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Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Originally publishedhttps://doi.org/10.1161/CIR.0000000000000659Circulation. 2019;139:e56–e528

Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.

Table of Contents

  • Summary e57

  • 1. About These Statistics e67

  • 2. Cardiovascular Health e70

  • Health Behaviors

  • 3. Smoking/Tobacco Use e87

  • 4. Physical Inactivity e99

  • 5. Nutrition e119

  • 6. Overweight and Obesity e138

  • Health Factors and Other Risk Factors

  • 7. High Blood Cholesterol and Other Lipids e161

  • 8. High Blood Pressure e174

  • 9. Diabetes Mellitus e193

  • 10. Metabolic Syndrome e212

  • 11. Kidney Disease e233

  • 12. Sleep e249

  • Cardiovascular Conditions/Diseases

  • 13. Total Cardiovascular Diseases e257

  • 14. Stroke (Cerebrovascular Disease) e281

  • 15. Congenital Cardiovascular Defects and Kawasaki Disease e327

  • 16. Disorders of Heart Rhythm e346

  • 17. Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies e377

  • 18. Subclinical Atherosclerosis e401

  • 19. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e415

  • 20. Cardiomyopathy and Heart Failure e438

  • 21. Valvular Diseases e455

  • 22. Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension e472

  • 23. Peripheral Artery Disease and Aortic Diseases e481

  • Outcomes

  • 24. Quality of Care e497

  • 25. Medical Procedures e511

  • 26. Economic Cost of Cardiovascular Disease e516

  • Supplemental Materials

  • 27. At-a-Glance Summary Tables e522

  • 28. Glossary e526

Summary

Each year, the American Heart Association (AHA), in conjunction with 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 cardiovascular risk factors in the AHA’s My Life Check − Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, 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) produces immense health and economic burdens in the United States and globally. The Statistical 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 [CHD], heart failure [HF], valvular disease, venous disease, and peripheral arterial disease) and the associated outcomes (including quality of care, procedures, and economic costs). Since 2007, the annual versions of the Statistical Update have been cited >20 000 times in the literature.

Figure.

Figure. AHA’s My Life Check – 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 mellitus.

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 data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, a new chapter on sleep, an enhanced focus on social determinants of health, a substantively expanded focus 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 Statistical Update.

Cardiovascular Health (Chapter 2)

  • New data expand the benefits of better cardiovascular health to include lower prevalence of aortic sclerosis and stenosis, improved prognosis after myocardial infarction (MI), lower risk of atrial fibrillation, and greater positive psychological functioning (dispositional optimism).

  • Among children, from 1999 to 2000 to 2015 to 2016, prevalence of nonsmoking, ideal total cholesterol, and ideal BP improved. For example, nonsmoking among children aged 12 to 19 years went from 76% to 94%. However, meeting ideal levels for physical activity, body mass index (BMI), and blood glucose did not improve. For example, prevalence of ideal BMI declined from 70% to 60% over the same time period.

Smoking/Tobacco Use (Chapter 3)

  • The prevalence of current smoking in the United States in 2016 was 15.5% for adults, and 3.4% of adolescents smoked cigarettes in the past month. Although there has been a consistent decline in tobacco use in the United States, significant disparities persist. Substantially higher tobacco use prevalence rates are observed in American Indian/Alaska Natives and lesbian, gay, bisexual, and transgender populations, as well as among individuals with low socioeconomic status, those with mental illness, individuals with HIV who are receiving medical care, and those who are active-duty military.

  • Tobacco use remains a leading cause of preventable death in the United States and globally. It was estimated to account for 7.1 million deaths worldwide in 2016.

  • Over the past 6 years, there has been a sharp increase in e-cigarette use among adolescents, and e-cigarettes are now the most commonly used tobacco product in this demographic.

  • Policy-level interventions such as Tobacco 21 Laws and MPOWER are being adopted and have been associated with reductions in tobacco use incidence and prevalence.

Physical Inactivity (Chapter 4)

  • The trends in the prevalence of self-reported inactivity among adults decreased from 1998 to 2016, with the largest drop occurring in the past decade, from 40.1% to 26.9% between 2007 and 2016, respectively. Despite this decrease in inactivity over recent years, currently, <23% of adults report participating in adequate leisure-time aerobic and muscle-strengthening activity to meet the 2008 federal guidelines for physical activity.

  • Converging evidence from epidemiological studies suggests that limiting sedentary time is associated with a lower risk of cardiovascular events and mortality after accounting for other traditional risk factors and physical activity levels.

  • A Nielsen report from 2017 suggests that technology use is changing rapidly, with potential implications for influencing sedentary behavior. Although adult television and tablet use has decreased modestly in recent years, adult smartphone use increased from the 2012 to 2014 period to 2017 by >1 hour each day.

Nutrition (Chapter 5)

  • In a 2013 to 2014 nationally representative sample of 827 nonpregnant, noninstitutionalized US adults, estimated mean sodium intake by 24-hour urinary excretion was 4205 mg/d for males and 3039 mg/d for females. In a diverse sample of 450 US adults in 3 geographic locations, ≈70% of sodium was added to food outside the home, 13% to 16% was inherent to food, 4% to 9% was added in home food preparation, 3% to 8% was added at the table, and <1% was from dietary supplements and home tap water; amounts varied modestly by race/ethnicity.

  • After a 1 peso per liter excise tax on sugar-sweetened beverages (SSBs) was implemented in Mexico in January 2014, SSB purchases were reduced by 5.5% after 1 year and 9.7% after 2 years compared with predicted SSB purchases based on pretax trends. The effect of the SSB tax was greatest among households of the lowest socioeconomic status. A similar 1 cent per ounce excise tax on SSBs was implemented in Berkeley, California, in January 2015, and SSB sales declined by 9.6% after 1 year compared with predicted SSB purchases based on pretax trends.

  • The Special Supplemental Nutrition Program for Women, Infants, and Children food package was revised in 2009 to include more fruits, vegetables, whole grains, and lower-fat milk. These food package revisions were associated with a significant improvement in Healthy Eating Index-2010 score (3.7 higher Healthy Eating Index points; 95% CI, 0.6–6.9). By contrast, participation in the Supplemental Nutrition Assistance Program (SNAP), which does not regulate nutritional quality, was associated with less healthy household purchases (15–20 more calories from SSBs per person per day, 174–195 more milligrams of sodium per person per day, and 0.52 fewer grams of fiber per person per day).

Overweight and Obesity (Chapter 6)

  • According to NHANES (National Health and Nutrition Examination Survey) 2015 to 2016, 39.6% of US adults and 18.5% of youths were obese, and 7.7% of adults and 5.6% of youth had severe obesity. The overall prevalence of obesity and severe obesity in youth (aged 2–19 years) did not increase significantly from 2007 to 2008 to 2015 to 2016. However, the age-standardized prevalence of obesity and severe obesity increased significantly in the past decade (from 2007–2008 to 2015–2016) among adults.

  • A recent mendelian randomization study of participants from 7 prospective cohorts demonstrated that genetic variants associated with higher BMI were significantly associated with incident atrial fibrillation, which supports a causal relationship between obesity and atrial fibrillation.

  • In a study of 189 672 participants from 10 US longitudinal cohort studies, obesity was associated with a shorter total longevity and greater proportion of life lived with CVD. Higher BMI was associated with a significantly higher risk of death attributable to CVD.

High Blood Cholesterol and Other Lipids (Chapter 7)

  • Between 1999 and 2016, mean total cholesterol levels declined overall and across all subgroups of race.

  • Recent data from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) indicate that even after accounting for access to medical care, there are disparities in the use of statins in individuals with diabetes mellitus (DM). White males with DM and low-density lipoprotein cholesterol >100 mg/dL were more likely to be prescribed statins (66.0%) than black males (57.8%), white females (55.0%), and black females (53.6%).

High Blood Pressure (Chapter 8)

  • In 2011 to 2014, the prevalence of hypertension among US adults was 45.6% (95% CI, 43.6%–47.6%) using the new BP thresholds from the 2017 American College of Cardiology/AHA guidelines versus 31.9% (95% CI, 30.1%–33.7%) using guideline thresholds from the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.

  • In prospective follow-up of the REGARDS, MESA (Multi-Ethnic Study of Atherosclerosis), and JHS (Jackson Heart Study) cohorts, 63.0% of incident CVD events occurred in participants with systolic BP (SBP) <140 mm Hg and diastolic BP <90 mm Hg.

  • US non-Hispanic (NH) blacks (13.2%) are more likely than NH Asians (11.0%), NH whites (8.6%), or Hispanics (7.4%) to use home BP monitoring on a weekly basis.

  • In 2015, the worldwide prevalence of SBP ≥140 mm Hg was estimated to be 20 526 per 100 000. This represents an increase from 17 307 per 100 000 in 1990. Also, the prevalence of SBP of 110 to 115 mm Hg or higher increased from 73 119 per 100 000 to 81 373 per 100 000 between 1990 and 2015. There were 3.47 billion adults worldwide with SBP of 110 to 115 mm Hg or higher in 2015.

  • Among African Americans in the JHS not taking antihypertensive medication, the prevalence of clinic hypertension (mean SBP ≥140 mm Hg or mean diastolic BP ≥90 mm Hg) was 14.3%, the prevalence of daytime hypertension (mean daytime SBP ≥135 mm Hg or mean daytime diastolic BP ≥85 mm Hg) was 31.8%, and the prevalence of nighttime hypertension (mean nighttime SBP ≥120 mm Hg or mean nighttime diastolic BP ≥70 mm Hg) was 49.4%. Among JHS participants taking antihypertensive medication, the prevalence estimates were 23.1% for clinic hypertension, 43.0% for daytime hypertension, and 61.7% for nighttime hypertension.

Diabetes Mellitus (Chapter 9)

  • On the basis of data from NHANES 2013 to 2016, of US adults, an estimated 26 million (9.8%) have diagnosed DM, 9.4 million (3.7%) have undiagnosed DM, and 91.8 million (37.6%) have prediabetes.

  • In 2017, the cost of DM was estimated at $327 billion (Table 9-1), up 26% from 2012, accounting for 1 in 4 healthcare dollars. Of these costs, $237 billion were direct medical costs and $90 billion resulted from reduced productivity.

  • On the basis of NHANES 2013 to 2016 data for adults with DM, 20.9% had their DM treated and controlled (fasting glucose <126 mg/dL), 45.2% had their DM treated but uncontrolled, 9.2% were aware they had DM but were not treated, and 24.7% were undiagnosed and not treated.

Metabolic Syndrome (Chapter 10)

  • The overall prevalence of metabolic syndrome has remained stable at 34.3% across all sex, age, and racial/ethnic groups since 2008 according to data from NHANES 2007 to 2014.

  • In a recent meta-analysis of 26 609 young adults (aged 18–30 years) across 34 studies, the prevalence of metabolic syndrome was 4.8% to 7% depending on the definition used.

  • In addition to well-established associations with poor CVD outcomes and all-cause mortality, the presence of metabolic syndrome also has been shown to be associated with poorer cancer outcomes, including increased risk of cancer recurrence, cancer-related mortality, and overall mortality.

Kidney Disease (Chapter 11)

  • According to the United States Renal Data System, the overall prevalence of chronic kidney disease in the United States among NHANES participants ≥20 years of age was 14.8% (95% CI, 13.6%–16.0%) in 2011 to 2014.

  • In 3 community-based cohort studies (JHS, Cardiovascular Health Study, and MESA), absolute incidence rates (per 1000 person-years) for HF, CHD, and stroke for participants with versus without chronic kidney disease were 22 versus 6.2 for HF, 24.5 versus 8.4 for CHD, and 13.4 versus 4.8 for stroke.

  • A recent meta-analysis of 43 studies examining associations between socioeconomic indicators (income, education, and occupation) found that lower socioeconomic status, particularly income, was associated with a higher prevalence of chronic kidney disease and faster progression to end-stage renal disease. This association was observed in higher- versus lower- or middle-income countries and was more pronounced in the United States, relative to Europe.

Sleep (Chapter 12)

  • Data from the Centers for Disease Control and Prevention indicated that the age-adjusted prevalence of healthy sleep duration (≥7 hours) was 65.2% for all Americans and was lower among Native Hawaiians/Pacific Islanders (53.7%), NH blacks (54.2%), multiracial NH people (53.6%), and American Indians/Alaska Natives (59.6%) compared with NH whites (66.8%), Hispanics (65.5%), and Asians (62.5%).

  • A meta-analysis of 43 studies indicated that both short sleep (<7 hours per night; relative risk [RR], 1.13; 95% CI, 1.10–1.17) and long sleep (>8 hours per night; RR, 1.35; 95% CI, 1.29–1.41) were associated with a greater risk of all-cause mortality. In addition, short sleep (<7 hours per night) was associated with total CVD (RR, 1.14; 95% CI, 1.09–1.20) and CHD (RR, 1.22; 95% CI, 1.13–1.31) but not stroke (RR, 1.09; 95% CI, 0.99–1.19). Long sleep duration was associated with total CVD (RR, 1.36; 95% CI, 1.26–1.48), CHD (RR, 1.21; 95% CI, 1.12–1.30), and stroke (RR, 1.45; 95% CI, 1.30–1.62).

  • A meta-analysis of 27 cohort studies found that mild obstructive sleep apnea (hazard ratio, 1.19; 95% CI, 0.86–1.65), moderate obstructive sleep apnea (1.28; 95% CI, 0.96–1.69), and severe obstructive sleep apnea (2.13; 95% CI, 1.68–2.68) were associated with all-cause mortality in a dose-response fashion. Only severe obstructive sleep apnea was associated with cardiovascular mortality (hazard ratio, 2.73; 95% CI, 1.94–3.85).

Total Cardiovascular Diseases (Chapter 13)

  • On the basis of NHANES 2013 to 2016 data, the prevalence of CVD (comprising CHD, HF, stroke, and hypertension) in adults ≥20 years of age is 48.0% overall (121.5 million in 2016) and increases with advancing age in both males and females. CVD prevalence excluding hypertension (CHD, HF, and stroke only) is 9.0% overall (24.3 million in 2016).

  • In 2016, 2 744 248 resident deaths were registered in the United States. Ten leading causes accounted for 74.1% of all registered deaths. The 10 leading causes of death in 2016 were the same as in 2015; these include heart disease (No. 1), cancer (No. 2), unintentional injuries (No. 3), chronic lower respiratory diseases (No. 4), stroke (No. 5), Alzheimer disease (No. 6), DM (No. 7), influenza and pneumonia (No. 8), kidney disease (No. 9), and suicide (No. 10). Seven of the 10 leading causes of death had a decrease in age-adjusted death rates. The age-adjusted death rates decreased 1.8% for heart disease, 1.7% for cancer, 2.4% for chronic lower respiratory diseases, 0.8% for stroke, 1.4% for DM, 11.2% for influenza and pneumonia, and 2.2% for kidney disease. The age-adjusted rate increased 9.7% for unintentional injuries, 3.1% for Alzheimer disease, and 1.5% for suicide.

  • In 2016, ≈17.6 million (95% CI, 17.3–18.1 million) deaths were attributed to CVD globally, which amounted to an increase of 14.5% (95% CI, 12.1%–17.1%) from 2006. The age-adjusted death rate per 100 000 population was 277.9 (95% CI, 272.1–284.6), which represents a decrease of 14.5% (95% CI, −16.2% to −12.5%) from 2006.

Stroke (Cerebrovascular Disease) (Chapter 14)

  • An estimated 7.0 million Americans ≥20 years of age self-report having had a stroke, and the overall stroke prevalence was an estimated 2.5%.

  • In the National (Nationwide) Inpatient Sample, hospitalizations for acute ischemic stroke increased significantly for both males and females and for certain racial/ethnic groups among younger adults aged 18 to 54 years. From 1995 through 2011 to 2012, stroke hospitalization rates almost doubled for males aged 18 to 34 and 35 to 44 years, with a 41.5% increase among males aged 35 to 44 years from 2003 to 2004 through 2011 to 2012.

  • In analyses using data from the Global Burden of Disease Study, ≈90% of the stroke risk could be attributed to modifiable risk factors (such as high BP, obesity, hyperglycemia, hyperlipidemia, and renal dysfunction), and 74% could be attributed to behavioral risk factors, such as smoking, sedentary lifestyle, and an unhealthy diet. Globally, 29% of the risk of stroke was attributable to air pollution.

  • Although global age-adjusted mortality rates for ischemic and hemorrhagic stroke decreased between 1990 and 2015, the absolute number of people who have strokes annually, as well as related deaths and disability-adjusted life-years lost, increased. The majority of global stroke burden is in low-income and middle-income countries.

  • In analyses of 1 165 960 Medicare fee-for-service beneficiaries hospitalized between 2009 and 2013 for ischemic stroke, patients treated at primary stroke centers certified between 2009 and 2013 had lower in-hospital (odds ratio [OR], 0.89; 95% CI, 0.85–0.94), 30-day (hazard ratio, 0.90; 95% CI, 0.89–0.92), and 1-year (hazard ratio, 0.91; 95% CI, 0.90–0.92) mortality than those treated at noncertified hospitals, after adjustment for demographic and clinical factors. Hospitals certified between 2009 and 2013 also had lower in-hospital and 30-day mortality than centers certified before 2009.

Congenital Cardiovascular Defects and Kawasaki Disease (Chapter 15)

  • Although estimates of birth prevalence/overall prevalence of congenital cardiovascular defects appear relatively stable, a general trend toward improved outcome/survival continues, which has led to an expanding population of adult congenital heart disease patients.

  • Although there remains increased mortality in patients with congenital cardiovascular defects compared with the general population, the standardized mortality ratios after congenital heart disease surgery continue to decrease. In a recent study from the Pediatric Cardiac Care Consortium’s US-based multicenter data registry, which examined 35 998 patients with a median follow-up of 18 years, the overall standardized mortality ratio was 8.3% (95% CI, 8.0%–8.7%).

Disorders of Heart Rhythm (Chapter 16)

  • The lifetime risk of atrial fibrillation recently has been estimated to be ≈1 in 3 among whites and 1 in 5 among blacks in the United States.

  • Individuals with optimal cardiovascular health have a 32% lower risk of atrial fibrillation.

  • Approximately 0.7 million (13%) of the ≈5.3 million cases of atrial fibrillation in the United States are undiagnosed.

  • Obese individuals have a 51% increased risk of developing atrial fibrillation compared with their nonobese counterparts.

  • Patients with atrial fibrillation admitted to rural hospitals had a 17% higher risk of death than those admitted to urban hospitals.

Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies (Chapter 17)

  • Prevalence of reported current training in cardiopulmonary resuscitation was 18%, and prevalence of having cardiopulmonary resuscitation training at some point was 65% in a survey of 9022 people in the United States in 2015. The prevalence of cardiopulmonary resuscitation training was lower in Hispanic/Latino people, older people, people with less formal education, and the lower-income group.

  • Incidence of emergency medical services–assessed out-of-hospital cardiac arrest in people of any age was 110.8 per 100 000 population (95% CI, 108.9–112.6), or 356 461 people (quasi-CI, 350 349–362 252) based on extrapolation from the ROC registry (Resuscitation Outcomes Consortium) of out-of-hospital cardiac arrest to the total population of the United States (325 193 000 as of June 9, 2017).

  • Survival of hospitalization after cardiac arrest varied between academic medical centers and was higher in hospitals with higher cardiac arrest volume, higher surgical volume, greater availability of invasive cardiac services, and more affluent catchment areas.

Subclinical Atherosclerosis (Chapter 18)

  • Coronary computed tomographic angiography, which includes assessment of the severity of coronary artery stenosis, plaque composition, and coronary segment location, does not offer additional prognostic value for all-cause mortality beyond traditional risk factors and coronary calcium in asymptomatic individuals.

  • In contrast to the US population, the majority (≈85%) of middle-aged people living a forager-horticulturalist lifestyle in the Bolivian Amazon remain free of coronary artery calcium, which indicates that coronary atherosclerosis can typically be avoided by maintaining a low lifetime burden of risk factors. Even among those Bolivian Amazon individuals >75 years of age, 65% remained free of coronary artery calcium.

Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris (Chapter 19)

  • Data from the BRFSS (Behavioral Risk Factor Surveillance System) 2016 survey indicated that 4.4% of respondents had been told that they had had an MI and 4.1% of respondents had been told that they had angina or CHD.

  • From 2006 to 2016, the annual death rate attributable to CHD declined 31.8%. CHD age-adjusted death rates per 100 000 were 132.3 for NH white males, 146.5 for NH black males, and 95.6 for Hispanic males; for NH white females, the rate was 67.9; for NH black females, it was 85.4; and for Hispanic females, it was 54.6 (unpublished National Heart, Lung, and Blood Institute tabulation).

  • Compared with nonparticipants, participants in SNAP have twice the risk of CVD mortality, which likely reflects differences in socioeconomic, environmental, and behavioral characteristics.

  • In the BRFSS from 2005 to 2015, <40% of patients self-reported participation in cardiac rehabilitation after an acute MI. Between 2011 and 2015, compared with patients who did not participate in cardiac rehabilitation, those who declared such participation were less likely to be female (OR, 0.76; 95% CI, 0.65–0.90; P=0.002) or black (OR, 0.70; 95% CI, 0.53–0.93; P=0.014), were less well educated (high school versus college graduate: OR, 0.69; 95% CI, 0.59–0.81; P<0.001 and less than high school versus college graduate: OR, 0.47; 95% CI, 0.37–0.61; P<0.001), and were more likely to be retired or self-employed (OR, 1.39; 95% CI, 1.24–1.73; P=0.003).

Cardiomyopathy and Heart Failure (Chapter 20)

  • The prevalence of HF continues to rise over time with the aging population. In NHANES data, an estimated 6.2 million American adults ≥20 years of age (2.2%) had HF between 2013 and 2016 compared with an estimated 5.7 million between 2009 and 2012.

  • Primary prevention of HF can be augmented by greater adherence to the Life’s Simple 7 goals; optimal profiles in smoking, BMI, physical activity, diet, cholesterol, BP, and glucose are associated with a lower lifetime risk of HF and more favorable cardiac structure and functional parameters by echocardiography.

  • Of incident hospitalized HF events, approximately half are characterized by reduced ejection fraction and the other half by preserved ejection fraction. The prevalence of HF with preserved ejection fraction, compared with prevalence of HF with reduced ejection fraction, appears to be increasing over time along with aging of the population.

Valvular Diseases (Chapter 21)

  • 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. In 2015, 33.4 million people were estimated to be living with rheumatic heart disease around the world, with sub-Saharan Africa, South Asia, and Oceania having the highest concentration of disability-adjusted life-years attributable to rheumatic heart disease.

  • Admissions for endocarditis related to injection drug use have risen in recent years in parallel with the opioid drug crisis. The prevalence of documented intravenous drug use among people admitted to a hospital because of endocarditis in the National (Nationwide) Inpatient Sample rose from 4.3% in 2008 to 10% in 2014.

Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension (Chapter 22)

  • Traditional atherosclerotic risk factors, including hypertension, hyperlipidemia, and DM, were not associated with risk of venous thromboembolism in a 2017 individual-level meta-analysis of >240 000 participants from 9 cohorts. Cigarette smoking was associated with provoked but not with unprovoked venous thromboembolism events.

  • Emerging evidence suggests that autoimmune disease, such as lupus and Sjögren syndrome, could be risk factors for venous thromboembolism.

  • African Americans present with higher-severity chronic venous insufficiency and have less improvement with radiofrequency ablation.

Peripheral Artery Disease and Aortic Diseases (Chapter 23)

  • A recent Danish trial in men aged 65 to 74 years reported that screening of peripheral artery disease (with ankle-brachial index), abdominal aortic aneurysm (with abdominal ultrasound), and hypertension followed by optimal care resulted in a 7% lower risk of 5-year mortality compared with no screening.

  • African Americans have a 37% higher amputation risk than white individuals. In adjusted analyses, lower socioeconomic status was associated with a 12% higher risk for amputation.

  • In 2017, the Centers for Medicare & Medicaid Services decided to cover supervised exercise therapy (up to 36 sessions over 12 weeks) for eligible symptomatic peripheral artery disease patients with intermittent claudication.

Quality of Care (Chapter 24)

  • Quality and performance measures for MI have been relatively stable in recent years but have improved longitudinally since data collection began.

  • Among hospitals that care for Medicare fee-for-service beneficiaries, the implementation of hospital readmission reduction programs was associated with a reduction in 30-day and 1-year hospitalization rates but an increase in 30-day and 1-year mortality.

  • According to national Medicare data from July 2015 through June 2016, the median (interquartile range) hospital risk-standardized mortality rate for MI was 13.1% (12.6%, 13.5%), and the median (interquartile range) risk-standardized 30-day readmission rate was 15.8% (15.5%, 16.2%).

  • According to national Medicare data from July 2015 through June 2016, the median (interquartile range) hospital risk-standardized mortality rate for HF was 11.6% (10.8%, 12.4%), and the median (interquartile range) risk-standardized 30-day readmission rate was 21.4% (20.8%, 22.1%).

Medical Procedures (Chapter 25)

  • Data from the Society of Thoracic Surgeons Adult Cardiac Surgery Database indicate that a total of 159 869 procedures involved isolated coronary artery bypass grafting in 2016.

  • In 2017, 3244 heart transplantations were performed in the United States, the most ever.

Economic Cost of Cardiovascular Disease (Chapter 26)

  • The average annual direct and indirect cost of CVD and stroke in the United States was an estimated $351.2 billion in 2014 to 2015.

  • The estimated direct costs of CVD and stroke increased from $103.5 billion in 1996 to 1997 to $213.8 billion in 2014 to 2015.

  • Between 2015 and 2035, the projected total (direct and indirect) costs of total CVD are estimated to remain relatively stable for 18- to 44-year-olds, increase slightly for 45- to 64 year-olds, and increase sharply for 65- to 79-year-olds and adults aged ≥80 years.

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. The 2019 annual Statistical Update is the product of a full year’s worth of effort by dedicated volunteer physicians and scientists, committed government professionals, and 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

Salim S. Virani, MD, PhD, FAHA, Chair Elect

Paul Muntner, PhD, FAHA, Vice Chair

Connie W. Tsao, MD, MPH, Vice Chair Elect

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

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

Acknowledgments

We wish to thank our colleagues: Lucy Hsu, Michael Wolz, Sean Coady, and Dr Gina Wei, National Heart, Lung, and Blood Institute; Ian Golnik and Kathleen Smith, AHA; and all the dedicated staff of the Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute for their valuable comments and contributions.

Disclosures

Writing Group Disclosures

Writing Group MemberEmploymentResearch GrantOther Research SupportSpeakers’ Bureau/HonorariaExpert WitnessOwnership InterestConsultant/Advisory BoardOther
Emelia J. BenjaminBoston University School of Medicine, Cardiology DepartmentAmerican Heart Association; NIH/NHLBI; RWJF; AHA/NIHNoneNoneNoneNoneNoneNone
Paul MuntnerUniversity of Alabama at Birmingham, Department of EpidemiologyAmgenNoneNoneNoneNoneNoneNone
Alvaro AlonsoEmory University, Department of EpidemiologyNIH; American Heart AssociationNoneNoneNoneNoneNoneNone
Marcio S. BittencourtUniversity of Sao PauloSanofi*NoneNoneNoneNoneNoneNone
Clifton W. CallawayUniversity of Pittsburgh, Department of Emergency Medicine
April P. CarsonUniversity of Alabama at Birmingham, Department of EpidemiologyCenters for Disease Control and Prevention; National Heart, Lung, and Blood Institute; National Institute for Diabetes and Digestive and Kidney Diseases; Amgen, IncNoneNoneNoneNoneNoneNone
Alanna M. ChamberlainMayo Clinic Health Sciences ResearchEpidStat InstituteNoneNoneNoneNoneNoneNone
Alexander R. ChangGeisinger Health System NephrologyNoneNoneNoneNoneNoneNoneNone
Susan ChengBrigham and Women’s Hospital, Cardiovascular Medicine
Sandeep R. DasUniversity of Texas Southwestern Medical Center, Department of Internal MedicineNoneNoneNoneNoneNoneNoneNone
Francesca N. DellingUniversity of California San Francisco, Cardiovascular ResearchNHLBINoneNoneNoneNoneNoneNone
Luc DjousseVA Boston Healthcare System MAVERICAmerican Egg Board; NIHNoneNoneNoneNoneNoneAmerican Egg Board (salary)
Mitchell S.V. ElkindColumbia University, Department of NeurologyBMS-Pfizer Alliance for Eliquis*; Roche*; NINDSNoneNoneAuxilium; Merck/Organon; LivaNova (Sorin)NoneAbbott*; Vascular Dynamics*AHA/ASA (board member)*; UpToDate (royalties)*; Biogen*; Medtronic*
Jane F. FergusonVanderbilt University Medical Center, Division of Cardiovascular MedicineNoneNoneNoneNoneNoneNoneNone
Myriam FornageUniversity of Texas Health Science Center at Houston, Institute of Molecular MedicineNoneNoneNoneNoneNoneNoneNone
Lori Chaffin JordanVanderbilt University Medical Center, Department of PediatricsNoneNoneNoneNoneNoneNoneNone
Sadiya S. KhanNorthwestern UniversityNIHNoneNoneNoneNoneNoneNone
Brett M. KisselaUniversity of Cincinnati College of MedicineNoneNoneNoneNoneNoneNoneNone
Kristen L. KnutsonUniversity of Chicago Health StudiesNoneNoneNoneNoneNonePfizer*None
Tak W. KwanMount Sinai Beth Israel, Department of CardiologyNoneNoneNoneNoneNoneNoneNone
Daniel T. LacklandMedical University of South Carolina, Department of NeurologyNoneNoneNoneNoneNoneNoneNone
Tené T. LewisEmory University, Rollins School of Public Health, Department of EpidemiologyNoneNoneNoneNoneNoneNoneNone
Judith H. LichtmanYale School of Public Health, Department of Chronic Disease EpidemiologyNIH*NoneNoneNoneNoneNoneNone
Chris T. LongeneckerCase Western Reserve University, School of Medicine, CardiologyMedtronic Philanthropy*; Gilead Sciences*NoneNoneNoneNoneNoneNone
Matthew Shane LoopUniversity of North Carolina at Chapel HillNHLBI (HCHS/SOL contract); NHLBI (ARIC contract); DENKA-SEIKEN; Puget Sound BloodworksNoneNoneNoneNoneNoneNone
Pamela L. LutseyUniversity of Minnesota, Division of Epidemiology and Community HealthNIHNoneNoneNoneNoneNoneNone
Seth S. MartinJohns Hopkins School of Medicine, Department of CardiologyNoneNoneNoneNoneNoneNoneNone
Kunihiro MatsushitaJohns Hopkins Bloomberg School of Public Health, Department of EpidemiologyFukuda DenshiNoneNoneNoneNoneBristol-Myers Squibb*; Fukuda Denshi*None
Andrew E. MoranColumbia University MedicineNoneNoneNoneNoneNoneNoneNone
Michael E. MussolinoNIHNoneNoneNoneNoneNoneNoneNone
Martin O’FlahertyUniversity of Liverpool, Department of Public Health and PolicyNoneNoneNoneNoneNoneNoneNone
Ambarish PandeyUniversity of Texas Southwestern Medical Center, CardiologyTexas Health Resources Clinical ScholarshipNoneNoneNoneNoneNoneNone
Amanda M. PerakLurie Children’sNoneNoneNoneNoneNoneNoneNone
Wayne D. RosamondGillings School of Global Public Health, University of North Carolina, Department of EpidemiologyNoneNoneNoneNoneNoneNoneNone
Gregory A. RothUniversity of Washington, Department of Medicine–CardiologyNHLBI; Cardiovascular Medical Research and Education FoundationNoneNoneNoneNoneNoneNone
Uchechukwu K.A. SampsonNew York University, Department of Population HealthNoneNoneNoneNoneNoneNoneNone
Gary M. SatouUCLANoneNoneNoneNoneNoneNoneNone
Emily B. SchroederKaiser Permanente Colorado Institute for Health ResearchNoneNoneNoneNoneNoneNoneNone
Svati H. ShahDuke University MedicineNoneNoneNoneNoneNoneNoneNone
Nicole L. SpartanoBoston University, Department of Preventative Medicine and EpidemiologyAlzheimer’s Association; American Heart AssociationNoneNoneNoneNoneNoneNone
Andrew StokesBoston University Global HealthJohnson & Johnson, IncNoneNoneNoneNoneNoneNone
David L. TirschwellHarborview Medical Center, Department of NeurologyNoneNoneNoneNoneNoneNoneNone
Connie W. TsaoBeth Israel Deaconess Medical Center Department of MedicineNoneNoneNoneNoneNoneNoneNone
Mintu P. TurakhiaStanford University School of Medicine; Veterans Affairs Palo Alto Health Care System; Center for Digital HealthJanssen; Apple; AstraZeneca; AHANoneNoneNoneAliveCor*Medtronic*; Abbott*; iBeat*JAMA Cardiology (income as editor)*
Lisa B. VanWagnerNorthwestern University MedicineNIH; Gore MedicalNoneSalix Pharmaceuticals*NoneNoneNoneNone
Salim S. ViraniMichael E. DeBakey VA Medical Center, Baylor College of MedicineNoneNoneNoneNoneNoneNoneNone
John T. WilkinsNorthwestern University Feinberg School of Medicine, Preventive MedicineNIH K23NoneNoneNoneNoneNoneNone
Sally S. WongAmerican Heart AssociationNoneNoneNoneNoneNoneNoneHunter College, CUNY (salary)*

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

*Modest.

†Significant.

Footnotes

https://www.ahajournals.org/journal/circ

The views expressed in this manuscript 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, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O’Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS; on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation. 2019;139:e56–e528. doi: 10.1161/CIR.0000000000000659.

The expert peer review of AHA-commissioned documents (eg, scientific statements, clinical practice guidelines, systematic reviews) 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 https://www.heart.org/permissions. A link to the “Copyright Permissions Request Form” appears in the second paragraph (https://www.heart.org/en/about-us/statements-and-policies/copyright-request-form).

Reference

1. About These Statistics

The AHA works with the NHLBI 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 28 of this document, the Glossary.

The surveys used are the following:

  • ARIC—CHD and HF incidence rates

  • BRFSS—ongoing telephone health survey system

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

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

  • 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

  • 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

  • NIS of the Agency for Healthcare Research and Quality—hospital inpatient discharges, procedures, and charges

  • United States Renal Data System—kidney disease prevalence

  • 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, including MI, AP, HF, stroke, and hypertension.

A major emphasis of this Statistical Update is to present the latest estimates of the number of people in the United States who have specific conditions to provide a realistic estimate of burden. Most estimates based on NHANES prevalence rates are based on data collected from 2013 to 2016. These are applied to census population estimates for 2016. 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.

A major enhancement in the 2019 Statistical Update is the addition of a new chapter, Sleep (Chapter 12). Also this year, there is an emphasis on social determinants of health that are built across the various chapters, and global estimates are provided where available.

Risk Factor Prevalence

The NHANES 2013 to 2016 data are used in this Update to present estimates of the percentage of people with high lipid values, DM, overweight, and obesity. The NHIS 2015 data are 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 2016 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 8 (High Blood Pressure) and Chapter 20 (Cardiomyopathy and Heart Failure). High BP, or hypertension, increases the mortality risks of CVD and other diseases, and HF should be selected as an underlying cause only when the true underlying cause is not known. In this Update, hypertension and HF death rates are presented in 2 ways: (1) As nominally classified as the underlying cause and (2) as any-mention mortality.

National and state mortality data presented according to the underlying cause of death were computed from the mortality tables of the NCHS/CDC website or the CDC compressed mortality file. Any-mention numbers of deaths were tabulated from the electronic mortality files of the NCHS/CDC website.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 20161 in the computation of morbidity data. NCHS/CDC population estimates2 for 2016 were used in the computation of death rate data. The Census Bureau website 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 HCUP 2014. Ambulatory care visit data include patient visits to primary providers’ 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 2015 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. If 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 year 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 2014 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 2016. For disease and risk factor prevalence, most rates in this report are calculated from the 2013 to 2016 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US resident population on July 1, 2016, 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 2014. Numbers of visits to primary providers’ offices and hospital EDs are for 2015, whereas hospital outpatient department visits are for 2011. Except as noted, economic cost estimates are for 2014 to 2015.

Cardiovascular Disease

For data on hospitalizations, primary provider 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 only 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://newsroom.heart.org/connect 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 the journal Circulation.

Abbreviations Used in Chapter 1

AHAAmerican Heart Association
APangina pectoris
ARICAtherosclerosis Risk in Communities Study
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CHSCardiovascular Health Study
CVDcardiovascular disease
DMdiabetes mellitus
EDemergency department
FHSFramingham Heart Study
GCNKSSGreater Cincinnati/Northern Kentucky Stroke Study
HCUPHealthcare Cost and Utilization Project
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
NHISNational Health Interview Survey
NHLBINational Heart, Lung, and Blood Institute
NINDSNational Institute of Neurological Disorders and Stroke
NISNational (Nationwide) Inpatient Sample
PADperipheral artery disease
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

References

2. Cardiovascular Health

See Tables 2-1 through 2-6 and Charts 2-1 through 2-12

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

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 al.1 Copyright © 2010, 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 the American College of Cardiology Foundation. Copyright © 2011, the American College of Cardiology Foundation.

By 2020, to improve the cardiovascular health of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.1

These goals introduced 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 for children.

This concept of cardiovascular health represented 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 has evaluated and published 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 (summaries below) 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 (CAC) prevalence and progression; with physical functional impairment and frailty4; and with cognitive decline and depression.4,5 Similar relationships have also been seen in non-US populations.4–9

  • 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.10

  • A recent study in blacks found that risk of incident HF was 61% lower among those with ≥4 ideal cardiovascular health metrics than among those with 0 to 2 ideal metrics.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

  • Analyses from the US Burden of Disease Collaborators demonstrated that poor levels of each of the 7 health factors and behaviors resulted in 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.13

  • A stepwise association was present between the number of ideal cardiovascular health metrics and risk of death based on NHANES 1988 to 2006 data.14 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.14

  • 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 a 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).15

  • The adjusted population attributable fractions for CVD mortality were as follows14:

    • — 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 follows14:

    • — 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.16

  • 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 not smoking) 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.17

  • Better cardiovascular health is associated with less incident HF,18 less subclinical vascular disease,19,20 better global cognitive performance and cognitive function,21,22 lower prevalence23 and incidence24 of depressive symptoms, lower loss of physical functional status,25 longer leukocyte telomere length,26 less end-stage renal disease,27 and less pneumonia, chronic obstructive pulmonary disease,28 VTE/PE,29 lower prevalence of aortic sclerosis and stenosis,30 better prognosis after MI,31 and lower risk of AF.32 In addition, a recent study among a sample of Hispanics/Latinos residing in the United States reported that a measure of greater positive psychological functioning (dispositional optimism) was associated with higher cardiovascular health scores as defined by the AHA.33

  • On the basis of NHANES 1999 to 2006 data, 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.34

  • Cardiovascular health metrics are also associated with lower healthcare costs. A recent report from a large, ethnically diverse insured population35 found that people with 6 or 7 and those with 3 to 5 of the cardiovascular health metrics in the ideal category had a $2021 and $940 lower annual mean healthcare expenditure, respectively, than those with 0 to 2 ideal health metrics.

Cardiovascular Health: Current Prevalence

(Table 2-2 and Charts 2-2 through 2-9)

  • 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 is worse than in adults.

  • Among US children aged 12 to 19 years (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 >85% for the smoking, BP, and fasting glucose metrics (unpublished AHA tabulation).

  • 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 78% for never having smoked or being a former smoker who has quit for >12 months. In 2015 to 2016, only about half (49%) of all adults had ideal levels of TC (<200 mg/dL).

  • Age-standardized and age-specific prevalence estimates for ideal cardiovascular health and for ideal levels of each of its components are shown for 2013 to 2014 and 2015 to 2016 in Table 2-2. NHANES 2013 to 2014 data were used for some of the statistics that required nutritional data and for DM. The prevalence of ideal levels across 7 health factors and health behaviors generally was lower with increasing age. The exception was diet, for which prevalence of ideal levels was highest in older adults but still very low (<1%).

  • 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 (of 7 possible) in 2013 to 2014.

    • — Few US children 12 to 19 years of age (≈4%) meet <2 criteria for ideal cardiovascular health.

    • — Approximately half of US children (48%) meet 3 or 4 criteria for ideal cardiovascular health, and ≈47% meet 5 or 6 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 in 2013 to 2014, overall and stratified by age, sex, and race.

    • — Approximately 2% of US adults meet 0 of the 7 criteria at ideal levels, and another 15% meet only 1 of 7 criteria. Having ≤1 ideal metric is much more common among adults (17%) than among children (12–19 years of age), for whom having ≤1 ideal metric is very rare (<1%).

    • — Most US adults (≈62%) have 3 or fewer cardiovascular metrics in the ideal cardiovascular health range.

    • — Approximately 13% of US adults meet ideal levels in 5 categories, 5% have 6 ideal metrics, and virtually 0% meet all 7 criteria at ideal levels.

    • — Presence of ideal cardiovascular health by age and sex is shown in 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. Having ≥4 ideal metrics is most common among Asians (48%), followed by whites (38%), Hispanics (34%), blacks (30%), and others (24%).

  • Chart 2-7 displays the age-standardized percentages of US adults and the percentages of children who have ≥5 of the metrics (of 7 possible) at ideal levels in 2007 to 2008 and 2013 to 2014.

    • — Currently, nearly half (47%) of US children 12 to 19 years of age have ≥5 metrics at ideal levels, with similar prevalence in boys (49%) and girls (46%).

    • — In comparison, only 18% of US adults have ≥5 metrics at ideal levels, with lower prevalence in males (15%) than in females (22%).

    • — All populations showed improvement compared with 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 adults, NH Asians tend to have a higher prevalence of having ≥5 metrics at ideal levels than other racial/ethnic groups. In children, the prevalence of ≥5 metrics at ideal levels is highest for NH whites (53%), followed by NH Asians (48%), Hispanics (40%), and NH blacks (36%).

  • Chart 2-9 displays the age-standardized percentages of US adults meeting different numbers of criteria for both poor and ideal cardiovascular health in 2013 to 2014. 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 36% of US adults have ≥3 metrics at poor levels.

    • — Few US adults (3%) have ≥5 metrics at poor levels.

    • — More US adults have 4 to 6 ideal metrics than 4 to 6 poor metrics.

Table 2-2. Prevalence of Ideal Cardiovascular Health and Its Components in the US Population in Selected Age Strata: NHANES 2013 to 2014 and 2015 to 2016

NHANES CycleAge 12–19 yAge ≥20 y*Age 20–39 yAge 40–59 yAge ≥60 y
Ideal cardiovascular health profile (7/7)2013–20140.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)
 ≥6 Ideal2013–20149.4 (1.3)5.1 (0.4)9.0 (0.9)4.2 (0.6)0.6 (0.3)
 ≥5 Ideal2013–201447.2 (2.0)18.1 (1.0)30.8 (1.8)13.3 (1.5)5.0 (0.9)
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/dL2015–201677.7 (1.3)49.4 (1.1)72.9 (1.4)39.0 (1.7)25.2 (1.2)
 SBP <120/DBP <80 mm Hg2015–201685.2 (1.0)41.0 (1.2)61.7 (1.7)34.1 (2.0)15.6 (2.1)
 Nonsmoker2015–201693.6 (0.9)78.8 (1.0)75.0 (1.4)77.0 (1.5)86.5 (1.4)
 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)2013–20140.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)
 PA at goal2013–201427.7 (1.2)36.7 (1.1)45.0 (2.0)34.2 (1.6)26.7 (1.5)
 Nonsmoker2015–201691.4 (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 met2013–20140.0 (0.0)0.2 (0.1)0.0 (0.0)0.2 (0.1)0.4 (0.2)
  F&V ≥4.5 C/d2013–20146.5 (1.4)10.3 (0.7)8.5 (1.0)9.7 (1.2)13.8 (1.7)
  Fish ≥2 svg/wk2013–20148.5 (1.4)20.1 (1.6)16.4 (1.7)21.8 (2.1)23.1 (2.2)
  Sodium <1500 mg/d2013–20140.5 (0.5)0.6 (0.2)0.9 (0.3)0.6 (0.4)0.2 (0.1)
  SSB <36 oz/wk2013–201437.0 (2.3)51.2 (1.9)41.8 (1.9)53.0 (3.3)64.5 (2.8)
  Whole grains ≥3 1-oz/d2013–20143.4 (1.1)7.1 (0.4)5.3 (0.8)6.6 (0.6)10.4 (1.1)
Secondary diet metrics
 Nuts/legumes/seeds ≥4 svg/wk2013–201431.7 (1.9)49.7 (1.3)46.8 (2.1)51.0 (2.3)53.5 (2.4)
 Processed meats ≤2 svg/wk2013–201444.4 (3.2)45.0 (1.3)44.7 (1.5)47.3 (2.4)41.4 (2.3)
 SFat <7% total kcal2013–20148.9 (1.3)9.2 (0.4)9.7 (0.9)9.4 (0.9)8.4 (1.2)

Values are % (standard error). BMI indicates body mass index; DBP, diastolic blood pressure; F&V, fruits and vegetables; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c (glycosylated hemoglobin); NHANES, National Health and Nutrition Examination Survey; PA, physical activity; SBP, systolic blood pressure; SFat, saturated fat; SSB, sugar-sweetened beverages; and svg, servings.

*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.

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 AHA 2020 goals, among US children aged 12 to 19 years. AHA indicates American Heart Association. *Healthy Diet Score reflects 2013 to 2014 NHANES (National Health and Nutrition Examination Survey). Source: National Center for Health Statistics, NHANES, 2015 to 2016.

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 AHA 2020 goals among US adults aged 20 to 49 and ≥50 years. AHA indicates American Heart Association. *Healthy Diet Score reflects 2013 to 2014 NHANES (National Health and Nutrition Examination Survey). Source: National Center for Health Statistics, NHANES, 2015 to 2016.

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, 2013 to 2014.

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, 2013 to 2014.

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 2013 to 2014.

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, 2007 to 2008 and 2013 to 2014.

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, 2013 to 2014.

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 AHA 2020 goals among US adults aged ≥20 years. AHA indicates American Heart Association. Source: National Center for Health Statistics, National Health and Nutrition Examination Survey, 2013 to 2014.

Cardiovascular Health: Trends Over Time

(Charts 2-10 and 2-11)

  • The trends over the past decade in each of the 7 cardiovascular health metrics (for diet, trends from 1999–2000 through 2015–2016) are shown in Chart 2-10 (for children 12–19 years of age) and Chart 2-11 (for adults ≥20 years of age).

    • — Among children, from 1999 to 2000 to 2015 to 2016, the prevalence of nonsmoking, ideal TC, and ideal BP improved. For example, the prevalence of nonsmoking among children aged 12 to 19 years increased from 76% to 94%, and for ideal TC, the prevalence increased from 72% to 78%. However, there were no improvements in meeting ideal levels for PA, BMI, and blood glucose. For example, the prevalence of ideal BMI declined from 70% in 1999 to 2000 to 60% in 2015 to 2016.

    • — Among adults, the prevalence of nonsmoking and ideal TC, BP, and PA improved. For example, nonsmoking increased from 73% of the adult population in 1999 to 2000 to 79% in 2015 to 2016. For TC, the prevalence of ideal levels increased from 45% of the adult population in 1999 to 2000 to 49% of the adult population in 2015 to 2016.

  • 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.36 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.36

  • On the basis of these projections for 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).37

Chart 2-10.

Chart 2-10. 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. Data for the Healthy Diet Score, based on a 2-day average intake, were 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. NHANES indicates National Health and Nutrition Examination Survey. *Because of changes in the physical activity questionnaire between different cycles of NHANES, trends over time for this indicator should be interpreted with caution, and statistical comparisons should not be attempted. Source: National Center for Health Statistics, NHANES, 1999 to 2000 through 2015 to 2016.

Chart 2-11.

Chart 2-11. Age-standardized trends in prevalence of meeting criteria for ideal cardiovascular health for each of the 7 metrics among US adults aged ≥20 years. Data for the Healthy Diet Score, based on a 2-day average intake, were 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. NHANES indicates National Health and Nutrition Examination Survey. *Because of changes in the physical activity questionnaire between different cycles of NHANES, trends over time for this indicator should be interpreted with caution, and statistical comparisons should not be attempted. Source: National Center for Health Statistics, NHANES, 1999 to 2000 through 2015 to 2016.

Achieving the 2020 Impact Goals1

(Tables 2-3 through 2-6 and Chart 2-12)

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.

Table 2-3. Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 Years Old: NHANES 2015 to 2016

%
Percent BP ideal among adults, 2015–201641.0
20% Relative increase49.2
Percent whose BP would be ideal if population mean BP were lowered by*:
 2 mm Hg44.6
 3 mm Hg47.2
 4 mm Hg49.0
 5 mm Hg52.0

Standardized to the age distribution of the 2000 US standard population. BP indicates blood pressure; and NHANES, National Health and Nutrition Examination Survey.

*Reduction in BP = (observed average systolic BP − X mm Hg) and (observed average diastolic BP − X mm Hg).

Chart 2-12.

Chart 2-12. Prevalence of ideal, intermediate, and poor cardiovascular health metrics in 2006 (AHA 2020 Strategic 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. AHA indicates American Heart Association. Reprinted from Huffman et al.36 Copyright © 2012, American Heart Association, Inc.

  • Taken together, the 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 by the year 2020 (Chart 2-12).

  • 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 2015 to 2016, the current prevalence of ideal levels of BP among US adults is 41%. To achieve the 2020 goals, a 20% relative improvement would require an increase in this proportion to 49.2% by 2020 (41% × 1.20). On the basis of NHANES data, a reduction in population mean BP of just 5 mm Hg would result in 52% of US adults having ideal levels of BP, which represents a 27% 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.38

  • A range of complementary strategies and approaches can lead to improvements in cardiovascular health.37 These include the following:

    • — Individual-focused approaches that target lifestyle and treatments at the individual level (Table 2-4).

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

    • — Population approaches that 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.

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 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.40 Copyright © 2010, American Heart Association, Inc.

Table 2-5. Evidence-Based Healthcare Systems Approaches to Support and Facilitate Improvements in Health Behaviors and Health Factors41–43

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)§
 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)
Physical activity Continued
 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 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 al.41 Copyright © 2012, American Heart Association, Inc.

Abbreviations Used in Chapter 2

AFatrial fibrillation
AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
CACcoronary artery calcification
CHDcoronary heart disease
CIconfidence interval
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
IHDischemic heart disease
IMTintima-media thickness
MImyocardial infarction
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
PAphysical activity
PEpulmonary embolism
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SFatsaturated fat
SSBsugar-sweetened beverage
TCtotal cholesterol
VTEvenous thromboembolism

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3. Smoking/Tobacco Use

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

Tobacco use is one of the leading preventable causes of death in the United States and globally.1 Tobacco smoking, the most common form of tobacco use, is a major risk factor for CVD and stroke.2 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 in Life’s Simple 7.3 Unless otherwise stated, throughout the rest of the chapter we will report tobacco use and smoking estimates from the NSDUH4 for adolescents (12–17 years of age) and from the NHIS5 for adults (≥18 years of age), because these data sources have more recent data.

Table 3-1. Deaths Caused by Tobacco Smoke Worldwide, by Sex

Males and FemalesMalesFemales
Total number of deaths in 2015, millions7.2 (6.5 to 7.8)5.2 (4.7 to 5.7)1.9 (1.7 to 2.2)
Percent change in total number from 1990 to 201521.5 (15.8 to 27.2)26.4 (20.4 to 32.9)9.9 (1.2 to 19.1)
Percent change in total number from 2005 to 20154.2 (0.9 to 7.6)6.8 (3.1 to 10.7)-2.3 (-7.5 to 3.7)
Mortality rate per 100 000 in 2015110.7 (101.0 to 120.3)177.2 (160.2 to 193.7)55.2 (48.8 to 61.9)
Percent change in rate from 2005 to 2015−20.3 (−22.8 to −17.7)−18.9 (−21.7 to −16.0)−25.2 (−29.1 to −20.5)
Percent change in rate from 1990 to 2015−33.3 (−36.4 to −30.1)−32.8 (−35.8 to −29.5)−38.4 (−43.7 to −33.2)
PAF in 2015, %12.8 (11.7 to 13.9)16.9 (15.4 to 18.4)7.8 (6.9 to 8.7)
Percent change in PAF from 1990 to 20154.4 (−0.1 to 8.5)3.7 (−0.1 to 7.4)−0.4 (−7.9 to 7.0)
Percent change in PAF from 2005 to 20150.1 (−2.4 to 2.6)−0.1 (−2.4 to 2.2)−3.0 (−7.4 to 2.2)

Rates are most current data available as of 2015. Rates are per 100 000 people. Values in parentheses represent 95% CIs. PAF indicates population attributable fraction.

Source: Global Burden of Disease Study 2015, Institute of Health Metrics and Evaluation.62

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 and 2015). 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 might 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.63

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 exponentially, giving rise to the more general term electronic nicotine delivery systems.6 Use of cigars, cigarillos, filtered cigars, and hookah also has become increasingly common in recent years. Thus, each section below will address the most recent statistical estimates for combustible cigarettes, electronic nicotine delivery systems, and other forms of tobacco use if such estimates are available.

Prevalence

(Charts 3-1 through 3-4)

Youth
  • Prevalence of cigarette use in the past month for adolescents aged 12 to 17 years by sex and race/ethnicity in 2014 and 2015 is shown in Chart 3-1.

  • According to the NSDUH 2016 data, for adolescents aged 12 to 17 years4:

    • — 5.3% (1 319 541) used tobacco products, and 3.4% (846 498) smoked cigarettes in the past month.

    • — 1.4% (348 558) used smokeless tobacco in the past month.

    • — Of adolescents who smoked, 15% (126 975) smoked cigarettes daily.

    • — 1.8% (448 146) were current cigar smokers, and 0.5% (124 485) were current pipe tobacco smokers.

  • In 2015, tobacco use within the past month for youth 12 to 17 years of age varied slightly by region: 6.2% in the Northeast, 7.0% in the Midwest, 6.0% in the South, and 4.9% in the West.4

  • In 2015, 21.6% of adults aged 18 to 20 years were current cigarette smokers compared with 8.7% of adolescents aged 16 to 17 years.4

  • The PATH study assessed the use of different types of tobacco products, including cigarettes, e-cigarettes, cigars, cigarillos, filtered cigars, pipe tobacco, hookah, snus pouches, smokeless tobacco, dissolvable tobacco, bidis, and kreteks. This study estimated that in 2013 to 2014, 8.9% of youth aged 12 to 18 years used some form of tobacco in the past 30 days, and 1.6% were daily users.7

  • Data from the National Youth Tobacco Survey indicate that the percentage of high school students who used e-cigarettes (11.3%) exceeded the proportion using cigarettes (8.0%) in 2016 (Chart 3-2).8

Chart 3-2.

Chart 3-2. Estimated percentage of high school students who have used any tobacco products,* ≥2 tobacco products,†‡ and select tobacco products§ in the past 30 days (National Youth Tobacco Survey, 2011–2016). *Any tobacco use is defined as past 30-day use of cigarettes, cigars, smokeless tobacco, electronic cigarettes (e-cigarettes), hookahs, pipe tobacco, and/or bidis. †Use of ≥2 tobacco products is defined as past 30-day use of ≥2 of the following product types: cigarettes, cigars, smokeless tobacco, e-cigarettes, hookahs, pipe tobacco, or bidis. ‡Use of ≥2 tobacco products demonstrated a nonlinear change (P<0.05). §E-cigarettes and hookahs demonstrated a linear increase (P<0.05). Cigarettes, cigars, and smokeless tobacco demonstrated a linear decrease (P<0.05). Pipe tobacco and bidis demonstrated a nonlinear decrease (P<0.05). Data derived from the Centers for Disease Control and Prevention, National Youth Tobacco Survey.64

Adults
  • According to the NHIS 2016 data, among adults ≥18 years of age6:

    • — 15.5% of adults (37.8 million) are current smokers.

    • — 17.5% of males and 13.5% of females are current smokers.

    • — 13.1% of those 18 to 24 years of age, 17.6% of those 25 to 44 years of age, 18.0% of those 45 to 64 years of age, and 8.8% of those ≥65 years old are current smokers.

    • — 31.8% of American Indians or Alaska Natives, 16.5% of blacks, 9% of Asians, 10.7% of Hispanics, and 16.6% of whites are current smokers.

    • — 25.3% of people below the poverty level based on family income and family size are current smokers.

  • 20.5% of lesbian/gay/bisexual individuals are current smokers.

  • By region, the prevalence of current cigarette smokers was highest in the Midwest (18.5%) and lowest in the West (12.3%).7

  • In 2009, 42.4% of adults with HIV receiving medical care were current smokers.9

  • Using data from BRFSS 2016, the state with the highest age-adjusted percentage of current cigarette smokers was West Virginia (26.2%). The state with the lowest age-adjusted percentage of current cigarette smokers was Utah (8.7%) (Chart 3-3).9a

  • In 2016, smoking prevalence was higher among adults ≥18 years of age who reported having a disability or activity limitation (21.2%) than among those reporting no disability or limitation (14.4%).7

  • 9.3% of males and 33.6% of females with mental illness were current smokers.10

  • Among females who gave birth in 2016, 7.2% smoked cigarettes during pregnancy. Smoking prevalence during pregnancy was greatest for females aged 20 to 24 years (10.7%), followed by females aged 15 to 19 years (8.5%) and 25 to 29 years (8.2%).11 Rates were highest among NH American Indian or Alaska Native females (16.7%) and lowest in NH Asian females (0.6%). With respect to differences by education, cigarette smoking prevalence was highest among females who completed high school (12.2%), followed by females with less than high school education (11.7%).

  • The PATH study assessed the use of different types of tobacco products, including cigarettes, e-cigarettes, cigars, cigarillos, filtered cigars, pipe tobacco, hookah, snus pouches, smokeless tobacco, dissolvable tobacco, bidis, and kreteks. This study estimated that in 2013 to 2014, 34.8% of adult males and 20.8% of adult females were current users of some form of tobacco.7

  • E-cigarette prevalence in 2016 is shown in Chart 3-4.

Chart 3-3.

Chart 3-3. Age-adjusted prevalence (%) of current cigarette smoking for adults, by state: United States (BRFSS, 2016). BRFSS indicates Behavior Risk Factor Surveillance System; GU, Guam; PR, Puerto Rico; and VI, Virgin Islands. Data derived from the Centers for Disease Control and Prevention.65

Chart 3-4.

Chart 3-4. Prevalence (age-adjusted) of current e-cigarette use (BRFSS, 2016). BRFSS indicates Behavior Risk Factor Surveillance System; GU, Guam; PR, Puerto Rico; and VI, Virgin Islands.

Incidence

  • According to the 2015 NSDUH, approximately 1.96 million people ≥12 years of age had smoked cigarettes for the first time within the past 12 months, a decrease from 2.3 million in 2012.4 The 2015 estimate averages out to ≈5390 new cigarette smokers every day. Of new smokers in 2015, 823 000 (42.1%) were 12 to 17 years old, 762 000 (39%) were 18 to 20 years old, and 287 000 (14.7%) were 21 to 25 years old; only 84 000 (4.3%) were ≥26 years when they first smoked cigarettes.

  • The number of new smokers 12 to 17 years of age (823 000) decreased from 2002 (1.3 million); however, new smokers 18 to 25 years of age increased from ≈600  000 in 2002 to 1.05 million in 2015.

  • According to the NHIS, in 2015, the average age for initiation of cigarette use was 17.9 years.5

  • According to data from PATH, in youth 12 to 17 years of age, use of an e-cigarette was independently associated with combustible cigarette use 1 year later (OR, 1.87; 95% CI, 1.15–3.05). For youth who tried hookah, noncigarette combustible tobacco, or smokeless tobacco, a similar strength of association for tobacco use at 1 year was observed.12

Lifetime Risk and Cumulative Incidence in Youth (12 to 17 Years Old) in 2015

  • Per NSDUH data for individuals aged 12 to 17 years, overall, the lifetime use of tobacco products declined from 18.5% to 17.3%, with lifetime cigarette use declining from 14.2% to 13.2% during the same time period (P<0.05 for both).4

  • The lifetime use of tobacco products among adolescents 12 to 17 years old varied by the following4:

    • — Sex: Lifetime use was higher among boys (19.1%) than girls (15.3%).

    • — Race/ethnicity: Lifetime use was highest among whites (19.9%), followed by American Indians or Alaska Natives (19.6%), Hispanics or Latinos (14.5%), African Americans (13.8%), and Asians (7.7%).

    • — Geographic division: The highest lifetime use was observed in the South (East South Central 22.8%), and the lowest was observed in the Pacific West (13.0%).

Adults

  • According to NSDUH data, the lifetime use of tobacco products in individuals aged ≥18 years declined significantly (P<0.01) between 2014 and 2015, from 71.1% to 68.7%, with cigarette use declining in the same interval from 65.9% to 63.1% (both P<0.01).4 Similar to the patterns in youth, lifetime risk of tobacco products varied by demographic factors:

    • — Sex: Lifetime use was higher in males (78.1%) than females (60.0%).

    • — Race/ethnicity: Lifetime use was highest in American Indians or Alaska Natives (75.9%) and whites (75.9%), followed by blacks (58.4%), Native Hawaiian or Other Pacific Islander (56.8%), Hispanics or Latinos (56.7%), and Asians (37.9%).

  • In 2015, the lifetime use of smokeless tobacco for adults ≥18 years of age was 17.4%.

  • Lifetime tobacco use for people with psychiatric diagnoses was 56.1%, 55.6%, and 70.1% in patients with mood disorders, anxiety disorders, and schizophrenia, respectively.13

Mortality

  • Of risk factors evaluated by the US Burden of Disease Collaborators, tobacco use was the second-leading risk factor for death in the United States and the leading cause of DALYs, accounting for 11% of DALYs, in 2016.14

  • Overall mortality among US smokers is 3 times higher than that for never-smokers.15

  • On average, male smokers die 12 years earlier than male never-smokers, and female smokers die 11 years earlier than female never-smokers.9a,16

  • Increased CVD mortality risks persist for older (≥60 years old) smokers as well. A 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, 1.82–2.36) compared with never-smokers and 1.37 (95% CI, 1.25–1.49) compared with former smokers.18

  • In a sample of Native Americans (SHS), 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.19

  • 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.20

  • If current smoking trends continue, 5.6 million US children will die of smoking prematurely during adulthood.21

Secular Trends

(Charts 3-2 and 3-5)

Youth

The percentage of adolescents (12–17 years old) who reported smoking in the past month declined from 13% in 2002 to 3.4% in 2016 (Chart 3-5).22 The percentages for daily cigarette use among past-month cigarette-smoking adolescents were 31.8% in 2002 and 15% in 2016. Among high school students between 2011 and 2016, past-month cigarette smoking declined from 15.8% to 8.0% (Chart 3-2).8

Chart 3-5.

Chart 3-5. Past month cigarette use among people ≥12 years of age, by age group: percentages, 2002 to 2016 (NHIS, 2002–2016; NSDUH, 2002–2016). 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).63

Adults

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 13.6% in 2015, according to NHIS data.10,12 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 to secular declines in the HD death rate.16

  • On the basis of weighted NHIS data, the current smoking status among 18- to 24-year-old men declined 46.5%, from 28.0% in 2005 to 15.0% in 2015; for 18- to 24-year-old females, smoking declined 47.0%, from 20.7% to 11.0%, over the same time period.10 On the basis of age-adjusted estimates in 2015, among people ≥65 years of age, 9.7% of males and 8.3% of females were current smokers.

  • From 2005 to 2015, adjusted prevalence rates for tobacco use in individuals with serious psychological distress (according to the Kessler Scale) went from 41.9% to 40.6%, which represents a nonsignificant decline; however, rates for people without serious psychological stress declined significantly, from 20.3% to 14.0%.10

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.21 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.23

  • 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.21

  • Cigarette smoking and other traditional CHD risk factors might have a synergistic interaction in HIV-positive individuals.24

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

  • 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 SBP26 and oral contraceptive use.27,28

  • 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.26

  • Current smokers have a 2- to 4-times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.29,30

  • Short-term exposure to water pipe smoking is associated with a significant increase in SBP and heart rate compared with nonsmoking control subjects,31 but long-term effects remain unclear. Current use of smokeless tobacco is associated with an increased risk of CVD events in cigarette nonsmokers.32

  • The CVD risks associated with e-cigarette use are not known.33,34

Healthcare Utilization: Hospital Discharges/Ambulatory Care Visits

Cost
  • Each year from 2005 to 2009, US smoking-attributable economic costs were between $289 billion and $333 billion, including $133 billion to $176 billion for direct medical care of adults and $151 billion for lost productivity related to premature death.16

  • In the United States, cigarette smoking was associated with 8.7% of annual aggregated healthcare spending from 2006 to 2010.35

  • In 2016, $9.5 billion was spent on marketing cigarettes and smokeless tobacco in the United States.36

  • 249 billion cigarettes were sold in the United States in 2017, which is a 3.5% decrease from the number sold in 2016.36

  • Cigarette prices in the United States increased steeply between the early 1980s and 2016, in large part because of excise taxes on tobacco products.36

Smoking Prevention

Tobacco 21 laws increase the minimum age of sale for tobacco products from 18 to 21 years.

  • 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.37

  • In several towns where Tobacco 21 laws have been enacted, 47% reductions in smoking prevalence among high school students have been reported.36

  • Furthermore, the National Academy 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 2019.38

  • As of March 30, 2018, 5 states (California, New Jersey, Oregon, Hawaii, and Maine) and at least 300 localities, including New York City, Chicago, San Antonio, Boston, Cleveland, and both Kansas Cities, have set the minimum age for the purchase of tobacco to 21 years.39

Awareness, Treatment, and Control

Smoking Cessation
  • According to NHIS 2015 data, 59.1% of adult ever-smokers had stopped smoking.40

    • — The majority (68.0%) of adult smokers wanted to quit smoking; 55.4% had tried in the past year, 7.4% had stopped recently, and 57.2% had received healthcare provider advice to quit.

    • — Receiving advice to quit smoking was lower in uninsured smokers and varied by race, with a lower prevalence in Asian (34.2%), American Indian/Alaska Native (38.1%), and Hispanic (42.2%) smokers than in white smokers (60.2%).

    • — The period from 2000 to 2015 revealed significant increases in the prevalence of smokers who had tried to quit in the past year, had stopped recently, had a health professional recommend quitting, or had used cessation counseling or medication.

    • — In 2015, less than one-third of smokers attempting to quit used evidence-based therapies: 4.7% used both counseling and medication, 6.8% used counseling, and 29.0% used medication (16.6% nicotine patch, 12.5% gum/lozenges, 2.4% nicotine spray/inhaler, 2.7% bupropion, and 7.9% varenicline).

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

    • — There is no convincing evidence to date that 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.15

    • — Quitting smoking at any age significantly lowers mortality from smoking-related diseases, and the risk declines more the longer the time since quitting smoking.2 Cessation appears to have both short-term (weeks to months) and long-term (years) benefits for lowering CVD risk. Overall, CVD 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.15

  • Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.41

  • 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; NNT=6.8). Continuous abstinence rates and reduction rates (≥50% of daily cigarette consumption) were also higher in the varenicline group.42

  • The EAGLES trial43 demonstrated the 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.43

  • 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.44

  • An RCT demonstrated the effectiveness of individual- and group-oriented financial incentives for tobacco abstinence through at least 12 months of follow-up.45

  • 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.40,46

  • 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.47

  • 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.48

Electronic Cigarettes

(See Charts 3-2 and 3-5)

  • 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.

  • Current e-cigarette user prevalence for 2016 is shown in Chart 3-4.

  • According to the National Youth Tobacco Survey, in 2016, e-cigarettes were the most commonly used tobacco products in youth: in the prior 30 days, 4.3% of middle school and 11.3% of high school students endorsed use (Chart 3-2).8 Despite a general trend of increased use between 2011 and 2016, the prevalence of e-cigarette use decreased between 2015 and 2016, from 5.3% to 4.3% in middle school students and from 16.0% to 11.3% in high school students. Among high school students, rates of use were most pronounced in males (13.1%) and NH whites (13.7%). In middle school students, higher rates were observed in males (5.1%) than females (3.4%) and in Hispanics (5.6%) than in other racial/ethnic groups (3.7% in NH white students and 4.0% in NH black students)

  • In 2014, 18.3 million US middle and high school students (68.9%) were exposed to e-cigarette advertising.4,49

  • Among US adults, awareness and use of e-cigarettes has increased considerably.50 In 2014, 12.6% of all adults and nearly half (49%) of current daily cigarette smokers had tried an e-cigarette.51

  • According to NHIS 2014 data, among US working adults, 3.8% (≈5.5 million) currently used e-cigarettes. The use of e-cigarettes was significantly higher among current smokers (16.2%) than among past smokers (4.3%) or never-smokers (0.5%). Similarly, prevalence was significantly higher among males (4.5%), NH whites (4.5%), younger adults (18–24 years; 5.1%), individuals without health insurance (5.9%), individuals with incomes <$35 000, and those residing in the Midwest (4.5%).52

  • Effective August 8, 2016, the US Food and Drug Administration’s Deeming Rule prohibited sale of e-cigarettes to individuals <18 years of age.53

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%.21

    • — Exposure to secondhand smoke increases the risk of stroke by 20% to 30%, and it is associated with increased mortality (adjusted mortality rate ratio, 2.11) after a stroke.51

  • A 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.52

  • A meta-analysis of 24 studies demonstrates that secondhand smoke can increase risks for preterm birth by 20%.53

  • As of April 1, 2018, 11 states (California, Connecticut, Delaware, Hawaii, Maine, New Jersey, New York, North Dakota, Oregon, Utah, and Vermont), the District of Columbia, and Puerto Rico have passed comprehensive smoke-free indoor air laws that include e-cigarettes. These laws prohibit smoking and the use of e-cigarettes in indoor areas of private work sites, restaurants, and bars.39

  • Pooled data from 17 studies in North America, Europe, and Australia suggest that smoke-free legislation can reduce the incidence of acute coronary events by 10%.54

  • 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 higher for NH blacks (46.8%) than for NH 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).55

Family History and Genetics

  • Genetic factors might contribute to smoking behavior; several loci have been identified that are associated with smoking initiation, number of cigarettes smoked per day, and smoking cessation.55

  • Genetics might also modify adverse cardiovascular health outcomes among smokers, with variation in ADAMTS7 associated with loss of cardioprotection in smokers.56

Global Burden of Tobacco Use

(See Table 3-1 and Chart 3-6)

  • Although tobacco use in the United States has been declining, the absolute number of tobacco users worldwide has climbed steeply.42

  • On the basis of the GBD synthesis of >2800 data sources, the age-standardized global prevalence of daily smoking in 2016 was 25.1% (95% UI, 22.7%–28.7%) in males and 7.9% (95% UI, 6.5%–10.6%) in females. The investigators estimate that since 1990, smoking rates have declined globally by 29.6% in males and 28.6% in females.1

  • The GBD 2016 study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 315 diseases and injuries in 195 countries and territories. Eastern and Central Europe, East Asia, Southeast Asia, and southern sub-Saharan Africa have the highest mortality rates attributable to tobacco (Chart 3-6).57

  • The number of smokers was estimated to have grown from 721 million in 1980 to 967 million in 2012.58

  • Worldwide, ≈80% of smokers live in low- and middle-income countries.59

  • Tobacco smoking (including secondhand smoke) caused an estimated 7.1 million deaths in 2016. Among the leading risk factors for death, smoking ranked fourth in DALYs globally.14

  • The WHO estimated that the economic cost of smoking-attributable diseases accounted for US $422 billion, which represented ≈5.7% of global health expenditures.60 The total economic costs, including both health expenditures and lost productivity, amounted to approximately US $1436 billion, which was roughly equal to 1.8% of the world’s annual gross domestic product. The WHO further estimated that 40% of the expenditures were in developing countries.

  • To help combat the global problem of tobacco exposure, in 2003 the WHO adopted the Framework Convention on Tobacco Control treaty. From this emerged a set of evidence-based policies with the goal of reducing the demand for tobacco, entitled MPOWER. MPOWER policies outline the following strategies for nations to reduce tobacco use: (1) monitor tobacco use and prevention policies; (2) protect individuals from tobacco smoke; (3) offer to help with tobacco cessation; (4) warn about tobacco-related dangers; (5) enforce bans on tobacco advertising; (6) raise taxes on tobacco; and (7) reduce the sale of cigarettes. More than half of all nations have implemented at least 1 MPOWER policy.61

Chart 3-6.

Chart 3-6. Age-standardized global mortality rates attributable to tobacco per 100 000, both sexes, 2016. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Micronesia, Federated States of; GRD, Grenada; KIR, Kiribati; LCA, Saint Lucia; MDV, Maldives; MHL, Marshall Islands; MLT, Malta; MUS, Mauritius; SGP, Singapore; SLB, Solomon Islands; SYC, Seychelles; TLS, Timor-Leste; TON, Tonga; TTO, Trinidad and Tobago; VCT, Saint Vincent and the Grenadines; VUT, Vanuatu; W Africa, West Africa; and WSM, Samoa. Data derived from Global Burden of Disease Study 2016 with permission.57 Copyright © 2017, University of Washington.

Abbreviations Used in Chapter 3

ACSacute coronary syndrome
AHAAmerican Heart Association
AIANAmerican Indian or Alaska Native
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DALYdisability-adjusted life-year
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
GBDGlobal Burden of Disease
HDheart disease
HIVhuman immunodeficiency virus
HRhazard ratio
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
NNTnumber needed to treat
NSDUHNational Survey on Drug Use and Health
ORodds ratio
PAFpopulation attributable fraction
PARpopulation attributable risk
PATHPopulation Assessment of Tobacco and Health
RCTrandomized controlled trial
RRrelative risk
SAHsubarachnoid hemorrhage
SBPsystolic blood pressure
SHSStrong Heart Study
UIuncertainty interval
WHOWorld Health Organization

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4. Physical Inactivity

See Charts 4-1 through 4-13

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 for PA recommend that children get at least 60 minutes of PA daily (including aerobic and muscle- and bone-strengthening activity).3 In 2015, on the basis of survey interviews,4 only 27.1% of high school students reported achieving at least 60 minutes of daily PA, which is likely an overestimation of those actually meeting the guidelines.5,6 The 2008 federal 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 a nationally representative sample of adults, only 22.5% of adults reported participating in adequate leisure-time aerobic and muscle-strengthening activity to meet these criteria (Chart 4-1), but they were not asked to report activity accumulated during occupational, transportation, or domestic duties.7 Being physically active is an important aspect of overall health. PA not only reduces premature mortality but also improves risk factors for CVD (such as HBP and high cholesterol) and reduces the likelihood of diseases related to CVD, including CHD, HF, stroke, type 2 DM, and sudden heart attacks.3 Benefits from PA are seen for all ages and groups, including children and older adults, pregnant females, and people with disabilities and chronic conditions. Therefore, the 2008 federal guidelines recommend being as physically active as abilities and conditions allow and, if not currently meeting the recommendations, increasing PA gradually.3 Ahead of the new 2018 federal guidelines, the Physical Activity Guidelines Advisory Committee published a report providing an even clearer message for individuals not meeting the 150 minutes of moderate PA per week guideline, stating that even small increases in moderate-intensity PA or replacing sedentary time with light-intensity PA could provide health benefits.8

Chart 4-1.

Chart 4-1. Prevalence of meeting both the aerobic and muscle-strengthening guidelines for the 2008 Physical Activity Guidelines for Americans among adults ≥18 years of age, overall and by sex and race/ethnicity. Data are age adjusted for adults ≥18 years of age. NH indicates non-Hispanic. Source: National Health Interview Survey, 2016 (National Center for Health Statistics).7

Defining and Measuring PA

There are 4 dimensions of PA (mode or type, frequency, duration, and intensity) and 4 common domains for adults (occupational, domestic, transportation, and leisure time).5 For children, there are additional considerations of structured PA in schools and communities. 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 can sometimes be compensated for by a decrease in another domain, ideally data will be collected on all dimensions and domains of PA.5

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 and objective methods have found that there is marked discordance between self-reported and measured PA, with respondents often overstating their PA, especially the intensity.5,6

Another consideration in the measurement of PA is that surveys often ask only about leisure-time PA, which represents PA obtained from a single domain. People who obtain high PA in other domains might be less likely to engage in leisure-time PA. Although they might meet the federal PA guidelines, people who spend considerable time and physical effort in occupational, domestic, or transportation activities/domains might be less likely to be identified as meeting the guidelines.

PA and cardiorespiratory fitness provide distinct metrics in assessment of CVD risk.9 Poor cardiorespiratory (or aerobic) fitness might be a stronger predictor of adverse cardiometabolic and cardiovascular outcomes such as CHD, stroke, and HF than traditional risk factors.10–12 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.13 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.9 Unlike health behaviors such as PA and risk factors that are tracked by federally funded programs (NHIS, NHANES, etc),6,14 there are no national data on adult cardiorespiratory fitness, although the development of a national cardiorespiratory fitness registry has been proposed.9 Such additional data on the cardiorespiratory fitness levels of Americans could give a fuller and more accurate picture of physical fitness levels.9

Prevalence

Youth
Meeting the Activity Recommendations

(Charts 4-2 through 4-4)

  • On the basis of self-reported PA (YRBSS, 2015)4:

    • — The prevalence of high school students who met aerobic 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 (23.5%) grades. At each grade level, the prevalence was higher in boys than in girls.

    • — More than double the percentage of high school–aged boys (36.0%) than girls (17.7%) reported having been physically active ≥60 minutes per day on all 7 days (Chart 4-2).

      • ▪ The prevalence of students meeting activity recommendations on ≥5 days per week was higher among NH white boys (62.0%), NH black boys (52.2%), and Hispanic boys (53.5%) than NH white girls (43.5%), NH black girls (33.4%), and Hispanic girls (33.1%) (Chart 4-2).

    • — 14.3% of students reported that they did not participate in ≥60 minutes of any kind of PA on any 1 of the previous 7 days. Girls were more likely than boys to report this level of inactivity (17.5% versus 11.1%), with black girls reporting the highest rate of inactivity (25.2%) (Chart 4-3).

  • With regard to objectively measured moderate to vigorous PA (based on age-specific criteria for accelerometer cutpoints; NHANES, 2003–2004)6:

    • — Only 8% of 12- to 19-year-olds accumulated ≥60 minutes of moderate to vigorous PA on ≥5 days per week, whereas 42% of 6- to 11-year-olds achieved similar activity levels.6

    • — More boys than girls met PA recommendations (≥60 minutes of moderate to vigorous activity) on ≥5 days per week.6

  • With regard to objectively measured cardiorespiratory fitness (NHANES, 2012)15:

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

  • With regard to self-reported muscle-strengthening activities (YRBSS, 2015)4:

    • — The proportion of high school students who participated in muscle-strengthening activities on ≥3 days of the week was 53.4% nationwide and declined from 9th grade (males 64.9%, females 48.2%) to 12th grade (males 59.9%, females 39.9%).

    • — More high school boys (63.7%) than girls (42.7%) reported having participated in muscle-strengthening activities on ≥3 days of the week.

Chart 4-2.

Chart 4-2. Prevalence of students in grades 9 to 12 who were active at least 60 min/d on all 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 all 7 days preceding the survey. NH indicates non-Hispanic. Source: Youth Risk Behavior Surveillance Survey, 2015.4

Chart 4-3.

Chart 4-3. 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, 2015.4

Chart 4-4.

Chart 4-4. 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, 2012.15

Structured Activity Participation in Schools and Sports
  • In 2015, only 29.8% of students attended physical education classes in school daily (33.8% of boys and 25.5% of girls; YRBSS).4

  • 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).4

  • 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).4

Television/Video/Computers

(Chart 4-5)

Research suggests that screen time (watching television or using a computer) can lead to less PA among children.16 In addition, television viewing time is associated with poor nutritional choices, overeating, and weight gain (Chapter 5, Nutrition).

Chart 4-5.

Chart 4-5. 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. *For something other than school work. Source: Youth Risk Behavior Surveillance Survey, 2015.4

  • In 2015 (YRBSS)4:

    • — Nationwide, 41.7% of high school students used a computer for activities other than school work (eg, videogames or other computer games) for ≥3 hours per day on an average school day.

    • — The prevalence of using computers ≥3 hours per day (for activities other than school work) was highest among NH black girls (48.4%), followed by Hispanic girls (47.4%), Hispanic boys (45.1%), NH black boys (41.2%), NH white boys (38.9%), and NH white girls (38.3%) (Chart 4-5).

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

  • A report from the Kaiser Family Foundation (using data from 2009) reported that 8- to 18-year-olds spent an average of 33 minutes per day talking on the phone and 49 minutes using their phone to access media (music, games, or videos).17 In addition to other cell phone use, 7th to 12th graders spent an average of 95 minutes per day text messaging. Surveys such as YRBSS have not historically asked about cell phone use specifically and are thus likely underestimates of total screen-time use.

Adults
Meeting the Activity Recommendations

(Charts 4-1 and 4-6 through 4-10)

  • With regard to self-reported leisure-time aerobic and muscle-strengthening PA (NHIS, 2016)7:

    • — 22.5% of adults met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness, based on only reporting leisure-time activity (Chart 4-1).

  • For self-reported leisure-time aerobic PA (NHIS, 2016)7:

    • — 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) through leisure-time activities was 59.7% and 53.6% for NH white males and females, 51.0% and 39.1% for NH black males and females, and 46.4% and 41.8% for Hispanic males and females, respectively. Among both males and females, NH whites were more likely to meet the PA aerobic guidelines with leisure-time activity than NH blacks and Hispanics. For each racial/ethnic group, males had higher PA than females (Chart 4-6).

    • — Among adults ≥25 years of age, 32.4% of participants with no high school diploma, 40.8% of those with a high school diploma or GED high school equivalency credential, 51.4% of those with some college, and 64.9% of those with a bachelor’s degree or higher met the federal guidelines for aerobic PA through leisure-time activities (Chart 4-7).

  • Adults residing in urban areas (metropolitan statistical areas) are more likely to meet the federal aerobic PA guidelines through leisure-time activities than those residing in rural areas (53.7% versus 46.2%) (Chart 4-8).7

  • Adults living below 200% of the poverty level are less likely to meet the federal PA guidelines through leisure-time activities than adults living at >200% above the poverty level (Chart 4-9).7

  • 13.5 % of people with disabilities and 24.3% of people without disabilities meet both the aerobic and muscle-strengthening guidelines (Chart 4-10).7

  • In 2016, 26.9% of adults reported that they do not engage in leisure-time PA (no sessions of leisure time PA of ≥10 minutes in duration; Chart 4-11).7

  • 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 for 30 minutes (in sessions of ≥10 minutes) on ≥5 of 7 days.6 It is also important to consider that using data accumulated only in ≥10-minute bouts could remove up to 75% of moderate activity.18

    • — These data also revealed that rural US residents performed less moderate to vigorous PA than urban residents, but rural residents spent more time in lighter-intensity PA (accelerometer counts per minute: 760–2020) than their urban resident counterparts.19

    • — In a review examining self-reported versus directly 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.20 Among males, self-reported PA was 44% greater than directly measured values; among females, self-reported activity was 138% greater than directly measured PA.20

  • With regard to objectively measured moderate to vigorous PA (accelerometer counts per minute >2020; NHANES, 2003–2004)6:

    • — Among those ≥60 years of age, adherence to PA recommendations was 2.5% in males and 2.3% in females.6

    • — In contrast to self-reported PA, which suggested that NH whites had the higher levels of PA,14 data from objectively measured PA revealed that Hispanic participants had higher total PA and moderate to vigorous PA compared with NH white or black participants (≥20 years old).6,21

  • Levels of activity declined sharply after the age of 50 years in all groups.18 In a recent study of almost 5000 British males, in those with low PA in midlife, retirement and the development of cardiovascular-related conditions were identified as factors predicting a decrease in PA over 20 years of follow-up, but for males who were more active in middle age, retirement was observed to be a time of increasing PA.22

  • A Nielsen Report using data from 2017 reported that adults spent an average of 5 hours 5 minutes per day watching television (including live television and other television-connected devices such as DVDs or playing video games on a console) and an hour and a half each day on computers or tablets.23 Adult smartphone app/web use was reported as 2 hours 28 minutes per day using data collected from 12 500 smartphone users in 2017.23 These technology use behaviors could influence time spent in PA and sedentary time.

    • — Of particular concern, black adults spent an average of 7 hours 13 minutes per day watching television. Black and Hispanic adults had the highest smartphone use compared with other racial/ethnic groups.23

Chart 4-6.

Chart 4-6. Prevalence of meeting the aerobic guideline of the 2008 Physical Activity Guidelines for Americans among adults ≥18 years of age by race/ethnicity and sex (NHIS, 2016). 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, 2016 (National Center for Health Statistics).7

Chart 4-7.

Chart 4-7. Prevalence of meeting the aerobic guideline of the 2008 Physical Activity Guidelines for Americans among adults ≥25 years of age by educational attainment (NHIS, 2016). 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, 2016 (National Center for Health Statistics).7

Chart 4-8.

Chart 4-8. Prevalence of meeting the aerobic guideline for the 2008 Physical Activity Guidelines for Americans among adults ≥18 years of age by location of residence (NHIS, 2016). 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, 2016 (National Center for Health Statistics).7

Chart 4-9.

Chart 4-9. Prevalence of meeting the aerobic and muscle-strengthening guidelines for the 2008 Physical Activity Guidelines for Americans among adults ≥18 years of age by poverty level and type of activity (NHIS, 2016). 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 performing muscle-strengthening activities at least 2 days per week. NHIS indicates National Health Interview Survey. Source: NHIS, 2016 (National Center for Health Statistics).7

Chart 4-10.

Chart 4-10. Prevalence of meeting both the aerobic and muscle-strengthening guidelines for the 2008 Physical Activity Guidelines for Americans among adults ≥18 years of age by disability status. 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. Source: National Health Interview Survey, 1998 to 2016 (National Center for Health Statistics).7

Chart 4-11.

Chart 4-11. Trends in the prevalence of physical inactivity among adults ≥18 years of age, overall and by sex (NHIS, 1998–2016). Percentages are age adjusted. Physical inactivity is defined as reporting no engagement in leisure-time physical activity in bouts lasting ≥10 minutes. NHIS indicates National Health Interview Survey. Source: NHIS, 1998 to 2016 (National Center for Health Statistics).7

Structured Activity Participation in Leisure-Time, Domestic, Occupational, and Transportation Activities
  • Individuals from urban areas who participated in NHANES 2003 to 2006 reported participating in more transportation activity, but rural individuals reported spending more time in household PA and more total PA than urban individuals, possibly explaining the higher levels of light activity of rural individuals observed by objective methods.19

  • At this time, it is unclear which construct of PA (domestic, occupational, or transportation) contributes to the higher objectively measured PA21 but lower subjectively measured PA14 for Hispanic individuals, or whether these differences are caused by overreporting or underreporting of leisure-time PA.

  • A 1-day assessment indicated that the mean prevalence of any active transportation was 10.3% using 2012 data from the American Time Use Study. NH whites reported the lowest active transport, only 9.2%, of any racial/ethnic group. Roughly 11.0% of Hispanics, 13.4% of NH blacks, and 15.0% of other NH individuals reported participating in any active transportation on the previous day.24

Mortality

Self-Reported Physical Inactivity and Mortality
  • Physical inactivity is the fourth-leading risk factor for global death, responsible for 1 to 2 million deaths annually.25,26 The adjusted population attributable fraction for achieving <150 minutes of moderate to vigorous PA per week was 8.0% for all-cause and 4.6% for major CVD in a study of 17 low-, middle-, and high-income countries in 130 843 participants without preexisting CVD.27

  • A similar analysis in the US using NHIS data from 1990 to 1991 (N=67 762) found that after 20 years of follow-up, 8.7% of all-cause mortality was attributed to levels of PA that were <150 minutes of moderate-intensity equivalent activity per week.28

  • A study of US adults that linked a large, nationally representative sample of 10 535 participants (NHANES) to death records found that meeting the aerobic PA guidelines reduced all-cause mortality, with an HR of 0.64 (95% CI, 0.52–0.79), after adjustment for potential confounding factors. Furthermore, for adults not meeting the aerobic PA guidelines, engaging in muscle-strengthening activity ≥2 times a week was associated with a 44% lower adjusted HR for all-cause mortality.29

  • 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.30

  • In a pooled study of >600 000 participants,31 an inverse dose-response relationship was observed between level of self-reported leisure-time PA (HR, 0.80 [95% CI, 0.78–0.82] for less than the recommended minimum of the PA guidelines; HR, 0.69 [95% CI, 0.67–0.70] for 1–2 times the recommended minimum; and HR, 0.63 [95% CI, 0.62–0.65] for 2–3 times the minimum) and mortality, with the upper threshold for mortality benefit occurring at 3 to 5 times the PA recommendations (HR, 0.61; 95% CI, 0.59–0.62). Furthermore, there was no evidence of harm associated with performing ≥10 times the recommended minimum (HR, 0.68; 95% CI, 0.59–0.78).31

  • Similarly, 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 (95% CI, 0.61–0.71) for those reporting 10 to 149 min/wk, 0.53 (95% CI, 0.48–0.57) for those reporting 150 to 299 min/wk, and 0.46 (95% CI, 0.43–0.49) for those reporting ≥300 min/wk of activity.32

  • In the Women’s Health Study (N=28 879; mean age, 62 years), females participating in strength training (1–19, 20–59, and 60–149 min/wk compared with 0 min/wk) had lower risk of all-cause mortality (HR [95% CI], 0.73 [0.65–0.82], 0.71 [0.62–0.82], and 0.81 [0.67–0.97], respectively), but performing ≥150 min/wk strength training was not associated with lower risk of all-cause mortality (HR, 1.10; 95% CI, 0.77–1.56) because of very wide CIs.33

  • A meta-analysis also revealed an association between participating in more transportation-related PA and lower all-cause mortality risk.34 In contrast, higher occupational PA has been associated with higher mortality in males but not females.35 It is unclear whether confounding factors such as fitness, SES, or other domains of PA might impact this relationship.

  • In a longitudinal cohort study of 263 540 participants from the UK Biobank cohort, commuting by bicycle was associated with a lower risk of CVD mortality and all-cause mortality (HR, 0.48 and 0.59, respectively). Commuting by walking was associated with a lower risk of CVD mortality (HR, 0.64) but not all-cause mortality.36

  • 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 CVD and of death attributable to all causes.37

  • In the Southern Community Cohort Study of 63 308 individuals 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 both black and white adults. Both PA (beneficial) and sedentary time (detrimental) were associated with mortality risk.38

  • In a meta-analysis of 13 studies evaluating the association between sedentary time and all-cause mortality, higher sedentary time was associated with a 22% higher risk of all-cause mortality (HR, 1.22; 95% CI, 1.09–1.41). This association was more pronounced at lower levels of PA than at higher levels.39

  • A meta-analysis that included >1 million participants across 16 studies compared the risk associated with sitting time and television viewing in physically active and inactive study participants. For inactive individuals (defined as the lowest quartile of PA), those sitting >8 h/d had a higher all-cause mortality risk than those sitting <4 h/d. For active individuals (top quartile for PA), sitting time was not associated with all-cause mortality, but active people who watched television ≥5 h/d did have higher mortality risk.40

Objectively Measured Physical Inactivity/Sedentary Time and Mortality
  • In a subsample of NHANES (participants with objectively measured PA and between the ages of 50 and 79 years [N=3029]), models that replaced sedentary time with 10 min/d of moderate to vigorous PA were associated with lower all-cause mortality (HR, 0.70; 95% CI, 0.57–0.85) after 5 to 8 years of follow-up. Even substituting in 10 min/d of light activity was associated with lower all-cause mortality (HR, 0.91; 95% CI, 0.86–0.96).41

  • In an analysis from the Women’s Health Study, objective measures of PA and sedentary behavior using an accelerometer were associated with all-cause mortality. The highest levels of overall PA volume, as measured by the accelerometer, were associated with 60% to 70% lower risk of all-cause mortality. This inverse association between overall PA and all-cause mortality was largely driven by the moderate to vigorous PA levels; light PA or sedentary behavior was not associated with mortality risk in this cohort after accounting for moderate to vigorous PA.42

  • In a cohort study of 7985 middle- and older-aged US adults, the REGARDS study objectively measured total sedentary time (HR [95% CI] for highest versus lowest quartile of total sedentary time, 2.63 [1.60–4.30]) and longer sedentary bouts (HR, 1.96; 95% CI, 1.31–2.93) were both associated with higher risk of all-cause mortality.43

Cardiorespiratory Fitness and Mortality
  • 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.44

  • In a longitudinal cohort study from the UK Biobank data, the association between PA and all-cause mortality was strongest among those with lowest hand-grip strength and lowest cardiorespiratory fitness, which suggests that strength and possibly cardiorespiratory fitness could moderate the association between PA and mortality.45

  • In a retrospective cohort study of 57 085 individuals who were clinically referred for stress testing (but without established CAD or HF), cardiorespiratory fitness–associated “biologic age” was a stronger predictor of mortality over 10 years of follow-up than chronological age.46

Secular Trends

Youth

In 2015 (YRBSS)4:

  • Among students nationwide, there was a significant increase in the number of individuals reporting participation 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 number of youth reporting having used computers not for school work for ≥3 h/d 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%).

    • — From 2004 to 2009, the Kaiser Family Foundation reported that the proportion of 8- to 18-year-olds who owned their own cell phone increased from 39% to 66%,17 which could also contribute to higher exposure to screen time in children.

  • Nationwide, the number of high school students who reported attending physical education classes at least once per week did not change substantively between 2013 (48.0%) and 2015 (51.6%).

    • — The number of high school students reporting attending daily physical education classes changed in nonlinear ways over time. Attendance initially decreased from 1991 to 1995 (from 41.6% to 25.4%) and did not substantively change between 1995, 2013, and 2015 (25.4%, 29.4%, and 29.8%, respectively).

  • The prevalence of high school students 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% (Chart 4-3), down from 52.4% in 1999 to 2000.15

Adults

(Charts 4-11 and 4-12)

  • The prevalence of physical inactivity among adults ≥18 years of age, overall and by sex, has decreased from 1998 to 2016, with the largest drop occurring in the past decade, from 40.2% to 26.9% between 2005 and 2016, respectively (Chart 4-11).7,47 The prevalence of physical inactivity has surpassed the target for Healthy People 2020, which was 32.6%.47

  • 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.48

  • The age-adjusted percentage of US adults who reported meeting both the muscle-strengthening and aerobic guidelines increased from 14.4% in 1998 to 21.4% in 2015 (Chart 4-12).14 The percentage of US adults who reported meeting the aerobic guideline increased from 40.1% in 1998 to 49.7% in 2015.14,49

  • Although it appears that leisure-time PA has been increasing in recent years, trends in technology behavior could influence both PA and sedentary time. Nielsen reports of adult smartphone app/web use comparing data collected in 2012 and 2014 (48 min/d and 1 hour 25 minutes per day, respectively)47 to 2017 (2 hours 28 minutes per day)50 suggest extreme increases in use over the past few years. Although they acknowledge that there were inconsistent methods in data collection among these different reports, the reported changes in technology behavior over such a short period of time are striking.

    • — During this time period, from 2012 to 2017, television viewing decreased from 5 hours 28 minutes per day to 5 hours 5 minutes per day. Time spent on a computer decreased from 1 hour 3 minutes to <52 minutes in 2017. However, in 2017, tablet use was also measured and contributed to screen time, at 34 min/d.

    • — The relationships between changes in technology habits and sedentary time have not been measured systematically.

Chart 4-12.

Chart 4-12. Trends in meeting the physical activity guidelines of the 2008 Federal Physical Activity Guidelines for Americans through leisure-time activity only among adults ≥18 years of age by type of activity (NHIS, 1998–2015). Source: NHIS, 1998 to 2015 (National Center for Health Statistics).

Complications of Physical Inactivity: The Cardiovascular Health Impact

Youth
  • In a study from the NHANES cohort, of participants aged 6 to 17 years with objective measurement of PA levels by accelerometer, young participants with the highest levels of PA had lower SBP, lower glucose levels, and lower insulin levels than participants in the lowest PA group.51

  • Similarly, a higher amount of objectively measured sedentary duration assessed by accelerometer among children aged 10 to 14 years old is associated with greater odds of hypertriglyceridemia and cardiometabolic risk.52

  • For elementary school children, engagement in organized sports for ≈1 year was associated with lower clustered cardiovascular risk.53

  • In a study of 36 956 Brazilian adolescents, self-reported higher moderate to vigorous PA levels and lower screen time were associated with lower cardiometabolic risk. Furthermore, the association of screen time with cardiometabolic risk was modified by BMI. In contrast, the association between moderate to vigorous PA and cardiometabolic risk was independent of BMI.54

  • In a prospective study of 700 Norwegian 10-year-old children with objective measures of PA, higher levels of moderate PA at baseline were associated with lower triglyceride levels and lower insulin resistance at 7-month follow-up. In contrast, sedentary time duration was not associated with cardiometabolic risk factors on follow-up.55

Adults
Cardiovascular and Metabolic Risk
  • 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 intermediate physiological outcomes. 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 0.14 mmol/L, LDL-C levels by 0.10 mmol/L, triglyceride levels by 0.09 mmol/L, SBP by 2.06 mm Hg, DBP by 1.30 mm Hg, fasting glucose by 0.10 mmol/L, DM incidence by an RR of 0.54, and weight by a standardized difference of 0.24.56

  • Results from NHANES 2011 to 2014 demonstrated that the prevalence of low HDL-C was higher among adults who reported not meeting PA guidelines (21.0%) than among adults meeting guidelines (17.7%).57

  • Engaging in active transport to work has been associated with lower cardiovascular risk factors.

    • — In a large Swedish cohort of 23 732 individuals, bicycling to work at baseline was associated with a lower odds of developing incident obesity, hypertension, hypertriglyceridemia, and impaired glucose tolerance at 10 years’ follow-up than among those using passive modes of transportation.58

  • A total of 120 to 150 min/wk of moderate-intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.3

  • Even lighter-intensity activities, such as yoga, were reported to improve BMI, BP, triglycerides, LDL-C, and HDL-C but not fasting blood glucose in a meta-analysis of 32 RCTs comparing yoga to nonexercise control groups.59

  • 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-U higher BMI, a 0.57-cm larger WC, and 0.44% more body fat. Both higher sedentary leisure time and lower PA were independently associated with an increased BMI.60

  • In a dose-response meta-analysis of 22 studies with 330 222 participants evaluating the association between PA levels and risk of hypertension, each 10 MET h/wk higher level of leisure time PA was associated with a 6% lower risk of hypertension (RR, 0.94; 95% CI, 0.92–0.96)].61

  • In a meta-analysis of 17 trials with 5075 pregnant female participants that evaluated the effects of exercise during pregnancy, aerobic exercise for ≈30 to 60 minutes 2 to 7 times per week during pregnancy was associated with significantly lower risk of gestational hypertensive disorders (RR, 0.70; 95% CI, 0.53–0.83).62

  • In a population-based study of Hispanic/Latino adults with objective assessment of sedentary time, higher levels of sedentary time were associated with lower levels of HDL, higher triglycerides, and higher measures of insulin resistance after adjustment for PA levels. Furthermore, the accrual of prolonged and uninterrupted bouts of sedentary time was particularly associated with greater abnormalities in measures of glucose regulation.63,64

Cardiovascular Events
  • In a dose-response meta-analysis of 9 prospective cohort studies (N=720 425), higher levels of sedentary time were associated with greater risk of CVD in a nonlinear relationship (HR for highest versus lowest sedentary time, 1.14; 95% CI, 1.09–1.19).65

  • 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.66

  • In a study that followed 1.1 million females in the United Kingdom without prior vascular disease for an average of 9 years, those who reported moderate activity were found to be at lower risk of CHD, a cerebrovascular event, or a thrombotic event. However, strenuous PA was not found to be as beneficial as moderate PA.67

  • In a prospective cohort study of 168 916 participants from 17 countries, compared with low levels of self-reported PA (<150 min/wk of moderate-intensity PA), moderate (150–750 min/wk) and high (>750 min/wk) levels of PA were associated with a graded lower risk of major cardiovascular events (HR [95% CI] high versus low: 0.75 [0.69–0.82]; moderate versus low: 0.86 [0.78–0.93]; high versus moderate: 0.88 [0.82–0.94]) over an average 6.9 years of follow-up time.27

  • In the 2-year LIFE study of older adults (mean age, 78.9 years), higher levels of PA, measured by accelerometer, were associated with lower risk of adverse cardiovascular events.68

  • In a dose-response meta-analysis of 12 prospective cohort studies (n=370 460), there was an inverse dose-dependent association between PA levels and risk of HF. PA levels at the guideline-recommended minimum (500 MET min/wk) were associated with 10% lower risk of HF. PA at twice and 4 times the guideline-recommended levels was associated with 19% and 35% lower risk of HF, respectively.69

  • Furthermore, a recent individual level pooled analysis of 3 large cohort studies demonstrated that the strong, dose-dependent association between higher PA levels and lower risk of HF is largely driven by lower risk of HF with preserved EF but not HF with reduced EF.70

  • In a large clinical trial (NAVIGATOR) involving 9306 people with impaired glucose tolerance, ambulatory activity as assessed by pedometer at baseline and 12 months was found to be inversely associated with risk of a cardiovascular event.71

  • Domains of PA, other than leisure time, are understudied and often overlooked. A meta-analysis reported a protective relation of transportation activity to cardiovascular risk, which was greater in women.72 However, higher occupational PA has recently been associated with higher MI incidence in males 19 to 70 years old.35,73 These relationships require further investigation, because a protective association of occupational activity with MI has been reported in young males (19–44 years).73

  • A recent analysis from the Rotterdam Study evaluated the contribution of specific PA types on CVD-free life expectancy. Higher levels of cycling were associated with a greater CVD-free life span in males (3.1 years) and females (2.4 years). Furthermore, high domestic work in females (2.4 years) and high gardening in males (2 years) was also associated with an increased CVD-free life span.74

  • Cardiorespiratory fitness and PA levels are important determinants of HF risk in the general population. In the Cooper Center Longitudinal Study population, higher levels of cardiorespiratory fitness in midlife were associated with lower risk of HF, MI, and stroke.75

    • — The inverse association between higher fitness levels and risk of HF (HR per 1-MET higher fitness level, 0.79; 95% CI, 0.75–0.83 for males) was stronger than observed for risk of MI (HR, 0.91; 95% CI, 0.87–0.95).75

    • — Cardiorespiratory fitness accounted for 47% of the HF risk associated with higher BMI levels.11

    • — Improvement in cardiorespiratory fitness in middle age was also strongly associated with lower risk of HF among the Cooper Center Longitudinal Study participants (HR per 1-MET increase in fitness levels, 0.83; 95% CI, 0.74–0.93).76

  • Lower levels of cardiorespiratory fitness have also been associated with higher risk of HF in a recent study of 21 080 veterans, with a 91% higher risk of HF noted among low-fit participants (HR, 1.91; 95% CI, 1.74–2.09).77

  • In a Swedish cohort of 773 925 young males without history of VTE, cardiorespiratory fitness was associated with a reduced risk of VTE (HR, 0.81; 95% CI, 0.78–0.85) at ≥20 years of follow-up.78

  • In 5962 veterans, lower exercise capacity was associated with a higher risk of developing AF. For every 1-MET increase in exercise capacity, the risk of developing AF was 21% lower (HR, 0.79; 95% CI, 0.76–0.82).79

Secondary Prevention
  • A Cochrane systematic review of 63 studies concluded that exercise-based cardiac rehabilitation programs for CHD patients reduced cardiovascular mortality and hospital admissions but not overall mortality.80

  • In a prospective study that monitored 902 HF patients (with preserved or reduced EF) for 3 years, reporting participation in any PA (≥1 min/wk) was associated with a lower risk of cardiac death and all-cause death than no PA. Less television screen time (<2 versus >4 h/d) was also associated with lower all-cause death.81

  • In a prospective cohort study of 15 486 participants with stable CAD from 39 countries, higher levels of PA were associated with lower risk of mortality such that doubling the exercise volume was associated with 10% lower risk of all-cause mortality after adjustment for potential confounders.82

    • — Among 1746 CAD patients followed up for 2 years, those who remained inactive or became inactive had a 4.9- and 2.4-fold higher risk of cardiac death, respectively, than patients who remained at least irregularly active during the follow-up period.83

    • — In a prospective cohort study of 3307 individuals with CHD, participants who maintained high PA levels over longitudinal follow-up had a lower risk of mortality than those who were inactive over time (HR, 0.64; 95% CI, 0.50–0.83).84

  • In a cohort of patients with HF and preserved EF, compared with high levels of self-reported PA, poor and intermediate levels were associated with higher risk of HF hospitalization (HR [95% CI], 1.93 [1.16–3.22] for poor versus high PA and 1.84 [1.02–3.31] for intermediate versus high PA) and cardiovascular mortality (HR [95% CI], 4.36 [1.37–13.83] for poor versus high PA and 4.05 [1.17–14.04] for intermediate versus high PA).85

  • Using data from a registry of stable outpatients with symptomatic coronary disease, cerebrovascular disease, or PAD, the mortality rate of patients with a recent MI was significantly lower in patients who participated in supervised (N=593) versus unsupervised (N=531) exercise programming.86

  • Early mortality after a first MI was lower for patients who had higher exercise capacity before the MI event. Every 1-MET-higher exercise capacity before the MI was associated with an 8% to 10% lower risk of mortality at 28 days, 90 days, and 365 days after MI.87 A study of 3572 patients with recent MI demonstrated significant sex differences in PA after AMI. Females were more likely to be inactive than males within 12 months after the AMI episode (OR, 1.37; 95% CI, 1.21–1.55).88

  • A recent study of participants included in the WHI observational study who experienced a clinical MI during the study demonstrated that compared with those who maintained low PA levels after the MI event, participants had lower risk of mortality with improvement in PA levels (HR, 0.54; 95% CI, 0.36–0.86) or with sustained high PA levels (HR, 0.52; 95% CI, 0.36–0.73).89

  • Among 2370 individuals with CVD who responded to the Taiwan National Health Interview Survey, achieving more total PA, leisure-time PA, and domestic and work-related PA was associated with lower mortality at 7-year follow-up.90

Costs

  • The economic consequences of physical inactivity are substantial. Using data derived primarily from WHO publications and data warehouses, one study estimated that 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.91

  • A global analysis of 142 countries (93.2% of the world’s population) concluded that physical inactivity cost healthcare systems $53.8 billion in 2013, including $9.7 billion paid by individual households.92

  • A study of American adults reported that inadequate levels of aerobic PA (after adjustment 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).93

  • 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 in 2012) 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).94

  • 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.95

  • Interventions and community strategies to increase PA have been shown to be cost-effective in terms of reducing medical costs96:

    • — Nearly $3 in medical cost savings is realized for every $1 invested in building bike and walking trails.

    • — The incremental cost-effectiveness ratio ranges 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.

Strategies to Prevent Physical Inactivity

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.97 There are roles for communities, schools, and worksites.

Communities
  • Community-level interventions have been shown to be effective in promoting increased PA. Communities can encourage walking with street design that includes sidewalks, improved street lighting, and landscaping design that reduces traffic speed to improve pedestrian safety. Higher neighborhood walkability has been associated with lower prevalence of overweight, obesity, and lower incidence of DM.98 Moving to a walkable neighborhood was associated with a lower risk for incident hypertension in the Canadian Community Health Survey.99

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

  • Educating the public on the recommended PA guidelines could 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).100

Schools
  • Schools can provide opportunities for PA through physical education, recess, before- and after-school activity programs, and PA breaks.101

  • According to the School Health Policies and Practices Study, <5% of elementary schools and junior and senior high schools required daily physical education in 2014.47

  • In 2012, the School Health Policies and Practices Study also reported that 58.9% of school districts required regular elementary school recess.47

  • Healthy afterschool programs and active school day policies have been shown to be cost-effective solutions to increase PA and prevent childhood obesity.102

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

  • Worksite interventions for sedentary occupations, such as providing “activity-permissive” workstations and email contacts that promote breaks, have reported increased occupational light activity, and the more adherent individuals observed improvements in cardiometabolic outcomes.103,104

Family History and Genetics

  • It is clear that environmental factors can play a role in PA and sedentary behavior and the context in which these behaviors occur. However, PA and sedentary behavior can also be determined in part by genetics, with heritability estimates of up to 47%, although few loci have been identified or replicated.105,106

Global Burden

(See Chart 4-13)

  • 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.107

  • 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.108

  • The GBD 2016 Study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 315 diseases and injuries in 195 countries and territories. Mortality rates attributable to low PA are high in Eastern Europe, the North Africa/Middle East region, and the Pacific Island countries (Chart 4-13).109

Chart 4-13.

Chart 4-13. Age-standardized global mortality rates attributable to low physical activity per 100 000, both sexes, 2016. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Micronesia, Federated States of; GRD, Grenada; KIR, Kiribati; LCA, Saint Lucia; MDV, Maldives; MHL, Marshall Islands; MLT, Malta; MUS, Mauritius; SGP, Singapore; SLB, Solomon Islands; SYC, Seychelles; TLS, Timor-Leste; TON, Tonga; TTO, Trinidad and Tobago; VCT, Saint Vincent and the Grenadines; VUT, Vanuatu; W Africa, West Africa; and WSM, Samoa. Data derived from Global Burden of Disease Study 2016 with permission.109 Copyright © 2017, University of Washington.

Abbreviations Used in Chapter 4

AFatrial fibrillation
AHAAmerican Heart Association
AMIacute myocardial infarction
BMIbody mass index
BPblood pressure
CADcoronary artery disease
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DBPdiastolic blood pressure
DMdiabetes mellitus
EDemergency department
EFejection fraction
GBDGlobal Burden of Disease
GEDGeneral Educational Development
HBPhigh blood pressure
HDLhigh-density lipoprotein
HDL-Chigh-density lipoprotein cholesterol
HFheart failure
HRhazard ratio
LDL-Clow-density lipoprotein cholesterol
LIFELifestyle Interventions and Independence for Elders
METmetabolic equivalent
MImyocardial infarction
MSAMetropolitan Statistical Area
NAVIGATORLong-term Study of Nateglinide + Valsartan to Prevent or Delay Type II Diabetes Mellitus and Cardiovascular Complications
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
ORodds ratio
PAphysical activity
PADperipheral artery disease
PARpopulation attributable risk
QALYquality-adjusted life-year
RCTrandomized controlled trial
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SDstandard deviation
SESsocioeconomic status
TCtotal cholesterol
VTEvenous thromboembolism
WCwaist circumference
WHIWomen’s Health Initiative
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

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  • 107. 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)17018-9CrossrefMedlineGoogle Scholar
  • 108. Wen CP, Wu X. Stressing harms of physical inactivity to promote exercise.Lancet. 2012; 380:192–193. doi: 10.1016/S0140-6736(12)60954-4CrossrefMedlineGoogle Scholar
  • 109. Global Burden of Disease Study 2016. Global Burden of Disease Study 2016 (GBD 2016) results. Seattle, WA: Institute for Health Metrics and Evaluation (IHME), University of Washington; 2016. http://ghdx.healthdata.org/gbd-results-tool. Accessed September 7, 2018.Google Scholar

5. Nutrition

See Table 5-1 and Charts 5-1 through 5-8

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.

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 metrics
0 (worst) to 100 (best)§
Ideal: 80–100
Intermediate: 40–79
Poor: <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: AHA’s My Life Check – Life’s Simple 71; Lloyd-Jones et al2; Rehm et al.4

Chart 5-1.

Chart 5-1. Trends in prevalence of poor AHA healthy diet score, by race/ethnicity. Components of AHA healthy diet score are defined in Table 5-1. Poor diet was defined as <40% adherence, based on primary AHA continuous diet score.4 AHA indicates American Heart Association.

Prevalence and Trends in the AHA 2020 Healthy Diet Metrics

(See Table 5-1 and Charts 5-1 and 5-2)

The AHA’s 2020 Impact Goals prioritize improving cardiovascular health,1 which includes following a healthy diet pattern characterized by 5 primary and 3 secondary metrics (Table 5-1) that should be consumed within the context 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 toward 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).

Using the alternative scoring system, the mean AHA healthy diet score improved between 2003 to 2004 and 2011 to 2012 in the United States in both children and adults.3 In children, the poor diet (<40% adherence), based on the AHA healthy diet score, decreased from 69.2% to 54.6%. In adults, the prevalence of a poor diet decreased from 50.3% to 41.0%.4 Improvements were largely attributable to increased whole grain consumption and decreased SSB consumption in both children and adults.4 Among adults, other significant improvements included increased consumption of nuts, seeds, and legumes (0.54 to 0.81 servings/d) and decreased consumption of 100% fruit juice (0.43 to 0.32 servings/d) and white potatoes (0.39 to 0.32 servings/d).4 No major improvements in consumption of sodium, fish, fruits and vegetables, processed meats, and saturated fat were noted.

Smaller improvements in AHA healthy diet scores were seen in minority groups and those with lower income or education (Charts 5-1 and 5-2).4 For example, the proportion with a poor diet (<40% adherence) decreased from 50.5% to 35.7% in adults with income-to-poverty ratio ≥3.0, but only from 67.8% to 60.6% in adults with income-to-poverty ratio <1.3 (Chart 5-2).

Chart 5-2.

Chart 5-2. Trends in prevalence of poor AHA healthy diet score, by ratio of family income to poverty level (<1.30, 1.30–1.849, 1.85–2.99, ≥3.0). Components of AHA healthy diet score are defined in Table 5-1. Poor diet was defined as <40% adherence, based on primary AHA continuous diet score.4 AHA indicates American Heart Association.

Global Trends in Key Dietary Factors

Globally, between 1999 and 2010, SSB intake increased in several countries.5 SSB 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 among 187 countries.

A number of countries and US cities have implemented SSB taxes.6 In Mexico, a 1 peso per liter excise tax was implemented in January 2014. In a study using store purchase data from 6645 Mexican households, posttax volume of beverages purchased decreased by 5.5% in 2014 and by 9.7% in 2015 compared with predicted volume of beverages purchased based on pretax trends. Although all socioeconomic groups experienced declines in SSB purchases, the lowest socioeconomic group had the greatest decline in SSB purchases (9.0% in 2014 and 14.3% in 2015).7 In Berkeley, CA, a 1 cent per ounce SSB excise tax was implemented in January 2015.8 Using store-level data, posttax year 1 SSB sales declined by 9.6% compared with predicted SSB sales based on pretax trends. By comparison, SSB sales increased by 6.9% in non-Berkeley stores in adjacent cities.

In 2010, mean sodium intake among adults worldwide was 3950 mg/d.9 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.9

In a systematic review of population-level sodium initiatives, reduction in mean sodium intake occurred in 5 of 10 initiatives.10 Successful population-level sodium initiatives tended to use multiple strategies and included structural activities, such as food product reformulation. For example, Finland initiated a nationwide campaign in the late 1970s through public education, collaboration with the food industry, and salt labeling legislation. From 1979 to 2002, mean 24-hour urine sodium excretion in population-based samples decreased in Finnish males (5.1 to 3.9 g/d) and females (4.1 to 3.0 g/d), with concurrent decreases in mean SBP and prevalence of hypertension.11,12 Similarly, the United Kingdom initiated a nationwide salt reduction program in 2003 to 2004 that included consumer awareness campaigns, progressively lower salt targets for various food categories, clear nutritional labeling, and working with industry to reformulate foods. Mean sodium intake in the United Kingdom decreased by 15% from 2003 to 2011,13 along with concurrent decreases in BP (3.0/1.4 mm Hg) in patients not taking antihypertensive medication, stroke mortality (42%), and CHD mortality (40%) (P<0.001 for all comparisons); these findings remained statistically significant after adjustment for changes in demographics, BMI, and other dietary factors.

Dietary Habits in the United States: Current Intakes

Foods and Nutrients
Adults

The average dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health based on data from 2011 to 2012 NHANES is detailed below3:

  • Consumption of whole grains was 1.1 servings per day by NH white males and females, 0.8 to 0.9 servings per day by NH black males and females, and 0.6 to 0.8 servings per day by Mexican American males and females. For each of these groups, <10% of adults in 2011 to 2012 met guidelines of ≥3 servings per day.

  • Fruit consumption ranged from 1.0 to 1.6 servings per day in these racial or ethnic subgroups: ≈9% of NH whites, 7% of NH 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 NH whites, doubled in NH blacks, and more than doubled in Mexican Americans.

  • Nonstarchy vegetable consumption ranged from 1.7 to 2.7 servings per day. Across all racial/ethnic subgroups, NH white females were the only group meeting the target of consuming ≥2.5 cups per day.

  • 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 NH black females and NH 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 racial or ethnic subgroup consumed ≥2 servings per week.

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

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

  • Consumption of processed meats was lowest among Mexican American females (1.1 servings per week) and highest among NH black and NH white males (≈2.5 servings per week). Between 57% (NH white males) and 79% (Mexican American females) of adults consumed ≤2 servings per week.

  • Consumption of SSBs ranged from 6.8 servings per week among NH white females to nearly 12 servings per week among Mexican American males. Females generally consumed less than males. Some adults, from 33% of Mexican American males to 65% of NH white females, consumed <36 oz/wk.

  • 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 <2.5 servings per week.

  • Consumption of eicosapentaenoic acid and docosahexaenoic acid ranged from 0.058 to 0.117 g/d in each sex and racial or ethnic subgroup. Fewer than 8% of NH whites, 14% of NH blacks, and 11% of Mexican Americans consumed ≥0.250 g/d.

  • One-third to one-half of adults in each sex and racial 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 NH whites, 4% to 5% of blacks, and 13% to 14% of Mexican Americans consumed ≥28 g of dietary fiber per day.

  • Only ≈6% to 8% of adults in each age and racial or ethnic subgroup consumed <2.3 g of sodium per day. Estimated mean sodium intake in the US by 24-hour urinary excretion was 4205 mg/d for males and 3039 mg/d for females in 2013 to 2014. Estimates of sodium intake by race, sex, and source are shown in Charts 5-3 and 5-4.14 Sodium added to food outside the home accounts for more than two-thirds of total sodium intake in the United States (Chart 5-4).15 Top sources of sodium intake vary by race/ethnicity, with the largest contributor being yeast breads for NH whites, sandwiches for NH blacks, burritos and tacos for Hispanics, and soups for NH Asians.16

Chart 5-3.

Chart 5-3. Estimated mean sodium intake in the United States by 24-hour urinary excretion. Estimates based on nationally representative sample of 827 nonpregnant, noninstitutionalized US adults aged 20 to 69 years who completed a 24-hour urine collection in NHANES 2013 to 2014.14 NHANES indicates National Health and Nutrition Examination Survey.

Chart 5-4.

Chart 5-4. Sources of sodium intake in US adults in 3 geographic regions. Sources of sodium intake determined by four 24-hour dietary recalls with special procedures, in which duplicate samples of salt added to food at the table and in home food preparation were collected in 450 adults recruited in 3 geographic regions (Birmingham, AL, Palo Alto, CA, Minneapolis-St. Paul, MN) with equal numbers of males and females from 4 racial/ethnic groups (Asians, blacks, Hispanics, non-Hispanic whites).15

Children and Teenagers

On the basis of NHANES 2011 to 2012, the average dietary consumption by US children and teenagers of selected foods and nutrients related to cardiometabolic health is detailed below3:

  • Whole grain consumption was <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.17

  • 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 of age, and 15% of those 15 to 19 years of age.

  • 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.

  • 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.

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

  • 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.

  • Consumption of processed meats ranged from 1.4 to 2.3 servings per week, and the majority of children consumed <2 servings per week of processed meats.

  • Consumption of SSBs 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 it was 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.

  • 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 <2.5 servings per week.

  • 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 ≥0.250 g/d.

  • Consumption of SFAs 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 SFAs, and ≈70% to 80% consumed <300 mg of dietary cholesterol per day.

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

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

Impact on US Mortality

(See Chart 5-5)

Comparable risk assessment methods and nationally representative data were used to estimate the impact of 10 specific dietary factors on cardiometabolic mortality in the United States in 2002 and 2012 (Chart 5-5).18 In 2012, 318 656 (45.4%) of 702 308 cardiometabolic deaths were estimated to be attributable to poor dietary habits. The largest numbers of deaths attributable to diet were estimated to be from high sodium intake (66 508; 9.5% of all cardiometabolic deaths), low consumption of nuts/seeds (59 374; 8.5%), high consumption of processed meats (57 766; 8.2%), low intake of seafood omega-3 fats (54 626; 7.8%), low consumption of vegetables (53 410; 7.6%) and fruits (52 547; 7.5%), and high consumption of SSBs (51 694; 7.4%). Between 2002 and 2012, population-adjusted US cardiometabolic deaths decreased by 26.5%, with declines in estimated diet-associated cardiometabolic deaths for PUFAs (−20.8%), nuts/seeds (−18.0%), and SSBs (−14.5%) and increases in diet-associated cardiometabolic deaths for sodium (5.8%) and unprocessed red meats (14.4%). Estimated cardiometabolic mortality related to whole grains, fruits, vegetables, seafood, omega-3 fats, and processed meats remained relatively stable.

Chart 5-5.

Chart 5-5. Absolute and proportional cardiometabolic disease mortality associated with overall suboptimal diet in the United States in 2012 by population subgroups. Bars represent absolute number (left) and percentage (right) of cardiometabolic deaths jointly related to suboptimal intakes of 10 dietary factors. The 10 factors were low intakes of fruits, vegetables, nuts/seeds, whole grains, seafood omega-3 fats, and polyunsaturated fats (replacing saturated fats) and high intakes of sodium, unprocessed red meats, processed meats, and sugar-sweetened beverages. Error bars indicate 95% uncertainty intervals. CHD indicates coronary heart disease; and CVD, cardiovascular disease. Reprinted with permission from Micha et al.18 Copyright © 2017, American Medical Association. All rights reserved.

Cost

(See Chart 5-6)

The US Department of Agriculture reported that the Consumer Price Index for all food increased by 0.9% in 2017.19 Prices for foods eaten at home decreased by 0.2% in 2017, whereas prices for foods eaten away from home increased by 2.3%.12 Using data from Euromonitor International, the US Department of Agriculture calculated the share of consumer expenditures attributed to food in multiple countries in 2016. The proportion of consumer expenditures spent on food ranged from 6.3% in the United States to 9.1% in Canada, 23.1% in Mexico, and 58.9% in Nigeria (Chart 5-6).20

Chart 5-6.

Chart 5-6. Proportion of consumer expenditures spent on food at home in selected countries. Data computed by the US Department of Agriculture Economic Research Service, August 2017.120

Cost of a Healthy Diet
  • A meta-analysis of price comparisons of healthy versus unhealthy diet patterns found that the healthiest diet patterns cost, on average, ≈$1.50 more per person per day to consume.21

  • 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.22

  • In a 1-year (2013–2014) RCT of 30 after-school programs in South Carolina, site leaders in the intervention group received assistance in establishing snack budgets and menus and identifying low-cost outlets to purchase snacks that met healthy eating standards. The intervention was successful in increasing the number of days fruits and vegetables were served (3.9 versus 0.7 d/wk) and decreasing the number of days SSBs (0.1 versus 1.8 d/wk) and sugary foods (0.3 versus 2.7 d/wk) were served.23 Cost in the intervention group was minimized by identifying low-cost grocery outlets or large bulk warehouse stores; cost increased by $0.02 per snack in the intervention group compared with a $0.01 per snack decrease in the control group.

Cost-Effectiveness of Sodium Reduction
  • In a cost-effectiveness analysis using the Coronary Heart Disease Policy Model, 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 92 000.24 The projected benefits would be greater in blacks than in nonblacks. If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 billion to $24 billion annually.24

  • A global cost-effectiveness analysis modeled the cost-effectiveness of a so-called soft regulation national policy to reduce sodium intake in countries around the world, based on the United Kingdom experience (government-supported industry agreements, government monitoring of industry compliance, public health campaign).25 Model estimates were based on sodium intake, BP, and CVD data from 183 countries. Country-specific cost data were used to estimate the CER, defined as purchasing power parity adjusted international dollars (I$, equivalent to country-specific purchasing power of $1 US) per DALY saved over 10 years. Globally, the estimated average CER was I$204 per DALY (95% CI, I$149–I$322) saved. The estimated CER was highly favorable in high-, middle-, and low-income countries.

Secular Trends

Trends in Dietary Patterns

(Chart 5-7)

In addition to individual foods and nutrients, overall dietary patterns can be another useful tool for assessing diet quality.26 Different dietary patterns have been defined, such as Mediterranean, DASH-type, HEI-2010, Alternate HEI, Western, prudent, and vegetarian patterns. The original DASH diet was low fat; a higher-MUFA DASH-type diet is even more healthful and similar to a traditional Mediterranean dietary pattern.27

Between 1999 and 2010, the average Alternate HEI–2010 score of US adults improved from 39.9 to 46.8.28 This was related to reduced intake of trans fat (accounting for more than half of the improvement), SSBs, and fruit juice, as well as an increased intake of whole fruit, whole grains, PUFAs, and nuts and legumes. Adults with greater family income and education had higher scores, and the gap between low and high SES widened over time, from 3.9 points in 1999 to 2000 to 7.8 points in 2009 to 2010.

Between 1999 and 2012, the mean HEI-2010 score in US children and adolescents aged 2 to 18 years improved from 42.5 to 50.9.29 One-third of the improvement was attributable to reduction in empty calorie intake; other HEI categories that improved included whole grains, fruit, seafood and plant proteins, greens and beans, and fatty acids (Chart 5-7). Participants in the National School Lunch Program and the School Breakfast Program had lower HEI-2010 scores than nonparticipants. There was also a trend toward lower HEI-2010 in SNAP participants after the 2003 to 2004 cycle. HEI-2010 scores were consistently lower from 1999 to 2012 in NH blacks (39.6–48.4) than in NH whites (42.1–50.2) and were highest in Mexican Americans (44.1–51.9). In a study that used household store purchase data (N=98 256 household-by-quarter observations), SNAP participants purchased more calories from SSBs (15–20 kcal per person per day), more sodium (174–195 mg per person per day), and fewer calories from fiber (−0.52 kcal per person per day) than income-eligible and higher-income nonparticipants.30

Chart 5-7.

Chart 5-7. Mean Healthy Eating Index (HEI)-2010 component scores in children and adolescents aged 2 to 18 years according to NHANES survey cycles. Sizes of the study population were as follows: N=3590 for 1999 to 2000, N=4039 for 2001 to 2002, N=6841 for 2003 to 2004, N=7215 for 2005 to 2006, N=5402 for 2007 to 2008, N=5751 for 2009 to 2010, and N=5649 for 2011 to 2012. For total fruit, whole fruit, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids, higher scores corresponded to higher intakes. For refined grains, sodium, and empty calories, higher scores corresponded to lower intakes. The HEI-2010 component score increased by 3.8 points for empty calories; 1.1 points for whole grains; 0.9 points for dairy; 0.8 points for whole fruit; 0.6 points for total fruit; 0.5 points for seafood and plant proteins, greens and beans, and fatty acids; 0.4 points for total protein foods (all P for linear trend <0.001); and 0.2 points for refined grains (P for linear trend=0.01). The HEI-2010 component score decreased by 0.2 points for sodium (P for trend <0.001). There was no significant improvement for total vegetable intake over the period. Reprinted from Gu et al29 by permission of Oxford University Press.

The impact of the October 2009 Special Supplemental Nutrition Program for Women, Infants, and Children food package revision (more fruits, vegetables, whole grains, and lower-fat milk) was examined using 2003 to 2008 and 2011 to 2012 NHANES data in 2- to 4-year-old children from low-income households.31 The Women, Infants, and Children food package revisions were associated with significant improvements in HEI-2010 score (3.7-higher HEI points; 95% CI, 0.6–6.9), with the greatest improvement coming from a 3.4-fold increase (95% CI, 1.3–9.4) in the greens and beans category.

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, SSBs).32 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 Energy Intake

Until 1980, total energy intake remained relatively constant33; however, data from NHANES indicate that average energy intake among US adults increased from 1971, peaked at 2004, and has since stabilized. Average total energy intakes among US white adults in 1971, 2004, and 2010 were 1992, 2283, and 2200 kcal/d, respectively. Average total energy intakes among US black adults in 1971, 2004, and 2010 were 1780, 2169, and 2134 kcal/d, respectively. This rise in energy intake was primarily attributable to increased carbohydrate intake, particularly of starches, refined grains, and sugars.34,35

In a study using 4 nationally representative US Department of Agriculture surveys of food intake among US children,36 average portion sizes among US children increased for many foods between 1977 and 2006. For example, pizza portion size increased from 406 to 546 calories, with much of this increase occurring from the 1990s to the 2000s. Portion sizes for other foods increased, including Mexican food (from 373 to 512 calories), cheeseburgers (from 380 to 473 calories), soft drinks (from 121 to 155 calories), fruit drinks (from 106 to 133 calories), and salty snacks (from 124 to 165 calories). French fry portion sizes increased at fast food locations but not stores and restaurants. Soft drink and pizza portion sizes increased at all food sources (stores, restaurants, and fast food locations).

In a quantitative analysis using various US surveys between 1977 and 2010, the relationships of national changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.37 Total energy intake increased by 108 kcal/day over this time period. Changes in energy density were not consistently linked to energy intake over time. Rather, main contributors to temporal changes in caloric intake included an increase in the number of eating occasions from 3.9 to 5.1 from 1977 to 2010 and decreases in average portion size of foods and beverages since 1989 to 1991.

Cardiovascular Health Impact of Dietary Patterns

Cardiovascular and Metabolic Risk
  • In a systematic review and meta-analysis, RCTs in children demonstrated reductions in BMI gain when SSBs were replaced with noncaloric beverages, and RCTs in adults showed weight gain when SSBs were added.38

  • In a meta-analysis of 61 trials (N=2582), tree nut consumption lowered TC by 4.7 mg/dL, LDL-C by 4.8 mg/dL, apolipoprotein B by 3.7 mg/dL, and triglycerides by 2.2 mg/dL. No heterogeneity by nut type was observed.39

  • After intentional weight loss in 21 overweight/obese young adults, an isocaloric low-carbohydrate diet resulted in smaller declines in total energy expenditure than a low-fat diet, with a mean difference of >300 kcal/d.40

  • In a randomized trial of 609 nondiabetic participants with BMI 28 to 40 kg/m2 that compared the effects of healthy low-fat versus healthy low-carbohydrate weight loss diets, weight loss at 12 months did not differ between groups. Neither genotype pattern (3 SNP multilocus genotype responsiveness pattern) nor insulin secretion (30 minutes after glucose challenge) modified the effects of diet on weight loss.41

  • In the PREDIMED RCT, 7447 adults with type 2 DM or ≥3 CVD risk factors were randomized to 3 arms: Mediterranean diet supplemented by extra-virgin olive oil, Mediterranean diet supplemented with nuts, or control diet (advice to reduce dietary fat).42 A significantly smaller decrease in central adiposity occurred in the Mediterranean diet with olive oil arm (−0.55 cm; 95% CI, −1.16 to −0.06) and the Mediterranean diet with nuts arm (−0.94 cm; 95% CI, −1.60 to −0.27) than in the control arm. In a subgroup analysis of 3541 patients in PREDIMED without DM, HRs for incident DM were 0.60 (95% CI, 0.43–0.85) for the Mediterranean diet with olive oil arm and 0.82 (0.61–1.10) for the Mediterranean diet with nuts arm compared with the control arm.

  • In a randomized crossover trial of 118 overweight omnivores at low-moderate CVD risk, a reduced-calorie lacto-ovo vegetarian diet was compared to a reduced-calorie Mediterranean diet by providing face-to-face, individual counseling sessions. Both diets were equally successfully in reducing body weight and fat mass. LDL-C, uric acid, and vitamin B12 were lower during the vegetarian diet, whereas triglycerides were lower during the Mediterranean diet, without substantial differences between oxidative stress markers and inflammatory cytokines.43

  • In a meta-analysis of 34 RCTs with modest reduction of sodium for ≥4 weeks, a 100-mmol/d (2300-mg/d) reduction in sodium was associated with a 5.8-mm Hg lower SBP.44 The effects of sodium reduction on BP were stronger in individuals who were older, hypertensive, and black.45,46

  • Compared with a usual Western diet, a DASH-type dietary pattern with low sodium reduced SBP by 5.3, 7.5, 9.7, and 20.8 mm Hg in adults with baseline SBP <130, 130–139, 140–149, and ≥150 mm Hg, respectively.47

  • Compared with a higher-carbohydrate DASH diet, a DASH-type diet with higher protein lowered BP by 1.4 mm Hg, LDL by 3.3 mg/dL, and triglycerides by 16 mg/dL but also lowered HDL by 1.3 mg/dL. Compared with a higher-carbohydrate DASH diet, a DASH-type diet with higher unsaturated fat lowered BP by 1.3 mm Hg, increased HDL by 1.1 mg/dL, and lowered triglycerides by 10 mg/dL.48 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the higher-carbohydrate DASH diet.49

  • In a systematic review and meta-analysis of controlled clinical trials of dietary pattern interventions, the DASH diet had the largest net effect on SBP (−7.6 mm Hg) and DBP (−4.2 mm Hg), whereas the Mediterranean diet had an effect on DBP (−1.4 mm Hg) but not SBP.50

  • In a meta-analysis of 60 randomized controlled feeding trials, consumption of 1% of calories from SFAs in place of carbohydrates raised LDL-C concentrations but also raised HDL-C and lowered triglycerides, with no significant effects on apolipoprotein B concentrations.51

  • In a randomized crossover trial of 92 adults with abdominal obesity, LDL was highest after a butter-rich diet, followed by a cheese-rich diet, high-carbohydrate diet, MUFA-rich diet, and PUFA-rich diet. The butter-rich and cheese-rich diets similarly increased HDL (+4.7% and +3.8%, respectively), compared with the high-carbohydrate diet.52

  • In a meta-analysis of RCTs, consumption of 1% of calories from trans fat in place of SFAs, MUFAs, or PUFAs, 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.53

  • In a meta-analysis of 70 RCTs, consumption of eicosapentaenoic acid and docosahexaenoic acid lowered BP by 1.5/1.0 mm Hg.54

  • A meta-analysis of 102 randomized controlled feeding trials evaluated the effects of exchanging different dietary fats and carbohydrates on markers of glucose-insulin homeostasis.55 Replacing 5% energy from carbohydrates with SFAs generally had no significant effects, whereas replacing carbohydrates with unsaturated fats lowered both HbA1c and insulin. On the basis of “gold standard” short-term insulin response in 10 trials, PUFAs improved insulin secretion compared with carbohydrates, SFAs, and even MUFAs.

Cardiovascular Events

Fats and Carbohydrates
  • In the WHI RCT (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 follow up of 8.1 years.56 This was consistent with null results of 4 prior RCTs and multiple large prospective cohort studies that indicated little effect of total fat consumption on CVD risk.57

  • In a meta-analysis of 21 studies, SFA consumption was not associated with increased risk of CHD, stroke, or total CVD.58 In comparison, in a pooled individual-level analysis of 11 prospective cohort studies, the specific exchange of PUFA consumption in place of SFAs was associated with lower CHD risk, with 13% lower risk for each 5% energy exchange (RR, 0.87; 95% CI, 0.70–0.97).59 These findings are consistent with a meta-analysis of RCTs in which increased PUFA consumption in place of SFAs reduced CHD events, with 10% lower risk for each 5% energy exchange (RR, 0.90; 95% CI, 0.83–0.97).60 Replacing SFAs with MUFAs was not significantly associated with CHD risk.59

  • In a meta-analysis of 13 prospective cohort studies, increased intake of PUFAs was associated with lower risk of CHD, whether it replaced SFAs or carbohydrates.61

  • 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).62

  • 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).63,64

  • In a prospective cohort study of urban Chinese females (N=64 328), high glycemic index and glycemic load were associated with increased risk of stroke. Compared with the lowest 10th percentiles, risks for the 90th percentiles of glycemic index and glycemic load were 1.19 (95% CI, 1.04–1.36) and 1.27 (95% CI, 1.04–1.54), respectively.65

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), a 5% lower risk of stroke (RR, 0.95; 95% CI, 0.92–0.97), and a 4% lower risk of cardiovascular mortality (RR, 0.96; 95% CI, 0.92–0.99).66–68

  • In a prospective study of 512 891 adults in China (only 18% consumed fresh fruit daily), individuals who ate fresh fruit daily had 40% lower risk of CVD death (RR, 0.60; 95% CI, 0.54–0.67), 34% lower risk of incident CHD (RR, 0.66; 95% CI, 0.58–0.75), 25% lower risk of ischemic stroke (RR, 0.75; 95% CI, 0.72–0.79), and 36% lower risk of hemorrhagic stroke (RR, 0.64; 95% CI, 0.56–0.74).69

  • In a meta-analysis of 45 prospective studies, whole grain intake was associated with a lower risk of CHD (HR, 0.81; 95% CI, 0.75–0.87) and CVD (HR, 0.78, 95% CI, 0.73–0.85) but was not significantly associated with stroke (HR, 0.88; 95% CI, 0.75–1.03).70

  • In a meta-analysis of 16 prospective cohort studies, fish consumption was associated with significantly lower risk of CHD mortality.71 Compared with no consumption, consumption of an estimated 0.250 g/d of long-chain omega-3 fatty acids was associated with 35% lower risk of CHD death (P<0.001).

  • Among 16 479 males and females in the REGARDS study, individuals who consumed ≥2 servings of fried fish per week had a greater risk of CVD over 5.1 years of follow-up than those who consumed <1 serving per month (HR, 1.63; 95% CI, 1.11–2.40).72

  • 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).73

  • In a study of 169 310 female nurses and 41 526 male physicians, consumption of 1 serving of nuts ≥5 times per week was associated with lower risk of CVD (HR, 0.86; 95% CI, 0.79, 0.93) and CHD (HR, 0.80; 95% CI, 0.72, 0.89), compared with those who never or almost never consumed nuts. Results were largely consistent for peanuts, tree nuts, and walnuts.74

  • In a meta-analysis of 5 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).75

  • Results from a meta-analysis of 17 prospective observational studies showed that neither dairy consumption nor dairy fat was significantly associated with higher or lower risk of CHD.76

  • In a meta-analysis of 15 country-specific observational cohorts, consumption of butter had small or neutral overall associations with mortality, CVDs, and DM.77

Sodium and Potassium
  • Nearly all observational studies demonstrate an association between higher estimated sodium intakes (eg, >4000 mg/d) and a higher risk of CVD events, in particular stroke.78–84 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.

  • An AHA science advisory suggested that variation in methodology might account for inconsistencies in the relationship between sodium and CVD in observational studies. Increased risk at low sodium intake in some observational studies could be related to reverse causation (illness causing low intake), imprecise estimation of sodium intake through a single dietary recall or a single urine excretion.82

  • Post hoc analyses of the TOHP with 10 to 15 years of follow-up found that participants randomized to sodium reduction had a 25% decrease in CVD risk (RR, 0.75; 95% CI, 0.57–0.99) compared with those randomized to control.83

  • In an observational analysis of TOHP participants not assigned to an active sodium reduction intervention, sodium-potassium ratio was linearly associated with risk of CVD over 10 to 15 years of follow-up (RR, 1.24 per unit; 95% CI, 1.05–1.46; P=0.01).83

  • In a longer-term (median 24 years) post hoc analysis of the TOHP (median of five 24-hour urine measurements), every 1-U increase in sodium-potassium ratio was associated with a 13% higher risk of death (HR, 1.13; 95% CI, 1.01–1.27; P=0.04).84

Dietary Supplement Trends and Outcomes

Use of dietary supplements is common in the United States among both adults and children despite lack of evidence to support the use of most dietary supplements in reducing risks of CVD or death85:

  • 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).86 Supplement use is associated with older age, white race, higher education, greater PA, moderate alcohol consumption, lower BMI, abstinence from smoking, having health insurance, and higher intake of most vitamins and minerals from food.87,88

  • A meta-analysis of 4 RCTs and 27 prospective cohort and nested case-control studies found no significant effect of calcium supplements or calcium plus vitamin D supplements with CVD events or mortality.89

  • Observational studies have found that the antioxidants vitamin C, beta-carotene, and vitamin E are associated with lower risk of CHD and mortality, but RCTs providing antioxidant supplementation have demonstrated no benefit on CVD outcomes or mortality.90–95

  • A 2017 AHA scientific advisory statement summarized available evidence and suggested fish oil supplementation only for secondary prevention of CHD and SCD (Class IIa recommendation) and for secondary prevention of outcomes in patients with HF (Class IIa recommendation).96

  • A meta-analysis of 77 917 participants in 10 RCTs with ≥500 participants treated for ≥1 year found that fish oil supplementation (eicosapentaenoic acid dose range 226–1800 mg/d; docosahexaenoic acid dose range 0–1700 mg/d) had no significant effect on CHD death (RR, 0.94; 95% CI, 0.81–1.03), nonfatal MI (RR, 0.97; 95% CI, 0.87–1.08), or any CHD events (RR, 0.97; 95% CI, 0.93–1.01).97

Dietary Patterns

The 2015 US Dietary Guidelines Advisory Committee summarized the evidence for benefits of healthful diet patterns on a range of cardiometabolic and other disease outcomes.17 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. The 2015 US Dietary Guidelines also describe a healthy vegetarian dietary pattern, which includes more legumes, soy products, nuts and seeds, and whole grains but does not include meats, poultry, or seafood.17

  • In a meta-analysis of 8 observational studies (3 Seventh-day Adventist cohorts [N=110 723] and 5 other cohorts [N=72 598]), vegetarians had a 40% lower risk of CHD in the Seventh-day Adventist studies (RR, 0.60; 95% CI, 0.43–0.80) and a 16% lower risk of CHD (RR, 0.84; 95% CI, 0.74–0.96) in the other studies.98

  • In a cohort of 200 272 US males and females, greater adherence to a plant-based dietary pattern, defined by higher intake of plant-based foods and low intake of animal based foods, was associated with a 20% lower risk of DM (HR, 0.80; 95% CI, 0.74–0.87).99

  • In a cohort of 380 296 US males and females, greater versus lower adherence to a Mediterranean dietary pattern was associated with a 22% lower cardiovascular mortality (RR, 0.78; 95% CI, 0.69–0.87).100 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke101 and for the DASH-type dietary pattern.102

  • In a cohort of 72 113 US female nurses, a dietary pattern characterized by higher intakes of vegetables, fruits, legumes, fish, poultry, and whole grains was associated with a 28% lower cardiovascular mortality (RR, 0.72; 95% CI, 0.60–0.87), whereas a dietary pattern characterized by higher intakes of processed meat, red meat, refined grains, french fries, and sweets/desserts was associated with a 22% higher cardiovascular mortality (RR, 1.22; 95% CI, 1.01–1.48).103 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.104–110

  • The observational findings for benefits of a healthy food–based dietary pattern have been confirmed in 2 randomized clinical trials, including a small secondary prevention trial in France among patients with recent MI111 and a large primary prevention trial in Spain among patients with CVD risk factors.42 The latter trial, PREDIMED, demonstrated an ≈30% reduction in the risk of stroke, MI, and death attributable to cardiovascular causes in those patients randomized to Mediterranean-style diets supplemented with extra-virgin olive oil or mixed nuts.42

Family History and Genetics

  • Although the interaction between genetics and nutrition is complex, genetic factors may contribute to food preferences and modulate the association between dietary components and adverse cardiovascular health outcomes.112,113

Nutrition and Disparities in CVD Health

  • 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.114–116

  • 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.117

  • Disparities may be driven in part by overabundance of unhealthy food options. In a study of neighborhood-level data from 4 US cities (Birmingham, AL, Chicago, IL, Minneapolis, MN, and Oakland, CA), past neighborhood-level income was inversely associated with current density of convenience stores.118 In low-income neighborhoods, the percentage of white population was inversely associated with density of fast food restaurants and smaller grocery stores.

  • In a study using NHANES and Nielsen Homescan data to examine disparities in calories from store-bought consumer packaged goods over time, calories from store-bought beverages decreased between 2003 to 2006 and 2009 to 2012. However, the decline in calories from consumer packaged goods was slower for NH blacks, Mexican Americans, and lowest-income households.119

Global Burden

(See Chart 5-8)

  • A report from the GBD Study estimated the impact of 14 specific dietary factors on mortality worldwide in 2005 to 2015. In 2015, a total of 6.9 million male deaths (237.4 deaths per 100 000) and 5.2 million female deaths (147.0 deaths per 100 000) worldwide were estimated to be attributable to poor dietary habits. The population attributable fraction was 22.4% for males and 20.7% for females. Although the estimated mortality rate attributable to poor dietary factors decreased by 15.0% in the worldwide population from 2005 to 2015, the population attributable fraction increased by 7.9% over the same time period.120

  • The GBD 2016 Study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 315 diseases and injuries in 195 countries and territories. Mortality rates attributable to dietary risks were highest in Eastern Europe, Russia, and Central Asia (Chart 5-8).121

Chart 5-8.

Chart 5-8. Age-standardized global mortality rates attributable to dietary risks per 100 000, both sexes, 2016. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Micronesia, Federated States of; GRD, Grenada; KIR, Kiribati; LCA, Saint Lucia; MDV, Maldives; MHL, Marshall Islands; MLT, Malta; MUS, Mauritius; SGP, Singapore; SLB, Solomon Islands; SYC, Seychelles; TLS, Timor-Leste; TON, Tonga; TTO, Trinidad and Tobago; VCT, Saint Vincent and the Grenadines; VUT, Vanuatu; W Africa, West Africa; and WSM, Samoa. Data derived from Global Burden of Disease Study 2016 with permission.121 Copyright © 2017, University of Washington.

Abbreviations Used in Chapter 5

AHAAmerican Heart Association
BMIbody mass index
BPblood pressure
CERcost-effectiveness ratio
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
DALYdisability-adjusted life-year
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
GBDGlobal Burden of Disease
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDLhigh-density lipoprotein
HDL-Chigh-density lipoprotein cholesterol
HEIHealthy Eating Index
HFheart failure
HRhazard ratio
LDLlow-density lipoprotein
LDL-Clow-density lipoprotein cholesterol
MImyocardial infarction
MUFAmonounsaturated fatty acid
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
PAphysical activity
PREDIMEDPrevención con Dieta Mediterránea
PUFApolyunsaturated fatty acid
RCTrandomized controlled trial
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SCDsudden cardiac death
SESsocioeconomic status
SFAsaturated fatty acid
SNAPSupplemental Nutrition Assistance Program
SNPsingle-nucleotide polymorphism
SSBsugar-sweetened beverage
TCtotal cholesterol
TOHPTrials of Hypertension Prevention
WHIWomen’s Health Initiative

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