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Heart Disease and Stroke Statistics—2021 Update

A Report From the American Heart Association
Originally publishedhttps://doi.org/10.1161/CIR.0000000000000950Circulation. 2021;143:e254–e743

Abstract

Background:

The American Heart Association, in conjunction with the National Institutes of Health, annually reports 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 2021 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, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

Results:

Each of the 27 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, health care 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 e255

  • 1. About These Statistics e266

  • 2. Cardiovascular Health e270

  • Health Behaviors

  • 3. Smoking/Tobacco Use e291

  • 4. Physical Inactivity e307

  • 5. Nutrition e327

  • 6. Overweight and Obesity e351

  • Health Factors and Other Risk Factors

  • 7. High Blood Cholesterol and Other Lipids e367

  • 8. High Blood Pressure e380

  • 9. Diabetes e398

  • 10. Metabolic Syndrome e418

  • 11. Adverse Pregnancy Outcomes e439

  • 12. Kidney Disease e456

  • 13. Sleep e469

  • Cardiovascular Conditions/Diseases

  • 14. Total Cardiovascular Diseases e478

  • 15. Stroke (Cerebrovascular Diseases and Vascular Contributions to Brain Health) e498

  • 16. Congenital Cardiovascular Defects and Kawasaki Disease e541

  • 17. Disorders of Heart Rhythm e559

  • 18. Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies e590

  • 19. Subclinical Atherosclerosis e613

  • 20. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e626

  • 21. Cardiomyopathy and Heart Failure e649

  • 22. Valvular Diseases e665

  • 23. Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension e683

  • 24. Peripheral Artery Disease and Aortic Diseases e692

  • Outcomes

  • 25. Quality of Care e714

  • 26. Medical Procedures e728

  • 27. Economic Cost of Cardiovascular Disease e733

  • Supplemental Materials

  • 28. At-a-Glance Summary Tables e737

  • 29. Glossary e741

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 (HD), 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, 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, health care 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 HD, rhythm disorders, subclinical atherosclerosis, coronary HD [CHD], heart failure [HF], valvular HD, 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, do not smoke or use smokeless tobacco, eat a heart-healthy diet, keep blood pressure healthy, and learn about blood sugar and diabetes.1 AHA indicates American Heart Association; HDL, high-density lipoprotein; and LDL, low-density lipoprotein.

Each annual version of the Statistical Update undergoes revisions to include the newest nationally representative available 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. Below are a few highlights from this year’s Statistical Update. Please see each chapter for references and additional information.

Cardiovascular Health (Chapter 2)

  • The 5 US states with the highest health-adjusted life expectancy at birth include Hawaii, Minnesota, California, Connecticut, and Nebraska. The 5 US states with the lowest health-adjusted life expectancy at birth include West Virginia, Kentucky, Alabama, Oklahoma, and Louisiana.

  • High body mass index, high fasting plasma glucose, and smoking are the first, second, and third leading years lived with disability and injury risk factors in the United States in both 1990 and 2019, whereas smoking dropped from first to third leading years lived with disability and injury risk factor during this time period.

  • Smoking and high systolic BP remained the first and second leading years of life lost risk factors in the United States in both 1990 and 2019.

  • High systolic BP and smoking are the first and second leading years of life lost risk factors globally in 2019.

  • High fasting plasma glucose and high body mass index were the first and second leading years lived with disability and injury risk factors globally in 2019.

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 2.3% and 5.8%, respectively, in 2019.

  • 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 Native individuals and lesbian, gay, and bisexual populations.

  • Over the past 8 years, there has been a sharp increase in electronic cigarette use among adolescents, increasing from 1.5% to 27.4% between 2011 and 2019; electronic cigarettes are now the most commonly used tobacco product in this demographic.

  • Tobacco use was the second leading cause of disability-adjusted life-years in the United States in 2016. Globally, smoking accounted for 8.7 million deaths worldwide in 2019.

  • Tobacco 21 legislation was signed into law in December 2019, increasing the federal minimum age for sale of tobacco products from 18 to 21 years. In January 2020, the US Food and Drug Administration issued a policy prioritizing enforcement against the development and distribution of certain unauthorized flavored electronic cigarette products such as fruit and mint flavors (ie, any flavors other than tobacco and menthol).

Physical Inactivity (Chapter 4)

  • In a nationally representative sample of high school students in 2017, 26.1% reported achieving at least 60 minutes of daily physical activity.

  • In a nationally representative sample of adults in 2018, 24.0% reported participating in adequate leisure-time aerobic and muscle-strengthening activity to meet US recommendations for physical activity.

  • In a harmonization of 8 prospective studies using accelerometry to assess movement, over a median of 5.8 years of follow-up, the highest quartile of light and moderate to vigorous physical activity compared with the lowest quartile (least active) was associated with a lower risk of all-cause mortality. Time in sedentary behavior was associated with a higher risk of all-cause mortality compared with the lowest quartile (least sedentary).

Nutrition (Chapter 5)

  • According to NHANES (National Health and Nutrition Examination Survey; 2015–2016), <10% adults met the guidelines for whole grains (≥3 servings per day), whole fruits (≥2 cups per day), and nonstarchy vegetables (≥2.5 cups per day).

  • According to the AHA primary diet score, 47.8% of US adults had poor diet quality in 2015 to 2016. On the basis of the secondary score, 36.4% of US adults had poor diet quality in 2015 to 2016.

  • In a large primary prevention trial among patients with CVD risk factors, patients randomized to unrestricted-calorie Mediterranean-style diets supplemented with extra-virgin olive oil or mixed nuts had a ≈30% reduction in the risk of stroke, myocardial infarction, and death attributable to cardiovascular causes, without changes in body weight.

Overweight and Obesity (Chapter 6)

  • According to NHANES 2015 to 2018, among US adults ≥20 years of age, the age-adjusted prevalence of obesity was 39.9% in males and 41.1% in females; the prevalence of extreme obesity was 6.2% in males and 10.5% in females; the overall prevalence of obesity among youth 2 to 19 years of age was 19.0%.

  • In a study of 2625 participants with new-onset diabetes pooled from 5 longitudinal cohort studies, rates of total, CVD, and non-CVD mortality were higher among normal-weight people than among overweight participants and participants with obesity, with adjusted hazard ratios (HRs) of 2.08, 1.52, and 2.32, respectively.

  • In the Systolic Blood Pressure Intervention Trial (SPRINT), there was a J-shaped associated between body mass index and all-cause mortality and risk of stroke. An increased risk of stroke was also seen when participants with obesity were compared with normal-weight participants in the Copenhagen City Heart Study (HR, 1.4) and the Copenhagen General Population Study (HR, 1.1).

  • In a retrospective cohort study of individuals with a median follow-up of 3.9 years, patients in the bariatric surgery group had a cumulative incidence of major adverse cardiac events of 30.8% compared with 47.7% among matched patients who did not undergo bariatric surgery.

High Blood Cholesterol and Other Lipids (Chapter 7)

  • The Healthy People 2020 target is a mean population total cholesterol level of 177.9 mg/dL for adults, which had not been achieved among the population of US adults or in any race/ethnicity subgroup as of 2015 to 2018 NHANES data. Conversely, the Healthy People 2020 target of ≤13.5% for the proportion of adults with high total cholesterol ≥240 mg/dL has been achieved as of the combined period 2015 to 2018 for adults overall and all race-sex subgroups.

  • Long-term exposure to even modestly elevated cholesterol levels can lead to CHD later in life. In an analysis of time-weighted average exposures to low-density lipoprotein cholesterol (LDL-C) during young (18–39 years of age) versus later (≥40 years of age) adulthood among 36 030 participants from 6 US cohorts, CHD rates were significantly elevated among individuals who had young adult LDL-C ≥100 (versus <100) mg/dL, independently of later adult exposures (adjusted HR, 1.64). Specifically, compared with LDL-C <100 mg/dL, adjusted HRs were as follows: for LDL-C 100 to 129 mg/dL, 1.62; for LDL-C 130 to 159 mg/dL, 1.89; and for LDL-C ≥160 mg/dL, 2.03.

  • In a 20-year follow-up study, early initiation of statin treatment among 214 children with familial hypercholesterolemia was associated with a decrease in LDL-C by 32%, slowed progression of subclinical atherosclerosis, and lower cumulative incidence by 39 years of age of cardiovascular events compared with affected parents (0% versus 7% and 1% versus 26% of fatal and nonfatal cardiovascular events, respectively).

  • Among 5693 participants in PALM (Patient and Provider Assessment of Lipid Management), a nationwide registry of ambulatory community practices, females were less likely than males to receive statin dosing at the guideline-recommended intensity (36.7% versus 45.2%; P<0.001) and were more likely to not ever have been offered statin therapy despite being eligible (18.6% versus 13.5%) compared with males.

High Blood Pressure (Chapter 8)

  • Analysis of NHANES 1999 to 2002, 2007 to 2010, and 2015 to 2018 found large increases in hypertension awareness, treatment, and control (≈10%) within each race/ethnicity and sex subgroup except for Black females. Among Black females, levels of hypertension awareness, treatment, and control increased between 1999 to 2002 and 2007 to 2010 but decreased between 2007 to 2010 and 2015 to 2018.

  • With the use of 2017 guidelines from the American Academy of Pediatrics, analysis of data for children and adolescents 8 to 17 years of age (n=12 249) from NHANES 2003 to 2004 through NHANES 2015 to 2016 found that the prevalence of either elevated BP or hypertension (combined) significantly declined from 16.2% in 2003 to 2004 to 13.3% in 2015 to 2016 and the prevalence of hypertension declined from 6.6% to 4.5% in this age group.

  • In NHDS (National Hospital Discharge Survey) data compiled by the Centers for Disease Control and Prevention, chronic hypertension in pregnancy (defined as systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg either before pregnancy or up to the first 20 weeks during pregnancy) increased >13-fold between 1970 and 2010. Black women had a persistent 2-fold higher rate of chronic hypertension compared with White women over the 40-year period.

Diabetes (Chapter 9)

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

  • ▪ The age-adjusted prevalence of diagnosed diabetes in adults ≥18 years of age increased from 6.4% in 1999 to 2002 to 9.4% in 2013 to 2016. In contrast, the age-adjusted prevalence of undiagnosed diabetes was similar from 1999 to 2002 (3.1%) and 2013 to 2016 (2.6%).

  • ▪ Among adults with diagnosed diabetes in NHANES 2013 to 2016, 9.9% had a hemoglobin A1c ≥10.0%, and this was more prevalent among adults 18 to 44 years of age (16.3%) than adults ≥65 years of age (4.3%).

  • ▪ In NHIS (National Health Interview Survey) 2013 to 2017, adults with diabetes <65 years of age were more likely to report overall financial hardship from medical bills (41.1%) than adults with diabetes ≥65 years of age (20.7%). The prevalence of cost-related medication nonadherence was 34.7% and delayed medical care was 55.5% among adults with diabetes <65 years of age.

  • ▪ In 2016, of 154 health conditions evaluated, diabetes had the highest public insurance spending ($55.4 billion).

Metabolic Syndrome (Chapter 10)

  • Uncertainty remains concerning the definition of the obesity component of metabolic syndrome (MetS) in the pediatric population because it is age dependent. Therefore, use of body mass index percentiles and waist-height ratio has been recommended. According to the Centers for Disease Control and Prevention and FitnessGram standards for pediatric obesity, the prevalence of MetS in obese youth ranges from 19% to 35%.

  • On the basis of NHANES 2007 to 2014, the overall prevalence of MetS 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.

  • Each 1000-steps-per-day increase is associated with lower odds of having MetS (odds ratio, 0.90) in American men.

  • In a meta-analysis including 17 prospective longitudinal studies with 602 195 women and 15 945 cases of breast cancer, MetS was associated with increased risk of incident breast cancer in postmenopausal women (adjusted relative risk, 1.25) but significantly reduced breast cancer risk in premenopausal women (adjusted relative risk, 0.82). Further analyses showed that the association between MetS and increased risk of breast cancer was observed only among White and Asian women, whereas there was no association in Black women.

Adverse Pregnancy Outcomes (Chapter 11)

  • Adverse pregnancy outcomes (including hypertensive disorders of pregnancy, gestational diabetes, preterm birth, and small for gestational age at birth) occur in 10% to 20% of pregnancies.

  • According to a meta-analysis of individual participant data from 265 270 females from 39 European, North American, and Oceanic cohort studies, risk of adverse pregnancy outcomes was greater with higher categories of prepregnancy body mass index and greater degree of gestational weight gain, with an adjusted odds ratio of 2.51 for women with prepregnancy obesity and high gestational weight gain.

  • On the basis of a meta-analysis of 9 studies, gestational hypertension was associated with a 67% higher risk of subsequent CVD, and preeclampsia was associated with a 75% higher risk of subsequent CVD-related mortality.

  • Among 2 141 709 live-born singletons in the Swedish Birth Registry from 1973 to 1994 followed up through 2015 (maximum, 43 years of age), gestational age at birth was inversely associated with risk for premature CHD (adjusted HRs at 30–43 years of age versus full-term [39–41 weeks] births: for preterm [<37 weeks], 1.53; for early term [37–38 weeks], 1.19).

Kidney Disease (Chapter 12)

  • Overall prevalence of chronic kidney disease (estimated glomerular filtration rate <60 mL·min−1·1.73 m−2 or albumin-to-creatinine ratio ≥30 mg/g) was 14.8% (2013–2016).

  • Incidence of end-stage kidney disease in the United States is projected to increase 11% to 18% through 2030.

  • In NHANES 1999 to 2014, 34.9% of adults with chronic kidney disease used an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker.

  • Rates of stress testing among Medicare beneficiaries declined from 2008 to 2012, but rates were 5% to 15% higher for those with chronic kidney disease and end-stage kidney disease than for those without chronic kidney disease.

Sleep (Chapter 13)

  • Analysis of 2018 BRFSS (Behavioral Risk Factor Surveillance System) data indicated that the proportion of adults reporting inadequate sleep (<7 hours) was 35.4%. Older people (>65 years of age) were less likely to report sleeping <7 hours, and younger males (<45 years of age) were more likely to report sleeping <7 hours.

  • In the 2018 BRFSS, non-Hispanic Black people had the highest percentage of respondents reporting sleeping <7 hours per night (45.4%), whereas non-Hispanic White people had the lowest percentage (33.2%) of respondents reporting sleeping <7 hours.

  • A meta-analysis of 15 prospective studies observed a significant association between the presence of obstructive sleep apnea and the risk of cerebrovascular disease (HR, 1.94).

  • An analysis of the global prevalence and burden of obstructive sleep apnea estimated that 936 million males and females 30 to 69 years of age have mild to severe obstructive sleep apnea (apnea-hypopnea index ≥5) and 425 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 14)

  • On the basis of NHANES 2015 to 2018 data, the prevalence of CVD (comprising CHD, HF, stroke, and hypertension) in adults ≥20 years of age is 49.2% overall (126.9 million in 2018) and increases with age in both males and females. CVD prevalence excluding hypertension (CHD, HF, and stroke only) is 9.3% overall (26.1 million in 2018).

  • From the combination of estimates from NHANES, REGARDS (Reasons for Geographic and Racial Differences in Stroke), and randomized controlled trials for BP-lowering treatments, it was estimated that achieving the 2017 American College of Cardiology/AHA BP goals could prevent 3.0 million (uncertainty range, 1.1 million–5.1 million) CVD events (CHD, stroke, and HF) compared with current BP levels, but achieving the 2017 American College of Cardiology/AHA BP goals could also increase serious adverse events by 3.3 million (uncertainty range, 2.2 million–4.4 million).

  • The US IMPACT (International Model for Policy Analysis of Agricultural Commodities and Trade) Food Policy Model, a computer simulation model, projected that a national policy combining a 30% fruit and vegetable subsidy targeted to low-income Supplemental Nutrition Assistance Program recipients and a population-wide 10% price reduction in fruits and vegetables in the remaining population could prevent ≈230 000 deaths by 2030 and reduce the socioeconomic disparity in CVD mortality by 6%.

Stroke (Cerebrovascular Diseases and Vascular Contributions to Brain Health) (Chapter 15)

  • In a county-level study, stroke mortality rates among US adults 35 to 64 years of age increased from 14.7 per 100 000 in 2010 to 15.4 per 100 000 in 2016. Rates decreased among adults ≥65 years of age from 299.3 per 100 000 in 2010 to 271.4 per 100 000 in 2016.

  • In a meta-analysis of 35 studies (n=2 458 010 patients), perioperative or postoperative atrial fibrillation (AF) was associated with an increased risk of early stroke (odds ratio, 1.62) and later stroke (HR, 1.37). This risk was found in both patients undergoing noncardiac surgery (HR, 2.00) and those undergoing cardiac surgery (HR, 1.20).

  • An analysis of the NHIS demonstrated that awareness of stroke symptoms and signs among US adults improved from 2009 to 2014. In 2014, 68.3% of the survey respondents were able to recognize 5 common stroke symptoms, and 66.2% demonstrated knowledge of all 5 stroke symptoms and the importance of calling 9-1-1.

  • In a meta-analysis of 9 studies, subclinical or silent brain infarcts were associated with decline in cognitive function on the Mini-Mental State Examination score (standardized mean difference, −0.47). In the same meta-analysis, among 4 studies, subclinical or silent brain infarcts were associated with cognitive dysfunction on the Montreal Cognitive Assessment Scale (standardized mean difference, −3.36).

Congenital Cardiovascular Defects and Kawasaki Disease (Chapter 16)

  • In 2018, the age-adjusted death rate attributable to congenital cardiovascular defects in the United States was 0.9 per 100 000. The death rate was higher for males than females.

  • In a recent study, adults with congenital cardiovascular defects requiring hospital admission for HF demonstrated higher odds of longer length of stay, incident arrhythmias, and in-hospital mortality compared with adults with HF without congenital cardiovascular defects.

  • The incidence of Kawasaki disease appears to be rising worldwide, with potential contributions from improved recognition, more frequent diagnosis of incomplete Kawasaki disease, and true increasing incidence.

Disorders of Heart Rhythm (Chapter 17)

  • In 2018, 53 895 deaths had arrhythmias as the primary cause of death, and 564 182 included arrhythmia as one of the causes of death.

  • The prevalence of AF in the United States was estimated to be 5.2 million in 2010, increasing to 12.1 million in 2030. In the United States, 1.2 million people were newly diagnosed with AF in 2010. This number is projected to increase to 2.6 million by 2030.

  • The lifetime risk of AF has been estimated to be 1 in 3 among White people and 1 in 5 among Black people.

  • Hypertension accounts for the largest proportion of AF (≈22%), followed by obesity, smoking, cardiac disease, and diabetes.

  • A study examining public and private health insurer records from 1996 to 2016 reported that AF was 33rd in spending for health conditions with an estimated $28.4 billion in 2016 dollars, with an annualized rate of change of 3.4% during this period.

  • In a controlled trial randomizing alcohol drinkers with paroxysmal AF either to alcohol abstinence or to continue their usual alcohol consumption, AF recurred in 53% of the abstinence group and 73% of the control group. Compared with the control group, the abstinence group had a significantly longer duration without AF recurrence (HR, 0.55) and significantly lower AF burden (median percent time in AF, 0.5% versus 1.2%).

Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies (Chapter 18)

  • Sudden cardiac arrest and sudden cardiac death result from many different disease processes, each of which can have different risk factors. Among patients with out-of-hospital cardiac arrest (OHCA) resuscitated and hospitalized from 2012 to 2016, acute coronary syndrome and other cardiac causes accounted for the largest proportion of causes. Among patients with in-hospital cardiac arrest, respiratory failure was the most common cause.

  • Among 5869 autopsied subjects with sudden cardiac death, excluding cases with noncardiac causes of death, in Finland between 1998 and 2017, ischemic cardiac disease represented 4392 (74.8%) and nonischemic cardiac diseases represented 1477 (25.2%). Over time, the proportion of ischemic sudden cardiac death declined from 78.8% (1998–2002) to 72.4% (2013–2017).

  • According to multiple studies, sudden cardiac arrest is more common in males than in females. Females compared with males with OHCA are older, less likely to present with shockable rhythms, and less likely to collapse in public. Despite these factors that would reduce survival, females have equivalent or higher rates of survival to hospital discharge or to 30 days relative to males.

  • Incidence of emergency medical services–treated OHCA in people of any age is 76.5 individuals per 100 000 population according to the 2019 CARES (Cardiac Arrest Registry to Enhance Survival) registry, with >2-fold variation between states (range, 41.8–126.1). Survival after emergency medical services–treated OHCA was 10.6% in the 2019 CARES registry, with variation between states reporting data.

Subclinical Atherosclerosis (Chapter 19)

  • Among 1585 participants free of CHD and free of MetS, those who were obese had a higher prevalence of coronary artery calcification than individuals with a normal weight, with a prevalence ratio of 1.59.

  • A single-nucleotide-polymorphism genetic risk score for type 2 diabetes composed of 48 variants was associated with carotid plaque and atherosclerotic CVD events in ≈160 000 individuals, suggesting a causal role between genetic predisposition to type 2 diabetes and atherosclerotic CVD.

  • In overweight and obese children 6 to 13 years of age, greater nut consumption was associated with lower carotid intima-media thickness (β=0.135 mm) when controlled for confounders.

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

  • Data from the National Center for Health Statistics on trends in CHD death rates from 1999 to 2009 indicate disparities in the trends by rural-urban status. An overall 40% decline in the rate of CHD death was observed; however, the decline was greater in urban areas (large metropolitan: 42% decline; from 284 to 164 per 100 000 from 1999–2009; medium metropolitan: 40% decline; from 244 to 147 per 100 000) compared with rural areas (35% decline; from 266 to 173 per 100 000).

  • According to the Centers for Medicare & Medicaid Services Hospital Inpatient Quality Reporting Program data on 2363 hospitals in 2018, the average 30-day mortality after acute myocardial infarction was 13.6%, with higher mortality observed in rural hospitals (from 13.4% to 13.8% for the most urban to most rural hospitals).

  • The rapid increase in the population ≥65 years of age has resulted in a slowing of HD mortality. According to the Centers for Disease Control and Prevention WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiological Research) data from 2011 through 2017, a deceleration in the decline in HD mortality was observed with a <1% annualized decrease. The increase in the growth of the population ≥65 years of age, combined with the slowing of the decrease in HD mortality, resulted in an increase in the absolute number of HD deaths since 2011 (50 880 deaths; 8.5% total increase). However, the age-adjusted mortality for CHD continued to decline (2.7% annualized decrease) and the absolute number of CHD deaths declined (2.5% total decrease over the time period) between 2011 to 2017.

  • Among 366 103 Medicare fee-for-service beneficiaries eligible for cardiac rehabilitation in 2016, only 24.4% participated in cardiac rehabilitation; among those who participated, the mean number of days to initiation was 47.0, and 26.9% completed cardiac rehabilitation with ≥36 sessions. Participation decreased with increasing age and was lower in females, Hispanic individuals, Asian individuals, those eligible for dual Medicare/Medicaid coverage, and those with ≥5 comorbidities.

Cardiomyopathy and Heart Failure (Chapter 21)

  • The prevalence of HF continues to rise over time, with aging of the population. An estimated 6 million American adults ≥20 years of age had HF according to 2015 to 2018 data. Prevalence is higher in women than men ≥80 years of age; overall prevalence is especially high in both Black females and Black males.

  • 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 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 HD is most prevalent in Europe and North America. Valvular HD is more common in East Asia and Asia-Pacific countries.

Valvular Diseases (Chapter 22)

  • The incidence of valvular HD is 64 per 100 000 person-years, with aortic stenosis (47.2%), mitral regurgitation (24.2%), and aortic regurgitation (18.0%) contributing most of the valvular diagnoses.

  • In 1950, ≈15 000 Americans died of rheumatic fever/rheumatic HD compared with ≈3400 annually in the present era. Recent declines in mortality have been slowest in the South compared with other regions.

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

  • In 2016, there were an estimated ≈1 220 000 total venous thromboembolism cases in the United States.

  • According to 2018 data, ≈25 000 deaths (any mention) resulted from pulmonary hypertension.

  • Hospitalized patients are at particularly high risk of venous thromboembolism; asymptomatic deep vein thrombosis was associated with 3-fold greater risk of death among acutely ill hospitalized patients.

  • In the ARIC study (Atherosclerosis Risk in Communities), the presence of HF was associated with a 3-fold greater venous thromboembolism risk. The association was present for HF with both preserved and reduced ejection fraction.

Peripheral Artery Disease and Aortic Diseases (Chapter 24)

  • The lifetime risk (80-year horizon) of peripheral arterial disease was estimated at ≈19%, 22%, and 30% in White, Hispanic, and Black individuals, respectively, from pooled data from 6 US community-based cohorts.

  • A large-scale genome-wide association study in >31 000 peripheral artery disease cases and >211 000 controls from the Million Veterans Program and the UK Biobank identified 18 new peripheral arterial disease loci. Eleven of the loci were associated with disease in 3 vascular beds, including LDLR, LPA, and LPL, whereas 4 of the variants were specific for peripheral arterial disease (including variants in TCF7L2 and F5).

  • Patients with microvascular disease, defined as retinopathy, neuropathy, and nephropathy, were at increased risk for amputation (HR, 3.7), independently of traditional risk factors and prevalent peripheral arterial disease, among 135 674 patients in the Veterans Aging Cohort Study (enrollment 2003–2014).

  • Between 1999 and 2016, deaths resulting from ruptured thoracic aortic aneurysm and abdominal aortic aneurysm declined significantly from 5.5 to 1.8 million and 26.3 to 7.9 per million, respectively, according to US National Vital Statistics data.

Quality of Care (Chapter 25)

  • Among hospitals that care for Medicare fee-for-service beneficiaries, the implementation of hospital readmission reduction programs for acute myocardial infarction was associated with a reduction in 30-day postdischarge mortality.

  • For HF, the Hospital Readmissions Reduction Program was associated with a reduction in 1-year risk adjusted readmission rate.

  • Higher quality of care for OHCA is associated with an increase in adjusted survival to discharge and adjusted rates of favorable neurological outcome.

Medical Procedures (Chapter 26)

  • Data from the Society of Thoracic Surgeons Congenital Heart Surgery Database indicate that a total of 123 777 congenital heart surgeries were performed from January 2015 to December 2018 and that delayed sternal closure was the most common primary procedure.

  • In 2019, 3552 heart transplantations were performed in the United States, the most ever.

Economic Cost of Cardiovascular Disease (Chapter 27)

  • The average annual direct and indirect cost of CVD in the United States was an estimated $363.4 billion in 2016 to 2017.

  • The estimated direct costs of CVD increased from $103.5 billion in 1996 to 1997 to $216.0 billion in 2016 to 2017.

  • By event type, hospital inpatient stays accounted for the highest direct cost ($96.2 billion) in 2016 to 2017.

Conclusions

The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on HD and stroke in the United States to provide the most current information available in the Statistical Update. The 2021 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

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

Debra G. Heard, PhD, AHA Consultant

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

Acknowledgments

The Writing Group wishes to thank their colleagues Lucy Hsu and Michael Wolz at the National Heart, Lung, and Blood Institute; Celine Barthelemy and Nikki DeCleene 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 2021 American Heart Association (AHA) Statistical Update uses updated language surrounding race and ethnicity to honor the people belonging to each group. Instead of referring to a specific group with only the name of their race or ethnicity, we have identified each race or ethnic classification with terms such as “Asian people,” “Black adults,” “Hispanic youth,” “White females,” or similar terms.

As the AHA continues its focus on health equity to address structural racism, we are working actively to reconcile language used in previously published data sources and studies as we compile this information in the annual Statistical Update. We strive to use the racial and ethnic terms from the original data sources or published studies (mostly from the past 5 years), which may not be as inclusive as the terms now used in 2021. As style guidelines for scientific writing evolve, they will serve as guidance for data sources and publications and how they are cited in future Statistical Update publications.

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 email .

The American Heart Association requests that this document be cited as follows: Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang N-Y, 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—2021 update: a report from the American Heart Association. Circulation. 2021;143:e254–e743. doi: 10.1161/CIR.0000000000000950

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.”

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Reference

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 29 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, mortality, YLL, and YLD

  • 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 from data collected from 2015 to 2018. These are applied to census population estimates for 2018. 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 be evaluated only by comparing prevalence rates estimated from surveys conducted in different years.

In the 2021 Statistical Update, there is an emphasis on social determinants of health that are built across the various chapters, and global estimates are provided when 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 LDL-C and diabetes. NHANES 2015 to 2018 data are used to present estimates of the percentage of people with overweight, obesity, and high TC and HDL-C. BRFSS 2018 data are used for the prevalence of sleep issues. NHIS 2018, BRFSS 2017 and 2018, and NYTS 2018 data are used for the prevalence of cigarette smoking. The prevalence of physical inactivity is obtained from 2017 YRBSS and 2017 and 2018 NHIS.

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 2018 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 21 (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 20183 in the computation of morbidity data. CDC/NCHS population estimates4 for 2018 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.5 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 NAMCS6 and 2016 NHAMCS7 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.8 Effective with mortality data for 1999, ICD-10 is used.9 Beginning in 2016, ICD-10-CM is used for hospital inpatient stays and ambulatory care visit data.10

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.11 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 2018. For disease and risk factor prevalence, most rates in this report are calculated from the 2015 to 2018 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US resident population on July 1, 2018, 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 2016 to 2017.

Cardiovascular Disease

For data on hospitalizations, primary provider office visits, and mortality, total CVD is defined according to ICD codes given in Chapter 14 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. The study seeks to produce consistent and comparable estimates of population health over time and across locations, including summary metrics such as disability-adjusted life years and HALE. Results are made available to policy makers, researchers, governments, and the public with the overarching goals of improving population health and reducing health disparities.

GBD 2019, the study’s most recent iteration, was produced by the collective efforts of >5000 researchers in >150 countries. Estimates were produced for 369 diseases and injuries and 87 risk factors. Detailed methods and results can be found via the study’s online data visualization tools and across a range of peer-reviewed scientific research articles that can be found cited in this publication.

During each annual cycle of the GBD study, population health estimates are reproduced for the full-time series. For GBD 2019, estimates were produced for 1990 to 2019 for 204 countries and territories, stratified by age and sex, with subnational estimates made available for an increasing number of countries. Improvements in statistical and geospatial modeling methods and the addition of new data sources may 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 2019 resources, data visualizations, and most recent publications, please visit the study’s website.12–14

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 and Health. 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 Epidemiological Research
CHDcoronary heart disease
CHSCardiovascular Health Study
CVDcardiovascular disease
EDemergency department
FHSFramingham Heart Study
GBDGlobal Burden of Disease Study
GCNKSSGreater Cincinnati/Northern Kentucky Stroke Study
GWTGGet With The Guidelines
HALEhealthy life expectancy
HBPhigh blood pressure
HCUPHealthcare Cost and Utilization Project
HDL-Chigh-density lipoprotein cholesterol
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
LDL-Clow-density lipoprotein cholesterol
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
NYTSNational Youth Tobacco Survey
TCtotal cholesterol
USRDSUnited States Renal Data System
WHOWorld Health Organization
YLDyears lived with disability and injury
YLLyears of life lost
YRBSSYouth Risk Behavior Surveillance System

References

2. Cardiovascular Health

See Tables 2-1 through 2-10 and Charts 2-1 through 2-5

In 2010, the AHA released an Impact Goal that included 2 objectives that would guide organizational priorities over the next decade: “By 2020, to improve the cardiovascular health of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.”1

Chart 2-1.

Chart 2-1. Prevalence estimates of poor, intermediate, and ideal cardiovascular health (CVH) for each component of CVH among US children 12 to 19 years of age and US adults ≥20 years of age, 2015 to 2016 and 2017 to 2018. *2015 to 2016 data for both age groups for healthy diet score and diabetes and for 12 to 19 years of age for physical activity. All other data are from 2017 to 2018. Data collection methodology for physical activity was changed in 2017 to 2018 for participants <18 years of age, resulting in an inability to estimate prevalence of ideal physical activity levels in this age group during this cycle. †Categories defined by either fasting plasma glucose or hemoglobin A1c on the basis of data availability. Prevalence estimates for adults ≥20 years of age are age adjusted. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016 and 2017 to 2018.54

The concept of CVH was introduced in this goal and characterized by 7 components (Life’s Simple 7)2 that include health behaviors (diet quality, PA, smoking) and health factors (blood cholesterol, BMI, BP, blood glucose). For an individual to have ideal CVH overall, they must have an absence of clinically manifest CVD and the simultaneous presence of optimal levels of all 7 CVH components, including abstinence from smoking, a healthy diet pattern, sufficient PA, normal body weight, and normal levels of TC, BP, and FPG (in the absence of medication treatment; Table 2-1). Because ideal CVH is rare, the distribution of the 7 CVH components is also described with the use of the categories poor, intermediate, and ideal.1Table 2-1 provides the specific definitions for these categories for each CVH component in both adults and youth.

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
PA
  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
BP
  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§
  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; BP, blood pressure; CVH, cardiovascular health; DBP, diastolic blood pressure; ellipses (…), data not available; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin or hemoglobin A1c; PA, physical activity; 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 PA to maintain normal body weight.

‡ In the context of a healthy dietary pattern that is consistent with a DASH (Dietary Approaches to Stop Hypertension)–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 with a continuous American Heart Association diet score, scaled from 0 to 100 (see Chapter 5, 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 to reflect this addition.

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

From 2011 to 2020, this chapter in the annual Statistical Update has published national prevalence estimates for CVH to inform progress toward improvements in the prevalence of CVH. This year, updates to this chapter include prevalence estimates for components of CVH for which newly released NHANES data from 2017 to 2018 were available. New additions this year also include 10-year differences in the leading causes and risk factors for YLDs and YLLs, which highlight the influence of the components of CVH on premature death and disability in populations.

Relevance of Ideal CVH

  • 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, IHD mortality, CVD, and HF; with subclinical measures of atherosclerosis such as carotid IMT, arterial stiffness, and CAC prevalence and progression; with physical functional impairment and frailty; with cognitive decline and depression; and with longevity.6–12 Similar relationships have also been seen in non-US populations.6,7,13–23

  • A large Hispanic/Latino cohort study in the United States confirmed the associations between CVD and status of CVH components in this population and found that the levels of CVH components compared favorably with existing national estimates; however, some of the associations varied by sex and heritage.7

  • A study of Black people 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.8

  • Ideal health behaviors and ideal health factors are each independently associated with lower CVD risk in a stepwise fashion; across any level of health behaviors, health factors are associated with incident CVD, and conversely, across any level of health factors, health behaviors are associated with incident CVD.24

  • 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 leading risk factor related to overall disease burden was suboptimal diet, followed by tobacco smoking, high BMI, raised BP, high FPG, and physical inactivity.25

  • A 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.26

  • The adjusted PAFs for CVD mortality for individual components of CVH have been reported as follows27:

    • — 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

  • Several studies have been published in which investigators have assigned individuals a CVH score ranging from 0 to 14 on the basis of the sum of points assigned to each component of CVH (poor=0, intermediate=1, ideal=2 points). With 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.28

  • 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.29

  • Better CVH as defined by the AHA is associated with lower incidence of HF,6,8,9,11,19 less subclinical vascular disease,12,17,20,30,31 better global cognitive performance and cognitive function,18,32,33 lower hazard of subsequent dementia,34 lower prevalence10 and incidence35 of depressive symptoms, lower loss of physical functional status,36 longer leukocyte telomere length,37 less ESRD,38 less pneumonia, less chronic obstructive pulmonary disease,39 less VTE/PE,40 lower prevalence of aortic sclerosis and stenosis,41 lower risk of calcific aortic valve stenosis,42 better prognosis after MI,43 lower risk of AF,44 and lower odds of having elevated resting heart rate.45 In addition, a study among a sample of Hispanic/Latino people residing in the United States reported that greater positive psychological functioning (dispositional optimism) was associated with higher CVH scores as defined by the AHA.46 A study in college students found that both handgrip strength and muscle mass are positively associated with greater numbers of ideal CVH components,47 and a cross-sectional study found that greater cardiopulmonary fitness, upper-body flexibility, and lower-body muscular strength are associated with better CVH components in perimenopausal females.48 Furthermore, studies demonstrate that higher quality of life scores are associated with better CVH metrics,49 providing additional evidence to support the benefits of ideal CVH on general health and quality of life.

  • According to 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.50 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.51 Having more ideal CVH components in middle age is also associated with lower non-CVD and CVD health care costs in later life.52 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.52

  • A report from a large, ethnically diverse insured population 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 health care expenditure, respectively, than those with 0 to 2 ideal health components.53

CVH: Prevalence (NHANES 2015–2016 and 2017–2018)

(See Table 2-2 and Charts 2-1 through 2-3)

  • The national prevalence estimates for children (12–19 years of age) and adults (≥20 years of age) who meet ideal, intermediate, and poor levels of each of the 7 CVH components are displayed in Chart 2-1.54 The most current estimates at time of publication were based on data from NHANES 2017 to 2018 for smoking, BMI, PA (for adults), TC, and BP and data from NHANES 2015 to 2016 for PA (for children), diet, and diabetes status. NHANES 2017 to 2018 survey changed the PA assessments for children, so the PA status for children was updated through 2016 only.

  • 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 2-1), 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 >86% for smoking, BP, and diabetes components (unpublished AHA tabulation).

  • Among US adults (Chart 2-1), the lowest prevalence of ideal levels for CVH components is <1% for the Healthy Diet Score in adults ≥20 years of age. The highest prevalence of ideal levels for a CVH component is for smoking (79.8% of adults report never having smoked or being a former smoker who has quit for >12 months). In 2017 to 2018, 52.4% of adults 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 2015 to 2016 and 2017 to 2018 are displayed in Table 2-2.

  • In 2015 to 2018, all individual components of CVH among adults were highest in the youngest age groups (20–39 years of age) and were lowest in the oldest age group (≥60 years of age), except the Healthy Diet Score, for which prevalence of ideal levels was highest in older adults but still <1% according to the 2015 to 2016 NHANES data.

  • Chart 2-2 displays the unadjusted prevalence estimates of ideal levels of CVH components for the population of US children (12–19 years of age) by race/ethnicity.

    • — Majority of US children 12 to 19 years of age met ideal criteria for smoking (93.4%–97.4%), BP (80.1%–89.6%), and TC (73.4%–80.0%) in 2017 to 2018 across race/ethnicity subgroups.

    • — Majority of US children 12 to 19 years of age met ideal criteria for diabetes (73.6%–88.0%) in 2015 to 2016 across race/ethnicity groups.

    • — Of US children 12 to 19 years of age, 46.8% to 76.2% met ideal criteria for BMI in 2017 to 2018, whereas only 23.8% to 27.8% of US children met ideal criteria for PA in 2015 to 2016 across race/ethnicity categories.

    • — Few US children 12 to 19 years of age (<1%) met ideal criteria for Healthy Diet Score in 2015 to 2016 across all race/ethnicity groups.

  • Chart 2-3 displays the unadjusted prevalence estimates of ideal levels of CVH components for the population of US adults ≥20 years of age by race/ethnicity.

    • — Majority of US adults ≥20 years of age met ideal criteria for smoking (75.9%–90.4%) in 2017 to 2018 across race/ethnicity subgroups.

    • — Fewer than a quarter to a little more than half of US adults ≥20 years of age met ideal criteria for BMI (15.2%–50.5%), TC (49.8%–57.7%), PA (30.4%–42.7%), and BP (31.5%–44.4%) in 2017 to 2018 across race/ethnicity groups.

    • — Of US adults ≥20 years of age, 42.0% to 59.7% met ideal criteria for diabetes in 2015 to 2016 across race/ethnicity categories.

    • — Few US adults ≥20 years of age (0.1%–1.6%) met ideal criteria for Healthy Diet Score in 2015 to 2016 across all race/ethnicity groups.

Chart 2-2.

Chart 2-2. Prevalence estimates of poor, intermediate, and ideal cardiovascular health (CVH) for each component of CVH by race/ethnicity among US children 12 to 19 years, 2015 to 2016 and 2017 to 2018. MA indicates Mexican American; NH, non-Hispanic; NHB, non-Hispanic Black; and NHW, non-Hispanic White. *Data from 2015 to 2016. All other data are from 2017 to 2018. †Categories defined by either fasting plasma glucose or hemoglobin A1c on the basis of data availability. Prevalence estimates for adults ≥20 years of age are age adjusted. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016 and 2017 to 2018.54

Chart 2-3.

Chart 2-3. Age-adjusted prevalence estimates of poor, intermediate, and ideal cardiovascular health (CVH) for each component of CVH by race/ethnicity among US adults ≥20 years of age, 2015 to 2016 and 2017 to 2018. MA indicates Mexican American; NH, non-Hispanic; NHB, non-Hispanic Black; and NHW, non-Hispanic White. *Data from 2015 to 2016. All other data are from 2017 to 2018. †Categories defined by either fasting plasma glucose or hemoglobin A1c on the basis of data availability. Source: Unpublished American Heart Association tabulation using National Health and Nutrition Examination Survey, 2015 to 2016 and 2017 to 2018.54

Table 2-2. Prevalence of Ideal CVH and Its Components in the US Population in Selected Age Strata: NHANES 2015 to 2016 and 2017 to 2018

NHANES yearsAge 12–19 yAge ≥20 y*Age 20–39 yAge 40–59 yAge ≥60 y
Ideal CVH factors
  TC2017–201877.2 (1.7)52.4 (1.5)74.0 (1.8)44.8 (2.6)25.5 (1.5)
  BP2017–201889.1 (1.3)40.8 (1.4)61.6 (1.9)34.0 (2.4)15.1 (1.3)
  Diabetes2015–201686.2 (1.4)58.4 (1.4)79.3 (1.1)51.2 (2.5)32.4 (1.6)
Ideal health behaviors
  PA2017–2018NA38.3 (1.7)48.4 (2.3)33.9 (2.2)29.3 (2.6)
  Smoking2017–201895.7 (1.1)79.8 (1.3)74.3 (2.2)80.1 (1.7)87.8 (1.0)
  BMI2017–201863.4 (1.8)26.4 (1.3)33.6 (2.1)22.7 (2.0)21.9 (1.1)
  4 or 5 Healthy diet goals met2015–20160.0 (0.0)0.3 (0.1)0.1 (0.1)0.1 (0.1)0.7 (0.3)
  F&V ≥4.5 cups/d2015–20163.7 (0.9)10.2 (0.6)7.8 (0.9)11.1 (1.4)13.8 (1.1)
  Fish ≥2 svg/wk2015–20167.6 (1.0)18.0 (1.7)15.9 (2.5)19.3 (2.3)18.7 (1.6)
  Sodium <1500 mg/d2015–20160.6 (0.3)0.7 (0.2)0.8 (0.3)0.9 (0.4)0.2 (0.1)
  SSB <450 kcal/wk2015–201640.4 (2.6)53.3 (1.7)47.7 (2.9)51.6 (2.3)66.5 (2.7)
  Whole grains ≥3 one-ounce svg/d2015–20166.8 (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/wk2015–201636.7 (2.4)52.4 (1.7)48.9 (3.0)54.9 (2.3)54.1 (1.8)
  Processed meats ≤2 svg/wk2015–201639.2 (2.8)44.0 (0.9)45.4 (1.1)44.0 (1.7)41.9 (2.6)
  SFat <7% total kcal2015–20164.5 (1.0)8.4 (0.5)8.8 (1.1)8.9 (0.7)6.8 (0.9)

Values are percent (standard error).

BMI indicates body mass index; BP, blood pressure; CVH, cardiovascular health; F&V, fruits and vegetables; NA, not available; NHANES, National Health and Nutrition Examination Survey; PA, physical activity; SFat, saturated fat; SSB, sugar-sweetened beverage; 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 2015 to 2016 and 2017 to 2018.54

CVH: Trends Over Time

(See Charts 2-4 and 2-5)

  • The trends in prevalence of meeting ideal criteria for the individual components of CVH from 1999 to 2000 to 2017 to 2018 (for diet, trends from 2003–2004 through 2015–2016) are shown in Chart 2-4 for children (12–19 years of age) and in Chart 2-5 for adults (≥20 years of age).

    • — Among children 12 to 19 years of age from 1999 to 2000 to 2017 to 2018, the prevalence of meeting ideal criteria for smoking and BP has consistently improved, increasing from 76.4% to 95.7% for nonsmoking and from 83.6% to 89.1% for ideal BP. For ideal TC, the prevalence increased from 72.0% to 77.2%. However, a decline in prevalence of ideal levels was observed for BMI, from 69.8% in 1999 to 2000 to 60.1% in 2015 to 2016, although it rebounded slightly to 63.3% in 2017 to 2018.

    • — From 1999 to 2000 to 2015 to 2016, declines in prevalence of ideal levels were observed for PA (38.4% to 25.4%) and diabetes (92.4% to 86.2%) among children.

    • — Among adults, from 1999 to 2000 to 2017 to 2018, the prevalence of meeting ideal criteria for smoking, TC, and BP increased. For example, the prevalence of being a never smoker or having quit ≥1 year prior increased from 72.9% to 79.8%. Over the 18-year period, the prevalence of meeting criteria for ideal TC increased from 45.1% to 52.4%. However, declines in prevalence of ideal levels were observed for PA (from 40.2% to 38.3%) and BMI (from 36.3% to 26.4%) among adults during this period.

    • — Similar to trends observed in children, a decline in prevalence of ideal levels was observed for diabetes among adults, from 69.1% in 1999 to 2000 to 58.4% in 2015 to 2016.

Chart 2-4.

Chart 2-4. Trends in prevalence (unadjusted) of meeting ideal criteria for individual components of cardiovascular health (CVH) among US children 12 to 19 years of age, 1999 to 2000 through 2017 to 2018. Error bars represent 95% CI. Data for the Healthy Diet Score, based on a 2-day average intake, were available only for the 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, 2011 to 2012, 2013 to 2014, and 2015 to 2016 National Health and Nutrition Examination Survey (NHANES) cycles at the time of this analysis. Data on diet and diabetes were not available for NHANES 2017 to 2018 at the time of these analyses. BMI indicates body mass index; BP, blood pressure; and PA, physical activity. *Because of changes in the PA 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. Data collection methodology for PA was changed in 2017 to 2018 for participants <18 years of age, resulting in an inability to estimate prevalence of ideal PA levels in this age group during this cycle. Source: Unpublished American Heart Association tabulation using NHANES, 1999 to 2000 through 2017 to 2018.54

Chart 2-5.

Chart 2-5. 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 2017 to 2018. Error bars represent 95% CI. Data for the Healthy Diet Score, based on a 2-day average intake, were available only for the 2003 to 2004, 2005 to 2006, 2007 to 2008, 2009 to 2010, 2011 to 2012, 2013 to 2014, and 2015 to 2016 National Health and Nutrition Examination Survey (NHANES) cycles at the time of this analysis. Data on diet and diabetes were not available for NHANES 2017 to 2018 at the time of this analysis. BMI indicates body mass index; BP, blood pressure; and PA, physical activity.*Because of changes in the PA questionnaire between NHANES cycles 1999 to 2006 and 2007 to 2018, 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 2017 to 2018.54

Trends in Risk Factors and Causes for YLL and YLD in the United States: 1990 to 2019

(See Tables 2-3 through 2-6)

  • The leading risk factors for YLLs from 1990 to 2019 in the United States are presented in Table 2-3.

    • — Smoking and high SBP remained the first and second leading YLL risk factors in both 1990 and 2019. Age-standardized rates of YLL attributable to smoking declined by 46.4%, whereas age-standardized rates attributable to high SBP declined 45.8%.

    • — From 1990 to 2019, YLLs caused by drug use rose from 18th to 5th leading YLL risk factor with a 242.3% increase in the age-standardized YLL rate.

  • The leading causes of YLLs from 1990 to 2019 in the United States are presented in Table 2-4.

    • — IHD and tracheal, bronchus, and lung cancer were the first and second leading YLL causes in both 1990 and 2019. Age-standardized YLL rates attributable to IHD declined 50.9%, whereas age-standardized YLL rates resulting from tracheal, bronchus, and lung cancer declined 36.1%.

    • — From 1990 to 2019, opioid use disorders rose from 46th to 4th leading YLL cause with a 799.2% increase in the age-standardized YLL rate. Type 2 diabetes also rose from 12th to 6th leading YLL cause, whereas Alzheimer disease and other dementias also rose from 15th to 7th leading YLL cause.

    • — The leading risk factors for YLDs from 1990 to 2019 in the United States are presented in Table 2-5.

    • — High BMI, high FPG, and smoking are among the first, second, and third leading YLD risk factors in both 1990 and 2019, with high BMI and high FPG rising in ranking while smoking dropped from the first to third leading YLD risk factor during this time period. Age-standardized YLD rates attributable to smoking declined by 25.8%, while age-standardized rates attributable to high BMI and high FPG increased by 44.4% and 47.4%, respectively, between 1990 and 2019.

  • The leading causes of YLDs from 1990 to 2019 in the United States are presented in Table 2-6.

    • — Low back pain and other musculoskeletal disorders were the first and second leading causes of YLDs in both 1990 and 2019. The age-standardized rates of YLD attributable to low back pain decreased 12.5%, whereas age-standardized YLD rates for other musculoskeletal disorders increased 44.2%.

    • — From 1990 to 2019, type 2 diabetes rose from the ninth to third leading YLD cause with a 55.8% increase in the age-standardized YLD rates.

    • — Opioid use disorders rose from 16th to 4th leading YLD cause between 1990 and 2019 with a 288.7% increase in age-standardized rates of YLD.

Table 2-3. The Leading 20 Risk Factors of YLL and Death in the United States: Rank, Number, and Percentage Change, 1990 and 2019

Risk factors for disabilityYLL rank (for total number)Total No. of YLLs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)Corresponding total No. of deaths, in thousands (95% UI)Corresponding percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLLsAge-standardized YLL rate19902019Total No. of deathsAge-standardized death rate
Smoking1111 005.06 (10 692.42 to 11 351.22)10 371.03 (10 ,017.19 to 10 728.28)−5.76% (−8.46% to −2.93%)−46.43% (−47.91% to −44.85%)515.41 (496.77 to 537.03)527.74 (505.55 to 550.83)2.39% (−1.3% to 6.28%)−42.21% (−44.18% to −40.15%)
High SBP228466.11 (7465.95 to 9424.27)7815.63 (6814.38 to 8821.87)−7.68% (−13.09% to −2.58%)−45.76% (−48.82% to −42.81%)503.63 (425.60 to 573.56)495.20 (407.47 to 574.65)−1.67% (−9.73% to 6.05%)−45.94% (−49.57% to −42.07%)
High BMI434994.23 (3131.76 to 6877.86)7778.57 (5416.09 to 9912.24)55.75% (41.31% to 80.47%)−9.18% (−17.75% to 5.86%)232.16 (138.00 to 334.08)393.86 (257.61 to 528.44)69.65% (52.54% to 98.96%)−5.82% (−15.3% to 10%)
High FPG544664.81 (3563.73 to 6006.04)7121.62 (5548.50 to 9006.14)52.67% (37.87% to 68%)−12.25% (−20.59% to −3.79%)263.41 (193.27 to 355.67)439.38 (320.11 to 582.66)66.81% (48.24% to 85.48%)−8.01% (−17.9% to 2.09%)
Drug use185999.47 (899.54 to 1135.28)4265.41 (4080.78 to 4494.41)326.77% (277.64% to 372.57%)242.34% (202.34% to 280.43%)24.76 (22.26 to 27.73)104.74 (100.39 to 109.98)323.09% (280.5% to 364.71%)214.02% (181.7% to 245.57%)
Alcohol use662708.90 (2327.61 to 3129.89)3936.71 (3457.94 to 4524.58)45.33% (30.7% to 60.18%)−5.97% (−14.74% to 2.75%)76.48 (61.08 to 93.37)136.66 (115.68 to 162.66)78.69% (54.74% to 108.25%)6.66% (−6.18% to 22.33%)
High LDL cholesterol376291.91 (5210.65 to 7354.85)3863.72 (3077.21 to 4730.88)−38.59% (−43.38% to −34.18%)−63.6% (−66.17% to −61.13%)353.09 (267.44 to 443.65)226.34 (158.85 to 304.37)−35.9% (−43.1% to −29.38%)−64.86% (−68.02% to −61.77%)
Kidney dysfunction782138.32 (1781.84 to 2527.38)3159.52 (2795.42 to 3536.01)47.76% (37.73% to 60.92%)−13.36% (−19.3% to −5.75%)138.81 (111.85 to 167.70)214.74 (182.32 to 248.84)54.71% (43.24% to 69.01%)−15% (−20.89% to −6.95%)
Diet low in whole grains991897.21 (868.61 to 2445.35)1778.79 (855.23 to 2258.78)−6.24% (−10% to 0.74%)−44.83% (−47.05% to −40.69%)103.24 (46.57 to 133.79)102.25 (48.18 to 131.55)−0.96% (−5.31% to 6.17%)−45.32% (−47.42% to −41.37%)
Low temperature13101320.06 (1079.50 to 1579.76)1734.12 (1488.09 to 1989.52)31.37% (21.84% to 42.8%)−28.03% (−33.6% to −21.47%)92.53 (76.50 to 108.86)123.09 (104.13 to 141.28)33.02% (24.01% to 42.4%)−28.1% (33.15% to 22.91%)
Diet low in legumes12111471.67 (348.59 to 2464.41)1299.03 (337.88 to 2145.69)−11.73% (−15.97% to 2.02%)−48.26% (−50.62% to −39.91%)80.91 (20.30 to 134.49)76.84 (19.83 to 126.33)−5.03% (−10.1% to 8.8%)−48.05% (−50.45% to −41.09%)
Diet high in red meat16121258.35 (677.77 to 1830.45)1268.70 (754.94 to 1787.30)0.82% (−7.68% to 16.14%)−40.06% (−45.03% to −30.7%)59.84 (31.13 to 88.85)65.65 (37.01 to 94.39)9.71% (−0.52% to 29.65%)−38.55% (−44.31% to −27.11%)
Diet high in trans fatty acids14131311.91 (77.03 to 1776.96)1097.24 (55.44 to 1490.02)−16.36% (−24.34% to −12.35%)−50.97% (−55.84% to −48.6%)71.37 (4.33 to 97.34)64.39 (3.44 to 88.07)−9.78% (−18.55% to −4.86%)−50.56% (−55.32% to −48.06%)
Diet high in processed meat1914850.40 (283.64 to 1366.73)969.35 (405.97 to 1459.61)13.99% (−0.22% to 53.8%)−32.69% (−41.36% to −9.36%)42.16 (13.90 to 69.60)50.90 (20.97 to 78.62)20.71% (5.93% to 59.18%)−32.15% (−40.76% to −9.05%)
Ambient particulate matter pollution8152001.60 (842.72 to 3490.50)931.95 (526.95 to 1361.42)−53.44% (−76.57% to 3.52%)−71.21% (−84.9% to −39.42%)95.26 (37.62 to 171.26)47.79 (26.06 to 71.53)−49.84% (−75.93% to 18.1%)−71.29% (−85.9% to −33.4%)
Diet high in sodium2416574.46 (36.43 to 1999.45)914.24 (61.08 to 2622.57)59.15% (25.57% to 270.02%)−4.75% (−25.72% to 132.21%)31.62 (2.16 to 113.50)48.50 (3.26 to 151.35)53.38% (23.18% to 208.55%)−13.04% (−30.53% to 82.94%)
Low birth weight10171512.98 (1436.65 to 1601.27)853.24 (778.57 to 935.91)−43.61% (−49.31% to −37.44%)−38.47% (−44.69% to −31.75%)17.04 (16.18 to 18.03)9.61 (8.77 to 10.54)−43.62% (−49.32% to −37.46%)−38.49% (−44.71% to −31.77%)
Short gestation11181492.43 (1415.76 to 1577.76)830.26 (756.11 to 909.70)−44.37% (−49.91% to −38.33%)−39.3% (−45.36% to −32.72%)16.81 (15.94 to 17.77)9.35 (8.51 to 10.24)−44.38% (−49.92% to −38.35%)−39.32% (−45.37% to −32.74%)
Secondhand smoke17191072.52 (858.49 to 1288.00)765.32 (597.81 to 943.60)−28.64% (−35.48% to −21.24%)−58.57% (−62.38% to −54.53%)44.43 (35.48 to 53.61)35.58 (27.27 to 44.12)−19.92% (−28.44% to −10.64%)−55.34% (−59.81% to −50.32%)
Diet low in fruits2120845.55 (505.63 to 1141.76)745.10 (463.85 to 1006.64)−11.88% (−21.92% to 0.05%)−47.98% (−53.6% to −41.37%)42.79 (25.00 to 57.89)40.17 (24.61 to 54.38)6.13% (−18.07% to 9.22%)−47.6% (−53.99% to −39.31%)

BMI indicates body mass index; FPG, fasting plasma glucose; LDL, low-density lipoprotein; SBP, systolic blood pressure; UI, uncertainty interval; and YLLs, years of life lost to premature mortality.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.66 Printed with permission. Copyright © 2020, University of Washington.

Table 2-4. The Leading 20 Causes of YLL and Death in the United States: Rank, Number, and Percent Change, 1990 and 2019

Diseases and injuriesYLL rank (for total number)Total No. of YLLs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)Corresponding total No. of deaths, in thousands (95% UI)Corresponding percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLLsAge-standardized YLL rate19902019Total No. of deathsAge-standardized death rate
IHD1110 181.09 (9690.92 to 10 439.15)8651.61 (8081.02 to 9124.13)−15.02% (−17.54% to −11.72%)−50.89% (−52.28% to −48.96%)604.09 (558.11 to 627.32)557.65 (496.86 to 594.41)−7.69% (−11.14% to −3.43%)−49.86% (−51.39% to −47.6%)
Tracheal, bronchus, and lung cancer223559.62 (3479.49 to 3617.41)4124.65 (3950.45 to 4261.93)15.87% (11.75% to 19.93%)−36.1% (−38.35% to −33.86%)156.26 (151.01 to 159.34)206.20 (193.72 to 214.28)31.96% (26.46% to 37.09%)−26.83% (−29.74% to −24.01%)
Chronic obstructive pulmonary disease431592.74 (1505.38 to 1778.28)3100.42 (2620.31 to 3305.63)94.66% (63.07% to 109.95%)11.21% (−6.25% to 19.76%)90.48 (83.71 to 103.20)195.83 (161.22 to 212.29)116.42% (72.76% to 137.51%)21.67% (−2.03% to 33%)
Opioid use disorders464219.00 (209.51 to 229.51)286.80 (2182.91 to 2418.61)944.2% (875.88% to 1027.46%)799.2% (738.44% to 878.48%)4.35 (4.18 to 4.55)47.34 (45.39 to 49.24)987.66% (922.91% to 1054.34%)795.34% (741.01% to 859.05%)
Colon and rectum cancer751291.48 (1249.20 to 1320.46)1640.65 (1574.85 to 1689.21)27.04% (23.7% to 30.48%)−24.11% (−26.08% to −21.94%)65.58 (61.89 to 67.69)84.03 (77.99 to 87.52)28.12% (24.34% to 31.56%)−26.31% (−28.25% to −24.39%)
Type 2 diabetes126856.92 (809.02 to 882.74)1365.65 (1299.49 to 1422.98)59.37% (54.2% to 65.34%)−7.31% (−10.46% to −3.84%)43.92 (40.93 to 45.55)73.41 (67.73 to 76.76)67.15% (61.31% to 72.93%)−5.46% (−8.66% to 2.26%)
Alzheimer disease and other dementias157743.80 (180.25 to 2011.60)139.08 (333.70 to 3431.38)80.03% (65.82% to 99.45%)−3.65% (−10.86% to 5.5%)73.08 (18.40 to 194.71)143.92 (37.07 to 354.96)96.94% (80.52% to 119.01%)−1.92% (−9.65% to 7.87%)
Motor vehicle road injuries381836.51 (1812.57 to 1864.76)1231.24 (1152.15 to 1272.09)−32.96% (−37.75% to −30.48%)−46.42% (−50.42% to −44.35%)35.67 (35.13 to 36.27)28.25 (26.71 to 29.14)−20.82% (−25.88% to −18.17%)−42.5% (−46.41% to −40.47%)
Breast cancer991199.58 (1165.78 to 1222.05)1212.43 (1157.03 to 1261.82)1.07% (−3% to 4.94%)−40.05% (−42.49% to −37.71%)48.21 (45.76 to 49.51)55.02 (51.01 to 57.90)14.12% (9.23% to 18.83%)−35.5% (−38.05% to −33.07%)
Lower respiratory infections8101223.88 (1159.84 to 1261.53)1210.65 (1124.89 to 1262.59)−1.08% (−4.06% to 1.99%)−40.39% (−42.03% to −38.65%)72.72 (66.22 to 76.44)81.92 (72.24 to 87.40)12.66% (8.1% to 16.85%)−38.93% (−40.75% to −36.94%)
Ischemic stroke6111324.40 (1218.20 to 1381.45)1185.52 (1045.83 to 1295.90)−10.49% (−15.56% to −3.94%)−50.06% (−52.58% to −46.54%)103.35 (92.02 to 109.29)108.95 (92.44 to 120.30)5.42% (−1.45% to 14.3%)−44.68% (−47.72% to −40.18%)
Pancreatic cancer1712587.36 (568.59 to 599.72)1134.93 (1078.47 to 1178.70)93.23% (85.27% to 100.27%)10.36% (5.85% to 14.28%)28.60 (27.10 to 29.43)57.49 (53.67 to 60.25)101.03% (92.1% to 109.18%)14.29% (9.49% to 18.74%)
ICH1413772.31 (741.63 to 799.80)1099.70 (1033.09 to 1188.13)42.39% (35.89% to 50.11%)−16.7% (−20.47% to −12.21%)38.33 (35.84 to 39.86)59.73 (54.34 to 64.89)55.82% (47.69% to 66.31%)−12.28% (−16.49% to −6.65%)
Self-harm by other specified means1614686.74 (629.95 to 767.19)961.37 (835.09 to 1004.91)39.99% (28.48% to 45.86%)12.77% (3.34% to 17.66%)14.65 (13.31 to 16.22)21.98 (19.00 to 23.04)50.1% (40.1% to 55.9%)12.88% (4.55% to 17.5%)
Hypertensive HD2315447.65 (373.87 to 469.58)957.73 (599.24 to 1027.23)113.95% (43.15% to 126.64%)29.98% (−15.61% to 38.05%)23.73 (20.11 to 25.47)52.96 (35.45 to 57.78)123.18% (58.64% to 136.08%)23.67% (−13.76% to 30.56%)
Self-harm by firearm1316853.20 (767.29 to 906.88)895.00 (844.35 to 1014.78)4.9% (1.11% to 13.45%)−20.52% (−23.51% to −13.82%)19.32 (17.67 to 20.57)23.36 (22.13 to 26.18)20.95% (17.12% to 28.48%)−16.01% (−18.8% to −10.1%)
Cirrhosis and other chronic liver diseases caused by hepatitis C2417434.18 (390.04 to 483.14)839.29 (746.47 to 938.91)93.3% (82.11% to 103.87%)19.63% (14.07% to 25.01%)14.46 (12.96 to 16.10)29.91 (26.55 to 33.43)106.84% (97.17% to 116.53%)23.07% (18.06% to 28.21%)
Endocrine, metabolic, blood, and immune disorders3518272.90 (226.89 to 362.60)772.39 (598.36 to 893.98)183.04% (139% to 197.28%)77.55% (62.97% to 84.21%)8.68 (7.45 to 12.18)34.54 (24.72 to 37.44)297.78% (180.95% to 332.08%)123.05% (67.99% to 138.77%)
Physical violence by firearm1119980.04 (963.97 to 993.74)735.86 (682.89 to 761.54)−24.92% (−29.57% to −22.24%)−34.98% (−39.02% to −32.65%)16.74 (16.47 to 16.96)13.00 (12.12 to 13.43)−22.33% (−26.91% to −19.9%)−35.1% (−39.01% to −32.96%)
Prostate cancer1820581.18 (403.13 to 650.19)712.79 (628.11 to 1037.53)22.65% (9.65% to 66.94%)−29.34% (−36.77% to −4.07%)36.24 (25.66 to 40.65)48.32 (41.35 to 70.59)33.36% (19.07% to 78.37%)−24.46% (−32.33% to 1.1%)

HD indicates heart disease; ICH, intracerebral hemorrhage; IHD, ischemic heart disease; UI, uncertainty interval; and YLLs, years of life lost to premature mortality.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.67 Printed with permission. Copyright © 2020, University of Washington.

Table 2-5. The Leading 20 Risk Factors for YLDs in the United States: Rank, Number, and Percentage Change, 1990 and 2019

Risk factors for disabilityYLD rank (for total number)Total No. of YLDs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLDsAge-standardized YLD rate
High BMI212014.44 (1191.63 to 3041.53)4757.53 (3035.97 to 6728.53)136.17% (116.67% to 171.6%)44.45% (32.86% to 65.18%)
High FPG321473.97 (1043.23 to 1958.70)3705.54 (2636.55 to 4926.74)151.4% (140.32% to 165.13%)47.37% (40.86% to 54.89%)
Smoking132927.37 (2152.15 to 3726.22)3580.31 (2711.48 to 4421.59)22.3% (15.58% to 30.13%)−25.75% (−29.66% to −21.37%)
Drug use541031.70 (712.04 to 1385.17)3009.85 (2080.84 to 4025.99)191.74% (158.71% to 224.78%)148.76% (118.72% to 178.48%)
High SBP65884.49 (639.70 to 1142.32)1287.04 (929.96 to 1667.98)45.51% (35.52% to 55.15%)−13.11% (−18.82% to −7.75%)
Alcohol use461102.64 (760.00 to 1520.68)1259.73 (879.63 to 1722.34)14.25% (4.96% to 25.06%)−16.46% (−21.27% to −11.03%)
Occupational ergonomic factors77769.12 (531.07 to 1052.57)909.32 (640.04 to 1206.98)18.23% (8.01% to 30.5%)−14.3% (−21.29% to −6.44%)
Low bone mineral density88411.39 (289.23 to 569.28)782.17 (549.97 to 1077.01)90.13% (85.32% to 95.57%)6.66% (4.03% to 9.54%)
Kidney dysfunction99399.32 (297.80 to 524.36)775.02 (582.79 to 1002.90)94.08% (83.38% to 105.14%)19.75% (14.04% to 25.57%)
Diet high in red meat1410230.60 (158.70 to 317.03)485.27 (322.95 to 687.22)110.44% (91.62% to 126.96%)25.76% (15.64% to 34.5%)
Diet high in processed meat1711172.86 (104.84 to 255.78)471.02 (287.52 to 692.65)172.5% (148.34% to 205.98%)58.21% (44.23% to 76.99%)
Short gestation1012371.84 (284.50 to 469.16)468.88 (365.55 to 581.92)26.1% (16.16% to 36.48%)4.21% (−3.87% to 12.88%)
Low birth weight1113371.84 (284.50 to 469.16)468.88 (365.55 to 581.92)26.1% (16.16% to 36.48%)4.21% (−3.87% to 12.88%)
High LDL cholesterol1314297.03 (185.95 to 446.89)303.55 (190.21 to 472.68)2.19% (−8.4% to 12.75%)−37.09% (−43.62% to −30.57%)
Ambient particulate matter pollution1215308.85 (111.01 to 556.89)291.90 (139.49 to 500.08)−5.49% (−55.19% to 120.72%)−44.15% (−73.38% to 30.06%)
Bullying victimization2216132.13 (29.00 to 322.15)268.38 (58.82 to 613.61)103.12% (81.47% to 133.27%)81.82% (61.43% to 105.89%)
Occupational injuries1517196.96 (134.56 to 279.88)265.30 (176.61 to 390.65)34.7% (5.8% to 73.94%)0.01% (−21.72% to 29.35%)
Childhood sexual abuse1918164.32 (72.88 to 313.28)251.15 (121.67 to 443.14)52.84% (27.67% to 94.68%)22.66% (3.32% to 54.56%)
Intimate partner violence2019161.94 (26.50 to 326.56)250.12 (31.52 to 514.75)54.45% (27.68% to 63.76%)23.3% (−4.55% to 30.31%)
Secondhand smoke1620173.12 (106.23 to 245.30)246.72 (146.07 to 362.41)42.51% (23% to 59.97%)−16.37% (−27.46% to −6.05%)

BMI indicates body mass index; FPG, fasting plasma glucose; LDL, low-density lipoprotein; SBP, systolic blood pressure; UI, uncertainty interval; and YLDs, years of life lived with disability or injury.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.66 Printed with permission. Copyright © 2020, University of Washington.

Table 2-6. The Leading 20 Causes for YLDs in the United States: Rank, Number, and Percent Change, 1990 and 2019

Diseases and injuriesYLD rank (for total number)Total number of YLDs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLDsAge-standardized YLD rate
Low back pain114504.86 (3168.68 to 6039.64)5697.15 (4114.14 to 7474.69)26.47% (18.72% to 34.96%)−12.46% (−17.42% to −7.02%)
Other musculoskeletal disorders221731.90 (1200.59 to 2420.19)3530.50 (2522.22 to 4747.29)103.85% (83.83% to 126.23%)44.17% (30.42% to 59.6%)
Type 2 diabetes931030.39 (715.25 to 1387.82)2761.76 (1939.08 to 3738.03)168.03% (153.55% to 185.2%)55.84% (47.58% to 65.14%)
Opioid use disorders164554.70 (366.80 to 787.88)2489.58 (1684.54 to 3394.11)348.82% (308.52% to 396.89%)288.67% (253.85% to 332.48%)
Major depressive disorder451341.83 (930.71 to 1837.66)2242.30 (1552.73 to 3056.52)67.11% (62.83% to 72.26%)33.07% (29.58% to 36.62%)
Age-related and other hearing loss561340.58 (932.94 to 1865.97)2187.37 (1524.78 to 3048.08)63.17% (58.93% to 67.46%)−1.4% (−3.46% to 0.7%)
Migraine371671.80 (241.76 to 3778.40)2078.81 (333.85 to 4660.27)24.35% (18.96% to 37.7%)−2.61% (−5.89% to 1.17%)
Neck pain781201.62 (792.53 to 1709.09)2043.52 (1392.66 to 2886.40)70.06% (55.99% to 82.82%)18.41% (9.89% to 27.58%)
Chronic obstructive pulmonary disease891111.88 (924.35 to 1262.67)1921.11 (1606.46 to 2147.99)72.78% (66.73% to 79.98%)−0.62% (−3.94% to 3.51%)
Anxiety disorders6101331.27 (932.18 to 1816.40)1872.34 (1314.62 to 2530.62)40.64% (37% to 44.94%)8.41% (6.85% to 10.06%)
Falls1011971.06 (690.51 to 1336.57)1594.64 (1136.33 to 2190.22)64.22% (57.72% to 71.62%)0.07% (−2.87% to 3.35%)
Asthma1112904.55 (587.17 to 1330.72)1296.66 (857.41 to 1849.88)43.35% (31.26% to 56.15%)11.01% (1.8% to 21.71%)
Schizophrenia1313767.43 (562.88 to 970.69)993.34 (732.79 to 1243.07)29.44% (25.28% to 34.45%)−1.22% (−3.13% to 0.79%)
Osteoarthritis hand1814486.85 (249.46 to 1017.65)930.08 (466.70 to 1964.92)91.04% (74.27% to 108.64%)7.82% (−0.72% to 17.23%)
Ischemic stroke1515559.93 (399.70 to 724.14)870.59 (628.48 to 1114.77)55.48% (47.94% to 63.39%)−5.16% (−9.35% to −0.14%)
Alcohol use disorders1216785.98 (523.84 to 1106.57)784.98 (538.64 to 1092.19)−0.13% (−5.58% to 5.53%)−21.58% (−24.39% to −18.84%)
Osteoarthritis knee1917450.96 (227.51 to 906.41)759.11 (380.59 to 1527.66)68.33% (62.62% to 75.07%)−2.68% (−6.62% to 1.66%)
Endocrine, metabolic, blood, and immune disorders1418629.50 (428.40 to 868.36)726.71 (500.66 to 990.69)15.44% (6.81% to 23.95%)−23.84% (−29.21% to −18.2%)
Alzheimer’s disease and other dementias2219391.77 (276.91 to 523.54)687.80 (497.57 to 889.29)75.56% (59.97% to 94.86%)−3.82% (−12.02% to 6.33%)
Edentulism1720491.91 (304.02 to 742.02)668.95 (424.02 to 985.05)35.99% (29.73% to 43.73%)−17.13% (−22.52% to −10.71%)

UI indicates uncertainty interval; and YLDs, years of life lived with disability or injury.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.67 Printed with permission. Copyright © 2020, University of Washington.

Trends in Global Risk Factors and Causes for YLL and YLD: 1990 to 2019

(See Tables 2-7 through 2-11)

  • The leading global YLL risk factors from 1990 to 2019 are presented in Table 2-7.

    • — High SBP and smoking were the first and second leading YLL risk factors globally in 2019. Age-standardized YLL rates attributable to HBP and smoking declined 29.0% and 41.3%, respectively, between 1990 and 2019.

    • — From 1990 to 2019, high FPG rose from 14th to 5th leading risk factor of global YLLs with a 1.5% decrease in the age-standardized YLL rates over this period.

  • The leading global YLL causes from 1990 to 2019 are presented in Table 2-8.

    • — IHD rose from the third to first leading global YLL cause between 1990 and 2019, whereas age-standardized YLL rates declined by 29.1% during this period. This shift resulted in lower respiratory infections moving from first to second leading cause, and age-standardized YLL rates declined 62.7%.

    • — ICH and ischemic stroke rose from 9th to 4th and from 13th to 8th leading cause of global YLL, respectively, between 1990 and 2019.

    • — Type 2 diabetes also rose from 28th to 14th leading global YLL cause, showing a 9.1% increase in age-standardized YLL rate.

  • The leading global risk factors for YLDs from 1990 to 2019 are presented in Table 2-9.

    • — High FPG and high BMI were the first and second leading YLD risk factors globally in 2019, replacing iron deficiency and smoking, which ranked fourth and third, respectively, in 2019. Age-standardized YLD rates attributable to high FPG and high BMI increased 44.1.% and 60.2%, respectively, whereas age-standardized global YLD rates attributable to smoking and iron deficiency deceased 22.9% and 16.7%, respectively.

    • — Ambient particulate matter pollution rose from 17th to 8th leading global risk factor for YLD, resulting in a 64.9% increase in the age-standardized global YLD rates.

  • The leading global causes of YLDs from 1990 to 2019 are presented in Table 2-10.

    • — Low back pain and migraine were the first and second leading global causes of YLDs in both 1990 and 2019. The age-standardized rates of YLD attributable to low back pain decreased 16.3%, whereas rates for migraine increased 1.5% across the same time period.

    • — From 1990 to 2019, type 2 diabetes rose from 10th to 6th leading global cause of YLD during this time period, with a 50.2% increase in the age-standardized global YLD rate.

Table 2-7. The Leading 20 Global Risk Factors of YLL and Death: Rank, Number, and Percentage Change, 1990 and 2019

Risk factors for disabilityYLL rank (for total number)Total No. of YLLs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)Corresponding total No. of deaths, in thousands (95% UI)Corresponding percent change, 1990–2019 (95% UI)
1990201919902019Total Number of YLLsAge-standardized YLL rate19902019Total No. of deathsAge-standardized death rate
High SBP61143603.62 (129 333.91 to 157 734.25)214 260.28 (191 165.39 to 236 748.61)49.2% (38.51% to 59.21%)−28.96% (−33.93% to −24.37%)6787.71 (6072.71 to 7495.92)10 845.60 (9514.14 to 12 130.85)59.78% (49.19% to 69.4%)−29.81% (−34.25% to −25.76%)
Smoking72140 203.56 (132 792.85 to 147 036.56)168 238.03 (155 801.16 to 180 393.21)20% (10.41% to 30.71%)−41.31% (−45.98% to −36.16%)5868.49 (5578.08 to 6152.89)7693.37 (7158.45 to 8200.59)31.1% (21.21% to 42.07%)−38.67% (−43.11% to −33.68%)
Low birth weight23269 478.56 (250 822.80 to 288 996.54)151 317.48 (128 528.30 to 179 613.60)−43.85% (−52.35% to −33.52%)−43.1% (−51.71% to −32.64%)3033.43 (2823.41 to 3253.23)1703.12 (1446.63 to 2021.58)−43.85% (−52.35% to −33.53%)−43.11% (−51.72% to −32.65%)
Short gestation34221 314.76 (206 273.76 to 238 540.80)128 741.23 (109 481.34 to 153 683.78)−41.83% (−50.32% to −30.76%)−41.05% (−49.66% to −29.84%)2491.34 (2321.98 to 2685.26)1449.04 (1232.27 to 1729.80)−41.84% (−50.33% to −30.77%)−41.06% (−49.67% to −29.85%)
High FPG14561 627.96 (51 459.07 to 74 728.01)126 654.90 (104 234.74 to 153 148.03)105.52% (91.63% to 119.7%)−1.5% (−7.92% to 5.66%)2910.09 (2340.62 to 3753.67)6501.40 (5110.28 to 8363.05)123.41% (108.53% to 138.04%)−1.46% (−7.48% to 5.12%)
High BMI16654 375.58 (30 163.43 to 84 361.01)119 383.76 (79 596.11 to 163 875.52)119.55% (88.91% to 166.91%)8.27% (−6.61% to 31.18%)2198.13 (1205.50 to 3432.16)5019.36 (3223.36 to 7110.74)128.35% (101.34% to 170.06%)4.93% (−7.26% to 24.58%)
Ambient particulate matter pollution13766 492.55 (44 569.97 to 94 108.79)104 895.28 (84 911.25 to 123 445.01)57.75% (20.29% to 113.82%)−4.23% (−24.76% to 26.13%)2047.17 (1454.74 to 2739.85)4140.97 (3454.41 to 4800.29)102.28% (60.27% to 160.61%)−0.92% (−19.85% to 26.25%)
High LDL cholesterol12866 683.88 (56 074.15 to 79 392.34)92 904.81 (75 590.22 to 111 436.78)39.32% (28.6% to 48.91%)−33.26% (−37.98% to −28.66%)3002.61 (2350.83 to 3761.88)4396.98 (3301.26 to 5651.79)46.44% (35.21% to 55.63%)−36.74% (−40.61% to −33.09%)
Household air pollution from solid fuels49200 169.50 (154 731.29 to 248 560.54)83 565.87 (60 754.11 to 108 481.62)−58.25% (−66.65% to −48.52%)−69.1% (−74.78% to −62.42%)4358.21 (3331.29 to 5398.69)2313.99 (1631.34 to 3118.14)−46.91% (−58.07% to −34.49%)−69.88% (−75.85% to −63.27%)
Child wasting110292 012.74 (241 855.36 to 351 715.87)79 187.22 (61 262.34 to 100 812.43)−72.88% (−78.47% to −66.32%)−73.89% (−79.28% to −67.54%)3430.42 (2851.24 to 4125.93)993.05 (786.46 to 1245.24)−71.05% (−76.85% to −64.32%)−73.05% (−78.35% to −66.7%)
Alcohol use151155 971.37 (49 934.31 to 62 781.18)75 813.95 (66 966.44 to 85 498.40)35.45% (23.85% to 47.91%)−25.69% (−32.08% to −18.91%)1639.87 (1442.38 to 1845.20)2441.97 (2136.99 to 2784.90)48.91% (35.99% to 63.1%)−23.77% (−30.55% to −16.4%)
Kidney dysfunction191237 087.06 (32 724.00 to 41 606.93)65 204.46 (57 219.63 to 73 512.12)75.81% (64.57% to 87.42%)−11.26% (−17.07% to −5.57%)1571.72 (1344.42 to 1805.60)3161.55 (2723.36 to 3623.81)101.15% (88.45% to 112.88%)−10.02% (−15.49% to −4.64%)
Unsafe water source513153 905.20 (115 315.56 to 190 197.92)57 641.09 (41 786.87 to 75 887.40)−62.55% (−71.19% to −49.83%)−68.27% (−75.24% to −57.55%)2442.07 (1764.95 to 3147.03)1230.15 (817.82 to 1788.90)−49.63% (−61.95% to −29.85%)−65.76% (−73.6% to −53.37%)
Unsafe sex251418 492.16 (14 813.00 to 23 832.65)41 999.23 (37 398.24 to 49 078.72)127.12% (100.78% to 162.48%)35.87% (21.91% to 54.45%)429.99 (356.20 to 533.21)984.37 (904.99 to 1106.17)128.93% (102.2% to 164.15%)27.64% (13.89% to 44.6%)
Diet high in sodium201531 285.63 (10 435.19 to 63 583.27)40 722.69 (11 550.13 to 86 326.74)30.16% (−3.03% to 47.85%)−36.45% (−52.02% to −28.15%)1320.34 (412.33 to 2796.87),885.36 (476.84 to 4194.71)42.79% (4.76% to 61.05%)−34.18% (−50.81% to −26.58%)
Diet low in whole grains221626 467.42 (12 815.63 to 33 041.82)38 954.84 (19 130.31 to 49 094.51)47.18% (37.22% to 57.73%)−28.99% (−33.76% to −24.05%)1178.22 (579.63 to 1474.66)1844.84 (921.29 to 2338.61)56.58% (47.07% to 65.85%)−31.16% (−35.14% to −27.26%)
Unsafe sanitation917115 547.43 (92 118.35 to 138 980.27)37 183.90 (29 008.07 to 48 393.08)−67.82% (−75.33% to −56.89%)−72.65% (−78.73% to −63.04%)1836.46 (1390.57 to 2325.10)756.58 (542.45 to 1095.44)−58.8% (−68.54% to −43.12%)−71.89% (−78.23% to −62.13%)
No access to handwashing facility101880 929.22 (58 183.31 to 102 881.65)32 224.40 (22 228.24 to 42 981.39)−60.18% (−67.34% to −51.09%)−65.26% (−71.61% to −57.2%)1200.09 (854.11 to 1553.29)627.92 (427.17 to 846.29)−47.68% (−56.38% to −36.7%)−62.55% (−68.93% to −54.77%)
Secondhand smoke181944 029.71 (31 252.42 to 57 353.06)31 489.25 (24 218.79 to 38 792.35)−28.48% (−39.18% to −15.29%)−54.89% (−60.57% to −48.97%)1161.96 (878.27 to 1431.85)1304.32 (1006.96 to 1605.39)12.25% (1.01% to 25.04%)−42.45% (−47.47% to −36.76%)
Low temperature212026 827.37 (20 973.96 to 33 715.52)25 954.68 (21 667.68 to 30 902.49)−3.25% (−18.13% to 13.86%)−51.56% (−57.31% to −45.99%)1276.64 (1092.81 to 1461.24)1652.98 (1413.03 to 1913.43)29.48% (18.11% to 41.67%)−43.63% (−47.8% to −38.92%)

BMI indicates body mass index; FPG, fasting plasma glucose; LDL, low-density lipoprotein; SBP, systolic blood pressure; UI, uncertainty interval; and YLLs, years of life lost because of premature mortality.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.66 Printed with permission. Copyright © 2020, University of Washington.

Table 2-8. The Leading 20 Global Causes of YLL and Death: Rank, Number, and Percentage Change, 1990 and 2019

Diseases and injuriesYLL rank (for total number)Total No. of YLLs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)Corresponding total No. of deaths, in thousands (95% UI)Corresponding percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLLsAge-standardized YLL rate19902019Total No. of deathsAge-standardized death rate
IHD31118 399.43 (113 795.23 to 122 787.19)176 634.92 (165 028.83 to 188 453.38)49.19% (38.17% to 59.29%)−29.14% (−34.13% to −24.56%)5695.89 (5405.19 to 5895.40)9137.79 (8395.68 to 9743.55)60.43% (50.23% to 69.14%)−30.8% (−34.83% to −27.17%)
Lower respiratory infections12223 807.88 (198 291.93 to 258 361.55)96 536.65 (84 197.05 to 112 404.97)−56.87% (−64.43% to −47.7%)−62.66% (−69.13% to −55.03%)3320.01 (3018.49 to 3715.06)2493.20 (2268.18 to 2736.18)−24.9% (−34.42% to −15.39%)−48.54% (−53.95% to −42.93%)
Diarrheal diseases23182 456.67 (146 519.78 to 217 965.17)69 887.49 (54 617.33 to 92 161.23)−61.7% (−70.34% to −49.12%)−67.6% (−74.63% to −56.89%)2896.27 (2222.66 to 3644.59)1534.44 (1088.68 to 2219.10)−47.02% (−59.64% to −27.06%)−64.05% (−72.05% to −51.35%)
ICH9452 648.78 (48 739.14 to 57 507.05)65 306.22 (60 073.84 to 70 392.27)24.04% (10.38% to 35.4%)−37.37% (−44.17% to −31.5%)2099.76 (1932.53 to 2328.41)2886.20 (2644.48 to 3099.35)37.45% (21.73% to 50.92%)−35.61% (−42.76% to −29.23%)
Neonatal preterm birth45112 709.17 (103 574.46 to 122 915.10)58 942.91 (49 829.35 to 70 084.83)−47.7% (−56.13% to −37.42%)−47.02% (−55.56% to −36.61%)1269.04 (1166.14 to 1383.98)663.52 (560.96 to 788.95)−47.71% (−56.14% to −37.44%)−47.04% (−55.57% to −36.63%)
Chronic obstructive pulmonary disease11648 769.20 (40 770.89 to 52 860.94)54 594.90 (48 711.47 to 59 513.37)11.95% (−0.47% to 35.12%)−46.81% (−52.61% to −36.11%)2520.22 (2118.06 to 2719.39)3280.64 (2902.85 to 3572.37)30.17% (15.74% to 55.05%)−41.74% (−48.03% to −31.07%)
Neonatal encephalopathy caused by birth asphyxia and trauma6771 832.72 (64 553.03 to 80 228.20)50 368.25 (42 242.80 to 59 745.92)−29.88% (−41.7% to −15.68%)−28.91% (−40.9% to −14.52%)808.68 (726.80 to 903.20)566.98 (475.54 to 672.55)−29.89% (−41.71% to −15.69%)−28.92% (−40.91% to −14.54%)
Ischemic stroke13834 004.54 (31 954.95 to 37 258.43)50 349.74 (46 232.45 to 54 066.67)48.07% (32.31% to 61.3%)−33.35% (−40% to −27.56%)2049.67 (1900.02 to 2234.21)3293.40 (2973.54 to 3536.08)60.68% (45.83% to 74.65%)−33.64% (−39.16% to −28.15%)
Tracheal, bronchus, and lung cancer19926 859.81 (25 598.42 to 28 199.92)45 313.75 (41 866.20 to 48 831.01)68.7% (52.68% to 85.03%)−16.34% (−24.19% to −8.38%)1065.14 (1019.22 to 1117.18)2042.64 (1879.24 to 2193.27)91.77% (74.52% to 108.97%)−7.77% (−15.93% to 0.23%)
Malaria81063 480.60 (34 802.94 to 103 091.05)43 824.70 (21 055.36 to 77 962.79)−30.96% (−58.84% to 6.4%)−39.03% (−63.65% to −6.42%)840.55 (463.32 to 1356.07)643.38 (301.60 to 1153.66)−23.46% (−54.89% to 18.46%)−37.93% (−63.46% to −4.52%)
Drug-susceptible tuberculosis51174 658.58 (68 441.13 to 81 346.25)38 431.33 (33 206.79 to 43 219.46)−48.52% (−55.92% to −40.77%)−67.54% (−72.12% to −62.69%)1760.71 (,610.86 to 1908.32)1061.29 (924.21 to 1186.12)−39.72% (−48.03% to −30.36%)−66.82% (−71.34% to −61.52%)
Other neonatal disorders121247 950.24 (40 831.64 to 57 251.83)33 099.91 (27 646.20 to 40 129.55)−30.97% (−48% to −11.34%)−30.12% (−47.35% to −10.26%)539.95 (459.81 to 644.56)372.68 (311.26 to 451.84)−30.98% (−48% to −11.37%)−30.13% (−47.36% to −10.29%)
HIV/AIDS resulting in other diseases321312 728.09 (9716.63 to 17 727.71)32 470.01 (26 796.66 to 40 802.58)155.11% (119.22% to 204.68%)77.01% (51.97% to 111.74%)216.91 (162.89 to 308.68)646.76 (551.85 to 780.47)198.17% (147.74% to 269.45%)94.13% (61.07% to 141.2%)
Type 2 diabetes281413 851.47 (13 104.90 to 14 647.61)31 149.12 (29 302.02 to 33 148.25)124.88% (110.14% to 141.3%)9.11% (2.06% to 16.65%)606.41 (573.07 to 637.51)1472.93 (1371.94 to 1565.86)142.9% (128.32% to 158.37%)10.77% (4.42% to 17.44%)
Self-harm by other specified means151532 879.52 (29 065.89 to 35 287.35)30 986.82 (27 870.17 to 34 246.63)−5.76% (−14.84% to 4.31%)−38.8% (−44.56% to −32.43%)687.85 (607.61 to 736.36)706.33 (633.90 to 777.33)2.69% (−6.38% to 13.66%)−38.83% (−43.96% to −32.27%)
Colon and rectum cancer341612 013.14 (11 481.93 to 12 503.78)23 218.75 (21 662.64 to 24 591.16)93.28% (79.51% to 106.26%)−5.29% (−11.8% to 0.81%)518.13 (493.68 to 537.88)1085.80 (1002.80 to 1149.68)109.56% (96.2% to 121.74%)−4.37% (−10.03% to 0.93%)
Motor vehicle road injuries211722 260.33 (19 219.44 to 25 401.32)21 982.25 (19 334.80 to 24 633.49)−1.25% (−14.6% to 15.23%)−30.61% (−39.82% to −19.51%)399.99 (349.88 to 452.26)448.73 (396.67 to 500.41)12.19% (−2.49% to 28.58%)−27.7% (−37.11% to −17.51%)
Stomach cancer241820 241.69 (19 030.22 to 21 513.16)21 872.43 (19 972.71 to 23 712.52)8.06% (−2.52% to 19.94%)−45.85% (−51.1% to −39.99%)788.32 (742.79 to 834.00)957.19 (870.95 to 1034.65)21.42% (10.17% to 33.59%)−41.98% (−47.18% to −36.33%)
Neonatal sepsis and other neonatal infections201923 105.79 (18 521.37 to 26 599.32)20 118.04 (16 896.71 to 24 474.48)−12.93% (−29.92% to 11.86%)−11.91% (−29.12% to 13.14%)260.15 (208.54 to 299.46)226.52 (190.25 to 275.55)−12.93% (−29.93% to 11.86%)−11.91% (−29.12% to 13.15%)
Hypertensive HD312013 303.40 (10 669.61 to 14 984.15)19 991.58 (14 951.10 to 22 179.67)50.27% (31.09% to 74.64%)−28.13% (−38.1% to −17.04%)654.91 (530.57 to 732.73)1156.73 (859.83 to 1278.56)76.63% (49.7% to 103.4%)−21.49% (−35.18% to −10.13%)

HD indicates heart disease; ICH, intracerebral hemorrhage; IHD, ischemic heart disease; UI, uncertainty interval; and YLLs, years of life lost to premature mortality.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.67 Printed with permission. Copyright © 2020, University of Washington.

Table 2-9. The Leading 20 Global Risk Factors for YLDs: Rank, Number, and Percentage Change, 1990 and 2019

Risk factors for disabilityYLD rank (for total number)Total No. of YLDs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLDsAge-standardized YLD rate
High FPG3115 581.99 (11 024.37 to 20 775.85)45 413.83 (31 849.57 to 60 894.87)191.45% (186.87% to 196.13%)44.07% (41.68% to 46.29%)
High BMI4212 907.42 (6 901.43 to 20 969.73)40 881.60 (24 508.83 to 60 876.50)216.73% (178.46% to 276.78%)60.16% (41.28% to 90.24%)
Smoking2320 484.09 (15 154.19 to 26 177.63)31 556.71 (23 686.35 to 40 009.32)54.05% (49.57% to 59.1%)−22.88% (−24.83% to −20.74%)
Iron deficiency1425 379.25 (16 986.41 to 36 524.20)28 798.47 (19 425.22 to 41 491.77)13.47% (10.15% to 16.89%)−16.67% (−19.02% to −14.23%)
High SBP7510 128.23 (7295.78 to 13 093.83)21 164.35 (15 195.78 to 27 235.49)108.96% (102.17% to 116.39%)0.98% (−2.31% to 4.4%)
Alcohol use5611 836.52 (8147.05 to 16 305.10)17 182.28 (12 000.25 to 23 497.81)45.16% (39.58% to 51.25%)−13.47% (−15.96% to −10.79%)
Occupational ergonomic factors6711 784.36 (8098.99 to 15 893.42)15 310.68 (10 544.90 to 20 762.41)29.92% (24.65% to 34.57%)−24.61% (−26.93% to −22.45%)
Ambient particulate matter pollution1783985.80 (2637.74 to 5634.02)13 320.10 (9643.12 to 17 166.65)234.19% (172.63% to 322.4%)64.91% (34.85% to 107.76%)
Drug use997479.41 (5163.69 to 10 042.08)12 664.94 (8804.75 to 16 725.98)69.33% (60.93% to 78.15%)14.49% (9.59% to 19.37%)
Kidney dysfunction14105003.27 (3651.06 to ,508.03)11 282.48 (8232.55 to 14 676.40)125.5% (118.26% to 132.74%)20.24% (16.89% to 23.23%)
Short gestation12115054.73 (3854.95 to 6433.30)9673.88 (7598.43 to 12 021.19)91.38% (75.26% to 106.94%)43.44% (31.94% to 54.79%)
Low birth weight13125054.73 (3854.95 to 6433.30)9673.88 (7598.43 to 12 021.19)91.38% (75.26% to 106.94%)43.44% (31.94% to 54.79%)
Low bone mineral density16134082.06 (2923.34 to 5511.96)8620.52 (6115.78 to 11 640.10)111.18% (108.01% to 114.56%)−1.7% (−2.77% to −0.66%)
Household air pollution from solid fuels8148277.99 (5837.95 to 11 127.29)7908.60 (5254.80 to 11 299.35)−4.46% (−20.63% to 15.04%)−52.14% (−60.18% to −42.55%)
Unsafe water source11156054.63 (3781.50 to 8815.37)7455.38 (4530.39 to 10 914.15)23.14% (16.02% to 29.05%)−11.82% (−16.58% to −8.1%)
Occupational noise18163933.44 (2688.10 to 5599.97)7001.45 (4760.56 to 10 059.34)78% (71.39% to 83.61%)−1.71% (−4.07% to 0.35%)
Occupational injuries10176779.60 (4833.81 to 9123.27)6842.83 (4831.64 to 9300.85)0.93% (−10.59% to 13.14%)−39.26% (−46.08% to −31.85%)
High LDL cholesterol22183035.02 (1990.11 to 4342.73)5713.21 (3677.82 to 8268.24)88.24% (82.75% to 94.36%)−7.77% (−9.68% to −6.05%)
Secondhand smoke24192652.31 (1685.26 to 3741.03)5512.81 (3246.56 to 8105.45)107.85% (84.4% to 123.61%)6.66% (−4.51% to 14.89%)
Unsafe sex32201609.09 (1135.71 to 2172.24)4646.23 (3296.41 to 6215.68)188.75% (161.84% to 225.83%)80.75% (63.79% to 103.78%)

BMI indicates body mass index; FPG, fasting plasma glucose; LDL, low-density lipoprotein; SBP, systolic blood pressure; UI, uncertainty interval; and YLDs, years of life lived with disability or injury.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.66 Printed with permission. Copyright © 2020, University of Washington.

Table 2-10. The Leading 20 Global Causes for YLDs: Rank, Number, and Percentage Change, 1990 and 2019

Diseases and injuriesYLD rank (for total number)Total No. of YLDs, in thousands (95% UI)Percent change, 1990–2019 (95% UI)
1990201919902019Total No. of YLDsAge-standardized YLD rate
Low back pain1143 361.65 (30 529.53 to 57 934.97)63 685.12 (44 999.20 to 85 192.92)46.87% (43.31% to 50.52%)–16.34% (–17.12% to –15.55%)
Migraine2226 863.35 (3969.24 to 61 445.23)42 077.67 (6418.38 to 95 645.21)56.64% (52.61% to 62.08%)1.54% (–4.43% to 3.27%)
Age-related and other hearing loss5322 008.10 (14 914.22 to 31 340.37)40 235.30 (27 393.19 to 57 131.94)82.82% (75.22% to 88.94%)–1.82% (–3.65% to -0.14%)
Other musculoskeletal disorders7416 608.89 (11 264.34 to 23 176.10)38 459.70 (26 253.49 to 53 553.79)131.56% (124.6% to 139.54%)32.24% (28.82% to 36.45%)
Major depressive disorder4523 461.28 (16 026.05 to 32 502.66)37 202.74 (25 650.21 to 51 217.04)58.57% (53.61% to 62.96%)–2.83% (–4.06% to –1.63%)
Type 2 diabetes10611 626.63 (7964.90 to 15 799.45)35 150.63 (23 966.55 to 47 810.13)202.33% (197.13% to 207.63%)50.23% (48.08% to 52.22%)
Anxiety disorders6718 661.02 (12 901.15 to 25 547.29)28 676.05 (19 858.08 to 39 315.12)53.67% (48.76% to 59.06%)–0.12% (–0.95% to 0.74%)
Dietary iron deficiency3825 069.79 (16 835.78 to 36 058.21)28 534.68 (19 127.59 to 41 139.28)13.82% (10.49% to 17.17%)–16.39% (–18.72% to –14%)
Neck pain9912 393.48 (8128.87 to 17 740.32)22 081.32 (14 508.24 to 31 726.93)78.17% (69.45% to 87.06%)–0.34% (–2.47% to 1.85%)
Falls81012 639.31 (8965.44 to 17 334.90)21 383.29 (15 161.79 to 29 501.22)69.18% (65.42% to 73.71%)–7% (–8.56% to –5.35%)
Chronic obstructive pulmonary disease131110 472.74 (8682.19 to 11 830.68)19 837.47 (16 596.49 to 22 441.73)89.42% (85.38% to 93.59%)–4.85% (–6.64% to –2.98%)
Endocrine, metabolic, blood, and immune disorders111211 022.44 (7513.64 to 15 340.32)18 000.31 (12 249.60 to 24 962.91)63.31% (59.14% to 67.48%)–4.64% (–6.09% to –3.38%)
Other gynecological diseases121310 812.95 (7041.93 to 15 340.80)16 382.52 (10 628.96 to 23 352.28)51.51% (48.55% to 54.4%)–9.37% (–11.11% to –7.59%)
Schizophrenia14149131.34 (6692.14 to 11 637.63)15 107.25 (11 003.87 to 19 206.79)65.44% (62.36% to 68.86%)–0.56% (–1.57% to 0.38%)
Ischemic stroke18156499.45 (4626.50 to 8367.19)13 128.53 (9349.92 to 16 930.38)101.99% (97.41% to 106.95%)0.07% (–1.76% to 1.95%)
Osteoarthritis knee25165184.78 (2569.34 to 10 565.52)11 534.02 (5719.12 to 23 489.98)122.46% (120.76% to 124.08%)7.8% (7.1% to 8.44%)
Diarrheal diseases16178035.21 (5544.86 to 11 122.17)11 030.29 (7631.54 to 15 146.75)37.27% (33.79% to 41.16%)–2.63% (–4.19% to –1.02%)
Alcohol use disorders17187875.53 (5287.35 to 11 122.36)10 732.01 (7253.40 to 15 212.46)36.27% (31.35% to 41.08%)–15.49% (–16.83% to –14.07%)
Asthma15198832.45 (5776.18 to 13 071.58)10 196.26 (6654.65 to 15 061.36)15.44% (12.66% to 18.69%)–23.4% (–26.63% to –20.2%)
Neonatal preterm birth26205054.73 (3854.95 to 6433.30)9673.88 (7598.43 to 12 021.19)91.38% (75.26% to 106.94%)43.44% (31.94% to 54.79%)

UI indicates uncertainty interval; and YLDs, years of life lived with disability or injury.

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.67 Printed with permission. Copyright © 2020, University of Washington.

Furthering the AHA’s Impact Through Continued Efforts to Improve CVH

(See Tables 2-3 through 2-6)

  • Renewed efforts to maintain and improve CVH will be foundational to successful reductions in mortality and disability in the United States and globally. Individuals with more favorable levels of CVH have significantly lower risk for several of the leading causes of death, including IHD,24 Alzheimer disease,55 stroke,56,57 CKD,58 diabetes,59,60 breast cancer,61,62 and atrial fibrillation (Tables 2-3 and 2-4). In addition, 6 of the 10 leading US risk factors for YLL and 4 of the 10 leading risk factors for YLD in 2019 were also components of CVH (Tables 2-3 and 2-5). Taken together, these data demonstrate the tremendous importance of continued efforts to improve CVH.

  • The expanding efforts of the AHA and American Stroke Association in areas of brain health are also well poised to drive toward improvement in several leading causes of death and disability that influence YLLs and YLDs, including stroke, Alzheimer disease, depression and anxiety disorders, and alcohol and substance use disorders.

  • Despite improvements observed in CVH and brain health over the past decade, further progress is needed to more fully realize these benefits for all Americans. Details are described in the AHA presidential advisory on brain health.63

Global Efforts to Improve CVH

(See Tables 2-7 through 2-10)

  • Renewal of efforts to improve CVH is a continuing challenge that requires collaboration throughout the global community in ways that aim targeted skills and resources at improving the top causes and risk factors for death and disability in countries. Such efforts are required in countries at all income levels with an emphasis on efforts to halt the continued worsening of the components of CVH (Tables 2-7 through 2-10).

  • Many challenges exist related to implementation of prevention and treatment programs in international settings; some challenges are unique to individual countries/cultures, whereas others are universal. Partnerships and collaborations with local, national, regional, and global partners are foundational to effectively addressing relevant national health priorities in ways that facilitate contextualization within individual countries and cultures.

Abbreviations Used in Chapter 2

AFatrial fibrillation
AHAAmerican Heart Association
AIDSautoimmune deficiency syndrome
BMIbody mass index
BPblood pressure
CACcoronary artery calcification
CIconfidence interval
CKDchronic kidney disease
CVDcardiovascular disease
CVHcardiovascular health
DALYdisability-adjusted life-year
DASHDietary Approaches to Stop Hypertension
DBPdiastolic blood pressure
ESRDend-stage renal disease
F&Vfruits and vegetables
FPGfasting plasma glucose
GBDGlobal Burden of Disease Study
HbA1chemoglobin A1c (glycosylated hemoglobin)
HBPhigh blood pressure
HDheart disease
HFheart failure
HIVhuman immunodeficiency virus
HRhazard ratio
ICHintracerebral hemorrhage
IHDischemic heart disease
IMTintima-media thickness
LDLlow-density lipoprotein
MAMexican American
MImyocardial infarction
NAnot available
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHBnon-Hispanic Black
NHWnon-Hispanic White
PAphysical activity
PAFpopulation attributable fraction
PEpulmonary embolism
REGARDSReasons for Geographic and Racial Differences in Stroke
RRrelative risk
SBPsystolic blood pressure
SFatsaturated fat
SSBsugar-sweetened beverage
svgServings
TCtotal cholesterol
UIuncertainty interval
VTEvenous thromboembolism
YLDyears lived with disability and injury
YLLyears of life lost

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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. Cigarette 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 this chapter, we 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

Table 3-1. Deaths Caused by Tobacco Worldwide by Sex, 2019

Both sexes (95% UI)Males (95% UI)Females (95% UI)
Total No. of deaths, millions8.7 (8.1 to 9.3)6.6 (6.0 to 7.1)2.1 (2.0 to 2.3)
Percent change in total number 1990–201928.6 (19.5 to 38.8)31.7 (20.2 to 45.0)19.8 (10.2 to 29.6)
Percent change in total number 2010–201910.0 (3.3 to 17.2)9.8 (1.6 to 18.7)10.7 (3.8 to 18.1)
Mortality rate per 100 000, age-standardized108.6 (101.3 to 115.9)180.6 (166.1 to 194.8)49.2 (44.8 to 53.7)
Percent change in rate, age standardized 1990–2019−38.9 (−43.2 to −34.2)−39.2 (−44.4 to −33.4)−42.6 (−47.0 to −38.1)
Percent change in rate, age standardized 2010–2019−15.1 (−20.1 to −9.7)−15.6 (−21.8 to −9.0)−14.9 (−20.1 to −9.3)
PAF, all ages, %15.4 (14.6 to 16.2)21.4 (20.5 to 22.3)8.3 (7.7 to 8.9)
Percent change in PAF, all ages 1990–20196.1 (0.8 to 12.1)7.5 (2.5 to 13.7)0.1 (−5.4 to 6.4)
Percent change in PAF, all ages 2010–20192.6 (−1.3 to 6.6)3.4 (−0.6 to 7.1)2.1 (−1.1 to 5.5)

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

Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.99 Printed with permission. Copyright © 2020, University of Washington.

Chart 3-1.

Chart 3-1. Prevalence (percent) of tobacco use in the United States in the past 30 days by product,* school level, sex, and race/ethnicity† (NYTS, 2019). Data in (A) relate to high school students and (B) relate to middle school students. Because of methodological differences among the NSDUH, the YRBSS, 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 of age, who are legally permitted to smoke and have higher rates of smoking. e-Cigarette indicates electronic cigarette; NSDUH, National Survey on Drug Use and Health; NYTS, National Youth Tobacco Survey; and YRBSS, Youth Risk Behavior 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 1 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 1 day?” †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 days in the past 30 days. §Any combustible tobacco product use was defined as use of cigarettes, cigars, hookahs, pipe tobacco, or bidis on ≥1 days 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 days in the past 30 days. Source: Data derived from Wang et al.7

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 addresses 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 Chart 3-1)

Youth
  • Prevalence of cigarette use in the past 30 days for middle and high school students by sex and race/ethnicity in 2019 is shown in Chart 3-1.

  • In 20197:

    • — 31.2% (95% CI, 29.1%–33.5%) of high school students (corresponding to 4.7 million users) and 12.5% (95% CI, 11.2%–13.9%) of middle school students (corresponding to 1.5 million users) used any tobacco products. In addition, 5.8% (95% CI, 4.6%–7.3%) of high school students (860 000 users) and 2.3% (95% CI, 1.8%–2.9%) of middle school students (270 000 users) smoked cigarettes in the past 30 days.

    • — 4.8% (95% CI, 3.7%–6.3%) of high school students (720 000 users) and 1.8% (95% CI, 1.4%–2.2%) of middle school students (210 000) used smokeless tobacco in the past 30 days.

    • — 7.6% (95% CI, 6.6%–8.8%) of high school students (1.1 million users) and 2.3% (95% CI, 1.9%–2.9%) of middle school students (270 000 users) used cigars in the past 30 days.

  • Of youth who smoked cigarettes in the past 30 days in 2019, 28.9% (95% CI, 23.1%–35.5%) of middle and high school students (corresponding to 330 000 users) reported smoking cigarettes on 20 to 30 days of the past 30 days.7

  • In 2019, tobacco use within the past month for middle and high school students varied by race/ethnicity: The prevalence of past 30-day cigarette use was 5.0% (95% CI, 3.9%–6.4%) in NH White youth compared with 3.1% (95% CI, 2.3–4.1%) in NH Black youth and 3.6% (95% CI, 2.8%–4.5%) in Hispanic youth. For cigars, the respective percentages were 5.1% (95% CI, 4.3%–6.1%), 8.6% (7.0%–10.6%), and 4.8% (95% CI, 3.9%–5.9%).7

  • The percentage of high school (27.5% or 4 110 000 users) and middle school (10.5% or 1 240 000 users) students who used e-cigarettes in the past 30 days exceeded the proportion using cigarettes in 2019 (Chart 3-1).

Adults
(See Charts 3-2 and 3-3)
  • According to the NHIS 2018 data, among adults ≥18 years of age4:

    • — 13.7% (95% CI, 13.1%–14.3%) of adults reported cigarette use every day or some days.

    • — 15.6% (95% CI, 14.8%–16.5%) of males and 12.0% (95% CI, 11.2%–12.7%) of females reported cigarette use every day or some days.

    • — 7.8% of those 18 to 24 years of age, 16.5% of those 25 to 44 years of age, 16.3% of those 45 to 64 years of age, and 8.4% of those ≥65 years of age reported cigarette use every day or some days.

    • — 22.6% of NH American Indian or Alaska Native adults, 14.6% of NH Black adults, 7.1% of NH Asian adults, 9.8% of Hispanic adults, and 15.0% of NH White adults reported cigarette use every day or some days.

    • — By annual household income, reported cigarette use every day or some days was 21.3% of people with <$35 000 income compared with 14.9% of those with income of $35 000 to $74 999, 13.3% of those with income of $75 000 to $99 999, and 7.3% of those with income ≥$100 000.

    • — In adults ≥25 years of age, the percentage reporting current cigarette use was 21.8% for those with <12 years of education, 36.0% in those with a General Educational Development high school equivalency, 19.7% among those with a high school diploma, 18.3% among those with some college, 14.8% among those with an associate’s degree, and 7.1% among those with an undergraduate degree compared with 3.7% among those with a graduate degree.

    • — 20.6% of lesbian/gay/bisexual individuals were current smokers compared with 13.5% of heterosexual/straight individuals.

    • — By region, the prevalence of current cigarette smokers was highest in the Midwest (16.2%) and South (14.8%) and lowest in the Northeast (12.5%) and West (10.7%).4

  • According to data from BRFSS 2018, the state with the highest age-adjusted percentage of current cigarette smokers was West Virginia (26.8%). The states with the lowest age-adjusted percentage of current cigarette smokers were Utah (9.0%) and California (11.4%; Chart 3-2).8

  • In 2018, smoking prevalence was higher among adults ≥18 years of age who reported having a disability or activity limitation (19.2%) than among those reporting no disability or limitation (13.1%).4

  • Among individuals reporting serious psychological distress, 31.6% were current smokers compared with 13.0% of 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%).9 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 3-3. Comparing e-cigarette prevalence across the 50 states shows that 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 3-2.

Chart 3-2. Age-adjusted prevalence (%) of current cigarette smoking for US adults by state (BRFSS, 2018). 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, 2018.8

Chart 3-3.

Chart 3-3. Prevalence (age-adjusted) of current electronic 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, 2017.8

Incidence

  • According to the 2018 NSDUH, ≈1.83 million people ≥12 years of age had smoked cigarettes for the first time within the past 12 months compared with 1.90 million in 2017 (2018 NSDUH Table 4.2B).10 Of new smokers in 2018, 571 000 were 12 to 17 years of age, 781 000 were 18 to 20 years of age, and 360 000 were 21 to 25 years of age; only 113 000 were ≥26 years of age when they first smoked cigarettes.

  • The number of new smokers 12 to 17 years of age in 2018 (571 000) decreased from 2017 (604 000). The number of new smokers 18 to 25 years of age in 2018 (1.14 million) also decreased from 2017 (1.15 million) (2018 NSDUH Table 4.2B).10

  • 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 use of combustible cigarettes (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.11

Lifetime Risk

Youth
  • Per NSDUH data for individuals 12 to 17 years of age, overall, the lifetime use of tobacco products declined from 14.9% to 13.4% between 2017 and 2018, with lifetime cigarette use declining from 10.8% to 9.6% during the same time period (2018 NSDUH Tables 2.2B and 2.3B).10

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

      • ▪ Sex: Lifetime use was higher among males (14.7%) than females (12.0%; 2018 NSDUH Table 2.8B).10

      • ▪ Race/ethnicity: Lifetime use was highest among American Indian and Alaska Native adolescents (18.7%), followed by NH White adolescents (16.3%), Hispanic or Latino adolescents (10.8%), NH Black adolescents (9.8%), and NH Asian adolescents (4.6%; 2018 NSDUH Table 2.8B).10

Adults
  • According to NSDUH data, the lifetime use of tobacco products in individuals ≥18 years of age did not decline significantly between 2017 (67.5%) and 2018 (66.3%). Lifetime cigarette use declined in a similar interval from 61.8% to 60.3% (2018 NSDUH Tables 2.2B and 2.3B).10 Similar to the patterns in youth, lifetime risk of tobacco products varied by demographic factors (2018 NSDUH Table 2.8B)10:

    • — Sex: Lifetime use was higher in males (75.0%) than females (58.2%).

    • — Race/ethnicity: Lifetime use was highest in American Indian or Alaska Native adults (78.2%) and NH White adults (74.1%), followed by Native Hawaiian or Other Pacific Islander adults (69.7%), Hispanic or Latino adults (51.6%), NH Black adults (55.1%), and NH Asian adults (40.1%).

  • In 2018, the lifetime use of smokeless tobacco for adults ≥18 years of age was 16.7% (2018 NSDUH Table 2.1B).

Secular Trends

(See Chart 3-4)

Youth

The percentage of adolescents (12–17 years of age) who reported smoking cigarettes in the past month declined from 13.0% in 2002 to 2.7% in 2018 (NSDUH Table 7.6B10; Chart 3-4). The percentages for daily cigarette use among those with past-month cigarette smoking in 12- to 17-year-olds were 31.5% in 2002 and 14.8% in 2018.10,12 Trends in e-cigarette use and other tobacco product use among high school students between 2011 and 2018 are shown in Chart 3-5.

Chart 3-4.

Chart 3-4. Past-month cigarette use among people ≥12 years of age, by age group: percentages, 2002 to 2018, United States (NHIS, 2002–2018; NSDUH, 2002–2018). NHIS indicates National Health Interview Survey; and NSDUH, National Survey on Drug Use and Health. Source: Reprinted from NSDUH.104

Chart 3-5.

Chart 3-5. 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-Cigarette 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 days in the past 30 days. †Any combustible tobacco product use was defined as use of cigarettes, cigars, hookahs, pipe tobacco, or bidis on ≥1 days 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 days 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

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 15.6% in 2018 and from 34% of females in 1965 to 12.0% in 2018, according to NHIS data.4,13 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 47.5%, from 28.0% in 2005 to 14.7% in 2016; for 18- to 24-year-old females, smoking declined 44.4%, from 20.7% to 11.5%, over the same time period.15

  • According to data from the BRFSS, the prevalence of e-cigarette use increased from 4.3% to 4.8% between 2016 and 2018 in US adults. Increases in e-cigarette use over this period were significant for middle-aged adults, women, and former smokers.16

  • 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.17 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.18

  • 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 diabetes.17

  • Cigarette smoking and other traditional CHD risk factors might have a synergistic interaction in HIV-positive individuals.19

  • Among the US Black population, cigarette use is associated with elevated measures of subclinical PAD in a dose-dependent manner. Current smokers had an increased adjusted odds of ABI <1 (OR, 2.2 [95% CI, 1.5–3.3]).20

  • 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]).21

  • 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.22

  • 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.23

  • Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.22,24

  • A meta-analysis of 26 studies reported that compared with never smoking, current smoking (RR, 1.75 [95% CI, 1.54–1.99]) and former smoking (RR, 1.16 [95% CI, 1.08–1.24]) were associated with increased risk of HF.25 In MESA, compared with never smoking, current smoking was associated with an adjusted doubling in incident HF (HR, 2.05 [95% CI, 1.36–3.09]). The increased risk was similar for HFpEF (HR, 2.51) and HFrEF (HR, 2.58).26

  • Short-term exposure to water pipe smoking is associated with a significant increase in SBP, DBP, and heart rate compared with nonsmoking control subjects,27 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 using. The CVD rate was 11.3 per 1000 person-years in never users and 21.4 in current users of smokeless tobacco.28

  • The long-term CVD risks associated with e-cigarette use are not known because of a lack of longitudinal data.29,30 However, e-cigarette use has been linked to elevated levels of preclinical biomarkers associated with cardiovascular injury such as markers for sympathetic activation, oxidative stress, inflammation, thrombosis, and vascular dysfunction.31 In addition, daily and some-day use of e-cigarettes may be associated with MI and CHD.32,33

  • Dual use of e-cigarettes and combustible cigarettes was associated with significantly higher odds of CVD (OR, 1.36 [95% CI, 1.18–1.56]) compared with exclusive combustible cigarette use.33 The association of dual use (relative to exclusive cigarette use) with CVD was 1.57 (95% CI, 1.18–2.07) for daily e-cigarette users and 1.31 (95% CI, 1.13–1.53) for occasional e-cigarette users.

Family History and Genetics

  • Genetic factors contribute to smoking behavior; common and rare variants in several loci have been found to be associated with smoking initiation, number of cigarettes smoked per day, and smoking cessation.34,35

  • Genetics might also modify adverse CVH outcomes among smokers, with variation in ADAMTS7 associated with loss of cardioprotection in smokers.36

Smoking Prevention

Tobacco 21 legislation was signed into law on December 20, 2019, increasing the federal minimum age for sale of tobacco products from 18 to 21 years.37

  • Such legislation is likely to 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 years. 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.38

    • — Another study using BRFSS 2011 to 2016 data before the federal legislation found that metropolitan and micropolitan statistical areas with local Tobacco 21 policies yielded significant reductions in smoking among youth 18 to 20 years of age.39

  • In addition, in several towns where Tobacco 21 laws were enacted before federal legislation, reductions of up to 47% in smoking prevalence among high school students have been reported.40 Furthermore, the National Academy of Medicine estimates that the nationwide Tobacco 21 law could result in 249 000 fewer premature deaths, 45 000 fewer lung cancer deaths, and 4.2 million fewer life-years lost among Americans born between 2010 and 2019.40

  • Before the federal minimum age of sale increase, 19 states (Hawaii, California, New Jersey, Oregon, Maine, Massachusetts, Illinois, Virginia, Delaware, Arkansas, Texas, Vermont, Connecticut, Maryland, Ohio, New York, Washington, Pennsylvania, and Utah), Washington, DC, and at least 470 localities (including New York City, NY; Chicago, IL; San Antonio, TX; Boston, MA; Cleveland, OH; and both Kansas Cities [Kansas and Missouri]) passed legislation setting the minimum age for the purchase of tobacco to 21 years.41

Awareness, Treatment, and Control

Smoking Cessation
  • According to NHIS 2017 data, 61.7% of adult ever smokers had stopped smoking; the quit rate has increased 6 percentage points since 2012 (55.1%).42

    • — Between 2011 and 2017, according to BRFSS surveys, quit attempts varied by state, with quit attempts increasing in 4 states (Kansas, Louisiana, Virginia, and West Virginia), declining in 2 states (New York and Tennessee), and not changing significantly in 44 states. In 2017 the quit attempts over the past year were highest in Guam (72.3%) and lowest in Wisconsin (58.6%), with a median of 65.4%.43

    • — According to NHIS 2015 data, 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 health care provider advice to quit.44 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. 9%).

  • Data from clinical settings suggest wide variation in counseling practices related to smoking cessation. In a study based on national registry data, only 1 in 3 smokers who visited a cardiology practice received smoking cessation assistance.45

  • According to cross-sectional MEPS data from 2006 to 2015, receiving advice to quit increased over time from 60.2% in 2006 to 2007 to 64.9% in 2014 to 2015. In addition, in 2014 to 2015, use of prescription smoking cessation medicine was significantly lower among NH Black (OR, 0.51 [95% CI, 0.38–0.69]), NH Asian (OR, 0.31 [95% CI, 0.10–0.93]), and Hispanic (OR, 0.53 [95% CI, 0.36–0.78]) individuals compared with White individuals. Use of prescription smoking cessation medicine was also significantly lower among those without health insurance (OR, 0.58 [95% CI, 0.41–0.83]) and higher among females (OR, 1.28 [95% CI, 1.10–1.52]).46 In 2014 to 2015, receipt of doctor’s advice to quit among US adult smokers was significantly lower in NH Black (59.7 [95% CI, 56.1–63.1]) and Hispanic (57.9 [95% CI, 53.5–62.2]) individuals compared with NH White individuals (66.6 [95% CI, 64.1–69.1]).

    • — 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.44

    • — 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).44

  • 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.47,48

    • — Quitting smoking at any age significantly lowers mortality from smoking-related diseases, and the risk declines with 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.49

    • — 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.47

    • — Among those with a cumulative smoking history of at least 20 pack-years, individuals who quit smoking had a significantly lower risk of CVD within 5 years of smoking cessation compared with current smokers. However, former smokers’ CVD risks remained significantly higher than risks for never smokers beyond 5 years after smoking cessation.50

  • Cessation medications (including sustained-release bupropion, varenicline, nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.51,52

  • 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; number needed to treat, 6.8). Continuous abstinence rates and reduction rates (≥50% of daily cigarette consumption) were also higher in the varenicline group.53

  • The EAGLES trial54 demonstrated the efficacy and safety of 12 weeks of varenicline, bupropion, or nicotine patch in motivated-to-quit patients who smoked 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.54

  • Extended use of a nicotine patch (24 weeks compared with 8 weeks) has been demonstrated to be safe and efficacious in randomized clinical trials.55

  • An RCT demonstrated the effectiveness of individual- and group-oriented financial incentives for tobacco abstinence through at least 12 months of follow-up.56

  • In addition to medications, smoke-free policies, increases in tobacco prices, cessation advice from health care professionals, and quit lines and other counseling have contributed to smoking cessation.44,57

  • 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.58

  • 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.59

  • A randomized trial of e-cigarettes and behavioral support versus nicotine-replacement therapy and behavioral support in adults attending the UK 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.60

  • Observational evidence suggests that daily use of e-cigarettes is associated with increased likelihood of combustible cigarette smoking abstinence. However, some-day use of e-cigarettes is not associated with smoking abstinence or reduction.61

Mortality

  • According to the 2020 Surgeon General’s report on smoking cessation, >480 000 Americans die as a result of cigarette smoking and >41 000 die of secondhand smoke exposure each year, ≈1 in 5 deaths annually.

  • 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 2016.62 Overall mortality among US smokers is 3 times higher than that for never smokers.47

  • On average, on the basis of 2016 data, male smokers die 12 years earlier than male never smokers, and female smokers die 11 years earlier than female never smokers.14,63

  • Increased CVD mortality risks persist for older (≥60 years of age) 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.64

  • In a sample of Native American individuals (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.65

  • 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.66

  • If current smoking trends continue, 5.6 million US children will die of smoking prematurely during adulthood.17

Electronic Cigarettes

(See Charts 3-1 and 3-4)

  • Electronic nicotine delivery systems, more commonly called e-cigarettes, are battery-operated devices that deliver nicotine, flavors, and other chemicals to the user in an aerosol. Although e-cigarettes were introduced into the United States only 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 intensity of nicotine delivery, which approximates the experience of combustible cigarettes.67

  • e-Cigarette use has become prevalent among never smokers. In 2016, an estimated 1.9 million tobacco users exclusively used e-cigarettes in the United States. Of these exclusive e-cigarette users, 60% were <25 years of age.68

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

  • According to the NYTS, in 2019, e-cigarettes were the most commonly used tobacco products in youth: In the past 30 days, 10.5% (1.2 million) of middle school and 27.5% (4.1 million) of high school students endorsed use (Chart 3-1).7 A significant nonlinear increase in current e-cigarette use in high school students was observed between 2011 (1.5%) and 2019 (27.4%).7,69 A significant increase in current e-cigarette use also was observed for middle school students, for whom the corresponding values were 0.6% and 10.5% in the 2 periods.3,7 Among high school students, rates of use were approximately equal between males (27.6%) and females (27.4%) and most pronounced among NH White students (32.4%). In middle school students, slightly higher rates were observed in females (10.8%) and in Hispanic students (13.1%).7

  • Frequent use of e-cigarettes among high school students who were current e-cigarette users increased from 27.7% in 2018 to 34.2% in 2019. In middle school students, the percentage using frequently among current e-cigarette users increased from 16.2% in 2018 to 18.0% in 2019.3,7

  • In 2016, 20.5 million US middle and high school students (80%) were exposed to e-cigarette advertising.70

  • Among US adults, awareness and use of e-cigarettes have increased considerably.71 In 2018, the prevalence of current e-cigarette use in adults, defined as use every day or on some days, was 3.2% according to data from the NHIS. The prevalence of current e-cigarette use was highest in individuals 18 to 24 years of age (7.6%) and in those with serious psychological distress (6.2%).4

  • 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%).72

  • e-Cigarettes contain lower levels of most tobacco-related toxic constituents compared with traditional cigarettes,73 including volatile organic compounds.74,75 However, nicotine levels have been found to be consistent across long-term cigarette and long-term e-cigarette users.31,76

  • e-Cigarette use has a significant cross-sectional association with a less favorable perception of physical and mental health and with depression.77,78

  • According to the BRFSS 2016 and 2017, e-cigarettes are associated with a 39% increased odds of self-reported asthma (OR, 1.39 [95% CI, 1.15–1.68]) and self-reported chronic obstructive pulmonary disease (OR, 1.75 [95% CI, 1.25–2.45]) among never users of combustible cigarette.79,80 There is a dose-response relationship such that higher frequency of e-cigarette use was associated with more asthma or chronic obstructive pulmonary disease.

  • An outbreak of e-cigarette or vaping product use–associated lung injury peaked in September 2019 after increasing rapidly between June and August 2019. Surveillance data and product testing indicate that tetrahydrocannabinol-containing e-cigarettes or vaping products are linked to most e-cigarette or vaping product use–associated lung injury cases. In particular, vitamin E acetate, an additive in some tetrahydrocannabinol-containing e-cigarettes or vaping, has been identified as the primary source of risk, although exposure to other e-cigarette– or vaping-related toxicants may also play a role. As of February 18, 2020, a total of 2807 hospitalized e-cigarette or vaping product use–associated lung injury cases or deaths have occurred in the United States.81

  • Effective August 8, 2016, the FDA’s Deeming Rule prohibited sale of e-cigarettes to individuals <18 years of age.82

  • In January 2020, the FDA issued a policy prioritizing enforcement against the development and distribution of certain unauthorized flavored e-cigarette products such as fruit and mint flavors (ie, any flavors other than tobacco and menthol).83

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%.17

    • — 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.84

  • 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.85

  • A meta-analysis of 24 studies demonstrated that secondhand smoke can increase risks for preterm birth by 20%.86

  • A study using the Framingham Offspring cohort found that there was an 18% increase in AF among offspring for every 1–cigarette pack per day increase in parental smoking. In addition, offspring with parents who smoked had 1.34 (95% CI, 1.17–1.54) times the odds of smoking compared with offspring with nonsmoking parents.87

  • As of December 31, 2019, 14 states (California, Colorado, Delaware, Hawaii, Massachusetts, New Jersey, New Mexico, New York, North Dakota, Oregon, Rhode Island, South Dakota, 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 worksites, restaurants, and bars.41,88

  • 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]).89

  • 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 Black individuals (46.8%) than for NH White individuals (21.8%) and Mexican American individuals (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).90

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 resulting 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 health care spending from 2006 to 2010, which represented roughly $170 billion per year, 60% of which was paid by public programs (eg, Medicare and Medicaid).91

  • According to the CDC and Federal Trade Commission, the tobacco industry spends about $9.06 billion on cigarette and smokeless tobacco advertising annually, equivalent to $25 million per day.92

  • In 2018, 216.9 billion cigarettes were sold by major manufacturers in the United States, which represents a 5.3% decrease (12.2 billion units) from 2017.93

  • Cigarette prices in the United States increased steeply between the early 1970s and 2018, 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 2018, the average cost was $6.90 and average tax $2.82.94

  • From 2012 through 2016, e-cigarette sales significantly increased while national e-cigarette prices significantly decreased. Together, these trends highlight the rapidly changing landscape of the US e-cigarette marketplace.94

  • Despite the morbidity and mortality resulting from tobacco use, Dieleman et al95 estimated that tobacco interventions were among the bottom third of health care expenditures of the 154 health conditions they analyzed. They estimated that in 2019 the United States spent $1.9 billion (95% CI, $1.5–$2.3 billion) on tobacco interventions, the majority (75.6%) on individuals 20 to 64 years of age. Almost half of the funding (48.5%) for the intervention came from public insurance.

Global Burden of Tobacco Use

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

  • According to 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.96

  • The GBD 2019 study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 369 diseases and injuries and 87 risk factors in 204 countries and territories. Oceania, East and Central 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 male. The annualized rate of change in smoking prevalence between 1990 to 2015 was −1.7% in females and −1.3% in males.97

  • Worldwide, ≈80% of smokers live in low- and middle-income countries.98

  • Tobacco (including smoking, secondhand smoke, and chewing tobacco) caused an estimated 8.7 million deaths globally in 2019 (6.6 million males and 2.1 million females; Table 3-1).99 GBD investigators estimated that in 2019 tobacco was the second leading risk of mortality (high SBP was number 1), and tobacco ranked third in DALYs globally.99

  • 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.100 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.72,101 In 2018, population cost coverage (either partial or full) for quit interventions increased to 78% in middle-income countries and to 97% in high-income countries; 5 billion people are now covered by at least 1 MPOWER measure. However, only 23 countries offered comprehensive cessation support in the same year.102

  • The CDC examined data from 28 countries from the 2008 to 2016 Global Adult Tobacco Survey and reported that the median prevalence of tobacco smoking was 22.5% with wide heterogeneity (3.9% in Nigeria to 38.2% in Greece). Among current smokers, quit attempts over the prior 12 months also varied with a median of 42.5% (ranging from 14.4% in China to 59.6% in Senegal). Knowledge that smoking causes heart attacks (median, 83.6%; range, 38.7% in China to 95.5% in Turkey) and stroke (median 73.6%; range, 27.2% in China to 89.2% in Romania) varied widely across countries.103

Chart 3-6.

Chart 3-6. Age-standardized global mortality rates attributable to tobacco per 100 000, both sexes, 2019. Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.99 Printed with permission. Copyright © 2020, University of Washington. Detailed results are available on the GBD website.105

References

Abbreviations Used in Chapter 3

ABIankle-brachial index
ACSacute coronary syndrome
AHAAmerican Heart Association
BRFSSBehavioral Risk Factor Surveillance System
CDCCenters for Disease Control and Prevention
CHDcoronary heart disease
CIconfidence interval
CVDcardiovascular disease
CVHcardiovascular health
DALYdisability-adjusted life-year
DBPdiastolic blood pressure
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
FDAUS Food and Drug Administration
GBDGlobal Burden of Disease Study
HDheart disease
HFheart failure
HFpEFheart failure with preserved ejection fraction
HFrEFheart failure with reduced ejection fraction
HIVhuman immunodeficiency virus
HRhazard ratio
MEPSMedical Expenditure Panel Survey
MESAMulti-Ethnic Study of Atherosclerosis
MImyocardial infarction
MPOWERMonitor tobacco use and prevention policies
NHnon-Hispanic
NHANESNational Health and Nutrition Examination Survey
NHISNational Health Interview Survey
NSDUHNational Survey on Drug Use and Health
NYTSNational Youth Tobacco Survey
ORodds ratio
PADperipheral artery disease
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
SHSStrong Heart Study
UIuncertainty interval
WHOWorld Health Organization
YRBSSYouth Risk Behavior Survey

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4. PHYSICAL INACTIVITY

See Charts 4-1 through 4-13

Physical inactivity is defined as an insufficient level to meet the current PA recommendations.1 Physical inactivity is a major risk factor for incident CVD (eg, CHD, stroke, PAD, HF).2 Achieving the guideline recommendations for PA is one of the AHA’s 7 components of ideal CVH for both children and adults.3

Chart 4-1.

Chart 4-1. Prevalence of meeting both the aerobic and muscle-strengthening guidelines among US adults ≥18 years of age, overall and by sex and race/ethnicity, 2018. Error bars represent 95% confidence intervals. Data are age adjusted to the year 2000 standard population for adults ≥18 years of age. The 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination (eg, aerobic guideline). The 2018 Physical Activity Guidelines for Americans also recommend engaging in muscle-strengthening activities ≥2 d/wk (eg, muscle-strengthening guideline). NH indicates non-Hispanic. Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2018.24

The 2018 Physical Activity Guidelines for Americans recommend that children and adolescents accumulate at least 60 minutes of PA daily (including aerobic and muscle- and bone-strengthening activity).4 In 2017, on the basis of survey interviews,5 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.6

The 2018 Physical Activity Guidelines for Americans4 and the 2019 CVD Primary Prevention Clinical Practice Guidelines7 recommend that adults accumulate at least 150 min/wk of moderate-intensity or 75 min/wk of vigorous-intensity aerobic activity (or an equivalent combination) and perform muscle-strengthening activities at least 2 d/wk. For many people, examples of moderate-intensity activities include walking briskly or raking the yard, and examples of vigorous-intensity activities include jogging, carrying loads upstairs, or shoveling snow. In a nationally representative sample of adults in 2018, only 24.0% reported participating in adequate leisure-time aerobic and muscle-strengthening activity to meet these criteria (Chart 4-1).8

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, diabetes, and obesity) 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 observed across the life span, including for children and older adults, pregnant females, and people with disabilities and chronic conditions. Therefore, the 2018 Physical Activity Guidelines for Americans recommend being as physically active as abilities and conditions allow and that some PA is better than none.4 Even small increases in moderate-intensity PA or replacing sedentary behavior (defined as “any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting, reclining, or lying posture”1) with light-intensity PA could provide health benefits.4,9

Defining and Measuring PA

There are several PA dimensions (eg, mode or type, frequency, duration, and intensity) and PA domains (eg, occupational, domestic, transportation, and leisure time). There are additional considerations of where PA occurs such as in homes, worksites, schools, and communities. The federal guidelines specify the suggested frequency, duration, and intensity of PA and focus on 2 types: aerobic and strengthening.

There are 2 broad categories of methods to assess PA: (1) self-reported methods that use questionnaires and diaries/logs and (2) device-based methods that use wearables (eg, pedometers, accelerometers). Studies that compare the findings between methods show that there is marked discordance between self-reported and measured PA, with respondents often overstating their PA compared with device-based measures.6

Another consideration in the measurement of PA is that surveys often ask only about leisure-time PA, which represents PA obtained from a single domain. People who obtain high PA in other domains might be less likely to engage in leisure-time PA. For example, 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 when assessments focused only on leisure-time PA are used.12

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 such as a genetic predisposition to perform aerobic exercise can contribute.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 often stronger than the relationship of PA to clinical outcomes.13 The WHO created an action plan to improve cardiorespiratory fitness globally with a goal to reduce the prevalence of insufficient PA by 15% by 2030.16

Prevalence

Youth
(See Charts 4-2 through 4-5)
  • On the basis of self-reported PA (YRBSS, 2017)5:

    • — 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 was lower with each successive grade (from 9th [30.6%] to 12th [22.9%] grades). At each grade level, the prevalence was higher in boys than in girls.

    • — The prevalence of high school students who met PA recommendations on all 7 d/wk or on at least 5 of 7 d/wk was higher among boys than girls overall and stratified by race/ethnicity (Chart 4-2).

    • — Among high school students, 15.4% 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 not meeting recommendations on any day (19.5% versus 11.0%), with NH Black girls reporting the highest prevalence of inactivity (26.6%; Chart 4-3).

    • — Among high school students, 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 than among female students (Chart 4-4).

  • With the use of accelerometry (NHANES, 2003–2006),17 youth 6 to 19 years of age had a median of 53 min/d of moderate to vigorous PA.

    • — These levels of moderate to vigorous PA in youth were lower in girls and lower with greater age, with median values ranging from 82 to 138 min/d in boys 6 to 9 years of age and 64 to 111 min/d in girls 6 to 9 years of age, 39 to 67 min/d in boys 10 to 13 years of age and 20 to 49 min/d in girls 10 to 13 years of age, and 29 to 33 min/d in boys 14 to 17 years of age and 14 to 16 min/d in girls 14 to 17 years of age.

  • With regard to 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)5:

    • — The proportion of high school students who participated in muscle-strengthening activities on ≥3 d/wk was 51.1% nationwide and was lower with successively higher grades (9th grade: males, 66.4%, females, 49.3%; 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 d/wk.

Chart 4-2.

Chart 4-2. Prevalence of US students in grades 9 to 12 who were active at least 60 min/d on at least 5 and all 7 days by race/ethnicity and sex, 2017. Error bars represent 95% confidence intervals. This time included physical activity that increased heart rate and breathing some of the time during the 7 days before the survey. NH indicates non-Hispanic. Source: Data derived from Kann et al5 using Youth Risk Behavior Surveillance System, 2017.130

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. Error bars represent 95% confidence intervals. This time included physical activity that increased heart rate and breathing some of the time during the 7 days before the survey. NH indicates non-Hispanic. Source: Data derived from Kann et al5 using Youth Risk Behavior Surveillance System, 2017.130

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. Error bars represent 95% confidence intervals. This time included physical activity that increased heart rate and breathing some of the time during the 7 days before the survey. Source: Data derived from Kann et al5 using Youth Risk Behavior Surveillance System, 2017.130

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. Error bars represent 95% confidence intervals. Source: Data derived from Gahche et al18 using National Health and Nutrition Examination Survey, National Youth Fitness Survey, 2012.131

Structured Activity Participation in Schools and Sports
  • Only 29.9% of students attended physical education classes in school daily (34.7% of boys and 25.3% of girls; YRBSS, 2017).5

  • Daily physical education class participation was lower with successively higher grades 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, 2017).5

  • 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, 2017).5

  • 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).

    • — Nationwide, 43.0% of high school students used a computer, tablet, or smartphone for activities other than school work (eg, video games, texting, YouTube, or social media) for ≥3 h/d on an average school day (YRBSS, 2017).5

    • — Among high school students, 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%) (YRBSS, 2017).5 The prevalence of playing video games or using a computer ≥3 h/d (for activities other than schoolwork) was higher among boys and girls (Chart 4-6) (YRBSS, 2017).5

  • A nationally representative survey conducted in 2015 of 2658 US children 8 to 18 years of age indicated that tweens (8–12 years of age) use entertainment media (eg, television, video games, internet, music, social media) on average 5 hours 55 minutes per day whereas teenagers (13–18 years of age) average 8 hours 56 minutes per day outside of school or homework.21 Total screen time is higher for teenagers (6 hours 40 minutes) than for tweens (4 hours 36 minutes).21

  • A nationally representative survey conducted in 2017 of 1454 parents of US children ≤8 years of age indicated that on average children spend 2 hours 19 minutes per day on screen media.22 Children ≤2 years of age spend on average 42 min/d on screen media.22 Despite recommendations by the American Academy of Pediatrics23 to refrain from media use 1 hour before bedtime, 49% of children ≤8 years of age watched television or videos or played video games in the hour before bedtime.22

Chart 4-6.

Chart 4-6. Percentage of US students in grades 9 to 12 who played video or computer games or used a computer* for ≥3 hours on an average school day by race/ethnicity and sex, 2017. Error bars represent 95% confidence intervals. NH indicates non-Hispanic. *For something other than schoolwork. Source: Data derived from Kann et al5 using Youth Risk Behavior Surveillance System, 2017.130

Adults
(See Charts 4-7 through 4-13)
  • For self-reported leisure-time aerobic PA (NHIS, 2018)8,24:

    • — The age-adjusted proportion who reported meeting the 2018 aerobic PA guidelines for Americans (≥150 minutes of moderate PA, ≥75 minutes of vigorous PA, or an equivalent combination each week) through leisure-time activities is shown in Chart 4-7. Among both males and females, NH White adults were more likely to meet the PA aerobic guidelines with leisure-time activity than NH Black and Hispanic adults. For each racial/ethnic group, males had higher PA than females.25

  • Adults with disabilities were less likely to meet the federal aerobic PA guidelines through leisure-time activities than those without disabilities (Chart 4-11).8 This pattern was similar for meeting recommendations for both aerobic and strengthening.

  • In 2018, 25.4% of adults did not engage in leisure-time PA (no sessions of leisure-time PA of ≥10 minutes in duration; Chart 4-12).8

  • From accelerometer-assessed PA (NHANES, 2005–2006),26 US adults were estimated to participate in 45.1 min/wk (SE, 4.6 min/wk) of moderate PA and 18.6 min/wk (SE, 6.6 min/wk) of vigorous PA. Levels of moderate and vigorous PA were lower in older adults (60–69 years of age; moderate, 32.7 min/wk [SE, 3.6 min/wk]; vigorous, 1.4 min/wk [SE, 0.7 min/wk]) compared with adults in younger age groups (eg, 40–49 years of age; moderate, 54.1 min/wk [SE, 12.8 min/wk]; vigorous, 24.9 min/wk [SE, 16.6 min/wk]).

    • — Accelerometer data (2003–2006) also revealed that rural US adults performed less moderate to vigorous PA than urban adults, but rural adults spent more time in lighter-intensity PA (accelerometer counts per minute, 760–2020) than their urban adult counterparts.27

    • — In contrast to self-reported PA, which suggested that NH White individuals had higher levels of PA,28 data from accelerometer-assessed PA revealed that Mexican American adults had higher total PA and moderate to vigorous PA than NH White or Black adults (≥20 years of age).26

  • In a 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. However, for males who were more active in middle age, retirement is associated with higher PA.29

  • A report using data from 2018 indicated that US adults spent on average 10.5 h/d connected to media (eg, television, radio, smartphone, tablet, internet on computer), with adults 50 to 64 years of age spending the most time per day on media compared with any other age group.30 This same report estimated that on average Black adults spent 12 hours 58 minutes, Hispanic adults spent 9 hours 17 minutes, and Asian American adults spent 6 hours 46 minutes per day connected to media. These habits affect time available for PA and contribute to sedentary behavior.

Chart 4-7.

Chart 4-7. Prevalence of meeting the aerobic guidelines among US adults ≥18 years of age by race/ethnicity and sex, 2018. Error bars represent 95% confidence intervals. Percentages are age adjusted. The aerobic guidelines of the 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination. NH indicates non-Hispanic. Source: American Heart Association unpublished tabulation of National Health Interview Survey, 2018.24

Pregnancy and Postpartum
  • PA is recommended for pregnant females without obstetric or medical complications.4,31,32 Several reviews of the literature that supported these guidelines indicate that PA during pregnancy can decrease the odds of excessive gestational weight gain,33,34 gestational diabetes,33,35 preeclampsia and gestational hypertension,35 and depressive symptoms.36 PA also can assist with postpartum weight retention34 and postpartum depressive symptoms.33

  • US estimates from NHANES (2007–2014) indicate that 12.7% to 45.0% of pregnant females meet the 2015 American College of Obstetrics and Gynecology guidelines.37 Accelerometer-assessed PA measures from NHANES (2003–2006) indicate that the population of US pregnant females averaged 12 min/d of moderate activity and 57% of their monitored day in sedentary behavior (average, 424 min/d).38

  • For more information, see Chapter 11 on pregnancy.

Structured Activity Participation in Leisure-Time, Domestic, Occupational, and Transportation Activities
  • Individuals from urban areas who participated in NHANES (2003–2006) 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 accelerometry.27

    • — 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.39

  • A 1-day assessment indicated that the mean prevalence of any active transportation was 10.3% on the basis of 2012 data from the American Time Use Study. NH White individuals reported the lowest active transport (9.2%), followed by 11.0% of Hispanic individuals, 13.4% of NH Black individuals, and 15.0% of other NH individuals.40

Sitting Time
  • According to data from the 2015 to 2016 NHANES, prevalence of time spent sitting >8 h/d was reported at 25.7% and was successively higher with older age.41

Secular Trends

Youth
Physical Activity
  • Among students nationwide, there was a significant increase in the proportion reporting participation in muscle-strengthening activities on ≥3 d/wk, from 47.8% in 1991 to 51.1% in 2017; however, the prevalence did not change substantively from 2011 (55.6%) to 2017 (51.1%).5,42

  • Nationwide, the number of high school students who reported attending physical education classes at least once per week (on an average week while in school) did not change substantively between 1991 (48.9%) and 2017 (51.7%).5,42 Similar patterns were observed for attending physical education classes on all 5 days.

  • 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%).5,42

Cardiorespiratory Fitness
  • 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%, down from 52.4% in the combined years from 1999 to 2000.18

Television/Video/Computers
  • According to NHANES, sitting and watching television or videos at least 2 h/d remained high over time for youth 5 to 11 years of age (65.5% in 2001–2002 to 62.2% in 2015–2016) and youth 12 to 19 years of age (64.2% in 2003–2004 to 59.4% in 2015–2016).43

  • A significant increase occurred in the number of youth reporting having used computers for something other than schoolwork for ≥3 h/d in 2017 (43.0%) compared with 2003 (22.1%).5,42

  • A nationally representative survey of parents to children ≤8 years of age indicated that smartphone ownership in the home has risen from 41% in 2011 to 95% in 2017; tablet ownership also rose from 8% in 2011 to 78% in 2017.22 Among children ≤8 years of age, the amount of screen time was similar for 2011 (2 hours 16 minutes) and 2017 (2 hours 19 minutes), but the type of media accessed was shifting.22

Adults

  • The prevalence of physical inactivity among adults ≥18 years of age, overall and by sex, has decreased from 1998 to 2018 (Chart 4-12).44

  • The age-adjusted percentage of US adults who reported meeting both the muscle-strengthening and aerobic guidelines increased from 18.2% in 2008 to 24.0% in 2018.44 The percentage of US adults who reported meeting the aerobic guidelines increased from 43.5% in 2008 to 54.2% in 2018.44

  • 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% (2005–2015).45

  • According to NHANES, sitting and watching television or videos at least 2 h/d remained high over time for adults ≥20 years of age (64.7% in 2003–2004 to 65.1% in 2015–2016).43 Nielsen reports of adult smartphone app/web use comparing data collected in 2012 (48 min/d)46 to 2017 (2 hours 28 minutes per day)47 suggest large 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.

Social Determinants

  • The proportion of adults ≥25 years of age who met the 2018 PA guidelines for aerobic PA through leisure-time activities was higher with successively higher educational attainment (Chart 4-8).8 This pattern was similar for meeting recommendations for both aerobic and strengthening activities.

  • Adults residing in urban areas (metropolitan statistical areas) were more likely to meet the federal aerobic PA guidelines through leisure-time activities than those residing in rural areas (55.2% versus 47.5%; Chart 4-9).8 This pattern was similar for meeting recommendations for both aerobic and strengthening activities.

  • Categories of adults living above the poverty level were successively more likely to meet the federal aerobic PA guidelines through leisure-time activities than those living below the poverty level (<100%) (Chart 4-10).8 When considering meeting both the aerobic and strengthening PA recommendations, the stepwise pattern persisted, with a higher percent of adults meeting recommendations the further away from the poverty line of 100%.

  • In an analysis from the NIH-AARP Diet and Health Study, severe neighborhood socioeconomic deprivation was prospectively associated with less exercise time in hours (highest quintile versus lowest quintile, −0.85 [95% CI, −0.95 to −0.75]) among 136 526 participants 51 to 70 years of age.48

Chart 4-8.

Chart 4-8. Prevalence of meeting the aerobic guidelines among US adults ≥25 years of age by educational attainment, 2018. Error bars represent 95% confidence intervals. Data are age adjusted to the year 2000 standard population for adults ≥18 years of age. The 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination (eg, aerobic guideline). The 2018 Physical Activity Guidelines for Americans also recommend engaging in muscle-strengthening activities ≥2 d/wk (eg, muscle-strengthening guideline). Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2018.24

Chart 4-9.

Chart 4-9. Prevalence of meeting the aerobic guidelines among US adults ≥18 years of age by location of residence, 2018. Error bars represent 95% confidence intervals. Data are age adjusted to the year 2000 standard population for adults ≥18 years of age. The 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for ≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination (eg, aerobic guideline). The 2018 Physical Activity Guidelines for Americans also recommend engaging in muscle-strengthening activities ≥2 d/wk (eg, muscle-strengthening guideline). Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2018.24

Chart 4-10.

Chart 4-10. Prevalence of meeting the aerobic and muscle-strengthening guidelines among US adults ≥18 years of age by family income (percent of poverty threshold), 2018. Error bars represent 95% confidence intervals. Data are age adjusted to the year 2000 standard population for adults ≥18 years of age. The 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination (eg, aerobic guideline). The 2018 Physical Activity Guidelines for Americans also recommend engaging in muscle-strengthening activities ≥2 d/wk (eg, muscle-strengthening guideline). Poverty status is based on family income and family size using the US Census Bureau poverty thresholds. Source: Data derived from Healthy People 20208 using National Health Interview Survey, 2018.24

Chart 4-11.

Chart 4-11. Prevalence of meeting both the aerobic and muscle-strengthening guidelines among US adults ≥18 years of age by disability status, 2017. Error bars represent 95% confidence intervals. Percentages are age adjusted. The 2018 Physical Activity Guidelines for Americans recommend engaging in moderate leisure-time physical activity for≥150 min/wk, vigorous activity for ≥75 min/wk, or an equivalent combination (eg, aerobic guideline). The 2018 Physical Activity Guidelines for Americans also recommend engaging in muscle-strengthening activities ≥2 d/wk (eg, muscle-strengthening guideline). Source: Data derived from Healthy People 20208 using 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 2018. Data are age adjusted to the year 2000 standard population for adults ≥18 years of age. 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 2018.24

Family History and Genetics

  • Genetic factors contribute to the propensity to exercise.49-51 More work is needed to identify genetic factors that contribute to higher PA or physical inactivity.49

Prevention of Physical Inactivity

The US Surgeon General has introduced Step It Up! A Call to Action to Promote Walking and Walkable Communities in recognition of the importance of PA.52 There are roles for communities, schools, and worksites.

Communities
  • Community-level interventions are effective in promoting PA. Communities can encourage walking with street design that includes sidewalks and improved street lighting and landscaping design that reduces traffic speed to improve pedestrian safety.53

  • Higher neighborhood walkability has been associated with lower prevalence of overweight and obesity and lower incidence of diabetes.54 Moving to a walkable neighborhood was associated with a lower risk for incident hypertension in the Canadian Community Health Survey.55

Schools
  • Schools can provide opportunities for PA through physical education, recess, before- and after-school activity programs, and PA breaks, as well as offering a place for PA for the community.56

  • Requiring daily physical education in US middle schools decreased from 10.5% in 2006 to 3.4% in 2014 and in US high schools increased from 2.1% in 2006 to 4.0% in 2014.57 The proportion of students in grades 9 to 12 who participated in daily physical education did not meaningfully change between 2009 (33.3%) and 2013 (29.4%).57

  • In 2012, the School Health Policies and Practices Study also reported that 58.9% of school districts required regular elementary school recess, a proportion similar to that in 2006 (57.1%).57

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.58,59

Mortality

Self-Reported PA, Sedentary Behavior, and Mortality
  • In an analysis from NHIS, among 67 762 adults with >20 years of follow-up, 8.7% of all-cause mortality was attributed to a PA level of <150 min/wk of moderate-intensity PA.60

  • A meta-analysis of 9 cohort studies, representing 122 417 adults ≥60 years of age, found that as little as 15 minutes of daily moderate to vigorous PA reduced all-cause mortality.61 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 below the amount recommended by the federal guidelines.

  • In a pooled study of >600 000 adults,62 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]). There was no evidence of harm associated with performing ≥10 times the recommended minimum (HR, 0.68 [95% CI, 0.59–0.78]).62

  • 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 of strength training was not associated with lower risk of all-cause mortality (HR, 1.10 [95% CI, 0.77–1.56]).63 The HRs were adjusted for potential confounders and aerobic activity.

  • A meta-analysis of 23 studies revealed an association between participating in more transportation-related PA and lower all-cause mortality, CVD, and diabetes.64

  • In the UK Biobank of 263 540 participants, 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.65

  • A meta-analysis including 193 696 adults reported that high occupational PA was associated with a greater risk of all-cause mortality in males (HR, 1.18 [95% CI, 1.05–1.34]) compared with low occupational PA.66 However, a nonsignificant decrease in all-cause mortality was observed among females with high occupational PA (HR, 0.90 [95% CI, 0.80–1.01]) compared with those with low occupational PA. It is unclear whether factors such as fitness, SES, preexisting CVD, type of occupation, and other domains of PA may modify this relationship.

  • In a meta-analysis of 13 studies, higher sedentary behavior 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 of PA.67

  • 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.68

  • In a prospective US cohort study (CPS-II) of 127 544 adults, prolonged leisure-time sitting (≥6 h/d versus <3 h/d) was associated with higher risk of mortality from all causes and CVD (including CHD and stroke-specific mortality).69

  • With the use of 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-intensity 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.70

Device-Measured PA, Sedentary Behavior, and Mortality
  • Among 3029 NHANES adults 50 to 79 years of age in 2003 to 2006, 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 10 min/d of light activity was associated with lower all-cause mortality (HR, 0.91 [95% CI, 0.86–0.96]).71

  • In a landmark harmonization effort of 8 prospective studies with accelerometry, over a median of 5.8 years of follow-up, the highest quartile of light (HR, 0.38–0.60) and moderate to vigorous (HR, 0.52–0.64) PA compared with the lowest quartile (least active) was associated with a lower risk of all-cause mortality.72 Time in sedentary behavior was associated with a higher risk of all-cause mortality (HR, 1.28–2.63 across quartiles) compared with the lowest quartile (least sedentary).

  • Step counting is recommended as an effective method for translating PA guidelines and monitoring PA levels because of its simplicity and the increase in step-counting devices.9 All longitudinal studies included in a systematic review reported a favorable dose-response relationship between daily step counts and all-cause mortality (HR, 0.94 [95% CI, 0.90–0.98] per 1000–steps per day increase).73 Among older females, having as few as 4400 steps per day was associated with lower mortality.74 More evidence is needed to set target volumes of PA based on steps per day and to determine the role of cadence (steps per minute; a proxy for intensity of ambulation) in these relationships.9,73

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.75

  • Among a Swedish cohort of 266 109 adults 18 to 74 years of age, risk of CVD morbidity and all-cause mortality decreased 2.6% and 2.3% per 1–mL·min−1·kg−1 increase, respectively, in cardiorespiratory fitness estimated from a submaximal bicycle test.76 The risk reduction with higher cardiorespiratory fitness was observed for both males and females across ages.

  • In the UK Biobank, 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.77

Benefits of PA and Complications of Inactivity

Youth
Benefits
  • In a study of 36 956 Brazilian adolescents, higher self-reported 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.78

  • In a prospective study of 700 Norwegian 10-year-old children, higher levels of accelerometer-assessed moderate PA at baseline were associated with lower triglyceride levels and lower insulin resistance at the 7-month follow-up. In contrast, sedentary duration was not associated with cardiometabolic risk factors at follow-up.79

  • Among the NHANES 2003 to 2006 cohort of youths 6 to 17 years of age, those with the highest levels of accelerometer-assessed PA had lower SBP, lower glucose levels, and lower insulin levels than youths in the lowest PA group.80

Complications
  • A higher amount of accelerometer-measured sedentary duration among children 0 to 14 years of age is associated with greater odds of hypertriglyceridemia and cardiometabolic risk.81

Adults
Cardiovascular and Metabolic Risk Factors
Benefits
  • 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).82

  • 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 the 10-year follow-up compared with using passive modes of transportation.83

  • Even lighter-intensity activities such as yoga were reported to improve BMI, BP, triglycerides, LDL-C, and HDL-C but not FPG in a meta-analysis of 32 RCTs comparing yoga with nonexercise control.84

  • 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]).85

  • A systematic review reported favorable dose-response relationships between daily step counts and both type 2 diabetes (25% reduction in 5-year dysglycemia incidence per 2000-step/d increase) and MetS (29% reduction in 6-year metabolic score per 2000-step/d increase).73

  • Intermittent breaks of 10 minutes of 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.86

Complications
  • 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%).87

  • In a population-based study of Hispanic/Latino adults, higher levels of sedentary time were associated cross-sectionally 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.88,89

Pregnancy
  • 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]).90

Cardiovascular Events
Benefits
  • A study of the factors related to declining CVD among Norwegian adults ≥25 years of age found that increased PA (≥1 h/wk of strenuous PA) accounted for 9% of the decline in hospitalized and nonhospitalized fatal and nonfatal CHD events.91

  • 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) intensity levels of PA were associated with a graded lower risk of major cardiovascular events (HR for 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.92

  • In the 2-year LIFE study of older adults (mean age, 78.9 years), higher levels of accelerometer-assessed PA and daily steps were associated with lower risk of adverse cardiovascular events.93

  • A systematic review reported a favorable dose-response relationship between daily step counts and cardiovascular events (defined as cardiovascular death, nonfatal MI, or nonfatal stroke; 8% yearly rate reduction per 2000–steps per day increase).73

  • In the WHI, every 1-h/d increase in accelerometer-assessed light-intensity PA was associated with a lower risk of CHD (HR, 0.86 [95% CI, 0.73–1.00]) and lower CVD (HR, 0.92 [95% CI, 0.85–0.99]).94

  • Domains of PA other than leisure time are understudied. A meta-analysis reported a protective relationship between transportation activity and cardiovascular risk, which was greater in females.95 However, higher occupational PA has been associated with higher MI incidence in males 19 to 70 years of age.96,97 These relationships require further investigation because a protective association of occupational activity with MI has been reported in young males (19–44 years of age).97

  • The Rotterdam Study evaluated the contribution of specific PA types to 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 levels of domestic work in females (2.4 years) and high levels of gardening in males (2 years) were also associated with an increased CVD-free life span.98

  • With an average of 27 years of follow-up, estimates from 13 534 ARIC participants indicated that those who engaged in past-year leisure-time PA at least at median levels had a longer life expectancy free of nonfatal CHD (1.5–1.6 years), stroke (1.8 years), and HF (1.6–1.7 years) compared with those who did not engage in leisure-time PA.99 In addition, those watching less television had longer life expectancy free of CHD, stroke, and HF of close to 1 year.

Complications
  • In a dose-response meta-analysis of 9 prospective cohort studies (n=720 425), higher levels of sedentary behavior were associated with greater risk of CVD in a nonlinear relationship (HR for highest versus lowest sedentary behavior, 1.14 [95% CI, 1.09–1.19]).100

Heart Failure
  • In a meta-analysis of 12 prospective cohort studies (n=370 460), there was an inverse dose-dependent association between self-reported PA 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 2 and 4 times the guideline-recommended levels was associated with 19% and 35% lower risk of HF, respectively.101

  • Furthermore, an individual-level pooled analysis of 3 large cohort studies demonstrated that the strong, dose-dependent association between higher self-reported leisure-time PA and lower risk of HF is driven largely by lower risk of HFpEF but not HFrEF.102

  • In a prospective study that monitored 902 patients with HF (with HFpEF or HFrEF) 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 h/d versus >4 h/d) also was associated with lower all-cause death.103

  • Lower levels of cardiorespiratory fitness have also been associated with higher risk of HF in a 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]).104

Secondary Prevention
  • Cardiac rehabilitation, a multicomponent intervention that includes aerobic exercise and strengthening, is recommended for those with CVD to reduce hospital admissions, secondary events, and mortality.105,106 Underuse of cardiac rehabilitation remains a persistent problem; newer approaches such as home-based cardiac rehabilitation are being explored.106 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.107

  • 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.108

    • — Among 1746 patients with CAD 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.109

    • — 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]).110

  • Using data from a registry of stable outpatients with symptomatic coronary disease, cerebrovascular disease, or PAD showed that 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.111

  • 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, 90, and 365 days after MI.112 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]).113

  • A study of women in the WHI observational study who experienced a clinical MI demonstrated that 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]) compared with those who maintained low PA levels after an MI.114

  • Among 2370 individuals with CVD who responded to the Taiwan NHIS, achieving more total PA, leisure-time PA, and domestic and work-related PA was associated with lower mortality at the 7-year follow-up.115

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 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 the Life’s Simple 7 strategies promotes achievement of adequate PA.10 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.116 Results from intervention trials have been more inconsistent.117–120 However, there have been some promising results from a study that observed better executive function in those who adhered to a multidomain (exercise, cognitive training, and Mediterranean diet) intervention for 2 years.117

  • Evidence from meta-analyses in patients with stroke 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 patients with stroke (Hedges g, 0.30 [95% CI, 0.14–0.47]) in a meta-analysis representing data from 736 participants.121 Another meta-analysis of studies involving patients with stroke 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.122

Costs

  • The economic consequences of physical inactivity are substantial. A global analysis of 142 countries (93.2% of the world’s population) concluded that physical inactivity cost health care systems $53.8 billion in 2013, including $9.7 billion paid by individual households.123

  • 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 health care expenditures (including expenditures for inpatient, outpatient, ED, office-based, dental, vision, home health, prescription drug, and other services).124

  • An evaluation of health care 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. Health care 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).125

  • Interventions and community strategies to increase PA have been shown to be cost-effective in terms of reducing medical costs126,127:

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

    • — The ICER 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 females reporting insufficient PA is 8% higher than males, globally.128

  • The GBD 2019 study used statistical models and data on incidence, prevalence, case fatality, excess mortality, and cause-specific mortality to estimate disease burden for 369 diseases and injuries and 87 risk factors in 204 countries and territories.129 Mortality rates attributable to low PA are highest in North Africa and the Middle East (Chart 4-13).

  • Physical inactivity was responsible for 831 502 deaths in 2019.129 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.92

Chart 4-13.

Chart 4-13. Age-standardized global mortality rates attributable to low physical activity per 100 000, both sexes, 2019. Source: Data derived from Global Burden of Disease Study 2019, Institute for Health Metrics and Evaluation, University of Washington.129 Printed with permission. Copyright © 2020, University of Washington. Detailed results are available on the Global Burden of Disease Study website.132

Abbreviations Used in Chapter 4

AHAAmerican Heart Association
AMIacute myocardial infarction
appapplication
ARICAtherosclerosis Risk in Communities study
BMIbody mass index
BPblood pressure
CADcoronary artery disease
CHDcoronary heart disease
CIconfidence interval
CPS-IICancer Prevention Study II
CVDcardiovascular disease
CVHcardiovascular health
DBPdiastolic blood pressure
EDemergency department
FPGfasting plasma glucose
GBDGlobal Burden of Disease Study
HBPhigh blood pressure
HDL-Chigh-density lipoprotein cholesterol
HFheart failure
HFpEFheart failure with preserved ejection fraction
HFrEFheart failure with reduced ejection fraction
HRhazard ratio
ICERincremental cost-effectiveness ratio
LDL-Clow-density lipoprotein cholesterol
LIFELifestyle Interventions and Independence for Elders
METmetabolic equivalent
MetSmetabolic syndrome
MImyocardial infarction
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
RRrelative risk
SBPsystolic blood pressure
SEstandard error
SESsocioeconomic status
WCwaist circumference
WHIWomen’s Health Initiative
WHOWorld Health Organization
WHSWomen’s Health Study
YRBSSYouth Risk Behavior Surveillance System

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