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Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association

Originally publishedhttps://doi.org/10.1161/CIR.0000000000000757Circulation. 2020;141:e139–e596

Abstract

Background:

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

Methods:

The American Heart Association, 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 annual Statistical Update. The 2020 Statistical Update is the product of a full year’s worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year’s edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association’s 2020 Impact Goals.

Results:

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

Conclusions:

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.

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

Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter.

  • Summary e140

  • 1. About These Statistics e152

  • 2. Cardiovascular Health e155

  • Health Behaviors

  • 3. Smoking/Tobacco Use e171

  • 4. Physical Inactivity e185

  • 5. Nutrition e207

  • 6. Overweight and Obesity e224

  • Health Factors and Other Risk Factors

  • 7. High Blood Cholesterol and Other Lipids e241

  • 8. High Blood Pressure e254

  • 9. Diabetes Mellitus e271

  • 10. Metabolic Syndrome e291

  • 11. Kidney Disease e312

  • 12. Sleep e325

  • Cardiovascular Conditions/Diseases

  • 13. Total Cardiovascular Diseases e333

  • 14. Stroke (Cerebrovascular Disease) e354

  • 15. Congenital Cardiovascular Defects and Kawasaki Disease e400

  • 16. Disorders of Heart Rhythm e417

  • 17. Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies e447

  • 18. Subclinical Atherosclerosis e470

  • 19. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e485

  • 20. Cardiomyopathy and Heart Failure e507

  • 21. Valvular Diseases e524

  • 22. Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension e540

  • 23. Peripheral Artery Disease and Aortic Diseases e550

  • Outcomes

  • 24. Quality of Care e565

  • 25. Medical Procedures e579

  • 26. Economic Cost of Cardiovascular Disease e584

  • Supplemental Materials

  • 27. At-a-Glance Summary Tables e590

  • 28. Glossary e594

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 (Figure), 1 which include core health behaviors (smoking, physical activity [PA], diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions. Cardiovascular disease (CVD) 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, heart failure [HF], valvular disease, venous disease, and peripheral artery 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.1 AHA indicates American Heart Association; HDL, high-density lipoprotein cholesterol; and LDL, low-density lipoprotein cholesterol. Copyright © 2019, American Heart Association, Inc.

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, an enhanced focus on social determinants of health, a 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. Please see each chapter for references and additional information.

Cardiovascular Health (Chapter 2)

  • Between 1999 to 2000 and 2015 to 2016, the prevalence of ideal levels for several cardiovascular health components improved for US children (12–19 years of age), including nonsmoking, total cholesterol, and BP. Although no notable changes were observed in the prevalence of an ideal score for the healthy diet score among children across this time frame, the prevalence of ideal levels of body mass index, PA, and diabetes mellitus (DM) declined.

  • Adults (≥20 years of age) also showed improvement in cardiovascular health components, with an increased prevalence of meeting ideal criteria for smoking, total cholesterol, BP, and PA. There were declines in the prevalence of ideal levels for body mass index and DM.

  • At all levels of household income–to–poverty ratio between 2003 to 2004 and 2015 to 2016, the highest prevalence of meeting ideal criteria for ≥5 cardiovascular health components was observed in adults with the highest levels of education. Recent research expands the general health benefits of ideal cardiovascular health to include improved psychological and muscle strength benefits.

Smoking/Tobacco Use (Chapter 3)

  • The prevalence of cigarette use in the past 30 days among middle and high school students in the United States was 1.8% and 8.1%, respectively, in 2018.

  • Although there has been a consistent decline in adult and youth cigarette 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, and individuals with HIV who are receiving medical care.

  • Over the past 7 years, there has been a sharp increase in electronic cigarette use among adolescents, from 1.5% to 20.8% between 2011 and 2018, and electronic cigarettes are now the most commonly used tobacco product in this demographic.

  • Tobacco use is the leading cause of disability-adjusted life-years in the United States. Globally, smoking is the second-leading cause of death, accounting for 8.1 million deaths worldwide in 2017.

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

Physical Inactivity (Chapter 4)

  • According to the National Health Interview Survey, the prevalence of self-reported physical inactivity has declined sharply among adults, from 40.2% to 25.9% between 2005 and 2017, decreasing below the target for Healthy People 2020, which was 32.6%.

  • The prevalence of high school students meeting the aerobic PA recommendations of ≥60 minutes of moderate to vigorous PA on all 7 days of the week was 26.1% nationwide, reported in the 2017 Youth Risk Behavior Survey System. Girls were less likely than boys to achieve these guidelines (17.5% versus 35.3%, respectively). Strikingly, only 14.7% of gay, lesbian, and bisexual students compared with 28.5% of heterosexual students met aerobic PA guidelines.

  • Evidence has suggested a role for light-intensity PA in preventing CVD. In a recent study from the Women’s Health Initiative, every hour per day more of light-intensity PA was associated with lower coronary heart disease (hazard ratio [HR], 0.86 [95% CI, 0.73–1.00]; P=0.05) and lower CVD (HR, 0.92 [95% CI, 0.85–0.99]; P=0.03).

  • In the Cancer Prevention Study II, among participants with the lowest level of PA, replacing 30 minutes per day of sitting with light-intensity PA or moderate to vigorous PA was associated with 14% (HR, 0.86 [95% CI, 0.81–0.89]) or 45% (HR, 0.55 [95% CI, 0.47–0.62]) lower mortality, respectively. For the individuals with the highest PA levels, substitution (replacing 30 min/day of sitting with light-intensity PA or moderate to vigorous PA) was not associated with differences in mortality risk.

Nutrition (Chapter 5)

  • The mean AHA healthy diet score improved between 2003 to 2004 and 2015 to 2016 in US adults, although disparities persisted. The proportion with a poor diet decreased from 64.7% to 58.3% for non-Hispanic blacks, from 66.0% to 57.5% for Mexican Americans, and from 54.0% to 45.9% for non-Hispanic whites. Improvements were largely attributable to increased consumption of whole grains, nuts, seeds, and legumes, as well as decreased consumption of sugar-sweetened beverages.

  • A 2-by-2 factorial randomized clinical trial of 25 871 adults (males ≥50 years of age and females ≥55 years of age) found that neither daily supplementation with 2000 IU of vitamin D nor 1 g of marine n-3 fatty acids had an effect on major cardiovascular events (vitamin D: HR, 0.97 [95% CI, 0.85–1.12]; marine n-3 fatty acids: HR, 0.92 [95% CI, 0.80–1.06]), invasive cancer, or any secondary outcomes.

  • Using a comparative risk assessment approach, the Global Burden of Disease 2017 Study estimated that 11 million deaths (95% uncertainty interval [UI], 10–12 million; 22% of all deaths) and 255 million disability-adjusted life-years (95% UI, 234–274 million; 15% of all disability-adjusted life-years) were attributable to 15 dietary risks in 2017. The leading dietary risk factors were high sodium intake (3 million deaths; 95% UI, 1–5 million deaths), low whole grain intake (3 million deaths; 95% UI, 2–4 million deaths), and low fruit intake (2 million deaths; 95% UI, 1–4 million deaths). Age-standardized diet-related death rates decreased between 1990 and 2017 from 406 (95% UI, 381–430) to 275 (95% UI, 258–292) deaths per 100 000 population, although the proportion of deaths attributable to dietary risks was largely stable.

  • In a pooled analysis of 30 904 participants from 3 cohort studies, the interactions between a genetic risk score composed of 97 body mass index–associated variants and 3 diet-quality scores were examined. The relationship between genetic predisposition for obesity and body mass index was attenuated by higher diet quality. For example, a 10-U increase in the genetic risk score was associated with a 0.84-U (95% CI, 0.72–0.96) increase in body mass index for those in the highest tertile of Alternate Healthy Eating Index score compared with a 1.14-U (95% CI, 0.99–1.29) increase in body mass index for those in the lowest tertile of Alternate Healthy Eating Index score.

Overweight and Obesity (Chapter 6)

  • According to 2015 to 2016 data from NHANES (National Health and Nutrition Examination Survey), the overall prevalence of obesity (≥95th percentile) among youth was 18.5%. By age group, the prevalence of obesity for children 2 to 5 years of age was 13.9%; for children 6 to 11 years of age, the prevalence was 18.4%; and for adolescents 12 to 19 years of age, the prevalence was 20.6%. According to 2013 to 2016 data from NHANES, the prevalence of obesity among adults was 38.3% (36.0% of males and 40.4% of females), including 7.7% with class III obesity or a body mass index ≥40 kg/m2 (5.5% of males and 9.8% of females).

  • Long-term follow-up of the Longitudinal Assessment of Bariatric Surgery-2 study, a multicenter observational cohort study of 1300 participants who underwent bariatric surgery, demonstrated that most participants maintained the majority of their weight loss. However, at 7 years after surgery, lower prevalence rates of DM and hypertension were achieved only among those who underwent Roux-en-Y gastric bypass and not among those who underwent laparoscopic gastric banding.

  • In 10 large population cohorts in the United States, individual-level data from adults 20 to 79 years of age with 3.2 million person-years of follow-up (1964–2015) demonstrated that overweight and obesity were associated with earlier development of CVD and reinforced the greater mortality associated with obesity.

  • Among older adults in the Multi-Ethnic Study of Atherosclerosis, approximately half of participants with metabolically healthy obesity developed metabolic syndrome (MetS) and had increased odds of CVD compared with those with stable metabolically healthy obesity or healthy normal weight. This suggests that metabolically healthy obesity is not a low-risk state.

High Blood Cholesterol and Other Lipids (Chapter 7)

  • From 2007 to 2008 to 2015 to 2016, the proportion of US youths 6 to 19 years of age with all ideal levels of total cholesterol, high-density lipoprotein cholesterol, and non–high-density lipoprotein cholesterol increased significantly, from 42.1% (95% CI, 39.6%–44.7%) to 51.4% (95% CI, 48.5%–54.2%). Conversely, from 2007 to 2010 to 2013 to 2016, the proportion of youths with at least 1 of these lipids at adverse levels decreased, from 23.1% (95% CI, 21.5%–24.7%) to 19.2% (95% CI, 17.6%–20.8%).

  • In a recent meta-analysis in which low-density lipoprotein cholesterol levels were lowered from a baseline of 63 mg/dL to an end result of 21 mg/dL, major vascular events were consistently reduced (relative risk per 38.7-mg/dL reduction, 0.79 [95% CI, 0.71–0.87]) without adverse effects.

  • In the Health Survey for England and the Scottish Health Survey, a U-shaped association of all-cause mortality was seen with the lowest high-density lipoprotein cholesterol level (<58 mg/dL; HR, 1.23 [95% CI, 1.06–1.44]) and the highest high-density lipoprotein cholesterol level (≥77 mg/dL; HR, 1.25 [95% CI, 0.97–1.62]).

High Blood Pressure (Chapter 8)

  • Data from 13 160 participants in cohorts in the Cardiovascular Lifetime Risk Pooling Project (ie, the Framingham Offspring Study, the Coronary Artery Risk Development in Young Adults study, and the ARIC [Atherosclerosis Risk in Communities] study) found that the lifetime risk of hypertension from 20 to 85 years of age using 2017 American College of Cardiology (ACC)/AHA guidelines was 86.1% (95% CI, 84.1%–88.1%) for black males, 85.7% (95% CI, 84.0%–87.5%) for black females, 83.8% (95% CI, 82.5%–85.0%) for white males, and 69.3% (95% CI, 67.8%–70.7%) for white females.

  • Among 60 027 participants in the Norwegian Mother and Child Cohort Study who were normotensive before pregnancy, the population attributable fraction for pharmacologically treated hypertension within 10 years postpartum was 28.6% (95% CI, 25.5%–30.3%) for complications of pregnancy (preeclampsia/eclampsia, gestational hypertension, preterm delivery, and pregestational or gestational DM).

  • In the HCHS/SOL (Hispanic Community Health Study/Study of Latinos) Sueño Sleep Ancillary Study of Hispanics (N=2148), a 10% higher sleep fragmentation and frequent napping versus not napping were associated with a 5.2% and 11.6% higher prevalence of hypertension, respectively. A 10% higher sleep efficiency was associated with a 7.2% lower prevalence of hypertension.

Diabetes Mellitus (Chapter 9)

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

  • Among adults without DM in NHANES 2007 to 2012, 37.8% met the moderate-intensity PA goal of 150 min/wk and 58.6% met the weight loss or maintenance goal for DM prevention. Adults with prediabetes were less likely to meet the PA and weight goals than adults with normal glucose levels.

  • On the basis of NHANES 2013 to 2016 data for adults with DM, 20.9% had their DM treated and controlled, 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.

  • Among Medicare Advantage patients with DM from 2006 to 2013, use of metformin increased from 47.6% to 53.5%, dipeptidyl peptidase 4 inhibitor use increased from 0.5% to 14.9%, insulin use increased from 17.1% to 23.0%, use of sulfonylureas decreased from 38.8% to 30.8%, and use of thiazolidinediones decreased from 28.5% to 5.6%.

Metabolic Syndrome (Chapter 10)

  • On the basis of NHANES 1999 to 2014, the prevalence of MetS in adolescents 12 to 19 years of age in the United States varied by geographic region. MetS prevalence was lower in the Northeast (6.25% [95% CI, 4.14%–8.36%]) and West (6.31% [95% CI, 4.73%–7.89%]) regions and higher in the South (7.57% [95% CI, 5.80%–9.33%]) and Midwest (11.42% [95% CI, 8.11%–14.72%]).

  • On the basis of NHANES 2007 to 2014, the overall prevalence of MetS in adults was 34.3% and was similar for males (35.3%) and females (33.3%). The prevalence of MetS increased with age, from 19.3% among people 20 to 39 years of age to 37.7% for people 40 to 59 years of age and 54.9% among people ≥60 years of age.

  • Secular trends in MetS differ based on the definition used. Using the harmonized MetS criteria, the prevalence of MetS increased from 25.3% in NHANES 1988 to 1994 to 34.2% in NHANES 2007 to 2012. In contrast, using Adult Treatment Panel III criteria, the prevalence of MetS was stable overall in NHANES 2003 to 2014.

  • In the ARIC study (1987–1998), the prevalence of MetS increased from 33% to 50% over the mean 10-year follow-up, with differences by age and sex. The prevalence of MetS was lower in black males than in white males at all time points and for all ages across the study. Black females had higher prevalence of MetS than white females at baseline and subsequent time points for all ages except for those >60 years of age.

Kidney Disease (Chapter 11)

  • Using data from NHANES 2013 to 2016, the United States Renal Data System has estimated the prevalence of chronic kidney disease by estimated glomerular filtration rate and albuminuria categories. The overall prevalence of chronic kidney disease (estimated glomerular filtration rate <60 mL·min−1·1.73 m−2 or albumin/creatinine ratio ≥30 mg/g) in 2013 to 2016 was 14.8%.

  • Chronic kidney disease is a risk factor for incident and recurrent coronary events, stroke, HF, venous thromboembolism, and atrial fibrillation (AF). The association of reduced estimated glomerular filtration rate with cardiovascular risk is generally similar across age, race, and sex subgroups, although albuminuria tends to be a stronger risk factor for females than for males and for older (>65 years of age) versus younger people.

  • In a nationwide US cohort that included 4726 participants with chronic kidney disease, only 2366 (50%) self-reported taking statins, whereas an additional 1984 participants (42%) met recommendations for statin treatment according to the 2013 ACC/AHA guideline on treatment of blood cholesterol but did not report using statins.

  • In 2015, admissions for CVD accounted for 27% of all inpatient spending for patients with end-stage renal disease.

Sleep (Chapter 12)

  • A systematic review estimated the prevalence of obstructive sleep apnea in cerebrovascular disease in 3242 patients who had either cerebral infarction, transient ischemic attack, ischemic stroke, or hemorrhagic stroke and found that the pooled prevalence of obstructive sleep apnea (defined as apnea-hypopnea index >10 events/hour) was 62% (95% CI, 55%–69%), and the pooled prevalence of severe obstructive sleep apnea (apnea-hypopnea index >30) was 30% (95% CI, 23%–37%).

  • The deepest stage of non–rapid eye movement sleep, also called slow-wave sleep, is thought to be a restorative stage of sleep. In the Sleep Heart Health Study, which used in-home polysomnography to characterize sleep, it was found that participants with a lower proportion of slow-wave sleep had significantly greater odds of incident hypertension (quartile 1 versus quartile 3: odds ratio [OR], 1.69 [95% CI, 1.21–2.36]).

  • A meta-analysis analyzed data from 9 cohort studies with 2755 participants that described the association between obstructive sleep apnea and major adverse cardiovascular events after percutaneous coronary intervention with stenting and found that obstructive sleep apnea was associated with a significant increased risk of major adverse cardiovascular events (pooled relative risk, 1.96; 95% CI, 1.36–2.81).

  • Analysis of direct and indirect costs related to inadequate sleep in Australia suggested that the cost for a population the size of the United States would be more than approximately $585 billion for 2016 to 2017.

  • A recent analysis of the global prevalence and burden of obstructive sleep apnea estimated that 936 million (95% CI, 903–970 million) males and females 30 to 69 years of age have mild to severe obstructive sleep apnea (apnea-hypopnea index ≥5), and 425 million (95% CI, 399–450 million) have moderate to severe obstructive sleep apnea (apnea-hypopnea index ≥15) globally. The prevalence was highest in China, followed by the United States, Brazil, and India.

Total Cardiovascular Diseases (Chapter 13)

  • On the basis of the 2017 National Health Interview Survey, the age-adjusted prevalence of all types of heart disease was 10.6%; the corresponding age-adjusted prevalence of heart disease among whites, blacks, Hispanics, and Asians was 11.0%, 9.7%, 7.4%, and 6.1%, respectively. The age-adjusted prevalence of heart disease, coronary artery disease, hypertension, and stroke was higher in males (11.8%, 7.2%, 26.0%, and 3.3%, respectively) than females (9.5%, 4.2%, 23.1%, and 2.5%, respectively).

  • A recent study using the Global Burden of Disease methodology examined the burden of CVD among US states and found that a large proportion of CVD is attributable to (in decreasing order of contribution) dietary risks, high systolic BP, high body mass index, high total cholesterol level, high fasting plasma glucose level, tobacco smoking, and low levels of PA.

  • In 2017, 2 813 503 resident deaths were registered in the United States, which exceeds the 2016 figure by 69 255 deaths. Ten leading causes accounted for 74.0% of all registered deaths. The 10 leading causes of death in 2017 were the same as in 2016; 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 an increase in age-adjusted death rates. The age-adjusted rate increased 4.2% for unintentional injuries, 2.3% for Alzheimer disease, 3.7 % for suicide, 2.4% for DM, 5.9% for influenza and pneumonia, 0.7% for chronic lower respiratory disease, and 0.8% for stroke. The age-adjusted death rates decreased 2.1% for cancer but did not change appreciably for heart disease or kidney disease.

  • In 2017, ≈17.8 million (95% CI, 17.5–18.0 million) deaths were attributed to CVD globally, which amounted to an increase of 21.1% (95% CI, 19.7%–22.6%) from 2007. The age-adjusted death rate per 100 000 population was 233.1 (95% CI, 229.7–236.4), which represents a decrease of 10.3% (95% CI, −11.4% to −9.3%) from 2007. Overall, the crude prevalence of CVD was 485.6 million cases (95% CI, 468.0–505.0 million) in 2017, an increase of 28.5% (95% CI, 27.7%–29.4%) compared with 2007. However, the age-adjusted prevalence rate was 6081.6 (95% CI, 5860.8–6320.8) per 100 000, an increase of 0.2% (95% CI, −0.4% to 0.80%) from 2007.

Stroke (Cerebrovascular Disease) (Chapter 14)

  • Despite encouraging data about declining stroke incidence, on a global level the aging population and accumulating risk factors contribute to an increasing lifetime risk of stroke. Per the Global Burden of Disease 2016 Lifetime Risk of Stroke Collaborators, the mean global lifetime risk of stroke increased from 22.8% in 1990 to 24.9% in 2016, a relative increase of 8.9% (95% CI, 6.2%–11.5%) after accounting for the competing risk of death of any cause other than stroke.

  • A mendelian randomization study among almost 500 000 Chinese individuals found that genetic markers predictive of low-density lipoprotein cholesterol levels were directly associated with ischemic stroke and inversely associated with intracerebral hemorrhage, thus providing causal evidence of opposing effects of low-density lipoprotein cholesterol levels on the 2 most common stroke types.

  • The largest multiethnic genome-wide association study of stroke conducted to date reported 32 genetic loci, including 22 not previously reported. These novel loci point to a major role of cardiac mechanisms beyond established sources of cardioembolism. Approximately half of the stroke genetic loci share genetic associations with other vascular traits, most notably BP.

  • Among 81 714 females in the Women’s Health Initiative prospective cohort study, those who consumed ≥2 artificially sweetened beverages daily, on average, had an elevated risk of all stroke (adjusted HR, 1.23 [95% CI, 1.02–1.47]) and ischemic stroke (adjusted HR, 1.31 [95% CI 1.06–1.63]) compared with those who consumed <1 artificially sweetened beverage weekly, after adjustment for demographics, CVD history, risk factors, body mass index, health behaviors, and overall diet quality.

  • As the US population ages, the number of people with Alzheimer disease will increase dramatically from 2010 to 2050. According to a modeling study based on estimates in a population of 10 800 participants from the Chicago Health and Aging Project in the United States, in 2010 there were 4.7 million individuals ≥65 years of age with Alzheimer disease (95% CI, 4.0–5.5 million); by 2050, the number of people with Alzheimer disease is projected to be 13.8 million, with 7.0 million ≥85 years of age.

Congenital Cardiovascular Defects and Kawasaki Disease (Chapter 15)

  • Increasingly, social determinants are being identified as playing an important role in outcomes from congenital cardiovascular defects. One recent, large review of >15 000 infants demonstrated improved survival among patients with fathers >35 years old (versus younger); survival was also impacted by factors such as maternal education, race/ethnicity, and marital status.

  • In 2016, there were 6000 all-listed diagnoses hospital discharges for Kawasaki disease in the United States, and in 2017, US mortality attributable to Kawasaki disease was 5 patients for underlying mortality and 10 patients for all-cause mortality.

Disorders of Heart Rhythm (Chapter 16)

  • Higher levels of cardiovascular health are associated with decreased risk of developing AF. An analysis of the ARIC study described that individuals with average and optimal cardiovascular health had a 41% and 62% lower risk of AF, respectively, than those with inadequate cardiovascular health.

  • High atrial rate episodes detected by cardiac implantable electronic devices are associated with higher risk of clinical AF (OR, 5.7 [95% CI, 4.0–8.0]) and higher risk of stroke (OR, 2.4 [95% CI, 1.8–3.3]), according to a meta-analysis.

  • Racial disparities exist in the treatment of patients with AF. In the ORBIT-AF II registry (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation), black patients were 27% less likely than their white counterparts to receive direct oral anticoagulants if an anticoagulant was prescribed. Black and Hispanic patients were more likely than their white counterparts to receive inappropriate doses of direct oral anticoagulants.

  • In a cohort of new patients with AF at the University of Pennsylvania who did not have a history of remote stroke, blacks with new-onset AF were more likely to have an ischemic stroke before or after the diagnosis of AF. The rate of ischemic stroke per year after AF diagnosis was 1.5% in whites and 2.5% in blacks.

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

  • In 2017, primary-cause sudden cardiac death (SCD) mortality was 18 835, and any-mention SCD mortality in the United States was 379 133. The any-mention age-adjusted annual SCD rate is 97.1 (95% CI, 96.8–97.4) per 100 000 population.

  • SCD appeared among the multiple causes of death on 13.5% of death certificates in 2017 (379 133 of 2 813 503), which suggests that 1 of every 7.4 people in the United States died of SCD.

  • Incidence of emergency medical services–treated out-of-hospital cardiac arrest in people of any age is 74.3 individuals per 100 000 population based on the 2018 CARES (Cardiac Arrest Registry to Enhance Survival), with >2-fold variation between states (range, 51.6–128.3 per 100 000 population).

  • In the National Emergency Department Sample for 2016, the weighted national estimate of emergency department visits with a principal diagnosis of either cardiac arrest or ventricular fibrillation/flutter was 183 629 (rate of 56.8 per 100 000 people). Of these, 15.8% (29 096) were admitted to the same hospital or transferred to another hospital.

Subclinical Atherosclerosis (Chapter 18)

  • The 2018 Cholesterol Clinical Practice Guideline and the 2019 CVD Primary Prevention Clinical Practice Guideline advise that the use of coronary artery calcium is reasonable in intermediate-risk or selected borderline-risk adults if the decision about statin therapy remains uncertain after calculation of the 10-year atherosclerotic cardiovascular disease risk and after accounting for risk-enhancing factors.

  • Compared with individuals who sleep 7 to 8 hours per night, and with adjustment for conventional risk factors, people who sleep <6 hours per night have a 1.27 greater odds of noncoronary atherosclerosis.

  • Older adult females who consumed ≥3 servings of vegetables each day had an ≈5.0% lower amount of carotid atherosclerosis than females who consumed <2 servings of vegetables.

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

  • The awareness of 5 common heart attack symptoms (jaw, neck, or back discomfort; weakness or lightheadedness; chest discomfort; arm or shoulder discomfort; and shortness of breath) is higher in females than in males (54.4% versus 45.6%) and in whites (54.8%) than in blacks (43.1%), Asians (33.5%), and Hispanics (38.9%).

  • Among patients hospitalized for ST-segment–elevation myocardial infarction, lack of health insurance (OR, 1.77 [95% CI, 1.72–1.82]; P<0.001) and below-median income (OR, 1.08 [95% CI, 1.07–1.09]; P<0.001) are independent predictors of in-hospital mortality.

  • Neighborhood socioeconomic status is associated with outcomes in patients with acute myocardial infarction. Compared with those in the highest quintile of neighborhood socioeconomic status, those residing in the most disadvantaged quintile experience higher rates of in-hospital death (OR, 1.10 [95% CI, 1.02–1.18]) and major bleeding (OR, 1.10 [95% CI, 1.05–1.15]).

  • Females experience longer door-to-balloon times and lower rates of guideline-directed medical therapy than males. In-hospital mortality is higher in females than in males with ST-segment–elevation myocardial infarction (7.4% versus 4.6%) and non–ST-segment–elevation myocardial infarction (4.8% versus 3.9%).

Cardiomyopathy and Heart Failure (Chapter 20)

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

  • 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.

  • The prevalence of HF is highly variable across the world, with the lowest in sub-Saharan Africa. Prevalence of HF risk factors also varies worldwide, with hypertension being most common in Latin America, the Caribbean, Eastern Europe, and sub-Saharan Africa. Ischemic heart disease is most prevalent in Europe and North America. Valvular heart disease is more common in East Asia and Asia-Pacific countries.

Valvular Diseases (Chapter 21)

  • In high-risk patients with severe aortic stenosis, recent studies have shown that transcatheter aortic valve replacement is comparable to surgical aortic valve replacement in terms of mortality at 1 and 5 years. In patients at intermediate surgical risk, transcatheter aortic valve replacement and surgical aortic valve replacement have similar rates of death attributable to any cause or debilitating stroke at 2 years. In subjects at low surgical risk, transcatheter aortic valve replacement has lower rates of death, stroke, or rehospitalization at 2 years than surgical aortic valve replacement.

  • Percutaneous mitral valve repair techniques for primary or degenerative mitral regurgitation have become a common treatment option for high-risk patients not deemed candidates for surgical repair. Data from the Society for Thoracic Surgeons/ACC Transcatheter Valve Therapy Registry on patients commercially treated with the MitraClip percutaneous mitral valve repair device showed reduction in the severity of mitral regurgitation and procedural success in >90% of cases, although mitral valve dysfunction at 12 months is more common with percutaneous mitral valve repair than with surgical repair.

  • The role of the MitraClip in secondary mitral regurgitation was investigated in 2 recently published randomized clinical trials with divergent results. The MITRA-FR trial (Percutaneous Repair With the MitraClip Device for Severe Functional/Secondary Mitral Regurgitation) did not show a significant difference in the combined end point of death or rehospitalization for HF at 1 year between the group treated with MitraClip and the group treated with optimal medical therapy and cardiac resynchronization alone. However, the COAPT trial (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy) demonstrated a significant reduction in rehospitalization because of HF and mortality at 2 years with the MitraClip. Such divergent results may be related to differences in sample characteristics and size, duration of follow-up, and primary end point.

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

  • In 2016, there were an estimated 1 220 000 cases of venous thromboembolism.

  • According to combined data from the Emerging Risk Factors Collaboration and the UK Biobank, among traditional atherosclerotic risk factors, age and obesity were associated with increased venous thromboembolism risk; for hypertension and dyslipidemia, there was no association; and for DM, the results were inconsistent.

  • In a meta-analysis of patients with deep vein thrombosis who underwent ultrasonography at least 6 weeks after their deep vein thrombosis, those with residual vein thrombosis had 2-fold greater risk of postthrombotic syndrome, whereas those with venous reflux at the popliteal level had 34% greater postthrombotic syndrome risk.

  • In a cohort of 23 329 patients with first venous thromboembolism, cumulative incidence of chronic thromboembolic pulmonary hypertension was 1.3% and 3.3% at 2 and 10 years after pulmonary embolism and 0.3% and 1.3% after deep vein thrombosis, respectively.

Peripheral Artery Disease and Aortic Diseases (Chapter 23)

  • A recent trial demonstrated that a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, evolocumab, reduced the risk of major adverse limb events, including acute limb ischemia, major amputation, and urgent revascularization (HR, 0.58 [95% CI, 0.38–0.88]), among patients with a history of myocardial infarction, stroke, or peripheral artery disease.

  • A recent study with ≈28 000 patients with a history of CVD demonstrated that patients with symptomatic peripheral artery disease but no prior myocardial infarction or stroke had an ≈2 times higher risk of CVD events than those with prior myocardial infarction or stroke but no symptomatic peripheral artery disease.

  • A recent report from the Nationwide Inpatient Sample demonstrated that the rate of nontraumatic lower-extremity amputation had increased by 50% between 2009 and 2015 in adults with DM, despite previously declining trends.

Quality of Care (Chapter 24)

  • The 30-day postdischarge mortality rate in acute myocardial infarction has decreased in recent years to ≈12%. The Hospital Readmissions Reduction Program did not change this trend, and the rates of reduction remained constant when comparing time before and after the program’s initiation.

  • There has been controversy concerning the impact of the Hospital Readmissions Reduction Program for patients hospitalized with HF. Although some studies suggested the program was associated with an increase in mortality (HR, 1.10 [95% CI, 1.06–1.14]), studies using other methods suggested no change in mortality.

  • For individuals with stroke, admission to institutions participating in the Get With The Guidelines–Stroke program was associated with several positive changes in management, including higher rates of tissue plasminogen activator use, education on risk factors, evaluation for swallowing, lipid evaluation, and neurology evaluation, as well as more appropriate referral for hospice.

Medical Procedures (Chapter 25)

  • Data from the Society of Thoracic Surgeons Congenital Heart Surgery Database indicate that a total of 122 459 congenital heart surgeries were performed from July 2014 to June 2018.

  • In 2018, 3408 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 $351.3 billion in 2014 to 2015.

  • The estimated direct costs of CVD in the United States 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 ≥80 years of age.

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 2020 annual Statistical Update is the product of a full year’s worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.

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

Connie W. Tsao, MD, MPH, Vice Chair

Connie W. Tsao, MD, MPH, Vice Chair Medicine Advisor

Debra G. Heard, PhD, AHA Consultant

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

Acknowledgments

The Writing Group wishes to thank their colleagues Lucy Hsu and Michael Wolz at the National Heart, Lung, and Blood Institute, John Omura at the Centers for Disease Control and Prevention, Celine Barthelemy at the Institute for Health Metrics and Evaluation at the University of Washington, and Christina Koutras and Fran Thorpe at the American College of Cardiology for their valuable comments and contributions.

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 https://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: Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, 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, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW; on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2020 update: a report from the American Heart Association. Circulation. 2020;141:e139–e596. doi: 10.1161/CIR.0000000000000757.

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 https://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).

References

1. About These Statistics

The AHA works with the NHLBI 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 and data sources used are the following:

  • ACC NCDR’s Chest Pain–MI Registry (formerly the ACTION Registry)—quality information for AMI

  • ARIC—CHD and HF incidence rates

  • BRFSS—ongoing telephone health survey system

  • GBD—global disease prevalence and mortality

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

  • GWTG—quality information for resuscitation, HF, and stroke

  • HCUP—hospital inpatient discharges and procedures

  • 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

  • NAMCS—physician office visits

  • NHAMCS—hospital outpatient and ED visits

  • NHANES—disease and risk factor prevalence and nutrition statistics

  • NHIS—disease and risk factor prevalence

  • NVSS—mortality for United States

  • USRDS—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 CDC/NCHS conducts health examination and health interview surveys that provide estimates of the prevalence of diseases and risk factors. In this Statistical 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.

In the 2020 Statistical Update, 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 Statistical Update to present estimates of the percentage of people with high lipid values, DM, overweight, and obesity. NHANES 2015 to 2016 and BRFSS 2017 data are used for the prevalence of sleep issues. The NHIS 2016 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 2017 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 Statistical 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). HBP, or hypertension, increases the mortality risks of CVD and other diseases, and HF should be selected as an underlying cause only when the true underlying cause is not known. In this Statistical 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 obtained from the CDC WONDER website or the CDC NVSS mortality file.1 Any-mention numbers of deaths were tabulated from the CDC WONDER website or CDC NVSS mortality file.2

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 20163 in the computation of morbidity data. CDC/NCHS population estimates4 for 2017 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 principal (first-listed) diagnosis, and procedures are listed according to all-listed procedures (principal and secondary). These estimates are from the 2016 HCUP. Ambulatory care visit data include patient visits to primary providers’ offices and hospital outpatient departments and EDs. Ambulatory care visit data reflect the primary (first-listed) diagnosis. These estimates are from the 2016 NAMCS and 2016 NHAMCS of the CDC/NCHS. Data for community health centers are included in 2016 NAMCS estimates. Readers comparing data across years should note that beginning October 1, 2015, a transition was made from ICD-9 to ICD-10. This should be kept in mind, because coding changes could affect some statistics, especially when comparisons are made across these years.

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 CDC/NCHS are applied as noted.5 Effective with mortality data for 1999, ICD-10 is used.6 Beginning in 2016, ICD-10-CM is used for hospital inpatient stays and ambulatory care visit data.7

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.8 International mortality data are age adjusted to the European standard population. 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 Statistical 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 2017. 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 2016. Numbers of visits to primary providers’ offices and hospital EDs are for 2016, 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, total CVD is defined according to ICD codes given in Chapter 13 of the present document. This definition includes all diseases of the circulatory system. Unless otherwise specified, estimates for total CVD do not include congenital CVD. Prevalence of total CVD includes people with hypertension, CHD, stroke, and HF.

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.

Global Burden of Disease

The GBD Study is an ongoing global effort to measure death and disability attributable to diseases, injuries, and risk factors for all countries, from 1990 to the present day. GBD 2017, the most recent iteration of the study, was produced by the collective efforts of >3600 researchers in >145 countries. Estimates were produced for 350 diseases and injuries and 84 risk factors. Detailed methods and results can be found via the study’s online data visualization tools, as well as across a range of peer-reviewed scientific research articles, which can be found cited elsewhere in this publication.

For GBD 2017, estimates were produced for 1990 to 2017 for 195 countries and territories, stratified by age and sex, with subnational estimates made available in an increasing number of countries. Improvements to statistical and geospatial modeling methods, as well as the addition of new data sources, could lead to changes in results across GBD Study cycles for both the most recent and earlier years.

For more information about the GBD, and to access GBD 2017 resources, data visualizations, and most recent publications, please visit the study’s website.9

Contacts

If you have questions about statistics or any points made in this Statistical 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 Statistical 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

ACCAmerican College of Cardiology
ACTIONAcute Coronary Treatment and Intervention Outcomes Network
AHAAmerican Heart Association
AMIacute myocardial infarction
APangina pectoris
ARICAtherosclerosis Risk in Communities Study
BPblood pressure
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CDC WONDERCenters for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research
CHDcoronary heart disease
CHSCardiovascular Health Study
CVDcardiovascular disease
DMdiabetes mellitus
EDemergency department
FHSFramingham Heart Study
GBDGlobal Burden of Disease study
GCNKSSGreater Cincinnati/Northern Kentucky Stroke Study
GWTGGet With The Guidelines
HBPhigh blood pressure
HCUPHealthcare Cost and Utilization Project
HFheart failure
ICDInternational Classification of Diseases
ICD-9International Classification of Diseases, 9th Revision
ICD-10International Classification of Diseases, 10th Revision
ICD-10-CMInternational Classification of Diseases, 10th Revision, Clinical Modification
MEPSMedical Expenditure Panel Survey
MImyocardial infarction
NAMCSNational Ambulatory Medical Care Survey
NCDRNational Cardiovascular Data Registry
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
NVSSNational Vital Statistics System
USRDSUnited States Renal Data System
WHOWorld Health Organization
YRBSSYouth Risk Behavior Surveillance System

References

2. Cardiovascular Health

See Tables 2-1 and 2-2 and Charts 2-1 through 2-15

In 2010, the AHA created a new set of central Strategic Impact Goals to drive organizational priorities for the decade to come:

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

This impact goal introduced a new concept of CVH, characterized by 7 components (Life’s Simple 7),2 including 4 health behaviors (diet quality, PA, smoking, BMI) and 3 health factors (blood cholesterol, BP, blood glucose). Ideal CVH is defined by the absence of clinically manifest CVD together with the simultaneous presence of optimal levels of all 7 components, including not smoking and having a healthy diet pattern, sufficient PA, normal body weight, and normal levels of TC, BP, and FPG (in the absence of medication treatment for these 3 factors; Table 2-1). Because a wide spectrum exists in potential levels of CVH, and the ideal CVH profile is known to be rare in the US population, the broader spectrum of CVH can also be represented using categorical ranges for 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 categories for each of the 7 components of CVH for both adults and children.

Table 2-1. This table lists definitions of poor, intermediate, and ideal for each component of cardiovascular health including current smoking, body mass index, physical activity, healthy diet score, total cholesterol, blood pressure, and diabetes.

Table 2-1. Definitions of Poor, Intermediate, and Ideal for Each Component of CVH

Level of CVH 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 score, No. of components
 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)
TC, 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
Diabetes mellitus§
 Adults ≥20 y of ageFPG ≥126 mg/dL or HbA1c ≥6.5%FPG 100–125 mg/dL or HbA1c 5.7%–6.4% or treated to goalFPG <100 mg/dL or HbA1c <5.7%
 Children 12–19 y of ageFPG ≥126 mg/dL or HbA1c ≥6.5%FPG 100–125 mg/dL or HbA1c 5.7%–6.4% or treated to goalFPG <100 mg/dL or HbA1c <5.7%

BMI indicates body mass index; CVH, cardiovascular health; DBP, diastolic blood pressure; ellipses (…), data not available; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin or hemoglobin A1c; SBP, systolic blood pressure; and TC, total cholesterol.

*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 also be described using a continuous American Heart Association diet score, scaled from 0 to 100 (see chapter on Nutrition).

§FPG is solely used to determine poor, intermediate, and ideal status for American Heart Association Strategic Impact Goal monitoring purposes. For population surveillance purposes, use of HbA1c was added to define poor, intermediate, and ideal levels of this component, and the name was changed to “Diabetes mellitus” to reflect this addition.

Source: Modified from Lloyd-Jones et al.1 Copyright © 2010, American Heart Association, Inc.

This concept of CVH represented a new focus for the AHA, with 3 central and novel emphases:

  • An expanded focus on CVD prevention and promotion of CVH as an active, positive, and achievable pursuit, 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 levels of health factors (optimal blood lipids, BP, glucose levels) throughout the lifespan.

  • Population-level health-promotion strategies to shift the majority of the public toward greater CVH, 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 surveillance estimates for the prevalence of CVH 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 CVH status, which includes symptom burden, functional status, and health-related quality of life, as an indicator of CVH in future organizational goal setting.3

Relevance of Ideal CVH

  • Since the AHA announced its 2020 Impact Goals, multiple independent investigations (summaries of which are provided in this chapter) have confirmed the importance of having ideal levels of these components, along with the overall concept of CVH. Findings include strong inverse, stepwise associations in the United States of the number of CVH components at ideal levels with all-cause mortality, CVD mortality, and HF; with subclinical measures of atherosclerosis such as carotid IMT, arterial stiffness, and 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.4–10

  • A study in a large Hispanic/Latino cohort study in the United States found that associations between CVD and status of CVH components compared favorably with existing national estimates; however, some of the associations varied by sex and heritage.10

  • A study in blacks found that risk of incident HF was 61% lower among those with ≥4 ideal CVH components than among those with 0 to 2 ideal components.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 CVH components 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 FPG, and physical inactivity.13

  • An inverse stepwise association was present between the number of ideal CVH components and risk of death based on NHANES data from 1988 to 2006.14 The HRs for people with 6 or 7 ideal health metrics compared with 0 ideal health components 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 having the highest number of ideal CVH components 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]) compared with individuals with the lowest number of ideal components.15

  • The adjusted PAFs for CVD mortality for individual components of CVH have been reported 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 PAFs for IHD mortality for individual components of CVH 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

  • Several studies have been published in which investigators have assigned individuals a CVH score ranging from 0 to 14 based on the sum of points assigned to each component of CVH (poor=0, intermediate=1, ideal=2 points). Using this approach, data from the REGARDS cohort were used to demonstrate an inverse stepwise association between a higher CVH score component and lower incidence of stroke. On the basis of this score, every unit increase in CVH 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 CVH 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 CVH factors. 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 CVH as defined by the AHA is associated with lower incidence of 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 ESRD,27 less pneumonia, less chronic obstructive pulmonary disease,28 less VTE/PE,29 lower prevalence of aortic sclerosis and stenosis,30 lower risk of calcific aortic valve stenosis,31 better prognosis after MI,32 and lower risk of AF.33 In addition, a study among a sample of Hispanics/Latinos residing in the United States reported that greater positive psychological functioning (dispositional optimism) was associated with higher CVH scores as defined by the AHA.34 A recent study in college students found that both handgrip strength and muscle mass are positively associated with greater numbers of ideal CVH components, providing further evidence of the general health benefits of ideal CVH.35

  • 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 CVH as measured by Life’s Simple 7 scores.36 In addition, neighborhood factors and contextual relationships have been found to be related to health disparities in CVH, but more research is needed to better understand these complex relationships.37 Having more ideal CVH components in middle age is also associated with lower non-CVD and CVD healthcare costs in later life.38 An investigation of 4906 participants in the Cooper Center Longitudinal Study reported that participants with ≥5 ideal CVH components exhibited 24.9% (95% CI, 11.7%–36.0%) lower median annual non-CVD costs and 74.5% (95% CI, 57.5%–84.7%) lower median CVD costs than those with ≤2 ideal CVH components.38

  • A more recent report from a large, ethnically diverse insured population39 found that people with 6 or 7 and those with 3 to 5 of the CVH components in the ideal category had a $2021 and $940 lower annual mean healthcare expenditure, respectively, than those with 0 to 2 ideal health components.

Chart 2-1.

Chart 2-1. Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors. Source: Reprinted from Folsom et al12 with permission from the American College of Cardiology Foundation. Copyright © 2011, the American College of Cardiology Foundation.

CVH: Prevalence (NHANES 2015–2016)

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

  • The most recent national prevalence estimates for children (12–19 years of age) and adults (20–49 years of age and ≥50 years of age) who meet ideal, intermediate, and poor levels of each of the 7 CVH components are displayed in Charts 2-2 and 2-3, respectively.40 The most current estimates at time of publication were based on data from NHANES 2015 to 2016.

  • For most components of CVH, prevalence of ideal levels is higher in US children (12–19 years of age) than in US adults (≥20 years of age), except for the Healthy Diet Score and PA, for which prevalence of ideal levels in children is lower than in adults.

  • Among US children (12–19 years of age; Chart 2-2), the unadjusted prevalence of ideal levels of CVH components currently varies from <1% for the Healthy Diet Score (ie, <1 in 100 US children meets at least 4 of the 5 dietary components) to >85% for smoking, BP, and DM components (unpublished AHA tabulation).

  • Among US adults (Chart 2-3), the lowest prevalence of ideal levels for CVH components is <1% for the Healthy Diet Score in adults 20 to 49 years of age and ≥50 years of age. The highest prevalence of ideal levels for a CVH component is for smoking (76.6% of adults 20–49 years of age and 81.6% of adults ≥50 years of age report never having smoked or being a former smoker who has quit for >12 months). In 2015 to 2016, 64.6% of adults 20 to 49 years of age and 27.2% of adults ≥50 years of age had ideal levels of TC (<200 mg/dL).

  • Age-standardized and age-specific prevalence estimates for ideal CVH and for ideal levels of individual CVH components for 2013 to 2014 and 2015 to 2016 are displayed in Table 2-2.

  • In 2015 to 2016, the prevalence of ideal levels of ≥5 or ≥6 CVH components among adults was highest in the youngest age groups (20–39 years of age) and was lowest in the oldest age group (≥60 years of age). A similar pattern occurred for all individual components of CVH except the Healthy Diet Score, for which prevalence of ideal levels was highest in older adults but still <1%.

  • Chart 2-4 displays the prevalence estimates for the population of US children (12–19 years of age) meeting ideal criteria for different numbers of CVH components, with a range of 0 to 7 ideal components.

    • — Few US children 12 to 19 years of age (3.8%) meet ideal criteria for 2 or fewer components of ideal CVH.

    • — Approximately half of US children (50.4%) meet ideal criteria for 3 or 4 components of CVH, and 45.0% meet ideal criteria for ≥5 components of CVH.

    • — <1% of children meet ideal criteria for all 7 components of CVH.

  • Charts 2-5 and 2-6 display the age-standardized prevalence estimates of US adults meeting ideal criteria for different numbers of CVH components in 2015 to 2016, overall and stratified by age, sex, and race/ethnicity.

    • — 2.5% of US adults (≥20 years of age) meet ideal criteria for 0 of the 7 components of CVH, whereas 15.3% meet only 1 of 7 criteria. Having ≤1 ideal CVH component is much less common among younger adults (20–39 years of age), at 4.5%, compared with older adults (≥60 years of age), for whom having ≤1 ideal metric is more common (28.6%).

    • — NH Asians have the highest prevalence of meeting ideal criteria for ≥5 components of CVH compared with other racial/ethnic groups.

  • Chart 2-7 displays the age-standardized and age-specific prevalence estimates of meeting ideal criteria for ≥5 components of CVH in US adults (≥20 years of age) and US children (12–19 years of age), stratified by race and ethnicity.

    • — In adults, the prevalence of ≥5 metrics at ideal levels is highest for NH Asians (26.0%), followed by NH whites (19.2%), Hispanics (12.4%), and NH blacks (11.8%).

    • — In children, the differences by race/ethnicity are similar to those of adults: prevalence of ≥5 metrics at ideal levels is highest for NH Asians (63.4%), followed by NH whites (48.8%), Hispanics (40.6%), and NH blacks (35.2%).

  • Chart 2-8 displays the age-standardized and age-specific prevalence of meeting criteria for ideal levels of ≥5 components of CVH in US adults (≥20 years of age) and US children (12–19 years of age) in 2015 to 2016, stratified by race and ethnicity.

    • — In both males and females and in both adults and children, higher prevalence of meeting ideal criteria for ≥5 components of CVH was observed in 2015 to 2016 than in 2007 to 2008, although differences were not statistically significant.

  • Chart 2-9 displays the age-standardized percentages of US adults meeting poor or ideal criteria for different numbers of CVH components in 2015 to 2016. Attaining the AHA 2020 Strategic Impact Goal for CVH is predicated on reducing the relative prevalence of individuals with poor levels of CVH while increasing the relative percentage of those with ideal levels for each of the 7 components.

    • — Approximately two-thirds (63.2%) of US adults have ≤2 components at poor levels.

    • — Conversely, 40.6% of adults have ≤2 components at ideal levels; approximately half (45.9%) of US adults have between 2 and 3 ideal CVH components.

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

  • Chart 2-10 displays the age-standardized percentages of US adults meeting ideal criteria for ≥5 components of CVH, stratified by both educational attainment and household income-to-poverty ratio in 2015 to 2016.

    • — The lowest prevalence (5.7%) of meeting ideal criteria for ≥5 components of CVH was observed in adults with both the lowest level of education (<high school) and lowest household income (<100%).

    • — At all levels of household income-to-poverty ratio, the highest prevalence of meeting ideal criteria for ≥5 ideal CVH components was observed in those with the highest level of educational attainment (college graduate; 24.2%–30.3%).

Table 2-2. This table lists the prevalence of ideal cardiovascular health, ideal health factors, and ideal health behaviors in the US population in age strata from 12 years of age to 60 years of age and older. Information in this table details where improvements would be helpful for different age strata.

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

Age 12–19 yAge ≥20 y*Age 20–39 yAge 40–59 yAge ≥60 y
Ideal CVH profile (7/7)0.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)0.0 (0.0)
 ≥6 Ideal7.2 (1.1)5.3 (0.5)11.0 (1.0)2.2 (0.6)0.7 (0.4)
 ≥5 Ideal45.0 (2.7)17.4 (0.7)31.6 (1.3)10.6 (1.0)4.1 (0.8)
Ideal health factors (4/4)54.9 (2.4)17.0 (0.6)31.4 (1.3)10.2 (1.1)2.5 (0.8)
 TC <200 mg/dL77.7 (1.3)49.4 (1.1)72.9 (1.4)39.0 (1.7)25.2 (1.2)
 SBP <120/DBP <80 mm Hg85.2 (1.0)41.0 (1.2)61.7 (1.7)34.1 (2.0)15.6 (2.1)
 FPG <100 mg/dL and HbA1c <5.7%86.2 (1.4)58.4 (1.4)79.3 (1.1)51.2 (2.5)32.4 (1.6)
Ideal health behaviors (4/4)0.0 (0.0)0.1 (0.0)0.0 (0.0)0.0 (0.0)0.2 (0.1)
 PA at goal25.4 (1.6)41.5 (1.7)52.4 (1.7)37.9 (2.3)28.2 (1.9)
 Nonsmoker93.6 (0.9)78.8 (1.0)75.0 (1.4)77.0 (1.5)86.5 (1.4)
 BMI <25 kg/m260.1 (1.9)28.7 (1.6)35.2 (1.8)25.2 (2.1)24.0 (2.1)
 4 or 5 Diet goals met0.0 (0.0)0.3 (0.1)0.1 (0.1)0.1 (0.1)0.7 (0.3)
  F&V ≥4.5 C/d3.7 (0.9)10.2 (0.6)7.8 (0.9)11.1 (1.4)13.8 (1.1)
  Fish ≥2 svg/wk7.6 (1.0)18.0 (1.7)15.9 (2.5)19.3 (2.3)18.7 (1.6)
  Sodium <1500 mg/d0.6 (0.3)0.7 (0.2)0.8 (0.3)0.9 (0.4)0.2 (0.1)
  SSB <36 oz/wk40.4 (2.6)53.3 (1.7)47.7 (2.9)51.6 (2.3)66.5 (2.7)
  Whole grains ≥3 1-oz/d6.8 (0.8)7.1 (0.6)5.9 (1.2)6.5 (0.9)9.5 (1.1)
Secondary diet metrics
  Nuts/legumes/seeds ≥4 svg/wk36.7 (2.4)52.4 (1.7)48.9 (3.0)54.9 (2.3)54.1 (1.8)
  Processed meats ≤2 svg/wk39.2 (2.8)44.0 (0.9)45.4 (1.1)44.0 (1.7)41.9 (2.6)
  SFat <7% total kcal4.5 (1.0)8.4 (0.5)8.8 (1.1)8.9 (0.7)6.8 (0.9)

Values are % (SE). BMI indicates body mass index; CVH, cardiovascular health; 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; svg, servings; and TC, total cholesterol.

*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.

Source: Unpublished American Heart Association tabulation using NHANES 2013 to 2014 and 2015 to 2016.40

Chart 2-2.

Chart 2-2. Prevalence (unadjusted) estimates of poor, intermediate, and ideal cardiovascular health (CVH) for each component of CVH among US children 12 to 19 years of age, 2015 to 2016.*Categories defined by either fasting plasma glucose or HbA1c based on data availability. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

Chart 2-3.

Chart 2-3. Prevalence (unadjusted) estimates of poor, intermediate, and ideal cardiovascular health (CVH) for each component of CVH among US adults 20 to 49 years of age and ≥50 years of age, 2015 to 2016. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

Chart 2-4.

Chart 2-4. Prevalence (unadjusted) of US children 12 to 19 years of age meeting ideal criteria for different numbers of cardiovascular health components, overall and by sex, 2015 to 2016. Error bars represent 95% CI. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

Chart 2-5.

Chart 2-5. Age-standardized prevalence estimates of US adults ≥20 years of age meeting ideal criteria for different numbers of cardiovascular health components, overall and by sex and age groups, 2015 to 2016. Error bars represent 95% CI. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

Chart 2-6.

Chart 2-6. Age-standardized prevalence estimates of US adults ≥20 years of age meeting ideal criteria for different numbers of components of cardiovascular health, overall and by race/ethnicity, 2013 to 2014. Error bars represent 95% CI. NH indicates non-Hispanic. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2013 to 2014.40

Chart 2-7.

Chart 2-7. Prevalence of meeting ≥5 criteria for ideal cardiovascular health among US adults ≥20 years of age (age-standardized) and US children 12 to 19 years of age by race/ethnicity, 2007 to 2008 and 2015 to 2016. Error bars represent 95% CI. NH indicates non-Hispanic. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2007 to 2008 and 2015 to 2016.40

Chart 2-8.

Chart 2-8. Prevalence of meeting idea criteria for ≥5 components of cardiovascular health among US adults ≥20 years of age (age-standardized) and US children 12 to 19 years of age between 2007 to 2008 and 2015 to 2016, overall and by sex. Error bars represent 95% CI. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2007 to 2008 and 2015 to 2016.40

Chart 2-9.

Chart 2-9. Age-standardized prevalence estimates of US adults (≥20 years of age) meeting ideal or poor criteria for different numbers of cardiovascular health components, 2015 to 2016. Error bars represent 95% CI. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

Chart 2-10.

Chart 2-10. Age-standardized prevalence of meeting ideal criteria for ≥5 components of cardiovascular health among US adults ≥20 years of age by educational attainment and household income-to-poverty ratio, 2015 to 2016. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016.40

CVH: Trends Over Time

(See Charts 2-11 through 2-14)

  • The trends in prevalence of meeting ideal criteria for the individual components of CVH from 1999 to 2000 to 2015 to 2016 (for diet, trends from 2003–2004 through 2015–2016) are shown in Chart 2-11 for children (12–19 years of age) and in Chart 2-12 for adults (≥20 years of age).

    • — Among children from 1999 to 2000 to 2015 to 2016, the prevalence of meeting ideal criteria for smoking and BP have consistently improved. For example, the prevalence of nonsmoking among children 12 to 19 years of age increased from 76.4% to 93.6%. For ideal TC, the prevalence increased from 72.0% to 77.7%. However, declines in prevalence of ideal levels were observed for PA (38.4% to 25.4%), BMI (69.8% to 60.1%), and DM (92.4% to 86.2%).

    • — From 1999 to 2000 to 2015 to 2016, the prevalence of meeting ideal criteria for smoking, TC, BP, and PA increased among adults. For example, the prevalence of being a never-smoker or having quit ≥1 year prior increased from 72.9% to 78.8%. Over the 18-year period, the prevalence of meeting criteria for ideal TC increased from 45.1% to 49.4%. Similar to trends observed in children, declines in prevalence of ideal levels were observed for BMI (from 36.3% to 28.7%), and DM (from 69.1% to 58.4%) among adults.

  • The prevalence trends from 2003 to 2004 to 2015 to 2016 for meeting ideal criteria for ≥5 components of CVH according to categories of household income (ie, household income-to-poverty ratio) among adults (≥20 years of age) are shown in Chart 2-13.

    • — Across all time points, the highest prevalence of meeting ideal criteria for ≥5 components of CVH was observed among individuals in the highest category of household income-to-poverty ratio (≥500%); higher household income-to-poverty ratio is consistently associated with a higher prevalence of meeting ideal criteria for ≥5 components of CVH.

    • — Between 2003 and 2016, little variation was observed in prevalence of meeting ideal criteria for ≥5 components of CVH across categories of household income-to-poverty ratio; the greatest increases were observed among individuals with 300% to 499% household income-to-poverty ratio (15.3% to 20.3%).

  • The prevalence trends from 2003 to 2004 to 2015 to 2016 for meeting ideal criteria for ≥5 components of CVH according to categories of educational attainment among adults (≥20 years of age) are shown in Chart 2-14.

    • — Trends in prevalence for meeting ideal criteria for ≥5 components of CVH according to category of educational attainment are similar to those observed for household income: higher educational attainment is consistently associated with a higher prevalence of meeting ideal criteria for ≥5 components of CVH.

    • — Across all time points, the highest prevalence of meeting ideal criteria for ≥5 components of CVH was observed among individuals in the highest category of educational attainment (college graduate). However, ≤10% of individuals with educational attainment of high school or less exhibit ≥5 ideal CVH components over time.

  • If trends in CVH metrics established on the basis of NHANES data from 1988 to 2008 continued forward, the estimated CVH in the United States is projected to improve by 6% by 2020, short of the AHA’s goal of 20% improvement.41 On the basis of these trend estimates 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.41

  • On the basis of these projections for CVH 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).42

Chart 2-11.

Chart 2-11. Trends in prevalence (unadjusted) of meeting ideal criteria for individual components of cardiovascular health (CVH) among US children (12–19 years of age), 1999 to 2000 through 2015 to 2016. Error bars represent 95% CI. 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. BMI indicates body mass index; BP, blood pressure; NHANES, National Health and Nutrition Examination Survey; and PA, physical activity. *Because of changes in the physical activity questionnaire between NHANES cycles 1999 to 2006 and 2007 to 2016, prevalence trends over time for this CVH component should be interpreted with caution, and statistical comparisons should not be attempted. Trend lines are absent between these time frames as an indicator of this issue. Source: Unpublished American Heart Association tabulation using NHANES, 1999 to 2000 through 2015 to 2016.40

Chart 2-12.

Chart 2-12. Age-standardized trends in prevalence of meeting ideal criteria for individual components of cardiovascular health (CVH) among US adults (≥20 years of age), 1999 to 2000 through 2015 to 2016. Error bars represent 95% CI. 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. BMI indicates body mass index; BP, blood pressure; NHANES, National Health and Nutrition Examination Survey; and PA, physical activity. *Because of changes in the physical activity questionnaire between NHANES cycles 1999 to 2006 and 2007 to 2016, prevalence trends over time for this CVH component should be interpreted with caution, and statistical comparisons should not be attempted. Trend lines are absent between these time frames as an indicator of this issue. Source: Unpublished American Heart Association tabulation using NHANES, 1999 to 2000 through 2015 to 2016.40

Chart 2-13.

Chart 2-13. Age-standardized trends in prevalence of meeting ideal criteria for ≥5 components of cardiovascular health among US adults (≥20 years of age) by household IPR, 2003 to 2004 through 2015 to 2016. Error bars represent 95% CI. IPR indicates income-to-poverty ratio. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2003 to 2004 through 2015 to 2016.40

Chart 2-14.

Chart 2-14. Age-standardized trends in prevalence of meeting ideal criteria for ≥5 components of cardiovascular health among US adults (≥20 years of age) by educational attainment, 2003 to 2004 through 2015 to 2016. Error bars represent 95% CI. Grad indicates graduate. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2003 to 2004 through 2015 to 2016.40

Achieving the 2020 Impact Goals

(See Chart 2-15)

To achieve the AHA’s 2020 Impact Goals of reducing deaths attributable to CVD and stroke by 20%,1 continued emphasis is needed on the treatment of acute CVD events and on secondary prevention through treatment and control of health behaviors and risk factors.

Chart 2-15.

Chart 2-15. Prevalence of ideal, intermediate, and poor cardiovascular health (CVH) metrics in the United States in 2006 (AHA 2020 Strategic Impact Goals baseline year) and 2020 projections assuming current trends continue. The 2020 targets for each CVH metric assume a 20% relative increase in ideal CVH prevalence metrics and a 20% relative decrease in poor CVH prevalence metrics for males and females. AHA indicates American Heart Association. Source: Reprinted from Huffman et al.41 Copyright © 2012, American Heart Association, Inc.

  • Taken together, the data continue to demonstrate the tremendous relevance of the AHA’s 2020 Impact Goals both for improved CVH and health in general. However, progress in health behavior and risk profiles in the United States is needed to more fully realize these benefits for all Americans (Chart 2-15).

  • For each CVH 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, 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.43–45

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

    • — Individual-focused approaches that target lifestyle and treatments at the individual level.

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

    • — Population approaches that target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation.

  • 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 CVH metrics can be improved and deserve major focus.

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
CVHcardiovascular health
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DMdiabetes mellitus
ESRDend-stage renal disease
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
PAFpopulation attributable fraction
PEpulmonary embolism
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SEstandard error
SFatsaturated fat
SSBsugar-sweetened beverage
svgservings
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. Tobacco smoking, the most common form of tobacco use, is a major risk factor for CVD and stroke.1 The AHA has identified never having tried smoking or never having smoked a whole cigarette (for children) and never having smoked or having quit >12 months ago (for adults) as 1 of the 7 components of ideal CVH in Life’s Simple 7.2 Unless otherwise stated, throughout the rest of the chapter we will report tobacco use and smoking estimates from the NYTS3 for adolescents and from the NHIS4 for adults (≥18 years of age), because these data sources have more recent data. As a survey of middle and high school students, the NYTS may not be generalizable to youth who are not enrolled in school; however, in 2016, 97% of youth 10 to 17 years of age were enrolled in school, which indicates that the results of the NYTS are likely broadly applicable to US youth.3

Other forms of tobacco use are becoming increasingly common. 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.5 A notable evolution in electronic nicotine delivery systems technology and marketing has occurred recently with the advent of “pod mods,” small rechargeable devices that deliver high levels of nicotine derived from nicotine salts in loose-leaf tobacco.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

(See Charts 3-1 through 3-4)

Youth
  • Prevalence of cigarette use in the past 30 days for middle and high school students by sex and race/ethnicity in 2018 is shown in Chart 3-1.

  • In 20183:

    • — 27.1% (95% CI, 25.3%–29.0%) of high school students (corresponding to 4 040 000 users) and 7.2% (95% CI, 6.3%–8.1%) of middle school students (corresponding to 840 000 users) used any tobacco products. Additionally, 8.1% (95% CI, 7.1%–9.3%) of high school students (1 180 000 users) and 1.8% (95% CI, 1.4%–2.2%) of middle school students (200 000 users) smoked cigarettes in the past 30 days.

    • — 5.9% (95% CI, 5.0%–7.0%) high school students (870 000 users) and 1.8% (95% CI, 1.5%–2.3%) of middle school students (210 000) used smokeless tobacco in the past 30 days.

    • — 7.6% (95% CI, 6.7%–8.6%) of high school students (1 100 000 users) and 1.6% (95% CI, 1.3%–2.1%) of middle school students (190 000 users) used cigars in the past 30 days.

  • Of youth who smoked cigarettes in the past 30 days in 2015 to 2017, 19.4% (95% CI, 17.1%–22.0%) of high school students (corresponding to 230 000 users) and 12.8% (95% CI, 10.0%–16.3%) of middle school students (corresponding to 30 000 users) reported smoking cigarettes every day in the past 30 days.7

  • In 2018, tobacco use within the past month for high school students varied by race/ethnicity: the prevalence of past 30-day cigarette use was 9.9% (95% CI, 8.5%–11.6%) in NH white youth compared with 3.2% (95% CI, 2.3%–4.6%) in black and 7.2% (95% CI, 5.8%–8.8%) in Hispanic youth. For cigars, the respective percentages were 7.8% (95% CI, 6.7%–9.1%), 9.2% (6.8%–12.4%), and 7.3% (95% CI, 5.9%–9.1%).3

  • The percentage of high school students who used e-cigarettes (20.8% or 3 050 000 users) exceeded the proportion using cigarettes (8.1% or 1 180 000 users) in 2018 (Chart 3-2).

Chart 3-1.

Chart 3-1. Prevalence (%) of cigarette use in the United States in the past 30 days, by product,* school level, sex, and race/ethnicity† (NYTS, 2011–2018). Data in (A) relate to high school students and (B) relate to middle school students. Because of methodological differences among the NSDUH, the Youth Risk Behavior Survey, the NYTS, 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. E-cigarettes indicates electronic cigarettes; NSDUH, National Survey on Drug Use and Health; and NYTS, National Youth Tobacco Survey. *Past 30-day use of e-cigarettes was determined by asking, “During the past 30 days, on how many days did you use e-cigarettes?” Past 30-day use of cigarettes was determined by asking, “During the past 30 days, on how many days did you smoke cigarettes?” Past 30-day use of cigars was determined by asking, “During the past 30 days, on how many days did you smoke cigars, cigarillos, or little cigars?” Past 30-day use of hookah was determined by asking, “During the past 30 days, on how many days did you smoke tobacco in a hookah or waterpipe?” Smokeless tobacco was defined as use of chewing tobacco, snuff, dip, snus, or dissolvable tobacco products. Past 30-day use of smokeless tobacco was determined by asking the following question for use of chewing tobacco, snuff, and dip: “During the past 30 days, on how many days did you use chewing tobacco, snuff, or dip?” and the following question for use of snus and dissolvable tobacco products: “In the past 30 days, which of the following products did you use on at least one day?” Responses from these questions were combined to derive overall smokeless tobacco use. Past 30-day use of pipe tobacco (not hookahs) was determined by asking, “In the past 30 days, which of the following products have you used on at least one day?” †Blacks, whites, and others are non-Hispanic; Hispanic people could be of any race. ‡Any tobacco product use was defined as use of any tobacco product (e-cigarettes, cigarettes, cigars, smokeless tobacco, hookahs, pipe tobacco, or bidis) on ≥1 day in the past 30 days. §Any combustible tobacco product use was defined as use of cigarettes, cigars, hookahs, pipe tobacco, or bidis on ≥1 day in the past 30 days. ‖Use of ≥2 tobacco products was defined as use of ≥2 tobacco products (e-cigarettes, cigarettes, cigars, smokeless tobacco, hookahs, pipe tobacco, or bidis) on ≥1 day in the past 30 days. Source: Data derived from Gentzke et al.3

Chart 3-2.

Chart 3-2. Estimated percentage of US high school students who currently use any tobacco product,* any combustible tobacco product,† ≥2 tobacco product types,‡ and selected tobacco products (NYTS, 2011–2018).§‖¶ E-cigarettes indicates electronic cigarettes; and NYTS, National Youth Tobacco Survey. *Any tobacco product use was defined as use of e-cigarettes, cigarettes, cigars, hookahs, smokeless tobacco, pipe tobacco, or bidis (small brown cigarettes wrapped in a leaf) on ≥1 day in the past 30 days. †Any combustible tobacco product use was defined as use of cigarettes, cigars, hookahs, pipe tobacco, or bidis on ≥1 day in the past 30 days. ‡Use of ≥2 tobacco product types was defined as use of ≥2 of the following tobacco products: e-cigarettes, cigarettes, cigars, hookahs, smokeless tobacco, pipe tobacco, or bidis on ≥1 day in the past 30 days. §During 2017 to 2018, current use of any tobacco product, ≥2 types of tobacco products, and e-cigarettes significantly increased (P<0.05). ‖During 2011 to 2018, current use of combustible tobacco products, ≥2 types of tobacco products, cigars, smokeless tobacco, and pipe tobacco exhibited linear decreases (P<0.05). Current use of cigarettes exhibited a nonlinear decrease (P<0.05). Current use of hookahs exhibited a nonlinear change (P<0.05). Current use of e-cigarettes exhibited a nonlinear increase (P<0.05). No significant trend in use of any tobacco product overall was observed. ¶Beginning in 2015, the definition of smokeless tobacco included chewing tobacco/snuff/dip, snus, and dissolvable tobacco to better reflect this class of tobacco products. Thus, estimates for individual smokeless tobacco products (chewing tobacco/snuff/dip, snus, and dissolvable tobacco) are not reported. This definition was applied across all years (2011–2018) for comparability purposes. Source: Reprinted from Gentzke et al.3

Adults
  • According to the NHIS 2017 data, among adults ≥18 years of age4:

    • — 14.0% (95% CI, 13.4%–14.6%) of adults reported cigarette use “every day” or “some days.”

    • — 15.8% (95% CI, 15.0%–16.7%) of males and 12.2% (95% CI, 11.4%–13.0%) of females reported cigarette use “every day” or “some days.”

    • — 10.4% of those 18 to 24 years of age, 16.1% of those 25 to 44 years of age, 16.5% of those 45 to 64 years of age, and 8.2% of those ≥65 years old reported cigarette use “every day” or “some days.”

    • — 24.0% of NH American Indians or Alaska Natives, 14.9% of NH blacks, 7.1% of NH Asians, 9.9% of Hispanics, and 15.2% of NH whites reported cigarette use “every day” or “some days.”

    • — By annual household income, reported cigarette use “every day” or “some days” was 21.4% of people with <$35 000 income compared with 15.3% of those with income of $35 000 to $74 999, 11.8% with income $75 000 to $99 999, and 7.6% with income ≥$100 000.

    • — In adults ≥25 years of age, the percentage reporting current cigarette use was 23.1% for those with <12 years of education and 36.8% in those with a General Educational Development high school equivalency, compared with 4.1% among those with a graduate degree.

    • — 20.3% of lesbian/gay/bisexual individuals are current smokers compared with 13.7% of heterosexual/straight individuals.

    • — By region, the prevalence of current cigarette smokers was highest in the Midwest (16.9%) and South (15.5%) and lowest in the Northeast (11.2%) and West (11.0%).4

  • The 2009 NHIS report estimated that 42.4% of adults with HIV receiving medical care were current smokers compared with 20.6% of all US adults.8

  • Using data from BRFSS 2017, the state with the highest age-adjusted percentage of current cigarette smokers was West Virginia (28.0%). The states with the lowest age-adjusted percentage of current cigarette smokers were Utah (8.8%) and California (11.5%; Chart 3-3).9

  • In 2017, smoking prevalence was higher among adults ≥18 years of age who reported having a disability or activity limitation (20.7%) than among those reporting no disability or limitation (13.3%).4

  • Among individuals reporting serious psychological distress, 35.2% were current smokers compared with 13.2% in those without serious psychological distress.4

  • Among females who gave birth in 2016, 7.2% smoked cigarettes during pregnancy. Smoking prevalence during pregnancy was greatest for females 20 to 24 years of age (10.7%), followed by females 15 to 19 years of age (8.5%) and 25 to 29 years of age (8.2%).10 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%).

  • E-cigarette prevalence in 2017 is shown in Chart 3-4. Comparing e-cigarette prevalence across the 50 states, the average age-adjusted prevalence was 5.3%. The lowest age-adjusted prevalence was observed in California (3.2%), and the highest prevalence was observed in Oklahoma (7.5%). The age-adjusted prevalence was 1.3% in Puerto Rico.

Chart 3-3.

Chart 3-3. Age-adjusted prevalence (%) of current cigarette smoking for US adults, by state (BRFSS, 2017). White space between the map and legend has been removed. Icons and drop-down menus for interactive tools have been removed. BRFSS indicates Behavior Risk Factor Surveillance System. Source: BRFSS Prevalence and Trends Data, 2017.9

Chart 3-4.

Chart 3-4. Prevalence (age-adjusted) of current e-cigarette use, United States (BRFSS, 2017). White space between the map and legend has been removed. Icons and drop-down menus for interactive tools have been removed. BRFSS indicates Behavior Risk Factor Surveillance System. Source: BRFSS Prevalence and Trends Data, 2017.9

Incidence

  • According to the 2017 NSDUH, ≈1.90 million people ≥12 years of age had smoked cigarettes for the first time within the past 12 months, compared with 1.78 million in 2016 (2017 NSDUH Table 4.2B).11 Of new smokers in 2017, 604 000 (31.8%) were 12 to 17 years of age, 817 000 (43.0%) were 18 to 20 years of age, and 335 000 (17.7%) were 21 to 25 years of age; only 142 000 (7.5%) were ≥26 years of age when they first smoked cigarettes.

  • The number of new smokers 12 to 17 years of age in 2017 (604 000) decreased from 2016 (723 000 million); however, new smokers 18 to 25 years of age increased from ≈978 000 million in 2002 to 1.15 million in 2017 (2017 NSDUH Table 4.2B).11

  • According to data from the PATH Study between 2013 and 2016, in youth 12 to 15 years of age, use of an e-cigarette was independently associated with new ever combustible cigarette use (OR, 4.09 [95% CI, 2.97–5.63]) and past 30-day use (OR, 2.75 [95% CI, 1.60–4.73]) at 2 years of follow-up. For youth who tried another non–e-cigarette tobacco product, a similar strength of association for cigarette use at 2 years was observed.12 Approximately 45 000 new current smokers may have started smoking cigarettes after initiating e-cigarette use over the 2-year period between 2013 to 2014 and 2015 to 2016.12

Lifetime Risk

Youth
  • Per NSDUH data for individuals 12 to 17 years of age, overall, the lifetime use of tobacco products declined from 15.3% to 14.9% between 2016 and 2017, with lifetime cigarette use declining from 11.6% to 10.8% during the same time period (2017 NSDUH Tables 2.21B and 2.16B).11

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

      • ▪ Sex: Lifetime use was higher among boys (17.0%) than girls (12.7%; 2017 NSDUH Table 2.21B).

      • ▪Race/ethnicity: Lifetime use was highest among American Indians and Alaska Natives (26.7%), followed by whites (17.9%), Hispanics or Latinos (12.3%), blacks (11.0%), and Asians (4.7%; 2017 NSDUH Table 2.21B).

Adults
  • According to NSDUH data, the lifetime use of tobacco products in individuals ≥18 years of age did not decline significantly between 2016 and 2017, from 67.7% to 67.5%, with lifetime cigarette use declining in the same interval from 62.1% to 61.8% (2017 NSDUH Table 2.6B).11 Similar to the patterns in youth, lifetime risk of tobacco products varied by demographic factors:

    • — Sex: Lifetime use was higher in males (76.2%) than females (59.3%).

    • — Race/ethnicity: Lifetime use was highest in American Indians or Alaska Natives (81.2%) and whites (75.2%), followed by Native Hawaiian or Other Pacific Islander (56.4%), Hispanics or Latinos (55.3%), blacks (54.8%), and Asians (39.1%).

  • In 2017, the lifetime use of smokeless tobacco for adults ≥18 years of age was 17.3%.

Secular Trends

(See Chart 3-5)

Youth

The percentage of adolescents (12–17 years old) who reported smoking cigarettes in the past month declined from 13% in 2002 to 3.4% in 2016 (Chart 3-5). The percentages for daily cigarette use among those with past-month cigarette smoking in 12- to 17-year-olds were 31.8% in 2002 and 15.0% in 2016.13

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. Source: Reprinted from NSDUH.13

Chart 3-6.

Chart 3-6. Age-standardized global mortality rates attributable to tobacco per 100 000, both sexes, 2017. East and Southeast Asia and Eastern Europe have the highest mortality rates attributable to tobacco. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Federated States of Micronesia; 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 2017, Institute for Health Metrics and Evaluation, University of Washington.64 Printed with permission. Copyright © 2018, University of Washington.

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 15.8% in 2017 and from 34% of females in 1965 to 12.2% in 2017, according to NHIS data.4 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.14

  • On the basis of weighted NHIS data, the current smoking status among 18- to 24-year-old males 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.15 On the basis of age-adjusted estimates in 2015, among people ≥65 years of age, 9.7% of males and 7.3% 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%.15

CVH 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.16 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 were reported in a systematic review of regular cigar smoking.17

  • 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.16

  • Cigarette smoking and other traditional CHD risk factors might have a synergistic interaction in HIV-positive individuals.18

  • 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]).19

  • Cigarette smoking is a risk factor for both ischemic stroke and SAH in adjusted analyses and has a synergistic effect on other stroke risk factors such as oral contraceptive use.20

  • 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.21

  • Current smokers have a 2- to 4-times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.20,22

  • Short-term exposure to water pipe smoking is associated with a significant increase in SBP and heart rate compared with nonsmoking control subjects,23 but long-term effects remain unclear. Current use of smokeless tobacco was associated with an adjusted 1.27-fold increased risk of CVD events compared with never-users. Per 1000 person-years, the CVD rate was 11.3 in never-users and 21.4 in current users of smokeless tobacco.24

  • The CVD risks associated with e-cigarette use are not known.25,26

Family History and Genetics

  • Genetic factors might contribute to smoking behavior; common and rare variants in several loci have been found to associate with smoking initiation, number of cigarettes smoked per day, and smoking cessation.27,28

  • Genetics might also modify adverse CVH outcomes among smokers, with variation in ADAMTS7 associated with loss of cardioprotection in smokers.29

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. For instance, investigators compared smoking rates in Needham, MA, after introduction of an ordinance that raised the minimum purchase age to 21. The 30-day smoking rate in Needham declined from 13% to 7% between 2006 and 2010, compared with a decline from 15% to 12% (P<0.001) in 16 surrounding communities.30

  • In several towns where Tobacco 21 laws have been enacted, 47% reductions in smoking prevalence among high school students have been reported.31 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 2019.31

  • As of May 17, 2019, 14 states (Hawaii, California, New Jersey, Oregon, Maine, Massachusetts, Illinois, Virginia, Delaware, Arkansas, Vermont, Maryland, Washington, and Utah) and at least 470 localities (including New York City, New York; Chicago, Illinois; San Antonio, Texas; Boston, Massachusetts; Cleveland, Ohio; and both Kansas Cities [Kansas and Missouri]), have set the minimum age for the purchase of tobacco to 21 years.32

Awareness, Treatment, and Control

Smoking Cessation
  • According to NHIS 2015 data, 59.1% of adult ever-smokers had stopped smoking.33

    • — 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 among uninsured smokers (44.1%) than among those with health insurance coverage through Medicaid or those who were dual eligible for coverage (both Medicaid and Medicare; 59.5%). Receiving advice to quit also 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, fewer 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.

    • — In several studies, a dose-response relationship has been seen among current smokers between the number of cigarettes smoked per day and CVD incidence.34,35

    • — Quitting smoking at any age significantly lowers mortality from smoking-related diseases, and the risk declines more the longer the time since quitting smoking.1 Cessation appears to have both short-term (weeks to months) and long-term (years) benefits for lowering CVD risk.

    • — Smokers who quit smoking at 25 to 34 years of age gained 10 years of life compared with those who continued to smoke. Those 35 to 44 years of age gained 9 years, those 45 to 54 years of age gained 6 years, and those 55 to 64 years of age gained 4 years of life, on average, compared with those who continued to smoke.34

  • Cessation medications (including sustained-release bupropion, varenicline, and nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.36,37

  • 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. The abstinence rates at 24 weeks were higher in the varenicline (47.3%) than the placebo (32.5%) 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.38

  • The EAGLES trial39 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.39

  • Extended use of a nicotine patch (24 weeks compared with 8 weeks) has been demonstrated to be safe and efficacious in randomized clinical trials.40

  • An RCT demonstrated the effectiveness of individual- and group-oriented financial incentives for tobacco abstinence through at least 12 months of follow-up.41

  • 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.33,42

  • 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. Investigators estimated that the Tips campaign cost about $48 million, saved ≈179 099 QALYs, and prevented ≈17 000 premature deaths in the United States.43

  • 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.44

  • A randomized trial of e-cigarettes and behavioral support versus nicotine-replacement therapy and behavioral support in adults attending the United Kingdom’s National Health Service stop-smoking services found that 1-year cigarette abstinence rates were 18% in the e-cigarette group compared with 9.9% in the nicotine-replacement therapy group (RR, 1.83 [95% CI, 1.30–2.58]; P<0.001). However, among participants abstinent at 1 year, in the nicotine-replacement therapy group only 9% were still using nicotine-replacement therapy, whereas 80% of those in the e-cigarette group were still using e-cigarettes.45

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 2016.46 Overall mortality among US smokers is 3 times higher than that for never-smokers.34

  • On average, based on 2016 data, male smokers die 12 years earlier than male never-smokers, and female smokers die 11 years earlier than female never-smokers.14,47

  • Increased CVD mortality risks persist for older (≥60 years old) smokers as well. A meta-analysis of 25 studies 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.48

  • 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.49

  • 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.50

  • If current smoking trends continue, 5.6 million US children will die of smoking prematurely during adulthood.16

Electronic Cigarettes

(See Charts 3-2 and 3-4)

  • 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 only introduced into the United States around 2007, there are currently >450 e-cigarette brands on the market, and sales in the United States were projected to be $2 billion in 2014. In 2015, Juul came on the market and has rapidly become the most popular e-cigarette product sold in the United States. The popularity of the Juul likely relates to several factors, including its slim and modern design, appealing flavors, and the intensity of nicotine delivery, which approximates the experience of combustible cigarettes.51

  • Current e-cigarette user prevalence for 2017 in the United States is shown in Chart 3-4.

  • According to the NYTS, in 2018, e-cigarettes were the most commonly used tobacco products in youth: in the prior 30 days, 4.9% (570 000) of middle school and 20.8% (3.05 million) of high school students endorsed use (Chart 3-2).3 A significant nonlinear increase in current e-cigarette use in high school students was observed between 2011 (1.5%) and 2018 (20.8%). A significant increase in current e-cigarette use was also observed for middle school students, where the corresponding values were 0.6% and 4.9% in the 2 periods. Among high school students, rates of use were most pronounced in males (22.6%) and NH whites (26.8%). In middle school students, slightly higher rates were observed in males (5.1%) and in Hispanics (6.6%).

  • Between 2017 and 2018, frequent use of e-cigarettes among high school students who were current e-cigarette users increased significantly by 38.5% (from 20.0% to 27.7%). In middle school students, the percentage using frequently among current e-cigarette users increased by 16.2%.3

  • In 2016, 20.5 million US middle and high school students (80%) were exposed to e-cigarette advertising.52

  • Among US adults, awareness and use of e-cigarettes has increased considerably.53 In 2016, the prevalence of current e-cigarette use in adults, defined as use every day or on some days, was 4.5% according to data from the BRFSS. The prevalence of current e-cigarette use was highest in individuals 18 to 24 years of age (9.2%) and in current combustible cigarette users (14.4%).54

  • According to BRFSS 2016, current use of e-cigarettes in adults ≥18 years of age was higher in sexual and gender minority individuals. With respect to sexual orientation, 9.0% of bisexual and 7.0% of lesbian/gay individuals were current e-cigarette users compared with 4.6% of heterosexual people. Individuals who were transgender (8.7%) were current e-cigarette users at a higher rate than cisgender individuals (4.7%). Across US states, the highest prevalence of current e-cigarette use was observed in Oklahoma (7.0%) and the lowest in South Dakota (3.1%).54

  • Effective August 8, 2016, the US Food and Drug Administration’s Deeming Rule prohibited sale of e-cigarettes to individuals <18 years of age.55

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%.16

    • — 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.56

  • 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.57

  • A meta-analysis of 24 studies demonstrated that secondhand smoke can increase risks for preterm birth by 20%.58

  • 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.32

  • 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% (RR, 0.90 [95% CI, 0.86–0.94]).59

  • 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).60

Cost

  • According to the Surgeon General’s 50th anniversary report on the health consequences of smoking, the estimated annual cost attributable to smoking from 2009 to 2012 was between $289 and $332.5 billion; direct medical care for adults accounted for $132.5 to $175.9 billion, lost productivity because of premature death accounted for $151 billion (estimated from 2005–2009), and lost productivity from secondhand smoke accounted for $5.6 billion (in 2006).14

  • In the United States, cigarette smoking was associated with 8.7% of annual aggregated healthcare spending from 2006 to 2010, which represented roughly $170 billion per year, 60% of which was paid by public programs (eg, Medicare and Medicaid).61

  • In 2016, $9.5 billion was spent on marketing cigarettes and smokeless tobacco in the United States.62

  • 249 billion cigarettes were sold in the United States in 2017, which is a 3.5% decrease from the number sold in 2016.63

  • Cigarette prices in the United States increased steeply between the early 1970s and 2016, in large part because of excise taxes on tobacco products. Per pack in 1970, the average cost was $0.38, and tax was $0.18, whereas in 2016 the average cost was $6.43, and average tax $2.85.63

Global Burden of Tobacco Use

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

Table 3-1. This table lists the total number of deaths worldwide, mortality rate, and population attributable fraction percent caused by tobacco in 2017, as well as the percent change from 2007 and 1990. The 8.1 million deaths attributable to tobacco in 2017 represent an 11.3 percent increase from 2007.

Table 3-1. Deaths Caused by Tobacco Worldwide, by Sex, 2017

Both Sexes (95% UI)Males (95% UI)Females (95% UI)
Total number (millions) deaths8.1 (7.8 to 8.4)6.2 (6.0 to 6.4)1.9 (1.8 to 2.1)
Percent change in total number, 2007 to 201711.3 (9.1 to 13.4)12.6 (10.1 to 15.1)7.3 (4.0 to 10.4)
Percent change in total number, 1990 to 201720.8 (17.1 to 24.6)27.9 (23.9 to 31.9)2.5 (−2.8 to 8.4)
Mortality rate per 100 000103.2 (99.0 to 107.3)173.5 (166.9 to 180.1)44.7 (41.5 to 48.2)
Percent change rate, 2007 to 2017−15.9 (−17.6 to −14.4)−15.6 (−17.5 to −13.8)−19.1 (−21.5 to −16.8)
Percent change rate, 1990 to 2017−39.2 (−40.9 to −37.4)−37.5 (−39.4 to −35.7)−47.8 (−50.4 to −44.9)
PAF, %14.5 (13.9 to 15.0)20.4 (19.7 to 21.1)7.5 (6.9 to 8.0)
Percent change in PAF, 2007 to 20171.9 (0.3 to 3.5)3.2 (1.6 to 4.9)−2.0 (−4.5 to 0.4)
Percent change in PAF, 1990 to 20170.4 (−2.4 to 3.2)4.8 (2.1 to 7.5)−13.5 (−17.8 to −8.9)

PAF indicates population attributable fraction; and UI, uncertainty interval.

Source: Data derived from Global Burden of Disease Study 2017, Institute for Health Metrics and Evaluation, University of Washington.64 Printed with permission. Copyright © 2018, University of Washington.

Abbreviations Used in Chapter 3

ACSacute coronary syndrome
AHAAmerican Heart Association
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CVHcardiovascular health
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
e-cigaretteelectronic cigarette
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
NYTSNational Youth Tobacco Survey
ORodds ratio
PAFpopulation attributable fraction
PARpopulation attributable risk
PATHPopulation Assessment of Tobacco and Health
QALYquality-adjusted life-year
RCTrandomized controlled trial
RRrelative risk
SAHsubarachnoid hemorrhage
SBPsystolic blood pressure
SEstandard error
SHSStrong Heart Study
UIuncertainty interval
WHOWorld Health Organization
  • Although tobacco use in the United States has been declining, the absolute number of tobacco users worldwide has climbed steeply.64

  • On the basis of the GBD synthesis of >2800 data sources, the age-standardized global prevalence of daily smoking in 2017 was 8.7% (95% UI, 7.72%–9.79%) in males and 1.76% (95% UI, 1.52%–2.02%) in females. The investigators estimate that since 1990, smoking rates have declined globally by 23% in males and 42% in females.65

  • The GBD 2017 study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 359 diseases and injuries in 195 countries and territories. East and Southeast Asia and Eastern Europe have the highest mortality rates attributable to tobacco (Chart 3-6).

  • In 2015, there were a total of 933.1 million (95% UI, 831.3–1054.3 million) smokers globally, of whom 82.3% were men. The annualized rate of change in smoking prevalence between 1990 to 2015 was −1.7% in women and −1.3% in men.66

  • Worldwide, ≈80% of smokers live in low- and middle-income countries.67

  • Tobacco (including smoking, secondhand smoke, and chewing tobacco) caused an estimated 8.1 million deaths globally in 2017 (6.2 million men and 1.9 million women; Table 3-1). GBD investigators estimated that in 2017, smoking tobacco was the second-leading risk of mortality (high SBP was number 1), and smoking tobacco ranked fourth in DALYs globally.64,65

  • The WHO estimated that the economic cost of smoking-attributable diseases accounted for US $422 billion in 2012, which represented ≈5.7% of global health expenditures.68 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.54,69

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4. Physical Inactivity

See Charts 4-1 through 4-13

Physical inactivity is a major risk factor for CVD (eg, CHD, stroke, PAD, HF).1 Achieving the guideline recommendations for PA is one of the AHA’s 7 components of ideal CVH for both children and adults.2 The AHA and 2018 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 2017, on the basis of survey interviews,4 only 26.1% 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 2018 federal guidelines3 and the 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease7 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. In a nationally representative sample of adults, only 24.3% reported participating in adequate leisure-time aerobic and muscle-strengthening activity to meet these criteria (Chart 4-1),8 but they were not asked to report activity accumulated during occupational, transportation, or domestic duties.

Chart 4-1.

Chart 4-1. Prevalence of meeting both the aerobic and muscle-strengthening guidelines for the 2018 Physical Activity Guidelines for Americans among US adults ≥18 years of age, overall and by sex and race/ethnicity, 2017. Data are age adjusted for adults ≥18 years of age. NH indicates non-Hispanic.Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2017.

Being physically active is an important aspect of overall health. Meeting recommendations for PA not only reduces premature mortality but also improves risk factors for CVD (such as HBP, type 2 DM, and high cholesterol) and reduces the likelihood of diseases related to CVD, including CHD, HF, stroke, and aging-related diseases, such as dementia.7,9–11 Benefits from PA are seen for all ages and groups, including children and older adults, pregnant women, and people with disabilities and chronic conditions. Therefore, the 2018 federal guidelines recommend being as physically active as abilities and conditions allow and that some PA is better than none.3 The PA Guidelines Advisory Committee published a scientific report providing a 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.9

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.5,6,12

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.13 Poor cardiorespiratory (or aerobic) fitness might be a stronger predictor of adverse cardiovascular outcomes than traditional risk factors.14 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.15 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.13 Unlike health behaviors such as PA and risk factors that are tracked by federally funded programs (NHIS, NHANES, etc), there are no national data on adult cardiorespiratory fitness to-date, but the WHO has recently created a global action plan to approach cardiorespiratory fitness globally with a goal to reduce the prevalence of insufficient PA by 15% by 2030.13,16 Such additional data on the cardiorespiratory fitness levels of populations could give a fuller and more accurate picture of physical fitness levels.13

Prevalence

Youth
Meeting the Activity Recommendations

(See Charts 4-2 through 4-5)

  • On the basis of self-reported PA (YRBSS, 2017)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 26.1% nationwide and declined from 9th (30.6%) to 12th (22.9%) grades. At each grade level, the prevalence was higher in boys than in girls.

    • — Almost double the percentage of high school–aged boys (35.3%) than girls (17.5%) reported having been physically active ≥60 min/d 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 (59.4%), NH black boys (54.5%), and Hispanic boys (52.6%) than NH white girls (38.8%), NH black girls (29.9%), and Hispanic girls (36.9%; Chart 4-2).

    • — 15.4% 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 (19.5% versus 11.0%), with black girls reporting the highest rate of inactivity (26.6%; Chart 4-3).

    • — In the 2017 YRBSS, 28.5% of heterosexual students, 14.7% of gay, lesbian, and bisexual students, and 19.0% of students not sure about their sexual identity reported being physically active for at least 60 min/d on all 7 days. The difference between prevalence of being physically active in heterosexual versus gay, lesbian, and bisexual students was larger among male students (37.0% versus 15.0%, respectively) than among female students (19.0% versus 14.3%, respectively; Chart 4-4).

  • With regard to objectively measured moderate to vigorous PA (based on age-specific criteria for accelerometer cut points; NHANES, 2003–2004)17:

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

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

  • With regard to objectively measured cardiorespiratory fitness (NHANES, 2012)18:

    • — For adolescents 12 to 15 years of age, boys in all age groups were more likely to have adequate levels of cardiorespiratory fitness than girls (Chart 4-5).

  • With regard to self-reported muscle-strengthening activities (YRBSS, 2017)4:

    • — The proportion of high school students who participated in muscle-strengthening activities on ≥3 days of the week was 51.1% nationwide and declined from 9th grade (males 66.4%, females 49.3%) to 12th grade (males 56.6%, females 36.1%).

    • — More high school boys (62.1%) than girls (40.8%) reported having participated in muscle-strengthening activities on ≥3 days of the week.

Chart 4-2.

Chart 4-2. Prevalence of US students in grades 9 to 12 who were active at least 60 min/d on all 7 days by race/ethnicity and sex, 2017. “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: Data derived from Kann et al4 using Youth Risk Behavior Surveillance System, 2017.

Chart 4-3.

Chart 4-3. Prevalence of US 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, 2017. NH indicates non-Hispanic. Source: Data derived from Kann et al4 using Youth Risk Behavior Surveillance System, 2017.

Chart 4-4.

Chart 4-4. Prevalence of US students in grades 9 to 12 who were active at least 60 min/d on all 7 days by sexual identity and sex, 2017. “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. Source: Data derived from Kann et al4 using Youth Risk Behavior Surveillance System, 2017.

Chart 4-5.

Chart 4-5. Prevalence of US children 12 to 15 years of age who had adequate levels of cardiorespiratory fitness by sex and age, 2012. Source: Data derived from Gahche et al18 using National Health and Nutrition Examination Survey, National Youth Fitness Survey, 2012.

Structured Activity Participation in Schools and Sports
  • In 2017, only 29.9% of students attended physical education classes in school daily (34.7% of boys and 25.3% of girls; YRBSS).4

  • Daily physical education class participation declined from the 9th grade (45.5% for boys, 39.2% for girls) through the 12th grade (26.5% for boys, 15.9% for girls; YRBSS).4

  • Just over half (54.3%) of high school students played on at least 1 school or community sports team in the previous year: 49.3% of girls and 59.7% of boys (YRBSS).4

  • Data from the 2017 SummerStyles survey demonstrated that only 16.5% of parents (n=1137) reported that their child walked to school and reported safety concerns and living too far away as barriers limiting commuting as a means of engaging in an active lifestyle.19

Television/Video/Computers

(See Chart 4-6)

  • Research suggests that screen time (watching television or using a computer) can lead to less PA among children.20 In addition, television viewing time is associated with poor nutritional choices, overeating, and weight gain (Chapter 5, Nutrition).

  • In 2017 (YRBSS)4:

    • — Nationwide, 43.0% of high school students used a computer, tablet, or smartphone for activities other than schoolwork (eg, videogames, texting, YouTube, or social media) for ≥3 h/d on an average school day.

    • — The prevalence of using a computer, tablet, or smartphone ≥3 h/d (for activities other than schoolwork) was highest among NH black boys (47.7%), followed by Hispanic girls (46.8%), NH black girls (46.7%), Hispanic boys (43.9%), NH white boys (41.7%), and NH white girls (39.6%; Chart 4-6).

    • — The prevalence of watching television ≥3 h/d was highest among NH black boys (37.8%) and girls (32.8%), followed by Hispanic boys (21.9%) and girls (19.5%) and NH white girls (18.4%) and boys (16.9%).

  • A report from the Kaiser Family Foundation (using data from 2009) reported that 8- to 18-year-olds spent an average of 33 min/d talking on the phone and 49 minutes using their phone to access media (music, games, or videos).21 In addition to other cell phone use, 7th to 12th graders spent an average of 95 min/d 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.

Chart 4-6.

Chart 4-6. Percentage of US students in grades 9 to 12 who used a computer* for ≥3 hours on an average school day by race/ethnicity and sex, 2017. NH indicates non-Hispanic. *For something other than schoolwork. Source: Data derived from Kann et al4 using Youth Risk Behavior Surveillance System, 2017.

Adults
Meeting the Activity Recommendations

(See Charts 4-1 and 4-7 through 4-12)

  • With regard to self-reported leisure-time aerobic and muscle-strengthening PA (NHIS, 2017):

    • — 24.3% of adults met the 2018 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, 2017):

    • — The age-adjusted proportion who reported meeting the 2018 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 62.0% and 55.1% for NH white males and females, 52.7% and 37.0% for NH black males and females, and 49.2% and 40.9% 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 4-7).22

    • — Among adults ≥25 years of age, 32.8% of participants with no high school diploma, 42.7% of those with a high school diploma or General Educational Development high school equivalency credential, 50.6% of those with some college, 54.5% of those with an associate’s degree, 64.1% of those with a bachelor’s degree, and 69.6% of those with an advanced degree met the federal guidelines for aerobic PA through leisure-time activities (Chart 4-8).

  • 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 (55.0% versus 47.2%; Chart 4-9).

  • 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 4-10).

  • In an analysis from the NIH-AARP Diet and Health Study, severe neighborhood socioeconomic deprivation was prospectively associated with less exercise time (highest quintile versus lowest quintile, −0.85 [95% CI, −0.95 to −0.75]) among 136 526 participants 51 to 70 years of age.23

  • 13.0% of people with disabilities and 27.1% of people without disabilities meet both the aerobic and muscle-strengthening guidelines (Chart 4-11).24

  • In 2017, 25.9% of adults reported that they do not engage in leisure-time PA (no sessions of leisure-time PA of ≥10 minutes in duration; Chart 4-12).

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

    • — The number of individuals meeting the federal PA guidelines, including all moderate to vigorous PA as recommended by the 2018 PA guidelines, declines from middle age (57% of 40- to 49-year-olds) to older age (26% of 60- to 69-year-olds).6 Previous reports estimated that there were lower numbers of adults meeting the 2008 PA guidelines, which recommended only counting moderate to vigorous PA accumulated in 10-minute bouts (removing up to 75% of moderate activity).25

    • — These accelerometer 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/min, 760–2020) than their urban resident counterparts.26

    • — On average, males and females self-report at least 5 times more moderate to vigorous PA than what is captured objectively when they wear an accelerometer using a traditional definition (≥2020 accelerometer counts/min),6 but the magnitude of difference varies widely if using a different accelerometer definition of moderate to vigorous PA.12

    • — In contrast to self-reported PA, which suggested that NH whites had higher levels of PA,24 data from objectively measured PA revealed that Hispanic participants had higher total PA and moderate to vigorous PA than NH white or black participants (≥20 years old).6

  • In a recent study of almost 5000 British males, among 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.27

  • 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.28 Adult smartphone app/web use was reported as 2 hours 28 minutes per day using data collected from 12 500 smartphone users in 2017.28 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.28

Chart 4-7.

Chart 4-7. Prevalence of meeting the aerobic guidelines of the 2018 Physical Activity Guidelines for Americans among US adults ≥18 years of age by race/ethnicity and sex, 2017. Percentages are age adjusted. The aerobic guidelines of the 2018 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. Source: American Heart Association unpublished tabulation of National Health Interview Survey, 2017.22

Chart 4-8.

Chart 4-8. Prevalence of meeting the aerobic guidelines of the 2018 Physical Activity Guidelines for Americans among US adults ≥25 years of age by educational attainment, 2017. Percentages are age adjusted. The aerobic guidelines of the 2018 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: Data derived from Healthy People 20208 using National Health Interview Survey, 2017.

Chart 4-9.

Chart 4-9. Prevalence of meeting the aerobic guidelines for the 2018 Physical Activity Guidelines for Americans among US adults ≥18 years of age by location of residence, 2017. Percentages are age adjusted. The aerobic guidelines of the 2018 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. Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2017.

Chart 4-10.

Chart 4-10. Prevalence of meeting the aerobic and muscle-strengthening guidelines for the 2018 Physical Activity Guidelines for Americans among US adults ≥18 years of age by poverty level and type of activity, 2017.Percentages of American adults meeting guidelines are presented for (A) adults <100% poverty, (B) adults 100% to <200% poverty, and (C) adults ≥200% poverty. Percentages are age adjusted. The aerobic guidelines of the 2018 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. Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2017.

Chart 4-11.

Chart 4-11. Prevalence of meeting both the aerobic and muscle-strengthening guidelines for the 2018 Physical Activity Guidelines for Americans among US adults ≥18 years of age by disability status, 2017. 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, 2017.24

Chart 4-12.

Chart 4-12. Trends in the prevalence of physical inactivity among US adults ≥18 years of age, overall and by sex, 1998 to 2017. Percentages are age adjusted. Physical inactivity is defined as reporting no engagement in leisure-time physical activity in bouts lasting ≥10 minutes. Source: Data derived from Healthy People 20208 using National Health Interview Survey, 1998 to 2017.

Structured Activity Participation in Leisure-Time, Domestic, Occupational, and Transportation Activities and Sitting Time
  • 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.26

    • — The prevalence of walking for transportation also varies by geographic location, ranging from 43.5% of individuals living in New England reporting any walking for transportation compared with 17.8% of individuals living in the East South Central region of the United States.29

  • At this time, it is unclear which construct of PA (domestic, occupational, or transportation) contributes to the higher objectively measured PA30 but lower subjectively measured PA24 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.31

  • Using data from the 2015 to 2016 NHANES, prevalence of time spent sitting >8 h/d was reported at 25.7% (95% CI, 23.0%–28.5%) and increased with increasing age.32

Secular Trends

Youth

In 2017 (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 51.1% in 2017; however, the prevalence did not change substantively from 2013 (51.7%) to 2017 (51.1%).4,33

  • A significant increase occurred in the number of youth reporting having used computers for something other than schoolwork for ≥3 h/d compared with 2003 (22.1% versus 43.0% in 2017). The prevalence did not change significantly from 2015 to 2017 (41.7% versus 43.0%).4

    • — 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%,21 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 2017 (51.7%).4,33

    • — 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 2017 (25.4%, 29.4%, and 29.9%, respectively).

  • The prevalence of high school students playing ≥1 team sport in the past year did not substantively change between 1999 (55.1%) and 2017 (54.3%).4 In 2012, the prevalence of adolescents 12 to 15 years of age with adequate levels of cardiorespiratory fitness (based on age- and sex-specific standards) was 42.2% (Chart 4-5), down from 52.4% in 1999 to 2000.18

Adults

(See Chart 4-12)

  • The prevalence of physical inactivity among adults ≥18 years of age, overall and by sex, has decreased from 1998 to 2017, with the largest drop occurring in the past decade, from 40.2% to 25.9% between 2005 and 2017, respectively (Chart 4-12).

  • The prevalence of physical inactivity has surpassed the target for Healthy People 2020, which was 32.6%.34

  • 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 and 24.4% in 2017.24 The percentage of US adults who reported meeting the aerobic guidelines increased from 40.1% in 1998 to 49.7% in 2015 and 54.0% in 2017.24,35

  • The increase in those meeting the aerobic guidelines may be explained in part by the increased prevalence in self-reported transportation walking from 28.4% to 31.7% and leisure walking from 42.1% to 52.1% (2005 to 2015).36

  • 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)37 to 2017 (2 hours 28 minutes per day)28 suggest extreme increases in use over the past few years. Although they acknowledge that there were inconsistent methods of 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.

Promotion of PA

The US Surgeon General has introduced “Step It Up!, a Call to Action to Promote Walking and Walkable Communities” in recognition of the importance of PA.38 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.39 Moving to a walkable neighborhood was associated with a lower risk for incident hypertension in the Canadian Community Health Survey.40

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

Schools
  • Schools can provide opportunities for PA through physical education, recess, before- and after-school activity programs, and PA breaks.41

  • According to the School Health Policies and Practices Study, <5% of elementary schools and junior and senior high schools required daily physical education in 2014.34

  • In 2012, the School Health Policies and Practices Study also reported that 58.9% of school districts required regular elementary school recess.34

  • Healthy after-school programs and active school-day policies have been shown to be cost-effective solutions to increase PA and prevent childhood obesity.42

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.43,44

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.45–47

Mortality

Self-Reported Physical Inactivity and Mortality
  • In analysis from NHIS with >20 years of follow-up, 8.7% of all-cause mortality was attributed to a PA level of <150 minutes of moderate-intensity equivalent activity per week (N=67 762).48

  • 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.49

  • In a pooled study of >600 000 participants,50 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]).50

  • 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.51

  • In the WHS (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, 0.73 [95% CI, 0.65–0.82]; HR, 0.71 [95% CI, 0.62–0.82]; and HR, 0.81 [95% CI, 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.52

  • A meta-analysis also revealed an association between participating in more transportation-related PA and lower all-cause mortality risk.53 In contrast, higher occupational PA has been associated with higher mortality in males but not females.54 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.55

  • In a meta-analysis of 13 studies, 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.56

  • 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.57

  • In a prospective US cohort study (CPS-II), prolonged leisure-time sitting (≥6 versus <3 h/d) was associated with higher risk of mortality from all causes and CVD (including CHD and stroke-specific mortality) among 127 554 males and females free of chronic disease at baseline.58

  • Using an isotemporal substitution approach in a subsample of the CPS-II, among participants with the lowest level of PA, replacing 30 min/d of sitting with light-intensity PA or moderate to vigorous PA was associated with 14% (HR, 0.86 [95% CI, 0.81–0.89]) or 45% (HR, 0.55 [95% CI, 0.47–0.62]) lower mortality, respectively. For the individuals with the highest PA levels, substitution was not associated with differences in mortality risk.59

Objectively Measured Physical Inactivity/Sedentary Time and Mortality
  • In a subsample of NHANES 2003 to 2006 (participants with objectively measured PA and between 50 and 79 years of age [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 minutes of light activity per day was associated with lower all-cause mortality (HR, 0.91 [95% CI, 0.86–0.96]).60

  • In an analysis from the WHS, 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.61

  • In the REGARDS cohort study of 7985 middle- and older-aged US adults, objectively measured total sedentary time was associated with higher risk of all-cause mortality, with an HR for highest versus lowest quartile of total sedentary time of 2.63 (95% CI, 1.60–4.30) and longer sedentary bouts (HR, 1.96 [95% CI, 1.31–2.93]).62

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.63

  • 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.64

  • 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.65

Complications of Physical Inactivity: The CVH Impact

Youth
  • In a study from the NHANES 2003 to 2006 cohort of participants 6 to 17 years of age with objective measurement of PA levels by accelerometer, those with the highest levels of PA had lower SBP, lower glucose levels, and lower insulin levels than participants in the lowest PA group.66

  • Similarly, a higher amount of objectively measured sedentary duration assessed by accelerometer among children 0 to 14 years of age is associated with greater odds of hypertriglyceridemia and cardiometabolic risk.67

  • For elementary school children, engagement in organized sports for ≈1 year was associated with lower clustered cardiovascular risk.68

  • In a study of 36 956 Brazilian adolescents, self-reported higher moderate to vigorous PA levels and lower amounts of 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.69

  • 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.70

Adults
Cardiovascular and Metabolic Risk
  • In a meta-analysis of 11 studies investigating the role of exercise among individuals with MetS, aerobic exercise significantly improved DBP (−1.6 mm Hg, P=0.01), WC (−3.4 cm, P<0.01), fasting glucose (−0.15 mmol/L, P=0.03), and HDL-C (0.05 mmol/L, P=0.02).71

  • 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%).72

  • 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.73

  • 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.74

  • In a dose-response meta-analysis of 29 studies with 330 222 participants that evaluated 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]).75

  • In a meta-analysis including 7 trials with 2517 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]).76

  • 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-C, 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.77,78

  • Intermittent breaks of 10 minutes standing or desk pedaling during each hour of sitting were insufficient to prevent endothelial dysfunction that developed over a period of 4 hours of sitting.79

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]).80

  • 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.81

  • 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.82

  • In a prospective cohort study of 130 843 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 high versus low, 0.75 [95% CI, 0.69–0.82]; moderate versus low, 0.86 [95% CI, 0.78–0.93]; high versus moderate, 0.88 [95% CI, 0.82–0.94]) over an average 6.9 years of follow-up time.83

  • 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.84

  • 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.85

  • 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.86

  • In a large clinical trial (NAVIGATOR) involving 9306 people with impaired glucose tolerance, ambulatory activity (in steps per day) as assessed by pedometer at baseline and 12 months was inversely associated with risk of a cardiovascular event.87

  • In the WHI, every hour per day more of light-intensity PA was associated with lower CHD (HR, 0.86 [95% CI, 0.73–1.00]; P=0.05) and lower CVD (HR, 0.92 [95% CI, 0.85–0.99]; P=0.03).88

  • 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 females.89 However, higher occupational PA has recently been associated with higher MI incidence in males 19 to 70 years old.54,90 These relationships require further investigation, because a protective association of occupational activity with MI has been reported in young males (19–44 years).90

  • 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) were also associated with an increased CVD-free life span.91

  • 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.92

    • — 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]).92

    • — Cardiorespiratory fitness accounted for 47% of the HF risk associated with higher BMI levels.93

    • — 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]).94

  • 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-fitness participants (HR, 1.91 [95% CI, 1.74–2.09]).95

  • 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.96

  • 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]).97

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.98

  • 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.99

  • 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.100

    • — 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.101

    • — 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]).102

  • 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, 1.93 [95% CI, 1.16–3.22] for poor versus high PA and HR, 1.84 [95% CI, 1.02–3.31] for intermediate versus high PA) and cardiovascular mortality (HR, 4.36 [95% CI, 1.37–13.83] for poor versus high PA and HR, 4.05 [95% CI, 1.17–14.04] for intermediate versus high PA).103

  • 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.104

  • 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.105 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]).106

  • 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]).107

  • 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.108

Brain Health
  • Growing evidence suggests a link between vascular risk factors, cardiovascular/cerebrovascular disease and poor brain health, leading to cognitive and motor dysfunction. The AHA has proposed to use the Life’s Simple 7 strategy not only to decrease cardiovascular risk, but also to maintain optimal brain health.10

  • One of Life’s Simple 7 strategies promotes achievement of adequate PA. Results from a meta-analysis including >33 000 participants suggest that individuals who self-report high PA levels have a 38% lower risk of cognitive decline.109 Results from intervention trials have been more inconsistent.110–113 However, there have been some promising results, including the FINGER study, which observed better executive function in those who adhered to a multidomain (exercise, cognitive training, and Mediterranean diet) intervention for 2 years.110

  • Evidence from meta-analyses in stroke patients suggests that PA rehabilitation may also improve cognitive and motor function outcomes. An overall positive effect of PA training on cognitive performance was observed in stroke patients (Hedges’ g, 0.30 [95% CI, 0.14–0.47]) in a meta-analysis representing data from 736 participants.114 Another recent meta-analysis of studies involving stroke patients observed that treadmill training improved motor function compared with no training (standard mean difference, 0.60 [95% CI, 0.55–0.66]), with similar results in both low- and high-intensity and volume rehabilitation programs.115

Costs

  • The economic consequences of physical inactivity are substantial. Using data derived primarily from WHO publications and data warehouses, one study estimated that the economic costs of physical inactivity account for 1.5% to 3.0% of total direct healthcare expenditures in developed countries such as the United States.116

  • 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.117

  • 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).118

  • 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 ≥40 years of age with CVD, the highest marginal expenditures ($2853 per person 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).119

  • 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.120

  • Interventions and community strategies to increase PA have been shown to be cost-effective in terms of reducing medical costs121:

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

    • — The incremental CER 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.

Global Burden

(See Chart 4-13)

  • Prevalence of physical inactivity in 2016 was reported to be 27.5% (95% CI, 25.0%–32.2%) of the population globally. These rates have not changed substantially since 2001, at which time prevalence of physical inactivity was 28.5% (95% CI, 23.9%–33.9%). Critically, it appears that the number of women reporting insufficient PA is 8% higher than men, globally.122

  • The GBD 2017 Study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 359 diseases and injuries in 195 countries and territories.123 Mortality rates attributable to low PA are highest in North Africa and the Middle East and in Central and Eastern Europe (Chart 4-13).

  • Physical inactivity is among the leading behavioral risk factors for global death, responsible for 1.3 million deaths annually.124 Other leading risk factors include diet, alcohol, tobacco, and child and maternal malnutrition. The adjusted PAF 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.83

Chart 4-13.

Chart 4-13. Age-standardized global mortality rates attributable to low physical activity per 100 000, both sexes, 2017. Mortality rates attributable to low physical activity are highest in North Africa and the Middle East and in Central and Eastern Europe. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Federated States of Micronesia; 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. Source: Data derived from Global Burden of Disease Study 2017, Institute for Health Metrics and Evaluation, University of Washington.123 Printed with permission. Copyright © 2018, University of Washington.

Abbreviations Used in Chapter 4

ACCAmerican College of Cardiology
AFatrial fibrillation
AHAAmerican Heart Association
AMIacute myocardial infarction
appapplication
BMIbody mass index
BPblood pressure
CADcoronary artery disease
CERcost-effectiveness ratio
CHDcoronary heart disease
CIconfidence interval
CPS-IICancer Prevention Study II
CVDcardiovascular disease
CVHcardiovascular health
DBPdiastolic blood pressure
DMdiabetes mellitus
EDemergency department
EFejection fraction
FINGERFinnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability
GBDGlobal Burden of Disease
HBPhigh blood pressure
HDL-Chigh-density lipoprotein cholesterol
HFheart failure
HRhazard ratio
LDL-Clow-density lipoprotein cholesterol
LIFELifestyle Interventions and Independence for Elders
METmetabolic equivalent
MetSmetabolic syndrome
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
NIH-AARPNational Institutes of Health–American Association of Retired Persons
ORodds ratio
PAphysical activity
PADperipheral artery disease
PAFpopulation attributable fraction
QALYquality-adjusted life-year
RCTrandomized controlled trial
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SESsocioeconomic status
VTEvenous thromboembolism
WCwaist circumference
WHIWomen’s Health Initiative
WHOWorld Health Organization
WHSWomen’s Health Study
YRBSSYouth Risk Behavior Surveillance System

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5. Nutrition

See Tables 5-1 through 5-3 and Charts 5-1 through 5-6

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 CVH.

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 CVH,1 which includes following a healthy diet pattern characterized by 5 primary and 3 secondary metrics (Table 5-1) that should be consumed within a context that is appropriate in energy balance and consistent with a DASH-type eating plan.1

Table 5-1. This table shows the American Heart Association targets, consumption ranges for the Alternative Healthy Diet Score, and the Alternative Scoring range for primary diet metrics and secondary diet metrics.

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

AHA TargetConsumption Range for
Alternative Healthy Diet Score*
Alternative Scoring
Range*
Primary dietary metrics
 Fruits and vegetables≥4.5 cups/d0 to ≥4.5 cups/d0–10
 Fish and shellfish2 or more 3.5-oz servings/wk
(≥200 g/wk)
0 to ≥7 oz/wk0–10
 Sodium≤1500 mg/d≤1500 to >4500 mg/d10–0
 SSBs≤36 fl oz/wk≤36 to >210 fl oz/wk10–0
 Whole grains3 or more 1-oz-equivalent servings/d0 to ≥3 oz/d0–10
Secondary dietary metrics
 Nuts, seeds, and legumes≥4 servings/wk (nuts/seeds: 1 oz; legumes: ½ cup)0 to ≥4 servings/d0–10
 Processed meats2 or fewer 1.75-oz servings/wk (≤100 g/wk)≤3.5 to >17.5 oz/wk10–0
 Saturated fat≤7% energy≤7 to >15 (% energy)10–0
AHA Diet Score (primary)Ideal: 4 or 5 dietary targets (≥80%)
Intermediate: 2 or 3 dietary targets (40%–79%)
Poor: <2 dietary targets (<40%)
Sum of scores for primary metrics0 (worst) to 100 (best)§
Ideal: 80–100
Intermediate: 40–79
Poor: <40
AHA Diet Score (secondary)Ideal: 4 or 5 dietary targets (≥80%)
Intermediate: 2 or 3 dietary targets (40%–79%)
Poor: <2 dietary targets (<40%)
Sum of scores for primary and secondary metrics0 (worst) to 100 (best)§
Ideal: 80–100
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 fruit 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.84

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 2015 to 2016 in the United States for adults. In adults, the prevalence of a poor diet improved from 56.0% to 47.8% for the primary score and 43.7% to 36.4% for the secondary score (Table 5-2). Changes in score were largely attributable to increased consumption of whole grains and nuts, seeds, and legumes and decreased consumption of SSBs. No significant changes were observed for consumption of total fruits and vegetables, fish and shellfish, sodium, processed meat, and saturated fat.

Table 5-2. This table shows trends in key dietary components and diet quality by primary and secondary score at all 2-year NHANES intervals between 2003 and 2016.

Table 5-2. Trends in Key Dietary Components Among US Adults, NHANES 2003 to 2004 to NHANES 2015 to 2016

AHA ScoreSurvey-Weighted Mean/Percentages (95% CI)*
2003–20042005–20062007–20082009–20102011–20122013–20142015–2016P for Trend
Primary19.0
(18.1–20.0)
19.9
(19.2–20.6)
19.5
(18.7–20.3)
20.9
(20.5–21.4)
21.2
(20.4–21.9)
21.0
(20.3–21.7)
20.8
(19.9–21.6)
<0.001
 Fruits and vegetables5.0
(4.7–5.3)
5.0
(4.8–5.3)
4.9
(4.7–5.2)
5.1
(4.9–5.3)
5.1
(4.9–5.3)
4.9
(4.7–5.0)
4.8
(4.5–5.0)
0.18
 Whole grains2.1
(1.9–2.3)
2.4
(2.3–2.6)
2.4
(2.2–2.6)
2.8
(2.7–2.9)
3.1
(2.9–3.3)
3.0
(2.8–3.1)
3.0
(2.8–3.2)
<0.001
 Fish and shellfish2.5
(2.2–2.8)
2.6
(2.4–2.8)
2.5
(2.2–2.7)
2.8
(2.4–3.1)
2.5
(2.2–2.8)
2.5
(2.2–2.9)
2.3
(1.9–2.6)
0.23
 SSBs5.6
(5.2–6.0)
6.3
(6.0–6.6)
6.2
(5.9–6.5)
6.6
(6.4–6.8)
6.7
(6.4–7.0)
6.9
(6.5–7.3)
7.1
(6.8–7.3)
<0.001
 Sodium3.8
(3.6–3.9)
3.5
(3.4–3.6)
3.5
(3.4–3.6)
3.6
(3.5–3.8)
3.8
(3.7–3.9)
3.8
(3.6–3.9)
3.7
(3.5–3.8)
0.17
Secondary34.6
(33.4–35.8)
35.6
(34.5–36.6)
35.5
(34.2–36.7)
37.3
(36.6–38.0)
38.0
(36.9–39.2)
37.5
(36.6–38.3)
37.1
(35.8–38.3)
<0.001
 Nuts, seeds and legumes4.1
(3.9–4.4)
4.4
(4.1–4.7)
4.3
(3.9–4.7)
4.4
(4.2–4.6)
4.8
(4.6–5.0)
4.7
(4.4–5.0)
5.0
(4.6–5.4)
<0.001
 Processed meat6.6
(6.4–6.8)
6.5
(6.1–6.8)
6.7
(6.5–6.9)
6.6
(6.4–6.9)
6.7
(6.4–6.9)
6.7
(6.5–7.0)
6.7
(6.5–7.0)
0.09
 Saturated fat4.9
(4.7–5.1)
4.8
(4.7–5.0)
5.0
(4.8–5.2)
5.3
(5.1–5.5)
5.4
(5.2–5.6)
5.0
(4.8–5.2)
4.5
(4.3–4.8)
0.48
Diet quality by primary and secondary scores (%)
 Primary score
  Poor56.0
(51.6–60.2)
52.4
(48.3–56.5)
53.9
(49.9–57.9)
47.8
(45.3–50.3)
45.8
(41.8–49.9)
46.6
(42.7–50.7)
47.8
(43.1–52.6)
<0.001
  Intermediate43.4
(39.2–47.6)
46.9
(43.0–50.8)
45.3
(41.5–49.1)
50.7
(48.0–53.3)
52.7
(48.8–56.6)
51.8
(47.7–55.9)
50.8
(46.2–55.4)
0.001
  Ideal0.7
(0.5–1.0)
0.7
(0.4–1.3)
0.8
(0.5–1.6)
1.5
(1.0–2.2)
1.5
(0.9–2.4)
1.6
(1.0–2.5)
1.4
(1.0–2.1)
0.001
 Secondary score
  Poor43.7
(39.6–47.8)
41.7
(38.1–45.4)
41.3
(37.1–45.7)
36.1
(34.0–38.3)
33.9
(31.2–36.7)
35.8
(33.3–38.3)
36.4
(32.6–40.4)
<0.001
  Intermediate55.2
(51.2–59.2)
56.8
(53.1–60.4)
57.5
(53.1–61.7)
61.6
(59.3–63.8)
64.1
(61.6–66.5)
62.0
(59.5–64.4)
62.0
(58.1–65.7)
<0.001
  Ideal1.1
(0.7–1.7)
1.5
(1.0–2.2)
1.3
(0.9–1.8)
2.3
(1.5–3.3)
2.0
(1.4–2.9)
2.3
(1.8–2.9)
1.6
(1.0–2.5)
0.02

AHA indicates American Heart Association; NHANES, National Health and Nutrition Examination Survey; and SSBs, sugar sweetened beverages.

*All dietary variables were adjusted for energy to 2000 kcal/d using the residual method before the analysis. Each AHA consumption target was evaluated based on a continuous scoring system. Intake of each dietary component was scored from 0 to 10 (beneficial components) and from 10 to 0 (harmful components). For beneficial dietary components, individuals with zero intake received the lowest score (0). For harmful dietary components, the lowest score (0) was assigned to a higher level approximately equivalent to the 80th to 90th percentile of intake among US adults and rounded to a practical value (eg, 4500 mg/d sodium, one 50-g serving/d of processed meat, two 8-oz servings/d of sugar-sweetened beverages, and 15% energy of saturated fat). Intermediate dietary intake was scored linearly between 0 and 10. For example, an adult consuming 3000 mg/d of sodium would receive 5 sodium points (ie, his or her sodium consumption was halfway between 1500 mg/d and the maximum value of 4500 mg/d).

Source: Unpublished analyses courtesy of Dr Junxiu Liu, Tufts University, using NHANES, 2003 to 2016. 85

Similar changes in AHA healthy diet scores between 2003 to 2004 and 2015 to 2016 were seen in minority groups and those with lower income or education, although significant disparities persisted (Charts 5-1 and 5-2). The proportion with a poor diet decreased from 64.7% to 58.3% for NH blacks, from 66.0% to 57.5% for Mexican Americans, and from 54.0% to 45.9% for NH whites (Chart 5-1). The proportion with a poor diet (<40% adherence) decreased from 50.7% to 38.8% in adults with income-to-poverty ratio ≥3.0, but only from 67.7% to 59.7% in adults with income-to-poverty ratio <1.3 (Chart 5-2).

Chart 5-1.

Chart 5-1. Trends in prevalence of poor AHA healthy diet score, by race/ethnicity, United States, 2003 to 2016. 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. AHA indicates American Heart Association; and NH, non-Hispanic. Source: Unpublished analyses courtesy of Dr Junxiu Liu, Tufts University, using National Health and Nutrition Examination Survey data, 2003 to 2016.85

Chart 5-2.

Chart 5-2. Trends in prevalence of poor AHA healthy diet score in the United States, by ratio of family income to poverty level, 2003 to 2016. 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. AHA indicates American Heart Association. Source: Unpublished analyses courtesy of Dr Junxiu Liu, Tufts University, using National Health and Nutrition Examination Survey data, 2003 to 2016.85

Dietary Habits in the United States: Current Intakes of Foods and Nutrients

Adults

(See Table 5-3)

The average dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health based on data from 2015 to 2016 NHANES is detailed below (Table 5-3):

Table 5-3. This detailed table shows the population mean consumption of food groups and key nutrients by sex and race/ethnicity from 2015 to 2016. This table shows that non-Hispanic white males have the highest consumption of whole grains. Mexican American females have the highest consumption of total fruit. Non-Hispanic white females have the highest consumption of non-starchy vegetables. Non-Hispanic black males have the highest consumption of sugar sweetened beverages. Many additional categories are reported, with their exact consumption levels, on this table.

Table 5-3. Population Mean Consumption* of Food Groups and Nutrients of Interest by Sex and Race/Ethnicity Among US Adults ≥20 Years of Age, NHANES 2015 to 2016

NH White MalesNH Black MalesMexican American MalesNH White FemalesNH Black FemalesMexican American Females
Average Consumption% Meeting GuidelinesAverage Consumption% Meeting GuidelinesAverage Consumption% Meeting GuidelinesAverage Consumption% Meeting GuidelinesAverage Consumption% Meeting GuidelinesAverage Consumption% Meeting Guidelines
Foods
 Whole grains, servings/d1.1±0.77.50.7±1.55.50.6±1.13.00.9±0.64.70.8±1.44.00.7±1.13.2
 Whole fruit, servings/d1.4±1.210.01.0±2.04.31.4±2.19.51.5±1.19.91.1±26.81.6±2.38.7
 Total fruit, servings/d1.8±1.413.71.7±2.68.92±2.716.51.9±1.212.01.9±2.514.62.2±2.918.6
 Nonstarchy vegetables, servings/d2.2±1.26.41.6±1.92.22±1.83.22.4±1.39.11.8±1.83.22.3±26.0
 Starchy vegetables, servings/d0.9±0.7NA1.0±1.5NA0.5±1NA0.9±0.7NA0.9± 1.2NA0.8± 1.2NA
 Legumes, servings/wk1.5±2.129.91.0±3.415.23.4±6.446.41.2± 1.625.31.1± 3.221.22.9±5.745.6
 Fish and shellfish, servings/wk1.0±1.816.01.4±3.921.11.2±4.118.21.0±1.518.61.8±4.124.51.2±417.3
 Nuts and seeds, servings/wk5.8±6.537.32.8±9.513.42.5±7.520.56.2±6.136.83.4±8.320.53±8.917.5
 Unprocessed red meats, servings/wk3.5±2.7NA3.4±5.7NA3.9±5.1NA2.4±1.9NA2.4±3.6NA3.1±4.5NA
 Processed meat, servings/wk2.5±1.956.72.1±3.262.01.8±3.167.21.8±1.565.71.4±2.470.91±1.879.8
 SSBs, servings/wk8.3±8.757.610.3±12.532.910±12.439.25.8±6.667.79.7±13.541.28±12.645.3
 Sweets and bakery desserts, servings/wk3.7±3.657.83.3±6.862.24.2±7.661.74.2±4.156.23.7±7.259.14.7±8.552.2
 Refined grain, servings/d4.8±1.49.45.2±3.17.57.0 ±3.20.824.8±1.49.84.9±2.67.16.7±3.53.0
Nutrients
 Total calories, kcal/d2418±522NA2211±1086NA2485±1140NA1742±344NA1762±824NA1852±803NA
 EPA/DHA, mg/d0.079±0.1039.00.101±0.24710.60.075±0.1596.90.084±0.1118.80.103±0.2518.20.090±0.2417.9
 ALA, g/d1.65±0.5542.41.69±1.1243.81.56±0.7341.61.95±0.7187.91.86±1.0286.71.72±0.8887.1
 n-6 PUFA, % energy7.4± 2.9NA8.8 ± 6.8NA7.3±5.8NA11.6±5.1NA11.9±14.8NA10.1±6.7NA
 Saturated fat, % energy12±226.011±436.211.4±3.630.712±2.126.810.9±3.937.311.2±3.737.5
 Ratio of (PUFAs + MUFAs)/SFAs1.8±0.612.62.2±1.625.11.8±1.313.62.3±0.829.72.6±2.240.02.3±1.431.3
 Dietary cholesterol, mg/d280±10766.2313±21654.6331±21354.9307±11561.9315±19955.6342±24454.3
 Carbohydrate, % energy45.3±6.2NA46.3±12.2NA47.3±10.6NA46.2±5.8NA48.7±11.3NA49.3±10.5NA
 Dietary fiber, g/d16.4±4.86.714.1±8.34.818.2±9.79.717.8±4.710.015±8.14.720.2±1014.8
 sodium, g/d3.4±0.587.73.5±1.114.73.4±1.067.43.5±0.545.63.4±0.917.03.4±0.984.4
 Added sugar, % energy11.1±9.536.913.8±17.523.010.8±13.238.116.7±9.620.022.1±33.611.815.3±16.522.6

Values for average consumption are mean±SD. Data are from NHANES 2015 to 2016, derived from two 24-hour dietary recalls per person, with population SD adjusted for within-person vs between-person variation. All values are energy adjusted by individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d. The calculations for foods use the US Department of Agriculture (USDA)’s Food Patterns Equivalent Database on composition of various mixed dishes, which incorporates partial amounts of various foods (eg, vegetables, nuts, processed meats) in mixed dishes; in addition, the characterization of whole grains is now derived from the USDA database instead of the ratio of total carbohydrate to fiber.

ALA indicates α-linoleic acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; MUFA, monounsaturated fatty acid; NA, not available; NH, non-Hispanic; NHANES, National Health and Nutrition Examination Survey; n-6-PUFA, ω-6-polyunsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; and SSBs, sugar-sweetened beverages.

*All intakes and guidelines adjusted to a 2000 kcal/d diet. Servings are defined as follows: whole grains, 1-oz equivalents; fruits and vegetables, 1/2-cup equivalents; legumes, 1/2 cup; fish/shellfish, 3.5 oz or 100 g; nuts and seeds, 1 oz; unprocessed red or processed meat, 3.5 oz or 100 g; sugar-sweetened beverages, 8 fl oz; sweets and bakery desserts, 50 g. Guidelines defined as follows: whole grains, 3 or more 1-oz equivalent (eg, 21 g of whole wheat bread, 82 g of cooked brown rice, 31 g of Cheerios) servings/d; fruits, ≥2 cups/d; nonstarchy vegetables, ≥2.5 cups/d; legumes, ≥1.5 cups/wk; fish or shellfish, 2 or more 100-g (3.5-oz) servings/wk; nuts and seeds, 4 or more 1-oz servings/wk; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings/wk (1/4 of discretionary calories); sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding 100% fruit juices), ≤36 oz/wk (≈1/4 of discretionary calories); sweets and bakery desserts, 2.5 or fewer 50-g servings/wk (≈1/4 of discretionary calories); EPA/DHA, ≥0.250 g/d51; ALA, ≥1.6/1.1 g/d (males/females); saturated fat, <10% energy; dietary cholesterol, <300 mg/d; dietary fiber, ≥28 g/d; sodium, <2.3 g/d; ratio of (PUFAs + MUFAs)/SFAs ≥2.5; added sugars ≤6.5% total energy intake. No dietary targets are listed for starchy vegetables and unprocessed red meats because of their positive association with long-term weight gain and their positive or uncertain relation with diabetes mellitus and cardiovascular disease.

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

Source: Unpublished analyses courtesy of Dr Junxiu Liu, Tufts University, using NHANES, 2015 to 2016.85

  • Consumption of whole grains was 1.1 and 0.9 servings per day by NH white males and females, 0.7 and 0.8 servings per day by NH black males and females, and 0.6 and 0.7 servings per day by Mexican American males and females, respectively. For each of these groups, <10% of adults in 2011 to 2012 met guidelines of ≥3 servings per day.

  • Whole fruit consumption ranged from 1.0 to 1.6 servings per day in racial or ethnic subgroups: ≈10% of NH whites, ≈6% of NH blacks, and ≈9% 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 increased to ≈13% in NH whites, ≈12% in NH blacks, and ≈18% in Mexican Americans.

  • Nonstarchy vegetable consumption ranged from 1.6 to 2.4 servings per day in these racial or ethnic subgroups: ≈8% of NH whites, ≈3% of NH blacks, and ≈5% of Mexican Americans met guidelines of ≥2.5 cups per day.

  • Consumption of fish and shellfish ranged from 1.0 to 1.8 servings per week in these racial or ethnic subgroups: ≈17% of NH whites, ≈23% of NH blacks, and ≈18% of Mexican Americans met guidelines of ≥2 servings per week.

  • Weekly consumption of nuts and seeds was ≈6 servings among NH whites and ≈3 servings among NH blacks and Mexican Americans. Approximately 1 in 3 whites, 1 in 6 NH blacks, and 1 in 5 Mexican Americans met guidelines of ≥4 servings per week.

  • Consumption of processed meats was lowest among Mexican American females (1.0 servings per week) and highest among NH white males (≈2.5 servings per week). Between 57% (NH white males) and 80% (Mexican American females) of adults consumed ≤2 servings per week.

  • Consumption of SSBs ranged from 5.8 servings per week among NH white females to 10 servings per week among NH black males and females and Mexican American males. The majority of NH whites (≈63%) consumed <36 oz/wk, whereas only the minority of NH blacks (37%) and Mexican Americans (42%) met this target.

  • Consumption of sweets and bakery desserts ranged from 4.7 servings per week among Mexican American females to 3.3 servings per week among NH black males. The majority of NH whites, NH blacks, and Mexican Americans consumed <2.5 servings per week.

  • The proportion of total energy intake from added sugars ranged from 10.8% for Mexican American males to 22.1% for NH black females. Between 12% of NH black females and 38.1% of Mexican American males consumed ≤6.5% of total energy intake from added sugars.

  • Consumption of eicosapentaenoic acid and docosahexaenoic acid ranged from 0.075 to 0.103 g/d in each sex and racial or ethnic subgroup. Fewer than ≈9% of NH whites, ≈9% of NH blacks, and ≈7% of Mexican Americans consumed ≥0.250 g/d.

  • One-quarter to two-fifths 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.

  • The ratio of (PUFAs+MUFAs)/SFAs ranged from 1.8 in NH white males and Mexican American males to 2.6 in NH black females. The proportion with a ratio ≥2.5 ranged from 40% in NH black females to 12.6% in NH white males.

  • Only ≈8% of NH whites, ≈5% of blacks, and ≈12% of Mexican Americans consumed ≥28 g of dietary fiber per day.

  • Only ≈4% to ≈8% of adults in each racial or ethnic subgroup consumed <2.3 g of sodium per day. Estimated mean sodium intake in the United States 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. Sodium added to food outside the home accounts for more than two-thirds of total sodium intake in the United States (Chart 5-4).3 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.4

Chart 5-3.

Chart 5-3. Estimated mean sodium intake by 24-hour urinary excretion, United States, 2013 to 2014. Estimates based on nationally representative sample of 827 nonpregnant, noninstitutionalized US adults 20 to 69 years of age who completed a 24-hour urine collection in NHANES 2013 to 2014. NHANES indicates National Health and Nutrition Examination Survey. Source: Data derived from Cogswell et al86 using NHANES 2013 to 2014.85

Chart 5-4.

Chart 5-4. Sources of sodium intake in adults in 3 geographic regions in the United States, 2013 to 2014. 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; and Minneapolis-St. Paul, MN) with equal numbers of males and females from 4 racial/ethnic groups (Asians, blacks, Hispanics, non-Hispanic whites). Source: Reprinted from Harnack et al.3 Copyright © 2017, American Heart Association, Inc.

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 below5:

  • 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.

  • 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 versus 6.0 servings per week) and 10- to 14-year-old (11.6 versus 9.7 servings per week) groups, but it was higher in girls than in boys in the 15- to 19-year-old group (14 versus 12.4 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 (6.6 to 8.3 servings per week) 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.

Secular Trends

In addition to individual foods and nutrients, overall dietary patterns can be another useful tool for assessing diet quality.6 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.7 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. Different dietary patterns have been defined, such as HEI-2010, AHEI, Mediterranean, DASH-type, Western, prudent, and vegetarian patterns.

Between 1999 and 2010, the average AHEI-2010 score of US adults improved from 39.9 to 46.8.8 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 2 to 18 years of age improved from 42.5 to 50.9.9 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. 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 scores in Supplemental Nutrition Assistance Program participants after the 2003 to 2004 cycle. HEI-2010 scores have remained consistently lower in NH blacks (1999–2000: 39.6; 2011–2012: 48.4) than in NH whites (1999–2000: 42.1; 2011–2012: 50.2) and highest in Mexican Americans (1999–2000: 44.1; 2011–2012: 51.9). In a study that used household store purchase data (N=98 256 household-by-quarter observations), Supplemental Nutrition Assistance Program 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.10

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.11 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.

In a study using data from the Food and Agriculture Organization Food Balance Sheets from 1961 to 1965, 2000 to 2003, and 2004 to 2011 in 41 countries, a Mediterranean adequacy index was calculated based on available energy intake for food groups consistent or inconsistent with the Mediterranean dietary pattern.12 Adherence to the Mediterranean dietary pattern decreased from 1961 to 1965 to 2000 to 2003, with stabilization overall from 2004 to 2011.

Trends in Dietary Supplement Intake

(See Chart 5-5)

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 death.13 From 1999 to 2000 to 2011 to 2012, use of multivitamins/multiminerals decreased from 37% to 31%, use of omega-3 fatty acids increased from 1.4% to 11%, and use of vitamin D supplements remained stable (34% to 38%; Chart 5-5). Fifty-two percent of US adults reported using any supplement, including multivitamins/multiminerals (31%), vitamin D (38%), and omega-3 fatty acids (11%).14 Trends in any supplement use over time were increasing in older adults, stable among middle-aged adults, and decreasing in younger adults.

Chart 5-5.

Chart 5-5. Trends in use of MVMM, vitamin D, and n-3 fatty acid supplements among adults in the United States (NHANES, 1999–2012). MVMM indicates multivitamin/mineral; and NHANES, National Health and Nutrition Examination Survey. Source: Data derived from Kantor et al.14

Social Determinants

  • 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.15–17

  • 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.18

  • Disparities may be driven in part by overabundance of unhealthy food options. In a study of neighborhood-level data from 4 US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California), past neighborhood-level income was inversely associated with current density of convenience stores.19 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.20

Genetics/Family History

  • Genetic factors may contribute to food preferences and modulate the association between dietary components and adverse CVH outcomes.21–23 However, there is a paucity of gene-diet interaction studies with independent replication to support personalizing dietary recommendations according to genotype.

  • In a randomized trial of 609 overweight-obese, nondiabetic participants that compared the effects of healthy low-fat versus healthy low-carbohydrate weight loss diets, 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.24

  • The interactions between a GRS composed of 97 BMI-associated variants and 3 diet-quality scores were examined in a pooled analysis of 30 904 participants from the Nurses’ Health Study, the Health Professional Follow-up Study, and the Women’s Genome Health Study. Higher diet quality was found to attenuate the association between GRS and BMI (P for interaction terms <0.005 for AHEI-2010 score, Alternative Mediterranean Diet score, and DASH diet score).25 A 10-U increase in the GRS was associated with a 0.84-U (95% CI, 0.72–0.96) increase in BMI for those in the highest tertile of AHEI score, compared with a 1.14-U (95% CI, 0.99–1.29) increase in BMI in those in the lowest tertile of AHEI score.

Impact on US Mortality

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 2012.26 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.

CVH Impact of Diet

Dietary Patterns
  • 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 MI27 and a large primary prevention trial in Spain among patients with CVD risk factors.28 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.

  • In a randomized crossover trial of 118 overweight omnivores at low-moderate CVD risk, a reduced-calorie lacto-ovo vegetarian diet was compared with 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.29

  • In the PREDIMED RCT, a significantly smaller decrease in central adiposity occurred in the Mediterranean diet with nuts group (−0.92 cm [95% CI, −1.6 to −0.2 cm]) but not the Mediterranean diet with olive oil group (−0.47 [95% CI, −1.1 to 0.2 cm]) compared with the control group.30 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 (95% CI, 0.61–1.10) for the Mediterranean diet with nuts arm compared with the control arm.

  • 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 to 139, 140 to 149, and ≥150 mm Hg, respectively.31

  • Compared with a higher-carbohydrate DASH diet, a DASH-type diet with higher protein lowered BP by 1.4 mm Hg, LDL-C by 3.3 mg/dL, and triglycerides by 16 mg/dL but also lowered HDL-C 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-C by 1.1 mg/dL, and lowered triglycerides by 10 mg/dL.32 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the higher-carbohydrate DASH diet.33

  • 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.34

  • 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.35

  • 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]).36

Fats and Carbohydrates
  • In meta-analyses of RCTs comparing higher versus lower fiber intake, higher fiber intake lowered body weight (−0.37 kg [95% CI, −0.63 to −0.11 kg]), TC (−0.15 mmol/L [95% CI, −0.22 to −0.07 mmol/L]), and SBP (−1.27 mm Hg [95% CI, −2.50 to −0.04 mm Hg]) and tended to lower HbA1c (−0.54% [95% CI, −1.28% to 0.20%]).37 In similar meta-analyses of RCTs for whole grains and glycemic index, higher whole grain intake only significantly reduced body weight (−0.62 kg [95% CI, −1.19 to −0.05 kg]), whereas no consistent health effects were found for glycemic index.

  • In meta-analyses of observational studies, higher total dietary fiber intake was associated with a lower risk of incident CHD (RR, 0.76 [95% CI, 0.69–0.83]), CHD mortality (RR, 0.69 [95% CI, 0.60–0.81]), and incident stroke (RR, 0.78 [95% CI, 0.69–0.88]).37 Higher whole grain intake was associated with a lower risk of incident CHD (RR, 0.80 [95% CI, 0.70–0.91]), CHD mortality (RR, 0.66 [95% CI, 0.56–0.77]), and stroke death (RR, 0.74 [95% CI, 0.58–0.94]). Evidence for associations between glycemic index, glycemic load, and source of dietary fiber and CVD outcomes was less robust.

  • 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.24

  • 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 Lp(a) levels by 3.8, 1.4, and 1.1 mg/L.38

  • 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.39 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.

  • In a randomized crossover trial of 92 adults with abdominal obesity, LDL-C 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-C (by 4.7% and 3.8%, respectively) compared with the high-carbohydrate diet.40

  • 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.41

  • 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.42

  • In a meta-analysis of RCTs in which increased PUFA consumption in place of SFAs reduced CHD events, there was 10% lower risk for each 5% energy exchange (RR, 0.90 [95% CI, 0.83–0.97]).43

  • 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.44

Foods and Beverages
  • 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.45

  • In a meta-analysis of 16 prospective cohort studies, each daily serving of fruits or vegetables was associated with a 4% lower risk of cardiovascular mortality (RR, 0.96 [95% CI, 0.92–0.99]).46

  • 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]).47

  • 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]).48

  • In a meta-analysis of 14 prospective cohort studies, every 20 g/d higher intake of fish was associated with 4% reduced risk of CVD mortality (RR, 0.96 [95% CI, 0.94–0.98]).49 The association was stronger in Asian cohorts than Western cohorts. 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]).50

  • 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 was associated with a higher incidence of CHD (RR, 1.42 [95% CI, 1.07–1.89]).51

  • In a study of 169 310 female nurses and 41 526 male health professionals, 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.52

  • 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]).53

  • 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.54

Sodium and Potassium
  • 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.55 The effects of sodium reduction on BP appear to be stronger in individuals who are older, hypertensive, and black.56,57

  • 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.58–64 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) or imprecise estimation of sodium intake through a single dietary recall or a single urine excretion.62

  • 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.63

  • 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).63

  • 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).64

Dietary Supplements
  • In an RCT of 15 480 adults with DM and no history of ASCVD, 1 g of n-3 fatty acids had no effect on first serious vascular event (RR, 0.97 [95% CI, 0.87–1.08]) or a composite outcome of first serious vascular event or revascularization (RR, 1.00 [95% CI, 0.91–1.09]) or mortality (RR, 0.95 [95% CI, 0.86–1.05]) compared with placebo (1 g of olive oil).65

  • In an RCT of 25 871 adults (males ≥50 years of age and females ≥55 years of age), the effects of daily supplementation of 2000 IU of vitamin D and 1 g of marine n-3 fatty acids were examined on prevention of cancer and CVD.66 Vitamin D had no effect on major cardiovascular events (HR, 0.97 [95% CI, 0.85–1.12]), cancer (HR, 0.96 [95% CI, 0.88–1.06]), or any secondary outcomes. Marine n-3 fatty acid supplementation had no effect on major cardiovascular events (HR, 0.92 [95% CI, 0.80–1.06]), invasive cancer (HR, 1.03 [95% CI, 0.93–1.13]), or any secondary outcomes.

  • 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).67

  • 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]).68

  • Meta-analyses of RCTs examining the effects of multivitamins, vitamin D, calcium, vitamin C, B-complex, antioxidants, and vitamin B3 (niacin) have demonstrated no salutary cardiovascular benefits.69 Meta-analyses of folic acid RCTs suggested reductions in stroke risk (RR, 0.80 [95% CI, 0.69–0.93]) and CVD (RR, 0.83 [95% CI, 0.73–0.93]), although the benefit was mainly driven by the China Stroke Primary Prevention Trial, a large RCT of 20 702 adults with hypertension and no history of stroke or MI.70

Cost

The US Department of Agriculture reported that the Consumer Price Index for all food increased by 1.4% in 2018.71 Prices for foods eaten at home increased by 0.4% in 2018, whereas prices for foods eaten away from home increased by 2.6%. 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.72

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.73

  • 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.74 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
  • 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).75 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.

Global Trends in Key Dietary Factors

Analysis of SSB sales data suggests that the regions in the world with the highest SSB consumption are North America, Latin America, Australasia, and Western Europe.76 A number of countries and US cities have implemented SSB taxes. 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).77 In Berkeley, CA, a 1 cent per ounce SSB excise tax was implemented in January 2015.78 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.79 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.

In a systematic review of population-level sodium initiatives, reduction in mean sodium intake occurred in 5 of 10 initiatives.80 Successful population-level sodium initiatives tended to use multiple strategies and included structural activities, such as food product reformulation. For example, 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 2011,81 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.

Global Burden

(See Chart 5-6)

  • The GBD 2017 Study82 used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 359 diseases and injuries in 195 countries and territories. The age-standardized mortality attributable to dietary risks is highest in Oceania and Central Asia (Chart 5-6).

  • An updated report from the GBD 2017 Study estimated the impact of 15 dietary risk factors on mortality and DALYs worldwide, using a comparative risk assessment approach.83 In 2017, an estimated 11 million deaths (95% UI, 10–12 million; 22% of all deaths) and 255 million DALYs (95% UI, 234–274 million; 15% of all DALYs) were attributable to dietary risks. The leading dietary risk factors were high sodium intake (3 million [95% UI, 1–5 million] deaths), low whole grain intake (3 million [95% UI, 2–4 million] deaths), and low fruit intake (2 million [95% UI, 1–4 million] deaths). Low-middle Socio-demographic Index and high-middle Socio-demographic Index countries had the highest age-standardized rates of diet-related deaths (344 [95% UI, 319–369] and 347 [95% UI, 324–369] deaths per 100 000 population), whereas high Socio-demographic Index countries had the lowest age-standardized rates of diet-related deaths (113 [95% UI, 104–122] deaths per 100 000 population). Age-standardized diet-related death rates decreased between 1990 to 2017 from 406 (95% UI, 381–430) to 275 (95% UI, 258–292) deaths per 100 000 population, although the proportion of deaths attributable to dietary risks was largely stable.

Chart 5-6.

Chart 5-6. Age-standardized global mortality rates attributable to dietary risks per 100 000, both sexes, 2017. The age-standardized mortality attributable to dietary risks is highest in Oceania and Central Asia. Country codes: ATG, Antigua and Barbuda; BRB, Barbados; COM, Comoros; DMA, Dominica; E Med., Eastern Mediterranean; FJI, Fiji; FSM, Federated States of Micronesia; 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. Source: Data derived from Global Burden of Disease Study 2017, Institute for Health Metrics and Evaluation, University of Washington.82 Printed with permission. Copyright © 2018, University of Washington.

Abbreviations Used in Chapter 5

AHAAmerican Heart Association
AHEIAlternate Healthy Eating Index
ALAα-linoleic acid
ASCVDatherosclerotic cardiovascular disease
BMIbody mass index
BPblood pressure
CERcost-effectiveness ratio
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
CVHcardiovascular health
DALYdisability-adjusted life-year
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
DHAdocosahexaenoic acid
DMdiabetes mellitus
EPAeicosapentaenoic acid
GBDGlobal Burden of Disease
GRSgenetic risk score
HbA1chemoglobin A1c (glycosylated hemoglobin)
HDL-Chigh-density lipoprotein cholesterol
HEIHealthy Eating Index
HFheart failure
HRhazard ratio
LDL-Clow-density lipoprotein cholesterol
Lp(a)lipoprotein(a)
MImyocardial infarction
MUFAmonounsaturated fatty acid
MVMMmultivitamin/mineral
NAnot available
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
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
SDstandard deviation
SESsocioeconomic status
SFAsaturated fatty acid
SNPsingle-nucleotide polymorphism
SSBsugar-sweetened beverage
TCtotal cholesterol
TOHPTrials of Hypertension Prevention
UIuncertainty interval
WHIWomen’s Health Initiative

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