroups meeting guidelines of ≥2.5 cups per day.
Average consumption of fish and shellfish was low, ranging between 0.3 and 0.8 servings per week in all age and sex groups. Among all ages, only 4% to 11% of youth consumed ≥2 servings per week.
Average consumption of nuts, seeds, and beans ranged from 2.3 to 3.0 servings per week among different age and sex groups. The distribution of consumption tended to be skewed to the right, and only between 1 in 4 and 1 in 5 of children in different age and sex subgroups consumed ≥4 servings per week.
Average consumption of processed meats ranged from 1.4 to 2.4 servings per week and was up to 10 fold higher than the average consumption of fish and shellfish. The distribution of consumption tended to be skewed to the right, and the majority of children consumed no more than 2 servings per week.
Average consumption of sugar-sweetened beverages was higher in boys than in girls and increased with age, from ≈7 to 8 servings (8 fl oz) per week in 5- to 9-year-olds, 9 to 11 servings per week in 10- to 14-year-olds, and 14 to 17 servings per week in 15- to 19-year-olds (each energy adjusted to 2000 kcal/d). This was generally considerably higher than the average consumption of whole grains, fruits, vegetables, fish and shellfish, or nuts, seeds, and beans. Less than half of children 5 to 9 years of age and only 1 in 5 boys 15 to 19 years of age consumed <4.5 servings per week.
Average consumption of sweets and bakery desserts was highest (≈9 servings per week) in 5- to 9-year-olds, about 8 servings per week in 10- to 14-year-olds, and 5 to 8 servings per week in 15- to 19-year-olds. Only about 1 in 4 children 5 to 14 years of age, and 1 in 3 youths 15 to 19 years of age, consumed no more than 2.5 servings per week.
Average consumption of eicosapentaenoic acid and docosahexaenoic acid was low, ranging from 39 to 63 mg/d in boys and girls at all ages. Fewer than 6% of children and teenagers at any age consumed ≥250 mg/d.
Average consumption of saturated fat was ≈11% of calories, and average consumption of dietary cholesterol ranged from 225 to 250 mg/d. Approximately 30% to 40% of youth consumed <10% energy from saturated fat, and >75% consumed <300 mg of dietary cholesterol per day.
Average consumption of dietary fiber ranged from 14 to 15 g/d. Less than 2% of children in all age and sex subgroups consumed ≥28 g/d.
Average consumption of sodium ranged from 3.3 to 3.5 g/d. Only between 2% and 9% of children in different age and sex subgroups consumed <2.3 g/d.
In children and teenagers, average daily caloric intake is higher in boys than in girls and increases with age in boys.

Dietary Patterns

In addition to individual foods and nutrients, overall dietary patterns can be used to assess more global dietary quality. Different dietary patterns have been defined, including the HEI, Alternative HEI, Western versus prudent dietary patterns, Mediterranean dietary pattern, and DASH-type diet. The higher-monounsaturated-fat DASH-type diet is generally similar to a traditional Mediterranean dietary pattern.1
In 1999 to 2004, only 19.4% of hypertensive US adults were following a DASH-type diet (based on intake of fiber, magnesium, calcium, sodium, potassium, protein, total fat, saturated fat, and cholesterol). This represented a decrease from 26.7% of hypertensive US adults in 1988 to 1994.2
Among older US adults (≥60 years of age) in 1999 to 2002, 72% met guidelines for dietary cholesterol intake, but only between 18% and 32% met guidelines for the HEI food groups (meats, dairy, fruits, vegetables, and grains). On the basis of the HEI score, only 17% of older US adults consumed a good-quality diet. Higher HEI scores were seen in white adults and individuals with greater education; lower HEI scores were seen in black adults and smokers.3

Dietary Supplements

Use of dietary supplements is common in the United States among both adults and children:
Approximately half of US adults in 2007 to 2010 used ≥1 dietary supplement, with the most common supplement being multivitamin-multimineral products (32% of men and women reporting use).4 It has been shown that most supplements are taken daily and for ≥2 years.5 Supplement use is associated with older age, higher education, greater PA, moderate alcohol consumption, lower BMI, abstinence from smoking, having health insurance, and white race.4,5 Previous research also suggests that supplement users have higher intakes of most vitamins and minerals from their food choices alone than nonusers.6,7 The primary reasons US adults in 2007 to 2010 reported for using dietary supplements were to “improve overall health” (45%) and to “maintain health” (33%).4
One third (32%) of US children (birth to 18 years of age) used dietary supplements in 1999 to 2002, with the highest use (48.5%) occurring among 4- to 8-year-olds. The most common supplements were multivitamins and multiminerals (58% of supplement users). The primary nutrients supplemented (either by multivitamins or individual vitamins) included vitamin C (29% of US children), vitamin A (26%), vitamin D (26%), calcium (21%), and iron (19%). Supplement use was associated with higher family income, a smoke-free home environment, lower child BMI, and less screen time (television, video games, or computers).8
In a 2005 to 2006 telephone survey of US adults, 41.3% were making or had made in the past a serious weight-loss attempt. Of these, one third (33.9%) had used a dietary supplement for weight loss, with such use being more common in women (44.9%) than in men (19.8%) and in blacks (48.7%) or Hispanics (41.6%) than in whites (31.2%); in those with high school education or less (38.4%) than in those with some college or more (31.1%); and in those with household income <$40 000 per year (41.8%) than in those with higher incomes (30.3%).9
Multiple trials of most dietary supplements, including folate, vitamin C, and vitamin E, have generally shown no significant benefits for CVD risk, and even potential for harm.10 For example, a multicenter randomized trial in patients with diabetic nephropathy found that B vitamin supplementation (folic acid 2.5 mg/d, vitamin B6 25 mg/d, and vitamin B12 1 mg/d) decreased GFR and increased risk of MI and stroke compared with placebo.11
Fish oil supplements at doses of 1 to 2 g/d have shown CVD benefits in 2 large randomized, open-label trials and 1 large randomized, placebo-controlled trial (GISSI-Prevenzione, Japan Eicosapentaenoic Acid Lipid Intervention Study, and GISSI-HF),12–14 but several other trials of fish oil have not shown significant effects on CVD risk.15 A meta-analysis of all randomized controlled clinical trials demonstrated a significant reduction for cardiac mortality but no statistically significant effects on other CVD end points.16

Trends in Energy Balance and Adiposity

(See Chapter 6 on Overweight and Obesity.)
The average US adult gains ≈1 lb per year. Energy balance, or consumption of total calories appropriate for needs, is determined by the balance of average calories consumed versus expended. This balance depends on multiple factors, including calories consumed, PA, body size, age, sex, and underlying basal metabolic rate. Thus, one individual may consume relatively high calories but have negative energy balance (as a result of even greater calories expended), whereas another individual may consume relatively few calories but have positive energy balance (because of low calories expended). Given such variation, the most practical and reasonable method to assess energy balance in populations is to assess changes in weight over time. Growing evidence indicates that, calorie for calorie, certain foods may be more highly obesogenic; others, modestly obesogenic; others, relatively neutral; and still others, actually protective against weight gain when their consumption is increased. These varying effects appear to relate to complex physiological responses to different foods and drinks, including responses related to hunger, satiety, brain reward, hepatic de novo lipogenesis, visceral adiposity, interactions with the intestinal inflammasome and microbiome, and metabolic expenditure (calories expended). This evidence is detailed below.
The US obesity epidemic began in approximately 1980, with dramatic increases in ensuing years in obesity compared to prior decades among both children and adults across broad cross sections of sex, race/ethnicity, geographic residence, and socioeconomic status. In more recent years, rates of obesity and overweight among both US adults and children have begun to level off.17 Examination of trends in diet, activity, and other factors from 1980 to the present is important to elucidate the drivers of this remarkably recent epidemic.
Until 1980, total energy intake remained relatively constant.18,19 Data from NHANES indicate that between 1971 and 2004, average total energy consumption among US adults increased by 22% in women (from 1542 to 1886 kcal/d) and by 10% in men (from 2450 to 2693 kcal/d).20 These increases are supported by data from 2 older surveys, the Nationwide Food Consumption Survey (1977–1978) and the Continuing Surveys of Food Intake (1989–1998).21 However, recent data show that energy intake appeared relatively stable among US adults during 1999 to 2008.22
Another analysis of national data estimated that increases in energy intake between 1980 and 1997 were primarily attributable to increases in dietary carbohydrate.23 Specifically, nearly 80% of the increase in total energy came from carbohydrates, 12% from protein, and only 8% from fat. These increases in calories were primarily attributable to greater refined carbohydrate intake, particularly of starches, refined grains, and sugars (see Trends in Specific Dietary Habits).
Other specific changes related to increased caloric intake in the United States since 1980 include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, and commercially prepared (especially fast-food) meals.21,24–29 In more recent years, intakes of sugar-sweetened beverages are decreasing nationally.
Between 1977 and 1996, the average portion sizes for many foods increased at fast-food outlets, other restaurants, and home. Based on one study, these included a 33% increase in the average portion of Mexican food (from 408 to 541 calories), a 34% increase in the average portion of cheeseburgers (from 397 to 533 calories), a 36% increase in the average portion of french fries (from 188 to 256 calories), and a 70% increase in the average portion of salty snacks such as crackers, potato chips, pretzels, puffed rice cakes, and popcorn (from 132 to 225 calories).21
In one analysis, among US children 2 to 7 years of age, an estimated energy imbalance of only 110 to 165 kcal/d (the equivalent of one 12- to 16-oz bottle of soda/cola) was sufficient to account for the excess weight gain between 1988 and 1994 and 1999 and 2002.30
In a quantitative analysis using various US surveys between 1977 and 2010, the relations of national changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.18,19 Changes in energy density were not consistently linked to energy intake over time, whereas increases in both portion size and number of eating occasions were linked to greater energy intake.
Among US children 2 to 18 years of age, increases in energy intake between 1977 and 2006 (179 kcal/d) were entirely attributable to substantial increases in energy eaten away from home (255 kcal/d).31 The percentage of energy eaten away from home increased from 23.4% to 33.9% during this time, with a shift toward energy from fast food as the largest contributor to foods away from home for all age groups.
A county-level investigation based on BRFSS and NHANES data found that prevalence of sufficient PA in the United States actually increased from 2001 to 2009 but that this was matched by increases in obesity in almost all counties during the same time period, with low correlation between level of PA and obesity in US counties.32

Determinants: Nutrients

For weight loss among overweight and obese individuals, low-carbohydrate, higher-fat diets achieve greater weight loss than low-fat, higher-carbohydrate diets.33
In ad libitum (not energy restricted) diets, intake of dietary sugars is positively linked to weight gain.34 However, isoenergetic exchange of dietary sugars with other carbohydrates had no relationship with body weight,34 which suggests that all refined carbohydrates may be similarly obesogenic.
In pooled analyses across 3 prospective cohort studies of US men and women, increased glycemic index and glycemic load were independently associated with greater weight gain over time.35
At the individual food level, energy density (total calories per gram of food) is not consistently linked with weight gain or obesity. For example, nuts have relatively high energy density and are inversely linked to weight gain, whereas sugar-sweetened beverages have low energy density and increase obesity. National changes in energy density over time are not consistently linked to changes in energy intake.

Determinants: Foods

In an analysis of >120 000 US men and women in 3 separate US cohorts followed up for up to 20 years, changes in intakes of different foods and beverages were linked to long-term weight gain in different ways.36 Foods and beverages most positively linked to weight gain included refined grains, starches, and sugars, including potatoes, white bread, white rice, low-fiber breakfast cereals, sweets/desserts, and sugar-sweetened beverages, as well as red and processed meats. In contrast, increased consumption of several other foods, including nuts, whole grains, fruits, vegetables, and yogurt, was linked to relative weight loss over time. These findings indicate that attention to dietary quality, not simply counting total calories, is crucial for energy balance.36
In both adults and children, intake of sugar-sweetened beverages has been linked to weight gain and obesity.37 Randomized trials in children demonstrate reductions in obesity when sugar-sweetened beverages are replaced with noncaloric beverages.37

Determinants: Mechanisms

Diet quality influences activation of brain reward centers, such as the nucleus accumbens. Isocaloric meals richer in rapidly digestible carbohydrate increased hunger and stimulated brain regions associated with reward and craving compared with isocaloric meals that had identical macronutrient content, palatability, and sweetness but were lower in rapidly digestible carbohydrate.38
Dietary factors that stimulate hepatic de novo lipogenesis, such as rapidly digestible grains, starches, and sugars, as well as trans fat, appear more strongly related to weight gain.36,39
In animal experiments, probiotics in yogurt alter gut immune responses and protect against obesity and nonalcoholic fatty acid liver disease.40–42
Diet quality may also influence energy expenditure. After intentional weight loss, isocaloric diets higher in fat and lower in rapidly digestible carbohydrates produced significantly smaller declines in total energy expenditure than low-fat, high-carbohydrate diets, with a mean difference of >300 kcal/d.43
Other possible nutritional determinants of positive energy balance (more calories consumed than expended), as determined by adiposity or weight gain, include larger portion sizes, skipping breakfast, consumption of fast food, and eating foods prepared outside the home, but evidence for relevance of these factors has been inconsistent.44–47

Determinants: Other

Although sedentary activity has been hypothesized to be linked to weight gain because of changes in metabolism, the strongest and most consistent associations are seen for television watching as opposed to other sedentary activities. In 2 randomized controlled trials, the effects of television watching on obesity were mediated by changes in diet rather than by changes in PA, which may be related to greater snacking/eating in front of the television, as well as the influence of television advertising on poor food choices overall.36,48–52
PA influences adiposity, as covered in Chapter 4 of this update.
Lower average sleep duration is consistently linked to greater adiposity in both children and adults, and short-term trials demonstrate effects of insufficient sleep on hunger, food choices, and leptin/ghrelin concentrations.53
Societal and environmental factors independently associated with diet quality, adiposity, and/or weight gain include education, income, race/ethnicity, and (at least cross-sectionally) availability of supermarkets.10,54,55
Other local food-environment characteristics, such as availability of grocery stores (ie, smaller stores than supermarkets), convenience stores, and fast food restaurants, are not consistently associated with diet quality or adiposity.56

Trends in Specific Dietary Habits

Several changes in foods and nutrients have occurred over time. Selected changes are highlighted below.

Trends in Nutrients

Starting in 1977 and continuing until the most recent dietary guidelines revision in 2010, a major focus of US dietary guidelines was reduction of dietary fats.57 During this time, average total fat consumption declined as a percent of calories from 36.9% to 33.4% in men and from 36.1% to 33.8% in women.20 However, more recent analyses show that there were no significant trends in total fat intake among US adults from 1999 to 2008.22
Dietary guidelines during this time also emphasized carbohydrate consumption as the base of one’s dietary pattern58 and more recently specified the importance of complex rather than refined carbohydrates (eg, as the base of the Food Guide Pyramid).57 From 1971 to 2004, total carbohydrate intake increased from 42.4% to 48.2% of calories in men and from 45.4% to 50.6% of calories in women.20 Evaluated as absolute intakes, the increase in total calories consumed during this period was attributable primarily to the greater consumption of carbohydrates, both as foods (starches and grains) and as beverages.59,60 However, more recent analyses show that there has been a decrease in carbohydrate intake (expressed as percentage of energy) among US adults from 1999 to 2008.22

Trends in Sugar-Sweetened Beverages

(See Chart 5-2.)
Between 1965 and 2002, the average percentage of total calories consumed from beverages in the United States increased from 11.8% to 21.0% of energy, which represents an overall absolute increase of 222 kcal/d per person.28 This increase was largely caused by increased consumption of sugar-sweetened beverages and alcohol: Average consumption of fruit juices went from 20 to 39 kcal/d; of milk, from 125 to 94 kcal/d; of alcohol, from 26 to 99 kcal/d; of sweetened fruit drinks, from 13 to 38 kcal/d; and of soda/cola, from 35 to 143 kcal/d.18
In addition to increased overall consumption, the average portion size of a single sugar-sweetened beverage increased by >50% between 1977 and 1996, from 13.1 to 19.9 fl oz.21
Among children and teenagers (2–19 years of age), the largest increases in consumption of sugar-sweetened beverages between 1988 to 1994 and 1999 to 2004 were seen among black and Mexican American youths compared with white youths.29
In contrast, between 1999 and 2010, sugar-sweetened beverage intake decreased among both youth and adults in the United States, consistent with increased attention to their importance as a cause of obesity. In 2009 to 2010, youth and adults consumed a daily average of 155 and 151 kcal from sugar-sweetened beverages, respectively, a decrease from 1999 to 2000 of 68 and 45 kcal/d, respectively.61 This reduction parallels the plateau of the obesity epidemic in US youth.17

Trends in Fruits and Vegetables

Between 1994 and 2005, the average consumption of fruits and vegetables declined slightly, from a total of 3.4 to 3.2 servings per day. The proportions of men and women consuming combined fruits and vegetables ≥5 times per day were low (≈20% and 29%, respectively) and did not change during this period.62

Morbidity and Mortality

Effects on Cardiovascular Risk Factors and Type 2 DM

Dietary habits affect multiple cardiovascular risk factors, including both established risk factors (SBP, DBP, LDL cholesterol levels, HDL cholesterol levels, glucose levels, and obesity/weight gain) and novel risk factors (eg, inflammation, cardiac arrhythmias, endothelial cell function, triglyceride levels, lipoprotein[a] levels, and heart rate):
A DASH dietary pattern with low sodium reduced SBP by 7.1 mm Hg in adults without hypertension and by 11.5 mm Hg in adults with hypertension.63
Compared with the low-fat DASH diet, DASH-type diets that increased consumption of either protein or unsaturated fat had similar or greater beneficial effects on CVD risk factors. Compared with a baseline usual diet, each of the DASH-type diets, which included various percentages (27%–37%) of total fat and focused on whole foods such as fruits, vegetables, whole grains, and fish, as well as potassium and other minerals and low sodium, reduced SBP by 8 to 10 mm Hg, DBP by 4 to 5 mm Hg, and LDL cholesterol by 12 to 14 mg/dL. The diets that had higher levels of protein and unsaturated fat also lowered triglyceride levels by 16 and 9 mg/dL, respectively.64 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the low-fat/high-carbohydrate DASH diet.65
In a meta-analysis of 60 randomized controlled feeding trials, consumption of 1% of calories from saturated fat in place of carbohydrate raised LDL cholesterol concentrations but also raised HDL cholesterol and lowered triglycerides, with no significant effects on apolipoprotein B concentrations.66
In a meta-analysis of randomized controlled trials, consumption of 1% of calories from trans fat in place of saturated fat, monounsaturated fat, or polyunsaturated fat, respectively, increased the ratio of total to HDL cholesterol by 0.031, 0.054, and 0.67; increased apolipoprotein B levels by 3, 10, and 11 mg/L; decreased apolipoprotein A-1 levels by 7, 5, and 3 mg/L; and increased lipoprotein(a) levels by 3.8, 1.4, and 1.1 mg/L.67
In meta-analyses of randomized controlled trials, consumption of eicosapentaenoic acid and docosahexaenoic acid for 212 weeks lowered SBP by 2.1 mm Hg68 and lowered resting heart rate by 2.5 beats per minute.69
In a pooled analysis of 25 randomized trials totaling 583 men and women both with and without hypercholesterolemia, nut consumption significantly improved blood lipid levels.70 For a mean consumption of 67 g of nuts per day, total cholesterol was reduced by 10.9 mg/dL (5.1%), LDL cholesterol by 10.2 mg/dL (7.4%), and the ratio of total cholesterol to HDL cholesterol by 0.24 (5.6% change; P<0.001 for each). Triglyceride levels were also reduced by 20.6 mg/dL (10.2%) in subjects with high triglycerides (2150 mg/dL). Different types of nuts had similar effects.70
A review of cross-sectional and prospective cohort studies suggests that higher intake of sugar-sweetened beverages is associated with greater visceral fat and higher risk of type 2 DM.71
In a randomized controlled trial, compared with a low-fat diet, 2 Mediterranean dietary patterns that included either virgin olive oil or mixed nuts lowered SBP by 5.9 and 7.1 mm Hg, plasma glucose by 7.0 and 5.4 mg/dL, fasting insulin by 16.7 and 20.4 pmol/L, the homeostasis model assessment index by 0.9 and 1.1, and the ratio of total to HDL cholesterol by 0.38 and 0.26 and raised HDL cholesterol by 2.9 and 1.6 mg/dL, respectively. The Mediterranean dietary patterns also lowered levels of CRP, interleukin-6, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1.72
Among 24 prospective cohort studies, greater consumption of refined carbohydrates and sugars, as measured by higher glycemic load, was positively associated with risk of type 2 DM: for each 100-g increment, 45% higher risk was seen (95% CI, 1.31–1.61) for a 100-g increment in glycemic load (P<0.001; n=24 studies, 7.5 million person-years of follow-up).73
In one meta-analysis of observational studies and trials, greater consumption of nuts was linked to lower incidence of type 2 DM (RR per 4 weekly 1-oz servings, 0.87; 95% CI, 0.81–0.94).74

Effects on Cardiovascular Outcomes

Because dietary habits affect a broad range of established and novel risk factors, estimation of the impact of nutritional factors on cardiovascular health by considering only a limited number of pathways (eg, only effects on lipids, BP, and obesity) will systematically underestimate or even misconstrue the actual total impact on cardiovascular health. Randomized controlled trials and prospective observational studies have been used to quantify the total effects of dietary habits on clinical outcomes.
Fats and Carbohydrates
In the WHI randomized clinical trial (n=48 835), reduction of total fat consumption from 37.8% energy (baseline) to 24.3% energy (at 1 year) and 28.8% energy (at 6 years) had no effect on incidence of CHD (RR, 0.98; 95% CI, 0.88–1.09), stroke (RR, 1.02; 95% CI, 0.90–1.15), or total CVD (RR, 0.98; 95% CI, 0.92–1.05) over a mean of 8.1 years.75 This was consistent with null results of 4 prior randomized clinical trials and multiple large prospective cohort studies that indicated little effect of total fat consumption on CVD risk.76
In 3 separate meta-analyses of prospective cohort studies, the largest of which included 21 studies with up to 2 decades of follow-up, saturated fat consumption overall had no significant association with incidence of CHD, stroke, or total CVD.77–79 In comparison, in a pooled individual-level analysis of 11 prospective cohort studies, the specific exchange of polyunsaturated fat consumption in place of saturated fat was associated with lower CHD risk, with 13% lower risk for each 5% energy exchange (RR, 0.87; 95% CI, 0.70–0.97).80 These findings are consistent with a meta-analysis of randomized controlled trials in which increased polyunsaturated fat consumption in place of saturated fat reduced CHD events, with 10% lower risk for each 5% energy exchange (RR, 0.90; 95% CI, 0.83–0.97).81
In a pooled analysis of individual-level data from 11 prospective cohort studies in the United States, Europe, and Israel that included 344 696 participants, each 5% higher energy consumption of carbohydrate in place of saturated fat was associated with a 7% higher risk of CHD (RR, 1.07; 95% CI, 1.01–1.14).80 Each 5% higher energy consumption of monounsaturated fat in place of saturated fat was not significantly associated with CHD risk.80 A more recent meta-analysis of prospective cohort studies found that increased intake of polyunsaturated fats was associated with lower risk of CHD, whether replacing saturated fat or carbohydrate.80a
Together these findings suggest that reducing saturated fat without specifying the replacement may have minimal effects on CHD risk, whereas increasing polyunsaturated fats from vegetable oils will reduce CHD, whether replacing saturated fat or carbohydrate.1
In a meta-analysis of prospective cohort studies, each 2% of calories from trans fat was associated with a 23% higher risk of CHD (RR, 1.23; 95% CI, 1.11–1.37).82
In meta-analyses of prospective cohort studies, greater consumption of refined complex carbohydrates, starches, and sugars, as assessed by glycemic index or load, was associated with significantly higher risk of CHD and DM. When the highest category was compared with the lowest category, risk of CHD was 36% greater (glycemic load: RR, 1.36; 95% CI, 1.13–1.63), and risk of DM was 40% greater (glycemic index: RR, 1.40; 95% CI, 1.23–1.59).83,84
Foods and Beverages
In meta-analyses of prospective cohort studies, each daily serving of fruits or vegetables was associated with a 4% lower risk of CHD (RR, 0.96; 95% CI, 0.93–0.99) and a 5% lower risk of stroke (RR, 0.95; 95% CI, 0.92–0.97).85,86
In a meta-analysis of prospective cohort studies, greater whole grain intake (2.5 compared with 0.2 servings per day) was associated with a 21% lower risk of CVD events (RR, 0.79; 95% CI, 0.73–0.85), with similar estimates in men and women and for various outcomes (CHD, stroke, and fatal CVD). In contrast, refined grain intake was not associated with lower risk of CVD (RR, 1.07; 95% CI, 0.94–1.22).87
In a meta-analysis of 16 prospective cohort studies that included 326 572 generally healthy individuals in Europe, the United States, China, and Japan, fish consumption was associated with significantly lower risk of CHD mortality.88 Compared with no consumption, an estimated 250 mg of long-chain omega-3 fatty acids per day was associated with 35% lower risk of CHD death (P<0.001).
In a meta-analysis of prospective cohort and case-control studies from multiple countries, consumption of unprocessed red meat was not significantly associated with incidence of CHD. In contrast, each 50-g serving per day of processed meats (eg, sausage, bacon, hot dogs, deli meats) was associated with a higher incidence of CHD (RR, 1.42; 95% CI, 1.07–1.89).89
In a meta-analysis of prospective cohort studies that included 442 101 participants and 28 228 DM cases, unprocessed red meat consumption was associated with a higher risk of DM (RR, 1.19; 95% CI, 1.04–1.37, per 100 g/d). On a per g/d basis, risk of DM was nearly 7-fold higher for processed meat consumption (RR, 1.51; 95% CI, 1.25–1.83, per 50 g/d).90
In a meta-analysis of 6 prospective observational studies, nut consumption was associated with lower incidence of fatal CHD (RR per 4 weekly 1-oz servings, 0.76; 95% CI, 0.69–0.84) and nonfatal CHD (RR, 0.78; 95% CI, 0.67–0.92).74 Nut consumption was not significantly associated with stroke risk based on 4 studies.74
In a meta-analysis of 6 prospective observational studies, consumption of legumes (beans) was associated with lower incidence of CHD (RR per 4 weekly 100-g servings, 0.86; 95% CI, 0.78–0.94).74
Higher consumption of dairy or milk products is associated with lower incidence of DM and trends toward lower risk of stroke.70,83,84 The inverse associations with DM appear strongest for both yogurt and cheese.91
Dairy consumption is not significantly associated with higher or lower risk of CHD.78,92
Among 88 520 generally healthy women in the Nurses’ Health Study who were 34 to 59 years of age in 1980 and were followed up from 1980 to 2004, regular consumption of sugar-sweetened beverages was independently associated with higher incidence of CHD, with 23% and 35% higher risk with 1 and ≥2 servings per day, respectively, compared with <1 per month.93 Among the 15 745 participants in the ARIC study, the OR for developing CHD was 2.59 for participants who had a serum uric acid level >9.0 mg/dL and who drank >1 sugar-sweetened soda per day.94
Sodium and Potassium
Lower estimated consumption of dietary sodium was not associated with lower CVD mortality among adults 30 years of age and older with no history of CVD events in NHANES,95 although such findings may be limited by changes in behaviors that could result from underlying risk (reverse causation). In a post hoc analysis of the Trials of Hypertension Prevention, participants randomized to low-sodium interventions had a 25% lower risk of CVD (RR, 0.75; 95% CI, 0.57–0.99) after 10 to 15 years of follow-up after the original trials.96
In a meta-analysis of small randomized trials of sodium reduction of ≥6 months’ duration, nonsignificant trends were seen toward fewer CVD events in subjects with normal BP (RR, 0.71; 95% CI, 0.42–1.20; n=200 events) or hypertension (RR, 0.84; 95% CI, 0.57–1.23; n=93 events), but findings were not statistically significant, with relatively low statistical power because of the small numbers of events. Sodium restriction increased total mortality in trials of patients with CHF (RR, 2.59; 95% CI, 1.04–6.44), but these data were based on very few events (n=21 deaths).97
In a meta-analysis of 13 prospective cohorts that included 177 025 participants and >11 000 vascular events, higher sodium consumption was associated with greater risk of stroke (pooled RR, 1.23; 95% CI, 1.06–1.43; P=0.007) and a trend toward higher risk of CVD (1.14; 95% CI, 0.99–1.32; P=0.07). These associations were greater with larger differences in sodium intake and longer follow-up.98
In a meta-analysis of 15 prospective cohort samples that included 247 510 participants and 7066 strokes, 3058 CHD events, and 2497 total CVD events, each 1.64-g/d (42 mmol/d) higher potassium intake was associated with a 21% lower risk of stroke (RR, 0.79; 95% CI, 0.68–0.90) and trends toward lower risk of CHD and total CVD.99
Dietary Patterns
In a cohort of 380 296 US men and women, greater versus lower adherence to a Mediterranean dietary pattern, characterized by higher intakes of vegetables, legumes, nuts, fruits, whole grains, fish, and unsaturated fat and lower intakes of red and processed meat, was associated with a 22% lower cardiovascular mortality (RR, 0.78; 95% CI, 0.69–0.87).100 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke101 and for the DASH-type dietary pattern.102
In a cohort of 72 113 US female nurses, a dietary pattern characterized by higher intakes of vegetables, fruits, legumes, fish, poultry, and whole grains was associated with a 28% lower cardiovascular mortality (RR, 0.72; 95% CI, 0.60–0.87), whereas a dietary pattern characterized by higher intakes of processed meat, red meat, refined grains, french fries, and sweets/desserts was associated with a 22% higher cardiovascular mortality (RR, 1.22; 95% CI, 1.01–1.48).103 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.104–110
The observational findings for benefits of a healthy food–based dietary pattern have been confirmed in 2 randomized clinical trials, including a small secondary prevention trial in France among patients with recent MI111 and a large primary prevention trial in Spain among patients with CVD risk factors.112 The latter trial, PREDIMED, demonstrated a 30% reduction in the risk of stroke, MI, and death attributable to cardiovascular causes in those patients randomized to Mediterranean-style diets.
Impact on US Mortality
One report used consistent and comparable risk assessment methods and nationally representative data to estimate the impact of all major modifiable risk factors on mortality and morbidity in the United States in 1990 and in 2010.113 Suboptimal dietary habits were the leading cause of both mortality and DALY lost, exceeding even tobacco. In 2010, a total of 678 000 deaths of all causes were attributable to suboptimal diet.
A previous investigation reported the estimated mortality effects of several specific dietary risk factors in 2005 in the United States. High dietary salt consumption was estimated to be responsible for 102 000 annual deaths, low dietary omega-3 fatty acids for 84 000 annual deaths, high dietary trans fatty acids for 82 000 annual deaths, and low consumption of fruits and vegetables for 55 000 annual deaths.114

Cost

(See Chart 5-3.)
The US Department of Agriculture forecast that the Consumer Price Index for all food would increase 3.0% to 4.0% in 2013 as retailers continued to pass on higher commodity and energy costs to consumers in the form of higher retail prices. The Consumer Price Index for food increased 3.7% in 2011. Prices for foods eaten at home increased 4.8% in 2011, whereas prices for foods eaten away from home increased by 1.9%.115
The proportion of total US food expenditures for meals outside the home, as a share of total food dollars, increased from 27% in 1961 to 40% in 1981 to 49% in 2011.58
The proportion of sales of meals and snacks from fast-food restaurants compared with total meals and snacks away from home increased from 5% in 1958 to 29% in 1982 to 36% in 2011.115
As a proportion of income, food has become less expensive over time in the United States. As a share of personal disposable income, average (mean) total food expenditures by families and individuals have decreased from 22.3% (1949) to 18.1% (1961) to 14.9% (1981) to 11.3% (2011). For any given year, the share of disposable income spent on food is inversely proportional to absolute income. The share increases as absolute income levels decline.115
Among 153 forms of fruits and vegetables priced with 2008 Nielsen Homescan data, price and calorie per portion of 20 fruits and vegetables were compared with 20 common snack foods such as cookies, chips, pastries, and crackers. Average price per portion of fruits and vegetables was 31 cents with an average of 57 calories per portion, compared with 33 cents and 183 calories per portion for snack foods.115
An overview of the costs of various strategies for primary prevention of CVD determined that the estimated costs per year of life gained were between $9800 and $18 000 for statin therapy, ≈$1500 for nurse screening and lifestyle advice, $500 to $1250 for smoking cessation, and $20 to $900 for population-based healthy eating.116
Each year, >$33 billion in medical costs and $9 billion in lost productivity resulting from HD, cancer, stroke, and DM are attributed to poor nutrition.117–120
Two separate cost-effectiveness analyses estimated that population reductions in dietary salt would not only be cost-effective but actually cost-saving.121,122 In 1 analysis, a 1.2-g/d reduction in dietary sodium was projected to reduce US annual cases of incident CHD by 60 000 to 120 000, stroke by 32 000 to 66 000, and total mortality by 44 000 to 92 000.122 If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 to $24 billion annually.122 Such an intervention would be more cost-effective than using medications to lower BP in all people with hypertension.

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6. Overweight and Obesity

Chart 6-1. Prevalence of overweight and obesity among students in grades 9 through 12 by sex and race/ethnicity. NH indicates non-Hispanic. Data derived from Kann et al (Table 101).87
Chart 6-2. Age-adjusted prevalence of obesity in adults 20 to 74 years of age by sex and survey year (National Health Examination Survey: 1960–1962; National Health and Nutrition Examination Survey: 1971–1974, 1976–1980, 1988–1994, 1999–2002, 2003-2006, and 2009–2012). Obesity is defined as body mass index of 30.0 kg/m2. Data derived from Health, United States, 2013 (National Center for Health Statistics).88
Chart 6-3. Trends in the prevalence of obesity among US children and adolescents by age and survey year (National Health and Nutrition Examination Survey:1988–1994, 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, and 2011–2012). Red line indicates ages 2 to 5 years; blue line, ages 6 to 11 years; and green line, ages 12 to 19 years. Data derived from Health, United States, 2013 (National Center for Health Statistics).88
Table 6-1. Overweight and Obesity
 Prevalence of Overweight and Obesity, 2009–2012, Age >20 yPrevalence of Obesity, 2009–2012, Age >20 yPrevalence of Overweight and Obesity 2011–2012, Ages 2–19 yPrevalence of Obesity, 2011–2012, Ages 2–19 yCost, 2008*
Both sexes, n (%)159 200 000 (68.5)81 800 000 (35.2)23 700 000 (31.8)12 600 000 (16.9)$147 Billion
 Males81 500 000 (72.5)38 600 000 (34.4)12 200 000 (32.0)6 300 000 (16.7)
 Females77 700 000 (64.7)43 200 000 (36.0)11 500 000 (31.6)6 300 000 (17.2)
NH white males, %72.734.227.812.6
NH white females, %61.232.529.215.6
NH black males, %69.437.934.419.9
NH black females, %81.957.536.120.5
Hispanic males, %80.138.440.724.1
Hispanic females, %76.342.937.020.6
Overweight and obesity in adults is defined as body mass index (BMI) ≥25 kg/m2. Obesity in adults is defined as BMI ≥30 kg/m2. In children, overweight and obesity are based on BMI-for-age values at or above the 85th percentile of the 2000 Centers for Disease Control and Prevention (CDC) growth charts. In children, obesity is based on BMI-for-age values at or above the 95th percentile of the CDC growth charts. In January 2007, the American Medical Association’s Expert Task Force on Childhood Obesity recommended new definitions for overweight and obesity in children and adolescents85; however, statistics based on this new definition are not yet available.
Ellipses (…) indicate data not available; and NH, non-Hispanic.
*
Data from Finkelstein et al.60
Sources: National Health and Nutrition Examination Survey (NHANES) 2009 to 2012 (adults), unpublished National Heart, Lung, and Blood Institute (NHLBI) tabulation; NHANES 2011 to 2012 (ages 2–19 years) from Ogden et al.86 Extrapolation for ages 2 to 19 years from NHLBI tabulation of US Census resident population on July 1, 2012.
Overweight and obesity are typically classified by use of BMI cutoffs, but variations in body fat distribution (eg, larger waist circumference) are also associated with increased cardiovascular risk.1 Overweight and obesity are major risk factors for CVD, including CHD, stroke,2,3 AF,4 VTE,5 and CHF. The AHA has identified BMI <85th percentile (for children) and <25 kg/m2 (for adults aged ≥20 years) as 1 of the 7 components of ideal cardiovascular health.6 In 2011 to 2012, 64.7% of children and 31.3% of adults met these criteria (Chapter 2, Cardiovascular Health).
Abbreviations Used in Chapter 6
AFatrial fibrillation
AFFIRMAtrial Fibrillation Follow-up Investigation of Rhythm Management
AHAAmerican Heart Association
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CACcoronary artery calcification
CADcoronary artery disease
CARDIACoronary Artery Risk Development in Young Adults
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHFcongestive heart failure
CIconfidence interval
CVDcardiovascular disease
DALYdisability-adjusted life-year
DMdiabetes mellitus
FHSFramingham Heart Study
HbA1chemoglobin A1c
HDLhigh-density lipoprotein
HRhazard ratio
HUNT 2Nord-Trøndelag Health Study
IMTintima-media thickness
MEPSMedical Expenditure Panel Survey
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
NCDRNational Cardiovascular Data Registry
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
PAphysical activity
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
STEMIST-segment–elevation myocardial infarction
Teen-LABSTeen Longitudianl Assessment of Bariatric Surgery
VTEvenous thromboembolism
WHIWomen’s Health Initiative

Prevalence

Youth

(See Table 6-1 and Chart 6-1.)
According to 2011 to 2012 data from NHANES (NCHS), the overall prevalence of overweight and obesity in children aged 2 to 19 years is 31.8% based on a BMI-for-age value ≥85th percentile of the 2000 CDC growth charts. The overall prevalence of overweight and obesity in children aged 2 to 5 years of age was 21.8% for non-Hispanic white boys and 19.9% for non-Hispanic white girls, 22.2% for non-Hispanic black boys and 21.6% for non-Hispanic black girls, 8.3% for Asian boys and 9.7% for Asian girls, and 31.4% for Hispanic boys and 28.1% for Hispanic girls.7 In children 6 to 11 years of age, the prevalence was 26.5% for non-Hispanic white boys and 32.7% for non-Hispanic white girls, 39.3% for non-Hispanic black boys and 36.9% for non-Hispanic black girls, 24.5% for Asian boys and 14.9% for Asian girls, and 48.7% for Hispanic boys and 43.6% for Hispanic girls. For those 12 to 19 years of age, the prevalence was 31.5% for non-Hispanic white boys and 31.0% for non-Hispanic white girls, 37.3% for non-Hispanic black boys and 42.5% for non-Hispanic black girls, 33.9% for Asian boys and 15% for Asian girls, and 39.6% for Hispanic boys and 36.5% for Hispanic girls.7
According to 2011 to 2012 data from NHANES (NCHS), the overall prevalence of obesity in children aged 2 to 19 years was 16.9% based on a BMI-for-age value ≥95th percentile of the 2000 CDC growth charts. Among children aged 2 to 5 years of age, the prevalence of obesity was 6.3% for non-Hispanic white boys and 0.6% for non-Hispanic white girls, 9.0% for non-Hispanic black boys and 13.9% for non-Hispanic black girls, 1.9% for Asian boys and 4.7% for Asian girls, and 18.0% for Hispanic boys and 15.2% for Hispanic girls.7 In children 6 to 11 years of age, the prevalence of obesity was 8.8% for non-Hispanic white boys and 17.9% for non-Hispanic white girls, 25.9% for non-Hispanic black boys and 21.7% for non-Hispanic black girls, 13.2% for Asian boys and 3.7% for Asian girls, and 28.6% for Hispanic boys and 23.4% for Hispanic girls. For those 12 to 19 years of age, the prevalence was 18.3% for non-Hispanic white boys and 20.9% for non-Hispanic white girls, 21.4% for non-Hispanic black boys and 22.7% for non-Hispanic black girls, 14.8% for Asian boys and 7.3 for Asian girls, and 23.9% for Hispanic boys and 21.3% for Hispanic girls.7
Childhood sociodemographic factors may contribute to sex disparities in obesity prevalence. A study of data from the National Longitudinal Study of Adolescent Health (Add Health) found that parental education consistently modified sex disparity in blacks. The sex gap was largest in those with low parental education (16.7% of men compared with 45.4% of women were obese) and smallest in those with high parental education (28.5% of men compared with 31.4% of women were obese). In whites, there was little overall sex difference in obesity prevalence.8
The obesity epidemic is disproportionally more rampant among children living in low-income, low-education, and higher-unemployment households, according to data from the National Survey of Children’s Health.9 Data from 2011 show that among low-income preschool children, American Indians/Alaskan Natives have an obesity rate of 17.7%, whereas rates are 14.7% for Hispanics, 10.6% for non-Hispanic blacks, 10.3% for non-Hispanic whites, and 9.3% for Asian/Pacific Islanders.10 According to 1999 to 2008 NHANES survey data, lowest-income girls had an obesity prevalence of 17.9% compared with 13.1% among those with higher income; similar observations were observed for boys (20.6% versus 15.6%, respectively).11
NHANES 2003 to 2004 and 2005 to 2006 data were used to determine overweight and obesity prevalence in rural versus urban youth; the results showed that 39% of rural versus 32% of urban children had BMI >85th percentile.12
A recent AHA Scientific Statement regarding severe obesity in children and adolescents recommended that for children and adolescents, the definition of severe obesity should include class II obesity, defined as BMI ≥120% of the 95th percentile for age and sex, or BMI ≥35 kg/m2.13 By this definition, in NHANES 1999 to 2006, the prevalence of severe obesity for those aged 2 to 19 years was 5.1% in boys and 4.7% in girls.14 According to NHANES data from 2011 to 2012, 5.9% of children aged 2 to 19 had class II obesity, defined as BMI ≥120% of the 95th percentile for age and sex, or BMI ≥35 kg/m2, and 2.1% had class III obesity, defined as BMI ≥140% of the 95th percentile for age and sex, or BMI ≥40 kg/m2.15
According to the National Longitudinal Study of Adolescent Health, compared with those with normal weight or those who were overweight, obese adolescents had a 16-fold increased risk of having severe obesity (BMI ≥40 kg/m2) as adults. Furthermore, the majority (70.5%) of adolescents with severe obesity maintained this weight status into adulthood.16

Adults

(See Table 6-1 and Chart 6-2.)
According to NHANES 2009 to 2012 (unpublished NHLBI tabulations of measured height and weight):
—Overall, 69% of US adults were overweight or obese (73% of men and 65% of women).
—Among men, Hispanics (80%) and non-Hispanic whites (73%) were more likely to be overweight or obese than non-Hispanic blacks (69%).
—Among women, non-Hispanic blacks (82%) and Hispanics (76%) were more likely to be overweight or obese than non-Hispanic whites (61%).
—Among US adults, 35% were obese (34% of men and 36% of women).
—Among men, Hispanics and non-Hispanic blacks (38%) were more likely to be obese than non-Hispanic whites (34%).
—Among women, non-Hispanic blacks (58%) and Hispanics (43%) were more likely to be obese than non-Hispanic whites (33%).
On the basis of self-reported weights and heights from the 2013 NHIS17:
—Blacks ≥18 years of age (27.6%), American Indians or Alaska Natives (23.2%), and whites (35.8%) were less likely than Asians (57.4%) to be at a healthy weight. Blacks ≥18 years of age (36.3%) and American Indians or Alaska Natives (46.5%) were more likely to be obese than were whites (27.9%) and Asians (10.8%).
In 2004 to 2006, most adults in Asian subgroups were in the healthy weight range, with rates ranging from 51% for Filipino adults to 68% for Chinese adults. Although the prevalence of obesity is low within the Asian adult population, Filipino adults (14%) were more than twice as likely to be obese (BMI ≥30 kg/m2) as Asian Indian (6%), Vietnamese (5%), or Chinese (4%) adults.18
As estimated from self-reported height and weight in the BRFSS/CDC survey in 2013, the prevalence of obesity ranged from 21.3% in Colorado to 35.3% in West Virginia and Mississippi.19 Additionally, no state met the Healthy People 2010 goal of reducing obesity to 15% of adults.20
According to NHANES 2007 to 2010 data, 35% of US adults >65 years of age were obese, which represents 13 million individuals.21
According to the 2008 National Healthcare Disparities Report (based on NHANES 2003–2006)22:
—Approximately 64.8% of obese adults were told by a doctor or health professional that they were overweight.
—The proportion of obese adults told that they were overweight was significantly lower for non-Hispanic blacks (60.5%) and Mexican Americans (57.1%) than for non-Hispanic whites (66.4%), for middle-income people than for high-income people (62.4% versus 70.6%), and for adults with less than a high school education than for those with any college education (59.2% versus 70.3%).

Trends

Youth

(See Chart 6-3.)
Among infants and children between 6 and 23 months of age, the prevalence of high weight for recumbent length was 7% in 1976 to 1980, 12% in 2003 to 2006 (NHANES, NCHS),23 and 8.1% in 2011 to 2012 (NHANES).7
According to NHANES data, overall obesity prevalence in youth between 2003 to 2004 and 2011 to 2012 was unchanged, although for children aged 2 to 5 years, the prevalence of obesity was decreased.7 Among adolescents, a socioeconomic gradient has been reported, in which the prevalence of obesity is decreasing among adolescents with high socioeconomic status but continues to increase among adolescents with low socioeconomic status.24 Furthermore, according to NHANES data, among children aged 2 to 19 years, the prevalence of severe obesity has increased during the past decade, particularly among adolescent boys.15

Adults

Forecasts through 2030 using the BRFSS 1990 to 2008 data set suggest that by 2030, 51% of the population will be obese, with 11% with severe obesity, an increase of 33% for obesity and 130% for severe obesity.25
According to NHANES data, there have been no overall changes in obesity prevalence in adults between 2003 to 200426 and 2011 to 2012.7 However, among women aged ≥60 years, the prevalence of obesity increased 6.6% from 2003–2004 to 2011–2012.7

Morbidity

Youth

Overweight children and adolescents are at increased risk for future adverse health effects, including the following27:
—Increased prevalence of traditional cardiovascular risk factors such as hypertension, hyperlipidemia, and DM.
—Poor school performance, tobacco use, alcohol use, premature sexual behavior, and poor diet.
—Other associated health conditions, such as asthma, hepatic steatosis, sleep apnea, stroke, some cancers (breast, colon, and kidney), renal insufficiency, musculoskeletal disorders, and gallbladder disease.
Data from 4 Finnish cohort studies examining childhood and adult BMI with a mean follow-up of 23 years found that overweight or obese children who remained obese in adulthood had increased risks of type 2 DM, hypertension, dyslipidemia, and carotid atherosclerosis; however, those who achieved normal weight by adulthood had risks comparable to individuals who were never obese.28
The CARDIA study showed that young adults who were overweight or obese had lower health-related quality of life than normal-weight participants 20 years later.29

Adults

Data from the FHS indicate that obesity is driving the doubling in the incidence of DM over the past 30 years, most dramatically during the 1990s and primarily among individuals with a BMI >30 kg/m2.30
Among 68 070 participants across multiple NHANES surveys, the decline in BP in recent birth cohorts is slowing, mediated by BMI.31
Cardiovascular risks may be even higher with severe obesity (class III, BMI ≥40 kg/m2) than with class I or class II obesity.32 Among 156 775 postmenopausal women in the WHI, for severe obesity versus normal BMI, HRs (95% CIs) for mortality were 1.97 (1.77–2.20) in white women, 1.55 (1.20–2.00) in African American women, and 2.59 (1.55–4.31) in Hispanic women; for CHD, HRs were 2.05 (1.80–2.35), 2.24 (1.57–3.19), and 2.95 (1.60–5.41) respectively; and for CHF, HRs were 5.01 (4.33–5.80), 3.60 (2.30–5.62), and 6.05 (2.49–14.69). However, CHD risk was strongly related to CVD risk factors across BMI categories, even in severe obesity, and CHD incidence was similar by race/ethnicity when adjusted for differences in BMI and CVD risk factors.32
In a meta-analysis from 58 cohorts, representing 221 934 people in 17 developed countries with 14 297 incident CVD outcomes, BMI, waist circumference, and waist-to-hip ratio were strongly associated with intermediate risk factors of SBP, DM, and total and HDL cholesterol. These risk factors, along with age, sex and smoking status, accounted for almost all of the association of BMI, waist circumference, and waist-to-hip ratio with CVD outcomes, so that they were only minimally associated with CVD outcomes after adjustment for those intermediate risk factors. Measures of adiposity also did not improve risk discrimination or reclassification when data on intermediate risk factors were included.33
Obesity is associated with subclinical atherosclerosis including CAC and carotid IMT, and this association persists after adjustment for CVD risk factors, as shown in MESA.34
The population attributable fraction for CHD associated with reducing current population mean BMI to 21 kg/m2 in the Asia-Pacific region ranged from 2% in India to 58% in American Samoa; the population attributable fraction for ischemic stroke ranged from 3% in India to 64% in American Samoa. These data from 15 countries show the proportion of CVD that would be prevented if the population mean BMI were reduced below the current overweight cut point.35
Obesity is also a strong predictor of sleep-disordered breathing, itself strongly associated with the development of CVD, as well as with myriad other health conditions, including numerous cancers, nonalcoholic fatty liver disease, gallbladder disease, musculoskeletal disorders, and reproductive abnormalities.36
A systematic review of prospective studies examining overweight and obesity as predictors of major stroke subtypes in >2 million participants over ≥4 years found an adjusted RR for ischemic stroke of 1.22 (95% CI, 1.05–1.41) in overweight individuals and an RR of 1.64 (95% CI, 1.36–1.99) for obese individuals relative to normal-weight individuals. RRs for hemorrhagic stroke were 1.01 (95% CI, 0.88–1.17) and 1.24 (95% CI, 0.99–1.54) for overweight and obese individuals, respectively. These risks were graded with increasing BMI and were independent of age, lifestyle, and other cardiovascular risk factors.37
A recent report from ARIC showed that VTE risk over 15.5 years (237 375 person-years) was associated with higher BMI (and current smoking) but not with other CVD risk factors.5
A recent meta-analysis of 15 prospective studies demonstrated the increased risk for Alzheimer disease or vascular dementia and any dementia was 1.35 and 1.26 for overweight, respectively, and 2.04 and 1.64 for obesity, respectively.38 The inclusion of obesity in dementia forecast models increases the estimated prevalence of dementia through 2050 by 9% in the United States and 19% in China.39
Ten-year follow-up data from the Swedish Obese Subjects intervention study indicated that to maintain a favorable effect on cardiovascular risk factors, more than the short-term goal of 5% weight loss is needed to overcome secular trends and aging effects.40
A randomized clinical trial of 130 severely obese adult individuals randomized to either 12 months of diet and PA or only 6 months of PA resulted in 12.1 and 9.9 kg, respectively, of weight loss at 1 year, with improvements in waist circumference, visceral fat, BP, and insulin resistance.41

Mortality

Elevated childhood BMIs in the highest quartile were associated with premature death as an adult in a cohort of 4857 American Indian children during a median follow-up of 23.9 years.42
According to NHIS data, among young adults aged 18 to 39 years, the HR for all-cause mortality was 1.07 (95% CI, 0.91–1.26) for overweight individuals, 1.41 (95% CI, 1.16–1.73) for obese individuals, and 2.46 for extremely obese individuals (95% CI, 1.91–3.16).43
Among adults, obesity was associated with nearly 112 000 excess deaths (95% CI, 53 754–170 064) relative to normal weight in 2000. Grade 1 obesity (BMI 30 to <35 kg/m2) was associated with almost 30 000 of these excess deaths (95% CI, 8534–68 220) and grade 2 to 3 obesity (BMI ≥35 kg/m2) with >82 000 (95% CI, 44 843–119 289). Underweight was associated with nearly 34 000 excess deaths (95% CI, 15 726–51 766). As other studies have found,44 overweight (BMI 25 to <30 kg/m2) was not associated with excess deaths.45
A recent systematic review (2.88 million individuals and >270 000 deaths) showed that relative to normal BMI (18.5 to <25 kg/m2), all-cause mortality was lower for overweight (HR, 0.94; 95% CI, 0.91–0.96) but was not elevated for grade 1 obesity (HR, 0.95; 95% CI, 0.88–1.01). All-cause mortality was higher for obesity (all grades; HR, 1.18; 95% CI, 1.12–1.25) and grades 2 and 3 obesity (HR, 1.29; 95% CI, 1.18–1.41).46
In a collaborative analysis of data from almost 900 000 adults in 57 prospective studies, mostly in western Europe and North America, overall mortality was lowest at a BMI of ≈22.5 to 25 kg/m2 in both sexes and at all ages, after exclusion of early follow-up and adjustment for smoking status. Above this range, each 5- kg/m2-higher BMI was associated with ≈30% higher all-cause mortality, and no specific cause of death was inversely associated with BMI. Below 22.5 to 25 kg/m2, the overall inverse association with BMI was predominantly related to strong inverse associations for smoking-related respiratory disease, and the only clearly positive association was for ischemic heart disease.47
In a meta-analysis of 1.46 million white adults, over a mean follow-up period of 10 years, all-cause mortality was lowest at BMI levels of 20.0 to 24.9 kg/m2. Among women, compared with a BMI of 22.5 to 24.9 kg/m2, the HRs for death were as follows: BMI 15.0 to 18.4 kg/m2, 1.47; 18.5 to 19.9 kg/m2, 1.14; 20.0 to 22.4 kg/m2, 1.0; 25.0 to 29.9 kg/m2, 1.13; 30.0 to 34.9 kg/m2, 1.44; 35.0 to 39.9 kg/m2, 1.88; and 40.0 to 49.9 kg/m2, 2.51. Similar estimates were observed in men.48
Overweight was associated with significantly increased mortality resulting from DM or kidney disease and was not associated with increased mortality resulting from cancer or CVD in an analysis of 2004 data from NHANES. Obesity was associated with significantly increased mortality caused by CVD, some cancers, and DM or kidney disease. Obesity was associated with 13% of CVD deaths in 2004.49
A BMI paradox has been reported, with higher-BMI patients demonstrating favorable outcomes in CHF, hypertension, peripheral vascular disease, and CAD; similar findings have been seen for percent body fat. In AFFIRM, a multicenter trial of AF, obese patients had lower all-cause mortality (HR, 0.77; P=0.01) than normal-weight patients after multivariable adjustment over a 3-year follow-up period.50
Interestingly, among 2625 participants with new-onset DM, rates of total, CVD, and non-CVD mortality were higher among normal-weight people compared with overweight/obese participants, with adjusted HRs of 2.08 (95% CI, 1.52–2.85), 1.52 (95% CI, 0.89–2.58), and 2.32 (95% CI, 1.55–3.48), respectively.51
Calculations based on NHANES data from 1978 to 2006 suggest that the gains in life expectancy from smoking cessation are beginning to be outweighed by the loss of life expectancy related to obesity.52
Because of the increasing prevalence of obesity, the number of quality-adjusted life-years lost as a result of obesity is similar to or greater than that lost as a result of smoking, according to data from the BRFSS.53
According to data from the NCDR, among patients presenting with STEMI and a BMI ≥40 kg/m2, in-hospital mortality rates were higher for patients with class III obesity (OR, 1.64; 95% CI, 1.32–2.03) when class I obesity was used as the referent.54
In a study of 22 203 women and men from England and Scotland, metabolically unhealthy obese individuals were at an increased risk of all-cause mortality compared with metabolically healthy obese individuals (HR, 1.72; 95% CI, 1.23–2.41).55
Recent estimates suggest that reductions in smoking, cholesterol, BP, and physical inactivity levels resulted in a gain of 2 770 500 life-years; however, these gains were reduced by a loss of 715 000 life-years caused by the increased prevalence of obesity and DM.56
In a comparison of 5 different anthropometric variables (BMI, waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio) in 62 223 individuals from Norway with 12 years of follow-up from the HUNT 2 study, the risk of death per SD increase in each measure was 1.02 (95% CI, 0.99–1.06) for BMI, 1.10 (95% CI, 1.06–1.14) for waist circumference, 1.01 (95% CI, 0.97–1.05) for hip circumference, 1.15 (95% CI, 1.11–1.19) for waist-to-hip ratio, and 1.12 (95% CI, 1.08–1.16) for waist-to-height ratio. For CVD mortality, the risk of death per SD increase was 1.12 (95% CI, 1.06–1.20) for BMI, 1.19 (95% CI, 1.12–1.26) for waist circumference, 1.06 (95% CI, 1.00–1.13) for hip circumference, 1.23 (95% CI, 1.16–1.30) for waist-to-hip ratio, and 1.24 (95% CI, 1.16–1.31) for waist-to-height ratio.57
However, because BMI and waist circumference are strongly correlated, large samples are needed to evaluate their independent contributions to risk.1,58 A recent pooled analysis of waist circumference and mortality in 650 386 adults followed up for a median of 9 years revealed that a 5-cm increment in waist circumference was associated with an increase in all-cause mortality at all BMI categories examined from 20 to 50 kg/m2.59 Similarly, in an analysis of postmenopausal women in the WHI limited to those with BMI ≥40 kg/m2, mortality, CHD, and CHF incidence all increased with waist circumference >115 and >122 cm compared with ≤108.4 cm.32

Cost

In 2008 US dollars, the estimated annual medical cost of obesity was $147 billion; the medical costs for those who were obese were $1429 higher than for those at normal weight.60
The total excess cost related to the current prevalence of adolescent overweight and obesity is estimated to be $254 billion ($208 billion in lost productivity secondary to premature morbidity and mortality and $46 billion in direct medical costs).61
If current trends in the growth of obesity continue, total healthcare costs attributable to obesity could reach $861 to $957 billion by 2030, which would account for 16% to 18% of US health expenditures.62
According to NHANES I data linked to Medicare and mortality records, obese 45-year-olds had lifetime Medicare costs of $163 000 compared with $117 000 among those with normal weight by the time they reached 65 years of age.63
According to 2006 MEPS and 2006 BRFSS data, annual medical expenditures would be 6.7% to 10.7% lower in the absence of obesity.64
According to data from the Medicare Current Beneficiary Survey from 1997 to 2006, in 1997, expenditures for a Part A and Part B services beneficiary were $6832 for a normal-weight individual, which was more than for overweight ($5473) or obese ($5790) individuals. However, over time, expenses increased more rapidly for overweight and obese individuals.65
The costs of obesity are high: Obese people pay on average $1429 (42%) more for healthcare costs than normal-weight individuals. For obese beneficiaries, Medicare pays $1723 more, Medicaid pays $1021 more, and private insurers pay $1140 more than for beneficiaries who are at normal weight. Similarly, obese people have 46% higher inpatient costs and 27% more outpatient visits and spend 80% more on prescription drugs.60

Bariatric Surgery

Patients with BMI >40 kg/m2 or >35 kg/m2 with an obesity-related comorbidity are eligible for gastric bypass surgery, which is typically performed as either a Roux-en-Y gastric bypass or a biliopancreatic diversion.
According to the 2006 NHDS, the incidence of bariatric surgery was estimated at 113 000 cases per year, with costs of nearly $1.5 billion annually.66
In a large bariatric surgery cohort, the prevalence of high 10-year predicted CVD risk was 36.5%,67 but 76% of those with low 10-year risk had high lifetime predicted CVD risk. The corresponding prevalence in US adults is 18% and 56%, respectively.68
Among obese Swedish patients undergoing bariatric surgery and followed up for up to 15 years, maximum weight loss was 32%. The risk of death was 0.76 among those who underwent bariatric surgery compared with matched control subjects.60 More recent data examining MI and stroke showed that bariatric surgery was associated with fewer CVD deaths (HR, 0.47; 95% CI, 0.29–0.76) and fewer strokes (HR, 0.67; 95% CI, 0.54–0.83) than in the control group. However, CVD risk was related to baseline CVD risk factors rather than to baseline BMI or 2-year weight change.69
Among 641 patients followed up for 10 years compared with 627 matched control subjects, after 2 years of follow-up, 72% of the surgically treated patients versus 21% of the control patients had remission of their DM; at 10 years of follow-up, results were 36% and 13%, respectively. Similar results have been observed for hypertension, elevated triglycerides, and low HDL cholesterol.70
According to retrospective data from the United States, among 9949 patients who underwent gastric bypass surgery, after a mean of 7 years, long-term mortality was 40% lower among the surgically treated patients than among obese control subjects. Specifically, cancer mortality was reduced by 60%, DM mortality by 92%, and CAD mortality by 56%. Nondisease death rates (eg, accidents, suicide) were 58% higher in the surgery group.71
A recent retrospective cohort from the Veterans Affairs medical system showed that in a propensity-matched analysis, bariatric surgery was not associated with reduced mortality compared with obese control subjects (time-adjusted HR, 0.94; 95% CI, 0.64–1.39).72
Two recent randomized controlled trials were performed that randomized bariatric surgery compared with intensive medical treatment among patients with type 2 DM. The first study randomized 150 patients and conducted 12-month follow-up; this study showed that glycemic control improved (6.4%) and weight loss was greater (29.4 versus 5.4 kg) in the surgical arm.73 The second trial randomized 60 patients to bariatric surgery versus medical therapy and conducted follow-up for 24 months. The results showed that DM remission occurred in 75% of the group that underwent gastric bypass surgery compared with 0% of those in the medical treatment arm, with HbA1c values of 6.35% in the surgical arm compared with 7.69% in the medical treatment arm.74
Of 120 patients with type 2 DM and a BMI between 30 and 39.9 kg/m2, 60 who were randomized to Roux-en-Y gastric bypass were almost 5-fold (OR, 4.8; 95% CI, 1.9–11.7) more likely to achieve an HbA1c <7.0% at 12-month follow-up. However, there were 22 serious adverse events in the intervention arm, including early and late perioperative complications and nutritional deficiencies.75
Adolescents (aged 10–19 years old) underwent bariatric surgery at a rate of 0.8/100 000 procedures, which increased to 2.3/100 000 in 2003 and remained constant by 2009 at 2.4/100 000.76 The Teen-LABS study recently reported a favorable short-term (30 day) complications profile of bariatric surgery among 242 patients aged 13 to 19 years.77
A recent cost-effectiveness study of laparoscopic adjustable gastric banding showed that after 5 years, $4970 was saved in medical expenses; if indirect costs were included (absenteeism and presenteeism), savings increased to $6180 and $10 960, respectively.78 However, when expressed per quality-adjusted life expectancy, only $6600 was gained for laparoscopic gastric bypass, $6200 for laparoscopic adjustable gastric band, and $17 300 for open Roux-en-Y gastric bypass, none of which exceeded the standard $50 000 per quality-adjusted life expectancy gained.79 Two other recent large studies failed to demonstrate a cost benefit for bariatric surgery versus matched patients over 6 years of follow-up.80,81 However, another study showed cost savings for bariatric surgery among patients with DM at baseline.82

Global Burden of High BMI and Obesity

Between 1980 and 2008, mean BMI has increased worldwide by 0.4 kg/m2 per decade for men and 0.5 kg/m2 per decade for women, with trends varying between nations. In 2008, an estimated 1.46 billion adults were overweight or obese. The prevalence of obesity was estimated at 205 million men and 297 million women. The highest prevalence of male obesity is in the United States, Southern and Central Latin America, Australasia, and Central and Western Europe, and the lowest prevalence is in South and Southeast Asia and East, Central, and West Africa. For women, the highest prevalence of obesity is in Southern and North Africa; the Middle East; Central and Southern Latin America; and the United States; and the lowest is in South, East, and Southeast Asia; the Asia-Pacific (high income); and East, Central, and West Africa.83
Between 1990 and 2010, estimated deaths attributable to high BMI increased 1.7-fold, from 1 963 549 to 3 371 232, and DALYs lost because of high BMI rose 1.8-fold, from 51 565 to 93 609. Therefore, between 1990 and 2010, high BMI went from tenth to sixth in ranking of contribution to the global burden of disease and was among the top 5 risk factors for global burden of disease in all regions except high-income Asia-Pacific; East, Southeast, and South Asia; and East, Central, and West sub-Saharan Africa.84

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7. Family History and Genetics

Table 7-1. OR for Combinations of Parental Heart Attack History
 OR (95% CI)
No family history1.00
One parent with heart attack ≥50 y of age1.67 (1.55–1.81)
One parent with heart attack <50 y of age2.36 (1.89–2.95)
Both parents with heart attack ≥50 y of age2.90 (2.30–3.66)
Both parents with heart attack, one <50 y of age3.26 (1.72–6.18)
Both parents with heart attack, both <50 y of age6.56 (1.39–30.95)
CI indicates confidence interval; and OR, odds ratio.
Data derived from Chow et al.5
Table 7-2. Validated SNPs for CAD, the Nearest Gene, and the OR From the CARDIoGRAMplusC4D Consortium
SNPChromosomeGeneEffect Size (OR)Effect Allele Frequency
rs6026331SORT11.120.77
rs174648571MIA31.050.87
rs171140361PPAP2B1.110.91
rs112065101PCSK91.060.84
rs48456251IL6R1.040.47
rs67258872WDR121.120.11
rs5151352APOB1.080.82
rs22526412ZEB2-AC074093.11.040.46
rs15611982VAMP5-VAMP8-GGCX1.050.45
rs65447132ABCG5-ABCG81.060.30
rs98188703MRAS1.070.14
rs76923874GUCY1A31.060.81
rs18784064EDNRA1.060.15
rs2739095SLC22A4-SLC22A51.090.14
rs122053316ANKS1A1.040.81
rs93696406PHACTR11.090.65
rs121902876TCF211.070.59
rs37982206LPA1.280.01
rs109477896KCNK51.060.76
rs42521206PLG1.060.73
rs115569247ZC3HC11.080.65
rs125398957-1.080.19
rs20239387HDAC91.070.10
rs2648LPL1.050.86
rs29540298TRIB11.040.55
rs13330499CDKN2A, CDKN2B1.230.47
rs5794599ABO1.070.21
rs250508310KIAA14621.060.42
rs50112010CXCL121.070.83
rs1241340910CYP17A1-CNNM2-NT5C21.100.89
rs224683310LIPA1.060.38
rs932624611ZNF259-APOA5-A4-C3-A11.090.10
rs97481911PDGFD1.070.29
rs318450412SH2B31.070.40
rs477314413COL4A1-COL4A21.070.42
rs931942813FLT11.050.32
rs289581114HHIPL11.060.43
rs717374315ADAMTS71.070.58
rs1751484615FURIN-FES1.050.44
rs228172717SMG6-SRR1.050.36
rs1293658717RASD1-SMCR3-PEMT1.060.59
rs1556317UBE2Z-GIP-ATP5G1-SNF81.040.52
rs112260819LDLR1.100.76
rs207565019ApoE-ApoC11.110.14
rs998260121KCNE21.130.13
CAD indicates coronary artery disease; CARDIoGRAMplusC4D, Coronary Artery Disease Genome-wide Replication and Meta-analysis (CARDIOGRAM) plus the Coronary Artery Disease (C4D) Genetics Consortium; OR, odds ratio; and SNP, single-nucleotide polymorphism.
Data derived from Deloukas et al.18
Table 7-3. Heritability of CVD Risk Factors From the FHS
TraitHeritability
ABI0.2135
SBP0.4236
DBP0.3936
Left ventricular mass0.24–0.3237
BMI0.37 (mean age 40 y)–0.52 (mean age 60 y)38
Waist circumference0.4139
Visceral abdominal fat0.3640
Subcutaneous abdominal fat0.5740
Fasting glucose0.3441
CRP0.3042
HbA1c0.2741
Triglycerides0.4843
HDL cholesterol0.5243
Total cholesterol0.5743
LDL cholesterol0.5943
Estimated GFR0.3344
ABI indicates ankle-brachial index; BMI, body mass index; CRP, C-reactive protein; CVD, cardiovascular disease; DBP, diastolic blood pressure; FHS, Framingham Heart Study; GFR, glomerular filtration rate; HbA1c, glycosylated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; and SBP, systolic blood pressure.
Biologically related first-degree relatives (siblings, offspring, and parents) share roughly 50% of their genetic variation with one another. This constitutes much greater sharing of genetic variation than with a randomly selected person from the population, and thus, familial aggregation of traits lends support for a genetic basis for the trait. Similarly, racial/ethnic minorities are more likely to share their genetic variation within their demographic than with other demographics. Familial aggregation of CVD may be related to aggregation of specific behaviors (eg, smoking, alcohol use) or risk factors (eg, hypertension, DM, obesity) that may themselves have environmental and genetic contributors. Unlike classic mendelian genetic risk factors, whereby usually 1 mutation directly causes 1 disease, a complex trait’s genetic contributors may increase risk without necessarily always causing the condition. The effect size of any specific contributor to risk may be small but widespread throughout a population, or may be large but affect only a small population, or may have an enhanced risk when an environmental contributor is present. We present a summary of evidence that a genetic risk for CVD is likely, as well as a summary of evidence on the most consistently replicated genetic markers for CHD and stroke identified to date. A comprehensive scientific statement on the role of genetics and genomics for the prevention and treatment of CVD is available elsewhere.1
Abbreviations Used in Chapter 7
AAAabdominal aortic aneurysm
ABIankle-brachial index
ACSacute coronary syndrome
AFatrial fibrillation
BMIbody mass index
CACcoronary artery calcification
CADcoronary artery disease
CARDIoGRAMplusC4DCoronary Artery Disease Genome-wide Replication and Meta-Analysis (CARDIOGRAM) plus the Coronary Artery Disease (C4D) Genetics Consortium
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
FHSFramingham Heart Study
GFRglomerular filtration rate
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDheart disease
HDLhigh-density lipoprotein
HFheart failure
HRhazard ratio
LDLlow-density lipoprotein
MImyocardial infarction
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
PADperipheral artery disease
SBPsystolic blood pressure
SEstandard error
SNPsingle-nucleotide polymorphism
VTEvenous thromboembolism

Family History

Prevalence

Among adults ≥20 years of age, 12.0% (SE 0.4%) reported having a parent or sibling with a heart attack or angina before the age of 50 years. The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 11.5% (SE 0.6%) for men, 14.6% (SE 0.8%) for women
—For non-Hispanic blacks, 9.1% (SE 0.8%) for men, 12.3% (SE 0.7%) for women
—For Hispanics, 7.6% (SE 0.7%) for men, 10.1% (SE 1.0%) for women
HD occurs as people age, so the prevalence of family history will vary depending on the age at which it is assessed. The breakdown of reported family history of heart attack by age of survey respondent in the US population as measured by NHANES is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—Age 20 to 39 years, 8.0% (SE 0.6%) for men, 9.7% (SE 0.8%) for women
—Age 40 to 59 years, 12.1% (SE 0.8%) for men, 15.2% (SE 1.4%) for women
—Age 60 to 79 years, 13.3% (SE 1.5%) for men, 16.6% (SE 1.3%) for women
—Age ≥80 years, 8.7% (SE 1.9%) for men, 15.5% (SE 2.4%) for women
In the multigenerational FHS, only 75% of participants with a documented parental history of a heart attack before age 55 years reported that history when asked.2

Impact of Family History

Coronary Heart Disease

Paternal history of premature heart attack has been shown to approximately double the risk of a heart attack in men and increase the risk in women by ≈70%.3,4
History of a heart attack in both parents increases the risk of heart attack, especially when 1 parent had a premature heart attack5 (Table 7-1).
Sibling history of CVD has been shown to increase the odds of CVD in men and women by 45% (OR, 1.45; 95% CI, 1.19–1.91) in models accounting for CVD risk factors.6
Family history of premature angina, MI, angioplasty, or bypass surgery increased the lifetime risk by ≈50% for both HD (from 8.9% to 13.7%) and CVD (from 14.1% to 21%) mortality.7
In a recent international study of individuals with premature ACS (age ≤55 years), more women (28%) than men (20%) had a family history of CAD (P=0.008). However, compared with patients without, patients with a family history of CAD had a higher prevalence of traditional CVD risk factors, including dyslipidemia and obesity. Women with a family history had a higher prevalence of each traditional risk factor (obesity, DM, dyslipidemia, and hypertension) except smoking.8

Other CVDs

A parental history of AF was associated with ≈80% increased odds of AF in men and women.9 The risk of AF was increased the younger the age of onset and the more family members affected.10 In a Swedish study, the odds of AF associated with familial AF (OR, 5.04; 95% CI, 4.26–5.82) were higher in people with a history of premature AF (diagnosed AF at age <50 years). Interestingly, there was modest spousal aggregation of AF, consistent with a contribution of shared environment to AF risk; the spousal OR for AF was 1.16 (95% CI, 1.13–1.19).11
A history of stroke in a first-degree relative increases the odds of stroke in men and women by ≈50%.12
A parental history of HF also is associated with an increased odds of offspring HF (multivariable-adjusted HR, 1.7; 95% CI, 1.11–2.60).13
In a Swedish population-based case control study, the risk of thoracic aortic disease increased the greater the number of affected relatives and the younger the individual affected. The OR was 5.8 (95% CI, 4.3–7.7) with 1 affected relative versus 20 (95% CI, 2.2–179) with at least 2 affected relatives.14
Similarly, the odds of having PAD were elevated (OR, 1.83; 95% CI, 1.03–3.26) in individuals with a family history of PAD.15
A family history of VTE is associated with a 2- to 3-fold odds of VTE, irrespective of identified known predisposing genetic factors.16,17

Genetics

Heart Disease

Genome-wide association is a robust technique to identify associations between genotypes and phenotypes. Table 7-2 presents results from the CARDIoGRAMplusC4D Consortium, which represents the largest genetic study of CAD to date. Although the ORs are modest, ranging from 1.06 to 1.51 per copy of the risk allele (individuals may harbor up to 2 copies of a risk allele), these are common alleles, which suggests that the attributable risk may be substantial. Additional analysis suggested that loci associated with CAD were involved in lipid metabolism and inflammation pathways.18
The relationship between genetic variants associated with CHD and measured CHD risk factors is complex, with some genetic markers associated with multiple risk factors and other markers showing no association with risk factors.19
Genetic markers discovered thus far have not been shown to add to cardiovascular risk prediction tools beyond current models that incorporate family history.20 Genetic markers also have not been shown to improve prediction of subclinical atherosclerosis beyond traditional risk factors.21 However, an association between genetic markers and CAC has been seen.22
The most consistently replicated genetic marker for HD in European-derived populations is located at 9p21.3. At this single-nucleotide polymorphism, ≈27% of the white population is estimated to have 0 risk alleles, 50% is estimated to have 1 risk allele, and the remaining 23% is estimated to have 2 risk alleles.23 In meta-analyses of individuals of East Asian ancestry, variants at 9p21.3 have also been reported to be associated with CHD (OR per risk allele, 1.3; 95% CI, 1.25–1.35).24
The 10-year HD risk for a 65-year-old man with 2 risk alleles at 9p21.3 and no other traditional risk factors is ≈13.2%, whereas a similar man with 0 alleles would have a 10-year risk of ≈9.2%. The 10-year HD risk for a 40-year-old woman with 2 alleles and no other traditional risk factors is ≈2.4%, whereas a similar woman with 0 alleles would have a 10-year risk of ≈1.7%.23
Variation at the 9p21.3 region also is associated with an increased risk of HF25 and sudden death.26 Associations have also been observed between the 9p21.3 region and CAC.27,28 Additionally, stronger associations have been found between variation at 9p21.3 and earlier27,28 and more severe29 heart attacks. Paradoxically, a recent meta-analysis reported that variants at 9p21.3 were associated with incident (HR, 1.19; 95% CI, 1.17–1.22) but not recurrent (HR, 1.01; 95% CI, 0.97–1.06) CHD events,30 which supports the genetic complexity of CHD. The biological mechanisms underpinning the association of genetic variation in the 9p21 region with disease outcomes are still under investigation.

Stroke

The same 9p21.3 region has also been associated with intracranial aneurysm,31 AAA,32 and ischemic stroke.33
For large-vessel ischemic stroke, an association for large-vessel stroke with histone deacetylase 9 on chromosome 7p21.1 has been identified (>9000 subjects) and replicated (>12 000 subjects).33,34

CVD Risk Factors

Heritability is the ratio of genetically caused variation to the total variation of a trait or measure. Table 7-3 presents heritability estimates for standard CVD risk factors using data generated from the FHS. These data suggest that most CVD risk factors have at least moderate heritability.

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Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng YC, Fornage M, Ikram MA, Malik R, Bevan S, Thorsteinsdottir U, Nalls MA, Longstreth W, Wiggins KL, Yadav S, Parati EA, Destefano AL, Worrall BB, Kittner SJ, Khan MS, Reiner AP, Helgadottir A, Achterberg S, Fernandez-Cadenas I, Abboud S, Schmidt R, Walters M, Chen WM, Ringelstein EB, O’Donnell M, Ho WK, Pera J, Lemmens R, Norrving B, Higgins P, Benn M, Sale M, Kuhlenbäumer G, Doney AS, Vicente AM, Delavaran H, Algra A, Davies G, Oliveira SA, Palmer CN, Deary I, Schmidt H, Pandolfo M, Montaner J, Carty C, de Bakker PI, Kostulas K, Ferro JM, van Zuydam NR, Valdimarsson E, Nordestgaard BG, Lindgren A, Thijs V, Slowik A, Saleheen D, Paré G, Berger K, Thorleifsson G, Hofman A, Mosley TH, Mitchell BD, Furie K, Clarke R, Levi C, Seshadri S, Gschwendtner A, Boncoraglio GB, Sharma P, Bis JC, Gretarsdottir S, Psaty BM, Rothwell PM, Rosand J, Meschia JF, Stefansson K, Dichgans M, Markus HS; Australian Stroke Genetics Collaborative, Wellcome Trust Case Control Consortium 2 (WTCCC2); International Stroke Genetics Consortium. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11:951–962.
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Murabito JM, Guo CY, Fox CS, D’AGOSTINO RB. Heritability of the ankle-brachial index: the Framingham Offspring study. Am J Epidemiol. 2006;164:963–968.
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Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17: genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension. 2000;36:477–483.
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Post WS, Larson MG, Myers RH, Galderisi M, Levy D. Heritability of left ventricular mass: the Framingham Heart Study. Hypertension. 1997;30:1025–1028.
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Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PWF, D’Agostino RB. Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study. Am J Hum Genet2002;71:1044–1050.
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Fox CS, Heard-Costa NL, Wilson PWF, Levy D, D’Agostino RB, Atwood LD. Genome-wide linkage to chromosome 6 for waist circumference in the Framingham Heart Study. Diabetes. 2004;53:1399–1402.
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Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48.
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Meigs JB, Panhuysen CIM, Myers RH, Wilson PWF, Cupples LA. A genome-wide scan for loci linked to plasma levels of glucose and HbA1c in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study. Diabetes. 2002;51:833–840.
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Schnabel RB, Lunetta KL, Larson MG, Dupuis J, Lipinska I, Rong J, Chen MH, Zhao Z, Yamamoto JF, Meigs JB, Nicaud V, Perret C, Zeller T, Blankenberg S, Tiret L, Keaney JF, Vasan RS, Benjamin EJ. The relation of genetic and environmental factors to systemic inflammatory biomarker concentrations. Circ Cardiovasc Genet. 2009;2:229–237.
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Kathiresan S, Manning A, Demissie S, D’Agostino R, Surti A, Guiducci C, Gianniny L, Burtt N, Melander O, Orho-Melander M, Arnett DK, Peloso GM, Ordovas JM, Cupples LA. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. BMC Med Genet 2007;8(suppl 1):S17.
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Fox CS, Yang Q, Cupples LA, Guo CY, Larson MG, Leip EP, Wilson PWF, Levy D. Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study. J Am Soc Nephrol. 2004;15:2457–2461.

8. High Blood Cholesterol and Other Lipids

Chart 8-1. Trends in mean serum total cholesterol among adolescents 12 to 19 years of age by race, sex, and survey year (National Health and Nutrition Examination Survey: 1988–1994, 1999–2006, and 2007–2012). Values are in mg/dL. Mex. Am. indicates Mexican American; and NH, non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 8-2. Age-adjusted trends in mean serum total cholesterol among adults ≥20 years old by race and survey year (National Health and Nutrition Examination Survey: 1988–1994, 1999–2006, and 2007–2012). Values are in mg/dL. NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 8-3. Age-adjusted trends in the prevalence of serum total cholesterol ≥200 mg/dL in adults ≥20 years of age by sex, race/ethnicity, and survey year (National Health and Nutrition Examination Survey 2007–2008, 2009–2010, and 2011–2012). NH indicates non-Hispanic.
Chart 8-4. Age-adjusted trends in the prevalence of serum total cholesterol ≥240 mg/dL in adults ≥20 years of age by sex, race/ethnicity, and survey year (National Health and Nutrition Examination Survey 2009–2010 and 2011-2012). NH indicates non-Hispanic. *Data not available for non-Hispanic Asians in 2009 to 2010. † 2009 to 2010 data are for Mexican Americans only.
Table 8-1. High Total and LDL Cholesterol and Low HDL Cholesterol
Population GroupPrevalence of Total Cholesterol ≥200 mg/dL, 2012 Age ≥20 yPrevalence of Total Cholesterol ≥240 mg/dL, 2012 Age ≥20 yPrevalence of LDL Cholesterol ≥130 mg/dL, 2012 Age ≥20 yPrevalence of HDL Cholesterol <40 mg/dL, 2012 Age ≥20 y
Both sexes, n (%)*100 100 000 (42.8)30 900 000 (13.1)73 500 000 (31.7)44 600 000 (19.9)
Males, n (%)*45 300 000 (40.4)13 000 000 (11.6)34 900 000 (31.0)32 400 000 (28.9)
Females, n (%)*54 830 000 (44.9)17 900 000 (14.4)38 600 000 (32.0)12 200 000 (10.4)
NH white males, %39.911.529.428.7
NH white females, %45.915.332.010.2
NH black males, %37.48.830.720.0
NH black females, %40.710.933.610.3
Hispanic males, %46.214.838.833.8
Hispanic females, %43.413.731.812.8
Prevalence of total cholesterol ≥200 mg/dL includes people with total cholesterol ≥240 mg/dL. In adults, levels of 200 to 239 mg/dL are considered borderline high. Levels of ≥240 mg/dL are considered high.
HDL indicates high-density lipoprotein; LDL, low-density lipoprotein; and NH, non-Hispanic.
*
Total data for total cholesterol are for Americans ≥20 years of age. Data for LDL cholesterol, HDL cholesterol, and all racial/ethnic groups are age adjusted for age ≥20 years.
Source for total cholesterol ≥200 mg/dL, ≥240 mg/dL, LDL, and HDL: National Health and Nutrition Examination Survey (2009–2012), National Center for Health Statistics, and National Heart, Lung, and Blood Institute. Estimates from National Health and Nutrition Examination Survey 2009 to 2012 (National Center for Health Statistics) were applied to 2012 population estimates.
High cholesterol is a major risk factor for CVD and stroke.1 The AHA has identified untreated total cholesterol <170 mg/dL (for children) and <200 mg/dL (for adults) as 1 of the 7 components of ideal cardiovascular health.2 In 2011 to 2012, 75.7% of children and 46.6% of adults met these criteria.

Prevalence of High Total Cholesterol

For information on dietary cholesterol, total fat, saturated fat, and other factors that affect blood cholesterol levels, see Chapter 5 (Nutrition).

Youth

(See Chart 8-1.)
Among children 6 to 11 years of age, the mean total cholesterol level is 160.2 mg/dL. For boys, it is 160.5 mg/dL; for girls, it is 159.8 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 158.6 mg/dL for boys and 158.2 mg/dL for girls
—For non-Hispanic blacks, 163.7 mg/dL for boys and 159.8 mg/dL for girls
—For Hispanics, 160.5 mg/dL for boys and 161.2 mg/dL for girls
Among adolescents 12 to 19 years of age, the mean total cholesterol level is 158.3 mg/dL. For boys, it is 155.2 mg/dL; for girls, it is 161.6 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 155.2 mg/dL for boys and 163.2 mg/dL for girls
—For non-Hispanic blacks, 153.9 mg/dL for boys and 158.6 mg/dL for girls
—For Hispanics, 157.0 mg/dL for boys and 160.4 mg/dL for girls
The prevalence of abnormal lipid levels among youths 12 to 19 years of age is 20.3%; 14.2% of normal-weight youths, 22.3% of overweight youths, and 42.9% of obese youths have ≥1 abnormal lipid level (NHANES 1999–2006, NCHS).3
Approximately 8.5% of adolescents 12 to 19 years of age have total cholesterol levels ≥200 mg/dL (NHANES 2009–2012, unpublished NHLBI tabulation).
Twenty percent of male adolescents and 27% of female adolescents have total cholesterol levels of 170 to 199 mg/dL.4
Among youths aged 6 to 19 years, there was a decrease in mean total cholesterol from 165 to 160 mg/dL and a decrease in the prevalence of elevated total cholesterol from 11.3% to 8.1% from 1988–1994 to 2007–2010.5
Mean non-HDL cholesterol (111.7 mg/dL) and prevalence of elevated non-HDL cholesterol both significantly decreased from the periods 1988–1994 to 2007–2010. In 2007 to 2010, 22% of youths had either a low HDL cholesterol level or a high non-HDL cholesterol level, which was lower than the 27.2% in 1988 to 1994.5
Among adolescents (aged 12–19 years) between 1988 to 1994 and 2007 to 2010, there was a decrease in mean LDL cholesterol from 95 to 90 mg/dL and a decrease in geometric mean triglycerides from 82 to 73 mg/dL. The prevalence of elevated LDL cholesterol and triglycerides also decreased significantly between 1988 to 1994 and 2007 to 2010.5
Fewer than 1% of adolescents are potentially eligible for pharmacological treatment on the basis of guidelines from the American Academy of Pediatrics.3,6
Abbreviations Used in Chapter 8
ACCAmerican College of Cardiology
AHAAmerican Heart Association
ASCVDatherosclerotic cardiovascular disease
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DALYdisability-adjusted life-year
DMdiabetes mellitus
HDLhigh-density lipoprotein
LDLlow-density lipoprotein
Mex. Am.Mexican American
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute
WHOWorld Health Organization

Adults

(See Table 8-1 and Charts 8-2 through 8-4.)
An estimated 30.9 million adults ≥20 years of age have serum total cholesterol levels ≥240 mg/dL (extrapolated for 2012 by use of NCHS/NHANES 2009–2012 data), with a prevalence of 13.1%.
Approximately 6.2% of adults ≥20 years of age have undiagnosed hypercholesterolemia, defined as a total cholesterol level ≥240 mg/dL and the participant having responded “no” to ever having been told by a doctor or other healthcare professional that the participant’s blood cholesterol level was high (NHANES 2009–2012, unpublished NHLBI tabulation).
In 2011 to 2012, an estimated 12.9% of US adults aged ≥20 years (11.1% of men and 14.4% of women) had high total cholesterol, which was unchanged since 2009 to 2010, according to NCHS/NHANES 2011 to 2012 data.7
—Non-Hispanic black adults had consistently lower percentages with high total cholesterol (9.8% overall, 7.4% for men, and 11.5% for women) than non-Hispanic white adults (13.5% overall, 11.6% for men, and 15.2% for women).7
—Overall, 14.2% of Hispanic adults had high total cholesterol.7
The age-adjusted mean total cholesterol level for adults ≥20 years of age declined linearly from 206 mg/dL (95% CI, 205–207 mg/dL) in 1988 to 1994 to 203 mg/dL (95% CI, 201–205 mg/dL) in 1999 to 2002 and to 196 mg/dL (95% CI, 195–198 mg/dL) in 2007 to 2010 (P<0.001 for linear trend).8
Data from NHANES 2007 to 2010 (NCHS) showed the serum total crude mean cholesterol level in adults to be 132 mg/dL for men and 197 mg/dL for women.8 Statistically significant declining trends in age-adjusted mean total cholesterol levels from 1988–1994 to 2007–2010 were observed in all sex and race/ethnicity subgroups except for Mexican American men (P=0.03). The Healthy People 2010 guideline9 of an age-adjusted mean total cholesterol level of ≤200 mg/dL has been achieved in adults, in men, in women, and in all race/ethnicity and sex subgroups.
Overall, the decline in cholesterol levels in recent years appears to reflect greater uptake of cholesterol-lowering medications rather than changes in dietary patterns.10
The declining total cholesterol level appears to reflect a worldwide trend; a report on trends in total cholesterol in 199 countries and territories indicated that total cholesterol declined in high-income regions of the world (Australasia, North America, and Western Europe).11 During the period from 1999 to 2006, 26.0% of adults had hypercholesterolemia, 9% of adults had both hypercholesterolemia and hypertension, 1.5% of adults had DM and hypercholesterolemia, and 3% of adults had all 3 conditions.12

Screening

Data from the 2013 BRFSS study of the CDC shows that the percentage of adults who had been screened for high cholesterol in the preceding 5 years ranged from 68.2% in Utah to 84.0% in Massachusetts. The median percentage among all 50 states was 76.4%.13
The percentage of adults who reported having had their cholesterol level checked increased from 68.6% during 1999 to 2000 to 74.8% during 2005 to 200614 and then declined to 69.4% in 2011 to 2012.7
Nearly 70% of adults (67% of men and nearly 72% of women) had been screened for cholesterol (defined as being told by a doctor their cholesterol was high and indicating they had their blood cholesterol checked <5 years ago) according to data from NHANES 2011 to 2012, which was unchanged since 2009 to 2010.7
—Among non-Hispanic whites, 71.8% were screened (70.6% of men and 72.9% of women).
—Among non-Hispanic blacks, 71.9% were screened (66.8% of men and 75.9% of women).
—Among non-Hispanic Asians, 70.8% were screened (70.6% of men and 70.9% of women).
—Among Hispanic adults, 59.3% were screened (54.6% of men and 64.2% of women). The percentage of adults screened for cholesterol in the past 5 years was lower for Hispanic adults than for non-Hispanic white, non-Hispanic black, and non-Hispanic Asian adults.7

Awareness

Data from the 2005 to 2008 BRFSS (CDC) survey in 2011 showed that among adults screened for high cholesterol, the percentage who had been told that they had high cholesterol ranged from 33.5% in Colorado to 42.3% in Mississippi. The median percentage among states was 38.4%.13 The percentage of adults reporting having been screened for high blood cholesterol within the preceding 5 years increased overall from 72.7% in 2005 to 76.0% in 2011.13
Among adults with hypercholesterolemia, 42% were told they had high total cholesterol in 1999 to 2000 compared with 50.4% during 2005 to 2006.14

Treatment

The ACC/AHA recently released a revised recommendation for statin treatment.15 Unlike previous recommendations, which had fixed LDL and non-HDL cholesterol goals, the ACC/AHA recommended lipid measurement at baseline, at 1 to 3 months after statin initiation, and then annually to check for the expected percentage decrease of LDL cholesterol levels (30% to 45% with a moderate-intensity statin and ≥50% with a high-intensity statin). They also recommended statin therapy in 4 identified groups in whom it has been clearly shown to reduce ASCVD risk. The 4 statin benefit groups are (1) people with clinical ASCVD, (2) those with primary elevations of LDL cholesterol >190 mg/dL, (3) people aged 40 to 75 years who have DM with LDL cholesterol 70 to 189 mg/dL and without clinical ASCVD, and (4) those without clinical ASCVD or DM with LDL cholesterol 70 to 189 mg/dL and estimated 10-year ASCVD risk >7.5%. Approximately 31.9% of the ASCVD-free, nonpregnant US population between 40 and 79 years of age has a 10-year risk of a first hard CHD event of ≥10% or has DM.16
According to a recent analysis of NHANES data from 2005 to 2010, the number of people eligible for statin therapy would rise from 43.2 million US adults (37.5%) to 50.6 million (48.6%) based on the new ACC/AHA guidelines for the management of blood cholesterol. Most of the increase comes from adults 60 to 75 years old without CVD who have a 10-year ASCVD risk >7.5%; the net number of new statin prescriptions could potentially increase by 12.8 million, including 10.4 million for primary prevention.17
NHANES data on the treatment of high LDL cholesterol showed an increase from 28.4% of people during 1999 to 2002 to 48.1% during 2005 to 2008.18
Self-reported use of cholesterol-lowering medications increased from 8.2% during 1999 to 2000 to 14% in 2005 to 200614 and reached 23% in 2007 to 2010.19

Adherence

Youth

The American Academy of Pediatrics recommends screening for dyslipidemia in children and adolescents who have a family history of dyslipidemia or premature CVD, those whose family history is unknown, and those youths with risk factors for CVD, such as being overweight or obese, having hypertension or DM, or being a smoker.3
Analysis of data from NHANES 1999 to 2006 showed that the overall prevalence of abnormal lipid levels among youths 12 to 19 years of age was 20.3%.3

Adults

New criteria from the “2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults”15 could result in >45 million middle-aged Americans who do not have CVD being recommended for consideration of statin therapy: 33.0 million are at ≥7.5% 10-year risk and 12.8 million are at >5.0% to 7.4% 10-year risk. This is approximately 1 in every 3 American adults, many of whom are already undergoing statin treatment under the previous US guidelines.20
On the basis of data from the 2005 to 2008 NHANES, an estimated 71 million US adults (33.5%) aged ≥20 years had high LDL cholesterol, but only 34 million (48.1%) were treated and only 23 million (33.2%) had their LDL cholesterol controlled.18
—The proportion of adults with high LDL cholesterol who were treated increased from 28.4% to 48.1% between the 1999 to 2002 and 2005 to 2008 study periods.
—Among adults with high LDL cholesterol, the prevalence of LDL cholesterol control increased from 14.6% to 33.2% between the periods. The prevalence of LDL cholesterol control was lowest among people who reported receiving medical care less than twice in the previous year (11.7%), being uninsured (13.5%), being Mexican American (20.3%), or having income below the poverty level (21.9%).

Global Burden of Hypercholesterolemia

Between 1980 and 2008, the mean age-adjusted total cholesterol level decreased from 4.72 to 4.64 mmol/L (95% CI, 4.51–4.76 mmol/L) for men and from 4.83 to 4.76 mmol/L (95% CI, 4.62–4.91 mmol/L) for women. Globally, mean total cholesterol changed little between 1980 and 2008, falling by <0.1 mmol/L per decade in men and women.21
Total cholesterol went from being the 14th leading risk factor in 1990 for the global burden of disease, as quantified by DALYs, to the number 15 risk factor in 2010.22
Raised cholesterol, defined as ≥190 mg/dL or ≥5.0 mmol/L, is estimated to cause 2.6 million deaths (4.5% of total deaths) and 29.7 million DALYs (2.0% of total DALYs).23
The prevalence of elevated total cholesterol was highest in the WHO European Region (54% for both sexes), followed by the WHO Region of the Americas (48% for both sexes). The WHO African Region and the WHO South-East Asia Region showed the lowest percentages (23% and 30%, respectively).23
Twenty-nine percent of ischemic heart disease DALYs can be attributed to high total cholesterol, the second-leading physiological risk factor.22

Lipid Levels

LDL (Bad) Cholesterol

Youth
There are limited data available on LDL cholesterol for children 6 to 11 years of age.
Among adolescents 12 to 19 years of age, the mean LDL cholesterol level is 89.3 mg/dL (boys, 88.3 mg/dL; girls, 90.3 mg/dL). The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 89.5 mg/dL for boys and 91.1 mg/dL for girls
—For non-Hispanic blacks, 86.7 mg/dL for boys and 90.9 mg/dL for girls
—For Hispanic Americans, 87.4 mg/dL for boys and 88.9 mg/dL for girls
High levels of LDL cholesterol occurred in 7.1% of male adolescents and 7.4% of female adolescents during 2009 to 2012 (unpublished NHLBI tabulation).
Adults
The mean level of LDL cholesterol for American adults ≥20 years of age was 115.8 mg/dL in 2009 to 2012 (unpublished NHLBI tabulation).
According to NHANES 2009 to 2012 (unpublished NHLBI tabulation):
—Among non-Hispanic whites, mean LDL cholesterol levels were 113.8 mg/dL for men and 116.8 mg/dL for women.
—Among non-Hispanic blacks, mean LDL cholesterol levels were 113.4 mg/dL for men and 115.5 mg/dL for women.
—Among Hispanics, mean LDL cholesterol levels were 120.1 mg/dL for men and 114.8 mg/dL for women.
The prevalence of high LDL cholesterol decreased from 59% in 1976 to 1980 to 42% in 1988 to 1994 and to 33% in 2001 to 2004, reaching 27% in 2007 to 2010. Between 1976 to 1980 and 2007 to 2010, the prevalence of high LDL cholesterol significantly decreased for men (from 65% to 31%), women (54% to 24%), and adults aged 40 to 64 years (56% to 27%) and 65 to 74 years (72% to 30%).19
The age-adjusted prevalence of high LDL cholesterol in US adults was 26.6% in 1988 to 1994 and 25.3% in 1999 to 2004 (NHANES/NCHS). Between 1988 to 1994 and 1999 to 2004, awareness increased from 39.2% to 63.0%, and use of pharmacological lipid-lowering treatment increased from 11.7% to 40.8%. LDL cholesterol control increased from 4.0% to 25.1% among those with high LDL cholesterol. In 1999 to 2004, rates of LDL cholesterol control were lower among adults 20 to 49 years of age than among those ≥65 years of age (13.9% versus 30.3%, respectively), among non-Hispanic blacks and Mexican Americans than among non-Hispanic whites (17.2% and 16.5% versus 26.9%, respectively), and among men than among women (22.6% versus 26.9%, respectively).24
Mean levels of LDL cholesterol decreased from 126.2 mg/dL during 1999 to 2000 to 115.5 mg/dL during 2011 to 2012. The age-adjusted prevalence of high LDL cholesterol decreased from 42.9% during 1999 to 2000 to 32.2% during 2011 to 2012 (unpublished NHLBI tabulation).
Data from NHANES 2005 to 2006 indicate that among those with elevated LDL cholesterol levels, 35.5% had not been screened previously, 24.9% were screened but not told they had elevated cholesterol, and 39.6% were treated inadequately.19

HDL (Good) Cholesterol

Youth
Among children 6 to 11 years of age, the mean HDL cholesterol level is 53.9 mg/dL. For boys, it is 55.4 mg/dL, and for girls, it is 52.4 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 55.1 mg/dL for boys and 52.5 mg/dL for girls
—For non-Hispanic blacks, 58.5 mg/dL for boys and 54.5 mg/dL for girls
—For Hispanics, 53.5 mg/dL for boys and 51.4 mg/dL for girls
Among adolescents 12 to 19 years of age, the mean HDL cholesterol level is 51.4 mg/dL. For boys, it is 49.4 mg/dL, and for girls, it is 53.4 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2009–2012, unpublished NHLBI tabulation):
—For non-Hispanic whites, 48.9 mg/dL for boys and 52.4 mg/dL for girls
—For non-Hispanic blacks, 52.6 mg/dL for boys and 55.1 mg/dL for girls
—For Hispanics, 48.1 mg/dL for boys and 53.6 mg/dL for girls
Low levels of HDL cholesterol occurred in 19.5% of male adolescents and 11.1% of female adolescents during 2009 to 2012 (NHANES 2009–2012, unpublished NHLBI tabulation).
Adults
The mean level of HDL cholesterol for American adults ≥20 years of age is 52.9 mg/dL (NHANES 2009–2012, unpublished NHLBI tabulation).
According to NHANES 2009 to 2012 (unpublished NHLBI tabulation):
—Among non-Hispanic whites, mean HDL cholesterol levels were 47.7 mg/dL for men and 58.5 mg/dL for women
—Among non-Hispanic blacks, mean HDL cholesterol levels were 51.9 mg/dL for men and 57.4 mg/dL for women
—Among Hispanics, mean HDL cholesterol levels were 45.4 mg/dL for men and 54.3 mg/dL for women.
Approximately 17% of adults (just over one quarter of men and <10% of women) had low HDL cholesterol during 2011 to 2012. The percentage of adults with low HDL cholesterol has decreased 20% since 2009 to 2010.7
—Among non-Hispanic whites, 17.1% (25.4% of men and 9.3% of women) had low HDL.
—Among non-Hispanic blacks, 12.7% (19.1% of men and 7.8% of women) had low HDL. The percentage of adults with low HDL cholesterol was lower in non-Hispanic black adults than in non-Hispanic white adults. These racial and ethnic differences were also observed in men but not in women.
—Among non-Hispanic Asians, 14.3% (24.5% of men and 5.1% of women) had low HDL. The prevalence of low HDL cholesterol was 5 times greater among non-Hispanic Asian men than women. Non-Hispanic Asian adults had consistently lower percentages of low HDL cholesterol than Hispanic adults.
The prevalence of low HDL cholesterol was 5 times higher in non-Hispanic Asian men (24.5%) than in non-Hispanic Asian women (5.1%).25
—Among Hispanic adults, 21.8% (32.6% of men and 11.3% of women) had low HDL. The percentage of adults with low HDL cholesterol was higher in Hispanic adults than in non-Hispanic black or non-Hispanic white adults. These racial and ethnic differences were also observed in men but not in women.

Triglycerides

Youth
There are limited data available on triglycerides for children 6 to 11 years of age.
Among adolescents 12 to 19 years of age, the geometric mean triglyceride level is 82.1 mg/dL. For boys, it is 84.6 mg/dL, and for girls, it is 79.5 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2009–2012):
—Among non-Hispanic whites, 83.1 mg/dL for boys and 82.4 mg/dL for girls
—Among non-Hispanic blacks, 70.4 mg/dL for boys and 62.2 mg/dL for girls
—Among Hispanics, 90.3 mg/dL for boys and 84.9 mg/dL for girls
High levels of triglycerides occurred in 10.0% of male adolescents and 6.5% of female adolescents during 2009 to 2012.
Adults
The geometric mean level of triglycerides for American adults ≥20 years of age is 108.8 mg/dL (NHANES 2009–2012, unpublished NHLBI tabulation).
Approximately 25.1% of adults had high triglyceride levels during 2009 to 2012 (NHANES 2009–2012, unpublished NHLBI tabulation).
Among men, the age-adjusted geometric mean triglyceride level is 117.2 mg/dL (NHANES 2009–2012, unpublished NHLBI tabulation), with the following racial/ethnic breakdown:
—117.7 mg/dL for non-Hispanic white men
—92.7 mg/dL for non-Hispanic black men
—134.7 mg/dL for Hispanic men
Among women, the age-adjusted geometric mean triglyceride level is 101.4 mg/dL (NHANES 2009–2012, unpublished NHLBI tabulation), with the following racial/ethnic breakdown:
—104.0 mg/dL for non-Hispanic white women
—83.5 mg/dL for non-Hispanic black women
—109.7 mg/dL for Hispanic women
Fewer than 3% of adults with a triglyceride level ≥150 mg/dL received pharmacological treatment during 1999 to 2004.26

References

1.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285:2486–2497.
2.
Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613.
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Centers for Disease Control and Prevention. Prevalence of abnormal lipid levels among youths: United States, 1999–2006 [published correction appears in MMWR Morb Mortal Wkly Rep. 2010;59:78]. MMWR Morb Mortal Wkly Rep. 2010;59:29.
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Shay CM, Ning H, Daniels SR, Rooks CR, Gidding SS, Lloyd-Jones DM. Status of cardiovascular health in US adolescents: prevalence estimates from the National Health and Nutrition Examination Surveys (NHANES) 2005–2010. Circulation. 2013;127:1369–1376.
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Kit BK, Carroll MD, Lacher DA, Sorlie PD, DeJesus JM, Ogden C. Trends in serum lipids among US youths aged 6 to 19 years, 1988–2010. JAMA. 2012;308:591–600.
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Ford ES, Li C, Zhao G, Mokdad AH. Concentrations of low-density lipoprotein cholesterol and total cholesterol among children and adolescents in the United States. Circulation. 2009;119:1108–1115.
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Carroll MD, Kit BK, Lacher DA, Yoon SSS. Total and high-density lipoprotein cholesterol in adults: National Health and Nutrition Examination Survey, 2011–2012. NCHS Data Brief. 2013;(132):1–8.
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Carroll MD, Kit BK, Lacher DA, Shero ST, Mussolino ME. Trends in lipids and lipoproteins in US adults, 1988–2010. JAMA. 2012;308:1545–1554.
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US Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Government Printing Office; 2000.
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Ford ES, Capewell S. Trends in total and low-density lipoprotein cholesterol among U.S. adults: contributions of changes in dietary fat intake and use of cholesterol-lowering medications. PLoS One. 2013;8:e65228.
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Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, Singh GM, Gutierrez HR, Lu Y, Bahalim AN, Farzadfar F, Riley LM, Ezzati M; on behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index). National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377:557–567.
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Fryar CD, Hirsch R, Eberhardt MS, Yoon SS, Wright JD. Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in US adults, 1999–2006. NCHS Data Brief. 2010;(36):1–8.
13.
Behavioral Risk Factor Surveillance System: prevalence and trends data. Centers for Disease Control and Prevention Web site. http://apps.nccd.cdc.gov/brfss/index.asp. Accessed November 12, 2014.
14.
Ford ES, Li C, Pearson WS, Zhao G, Mokdad AH. Trends in hypercholesterolemia, treatment and control among United States adults. Int J Cardiol. 2010;140:226–235.
15.
Stone NJ, Robinson JG, Lichtenstein AH, Merz CNB, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Watson K, Wilson PWF. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published correction appears in J Am Coll Cardiol. 2014;63(pt B):3024–3025]. J Am Coll Cardiol. 2014;63(pt B):2889–2934.
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Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Sorlie P, Stone NJ, Wilson PW. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [published correction appears in J Am Coll Cardiol. 2014;63(pt B):3026]. J Am Coll Cardiol. 2013.
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Pencina MJ, Navar-Boggan AM, D’Agostino RB, Williams K, Neely B, Sniderman AD, Peterson ED. Application of new cholesterol guidelines to a population-based sample. N Engl J Med. 2014;370:1422–1431.
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Centers for Disease Control and Prevention (CDC). Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol: United States, 1999–2002 and 2005–2008. MMWR Morb Mortal Wkly Rep. 2011;60:109–114.
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Kuklina EV, Yoon PW, Keenan NL. Trends in high levels of low-density lipoprotein cholesterol in the United States, 1999–2006. JAMA. 2009;302:2104–2110.
20.
Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet. 2013;382:1762–1765.
21.
Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365:217–223.
22.
Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A, Bell ML, Blore JD, Blyth F, Bonner C, Borges G, Bourne R, Boussinesq M, Brauer M, Brooks P, Bruce NG, Brunekreef B, Bryan-Hancock C, Bucello C, Buchbinder R, Bull F, Burnett RT, Byers TE, Calabria B, Carapetis J, Carnahan E, Chafe Z, Charlson F, Chen H, Chen JS, Cheng AT, Child JC, Cohen A, Colson KE, Cowie BC, Darby S, Darling S, Davis A, Degenhardt L, Dentener F, Des Jarlais DC, Devries K, Dherani M, Ding EL, Dorsey ER, Driscoll T, Edmond K, Ali SE, Engell RE, Erwin PJ, Fahimi S, Falder G, Farzadfar F, Ferrari A, Finucane MM, Flaxman S, Fowkes FG, Freedman G, Freeman MK, Gakidou E, Ghosh S, Giovannucci E, Gmel G, Graham K, Grainger R, Grant B, Gunnell D, Gutierrez HR, Hall W, Hoek HW, Hogan A, Hosgood HD, Hoy D, Hu H, Hubbell BJ, Hutchings SJ, Ibeanusi SE, Jacklyn GL, Jasrasaria R, Jonas JB, Kan H, Kanis JA, Kassebaum N, Kawakami N, Khang YH, Khatibzadeh S, Khoo JP, Kok C, Laden F, Lalloo R, Lan Q, Lathlean T, Leasher JL, Leigh J, Li Y, Lin JK, Lipshultz SE, London S, Lozano R, Lu Y, Mak J, Malekzadeh R, Mallinger L, Marcenes W, March L, Marks R, Martin R, McGale P, McGrath J, Mehta S, Mensah GA, Merriman TR, Micha R, Michaud C, Mishra V, Mohd Hanafiah K, Mokdad AA, Morawska L, Mozaffarian D, Murphy T, Naghavi M, Neal B, Nelson PK, Nolla JM, Norman R, Olives C, Omer SB, Orchard J, Osborne R, Ostro B, Page A, Pandey KD, Parry CD, Passmore E, Patra J, Pearce N, Pelizzari PM, Petzold M, Phillips MR, Pope D, Pope CA, Powles J, Rao M, Razavi H, Rehfuess EA, Rehm JT, Ritz B, Rivara FP, Roberts T, Robinson C, Rodriguez-Portales JA, Romieu I, Room R, Rosenfeld LC, Roy A, Rushton L, Salomon JA, Sampson U, Sanchez-Riera L, Sanman E, Sapkota A, Seedat S, Shi P, Shield K, Shivakoti R, Singh GM, Sleet DA, Smith E, Smith KR, Stapelberg NJ, Steenland K, Stockl H, Stovner LJ, Straif K, Straney L, Thurston GD, Tran JH, Van Dingenen R, van Donkelaar A, Veerman JL, Vijayakumar L, Weintraub R, Weissman MM, White RA, Whiteford H, Wiersma ST, Wilkinson JD, Williams HC, Williams W, Wilson N, Woolf AD, Yip P, Zielinski JM, Lopez AD, Murray CJ, Ezzati M, AlMazroa MA, Memish ZA. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 [published corrections appear in Lancet. 2013;381:1276 and Lancet. 2013;381:628]. Lancet. 2012;380:2224–2260.
23.
Alwan AGlobal Status Report on Noncommunicable Diseases 2010. Geneva, Switzerland: World Health Organization; 2011.
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Hyre AD, Muntner P, Menke A, Raggi P, He J. Trends in ATP-III-defined high blood cholesterol prevalence, awareness, treatment and control among U.S. adults. Ann Epidemiol. 2007;17:548–555.
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Aoki Y, Yoon SS, Chong Y, Carroll MD. Hypertension, abnormal cholesterol, and high body mass index among non-Hispanic Asian adults: United States, 2011–2012. NCHS Data Brief, no 140. 2014;(140):1–8.
26.
Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Hypertriglyceridemia and its pharmacologic treatment among US adults. Arch Intern Med. 2009;169:572–578.

9. High Blood Pressure

ICD-9 401 to 404, ICD-10 I10 to I15. See Tables 9-1 and 9-2 and Charts 9-1 through 9-5.
Chart 9-1. Prevalence of high blood pressure in adults ≥20 years of age by age and sex (National Health and Nutrition Examination Survey: 2007–2012). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, if the subject said “yes” to taking antihypertensive medication, or if the subject was told on 2 occasions that he or she had hypertension. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 9-2. Age-adjusted prevalence trends for high blood pressure in adults ≥20 years of age by race/ethnicity, sex, and survey (National Health and Nutrition Examination Survey: 1988–1994, 1999–2006, and 2007–2012). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, if the subject said “yes” to taking antihypertensive medication, or if the subject was told on 2 occasions that he or she had hypertension. NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 9-3. Extent of awareness, treatment, and control of high blood pressure by race/ethnicity (National Health and Nutrition Examination Survey: 2007–2012). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or if the subject said “yes” to taking antihypertensive medication. NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 9-4. Extent of awareness, treatment, and control of high blood pressure by age (National Health and Nutrition Examination Survey: 2007–2012). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or if the subject said “yes” to taking antihypertensive medication. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Chart 9-5. Extent of awareness, treatment, and control of high blood pressure by race/ethnicity and sex (National Health and Nutrition Examination Survey: 2007–2012). Hypertension is defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or if the subject said “yes” to taking antihypertensive medication. NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.
Table 9-1. High Blood Pressure
Population GroupPrevalence, 2012, Age ≥20 yMortality,* 2011, All AgesHospital Discharges, 2010, All AgesEstimated Cost, 2011
Both sexes80 000 000 (32.6%)65 123488 000$46.4 Billion
Males38 300 000 (33.5%)29 363 (45.0%)216 000
Females41 700 000 (31.7%)35 760 (55.0%)272 000
NH white males32.9%21 830
NH white females30.1%27 907
NH black males44.9%6610
NH black females46.1%6783
Hispanic males29.6%*
Hispanic females29.9%*
Asian1667
American Indian or Alaska Native26.2%§326
Hypertension is defined in terms of National Health and Nutrition Examination Survey blood pressure measurements and health interviews. A subject was considered hypertensive if systolic blood pressure was ≥140 mm Hg or diastolic blood pressure was ≥90 mm Hg, if the subject said “yes” to taking antihypertensive medication, or if the subject was told on 2 occasions that he or she had hypertension. Prevalence in American Indian or Alaska Natives is based on self-report data from the National Health Interview Survey, with hypertension defined as subjects having been told on ≥2 different visits that they had hypertension or high blood pressure.
Ellipses (…) indicate data not available; and NH, non-Hispanic.
*
Mortality data for the white, black, Asian or Pacific Islander, and American Indian or Alaska Native populations include deaths among people of Hispanic and non-Hispanic origin. Death rates for Hispanic, American Indian or Alaska Native, and Asian and Pacific Islander people should be interpreted with caution because of inconsistencies in reporting Hispanic origin or race on the death certificate compared with censuses, surveys, and birth certificates. Studies have shown underreporting on death certificates of American Indian or Alaska Native, Asian and Pacific Islander, and Hispanic decedents, as well as undercounts of these groups in censuses.
These percentages represent the portion of total high blood pressure mortality that is for males vs females.
Includes Chinese, Filipino, Hawaiian, Japanese, and other Asian or Pacific Islander.
§
National Health Interview Survey (2013), National Center for Health Statistics; data are weighted percentages for Americans ≥18 years of age. Persons had to have been told on 2 or more different visits that they had hypertension or high blood pressure to be classified as hypertensive.31
Sources: Prevalence: National Health and Nutrition Examination Survey (2009–2012), National Center for Health Statistics, and National Heart, Lung, and Blood Institute. Percentages for racial/ethnic groups are age adjusted for Americans ≥20 years of age. Age-specific percentages are extrapolated to the 2012 US population estimates. Mortality: Centers for Disease Control and Prevention/National Center for Health Statistics, 2011 Mortality Multiple Cause-of-Death–United States. These data represent underlying cause of death only. Hospital discharges: National Hospital Discharge Survey, National Center for Health Statistics; data include those discharged alive, dead, or status unknown. Cost: Medical Expenditure Panel Survey data include estimated direct costs for 2011; indirect costs calculated by National Heart, Lung, and Blood Institute for 2011.
Table 9-2. Hypertension Awareness, Treatment, and Control: NHANES 1999 to 2006 and 2007 to 2012, by Race/Ethnicity and Sex
 AwarenessTreatmentControl
 1999–20062007–20121999–20062007–20121999–20062007–2012
NH white males71.880.261.872.641.953.3
NH white females76.984.468.180.240.056.7
NH black male70.180.059.667.934.140.7
NH black females85.388.276.681.143.854.1
Mexican American males57.767.041.857.925.635.0
Mexican American females69.978.657.970.531.947.0
Values are percentages. Hypertension is defined in terms of NHANES blood pressure measurements and health interviews. A subject was considered hypertensive if systolic blood pressure was ≥140 mm Hg or diastolic blood pressure was ≥90 mm Hg, or if the subject said “yes” to taking antihypertensive medication.
NH indicates non-Hispanic; and NHANES, National Health and Nutrition Examination Survey.
Sources: NHANES (1999–2006, 2007–2012) and National Heart, Lung, and Blood Institute.
HBP is a major risk factor for CVD and stroke.1 The AHA has identified untreated BP <90th percentile (for children) and <120/<80 mm Hg (for adults aged ≥20 years) as 1 of the 7 components of ideal cardiovascular health.2 In 2011 to 2012, 82.3% of children and 42.2% of adults met these criteria (Chapter 2, Cardiovascular Health).
Abbreviations Used in Chapter 9
AHAAmerican Heart Association
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHFcongestive heart failure
CHSCardiovascular Health Study
CKDchronic kidney disease
CRPC-reactive protein
CVDcardiovascular disease
DALYdisability-adjusted life-year
DBPdiastolic blood pressure
DMdiabetes mellitus
EDemergency department
ESRDend-stage renal disease
FHSFramingham Heart Study
HBPhigh blood pressure
HDheart disease
ICD-9International Classification of Diseases, 9th Revision
ICD-9-CMInternational Classification of Diseases, Clinical Modification, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
JNCJoint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure
LDLlow-density lipoprotein
MEPSMedical Expenditure Panel Survey
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
NAMCSNational Ambulatory Medical Care Survey
NCHSNational Center for Health Statistics
NHnon-Hispanic
NHAMCSNational Hospital Ambulatory Medical Care Survey
NHANESNational Health and Nutrition Examination Survey
NHDSNational Hospital Discharge Survey
NHESNational Health Examination Survey
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NNHSNational Nursing Home Survey
PAphysical activity
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEARCHSearch for Diabetes in Youth Study

Prevalence

(See Table 9-1 and Chart 9-1.)
Surveillance definitions vary widely in the published literature.3
For surveillance purposes, the following definition of HBP has been proposed3:
—SBP ≥140 mm Hg or DBP ≥90 mm Hg or taking antihypertensive medicine, or
—Having been told at least twice by a physician or other health professional that one has HBP.
With this definition, the prevalence of hypertension (age adjusted) among US adults ≥20 years of age was estimated to be 32.6% in NHANES 2009 to 2012. This equates to an estimated 80.0 million adults ≥20 years of age who have HBP (38.3 million men and 41.7 million women), extrapolated to 2012 data (Table 9-1).
In 2009 to 2012, the age-adjusted prevalence of hypertension was 44.9% and 46.1% among non-Hispanic black men and women, respectively; 32.9% and 30.1% among non-Hispanic white men and women, respectively; and 29.6% and 29.9% among Hispanic men and women, respectively.
NHANES data show that a higher percentage of men than women have hypertension until 45 years of age. From 45 to 54 years of age and from 55 to 64 years of age, the percentages of men and women with hypertension are similar. After that, a higher percentage of women have hypertension than men (Chart 9-1).
The prevalence of hypertension increased between 1988 to 1994, 1999 to 2006, and 2007 to 2012 among non-Hispanic black men (37.5%, 39.5%, and 40.1%, respectively) and women (38.2%, 41.7%, and 42.9%, respectively), non-Hispanic men (25.6%, 28.7%, and 30.1%, respectively) and women (22.9%, 27.8%, and 27.7%, respectively), and Mexican American women (25.0%, 26.1%, and 27.0%, respectively) but not Mexican American men (26.9%, 24.3%, and 26.6%, respectively).
Data from NHANES 2011 to 2012 found that 17.2% of US adults are not aware they have hypertension.4
Data from the 2007 to 2008 BRFSS, NHIS, and NHANES surveys found 27.8%, 28.5%, and 30.7% of US adults, respectively, had been told they had hypertension.5
Among those 18 to 39 years of age, prevalence was 7.3%; among those 40 to 59 years of age, prevalence was 32.4%; and among those ≥60 years of age, prevalence was 65.0%.4
Oral contraceptive use was less common among women with than among those without hypertension.6
Data from NHANES 2011 to 2012 estimated the prevalence of hypertension in men and women ≥18 years of age to be 29.7% and 28.5%, respectively.4
Data from the 2013 BRFSS/CDC indicate that the percentage of adults ≥18 years of age who had been told that they had HBP ranged from 25.5% in Minnesota and Colorado to 38.3% in Louisiana. The mean percentage for the United States was 30.4%.7
According to 2003 to 2008 NHANES data, among US adults with hypertension, 11.8% met the criteria for resistant hypertension (SBP/DBP ≥140/90 mm Hg and reported use of antihypertensive medications from 3 different drug classes or drugs from ≥4 antihypertensive drug classes regardless of BP). This represents an increase from 5.5% in 1998 to 1994 and 8.5% in 1999 to 2004.8
The “2014 Evidence-Based Guideline for the Management of High Blood Pressure in Adults” report recommends a higher SBP threshold (150 mm Hg) for treatment initiation and goal attainment in adults ≥60 years of age without DM or CKD. Additionally, the SBP treatment/goal threshold increased from 130 to 140 mm Hg among individuals with DM or CKD. The DBP goal remained at 90 mm Hg.9 This change should have minimal impact on the percentage of US adults <60 years of age with hypertension.
—The prevalence of hypertension using the 2014 definition versus the JNC 7 definition declined from 20.3% to 19.2%.10 Among US adults ≥60 years of age, the percentage with hypertension decreased from 68.9% to 61.2% between JNC 7 and the 2014 definition, with above-goal BP declining from 41.3% to 20.9%.10
—In 2005 to 2010, more US adults ≥60 years of age had SBP ≥150 mm Hg than between 140 and 149 mm Hg.11
Projections show that by 2030, ≈41.4% of US adults will have hypertension, an increase of 8.4% from 2012 estimates (unpublished AHA computation, based on methodology described by Heidenreich et al12).

Older Adults

In 2009 to 2010, hypertension was among the diagnosed chronic conditions that were more prevalent among older (≥65 years of age) women than older men (57% prevalence for women, 54% for men). Ever-diagnosed conditions that were more prevalent among older men than older women included HD (37% for men, 26% for women) and DM (24% for men, 18% for women), on the basis of data from NHIS/NCHS.13
The age-adjusted prevalence of hypertension (both diagnosed and undiagnosed) in 2003 to 2006 was 75% for older women and 65% for older men on the basis of data from NHANES/NCHS.14
Data from the 2004 NNHS revealed the most frequent chronic medical condition among this nationally representative sample of long-term stay nursing home residents aged ≥65 years was hypertension (53% of men and 56% of women). In men, prevalence of hypertension decreased with increasing age.15
Among US adults ≥60 years of age in NHANES 2011 to 2012, prevalence of hypertension was 65.0%, awareness of hypertension was 86.1%, treatment for hypertension was 82.2%, and control of hypertension was 50.5%.4
Data from NHANES 2005 to 2010 found that 76.5% of US adults ≥80 years of age had hypertension. Of this population, 43.9% had isolated systolic hypertension and 2.0% had systolic and diastolic hypertension.16
In 2005 to 2010, 30.9% of US adults ≥80 years of age were taking ≥3 classes of antihypertensive medication. This represents an increase from 7.0% and 19.2% in 1988 to 1994 and 1999 to 2004, respectively.16

Children and Adolescents

Data from participants aged 12 to 19 years in the 2005 to 2010 NHANES found ideal BP (<95th percentile) to be present in 78% of males and 90% of females; poor BP (>95th percentile) was found in 2.9% of male and 3.7% of female participants.17
Analysis of data from NHANES III (1988–1994) and NHANES 1999 to 2008 found the prevalence of elevated BP (SBP or DBP ≥90th percentile or SBP/DBP ≥120/80 mm Hg) increased from 15.8% to 19.2% among boys and from 8.2% to 12.6% among girls.18
Among older children, male sex, black race/ethnicity, higher BMI, and higher sodium intake were independently associated with elevated BP for participants 8 to 17 years of age in NHANES 1999 to 2008.18
In a study of 199 513 children (aged 3 to 17 years) across 3 large, integrated healthcare delivery systems, 81.9% were normotensive (<90th percentile of BP), 12.7% had prehypertension (90th to 94th percentiles), and 5.4% had hypertension (≥95th percentile) based on a single visit. After 2 additional visits, the prevalence of hypertension (≥95th percentile at all 3 visits) was confirmed to be present in only 0.14% of the children. The prevalence of confirmed hypertension was higher in non-Hispanic blacks and Asian/Pacific Islanders than in whites and was higher at higher BMI percentiles.19
Analysis of the NHES, the Hispanic Health and Nutrition Examination Survey, and the NHANES/NCHS surveys of the NCHS (1963–2002) found that the BP, pre-HBP, and HBP trends in children and adolescents 8 to 17 years of age moved downward from 1963 to 1988 and upward thereafter. Pre-HBP and HBP increased 2.3% and 1%, respectively, between 1988 and 1999. Increased obesity (abdominal obesity more so than general obesity) partially explained the HBP and pre-HBP rise from 1988 to 1999. BP and HBP reversed their downward trends 10 years after the increase in the prevalence of obesity. In addition, an ethnic and sex gap appeared in 1988 for pre-HBP and in 1999 for HBP: Non-Hispanic blacks and Mexican Americans had a greater prevalence of HBP and pre-HBP than non-Hispanic whites, and the prevalence was greater in boys than in girls. In that study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.20
A study in Ohio of >14 000 children and adolescents 3 to 18 years of age who were observed at least 3 times between 1999 and 2006 found that 507 children (3.6%) had hypertension. Of these, 131 (26%) had been diagnosed and 376 (74%) were undiagnosed. In addition, 3% of those with hypertension had stage 2 hypertension, and 41% of those with stage 2 hypertension were undiagnosed. Criteria for prehypertension were met by 485 children. Of these, 11% were diagnosed. In this study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.21
Analysis of data from the SEARCH study, which included children 3 to 17 years of age with type 1 and type 2 DM, found the prevalence of elevated BP to be 5.9% among those with type 1 DM and 23.7% among those with type 2 DM.22
Longitudinal BP outcomes from the National Childhood Blood Pressure database (ages 13–15 years) were examined after a single BP measurement. Among those determined to have prehypertension, 14% of boys and 12% of girls had hypertension 2 years later; the overall rate of progression from prehypertension to hypertension was ≈7%.23

Race/Ethnicity and HBP

(See Table 9-1 and Chart 9-2.)
The prevalence of hypertension in blacks in the United States is among the highest in the world. From 1988 to 1994 through 1999 to 2002, the prevalence of HBP in adults increased from 35.8% to 41.4% among blacks, and it was particularly high among black women at 44.0%. Prevalence among whites also increased, from 24.3% to 28.1%.24
From 1999 to 2000 through 2009 to 2010, the prevalence of hypertension did not increase among non-Hispanic black men (38.0% and 39.6% in 1999–2000 and 2009–2010, respectively) or women (40.8% and 43.1% in 1999–2000 and 2009–2010, respectively).25
In 2011 to 2012, non-Hispanic blacks had a higher prevalence of hypertension (42.1%) than non-Hispanic whites (28.0%), Hispanics (24.7%), and non-Hispanic Asians (24.7%).4
Compared with whites, blacks develop HBP earlier in life, and their average BP is much higher.26,27
The incidence of hypertension is higher for blacks than whites through 75 years of age; for a 45-year-old without hypertension, the 40-year risk for hypertension is 92.7% among blacks, 92.4% among Hispanics, 86.0% among whites, and 84.1% among Asians.28
Compared with whites, blacks have a 1.3 times greater rate of nonfatal stroke, a 1.8 times greater rate of fatal stroke, a 1.5 times greater rate of death attributable to HD, and a 4.2 times greater rate of ESRD (fifth and sixth reports of the JNC).
The same increment in SBP is associated with a higher stroke risk for blacks than for whites.29
Higher SBP explains ≈50% of the excess risk among blacks compared with whites.30
Data from the 2013 NHIS showed that black adults 18 years of age were more likely (32.6%) to have been told on ≥2 occasions that they had hypertension than American Indian/Alaska Native adults (26.2%), white adults (22.8%), Hispanic or Latino adults (21.6%), or Asian adults (21.0%).31
In NHANES 2011 to 2012, age-adjusted awareness of hypertension was similar among non-Hispanic blacks (85.7%), non-Hispanic whites (82.7%), and Hispanics (82.2%) and lower among non-Hispanic Asians (72.8%).4
In the Hispanic Community Health Study/Study of Latinos, the age-standardized prevalence of hypertension ranged from a low of 19.9% among US men from South America to 32.6% among their counterparts from the Dominican Republic. For US women, the age-standardized prevalence of hypertension was lowest for those of South American descent (15.9%) and highest for their counterparts from Puerto Rico (29.1%).32
Among NHIS 1997 to 2005 respondents, in multivariate-adjusted analyses that controlled for sociodemographic and health-related factors, odds of self-reported hypertension were 67% higher among Dominicans and 20% to 27% lower among Mexicans/ Mexican Americans and Central/ South Americans than among non-Hispanic whites.33
Data from MESA found that being born outside the United States, speaking a language other than English at home, and living fewer years in the United States were each associated with a decreased prevalence of hypertension.34
Filipino (27%) and Japanese (25%) adults were more likely than Chinese (17%) or Korean (17%) adults to have ever been told that they had hypertension.35

Mortality

(See Table 9-1.)
In 2011, there were 65 123 deaths attributable to HBP. In 2011, there were 377 258 any-mention deaths for HBP. The 2011 death rate was 18.9. Death rates were 17.6 for white males, 47.1 for black males, 15.2 for white females, and 35.1 for black females.36
When any-mention mortality for 2011 was used, the overall death rate was 110.0. Death rates were 114.5 for white males, 212.8 for black males, 90.1 for Asian or Pacific Islander males, and 100.6 for American Indian or Alaska Native males (underestimated because of underreporting). In females, rates were 92.0 for white females, 157.9 for black females, 71.5 for Asian or Pacific Islander females, and 83.3 for American Indian or Alaska Native females (underestimated because of underreporting).37
From 2001 to 2011, the death rate attributable to HBP increased 13.2%, and the actual number of deaths rose 39.3% (NHLBI tabulation).36
A mathematical model was developed to estimate the number of deaths that potentially could be prevented annually by increasing the use of 9 clinical preventive services. The model predicted that a 10% increase in hypertension treatment would result in ≈14 000 deaths prevented.38
Data from the Harvard Alumni Health Study found that higher BP in early adulthood was associated several decades later with higher risk for all-cause mortality, CVD mortality, and CHD mortality but not stroke mortality.39
An analysis of NHANES I and III that compared mortality over time in hypertensive and nonhypertensive US adults found a reduction in the age-adjusted mortality rate from 18.8 per 1000 person-years for NHANES I (follow-up: 1971–1992) to 14.3 for NHANES III (follow-up: 1988–2006) among people with hypertension. The reduction was higher in men than in women but was similar for blacks and whites.40
Compared with other dietary, lifestyle, and metabolic risk factors, HBP is the leading cause of death in women and the second-leading cause of death in men, behind smoking.41
The CDC analyzed death certificate data from 1995 to 2002 (any-mention mortality; ICD-9 codes 401–404 and ICD-10 codes I10–I13). The results indicated that Puerto Rican Americans had a consistently higher hypertension-related death rate than all other Hispanic subpopulations and non-Hispanic whites. The age-standardized hypertension-related mortality rate was 127.2 per 100 000 population for all Hispanics, similar to that of non-Hispanic whites (135.9). The age-standardized rate for Hispanic females (118.3) was substantially lower than that observed for Hispanic males (135.9). Hypertension-related mortality rates for males were higher than rates for females for all Hispanic subpopulations. Puerto Rican Americans had the highest hypertension-related death rate among all Hispanic subpopulations (154.0); Cuban Americans had the lowest (82.5).42
Assessment of 30-year follow-up of the Hypertension Detection and Follow-up Program identified the long-term benefit of stepped care, as well as the increased survival for hypertensive African Americans, although disparities in death rates did persist.43
Assessment of the Charleston Heart Study and Evans County Heart Study identified the excess burden of elevated BP for African Americans and its effect on long-term health outcomes.44

Risk Factors

Numerous risk factors and markers for development of hypertension have been identified, including age, ethnicity, family history of hypertension and genetic factors, lower education and socioeconomic status, greater weight, lower PA, tobacco use, psychosocial stressors, sleep apnea, and dietary factors (including dietary fats, higher sodium intake, lower potassium intake, and excessive alcohol intake).
A study of related individuals in the NHLBI’s FHS suggested that different sets of genes regulate BP at different ages.45
Recent data from the Nurses’ Health Study suggest that a large proportion of incident hypertension in women can be prevented by controlling dietary and lifestyle risk factors.46
Risk prediction models for developing hypertension have been developed and validated. A commonly used risk prediction model was developed in the FHS and includes age, sex, SBP, DBP, BMI, smoking, and parental history of hypertension.47,48

Aftermath

Approximately 69% of people who have a first heart attack, 77% of those who have a first stroke, and 74% of those who have CHF have BP ≥140/90 mm Hg (NHLBI unpublished estimates from ARIC, CHS, and FHS Cohort and Offspring studies).
Data from FHS/NHLBI indicate that recent (within the past 10 years) and remote antecedent BP levels may be an important determinant of risk over and above the current BP level.49
Data from the FHS/NHLBI indicate that hypertension is associated with shorter overall life expectancy, shorter life expectancy free of CVD, and more years lived with CVD.50
—Total life expectancy was 5.1 years longer for normotensive men and 4.9 years longer for normotensive women than for hypertensive people of the same sex at 50 years of age.
—Compared with hypertensive men at 50 years of age, men with untreated BP <140/90 mm Hg survived on average 7.2 years longer without CVD and spent 2.1 fewer years of life with CVD. Similar results were observed for women.

Hospital Discharges/Ambulatory Care Visits

(See Table 9-1.)
From 2000 to 2010, the number of inpatient discharges from short-stay hospitals with HBP as the first-listed diagnosis increased from 457 000 to 488 000 (no significant difference; NCHS, NHDS). The number of all-listed discharges increased from 8 034 000 to 11 282 000 (NHLBI, unpublished data from the NHDS, 2010; diagnoses in 2010 were truncated at 7 diagnoses for comparability with earlier year).
Data from the Nationwide Inpatient Sample from the years 2000 to 2007 found the frequency of hospitalizations for adults aged ≥18 years of age with a hypertensive emergency increased from 101 to 111 per 100 000 in 2007 (average increase of 1.11%). In contrast to the increased number of hospitalizations, the all-cause in-hospital mortality rate decreased during the same period from 2.8% to 2.6%.51
Data from ambulatory medical care use estimates for 2010 showed that the number of visits for essential hypertension was 43 436 000. Of these, 38 916 000 were physician office visits, 940 000 were ED visits, and 3 580 000 were outpatient department visits (NAMCS and NHAMCS, NHLBI tabulation).
In 2010, there were 280 000 hospitalizations with a first-listed diagnosis of essential hypertension (ICD-9-CM code 401), but essential hypertension was listed as either a primary or a secondary diagnosis on 11 048 000 hospitalized inpatient visits (unpublished data from the NHDS, NHLBI tabulation).

Awareness, Treatment, and Control

(See Table 9-2 and Charts 9-3 through 9-5.)
Data from NHANES 2009 to 2012 showed that of those with hypertension who were ≥20 years of age, 82.7% were aware of their condition, 76.5% were under current treatment, 54.1% had their hypertension under control, and 45.9% did not have it controlled. Awareness and treatment of hypertension were higher at older ages. Hypertension control was higher in US adults 40 to 59 years of age (58.0%) and those ≥60 years of age (54.1%) than in their counterparts 20 to 39 years of age (35.4%). Non-Hispanic black adults were more aware of their hypertension than Hispanics (87.0% and 77.7%, respectively; NHLBI tabulation).
Data from NHANES 1999 to 2008 and BRFSS 1997 to 2009 showed awareness, treatment, and control of hypertension varied across the country and were highest in the southeastern United States.52
Analysis of NHANES 1999 to 2006 and 2009 to 2012 found the proportion of adults aware of their hypertension increased within each race-ethnicity/sex subgroup. Similarly, large increases in hypertension treatment and control (≈10%) occurred in each of these groups (Table 9-2).
According to data from NHANES 1999 to 2000 through 2009 to 2010, HBP control rates improved from 27.5% to 46.5%, treatment improved from 56.9% to 71.6%, and the control among those treated improved from 46.5% to 64.4%.53
In 2009 to 2010, controlled hypertension increased from 48.4% to 53.3%, respectively. Medication use to lower hypertension was lowest for those aged 18 to 39 years (46.0%) compared with those aged 40 to 59 years (77.1%) and those aged ≥60 years (80.7%). Non-Hispanic black adults were more likely to take antihypertensive medication than non-Hispanic whites or Hispanic adults (79.7%, 76.6%, and 69.6%, respectively).54
Data from the NHANES 2005 to 2010 show that among those ≥80 years of age, 79.4% of those with hypertension were aware of this condition, 57.4% were treated, and 39.8% had controlled their BP to JNC 7 targets.16
The change in SBP threshold from JNC 7 to the 2014 JAMA definition resulted in 5.8 million fewer US adults having antihypertensive medication treatment recommended to them, and 13.5 million fewer US adults taking treatment were recommended to be prescribed dose intensification or additional medication classes.10
Among a cohort of postmenopausal women taking hormone replacement, hypertension was the most common comorbidity, with a prevalence of 34%.55
A study of >300 women in Wisconsin showed a need for significant improvement in BP and LDL levels. Of the screened participants, 35% were not at BP goal, 32.4% were not at LDL goal, and 53.5% were not at both goals.56
In 2005, a survey of people in 20 states conducted by the BRFSS of the CDC found that 19.4% of respondents had been told on ≥2 visits to a health professional that they had HBP. Of these, 70.9% reported changing their eating habits; 79.5% reduced the use of or were not using salt; 79.2% reduced the use of or eliminated alcohol; 68.8% were exercising; and 73.4% were taking antihypertensive medication.57
Among 1509 NHANES 2005 to 2006 participants aged ≥30 years with hypertension, 24% were categorized as low risk, 21% as intermediate risk, and 23% as high risk according to Framingham global risk. Treatment for hypertension varied by risk category and ranged from 58% to 75%; hypertension control was 80% for those in the low-risk category and <50% for those in the high-risk category.58
According to data from NHANES 2001 to 2006, non-Hispanic blacks had 90% higher odds of poorly controlled BP than non-Hispanic whites. Among those who were hypertensive, non-Hispanic blacks and Mexican Americans had 40% higher odds of uncontrolled BP than non-Hispanic whites.59
According to data from NHANES 1998 to 2008 for adults with DM, prevalence of hypertension increased, whereas awareness, treatment, and control improved during these time periods; however, for adults 20 to 44 years of age, there was no evidence of improvement.60

Global Burden of Hypertension

In 2000, it was estimated that 972 million adults worldwide had hypertension.61
Between 1980 and 200862:
—The global mean age-adjusted SBP declined from 130.5 mm Hg in 1980 to 128.1 mm Hg in men and from 127.2 to 124.4 mm Hg in women.
—The global age-adjusted prevalence of uncontrolled hypertension decreased from 33% to 29% among men and from 29% to 25% among women.
—Because of population growth and aging, the number of people worldwide with uncontrolled hypertension (SBP ≥140 mm Hg or DBP ≥90 mm Hg) increased from 605 million to 978 million between 1980 and 2008.62
HBP went from being the fourth-leading risk factor in 1990, as quantified by DALYs, to being the number 1 risk factor in 2010.63
In 2010, HBP was 1 of the 5 leading risk factors in all regions with the exception of Oceania, Eastern sub-Saharan Africa, and Western sub-Saharan Africa.63

Cost

(See Table 9-1.)
The estimated direct and indirect cost of HBP for 2011 is $46.4 billion (MEPS, NHLBI tabulation).
Projections show that by 2030, the total cost of HBP could increase to an estimated $274 billion (unpublished AHA computation, based on methodology described in Heidenreich et al12).

Prehypertension

Prehypertension is untreated SBP of 120 to 139 mm Hg or untreated DBP of 80 to 89 mm Hg and not having been told on 2 occasions by a physician or other health professional that one has hypertension.
Among disease-free participants in NHANES 1999 to 2006, the prevalence of prehypertension was 36.3%. Prevalence was higher in men than in women. Furthermore, prehypertension was correlated with an adverse cardiometabolic risk profile.64
Follow-up of 9845 men and women in the FHS/NHLBI who attended examinations from 1978 to 1994 revealed that at 35 to 64 years of age, the 4-year incidence of hypertension was 5.3% for those with baseline BP <120/80 mm Hg, 17.6% for those with SBP of 120 to 129 mm Hg or DBP of 80 to 84 mm Hg, and 37.3% for those with SBP of 130 to 139 mm Hg or DBP of 85 to 89 mm Hg. At 65 to 94 years of age, the 4-year incidences of hypertension were 16.0%, 25.5%, and 49.5% for these BP categories, respectively.65
Among participants with and without prehypertension in MESA, 23.6% and 5.3%, respectively, developed hypertension over 4.8 years of follow-up.48
Data from FHS/NHLBI also reveal that prehypertension is associated with elevated relative and absolute risks for CVD outcomes across the age spectrum. Compared with normal BP (<120/80 mm Hg), prehypertension was associated with a 1.5- to 2-fold increased risk for major CVD events in those <60, 60 to 79, and ≥80 years of age. Absolute risks for major CVD associated with prehypertension increased markedly with age: 6-year event rates for major CVD were 1.5% in prehypertensive people <60 years of age, 4.9% in those 60 to 79 years of age, and 19.8% in those ≥80 years of age.66
In a study of NHANES 1999 to 2000 (NCHS), people with prehypertension were more likely than those with normal BP levels to have above-normal cholesterol levels (≥200 mg/dL) and to be overweight or obese, whereas the probability of current smoking was lower. People with prehypertension were 1.65 times more likely to have ≥1 of these adverse risk factors than were those with normal BP.67
In the REGARDS study, prehypertension was more common in blacks than whites and was more common among people with other risk factors, including DM and elevated CRP.68
A meta-analysis of 29 prospective cohort studies (including 1 010 858 participants) found prehypertension was associated with CVD incidence or death, stroke, and MI. The risk was particularly noted for those with BP values in the higher prehypertension range.69

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