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Heart Disease and Stroke Statistics—2017 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/CIR.0000000000000485Circulation. 2017;135:e146–e603

Table of Contents

Summary e147

1. About These Statistics e158

2. Cardiovascular Health e161

Health Behaviors

3. Smoking/Tobacco Usee 183

4. Physical Inactivitye 196

5. Nutrition e214

6. Overweight and Obesitye 240

Health Factors and Other Risk Factors

7. Family History and Genetics e263

8. High Blood Cholesterol and Other Lipids e270

9. High Blood Pressure e280

10. Diabetes Mellitus e295

11. Metabolic Syndrome e313

12. Chronic Kidney Disease e336

Cardiovascular Conditions/Diseases

13. Total Cardiovascular Diseases e349

14. Stroke (Cerebrovascular Disease) e374

15. Global CVD and Stroke e414

16. Congenital Cardiovascular Defects and Kawasaki Disease e423

17. Disorders of Heart Rhythm e440

18. Sudden Cardiac Arrest e468

19. Subclinical Atherosclerosis e487

20. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e505

21. Cardiomyopathy and Heart Failure e523

22. Valvular Diseases e539

23. Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension e548

24. Peripheral Artery Disease and Aortic Diseases e553

Outcomes

25. Quality of Care e565

26. Medical Procedures e583

27. Economic Cost of Cardiovascular Disease e589

Supplemental Materials

28. At-a-Glance Summary Tables e595

29. Glossary e601

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 in a single document the most up-to-date statistics related to heart disease, stroke, and the factors in the AHA’s Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity [PA], diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions. Cardiovascular disease (CVD) and stroke produce immense health and economic burdens in the United States and globally. The Update also presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure (HF), valvular disease, venous disease, and peripheral arterial disease) and the associated outcomes (including quality of care, procedures, and economic costs). Since 2006, the annual versions of the Statistical Update have been cited >20 000 times in the literature. In 2015 alone, the various Statistical Updates were cited ≈4000 times.

Figure.

Figure. Life’s Simple 7. Seven approaches to staying heart healthy: be active, keep a healthy weight, learn about cholesterol, don’t smoke or use smokeless tobacco, eat a heart-healthy diet, keep blood pressure healthy, and learn about blood sugar and diabetes.

Each annual version of the Statistical Update undergoes revisions to include the newest nationally representative data, add additional relevant published scientific findings, remove older information, add new sections or chapters, and increase the number of ways to access and use the assembled information. This year-long process, which begins as soon as the previous Statistical Update is published, is performed by the AHA Statistics Committee faculty volunteers and staff and government agency partners. This year’s edition includes new data on the monitoring and benefits of cardiovascular health in the population, new metrics to assess and monitor healthy diets, a new chapter on venous disease and pulmonary hypertension (PH), new information on stroke in young adults, an enhanced focus on underserved and minority populations, a substantively expanded focus and chapter on the global burden of CVD, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the AHA’s 2020 Impact Goals. Below are a few highlights from this year’s Update.

Current State of Cardiovascular Health in the United States: What’s New? (Chapter 2)

  • The AHA developed a Health Campaign for Life’s Simple 7, which emphasizes that adults and young people can live healthier lives by avoiding smoking and tobacco products, engaging in daily PA, eating a healthy diet, maintaining a healthy weight, and keeping cholesterol, BP, and glucose at healthy levels. New highlights from the cardiovascular health section include the following:

  • A recent meta-analysis of 9 prospective cohort studies involving 12 878 participants contributed new estimates of the importance of cardiovascular health metrics and risk for clinical events. The meta-analysis showed that achieving the greatest ideal cardiovascular health metrics was associated with a lower risk of stroke (relative risk, 0.31; 95% confidence interval [CI], 0.25–0.38), CVD (relative risk, 0.20; 95% CI, 0.11–0.37), cardiovascular mortality (relative risk, 0.25; 95% CI, 0.10–0.63), and all-cause mortality (relative risk, 0.55; 95% CI, 0.37–0.80).

  • The health benefits of pursuing cardiovascular health are observed across races/ethnicities and the nation. New data on measures of cardiovascular health in Hispanics find similar results as previous reports in non-Hispanic groups. Studies from non-US populations also support the importance of Life’s Simple 7 on future disease prevention.

  • Trends in improvements in overall cardiovascular health metrics are projected to reduce coronary heart disease deaths by 30% between 2010 and 2020.

  • The current evidence supports a range of complementary life course strategies to improve cardiovascular health in youth and adults as they age. Such approaches 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. The AHA and the literature support the importance of the following:

  • Individual-focused approaches, which target lifestyle and risk factor treatments at the individual level.

  • Healthcare systems approaches, which encourage, facilitate, and reward efforts by providers and patients to improve health behaviors and health factors.

  • Population approaches, which target lifestyle and treatments in schools, places of worship, workplaces, local communities, and states, as well as throughout the nation.

Smoking and Tobacco Use (Chapter 3)

  • In 2015, among adults ≥18 years of age, overall rates of tobacco use were estimated to be 15.2% (16.7% of males and 13.7% of females; National Health Interview Survey).

  • In the United States, substantially higher tobacco use rates are found in low socioeconomic status, Native American, and lesbian, gay, bisexual, or transgender people reporting disability or activity limitations, as well as mentally ill populations. There also is substantial regional variation in the percentage of current smokers.

    — The region with the highest rates is the Midwest (20.7%), and the state with the highest percentage was West Virginia (26.7%). The lowest percentages regionally were observed in the West (13.1%), and by state in Utah (9.7%).

  • In 2015, e-cigarettes were the most commonly used tobacco product among middle school (5.3%) and high school (16.0%) students. The risks for nicotine dependence and for CVD associated with regular e-cigarette use are unknown. Use of cigarillos or other mass marketed cigars, hookahs, and water pipes has also become increasingly common in the past few years.

  • In May 2016, the US Food and Drug Administration placed e-cigarettes under the same regulations and restrictions as traditional combustible cigarettes. Furthermore, Tobacco 21 legislation, which mandates a minimum age of 21 years to purchase tobacco, is becoming increasingly common in the United States.

Physical Inactivity (Chapter 4)

  • More Americans are meeting the federal PA guidelines. The age-adjusted percentage of US adults (≥18 years) who met both the muscle-strengthening and aerobic guidelines increased from 14.3% in 1998 to 21.6% in 2015. The percentage of US adults who met the aerobic guideline increased from 40.0% in 1998 to 49.8% in 2015.

  • In 2015, only 27.1% of high school students met activity recommendations of ≥60 minutes of PA on all 7 days of the week, and 14.3% of high school students reported that they were inactive on all of the previous 7 days.

  • Even low levels of leisure time PA (up to 75 minutes of brisk walking per week) were associated with reduced risk of mortality compared with participants who engaged in no PA.

  • A study of American adults reported that inadequate levels of aerobic PA (after adjustment for body mass index) were associated with an estimated 11.1% of aggregate healthcare expenditures.

Nutrition (Chapter 5)

  • The 2015 US Dietary Guidelines Advisory Committee recently concluded that a healthy dietary pattern is higher in vegetables, fruits, whole grains, low-fat or nonfat dairy, seafood, legumes, and nuts; moderate in alcohol (among adults); lower in red and processed meat; and low in sugar-sweetened foods and drinks and refined grains.

  • Between 2003 to 2004 and 2011 to 2012 in the United States, the mean AHA healthy diet score improved in both children and adults. The prevalence of an ideal healthy diet score (>80) increased from 0.2% to 0.6% in children and from 0.7% to 1.5% in adults. The prevalence of an intermediate healthy diet score (40–79) increased from 30.6% to 44.7% in children and from 49.0% to 57.5% in adults. These improvements were largely attributable to increased whole grain consumption and decreased sugar-sweetened beverage consumption in both children and adults.

  • Between 1999 and 2012, although AHA healthy diet scores tended to improve in all race/ethnicity, income, and education levels, many disparities present in earlier years widened over time, with generally smaller improvements seen in minority groups and those with lower income or education.

Overweight and Obesity (Chapter 6)

  • The prevalence of obesity among adults and youth in the United States increased significantly from 1999 to 2000 through 2013 to 2014. However, the increase in obesity prevalence began to level off and was not statistically significant for adults from the time period 2003 to 2004 through 2011 to 2012 and for youth from the time period 2003 to 2004 through 2013 to 2014.

  • Body mass index and waist circumference cut points in US guidelines underestimate obesity and CVD risk in Asian and South Asian populations.

  • Definitions of “metabolically healthy obesity” vary, and over time, a substantial proportion of those with metabolically healthy obesity transition to metabolically unhealthy. The risk of CVD events, particularly HF, may be increased with obesity even in the absence of metabolic risk factors.

Family History and Genetics (Chapter 7)

  • Among adults ≥20 years of age, 12.2% reported having a parent or sibling with a heart attack or angina before age 50 years, with the highest sex-specific prevalence observed among non-Hispanic white males and females.

High Blood Cholesterol and Other Lipids (Chapter 8)

  • Mean low-density lipoprotein cholesterol decreased from 126 mg/dL in 1999 to 2000 to 111 mg/dL in 2013 to 2014 among US adults. The age-adjusted prevalence of high low-density lipoprotein cholesterol decreased from 42.9% in 1999 to 2000 to 28.5% in 2013 to 2014.

  • Data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2000 to NHANES 2011 to 2012 show that the use of cholesterol-lowering treatment has increased substantially among adults, from 8% in 1999 to 2000 to 18% in 2011 to 2012. During this period, the use of statins increased from 7% to 17%.

  • From 1988 to 1994 to 2013 to 2014, mean serum total cholesterol for adolescents 12 to 19 years of age has decreased across all subgroups of race and sex.

High Blood Pressure (Chapter 9)

  • The age-adjusted prevalence of hypertension among US adults ≥20 years of age is estimated to be 34.0% in NHANES 2011 to 2014, which is equivalent to 85.7 million adults.

  • The prevalence of high BP or borderline high BP among US children and adolescents 8 to 17 years old is 11%.

  • The SPRINT (Systolic Blood Pressure Intervention Trial) demonstrated lower CVD and mortality risk with a systolic BP target goal of 120 mm Hg versus 140 mm Hg. It is estimated that 16.8 million US adults meet the SPRINT eligibility criteria.

  • The prevalence of apparent treatment-resistant hypertension was estimated from a meta-analysis to be 13.7%.

  • Controlling hypertension in all patients with CVD and stage 2 hypertension could be cost-saving.

Diabetes Mellitus (Chapter 10)

  • An estimated 23.4 million adults have diagnosed diabetes mellitus (DM), 7.6 million have undiagnosed DM, and 81.6 million have prediabetes.

  • Analyses of high school–aged blood donors in 2011 to 2012 reported that 10% had prediabetes hemoglobin A1c levels and an additional 0.6% had hemoglobin A1c ≥6.5%, the threshold endorsed to diagnose DM.

  • A recent large meta-analysis of randomized controlled trials showed that exercise may exert its favorable effects by significantly improving glucose tolerance and insulin resistance. The benefits of exercise were further supported by a large intervention project that showed that higher fitness was associated with a lower risk of incident DM regardless of demographic characteristics and baseline risk factors.

  • In 2014, there were 76 488 DM-related deaths.

Metabolic Syndrome (Chapter 11)

  • The prevalence of metabolic syndrome in youth ages 12 to 19 years old has decreased in NHANES 2009 to 2010 and 2011 to 2012. This important epidemiological statistic mirrors a previously documented plateau and decrease in the prevalence of metabolic syndrome in adults.

  • The decrease in metabolic syndrome in youth most closely correlates with rising high-density lipoprotein cholesterol and lowered triglyceride levels, which are potentially driven by decreased carbohydrate intake and increased unsaturated fat intake.

  • Despite these encouraging findings, recent data have confirmed that the severity of existing metabolic syndrome progresses with advancing age in approximately three quarters (76%) of adults, with faster progression of metabolic syndrome noted in women and younger people.

Chronic Kidney Disease (Chapter 12)

  • The total prevalence of chronic kidney disease is rising globally, primarily because of aging populations. The Global Burden of Disease study estimates that kidney disease is now the 19th-leading cause of death, up from the 36th-leading cause of death in 1990.

  • According to recent figures from the United States Renal Data System, the number of people with prevalent end-stage renal disease is increasing, with 661 648 prevalent cases as of December 31, 2013. However, the incidence rate has declined; 117 162 new cases were reported in 2013.

  • The prevalence of chronic kidney disease in adults ≥30 years of age is projected to increase to 14.4% in 2020 and 16.7% in 2030.

  • Cardiovascular risk in patients with kidney disease can now be classified as high, intermediate, and low according to estimated glomerular filtration rate and albuminuria categories defined by the Kidney Disease Improving Global Outcomes (KDIGO) working group.

Total Cardiovascular Diseases (Chapter 13)

  • An estimated 92.1 million US adults have at least 1 type of CVD. By 2030, 43.9% of the US adult population is projected to have some form of CVD.

  • From 2004 to 2014, death rates attributable to CVD declined 25.3%. The actual number of CVD deaths decreased 6.7%.

  • Globally, 80% of CVD deaths take place in low- and middle-income countries and occur almost equally in males and females.

Stroke and Cerebrovascular Disease (Chapter 14)

  • When considered separately from other CVDs, stroke ranks No. 5 among all causes of death, behind diseases of the heart, cancer, chronic lower respiratory disease, and unintentional injuries/accidents.

  • Globally, in 2013 there were 6.5 million stroke deaths, making stroke the second-leading cause of death behind ischemic heart disease.

  • Approximately 795 000 strokes occur in the United States each year. On average, every 40 seconds, someone in the United States has a stroke, and on average, every 4 minutes, someone dies of a stroke.

  • Approximately 60% of stroke deaths occurred outside of an acute care hospital.

  • A review of recent clinical trials identified the benefit of intense BP reduction, which reduced risks of stroke outcomes.

  • Adherence to a Mediterranean-style diet that was higher in nuts and olive oil was associated with a reduced risk of stroke.

  • One year after stroke, blacks were less likely to report independence in activities of daily living and instrumental activities of daily living than whites.

Global Cardiovascular Disease (Chapter 15)

  • In 2013, the highest prevalence of ischemic stroke (1015 to 1184 cases per 100 000 people) was in high-income countries (particularly in the United States), with the lowest (up to 339 per 100 000) in low- and middle-income countries.

  • CVD was the most common underlying cause of death in the world in 2013, accounting for an estimated 17.3 million (95% uncertainty interval, 16.5–18.1 million) of 54 million total deaths, or 31.5% (95% uncertainty interval, 30.3%-32.9%) of all global deaths.

  • Cost-effective medications such as aspirin, statins, and BP-lowering agents remain unaffordable for much of the world. New community health worker-based strategies to improve their delivery are proving to be highly effective.

Congenital Cardiovascular Defects and Kawasaki Disease (Chapter 16)

  • The mortality attributed to congenital cardiovascular defects decreases with later gestational age (to 40 weeks), which suggests early delivery will not benefit most patients with congenital cardiovascular defects.

  • Health outcomes are improving for congenital cardiovascular defects, and survival is increasing, leading to a population shift toward adulthood.

  • The rising population of adults with congenital heart disease adds to management complexity and emphasizes the need for coordinated care by adult congenital cardiovascular specialists.

Disorders of Heart Rhythm (Chapter 17)

  • The frequency and adverse consequences of clinically unrecognized and asymptomatic atrial fibrillation (AF) are increasingly reported, particularly in older adults. For instance, in a community-based study in Sweden, >7000 people 75 to 76 years of age were monitored intermittently; 3% had newly diagnosed AF, of whom only 17% had their AF detected by a screening ECG.

  • A recent meta-analysis from 4 large contemporary randomized trials revealed that AF is associated with systemic embolism, occurring at a rate of 0.24 per 100-person years compared with 1.92 for stroke per 100-person years.

  • Data from the Framingham Heart Study, the Atherosclerosis Risk in Communities study, the United Kingdom, and other sites suggest that the incidence and prevalence of AF are increasing over time.

Sudden Cardiac Arrest (Chapter 18)

  • In the 2015 CARES (Cardiac Arrest Registry to Enhance Survival) National Survival Report for emergency medical services–treated nontraumatic cardiac arrest, the survival rate to hospital discharge was 10.6% for adults >18 years old, 23.5% for children 13 to 18 years old, 16.6% for children >1 to 12 years old, and 6.2% for children <1 year old.

  • In 2015, Get With the Guidelines–Resuscitation reported the rate of survival to hospital discharge from pulseless in-hospital cardiac arrest in adults ≥18 years old was 23.8% (95% CI, 23.2%–24.3%], whereas in children 0 to 18 years old, it was 35.9% (95% CI, 31.4%–40.6%), and in neonates (0–30 days old), it was 24.2% (95% CI, 18.2%–31.4%).

Subclinical Atherosclerosis (Chapter 19)

  • Subclinical CVD is common among US adults living in rural areas; a study from central Appalachia reported 56% of participants had coronary artery calcium scores >0.

  • Coronary artery calcium scores >400 versus 0 are associated with an increased risk for cancer, chronic kidney disease, pneumonia, chronic obstructive pulmonary disease, and hip fracture.

  • Conflicting data have been reported on the contribution of carotid intima-media thickness to risk prediction. A recent study from a consortium of 14 population-based cohorts demonstrated little additive value of common carotid intima-media thickness to Framingham Risk Score for purposes of discrimination and reclassification as far as incident myocardial infarction (MI) and stroke were concerned. However, for those at intermediate risk, the addition of mean common carotid intima-media thickness to an existing cardiovascular risk score resulted in a small but statistically significant improvement in risk prediction.

Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris (Chapter 20)

  • A majority of MIs occur during a hospitalization for another reason, rather than being the cause of hospitalization.

  • Silent MIs (ie, MIs detected on ECG without a definite or probable hospitalized MI) account for almost 50% of incident MIs.

  • The percentage of US adults with a 10-year predicted CVD risk ≥20% decreased from 13.0% in 1999 to 2000 to 9.4% in 2011 to 2012.

  • Among US males and females <55 years old, coronary heart disease mortality did not decline between 1990 to 1999 and 2000 to 2011.

  • Between 2001 to 2003 and 2007 to 2009, age-adjusted mortality after MI decreased among white males, but no changes were present for white females or black males or females.

Cardiomyopathy and Heart Failure (Chapter 21)

  • On the basis of data from NHANES 2011 to 2014, an estimated 6.5 million Americans ≥20 years of age had HF. This represents an increase from an estimated 5.7 million US adults with HF based on NHANES 2009 to 2012 (NHLBI tabulation).

  • Five-year survival of HF diagnosis after an MI has also improved in 2001 to 2010 versus 1990 to 2000, from 54% to 61%.

  • Greater adherence to the AHA’s Life’s Simple 7 guidelines (better profiles in smoking, body mass index, PA, diet, cholesterol, BP, and glucose) is associated with a lower lifetime risk of HF and better cardiac structure and functional parameters by echocardiography.

  • Of incident hospitalized HF events, 53% had HF with reduced ejection fraction and 47% had preserved ejection fraction. Black males had the highest proportion of hospitalized HF with reduced ejection fraction (70%); white females had the highest proportion of hospitalized HF with preserved ejection fraction (59%).

Valvular Diseases (Chapter 22)

  • Although rheumatic heart disease is uncommon in high-income countries such as the United States, it remains an important cause of morbidity and mortality in low- and middle-income countries.

  • Both administrative and community-based data report that the incidence of infective endocarditis did not change after the publication of the 2007 AHA guidelines for management of infective endocarditis,2 which restricted the indications for antibiotic prophylaxis before dental procedures.

  • From the time of initial US Food and Drug Administration approval in late 2011 through 2014, more than 26 000 transcatheter aortic valve replacements were performed at 348 centers in 48 states in the United States. Two thirds of these patients were >80 years of age.

Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension (Chapter 23)

Venous Thrombosis
  • The main complications after venous thromboembolism are postthrombotic syndrome, which occurs in ≈40% of patients with deep vein thrombosis (DVT), and chronic thromboembolic PH, which occurs among 1.0% to 8.8% of those with pulmonary embolism.

  • Assuming 375 000 to 425 000 new cases of venous thromboembolism annually, the overall cost of venous thromboembolism was estimated at $7 billion to $10 billion annually.

New Section on Chronic Venous Insufficiency
  • Varicose veins affect 25 million US adults. More severe venous disease affects 6 million.

  • Venous ulcer is a substantial morbidity of chronic venous insufficiency. Estimated prevalence in adults is ≈0.3%, and incidence is ≈20% of those with chronic venous insufficiency. The estimated cost to treat venous ulcers in the United States is $1 billion annually.

  • Postthrombotic syndrome, a subset of chronic venous insufficiency, has risk factors that can be identified at the time of or after DVT, including recurrent ipsilateral DVT, obesity, more extensive DVT, poor quality of initial anticoagulation, ongoing symptoms or signs of DVT 1 month after diagnosis, and elevated D-dimer at 1 month.

New Section on PH
  • Risk factors are implicit in the World Health Organization disease classification of the 5 mechanistic subtypes of PH. The most common risk factors are left-sided heart disease and lung disease.

  • Mortality of PH depends on the cause and treatment. For example, an international prospective registry that included 679 patients with chronic thromboembolic PH estimated 3-year survival as 89% with pulmonary thromboendarterectomy and 70% without it.

  • In a study of 772 consecutive pulmonary embolism patients without major comorbidity such as cancer, the risk factors for chronic thromboembolic PH were unprovoked pulmonary embolism, hypothyroidism, symptom onset >2 weeks before pulmonary embolism diagnosis, right ventricular dysfunction on computed tomography or echocardiography, DM, and thrombolytic therapy or embolectomy. A risk prediction score that included these factors was able to predict a group with a chronic thromboembolic PH incidence of 10% (95% CI, 6.5%–15%).

  • Eighty percent of patients with PH live in developing countries, and the main cause of PH is heart and lung disease. Yet, schistosomiasis, rheumatic heart disease, HIV, and sickle cell disease remain prominent causes compared with high-income countries.

Peripheral Artery Disease and Aortic Diseases (Chapter 24)

  • From 2003 to 2011, there was a significant increase in endovascular treatment of critical limb ischemia (from 5.1% to 11.0%), which was accompanied by lower rates of in-hospital mortality and major amputation, as well as shorter hospital length of stay.

  • Endovascular repair may yield better outcomes in the first few years, but after 8 years of follow-up in one study, the open repair group and the endovascular repair group demonstrated similar survival. Of note, individuals in the endovascular repair group had a higher rate of eventual aneurysm rupture (5.4%) than patients who underwent open repair (1.4%).

Quality of Care (Chapter 25)

  • Overall, inpatient quality of care for patients with acute coronary syndromes, HF, and stroke continues to show gains, with compliance rates above 95% for some measures.

  • Although performance on inpatient quality-of-care measures or quality-of-care measures at discharge in patients after MI or stroke remains high (>90% for most measures), performance on outpatient quality-of-care measures, especially those that pertain to body mass index assessment and PA assessment in the outpatient setting, remains low.

  • Overall rates of bystander cardiopulmonary resuscitation remain low.

Medical Procedures (Chapter 26)

  • In 2015, 2804 heart transplantations were performed in the United States, the most ever.

Economic Cost of Cardiovascular Disease (Chapter 27)

  • CVD and stroke accounted for 14% of total health expenditures in 2012 to 2013, more than any major diagnostic group.

  • The annual direct and indirect cost of CVD and stroke in the United States was an estimated $316.1 billion in 2012 to 2013. This figure includes $189.7 billion in expenditures (direct costs, which include the cost of physicians and other professionals, hospital services, prescribed medication, and home health care, but not the cost of nursing home care) and $126.4 billion (indirect costs) in lost future productivity attributed to premature CVD and stroke mortality in 2012 to 2013.

  • Taking into account nursing home care costs, the total direct medical costs of CVD between 2012 to 2030 are projected to increase from $396 billion to $918 billion.

Conclusions

The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the Statistical Update. This annual Statistical Update is the product of a full year’s worth of effort by dedicated volunteer physicians and scientists, committed government professionals, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.

Emelia J. Benjamin, MD, ScM, FAHA, Chair

Paul Muntner, PhD, MHSc, Vice Chair

Sally S. Wong, PhD, RD, CDN, FAHA, AHA Science & Medicine Advisor

On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Note: Population data used in the compilation of NHANES prevalence estimates are for the latest year of the NHANES survey being used. Extrapolations for NHANES prevalence estimates are based on the census resident population for 2014 because this is the most recent year of NHANES data used in the Statistical Update.

Acknowledgments

We wish to thank our colleagues: Lucy Hsu and Michael Wolz, National Heart, Lung, and Blood Institute; Sheila Franco, National Center for Health Statistics; Lorena Egan, AHA; and the dedicated staff of the Centers for Disease Control and Prevention; the National Center for Health Statistics; and the National Heart, Lung, and Blood Institute for their valuable comments and contributions.

Footnotes

The views expressed in this document are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; the US Department of Health and Human Services; or the US Department of Veterans Affairs.

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.

A copy of the document is available at http://professional.heart.org/statements by using either “Search for Guidelines & Statements” or the “Browse by Topic” area. To purchase additional reprints, call 843-216-2533 or e-mail .

The American Heart Association requests that this document be cited as follows: Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jiménez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JHY, Alger HM, Wong SS, Muntner P; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation. 2017;135:e146-e603. DOI: 10.1161/CIR.0000000000000485.

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://professional.heart.org/statements. Select the “Guidelines & Statements” drop-down menu, then click “Publication Development.”

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.

Circulation is available at http://circ.ahajournals.org.

References

  • 1. Life’s Simple 7.http://www.heart.org/HEARTORG/Conditions/My-Life-Check—Lifes-Simple-7_UCM_471453_Article.jsp#.WBwQnvKQzio. Accessed November 3, 2016.Google Scholar
  • 2. Wilson W, Taubert KA, Gewitz M, Lockhart PB, Baddour LM, Levison M, Bolger A, Cabell CH, Takahashi M, Baltimore RS, Newburger JW, Strom BL, Tani LY, Gerber M, Bonow RO, Pallasch T, Shulman ST, Rowley AH, Burns JC, Ferrieri P, Gardner T, Goff D, Durack DT. Prevention of infective endocarditis: guidelines from the American Heart Association: a guideline from the American Heart Association Rheumatic Fever, Endocarditis, and Kawasaki Disease Committee, Council on Cardiovascular Disease in the Young, and the Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and the Quality of Care and Outcomes Research Interdisciplinary Working Group [published correction appears in Circulation. 2007;116:e376–e377].Circulation. 2007; 116:1736–1754. doi:10.1161/CIRCULATIONAHA.106.183095.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 Statistics Update. This chapter describes the most important sources and the types of data used from them. For more details, see Chapter 29 of this document, the Glossary.

The surveys used are the following:

  • BRFSS—ongoing telephone health survey system

  • GCNKSS—stroke incidence rates and outcomes within a biracial population

  • MEPS—data on specific health services that Americans use, how frequently they use them, the cost of these services, and how the costs are paid

  • NHANES—disease and risk factor prevalence and nutrition statistics

  • NHIS—disease and risk factor prevalence

  • NHDS—hospital inpatient discharges and procedures (discharged alive, dead, or status unknown)

  • NAMCS—physician office visits

  • National Home and Hospice Care Survey—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

  • National Nursing Home Survey—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 condition at a given point or period in time. The NCHS/CDC 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, which includes 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 2011 to 2014 (in most cases, these are the latest published figures). These are applied to census population estimates for 2014. 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 2011 to 2014 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 incidence is often discussed 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, the “any-mention” status). The number of deaths in 2014 with any mention of specific causes of death was tabulated by the NHLBI from the NCHS public-use electronic files on mortality.

The first set of statistics for each disease in this Update includes the number of deaths for which the disease is the underlying cause. Two exceptions are Chapter 9 (High Blood Pressure) and Chapter 20 (Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris). HBP, 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/CDC World Wide Web site or the CDC compressed mortality file. Any-mention numbers of deaths were tabulated from the electronic mortality files of the NCHS/CDC World Wide Web site.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 20141 in the computation of morbidity data. NCHS/CDC population estimates2 for 2014 were used in the computation of death rate data. The Census Bureau World Wide Web site 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/CDC unless otherwise noted. Ambulatory care visit data include patient visits to physician offices and hospital outpatient departments and EDs. Ambulatory care visit data reflect the first-listed (primary) diagnosis. These estimates are from the NAMCS and NHAMCS of the NCHS/CDC. Data for community health centers, which were included in estimates in previous years, were not available for 2012 NAMCS estimates included in this Update.

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/CDC are applied as noted.3 Effective with mortality data for 1999, we are using the 10th revision (ICD-10).4 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.5

Age Adjustment

Prevalence and mortality estimates for the United States or individual states comparing demographic groups or estimates over time are either age specific or age adjusted to the 2000 standard population by the direct method.6 International mortality data are age adjusted to the European standard.7 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 2013 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 2014. For disease and risk factor prevalence, most rates in this report are calculated from the 2011 to 2014 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US population in 2014, 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 and hospital EDs are for 2012, whereas hospital outpatient department visits are for 2011. Except as noted, economic cost estimates are for 2012 to 2013.

Cardiovascular Disease

For data on hospitalizations, physician office visits, and mortality, CVD is defined according to ICD codes given in Chapter 13 of the present document. This definition includes all diseases of the circulatory system, as well as congenital CVD. Unless otherwise 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/Ethnicity

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 and ethnic 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. Direct all media inquiries to News Media Relations at http://www.newsroom.heart.org/newsmedia/contacts or 214-706-1173.

The AHA works diligently to ensure that this Update is error free. If we discover errors after publication, we will provide corrections at http://www.heart.org/statistics and in 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
HBPhigh blood pressure
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
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NINDSNational Institute of Neurological Disorders and Stroke
PADperipheral artery disease
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

See Glossary (Chapter 29) for explanation of terms.

References

2. Cardiovascular Health

See Tables 2-1 through 2-6 and Charts 2-1 through 2-16

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 age*Tried 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
Physical activity
 Adults ≥20 y of ageNone1–149 min/wk moderate or 1–74 min/wk vigorous or 1–149 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 (AHA diet score)
 Adults ≥20 y of age<2 (0–39)2–3 (40–79)4–5 (80–100)
 Children 5–19 y of age<2 (0–39)2–3 (40–79)4–5 (80–100)
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; and SBP, systolic blood pressure.

*Age ranges in children for each metric depend on guidelines and data availability.

Represents appropriate energy balance, that is, appropriate dietary quantity and physical activity 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. The consistency of one’s diet with these dietary targets can be described using a continuous AHA diet score, scaled from 0 to 100 (see chapter on Nutrition).

Modified from Lloyd-Jones et al1 with permission. Copyright © 2010, American Heart Association, Inc.

Table 2-2. Prevalence of Ideal Cardiovascular Health and Its Components in the US Population in Selected Age Strata: NHANES 2011 to 2012 and 2013 to 2014

NHANES CycleAge 12—19 y, % (SE)Age ≥20 y, % (SE)*Age 20–39 y, % (SE)Age 40–59 y, % (SE)Age ≥60 y, % (SE)
Ideal CV health profile (7/7)2011–20120.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)
 ≥6 Ideal2011–201210.3 (1.7)4.3 (0.6)9.1 (1.4)1.8 (0.4)0.5 (0.3)
 ≥5 Ideal2011–201241.3 (2.3)16.9 (1.0)32.7 (2.5)8.9 (1.0)3.2 (1.1)
Ideal health factors (4/4)2013–201457.7 (2.1)18.4 (0.9)32.6 (2.1)13.1 (1.2)2.9 (0.5)
 Total cholesterol <200 mg/dL2013–201479.7 (0.9)50.1 (1.5)71.9 (2.2)41.9 (1.6)26.6 (1.8)
 SBP <120/DBP <80 mm Hg2013–201488.7 (1.1)45.4 (0.9)68.0 (1.6)40.2 (2.0)14.1 (1.1)
 Nonsmoker2013–201491.4 (1.4)77.1 (1.2)72.6 (1.4)74.7 (2.1)87.7 (1.2)
 FPG <100 mg/dL and HbA1c <5.7%2013–201487.6 (1.0)60.8 (1.1)78.5 (1.3)57.7 (1.8)35.4 (1.7)
Ideal health behaviors (4/4)2011–20120.0 (0.0)0.1 (0.1)0.0 (0.0)0.2 (0.1)0.1 (0.1)
 PA at goal2013–201427.7 (1.2)36.7 (1.1)45.0 (2.0)34.2 (1.6)26.7 (1.5)
 Nonsmoker2013–201491.4 (1.4)77.1 (1.2)72.6 (1.4)74.7 (2.1)87.7 (1.2)
 BMI <25 kg/m22013–201463.1 (2.4)29.6 (0.8)36.3 (1.5)25.4 (1.4)25.6 (1.1)
 4–5 Diet goals met2011–20120.0 (0.0)0.4 (0.1)0.1 (0.1)0.2 (0.1)0.8 (0.2)
  F&V ≥4.5 C/d2011–20123.9 (0.7)12.1 (1.1)7.9 (1.2)13.9 (1.5)17.1 (1.5)
  Fish ≥2 svg/wk2011–201210.0 (1.5)18.5 (1.6)16.3 (1.4)18.3 (2.4)22.5 (2.1)
  Sodium <1500 mg/d2011–20120.0 (0.0)0.6 (0.2)0.5 (0.3)1.1 (0.5)0.4 (0.2)
  SSB <36 oz/wk2011–201240.8 (2.4)55.9 (1.9)46.5 (2.5)56.1 (2.0)72.0 (2.1)
  WHLG ≥3 1-oz svg/d2011–20124.1 (1.4)7.7 (0.6)5.7 (0.9)6.6 (1.0)12.3 (1.2)
Secondary diet metrics
  Nuts/legumes/seeds ≥4 svg/wk2011–201242.1 (2.6)49.7 (0.9)47.8 (2.0)50.9 (1.7)50.9 (2.1)
  Processed meats ≤2 svg/wk2011–201241.2 (2.1)44.6 (1.4)43.5 (1.9)44.8 (2.0)46.0 (2.3)
  SFat <7% total kcal2011–20126.4 (0.9)10.4 (0.6)10.1 (0.8)10.5 (0.9)11.6 (1.4)

BMI indicates body mass index; CV, cardiovascular; DBP, diastolic blood pressure; FPG, fasting plasma glucose; F&V, fruits and vegetables; HbA1c, glycosylated hemoglobin; NHANES, National Health and Nutrition Examination Survey; PA, physical activity; SBP, systolic blood pressure; SE, standard error; SFat, saturated fat; SSB, sugar-sweetened beverages; svg, servings; and WHLG, whole grains.

*Standardized to the age distribution of the 2000 US standard population

Scaled to 2000 kcal/d and in the context of appropriate energy balance and a DASH (Dietary Approaches to Stop Hypertension)-type eating pattern.

Table 2-3. Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 Years Old: NHANES 2013 to 2014

%
Percent BP ideal among adults, 2013–201445.4
20% Relative increase54.4
Percent whose BP would be ideal if population mean BP were lowered by*
 2 mm Hg49.5
 3 mm Hg51.7
 4 mm Hg52.9
 5 mm Hg55.3

Reduction in BP=(observed average systolic BP−X mm Hg) and (observed average diastolic BP−X mm Hg). BP indicates blood pressure; and NHANES, National Health and Nutrition Examination Survey.

*Standardized to the age distribution of the 2000 US standard population.

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

• Set specific, shared, proximal goals (Class I; Level of Evidence A): Set specific, proximal goals with the patient, including a personalized plan to achieve the goals (eg, over the next 3 mo, increase fruits by 1 serving/d, reduce smoking by half a pack/d, or walk 30 min 3 times/wk).
• Establish self-monitoring (Class I; Level of Evidence A): Develop a strategy for self-monitoring, such as a dietary or physical activity diary or Web-based or mobile applications.
• Schedule regular follow-up (Class I; Level of Evidence A): Schedule regular follow-up (in person, telephone, written, and/or electronic), with clear frequency and duration of contacts, to assess success, reinforce progress, and set new goals as necessary.
• Provide feedback (Class I; Level of Evidence A): Provide feedback on progress toward goals, including using in-person, telephone, and/or electronic feedback.
• Increase self-efficacy (Class I; Level of Evidence A): Increase the patient’s perception that they can successfully change their behavior.*
• Use motivational interviewing (Class I; Level of Evidence A): Use motivational interviewing when patients are resistant or ambivalent about behavior change.
• Provide long-term support (Class I; Level of Evidence B): Arrange long-term support from family, friends, or peers for behavior change, such as in other workplace, school, or community-based programs.
• Use a multicomponent approach (Class I; Level of Evidence A): Combine ≥2 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 al.33 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 Factors3438

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

*The specific population interventions listed here are either a Class I or IIa recommendation with a Level of Evidence grade of either A or B.

At least some evidence from studies conducted in high-income Western regions and countries (eg, North America, Europe, Australia, New Zealand).

At least some evidence from studies conducted in high-income non-Western regions and countries (eg, Japan, Hong Kong, South Korea, Singapore).

§At least some evidence from studies conducted in low- or middle-income regions and countries (eg, Africa, China, Pakistan, India).

Based on cross-sectional studies only; only 2 longitudinal studies have been performed, with no significant relations seen.

Class IIa; Level of Evidence A for improving physical activity; Class IIb; Level of Evidence B for reducing adiposity.

Reprinted from Mozaffarian et al34 with permission. Copyright © 2012, American Heart Association, Inc.

Chart 2-1.

Chart 2-1. Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors.

Reprinted from Folsom et al12 with permission from Elsevier. Copyright © 2011, American College of Cardiology Foundation.

Chart 2-2.

Chart 2-2. Prevalence (unadjusted) estimates of poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US children aged 12 to 19 years.

*Healthy Diet Score reflects 2011 to 2012 NHANES.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2013 to 2014.

Chart 2-3.

Chart 2-3. Prevalence (unadjusted) estimates of poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US adults aged 20 to 49 and ≥50 years.

*Healthy Diet Score reflects 2011 to 2012 NHANES.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2013 to 2014.

Chart 2-4.

Chart 2-4. Proportion (unadjusted) of US children aged 12 to 19 years meeting different numbers of criteria for ideal cardiovascular health, overall and by sex.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012.

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 by age and sex subgroups.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012.

Chart 2-6.

Chart 2-6. 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.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012.

Chart 2-7.

Chart 2-7. Prevalence of meeting ≥5 criteria for ideal cardiovascular health among US adults aged ≥20 years (age-standardized) and US children aged 12 to 19 years, overall and by sex.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2007 to 2008 and 2011 to 2012.

Chart 2-8.

Chart 2-8. Prevalence of meeting ≥5 criteria for ideal cardiovascular health among US adults aged ≥20 years (age standardized) and US children aged 12 to 19 years, by race/ethnicity.

NH indicates non-Hispanic.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012.

Chart 2-9.

Chart 2-9. 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.

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey (NHANES) 2011 to 2012.

Chart 2-10.

Chart 2-10. Age-standardized cardiovascular health status by US states, BRFSS, 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.

BRFSS indicates Behavioral Risk Factor Surveillance System.

Reprinted from Fang et al28 with permission. Copyright © 2013, American Heart Association, Inc.

Chart 2-11.

Chart 2-11. Trends in prevalence (unadjusted) of meeting criteria for ideal cardiovascular health for each of the 7 metrics among US children aged 12 to 19 years.

*Because of changes in the physical activity questionnaire between different cycles of the National Health and Nutrition Examination Survey (NHANES), trends over time for this indicator should be interpreted with caution, and statistical comparisons should not be attempted.

Data for the Healthy Diet Score, based on a 2-day average intake, was only available for the 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010 and 2011 to 2012 NHANES cycles at the time of this analysis.

Source: National Center for Health Statistics, NHANES 1999 to 2000 through 2013 to 2014.

Chart 2-12.

Chart 2-12. Age-standardized trends in prevalence of meeting criteria for ideal cardiovascular health for each of the 7 metrics among US adults aged ≥20 years.

*Because of changes in the physical activity questionnaire between different cycles of the National Health and Nutrition Examination Survey (NHANES), trends over time for this indicator should be interpreted with caution, and statis-tical comparisons should not be attempted.

Data for the Healthy Diet Score, based on a 2-day average intake, was only available for the 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010 and 2011 to 2012 NHANES cycles at the time of this analysis.

Source: National Center for Health Statistics, NHANES 1999 to 2000 through 2013 to 2014.

Chart 2-13.

Chart 2-13. 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 males and females.

Reprinted from Huffman et al29 with permission. Copyright © 2012, American Heart Association, Inc.

Chart 2-14.

Chart 2-14. US age-standardized death rates* from cardiovascular diseases, 2000 to 2014.

CHD indicates coronary heart disease; and CVD, cardiovascular disease.

*Directly standardized to the age distribution of the 2000 US standard population. †Total CVD: International Classification of Diseases, 10th Revision (ICD-10) I00 to I99, Q20 to Q28. §Stroke (all cerebrovascular disease): ICD-10 I60 to I69. ¶CHD: ICD-10 I20 to I25. **Other CVD: ICD-10 I00 to I15, I26 to I51, I70 to I78, I80 to I89, I95 to I99.

Source: Centers for Disease Control and Prevention, National Vital Health Statistics System.31

Chart 2-15.

Chart 2-15. US age-standardized death rates* from cardiovascular disease by race/ethnicity, 2000 to 2014.

NH indicates non-Hispanic.

*Directly standardized to the age distribution of the 2000 US standard population. Total CVD: International Classification of Diseases, 10th Revision (ICD-10) I00 to I99, Q20 to Q28.

Source: Centers for Disease Control and Prevention, National Vital Statistics System.31

Chart 2-16.

Chart 2-16. US age-standardized death rates* from stroke by race/ethnicity, 2000 to 2014.

NH indicates non-Hispanic.

*Directly standardized to the age distribution of the 2000 US standard population. Stroke (all cerebrovascular disease): International Classification of Diseases, 10th Revision (ICD-10) I60 to I69.

Source: Centers for Disease Control and Prevention, National Vital Statistics System.31

In 2011, the AHA created a new set of central Strategic Impact Goals to drive organizational priorities for the current decade:

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 of cardiovascular health, characterized by 7 metrics (“Life’s Simple 7”2), including health behaviors (diet quality, PA, smoking, BMI) and health factors (blood cholesterol, BP, blood glucose). 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 not smoking and having a healthy diet pattern, sufficient PA, normal body weight, and normal levels of TC, BP, and fasting blood glucose, in the absence of drug treatment (Table 2-1). Because a spectrum of cardiovascular health is possible 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 and children.

This concept of cardiovascular health represents a new focus for the AHA, with 3 central and novel emphases:

  • An expanded focus on CVD prevention and promotion of positive “cardiovascular health,” in addition to the treatment of established CVD

  • Efforts to promote both healthy behaviors (healthy diet pattern, appropriate energy intake, PA, and nonsmoking) and healthy biomarker levels (optimal blood lipids, BP, glucose levels) throughout the lifespan

  • Population-level health promotion strategies to shift the majority of the public toward greater cardiovascular health, in addition to targeting those individuals at greatest CVD risk, because healthy lifestyles in all domains are uncommon throughout the US population

Beginning in 2011, and recognizing the time lag in the nationally representative US data sets, this chapter in the annual Statistical Update evaluates and publishes metrics and information to provide insights into both progress toward meeting the 2020 AHA goals and areas that require greater attention to meet these goals. The AHA has advocated for raising the visibility of patient-reported cardiovascular health status, which includes symptom burden, functional status, and health-related quality of life, as an indicator of cardiovascular health in future organizational goal setting.3

Relevance of Ideal Cardiovascular Health

  • Since the AHA announced its 2020 Impact Goals, multiple independent investigations have confirmed the importance of these metrics and the concept of cardiovascular health. Findings include strong inverse, stepwise associations in the United States of the metrics and cardiovascular health with all-cause mortality, CVD mortality, and HF; with preclinical measures of atherosclerosis such as carotid IMT arterial stiffness, and coronary artery calcium prevalence and progression; with physical functional impairment and frailty4; and with cognitive decline and depression.5,6 Similar relationships have also been seen in non-US populations.5,710

  • A recent study in a large Hispanic/Latino cohort study in the United States found that associations of CVD and cardiovascular health metrics compared favorably with existing national estimates; however, some of the associations varied by sex and heritage, providing important information to guide targeted health promotion efforts toward achieving 2020 goals.11

  • Ideal health behaviors and ideal health factors are each independently associated with lower CVD risk in a stepwise fashion (Chart 2-1). In other words, across any level of health behaviors, health factors are associated with incident CVD; conversely, across any level of health factors, health behaviors are still associated with incident CVD.12

  • In addition, only modest intercorrelations are apparent between different cardiovascular health metrics. On the basis of NHANES 1999 to 2002, these ranged from a correlation of −0.12 between PA and HbA1c to a correlation of 0.29 between BMI and HbA1c. Thus, substantial independent variation in each cardiovascular health component exists, and each is independently related to cardiovascular outcomes.13

  • These findings corroborate the independent value of targeting each of these 7 metrics as separate aims.

  • Analyses from the US Burden of Disease Collaborators demonstrated that each of the 7 health factors and behaviors caused substantial mortality and morbidity in the United States in 2010. The top risk factor related to overall disease burden was suboptimal diet, followed by tobacco smoking, high BMI, raised BP, high fasting plasma glucose, and physical inactivity.14

  • A stepwise association was present between the number of ideal cardiovascular health metrics and risk of death based on NHANES 1988 to 2006 data.15 The HRs for people with 6 or 7 ideal health metrics compared 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 IHD mortality.15 Ford et al13 demonstrated similar relationships.

  • A recent meta-analysis of 9 prospective cohort studies involving 12 878 participants reported that achieving the most ideal cardiovascular health metrics was associated with lower risk of all-cause mortality (RR, 0.55; 95% CI, 0.37–0.80), cardiovascular mortality (RR, 0.25; 95% CI, 0.10–0.63), CVD (RR, 0.20; 95% CI, 0.11–0.37), and stroke (RR, 0.31; 95% CI, 0.25–0.38).16

  • The adjusted population attributable fractions for CVD mortality were as follows15:

    — 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 IHD mortality were as follows15:

    — 34.7% (95% CI, 6.6%–57.7%) for HBP

    — 16.7% (95% CI, 6.4%–26.6%) for smoking

    — 20.6% (95% CI, 1.2%–38.6%) for poor diet

    — 7.8% (95% CI, 0%–22.2%) for insufficient PA

    — 7.5% (95% CI, 3.0%–14.7%) for abnormal glucose levels

  • Data from the REGARDS cohort also demonstrated a stepwise association between cardiovascular health metrics and incident stroke. Using a cardiovascular health score scale ranging from 0 to 14, every unit increase in cardiovascular health was associated with an 8% lower risk of incident stroke (HR, 0.92; 95% CI, 0.88–0.95), with a similar effect size for white (HR, 0.91; 95% CI, 0.86–0.96) and black (HR, 0.93; 95% CI, 0.87–0.98) participants.17

  • The Cardiovascular Lifetime Risk Pooling Project showed 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) 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, males with optimal risk factor profiles lived on average 14 years longer free of all CVD events, and 12 years longer overall, than people with ≥2 risk factors.18

  • Better cardiovascular health is associated with less incident HF,19 less subclinical vascular disease,20,21 better global cognitive performance and cognitive function,22,23 lower prevalence24 and incidence25 of depressive symptoms, and lower loss of physical functional status.26

  • The AHA’s 2020 Strategic Impact Goals are to improve cardiovascular health among all Americans. On the basis of NHANES 1999 to 2006, several social risk factors (low family income, low education level, minority race, and single-living status) were related to lower likelihood of attaining better cardiovascular health as measured by Life’s Simple 7 scores.27

Cardiovascular Health: Current Prevalence

(See Table 2-2 and Charts 2-2 through 2-10)

  • 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 (Chart 2-2) and for adults (Chart 2-3).

  • For most metrics, the prevalence of ideal levels of health behaviors and health factors is higher in US children than in US adults. The main exceptions are diet and PA, for which the prevalence of ideal levels in children are similar to (for PA) or worse (for diet) than in adults.

  • Among US children (Chart 2-2), 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 or a corresponding AHA diet score of at least 80) to >80% for the smoking, BP, and fasting glucose metrics.

  • Among US adults (Chart 2-3), the age-standardized prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from <1% for having a healthy diet pattern to up to 77% for never having smoked or being a former smoker who has quit for >12 months.

  • Age-standardized and age-specific prevalence estimates for ideal cardiovascular health and for ideal levels of each of its components are shown for 2011 to 2012 and 2013 to 2014 in Table 2-2. NHANES 2011 to 2012 data are used for some of the statistics that require nutritional data because 2013 to 2014 data have not been released. The prevalence of ideal levels across 7 health factors and health behaviors generally was lower with age, with much lower prevalence among older versus younger age groups. The exception was diet, for which prevalence of ideal levels was highest in older adults.

  • Chart 2-4 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 2011 to 2012.

    — Few US children (≈5%) meet only 0, 1, or 2 criteria for ideal cardiovascular health.

    — Approximately half of US children (54%) meet 3 or 4 criteria for ideal cardiovascular health, and ≈41% meet 5 or 6 criteria (mostly 5 criteria).

    — <1% of children meet all 7 criteria for ideal cardiovascular health.

  • Charts 2-5 and 2-6 display the age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2011 to 2012, overall and stratified by age, sex, and race.

    — Approximately 3% of US adults have 0 of the 7 criteria at ideal levels, and another 15% 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 ≈20% adults within each of these categories.

    — Approximately 13% of US adults have 5 criteria, 5% have 6 criteria, and virtually 0% have 7 criteria at ideal levels.

    — Presence of ideal cardiovascular health is both age and sex related (Chart 2-5). 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, females tend to have more metrics at ideal levels than do males.

    — Presence of ideal cardiovascular health also varies by race (Chart 2-6). Blacks and Hispanics 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 Hispanic adults have no more than 3 of 7 metrics at ideal levels.

  • Chart 2-7 displays the age-standardized percentages of US adults and percentages of children who have ≥5 of the metrics (of 7 possible) at ideal levels.

    — Approximately 41% of US children 12 to 19 years of age have ≥5 metrics at ideal levels, with similar prevalence in boys (42%) as in girls (41%).

    — In comparison, only 17% of US adults have ≥5 metrics at ideal levels, with lower prevalence in males (13%) than in females (21%).

    — All populations have improved since baseline year 2007 to 2008.

  • Chart 2-8 displays the age-standardized percentages of US adults and percentages of children by race/ethnicity who have ≥5 of the metrics (of 7 possible) at ideal levels.

    — In both children and adults, non-Hispanic Asians tend to have higher prevalence of having ≥5 metrics at ideal levels than other race/ethnic groups.

    — Approximately 4.7 in 10 non-Hispanic Asian children, 4.4 in 10 non-Hispanic white children, 3.5 in 10 non-Hispanic black children, and 3.7 in 10 Hispanic children have ≥5 metrics at ideal levels.

    — By comparison, among adults, ≈2.6 in 10 non-Hispanic Asians, 1.8 in 10 non-Hispanic whites, 1.3 in 10 Hispanics, and 1 in 10 non-Hispanic blacks have ≥5 metrics at ideal levels.

  • Chart 2-9 displays the age-standardized percentages of US adults who meet different numbers of criteria for both poor and ideal cardiovascular health. Meeting the AHA 2020 Strategic Impact Goals is predicated on reducing the relative percentage of those with poor levels while increasing the relative percentage of those with ideal levels for each of the 7 metrics.

    — Approximately 92% of US adults have ≥1 metric at poor levels.

    — Approximately 34% of US adults have ≥3 metrics at poor levels.

    — Few US adults (2.5%) 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 et al28 estimated the prevalence of ideal cardiovascular health by state (all 7 metrics at ideal level), which ranged from 1.2% (Oklahoma) to 6.9% (District of Columbia). Southern states tended to have higher percentages of poor cardiovascular health, lower percentages of ideal cardiovascular health, and lower mean cardiovascular health scores than New England and Western states (Chart 2-10).

Cardiovascular Health: Trends Over Time

(See Charts 2-11 through 2-13)

  • The trends over the past decade in each of the 7 cardiovascular health metrics (for diet, trends from 1999–2000 through 2013–2014) are shown in Chart 2-11 (for children 12–19 years of age) and Chart 2-12 (for adults ≥20 years of age).

    — The prevalence of both children and adults meeting the dietary goals improved between 2003 to 2004 and 2011 to 2012. The prevalence of ideal levels of diet (AHA diet score >80) increased from 0.2% to 0.6% in children and from 0.7% to 1.5% in adults (Chapter 5, Charts 5-2 and 5-3). The prevalence of intermediate levels of diet (AHA diet score 40–79) increased from 30.6% to 44.7% in children and from 49.0% to 57.5% in adults. These improvements were largely attributable to increased whole grain consumption and decreased sugar-sweetened beverage consumption in both children and adults, as well as small, nonsignificant trends in increased fruits and vegetables (Chapter 5, Charts 5-4 and 5-5). No major trends were evident in either children or adults meeting the target for consumption of fish or sodium.

    — Fewer children over time are meeting the ideal BMI metric, whereas more are meeting the ideal smoking and TC 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 2-13).29 On the basis of current trends among individual metrics, anticipated declines in prevalence of smoking, high cholesterol, and HBP (in males) would be offset by substantial increases in the prevalence of obesity and DM and smaller changes in ideal dietary patterns or PA.29

  • 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 TC, SBP, smoking, and PA (≈167 000 fewer deaths), offset by increases in DM and BMI (≈24 000 more deaths).30

CVD Mortality

(See Charts 2-14 through 2-16)

  • In 2014, the age-standardized death rate attributable to all CVD in the US population was 220.8 per 100 000, down 14.9% from 259.4 per 100 000 in 2007 (baseline data for the 2020 Impact Goals on CVD and stroke mortality)31 (Chart 2-14).

  • The age-standardized death rate in 2014 attributable to stroke was 36.5 per 100 000, a decrease of 16.1% from 2007. The death rate attributable to CHD in 2014 was 98.8 per 100 000, a reduction of 23.5% from 2007. The rate for other CVDs was 84.6 per 100 000 in 2014, similar to the rate in 200731 (Chart 2-14).

  • Between 2007 and 2013, CVD and stroke death rates decreased 12.5% and 16.1% respectively in non-Hispanic whites; 16.5% and 20.2% in non-Hispanic blacks; 18.1% and 17.3% in Hispanics; 15.0% and 19.6% in non-Hispanic Asian and Pacific Islanders; and 11.3% and 22.5% in non-Hispanic American Indian or Alaska Natives31 (Charts 2-15 and 2-16).

Achieving the 2020 Impact Goals

(See Tables 2-3 through 2-6)

  • To achieve the AHA’s 2020 Impact Goals of reducing deaths attributable to CVD and stroke by 20%, continued emphasis is needed on the treatment of acute CVD events and secondary prevention through treatment and control of health behaviors and risk factors.

  • Taken together, these data continue to demonstrate both the tremendous relevance of the AHA 2020 Impact Goals for cardiovascular health and the progress that will be needed to achieve these goals over by the year 2020.

  • For each cardiovascular health metric, modest shifts in the population distribution toward improved health would produce appreciable increases in the proportion of Americans in both ideal and intermediate categories. For example, on the basis of NHANES 2013 to 2014, the current prevalence of ideal levels of BP among US adults is 45.4%. To achieve the 2020 goals, a 20% relative improvement would require an increase in this proportion to 54.4% by 2020 (45.4% × 1.20). On the basis of NHANES data, a reduction in population mean BP of just 5 mm Hg would result in 55.3% of US adults having ideal levels of BP, which represents a 21.8% relative improvement in this metric (Table 2-3). Larger population reductions in BP would lead to even greater numbers of people with ideal levels of BP. 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.32

  • A range of complementary strategies and approaches can lead to improvements in cardiovascular health. These include 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 in the United States are health behaviors, including diet quality, PA, and body weight. However, each of the 7 cardiovascular health metrics can be improved and deserves major focus.

Abbreviations Used in Chapter 2

AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CIconfidence interval
CVcardiovascular
CVDcardiovascular disease
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
F&Vfruits and vegetables
FPGfasting plasma glucose
HbA1chemoglobin A1c (glycosylated hemoglobin)
HBPhigh blood pressure
HFheart failure
HRhazard ratio
ICD-10International Classification of Diseases, 10th Revision
IHDischemic heart disease
IMTintima-media thickness
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
PAphysical activity
REGARDSReasons for Geographic and Racial Differences in Stroke
SBPsystolic blood pressure
SEstandard error
SFatsaturated fat
SSBsugar-sweetened beverage
svgservings
TCtotal cholesterol
WHLFwhole grain

References

  • 1. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
  • 2. American Heart Association. My Life Check: Life’s Simple 7.American Heart Association Web site. http://mylifecheck.heart.org/. Accessed July 1, 2016.Google Scholar
  • 3. Rumsfeld JS, Alexander KP, Goff DC, Graham MM, Ho PM, Masoudi FA, Moser DK, Roger VL, Slaughter MS, Smolderen KG, Spertus JA, Sullivan MD, Treat-Jacobson D, Zerwic JJ; American Heart Association Council on Quality of Care and Outcomes Research, Council on Cardiovascular and Stroke Nursing, Council on Epidemiology and Prevention, Council on Peripheral Vascular Disease, and Stroke Council. Cardiovascular health: the importance of measuring patient-reported health status: a scientific statement from the American Heart Association.Circulation. 2013; 127:2233–2249. doi: 10.1161/CIR.0b013e3182949a2e.LinkGoogle Scholar
  • 4. Graciani A, García-Esquinas E, López-García E, Banegas JR, Rodríguez-Artalejo F. Ideal cardiovascular health and risk of frailty in older adults.Circ Cardiovasc Qual Outcomes. 2016; 9:239–245. doi: 10.1161/CIRCOUTCOMES.115.002294.LinkGoogle Scholar
  • 5. Shay CM, Gooding HS, Murillo R, Foraker R. Understanding and improving cardiovascular health: an update on the American Heart Association’s concept of cardiovascular health.Prog Cardiovasc Dis. 2015; 58:41–49. doi: 10.1016/j.pcad.2015.05.003.CrossrefMedlineGoogle Scholar
  • 6. Zhang N, Yang Y, Wang A, Cao Y, Li J, Yang Y, Zhang K, Zhang W, Wu S, Wang Z, Zhu M, Zhang Y, Wu S, Wang C, Zhao X. Association of ideal cardiovascular health metrics and cognitive functioning: the APAC study.Eur J Neurol. 2016; 23:1447–1454. doi: 10.1111/ene.13056.CrossrefMedlineGoogle Scholar
  • 7. Laitinen TT, Pahkala K, Magnussen CG, Oikonen M, Viikari JS, Sabin MA, Daniels SR, Heinonen OJ, Taittonen L, Hartiala O, Mikkilä V, Hutri-Kähönen N, Laitinen T, Kähönen M, Raitakari OT, Juonala M. Lifetime measures of ideal cardiovascular health and their association with subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study.Int J Cardiol. 2015; 185:186–191. doi: 10.1016/j.ijcard.2015.03.051.CrossrefMedlineGoogle Scholar
  • 8. Chang Y, Guo X, Chen Y, Guo L, Li Z, Yu S, Yang H, Sun G, Sun Y. Prevalence and Metrics Distribution of Ideal Cardiovascular Health: A Population-based, Cross-sectional Study in Rural China.Heart Lung Circ. 2016; 25:982–992. doi: 10.1016/j.hlc.2016.02.007.CrossrefMedlineGoogle Scholar
  • 9. Laitinen T. Cardiovascular health from childhood to adulthood: with special reference to early vascular changes: The Cardiovascular Risk in Young Finns Study [master’s thesis].Turku, Finland: University of Turku; 2015. http://www.doria.fi/bitstream/handle/10024/104430/AnnalesD%201174Laitinen.pdf?sequence=2. Accessed September 3, 2015.Google Scholar
  • 10. Younus A, Aneni EC, Spatz ES, Osondu CU, Roberson L, Ogunmoroti O, Malik R, Ali SS, Aziz M, Feldman T, Virani SS, Maziak W, Agatston AS, Veledar E, Nasir K. A Systematic review of the prevalence and outcomes of ideal cardiovascular health in US and non-US populations.Mayo Clin Proc. 2016; 91:649–670. doi: 10.1016/j.mayocp.2016.01.019.CrossrefMedlineGoogle Scholar
  • 11. González HM, Tarraf W, Rodríguez CJ, Gallo LC, Sacco RL, Talavera GA, Heiss G, Kizer JR, Hernandez R, Davis S, Schneiderman N, Daviglus ML, Kaplan RC. Cardiovascular health among diverse Hispanics/Latinos: Hispanic Community Health Study/Study of Latinos (HCHS/SOL) results.Am Heart J. 2016; 176:134–144. doi: 10.1016/j.ahj.2016.02.008.CrossrefMedlineGoogle Scholar
  • 12. Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD; ARIC Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence.J Am Coll Cardiol. 2011; 57:1690–1696. doi: 10.1016/j.jacc.2010.11.041.CrossrefMedlineGoogle Scholar
  • 13. Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States.Circulation. 2012; 125:987–995. doi: 10.1161/CIRCULATIONAHA.111.049122.LinkGoogle Scholar
  • 14. US Burden of Disease Collaborators. The state of US health, 1990–2010: burden of diseases, injuries, and risk factors.JAMA. 2013; 310:591–608. doi: 10.1001/jama.2013.13805.CrossrefMedlineGoogle Scholar
  • 15. 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. doi: 10.1001/jama.2012.339.CrossrefMedlineGoogle Scholar
  • 16. Fang N, Jiang M, Fan Y. Ideal cardiovascular health metrics and risk of cardiovascular disease or mortality: a meta-analysis.Int J Cardiol. 2016; 214:279–283. doi: 10.1016/j.ijcard.2016.03.210.CrossrefMedlineGoogle Scholar
  • 17. Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, Hong Y, Safford MM, Goyal A, Cushman M. Life’s Simple 7 and risk of incident stroke: the Reasons for Geographic and Racial Differences in Stroke study.Stroke. 2013; 44:1909–1914. doi: 10.1161/STROKEAHA.111.000352.LinkGoogle Scholar
  • 18. Wilkins JT, Ning H, Berry J, Zhao L, Dyer AR, Lloyd-Jones DM. Lifetime risk and years lived free of total cardiovascular disease.JAMA. 2012; 308:1795–1801. doi: 10.1001/jama.2012.14312.CrossrefMedlineGoogle Scholar
  • 19. Folsom AR, Shah AM, Lutsey PL, Roetker NS, Alonso A, Avery CL, Miedema MD, Konety S, Chang PP, Solomon SD. American Heart Association’s Life’s Simple 7: avoiding heart failure and preserving cardiac structure and function.Am J Med. 2015; 128:970–976.e2. doi: 10.1016/j.amjmed.2015.03.027.CrossrefMedlineGoogle Scholar
  • 20. Robbins JM, Petrone AB, Carr JJ, Pankow JS, Hunt SC, Heiss G, Arnett DK, Ellison RC, Gaziano JM, Djoussé L. Association of ideal cardiovascular health and calcified atherosclerotic plaque in the coronary arteries: the National Heart, Lung, and Blood Institute Family Heart Study.Am Heart J. 2015; 169:371–378.e1. doi: 10.1016/j.ahj.2014.12.017.CrossrefMedlineGoogle Scholar
  • 21. Saleem Y, DeFina LF, Radford NB, Willis BL, Barlow CE, Gibbons LW, Khera A. Association of a favorable cardiovascular health profile with the presence of coronary artery calcification.Circ Cardiovasc Imaging. 2015; 8:e001851. doi: 10.1161/CIRCIMAGING.114.001851.LinkGoogle Scholar
  • 22. Crichton GE, Elias MF, Davey A, Alkerwi A. Cardiovascular health and cognitive function: the Maine-Syracuse Longitudinal Study.PLoS One. 2014; 9:e89317. doi: 10.1371/journal.pone.0089317.CrossrefMedlineGoogle Scholar
  • 23. Thacker EL, Gillett SR, Wadley VG, Unverzagt FW, Judd SE, McClure LA, Howard VJ, Cushman M. The American Heart Association Life’s Simple 7 and incident cognitive impairment: the REasons for Geographic And Racial Differences in Stroke (REGARDS) study.J Am Heart Assoc. 2014; 3:e000635. doi: 10.1161/JAHA.113.000635.LinkGoogle Scholar
  • 24. Kronish IM, Carson AP, Davidson KW, Muntner P, Safford MM. Depressive symptoms and cardiovascular health by the American Heart Association’s definition in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study.PLoS One. 2012; 7:e52771. doi: 10.1371/journal.pone.0052771.CrossrefMedlineGoogle Scholar
  • 25. España-Romero V, Artero EG, Lee DC, Sui X, Baruth M, Ruiz JR, Pate RR, Blair SN. A prospective study of ideal cardiovascular health and depressive symptoms.Psychosomatics. 2013; 54:525–535. doi: 10.1016/j.psym.2013.06.016.CrossrefMedlineGoogle Scholar
  • 26. Dhamoon MS, Dong C, Elkind MS, Sacco RL. Ideal cardiovascular health predicts functional status independently of vascular events: the Northern Manhattan Study.J Am Heart Assoc. 2015; 4:. doi: 10.1161/JAHA.114.001322.LinkGoogle Scholar
  • 27. Caleyachetty R, Echouffo-Tcheugui JB, Muennig P, Zhu W, Muntner P, Shimbo D. Association between cumulative social risk and ideal cardiovascular health in US adults: NHANES 1999-2006.Int J Cardiol. 2015; 191:296–300. doi: 10.1016/j.ijcard.2015.05.007.CrossrefMedlineGoogle Scholar
  • 28. Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 states and the District of Columbia, 2009.J Am Heart Assoc. 2012; 1:e005371. doi: 10.1161/JAHA.112.005371.LinkGoogle Scholar
  • 29. Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM. Cardiovascular health behavior and health factor changes (1988-2008) and projections to 2020: results from the National Health and Nutrition Examination Surveys.Circulation. 2012; 125:2595–2602. doi: 10.1161/CIRCULATIONAHA.111.070722.LinkGoogle Scholar
  • 30. Huffman MD, Lloyd-Jones DM, Ning H, Labarthe DR, Guzman Castillo M, O’Flaherty M, Ford ES, Capewell S. Quantifying options for reducing coronary heart disease mortality by 2020.Circulation. 2013; 127:2477–2484. doi: 10.1161/CIRCULATIONAHA.112.000769.LinkGoogle Scholar
  • 31. Centers for Disease Control and Prevention. Compressed mortality file: underlying cause of death 1999–2014. CDC WONDER Online Database [database online].Atlanta, GA: Centers for Disease Control and Prevention. http://wonder.cdc.gov/mortSQl.html. Accessed July 1, 2016.Google Scholar
  • 32. Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease.N Engl J Med. 2010; 362:590–599. doi: 10.1056/NEJMoa0907355.CrossrefMedlineGoogle Scholar
  • 33. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE; American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association.Circulation. 2010; 122:406–441. doi: 10.1161/CIR.0b013e3181e8edf1.LinkGoogle Scholar
  • 34. Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, Franch HA, Jacobs DR, Kraus WE, Kris-Etherton PM, Krummel DA, Popkin BM, Whitsel LP, Zakai NA; American Heart Association Council on Epidemiology and Prevention, Council on Nutrition, Physical Activity and Metabolism, Council on Clinical Cardiology, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Peripheral Vascular Disease, and the Advocacy Coordinating Committee. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association.Circulation. 2012; 126:1514–1563. doi: 10.1161/CIR.0b013e318260a20b.LinkGoogle Scholar
  • 35. Bodenheimer T. Helping patients improve their health-related behaviors: what system changes do we need?Dis Manag. 2005; 8:319–330. doi: 10.1089/dis.2005.8.319.CrossrefMedlineGoogle Scholar
  • 36. Simpson LA, Cooper J. Paying for obesity: a changing landscape.Pediatrics. 2009; 123(suppl 5):S301–S307. doi: 10.1542/peds.2008-2780I.CrossrefMedlineGoogle Scholar
  • 37. Quist-Paulsen P. Cessation in the use of tobacco: pharmacologic and non-pharmacologic routines in patients.Clin Respir J. 2008; 2:4–10. doi: 10.1111/j.1752-699X.2007.00038.x.CrossrefMedlineGoogle Scholar
  • 38. Davis D, Galbraith R; American College of Chest Physicians Health and Science Policy Committee. Continuing medical education effect on practice performance: effectiveness of continuing medical education: American College of Chest Physicians Evidence-Based Educational Guidelines.Chest. 2009; 135(suppl):42S—48S. doi: 10.1378/chest.08-2517.CrossrefMedlineGoogle Scholar

3. Smoking/Tobacco Use

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

Table 3-1. Prevalence (%) of Current Cigarette Smoking for Adults ≥18 Years of Age by Sex, Race/Ethnicity, and Income Status (NHIS, 2015)

Population GroupPrevalence, 2015* (Age ≥18 y) (%)Cost7
Both sexes37 662 000 (15.2)$289 Billion per year
Males20 143 000 (16.7)
Females17 421 000 (13.7)
NH white males17.8
NH white females16.8
NH black males20.3
NH black females13.1
Hispanic or Latino males12.7
Hispanic or Latino females7.0
NH Asian-only males11.6
NH Asian-only females2.6
NH American Indian/Alaska Native–only males20.9
NH American Indian/Alaska Native–only females22.2
Living at <100% of poverty level27.8
Living at 100%–199% of poverty level23.6
Living at ≥200% of poverty level15.0

Percentages are age adjusted. Estimates for Asian-only and American Indian/Alaska Native–only include non-Hispanic and Hispanic people. Ellipses (…) indicate data not available; and NH, non-Hispanic.

*Rounded to the nearest thousand; based on total resident population.

Estimates are for 2011 to 2013.

Data derived from the National Center for Health Statistics.6

Chart 3-1.

Chart 3-1. Prevalence (%) of cigarette use in the past month for adolescents aged 12 to 17 years by sex and race/ethnicity (NSDUH, 2014).

Because of methodological differences among the NSDUH, the Youth Risk Behavior Survey, the National Youth Tobacco Survey, and other surveys, percentages of cigarette smoking measured by these surveys are not directly comparable. Notably, school-based surveys may include students who are 18 years old, who are legally permitted to smoke and have higher rates of smoking.

AIAN indicates American Indian or Alaska Native; NH, non-Hispanic; and NSDUH, National Survey on Drug Use and Health.

Data derived from Substance Abuse and Mental Health Services Administration, NSDUH.4

Chart 3-2.

Chart 3-2. Prevalence (%) of cigarette smoking for adolescents (past month) and adults (current) by sex and age (NHIS, 2003–2015; NSDUH, 2003–2014).

NSDUH indicates National Survey on Drug Use and Health; and NHIS, National Health Interview Survey.

Data derived from the Centers for Disease Control and Prevention/National Center for Health Statistics and the Substance Abuse and Mental Health Services Administration (NSDUH).4,6

Chart 3-3.

Chart 3-3. Long-term trend in current cigarette smoking prevalence (%) for adults ≥18 years of age by sex (NHIS, 1965–2015, selected years).

NHIS indicates National Health Interview Survey.

Data derived from the Centers for Disease Control and Prevention/National Center for Health Statistics, Health, United States, 2015 (NHIS).71

Chart 3-4.

Chart 3-4. Prevalence (%) of current cigarette smoking for adults ≥18 years of age by sex and race/ethnicity (NHIS, 2013–2015).

All percentages are age adjusted.

AIAN indicates American Indian/Alaska Native; NH, non-Hispanic; and NHIS, National Health Interview Survey.

Data derived from Centers for Disease Control and Prevention/National Center for Health Statistics, NHIS.6

Chart 3-5.

Chart 3-5. Prevalence (%) of current cigarette smoking for adults, by state: United States (BRFSS, 2014).

BRFSS indicates Behavior Risk Factor Surveillance System.

Data derived from the Centers for Disease Control and Prevention.8

Chart 3-6.

Chart 3-6. Percentage (%) of students who have ever tried electronic cigarettes by school level (National Youth Tobacco Survey, 2011–2013).

Data derived from the Centers for Disease Control, National Youth Tobacco Survey.56

Tobacco smoking, the most common form of tobacco use, is a major risk factor for CVD and stroke.1 The AHA has identified never having tried smoking or never having smoked a whole cigarette (for children) and never having smoked or having quit >12 months ago (for adults) as 1 of the 7 components of ideal cardiovascular health (Life’s Simple Seven).2 According to NHANES 2013 to 2014 data, 91.4% of adolescents and 77.1% of adults met these criteria. Throughout the rest of the chapter, smoking prevalence is estimated by the NSDUH for adolescents (12–17 years of age) and by the NHIS for adults (≥18 years of age).

Other forms of tobacco use are becoming increasingly common. Electronic cigarette (e-cigarette) use, which involves inhalation of a vaporized liquid that includes nicotine, solvents, and flavoring (“vaping”), has risen dramatically, particularly among young people. The variety of e-cigarette—related products has increased dramatically, giving rise to the more general term electronic nicotine delivery systems.3 Use of cigarillos and other mass market cigars, hookahs, and water pipes has also become increasingly common in recent years.

Prevalence

Youth

(See Charts 3-1 through 3-2)

  • According to the NSDUH 2014 data, for adolescents aged 12 to 17 years4:

    — In the past 30 days

    • 4.9% (1.2 million) currently smoked cigarettes.

    • Of adolescents who smoked, 24.1% (292 000) smoked cigarettes daily, and 11.9% smoked ≥1 pack per day in the past month.

    • 2.1% (521 000) were current cigar smokers, 0.7% (179 000) were current pipe tobacco smokers, and 2% (490 000) used smokeless tobacco.

    — The percentage reporting use of tobacco products in the past month decreased from 15.2% in 2002 to 7.0% in 2014.

    — Male adolescents were more likely than female adolescents to report use in the past month of cigars (2.7% compared with 1.5%) and smokeless tobacco (3.3% compared with 0.6%).

    — 18.5% had used tobacco products in their lifetime, and 12.7% had done so in the past year.

    — The prevalence of tobacco product use was higher in male adolescents (20.2%) than female adolescents (16.6%) over their lifetimes, as well as over the past year (14.3% of male adolescents, 11.0% of female adolescents) and month (8.2% of male adolescents, 5.8% of female adolescents).

    — The lifetime use of tobacco products in adolescents also varied by

    • Race/ethnicity: lifetime use was highest in American Indians or Alaskan Natives (27.1%), followed by whites (21.6%), Hispanics or Latinos (16.8%), blacks or African Americans (12.7%), and Asians (6.9%).

    • Geographic division: The highest lifetime use was observed in the South (East South Central 19.8%), and the lowest was observed in New England (10.6%).

    — White adolescents (9.1%) were more likely than Hispanic (5.0%), black or African American (4.0%), or Asian (1.9%) adolescents 12 to 17 years of age to report any tobacco use, which included cigarettes, cigars, and smokeless tobacco.

    — Current cigarette use increases sharply when it becomes legal for adolescents at 18 years of age. In 2014, 24.0 % of adults aged 18 to 20 years were current smokers compared with 10.2% of adolescents aged 16 to 17 years. In most states, the minimum age for purchasing tobacco products is 18 years of age. However, Alabama, Alaska, New Jersey, Utah, New York City, and some other communities have set higher minimum ages. The Institute of Medicine’s review of the literature concluded that increasing the minimum age to legally purchase tobacco products would likely prevent or delay tobacco use by adolescents and young adults, especially those aged 15 to 17 years.5

Adults

(See Table 3-1 and Charts 3-3 through 3-5)

Since the US Surgeon General’s first report on the health dangers of smoking, age-adjusted rates of smoking among adults have declined, from 51% of males smoking in 1965 to 16.7% in 2015 and from 34% of females in 1965 to 13.7% in 2015 (Table 3-1 and Chart 3-3; NHIS).6 The decline in smoking, along with other factors (including improved treatment and reductions in the prevalence of risk factors such as uncontrolled hypertension and high cholesterol), is a contributing factor in the sharp decline in the HD death rate during this period.7

  • According to the NHIS 2013–2015 data, among adults ≥18 years of age (Chart 3-4)6:

    — NH Asian males (13.4%) and Hispanic males (14.3%) were less likely to be current cigarette smokers than NH American Indian or Alaska Native males (25.6%), NH white males (19.8%), and NH black males (20.9%). Similarly, in 2015, NH Asian females (4.1%) and Hispanic females (7.1%) were less likely to be current cigarette smokers than NH black females (13.8%), NH white females (17.9%), and NH American Indian or Alaska Native females (24.8%).

  • By region, the prevalence of current cigarette smokers was highest in the Midwest (20.7%) and lowest in the West (13.1%).8,9

  • In 2014, according to the BRFSS, the states with the highest percentage of current cigarette smokers were West Virginia (26.7%), Kentucky (26.2%), Arkansas (24.7%), Tennessee (24.2%), Louisiana (24.0%), Mississippi (23.0%), Indiana (22.9%), and South Carolina (21.5%), compared with the US average of 16.8%8 (Chart 3-5).

  • In 2014, the states with the lowest percentage of current cigarette smokers were Utah (9.7%), Puerto Rico (11.3%), California (12.8%), Hawaii (14.1%), New York (14.4%), and Texas (14.5%); all percentages were significantly lower than the US average (16.8%)8,9 (Chart 3-5).

  • On the basis of NHIS data, current smoking status among 18- to 24-year-old males declined 21.1%, from 23.9% in 2005 to 18.5% in 2014, and for 18- to 24-year-old females, smoking declined 28.4%, from 20.7% to 14.8%, over the same time period.9 On the basis of age-adjusted estimates in 2014, among people ≥65 years of age, 9.8% of males and 7.5% of females were current smokers.9

  • Smoking prevalence increases as family income declines. Among adults ≥18 years of age living below the poverty level, 26.3% were current smokers in 2014. In comparison, among adults ≥18 years of age living at or above the poverty level, 15.2% were current smokers.9

  • In 2014, smoking prevalence was higher among adults ≥18 years of age who reported having a disability or activity limitation (21.9%) than among those reporting no disability or limitation (16.1%).9

  • People with mental illness are more than twice as likely to smoke as people without mental illness.10

  • Likewise, lifetime tobacco use for people with psychiatric diagnoses is high, with rates of 56.1%, 55.6%, and 70.1% in patients with mood disorders, anxiety disorders, and schizophrenia, respectively.11

  • From 2004 to 2011, adjusted prevalence rates for tobacco use in mentally ill populations did not decline significantly; however, rates for people who are not mentally ill declined from 19.2% to 16.5%.12

  • In 2012 to 2013, among females 15 to 44 years of age, past-month cigarette use was lower among those who were pregnant (15.4%) than among those who were not pregnant (24.0%). Rates were higher among females 18 to 25 years of age (21.0% versus 26.2% for pregnant and nonpregnant females, respectively) than among females 26 to 44 years of age (11.8% versus 25.4%, respectively). Smoking declines by pregnancy trimester, from 19.9% of females 15 to 44 years of age in the first trimester of pregnancy to 12.8% in the third trimester (NSDUH).13

Incidence

  • According to 2014 NSDUH4:

    — Approximately 2.16 million people ≥12 years of age smoked cigarettes for the first time within the past 12 months, down from 2.3 million in 2012. The 2012 estimate averages out to ≈5700 new cigarette smokers every day. Half of new smokers aged ≥12 in 2013 (38.7%) were <12 to 17 years of age when they first smoked cigarettes (NSDUH); 93.3% were 12 to 25 years of age. Unfortunately, the number of new smokers did not change significantly between 2013 and 2014.

    — The number of new smokers 12 to 17 years of age (83 800) was down from 2002 (1.3 million); new smokers 18 to 25 years of age increased from ≈600 000 in 2002 to 1.181 million in 2014.

  • In the NHIS, between 2005 and 2014:

    — Among people 12 to 49 years of age who had started smoking within the past 12 months, the average age of first cigarette use was 17.8 years, the same as in 2012.

Mortality

  • In 2010, tobacco smoking was the second-leading risk factor for death in the United States, after dietary risks.14

  • Smoking was responsible for >480 000 premature deaths in the United States annually from 2005 to 2009 among those ≥35 years of age. Furthermore, almost one third of deaths of CHD are attributable to smoking and secondhand smoke exposure.7

  • If current smoking trends continue, 5.6 million US children will die prematurely during adulthood of smoking.7

  • From 2005 to 2009, smoking during pregnancy resulted in an estimated 970 infant deaths annually.7

  • Each year from 2005 to 2009, an estimated 41 000 US deaths were attributable to exposure to secondhand smoke among those ≥35 years of age.7

  • In 2009, smoking was estimated to cause 3.3 million years of potential life lost for males and 2.2 million years for females, excluding deaths attributable to smoking-attributable residential fires and adult deaths attributable to secondhand smoke.7

  • Recent analysis found that in addition to the known risk of death attributable to CVD and stroke that is related to smoking, the risk of mortality attributable to hypertensive HD and hypertensive renal disease is related to smoking.15

  • On average, male smokers die 13.2 years earlier than male nonsmokers, and female smokers die 14.5 years earlier than female nonsmokers.1

  • Overall mortality among US smokers is 3 times higher than that for never-smokers.16

  • Increased CVD mortality risks persist for older (≥60 years old) smokers as well. A recent meta-analysis comparing CVD risks in 503 905 cohort participants ≥60 years of age reported an HR for cardiovascular mortality of 2.07 (95% CI, 1.82 to 2.36) compared with never smokers and 1.37 (95% CI, 1.25 to 1.49) compared with former smokers.17

  • In a sample of Native Americans (Strong Heart Study), among whom the prevalence of tobacco use is highest in the United States, the PAR for total mortality was 18.4% for males and 10.9% for females.18

  • Since the first report on the dangers of smoking was issued by the US Surgeon General in 1964, tobacco control efforts have contributed to a reduction of 8 million premature smoking-attributable deaths.19

Cardiovascular Health Impact

  • A 2010 report of the US Surgeon General on how tobacco causes disease summarized an extensive body of literature on smoking and CVD and the mechanisms through which smoking is thought to cause CVD.20 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. Similar health risks for CHD events are reported with regular cigar smoking as well.21

  • Smoking is an independent risk factor for CHD and appears to have a multiplicative effect with the other major risk factors for CHD: high serum levels of lipids, untreated hypertension, and DM.20

  • Cigarette smoking and other traditional CHD risk factors may have a synergistic interaction in HIV-positive individuals.22

  • A meta-analysis of 75 cohort studies (≈2.4 million individuals) demonstrated a 25% greater risk for CHD in female smokers when compared with male smokers (RR, 1.25; 95% CI, 1.12 to 1.39).23

  • Cigarette smoking is an independent risk factor for both ischemic stroke and SAH and has a synergistic effect on other stroke risk factors such as SBP24 and oral contraceptive use.25,26

  • A meta-analysis comparing pooled data of ≈3.8 million smokers and nonsmokers found a similar risk of stroke associated with current smoking in females and males.27

  • Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.28,29

  • Acute exposure to water pipe smoking is associated with a significant increase in SBP and heart rate compared with nonsmoking control subjects,30 but long-term effects remain unclear. Current use of smokeless tobacco is associated with an increased risk of CVD events in cigarette nonsmokers.31

Cost

  • Each year from 2005 to 2009, US smoking-attributable economic costs were between $289 billion and $333 billion, including $133 billion to $176 billion for direct medical care of adults and $151 billion for lost productivity related to premature death.7

  • In the United States, cigarette smoking is associated with 9% of annual aggregated healthcare spending.32

  • In 2008, $9.94 billion was spent on marketing cigarettes in the United States.33

  • Cigarette prices in the United States have increased 283% between the early 1980s and 2011, in large part because of excise taxes on tobacco products. Higher taxes have decreased cigarette consumption, which fell from ≈30 million packs sold in 1982 to ≈14 million packs sold in 2011.33

Smoking Prevention

  • Tobacco 21 Laws increase the minimum age of sale for tobacco products from 18 to 21 years old. Such legislation would likely reduce the rates of smoking during adolescence, a time during which the majority of smokers start smoking, by limiting access, because most people who buy cigarettes for adolescents are <21 years of age. In several towns where Tobacco 21 laws have been enacted, 47% reductions in smoking prevalence among high school students have been reported.34 Furthermore, the Institute of Medicine estimates that a nationwide Tobacco 21 law would result in 249 000 fewer premature deaths, 45 000 fewer lung cancer deaths, and 4.2 million fewer lost life-years among Americans born between 2010 and 2019.35 As of 2016, 125 localities and the states of Hawaii and California have adopted Tobacco 21 laws, and momentum is building. A federal Tobacco 21 law was introduced to the Congress in September 2015; review and a vote are pending.

Smoking Cessation

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

    — There is no convincing evidence to date that reducing the amount smoked by smoking fewer cigarettes per day reduces the risk of CVD, although in several studies a dose-response relationship has been seen among current smokers between the number of cigarettes smoked per day and CVD incidence.20

  • Quitting smoking at any age significantly lowers mortality from smoking-related diseases, and the risk declines more the longer the time since quitting smoking.36 Cessation appears to have both short-term (weeks to months) and long-term (years) benefits for lowering CVD risk. Overall, risk appears to approach that of nonsmokers after ≈10 years of cessation.

  • Smokers who quit smoking at 25 to 34 years of age gained 10 years of life compared with those who continued to smoke. Those aged 35 to 44 years gained 9 years and those aged 45 to 54 years gained 6 years of life, on average, compared with those who continued to smoke.16

  • 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).37

  • Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.38

  • EVITA was an RCT that examined the efficacy of varenicline versus placebo for smoking cessation among smokers who were hospitalized for ACS. At 24 weeks, rates of smoking abstinence and reduction were significantly higher among patients randomized to varenicline. Point-prevalence abstinence rates were 47.3% in the varenicline group and 32.5% in the placebo group (P=0.012; number needed to treat=6.8). Continuous abstinence rates and reduction rates (≥50% of daily cigarette consumption) were also higher in the varenicline group.39

  • The recently reported EAGLES trial demonstrated efficacy and safety of 12 weeks of varenicline, bupropion, or nicotine patch in motivated-to-quit smoking patients with major depressive disorder, bipolar disorder, anxiety disorders, posttraumatic stress disorder, obsessive-compulsive disorder, social phobia, psychotic disorders including schizophrenia and schizoaffective disorders, and borderline personality disorder. Of note, these participants were all clinically stable from a psychiatric perspective and were believed not to be at high risk for self-injury.40

  • Extended use of a nicotine patch (24 weeks compared with 8 weeks) has been demonstrated to be safe and efficacious in recent randomized clinical trials.41

  • An RCT demonstrated the effectiveness of individual- and group-oriented financial incentives for tobacco abstinence through at least 12 months of follow-up.42

  • In addition to medications, smoke-free policies, increases in tobacco prices, cessation advice from healthcare professionals, and quit-lines and other counseling have contributed to smoking cessation.37

  • In 2010, 52.4% of adult smokers reported having tried to quit smoking in the past year; 6.2% reported they had recently quit smoking. Of those who tried to quit smoking, 30.0% used cessation medications.37

  • The majority of ex-smokers report that they quit without any formal assistance.43

  • Mass media antismoking campaigns, such as the CDC’s Tips campaign (Tips From Former Smokers), have been shown to reduce smoking-attributable morbidity and mortality and are cost-effective.44

  • Despite states having collected $25.6 billion in 2012 from the 1998 Tobacco Master Settlement Agreement and tobacco taxes, <2% of those funds are spent on tobacco prevention and cessation programs. In addition, progress in passing and raising tobacco taxes and enacting smoke-free laws has slowed.45

  • In 2014, a major drug store chain, CVS Caremark Corporation, stopped selling tobacco products, a step that could reduce access to tobacco products and therefore encourage some smokers to quit.46

Electronic Cigarettes

(See Chart 3-6)

  • Electronic nicotine delivery systems, more commonly called electronic cigarettes or e-cigarettes, are battery-operated devices that deliver nicotine, flavors, and other chemicals to the user in an aerosol. Although e-cigarettes were introduced less than a decade ago, there are currently >450 e-cigarette brands on the market, and sales in the United States were projected to be $2 billion in 2014.47

  • Because these products have not been well studied, their risks and benefits are not fully understood, although they are thought to have a lower risk of harmful effects than conventional cigarettes.48,49 Specifically, the health risks from the inhaled nicotine, solvents, heavy metals, flavorings, and other chemicals in e-cigarettes are not entirely known.7,50,51 However, a recent cross-sectional study compared the effects of electronic cigarettes versus traditional cigarettes on markers of oxidative stress and endothelial function in healthy smokers and nonsmoker adults and found that e-cigarettes appeared to have a lesser impact.52 Until prospective cohort studies evaluating hard clinical outcomes become available, future studies are required to examine the association of e-cigarettes and markers reflecting long-term cardiovascular damage, such as coronary artery calcium.

  • In addition to uncertainty about the harmful effects to users, the risks associated with secondhand exposure to e-cigarettes have not been well studied.48,49,53 Moreover, there is also the potential for thirdhand exposure with e-cigarettes, which results from tobacco toxins remaining on the surfaces in areas where people have smoked. A study found that thirdhand exposure levels differed depending on the surface and the e-cigarette brand.54

  • E-cigarettes could play a beneficial role in helping smokers reduce or eliminate their conventional cigarette smoking; however, there are concerns that e-cigarettes could be a gateway to nicotine addiction and tobacco use by nonsmokers, especially teenagers, or could promote relapse among former smokers.7,50,51

  • Teenagers are increasingly trying e-cigarettes. In 2015, e-cigarettes were the most commonly used tobacco product among middle school (5.3%) and high school (16.0%) students.55 In 2013, 3.0% of middle school students had ever tried e-cigarettes, up from 1.4% in 2011. In 2013, 11.9% of high school students had ever tried e-cigarettes, up from 4.7% in 201156 (Chart 3-6).

  • In 2014, 18.3 million US middle and high school students were exposed to e-cigarette advertising.55 Among US adults during 2010 to 2013, awareness and use of e-cigarettes increased considerably, and more than one third of current cigarette smokers reported ever having used e-cigarettes.57

  • As of June 30, 2016, 46 states, Guam, and the US Virgin Islands had banned e-cigarettes sales to minors, and 8 states prohibited e-cigarette use in private worksites, restaurants, and bars.58

  • Many public health advocates are worried that e-cigarettes will reverse decades of efforts to denormalize smoking, which contributed to the decline in smoking.7,50,51,53

  • The answers to some of these questions may become clearer as the regulatory oversight of e-cigarettes becomes more defined.7 Currently, only e-cigarettes that are marketed for therapeutic purposes are regulated by the US Food and Drug Administration, but in April 2014, the US Food and Drug Administration proposed extending its tobacco product authorities to include e-cigarettes.59

  • In May 2016, the US Food and Drug Administration released its long-awaited final “deeming” regulations on noncigarette tobacco products, including e-cigarettes, cigars, little cigars, pipe tobacco, hookah pipes, and other tobacco products. Under this rule, e-cigarettes will be subjected to the same regulatory restriction as traditional cigarettes, including manufacture, marketing, and distribution. As a result, all e-cigarette products will need to undergo federal review to remain on the market, with the exception of products that entered the market before February 15, 2007. This action will also ban the sale of e-cigarettes to minors and require photo identification to purchase these products.60

Secondhand Smoke

  • Data from the US Surgeon General on the consequences of secondhand smoke indicate the following:

    — Nonsmokers who are exposed to secondhand smoke at home or at work increase their risk of developing CHD by 25% to 30%.20

    — 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.20

    — Exposure to secondhand smoke increases the risk of stroke by 20% to 30%.7

    — Nearly 34 000 premature deaths of HD occur each year in the United States among nonsmokers.7

  • These data are supported by a recent meta-analysis of 23 prospective and 17 case-control studies of cardiovascular risks associated with secondhand smoke exposure demonstrated 18%, 23%, 23%, and 29% increased risks for total mortality, total CVD, CHD, and stroke, respectively, in those exposed to secondhand smoke.61

  • Data from the 2008 BRFSS 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.62

  • As of June 2016, 27 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 June 2016, 9 states had no laws banning or restricting areas for smoking in private workplaces.63

  • In 2012, 30 of the 50 largest US cities prohibited indoor smoking in private workplaces, through either state or local ordinances.64

  • 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%.65

  • The percentage of the US nonsmoking population with serum cotinine ≥0.05 ng/mL (which indicates exposure to secondhand smoke) declined from 52.5% in 1999 to 2000 to 25.3% in 2011 to 2012, with declines occurring for both children and adults. During 2011 to 2012, the percentage of nonsmokers with detectable serum cotinine was 40.6% for those 3 to 11 years of age, 33.8% for those 12 to 19 years of age, and 21.3% for those ≥20 years of age. The percentage was also higher for non-Hispanic blacks (46.8%) than for non-Hispanic whites (21.8%) and Mexican Americans (23.9%). People living below the poverty level (43.2%) and those living in rental housing (36.8%) had higher rates of secondhand smoke exposure than their counterparts (21.1% of those living above the poverty level and 19.0% of those who owned their homes; NHANES).66

Global Burden of Smoking

  • Although tobacco use in the United States has been declining, tobacco use worldwide has climbed steeply and is currently responsible for 5 million deaths annually.45

  • The number of smokers was estimated to have grown from 721 million in 1980 to 967 million in 2012.67

  • Worldwide, tobacco smoking (including secondhand smoke) was 1 of the top 3 leading risk factors for disease and contributed to an estimated 6.2 million deaths in 2010.68

  • In 2004, 40% of children, 33% of male nonsmokers, and 35% of female nonsmokers were exposed to secondhand smoke, which was estimated to have caused 630 000 premature deaths.69

  • 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, 180 countries have ratified the WHO Framework Convention on Tobacco Control.70

Abbreviations Used in Chapter 3

ACSacute coronary syndrome
AHAAmerican Heart Association
AIANAmerican Indian or Alaska Native
AMIacute myocardial infarction
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DMdiabetes mellitus
EAGLESStudy Evaluating the Safety and Efficacy of Varenicline and Bupropion for Smoking Cessation in Subjects With and Without a History of Psychiatric Disorders
EVITAEvaluation of Varenicline in Smoking Cessation for Patients Post-Acute Coronary Syndrome
HDheart disease
HIVhuman immunodeficiency virus
HRhazard ratio
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
NSDUHNational Survey on Drug Use and Health
PARpopulation attributable risk
RCTrandomized controlled trial
RRrelative risk
SAHsubarachnoid hemorrhage
SBPsystolic blood pressure
WHOWorld Health Organization

References

  • 1. The 2004 United States Surgeon General’s Report: The Health Consequences of Smoking.N S W Public Health Bull. 2004; 15:107.MedlineGoogle Scholar
  • 2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
  • 3. Centers for Disease Control and Prevention (CDC). Electronic Nicotine Delivery Systems Key Facts.2014. https://chronicdata.cdc.gov/Policy/Electronic-Nicotine-Delivery-Systems-Key-Facts-Inf/nwhw-m4ki. Accessed June 8, 2016.Google Scholar
  • 4. Center for Behavioral Health Statistics and Quality. Behavioral Health Trends in the United States: Results From the 2014 National Survey on Drug Use and Health.2015. http://www.samhsa.gov/data/. Accessed August 25, 2016.Google Scholar
  • 5. Institute of Medicine. Public Health Implications of Raising the Minimum Age of Legal Access to Tobacco Products.Washington, DC: National Academy of Sciences; 2015.Google Scholar
  • 6. National Center for Health Statistics. National Health Interview Survey, 2015. Public-use data file and documentation: NCHS tabulations.http://www.cdc.gov/nchs/nhis/nhis_2015_data_release.htm. Accessed July 18, 2016.Google Scholar
  • 7. US Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014.Google Scholar
  • 8. Centers for Disease Control and Prevention. State Tobacco Activities Tracking and Evaluation (STATE) System. http://www.cdc.gov/statesystem/cigaretteuseadult.html. Accessed July 18, 2016.Google Scholar
  • 9. Jamal A, Homa DM, O’Connor E, Babb SD, Caraballo RS, Singh T, Hu SS, King BA. Current cigarette smoking among adults: United States, 2005-2014.MMWR Morb Mortal Wkly Rep. 2015; 64:1233–1240. doi: 10.15585/mmwr.mm6444a2.CrossrefMedlineGoogle Scholar
  • 10. Lasser K, Boyd JW, Woolhandler S, Himmelstein DU, McCormick D, Bor DH. Smoking and mental illness: a population-based prevalence study.JAMA. 2000; 284:2606–2610.CrossrefMedlineGoogle Scholar
  • 11. Martins SS, Gorelick DA. Conditional substance abuse and dependence by diagnosis of mood or anxiety disorder or schizophrenia in the U.S. population.Drug Alcohol Depend. 2011; 119:28–36. doi: 10.1016/j.drugalcdep.2011.05.010.CrossrefMedlineGoogle Scholar
  • 12. Cook BL, Wayne GF, Kafali EN, Liu Z, Shu C, Flores M. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation.JAMA. 2014; 311:172–182. doi: 10.1001/jama.2013.284985.CrossrefMedlineGoogle Scholar
  • 13. Substance Abuse and Mental Health Services Administration. Results From the 2013 National Survey on Drug Use and Health: Summary of National Findings [and detailed tables].Rockville, MD: Substance Abuse and Mental health Services Administration; 2014. http://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDUHresults2013.pdf. Accessed June 8, 2016.Google Scholar
  • 14. Murray CJ, Abraham J, Ali MK, et al. The state of US Health, 1990–2010: burden of diseases, injuries, and risk factors.JAMA. 2013; 310:591–608. doi: 10.1001/jama.2013.13805.CrossrefMedlineGoogle Scholar
  • 15. Carter BD, Abnet CC, Feskanich D, Freedman ND, Hartge P, Lewis CE, Ockene JK, Prentice RL, Speizer FE, Thun MJ, Jacobs EJ. Smoking and mortality: beyond established causes.N Engl J Med. 2015; 372:631–640. doi: 10.1056/NEJMsa1407211.CrossrefMedlineGoogle Scholar
  • 16. Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, McAfee T, Peto R. 21st-century hazards of smoking and benefits of cessation in the United States.N Engl J Med. 2013; 368:341–350. doi: 10.1056/NEJMsa1211128.CrossrefMedlineGoogle Scholar
  • 17. Mons U, Müezzinler A, Gellert C, Schöttker B, Abnet CC, Bobak M, de Groot L, Freedman ND, Jansen E, Kee F, Kromhout D, Kuulasmaa K, Laatikainen T, O’Doherty MG, Bueno-de-Mesquita B, Orfanos P, Peters A, van der Schouw YT, Wilsgaard T, Wolk A, Trichopoulou A, Boffetta P, Brenner H; CHANCES Consortium. Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: meta-analysis of individual participant data from prospective cohort studies of the CHANCES consortium.BMJ. 2015; 350:h1551. doi: 10.1136/bmj.h1551.CrossrefMedlineGoogle Scholar
  • 18. Zhang M, An Q, Yeh F, Zhang Y, Howard BV, Lee ET, Zhao J. Smoking-attributable mortality in American Indians: findings from the Strong Heart Study.Eur J Epidemiol. 2015; 30:553–561. doi: 10.1007/s10654-015-0031-8.CrossrefMedlineGoogle Scholar
  • 19. Holford TR, Meza R, Warner KE, Meernik C, Jeon J, Moolgavkar SH, Levy DT. Tobacco control and the reduction in smoking-related premature deaths in the United States, 1964-2012.JAMA. 2014; 311:164–171. doi: 10.1001/jama.2013.285112.CrossrefMedlineGoogle Scholar
  • 20. US Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2010.Google Scholar
  • 21. Chang CM, Corey CG, Rostron BL, Apelberg BJ. Systematic review of cigar smoking and all cause and smoking related mortality.BMC Public Health. 2015; 15:390. doi: 10.1186/s12889-015-1617-5.CrossrefMedlineGoogle Scholar
  • 22. Rasmussen LD, Helleberg M, May MT, Afzal S, Kronborg G, Larsen CS, Pedersen C, Gerstoft J, Nordestgaard BG, Obel N. Myocardial infarction among Danish HIV-infected individuals: population-attributable fractions associated with smoking.Clin Infect Dis. 2015; 60:1415–1423. doi: 10.1093/cid/civ013.CrossrefMedlineGoogle Scholar
  • 23. Huxley RR, Woodward M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and meta-analysis of prospective cohort studies.Lancet. 2011; 378:1297–1305. doi: 10.1016/S0140-6736(11)60781-2.CrossrefMedlineGoogle Scholar
  • 24. Nakamura K, Barzi F, Lam TH, Huxley R, Feigin VL, Ueshima H, Woo J, Gu D, Ohkubo T, Lawes CM, Suh I, Woodward M; Asia Pacific Cohort Studies Collaboration. Cigarette smoking, systolic blood pressure, and cardiovascular diseases in the Asia-Pacific region.Stroke. 2008; 39:1694–1702. doi: 10.1161/STROKEAHA.107.496752.LinkGoogle Scholar
  • 25. Ischaemic stroke and combined oral contraceptives: results of an international, multicentre, case-control study.WHO Collaborative Study of Cardiovascular Disease and Steroid Hormone Contraception. Lancet. 1996; 348:498–505.CrossrefMedlineGoogle Scholar
  • 26. Haemorrhagic stroke, overall stroke risk, and combined oral contraceptives: results of an international, multicentre, case-control study.WHO Collaborative Study of Cardiovascular Disease and Steroid Hormone Contraception. Lancet. 1996; 348:505–510.CrossrefMedlineGoogle Scholar
  • 27. Peters SA, Huxley RR, Woodward M. Smoking as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 81 cohorts, including 3,980,359 individuals and 42,401 strokes.Stroke. 2013; 44:2821–2828. doi: 10.1161/STROKEAHA.113.002342.LinkGoogle Scholar
  • 28. Meschia JF, Bushnell C, Boden-Albala B, Braun LT, Bravata DM, Chaturvedi S, Creager MA, Eckel RH, Elkind MS, Fornage M, Goldstein LB, Greenberg SM, Horvath SE, Iadecola C, Jauch EC, Moore WS, Wilson JA; American Heart Association Stroke Council; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; Council on Hypertension. Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American Heart Association/American Stroke Association.Stroke. 2014; 45:3754–3832. doi: 10.1161/STR.0000000000000046.LinkGoogle Scholar
  • 29. Shah RS, Cole JW. Smoking and stroke: the more you smoke the more you stroke.Expert Rev Cardiovasc Ther. 2010; 8:917–932. doi: 10.1586/erc.10.56.CrossrefMedlineGoogle Scholar
  • 30. Azar RR, Frangieh AH, Mroué J, Bassila L, Kasty M, Hage G, Kadri Z. Acute effects of waterpipe smoking on blood pressure and heart rate: a real-life trial.Inhal Toxicol. 2016; 28:339–342. doi: 10.3109/08958378.2016.1171934.CrossrefMedlineGoogle Scholar
  • 31. Yatsuya H, Folsom AR; ARIC Investigators. Risk of incident cardiovascular disease among users of smokeless tobacco in the Atherosclerosis Risk in Communities (ARIC) study.Am J Epidemiol. 2010; 172:600–605. doi: 10.1093/aje/kwq191.CrossrefMedlineGoogle Scholar
  • 32. Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. Annual healthcare spending attributable to cigarette smoking: an update.Am J Prev Med. 2015; 48:326–333. doi: 10.1016/j.amepre.2014.10.012.CrossrefMedlineGoogle Scholar
  • 33. US Department of Health and Human Services. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General, 2012.Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012. http://www.surgeongeneral.gov/library/reports/preventing-youth-tobacco-use/prevent_youth_by_section.html. Accessed May 30, 2012.Google Scholar
  • 34. Kessel Schneider S, Buka SL, Dash K, Winickoff JP, O’Donnell L. Community reductions in youth smoking after raising the minimum tobacco sales age to 21.Tob Control. 2016; 25:355–359. doi: 10.1136/tobaccocontrol-2014-052207.CrossrefMedlineGoogle Scholar
  • 35. Morain SR, Winickoff JP, Mello MM. Have Tobacco 21 laws come of age?N Engl J Med. 2016; 374:1601–1604. doi: 10.1056/NEJMp1603294.CrossrefMedlineGoogle Scholar
  • 36. Thun MJ, Carter BD, Feskanich D, Freedman ND, Prentice R, Lopez AD, Hartge P, Gapstur SM. 50-Year trends in smoking-related mortality in the United States.N Engl J Med. 2013; 368:351–364. doi: 10.1056/NEJMsa1211127.CrossrefMedlineGoogle Scholar
  • 37. Centers for Disease Control and Prevention (CDC). Quitting smoking among adults: United States, 2001–2010.MMWR Morb Mortal Wkly Rep. 2011; 60:1513–1519.MedlineGoogle Scholar
  • 38. Clinical Practice Guideline Treating Tobacco Use and Dependence 2008 Update Panel, Liaisons, and Staff. A clinical practice guideline for treating tobacco use and dependence: 2008 update: a U.S. Public Health Service report.Am J Prev Med. 2008; 35:158–176. doi: 10.1016/j.amepre.2008.04.009.CrossrefMedlineGoogle Scholar
  • 39. Eisenberg MJ, Windle SB, Roy N, Old W, Grondin FR, Bata I, Iskander A, Lauzon C, Srivastava N, Clarke A, Cassavar D, Dion D, Haught H, Mehta SR, Baril JF, Lambert C, Madan M, Abramson BL, Dehghani P; EVITA Investigators. Varenicline for smoking cessation in hospitalized patients with acute coronary syndrome.Circulation. 2016; 133:21–30. doi: 10.1161/CIRCULATIONAHA.115.019634.LinkGoogle Scholar
  • 40. Anthenelli RM, Benowitz NL, West R, St Aubin L, McRae T, Lawrence D, Ascher J, Russ C, Krishen A, Evins AE. Neuropsychiatric safety and efficacy of varenicline, bupropion, and nicotine patch in smokers with and without psychiatric disorders (EAGLES): a double-blind, randomised, placebo-controlled clinical trial.Lancet. 2016; 387:2507–2520. doi: 10.1016/S0140-6736(16)30272-0.CrossrefMedlineGoogle Scholar
  • 41. Schnoll RA, Goelz PM, Veluz-Wilkins A, Blazekovic S, Powers L, Leone FT, Gariti P, Wileyto EP, Hitsman B. Long-term nicotine replacement therapy: a randomized clinical trial.JAMA Intern Med. 2015; 175:504–511. doi: 10.1001/jamainternmed.2014.8313.CrossrefMedlineGoogle Scholar
  • 42. Halpern SD, French B, Small DS, Saulsgiver K, Harhay MO, Audrain-McGovern J, Loewenstein G, Brennan TA, Asch DA, Volpp KG. Randomized trial of four financial-incentive programs for smoking cessation.N Engl J Med. 2015; 372:2108–2117. doi: 10.1056/NEJMoa1414293.CrossrefMedlineGoogle Scholar
  • 43. Smith AL, Chapman S. Quitting smoking unassisted: the 50-year research neglect of a major public health phenomenon.JAMA. 2014; 311:137–138. doi: 10.1001/jama.2013.282618.CrossrefMedlineGoogle Scholar
  • 44. Xu X, Alexander RL, Simpson SA, Goates S, Nonnemaker JM, Davis KC, McAfee T. A cost-effectiveness analysis of the first federally funded antismoking campaign.Am J Prev Med. 2015; 48:318–325. doi: 10.1016/j.amepre.2014.10.011.CrossrefMedlineGoogle Scholar
  • 45. Antman E, Arnett D, Jessup M, Sherwin C. The 50th anniversary of the US Surgeon General’s report on tobacco: what we’ve accomplished and where we go from here.J Am Heart Assoc. 2014; 3:e000740. doi: 10.1161/JAHA.113.000740.LinkGoogle Scholar
  • 46. Frieden T, Yox S. CDC Reacts to CVS ending tobacco sales: An interview with CDC Director Tom Frieden, MD, MPH.February 6, 2014. Medscape. http://www.medscape.com/viewarticle/820269. Accessed June 9, 2016.Google Scholar
  • 47. Zhu SH, Sun JY, Bonnevie E, Cummins SE, Gamst A, Yin L, Lee M. Four hundred and sixty brands of e-cigarettes and counting: implications for product regulation.Tob Control. 2014; 23(suppl 3):iii3–9. doi: 10.1136/tobaccocontrol-2014-051670.CrossrefMedlineGoogle Scholar
  • 48. Farsalinos KE, Polosa R. Safety evaluation and risk assessment of electronic cigarettes as tobacco cigarette substitutes: a systematic review.Ther Adv Drug Saf. 2014; 5:67–86. doi: 10.1177/2042098614524430.CrossrefMedlineGoogle Scholar
  • 49. Pisinger C, Døssing M. A systematic review of health effects of electronic cigarettes.Prev Med. 2014; 69:248–260. doi: 10.1016/j.ypmed.2014.10.009.CrossrefMedlineGoogle Scholar
  • 50. Benowitz NL, Goniewicz ML. The regulatory challenge of electronic cigarettes.JAMA. 2013; 310:685–686. doi: 10.1001/jama.2013.109501.CrossrefMedlineGoogle Scholar
  • 51. Fairchild AL, Bayer R, Colgrove J. The renormalization of smoking? E-cigarettes and the tobacco “endgame” [published correction appears in N Engl J Med. 2014;370:2354].N Engl J Med. 2014; 370:293–295. doi: 10.1056/NEJMp1313940.CrossrefMedlineGoogle Scholar
  • 52. Carnevale R, Sciarretta S, Violi F, Nocella C, Loffredo L, Perri L, Peruzzi M, Marullo AG, De Falco E, Chimenti I, Valenti V, Biondi-Zoccai G, Frati G. Acute impact of tobacco vs electronic cigarette smoking on oxidative stress and vascular function.Chest. 2016; 150:606–612. doi: 10.1016/j.chest.2016.04.012.CrossrefMedlineGoogle Scholar
  • 53. Bhatnagar A, Whitsel LP, Ribisl KM, Bullen C, Chaloupka F, Piano MR, Robertson RM, McAuley T, Goff D, Benowitz N; American Heart Association Advocacy Coordinating Committee, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Quality of Care and Outcomes Research. Electronic cigarettes: a policy statement from the American Heart Association.Circulation. 2014; 130:1418–1436. doi: 10.1161/CIR.0000000000000107.LinkGoogle Scholar
  • 54. Goniewicz ML, Lee L. Electronic cigarettes are a source of thirdhand exposure to nicotine.Nicotine Tob Res. 2015; 17:256–258. doi: 10.1093/ntr/ntu152.CrossrefMedlineGoogle Scholar
  • 55. Singh T, Arrazola RA, Corey CG, Husten CG, Neff LJ, Homa DM, King BA. Tobacco use among middle and high school students–United States, 2011-2015.MMWR Morb Mortal Wkly Rep. 2016; 65:361–367. doi: 10.15585/mmwr.mm6514a1.CrossrefMedlineGoogle Scholar
  • 56. Centers for Disease Control and Prevention. National Youth Tobacco Survey (NYTS).http://www.cdc.gov/tobacco/data_statistics/surveys/nyts/. Accessed July 18, 2016.Google Scholar
  • 57. King BA, Patel R, Nguyen KH, Dube SR. Trends in awareness and use of electronic cigarettes among US adults, 2010-2013.Nicotine Tob Res. 2015; 17:219–227. doi: 10.1093/ntr/ntu191.CrossrefMedlineGoogle Scholar
  • 58. Centers for Disease Control and Prevention. STATE System E-Cigarette Fact Sheet.https://chronicdata.cdc.gov/Legislation/STATE-System-E-Cigarette-Fact-Sheet/qte6-7jwd. Accessed August 30, 2016.Google Scholar
  • 59. FDA proposes to extend its tobacco authority to additional tobacco products, including e-cigarettes [news release].Silver Spring, MD: US Food and Drug Administration; April 24, 2014. http://www.fda.gov/newsevents/newsroom/pressannouncements/ucm394667.htm. Accessed July 18, 2016.Google Scholar
  • 60. Food and Drug Administration, HHS. Deeming tobacco products to be subject to the federal Food, Drug, and Cosmetic Act, as amended by the Family Smoking Prevention and Tobacco Control Act; restrictions on the sale and distribution of tobacco products and required warning statements for tobacco products: final rule.Fed Regist. 2016; 81:28973–29106.MedlineGoogle Scholar
  • 61. Lv X, Sun J, Bi Y, Xu M, Lu J, Zhao L, Xu Y. Risk of all-cause mortality and cardiovascular disease associated with secondhand smoke exposure: a systematic review and meta-analysis.Int J Cardiol. 2015; 199:106–115. doi: 10.1016/j.ijcard.2015.07.011.CrossrefMedlineGoogle Scholar
  • 62. Centers for Disease Control and Prevention (CDC). State-specific secondhand smoke exposure and current cigarette smoking among adults: United States, 2008.MMWR Morb Mortal Wkly Rep. 2009; 58:1232–1235.MedlineGoogle Scholar
  • 63. Tynan MA, Holmes CB, Promoff G, Hallett C, Hopkins M, Frick B. State and local comprehensive smoke-free laws for worksites, restaurants, and bars: United States, 2015.MMWR Morb Mortal Wkly Rep. 2016; 65:623–626. doi: 10.15585/mmwr.mm6524a4.CrossrefMedlineGoogle Scholar
  • 64. Centers for Disease Control and Prevention (CDC). Comprehensive smoke-free laws: 50 largest U.S. cities, 2000 and 2012.MMWR Morb Mortal Wkly Rep. 2012; 61:914–917.MedlineGoogle Scholar
  • 65. Mackay DF, Irfan MO, Haw S, Pell JP. Meta-analysis of the effect of comprehensive smoke-free legislation on acute coronary events.Heart. 2010; 96:1525–1530. doi: 10.1136/hrt.2010.199026.CrossrefMedlineGoogle Scholar
  • 66. Homa DM, Neff LJ, King BA, Caraballo RS, Bunnell RE, Babb SD, Garrett BE, Sosnoff CS, Wang L; Centers for Disease Control and Prevention (CDC). Vital signs: disparities in nonsmokers’ exposure to secondhand smoke: United States, 1999-2012.MMWR Morb Mortal Wkly Rep. 2015; 64:103–108.MedlineGoogle Scholar
  • 67. Ng M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, Wollum A, Sanman E, Wulf S, Lopez AD, Murray CJ, Gakidou E. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012.JAMA. 2014; 311:183–192. doi: 10.1001/jama.2013.284692.CrossrefMedlineGoogle Scholar
  • 68. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A, Bell ML, Blore JD, Blyth F, Bonner C, Borges G, Bourne R, Boussinesq M, Brauer M, Brooks P, Bruce NG, Brunekreef B, Bryan-Hancock C, Bucello C, Buchbinder R, Bull F, Burnett RT, Byers TE, Calabria B, Carapetis J, Carnahan E, Chafe Z, Charlson F, Chen H, Chen JS, Cheng AT, Child JC, Cohen A, Colson KE, Cowie BC, Darby S, Darling S, Davis A, Degenhardt L, Dentener F, Des Jarlais DC, Devries K, Dherani M, Ding EL, Dorsey ER, Driscoll T, Edmond K, Ali SE, Engell RE, Erwin PJ, Fahimi S, Falder G, Farzadfar F, Ferrari A, Finucane MM, Flaxman S, Fowkes FG, Freedman G, Freeman MK, Gakidou E, Ghosh S, Giovannucci E, Gmel G, Graham K, Grainger R, Grant B, Gunnell D, Gutierrez HR, Hall W, Hoek HW, Hogan A, Hosgood HD, Hoy D, Hu H, Hubbell BJ, Hutchings SJ, Ibeanusi SE, Jacklyn GL, Jasrasaria R, Jonas JB, Kan H, Kanis JA, Kassebaum N, Kawakami N, Khang YH, Khatibzadeh S, Khoo JP, Kok C, Laden F, Lalloo R, Lan Q, Lathlean T, Leasher JL, Leigh J, Li Y, Lin JK, Lipshultz SE, London S, Lozano R, Lu Y, Mak J, Malekzadeh R, Mallinger L, Marcenes W, March L, Marks R, Martin R, McGale P, McGrath J, Mehta S, Mensah GA, Merriman TR, Micha R, Michaud C, Mishra V, Mohd Hanafiah K, Mokdad AA, Morawska L, Mozaffarian D, Murphy T, Naghavi M, Neal B, Nelson PK, Nolla JM, Norman R, Olives C, Omer SB, Orchard J, Osborne R, Ostro B, Page A, Pandey KD, Parry CD, Passmore E, Patra J, Pearce N, Pelizzari PM, Petzold M, Phillips MR, Pope D, Pope CA, Powles J, Rao M, Razavi H, Rehfuess EA, Rehm JT, Ritz B, Rivara FP, Roberts T, Robinson C, Rodriguez-Portales JA, Romieu I, Room R, Rosenfeld LC, Roy A, Rushton L, Salomon JA, Sampson U, Sanchez-Riera L, Sanman E, Sapkota A, Seedat S, Shi P, Shield K, Shivakoti R, Singh GM, Sleet DA, Smith E, Smith KR, Stapelberg NJ, Steenland K, Stöckl H, Stovner LJ, Straif K, Straney L, Thurston GD, Tran JH, Van Dingenen R, van Donkelaar A, Veerman JL, Vijayakumar L, Weintraub R, Weissman MM, White RA, Whiteford H, Wiersma ST, Wilkinson JD, Williams HC, Williams W, Wilson N, Woolf AD, Yip P, Zielinski JM, Lopez AD, Murray CJ, Ezzati M, AlMazroa MA, Memish ZA. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.Lancet. 2012; 380:2224–2260. doi: 10.1016/S0140-6736(12)61766-8.CrossrefMedlineGoogle Scholar
  • 69. Oberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries.Lancet. 2011; 377:139–146. doi: 10.1016/S0140-6736(10)61388-8.CrossrefMedlineGoogle Scholar
  • 70. World Health Organization. The WHO Framework Convention on Tobacco Control: An Overview.2003. http://www.who.int/fctc/about/en/index.html. Accessed July 18, 2016.Google Scholar
  • 71. National Center for Health Statistics. Health, United States, 2015: With Special Feature on Racial and Ethnic Health Disparities.Hyattsville, MD: National Center for Health Statistics; 2015. http://www.cdc.gov/nchs/data/hus/hus15.pdf. Accessed August 24, 2016.Google Scholar

4. Physical Inactivity

(See Table 4-1 and Charts 4-1 through 4-11)

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

Population GroupPrevalence, 2015 (Age ≥18 y), %
Age 18 y and over, age adjusted21.5
Age 18 y and over, crude20.9
Both sexes21.6
Males25.3
Females17.9
NH white23.4
NH black19.8
Hispanic or Latino16.8
Asian19.1
American Indian/Alaska Native18.9

“Met 2008 Federal Aerobic and Strengthening PA Guidelines for Adults” is defined as engaging in ≥150 min of moderate or 75 min of vigorous aerobic leisure-time physical activity per week (or an equivalent combination) and engaging in leisure-time strengthening physical activity at least twice a week. Data are age adjusted for adults ≥18 years of age.

NH indicates non-Hispanic; and PA, physical activity.

Source: National Health Interview Survey 2015 (National Center for Health Statistics).11

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 in the past 7 days by race/ethnicity and sex.

NH indicates non-Hispanic.

Source: Youth Risk Behavior Surveillance Survey 2015.8

Chart 4-2.

Chart 4-2. 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.

“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 min/d on 5 of the 7 days preceding the survey.

NH indicates non-Hispanic.

Source: Youth Risk Behavior Surveillance Survey 2015.8

Chart 4-3.

Chart 4-3. Percentage of students in grades 9 to 12 who used a computer for ≥3 hours on an average school day by race/ethnicity and sex.

NH indicates non-Hispanic.

Source: Youth Risk Behavior Surveillance Survey 2015.8

Chart 4-4.

Chart 4-4. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by sex and age (NHIS: 2015).

The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

NHIS indicates National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-5.

Chart 4-5. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by race/ethnicity and sex (NHIS: 2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

NH indicates non-Hispanic; and NHIS, National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-6.

Chart 4-6. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥25 years of age by educational attainment (NHIS: 2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

GED indicates General Educational Development; and NHIS, National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-7.

Chart 4-7. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by location of residence (NHIS: 2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

MSA indicates metropolitan statistical area; and NHIS, National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-8.

Chart 4-8. Prevalence of meeting the aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by disability status (NHIS: 2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

NHIS indicates National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-9.

Chart 4-9. Percent distribution of meeting the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by poverty level and type of activity (NHIS: 2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination and perform muscle-strengthening activities at least 2 d/wk.

NHIS indicates National Health Interview Survey.

Source: NHIS 2015 (National Center for Health Statistics).11

Chart 4-10.

Chart 4-10. Prevalence of children 12 to 15 years of age who had adequate levels of cardiorespiratory fitness, by sex and age (NHANES, National Youth Fitness Survey: 2012).

NHANES indicates National Health and Nutrition Examination Survey.

Source: NHANES, National Youth Fitness Survey 2012.12

Chart 4-11.

Chart 4-11. Trends in meeting the physical activity guidelines of the 2008 Federal Physical Activity Guidelines for Americans among adults ≥18 years of age by type of activity (NHIS: 1998-2015).

Percentages are age adjusted. The aerobic guidelines of the 2008 Federal Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk or vigorous activity ≥75 min/wk or an equivalent combination.

NH indicates non-Hispanic; and NHIS, National Health Interview Survey.

Source: NHIS 1998 to 2015 (National Center for Health Statistics).11

Being physically active improves health, and being inactive is unhealthy.3 PA reduces premature mortality. In addition, PA improves risk factors for CVD (such as HBP and high cholesterol) and reduces the likelihood of diseases related to CVD, including CHD, stroke, type 2 DM, and sudden heart attacks.3 Benefits from PA are seen for all ages and groups, including older adults, pregnant females, and people with disabilities and chronic conditions. Therefore, the federal guidelines recommend being as physically active as abilities and conditions allow and, if not currently meeting the recommendations, increasing PA gradually.

Physical inactivity is a major risk factor for CVD and stroke.1 Meeting the guidelines for PA is one of the AHA’s 7 components of ideal cardiovascular health for both children and adults.2 The AHA and 2008 federal guidelines on PA recommend that children get at least 60 minutes of PA daily (including aerobic and muscle- and bone-strengthening activity). The guidelines recommend that adults get at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic activity (or an equivalent combination) per week and perform muscle strengthening activities at least 2 days per week.3 In 2011 to 2012, on the basis of survey interviews, 36.5% of children and 44.0% of adults met these criteria (NHANES).

Defining and Measuring PA

There are 4 dimensions of PA (mode or type, frequency, duration, and intensity) and 4 common domains (occupational, domestic, transportation, and leisure time). The federal guidelines specify the suggested frequency, duration, and intensity of activity. Historically, recommendations on PA for health purposes have focused on leisure-time activity. However, because all domains of PA could have an impact on health, and because an increase in 1 domain may sometimes be compensated for by a decrease in another domain, ideally data will be collected on all dimensions and domains of PA.

There are 2 broad categories of methods to assess PA: (1) subjective methods that use questionnaires and diaries/logs and (2) objective methods that use wearable monitors (pedometers, accelerometers, etc). Studies that compare the findings between subjective methods (respondent reported) and objective methods (such as wearable monitors, like pedometers or accelerometers) have found that there is marked discordance between self-reported and measured PA, with respondents often overstating their PA, especially the intensity.4,5 The majority of studies linking inactivity/PA to cardiovascular outcomes obtain PA data from respondent reports, which may overestimate the amount or intensity of activity.

Another consideration in the measurement of PA is that surveys often ask only about leisure-time PA. PA also may come from occupational, domestic, and transportation responsibilities. People who get a lot of PA from these other responsibilities may be less like to engage in leisure-time PA, and yet they may meet the federal PA guidelines, but they may be less likely to be identified as meeting the guidelines.

Chronic physical inactivity contributes to poor levels of cardiorespiratory (or aerobic) fitness, which is a stronger predictor of adverse cardiometabolic and cardiovascular outcomes than traditional risk factors. Although both PA and cardiorespiratory fitness are inversely related to the risk of CVD and other clinical outcomes, they are distinct measures in the assessment of CVD risk.6 PA is a behavior that can potentially improve cardiorespiratory fitness. Although many studies have shown that increasing the amount and quality of PA can improve cardiorespiratory fitness, other factors can contribute, such as a genetic predisposition to perform aerobic exercise.7 Because cardiorespiratory fitness is directly measured and reflects both participation in PA and the state of physiological systems affecting performance, the relationship between cardiorespiratory fitness and clinical outcomes is stronger than the relationship of PA to a series of clinical outcomes.6 Unlike health behaviors such as PA and risk factors that are tracked by federally funded programs, there are no national data on cardiorespiratory fitness; the development of a national cardiorespiratory fitness registry has been proposed.6 Such additional data on the cardiorespiratory fitness levels of Americans may give a fuller and more accurate picture of physical fitness levels.6

Prevalence

Youth
Inactivity

(See Chart 4-1)

In 2015 (YRBSS)8:

  • Nationwide, 14.3% of adolescents reported that they were inactive on all of the previous 7 days (that is, 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 physical inactivity (17.5% versus 11.1%).

  • The prevalence of inactivity was highest among non-Hispanic black (25.2%) and Hispanic (19.2%) girls, followed by non-Hispanic black boys (16.2%), non-Hispanic white girls (14.3%), Hispanic boys (11.9%), and non-Hispanic white boys (8.8%). (Chart 4-1)

  • There was substantial variation by state. The state reporting the highest prevalence of inactivity was Mississippi (22.9%), whereas the lowest was Montana (10.7%).

Activity Recommendations

(See Chart 4-2)

  • In 2015 (YRBSS)8:

    — The prevalence of high school students who met activity recommendations of ≥60 minutes of PA on all 7 days of the week was 27.1% nationwide and declined from 9th (31.0%) to 12th (25.3%) grades. At each grade level, the prevalence was higher in boys than in girls.

    — More than double the percentage of high school boys (36.0%) than girls (17.7%) reported having been physically active ≥60 minutes per day on all 7 days.

    — The prevalence of students meeting currently recommended levels of physical activity (defined as having been physically active at least 60 minutes per day on at least 5 of the 7 days) was higher among non-Hispanic white boys (62.0%), non-Hispanic black boys (52.2%), and Hispanic boys (53.5%) than non-Hispanic white girls (43.5%), non-Hispanic black girls (33.4%), and Hispanic girls (33.1%) (Chart 4-2).

    — The proportion of students who participated in muscle strengthening activities on ≥3 days of the week was 53.4% nationwide and declined from 9th (males 67.3%, females 48.2%) to 12th (males 59.9%, females 39.9%) grades.

    — More high school boys (63.7%) than girls (42.7%) reported having participated in muscle-strengthening activities on ≥3 days of the week. At each grade level, the proportion was higher in boys than in girls.

  • 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 activity levels.4

  • More boys than girls met PA recommendations (≥60 minutes of moderate to vigorous activity on most days of the week) as measured by accelerometry.4

Television/Video/Computers

(See Chart 4-3)

Research suggests that screen time (watching television or using a computer) may lead to less PA among children.9 In addition, television viewing time is associated with poor nutritional choices, overeating, and weight gain (refer to Chapter 5, Nutrition).

  • In 2015 (YRBSS)8:

    — Nationwide, 41.7% 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. From 2003 to 2009, the computer usage rate increased significantly from 22.1% to 24.9%. The rate of computer use increased much more dramatically from 2009 to 2015 (24.9% to 41.7%).

    — The prevalence of using computers ≥3 hours per day (for activities other than school work) was highest among non-Hispanic black girls (48.4%), followed by Hispanic girls (47.4%), Hispanic boys (45.1%), non-Hispanic black boys (41.2%), non-Hispanic white boys (38.9%), and non-Hispanic white girls (38.3%) (Chart 4-3).

    — The prevalence of watching television ≥3 hours per day was highest among non-Hispanic black girls (41.5%) and boys (37.0%), followed by Hispanic girls (29.2%) and boys (27.4%) and non-Hispanic white boys (21.4%) and girls (18.8%).

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,10 in 2015 only 29.8% of students attended physical education classes in school daily (33.8% of boys and 29.6% of girls) (YRBSS).8

  • Daily physical education class participation declined from the 9th grade (44.6% for boys, 39.5% for girls) through the 12th grade (27.9% for boys, 16.0% for girls) (YRBSS).8

  • Just over half (57.6%) of high school students played on at least 1 school or community sports team in the previous year: 53.0% of girls and 62.2% of boys (YRBSS).8

Adults
Inactivity
  • According to 2015 data from the NHIS, of adults ≥18 years of age11:

    — 30.4% do not engage in leisure-time PA (“no leisure time PA/inactivity” refers to no sessions of light/moderate or vigorous PA of ≥10 minutes’ duration).

    — Inactivity was higher among females than males (31.7% versus 29.9%, age adjusted) and increased with age from 24.7% (ages 18–44 years) to 32.6% (ages 45–64 years), 36.3% (ages 65–74 years), and 53.0% (≥75 years of age).

    — Hispanic and non-Hispanic black adults were more likely to be inactive (38.8% and 39.0%, respectively) than were non-Hispanic white adults (27.0%) on the basis of age-adjusted estimates.

Activity Recommendations

(See Table 4-1 and Charts 4-4 through 4-9)

  • According to 2015 data from the NHIS, of adults ≥18 years of age11:

    — 21.6% met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness, based on leisure-time activity (Table 4-1).

    — The age-adjusted proportion who reported meeting 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 49.8%, with 53.0% of males and 46.9% of females meeting the aerobic guidelines.

    — The proportion of respondents who met the federal aerobic PA guidelines declined with age for both males and females. Among males, 60.0% of 18- to 44-year-olds met the aerobic activity guidelines compared with 31.1% of noninstitutionalized males ≥75 years of age. Among females, 52.9% of 18- to 44-year-olds met the aerobic activity guidelines compared with 24.4% of noninstitutionalized females ≥75 years of age11 (Chart 4-4).

    — Age-adjusted prevalence was 53.1% for non-Hispanic whites, 42.1% for non-Hispanic blacks, and 43.3% for Hispanics. Among both males and females, non-Hispanic whites were more likely to meet the PA aerobic guidelines with leisure-time activity than non-Hispanic blacks and Hispanics (Chart 4-5).11

    — Education was positively associated with meeting the federal guidelines. Among adults ≥25 years of age, 66.8% of participants with no high school diploma, 57.2% of those with a high school diploma or a General Educational Development (GED) high school equivalency credential, 46.9% of those with some college, and 35.0% of those with a bachelor’s degree or higher did not meet the full (aerobic and muscle-strengthening) federal PA guidelines.

    — For aerobic PA alone, among adults ≥25 years of age, 30.0% of participants with no high school diploma, 38.9% of those with a high school diploma or General Educational Development (GED) high school equivalency credential, 46.8% of those with some college, and 62.8% of those with a bachelor’s degree or higher met the federal guidelines11 (Chart 4-6).

    — Adults residing in urban (metropolitan statistical areas) are more likely to meet the federal aerobic PA guidelines than those residing in rural areas (51.2% versus 41.1%)11 (Chart 4-7).

    — The federal guidelines do not set different PA standards by age, disability status, or other factors. Instead, the federal guidelines recommend being as physically active as abilities and conditions permit. Adults with a disability (defined by the constructs any basic actions difficulty or complex activity limitation) were less likely to meet the aerobic PA guidelines than those without a disability (36.9% compared with 56.9%)3,11 (Chart 4-10).

    — Adults living at below 200% of the poverty level are less likely to meet the federal PA guidelines than adults living at >200% above the poverty level11 (Chart 4-9).

  • For adolescents ages 12 to 15 years, boys in all age groups were more likely to have adequate levels of cardiorespiratory fitness than girls12 (Chart 4-10).

  • Using the PA recommendations in effect at the time of the survey, adherence to PA recommendations was much lower when based on PA measured by accelerometer in NHANES 2003 to 20044:

    — Among adults 20 to 59 years of age, 3.8% of males and 3.2% of females 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 males and 2.3% in females.

  • Accelerometry data from NHANES 2003 to 2006 showed that males engaged in 35 minutes and females in 21 minutes of moderate activity per day. More than 75% of moderate activity was accumulated in 1-minute bouts. Levels of activity declined sharply after the age of 50 years in all groups.13

  • In a review examining self-reported versus actual measured PA (eg, accelerometers, pedometers, indirect calorimetry, doubly labeled water, heart rate monitor), 60% of respondents self-reported higher values of activity than what was measured by use of direct methods.14

  • Among males, self-reported PA was 44% greater than actual measured values; among females, self-reported activity was 138% greater than actual measured PA.14

Mortality

  • According to the WHO, physical inactivity is the fourth-leading risk factor for global death, responsible for 3.2 million deaths annually, including an estimated 670 000 premature deaths (people aged <60 years) and 30% of IHD burden.15

  • The Global Burden of Disease investigators estimated that worldwide, physical inactivity contributed to 2.2 million deaths in 2013.16

  • Adults ≥40 years of age who participated in moderate to vigorous PA once a week or more had a lower mortality risk than those who were inactive. Furthermore, older adults who engaged in moderate to vigorous PA ≥5 times per week had a significantly lower mortality risk than those who participated in a lower frequency of PA.17

  • A meta-analysis of 9 cohort studies, representing 122 417 patients, found that as little as 15 minutes of daily moderate to vigorous PA reduced all-cause mortality in adults ≥60 years of age. This protective effect of PA was dose dependent; the most rapid reduction in mortality per minute of added PA was for those at the lowest levels of PA. These findings suggest that older adults can benefit from PA time far below the amount recommended by the federal guidelines.18

  • A study of US adults that linked a large, nationally representative sample to death records found that meeting the aerobic PA guidelines reduced all-cause mortality, with an HR of 0.64, after adjustment for potential confounding factors.19

  • A pooled study of >600 000 participants found that even low levels of leisure-time PA (up to 75 minutes of brisk walking per week) were associated with reduced risk of mortality compared with participants with no PA. If this is a causal relationship, this low level of PA would confer a 1.8-year gain in life expectancy after age 40 years compared with no PA. For participants meeting the PA guidelines, the gain in life expectancy was 3.4 years over those with no PA.20

  • 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.21

  • 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.22

  • 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 task 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.23

  • In the Cooper Center Longitudinal Study, an analysis conducted on 16 533 participants revealed that across all risk factor strata, the presence of low cardiorespiratory fitness was associated with a greater risk of CVD death over a mean follow-up of 28 years.24

  • In the Southern Community Cohort Study of 63 308 individuals (with a large proportion of black adults) followed up for >6.4 years, more time spent being sedentary (>12 h/d versus <5.76 h/d) was associated with a 20% to 25% increased risk of all-cause mortality in blacks and whites. Both PA (beneficial) and sedentary time (detrimental) were associated with mortality risk.25

  • In a study involving 55 137 adults followed up over an average of 15 years, running even 5 to 10 min/d and at slow speeds (<6 mph) was associated with a markedly reduced risk of death attributable to all causes and CVD.26

  • A population-based cohort in New South Wales, Australia, of 204 542 adults followed up for an average of 6.5 years evaluated the relationship of PA to mortality risk. It found that compared with those who reported no moderate to vigorous PA, the adjusted HRs for all-cause mortality were 0.66 for those reporting 10–149 min/wk, 0.53 for those reporting 150–299 min/wk, and 0.46 for those reporting ≥300 min/wk of activity.27

  • An analysis of pooled data from 6 studies in the National Cancer Institute Cohort Consortium involving 661 137 males and females followed up for an average of 14.2 years revealed that compared with individuals reporting no leisure-time PA, an inverse dose-response relationship was observed between level of leisure-time PA (HR=0.80 for less than the recommended minimum of the PA guidelines, HR=0.69 for 1 to 2 times the recommended minimum, and HR=0.63 for 2 to 3 times the minimum) and mortality, with the upper threshold for mortality benefit occurring at 3 to 5 times the PA recommendations (HR, 0.61). Furthermore, there was no evidence of harm associated with performing ≥10 times the recommended minimum (HR, 0.69). Thus, meeting the 2008 minimum PA guidelines for Americans through either moderate- or vigorous-intensity activities was essentially associated with a nearly optimal reduction in mortality risk. This study supports the view that although healthcare professionals should encourage inactive people to become physically active, they should not discourage adults who are already very active at levels far above those recommended by the guidelines.28

  • A study that prospectively assessed the association of continuous inactivity and of changes in sitting time for 2 years with subsequent long-term all-cause mortality found that compared with people who remained consistently sedentary, the HRs for mortality were 0.91 in those who were newly sedentary, 0.86 in formerly sedentary individuals, and 0.75 in those who remained consistently nonsedentary. Thus, participants who reduced their sitting time over 2 years experienced a reduction in mortality.29

  • A meta-analysis of 17 eligible studies on PA in patients with DM revealed that the highest PA category in each study was associated with a lower RR (0.61) for all-cause mortality and CVD (0.71) than the lowest PA category. Although more PA was associated with larger reductions in future all-cause mortality and CVD, in patients with DM, any amount of habitual PA was better than inactivity.30

Costs

  • The economic consequences of physical inactivity are substantial. Using data derived primarily from WHO publications and data warehouses, one study estimated the economic costs of physical inactivity account for 1.5% to 3.0% of total direct healthcare expenditures in developed countries such as the United States.31

  • A study of Australian females aged 50 to 55 years found that healthcare costs were 26% higher in sedentary females than in moderately active females. Costs included visits to general practitioners, medical specialists, and outpatient pathology and radiology services.32

  • A study of American adults reported that inadequate levels of aerobic PA (after adjusting for BMI) were associated with an estimated 11.1% of aggregate healthcare expenditures (including expenditures for inpatient, outpatient, ED, office-based, dental, vision, home health, prescription drug, and other services).33

  • An evaluation of healthcare costs based on the cardiovascular risk factor profile (including ≥30 minutes of moderate to vigorous PA ≥5 times per week) found that among adults aged ≥40 years with CVD, the highest marginal expenditures ($2853) were for those not meeting the PA guidelines. Healthcare costs included hospitalizations, prescribed medications, outpatient visits (hospital outpatient visits and office-based visits), ED visits, and other expenditures (dental visits, vision aid, home health care, and other medical supplies).34

Secular Trends

Youth
  • In 2015 (YRBSS)8:

    — During the 2013 to 2015 time period, the percentage of adolescents not participating in ≥60 minutes of any kind of physical activity on at least 1 day did not substantively change, at 15.2% in 2013 and 14.3% in 2015.

    — 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 53.4% in 2015; however, the prevalence did not change substantively from 2013 (51.7%) to 2015 (53.4%).

    — A significant increase occurred in the prevalence of having used computers not for school work for ≥3 hours per day compared with 2003 (22.1% versus 41.7% in 2015). The prevalence increased from 2003 to 2009 (22.1% versus 24.9%) and then increased more rapidly from 2009 to 2015 (24.9% versus 41.7%).

    — Among adolescents nationwide, the prevalence of attending physical education classes at least once per week did not change substantively between 2013 (48.0%) and 2015 (51.6%).

    — Among high school students, the prevalence of attendance in daily physical education classes changed in nonlinear ways over time. Attendance initially decreased from 1991 to 1995 (from 41.6% to 25.4%) and was not substantively changed between 1995, 2013, and 2015 (25.4%, 29.4%, and 29.8%, respectively).

    — The prevalence of adolescents playing ≥1 team sport in the past year did not substantively change between 2013 (54.0%) and 2015 (57.6%).

  • In 2012, the prevalence of adolescents aged 12 to 15 years with adequate levels of cardiorespiratory fitness (based on age- and sex-specific standards) was 42.2% in 2012, down from 52.4% in 1999 to 2000.12 (Chart 4-10)

Adults

(See Chart 4-11)

  • Between 1988 to 1994 and 2001 to 2006 (NHANES), the percentage of adults who reported engaging in >12 bouts of PA per month declined from 57.0% to 43.3% in males and from 49.0% to 43.3% in females (crude percentages).35

  • The age-adjusted percentage of US adults who met both the muscle-strengthening and aerobic guidelines increased from 14.3% in 1998 to 21.6% in 2015 (Chart 4-11). The percentage of US adults who met the aerobic guideline increased from 40.0% in 1998 to 49.8% in 2015, and the percentage meeting the muscle-strengthening guideline increased from 17.7% in 1998 to 25.0% in 2015.11,36

  • 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.37

Cardiovascular Health Impact

Youth
  • Total and vigorous PA are inversely correlated with body fat and the prevalence of obesity.38

  • Among children 4 to 18 years of age, increased time spent engaging in moderate to vigorous PA was associated with improvements in waist circumference, SBP, fasting triglycerides, HDL-C, and insulin. These findings were significant regardless of the amount of the children’s sedentary time.39

  • 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.39

Adults
Cardiovascular and Metabolic Risk
  • Participants in the Diabetes Prevention Program randomized trial who met the 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.40

  • A review of the US Preventive Services Task Force recommendations examined the evidence on whether relevant counseling interventions for a healthful diet and PA in primary care modify self-reported behaviors, intermediate physiological outcomes, DM incidence, and cardiovascular morbidity or mortality in adults with CVD risk factors. It was concluded that after 12 to 24 months, intensive lifestyle counseling for individuals selected because of risk factors reduced TC levels by an average of 0.12 mmol/L, LDL-C levels by 0.09 mmol/L, SBP by 2.03 mm Hg, DBP by 1.38 mm Hg, fasting glucose by 0.12 mmol/L, DM incidence by an RR of 0.58, and weight outcomes by a standardized difference of 0.25.41

  • Weight loss from increased physical exercise, 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).42

  • A total of 120 to 150 min/wk of moderate-intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.43

  • In CARDIA, females who maintained high PA 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 males gained 2.6 fewer kilograms and 3.1 fewer centimeters than their lower-activity counterparts.44

  • In 3 US cohort studies, males and females 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.45

  • Among US males and females, 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.45

  • In a sample of 466 605 participants in the China Kadoorie Biobank study, a 1-SD (1.5 h/d) increase in sedentary time was associated with a 0.19-unit higher BMI, a 0.57-cm larger waist circumference, and 0.44% more body fat. Both sedentary leisure time and lower PA were independently associated with an increased BMI.46

Cardiovascular Events
  • The PA guidelines for adults cite evidence that ≈150 min/wk of moderate-intensity aerobic activity, compared with none, can reduce the risk of CVD.22

  • In the WHI observational study (n=71 018), sitting for ≥10 h/d compared with ≤5 h/d was associated with increased CVD risk (HR, 1.18) in multivariable models that included PA. Low PA was also associated with higher CVD risk. It was concluded that both low PA and prolonged sitting augment CVD risk.47

  • Longitudinal studies commonly report a graded, inverse association of PA amount and duration (ie, dosage) with incident CHD and stroke.48

  • In a meta-analysis of longitudinal studies among females, 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.49

  • A study of the factors related to declining CVD among Norwegian adults ≥25 years of age found that increased PA (≥1 hour of strenuous PA per week) accounted for 9% of the decline in hospitalized and nonhospitalized fatal and nonfatal CHD events.50

  • 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 males. This 22% reduction of risk can be explained in part by beneficial effects of PA on HDL-C, vitamin D, apolipoprotein B, and HbA1c.51

  • 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).52

  • In a study involving 1.1 million females without prior vascular disease and followed up over an average of 9 years, those who reported moderate activity were found to be at lower risk of CHD, a cardiovascular event, or a first thrombotic event. However, strenuous PA was not found to be as beneficial as moderate PA.53

  • In a large clinical trial (NAVIGATOR) involving 9306 people with impaired glucose tolerance, ambulatory activity assessed by pedometer at baseline and 12 months was found to be inversely associated with risk of a cardiovascular event.54

  • In the EPIC-Norfolk study, males and females with abdominal obesity with features of the metabolic syndrome who reported themselves to be physically very active were characterized by a lower (≈50%) risk of CHD than sedentary abdominally obese subjects with the metabolic syndrome.55

  • Self-reported low lifetime recreational activity has been associated with increased PAD.56

Primordial and Primary Prevention

  • Community-level interventions have been shown to be effective in promoting increased PA.

  • The US Surgeon General has introduced Step It Up!, a Call to Action to Promote Walking and Walkable Communities in recognition of the importance of PA.57 There are roles for the following:

    — Communities: Communities can encourage walking with street design that includes sidewalks, improved street lighting, and landscaping design that reduces traffic speed to improve pedestrian safety.

    — Schools: Schools can provide opportunities for PA through physical education, recess, after-school activity programs, and PA breaks. Despite these recommendations, only one-quarter of students participate in daily PA classes (see Structured Activity Participation).

    — Worksites: Worksites can offer access to on-site exercise facilities or employer-subsidized off-site exercise facilities to encourage PA among employees.

    — Community-wide campaigns: Such campaigns include a variety of strategies, such as media coverage, risk factor screening and education, community events, and policy or environmental changes.

  • A systematic review of population-based interventions to encourage PA found that improving biking trails, distributing pedometers, and school-based PA were most cost-effective.58

  • Educating the public on the recommended PA guidelines may increase adherence. In a study examining awareness of current US PA guidelines, only 33% of respondents had direct knowledge of the recommended dosage of PA (ie, frequency/duration).59

  • Interventions and community strategies to increase PA have been shown to be cost-effective in terms of reducing medical costs60:

    — 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 QALY gained from interventions such as pedometer or walking programs compared with no intervention, especially in high-risk groups.

Secondary Prevention

  • In a retrospective cohort study of 2086 patients (39% females, 56% white) who underwent clinical treadmill testing and had a first MI during follow-up (mean of 11 years), a higher baseline level of cardiorespiratory fitness was independently associated with a decreased risk of short-term mortality (28 days after a first MI).61

  • 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-1.62

  • 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 also improved brachial artery FMD, whereas resistance training was associated with better stair-climbing ability than control.63

  • On the basis of a meta-analysis of 34 RCTs, exercise-based cardiac rehabilitation after MI was associated with lower rates of reinfarction, cardiac mortality, and overall mortality.64

  • 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 LVEF and decreases in pro-BNP (40%), LV end-diastolic volume (18%), and LV end-systolic volume (25%) compared with control and endurance-training groups.65

  • Exercise training in patients with HF with preserved EF was associated with improved exercise capacity and favorable changes in diastolic function.66

Global Burden

  • 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, excessive alcohol intake, and psychosocial factors.67

  • Worldwide, the prevalence of physical inactivity (35%) is now greater than the prevalence of smoking (26%). On the basis of the HRs associated with these 2 behaviors (1.57 for smoking and 1.28 for inactivity), it was concluded that the PAR was greater for inactivity (9%) than for smoking (8.7%). Inactivity was estimated to be responsible for 5.3 million deaths compared with 5.1 million deaths for smoking.68

Abbreviations Used in Chapter 4

AHAAmerican Heart Association
BMIbody mass index
BNPB-type natriuretic peptide
CARDIACoronary Artery Risk Development in Young Adults
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EDemergency department
EFejection fraction
EPIC-NorfolkEuropean Prospective Investigation Into Cancer and Nutrition—Norfolk Cohort
FMDflow-mediated dilation
GEDGeneral Educational Development
HbA1chemoglobin A1c
HBPhigh blood pressure
HDL-Chigh-density lipoprotein cholesterol
HFheart failure
HRhazard ratio
IHDischemic heart disease
LDL-Clow-density lipoprotein cholesterol
LVleft ventricular
LVEFleft ventricular ejection fraction
MImyocardial infarction
MSAmetropolitan statistical area
NAVIGATORA Multinational, Randomized, Double-Blind, Placebo-Controlled, Forced-Titration, 2 × 2 Factorial Design Study of the Efficacy and Safety of Long-term Administration of Nateglinide and Valsartan in the Prevention of Diabetes and Cardiovascular Outcomes in Subjects With Impaired Glucose Tolerance (IGT)
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
PAphysical activity
PADperipheral artery disease
PARpopulation attributable risk
QALYquality-adjusted life-year
RCTrandomized controlled trial
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
TCtotal cholesterol
WHIWomen’s Health Initiative
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

References

  • 1. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE; American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association.Circulation. 2010; 122:406–441. doi: 10.1161/CIR.0b013e3181e8edf1.LinkGoogle Scholar
  • 2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
  • 3. US Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. http://www.health.gov/paguidelines/pdf/paguide.pdf. Accessed July 11, 2014.Google Scholar
  • 4. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer.Med Sci Sports Exerc. 2008; 40:181–188. doi: 10.1249/mss.0b013e31815a51b3.CrossrefMedlineGoogle Scholar
  • 5. Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, Richardson CR, Smith DT, Swartz AM; on behalf of the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health and Cardiovascular, Exercise, Cardiac Rehabilitation and Prevention Committee of the Council on Clinical Cardiology, and Council on Cardiovascular and Stroke Nursing. Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association.Circulation. 2013; 128:2259–2279. doi: 10.1161/01.cir.0000435708.67487.da.LinkGoogle Scholar
  • 6. Kaminsky LA, Arena R, Beckie TM, Brubaker PH, Church TS, Forman DE, Franklin BA, Gulati M, Lavie CJ, Myers J, Patel MJ, Piña IL, Weintraub WS, Williams MA; American Heart Association Advocacy Coordinating Committee, Council on Clinical Cardiology, and Council on Nutrition, Physical Activity and Metabolism. The importance of cardiorespiratory fitness in the United States: the need for a national registry: a policy statement from the American Heart Association.Circulation. 2013; 127:652–662. doi: 10.1161/CIR.0b013e31827ee100.LinkGoogle Scholar
  • 7. DeFina LF, Haskell WL, Willis BL, Barlow CE, Finley CE, Levine BD, Cooper KH. Physical activity versus cardiorespiratory fitness: two (partly) distinct components of cardiovascular health?Prog Cardiovasc Dis. 2015; 57:324–329. doi: 10.1016/j.pcad.2014.09.008.CrossrefMedlineGoogle Scholar
  • 8. Kann L, McManus T, Harris WA, Shanklin SL, Flint KH, Hawkins J, Queen B, Lowry R, Olsen EO, Chyen D, Whittle L, Thornton J, Lim C, Yamakawa Y, Brener N, Zaza S. Youth Risk Behavior Surveillance: United States, 2015.MMWR Surveill Summ. 2016; 65:1–174. doi: 10.15585/mmwr.ss6506a1.CrossrefGoogle Scholar
  • 9. Lieberman DA, Chamberlin B, Medina E, Franklin BA, Sanner BM, Vafiadis DK; Power of Play: Innovations in Getting Active Summit Planning Committee. The power of play: Innovations in Getting Active Summit 2011: a science panel proceedings report from the American Heart Association.Circulation. 2011; 123:2507–2516. doi: 10.1161/CIR.0b013e318219661d.LinkGoogle Scholar
  • 10. National Association for Sport and Physical Education. Moving Into the Future: National Standards for Physical Education. 2nd ed. Reston, VA: National Association for Sport and Physical Education; 2004.Google Scholar
  • 11. National Center for Health Statistics. National Health Interview Survey, 2015. Public-use data file and documentation: NCHS tabulations.http://www.cdc.gov/nchs/nhis/nhis_2015_data_release.htm. Accessed July 18, 2016.Google Scholar
  • 12. Gahche J, Fakhouri T, Carroll DD, Burt VL, Wang CY, Fulton JE. Cardiorespiratory fitness levels among U.S. youth aged 12–15 years: United States, 1999–2004 and 2012.NCHS Data Brief. 2014;(153):1–8.MedlineGoogle Scholar
  • 13. Luke A, Dugas LR, Durazo-Arvizu RA, Cao G, Cooper RS. Assessing physical activity and its relationship to cardiovascular risk factors: NHANES 2003-2006.BMC Public Health. 2011; 11:387. doi: 10.1186/1471-2458-11-387.CrossrefMedlineGoogle Scholar
  • 14. Prince SA, Adamo KB, Hamel ME, Hardt J, Connor Gorber S, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review.Int J Behav Nutr Phys Act. 2008; 5:56. doi: 10.1186/1479-5868-5-56.CrossrefMedlineGoogle Scholar
  • 15. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva, Switzerland: World Health Organization2009.Google Scholar
  • 16. GBD 2013 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013.Lancet. 2015; 386:2287–2323. doi: 10.1016/S0140-6736(15)00128-2.CrossrefMedlineGoogle Scholar
  • 17. Brown RE, Riddell MC, Macpherson AK, Canning KL, Kuk JL. The association between frequency of physical activity and mortality risk across the adult age span.J Aging Health. 2013; 25:803–814. doi: 10.1177/0898264313492823.CrossrefMedlineGoogle Scholar
  • 18. Hupin D, Roche F, Gremeaux V, Chatard JC, Oriol M, Gaspoz JM, Barthélémy JC, Edouard P. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged ≥60 years: a systematic review and meta-analysis.Br J Sports Med. 2015; 49:1262–1267. doi: 10.1136/bjsports-2014-094306.CrossrefMedlineGoogle Scholar
  • 19. Zhao G, Li C, Ford ES, Fulton JE, Carlson SA, Okoro CA, Wen XJ, Balluz LS. Leisure-time aerobic physical activity, muscle-strengthening activity and mortality risks among US adults: the NHANES linked mortality study.Br J Sports Med. 2014; 48:244–249. doi: 10.1136/bjsports-2013-092731.CrossrefMedlineGoogle Scholar
  • 20. Moore SC, Patel AV, Matthews CE, Berrington de Gonzalez A, Park Y, Katki HA, Linet MS, Weiderpass E, Visvanathan K, Helzlsouer KJ, Thun M, Gapstur SM, Hartge P, Lee IM. Leisure time physical activity of moderate to vigorous intensity and mortality: a large pooled cohort analysis.PLoS Med. 2012; 9:e1001335. doi: 10.1371/journal.pmed.1001335.CrossrefMedlineGoogle Scholar
  • 21. Grøntved A, Hu FB. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta-analysis.JAMA. 2011; 305:2448–2455. doi: 10.1001/jama.2011.812.CrossrefMedlineGoogle Scholar
  • 22. Schoenborn CA, Stommel M. Adherence to the 2008 adult physical activity guidelines and mortality risk.Am J Prev Med. 2011; 40:514–521. doi: 10.1016/j.amepre.2010.12.029.CrossrefMedlineGoogle Scholar
  • 23. Kokkinos P, Myers J, Faselis C, Panagiotakos DB, Doumas M, Pittaras A, Manolis A, Kokkinos JP, Karasik P, Greenberg M, Papademetriou V, Fletcher R. Exercise capacity and mortality in older men: a 20-year follow-up study.Circulation. 2010; 122:790–797. doi: 10.1161/CIRCULATIONAHA.110.938852.LinkGoogle Scholar
  • 24. Wickramasinghe CD, Ayers CR, Das S, de Lemos JA, Willis BL, Berry JD. Prediction of 30-year risk for cardiovascular mortality by fitness and risk factor levels: the Cooper Center Longitudinal Study.Circ Cardiovasc Qual Outcomes. 2014; 7:597–602. doi: 10.1161/CIRCOUTCOMES.113.000531.LinkGoogle Scholar
  • 25. Matthews CE, Cohen SS, Fowke JH, Han X, Xiao Q, Buchowski MS, Hargreaves MK, Signorello LB, Blot WJ. Physical activity, sedentary behavior, and cause-specific mortality in black and white adults in the Southern Community Cohort Study.Am J Epidemiol. 2014; 180:394–405. doi: 10.1093/aje/kwu142.CrossrefMedlineGoogle Scholar
  • 26. Lee DC, Pate RR, Lavie CJ, Sui X, Church TS, Blair SN. Leisure-time running reduces all-cause and cardiovascular mortality risk [published correction appears in J Am Coll Cardiol. 2014;64:1537].J Am Coll Cardiol. 2014; 64:472–481. doi: 10.1016/j.jacc.2014.04.058.CrossrefMedlineGoogle Scholar
  • 27. Gebel K, Ding D, Chey T, Stamatakis E, Brown WJ, Bauman AE. Effect of moderate to vigorous physical activity on all-cause mortality in middle-aged and older Australians [published correction appears in JAMA Intern Med. 2015;175:1248].JAMA Intern Med. 2015; 175:970–977. doi: 10.1001/jamainternmed.2015.0541.CrossrefMedlineGoogle Scholar
  • 28. Arem H, Moore SC, Patel A, Hartge P, Berrington de Gonzalez A, Visvanathan K, Campbell PT, Freedman M, Weiderpass E, Adami HO, Linet MS, Lee IM, Matthews CE. Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship.JAMA Intern Med. 2015; 175:959–967. doi: 10.1001/jamainternmed.2015.0533.CrossrefMedlineGoogle Scholar
  • 29. León-Muñoz LM, Martínez-Gómez D, Balboa-Castillo T, López-García E, Guallar-Castillón P, Rodríguez-Artalejo F. Continued sedentariness, change in sitting time, and mortality in older adults.Med Sci Sports Exerc. 2013; 45:1501–1507. doi: 10.1249/MSS.0b013e3182897e87.CrossrefMedlineGoogle Scholar
  • 30. Kodama S, Tanaka S, Heianza Y, Fujihara K, Horikawa C, Shimano H, Saito K, Yamada N, Ohashi Y, Sone H. Association between physical activity and risk of all-cause mortality and cardiovascular disease in patients with diabetes: a meta-analysis.Diabetes Care. 2013; 36:471–479. doi: 10.2337/dc12-0783.CrossrefMedlineGoogle Scholar
  • 31. Oldridge NB. Economic burden of physical inactivity: healthcare costs associated with cardiovascular disease.Eur J Cardiovasc Prev Rehabil. 2008; 15:130–139. doi: 10.1097/HJR.0b013e3282f19d42.CrossrefMedlineGoogle Scholar
  • 32. Brown WJ, Hockey R, Dobson AJ. Physical activity, body mass index and health care costs in mid-age Australian women.Aust N Z J Public Health. 2008; 32:150–155. doi: 10.1111/j.1753-6405.2008.00192.x.CrossrefMedlineGoogle Scholar
  • 33. Carlson SA, Fulton JE, Pratt M, Yang Z, Adams EK. Inadequate physical activity and health care expenditures in the United States.Prog Cardiovasc Dis. 2015; 57:315–323. doi: 10.1016/j.pcad.2014.08.002.CrossrefMedlineGoogle Scholar
  • 34. Valero-Elizondo J, Salami JA, Ogunmoroti O, Osondu CU, Aneni EC, Malik R, Spatz ES, Rana JS, Virani SS, Blankstein R, Blaha MJ, Veledar E, Nasir K. Favorable cardiovascular risk profile is associated with lower healthcare costs and resource utilization: the 2012 Medical Expenditure Panel Survey.Circ Cardiovasc Qual Outcomes. 2016; 9:143–153. doi: 10.1161/CIRCOUTCOMES.115.002616.LinkGoogle Scholar
  • 35. King DE, Mainous AG, Carnemolla M, Everett CJ. Adherence to healthy lifestyle habits in US adults, 1988-2006.Am J Med. 2009; 122:528–534. doi: 10.1016/j.amjmed.2008.11.013.CrossrefMedlineGoogle Scholar
  • 36. National Center for Health Statistics. Health, United States, 2015: With Special Feature on Racial and Ethnic Health Disparities. Hyattsville, MD: National Center for Health Statistics; 2015. http://www.cdc.gov/nchs/data/hus/hus15.pdf. Accessed June 15, 2016.Google Scholar
  • 37. Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, Giles WH, Capewell S. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000.N Engl J Med. 2007; 356:2388–2398. doi: 10.1056/NEJMsa053935.CrossrefMedlineGoogle Scholar
  • 38. Kim Y, Lee S. Physical activity and abdominal obesity in youth.Appl Physiol Nutr Metab. 2009; 34:571–581. doi: 10.1139/H09-066.CrossrefMedlineGoogle Scholar
  • 39. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A; International Children’s Accelerometry Database (ICAD) Collaborators. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents [published correction appears in JAMA. 2012;307:1915].JAMA. 2012; 307:704–712. doi: 10.1001/jama.2012.156.CrossrefMedlineGoogle Scholar
  • 40. Hamman RF, Wing RR, Edelstein SL, Lachin JM, Bray GA, Delahanty L, Hoskin M, Kriska AM, Mayer-Davis EJ, Pi-Sunyer X, Regensteiner J, Venditti B, Wylie-Rosett J. Effect of weight loss with lifestyle intervention on risk of diabetes.Diabetes Care. 2006; 29:2102–2107. doi: 10.2337/dc06-0560.CrossrefMedlineGoogle Scholar
  • 41. LeFevre ML; US Preventive Services Task Force. Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force Recommendation Statement.Ann Intern Med. 2014; 161:587–593. doi: 10.7326/M14-1796.CrossrefMedlineGoogle Scholar
  • 42. Shaw K, Gennat H, O’Rourke P, Del Mar C. Exercise for overweight or obesity.Cochrane Database Syst Rev. 2006;( 4):CD003817.Google Scholar
  • 43. US Department of Health and Human Services, Centers for Disease Control and Prevention. Physical activity and health: the benefits of physical activity.http://www.cdc.gov/physicalactivity/everyone/health/index.html#ReduceCardiovascularDisease. Accessed August 1, 2011.Google Scholar
  • 44. Hankinson AL, Daviglus ML, Bouchard C, Carnethon M, Lewis CE, Schreiner PJ, Liu K, Sidney S. Maintaining a high physical activity level over 20 years and weight gain [published correction appears in JAMA. 2011;305:150].JAMA. 2010; 304:2603–2610. doi: 10.1001/jama.2010.1843.CrossrefMedlineGoogle Scholar
  • 45. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men.N Engl J Med. 2011; 364:2392–2404. doi: 10.1056/NEJMoa1014296.CrossrefMedlineGoogle Scholar
  • 46. Du H, Bennett D, Li L, Whitlock G, Guo Y, Collins R, Chen J, Bian Z, Hong LS, Feng S, Chen X, Chen L, Zhou R, Mao E, Peto R, Chen Z; China Kadoorie Biobank Collaborative Group. Physical activity and sedentary leisure time and their associations with BMI, waist circumference, and percentage body fat in 0.5 million adults: the China Kadoorie Biobank study.Am J Clin Nutr. 2013; 97:487–496. doi: 10.3945/ajcn.112.046854.CrossrefMedlineGoogle Scholar
  • 47. Chomistek AK, Manson JE, Stefanick ML, Lu B, Sands-Lincoln M, Going SB, Garcia L, Allison MA, Sims ST, LaMonte MJ, Johnson KC, Eaton CB. Relationship of sedentary behavior and physical activity to incident cardiovascular disease: results from the Women’s Health Initiative.J Am Coll Cardiol. 2013; 61:2346–2354. doi: 10.1016/j.jacc.2013.03.031.CrossrefMedlineGoogle Scholar
  • 48. Carnethon MR. Physical activity and cardiovascular disease: how much is enough?Am J Lifestyle Med. 2009; 3(suppl):44S–49S. doi: 10.1177/1559827609332737.CrossrefMedlineGoogle Scholar
  • 49. Oguma Y, Shinoda-Tagawa T. Physical activity decreases cardiovascular disease risk in women: review and meta-analysis.Am J Prev Med. 2004; 26:407–418. doi: 10.1016/j.amepre.2004.02.007.CrossrefMedlineGoogle Scholar
  • 50. Mannsverk J, Wilsgaard T, Mathiesen EB, Løchen ML, Rasmussen K, Thelle DS, Njølstad I, Hopstock LA, Bønaa KH. Trends in modifiable risk factors are associated with declining incidence of hospitalized and nonhospitalized acute coronary heart disease in a population.Circulation. 2016; 133:74–81. doi: 10.1161/CIRCULATIONAHA.115.016960.LinkGoogle Scholar
  • 51. Chomistek AK, Chiuve SE, Jensen MK, Cook NR, Rimm EB. Vigorous physical activity, mediating biomarkers, and risk of myocardial infarction.Med Sci Sports Exerc. 2011; 43:1884–1890. doi: 10.1249/MSS.0b013e31821b4d0a.CrossrefMedlineGoogle Scholar
  • 52. Lee CD, Folsom AR, Blair SN. Physical activity and stroke risk: a meta-analysis.Stroke. 2003; 34:2475–2481. doi: 10.1161/01.STR.0000091843.02517.9D.LinkGoogle Scholar
  • 53. Armstrong ME, Green J, Reeves GK, Beral V, Cairns BJ; Million Women Study Collaborators. Frequent physical activity may not reduce vascular disease risk as much as moderate activity: large prospective study of women in the United Kingdom.Circulation. 2015; 131:721–729. doi: 10.1161/CIRCULATIONAHA.114.010296.LinkGoogle Scholar
  • 54. Yates T, Haffner SM, Schulte PJ, Thomas L, Huffman KM, Bales CW, Califf RM, Holman RR, McMurray JJ, Bethel MA, Tuomilehto J, Davies MJ, Kraus WE. Association between change in daily ambulatory activity and cardiovascular events in people with impaired glucose tolerance (NAVIGATOR trial): a cohort analysis.Lancet. 2014; 383:1059–1066. doi: 10.1016/S0140-6736(13)62061-9.CrossrefMedlineGoogle Scholar
  • 55. Broekhuizen LN, Boekholdt SM, Arsenault BJ, Despres JP, Stroes ES, Kastelein JJ, Khaw KT, Wareham NJ. Physical activity, metabolic syndrome, and coronary risk: the EPIC-Norfolk prospective population study.Eur J Cardiovasc Prev Rehabil. 2011; 18:209–217. doi: 10.1177/1741826710389397.CrossrefMedlineGoogle Scholar
  • 56. Wilson AM, Sadrzadeh-Rafie AH, Myers J, Assimes T, Nead KT, Higgins M, Gabriel A, Olin J, Cooke JP. Low lifetime recreational activity is a risk factor for peripheral arterial disease.J Vasc Surg. 2011; 54:427–432, 432.e1–4. doi: 10.1016/j.jvs.2011.02.052.CrossrefMedlineGoogle Scholar
  • 57. US Department of Health and Human Services. Step It Up! The Surgeon General’s Call to Action to Promote Walking and Walkable Communities.Washington, DC: US Department of Health and Human Services, Office of the Surgeon General;2015.Google Scholar
  • 58. Laine J, Kuvaja-Köllner V, Pietilä E, Koivuneva M, Valtonen H, Kankaanpää E. Cost-effectiveness of population-level physical activity interventions: a systematic review.Am J Health Promot. 2014; 29:71–80. doi: 10.4278/ajhp.131210-LIT-622.CrossrefMedlineGoogle Scholar
  • 59. Bennett GG, Wolin KY, Puleo EM, Mâsse LC, Atienza AA. Awareness of national physical activity recommendations for health promotion among US adults.Med Sci Sports Exerc. 2009; 41:1849–1855. doi: 10.1249/MSS.0b013e3181a52100.CrossrefMedlineGoogle Scholar
  • 60. Weintraub WS, Daniels SR, Burke LE, Franklin BA, Goff DC, Hayman LL, Lloyd-Jones D, Pandey DK, Sanchez EJ, Schram AP, Whitsel LP; American Heart Association Advocacy Coordinating Committee; Council on Cardiovascular Disease in the Young; Council on the Kidney in Cardiovascular Disease; Council on Epidemiology and Prevention; Council on Cardiovascular Nursing; Council on Arteriosclerosis; Thrombosis and Vascular Biology; Council on Clinical Cardiology, and Stroke Council. Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association.Circulation. 2011; 124:967–990. doi: 10.1161/CIR.0b013e3182285a81.LinkGoogle Scholar
  • 61. Shaya GE, Hung RK, Nasir K, Blumenthal RS, Keteyian SJ, Brawner CA, Qureshi W, Al-Mallah M, Blaha MJ. High cardiorespiratory fitness attenuates risk of short-term mortality after first myocardial infarction: the Henry Ford Hospital Exercise Testing (FIT) Project.Circulation. 2014; 130(suppl 2):A13463. Abstract.LinkGoogle Scholar
  • 62. Ranković G, Milicić B, Savić T, Dindić B, Mancev Z, Pesić G. Effects of physical exercise on inflammatory parameters and risk for repeated acute coronary syndrome in patients with ischemic heart disease.Vojnosanit Pregl. 2009; 66:44–48.CrossrefMedlineGoogle Scholar
  • 63. McDermott MM, Ades P, Guralnik JM, Dyer A, Ferrucci L, Liu K, Nelson M, Lloyd-Jones D, Van Horn L, Garside D, Kibbe M, Domanchuk K, Stein JH, Liao Y, Tao H, Green D, Pearce WH, Schneider JR, McPherson D, Laing ST, McCarthy WJ, Shroff A, Criqui MH. Treadmill exercise and resistance training in patients with peripheral arterial disease with and without intermittent claudication: a randomized controlled trial [published correction appears in JAMA. 2012; 307:1694].JAMA. 2009; 301:165–174. doi: 10.1001/jama.2008.962.CrossrefMedlineGoogle Scholar
  • 64. Lawler PR, Filion KB, Eisenberg MJ. Efficacy of exercise-based cardiac rehabilitation post-myocardial infarction: a systematic review and meta-analysis of randomized controlled trials.Am Heart J. 2011; 162:571–584.e2. doi: 10.1016/j.ahj.2011.07.017.CrossrefMedlineGoogle Scholar
  • 65. Wisløff U, Støylen A, Loennechen JP, Bruvold M, Rognmo Ø, Haram PM, Tjønna AE, Helgerud J, Slørdahl SA, Lee SJ, Videm V, Bye A, Smith GL, Najjar SM, Ellingsen Ø, Skjaerpe T. Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study.Circulation. 2007; 115:3086–3094. doi: 10.1161/CIRCULATIONAHA.106.675041.LinkGoogle Scholar
  • 66. Edelmann F, Gelbrich G, Düngen HD, Fröhling S, Wachter R, Stahrenberg R, Binder L, Töpper A, Lashki DJ, Schwarz S, Herrmann-Lingen C, Löffler M, Hasenfuss G, Halle M, Pieske B. Exercise training improves exercise capacity and diastolic function in patients with heart failure with preserved ejection fraction: results of the Ex-DHF (Exercise training in Diastolic Heart Failure) pilot study.J Am Coll Cardiol. 2011; 58:1780–1791. doi: 10.1016/j.jacc.2011.06.054.CrossrefMedlineGoogle Scholar
  • 67. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L; INTERHEART Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.Lancet. 2004; 364:937–952. doi: 10.1016/S0140-6736(04)17018-9.CrossrefMedlineGoogle Scholar
  • 68. Wen CP, Wu X. Stressing harms of physical inactivity to promote exercise.Lancet. 2012; 380:192–193. doi: 10.1016/S0140-6736(12)60954-4.CrossrefMedlineGoogle Scholar

5. Nutrition

See Tables 5-1 through 5-3 and Charts 5-1 through 5-7

Table 5-1. AHA Dietary Targets and Healthy Diet Score for Defining Cardiovascular Health

AHA TargetConsumption Range for Alternative Healthy Diet Score*Alternative Scoring Range*
Primary dietary metrics
 Fruits and vegetables≥4.5 cups/d0 to ≥4.5 cups/d0–10
 Fish and shellfish2 or more 3.5-oz servings/wk (≥200 g/wk)0 to ≥7 oz/wk0–10
 Sodium≤1500 mg/d≤1500 to >4500 mg/d10–0
 SSBs≤36 fl oz/wk≤36 to >210 fl oz/wk10–0
 Whole grains3 or more 1-oz-equivalent servings/d0 to ≥3 oz/d0–10
Secondary dietary metrics
 Nuts, seeds, and legumes≥4 servings/wk (nuts/seeds: 1 oz; legumes: ½ cup)0 to ≥4 servings/d0–10
 Processed meats2 or fewer 1.75-oz servings/wk (≤100 g/wk)≤3.5 to >17.5 oz/wk10–0
 Saturated fat≤7% energy≤7 to >15 (% energy)10–0
AHA Diet Score (primary)Ideal: 4 or 5 dietary targets (≥80%)Intermediate: 2 or 3 dietary targets (40%–79%)Poor: <2 dietary targets (<40%)Sum of scores for primary metrics0 (worst) to 100 (best)§Ideal: 80–100 Intermediate: 40–79 Poor: <40
AHA Diet Score (secondary)Ideal: 4 or 5 dietary targets (≥80%)Intermediate: 2 or 3 dietary targets (40%–79%)Poor: <2 dietary targets (<40%)Sum of scores for primary and secondary metrics0 (worst) to 100 (best)§ Ideal: 80–100Intermediate: 40–79Poor: <40

AHA indicates American Heart Association; and SSBs, sugar-sweetened beverages.

*Consistent with other dietary pattern scores, the highest score (10) was given for meeting or exceeding the AHA target (eg, at least 4.5 cups of fruits and vegetables per day; no more than 1500 mg/d of sodium), and the lowest score (0) was given for zero intake (protective factors) or for very high intake (harmful factors). The score for each metric was scaled continuously within this range. For harmful factors, the level of high intake that corresponded to a zero score was identified as approximately the 90th percentile distribution of US population intake.

Selected by the AHA based on evidence for likely causal effects on cardiovascular events, diabetes mellitus, or obesity; a general prioritization of food rather than nutrient metrics; consistency with US and AHA dietary guidelines; ability to measure and track these metrics in the US population; and parsimony, that is, the inclusion of as few components as possible that had minimal overlap with each other while at the same time having some overlap with the many other relevant dietary factors that were not included.2 The AHA dietary metrics should be targeted in the context of a healthy diet pattern that is appropriate in energy balance and consistent with a DASH (Dietary Approaches to Stop Hypertension)-type eating plan, including but not limited to these metrics.

Including up to one 8-oz serving per day of 100% fruit juice and up to 0.42 cups/d (3 cups/wk) of starchy vegetables such as potatoes or corn.

§The natural range of the primary AHA Diet Score is 0 to 50 (5 components), and the natural range of the secondary AHA Diet Score is 0 to 80 (8 components). Both scores are then rescaled to a range of 0 to 100 for comparison purposes. The ideal range of the primary AHA Diet Score corresponds to the AHA scoring system of meeting at least 4 of 5 binary dietary targets (≥80%), the intermediate range corresponds to meeting 2 or 3 dietary targets (40%–79%), and the poor range corresponds to meeting <2 dietary targets (<40%). The same ranges are used for the secondary AHA Diet Score for consistency and comparison.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Table 5-2. Dietary Consumption of Selected Foods and Nutrients Related to Cardiometabolic Health Among US Adults ≥20 Years of Age in 2011 to 2012

NH White MalesNH White FemalesNH Black MalesNH Black FemalesMexican American MalesMexican American Females
Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*
Foods
 Whole grains, servings/d1.1±1.09.01.1±0.87.30.9±1.27.70.8±0.74.80.6±0.53.90.8±0.76.1
 Total fruit, servings/d1.5±0.89.21.6±1.69.21±1.36.91.2±1.36.81.4±1.46.61.4±1.05.7
 Total fruits including 100% juices, servings/d2.0±1.715.32.2±1.9142±1.815.82±1.3141.9±1.516.42.1±1.514.5
 Nonstarchy vegetables, servings/d2.3±2.07.92.7±1.612.41.7±1.04.32.0±1.35.92.0±0.73.22.5±0.57.7
 Starchy vegetables, servings/d0.9±0.3NA0.8±0.3NA0.9±0.5NA0.9±0.4NA0.7±0.2NA0.7±0.3NA
 Legumes, servings/wk1.6±1.422.11.1±0.714.41.1±1.112.81.1±1.213.34.7±4.047.42.3±2.624.8
 Fish and shellfish, servings/wk1.1±1.116.71.0±0.916.51.7±2.723.31.9±0.926.71.7±1.719.70.8±0.114.6
 Nuts and seeds, servings/wk3.5±5.923.73.6±3.324.02.2±3.416.22.8±8.616.02.4±1.612.42.6±3.811.8
 Unprocessed red meat, servings/wk3.6±1.6NA2.5±1.3NA3.1±0.8NA2.6±1.3NA4.8±0.6NA3.2±1.5NA
 Processed meats, servings/wk2.4±1.457.41.6±1.069.52.5±1.5551.8±0.665.61.7±1.168.91.1±0.578.9
 Sugar-sweetened beverages, servings/wk9.1±11.050.96.8±8.365.411.1±8.932.311±8.033.911.7±7.732.78.4±6.642.7
 Sweets and bakery desserts, servings/wk6.4±3.633.17.3±4.131.85.8±4.042.76.4±3.037.43.9±2.554.05.8±0.638.5
Nutrients
 Total calories, kcal/d2482±551NA1789±421NA2354±682NA1853±514NA2576±506NA1848±467NA
 EPA/DHA, g/d0.083±0.0537.40.074±0.0546.40.101±0.089.90.110±0.1013.70.117±0.07510.70.058±0.0374.0
 ALA, g/d1.58±0.3541.91.75±0.3885.01.51±0.3735.51.72±0.4383.11.57±0.4641.21.66±0.2281.9
 n-6 PUFA, % energy7.0±1.4NA7.3±1.5NA7.2±1.2NA7.8±1.6NA6.8±1.4NA7.0±0.5NA
 Saturated fat, % energy11.0±1.836.310.7±1.843.110.2±1.847.110.3±1.546.310.7±1.442.010.9±2.446.9
 Dietary cholesterol, mg/d281±17767.7256±18071.4322±19358.9299±17663.1343±21651.6290±22168.7
 Total fat, % energy33.7±5.352.733.2±4.658.833.2±4.858.333.9±4.051.133.1±3.863.933.5±3.656.6
 Carbohydrate, % energy47.9±7.7NA49.8±7.2NA48.1±7.2NA50±4.9NA49.1±4.4NA50.5±5.7NA
 Dietary fiber, g/d17.2±6.37.318.4±6.210.314.3±4.63.715.2±4.65.319.3±5.913.418.9±5.814.2
 Sodium, g/d3.4±0.496.73.4±0.626.43.4±0.436.43.5±0.638.43.4±0.488.43.5±0.638.4

Values for average consumption are mean±SD. Data are from the National Health and Nutrition Examination Survey 2011 to 2012, derived from two 24-hour dietary recalls per person, with population standard deviations adjusted for within-person vs between-person variation (analyses courtesy of Dr Colin Rehm, Tufts University). All values are energy adjusted by individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d. Compared with 2014 and earlier American Heart Association Statistical Updates, the calculations for foods now use the US Department of Agriculture’s (USDA) Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats) in mixed dishes; in addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of total carbohydrate to fiber. ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; NH, non-Hispanic; and n-6-PUFA, ω-6-polyunsaturated fatty acid.

*All intakes and guidelines adjusted to a 2000 kcal/d diet. Servings are defined as follows: whole grains, 1-oz equivalents; fruits and vegetables, ½-cup equivalents; legumes, ½ cup; fish/shellfish, 3.5 oz or 100 g; nuts and seeds, 1 oz; unprocessed red or processed meat, 3.5 oz or 100 g; sugar-sweetened beverages, 8 fl oz; sweets and bakery desserts, 50 g. Guidelines defined as follows: whole grains, 3 or more 1-oz equivalent (eg, 21 g whole wheat bread, 82 g cooked brown rice, 31 g Cheerios) servings/d54; fruits, 2 or more cups/d54; nonstarchy vegetables, 2½ or more cups/d; legumes, 1.5 or more cups/wk54; fish or shellfish, 2 or more 100-g (3.5-oz) servings/wk54; nuts and seeds, 4 or more 1-oz servings/wk54; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings/wk (¼ of discretionary calories); sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding 100% fruit juices), ≤36 oz/wk (≈¼ of discretionary calories); sweets and bakery desserts, 2.5 or fewer 50-g servings/wk (≈¼ of discretionary calories); EPA/DHA, ≥0.250 g/d54; ALA, ≥1.6/1.1 g/d (males/females)54; saturated fat, <10% energy; dietary cholesterol, <300 mg/d54; total fat, 20% to 35% energy; dietary fiber, ≥28 g/d54; and sodium, <2.3 g/d.54 No dietary targets are listed for starchy vegetables and unprocessed red meats because of their positive association with long-term weight gain and their positive or uncertain relation with diabetes mellitus and cardiovascular disease.

Including white potatoes (chips, fries, mashed, baked, roasted, mixed dishes), corn, plantain, green peas, etc. Sweet potatoes, pumpkin, and squash are considered red-orange vegetables by the USDA and are included in nonstarchy vegetables.

Table 5-3. Dietary Consumption of Selected Foods and Nutrients Related to Cardiometabolic Health Among US Children and Teenagers in 2011 to 2012

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% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*Average Consumption% Meeting Guidelines*
Foods
 Whole grains, servings/d0.9±0.42.20.9±0.52.80.8±0.43.80.7±0.42.50.9±0.84.70.9±0.34.1
 Fruits, servings/d1.7±1.18.31.9±1.413.71.4±0.98.31.4±0.83.31.3±1.16.10.9±0.84.7
 Fruits including 100% fruit juice, servings/d2.7±1.322.32.7±1.523.12.1±1.216.32.0±1.116.52.0±0.414.31.6±1.211.2
 Nonstarchy vegetables, servings/d1.1±0.80.91.1±0.70.01.2±0.61.31.2±0.71.11.4±0.40.51.5±0.11.2
 Starchy vegetables, servings/d 0.6±0.2NA0.7±0.2NA0.7±0.3NA0.7±0.3NA0.8±0.6NA0.7±0.3NA
 Legumes, servings/wk1.2±1.714.20.7±0.89.21.0±1.013.91.2±1.516.61.3±1.415.40.9±0.49.6
 Fish/shellfish, servings/wk1.0±1.014.30.5±0.77.20.3±0.36.70.4±0.37.70.6±1.310.80.9±0.611.9
 Nuts and seeds, servings/wk2.3±4.615.42.1±1.513.31.1±0.86.41.3±0.210.91.8±1.317.42.7±2.012.9
 Unprocessed red meats, servings/wk1.7±0.7NA1.6±1.1NA2.3±1.5NA1.9±1.1NA3.6±1.8NA2.5±1.5
 Processed meats, servings/wk2.0±0.554.11.6±1.063.92.0±0.564.41.6±0.270.32.3±1.557.71.4±1.075.4
 Sugar-sweetened beverages, servings/wk7.7±6.244.26.0±3.852.511.6±5.320.79.7±7.937.812.4±5.824.514±6.032.8
 Sweets and bakery desserts, servings/wk8.0±1.827.07.7±2.815.58.3±4.529.16.6±2.531.24.7±3.542.06.0±3.538.7
Nutrients
 Total calories, kcal/d2048±457NA1767±238NA2145±497NA1745±343NA2444±664NA1838±287NA
 EPA/DHA, g/d0.063±0.0525.70.041±0.0242.10.034±0.0281.80.034±0.0252.10.065±0.0926.30.055±0.0455.2
 ALA, g/d1.39±0.2126.41.41±0.1776.71.36±0.1129.01.45±0.1672.71.45±0.1633.91.54±0.1776.5
 n-6 PUFA, % energy6.4±1.2NA6.7±1.2NA6.5±1.0NA6.7±1.2NA6.8±1.3NA7.1±0.8NA
 Saturated fat, % energy11.7±1.623.711.3±0.830.411.0±1.631.011.4±2.230.010.8±1.340.310.9±0.639.9
 Dietary cholesterol, mg/d215±14480.1212±14279.8242±17577.7226±14978.9269±15267.9269±20074.1
 Total fat, % energy33±4.166.333±2.763.531.9±3.169.032.6±0.660.632.5±3.857.832.6±2.966.1
 Carbohydrate, % energy53.9±2.7NA53.9±1.5NA54.7±4.0NA54±3.8NA51.8±3.0NA53.2±3.3NA
 Dietary fiber, g/d15.1±2.81.815.9±2.91.014.9±3.32.015.4±2.81.215.7±3.92.814.4±3.41.0
 Sodium, g/d3.2±0.417.23.1±0.4011.13.3±0.284.43.4±0.497.93.5±0.323.03.5±0.522.4

Values for average consumption are mean±SD. Data are from the National Health and Nutrition Examination Survey 2011 to 2012, derived from two 24-hour dietary recalls per person, with population standard deviations adjusted for within-person vs between-person variation (analyses courtesy of Dr Colin Rehm, Tufts University). All values are energy adjusted by individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d. Compared with 2014 and earlier American Heart Association Statistical Updates, the calculations for foods now use the US Department of Agriculture’s (USDA) Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats) in mixed dishes; in addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of total carbohydrate to fiber. ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NA, not available; and n-6-PUFA, ω-6-polyunsaturated fatty acid.

*All intakes and guidelines adjusted to a 2000 kcal/d diet. Servings defined as follows: whole grains, 1-oz equivalents; fruits and vegetables, ½-cup equivalents; legumes, ½ cup; fish/shellfish, 3.5 oz or 100 g; nuts and seeds, 1 oz; unprocessed red or processed meat, 3.5 oz or 100 g; sugar-sweetened beverages, 8 fl oz; sweets and bakery desserts, 50 g. Guidelines defined as follows: whole grains, 3 or more 1-oz equivalent (eg, 21 g whole wheat bread, 82 g cooked brown rice, 31 g Cheerios) servings/d54; fruits, 2 or more cups/d54; non-starchy vegetables, 2½ or more cups/d54; legumes, 1.5 or more cups/wk54; fish or shellfish, 2 or more 100-g (3.5-oz) servings/wk54; nuts and seeds, 4 or more 1-oz servings/wk54; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings/wk (¼ of discretionary calories)54; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding 100% fruit juices), ≤36 oz/wk (≈¼ of discretionary calories)14,54; sweets and bakery desserts, 2.5 or fewer 50-g servings/wk (≈¼ of discretionary calories)14,54; EPA/DHA, ≥0.250 g/d54; ALA, ≥1.6/1.1 g/d (males/females)54; saturated fat, <10% energy54; dietary cholesterol, <300 mg/d54; total fat, 20% to 35% energy54; dietary fiber, ≥28/d54; and sodium, <2.3 g/d.54 No dietary targets are listed for starchy vegetables and unprocessed red meats because of their positive association with long-term weight gain and their positive or uncertain relation with diabetes mellitus and cardiovascular disease.

Including white potatoes (chips, fries, mashed, baked, roasted, mixed dishes), corn, plantain, green peas, etc. Sweet potatoes, pumpkin, and squash are considered red-orange vegetables by the USDA and are included in nonstarchy vegetables.

Chart 5-1.

Chart 5-1. Trends in mean healthy diet scores for children and adults, NHANES 2003 to 2004 through 2011 to 2012.

Primary metrics include fruits/vegetables, whole grains, fish, sugar-sweetened beverages, and sodium. Secondary metrics include nuts, seeds, and legumes; processed meats; and saturated fats. Components of poor, intermediate, and ideal diet are defined in Table 5-1. Mean healthy diet scores based on the alternative scoring ranges described in Table 5-1.

NHANES indicates National Health and Nutrition Examination Survey.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Chart 5-2.

Chart 5-2. Healthy diet targets in children (5–19 years old) by survey year: NHANES 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, and 2011 to 2012.

Components of poor, intermediate, and ideal diet are defined in Table 5-1. Percentages based on the alternative scoring ranges described in Table 5-1.

NHANES indicates National Health and Nutrition Examination Survey.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Chart 5-3.

Chart 5-3. Healthy diet targets in adults (≥20 years of age) by survey year: NHANES 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, and 2011 to 2012.

Components of poor, intermediate, and ideal diet are defined in Table 5-1. Percentages based on the alternative scoring ranges described in Table 5-1.

NHANES indicates National Health and Nutrition Examination Survey.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Chart 5-4.

Chart 5-4. Trends in AHA-defined healthy diet score components for children (5–19 years old) by survey year: NHANES 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, and 2011 to 2012.

Unscaled dietary score (maximum=50). Mean healthy diet score components based on the alternative scoring ranges described in Table 5-1.

AHA indicates American Heart Association; max., maximum; and NHANES, National Health and Nutrition Examination Survey.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Chart 5-5.

Chart 5-5. Trends in AHA healthy diet score components for adults (<20 years of age) by survey year: NHANES 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, and 2011 to 2012.

Unscaled dietary score (maximum=50). Mean healthy diet score components based on the scoring ranges described in Table 5-1.

AHA indicates American Heart Association; and NHANES, National Health and Nutrition Examination Survey.

Sources: My Life Check: Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.3

Chart 5-6.

Chart 5-6. Percentage of sodium from dietary sources in the United States, 2005 to 2006.

Source: Applied Research Program, National Cancer Institute.4

Chart 5-7.

Chart 5-7. Total US food expenditures away from home and at home, 1977 and 2007.

Data derived from Davis et al.10

This chapter of the Update highlights national dietary habits, 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 and Trends in the AHA 2020 Healthy Diet Metrics

(See Table 5-1 and Charts 5-1 through 5-5)

  • The AHA’s 2020 Impact Goals include a new priority of improving cardiovascular health.1 The definition of cardiovascular health includes a healthy diet pattern, characterized by 5 primary and 3 secondary metrics (Table 5-1), that should be consumed within the context of a healthy dietary pattern that is appropriate in energy balance and consistent with a DASH-type eating plan.1

  • The AHA scoring system for ideal, intermediate, and poor diet patterns uses a binary-based scoring system, which awards 1 point for meeting the ideal target for each metric and 0 points otherwise.2 For better consistency with other dietary pattern scores such as DASH, an alternative continuous scoring system has been developed to measure small improvements over time towards the AHA ideal target levels (Table 5-1). The dietary targets remain the same, and progress toward each of these targets is assessed by use of a more granular range of 1 to 10 (rather than 0 to 1).

  • On the basis of the alternative scoring system, between 2003 to 2004 and 2011 to 2012 in the United States, the mean AHA healthy diet score improved in both children and adults (Chart 5-1). The prevalence of an ideal healthy diet score (>80) increased from 0.2% to 0.6% in children (Chart 5-2) and from 0.7% to 1.5% in adults (Chart 5-3). The prevalence of an intermediate healthy diet score (40–79) increased from 30.6% to 44.7% in children and from 49.0% to 57.5% in adults.3

    — These improvements were largely attributable to increased whole grain consumption and decreased sugar-sweetened beverage consumption in both children and adults (Charts 5-4 and 5-5).3 No major trends were evident in adults in progress toward the targets for consumption of fish or sodium.

  • Other significant changes between 1999 and 2012 included national increases in nuts, seeds, and legumes (0.26 servings/d) and whole fruit (0.15 servings/d) and decreases in 100% fruit juice (0.11 servings/d) and white potatoes (0.07 servings/d (95% CI).3

  • Although AHA healthy diet scores tended to improve in all race/ethnicity, income, and education levels, many disparities present in earlier years widened over time, with generally smaller improvements seen in minority groups and those with lower income or education.3 For example, among Americans with the highest family income (income-to-poverty ratio ≥3.0), the proportion with a poor diet decreased from 50.5% to 35.7%, whereas among those with lowest family income (income-to-poverty ratio <1.3), the proportion with a poor diet decreased from 67.8% to only 60.6%.

Dietary Habits in the United States: Current Intakes

Foods and Nutrients
Adults

(See Table 5-2 and Chart 5-6)

The dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-2 according to sex and race or ethnic subgroups.

  • Average daily energy intake among US adults was ≈2500 calories in males and 1800 calories in females.

  • Average consumption of whole grains was 1.1 servings per day by non-Hispanic white males and females, 0.8 to 0.9 servings per day by non-Hispanic black males and females, and 0.6 to 0.8 servings by Mexican American males and females. For each of these groups, fewer than 10% of adults met guidelines of ≥3 servings per day.

  • Average fruit consumption ranged from 1.0 to 1.6 servings per day in these sex and race or ethnic subgroups: ≈9% of non-Hispanic whites, 7% of non-Hispanic blacks, and 6% 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 nearly doubled in non-Hispanic whites, doubled in non-Hispanic blacks, and more than doubled in Mexican Americans.

  • Average nonstarchy vegetable consumption ranged from 1.7 to 2.7 servings per day. Across all race/ethnic subgroups, non-Hispanic white females were the only group meeting the target of consuming ≥2.5 cups per day.

  • Average consumption of fish and shellfish was lowest among Mexican American females and white females (0.8 and 1.0 servings per week, respectively) and highest among non-Hispanic black females and non-Hispanic black and Mexican American males (1.9 and 1.7 servings per week, respectively). Generally, only 15% to 27% of adults in each sex and race or ethnic subgroup consumed at least 2 servings per week.

  • Average weekly consumption of nuts and seeds was ≈3.5 servings among non-Hispanic whites and 2.5 servings among non-Hispanic blacks and Mexican Americans. Approximately 1 in 4 whites, 1 in 6 non-Hispanic blacks, and 1 in 8 Mexican Americans met guidelines of ≥4 servings per week.

  • Average consumption of unprocessed red meats was higher in males than in females, up to 4.8 servings per week in Mexican American males.

  • Average consumption of processed meats was lowest among Mexican American females (1.1 servings per week) and highest among non-Hispanic black and non-Hispanic white males (≈2.5 servings per week). Between 57% (non-Hispanic white males) and 79% (Mexican American females) of adults consumed 2 or fewer servings per week.

  • Average consumption of sugar-sweetened beverages ranged from 6.8 servings per week among non-Hispanic white females to nearly 12 servings per week among Mexican American males. Females generally consumed less than males. Some adults, 33% (Mexican American males) to 65% (non-Hispanic white females), consumed <36 oz/wk.

  • Average consumption of sweets and bakery desserts ranged from 3.9 servings per week (Mexican American males) to >7 servings per week (white females). Approximately 1 in 3 adults (1 in 2 Mexican American males) consumed no more than 2.5 servings per week.

  • Average consumption of eicosapentaenoic acid and docosahexaenoic acid ranged from 0.058 to 0.117 g/d in each sex and race or ethnic subgroup. Fewer than 8% of non-Hispanic whites, 14% of non-Hispanic blacks, and 11% of Mexican Americans consumed ≥0.250 g/d.

  • One third to one half of adults in each sex and race or ethnic subgroup consumed <10% of total calories from saturated fat, and approximately one half to two thirds consumed <300 mg of dietary cholesterol per day.

  • Only ≈7% to 10% of non-Hispanic whites, 4% to 5% of blacks, and 13% to 14% of Mexican Americans consumed ≥28 g of dietary fiber per day.

  • Only approximately 6% to 8% of adults in each age and race or ethnic subgroup consumed less than the recommended 2.3 g of sodium per day. Sodium is widespread in the US food supply, with diverse sources (Chart 5-6).4

Foods and Nutrients
Children and Teenagers

(See Table 5-3)

The dietary consumption by US children and teenagers of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-3:

  • Average whole grain consumption was low, <1 serving per day in all age and sex groups, with <5% 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: 1.7 to 1.9 servings per day in younger boys and girls (5–9 years of age), 1.4 servings per day in adolescent boys and girls (10–14 years of age), and 0.9 to 1.3 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: ≈8% to 14% in those 5 to 9 years of age, 3% to 8% in those 10 to 14 years of age, and 5% to 6% in those 15 to 19 years of age. When 100% fruit juices were included, the number of servings consumed increased by ≈50%, and proportions consuming ≥2 cups per day increased to nearly 25% of those 5 to 9 years of age, 20% of those 10 to 14 years, and 15% of those 15 to 19 years of age.

  • Average nonstarchy vegetable consumption was low, ranging from 1.1 to 1.5 servings per day, with <1.5% 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 1.0 servings per week in all age and sex groups. Among all ages, only 7% to 14% of youths consumed ≥2 servings per week.

  • Average consumption of nuts, seeds, and beans ranged from 1.1 to 2.7 servings per week among different age and sex groups, and generally fewer than 15% of children in different age and sex subgroups consumed ≥4 servings per week.

  • Average consumption of unprocessed red meats was higher in boys than in girls and increased with age, up to 3.6 and 2.5 servings per week in 15- to 19-year-old boys and girls, respectively.

  • Average consumption of processed meats ranged from 1.4 to 2.3 servings per week, and the majority of children consumed no more than 2 servings per week of processed meats.

  • Average consumption of sugar-sweetened beverages was higher in boys than in girls in the 5- to 9-year-old (7.7±6.2 versus 6.0±3.8 servings per week) and 10- to 14-year-old (11.6±5.3 versus 9.7±7.9 servings per week) groups, but higher in girls than in boys in the 15- to 19-year-old group (14±6.0 versus 12.4±5.8 servings per week). Only about half of children 5 to 9 years of age and one quarter of boys 15 to 19 years of age consumed <4.5 servings per week.

  • Average consumption of sweets and bakery desserts was higher among 5- to 9-year-old and 10- to 14-year-old boys and girls and modestly lower (4.7 to 6 servings per week) among 15- to 19-year-olds. A minority of children in all age and sex subgroups consumed no more than 2.5 servings per week.

  • Average consumption of eicosapentaenoic acid and docosahexaenoic acid was low, ranging from 0.034 to 0.065 g/d in boys and girls in all age groups. Fewer than 7% of children and teenagers at any age consumed ≥250 mg/d.

  • Average consumption of saturated fat was ≈11% of calories in boys and girls in all age groups, and average consumption of dietary cholesterol ranged from ≈210 to 270 mg/d, increasing with age. Approximately 25% to 40% of youths consumed <10% energy from saturated fat, and ≈70% to 80% consumed <300 mg of dietary cholesterol per day.

  • Average consumption of dietary fiber ranged from ≈14 to 16 g/d. Less than 3% of children in all age and sex subgroups consumed ≥28 g/d.

  • Average consumption of sodium ranged from 3.1 to 3.5 g/d. Only between 2% and 11% of children in different age and sex subgroups consumed <2.3 g/d.

  • Among children and teenagers, average daily caloric intake is higher in boys than in girls and increases with age in boys.

Impact on US Mortality

  • One report used consistent and comparable risk assessment methods and nationally representative data to estimate the impact of all major modifiable risk factors on mortality and morbidity in the United States in 1990 and in 2010.5 Among 17 leading risk factors in the United States in 2010, suboptimal dietary habits were the leading cause of both mortality and DALYs lost. 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.6

Cost

(See Chart 5-7)

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%.7

  • A meta-analysis of price comparisons of healthier versus unhealthier diet patterns found that the healthiest diet patterns cost, on average, approximately $1.50 more per person per day to consume.8

  • In an assessment of snacks served at YMCA afterschool programs from 2006 to 2008, healthful snacks were ≈50% more expensive ($0.26 per snack) than less healthful snacks.9 Higher snack costs were driven by serving fruit juice compared with water; serving refined grains without trans fat compared with refined grains with trans fats; and serving fruit and canned or frozen vegetables. Serving fresh vegetables (mostly carrots or celery) or whole grains did not alter price.

  • 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.7

  • 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 2011.10

  • 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 2011.7

  • 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.7

  • 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.11

  • Each year, more than $33 billion in medical costs and $9 billion in lost productivity resulting from HD, cancer, stroke, and DM are attributed to poor nutrition.1215

  • Two separate cost-effectiveness analyses estimated that population reductions in dietary salt would not only be cost-effective, but actually cost-saving.16,17 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 92 000.17 If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 to $24 billion annually.17 Such an intervention would be more cost-effective than using medications to lower BP in all people with hypertension.

Secular Trends

Trends in Dietary Patterns
  • In addition to individual foods and nutrients, overall dietary patterns can be very useful to assess diet quality.18 Different dietary patterns have been defined, such as Mediterranean, DASH-type, Healthy Eating Index–2010, Alternate Healthy Eating Index, Western, prudent, and vegetarian patterns. The original DASH diet was low fat; a higher-monounsaturated-fat DASH-type diet is even more healthful and similar to a traditional Mediterranean dietary pattern.19,20 The Healthy Eating Index–2010, which reflects compliance with the 2010 US Dietary Guidelines, exhibits a wide distribution among the US population, with a 5th percentile score of 31.7 and a 95th percentile score of 70.4 based on NHANES 2003 to 2004 data (theoretical maximum=100).21 Average diet quality is worse in males (score=49.8) than in females (52.7), in younger adults (45.4) than in older adults (56.1), and in smokers (45.7) than in nonsmokers (53.3).

  • Between 1999 and 2010, the average Alternate Healthy Eating Index–2010 score of US adults improved from 39.9 to 46.8.22 This was related to reduced intake of trans fat (accounting for more than half of the improvement), sugar-sweetened beverages, and fruit juice, as well as an increased intake of whole fruit, whole grains, polyunsaturated fatty acids, and nuts and legumes. Adults with greater family income and education had higher scores, and the gap between low and high socioeconomic status widened over time, from 3.9 points in 1999 to 2000 to 7.8 points in 2009 to 2010.

  • Worldwide, 2 separate, relatively uncorrelated dietary patterns can be characterized, 1 by greater intakes of health-promoting foods (eg, fruits, vegetables, nuts, fish), and 1 by lower intakes of less optimal foods (eg, processed meats, sugar-sweetened beverages).23 In 2010, compared with low-income nations, high-income nations had better diet patterns based on healthful foods, but substantially worse diet patterns based on unhealthful foods. Between 1990 and 2010, both types of dietary patterns improved in high-income Western countries but worsened or did not improve in low-income countries in Africa and Asia. Middle-income countries showed the largest improvements in dietary patterns based on healthful foods, but the largest deteriorations in dietary patterns based on unhealthful foods. Overall, global consumption of healthy foods improved, but was outpaced by increased intake of unhealthy foods in most world regions.

Trends in 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 males and females reporting such use).24 It has been shown that most supplements are taken daily and for ≥2 years.25 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.24,25 Previous research also suggests that supplement users have higher intakes of most vitamins and minerals from their food choices alone than nonusers.26,27 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%).24

  • 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).24,28 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 females (44.9%) than in males (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%).29

  • 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.30

  • 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),3133 but several other trials of fish oil have not shown significant effects on CVD risk.34 A meta-analysis of all RCTs demonstrated a significant reduction for cardiac mortality but no statistically significant effects on other CVD end points.35

Trends in Energy Balance and Adiposity
  • Energy balance, or consumption of total calories appropriate for needs, is determined by the balance of average calories consumed versus expended. This balance depends on multiple factors, including calories consumed, PA, body size, age, sex, and underlying basal metabolic rate. Thus, one person could consume relatively high calories but have negative energy balance (as a result of even greater calories expended), whereas another might consume relatively few calories but have positive energy balance (because of low calories expended). Given such variation, the most practical and reasonable method to assess energy balance in populations is to assess changes in weight over time. Growing evidence indicates that calorie for calorie, certain foods may be more highly obesogenic; others, modestly obesogenic; others, relatively neutral; and still others, actually protective against weight gain when their consumption is increased. These varying effects appear to relate to divergent influences on complex physiological pathways of long-term weight regulation, including those related to hunger, satiety, brain reward, hepatic de novo lipogenesis, adipocyte function, visceral adiposity, interactions with the gut inflammasome and microbiome, and energy expenditure. This evidence is detailed below.

  • The US overweight and obesity epidemic continues to be a public health concern. The age-adjusted prevalence of obesity in 2013 to 2014 was 35% among males and 40.4% among females. For females, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014, whereas there were no significant trends for males.36

  • In a nationally representative study between 1988 and 2014 of US children and adolescents aged 2 to 19 years, the prevalence of obesity in 2011 to 2014 was 17.0% and that of extreme obesity was 5.8%. By age group, the prevalence varied significantly. For children 2 to 5 years old, obesity prevalence was ≈9%; for children 6 to 11 years old, it was 17.5%; and for adolescents 12 to 19 years of age, it was 20.5%. For children 6 to 11 years old, the prevalence rose but leveled off in 2007 to 2008, but for adolescents (12 to 19 years old), the upward trajectory has continued.37

  • Until 1980, total energy intake remained relatively constant.38,39 However, data from NHANES indicate that between 1971 and 2004, average total energy intake among US adults increased by 22% in females (from 1542 to 1886 kcal/d) and by 10% in males (from 2450 to 2693 kcal/d).40 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).41 More recent data show that energy intake appears to have relatively stabilized among US adults between 1999 and 2008.42

  • Another analysis of national data estimated that increases in energy intake between 1980 and 1997 were primarily attributable to increases in dietary carbohydrates.43 Specifically, nearly 80% of the increase in total energy came from carbohydrates, 12% from protein, and only 8% from fat. These increases in calories were primarily attributable to greater refined carbohydrate intake, particularly of starches, refined grains, and sugars.

  • Other specific changes related to increased caloric intake in the United States since 1980 include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, and commercially prepared (especially fast-food) meals.41,4449 In more recent years, intakes of sugar-sweetened beverages have been decreasing nationally.22,50

  • Between 1977 and 1996, the average portion sizes for many foods increased at fast-food outlets, other restaurants, and home. On the basis of 1 study, these included a 33% increase in the average portion of Mexican food (from 408 to 541 calories), a 34% increase in the average portion of cheeseburgers (from 397 to 533 calories), a 36% increase in the average portion of french fries (from 188 to 256 calories), and a 70% increase in the average portion of salty snacks such as crackers, potato chips, pretzels, puffed rice cakes, and popcorn (from 132 to 225 calories).41

  • In 1 analysis, among U.S. 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 to 1994 and 1999 to 2002.51

  • In a quantitative analysis using various US surveys between 1977 and 2010, the relations of national changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.38,39 Changes in energy density were not consistently linked to energy intake over time, whereas increases in both portion size and number of eating occasions were linked to greater energy intake.

  • Among US children 2 to 18 years of age, increases in energy intake between 1977 and 2006 (179 kcal/d) were entirely attributable to substantial increases in energy eaten away from home (255 kcal/d).52 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 eaten away from home for all age groups.

  • A county-level investigation based on BRFSS and NHANES data found that prevalence of sufficient PA in the United States actually increased from 2001 to 2009, but that this was matched by increases in obesity in almost all counties during the same time period, with low correlation between level of PA and obesity in US counties.53

Trends in Specific Dietary Habits

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

Macronutrients
  • Starting in 1977 and continuing until the most recent dietary guidelines revision in 2015, a major focus of US dietary guidelines was reduction of dietary fats.54 Between 1977 and 1999, average total fat consumption declined as a percentage of calories from 36.9% to 33.4% in males and from 36.1% to 33.8% in females.55 After this significant decline, total fat consumption remained relatively stable among US adults from 1999 to 2008.42

  • Dietary guidelines during this time also emphasized eating a variety of foods with plenty of whole grain products, fruits, and vegetables while limiting sugar, fat, and other selected nutrients.10,54 Consistent with this message, from 1971 to 2004, total carbohydrate intake increased from 42.4% to 48.2% of calories in males and from 45.4% to 50.6% of calories in females.55 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.55,56 In more recent years, these trends have stabilized, with relatively stable intakes to slight declines in carbohydrate intake (expressed as percentage of energy) among US children and adults from 1999 to 2010, with corresponding slight increases in protein intake.57 However, intakes of whole grains, fruits, and vegetables have remained consistently stable and below levels recommended by the US Dietary Guidelines.

Sugar-Sweetened Beverages

(See Charts 5-4 and 5-6)

  • 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.48 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.38

  • 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.41

  • 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 youth compared with white youth.49

  • Between 2003 to 2004 and 2011 to 2012, there was significant progress toward success for both US adults and children in achieving the AHA 2020 dietary target of no more than 36 fl oz of sugar-sweetened beverages per week (Charts 5-4 and 5-5).

  • In contrast, between 1999 and 2010, sugar-sweetened beverage intake decreased among both youth and adults in the United States, consistent with increased attention to their importance as a cause of obesity. In 2009 to 2010, youth and adults consumed a daily average of 155 and 151 kcal from sugar-sweetened beverages, respectively, a decrease from 1999 to 2000 of 68 and 45 kcal/d, respectively.58 This reduction parallels the plateau of the obesity epidemic in US youth.59

  • Globally, between 1999 and 2010, sugar-sweetened beverage intake increased in several countries.60 Among adults, mean global intake was highest in males aged 20 to 39 years, at 1.04 8-oz servings per day. In comparison, globally, females >60 years of age had the lowest mean consumption at 0.34 servings per day. Sugar-sweetened beverage consumption was highest in the Caribbean, with adults consuming on average 2 servings per day, and lowest in East Asia, at 0.20 servings per day. Adults in the United States had the 26th-highest consumption of 187 countries.

Selected Foods

(See Charts 5-4 through 5-5)

  • Between 1994 and 2005, the average consumption of fruits and vegetables declined slightly, from a total of 3.4 to 3.2 times per day. The proportions of males and females consuming combined fruits and vegetables ≥5 times per day were low (≈20% and 29%, respectively) and did not change during this period.61

  • Between 2003 to 2004 and 2011 to 2012, there was no major change in the success of US adults or children in achieving the AHA 2020 dietary targets of 4.5 cups of total fruits and vegetables per day or 2 servings of fish per week. During this same period, there was significant progress toward success of both US adults and children in achieving the AHA 2020 dietary target of at least three 1-oz servings of whole grains per day (Charts 5-4 and 5-5).3

Sodium

(See Charts 5-4 through 5-5)

  • Although inconsistent methodology over time limits the ability to make strong conclusions, the current available data suggest that US sodium intake has remained relatively stable between 1957 and 2003.62

  • Worldwide in 2010, mean sodium intake among adults was 3950 mg/d, which corresponds to salt intake of ≈10 g/d.63 Across world regions, mean sodium intakes were highest in Central Asia (5510 mg/d) and lowest in Eastern sub-Saharan Africa (2180 mg/d). Across countries, the lowest observed mean national intakes were ≈1500 mg/d. Between 1990 and 2010, global mean sodium intake appeared to remain relatively stable, although data on trends in many world regions were suboptimal.63

Cardiovascular Health Impact

Dietary habits affect multiple cardiovascular risk factors, including both established risk factors (SBP, DBP, LDL-C levels, HDL-C 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).

Cardiovascular and Metabolic Risk
Overweight and Obesity
  • For short-term (up to 1–2 years) weight loss among overweight and obese individuals, the macronutrient composition of the diet has much less influence than compliance with the selected diet.64

  • In ad libitum (not energy restricted) diets, a low-carbohydrate (high fat) diet demonstrated better weight loss and reduced fat mass than a low-fat (high carbohydrate) diet at 1 year.65

  • In ad libitum (not energy restricted) diets, intake of dietary sugars was positively linked to weight gain.66 However, isoenergetic exchange of dietary sugars with other carbohydrates had no relationship with body weight,66 which suggests that all refined carbohydrates (complex starches and simple sugars) might be similarly obesogenic.

  • In pooled analyses across 3 prospective cohort studies of US males and females, increased glycemic index and glycemic load were independently associated with greater weight gain over time.67

  • Across types of foods, energy density (total calories per gram of food) is not consistently linked with weight gain or obesity. For example, nuts have relatively high energy density and are inversely linked to weight gain, and cheese has high energy density and appears relatively neutral, whereas sugar-sweetened beverages have low energy density and increase obesity.67 National changes in energy density over time are not consistently linked to changes in energy intake.38

  • In analyses of >120 000 US males and females 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.67,68 Foods and beverages most positively linked to weight gain included high-glycemic carbohydrates such as 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, legumes, fish, and yogurt, was linked to relative weight loss over time. These findings suggest that attention to food-based dietary quality, not simply counting total calories, is crucial for long-term weight homeostasis.68

  • In both adults and children, intake of sugar-sweetened beverages has been linked to weight gain and obesity.69 Randomized trials in children demonstrate reductions in obesity when sugar-sweetened beverages are replaced with noncaloric beverages.69

  • Diet quality influences activation of brain reward centers, such as the nucleus accumbens. Isocaloric meals richer in rapidly digestible carbohydrate increased hunger and stimulated brain regions associated with reward and craving compared with isocaloric meals that had identical macronutrient content, palatability, and sweetness but were lower in rapidly digestible carbohydrate.70

  • Dietary factors that stimulate hepatic de novo lipogenesis, such as rapidly digestible grains, starches, and sugars, as well as trans fat, appear more strongly related to weight gain.67,68,71

  • In animal experiments, probiotics in yogurt alter gut immune responses and protect against obesity and nonalcoholic fatty acid liver disease.7274

  • Diet quality might also influence energy expenditure. After intentional weight loss, isocaloric diets higher in fat and lower in rapidly digestible carbohydrates produced significantly smaller declines in total energy expenditure than low-fat, high-carbohydrate diets, with a mean difference of >300 kcal/d.64

  • Other possible nutritional determinants of positive energy balance (more calories consumed than expended), as determined by adiposity or weight gain, include larger portion sizes, skipping breakfast, consumption of fast food, and eating foods prepared outside the home, although the evidence for long-term relevance of these factors has been inconsistent.7578

  • Lower average sleep duration is consistently linked to greater adiposity in both children and adults, and short-term trials demonstrate effects of insufficient sleep on hunger, food choices, and leptin/ghrelin concentrations.79

  • Societal and environmental factors independently associated with diet quality, adiposity, or weight gain include education, income, race/ethnicity, and (at least cross-sectionally) neighborhood availability of supermarkets.8082

  • Other local food-environment characteristics, such as availability of grocery stores (ie, smaller stores than supermarkets), convenience stores, and fast-food restaurants, are not consistently associated with diet quality or adiposity and could be linked to social determinants of health for CVD.83

BP and Blood Cholesterol
  • Sodium linearly raises BP in a dose-dependent fashion, with stronger effects among older people, hypertensive people, and blacks,84 and induces additional BP-independent damage to renal and vascular tissues.85,86

  • Compared with a usual Western diet, a DASH-type 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.87

  • 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-C 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.88 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the low-fat/high-carbohydrate DASH diet.89

  • In a meta-analysis of 60 randomized controlled feeding trials, consumption of 1% of calories from saturated fat in place of carbohydrate raised LDL-C concentrations but also raised HDL-C and lowered triglycerides, with no significant effects on apolipoprotein B concentrations.90

  • In a meta-analysis of RCTs, consumption of 1% of calories from trans fat in place of saturated fat, monounsaturated fat, or polyunsaturated fat, respectively, increased the ratio of TC to HDL-C 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.91

  • In meta-analyses of RCTs, consumption of eicosapentaenoic acid and docosahexaenoic acid for 212 weeks lowered SBP by 2.1 mm Hg92 and lowered resting heart rate by 2.5 beats per minute.93

  • In a pooled analysis of 25 randomized trials totaling 583 males and females both with and without hypercholesterolemia, nut consumption significantly improved blood lipid levels.61,94 For a mean consumption of 67 g of nuts per day, TC was reduced by 10.9 mg/dL (5.1%), LDL-C by 10.2 mg/dL (7.4%), and the ratio of TC to HDL-C 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.94

  • In an RCT, 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 TC to HDL-C by 0.38 and 0.26 and raised HDL-C by 2.9 and 1.6 mg/dL, respectively. The Mediterranean dietary patterns also lowered levels of CRP, interleukin-6, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1.95

Blood Glucose
  • 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.96

  • Among 24 prospective cohort studies, greater consumption of refined carbohydrates and sugars, as measured by higher glycemic load, was positively associated with risk of type 2 DM: For each 100-g increment in glycemic load, 45% higher risk was seen (95% CI, 1.31–1.61; P<0.001; n=24 studies, 7.5 million person-years of follow-up).97

  • In one meta-analysis of observational studies and trials, greater consumption of nuts was linked to lower incidence of type 2 DM (RR per 4 weekly 1-oz servings, 0.87; 95% CI, 0.81–0.94).98

  • A meta-analysis of 102 randomized controlled feeding trials that included 239 diet arms and 4220 adults evaluated the effects of exchanging different dietary fats and carbohydrate on markers of glucose-insulin homeostasis.99 Although replacing 5% energy from carbohydrate with saturated fat generally had no significant effects, replacing carbohydrate with unsaturated fats lowered both HbA1c and insulin. Replacing saturated fat with polyunsaturated fat significantly lowered glucose, HbA1c, C-peptide, and the homeostatic model assessment of insulin resistance. On the basis of “gold standard” acute insulin response in 10 trials, polyunsaturated fat also significantly improved insulin secretion capacity (+0.5 pmol/L/min; 0.2, 0.8) whether it replaced carbohydrate, saturated fat, or even monounsaturated fat.

Cardiovascular Events

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. RCTs 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.100 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.101

  • 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.102104 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).105 These findings are consistent with a meta-analysis of RCTs 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).106

  • 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).105 Each 5% higher energy consumption of monounsaturated fat in place of saturated fat was not significantly associated with CHD risk.105 A more recent meta-analysis of prospective cohort studies found that increased intake of polyunsaturated fats was associated with lower risk of CHD, whether replacing saturated fat or carbohydrate.107 These findings suggest that reducing saturated fat without specifying the replacement might have minimal effects on CHD risk, whereas increasing polyunsaturated fats from vegetable oils will reduce CHD, whether replacing saturated fat or carbohydrate.20

  • 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).108

  • 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).109,110

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).111,112

  • 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 males and females 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).113

  • 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.114 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).115

  • 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).116

  • In a meta-analysis of 6 prospective observational studies, nut consumption was associated with lower incidence of fatal CHD (RR per 4 weekly 1-oz servings, 0.76; 95% CI, 0.69–0.84) and nonfatal CHD (RR, 0.78; 95% CI, 0.67–0.92).98 Nut consumption was not significantly associated with stroke risk based on 4 studies.98

  • In a meta-analysis of 6 prospective observational studies, consumption of legumes (beans) was associated with lower incidence of CHD (RR per 4 weekly 100-g servings, 0.86; 95% CI, 0.78–0.94).98

  • Higher consumption of dairy or milk products is associated with lower incidence of DM and trends toward lower risk of stroke.94,109,110 The inverse associations with DM appear strongest for both yogurt and cheese.117

  • Dairy consumption is not significantly associated with higher or lower risk of CHD.103,118

  • Among 88 520 generally healthy females 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.119 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.120

  • In a meta-analysis of 15 country-specific observational cohorts, which together included 636 151 unique participants, 6.5 million person-years of follow-up, and 28 271 total deaths, 9783 cases of incident CVD, and 23 954 cases of incident type 2 DM, consumption of butter had small or neutral overall associations with mortality, CVDs, and DM.121 For example, butter consumption was weakly associated with all-cause mortality (per 14 g [1 tbsp] per day: RR, 1.01; 95% CI, 1.00–1.03); was not associated with CVD (RR, 1.00; 95% CI, 0.98–1.02), CHD (RR, 0.99, 95% CI, 0.96–1.03), or stroke (RR, 1.01, 95% CI, 0.98–1.03); and was associated with lower risk of DM (RR, 0.96, 95% CI, 0.93–0.99).

Potassium and Sodium
  • Major dietary sources of potassium include vegetables, fruits, whole grains, legumes, nuts, and dairy. In randomized trials, potassium lowers BP, with stronger effects among hypertensive people and when dietary sodium intake is high.122 BP lowering is related to both increased urinary potassium excretion and a lower urine sodium-to-potassium ratio. Consistent with these benefits, potassium-rich diets are associated with lower risk of CVD, especially stroke.123

  • Nearly all observational studies demonstrate a positive association between higher estimated sodium intakes (eg, >4000 mg/d) and CVD events, in particular stroke.124,125 Some studies have also observed higher CVD risk at estimated low intakes (eg, <3000 g/d), which suggests a potential J-shaped relationship with risk.126128 Unique limitations in estimating sodium intake in observational studies, whether by urine collection or diet questionnaires, could explain the J shape seen in certain studies.129

  • During extended surveillance in a large sodium study that excluded sick people at baseline and collected multiple 24-hour urine samples per subject, individuals with sodium intake <2300 mg/d experienced 32% lower CVD risk than those who consumed 3600 to 4800 mg/d, with evidence for linearly decreasing risk.130

  • In ecological studies, the lowest mean intake level associated with both lower SBP and a lower age-BP slope was 614 mg/d.131

  • In well-controlled randomized feeding trials, the lowest tested intake for which BP reductions were clearly documented was 1500 mg/d.87

  • In meta-analyses of prospective observational studies, the lowest mean intakes associated with lower risk of CVD events ranged from 1787 to 2391 mg/d.124,125

  • 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.132

Dietary Patterns
  • The 2015 US Dietary Guidelines Advisory Committee recently summarized the evidence for benefits of healthful diet patterns on a range of cardiometabolic and other disease outcomes.18 They concluded that a healthy dietary pattern is higher in vegetables, fruits, whole grains, low-fat or nonfat dairy, seafood, legumes, and nuts; moderate in alcohol (among adults); lower in red and processed meat; and low in sugar-sweetened foods and drinks and refined grains.

  • In a cohort of 380 296 US males and females, 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).133 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke134 and for the DASH-type dietary pattern.135

  • 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).136 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.137143

  • 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 MI144 and a large primary prevention trial in Spain among patients with CVD risk factors.145 The latter trial, PREDIMED, demonstrated a 30% reduction in the risk of stroke, MI, and death attributable to cardiovascular causes in those patients randomized to Mediterranean-style diets rich in extra-virgin olive oil or mixed nuts.

Abbreviations Used in Chapter 5

AHAAmerican Heart Association
ALAα-linoleic acid
ARICAtherosclerosis Risk in Communities Study
BMIbody mass index
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CHDcoronary heart disease
CIconfidence interval
CRPC-reactive protein
CVDcardiovascular disease
DALYdisability-adjusted life-year
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DHAdocosahexaenoic acid
DMdiabetes mellitus
EPAeicosapentaenoic acid
GFRglomerular filtration rate
GISSIGruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDheart disease
HDL-Chigh-density lipoprotein cholesterol
HFheart failure
LDL-Clow-density lipoprotein cholesterol
max.maximum
MImyocardial infarction
n-6-PUFAω-6-polyunsaturated fatty acid
NAnot available
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
ORodds ratio
PAphysical activity
PREDIMEDPrevención con Dieta Mediterránea
RCTrandomized controlled trial
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
SSBsugar-sweetened beverage
TCtotal cholesterol
USDAUS Department of Agriculture
WHIWomen’s Health Initiative

References

  • 1. My Life Check: Life’s Simple 7. American Heart Association Web site. http://mylifecheck.heart.org/. Accessed June 13, 2016.Google Scholar
  • 2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
  • 3. Rehm CD, Peñalvo JL, Afshin A, Mozaffarian D. Dietary Intake Among US Adults, 1999-2012.JAMA. 2016; 315:2542–2553. doi: 10.1001/jama.2016.7491.CrossrefMedlineGoogle Scholar
  • 4. National Cancer Institute. Sources of sodium among the US population, 2005–06.Applied Research Program Web site. http://appliedresearch.cancer.gov/diet/foodsources/sodium/. Accessed June 14, 2016.Google Scholar
  • 5. US Burden of Disease Collaborators. The state of US Health, 1990–2010: burden of diseases, injuries, and risk factors.JAMA. 2013; 310:591–608. doi: 10.1001/jama.2013.13805.CrossrefMedlineGoogle Scholar
  • 6. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, Ezzati M. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors [published correction appears in PLoS Med. 2011;8].PLoS Med. 2009; 6:e1000058. doi: 10.1371/journal.pmed.1000058.CrossrefMedlineGoogle Scholar
  • 7. United States Department of Agriculture Economic Research Service. 2016. Food price outlook.http://www.ers.usda.gov/data-products/food-price-outlook.aspx. Accessed June 14, 2016.Google Scholar
  • 8. Rao M, Afshin A, Singh G, Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis.BMJ Open. 2013; 3:e004277. doi: 10.1136/bmjopen-2013-004277.CrossrefMedlineGoogle Scholar
  • 9. Mozaffarian RS, Andry A, Lee RM, Wiecha JL, Gortmaker SL. Price and healthfulness of snacks in 32 YMCA after-school programs in 4 US metropolitan areas, 2006-2008.Prev Chronic Dis. 2012; 9:E38.MedlineGoogle Scholar
  • 10. Davis C, Saltos E. Dietary recommendations and how they have changed over time., Frazao E, ed. In: America’s Eating Habits: Changes and Consequences. Washington, DC: US Department of Agriculture; 1999:33–50. Agriculture Information Bulletin No. 750.Google Scholar
  • 11. Brunner E, Cohen D, Toon L. Cost effectiveness of cardiovascular disease prevention strategies: a perspective on EU food based dietary guidelines.Public Health Nutr. 2001; 4:711–715.CrossrefMedlineGoogle Scholar
  • 12. US Department of Health and Human Services, Centers for Disease Control and Prevention. 2008. Preventing chronic diseases: investing wisely in health preventing obesity and chronic diseases through good nutrition and physical activity.http://atfiles.org/files/pdf/CDC-HHS.pdf. Accessed June 14, 2016.Google Scholar
  • 13. American Heart Association Nutrition Committee, Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, Franklin B, Kris-Etherton P, Harris WS, Howard B, Karanja N, Lefevre M, Rudel L, Sacks F, Van Horn L, Winston M, Wylie-Rosett J. Diet and lifestyle recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee. [published corrections appear in Circulation. 2006;114:e629 and Circulation. 2006;114:e27].Circulation. 2006; 114(1):82–96.MedlineGoogle Scholar
  • 14. International Society for the Study of Fatty Acids and Lipids. Recommendations for intake of polyunsaturated fatty acids in healthy adults.Devon, United Kingdom: International Society for the Study of Fatty Acids and Lipids; 2004. http://www.issfal.org/statements/pufa-recommendations/statement-3. Accessed June 14, 2016.Google Scholar
  • 15. Food and Nutrition Board, Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients).Washington, DC: Institute of Medicine, National Academies Press; 2005. https://fnic.nal.usda.gov/sites/fnic.nal.usda.gov/files/uploads/energy_full_report.pdf. Accessed June 14, 2016.Google Scholar
  • 16. Smith-Spangler CM, Juusola JL, Enns EA, Owens DK, Garber AM. Population strategies to decrease sodium intake and the burden of cardiovascular disease: a cost-effectiveness analysis.Ann Intern Med. 2010; 152:481–487, W170–W173. doi: 10.7326/0003-4819-152-8-201004200-00212.CrossrefMedlineGoogle Scholar
  • 17. Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease.N Engl J Med. 2010; 362:590–599. doi: 10.1056/NEJMoa0907355.CrossrefMedlineGoogle Scholar
  • 18. 2015 Dietary Guidelines Advisory Committee. 2015. Scientific Report of the 2015 Dietary Guidelines Advisory Committee.http://health.gov/dietaryguidelines/2015-scientific-report/PDFs/Scientific-Report-of-the-2015-Dietary-Guidelines-Advisory-Committee.pdf. Accessed June 13, 2016.Google Scholar
  • 19. Ahluwalia N, Andreeva VA, Kesse-Guyot E, Hercberg S. Dietary patterns, inflammation and the metabolic syndrome.Diabetes Metab. 2013; 39:99–110. doi: 10.1016/j.diabet.2012.08.007.CrossrefMedlineGoogle Scholar
  • 20. Mozaffarian D, Appel LJ, Van Horn L. Components of a cardioprotective diet: new insights.Circulation. 2011; 123:2870–2891. doi: 10.1161/CIRCULATIONAHA.110.968735.LinkGoogle Scholar
  • 21. Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, Casavale KO, Carroll RJ. The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans.J Nutr. 2014; 144:399–407. doi: 10.3945/jn.113.183079.CrossrefMedlineGoogle Scholar
  • 22. Wang DD, Leung CW, Li Y, Ding EL, Chiuve SE, Hu FB, Willett WC. Trends in dietary quality among adults in the United States, 1999 through 2010.JAMA Intern Med. 2014; 174:1587–1595. doi: 10.1001/jamainternmed.2014.3422.CrossrefMedlineGoogle Scholar
  • 23. Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, Mozaffarian D; Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment.Lancet Glob Health. 2015; 3:e132–e142. doi: 10.1016/S2214-109X(14)70381-X.CrossrefMedlineGoogle Scholar
  • 24. Bailey RL, Gahche JJ, Miller PE, Thomas PR, Dwyer JT. Why US adults use dietary supplements.JAMA Intern Med. 2013; 173:355–361. doi: 10.1001/jamainternmed.2013.2299.CrossrefMedlineGoogle Scholar
  • 25. Radimer K, Bindewald B, Hughes J, Ervin B, Swanson C, Picciano MF. Dietary supplement use by US adults: data from the National Health and Nutrition Examination Survey, 1999-2000.Am J Epidemiol. 2004; 160:339–349. doi: 10.1093/aje/kwh207.CrossrefMedlineGoogle Scholar
  • 26. Bailey RL, Fulgoni VL, Keast DR, Dwyer JT. Dietary supplement use is associated with higher intakes of minerals from food sources.Am J Clin Nutr. 2011; 94:1376–1381. doi: 10.3945/ajcn.111.020289.CrossrefMedlineGoogle Scholar
  • 27. Bailey RL, Fulgoni VL, Keast DR, Dwyer JT. Examination of vitamin intakes among US adults by dietary supplement use.J Acad Nutr Diet. 2012; 112:657–663.e4. doi: 10.1016/j.jand.2012.01.026.CrossrefMedlineGoogle Scholar
  • 28. Picciano MF, Dwyer JT, Radimer KL, Wilson DH, Fisher KD, Thomas PR, Yetley EA, Moshfegh AJ, Levy PS, Nielsen SJ, Marriott BM. Dietary supplement use among infants, children, and adolescents in the United States, 1999–2002.Arch Pediatr Adolesc Med. 2007; 161:978–985.CrossrefMedlineGoogle Scholar
  • 29. Pillitteri JL, Shiffman S, Rohay JM, Harkins AM, Burton SL, Wadden TA. Use of dietary supplements for weight loss in the United States: results of a national survey.Obesity (Silver Spring). 2008; 16:790–796. doi: 10.1038/oby.2007.136.CrossrefMedlineGoogle Scholar
  • 30. House AA, Eliasziw M, Cattran DC, Churchill DN, Oliver MJ, Fine A, Dresser GK, Spence JD. Effect of B-vitamin therapy on progression of diabetic nephropathy: a randomized controlled trial.JAMA. 2010; 303:1603–1609. doi: 10.1001/jama.2010.490.CrossrefMedlineGoogle Scholar
  • 31. Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico. Dietary supplementation with n-3 polyunsaturated fatty acids and vitamin E after myocardial infarction: results of the GISSI-Prevenzione trial: Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico [published corrections appear in Lancet. 2001;357:642 and Lancet. 2007;369:106].Lancet. 1999; 354:447–455.CrossrefMedlineGoogle Scholar
  • 32. Yokoyama M, Origasa H, Matsuzaki M, Matsuzawa Y, Saito Y, Ishikawa Y, Oikawa S, Sasaki J, Hishida H, Itakura H, Kita T, Kitabatake A, Nakaya N, Sakata T, Shimada K, Shirato K; Japan EPA Lipid Intervention Study (JELIS) Investigators. Effects of eicosapentaenoic acid on major coronary events in hypercholesterolaemic patients (JELIS): a randomised open-label, blinded endpoint analysis [published correction appears in Lancet. 2007;370:220].Lancet. 2007; 369:1090–1098. doi: 10.1016/S0140-6736(07)60527-3.CrossrefMedlineGoogle Scholar
  • 33. Tavazzi L, Maggioni AP, Marchioli R, Barlera S, Franzosi MG, Latini R, Lucci D, Nicolosi GL, Porcu M, Tognoni G; GISSI-HF Investigators. Effect of n-3 polyunsaturated fatty acids in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial.Lancet. 2008; 372:1223–1230. doi: 10.1016/S0140-6736(08)61239-8.CrossrefMedlineGoogle Scholar
  • 34. Mozaffarian D, Wu JH. Omega-3 fatty acids and cardiovascular disease: effects on risk factors, molecular pathways, and clinical events.J Am Coll Cardiol. 2011; 58:2047–2067. doi: 10.1016/j.jacc.2011.06.063.CrossrefMedlineGoogle Scholar
  • 35. Rizos EC, Ntzani EE, Bika E, Kostapanos MS, Elisaf MS. Association between omega-3 fatty acid supplementation and risk of major cardiovascular disease events: a systematic review and meta-analysis.JAMA. 2012; 308:1024–1033. doi: 10.1001/2012.jama.11374.CrossrefMedlineGoogle Scholar
  • 36. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obesity among adults in the United States, 2005 to 2014.JAMA. 2016; 315:2284–2291. doi: 10.1001/jama.2016.6458.CrossrefMedlineGoogle Scholar
  • 37. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, Flegal KM. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014.JAMA. 2016; 315:2292–2299. doi: 10.1001/jama.2016.6361.CrossrefMedlineGoogle Scholar
  • 38. Duffey KJ, Popkin BM. Energy density, portion size, and eating occasions: contributions to increased energy intake in the United States, 1977-2006.PLoS Med. 2011; 8:e1001050. doi: 10.1371/journal.pmed.1001050.CrossrefMedlineGoogle Scholar
  • 39. Duffey KJ, Popkin BM. Causes of increased energy intake among children in the U.S., 1977-2010.Am J Prev Med. 2013; 44:e1–e8. doi: 10.1016/j.amepre.2012.10.011.CrossrefMedlineGoogle Scholar
  • 40. National Center for Health Statistics. National Health Interview Survey, 2015. Public-use data file and documentation.http://www.cdc.gov/nchs/nhis/nhis_2015_data_release.htm. NCHS tabulations. Accessed August, 24, 2016.Google Scholar
  • 41. Nielsen SJ, Popkin BM. Patterns and trends in food portion sizes, 1977-1998.JAMA. 2003; 289:450–453.CrossrefMedlineGoogle Scholar
  • 42. Wright JD, Wang CY. Trends in intake of energy and macronutrients in adults from 1999–2000 through 2007–2008.NCHS Data Brief. 2010;(49):1–8.Google Scholar
  • 43. Gross LS, Li L, Ford ES, Liu S. Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment.Am J Clin Nutr. 2004; 79:774–779.CrossrefMedlineGoogle Scholar
  • 44. Kant AK, Graubard BI. Eating out in America, 1987-2000: trends and nutritional correlates.Prev Med. 2004; 38:243–249.CrossrefMedlineGoogle Scholar
  • 45. Briefel RR, Johnson CL. Secular trends in dietary intake in the United States.Annu Rev Nutr. 2004; 24:401–431. doi: 10.1146/annurev.nutr.23.011702.073349.CrossrefMedlineGoogle Scholar
  • 46. Kant AK, Graubard BI. Secular trends in patterns of self-reported food consumption of adult Americans: NHANES 1971-1975 to NHANES 1999-2002.Am J Clin Nutr. 2006; 84:1215–1223.CrossrefMedlineGoogle Scholar
  • 47. Popkin BM, Armstrong LE, Bray GM, Caballero B, Frei B, Willett WC. A new proposed guidance system for beverage consumption in the United States [published correction appears in Am J Clin Nutr. 2007;86:525].Am J Clin Nutr. 2006; 83:529–542.CrossrefMedlineGoogle Scholar
  • 48. Duffey KJ, Popkin BM. Shifts in patterns and consumption of beverages between 1965 and 2002.Obesity (Silver Spring). 2007; 15:2739–2747. doi: 10.1038/oby.2007.326.CrossrefMedlineGoogle Scholar
  • 49. Wang YC, Bleich SN, Gortmaker SL. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among US children and adolescents, 1988-2004.Pediatrics. 2008; 121:e1604–e1614. doi: 10.1542/peds.2007-2834.CrossrefMedlineGoogle Scholar
  • 50. Mesirow MS, Welsh JA. Changing beverage consumption patterns have resulted in fewer liquid calories in the diets of US children: National Health and Nutrition Examination Survey 2001–2010.J Acad Nutr Diet. 2015; 115:559–566.e4.CrossrefMedlineGoogle Scholar
  • 51. Wang YC, Gortmaker SL, Sobol AM, Kuntz KM. Estimating the energy gap among US children: a counterfactual approach.Pediatrics. 2006; 118:e1721–e1733. doi: 10.1542/peds.2006-0682.CrossrefMedlineGoogle Scholar
  • 52. Poti JM, Popkin BM. Trends in energy intake among US children by eating location and food source, 1977-2006.J Am Diet Assoc. 2011; 111:1156–1164. doi: 10.1016/j.jada.2011.05.007.CrossrefMedlineGoogle Scholar
  • 53. Dwyer-Lindgren L, Freedman G, Engell RE, Fleming TD, Lim SS, Murray CJ, Mokdad AH. Prevalence of physical activity and obesity in US counties, 2001-2011: a road map for action.Popul Health Metr. 2013; 11:7. doi: 10.1186/1478-7954-11-7.CrossrefMedlineGoogle Scholar
  • 54. US Department of Health and Human Services. Dietary Guidelines for Americans, 2010. https://health.gov/dietaryguidelines/dga2010/DietaryGuidelines2010.pdf. Accessed June 14, 2016.Google Scholar
  • 55. Centers for Disease Control and Prevention (CDC). Trends in intake of energy and macronutrients: United States, 1971–2000.MMWR Morb Mortal Wkly Rep. 2004; 53:80–82.MedlineGoogle Scholar
  • 56. Egan SK, Bolger PM, Carrington CD. Update of US FDA’s Total Diet Study food list and diets.J Expo Sci Environ Epidemiol. 2007; 17:573–582. doi: 10.1038/sj.jes.7500554.CrossrefMedlineGoogle Scholar
  • 57. Ervin RB, Ogden CL. Trends in intake of energy and macronutrients in children and adolescents from 1999–2000 through 2009–2010.NCHS Data Brief. 2013;(113):1–8.Google Scholar
  • 58. Kit BK, Fakhouri TH, Park S, Nielsen SJ, Ogden CL. Trends in sugar-sweetened beverage consumption among youth and adults in the United States: 1999-2010.Am J Clin Nutr. 2013; 98:180–188. doi: 10.3945/ajcn.112.057943.CrossrefMedlineGoogle Scholar
  • 59. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012.JAMA. 2014; 311:806–814. doi: 10.1001/jama.2014.732.CrossrefMedlineGoogle Scholar
  • 60. Singh GM, Micha R, Khatibzadeh S, Shi S, Lim S, Andrews KG, Engell RE, Ezzati M, Mozaffarian D; on behalf of the Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Global, regional, and national consumption of sugar-sweetened beverages, fruit juices, and milk: a systematic assessment of beverage intake in 187 countries.PLoS One. 2015; 10:e0124845. doi: 10.1371/journal.pone.0124845.CrossrefMedlineGoogle Scholar
  • 61. Blanck HM, Gillespie C, Kimmons JE, Seymour JD, Serdula MK. Trends in fruit and vegetable consumption among U.S. men and women, 1994-2005.Prev Chronic Dis. 2008; 5:A35.MedlineGoogle Scholar
  • 62. Bernstein AM, Willett WC. Trends in 24-h urinary sodium excretion in the United States, 1957-2003: a systematic review.Am J Clin Nutr. 2010; 92:1172–1180. doi: 10.3945/ajcn.2010.29367.CrossrefMedlineGoogle Scholar
  • 63. Powles J, Fahimi S, Micha R, Khatibzadeh S, Shi P, Ezzati M, Engell RE, Lim SS, Danaei G, Mozaffarian D; Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Global, regional and national sodium intakes in 1990 and 2010: a systematic analysis of 24 h urinary sodium excretion and dietary surveys worldwide.BMJ Open. 2013; 3:e003733. doi: 10.1136/bmjopen-2013-003733.CrossrefMedlineGoogle Scholar
  • 64. Ebbeling CB, Swain JF, Feldman HA, Wong WW, Hachey DL, Garcia-Lago E, Ludwig DS. Effects of dietary composition on energy expenditure during weight-loss maintenance.JAMA. 2012; 307:2627–2634. doi: 10.1001/jama.2012.6607.CrossrefMedlineGoogle Scholar
  • 65. Bazzano LA, Hu T, Reynolds K, Yao L, Bunol C, Liu Y, Chen CS, Klag MJ, Whelton PK, He J. Effects of low-carbohydrate and low-fat diets: a randomized trial.Ann Intern Med. 2014; 161:309–318. doi: 10.7326/M14-0180.CrossrefMedlineGoogle Scholar
  • 66. Te Morenga L, Mallard S, Mann J. Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies.BMJ. 2013; 346:e7492.CrossrefGoogle Scholar
  • 67. Smith JD, Hou T, Ludwig DS, Rimm EB, Willett W, Hu FB, Mozaffarian D. Changes in intake of protein foods, carbohydrate amount and quality, and long-term weight change: results from 3 prospective cohorts.Am J Clin Nutr. 2015; 101:1216–1224. doi: 10.3945/ajcn.114.100867.CrossrefMedlineGoogle Scholar
  • 68. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men.N Engl J Med. 2011; 364:2392–2404. doi: 10.1056/NEJMoa1014296.CrossrefMedlineGoogle Scholar
  • 69. Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis.Am J Clin Nutr. 2013; 98:1084–1102. doi: 10.3945/ajcn.113.058362.CrossrefMedlineGoogle Scholar
  • 70. Lennerz BS, Alsop DC, Holsen LM, Stern E, Rojas R, Ebbeling CB, Goldstein JM, Ludwig DS. Effects of dietary glycemic index on brain regions related to reward and craving in men.Am J Clin Nutr. 2013; 98:641–647. doi: 10.3945/ajcn.113.064113.CrossrefMedlineGoogle Scholar
  • 71. Kavanagh K, Jones KL, Sawyer J, Kelley K, Carr JJ, Wagner JD, Rudel LL. Trans fat diet induces abdominal obesity and changes in insulin sensitivity in monkeys.Obesity (Silver Spring). 2007; 15:1675–1684. doi: 10.1038/oby.2007.200.CrossrefMedlineGoogle Scholar
  • 72. Poutahidis T, Kleinewietfeld M, Smillie C, Levkovich T, Perrotta A, Bhela S, Varian BJ, Ibrahim YM, Lakritz JR, Kearney SM, Chatzigiagkos A, Hafler DA, Alm EJ, Erdman SE. Microbial reprogramming inhibits Western diet-associated obesity.PLoS One. 2013; 8:e68596. doi: 10.1371/journal.pone.0068596.CrossrefMedlineGoogle Scholar
  • 73. Park DY, Ahn YT, Park SH, Huh CS, Yoo SR, Yu R, Sung MK, McGregor RA, Choi MS. Supplementation of Lactobacillus curvatus HY7601 and Lactobacillus plantarum KY1032 in diet-induced obese mice is associated with gut microbial changes and reduction in obesity.PLoS One. 2013; 8:e59470. doi: 10.1371/journal.pone.0059470.CrossrefMedlineGoogle Scholar
  • 74. Ritze Y, Bárdos G, Claus A, Ehrmann V, Bergheim I, Schwiertz A, Bischoff SC. Lactobacillus rhamnosus GG protects against non-alcoholic fatty liver disease in mice.PLoS One. 2014; 9:e80169. doi: 10.1371/journal.pone.0080169.CrossrefMedlineGoogle Scholar
  • 75. Steenhuis IH, Vermeer WM. Portion size: review and framework for interventions.Int J Behav Nutr Phys Act. 2009; 6:58. doi: 10.1186/1479-5868-6-58.CrossrefMedlineGoogle Scholar
  • 76. Bezerra IN, Curioni C, Sichieri R. Association between eating out of home and body weight.Nutr Rev. 2012; 70:65–79. doi: 10.1111/j.1753-4887.2011.00459.x.CrossrefMedlineGoogle Scholar
  • 77. Lachat C, Nago E, Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P. Eating out of home and its association with dietary intake: a systematic review of the evidence.Obes Rev. 2012; 13:329–346. doi: 10.1111/j.1467-789X.2011.00953.x.CrossrefMedlineGoogle Scholar
  • 78. Mesas AE, Muñoz-Pareja M, López-García E, Rodríguez-Artalejo F. Selected eating behaviours and excess body weight: a systematic review.Obes Rev. 2012; 13:106–135. doi: 10.1111/j.1467-789X.2011.00936.x.CrossrefMedlineGoogle Scholar
  • 79. Magee L, Hale L. Longitudinal associations between sleep duration and subsequent weight gain: a systematic review [published correction appears in Sleep Med Rev. 2012;16:491].Sleep Med Rev. 2012; 16:231–241. doi: 10.1016/j.smrv.2011.05.005.CrossrefMedlineGoogle Scholar
  • 80. Li F, Harmer PA, Cardinal BJ, Bosworth M, Acock A, Johnson-Shelton D, Moore JM. Built environment, adiposity, and physical activity in adults aged 50-75.Am J Prev Med. 2008; 35:38–46. doi: 10.1016/j.amepre.2008.03.021.CrossrefMedlineGoogle Scholar
  • 81. Kumanyika S, Grier S. Targeting interventions for ethnic minority and low-income populations.Future Child. 2006; 16:187–207.CrossrefMedlineGoogle Scholar
  • 82. Sallis JF, Glanz K. The role of built environments in physical activity, eating, and obesity in childhood.Future Child. 2006; 16:89–108.CrossrefMedlineGoogle Scholar
  • 83. Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, Franch HA, Jacobs DR, Kraus WE, Kris-Etherton PM, Krummel DA, Popkin BM, Whitsel LP, Zakai NA; American Heart Association Council on Epidemiology and Prevention, Council on Nutrition, Physical Activity and Metabolism, Council on Clinical Cardiology, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Peripheral Vascular Disease, and the American Heart Association Advocacy Coordinating Committee. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association.Circulation. 2012; 126:1514–1563. doi: 10.1161/CIR.0b013e318260a20b.LinkGoogle Scholar
  • 84. Mozaffarian D, Fahimi S, Singh GM, Micha R, Khatibzadeh S, Engell RE, Lim S, Danaei G, Ezzati M, Powles J; Global Burden of Diseases Nutrition and Chronic Diseases Expert Group. Global sodium consumption and death from cardiovascular causes.N Engl J Med. 2014; 371:624–634. doi: 10.1056/NEJMoa1304127.CrossrefMedlineGoogle Scholar
  • 85. Sacks FM, Campos H. Dietary therapy in hypertension.N Engl J Med. 2010; 362:2102–2112. doi: 10.1056/NEJMct0911013.CrossrefMedlineGoogle Scholar
  • 86. Susic D, Frohlich ED. Salt consumption and cardiovascular, renal, and hypertensive diseases: clinical and mechanistic aspects.Curr Opin Lipidol. 2012; 23:11–16.CrossrefMedlineGoogle Scholar
  • 87. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, Obarzanek E, Conlin PR, Miller ER, Simons-Morton DG, Karanja N, Lin PH; DASH-Sodium Collaborative Research Group. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group.N Engl J Med. 2001; 344:3–10. doi: 10.1056/NEJM200101043440101.CrossrefMedlineGoogle Scholar
  • 88. Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, Conlin PR, Erlinger TP, Rosner BA, Laranjo NM, Charleston J, McCarron P, Bishop LM; OmniHeart Collaborative Research Group. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial.JAMA. 2005; 294:2455–2464. doi: 10.1001/jama.294.19.2455.CrossrefMedlineGoogle Scholar
  • 89. Gadgil MD, Appel LJ, Yeung E, Anderson CA, Sacks FM, Miller ERThe effects of carbohydrate, unsaturated fat, and protein intake on measures of insulin sensitivity: results from the OmniHeart trial.Diabetes Care. 2013; 36:1132–1137. doi: 10.2337/dc12-0869.CrossrefMedlineGoogle Scholar
  • 90. Mensink RP, Zock PL, Kester AD, Katan MB. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials.Am J Clin Nutr. 2003; 77:1146–1155.CrossrefMedlineGoogle Scholar
  • 91. Uauy R, Aro A, Clarke R, Ghafoorunissa , L’Abbe MR, Mozaffarian D, Skeaff CM, Stender S, Tavella M. WHO Scientific Update on trans fatty acids: summary and conclusions.Eur J Clin Nutr. 2009; 63:S68–S75.CrossrefGoogle Scholar
  • 92. Geleijnse JM, Giltay EJ, Grobbee DE, Donders AR, Kok FJ. Blood pressure response to fish oil supplementation: metaregression analysis of randomized trials.J Hypertens. 2002; 20:1493–1499.CrossrefMedlineGoogle Scholar
  • 93. Mozaffarian D, Geelen A, Brouwer IA, Geleijnse JM, Zock PL, Katan MB. Effect of fish oil on heart rate in humans: a meta-analysis of randomized controlled trials.Circulation. 2005; 112:1945–1952. doi: 10.1161/CIRCULATIONAHA.105.556886.LinkGoogle Scholar
  • 94. Sabaté J, Oda K, Ros E. Nut consumption and blood lipid levels: a pooled analysis of 25 intervention trials.Arch Intern Med. 2010; 170:821–827. doi: 10.1001/archinternmed.2010.79.CrossrefMedlineGoogle Scholar
  • 95. Estruch R, Martínez-González MA, Corella D, Salas-Salvadó J, Ruiz-Gutiérrez V, Covas MI, Fiol M, Gómez-Gracia E, López-Sabater MC, Vinyoles E, Arós F, Conde M, Lahoz C, Lapetra J, Sáez G, Ros E; PREDIMED Study Investigators. Effects of a Mediterranean-style diet on cardiovascular risk factors: a randomized trial.Ann Intern Med. 2006; 145:1–11.CrossrefMedlineGoogle Scholar
  • 96. Hu FB, Malik VS. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: epidemiologic evidence.Physiol Behav. 2010; 100:47–54. doi: 10.1016/j.physbeh.2010.01.036.CrossrefMedlineGoogle Scholar
  • 97. Livesey G, Taylor R, Livesey H, Liu S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies.Am J Clin Nutr. 2013; 97:584–596. doi: 10.3945/ajcn.112.041467.CrossrefMedlineGoogle Scholar
  • 98. Afshin A, Micha R, Khatibzadeh S, Mozaffarian D. Consumption of nuts and legumes and risk of incident ischemic heart disease, stroke, and diabetes: a systematic review and meta-analysis.Am J Clin Nutr. 2014; 100:278–288. doi: 10.3945/ajcn.113.076901.CrossrefMedlineGoogle Scholar
  • 99. Imamura F, Micha R, Wu JH, de Oliveira Otto MC, Otite FO, Abioye AI, Mozaffarian D. Effects of saturated fat, polyunsaturated fat, monounsaturated fat, and carbohydrate on glucose-insulin homeostasis: a systematic review and meta-analysis of randomised controlled feeding trials.PLoS Med. 2016; 13:e1002087. doi: 10.1371/journal.pmed.1002087.CrossrefMedlineGoogle Scholar
  • 100. Howard BV, Van Horn L, Hsia J, Manson JE, Stefanick ML, Wassertheil-Smoller S, Kuller LH, LaCroix AZ, Langer RD, Lasser NL, Lewis CE, Limacher MC, Margolis KL, Mysiw WJ, Ockene JK, Parker LM, Perri MG, Phillips L, Prentice RL, Robbins J, Rossouw JE, Sarto GE, Schatz IJ, Snetselaar LG, Stevens VJ, Tinker LF, Trevisan M, Vitolins MZ, Anderson GL, Assaf AR, Bassford T, Beresford SA, Black HR, Brunner RL, Brzyski RG, Caan B, Chlebowski RT, Gass M, Granek I, Greenland P, Hays J, Heber D, Heiss G, Hendrix SL, Hubbell FA, Johnson KC, Kotchen JM. Low-fat dietary pattern and risk of cardiovascular disease: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial.JAMA. 2006; 295:655–666.CrossrefMedlineGoogle Scholar
  • 101. Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases. Diet, nutrition and the prevention of chronic diseases: report of a joint WHO/FAO expert consultation, Geneva, 28 January – 1 February 2002.2003. http://www.who.int/dietphysicalactivity/publications/trs916/en/gsfao_introduction.pdf. Accessed June 14, 2016.Google Scholar
  • 102. Skeaff CM, Miller J. Dietary fat and coronary heart disease: summary of evidence from prospective cohort and randomised controlled trials.Ann Nutr Metab. 2009; 55:173–201. doi: 10.1159/000229002.CrossrefMedlineGoogle Scholar
  • 103. Mente A, de Koning L, Shannon HS, Anand SS. A systematic review of the evidence supporting a causal link between dietary factors and coronary heart disease.Arch Intern Med. 2009; 169:659–669. doi: 10.1001/archinternmed.2009.38.Crossref