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Heart Disease and Stroke Statistics—2014 Update

A Report From the American Heart Association
and on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Originally publishedhttps://doi.org/10.1161/01.cir.0000441139.02102.80Circulation. 2014;129:e28–e292

Table of Contents

Summary e29

1. About These Statistics e36

2. Cardiovascular Health e39

Health Behaviors

3. Smoking/Tobacco Use e60

4. Physical Inactivity e65

5. Nutrition e73

6. Overweight and Obesity e87

Health Factors and Other Risk Factors

7. Family History and Genetics e96

8. High Blood Cholesterol and Other Lipids e101

9. High Blood Pressure e107

10. Diabetes Mellitus e117

11. Metabolic Syndrome e129

12. Chronic Kidney Disease e137

Conditions/Diseases

13. Total Cardiovascular Diseases e142

14. Stroke (Cerebrovascular Disease) e166

15. Congenital Cardiovascular Defects and Kawasaki Disease e191

16. Disorders of Heart Rhythm e199

17. Subclinical Atherosclerosis e217

18. Coronary Heart Disease, Acute Coronary Syndrome, and

Angina Pectoris e227

19. Cardiomyopathy and Heart Failure e242

20. Valvular, Venous, and Aortic Diseases e248

21. Peripheral Artery Disease e256

Outcomes

22. Quality of Care e259

23. Medical Procedures e275

24. Economic Cost of Cardiovascular Disease e280

Supplemental Materials

25. At-a-Glance Summary Tables e285

26. Glossary e290

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 on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a critical resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best available national data on heart disease, stroke, and other cardiovascular disease–related morbidity and mortality and the risks, quality of care, use of medical procedures and operations, and costs associated with the management of these diseases in a single document. Indeed, since 1999, the Statistical Update has been cited >10 500 times in the literature, based on citations of all annual versions. In 2012 alone, the various Statistical Updates were cited ≈3500 times (data from Google Scholar). In recent years, the Statistical Update has undergone some major changes with the addition of new chapters and major updates across multiple areas, as well as increasing the number of ways to access and use the information assembled.

For this year’s edition, the Statistics Committee, which produces the document for the AHA, updated all of the current chapters with the most recent nationally representative data and inclusion of relevant articles from the literature over the past year. This year’s edition includes a new chapter on peripheral artery disease, as well as new data on the monitoring and benefits of cardiovascular health in the population, with additional 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.

The 2014 Update Expands Data Coverage of the Epidemic of Poor Cardiovascular Health Behaviors and Their Antecedents and Consequences

  • Adjusted estimated population attributable fractions for cardiovascular disease (CVD) mortality were as follows1: 40.6% (95% confidence interval [CI], 24.5%–54.6%) for high blood pressure; 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 physical activity; and 8.8% (95% CI, 2.1%–15.4%) for abnormal blood glucose levels.

  • Although significant progress has been made over the past 4 decades, in 2012, among Americans ≥18 years of age, 20.5% of men and 15.9% of women continued to be cigarette smokers. In 2011, 18.1% of students in grades 9 through 12 reported current cigarette use.

  • The percentage of the nonsmoking population with exposure to secondhand smoke (as measured by serum cotinine levels ≥0.05 ng/mL) declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008. More than half of children 3 to 11 years of age (53.6%) and almost half of those 12 to 19 years of age (46.5%) had detectable levels, compared with just over a third of adults 20 years of age and older (36.7%).

  • The proportion of youth (≤18 years of age) who report engaging in no regular physical activity is high, and the proportion increases with age.

  • In 2011, among adolescents in grades 9 through 12, 17.7% of girls and 10.0% of boys reported that they had not engaged in ≥60 minutes of moderate to vigorous physical activity (defined as any activity that increased heart rate or breathing rate) at least once in the previous 7 days, despite recommendations that children engage in such activity 7 days per week.

  • In 2012, 29.9% of adults reported engaging in no aerobic leisure-time physical activity.

  • In 2009 to 2010, <1% of Americans met at least 4 of 5 healthy dietary goals. Among adults aged ≥20 years, only 12.3% met recommended goals for fruits and vegetables; 18.3% met goals for fish; 0.6% met goals for sodium; 51.9% met goals for sugar-sweetened beverages; and 7.3% met goals for whole grains. These proportions were even lower in children, with only 29.4% of adolescents aged 12 to 19 years meeting goals for low sugar-sweetened beverage intake.

  • The estimated prevalence of overweight and obesity in US adults (≥20 years of age) is 154.7 million, which represented 68.2% of this group in 2010. Nearly 35% of US adults are obese (body mass index ≥30 kg/m2). Men and women of all race/ethnic groups in the population are affected by the epidemic of overweight and obesity.

  • Among children 2 to 19 years of age, 31.8% are overweight and obese (which represents 23.9 million children) and 16.9% are obese (12.7 million children). Mexican American boys and girls and African American girls are disproportionately affected. From 1971-1974 to 2007-2010, the prevalence of obesity in children 6 to 11 years of age has increased from 4.0% to 18.8%.

  • Obesity (body mass index ≥30 kg/m2) is associated with marked excess mortality in the US population. Even more notable is the excess morbidity associated with overweight and obesity in terms of risk factor development and incidence of diabetes mellitus, CVD end points (including coronary heart disease, stroke, and heart failure), and numerous other health conditions, including asthma, cancer, end-stage renal disease, degenerative joint disease, and many others.

Prevalence and Control of Cardiovascular Health Factors and Risks Remain an Issue for Many Americans

  • An estimated 31.9 million adults ≥20 years of age have total serum cholesterol levels ≥240 mg/dL, with a prevalence of 13.8%.

  • Based on 2007 to 2010 data, 33.0% of US adults ≥20 years of age have hypertension. This represents ≈78 million US adults with hypertension. The prevalence of hypertension is similar for men and women. African American adults have among the highest prevalence of hypertension (44%) in the world.

  • Among hypertensive Americans, ≈82% are aware of their condition and 75% are using antihypertensive medication, but only 53% of those with documented hypertension have their condition controlled to target levels.

  • In 2010, an estimated 19.7 million Americans had diagnosed diabetes mellitus, representing 8.3% of the adult population. An additional 8.2 million had undiagnosed diabetes mellitus, and 38.2% had prediabetes, with abnormal fasting glucose levels. African Americans, Mexican Americans, Hispanic/Latino individuals, and other ethnic minorities bear a strikingly disproportionate burden of diabetes mellitus in the United States.

  • The prevalence of diabetes mellitus is increasing dramatically over time, in parallel with the increases in prevalence of overweight and obesity.

Rates of Death Attributable to CVD Have Declined, but the Burden of Disease Remains High

  • The 2010 overall rate of death attributable to CVD was 235.5 per 100 000. The rates were 278.4 per 100 000 for white males, 369.2 per 100 000 for black males, 192.2 per 100 000 for white females, and 260.5 per 100 000 for black females.

  • From 2000 to 2010, death rates attributable to CVD declined 31.0%. In the same 10-year period, the actual number of CVD deaths per year declined by 16.7%. Yet in 2010, CVD (I00–I99; Q20–Q28) still accounted for 31.9% (787 650) of all 2 468 435 deaths, or ≈1 of every 3 deaths in the United States.

  • On the basis of 2010 death rate data, >2150 Americans die of CVD each day, an average of 1 death every 40 seconds. About 150 000 Americans who died of CVD in 2010 were <65 years of age. In 2010, 34% of deaths attributable to CVD occurred before the age of 75 years, which is before the current average life expectancy of 78.7 years.

  • Coronary heart disease alone caused ≈1 of every 6 deaths in the United States in 2010. In 2010, 379 559 Americans died of CHD. Each year, an estimated ≈620 000 Americans have a new coronary attack (defined as first hospitalized myocardial infarction or coronary heart disease death) and ≈295 000 have a recurrent attack. It is estimated that an additional 150 000 silent first myocardial infarctions occur each year. Approximately every 34 seconds, 1 American has a coronary event, and approximately every 1 minute 23 seconds, an American will die of one.

  • From 2000 to 2010, the relative rate of stroke death fell by 35.8% and the actual number of stroke deaths declined by 22.8%. 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 2010, stroke caused ≈1 of every 19 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.2

  • In 2010, 1 in 9 death certificates (279 098 deaths) in the United States mentioned heart failure. Heart failure was the underlying cause in 57 757 of those deaths in 2010. The number of any-mention deaths attributable to heart failure was approximately as high in 1995 (287 000) as it was in 2010 (279 000). 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.

The 2014 Update Provides Critical Data About Cardiovascular Quality of Care, Procedure Utilization, and Costs

In light of the current national focus on healthcare utilization, costs, and quality, it is critical to monitor and understand the magnitude of healthcare delivery and costs, as well as the quality of healthcare delivery, related to CVD risk factors and conditions. The Statistical Update provides these critical data in several sections.

Quality-of-Care Metrics for CVDs

Quality data are available from the AHA’s Get With The Guidelines programs for coronary heart disease, heart failure, and resuscitation and from the American Stroke Association/AHA’s Get With The Guidelines program for acute stroke. Similar data from the Veterans Healthcare Administration, national Medicare and Medicaid data, and Acute Coronary Treatment and Intervention Outcomes Network (ACTION)–Get With The Guidelines Registry data are also reviewed. These data show impressive adherence to guideline recommendations for many, but not all, metrics of quality of care for these hospitalized patients. Data are also reviewed on screening for CVD risk factor levels and control.

Cardiovascular Procedure Use and Costs

  • The total number of inpatient cardiovascular operations and procedures increased 28%, from 5 939 000 in 2000 to 7 588 000 in 2010 (National Heart, Lung, and Blood Institute computation based on National Center for Health Statistics annual data).

  • The total direct and indirect cost of CVD and stroke in the United States for 2010 is estimated to be $315.4 billion. This figure includes health expenditures (direct costs, which include the cost of physicians and other professionals, hospital services, prescribed medications, home health care, and other medical durables) and lost productivity that results from premature mortality (indirect costs).

  • By comparison, in 2008, the estimated cost of all cancer and benign neoplasms was $201.5 billion ($77.4 billion in direct costs, and $124 billion in mortality indirect costs). CVD costs more than any other diagnostic group.

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 Statistics Update.

This annual Statistical Update is the product of an entire year’s worth of effort by dedicated professionals, volunteer physicians and scientists, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.

Alan S. Go, MD

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 National Health and Nutrition Examination Survey (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 2010 because this is the most recent year of NHANES data used in the Statistical Update.

Writing Group Disclosures

Writing Group MemberEmploymentResearch GrantOther Research SupportSpeakers’ Bureau/HonorariaExpert WitnessOwnership InterestConsultant/Advisory BoardOther
Alan S. GoKaiser PermanenteNoneNoneNoneNoneNoneNoneNone
Dariush MozaffarianBrigham and Women’s Hospital, Harvard Medical School, and Harvard School of Public HealthNoneNoneAd hoc travel reimbursement and/or honoraria for one-time scientific presentations or reviews on diet and cardiometabolic diseases from Life Sciences Research Organization (10/12) and Bunge (4/13) (each)*NoneNoneAd hoc consulting fees from Amarin (9/13), Omthera (9/13), and Winston and Strawn LLP (9/13) (each)*; Advisory board: Unilever North America Scientific Advisory Board*Royalties from UpToDate, for an online chapter on fish oil*; Patent: Harvard University has filed a provisional patent application that has been assigned to Harvard University, listing Dr. Mozaffarian as a co-inventor to the US Patent and Trademark Office for use of trans-palmitoleic acid to prevent and treat insulin resistance, type 2 diabetes, and related conditions (no compensation)*
Véronique L. RogerMayo ClinicNIH†NoneNoneNoneNoneNoneNone
Emelia J. BenjaminBoston Uni versity School of Medicine2R01HL092577-05†; 1R01HL102214†; HHSN26820130047C†NoneNoneNoneNoneNIH, NHLBI Outside Safety & Monitoring Board for the Coronary Artery Risk Development in Young Adults [CARDIA] Study*;Honorarium, American Heart Association, Associate Editor, Circulation†None
Jarett D. BerryUT SouthwesternNHLBI†; AHA†NoneMerck†NoneNoneNoneNone
Michael J. BlahaJohns HopkinsNoneNoneNoneNoneNoneNoneNone
Shifan DaiCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Earl S. FordCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Caroline S. FoxNational Heart, Lung, and Blood InstituteNoneNoneNoneNoneNoneNoneNone
Sheila FrancoCenters for Disease Control and Prevention/National Center for Health StatisticsNoneNoneNoneNoneNoneNoneNone
Heather J. FullertonUniversity of California, San FranciscoNIH†; AHA†Private Philanthropy†NoneNoneNoneNoneNone
Cathleen GillespieCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Susan M. HailpernIndependent ConsultantNoneNoneNoneNoneNoneNoneNone
John A. HeitMayo ClinicNIH*NoneNoneNoneNoneDaiichi Sankyo*; Janssen Pharmaceutical*None
Virginia J. HowardUniversity of Alabama at BirminghamNIH†NoneNoneNoneNoneNoneNone
Mark D. HuffmanNorthwestern University Feinberg School of MedicineNational Heart, Lung, and Blood Institute†; Eisenberg Foundation†Fogarty International Center (travel)*; World Heart Federation (conference, travel, and contract proposal under development)†; American Heart Association (travel)*; Cochrane Heart Group (travel)*NoneNoneNoneNoneNone
Suzanne E. JuddUniversity of Alabama at BirminghamNIH†; diaDexus†NoneNoneNoneNonediaDexus†None
Brett M. KisselaUniversity of CincinnatiNIH†AbbVie and Reata*NoneNoneNoneAllergan*None
Steven J. KittnerUniversity of Maryland School of Medicine and Veterans Administration Health Care SystemNINDS Ischemic Stroke Genetics Consortium (U01NS069208)†NoneNoneNoneNoneNoneNone
Daniel T. LacklandMedical University of South CarolinaNoneNoneNoneNoneNoneNoneNone
Judith H. LichtmanYale UniversityAHA†; NIH†NoneNoneNoneNoneNoneNone
Lynda D. LisabethUniversity of MichiganR01 NS38916†; R01 NS062675*; R01 HL098065†; R01 NS070941†NoneNoneNoneNoneNoneNone
Rachel H. MackeyUniversity of PittsburghLipoScience Inc.†NoneNational Lipid Association*NoneNoneNoneNone
David J. MagidColorado Permanente Medical GroupNHLBI†; NIMH*; NIA*; AHRQ†; PCORI†; Amgen*NoneNoneNoneNoneNoneNone
Gregory M. MarcusUniversity of California, San FranciscoAmerican Heart Association†; Gilead Sciences†; Medtronic†; SentreHeart†NoneNoneNoneNoneInCarda*None
Ariane MarelliMcGill University Health CenterNoneNoneNoneNoneNoneNoneNone
David B. MatcharDuke University Medical Center/Duke-NUS Graduate Medical SchoolSingapore National Medical Research Council (NMRC)†NoneNoneNoneNoneNoneNone
Darren K. McGuireUT South western Medical CenterNoneAstra Zeneca*; Boehringer Ingelheim*; Bristol Myers Squibb*; Daiichi Sankyo*; Eli Lilly*; Genentech*; Glaxo Smith Kline*; F. Hoffmann LaRoche†; Merck*; Orexigen Therapeutic†; Takeda Pharmaceuticals North America*NoneTakeda Pharmaceuticals North America†NoneBoehringer Ingelheim*; Bristol Myers Squibb*; Genentech*; Janssen†; F. Hoffmann LaRoche*; Merck*; Sanofi Aventis*None
Emile R. Mohler IIIUniversity of PennsylvaniaGSK*; NIH*; Pluristem*NoneNoneNoneCytovas†; Floxmedical†Pfizer*; Takeda*None
Claudia S. MoyNational Institutes of HealthNoneNoneNoneNoneNoneNoneNone
Michael E. MussolinoNational Heart, Lung, and Blood InstituteNoneNoneNoneNoneNoneNoneNone
Robert W. NeumarUniversity of Michigan Health SystemNoneNoneNoneNoneNoneNoneNone
Graham NicholUniversity of WashingtonResuscitation Outcomes Consortium (NIH U01 HL077863-06) 2010-2015, Co-PI†; Dynamic AED Registry (Food and Drug Administration, Cardiac Science Corp., Philips Healthcare Inc., Physio-Control Inc., HealthSine Technologies Inc., ZOLL Inc) 2012-2016, PI*; Velocity Pilot Study of Ultrafast Hypothermia in Patients with ST-elevation Myocardial Infarction (Velomedix Inc.) 2012-2014, National Co-PI (Waived personal compensation)*Novel method of tracking location of medical devices in time and space. (Patent pending, assigned to University of Washington)*NoneNoneNoneMedic One Foundation Board of Directors (Money to Institution)*None
Dilip K. PandeyUniversity of Illinois at ChicagoNoneNoneNoneNoneNoneNoneNone
Nina P. PaynterBrigham and Women’s HospitalCelera†; National Institutes of Health†NoneNoneNoneNoneNoneNone
Matthew J. ReevesMichigan State UniversityNoneNoneNoneNoneNoneNoneNone
Paul D. SorlieNational Heart, Lung, and Blood Institute, NIHNoneNoneNoneNoneNoneNoneNone
Joel SteinColumbia UniversityNoneMyomo*; Tyromotion*QuantiaMD*NoneNoneMyomo*None
Amytis TowfighiUniversity of Southern CaliforniaAHA†; NIH/NINDS†NoneNoneNoneNoneNoneNone
Tanya N. TuranMedical University of South CarolinaNIH/NINDS K23 – CHIASM PI†NoneNoneExpert witness in Stroke-related medical malpractice cases*NoneBoehringer Ingelheim, BI1356/BI 10773 Trials – Clinical Endpoint Adjudication Committee†; Gore REDUCE Trial- Clinical Endpoint Adjudication Committee*; NIH/NINDS VERITAS study – Clinical Endpoint Adjudication Committee*None
Melanie B. TurnerAmerican Heart AssociationNoneNoneNoneNoneNoneNoneNone
Salim S. ViraniDepartment of Veterans Affairs, Baylor College of MedicineAgency for Health Care Research and Quality*; Department of Veterans Affairs†; NIH*; Roderick D. MacDonald Research Foundation†NoneNoneNoneNoneNoneNone
Nathan D. WongUniversity of California, IrvineBristol-Myers Squibb†; Regeneron†NoneNoneNoneNoneGenzyme*None
Daniel WooUniversity of CincinnatiNoneNoneNoneNoneNoneNoneNone

This table represents the relationships of writing group members that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all members of the writing group are required to complete and submit. A relationship is considered to be “significant” if (1) the person receives $10 000 or more during any 12-month period, or 5% or more of the person’s gross income; or (2) the person owns 5% or more of the voting stock or share of the entity, or owns $10 000 or more of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.

*Modest.

†Significant.

Acknowledgments

We wish to thank Lucy Hsu, Michael Wolz, Sean Coady, and Khurram Nasir for their valuable comments and contributions. We would like to acknowledge Lauren Rowell for her administrative assistance.

Footnotes

WRITING GROUP MEMBERS

*The findings and conclusions of this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.

The American Heart Association requests that this document be cited as follows: Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER 3rd, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014;129:e28–e292.

A copy of the document is available at http://my.americanheart.org/statements by selecting either the “By Topic” link or the “By Publication Date” link. To purchase additional reprints, call 843-216-2533 or e-mail .

Expert peer review of AHA Scientific Statements is conducted by the AHA Office of Science Operations. For more on AHA statements and guidelines development, visit http://my.americanheart.org/statements and select the “Policies and Development” link.

Permissions: Multiple copies, modification, alteration, enhancement, and/or distribution of this document are not permitted without the express permission of the American Heart Association. Instructions for obtaining permission are located at http://www.heart.org/HEARTORG/General/Copyright-Permission-Guidelines_UCM_300404_Article.jsp. A link to the “Copyright Permissions Request Form” appears on the right side of the page.

References

  • 1. 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.CrossrefMedlineGoogle Scholar
  • 2. Lackland DT, Roccella EJ, Deutsch A, Fornage M, George MG, Howard G, Kissela B, Kittner SJ, Lichtman JH, Lisabeth L, Schwamm LH, Smith EE, Towfighi A; on behalf of the American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Quality of Care and Outcomes and Research, and Council on Functional Genomics and Translational Biology. Factors influencing the decline in stroke mortality: a statement from the American Heart Association/American Stroke Association.Stroke. December 5, 2013. DOI: 10.1161/01.str.0000437068.30550.cf. http://stroke.ahajournals.org/lookup/doi/10.1161/01.str.0000437068.30550.cf. Accessed December 5, 2013.LinkGoogle Scholar

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 26 of this document, the Glossary.

The surveys used are:

  • 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 2007 to 2010 (in most cases, these are the latest published figures). These are applied to census population estimates for 2010. 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 2007 to 2010 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 2010 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 19 (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, the Health Data Interactive data system of the NCHS, 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 and from Health Data Interactive.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 2010 in the computation of morbidity data. NCHS population estimates for 2010 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 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 2010 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 2010. For disease and risk factor prevalence, most rates in this report are calculated from the 2007 to 2010 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US population in 2010, 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 2010.

Cardiovascular Disease

For data on hospitalizations, physician office visits, and mortality, CVD is defined according to ICD codes given in Chapter 26 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 26) for explanation of terms.

References

2. Cardiovascular Health

See Tables 2-1 through 2-8 and Charts 2-1 through 2-13.

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.

Table 2-2. Prevalence of Ideal Cardiovascular Health and its Components in the US Population, Overall and in Selected Age Strata From NHANES 2007 to 2008 and 2009 to 2010

Prevalence, %
Ages 12–19 y,2007–2008Ages 12–19 y,2009–2010Ages ≥20 y,*2007–2008Ages ≥20 y,*2009–2010Ages 20–39 y,2007–2008Ages 20–39 y,2009–2010Ages 40–59 y,2007–2008Ages 40–59 y,2009–2010Ages ≥60 y,2007–2008Ages ≥60 y,2009–2010
Ideal cardiovascular health profile (composite–all 7)0.00.00.00.10.00.30.00.00.00.0
≥6 Ideal cardiovascular health composite score8.218.43.64.57.17.82.12.90.10.8
≥5 Ideal cardiovascular health composite score39.848.515.817.229.729.39.711.12.95.8
Ideal health factors index (composite–all 4)35.548.413.915.827.729.57.39.21.02.4
Total cholesterol <200 mg/dL (untreated)69.669.546.347.064.167.137.136.529.929.6
SBP <120 mm Hg and DBP <80 mm Hg (untreated)82.385.743.844.663.865.236.940.514.615.8
Not current smoker (never or quit ≥12 mo)83.785.272.976.366.469.772.975.586.188.3
Fasting blood glucose <100 mg/dL76.287.952.057.567.474.045.654.031.935.4
Ideal health behaviors index (composite–all 4)0.00.00.10.20.10.60.00.00.00.0
PA at goal39.036.539.540.945.645.936.441.033.733.0
Not current smoker (never or quit ≥12 mo)83.785.272.976.366.469.772.975.586.188.3
BMI <25 kg/m262.564.231.931.039.137.728.027.725.325.3
4–5 Diet goals met0.00.10.30.50.30.70.10.50.50.3
Fruits and vegetables ≥4.5 cups/d7.97.612.313.711.711.511.413.715.817.0
Fish ≥2 3.5-oz servings/wk9.28.518.323.716.821.919.724.419.426.0
Sodium <1500 mg/d0.00.20.60.20.60.20.80.30.30.3
Sugar-sweetened beverages <36 oz/wk32.029.551.955.541.042.754.658.271.273.5
Whole grains (≥1.1 g of fiber per 10 g of carbohydrates) ≥3 1-oz equivalents/d3.25.87.311.27.011.27.110.48.411.9
Secondary dietary metrics
Nuts, legumes, seeds ≥4 servings/wk8.712.221.723.619.621.322.525.424.724.8
Processed meats <2 servings/wk56.353.257.658.054.054.759.758.761.162.3
Saturated fat <7% of total energy intake (kcal)4.58.18.711.79.313.58.010.29.011.3

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, except for dietary metrics.

Scaled for 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. Selected Secondary Metrics for Monitoring CVD, NHANES 2009 to 2010

In the Presence of CVDIn the Absence of CVD
No.*% (SE)†No.*% (SE)†
Total16 209 4747.2 (0.4)199 590 59692.8 (0.4)
CHD6 916 0123.2 (0.3)
Stroke5 717 7592.7 (0.2)
CHF4 320 2272.0 (0.3)
Acute MI6 929 9053.2 (0.3)
Health behaviors
Smoking
Current smoker or smokers who quit <12 mo ago3 127 27337.2 (4.9)40 760 06620.1 (0.9)
PA
PA: intermediate or poor‡11 813 01174.1 (5.1)115 561 98858.4 (1.5)
PA: none10 598 90864.5 (5.5)93 459 55647.3 (1.2)
Diet, No. of metrics
Total diet score 0–3 of 512 665 860100.0 (0.00)161 370 15499.7 (0.11)
Total diet score 0–1 of 59 540 53270.1 (4.69)127 156 29378.8 (1.42)
Overweight/obesity
Overweight or obese (BMI ≥25.0 kg/m2)12 621 70169.4 (4.1)134 879 71368.1 (1.3)
Obese (BMI ≥30.0 kg/m2)7 763 61149.0 (5.2)68 655 70234.7 (1.0)
Health factors
Hypertension
Prevalence of BP ≥140/90 mm Hg or taking medications10 591 17051.0 (5.0)53 523 89528.5 (0.9)
Awareness among those with hypertension10 071 34398.6 (0.3)42 436 78270.6 (3.2)
Treatment those with hypertension9 819 24497.4 (0.4)39 194 94861.4 (2.9)
BP control to <140/<90 mm Hg among treated6 886 17664.2 (9.5)27 323 64972.4 (2.4)
BP control to <140/<90 mm Hg among hypertensive6 886 17662.3 (9.4)27 323 64943.3 (2.8)
Hypercholesterolemia
Prevalence of total cholesterol ≥240 mg/dL or taking medications8 201 82937.1 (4.2)48 701 19825.7 (0.7)
Awareness among those with hypercholesterolemia7 742 12784.6 (8.0)35 174 93159.9 (2.6)
Treatment among those with hypercholesterolemia7 219 07879.3 (8.5)25 405 33438.7 (2.4)
Cholesterol control to <200 mg/dL among treated6 659 73295.0 (1.4)22 804 72490.4 (1.2)
Cholesterol control to <200 mg/dL among hypercholesterolemia6 659 73275.0 (8.9)22 804 72434.7 (2.4)
Diabetes mellitus
Prevalence of fasting glucose ≥125 mg/dL or taking medications4 769 75915.2 (2.2)21 078 44310.3 (1.1)
Awareness among diabetics4 006 15390.4 (2.3)14 242 76064.3 (4.6)
Treatment among diabetics3 935 44687.1 (3.2)13 391 29158.4 (5.3)
Blood glucose control among treated1 527 15132.6 (9.9)5 878 67645.0 (8.0)
Blood glucose control among diabetics1 527 15127.2 (8.7)5 878 67625.5 (5.9)

BMI indicates body mass index; BP, blood pressure; CHD, coronary heart disease; CHF, congestive heart failure; CVD, cardiovascular disease; MI, myocardial infarction; NHANES, National Health and Nutrition Examination Survey; PA, physical activity; and SE, standard error.

*Weighted sample size.

†Standardized to the age distribution of the 2000 US Standard population.

‡Moderate <150 min/wk AND Vigorous <75 min/wk AND Combined <150 min/wk.

Table 2-4. Evidence-Based Individual Approaches for Improving Health Behaviors and Health Factors in the Clinic Setting

• Set specific goals (Class IA). 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 per week).• Establish self-monitoring (Class IA). Develop a strategy for self-monitoring, such as a dietary or physical activity diary or Web-based or mobile applications.• Schedule follow-up (Class IA). 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 IA). Provide feedback on progress toward goals, including using in-person, telephone, and/or electronic feedback.• Increase self-efficacy (Class IA). Increase the patient’s perception that they can successfully change their behavior.*• Use motivational interviewing† (Class IA). Use motivational interviewing when patients are resistant or ambivalent about behavior change.• Provide long-term support (Class IB). 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 IA). 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 al16 with permission. Copyright © 2010, American Heart Association, Inc.

Table 2-5. Evidence-Based Healthcare Systems Approaches to Support and Facilitate Improvements in Health Behaviors and Health Factors17–21

• 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 2-6. 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 (IIa B) or as part of multicomponent strategies (I B)†‡§
On-site supermarket and grocery store educational programs to support the purchase of healthier foods (IIa B)†
Labeling and informationMandated nutrition facts panels or front-of-pack labels/icons as a means to influence industry behavior and product formulations (IIa B)†
Economic incentivesSubsidy strategies to lower prices of more healthful foods and beverages (I A)†
Tax strategies to increase prices of less healthful foods and beverages (IIa 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 (IIa 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 (I A)†
School garden programs, including nutrition and gardening education and hands-on gardening experiences (IIa A)†Fresh fruit and vegetable programs that provide free fruits and vegetables to students during the school day (IIa A)†
WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa 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 (IIa B)†
Local environmentIncreased availability of supermarkets near homes (IIa B)†‡
Restrictions and mandatesRestrictions on television advertisements for less healthful foods or beverages advertised to children (I B)†
Restrictions on advertising and marketing of less healthful foods or beverages near schools and public places frequented by youths (IIa B)†
General nutrition standards for foods and beverages marketed and advertised to children in any fashion, including on-package promotion (IIa B)†
Regulatory policies to reduce specific nutrients in foods (eg, trans fats, salt, certain fats) (I B)†§
Physical activity
Labeling and informationPoint-of-decision prompts to encourage use of stairs (IIa A)†
Economic incentivesIncreased gasoline taxes to increase active transport/commuting (IIa 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 (IIa A)†
Increased availability and types of school playground spaces and equipment (I B)†
Increased number of PE classes, revised PE curricula to increase time in at least moderate activity, and trained PE teachers at schools (IIa A/IIb A¶)†
Regular classroom physical activity breaks during academic lessons (IIa A)†§
WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa A)†
Structured worksite programs that encourage activity and also provide a set time for physical activity during work hours (IIa B)†
Improving stairway access and appeal, potentially in combination with “skip-stop” elevators that skip some floors (IIa B)†
Adding new or updating worksite fitness centers (IIa 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) (IIa B)†
Improved land-use design (eg, integration and interrelationships of residential, school, work, retail, and public spaces) (IIa B)†
Improved sidewalk and street design to increase active commuting (walking or bicycling) to school by children (IIa B)†
Improved traffic safety (IIa B)†
Improved neighborhood aesthetics (to increase activity in adults) (IIa B)†
Improved walkability, a composite indicator that incorporates aspects of land-use mix, street connectivity, pedestrian infrastructure, aesthetics, traffic safety, and/or crime safety (IIa B)†
Smoking
Media and educationSustained, focused media and educational campaigns to reduce smoking, either alone (IIa B) or as part of larger multicomponent population-level strategies (I A)†
Labeling and informationCigarette package warnings, especially those that are graphic and health related (I B)†‡§
Economic incentivesHigher taxes on tobacco products to reduce use and fund tobacco control programs (I A)†‡§
Schools and workplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa A)†
Local environmentReduced density of retail tobacco outlets around homes and schools (I B)†
Development of community telephone lines for cessation counseling and support services (I A)†
Restrictions and mandatesCommunity (city, state, or federal) restrictions on smoking in public places (I A)†
Local workplace-specific restrictions on smoking (I A)†‡§
Stronger enforcement of local school-specific restrictions on smoking (IIa B)†
Local residence-specific restrictions on smoking (IIa B)†§
Partial or complete restrictions on advertising and promotion of tobacco products (I B)†

PE indicates physical education.

*The specific population interventions listed here are either a Class I or IIa recommendation with an evidence grade of either A or B. The American Heart Association evidence grading system for class of recommendation and level of evidence is summarized in Table 2. Because implementation of population-level strategies does not require perfect evidence but rather consideration of risks versus benefits, associated costs, and alternate approaches, the absence of any specific strategy herein does not mean it should not also be considered for implementation. See the more detailed tables and text below for further information on the evidence for each of these interventions, as well as other strategies that were reviewed.

†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.

¶Evidence IIa A for improving physical activity; evidence IIb B for reducing adiposity.

Reprinted from Mozaffarian et al17 with permission. Copyright © 2012, American Heart Association, Inc.

Table 2-7. Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 Years of Age; NHANES 2009 to 2010

%
Percent BP ideal among adults, 2009–201044.26
20% Relative increase53.11
Percent of US adults whose BP would be ideal if population mean BP were lowered by*
2 mm Hg56.13
3 mm Hg59.49
4 mm Hg61.59
5 mm Hg65.31

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 2-8. AHA Advocacy and Policy Strategies Related to the 2020 Impact Goals for Ideal Cardiovascular Health

Measure of Cardiovascular HealthAdvocacy/Policy Solutions
Smoking statusIdeal for cardiovascular health:Adults: Never smoked or quit more than a year agoChildren: Never tried or never smoked a whole cigaretteFederal• Support the full, authorized funding level for the FDA’s Center for Tobacco Products and advocate for comprehensive implementation of FDA regulation of tobacco.• Implement clinical guidance and monitor health claims concerning smokeless tobacco and other “harm reduction” products.• Support the Tobacco Tax Equity Act that closes tax loopholes to ensure that all tobacco products are taxed at levels similar to the current tax rate for cigarettes.• Continue to advocate for ratification of WHO’s Framework Convention on Tobacco Control as part of the UN Political Declaration on Non-Communicable Diseases for implementation by all countries who are a party to the treaty.State• Establish, strengthen, and protect smoke-free air laws in compliance with the Fundamentals of Smokefree Workplace Laws guidelines.• Support tobacco-free secondary school, college, university, and hospital campuses.• Support significant increases in tobacco excise taxes on all tobacco products.• Establish and protect sustainable funding for tobacco prevention and cessation programs to levels that meet or exceed the CDC recommendations.• Provide comprehensive tobacco cessation benefits in Medicaid, Medicare, and private health insurance plans.• Eliminate tobacco sales in pharmacies and other health-related institutions.
Physical activityIdeal for cardiovascular health:Adults: At least 150 min of moderate or 75 min of vigorous PA each weekChildren: >60 min of moderate to vigorous PA per dayFederal• Preserve funding for Safe Routes to School and Complete Streets in transportation reauthorization.• Include PA in nutrition education funding for the Farm Bill Supplemental Nutrition Assistance Program.• Incorporate PA into electronic medical records.• Support implementation of the National Physical Activity Plan.• Increase the quality of physical education in schools and advocate for Physical Education for Progress grants to increase funding to schools to improve their PE programs.• Advocate for regular revision and update of the Physical Activity Guidelines for Americans.State• Implement shared use of school facilities within the community and support the construction of school fitness facilities.• Increase sports, recreational opportunities, parks, and green spaces in the community.• Support efforts to design workplaces, communities, and schools around active living and integrate PA opportunities throughout the day.• Provide safe routes to schools and school sites that offer walking/biking options for more students.• Support the creation of complete streets.• Support the use of zoning policy to increase access to safe places for recreation.• Create and maintain comprehensive worksite wellness programs.• Support the creation and implementation, through legislation and regulation (including licensing), of PA standards for preschool, day care, and other out-of-school care programs.• Require quality, more frequent PE in schools.• Promote efforts within the school environment that will lead to increased PA.
BMIIdeal for cardiovascular health:Adults: between 18.5 and 25 kg/m2Children: between the 15th and 85th percentileGo to http://www.americanheart.org/obesitypolicy for additional policy resourcesFederal• Provide obesity counseling and treatment coverage in the healthcare environment.• Provide robust surveillance and monitoring of obesity, diet, PA, and tobacco use.State• Provide robust coverage for guidelines-based prevention, diagnosis, and treatment of overweight and obesity in the healthcare environment.• Implement and monitor strong local wellness policies in all schools.• Ensure adequate funding and implementation of coordinated school health programs.• Establish comprehensive obesity prevention strategies in early childhood and day care programs.• Advocate for continued funding for obesity prevention research and work to ensure a strong evaluation component is a part of implementation of new laws and programs.
Healthy dietIdeal 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 per day• Sodium: <1500 mg/d• Sugar-sweetened beverages: <450 kcal (36 oz) per week. Go to http://www.americanheart.org/obesitypolicy for more specific policy resourcesFederal• Work to eliminate food deserts and improve access and affordability of healthy foods.• Strengthen nutrition standards in schools for meals and competitive foods and in all government nutrition assistance or feeding programs.• Improve food labeling to make the labels easier to read and convey more accurately the content of added sugars, trans fats, sodium, and whole grains in foods.• Implement menu labeling in restaurants.• Continue to support and monitor the removal of industrially produced trans fats from the food supply and ensure the use of healthy replacement oils.• Restrict the marketing and advertising of unhealthy food to children.• Support robust implementation of nutrition education and promotion in schools.• Reduce added sugar and sodium in the food supply.• Support the implementation and dissemination of procurement standards across federal agencies.• Ensure that diet counseling is a covered benefit in Medicare.State• Support the implementation of the reauthorization of the Federal Childhood Nutrition Act and new regulations concerning competitive foods and beverages and use all available techniques, including legislation, to encourage schools to take advantage of opportunities to provide even healthier options for children.• Support improvements in the school food environment just outside of school property, including corner stores and food trucks.• Support the creation and implementation of nutrition standards, through legislation and regulation (including licensing), for preschool and day care and other out-of-school care program meals.• Support opportunities for greater nutrition education in schools. Support opportunities to expand the availability of fruits, vegetables, and water, including policies that support expansion of school gardens and farm-to-school programs.• Support strategies that reduce sodium in the food supply.• Reduce trans fats in packaged foods, baked goods, restaurant meals, and school meal programs.• Support the elimination of food deserts through policies such as Healthy Food Financing that increase the availability of fruits, vegetables, and water in underserved neighborhoods.• Support the establishment of food procurement policies that meet the AHA or federal guidelines for government offices.• Support policies identified to reduce children’s exposure to marketing and advertising for unhealthy food.• Support policies that change relative prices of healthy versus unhealthy food items.• Support on a pilot basis the taxation of sugar-sweetened beverages to assess impact on health and consumer behavior, including 6 minimum criteria (at least a portion of the money is dedicated for HD and stroke prevention and/or obesity prevention; the tax is structured so as to result in an increase in price for sugar-sweetened beverages; tax is at least 1 cent/oz; there is money dedicated for evaluation with guidance that ensures rigorous evaluation, including health outcomes; there is a standard definition of “sugar-sweetened beverage”; and there is no sunset).• Support policies designed to encourage retailers to increase access to healthy foods while decreasing access to unhealthy foods.• Expand state participation in the Department of Defense Fresh Fruit and Vegetable program.
Total cholesterolIdeal for cardiovascular health:Adults: Total cholesterol <200 mg/dLChildren: <170 mg/dLFederal and state• Partner with Department of Health and Human Services to promote the Million Hearts Campaign through increased public awareness and partnership engagement, science and evaluation, clinical care improvement, patient outreach, and public policy.• Ensure adequate healthcare coverage for prevention and treatment of dyslipidemia.• Secure and protect dedicated state appropriations aligned with HD and stroke priorities, and work to support appropriate program implementation. Support other public health initiatives and evaluation targeted at HD, stroke, and related risk factors, as well as the disparities that exist in these areas.
Blood pressureIdeal for cardiovascular health:Adults: <120/80 mm HgChildren: <90th percentileFederal• Partner with the Department of Health and Human Services to promote the Million Hearts Campaign, as above.• Implement the Institute of Medicine’s recommendations to reduce sodium in the food supply.• Improve food labeling to increase consumer understanding of sodium levels in packaged foods.• Advocate for robust sodium limits in procurement standards, nutrition standards in schools, and other government feeding programs.State• Promote public funding for heart disease and stroke prevention programs.• Ensure the availability of essential CVD preventive benefits in private insurance and public health programs.
Fasting plasma glucoseIdeal for cardiovascular health:Children and adults: Fasting blood glucose <100 mg/dLFederal and state• Ensure adequate healthcare coverage for early treatment and prevention of diabetes mellitus.

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. At the time of press of this document, the AHA was in the process of updating its strategic policy agenda for 2014–2017.

AHA indicates American Heart Association; BMI, body mass index; CDC, Centers for Disease Control and Prevention; CVD, cardiovascular disease; DASH, Dietary Approaches to Stop Hypertension; FDA, US Food and Drug Administration; HD, heart disease; PA, physical activity; PE, physical education; UN, United Nations; and WHO, World Health Organization.

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.1Table 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).

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 towards 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.

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 76% 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 2007 to 2008 (baseline) and 2009 to 2010 in Table 2-2.

—In 2009 to 2010, 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 2008.

—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 (<7%) 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 (≥65%) have 2, 3, or 4 criteria at ideal cardiovascular health, with ≈1 in 5 adults within each of these categories.

—Approximately 13% of US adults meet 5 criteria, 4% 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 (46%) than in boys (51%).

—In comparison, only 17% of US adults have ≥5 metrics with ideal levels, with lower prevalence in men (11%) than in women (24%).

—Among adults, whites are more likely to have ≥5 metrics at ideal levels (19%) than are Mexican Americans (12%) or blacks (10%).

  • 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 35% 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 colleagues2 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).

  • The prevalence of poor health behaviors and health factors and their awareness, treatment, and control are displayed in Table 2-3 separately for those with and without self-reported CVD.

—Americans with CVD are much more likely to be current or former smokers than Americans without CVD.

—Approximately 20% of US adults are current smokers or have quit recently (<12 months ago).

—As measured by self-reported data, Americans with CVD are very likely to have intermediate or poor levels of PA (74.1%), whereas Americans without CVD still commonly have such levels (58.4%). Furthermore, 64.5% of those with CVD and 47.3% of those without CVD report engaging in no moderate or vigorous activity at all.

—Seventy percent of US adults with CVD and 79% of those without CVD meet 0 or only 1 of the 5 healthy diet metrics.

—Two thirds of US adults are overweight, with little difference by prevalent CVD. Half of all US adults with CVD and one third without CVD are obese.

—Hypertension is present in 28.5% of US adults without CVD and 51.0% of US adults with CVD. Of these, nearly all with CVD are aware of their hypertension (98.6%) and are receiving treatment (97.4%), but a much smaller proportion of those without CVD are aware (70.6%) or receiving treatment (61.4%).

—Both presence of hypercholesterolemia (total cholesterol ≥240 mg/dL or receiving medication) and DM (fasting glucose ≥126 mg/dL or receiving medications) and awareness and treatment of these conditions are similarly higher among those with CVD than among those without CVD.

Chart 2-1.

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 among US children aged 12 to 19 years, National Health and Nutrition Examination Survey 2009 to 2010.

Chart 2-2.

Chart 2-2. Age-standardized prevalence estimates for 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 years, National Health and Nutrition Examination Survey 2009 to 2010.

Chart 2-3.

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.

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.

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 from National Health and Nutrition Examination Survey 2009 to 2010.

Chart 2-6.

Chart 2-6. Prevalence estimates of meeting ≥5 criteria for ideal cardiovascular health among US adults aged ≥20 years (age standardized), overall and by sex and race, and US children aged 12 to 19 years (unadjusted), by sex, National Health and Nutrition Examination Survey 2009 to 2010.

Chart 2-7.

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.

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 al2 with permission. Copyright © 2012, American Heart Association, Inc.

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 BMI metric, whereas more are meeting the 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).3 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.3

  • 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).4

Chart 2-9.

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 2009 to 2010. *Because of changes in the physical activity questionnaire between different cycles of the NHANES survey, 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.

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 2009 to 2010. *Because of changes in the physical activity questionnaire between different cycles of the NHANES survey, 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.

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 al3 with permission. Copyright © 2012, American Heart Association, Inc.

Cardiovascular Diseases

  • In 2010, the age-standardized death rate attributable to all CVD was 236.6 per 100 000 (includes congenital CVD [ICD-10 I00-I99, Q20-Q28]; Chart 2-12), down 8.8% from 259.4 per 100 000 in 2007 (baseline data for the 2020 Impact Goals on CVD and stroke mortality).5

—Death rates in 2010 attributable to stroke, CHD, and other CVDs were 39.1, 113.6, and 82.7 per 100 000, respectively.5

  • Data from NHANES 2009 to 2010 reveal that overall, 7.2% of Americans self-reported having some type of CVD (Table 2-3), including 3.2% with CHD, 2.7% with stroke, and 2.0% with CHF (some individuals reported >1 condition).

Chart 2-12.

Chart 2-12. US age-standardized death rates* attributable to CVD, 2000 to 2010. *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 and 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, and I95 to I99. CHD indicates coronary heart disease; and CVD, cardiovascular disease. Source: Centers for Disease Control and Prevention, National Center for Health Statistics.5

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.

  • Bambs et al,6 Folsom et al,7 and Dong et al8 have all described the low prevalence (<1%) of ideal cardiovascular health, defined as being in the ideal category of all 7 AHA metrics in the Heart Strategies Concentrating on Risk Evaluation, ARIC, and NOMAS cohorts, respectively.

  • In ARIC and NOMAS, a stepwise inverse association was present between the number of ideal health metrics and incident CVD events (including CHD death, nonfatal MI, stroke, and HF) during 20 and 11 years of follow-up, respectively.7,8 For ARIC participants with 0, 1, 2, 3, 4, 5, 6, and 7 metrics at ideal levels, the age-, sex-, and race-adjusted rates of incident CVD incidence were 3.21, 2.19, 1.60, 1.20, 0.86, 0.64, 0.39, and 0 per 100 person-years, respectively.7 Findings were similar in the Aerobics Center Longitudinal Study, in which individuals with 6 to 7 ideal metrics had a 63% lower risk of CVD death (HR [95% CI], 0.37 [0.15, 0.95]) compared with individuals with 0 to 2 ideal metrics.9

  • A similar 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.10 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.10 Ford et al11 demonstrated similar relationships.

  • The adjusted population attributable fractions for CVD mortality were as follows10:

—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 follows10:

—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 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.12

  • 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.

  • 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.11

  • The AHA metrics may also be related to risk of noncardiovascular conditions. Rasmussen-Torvik et al13 demonstrated a graded, inverse association between ideal cardiovascular health and cancer incidence, with 51% lower risk among individuals with 6 or 7 ideal cardiovascular health metrics than among those with 0 ideal metrics. These results were only partially attenuated (25% lower risk) when smoking was removed from the sum of metrics. In contrast, Artero et al9 did not find a significant association between ideal cardiovascular health and death attributable to cancer in the Aerobics Center Longitudinal Study. The AHA cardiovascular health metrics have also been cross-sectionally associated with lower prevalence of depressive symptoms in the REGARDS cohort.14

  • 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, HBP, high fasting plasma glucose, and physical inactivity.14a

Chart 2-13.

Chart 2-13. Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors. Reprinted from Folsom et al7 with permission from Elsevier. Copyright © 2011, American College of Cardiology Foundation.

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 2-4)

—Healthcare systems approaches, which encourage, facilitate, and reward efforts by providers to improve health behaviors and health factors (Table 2-5)

—Population approaches, which target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation (Table 2-6)

  • 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 2-7). 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.15

  • The AHA has a broad range of policy initiatives to improve cardiovascular health and meet the 2020 Strategic Impact Goals (Table 2-8). Future Statistical Updates will update these initiatives and track progress toward the 2020 Impact Goals.

Abbreviations Used in Chapter 2

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
CIconfidence interval
CVDcardiovascular disease
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
FDAFood and Drug Administration
HbA1chemoglobin A1c
HBPhigh blood pressure
HDheart disease
HFheart failure
HRhazard ratio
ICDInternational Classification of Diseases
ICD-10International Classification of Diseases, 10th Revision
MImyocardial infarction
NHANESNational Health and Nutrition Examination Survey
NOMASNorthern Manhattan Study
PAphysical activity
PEphysical education
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEstandard error
UNUnited Nations
WHOWorld Health Organization

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3. Smoking/Tobacco Use

See Table 3-1 and Charts 3-1 and 3-2.

Table 3-1. Cigarette Smoking

Population GroupPrevalence, 2012 Age ≥18 y*6Cost18
Both sexes42 098 000 (18.1%)$193 Billion per year
Males22 983 000 (20.5%)
Females19 115 000 (15.9%)
NH white males22.0%
NH white females19.2%
NH black males21.6%
NH black females14.2%
Hispanic or Latino males16.6%
Hispanic or Latino females7.5%
Asian only (both sexes)10.4%
American Indian/Alaska Native only (both sexes)18.8%

Percentages are age adjusted. Estimates for Asian only and American Indian/Alaska Native only include non-Hispanic and Hispanic persons.

Ellipses (…) indicate data not available; and NH, non-Hispanic.

*Rounded to the nearest thousand.

Chart 3-1.

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, 2011). NH indicates non-Hispanic. Data derived from MMWR: Morbidity and Mortality Weekly Report.3

Chart 3-2.

Chart 3-2. Prevalence (%) of current smoking for adults >18 years of age by race/ethnicity and sex (National Health Interview Survey: 2009–2011). All percentages are age adjusted. AIAN indicates American Indian/Alaska Native; and NH, non-Hispanic. *Includes both Hispanics and non-Hispanics. Data derived from Centers for Disease Control and Prevention/National Center for Health Statistics, Health Data Interactive.10

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 2009 to 2010 data, 85.2% of children and 76.2% of adults met these criteria.

Prevalence

Youth

(See Chart 3-1.)

  • In 2011, in grades 9 through 12:

—18.1% of students reported current cigarette use (on ≥1 day during the 30 days before the survey), 13.1% of students reported current cigar use, and 7.7% of students reported current smokeless tobacco use. Overall, 23.4% of students reported any current tobacco use (YRBS; Chart 3-1).3

—Male students were more likely than female students to report current cigarette use (19.9% compared with 16.1%). Male students were also more likely than female students to report current cigar use (17.8% compared with 8.0%) and current smokeless tobacco use (12.8% compared with 2.2%; YRBS).3

—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 (26.5% compared with 20.5% for Hispanic students and 15.4% for non-Hispanic black students; YRBS).3

  • Among youths 12 to 17 years of age in 2011, 2.4 million (10.0%) used a tobacco product (cigarettes, cigars, or smokeless tobacco) in the past month, and 1.9 million (7.8%) used cigarettes. Cigarette use in the past month in this age group declined significantly from 13.0% in 2002 to 7.8% in 2011 (NSDUH).4

  • Data from the YRBS5 for students in grades 9 to 12 indicated the following:

—The percentage of students who reported ever trying cigarettes remained stable from 1991 to 1999 and then declined from 70.4% in 1999 to 44.7% in 2011.

—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 18.1% in 2011.

—The percentage who reported current frequent cigarette use (smoked on ≥20 of the 30 days before the survey) increased from 1991 to 1999 and then declined from 16.8% in 1999 to 6.4% in 2011.

  • In 2011, 49.9% 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 (53.9%) than among male student smokers (47.0%) and among white females (54.0%) and Hispanic females (55.9%) than among white males (46.3%) and Hispanic males (44.7%; YRBS).3

Adults

(See Table 3-1 and Chart 3-2.)

  • In 2012, among adults ≥18 years of age:

—20.5% of men and 15.9% of women were current cigarette smokers (NHIS).6

—The percentage of current cigarette smokers (18.1%) declined 25% since 1998 (24.1%).6,7

—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 2011, an estimated 68.2 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 2011 (from 28.6% to 26.5%; NSDUH).4

  • 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

  • In 2009 to 2011, among people ≥65 years of age, 8.9% of men and 8.7% of women were current smokers. In this age group, men were more likely than women to be former smokers (53.0% compared with 30.6%) on the basis of age-adjusted estimates (NHIS).10

  • In 2009 to 2011, among adults ≥18 years of age, Asian men (15.1%) and Hispanic men (16.3%) were less likely to be current cigarette smokers than non-Hispanic black men (23.2%), non-Hispanic white men (23.6%), and American Indian or Alaska Native men (23.7%) on the basis of age-adjusted estimates (NHIS). Similarly, in 2009 to 2011, Asian women (5.7%) and Hispanic women (8.9%) were less likely to be current cigarette smokers than non-Hispanic black women (16.9%), non-Hispanic white women (20.3%), and American Indian or Alaska Native women (23.6%; NHIS).10

  • In 2010 to 2011, among women 15 to 44 years of age, past-month cigarette use was lower for those who were pregnant (17.6%) than among those who were not pregnant (25.4%). This pattern was found for women 18 to 25 years of age (22.4% versus 29.9% for pregnant and nonpregnant women, respectively) and for women 26 to 44 years of age (14.3% versus 25.7%, respectively; NSDUH).4

Incidence

  • In 2011:

—Approximately 2.4 million people ≥12 years of age smoked cigarettes for the first time within the past 12 months, which was similar to the estimate in 2010. The 2011 estimate averages out to ≈6500 new cigarette smokers every day. Most new smokers (55.7%) in 2011 were <18 years of age when they first smoked cigarettes (NSDUH).4

—The number of new smokers <18 years of age (1.3 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 2011 (NSDUH).4

—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 17.2 years, similar to the average in 2010 (17.3 years).4

  • Data from 2002 to 2004 suggest that ≈1 in 5 nonsmokers 12 to 17 years of age is likely to start smoking. Youths in the Mexican subpopulations were significantly more susceptible (28.8%) to start smoking than those in non- Hispanic white (20.8%), non-Hispanic black (23.0%), Cuban (16.4%), Asian Indian (15.4%), Chinese (15.3%), and Vietnamese (13.8%) subpopulations. There was no significant difference in susceptibility to start smoking between boys and girls in any of the major populations or subpopulations (NSDUH).11

Morbidity

A 2010 report of the US Surgeon General on how tobacco causes disease summarizes an extensive body of literature on smoking and CVD and the mechanisms through which smoking is thought to cause CVD.12 Among its conclusions are the following:

  • 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.

  • 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).13

  • Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.14,15

  • Recent analysis has found that tobacco exposure is a top risk factor for disability in the United States, second only to dietary risks.16

  • Worldwide, tobacco smoking (including secondhand smoke) was 1 of the top 3 leading risk factors for disease in 2010.17

Mortality

  • In 2005, tobacco smoking was the cause of ≈467 000 adult deaths (19.1%) in the United States. Approximately one third of these deaths were related to CVD.18

  • During 2000 to 2004, ≈49 000 (11.1%) of cigarette smoking–related deaths were attributable to secondhand smoke.19

  • Each year from 2000 to 2004, smoking caused 3.1 million years of potential life lost for males and 2.0 million years for females, excluding deaths attributable to smoking-attributable residential fires and adult deaths attributable to secondhand smoke.19

  • From 2000 to 2004, smoking during pregnancy resulted in an estimated 776 infant deaths annually.17

  • During 2000 to 2004, cigarette smoking resulted in an estimated 269 655 deaths annually among males and 173 940 deaths annually among females.19

  • 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 deaths in the United States, after dietary risks.16

  • Overall mortality among US smokers is 3 times higher than that for never-smokers.20

  • Worldwide, tobacco smoking (including secondhand smoke) was estimated to contribute to 6.2 million deaths in 2010.17

Smoking Cessation

  • Smoking cessation reduces the risk of cardiovascular morbidity and mortality for smokers with and without CHD.

—There is no evidence to date that reducing the amount smoked by smoking fewer cigarettes per day reduces the risk of CVD.12

  • 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.20

  • 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).21

  • Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.22

  • 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.21

  • 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.21

  • 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.23

Secondhand Smoke

  • Data from a 2006 report of the US Surgeon General on the consequences of involuntary exposure to tobacco smoke12 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%.

—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.

  • 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 December 31, 2010, 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 December 31, 2010, an additional 10 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

  • Direct medical costs ($96 billion) and lost productivity costs ($97 billion) associated with smoking totaled an estimated $193 billion per year between 2000 and 2004.18

  • In 2008, $9.94 billion was spent on marketing cigarettes in the United States.29

  • Cigarette prices have increased 283% between the early 1980s and 2011, which contributed to decreased sales from ≈30 million packs sold in 1982 to ≈14 million packs sold in 2011.29

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
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

References

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4. Physical Inactivity

See Table 4-1 and Charts 4-1 through 4-5.

Table 4-1. Met 2008 Federal PA Guidelines for Adults

Population GroupPrevalence, 2012 (Age ≥18 y), %
Both sexes20.7
Males24.6
Females17.1
NH white only22.9
NH black only16.6
Hispanic or Latino15.7
American Indian/Alaska Native only18.7
Asian only17.1

“Met 2008 federal PA guidelines for adults” is defined as engaging in ≥150 min of moderate or 75 min of vigorous aerobic leisure-time physical activity per wk (or an equivalent combination) and engaging in leisure-time strengthening physical activity at least twice a wk.

Data are age adjusted for adults ≥18 y of age.

PA indicates physical activity; NH, non-Hispanic.

Source: National Health Interview Survey 2012 (National Center for Health Statistics).7

Chart 4-1.

Chart 4-1. Prevalence of students in grades 9 to 12 who did not participate in ≥60 minutes of physical activity on any day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.3

Chart 4-2.

Chart 4-2. Percentage of students in grades 9 to 12 who used a computer for ≥3 hours a day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.3

Chart 4-3.

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: 2011). “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.3

Chart 4-4.

Chart 4-4. Prevalence of children 6 to 19 years of age who attained sufficient moderate to vigorous physical activity to meet public health recommendations (≥60 minutes per day on 5 or more of the 7 days preceding the survey), by sex and age (National Health and Nutrition Examination Survey: 2003–2004). Source: Troiano et al.5

Chart 4-5.

Chart 4-5. 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: 2012). 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: Blackwell et al.7

Physical inactivity is a major risk factor for CVD and stroke.1 The AHA has identified ≥60 minutes of moderate- or vigorous-intensity activity every day (for children) and ≥150 min/wk of moderate-intensity activity or ≥75 min/wk of vigorous-intensity activity or a combination thereof (for adults) as 1 of the 7 components of ideal cardiovascular health.2 In 2009 to 2010, 36.5% of children and 41.1% of adults met these criteria.

Prevalence

Youth
Inactivity

(See Chart 4-1.)

In 20113:

  • Nationwide, 13.8% of adolescents 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 (17.7% versus 10.0%).

  • The prevalence of inactivity was highest among black (26.7%) and Hispanic (21.3%) girls, followed by white girls (13.7%), black boys (12.3%), Hispanic boys (10.7%), and white boys (8.5%).

Television/Video/Computers

(See Chart 4-2.)

In 20113:

  • Nationwide, 31.1% 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 or watching television ≥3 hours per day was highest among black (41.1%) and Hispanic (36.3%) boys, followed by white boys (33.3%), black girls (35.2%), Hispanic girls (28.3%), and white girls (22.6%).

  • 32.4% of adolescents watched television for ≥3 hours per day.

  • The prevalence of watching television ≥3 hours per day was highest among black girls (54.9%) and boys (54.4%), followed by Hispanic boys (38.4%) and girls (37.2%) and white boys (27.3%) and girls (23.9%).

  • Increased television time has significant nutritional associations with weight gain (refer to Chapter 5, Nutrition).

Activity Recommendations

(See Charts 4-3 and 4-4.)

  • In 2011, the proportion of students who met activity recommendations of ≥60 minutes of PA on 7 days of the week was 28.7% nationwide and declined from 9th (30.7%) to 12th (25.1%) grades. At each grade level, the proportion was higher in boys than in girls.3

  • In 2011, more high school boys (38.3%) than girls (18.5%) self-reported having been physically active ≥60 minutes per day on all 7 days; self-reported rates of activity were higher in white (30.4%) than in black (26.0%) or Hispanic (26.5%) adolescents.3

  • The 2010 National Youth Physical Activity and Nutrition Study showed that a total of 15.3% of high school students met the recommendations for aerobic activity, 51.0% met the recommendations for muscle-strengthening activity, and 12.2% met the recommendations for both aerobic and muscle-strengthening activities.4

  • 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.5

  • 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.5

  • More boys than girls met PA recommendations (≥60 minutes of moderate to vigorous activity on most days of the week) as measured by accelerometry.5

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,6 only 51.8% of students attended physical education classes in school daily (56.7% of boys and 46.7% of girls).3

  • Physical education class participation declined from the 9th through the 12th grades among boys and girls.3

  • Little more than half (58.4%) of high school students played on at least 1 school or community sports team in the previous year; however, the prevalence declined with increasing grade level, from 61.4% in the 9th grade to 52.5% in the 12th grade.3

Adults
Inactivity

According to 2012 data from the NHIS, in adults ≥18 years of age:

  • 29.9% 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).7

  • Inactivity was higher among women than men (31.0% versus 28.6%, age adjusted) and increased with age from 24.5% to 31.8%, 35.7%, and 51.4% among adults 18 to 44, 45 to 64, 65 to 74, and ≥75 years of age, respectively.7

  • Non-Hispanic black and Hispanic adults were more likely to be inactive (39.4% and 39.8%, respectively) than were non-Hispanic white adults (26.2%) on the basis of age-adjusted estimates.7

Activity Recommendations

(See Table 4-1 and Chart 4-5.)

According to 2012 data from the NHIS, in adults ≥18 years of age:

  • 20.7% met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness.7

  • 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 50.1%; 53.9% of men and 46.5% of women met the recommendations. Age-adjusted prevalence was 53.6% for non-Hispanic whites, 40.9% for non-Hispanic blacks, and 42.5% for Hispanics.7

  • The proportion of respondents who did not meet the federal aerobic PA guidelines increased with age from 43.8% of 18- to 44-year-olds to 71.9% of adults ≥75 years of age.7

  • Non-Hispanic black adults (59.1%) and Hispanic/Latino adults (57.4%) were more likely not to meet the federal aerobic PA guidelines than non-Hispanic white (46.4%) adults, according to age-adjusted estimates.7

  • The percentage of adults ≥25 years of age not meeting the full (aerobic and muscle-strengthening) federal PA guidelines was inversely associated with education; 66.4% of participants with no high school diploma, 57.6% of those with a high school diploma or a high school equivalency credential, 46.8% of those with some college, and 33.2% of those with a bachelor’s degree or higher did not meet the full federal PA guidelines.7

  • The proportion of adults ≥25 years of age who met the 2008 federal PA guidelines for aerobic activity was positively associated with education level: 62.9% of those with a college degree or higher met the PA guidelines compared with 31.5% of adults with less than a high school diploma.7

  • The proportion of adults reporting levels of PA consistent with the 2008 Physical Activity Guidelines for Americans remains low and decreases with age.8,9 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).10

  • 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.9

  • 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.9

  • 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 20045:

—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.11

  • 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.12

  • Among men, self-reported PA was 44% greater than actual measured values; among women, self-reported activity was 138% greater than actual measured PA.12

Trends

Youth

In 20113:

  • Among adolescents, there was a significant decrease in the prevalence of watching television ≥3 hours per day, from 42.8% in 1999 to 32.4%, although there was no significant decrease from the 2009 prevalence of 32.8%.

  • 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 55.6%.

  • Nationwide, the prevalence of adolescents using computers ≥3 hours per day increased from 21.1% in 2005 to 24.9% in 2009 and 31.1% in 2011.

  • Among adolescents nationwide, the prevalence of attending physical education classes at least once per week did not increase significantly, from 48.9% in 1991 to 51.8%.

  • The prevalence of adolescents playing ≥1 team sport in the past year increased from 55.1% in 1999 to 58.4%.

Adults
  • Between NHANES III (1988–1994) and NHANES 2001 to 2006, the non–age-adjusted proportion of adults who engaged 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.13

  • 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 2011.8,14

  • 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.15

CVD and Metabolic Risk Factors

Youth
  • More girls (67.9%) than boys (55.7%) reported having exercised to lose weight or to keep from gaining weight.3

  • 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.3

  • Total and vigorous PA are inversely correlated with body fat and the prevalence of obesity.16

  • 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.17

  • 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.17

Adults
  • Participants in the Diabetes Prevention Project 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.18

  • 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).19

  • A total of 120 to 150 minutes per week of moderate- intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.20

  • 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.21

  • Self-reported low lifetime recreational activity has been associated with increased PAD.22

  • 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.23

  • 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.23

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.24

  • 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.25

  • 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).26

  • 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.27

  • Longitudinal studies commonly report a graded, inverse association of PA amount and duration (ie, dose) with incident CHD and stroke.28

  • 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.29

  • 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.29

  • 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.30

  • 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.31

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-1.32

  • 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.33

  • 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.34

  • 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 left ventricular EF and decreases in pro-brain natriuretic peptide (40%), left ventricular end-diastolic volume (18%), and left ventricular end-systolic volume (25%) compared with control and endurance-training groups.35

  • Exercise training in patients with HF with preserved EF was associated with improved exercise capacity and favorable changes in diastolic function.36

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.37

  • Interventions and community strategies to increase physical activity have been shown to be cost-effective in terms of reducing medical costs38:

—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.

Abbreviations Used in Chapter 4

AHAAmerican Heart Association
CARDIACoronary Artery Risk Development in Young Adults
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EFejection fraction
FMDflow-mediated dilation
HbA1chemoglobin AIc
HDLhigh-density lipoprotein
HFheart failure
HRhazard ratio
MImyocardial infarction
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
PAphysical activity
PADperipheral artery disease
RRrelative risk
SBPsystolic blood pressure
WHOWorld Health Organization

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5. Nutrition

See Tables 5-1 and 5-2 and Charts 5-1 through 5-3.

Table 5-1. Dietary Consumption in 2009 to 2010 Among US Adults ≥20 Years of Age of Selected Foods and Nutrients Related to Cardiometabolic Health103–106

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/d1.1±0.910.41.1±0.88.40.8±0.77.40.8±0.78.12.2±1.429.02.1±1.526.9
Fruits, servings/d1.6±1.811.01.8±1.512.71.2±1.87.81.3±1.26.91.7±1.314.11.9±2.114.3
Fruits including 100% juices, servings/d2.6±2.325.92.7±2.026.53.0±2.428.62.9±2.127.13.2±2.329.33.4±2.636.9
Vegetables including starch, servings/d2.1±0.46.22.2±1.48.11.3±0.82.11.6±1.05.01.3±0.92.11.6±1.03.8
Vegetables including starch and juices/sauces, servings/d2.4±0.79.02.4±1.49.41.5±0.83.31.8±1.25.91.7±0.75.71.8±1.04.5
Fish and shellfish, servings/wk1.6±1.722.41.4±1.722.21.6±1.723.22.1±01.327.71.9±1.325.71.2±1.321.3
Nuts, legumes, and seeds, servings/wk2.6±2.921.72.8±4.222.82.4±4.217.32.6±4.619.37.5±4.644.25.3±4.637.4
Processed meats, servings/wk3.0±1.749.22.1±1.460.83.3±1.449.02.3±1.057.72.0±1.063.41.2±1.075.2
Sugar-sweetened beverages, servings/wk8.3±10.956.95.7±9.568.211.2±9.241.511.0±8.838.312.3±8.029.39.6±9.343.3
Sweets and bakery desserts, servings/wk5.9±3.940.46.9±4.434.76.3±3.942.26.1±4.041.14.4±1.049.44.6±3.247.8
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

Unpublished data from National Health and Nutrition Examination Survey 2009 to 2010, derived from two 24-h 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.

ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; NH, non-Hispanic; n-6-PUFA, ω-6-polyunsaturatedfatty acid; and SD, standard deviation.

*Guidelines adjusted to a 2000-kcal/d diet. Whole grains (characterized as minimum 1.1 g of fiber per 10 g of carbohydrate), 3 or more 1-oz equivalent (1 oz of bread; 1 cup of dry cereal; 1/2 cup of cooked rice, pasta, or cereal) servings per d (Dietary Guidelines for Americans)129; fish or shellfish, 2 or more 100-g (3.5-oz) servings per wk129; fruits, 2 cups per d125; vegetables, 2 1/2 cups per d, including up to 3 cups per wk of starchy vegetables125; nuts, legumes, and seeds, 4 or more 50-g servings per wk129; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings per wk (1/4 of discretionary calories)125; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding whole juices), ≤36 oz per wk (≈1/4 of discretionary calories)125, 129; sweets and bakery desserts, 2.5 or fewer 50-g servings per wk (≈1/4 of discretionary calories)125, 129; EPA/DHA, ≥0.250 g/d130; ALA, ≥1.6/1.1 g/d (men/women)126; saturated fat, <10% energy; dietary cholesterol, <300 mg/d125; total fat, 20% to 35% energy125; dietary fiber, ≥28/d125; and sodium, <2.3 g/d.125

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±0.52.90.8±0.54.70.9±0.85.30.7±0.33.40.9±0.65.20.9±1.16.9
Fruits, servings/d1.6±1.210.41.7±0.99.61.3±1.77.61.3±0.88.11.2±1.44.80.9±1.05.0
Fruits including 100% juices, servings/d3.2±1.834.83.3±1.731.62.6±2.324.12.7±3.224.72.6±2.423.42.3±1.121.8
Vegetables including starch, servings/d0.9±0.60.51.0±0.40.30.8±0.40.31.3±0.41.81.2±0.83.11.1±0.40.8
Vegetables including starch and juices/sauces, servings/d1.1±0.61.11.2±0.40.11.0±0.40.31.5±0.42.51.4±0.93.91.3±0.20.8
Fish and shellfish, servings/wk0.4±0.38.90.6±0.311.00.5±0.310.20.3±0.34.70.9±0.910.70.5±1.29.2
Nuts, legumes, and seeds, servings/wk1.4±1.612.51.9±1.514.41.5±0.712.81.7±0.711.41.5±0.512.41.9±0.513.5
Processed meats, servings/wk2.4±1.251.51.8±0.461.52.8±1.948.12.5±1.553.13.2±1.550.52.3±1.559.7
Sugar-sweetened beverages, servings/wk7.6±4.646.87.1±5.542.910.2±6.231.18.7±3.533.016.4±12.524.212.5±10.030.2
Sweets and bakery desserts, servings/wk8.9±2.324.59.6±2.320.87.3±2.524.97.7±2.526.65.3±2.938.67.6±3.936.9
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

Unpublished data from National Health and Nutrition Examination Survey 2009 to 2010, derived from two 24-h 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.

ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; n-6-PUFA, ω-6-polyunsaturatedfatty acid; and SD, standard deviation.

*For different age and sex subgroups here, the guideline cut points are standardized to a 2000-kcal/d diet to account for differences in caloric intake in these groups. Whole grains (characterized as minimum 1.1 g of fiber per 10 g of carbohydrate), 3 or more 1-oz equivalent (1 oz of bread; 1 cup of dry cereal; 1/2 cup of cooked rice, pasta, or cereal) servings per d (Dietary Guidelines for Americans)129; fish or shellfish, 2 or more 100-g (3.5-oz) servings per wk129; fruits, 2 cups per d125; vegetables, 2 1/2 cups per d, including up to 3 cups per wk of starchy vegetables125; nuts, legumes, and seeds, 4 or more 50-g servings per wk129; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings per wk (1/4 of discretionary calories)125; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding whole juices), ≤36 oz per wk (≈1/4 of discretionary calories)125,129; sweets and bakery desserts, 2.5 or fewer 50-g servings per wk (≈1/4 of discretionary calories)125,129; EPA/DHA, ≥0.250 g/d130; ALA, ≥1.6/1.1 g/d (men/women)126; saturated fat, <10% energy; dietary cholesterol, <300 mg/d125; total fat, 20% to 35% energy125; dietary fiber, ≥28/d125; and sodium, <2.3 g/d.125

Chart 5-1.

Chart 5-1. Age-adjusted trends in macronutrients and total calories consumed by US adults (20–74 years of age), 1971 to 2008. Data derived from National Center for Health Statistics14 and Wright and Wang.55

Chart 5-2.

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 Popkin62 and Kit et al.69 The 2010 data were only analyzed for soda/cola and sweetened fruit drinks.

Chart 5-3.

Chart 5-3. Total US food expenditures away from home and at home, 1977 and 2007. Data derived from Davis and Saltos.66

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

Foods and Nutrients: Adults

(See Table 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:

  • Average consumption of whole grains was 1.1 servings per day by white men and women and 0.8 servings per day by black men and women, with only between 7% and 10% of white and black adults meeting guidelines of ≥3 servings per day. Average whole grain consumption by Mexican Americans was ≈2 servings per day, with 27% to 29% consuming ≥3 servings per day.

  • Average fruit consumption ranged from 1.2 to 1.9 servings per day in these sex and race or ethnic subgroups: 11% to 13% of whites, 7% to 8% of blacks, and 14% 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 nearly quadrupled in blacks.

  • Average vegetable consumption ranged from 1.3 to 2.2 servings per day; 6% to 8% of whites, 2% to 5% of blacks, and 2 to 4% of Mexican Americans consumed ≥2.5 cups per day. The inclusion of vegetable juices and sauces generally produced little change in these consumption patterns.

  • Average consumption of fish and shellfish was lowest among Mexican American and white women (1.2 and 1.4 servings per week, respectively) and highest among black women (2.1 servings per week); ≈72% to 78% of all adults in each sex and race or ethnic subgroup consumed <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, legumes, and seeds was ≈2.5 servings per week among whites and blacks and 5 to 8 servings per week among Mexican Americans. Approximately 22% of whites, 18% of blacks, and 40% of Mexican Americans met guidelines of ≥4 servings per week.

  • Average consumption of processed meats was lowest among Mexican American women (1.2 servings per week) and highest among black men (3.3 servings per week). Between 49% (black men) and 75% (Mexican American women) of adults consumed 2 or fewer servings per week.

  • Average consumption of sugar-sweetened beverages ranged from ≈6 servings per week among white women to 12 servings per week among Mexican American men. Women generally consumed less than men. From 29% (Mexican American men) to 68% (white women) of adults consumed no more than 36 oz (4.5 8-oz servings) 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 two thirds of white women and more than half of all other sex and race groups consumed >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.

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, <1 serving per day in all age and sex groups, with <7% 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: 1.6 to 1.7 servings per day in younger boys and girls (5–9 years of age), 1.3 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: ≈10% in those 5 to 9 years of age, 8% 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 approximately one third of those 5 to 9 years of age and one fourth of those 10 to 14 years and 15 to 19 years of age.

  • Average vegetable consumption was low, ranging from 0.8 to 1.3 servings per day, with at most 3% 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 0.9 servings per week in all age and sex groups. Among all ages, only 5% to 11% of youth consumed ≥2 servings per week.

  • Average consumption of nuts, legumes, and seeds ranged from 1.4 to 1.9 servings per week among different age and sex groups. Only between 11% and 14% of children in different age and sex subgroups consumed ≥4 servings per week.

  • Average consumption of processed meats ranged from ≈2 to 3 servings per week; was generally higher than the average consumption of nuts, legumes, and seeds; and was up to 8 times higher than the average consumption of fish and shellfish. Approximately 40% and 50% of children consumed >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 per week in 5- to 9-year-olds, 9 to 10 servings per week in 10- to 14-year-olds, and 13 to 16 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, legumes, and seeds. Less than half of children 5 to 9 years of age and less than one quarter of boys 15 to 19 years of age consumed <4.5 servings per week.

  • Average consumption of sweets and bakery desserts was ≈9 to 10 servings per week in 5- to 9-year-olds, 7 to 8 servings per week in 10- to 14-year-olds, and 5 to 8 servings per week in 15- to 19-year-olds. From 61% (boys 15–19 years of age) to 79% (girls 5–9 years of age) of youths consumed >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.

Energy Balance

Energy balance, or consumption of total calories appropriate for needs, is determined by the balance of average calories consumed versus expended, with this balance depending 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 (Trends section).

  • Average daily caloric intake in the United States is ≈2500 calories in adult men and 1800 calories in adult women (Table 5-1). In children and teenagers, average caloric intake is higher in boys than in girls and increases with age in boys (Table 5-2). Trends in energy balance are described below. The average US adult gains ≈1 lb per year. 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.1 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.1

  • Diet quality also appears to 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.2 Similarly, isocaloric meals richer in rapidly digestible carbohydrate increased hunger and stimulated brain regions associated with reward and craving compared with isocaloric meals lower in rapidly digestible carbohydrate.3

  • Other nutritional determinants of positive energy balance (more calories consumed than expended), as determined by adiposity or weight gain, include larger portion sizes4,5 and greater consumption of fast food and commercially prepared meals.610

  • Preferences for portion size are associated with BMI, socioeconomic status, eating in fast food restaurants, and television watching.11,12 Portion sizes are larger at fast food restaurants than at home or at other restaurants.13

  • Between 1999 and 2004, 53% of Americans consumed an average of 1 to 3 restaurant meals per week, and 23% consumed ≥4 restaurant meals per week.14 Spending on food away from home, including restaurant meals, catered foods, and food eaten during out-of-town trips, increased from 26% of average annual food expenditures in 1970 to 42% in 2004.14

  • Macronutrient composition of the overall diet or of specific foods, such as percentage of calories from total fat, does not appear to be strongly associated with energy balance as ascertained by weight gain or loss.1,1517 In contrast, dietary quality as characterized by higher or lower intakes of specific foods and beverages is strongly linked to weight gain (see above).1

  • Emerging evidence suggests that consumption of trans fat may be associated with energy imbalance as assessed by changes in adiposity or weight, as well as more specific adverse effects on visceral adiposity.1820

  • Other individual factors associated with positive energy balance (weight gain) include greater television watching (with evidence that effects are mediated by diet, rather than physical inactivity, including greater snacking in front of the television and the influence of advertising on poor food choices)1,2125 and lower average sleep duration.1,26

  • Randomized controlled trials of weight loss in obese individuals generally show modestly greater weight loss with low-carbohydrate (high-fat) diets than with low-fat diets at 6 months, but at 1 year, such differences diminish, and a diet that focuses on dietary quality and whole foods may be most successful in the long term.2730

  • A comparison of BRFSS data in 1996 and 2003 suggested a shift in self-reported dietary strategies to lose weight, with the proportion focusing on calorie restriction increasing from 11.3% to 24.9% and the proportion focusing on restricting fat consumption decreasing from 41.6% to 29.1%.31

  • On the basis of BRFSS data from 2003, among all American adults who were overweight or obese, a higher proportion was trying to lose weight if also diagnosed with hypertension (58% trying to lose weight), DM (60%), or both diseases (72%) than adults with neither condition (50%).32

  • A 2007 to 2008 national survey of 1082 retail stores in 19 US cities found that energy-dense snack foods/beverages were present in 96% of pharmacies, 94% of gas stations, 22% of furniture stores, 16% of apparel stores, and 29% to 65% of other types of stores.33

  • Societal and environmental factors independently associated with energy imbalance (weight gain), via either increased caloric consumption or decreased expenditure, include education, income, race/ethnicity, and local conditions such as availability of grocery stores, types of restaurants, safety, parks and open spaces, and walking or biking paths.3436 PA is covered in Chapter 4 of this update.

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.37

  • 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 1994.38

  • 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.39

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).40 It has been shown that most supplements are taken daily and for ≥2 years.41 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.40,41 Previous research also suggests that supplement users have higher intakes of most vitamins and minerals from their food choices alone than nonusers.42,43 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%).40

  • 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).44

  • 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%).45

  • 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.36 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.46

  • 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),4749 but several other trials of fish oil have not shown significant effects on CVD risk.50 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.51

Trends

Energy Balance

(See Chart 5-1.)

Energy balance, or consumption of total calories appropriate for needs, has been steadily worsening in the United States over the past several decades, as evidenced by the dramatic increases over the past 30 years in overweight and obesity among both children and adults across broad cross sections of sex, race/ethnicity, geographic residence, and socioeconomic status. However, in more recent years, rates of obesity and overweight among both adults and children have begun to level off.5254

  • The US obesity epidemic began in approximately 1980, accelerated from 1990 to 2005, and may be slowing in more recent years. Examining trends in diet, activity, and other factors from 1980 to present is important to elucidate the drivers of this remarkably recent epidemic.

  • Although trends in total calories consumed are difficult to quantify exactly because of differing methods of serial national dietary surveys over time, multiple lines of evidence indicate that average total energy consumption has increased by ≥200 kcal/d per person in the past 3 decades.

  • 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).14 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).13 However, recent data show that energy intake appeared relatively stable among US adults during 1999 to 2008.55

  • The increases in calories consumed between 1971 and 2004 are attributable primarily to greater average carbohydrate intake, particularly of starches, refined grains, and sugars (Foods and Nutrients section). Other specific changes related to increased caloric intake in the United States include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, commercially prepared (especially fast-food) meals, and higher-energy-density foods.7,13,5660

  • Between 1977 and 1996, the average portion sizes for nearly all foods increased at fast-food outlets, other restaurants, and home. 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).13

  • 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 2002.61

  • In a quantitative analysis using various US surveys between 1977 and 2006, the relations of changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.62 Decreases in energy density were actually linked to lower total 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).63 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 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.64

Foods and Nutrients

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

Macronutrients

(See Chart 5-1.)

  • 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.65 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.14 However, more recent analyses show that there were no significant trends in total fat intake among US adults from 1999 to 2008.55

  • Dietary guidelines during this time also emphasized carbohydrate consumption as the base of one’s dietary pattern66 and more recently specified the importance of complex rather than refined carbohydrates (eg, as the base of the Food Guide Pyramid).65 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.14 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.67,68 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 2008.55

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.59 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.62

  • 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/d from sugar-sweetened beverages, respectively, a decrease from 1999 to 2000 of 68 and 45 kcal/d, respectively.69

  • 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.13

  • 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.60

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.70

Morbidity and Mortality

Effects on Cardiovascular Risk Factors

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.71

  • 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.72 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the low-fat/high-carbohydrate DASH diet.73

  • 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.74

  • In meta-analyses of randomized controlled trials, consumption of eicosapentaenoic acid and docosahexaenoic acid for 212 weeks lowered SBP by 2.1 mm Hg75 and lowered resting heart rate by 2.5 beats per minute.76

  • 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.77 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.77

  • 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.78 Two randomized trials have confirmed that reducing intake of sugar-sweetened beverages reduces weight gain in children.79,80

  • 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 C-reactive protein, interleukin-6, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1.81

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.82 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.83

  • 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.8486 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).87 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).88

  • 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).87 Each 5% higher energy consumption of monounsaturated fat in place of saturated fat was not significantly associated with CHD risk.87

  • 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.37

  • 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).89

  • 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).90,91

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).92,93

  • 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).94

  • 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.95 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).96

  • 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).97

  • In a meta-analysis of 6 prospective observational studies, nut consumption was associated with significantly lower incidence of CHD (comparing higher to low intake: RR, 0.70; 95% CI, 0.57–0.82).85

  • Higher consumption of dairy or milk products is associated with lower incidence of DM and trends toward lower risk of stroke.77,90,91 Some limited evidence suggests that these associations are stronger for low-fat dairy or milk than for other dairy products. Dairy consumption is not significantly associated with higher or lower risk of CHD.85,98

  • 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.99 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.100

Sodium and Potassium
  • Lower estimated consumption of dietary sodium was not associated with lower CVD mortality in NHANES,101 although such findings may be limited by changes in behaviors that 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.102

  • 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).103

  • 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.104

  • 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.105

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).106 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke107 and for the DASH-type dietary pattern.108

  • 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).109 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.110116

  • 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 MI117 and a large primary prevention trial in Spain among patients with CVD risk factors.118 The latter trial 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 2010.119 Suboptimal dietary habits were the leading cause of both mortality and disability-adjusted life-years 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.120

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%.121

  • 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 2011.66

  • 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 2011.121

  • 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.121

  • 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.121

  • 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.122

  • 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.123126

  • Two separate cost-effectiveness analyses estimated that population reductions in dietary salt would not only be cost-effective but actually cost-saving.127,128 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 92 000.128 If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 to $24 billion annually.128 Such an intervention would be more cost-effective than using medications to lower BP in all people with hypertension.

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
CVDcardiovascular disease
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
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
WHIWomen’s Health Initiative

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6. Overweight and Obesity

See Table 6-1 and Charts 6-1 through 6-3.

Table 6-1. Overweight and Obesity

Prevalence of Overweight and Obesity, 2007–2010, Age >20 yPrevalence of Obesity in Adults, 2007–2010, Age >20 yPrevalence of Overweight and Obesity in Children, 2009–2010, Ages 2–19 yPrevalence of Obesity in Children, 2009–2010, Ages 2–19 yCost, 2008*
Both sexes, n (%)154 700 000 (68.2)78 400 000 (34.6)23 900 000 (31.8)12 700 000 (16.9)$147 Billion
Males79 900 000 (72.9)36 800 000 (33.6)12 700 000 (33.0)7 200 000 (18.6)
Females74 800 000 (63.7)41 600 000 (35.6)11 200 000 (30.4)5 500 000 (15.0)
NH white males, %73.133.830.116.1
NH white females, %60.232.525.611.7
NH black males, %68.737.936.924.3
NH black females, %79.953.941.324.3
Mexican American males, %81.336.040.524.0
Mexican American females, %78.244.838.218.2

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 adolescents71; however, statistics based on this new definition are not yet available.

NH indicates non-Hispanic; ellipses (…), data not available.

*Data from Finkelstein et al.57

Sources: National Health and Nutrition Examination Survey (NHANES) 2007 to 2010 (adults), unpublished National Heart, Lung, and Blood Institute (NHLBI) tabulation; NHANES 2009 to 2010 (ages 2–19 y) from Ogden et al.4 Extrapolation for ages 2 to 19 y from NHLBI tabulation of US Census resident population on April 1, 2010.

Chart 6-1.

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 Eaton et al (Table 101).72

Chart 6-2.

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, and 2007–2010). Obesity is defined as body mass index of 30.0 kg/m2. Data derived from Health, United States, 2011 (National Center for Health Statistics).73

Chart 6-3.

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: 1971–1974, 1976–1980, 1988–1994, 1999–2002, 2003–2006, and 2007–2010). Data derived from Health, United States, 2011 (National Center for Health Statistics).73

Overweight and obesity are major risk factors for CVD and stroke.1,2 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.3 In 2009 to 2010, 64.2% of children and 31.1% of adults met these criteria (see Chapter 2, Cardiovascular Health).

Prevalence

Youth

(See Table 6-1 and Chart 6-1.)

  • The prevalence of overweight and obesity in children 2 to 5 years of age, based on a BMI-for-age value ≥85th percentile of the 2000 CDC growth charts, was 26% for non-Hispanic white boys and 21% for non-Hispanic white girls, 31% for non-Hispanic black boys and 27% for non-Hispanic black girls, and 34% for Mexican American boys and 33% for Mexican American girls according to 2009 to 2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 30% for non-Hispanic white boys and 25% for non-Hispanic white girls, 41% for non-Hispanic black boys and 44% for non-Hispanic black girls, and 39% for Mexican American boys and 40% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 32% for non-Hispanic white boys and 28% for non-Hispanic white girls, 37% for non-Hispanic black boys and 45% for non-Hispanic black girls, and 46% for Mexican American boys and 41% for Mexican American girls.4

  • The national prevalence of obesity in children 2 to 5 years of age, based on BMI-for-age values ≥95th percentile of the 2000 CDC growth charts, was 12% for non-Hispanic white boys and 6% for non-Hispanic white girls, 21% for non-Hispanic black boys and 17% for non-Hispanic black girls, and 19% for Mexican American boys and 12% for Mexican American girls according to 2009 to 2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 17% for non-Hispanic white boys and 11% for non-Hispanic white girls, 30% for non-Hispanic black boys and 28% for non-Hispanic black girls, and 22% for Mexican American boys and 22% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 18% for non-Hispanic white boys and 15% for non-Hispanic white girls, 23% for non-Hispanic black boys and 25% for non-Hispanic black girls, and 29% for Mexican American boys and 19% for Mexican American girls.4 Regional variation exists in these prevalences.

  • Overall, 18% of US children and adolescents 6 to 19 years of age have BMI-for-age values ≥95th percentile of the 2000 CDC growth charts for the United States (NHANES 2009–2010, NCHS).4

  • NHANES 2009 to 2010 found that 16.9% (95% CI, 15.4%–18.4%) of youth aged 2 to 19 years were obese, which was unchanged from NHANES 2007 to 2008. Rates of overweight and obesity (≥85th BMI percentile) were 39.1% for Hispanics, 39.4% for Mexican Americans, 27.9% for non-Hispanic whites, and 39.1% for non-Hispanic blacks.4

  • A study of >8500 4-year-olds in the Early Childhood Longitudinal Study, Birth Cohort (National Center for Education Statistics) found that 1 in 5 were obese. Almost 13% of Asian children, 16% of white children, nearly 21% of black children, 22% of Hispanic children, and 31% of American Indian children were obese. Children were considered obese if their BMI was ≥95th percentile on the basis of CDC BMI growth charts.5

  • 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.6

  • 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.7

  • 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.8

  • 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).9

  • According to the National Longitudinal Study of Adolescent Health, 1.0% of adolescents were severely obese in 1996 (defined as age <20 years and BMI ≥95th sex-specific BMI-for-age growth chart or BMI ≥30 kg/m2); the majority (70.5%) maintained this weight status into adulthood. Obese adolescents had a 16-fold increased risk of becoming severely obese adults compared with those with normal weight or those who were overweight.10

  • 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.11

Adults

(See Table 6-1 and Chart 6-2.)

  • According to NHANES 2007 to 2010 (unpublished NHLBI tabulations):

—Overall, 68% of US adults were overweight or obese (73% of men and 64% of women).

—Among men, Mexican-Americans (81%) and non-Hispanic whites (73%) were more likely to be overweight or obese than non-Hispanic blacks (69%).

—Among women, non-Hispanic blacks (80%) and Mexican-Americans (78%) were more likely to be overweight or obese than non-Hispanic whites (60%).

—Among US adults, 35% were obese (35% of men and 36% of women).

—Among men, non-Hispanic blacks (38%) and Mexican-Americans (36%) were more likely to be obese than non-Hispanic whites (34%).

—Among women, non-Hispanic blacks (54%) and Mexican-Americans (45%) were more likely to be obese than non-Hispanic whites (33%).

  • When estimates were based on self-reported height and weight in the BRFSS/CDC survey in 2011, the prevalence of obesity ranged from 20.7% in Colorado to 34.9% in Mississippi. The median percentage by state was 27.8%.12 Additionally, no state met the Healthy People 2010 goal of reducing obesity to 15% of adults.13

  • On the basis of self-reported weights and heights from the 2012 NHIS14:

—Blacks ≥18 years of age (27.9%), American Indians or Alaska Natives (26.6%), and whites (35.7%) were less likely than Asians (57.6%) to be at a healthy weight.

—Blacks ≥18 years of age (36.2%) and American Indians or Alaska Natives (41.2%) were more likely to be obese than were whites (28.0%) and Asians (9.9%).

  • 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.15

  • According to the 2008 National Healthcare Disparities Report (based on NHANES 2003–2006)16:

—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%).

  • As judged by an analysis of data from MESA, a large proportion of white, black, and Hispanic participants were overweight (60%–85%) or obese (30%–50%), whereas fewer Chinese American participants were overweight (33%) or obese (5%).17

  • According to NHANES 2007 to 2010 data, 35% of US adults >65 years of age were obese, which represents 13 million individuals.18

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 and 12% in 2003 to 2006 (NHANES, NCHS).19

  • The obesity epidemic in children continues to grow on the basis of recent data from the Bogalusa Heart Study. Compared with 1973 to 1974, the proportion of children 5 to 17 years of age who were obese was 5 times higher in 2008 to 2009.20

  • A comparison of NHANES 2009 to 2010 data with 1999 to 2000 data demonstrates an increase in obesity prevalence in male youth of 5% (OR, 1.05; 95% CI, 1.01–1.10) but not in female youth (OR, 1.02; 95% CI, 0.98–1.07).4

Adults
  • On the basis of 2009 self-reported BRFSS data, overall obesity prevalence was 26.7% in the United States, with rates of 27.4% in men and 26.0% in women. By race/ethnicity, the prevalence of obesity among non-Hispanic whites was 25.2%, whereas it was 36 .8% among non-Hispanic blacks and 30.7% among Hispanics. There was an inverse association by education level: College graduates had a 20.8% rate of obesity, whereas those who attained less than a high school education had an obesity prevalence of 32.9%.21

  • According to NHANES data, between 2009 and 2010, the prevalence of obesity remained steady among US adult men and women, with no significant change compared with 2003 to 2008.22 Among adults aged ≥65 years, the prevalence of obesity increased linearly for men between 1999 and 2010, but the increase among women was not statistically significant.18

  • 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.23

Morbidity

  • Overweight children and adolescents are at increased risk for future adverse health effects, including the following24:

—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 became normal weight by adulthood had risks comparable to individuals who were never obese.25

  • 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. On the basis of data from the Medical Outcomes Study 12-item short-form health survey, overweight and obese participants had lower multivariable-adjusted scores on the physical component summary score but not on the mental component summary score.25a

  • The increasing prevalence of obesity is driving an increased incidence of type 2 DM. Data from the FHS indicate a 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/m2.26

  • Among 68 070 participants across multiple NHANES surveys, the decline in BP in recent birth cohorts is slowing, mediated by BMI.27

  • 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 only minimally associated with cardiovascular outcomes after controlling for baseline SBP, DM, and total and HDL cholesterol in addition to age, sex, and smoking status. Measures of adiposity also did not improve risk discrimination or reclassification when risk factor data were included.28

  • 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.29

  • 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.30

  • 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.31

  • 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.32 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.33

  • 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.34

  • 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.35

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.36

  • 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).37

  • 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,38 overweight (BMI 25 to <30 kg/m2) was not associated with excess deaths.39

  • 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).40

  • 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.41

  • 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.42

  • 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 2004.43

  • 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.44

  • 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.45

  • 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.46

  • 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.47

  • 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.48

  • 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.49

  • 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.50

  • 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).51

Cost

  • 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.52

  • 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.53

  • 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).54

  • According to 2006 MEPS and 2006 BRFSS data, annual medical expenditures would be 6.7% to 10.7% lower in the absence of obesity.55

  • 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.56

  • 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.57

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.58

  • In a large bariatric surgery cohort, the prevalence of high 10-year predicted CVD risk was 36.5%,59 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.60

  • 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.57 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.61

  • 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.62

  • 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.63

  • 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).64

  • 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.65 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.66

  • 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.67

  • 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.68 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.69

  • 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 2.4/100 000.70

Abbreviations Used in Chapter 6

AFatrial fibrillation
AFFIRMAtrial Fibrillation Follow-up Investigation of Rhythm Management
AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
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
DMdiabetes mellitus
FHSFramingham Heart Study
HbA1chemoglobin A1c
HDLhigh-density lipoprotein
HRhazard ratio
HUNT 2Nord-Trøndelag Health Study
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

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7. Family History and Genetics

See Tables 7-1 through 7-3.

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; OR, odds ratio.

Data derived from Chow et al.4

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.9

Table 7-3. Heritability of CVD Risk Factors From the FHS

TraitHeritability
ABI0.2124
SBP0.4225
DBP0.3925
Left ventricular mass0.24 to 0.3226
BMI0.37 (mean age 40 y) to 0.52 (mean age 60 y)27
Waist circumference0.4128
Visceral abdominal fat0.3629
Subcutaneous abdominal fat0.5729
Fasting glucose0.3430
HbA1c0.2730
Triglycerides0.4831
HDL cholesterol0.5231
Total cholesterol0.5731
LDL cholesterol0.5931
Estimated GFR0.3332

ABI indicates ankle-brachial index; BMI, body mass index; 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, when a trait aggregates within a family, this lends evidence for a genetic risk factor 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. Although the breadth of all genetic research into CVD is beyond the scope of this chapter, 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 HD and stroke identified to date.

Family History

Prevalence
  • Among adults ≥20 years of age, 12.6% (SE 0.5%) 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 2007–2010, unpublished NHLBI tabulation):

—For non-Hispanic whites, 12.4% (SE 0.7%) for men, 14.9% (SE 0.9%) for women

—For non-Hispanic blacks, 8.1% (SE 0.8%) for men, 13.0% (SE 0.9%) for women

—For Mexican Americans, 8.1% (SE 0.9%) for men, 10.0% (SE 1.1%) for women

—For other Hispanics, 8.8% (SE 1.5%) for men, 12.0% (SE 1.2%) for women

—For other races, 8.7% (SE 2.1%) for men, 10.7% (SE 2.6%) for women

  • HD occurs as people age, and those without a family history of HD may survive longer, 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 in the US population as measured by NHANES is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Age 20 to 39 years, 8.4% (SE 0.9%) for men, 10.3% (SE 0.7%) for women

—Age 40 to 59 years, 12.8% (SE 0.9%) for men, 15.3% (SE 1.1%) for women

—Age 60 to 79 years, 13.7% (SE 0.9%) for men, 17.5% (SE 1.2%) for women

—Age ≥80 years, 9.8% (SE 1.5%) for men, 13.7% (SE 0.6%) 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.1

Impact of Family History
  • Premature paternal history of a heart attack has been shown to approximately double the risk of a heart attack in men and increase the risk in women by ≈70%.2,3

  • History of a heart attack in both parents increases the risk of heart attack, especially when 1 parent had a premature heart attack4 (Table 7-1).

  • Sibling history of HD has been shown to increase the odds of HD in men and women by ≈50%.5

  • Premature family history of angina, MI, angioplasty, or bypass surgery increased the lifetime risk by ≈50% for both HD (from 8.9% to 13.7%) and CVD mortality (from 14.1% to 21%).6

  • Similarly, parental history of AF is associated with ≈80% increased odds of AF in men and women,7 and a history of stroke in a first-degree relative increases the odds of stroke in men and women by ≈50%.8

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.9

  • 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.10

  • Genetic markers discovered thus far have not been shown to add to cardiovascular risk prediction tools beyond current models that incorporate family history.11 Genetic markers have also not been shown to improve prediction of subclinical atherosclerosis beyond traditional risk factors.12 However, an association between genetic markers and coronary calcification has been seen.13

  • 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.14

  • 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%.14

  • Variation at the 9p21 .3 region also increases the risk of HF15 and sudden death.16 Associations have also been observed between the 9p21.3 region and CAC.17,18 Additionally, stronger associations have been found between variation at 9p21.3 and earlier17,18 and more severe19 heart attacks. The biological mechanism underpinning the association of genetic variation in the 9p21 region with disease outcomes is still under investigation.

Stroke
  • The same 9p21.3 region has also been associated with intracranial aneurysm,20 AAA,21 and ischemic stroke.22

  • 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).22,23

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.

Abbreviations Used in Chapter 7

AAAabdominal aortic aneurysm
ABIankle-brachial index
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
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
FHSFramingham Heart Study
GFRglomerular filtration rate
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDheart disease
HDLhigh-density lipoprotein
HFheart failure
LDLlow-density lipoprotein
MImyocardial infarction
NHANESNational Health and Nutrition Examination Survey
NHLBINational Heart, Lung, and Blood Institute
ORodds ratio
SBPsystolic blood pressure
SEstandard error
SNPsingle-nucleotide polymorphism

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