Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association
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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).
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 2022 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 and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.
Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.
The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
Table of Contents
Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter.
Abbreviations Table e165
About These Statistics e170
Cardiovascular Health e173
Smoking/Tobacco Use e194
Physical Activity and Sedentary Behavior e210
Overweight and Obesity e246
Health Factors and Other Risk Factors
High Blood Cholesterol and Other Lipids e260
High Blood Pressure e274
Metabolic Syndrome e311
Adverse Pregnancy Outcomes e333
Kidney Disease e354
Total Cardiovascular Diseases e374
Stroke (Cerebrovascular Diseases) e391
Brain Health e427
Congenital Cardiovascular Defects and Kawasaki Disease e445
Disorders of Heart Rhythm e462
Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies e489
Subclinical Atherosclerosis e517
Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e528
Cardiomyopathy and Heart Failure e547
Valvular Diseases e562
Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension e580
Peripheral Artery Disease and Aortic Diseases e591
Quality of Care e611
Medical Procedures e625
Economic Cost of Cardiovascular Disease e630
At-a-Glance Summary Tables e633
Each year, the American Heart Association (AHA), in conjunction with the National Institutes of Health and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors in the AHA’s My Life Check−Life’s Simple 7 (Figure),1 which include core health behaviors (smoking, physical activity [PA], diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health (CVH). The Statistical Update represents a critical resource for the lay public, policymakers, 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 heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure [HF], valvular heart disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). Since 2007, the annual versions of the Statistical Update have been cited >20 000 times in the literature.
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, CIs for statistics reported below, and additional information.
Cardiovascular Health (Chapter 2)
A report pooled NHANES (National Health and Nutrition Examination Survey) 2011 to 2016 data and individual-level data from 7 US community-based cohort studies and estimated that 70.0% of major CVD events in the United States were attributable to low and moderate CVH; 2.0 million major CVD events could potentially be prevented each year if all US adults attain high CVH; and even a partial improvement in CVH scores to the moderate level among all US adults with low overall CVH could lead to a reduction of 1.2 million major CVD events annually.
The large number of individuals in the United States who contracted severe illness because of coronavirus disease 2019 (COVID-19) resulted in a huge mortality toll. As of March 2021, the cumulative number of COVID-19 deaths in the United States was ≈545 000, which equates to ≈166 cases per 100 000 people, with higher rates of deaths occurring among US counties with metropolitan areas (≈185 deaths per 100 000), with a high percentage (>45.5%) of the population that is non-Hispanic (NH) Black (≈200 deaths per 100 000), with a high proportion (>37%) of the population that is Hispanic (≈219 deaths per 100 000), or with a high percentage (>17.3%) of the population that are living in poverty (≈211 deaths per 100 000 people).
Because of the high COVID-19 mortality rates, life expectancy in the United States for the year 2020 has been estimated to decline with disproportionate impacts on populations with high COVID-19 mortality rates. Provisional US life expectancy estimates for January to June 2020 indicate that between 2019 and the first half of 2020, life expectancy decreased from 74.7 to 72.0 years for NH Black individuals, from 81.8 to 79.9 years for Hispanic individuals, and from 78.8 to 78.0 years for NH White individuals.
Smoking/Tobacco Use (Chapter 3)
The prevalence of cigarette use in the past 30 days among middle and high school students in the United States was 1.6% and 4.6%, respectively, in 2020.
Although there has been a consistent decline in adult and youth cigarette use in the United States in the past 2 decades, significant disparities persist. Substantially higher tobacco use prevalence rates are observed in American Indian/Alaska Native adults and youth and lesbian, gay, and bisexual adults.
Over the past 9 years, there has been a sharp increase in electronic cigarette use among adolescents, increasing from 1.5% to 19.6% between 2011 and 2020; electronic cigarettes are now the most commonly used tobacco product in this demographic.
Physical Activity and Sedentary Behavior (Chapter 4)
According to nationwide self-reported PA (YRBSS [Youth Risk Behavior Surveillance System], 2019), the prevalence of high school students who engaged in ≥60 minutes of PA on at least 5 days of the week was 44.1% and was lower with each successive grade (from 9th [49.1%]–12th [40.0%] grades).
From nationwide self-reported PA (NHIS, 2018), the age-adjusted proportion who reported meeting the 2018 aerobic PA guidelines for Americans was 54.2%.
An umbrella review of 24 systematic reviews of adults ≥60 years of age concluded that those who are physically active are at a reduced risk of CVD mortality (25%–40% risk reduction), all-cause mortality (22%–35%), breast cancer (12%–17%), prostate cancer (9%–10%), and depression (17%–31%) while experiencing better quality of life, healthier aging trajectories, and improved cognitive functioning.
Nutrition (Chapter 5)
Data from the Nurses’ Health Study (1984–2014) and Health Professionals Follow-up Study showed that daily intake of 5 servings of fruit and vegetables (versus 2 servings/d) was associated with 13% lower total mortality, 12% lower CVD mortality, 10% lower cancer mortality, and 35% lower respiratory disease mortality.
NHANES data and meta-analyses of prospective cohort studies show that higher intakes of total fat, polyunsaturated fatty acids, and monounsaturated fatty acids are associated with lower total mortality. However, the evidence for saturated fatty acid intake as a risk or protective factor for total and CVD mortality remains controversial.
Meta-analytic evidence from randomized clinical trials does not support vitamin D supplementation for improving cardiometabolic health in children and adolescents between 4 and 19 years of age.
Overweight and Obesity (Chapter 6)
From NHANES data, the overall prevalence of obesity and severe obesity in youth 2 to 19 years of age increased from 13.9% to 19.3% and 2.6% to 6.1% between 1999 to 2000 and 2017 to 2018. Over the same period, the prevalence of obesity and severe obesity increased from 14.0% to 20.5% and from 3.7% to 6.9% for males and from 13.8% to 18.0% and from 3.6% to 5.2% for females.
From NHANES data, among adults, from 1999 to 2000 through 2017 to 2018, the prevalence of obesity among males increased from 27.5% to 43.0% and severe obesity increased from 3.1% to 6.9%. The prevalence of obesity among females increased from 33.4% to 41.9% and severe obesity from 6.2% to 11.5%.
Significant increases in the prevalence of obesity were seen between 1999 to 2000 through 2017 to 2018 in all age-race and ethnicity groups except for NH Black males, in whom the prevalence increased from 1999 through 2006.
High Blood Cholesterol and Other Lipids (Chapter 7)
In 2015 to 2018, unfavorable lipid measures of low-density lipoprotein cholesterol ≥130 mg/dL were present in 6.1% of male adolescents and 3.0% of female adolescents 12 to 19 years of age, triglycerides ≥130 mg/dL were present in 9.7% of male adolescents and 6.6% of female adolescents, and high-density lipoprotein cholesterol measures <40 mg/dL were present in 18.4% of male adolescents and 7.4% of female adolescents.
In 2015 to 2018, total cholesterol ≥200 mg/dL was present in 38.1% of adults, low-density lipoprotein cholesterol ≥130 mg/dL was present in 27.8% of adults, triglycerides ≥150 mg/dL were present in 21.1% of adults, high-density lipoprotein cholesterol <40 mg/dL was present in 17.2% of adults.
High Blood Pressure (Chapter 8)
From 2009 to 2019, the death rate attributable to high BP increased 34.2%, and the actual number of deaths attributable to high BP rose 65.3%.
The 2019 age-adjusted death rate attributable primarily to high BP was 25.1 per 100 000 people. Age-adjusted death rates attributable to high BP (per 100 000 people) in 2019 were 25.7 for NH White males, 56.7 for NH Black males, 23.1 for Hispanic males, 17.4 for NH Asian/Pacific Islander males, 31.9 for NH American Indian/Alaska Native males, 20.6 for NH White females, 38.7 for NH Black females, 17.4 for Hispanic females, 14.5 for NH Asian/Pacific Islander females, and 22.4 for NH American Indian/Alaska Native females.
In an analysis of 18 262 adults ≥18 years of age with hypertension (defined as 140/90 mm Hg) in NHANES, the estimated age-adjusted proportion with controlled BP increased from 31.8% in 1999 to 2000 to 48.5% in 2007 to 2008, remained relatively stable at 53.8% in 2013 to 2014, but declined to 43.7% in 2017 to 2018.
Diabetes (Chapter 9)
In NHANES 2015 to 2018, an estimated 28.2 million adults (10.4%) had diagnosed diabetes, 9.8 million adults (3.8%) had undiagnosed diabetes, and 113.6 million adults (45.8%) had prediabetes.
In NHANES 2003 through 2016, among adults with diagnosed and undiagnosed diabetes, the proportion taking any medication increased from 58% in 2003 through 2004 to 67% in 2015 through 2016, with an increase in the use of metformin and insulin analogs and decrease in sulfonylureas, thiazolidinediones, and human insulin.
In NHANES 1988 through 2018, among adults with newly diagnosed type 2 diabetes, there was a significant increase in the proportion of individuals with hemoglobin A1c <7% (59.8% for 1998–1994 and 73.7% for 2009–2018) and decreases in mean hemoglobin A1c (7.0% and 6.7%), mean BP (130.1/77.5 and 126.0/72.1 mm Hg), and mean total cholesterol (219.4 and 182.4 mg/dL). The proportion with hemoglobin A1c <7.0%, BP <140/90 mm Hg, and total cholesterol <240 mg/dL improved from 31.6% to 56.2%.
Metabolic Syndrome (Chapter 10)
In the HELENA study (Healthy Lifestyle in Europe by Nutrition in Adolescence) among 1037 European adolescents 12.5 to 17.5 years of age, those with mothers with low education showed a higher metabolic syndrome (MetS) risk score (β estimate, 0.54) compared with those with highly educated mothers. Adolescents who accumulated >3 disadvantages (defined as parents with low education, low family affluence, migrant origin, unemployed parents, or nontraditional families) had a higher MetS risk score compared with those who did not experience disadvantage (β estimate, 0.69).
In HCHS/SOL (Hispanic Community Health Study/Study of Latinos), socioeconomic status was inversely associated with prevalent MetS among Hispanic/Latino adults of diverse ancestry groups. Higher income and education and full-time employment status versus unemployed status were associated with a 4%, 3%, and 24% decreased odds of having MetS, respectively. The association with income was significant only among females and those with current health insurance.
In combined analysis from ARIC (Atherosclerosis Risk in Communities) and JHS (Jackson Heart Study), among 13 141 White and Black individuals with a mean follow-up of 18.6 years, risk of ischemic stroke increased consistently with MetS severity z score (hazard ratio [HR], 1.75) for those above the 75th percentile compared with those below the 25th percentile. Risk was highest for White females (HR, 2.63), although without significant interaction by sex and race.
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.
Among 2304 female-newborn dyads in the multinational HAPO study (Hyperglycemia and Adverse Pregnancy Outcome), lower CVH (based on 5 metrics: body mass index, BP, cholesterol, glucose, and smoking) at 28 weeks’ gestation was associated with a higher risk of preeclampsia; adjusted relative risks were 3.13, 5.34, and 9.30 for females with ≥1 intermediate, 1 poor, or ≥2 poor (versus all ideal) CVH metrics during pregnancy, respectively.
In analyses of Swedish national birth register data (>2 million–>4 million individuals), gestational age at birth was inversely associated with the risks for type 1 diabetes, type 2 diabetes, hypertension, and lipid disorders among individuals born preterm versus term.
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 14.9% (2015–2018).
Age-, race-, and sex-adjusted prevalence of end-stage renal disease in the United States was 2242 per million people (in 2018) with highest rates among Black adults followed by American Indian/Alaska Native adults, Asian adults, and White adults.
Medicare spent $81 billion caring for people with chronic kidney disease and $49.2 billion on those with end-stage renal disease in 2018.
Sleep (Chapter 13)
In data from the 2014 BRFSS (Behavioral Risk Factor Surveillance System), 11.8% of people reported a sleep duration ≤5 hours, 23.0% reported 6 hours, 29.5% reported 7 hours, 27.7% reported 8 hours, 4.4% reported 9 hours, and 3.6% reported ≥10 hours. Overall, 65.2% met the recommended sleep duration of ≥7 hours.
Analysis of the UK Biobank study (N=468 941) found that participants who reported short sleep (<7 h/d) or long sleep (>9 h/d) had an increased risk of incident HF compared with normal sleepers (7–9 h/d). In males, the adjusted HR was 1.24 for short sleep and 2.48 for long sleep. In females, the adjusted HR was 1.39 for short sleep and 1.99 for long sleep.
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).
Total Cardiovascular Diseases (Chapter 14)
In the Cardiovascular Lifetime Risk Pooling Project among 30 447 participants from 7 US cohort studies, among individuals ≥60 years of age with low CVH, the 35-year risk of CVD was highest in White males (65.5%), followed by White females (57.1%), Black females (51.9%), and Black males (48.4%). These estimated risks accounted for competing risks of death caused by non-CVD causes.
In a meta-analysis of 14 studies that focused on CVD among individuals diagnosed with COVID-19, preexisting CVD had a relative risk of 2.25 for death resulting from COVID-19.
In 2020, ≈19 million deaths were attributed to CVD globally, which amounted to an increase of 18.7% from 2010.
Stroke (Cerebrovascular Diseases) (Chapter 15)
In the Greater Cincinnati Northern Kentucky Stroke Study, sex-specific ischemic stroke incidence rates declined significantly between 1993 to 1994 and 2015 for both males and females. In males, there was a decline from 282 to 211 per 100 000. In females, the decline was from 229 to 174 per 100 000. This trend was not observed for intracerebral hemorrhage or subarachnoid hemorrhage.
In the Northern Manhattan Study, among 3298 stroke-free participants followed up through 2019, Black and Hispanic females ≥70 years of age had higher risk of stroke compared with White females after controlling for age, sex, education, and insurance status (Black females/White females: HR, 1.76; Hispanic females/White females: HR, 1.77). This increased risk was not present among elderly Black or Hispanic males compared with White males.
Brain Health (Chapter 16)
A systematic analysis of data from the GBD study (Global Burden of Disease) showed that, in 2017, Alzheimer disease/Alzheimer disease and related dementia was the fourth most prevalent neurological disorder in the United States (2.9 million people). Among neurological disorders, Alzheimer disease/Alzheimer disease and related dementia was the leading cause of mortality in the United States (38 deaths per 100 000 population per year) ahead of stroke.
In 2017, Alzheimer disease/Alzheimer disease and related dementia had the fifth leading incidence rate of neurological disorders in the United States according to the GBD study data. The US age-standardized incidence rate of Alzheimer disease/Alzheimer disease and related dementia was 85 cases per 100 000 people).
In a meta-analysis of 12 randomized controlled trials (>92 000 participants; mean age, 69 years; 42% females), BP lowering with antihypertensive agents, compared with control, was associated with a lower risk of incident dementia or cognitive impairment (7.0% versus 7.5% of patients over a mean trial follow-up of 4.1 years; odds ratio [OR], 0.93; absolute risk reduction, 0.39%).
Congenital Cardiovascular Defects and Kawasaki Disease (Chapter 17)
The 2017 all-age prevalence of congenital cardiovascular defects in the United States was estimated at 466 566 individuals, with 279 320 (60%) of these under the age of <20 years of age. The 2017 global prevalence of congenital cardiovascular defects was estimated at 157 per 100 000 people. with the highest prevalence estimates in countries with a low sustainable development index (238 per 100 000 people) and the lowest in those with a high-middle or high sustainable development index (112 and 135 per 100 000 people, respectively).
Congenital cardiovascular defects appear to be more common among infants born to mothers with low socioeconomic status. In Ontario, mothers who lived in the lowest-income neighborhoods had higher risk of having an infant with a congenital cardiovascular defect compared with mothers living in the highest-income neighborhoods (OR, 1.29). Furthermore, this discrepancy between low and high was also found across measures of neighborhood education (OR, 1.34) and employment rate (OR, 1.18).
Since May 2020, the Centers for Disease Control and Prevention has been tracking reports of multisystem inflammatory syndrome in children. As of June 28, 2021, 4196 cases and 37 attributable deaths (0.89%) have been reported. Median age of cases was 9 years; 62% of cases have occurred in children who are Hispanic or Latino (1246 cases) or Black (1175 cases); 99% tested positive for severe acute respiratory syndrome coronavirus 2 (reverse transcription–polymerase chain reaction, serology, or antigen test); and 60% of reported patients were male.
Disorders of Heart Rhythm (Chapter 18)
A systematic review and meta-analysis of 18 published studies reported short-term and long-term associations of air pollution with atrial fibrillation (AF). For 10-mg/m3 increases in PM2.5 and PM10 concentrations, the OR of AF was 1.01 and 1.03, respectively. The corresponding ORs for long-term exposure were 1.07 for PM2.5 and 1.03 for PM10. SO2 and NO2 were also associated with AF in the short term: ORs for 10-ppb increments were 1.05 and 1.03, respectively.
A multicenter, open-label, randomized trial evaluated a 2-week continuous electrocardiographic patch and an automated home BP machine with oscillometric AF screening capability for the detection of AF compared with usual care over a 6-month period in participants ≥75 years of age with hypertension. AF detection was 5.3% in the screening group compared with 0.5% in the control group (risk difference, 4.8%; number needed to screen, 21). By 6 months, anticoagulation was more frequently prescribed in the intervention group (4.1% versus 0.9%; risk difference, 3.2%).
AF has been associated with increased mortality in patients with COVID-19. A meta-analysis of studies published in 2020, including 23 studies and 108 745 patients with COVID-19, showed that AF was associated with increased mortality (pooled effect size, 1.14).
Sudden Cardiac Arrest, Ventricular Arrhythmias, and Inherited Channelopathies (Chapter 19)
There was a 119% increase in out-of-hospital cardiac arrest during the pandemic compared with earlier control periods in a meta-analysis in 10 countries. For the patients with known outcomes (n=10 992), mortality was 85% compared with 62% for the control periods.
Coinciding with timing of the pandemic in the United States, CARES Registry (Cardiac Arrest Registry to Enhance Survival) data indicate increased delays to initiation of cardiopulmonary resuscitation for out-of-hospital cardiac arrest and reduced survival after out-of-hospital cardiac arrest. Accompanying these effects were reductions in the frequency of shockable rhythms, out-of-hospital cardiac arrest in public locations, and bystander automated external defibrillator use, whereas field termination of resuscitation efforts increased. There was no significant alteration in frequency of bystander cardiopulmonary resuscitation.
Survival to hospital discharge was 22.4% of 33 874 adult pulseless in-hospital cardiac arrests at 328 hospitals in Get With The Guidelines 2020 data. Among survivors, 79.5% had good functional status (Cerebral Performance Category 1 or 2) at hospital discharge.
Subclinical Atherosclerosis (Chapter 20)
In 3116 MESA (Multi-Ethnic Study of Atherosclerosis) participants (58±9 years of age, 63% females) who had no detectable coronary artery calcification (CAC) at baseline and were followed up over 10 years, CAC score >0, CAC score >10, and CAC score >100 were seen in 53%, 36%, and 8% of individuals at 10 years, respectively.
In a study with 12.3 years of mean follow-up, cancer-related mortality was 1.55-fold higher in individuals who had a CAC score ≥1000 at baseline compared with those who had a CAC score of 0 at baseline, after adjustment for age, sex, and risk factors.
In 9388 US and Finnish individuals with longitudinal measurement of CVD risk factors and carotid intima-media thickness, CVH declined from childhood to adulthood and was associated with thickening of the intima-media thickness.
Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris (Chapter 21)
In a European registry of high-volume percutaneous coronary intervention centers, the COVID-19 pandemic was associated with a significant increase in door-to-balloon and total ischemia times. Door-to-balloon time >30 minutes was 57.0% in the period of March to April 2020 compared with 52.9% in March to April 2019 (P=0.003), whereas total ischemia time >12 hours was 11.7% in the 2020 period compared with 9.1% in 2019 (P=0.001).
In a retrospective cohort study of Medicare fee-for-service patients (N=453 783) who were diagnosed with coronary artery disease, patients that received care at the most socioeconomically deprived practices had higher odds of being admitted for unstable angina (adjusted OR, 1.46) and higher 30-day mortality rates after acute myocardial infarction (adjusted OR, 1.31). After additional adjustment for patient-level area deprivation index, these associations were attenuated (unstable angina adjusted OR, 1.20; 30-day mortality after myocardial infarction adjusted OR, 1.31).
A pooled analysis of 21 randomized percutaneous coronary intervention trials including 32 877 patients (28% females) found that female sex was an independent risk factor for major adverse cardiovascular events (HR, 1.14) and ischemia-driven target lesion vascularization (HR, 1.23) but not of all-cause or cardiovascular mortality (HR, 0.91 and 0.97, respectively).
Cardiomyopathy and Heart Failure (Chapter 22)
The lifetime risk of HF remains high, with variation across racial and ethnic groups ranging from 20% to 45% after 45 years of age.
Secular trends show that the incidence of HF with preserved ejection fraction is increasing and, in contrast, the incidence of HF with reduced ejection fraction is decreasing, whereas both HF subtypes have similar all-cause mortality rates.
Contemporary HF with reduced ejection fraction guideline-directed medical therapy is estimated to reduce the hazard of cardiovascular death or HF hospitalization by up to 62% compared with limited conventional therapy.
Valvular Diseases (Chapter 23)
The number of elderly patients with calcific aortic stenosis is projected to more than double by 2050 in both the United States and Europe according to a simulation model in 7 decision analysis studies.
The pooled prevalence of all aortic stenosis in the elderly is 12.4%, and the prevalence of severe aortic stenosis is 3.4%. The annual volume of transcatheter aortic valve replacement (TAVR) has increased each year since 2011. After the US Food and Drug Administration approval of TAVR for low-risk patients in 2019, the TAVR volume exceeded all forms of surgical aortic valve replacement (n=72 991 versus n=57 626). From 2011 through 2018, extreme- and high-risk patients remained the largest cohort undergoing TAVR, but in 2019, the intermediate-risk cohort was the largest, and low-risk patients with a median 75 years of age increased to 8395, making up 11.5% of all patients undergoing TAVR.
Venous Thromboembolism (Deep Vein Thrombosis and Pulmonary Embolism), Chronic Venous Insufficiency, Pulmonary Hypertension (Chapter 24)
In 2018, there were an estimated ≈1 015 000 total venous thromboembolism cases in the United States.
In addition, 2019 data show that 37 571 deaths (any mention) resulted from pulmonary embolism and 27 574 deaths (any mention) resulted from pulmonary hypertension.
In the COVID-19 scenario, the incidence of venous thromboembolism was up to 31% in hospitalized patients. Among them, those who were admitted to the intensive care unit had a 2- to 3-fold greater risk of developing deep vein thrombosis or pulmonary embolism.
Peripheral Artery Disease and Aortic Diseases (Chapter 25)
From 2011 to 2019, the global prevalence of peripheral artery disease was 5.56% with a higher prevalence in high- compared with low- to middle-income countries (7.37% versus 5.09%, respectively). In 2015, it was estimated that 236.62 million people ≥25 years of age were living with peripheral artery disease.
In an analysis of 393 017 patients who underwent lower extremity arterial revascularization, 50 750 (12.9%) had at least 1 subsequent hospitalization for major adverse limb events.
In a population-based screening study of 14 989 participants 60 to 74 years of age, male sex (OR, 1.9), hypertension (OR, 1.8), and family history (OR, 1.6) were associated with a heightened risk of ascending thoracic aortic aneurysm. Diabetes was associated with a lower risk (OR, 0.8).
Quality of Care (Chapter 26)
Compared with 2019, a lower proportion of cases received bystander cardiopulmonary resuscitation in 2020, and use of automated external defibrillators was lower. There were also longer emergency medical services response times and lower survival to hospital discharge. Those are likely related to the COVID-19 pandemic.
In a Get With The Guidelines–HF study, inclusion in Medicare Advantage led to a higher proportion of discharge to home with no difference in mortality compared with fee-for-service programs.
In data from the PINNACLE Registry (Practice Innovation and Clinical Excellence), only about two-thirds of the individuals were treated with appropriate statin therapy as recommended in the American College of Cardiology/AHA guidelines. In addition, higher income was associated with higher likelihood of appropriate statin therapy.
Medical Procedures (Chapter 27)
As per the Society of Thoracic Surgeons/American College of Cardiology transcatheter valve therapy registry data, TAVR volumes continue to grow, with 13 723 TAVR procedures in 2011 to 2013 and 72 991 TAVR procedures in 2019. In 2019, 669 sites were performing TAVR. In 2019, TAVR volumes (n=72 991) exceeded the volumes for all forms of surgical aortic valve replacement (n=57 626).
In 2020, 3658 heart transplantations were performed in the United States, the most ever. The highest number of heart transplantations were performed in the states of California (496), Texas (302), Florida (288), and New York (250).
A global survey of 909 inpatient and outpatient centers performing cardiovascular diagnostic procedures in 108 countries compared procedural volumes for common cardiovascular diagnostic procedures between March 2019 and March 2020/April 2020. This survey indicated that cardiovascular diagnostic procedures decreased by 64% from March 2019 to April 2020.
Economic Cost of Cardiovascular Disease (Chapter 28)
The average annual direct and indirect cost of CVD in the United States was an estimated $378.0 billion in 2017 to 2018.
The estimated direct costs of CVD in the United States increased from $103.5 billion in 1996 to 1997 to $226.2 billion in 2017 to 2018.
By event type, hospital inpatient stays accounted for the highest direct cost ($99.6 billion) in 2017 to 2018 in the United States.
The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the Statistical Update. The 2022 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.
Connie W. Tsao, MD, MPH, FAHA, Chair
Seth S. Martin, MD, MHS, FAHA, 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
The Writing Group thanks its colleagues Lucy Hsu and Michael Wolz at the National Heart, Lung, and Blood Institute; the team at the Institute for Health Metrics and Evaluation at the University of Washington; Bryan McNally and Rabab Al-Araji at the CARES program; and Christina Koutras and Fran Thorpe at the American College of Cardiology for their valuable contributions and review.
|Writing group member||Employment||Research grant||Other research support||Speakers’ bureau/honoraria||Expert witness||Ownership interest||Consultant/advisory board||Other|
|Connie W. Tsao||Beth Israel Deaconess Medical Center||NIH/NHLBI†||None||None||None||None||None||None|
|Seth S. Martin||Johns Hopkins University School of Medicine||AHA (Health Tech SFRN Grant)†; NIH†; PCORI†; Apple (in-kind device support)†; Amgen†; the Pollin Digital Innovation Fund (philanthropic support)†; Sandra and Larry Small (philanthropic support)†; David and June Trone Family Foundation (philanthropic support)†||None||None||None||None||Amgen†; AstraZeneca*; Kaneka*; Sanofi*||None|
|Aaron W. Aday||Vanderbilt University Medical Center||NIH†||None||None||None||None||OptumCare†||None|
|Zaid I. Almarzooq||Brigham and Women’s Hospital||None||None||None||None||None||None||None|
|Alvaro Alonso||Emory University||NIH (Afib research)†; AHA (Afib research)†||None||None||None||None||None||None|
|Andrea Z. Beaton||The Heart Institute, Cincinnati Children’s Hospital Medical Center||AHA (Health Tech, SFRN Grant)†; Thrasher Research Fund*; Edwards Lifesciences*||None||None||None||None||None||None|
|Marcio S. Bittencourt||University of São Paulo (Brazil)||Sanofi (investigator-initiated research)*||None||Novonordisk*; Novartis*||None||None||Bayer*||None|
|Amelia K. Boehme||Columbia University||NIH†||None||None||None||None||None||None|
|Alfred E. Buxton||Beth Israel Deaconess Medical Center/Harvard Medical School||None||None||None||None||None||None||None|
|April P. Carson||University of Alabama at Birmingham||Amgen, Inc (investigator-initiated research funding)†||None||None||None||None||None||None|
|Yvonne Commodore-Mensah||Johns Hopkins University||None||None||None||None||None||None||None|
|Mitchell S.V. Elkind||Columbia University||BMS-Pfizer Alliance for Eliquis (study drug in kind to institution for NIH-funded clinical trial of stroke prevention; no personal compensation)†; Roche (ancillary funding of NIH-funded clinical trial of stroke prevention; no personal compensation)†||None||None||None||None||None||None|
|Kelly R. Evenson||University of North Carolina||NIH (funding for my research to my institution)†; Robert Wood Johnson Foundation (funding for research to my institution)*; US Department of Transportation (funding for research to my institution)†; NC Governor’s Highway Safety Program (funding for research to my institution)†||None||None||None||None||None||None|
|Chete Eze-Nliam||Cleveland Clinic||None||None||None||None||None||None||None|
|Jane F. Ferguson||Vanderbilt University Medical Center||NIH (PI on R01s relating to cardiometabolic disease)†||None||None||None||None||None||None|
|Giuliano Generoso||University Hospital, University of São Paulo Center for Clinical and Epidemiological Research (Brazil)||None||None||None||None||None||None||None|
|Jennifer E. Ho||Massachusetts General Hospital||NIH†; Bayer, AG†||EcoNugenics, Inc (research supplies)*||None||None||Pfizer, Inc. (immediate family members)†||None||Pfizer, Inc (salary--immediate family members, vice president, clinical research head)†|
|Rizwan Kalani||University of Washington||None||None||None||None||None||None||None|
|Sadiya S. Khan||Northwestern University||None||None||None||None||None||None||None|
|Brett M. Kissela||University of Cincinnati||NIH (PI or multiple PI of several grants)†||None||None||None||None||None||None|
|Kristen L. Knutson||Northwestern University Feinberg School of Medicine||NIH†||None||None||None||None||OneCare Media*; Sleep Research Society/SRS Foundation (on Board of Directors of SRS and president of SRSF)†||None|
|Deborah A. Levine||University of Michigan||NIH†||None||None||None||None||Northwestern*||None|
|Tené T. Lewis||Emory University, Rollins School of Public Health||None||None||None||None||None||None||None|
|Junxiu Liu||Tufts University||None||None||None||None||None||None||None|
|Matthew Shane Loop||University of North Carolina at Chapel Hill||None||None||None||None||None||None||None|
|Jun Ma||University of Illinois Chicago||NIH†; VA†; PCORI†||None||None||None||None||Health Mentor (San Jose, CA)*||None|
|Michael E. Mussolino||NIH National Heart, Lung, and Blood Institute||None||None||None||None||None||None||None|
|Sankar D. Navaneethan||Baylor College of Medicine||None||None||None||None||None||Bayer*; Boehringer Ingelheim*; Vifor Pharma*; Eli Lilly*||None|
|Amanda Marma Perak||Lurie Children’s Hospital and Northwestern University||None||None||None||None||None||None||None|
|Mary Rezk-Hanna||UCLA||NIH†; Tobacco-Related Disease Research Program†||None||None||None||None||None||None|
|Gregory A. Roth||University of Washington||NIH†; Cardiovascular Medical Education and Research Fund*||None||None||None||None||None||None|
|Emily B. Schroeder||Parkview Health||None||None||None||None||None||None||None|
|Svati H. Shah||Duke University||Verily, Inc†; AstraZeneca†; Lilly, Inc†||None||None||None||None||American Heart Association Board of Directors*||None|
|Evan L. Thacker||Brigham Young University||None||None||None||None||None||None||None|
|Lisa B. VanWagner||Northwestern University||W.L. Gore & Associates (money paid to institution, investigator-initiated grant for the use of TIPS in portal hypertension)†||None||None||Anderson, Moschetti and Taffany, PLLC*; Hamilton Law Firm*; Iliff, Meredith, Wildberger & Brennan, PC*||None||American Association for the Study of Liver Diseases (uncompensated, member of the Practice Guidelines Committee)*; American Society for Transplantation (uncompensated, chair of the Liver and Intestine Community of Practice)*; International Liver Transplantation Society (uncompensated, chair of Cardiovascular Special Interest Topic Group)*||None|
|Salim S. Virani||VA Medical Center Health Services Research and Development Center for Innovations, Baylor College of Medicine||None||None||None||None||None||None||None|
|Jenifer H. Voeks||Medical University of South Carolina||NIH (CREST-2 NINDS)†||None||None||None||None||None||None|
|Nae-Yuh Wang||The Johns Hopkins Medical Institutions||NIH (receiving support from multiple research grants to Johns Hopkins University)†; AHA (receiving research support through contract to Johns Hopkins University)†||None||None||None||None||None||None|
|Kristine Yaffe||UCSF||None||None||None||None||None||Eli Lilly*; Alector*||None|
- 1. American Heart Association. My Life Check–Life’s Simple 7. Accessed July 28, 2021. https://www.heart.org/en/healthy-living/healthy-lifestyle/my-life-check--lifes-simple-7Google Scholar
|6MWD||6-minute walk distance|
|AAA||abdominal aortic aneurysm|
|ACC||American College of Cardiology|
|ACCORD||Action to Control Cardiovascular Risk in Diabetes|
|ACS||acute coronary syndrome|
|ACTION||Acute Coronary Treatment and Intervention Outcomes Network|
|ADAMS||Aging, Demographics, and Memory Study|
|ADRD||Alzheimer disease and related dementia|
|AF||atrial fibrillation or atriofibrillation|
|AGES||Age, Gene/Environment Susceptibility|
|AHA||American Heart Association|
|AHEI||Alternative Health Eating Index|
|aHR||adjusted hazard ratio|
|AHS-2||Adventist Health Study 2|
|AIM-HIGH||Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides and Impact on Global Health Outcomes|
|aIRR||adjusted incidence rate ratio|
|AIS||acute ischemic stroke|
|AMI||acute myocardial infarction|
|ANOVA||analysis of variance|
|ANP||atrial natriuretic peptide|
|aOR||adjusted odds ratio|
|APO||adverse pregnancy outcome|
|ARGEN-IAM-ST||Pilot Study on ST Elevation Acute Myocardial Infarction|
|ARIC||Atherosclerosis Risk in Communities|
|ARIC-NCS||Atherosclerosis Risk in Communities Neurocognitive Study|
|ARIC-PET||Atherosclerosis Risk in Communities–Positron Emission Tomography|
|aRR||adjusted relative risk|
|ARVC||arrhythmogenic right ventricular cardiomyopathy|
|ASB||artificially sweetened beverage|
|ASCVD||atherosclerotic cardiovascular disease|
|ASD||atrial septal defect|
|ASPIRE||Assessing the Spectrum of Pulmonary Hypertension Identified at a Referral Centre Registry|
|ATP III||Adult Treatment Panel III|
|AUC||area under the curve|
|AVAIL||Adherence Evaluation After Ischemic Stroke Longitudinal|
|AWHS||Aragon Workers Health Study|
|BASIC||Brain Attack Surveillance in Corpus Christi|
|BEST||Randomized Comparison of Coronary Artery Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients With Multivessel Coronary Artery Disease|
|BiomarCaRE||Biomarker for Cardiovascular Risk Assessment in Europe|
|BioSHaRe||Biobank Standardization and Harmonization for Research Excellence in the European Union|
|BIOSTAT-CHF||Biology Study to Tailored Treatment in Chronic Heart Failure|
|BMI||body mass index|
|BNP||B-type natriuretic peptide|
|BRFSS||Behavioral Risk Factor Surveillance System|
|CABG||coronary artery bypass graft|
|CAC||coronary artery calcification|
|CAD||coronary artery disease|
|CAIDE||Cardiovascular Risk Factors, Aging and Dementia|
|CANHEART||Cardiovascular Health in Ambulatory Care Research Team|
|CARDIA||Coronary Artery Risk Development in Young Adults|
|CARDIoGRAM||Coronary Artery Disease Genome-Wide Replication and Meta-Analysis|
|CARDIoGRAMplusC4D||Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) plus the Coronary Artery Disease Genetics (C4D)|
|CARES||Cardiac Arrest Registry to Enhance Survival|
|CAS||carotid artery stenting|
|CASCADE FH||Cascade Screening for Awareness and Detection of Familial Hypercholesterolemia|
|CCD||congenital cardiovascular defect|
|CDC||Centers for Disease Control and Prevention|
|CDC WONDER||Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research|
|CHADS2||clinical prediction rule for estimating the risk of stroke based on congestive heart failure, hypertension, age ≥75 years, diabetes (1 point each), and prior stroke/transient ischemic attack/thromboembolism (2 points)|
|CHA2DS2-VASc||clinical prediction rule for estimating the risk of stroke based on congestive heart failure, hypertension, diabetes, and sex (1 point each); age ≥75 years and stroke/transient ischemic attack/thromboembolism (2 points each); plus history of vascular disease, age 65 to 74 years, and (female) sex category|
|CHAMP-HF||Change the Management of Patients With Heart Failure|
|CHAP||Chicago Health and Aging Project|
|CHARGE-AF||Cohorts for Heart and Aging Research in Genomic Epidemiology–Atrial Fibrillation|
|CHD||coronary heart disease|
|CHS||Cardiovascular Health Study|
|CKD||chronic kidney disease|
|CKD-EPI||Chronic Kidney Disease Epidemiology Collaboration|
|COAPT||Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation|
|COMPASS||Cardiovascular Outcomes for People Using Anticoagulation Strategies|
|CONFIRM||Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry|
|CORAL||Cardiovascular Outcomes in Renal Atherosclerotic Lesions|
|COVID-19||coronavirus disease 2019|
|CPAP||continuous positive airway pressure|
|CPS-II||Cancer Prevention Study II|
|CPVT||catecholaminergic polymorphic ventricular tachycardia|
|CROMIS-2||Clinical Relevance of Microbleeds in Stroke|
|CRUSADE||Can Rapid Risk Stratification of Unstable Angina Patient Suppress Adverse Outcomes With Early Implementation of the ACC/AHA Guidelines|
|CSC||comprehensive stroke center|
|CTEPH||chronic thromboembolic pulmonary hypertension|
|CVD PREDICT||Cardiovascular Disease Policy Model for Risk, Events, Detection, Interventions, Costs, and Trends|
|CVI||chronic venous insufficiency|
|DASH||Dietary Approaches to Stop Hypertension|
|DBP||diastolic blood pressure|
|DCCT/EDIC||Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications|
|DII||Dietary Inflammatory Index|
|DPP||Diabetes Prevention Program|
|DVT||deep vein thrombosis|
|EAGLES||Study Evaluating the Safety and Efficacy of Varenicline and Bupropion for Smoking Cessation in Subjects With and Without a History of Psychiatric Disorders|
|eGFR||estimated glomerular filtration rate|
|ELSA||English Longitudinal Study of Ageing|
|EMPHASIS-HF||Eplere in Mild Patients Hospitalization and Survival Study in Heart Failure|
|EMS||emergency medical services|
|EPIC||European Prospective Investigation Into Cancer and Nutrition|
|ERICA||Study of Cardiovascular Risks in Adolescents|
|ERP||early repolarization pattern|
|ERR||excess readmission ratio|
|ESRD||end-stage renal disease|
|EUCLID||Examining Use of Ticagrelor in PAD|
|EVEREST||Endovascular Valve Edge-to-edge Repair|
|EVEREST II HRS||Endovascular Valve Edge-to-Edge Repair High-Risk Study|
|EVITA||Effect of Vitamin D on Mortality in Heart Failure|
|EVITA||Evaluation of Varenicline in Smoking Cessation for Patients Post-Acute Coronary Syndrome|
|EXAMINE||Examination of Cardiovascular Outcomes With Alogliptin Versus Standard of Care|
|FANTASIIA||Atrial fibrillation: influence of the level and type of anticoagulation on the incidence of ischemic and hemorrhagic stroke|
|FDA||US Food and Drug Administration|
|FHS||Framingham Heart Study|
|FINRISK||Finnish Population Survey on Risk Factors for Chronic, Noncommunicable Diseases|
|FOURIER||Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk|
|FPG||fasting plasma glucose|
|FRS||Framingham Risk Score|
|FUTURE||Follow-up of TIA and Stroke Patients and Unelucidated Risk Factor Evaluation|
|FVL||factor V Leiden|
|GARFIELD-VTE||Global Anticoagulant Registry in the Field–Venous Thromboembolism|
|GBD||Global Burden of Disease|
|GCNKSS||Greater Cincinnati/Northern Kentucky Stroke Study|
|GFR||glomerular filtration rate|
|GISSI-3||Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto Miocardico|
|GLORIA-AF||Global Registry on Long-term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation|
|GRS||genetic risk score|
|GWAS||genome-wide association studies|
|GWG||gestational weight gain|
|GWTG||Get With The Guidelines|
|HANDLS||Health Aging in Neighborhoods of Diversity Across the Life Span|
|HAPIEE||Health, Alcohol and Psychosocial Factors in Eastern Europe|
|HAPO||Hyperglycemia and Adverse Pregnancy Outcome|
|HbA1c||hemoglobin A1c (glycosylated hemoglobin)|
|HBP||high blood pressure|
|HCHS/SOL||Hispanic Community Health Study/Study of Latinos|
|HCUP||Healthcare Cost and Utilization Project|
|HDL-C||high-density lipoprotein cholesterol|
|HDP||hypertensive disorders of pregnancy|
|Health ABC||Health, Aging, and Body Composition|
|HEI||Healthy Eating Index|
|HELENA||Healthy Lifestyle in Europe by Nutrition in Adolescence|
|HF-ACTION||Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training|
|HFmrEF||heart failure with midrange ejection fraction|
|HFpEF||heart failure with preserved ejection fraction|
|HFrEF||heart failure with reduced ejection fraction|
|HIV||human immunodeficiency virus|
|HLHS||hypoplastic left-heart syndrome|
|HPFS||Health Professionals Follow-Up Study|
|HPS||Heart Protection Study|
|HRRP||Hospital Readmissions Reduction Program|
|HRS||Health and Retirement Study|
|HYVET||Hypertension in the Very Elderly Trial|
|ICAD||International Children’s Accelerometry Database|
|ICD||International Classification of Diseases|
|ICD-9||International Classification of Diseases, 9th Revision|
|ICD-9-CM||International Classification of Diseases, 9th Revision, Clinical Modification|
|ICD-10||International Classification of Diseases, 10th Revision|
|ICD-10-CM||International Classification of Diseases, 10th Revision, Clinical Modification|
|ICE-PCS||International Collaboration on Endocarditis–Prospective Cohort Study|
|ICE-PLUS||International Collaboration on Endocarditis–PLUS|
|ICU||intensive care unit|
|IDACO||International Database on Ambulatory Blood Pressure Monitoring in Relation to Cardiovascular Outcomes|
|IE After TAVI||Infective Endocarditis After Transcatheter Aortic Valve Implantation and SwissTAVI as Swiss Transcatheter Aortic Valve Implantation|
|IHCA||in-hospital cardiac arrest|
|IHD||ischemic heart disease|
|IMPACT||International Model for Policy Analysis of Agricultural Commodities and Trade|
|IMPROVE||Carotid Intima–Media Thickness (IMT) and IMT–Progression as Predictors of Vascular Events in a High–Risk European Population|
|INTER-CHF||International Congestive Heart Failure|
|INTERMACS||Interagency Registry for Mechanically Assisted Circulatory Support|
|IRAD||International Registry of Acute Aortic Dissection|
|IRR||incidence rate ratio|
|JHS||Jackson Heart Study|
|LBW||low birth weight|
|LDL-C||low-density lipoprotein cholesterol|
|LEADER||Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results|
|LIBRA||Lifestyle for Brain Health|
|LIFE||Lifestyle Interventions and Independence for Elders|
|LOAD||late-onset Alzheimer disease|
|Look AHEAD||Look: Action for Health in Diabetes|
|LVAD||left ventricular assist device|
|LVEF||left ventricular ejection fraction|
|LVH||left ventricular hypertrophy|
|LQTS||long QT syndrome|
|MACE||major adverse cardiovascular event|
|MAP||Memory and Aging Project|
|MARS||Minority Aging Research Study|
|MCI||mild cognitive impairment|
|MDCS||Malmö Diet and Cancer Study|
|MEPS||Medical Expenditure Panel Survey|
|MESA||Multi-Ethnic Study of Atherosclerosis|
|MHO||metabolically healthy obesity|
|MIDA||Mitral Regurgitation International Database|
|MIDAS||Myocardial Infarction Data Acquisition System|
|MI-GENES||Myocardial Infarction Genes Study|
|MIND-China||Multimodal Interventions to Delay Dementia and Disability in Rural China|
|MIS-C||multisystem inflammatory syndrome in children|
|MITRA-FR||Percutaneous Repair With the MitraClip Device for Severe Functional/Secondary Mitral Regurgitation|
|MONICA||Monitoring Trends and Determinants of Cardiovascular Disease|
|MRI||magnetic resonance imaging|
|MTF||Monitoring the Future|
|MUSIC||Muerte Súbita en Insuficiencia Cardiaca|
|NAFLD||nonalcoholic fatty liver disease|
|NAMCS||National Ambulatory Medical Care Survey|
|NCDR||National Cardiovascular Data Registry|
|NCHS||National Center for Health Statistics|
|NHAMCS||National Hospital Ambulatory Medical Care Survey|
|NHANES||National Health and Nutrition Examination Survey|
|NHDS||National Hospital Discharge Survey|
|NHIS||National Health Interview Survey|
|NHLBI||National Heart, Lung, and Blood Institute|
|NIH-AARP||National Institutes of Health–American Association of Retired Persons|
|NIHSS||National Institutes of Health Stroke Scale|
|NINDS||National Institutes of Neurological Disorders and Stroke|
|NIPPON DATA||National Integrated Project for Prospective Observation of Noncommunicable Disease and Its Trends in Aged|
|NIS||National (Nationwide) Inpatient Sample|
|NOMAS||Northern Manhattan Study|
|NOTION||Nordic Aortic Valve Intervention|
|NSDUH||National Survey on Drug Use and Health|
|NSHDS||Northern Sweden Health and Disease Study|
|NSTEMI||non–ST-segment–elevation myocardial infarction|
|NT-proBNP||N-terminal pro-B-type natriuretic peptide|
|nuMoM2b||Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be|
|NVSS||National Vital Statistics System|
|ODYSSEY Outcomes||Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab|
|OHCA||out-of-hospital cardiac arrest|
|ORBIT-AF||Outcomes Registry for Better Informed Treatment of Atrial Fibrillation|
|OSA||obstructive sleep apnea|
|OVER||Open Versus Endovascular Repair|
|PAD||peripheral artery disease|
|PAF||population attributable fraction|
|PAH||pulmonary arterial hypertension|
|PALM||Patient and Provider Assessment of Lipid Management Registry|
|PAR||population attributable risk|
|PARADIGM||Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging|
|PARTNER||Placement of Aortic Transcatheter Valve|
|PATH||Population Assessment of Tobacco and Health|
|PCE||Pooled Cohort Equations|
|PCI||percutaneous coronary intervention|
|PCSK9||proprotein convertase subtilisin/kexin type 9|
|PESA||Progression of Early Subclinical Atherosclerosis|
|PHS||Physicians’ Health Study|
|PHIRST||Pulmonary Arterial Hypertension and Response to Tadalafil Study|
|PINNACLE||Practice Innovation and Clinical Excellence|
|PM2.5||fine particulate matter <2.5-μm diameter|
|POINT||Platelet-Oriented Inhibition in New TIA and Minor Ischemic Stroke|
|PPSW||Prospective Population Study of Women in Gothenburg|
|PRECOMBAT||Premier of Randomized Comparison of Bypass Surgery Versus Angioplasty Using Sirolimus Stents in Patients With Left Main Coronary Artery Disease|
|PREDIMED||Prevención con Dieta Mediterránea|
|PREMA||Prediction of Metabolic Syndrome in Adolescence|
|PREMIER||Lifestyle Interventions for Blood Pressure Control|
|PREVEND||Prevention of Renal and Vascular End-Stage Disease|
|PROFESS||Prevention Regimen for Effectively Avoiding Second Stroke|
|PUFA||polyunsaturated fatty acid|
|PURE||Prospective Urban Rural Epidemiology|
|QTc||corrected QT interval|
|RCT||randomized controlled trial|
|RE-LY||Randomized Evaluation of Long-Term Anticoagulation Therapy|
|REACH||Reduction of Atherothrombosis for Continued Health|
|REDINSCOR||Red Española de Insuficiencia Cardiaca|
|REGARDS||Reasons for Geographic and Racial Differences in Stroke|
|REMEDY||Global Rheumatic Heart Disease Registry|
|RENIS-T6||Renal Iohexol Clearance Survey in Tromsø 6|
|REVASCAT||Revascularization With Solitaire FR Device Versus Best Medical Therapy in the Treatment of Acute Stroke Due to Anterior Circulation Large Vessel Occlusion Presenting Within Eight Hours of Symptom Onset|
|REVEAL||Registry to Evaluate Early and Long-term PAH Disease Management|
|ROADMAP||Risk Assessment and Comparative Effectiveness of Left Ventricular Assist Device (LVAD) and Medical Management in Ambulatory Heart Failure Patients|
|ROC||Resuscitation Outcomes Consortium|
|ROS||Religious Orders Study|
|RSMR||risk-standardized mortality rate|
|RYR2||ryanodine receptor 2|
|SAFEHEART||Spanish Familial Hypercholesterolemia Cohort Study|
|SAGE||Study on Global Ageing and Adult Health|
|S.AGES||Sujets AGÉS–Aged Subjects|
|SAVE||Sleep Apnea Cardiovascular Endpoints|
|SAVR||surgical aortic valve replacement|
|SBP||systolic blood pressure|
|SCA||sudden cardiac arrest|
|SCD||sudden cardiac death|
|SCORE||Systematic Coronary Risk Evaluation|
|SDB||sleep disordered breathing|
|SEARCH||Search for Diabetes in Youth|
|SEMI-COVID-19||Sociedad Española de Medicina Interna Coronavirus Disease 2019|
|SFA||saturated fatty acid|
|SGA||small for gestational age|
|SHIP||Study of Health in Pomerania|
|SHS||Strong Heart Study|
|SILVER-AMI||Comprehensive Evaluation of Risk Factors in Older Patients With Acute Myocardial Infarction|
|SNAC-K||Swedish National Study on Aging and Care in Kungsholmen|
|SND||sinus node dysfunction|
|SOF||Study of Osteoporotic Fractures|
|SPRINT||Systolic Blood Pressure Intervention Trial|
|SPS3||Secondary Prevention of Small Subcortical Strokes|
|START||South Asian Birth Cohort|
|STEMI||ST-segment–elevation myocardial infarction|
|STS||Society of Thoracic Surgeons|
|SUN||Seguimiento Universidad de Navarra|
|SURTAVI||Surgical Replacement and Transcatheter Aortic Valve Implantation|
|SWAN||Study of Women’s Health Across the Nation|
|SWIFT PRIME||Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment|
|SwissTAVI||Swiss Transcatheter Aortic Valve Implantation|
|SYNTAX||Synergy Between PCI With Taxus and Cardiac Surgery|
|TAA||thoracic aortic aneurysm|
|TAVR||transcatheter aortic valve replacement|
|TdP||torsade de pointes|
|TECOS||Trial Evaluating Cardiovascular Outcomes With Sitagliptin|
|TGA||transposition of the great arteries|
|TGF||transforming growth factor|
|3C||Three City Study|
|TIA||transient ischemic attack|
|TODAY||Treatment Options for Type 2 Diabetes in Adolescents and Youth|
|TOF||tetralogy of Fallot|
|TOPCAT||Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist|
|tPA||tissue-type plasminogen activator|
|TRIUMPH||Treprostinil Sodium Inhalation Used in the Management of Pulmonary Arterial Hypertension|
|TVT||transcatheter valve therapy|
|USRDS||US Renal Data System|
|VBI||vascular brain injury|
|VITAL||Vitamin D and Omega-3 Trial|
|VOYAGER||Efficacy and Safety of Rivaroxaban in Reducing the Risk of Major Thrombotic Vascular Events in Subjects With Symptomatic Peripheral Artery Disease Undergoing Peripheral Revascularization Procedures of the Lower Extremities|
|VSD||ventricular septal defect|
|WHI||Women’s Health Initiative|
|WHICAP||Washington Heights-Hamilton Heights-Inwood Community Aging Project|
|WHO||World Health Organization|
|WHS||Women’s Health Study|
|WMD||weighted mean difference|
|WMH||white matter hyperintensity|
|YLD||years of life lived with disability or injury|
|YLL||years of life lost to premature mortality|
|YRBS||Youth Risk Behavior Survey|
|YRBSS||Youth Risk Behavior Surveillance System|
1. ABOUT THESE STATISTICS
The AHA works with the NHLBI to derive the annual statistics in the AHA Statistical Update. This chapter describes the most important sources and the types of data used from them. For more details, see Chapter 30 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, and healthy life expectancy
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 the United States
USRDS—kidney disease prevalence
WHO—mortality rates by country
YRBS—health-risk behaviors in youth and young adults
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 the 2022 Statistical Update is to present the latest estimates of the number of people in the United States who have specific conditions to provide a realistic estimate of burden. Most estimates based on NHANES prevalence rates are based on data collected from 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 2022 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 2015 to 2018 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 are used to present estimates of the percentage of people with overweight, obesity, and high total cholesterol and HDL-C. BRFSS 2019 data are used for the prevalence of sleep issues. The NHIS 2019 data, BRFSS 2019, and NYTS 2020 are used for the prevalence of cigarette smoking. The prevalence of physical inactivity is obtained from 2019 YRBS 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 AHA Statistical 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 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 the 2022 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 22 (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
In this publication, we have used national population estimates from the US Census Bureau for 20182 in the computation of morbidity data. CDC/NCHS population estimates3 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 2018 HCUP NIS. Ambulatory care visit data include patient visits to primary health care professionals’ offices and EDs. Ambulatory care visit data reflect the primary (first-listed) diagnosis. Primary health care professional office visit estimates are from the 2018 NAMCS of the CDC/NCHS. ED visit estimates are from the 2018 HCUP National ED Sample. 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 ninth and 10th ICD revisions, comparability ratios computed by the CDC/NCHS are applied as noted.4 Effective with mortality data for 1999, ICD-10 is used.5 Beginning in 2016, ICD-10-CM is used for hospital inpatient stays and ambulatory care visit data.6
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.7 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 the 2022 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 2019. 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 of hospital inpatient discharges for the United States are for 2018. The numbers of visits to primary health care professionals’ offices and hospital EDs are for 2018. Except as noted, economic cost estimates are for 2017 to 2018.
For data on hospitalizations, primary health care professional 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 and 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 AHA works with the Institute for Health Metrics and Evaluation to help derive annual statistics for the AHA Statistical Update. The Global Burden of Diseases, Injuries, and Risk Factors Study is an ongoing global effort to quantify health loss from hundreds of causes and risks from 1990 to the present for all countries. The study seeks to produce consistent and comparable estimates of population health over time and across locations, including summary metrics such as DALYs and healthy life expectancy. Results are made available to policymakers, researchers, governments, and the public with the overarching goals of improving population health and reducing health disparities.
GBD 2020, the most recent iteration of the study, was produced by the collective efforts of more than 7500 researchers in more than 150 countries. Estimates were produced for 370 causes and 88 risk factors.
During each annual GBD Study cycle, population health estimates are reproduced for the full time series. For GBD 2020, estimates were produced for 1990 to 2020 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 GBD and to access GBD resources, data visualizations, and most recent publications, please visit the study website.8–10
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 the 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.
- 1. Centers for Medicare & Medicaid Services. Decision memo for supervised exercise therapy (SET) for symptomatic peripheral artery disease (PAD) (CAG-00449N). May 25, 2017. Accessed July 1, 2021. https://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=287Google Scholar
- 2. US Census Bureau.US Census Bureau population estimates: historical data: 2000s. Accessed July 1, 2021. https://www.census.gov/programs-surveys/popest.htmlGoogle Scholar
Lloyd-Jones DMHong YLabarthe DMozaffarian DAppel LJVan Horn LGreenlund KDaniels SNichol GTomaselli GF; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703LinkGoogle Scholar
Fowkes FGRudan DRudan IAboyans VDenenberg JOMcDermott MMNorman PESampson UKWilliams LJMensah GA. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis.Lancet. 2013; 382:1329–1340. doi: 10.1016/S0140-6736(13)61249-0CrossrefMedlineGoogle Scholar
Cea-Soriano LFowkes FGRJohansson SAllum AMGarcía Rodriguez LA. Time trends in peripheral artery disease incidence, prevalence and secondary preventive therapy: a cohort study in the Health Improvement Network in the UK.BMJ Open. 2018; 8:e018184. doi: 10.1136/bmjopen-2017-018184CrossrefMedlineGoogle Scholar
- 6. National Center for Health Statistics. ICD-10-CM Official Guidelines for Coding and Reporting, FY 2019: Centers for Disease Control and Prevention website. Accessed July 19, 2021. https://www.cdc.gov/nchs/icd/data/10cmguidelines-FY2019-final.pdfGoogle Scholar
Anderson RNRosenberg HM. Age standardization of death rates: implementation of the year 2000 standard.Natl Vital Stat Rep1998; 47:1–16, 20.Google Scholar
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- 10. Global Burden of Disease Study, Institute for Health Metrics and Evaluation. University of Washington. Accessed August 1, 2021. http://ghdx.healthdata.org/Google Scholar
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 CVH of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.”1
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.
|Level of CVH for each metric|
|Adults ≥20 y of age||Yes||Former ≥12 mo||Never or quit >12 mo|
|Children 12–19 y of age*||Tried during the prior 30 d||…||Never tried; never smoked whole cigarette|
|Adults ≥20 y of age||≥30 kg/m2||25–29.9 kg/m2||<25 kg/m2|
|Children 2–19 y of age||>95th percentile||85th–95th percentile||<85th percentile|
|Adults ≥20 y of age||None||1–149 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 age||None||>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)|
|Adults ≥20 y of age||≥240||200–239 or treated to goal||<200|
|Children 6–19 y of age||≥200||170–199||<170|
|Adults ≥20 y of age||SBP ≥140 mm Hg or DBP ≥90 mm Hg||SBP 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 percentile||90th–95th percentile or SBP ≥120 mm Hg or DBP ≥80 mm Hg||<90th percentile|
|Adults ≥20 y of age||FPG ≥126 mg/dL or HbA1c ≥6.5%||FPG 100–125 mg/dL or HbA1c 5.7%–6.4% or treated to goal||FPG <100 mg/dL or HbA1c <5.7%|
|Children 12–19 y of age||FPG ≥126 mg/dL or HbA1c ≥6.5%||FPG 100–125 mg/dL or HbA1c 5.7%–6.4% or treated to goal||FPG <100 mg/dL or HbA1c <5.7%|
From 2011 to 2021, this chapter in the annual Statistical Update published national prevalence estimates for CVH based on released NHANES data to inform progress toward improvements in the prevalence of CVH. In 2021, 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, were also added.
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.3–8 Similar relationships have also been seen in non-US populations.3,4,9–22
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.4
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.5
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.23
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.24
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 having the lowest number of ideal components.25
The adjusted PAFs for CVD mortality for individual components of CVH have been reported as follows26:
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 a 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.27 CVH score and components were also shown to predict MACEs (first occurrence of MI, stroke, acute ischemic syndrome, coronary revascularization, or death) over a median follow-up of 12 years in a biracial community-based population.28
By combining the 7 CVH component scores and categorizing the total score to define overall CVH (low, 0–8 points; moderate, 9–11 points; high, 12–14 points), a report pooled NHANES 2011 to 2016 data and individual-level data from 7 US community-based cohort studies to estimate the age-, sex-, and race and ethnicity–adjusted PAF of major CVD events (nonfatal MI, stroke, HF, or CVD death) associated with CVH and found that 70.0% (95% CI, 56.5%–79.9%) of major CVD events in the United States were attributable to low and moderate CVH.29 According to the authors’ estimates, 2.0 (95% CI, 1.6–2.3) million major CVD events could potentially be prevented each year if all US adults attain high CVH, and even a partial improvement in CVH scores to the moderate level among all US adults with low overall CVH could lead to a reduction of 1.2 (95% CI, 1.0–1.4) million major CVD events annually.
A report from the Framingham Offspring Study showed increased risks of subsequent hypertension, diabetes, CKD, CVD, and mortality associated with having a shorter duration of ideal CVH in adulthood.30
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.31
Better CVH as defined by the AHA is associated with lower incidence of HF,3,5–7,22 less subclinical vascular disease,8,15,17,33,34 better global cognitive performance and cognitive function,16,35,36 lower hazard of subsequent dementia,37,38 lower prevalence39 and incidence40 of depressive symptoms, lower loss of physical functional status,41 longer leukocyte telomere length,42 less ESRD,43 less pneumonia, less chronic obstructive pulmonary disease,44 less VTE/PE,45 lower prevalence of aortic sclerosis and stenosis,46 lower risk of calcific aortic valve stenosis,47 better prognosis after MI,48 lower risk of AF,49 and lower odds of having elevated resting heart rate.50 Using the CVH scoring approach, the FHS demonstrated significantly lower odds of prevalent hepatic steatosis associated with more favorable CVH scores, and the decrease of liver fat associated with more favorable CVH scores was greater among people with a higher GRS for NAFLD.51 In addition, a study based on NHANES data showed significantly decreased odds of ocular diseases (OR, 0.91 [95% CI, 0.87–0.95]), defined as age-related macular degeneration, any retinopathy, and cataract or glaucoma, and odds of diabetic retinopathy (OR, 0.71 [95% CI, 0.66–0.76]) associated with each unit increase in CVH among US adults.52
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.53 A study in college students found that both handgrip strength and muscle mass were positively associated with greater numbers of ideal CVH components,54 and a cross-sectional study found that greater cardiopulmonary fitness, upper-body flexibility, and lower-body muscular strength were associated with better CVH components in perimenopausal females.55 Furthermore, higher quality of life scores were associated with better CVH metrics,56 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, underrepresented racial groups, and single-living status) were related to lower likelihood of attaining better CVH as measured by Life’s Simple 7 scores.57 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.58 A study focused on people with serious mental illness found that individuals of underrepresented races and ethnicities had significant lower CVH scores based on 5 of the Life’s Simple 7 components.59
Having more ideal CVH components in middle age has been associated with lower non-CVD and CVD health care costs in later life.60 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.60 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.61
CVH in the United States: Prevalence (NHANES 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 2-1.62 The most current estimates at the time of publication were based on data from NHANES 2017 to 2018. NHANES 2017 to 2018 survey changed the PA assessments for children, so the PA status for children was updated according to data from respondents who were 18 to 19 years of age.
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, for which prevalence of ideal levels in children is lower than in adults. For PA, the contrast for adults versus children is not clear because the prevalence estimate for children was from a subgroup of children only.
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 >79% for smoking, BP, and diabetes components (95.7%, 89.1%, and 79.0% respectively; 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 2017 to 2018 are displayed in Table 2-2.
In 2017 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 smoking and the Healthy Diet Score, for which prevalence of ideal levels was highest in older adults. For the Healthy Diet Score, all age groups had a prevalence of ideal level <1% according to the 2017 to 2018 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 and ethnicity.
Chart 2-3 displays the adjusted prevalence estimates of ideal levels of CVH components for the population of US adults ≥20 years of age by race and ethnicity.
|NHANES years||Age 12–19 y||Age ≥20 y*||Age 20–39 y||Age 40–59 y||Age ≥60 y|
|Ideal CVH factors|
|TC||2017–2018||77.2 (1.7)||52.4 (1.5)||74.0 (1.8)||44.8 (1.7)||25.5 (1.5)|
|BP||2017–2018||89.1 (1.3)||40.8 (1.4)||61.6 (1.9)||34.0 (2.6)||15.1 (1.3)|
|Diabetes||2017–2018||79.0 (2.0)||50.4 (1.2)||68.9 (1.8)||42.4 (2.5)||31.5 (2.0)|
|Ideal health behaviors|
|PA||2017–2018||54.0 (4.2)†||38.3 (1.3)||48.4 (2.3)||33.9 (2.2)||29.3 (2.6)|
|Smoking||2017–2018||95.7 (1.1)||79.8 (1.3)||74.3 (2.2)||80.1 (1.7)||87.8 (1.0)|
|BMI||2017–2018||63.4 (1.8)||26.4 (1.3)||33.6 (2.1)||21.9 (2.0)||21.9 (1.1)|
|4 or 5 Healthy diet goals met‡||2017–2018||0.0 (0.0)||0.2 (0.1)||0.1 (0.1)||0.3 (0.2)||0.4 (0.1)|
|F&V ≥4.5 cups/d||2017–2018||5.5 (1.0)||9.8 (0.8)||8.7 (0.9)||9.3 (1.5)||12.0 (1.5)|
|Fish ≥2 svg/wk||2017–2018||8.4 (1.2)||18.3 (1.1)||16.4 (1.7)||18.2 (2.3)||23.7 (2.1)|
|Sodium <1500 mg/d||2017–2018||0.2 (0.1)||0.5 (0.2)||0.4 (0.2)||0.7 (0.3)||0.2 (0.1)|
|SSB <450 kcal/wk||2017–2018||39.3 (2.6)||55.1 (2.3)||49.7 (2.4)||55.2 (3.3)||64.0 (2.2)|
|Whole grains ≥3 one-ounce svg/d||2017–2018||6.2 (1.0)||6.4 (0.8)||5.6 (1.0)||5.5 (1.3)||8.6 (1.1)|
|Secondary diet metrics|
|Nuts/legumes/seeds ≥4 svg/wk||2017–2018||34.2 (3.1)||49.6(1.7)||47.7 (2.2)||49.1 (2.3)||53.7 (2.9)|
|Processed meats ≤2 svg/wk||2017–2018||39.1 (2.3)||41.5 (0.8)||42.9 (1.9)||41.7 (2.3)||39.5 (1.9)|
|SFat <7% total kcal||2017–2018||6.8 (1.2)||7.0 (0.4)||7.4 (0.9)||8.0 (1.0)||5.3 (0.6)|
CVH in the United States: 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 2017–2018) are shown in Chart 2-4 for children (12–19 years of age) and in Chart 2-5 for adults (≥20 years of age).
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.
The leading causes of YLLs from 1990 to 2019 in the United States are presented in Table 2-4.
The leading causes of YLDs from 1990 to 2019 in the United States are presented in Table 2-6.
|Risk factors for disability||YLL 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)|
|1990||2019||1990||2019||Total No. of YLLs||Age-standardized YLL rate||1990||2019||Total No. of deaths||Age-standardized death rate|
|Smoking||1||1||11 005.06 (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 SBP||2||2||8466.11 (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 BMI||4||3||4994.23 (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 FPG||5||4||4664.81 (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 use||18||5||999.47 (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 use||6||6||2708.90 (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-C||3||7||6291.91 (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 dysfunction||7||8||2138.32 (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 grains||9||9||1897.21 (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 temperature||13||10||1320.06 (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 legumes||12||11||1471.67 (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 meat||16||12||1258.35 (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 acids||14||13||1311.91 (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 meat||19||14||850.40 (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 pollution||8||15||2001.60 (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 sodium||24||16||574.46 (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 weight||10||17||1512.98 (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 gestation||11||18||1492.43 (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 smoke||17||19||1072.52 (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 fruits||21||20||845.55 (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%)|
|Diseases and injuries||YLL 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)|
|1990||2019||1990||2019||Total No. of YLLs||Age-standardized YLL rate||1990||2019||Total No. of deaths||Age-standardized death rate|
|IHD||1||1||10 181.09 (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 cancer||2||2||3559.62 (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 disease||4||3||1592.74 (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 disorders||46||4||219.00 (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 cancer||7||5||1291.48 (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 diabetes||12||6||856.92 (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 dementias||15||7||743.80 (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 injuries||3||8||1836.51 (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 cancer||9||9||1199.58 (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 infections||8||10||1223.88 (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 stroke||6||11||1324.40 (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 cancer||17||12||587.36 (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%)|
|ICH||14||13||772.31 (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 means||16||14||686.74 (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 HD||23||15||447.65 (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 firearm||13||16||853.20 (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 C||24||17||434.18 (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 disorders||35||18||272.90 (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 firearm||11||19||980.04 (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 cancer||18||20||581.18 (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%)|
|Risk factors for disability||YLD rank (for total number)||Total No. of YLDs, in thousands (95% UI)||Percent change, 1990–2019 (95% UI)|
|1990||2019||1990||2019||Total No. of YLDs||Age-standardized YLD rate|
|High BMI||2||1||2014.44 (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 FPG||3||2||1473.97 (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%)|
|Smoking||1||3||2927.37 (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 use||5||4||1031.70 (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 SBP||6||5||884.49 (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 use||4||6||1102.64 (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 factors||7||7||769.12 (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 density||8||8||411.39 (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 dysfunction||9||9||399.32 (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 meat||14||10||230.60 (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 meat||17||11||172.86 (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 gestation||10||12||371.84 (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 weight||11||13||371.84 (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-C||13||14||297.03 (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 pollution||12||15||308.85 (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 victimization||22||16||132.13 (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 injuries||15||17||196.96 (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 abuse||19||18||164.32 (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 violence||20||19||161.94 (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 smoke||16||20||173.12 (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%)|
|Diseases and injuries||YLD rank (for total number)||Total No. of YLDs, in thousands (95% UI)||Percent change, 1990–2019 (95% UI)|
|1990||2019||1990||2019||Total No. of YLDs||Age-standardized YLD rate|
|Low back pain||1||1||4504.86 (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 disorders||2||2||1731.90 (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 diabetes||9||3||1030.39 (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 disorders||16||4||554.70 (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 disorder||4||5||1341.83 (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 loss||5||6||1340.58 (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%)|
|Migraine||3||7||1671.80 (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 pain||7||8||1201.62 (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 disease||8||9||1111.88 (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 disorders||6||10||1331.27 (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%)|
|Falls||10||11||971.06 (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%)|
|Asthma||11||12||904.55 (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%)|
|Schizophrenia||13||13||767.43 (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 in the hand||18||14||486.85 (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 stroke||15||15||559.93 (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 disorders||12||16||785.98 (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 in the knee||19||17||450.96 (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 disorders||14||18||629.50 (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 disease and other dementias||22||19||391.77 (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%)|
|Edentulism||17||20||491.91 (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%)|
Trends in Global Risk Factors and Causes for YLL and YLD: 1990 to 2019
(See Tables 2-7 through 2-10)
The leading global YLL risk factors from 1990 to 2019 are presented in Table 2-7.
The leading global YLL causes from 1990 to 2019 are presented in Table 2-8.
The leading global risk factors for YLDs from 1990 to 2019 are presented in Table 2-9.
The leading global causes of YLDs from 1990 to 2019 are presented in Table 2-10.
|Risk factors for disability||YLL 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)|
|1990||2019||1990||2019||Total No. of YLLs||Age-standardized YLL rate||1990||2019||Total No. of deaths||Age-standardized death rate|
|High SBP||6||1||143 603.62 (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%)|
|Smoking||7||2||140 203.56 (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 weight||2||3||269 478.56 (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 gestation||3||4||221 314.76 (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 FPG||14||5||61 627.96 (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 BMI||16||6||54 375.58 (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 pollution||13||7||66 492.55 (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-C||12||8||66 683.88 (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 fuels||4||9||200 169.50 (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 wasting||1||10||292 012.74 (241 855.36 to 351 715.87)||79 87.22 (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 use||15||11||55 971.37 (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 dysfunction||19||12||37 087.06 (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 source||5||13||153 905.20 (115 315.56 to 190 197.92)||57 641.09 (41 .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 sex||25||14||18 492.16 (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 sodium||20||15||31 285.63 (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 grains||22||16||26 467.42 (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 sanitation||9||17||115 547.43 (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 facility||10||18||80 929.22 (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 smoke||18||19||44 029.71 (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 temperature||21||20||26 827.37 (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%)|
|Diseases and injuries||YLL 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)|
|1990||2019||1990||2019||Total No. of YLLs||Age-standardized YLL rate||1990||2019||Total No. of deaths||Age-standardized death rate|
|IHD||3||1||118 399.43 (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 infections||1||2||223 807.88 (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 diseases||2||3||182 456.67 (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%)|
|ICH||9||4||52 648.78 (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 birth||4||5||112 709.17 (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 disease||11||6||48 769.20 (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 trauma||6||7||71 832.72 (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 stroke||13||8||34 004.54 (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 cancer||19||9||26 859.81 (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%)|
|Malaria||8||10||63 480.60 (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 tuberculosis||5||11||74 658.58 (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 disorders||12||12||47 950.24 (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 diseases||32||13||12 728.09 (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 diabetes||28||14||13 851.47 (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 means||15||15||32 879.52 (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 cancer||34||16||12 013.14 (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 injuries||21||17||22 260.33 (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 cancer||24||18||20 241.69 (19 .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 infections||20||19||23 105.79 (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 HD||31||20||13 303.40 (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%)|
|Risk factors for disability||YLD rank (for total number)||Total No. of YLDs, in thousands (95% UI)||Percent change, 1990–2019 (95% UI)|
|1990||2019||1990||2019||Total No. of YLDs||Age-standardized YLD rate|
|High FPG||3||1||15 581.99 (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 BMI||4||2||12 907.42 (6901.43 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%)|
|Smoking||2||3||20 484.09 (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 deficiency||1||4||25 379.25 (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 SBP||7||5||10 128.23 (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 use||5||6||11 836.52 (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 factors||6||7||11 784.36 (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 pollution||17||8||3985.80 (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 use||9||9||7479.41 (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 dysfunction||14||10||5003.27 (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 gestation||12||11||5054.73 (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 weight||13||12||5054.73 (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 density||16||13||4082.06 (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 fuels||8||14||8277.99 (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 source||11||15||6054.63 (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 noise||18||16||3933.44 (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 injuries||10||17||6779.60 (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-C||22||18||3035.02 (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 smoke||24||19||2652.31 (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 sex||32||20||1609.09 (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%)|
|Diseases and injuries||YLD rank (for total number)||Total No. of YLDs, in thousands (95% UI)||Percent change, 1990–2019 (95% UI)|
|1990||2019||1990||2019||Total No. of YLDs||Age-standardized YLD rate|
|Low back pain||1||1||43 361.65 (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%)|
|Migraine||2||2||26 863.35 (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 loss||5||3||22 008.10 (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 disorders||7||4||16 608.89 (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 disorder||4||5||23 461.28 (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 diabetes||10||6||11 626.63 (7964.90 to 15 799.45)||35 150.63 (23 966.55 to 47 .13)||202.33% (197.13% to 207.63%)||50.23% (48.08% to 52.22%)|
|Anxiety disorders||6||7||18 661.02 (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 deficiency||3||8||25 069.79 (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 pain||9||9||12 393.48 (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%)|
|Falls||8||10||12 639.31 (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 disease||13||11||10 472.74 (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 disorders||11||12||11 022.44 (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 diseases||12||13||10 812.95 (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%)|
|Schizophrenia||14||14||9131.34 (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 stroke||18||15||6499.45 (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 knee||25||16||5184.78 (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 diseases||16||17||8035.21 (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 disorders||17||18||7875.53 (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%)|
|Asthma||15||19||8832.45 (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 birth||26||20||5054.73 (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%)|
COVID-19 Mortality in the United States
The large number of individuals in the United States who contracted severe illness attributable to COVID-19 resulted in a huge mortality toll, with disproportionate rates of deaths occurring among US counties with metropolitan areas and with higher proportions of the population who are NH Black and Hispanic people and in poverty.
Impact of COVID-19 on Life Expectancy in the United States
As a result of the high COVID-19 mortality rates, life expectancy in the United States for 2020 has been estimated to decline with disproportionate impacts on populations with high COVID-19 mortality rates.
Provisional US life expectancy estimates for January to June 202064 indicate that between 2019 and the first half of 2020, life expectancy (at birth) decreased from 74.7 to 72.0 years (−2.7 years) for NH Black individuals. Life expectancy decreased from 81.8 to 79.9 years (−1.9 years) for Hispanic individuals and decreased from 78.8 to 78.0 years (−0.8 year) for NH White individuals.
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 and YLD, including IHD,23 Alzheimer disease,65 stroke,66,67 CKD,68 diabetes,69,70 and breast cancer71,72 (Tables 2-4 and 2-6). 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 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.73
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.
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3. SMOKING/TOBACCO USE
See Table 3-1 and Charts 3-1 through 3-5
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, including 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 7.2,3 Unless otherwise stated, throughout the rest of this chapter, we report tobacco use and smoking estimates from the NYTS2 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.2
|Both sexes (95% UI)||Males (95% UI)||Females (95% UI)|
|Total No. of deaths (millions), 2020||8.09 (3.18 to 12.76)||6.27 (2.24 to 9.88)||1.82 (0.83 to 2.95)|
|Percent change in total number, 1990–2020||31.44 (15.71 to 47.29)||36.43 (20.45 to 52.74)||16.73 (−1.23 to 41.09)|
|Percent change in total number, 2010–2020||10.51 (2.64 to 18.88)||11.34 (1.90 to 21.43)||7.72 (−0.56 to 15.81)|
|Mortality rate per 100 000, age standardized, 2020||98.79 (38.72 to 156.87)||169.11 (60.84 to 267.05)||40.88 (18.59 to 66.00)|
|Percent change in rate, age standardized, 1990–2020||−39.50 (−44.76 to −33.91)||−39.23 (−44.54 to −33.43)||−45.98 (−52.04 to −37.93)|
|Percent change in rate, age standardized, 2010–2020||−16.95 (−22.65 to −11.06)||−16.75 (−23.46 to −9.73)||−19.54 (−25.39 to −13.62)|
|PAF, all ages, 2020||14.26 (5.60 to 22.39)||20.29 (7.06 to 31.50)||7.05 (3.26 to 11.55)|
|Percent change in PAF, all ages, 1990–2020||4.90 (−6.04 to 16.13)||8.14 (−1.17 to 17.01)||−6.07 (−19.50 to 13.49)|
|Percent change in PAF, all ages, 2010–2020||1.71 (−3.01 to 6.80)||3.32 (−1.08 to 8.19)||−1.83 (−7.04 to 3.52)|
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 adults and high school –aged children. 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 from nicotine salts in loose-leaf tobacco.6 Use of cigars, cigarillos, filtered cigars, and hookah (ie, water pipe) 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.
(See Chart 3-1)
Prevalence of cigarette use in the past 30 days for middle and high school students by sex and race and ethnicity in 2020 is shown in Chart 3-1.
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.8
In 2020, tobacco use within the past month for middle and high school students varied by race and ethnicity: The prevalence of past 30-day cigarette use was 3.7% (95% CI, 2.8%–4.8%) in NH White youth compared with 2.5% (95% CI, 1.8%–3.5%) in NH Black youth and 3.6% (95% CI, 2.6%–4.9%) in Hispanic youth. For cigars, the respective percentages were 2.8% (95% CI, 2.1%–3.7%), 6.5% (95% CI, 5.2%–8.2%), and 4.0% (95% CI, 2.9%–5.4%).7
The percentage of high school (19.6% or 3 020 000 users) and middle school (4.7% or 550 000 users) students who used e-cigarettes in the past 30 days exceeded the proportion using cigarettes in 2020 (Chart 3-1).7
(See Charts 3-2 and 3-3)
According to the NHIS 2019 data, among adults ≥18 years of age9:
According to data from BRFSS 2019, the state with the highest age-adjusted percentage of current cigarette smokers was West Virginia (25.4%). The states with the lowest age-adjusted percentage of current cigarette smokers were Utah (7.9%) and California (10.1%; Chart 3-2).10
In 2019, smoking prevalence was higher among adults ≥18 years of age who reported having a disability or activity limitation (21.1%) than among those reporting no disability or limitation (13.3%).9
Among individuals who reported cigarette use every day or some days, 34.5% reported having severe generalized anxiety disorder, 27.0% reported having moderate generalized anxiety disorder, and 21.5% reported having mild generalized anxiety disorder compared with 12.0% who reported having no/minimal generalized anxiety disorder.9
Among females who gave birth in 2017, 6.9% smoked cigarettes during pregnancy. Smoking prevalence during pregnancy was greatest for females 20 to 24 years of age (9.9%), followed by females 15 to 19 years of age (8.3%) and 25 to 29 years of age (7.9%).11 Rates were highest among NH American Indian or Alaska Native females (15%) and lowest in NH Asian females (1%). With respect to differences by education, cigarette smoking prevalence was highest among females who completed high school (12.2%), and lowest among females with a master’s degree and higher (0.3%).
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.10
According to the 2019 NSDUH, ≈1.60 million people ≥12 years of age had smoked cigarettes for the first time within the past 12 months compared with 1.83 million in 2018 (2019 NSDUH Table 4.2B).12 Of new smokers in 2019, 541 000 were 12 to 17 years of age, 672 000 were 18 to 20 years of age, and 292 000 were 21 to 25 years of age; only 90 000 were ≥26 years of age when they first smoked cigarettes.
The number of new smokers 12 to 17 years of age in 2019 (541 000) decreased from 2018 (571 000). The number of new smokers 18 to 25 years of age in 2019 (964 000) also decreased from 2018 (1.14 million) (2019 NSDUH Table 4.2B).12
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.13
Per NSDUH data for individuals 12 to 17 years of age, overall, the lifetime use of tobacco products declined from 13.4% to 12.8% between 2018 and 2019, with lifetime cigarette use declining from 9.6% to 9.0% during the same time period (2019 NSDUH Tables 2.1B and 2.2B).12
According to NSDUH data, the lifetime use of tobacco products in individuals ≥18 years of age did not decline significantly between 2018 (66.3%) and 2019 (65.8%). Lifetime cigarette use declined in a similar interval from 60.3% to 59.5% (2019 NSDUH Tables 2.1B). Similar to the patterns in youth, lifetime risk of tobacco products varied by demographic factors (2019 NSDUH Table 2.8B)12:
In 2019, the lifetime use of smokeless tobacco for adults ≥18 years of age was 16.6% (2019 NSDUH Table 2.4B).12
(See Chart 3-4)
According to data from NSDUH (12–17 years of age) and MTF (8th and 10th grades combined), the percentage of adolescents who reported smoking cigarettes in the past month declined from 13.0% and 14.2% in 2002 to 2.3% and 2.9% in 2019, respectively (Chart 3-4).12,14 The percentages for daily cigarette use among those with past-month cigarette smoking in individuals 12 to 17 years of age were 31.5% in 2002 and 13.2% in 2019.12,15
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 The decline in smoking, along with other factors (including improved treatment and reductions in the prevalence of risk factors such as uncontrolled hypertension and high cholesterol), is a contributing factor to secular declines in the HD death rate.16
On the basis of weighted NHIS data (2019), the current smoking status among males 18 to 24 years of age declined from 28.0% in 2005 to 15.3% in 2019; for females 18 to 24 years of age, smoking declined from 20.7% to 12.7% over the same time period.9
According to data from the BRFSS, the prevalence of e-cigarette use increased from 4.3% to 4.5% between 2016 and 2019 in US adults. Increases in e-cigarette use over this period were significant for middle-aged adults, females, and former smokers.17
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.18 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.19
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.18
Cigarette smoking and other traditional CHD risk factors might have a synergistic interaction in HIV-positive individuals.20
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]).21
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]).22
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.23
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.24
Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.23,25 Among JHS participants without a history of stroke (N=4410), risk of stroke was higher among current smokers compared with individuals who never smoked (HR, 2.48; 95% CI, 1.60–3.83).26
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.27 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).28
Short-term exposure to hookah smoking is associated with a significant increase in BP and heart rate and changes in cardiac function and blood flow, similar to those associated with cigarette smoking.29 The short-term vascular impairment associated with hookah smoking is masked by the high levels of carbon monoxide–—a vasodilator molecule—released from the charcoal briquettes used to heat the flavored tobacco product.30 In a recent meta-analysis of 42 studies, compared with nonsmokers, hookah smokers had significantly lower HDL-C and higher LDL-C, triglycerides, and fasting glucose.31 The long-term effects of hookah smoking 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.32
The long-term CVD risks associated with e-cigarette use are not known because of a lack of longitudinal data.33,34 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.35 In addition, daily and some-day use of e-cigarettes may be associated with MI and CHD.36,37
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.37 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.
In a pooled analysis of data collected from 10 randomized trials (N=2564), smokers had a higher risk of death or HF hospitalization (HR, 1.49 [95% CI, 1.09–2.02]), as well as reinfarction (HR, 1.97 [95% CI, 1.17–3.33) after primary PCI in STEMI.38
Family History and Genetics
Genetic factors contribute to smoking behavior; in analyses of up to 346 813 participants, common and rare variants in dozens of loci have been found to be associated with smoking initiation, number of cigarettes smoked per day, and smoking cessation.39,40
Genetics might also modify adverse CVH outcomes among smokers, with variation in ADAMTS7 associated with loss of cardioprotection in smokers.41
Mendelian randomization analysis has linked genetic liability to smoking to ASCVD, including increased risk of PAD (OR, 2.13 [95% CI, 1.78–2.56]; P=3.6×10−16), CAD (OR, 1.48 [95% CI, 1.25–1.75]; P=4.4×10−6), and stroke (OR, 1.40 [95% CI, 1.02–1.92]; P=0.04).42
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.43
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.
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.47 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 2019.47
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.48
Awareness, Treatment, and Control
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%).49
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.52
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]).53 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%]).
Smoking cessation reduces the risk of cardiovascular morbidity and mortality for smokers with and without CHD.
Among 726 smokers included in the Wisconsin Smokers Health Study, smoking cessation was associated with less progression of carotid plaque but not IMT.58
Cessation medications (including sustained-release bupropion, varenicline, nicotine gum, lozenge, nasal spray, and patch) are effective for helping smokers quit.59,60
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.61
The EAGLES trial62 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.62
Extended use of a nicotine patch (24 compared with 8 weeks) has been demonstrated to be safe and efficacious in randomized clinical trials.63
An RCT demonstrated the effectiveness of individual- and group-oriented financial incentives for tobacco abstinence through at least 12 months of follow-up.64
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.51,65
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.66
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.67
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.68
In a meta-analysis of 55 observational studies and 9 RCTs, e-cigarettes were not associated with increased smoking cessation, but e-cigarette provision was associated with increased smoking cessation.69
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 2016.70 Overall mortality among US smokers is 3 times higher than that for never-smokers.54
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.16,71
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.72
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.73
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.74
If current smoking trends continue, 5.6 million US children will die of smoking prematurely during adulthood.18
E-Cigarettes and Vaping Products
(See Charts 3-1 and 3-3)
Electronic nicotine delivery systems are battery-operated devices that deliver nicotine, flavors, and other chemicals to the user in an aerosol without any combustion. Although e-cigarettes—the most common form of electronic nicotine delivery systems—were introduced into the United States only around 2007, there are currently >450 e-cigarette brands and vaping products on the market, and sales in the United States were projected to be $2 billion in 2014. Juul came on the market in 2015 and has rapidly become the most popular vaping 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.75 Besides e-cigarettes and Juul, e-hookahs (ie, e-waterpipes) are a new category of vaping devices recently patented by Philip Morris in 2019.76,77 Unlike e-cigarettes and Juul, e-hookahs are used through traditional water pipes, allowing the flavored aerosol to pass through the water-filled bowl before being inhaled.78 The popularity of e-hookahs is driven in part by unsubstantiated claims that the presence of water “filters out toxins,” rendering e-hookahs as healthier tobacco alternatives.79,80
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.81
Current e-cigarette user prevalence for 2017 in the United States is shown in Chart 3-3.
According to the NYTS, in 2020, e-cigarettes were the most commonly used tobacco products in youth: In the past 30 days, 4.7% (550 000) of middle school and 19.6% (3.0 million) of high school students endorsed use (Chart 3-1).7 An exponential increase in current e-cigarette use in high school students was observed between 2011 (1.5%) and 2020 (19.6%).7,82 A significant increase in current e-cigarette use also was observed for middle school students, for whom the corresponding values were 0.6% and 4.7% in the 2 periods.2,7 Among high school students, rates of use were slightly higher among males (20.4%) than females (18.7%) and most pronounced among NH White students (23.2%). In middle school students, rates of use were approximately equal between males (4.5%) and females (4.8%) and in Hispanic students (7.1%).7
According to the NYTS, current exclusive e-cigarette use among US youth who have never used combustibles, including cigarettes, increased exponentially from 2014 to 2019.83 Among high school students, current exclusive e-cigarette use increased from 1.4% (95% CI, 1.0%–2.1%) in 2014 to 9.2% (95% CI, 8.2%–10.2%) in 2019 and from 0.9% (95% CI, 0.6%–1.3%) in 2014 to 4.5% (95% CI, 3.7%–5.2%) in 2019 among middle school students.
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 frequently using e-cigarettes among current users increased from 16.2% in 2018 to 18.0% in 2019.2,8
Current use of e-cigarettes among high school students declined from 27.5% in 2019 to 19.6% in 2020.7 In middle school students, current e-cigarette use declined from 10.5% in 2019 to 4.7% in 2020.
In 2016, 20.5 million US middle and high school students (80%) were exposed to e-cigarette advertising.84
In 2019, the prevalence of current e-cigarette use in adults, defined as use every day or on some days, was 4.5% according to data from the NHIS. The prevalence of current e-cigarette use was highest in individuals 18 to 24 years of age (9.3%) and among those reporting severe generalized anxiety disorder (10.1%).9
According to data from BRFSS 2016 to 2018, current use of e-cigarettes in adults ≥18 years of age was higher in sexual and gender minority individuals.85,86 Data from 2017 and 2018 data sets show that the prevalence of current e-cigarette use among sexual and gender minority adults was 13.0% (95% CI, 12.0%–14.2%) versus 4.8% (95% CI, 4.6%–4.9%) among heterosexuals.85 In 2016, 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%).86
Limited data exist on the prevalence of other electronic nicotine delivery devices besides e-cigarettes. According to nationally representative data from the PATH study, in 2014 to 2015, 7.7% of youth 12 to 17 years of age reported ever e-hookah use.87 Among adults >18 years of age, 4.6% reported ever e-hookah use, and 26.8% of them reported current use.
E-cigarettes contain lower levels of most tobacco-related toxic constituents compared with traditional cigarettes,88 including volatile organic compounds.89,90 However, nicotine levels have been found to be consistent across long-term cigarette and long-term e-cigarette users.35,91
E-cigarette use has a significant cross-sectional association with a less favorable perception of physical and mental health and with depression.92,93
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.94,95 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.96
Effective August 8, 2016, the FDA’s Deeming Rule prohibited sale of e-cigarettes to individuals <18 years of age.97
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).98
According to data from the BRFSS 2016 and 2017, e-cigarette use among adults is associated with state-level regulations and policies regarding e-cigarettes: OR of 0.90 (95% CI, 0.83–0.98) for laws prohibiting e-cigarette use in indoor areas; OR of 0.90 (95% CI, 0.85–0.95) for laws requiring retailers to purchase a license to sell e-cigarettes; OR of 1.04 (95% CI, 0.99–1.09) for laws prohibiting self-service displays of e-cigarettes; OR of 0.86 (95% CI, 0.74–0.99) for laws prohibiting sales of tobacco products, including e-cigarettes, to people <21 years of age; and OR of 0.89 (95% CI, 0.83–0.96) for laws applying taxes to e-cigarettes.99
Data from the US Surgeon General on the consequences of secondhand smoke indicate the following:
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.101
A meta-analysis of 24 studies demonstrated that secondhand smoke can increase risks for preterm birth by 20%.102
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.103
As of September 30, 2020, 15 states (California, Colorado, Delaware, Hawaii, Massachusetts, Minnesota, 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.48,104
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]).105
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 24.7% in 2017 to 2018, with declines occurring for both children and adults. During 2017 to 2018, the percentage of nonsmokers with detectable serum cotinine was 38.2% for those 3 to 11 years of age, 33.2% for those 12 to 19 years of age, and 21.2% for those ≥20 years of age. The percentage was higher for NH Black individuals (48.0%) than for NH White individuals (22.0%) and Mexican American individuals (16.6%). People living below the poverty level (44.7%) had higher rates of secondhand smoke exposure than their counterparts (21.3% of those living above the poverty level; NHANES).106,107
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 attributable to premature death accounted for $151 billion (estimated from 2005–2009); and lost productivity resulting from secondhand smoke accounted for $5.6 billion (in 2006).16
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).108
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.109 In 2018, total US e-cigarette advertising expenditures (including print, radio, television, internet, and outdoors) were estimated to be $110 million, which increased remarkably from $48 million in 2017. 110
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 2017.111
Cigarette prices in the United States increased steeply between the early 1970s and 2018, in large part because of excise taxes on tobacco products. The increase in cigarette prices appeared to be larger than general inflation: 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 was $2.82.112
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.112
Despite the morbidity and mortality resulting from tobacco use, Dieleman et al113 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
The GBD 2020 study produces comprehensive and comparable estimates of disease burden for 370 reported causes and 88 risk factors for 204 countries and territories from 1990 to 2020. Oceania, East and Central Asia, and Central and Eastern Europe had the highest age-standardized mortality rates attributable to tobacco (Chart 3-5).
Tobacco caused 8.09 (95% UI, 3.18–12.76) million deaths in 2020, with 6.27 (95% UI, 2.24–9.88) million among males and 1.82 (95% UI, 0.83–2.95) million among females (Table 3-1).114
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.115
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.116
Worldwide, ≈80% of tobacco users live in low- and middle-income countries.117
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.118 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.86,119 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.120
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.121
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4. PHYSICAL ACTIVITY AND SEDENTARY BEHAVIOR
See Charts 4-1 through 4-9
PA is defined as any body movement produced by skeletal muscles that results in energy expenditure. In 1992, the AHA first published a position statement declaring lack of PA as a risk factor for the development of CHD.1 As the research accumulated, lack of PA was established as a major risk factor for CVD (eg, CHD, stroke, PAD, HF).2
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).3 In 2019, on the basis of survey interviews, only 23.2% of high school students reported achieving at least 60 minutes of daily PA,4 which is likely an overestimation of those actually meeting the guidelines.5 The 2018 Physical Activity Guidelines for Americans3 recommend that adults accumulate at least 150min/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. The 2019 CVD Primary Prevention Clinical Practice Guidelines6 support the aerobic recommendations. For many people, examples of absolutely defined 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). Achieving the guideline recommendations for PA is 1 of the AHA’s 7 components of ideal CVH for both children and adults.7
More recently, the 2020 WHO guidelines supported moderate to vigorous PA across all age groups and abilities,8 including those living with a disability.9 Even for those who cannot meet recommended levels of PA, being as physically active as abilities and conditions allow is still beneficial; some PA is better than none.3 Small increases in moderate-intensity PA or replacing sedentary behavior with light-intensity PA can provide health benefits.3,8–10 Cardiorespiratory fitness is the ability to perform whole-body, large-muscle exercise at moderate to vigorous levels of intensity for extended time periods.3 PA and cardiorespiratory fitness provide distinct metrics in assessment of CVD risk.11
Sedentary behavior is defined as “any waking behavior characterized by an energy expenditure ≤1.5 MET while in a sitting, reclining, or lying posture.”12 Sedentary behavior is a distinct construct from PA and is characterized by activities such as driving/riding in a vehicle, using a screen (eg, watching television, playing video games, using a computer), or reading. The WHO guidelines8 recommend reducing sedentary behaviors across all age groups and abilities, but precise guidance is not yet possible given the current state of the science.
Measuring PA and Sedentary Behavior
Several dimensions (eg, mode or type, frequency, duration, and intensity) and domains (eg, occupational, domestic, transportation, and leisure time) characterize PA. There are additional considerations of where PA occurs such as in homes, worksites, schools, and communities. The federal guidelines3 specify the suggested frequency, duration, and intensity of PA and focus on aerobic and strengthening modalities.
Measurement of PA can be defined by 2 broad assessment methods: (1) self-reported methods that use questionnaires and diaries/logs and (2) device-based methods that use wearables (eg, pedometers, accelerometers). Studies that have compared the findings between methods have shown that there is discordance between self-reported and measured PA, with respondents often overstating their PA compared with device-based measures.5 Sedentary behavior also has several dimensions (eg, type, frequency, duration) and domains (eg, driving/riding in a vehicle, using a screen, reading) that can also be assessed with both self-reported and device-based methods.
(See Chart 4-2)
Using parental report, from 2018 to 2019, the nationwide prevalence of youth who were active for ≥60 minutes every day of the week was higher for youth 6 to 11 years of age (28.3%) compared with youth 12 to 17 years of age (16.5%; Chart 4-2).13
Using nationwide self-reported PA (YRBSS, 2019)4:
With the use of accelerometry (NHANES, 2003–2006),14 youth 6 to 19 years of age had a median of 53 min/d of moderate to vigorous PA.
With regard to measured cardiorespiratory fitness (NHANES, 2012),15 for adolescents 12 to 15 years of age, boys at each age were more likely to have adequate levels of cardiorespiratory fitness than girls.
With regard to self-reported muscle-strengthening activities (YRBSS, 2019),4 the proportion of high school students who participated in muscle-strengthening activities (such as push-ups, sit-ups, or weight lifting) on ≥3 d/wk was 49.5% nationwide and was lower in 12th grade (45.9%) compared with 9th grade (52.4%). More high school boys (59.0%) than girls (39.7%) reported having participated in muscle-strengthening activities on ≥3 d/wk.
From a nonrepresentative sample of US parents of youth 5 to 13 years of age, there is an indication that PA declined from before COVID-19 to early COVID-19 in 2020.16 The longer-term impacts of the pandemic on PA and sedentary behavior patterns are not known.
Physical Education Classes and Organized Sports
Only 25.9% of students attended physical education classes in school daily (28.9% of boys and 22.8% of girls; YRBSS, 2019).4
Daily physical education class participation was lower with successively higher grades from the 9th grade (34.7%) through the 12th grade (19.7%; YRBSS, 2019).4
Just more than half (57.4%) of high school students played on at least 1 school or community sports team in the previous year (54.6% of girls and 60.2% of boys); this number was lower in 12th grade (49.8%) compared with 9th grade (61.9%; YRBSS, 2019).4
(See Charts 4-3 and 4-4)
Research suggests that screen time (watching television or using a computer) is associated with less PA among children.17 In addition, television viewing is associated with poor nutritional choices, overeating, and weight gain (Chapter 5, Nutrition).
Nationwide, 46.1% of high school students used a computer, tablet, or smartphone for activities other than school work (eg, video games, texting, social media) for ≥3 h/d on an average school day (YRBSS, 2019; Chart 4-3).4 The prevalence differed by race and ethnicity and was high among both boys (47.5%) and girls (44.6%; YRBSS, 2019).4
Among high school students, the prevalence of watching television ≥3 h/d was 19.8% (YRBSS, 2019; Chart 4-4).4 The prevalence varied by race and ethnicity and was higher among boys than girls. (31.6%).4
(See Charts 4-5 through 4-7)
According to NHIS (2018), for self-reported leisure-time aerobic PA:
According to NHANES (2003–2006), adults from urban areas reported more transportation activity, but adults from rural areas reported spending more time in household PA and total PA than individuals from urban areas.20
According to NHIS (2015), the prevalence of any walking for transportation in the United States varied by geographic location, ranging from 17.8% for adults living in the East South Central region to 43.5% for adults living in New England.21
From NHIS (2018) data, 25.4% of adults did not engage in leisure-time PA (no sessions of leisure-time PA of ≥10 minutes in duration).22 Trends in physical inactivity over time (1998–2018) are shown in Chart 4-7.
According to accelerometer-assessed PA (NHANES, 2005–2006),23 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 (NHANES, 2003–2006) also revealed that rural-dwelling adults were generally more active than urban-dwelling adults (mean, 325 bout min/d versus 314 bout min/d).20 Self-reported data from the same sample indicated higher total (438 min/wk versus 371 min/wk) and household PA (202 min/wk versus 124 min/wk), similar leisure PA (207 min/wk versus 206 min/wk), and lower transportation PA (30 min/wk versus 41 min/wk) among rural- compared with urban-dwelling adults.
In a nonrepresentative sample of adults from 14 countries, a cross-sectional study indicated that self-reported PA declined from before to after COVID-19 restrictions in 2020.24 The decline was greater for occupational activity compared with leisure activity, for more compared with less active adults, and for younger compared with older adults.
Activity tracker companies also documented declines in PA among their users during the COVID-19 pandemic. Comparing the week of March 22, 2020, with the same week in 2019 showed that Fitbit-measured steps declined worldwide (eg, declined 24% Argentina, 4% Australia, 15% Brazil, 14% Canada, 16% China, 13% Mexico, 14% Norway, 7% South Africa, 38% Spain, 9% United Kingdom, 12% United States), with the greatest decline occurring in Europe.25 Users of Garmin activity trackers also documented a decline in average daily steps during the month of March 2020 both globally and for the United States, as well as a shift to indoor fitness-oriented activities.26 The total number of steps decreased by 7.3% from 2019 to 2020 for Garmin users.27 It is important to note that those who own and wear activity trackers are not representative of the general population.28,29
According to NHANES (2015–2016), 25.7% reported sitting >8 h/d; the time spent sitting was successively higher with older age.30
A Nielsen report indicated that in January 2020 US adults spent on average 12 hours 21 minutes connected to media (eg, television, radio, smartphone, tablet, internet on computer), higher than in January 2018 (11 hours 6 minutes) and January 2019 (11 hours 27 minutes).31 These habits affect time available for PA and contribute to sedentary behavior.
PA Trends Using YRBS Data
Among high school students nationwide, the prevalence of being physically active for ≥60 minutes for at least 5 d/wk decreased from 49.5% in 2011 to 44.1% in 2019.32 Similarly, the prevalence of being physically active for ≥60 minutes on all 7 days in a week decreased from 28.7% in 2011 to 23.2% in 2019.32
Nationwide, the prevalence 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 2019 (52.2%).32 However, the prevalence of attending physical education classes on all 5 days of the week decreased from 1991 (41.6%) to 2019 (25.9%).
The prevalence of high school students playing ≥1 team sports in the past year did not substantively change between 1999 (55.1%) and 2019 (57.4%).32
Sedentary Behavior Trends Using YRBS Data
Among high school students nationwide, the prevalence of playing video or computer games or using a computer ≥3 hours/d increased from 22.1% in 2003 to 46.1% in 2019.32 However, watching television for ≥3 h/d decreased from 42.8% in 1999 to 19.8% in 2019.
(See Chart 4-7)
PA Trends Using NHIS Data
The prevalence of physical inactivity among adults ≥18 years of age, overall and by sex, decreased from 1998 to 2018 (Chart 4-7).
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 2018.33 The percentage of US adults who reported meeting the aerobic guidelines increased from 43.5% in 2008 to 54.2% in 2018.33
Sedentary Behavior Trends Using NHANES Data
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).35
Social Determinants of Health
(See Chart 4-8)
The proportion of adults ≥25 years of age who met the 2018 guidelines for aerobic PA was higher with successively higher educational attainment category (Chart 4-8). This pattern was similar for meeting recommendations for both aerobic and strengthening activities.
In 26 high- and 34 middle-income countries between 2001 and 2016, the levels of insufficient PA were greater when there were greater income inequalities (defined as the difference between those with the highest and lowest incomes).36
Genetics and Family History
Genetic factors have been shown to contribute to the propensity to exercise; however, more work is needed to identify genetic factors that contribute to PA.37,38
Genome-wide association analysis in >377 000 individuals identified multiple variants associated with habitual PA, including CADM2 and APOE.37
A GWAS of 91 105 individuals with device-measured PA identified 14 significant loci.39
Multiethnic analysis of >20 000 individuals identified several loci associated with leisure-time PA in individuals of European and African ancestry.40 Specifically, 4 previous loci (GABRG3, CYP19A1, PAPSS2 and CASR) were replicated. Among African Americans, 2 variants were identified (rs116550874 and rs3792874) and among European Americans, 1 variant was identified (rs28524846) as being associated with leisure-time PA.
Genetic variants have been identified, but few have been replicated by other studies.41
Promotion of PA
The US Surgeon General supports Step It Up! A Call to Action to Promote Walking and Walkable Communities in recognition of the importance of PA.42 There are opportunities for positive changes in communities, schools, and worksites to support walking.
Community-level interventions are effective in promoting PA.43 Communities can encourage walking with street design that includes sidewalks, improved street lighting, and landscaping design that reduces traffic speed to improve pedestrian safety.44 Nationwide, in 2017, the most prominent barriers to bicycling included heavy traffic and lack of separated paths or trails.45 In a qualitative study across 10 US cities, other barriers to bicycling were identified.46
Park prescriptions, which prescribe PA in local parks, may increase park use, time spent in parks, and recreational PA.47
The COVID-19 pandemic affected walking and bicycling for transportation and leisure through environmental and policy changes designed to limit or accommodate shifting users.48 The short- and long-term impacts of the environmental and policy changes on representative patterns of walking and bicycling are not yet known.
Schools can provide opportunities for PA through physical education, recess, before- and after-school activity programs, and PA breaks, as well as offering by a place for PA for the community.49
Worksites can offer access to onsite exercise facilities or employer-subsidized offsite 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.50,51
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.52
A meta-analysis of 23 studies revealed an association between participating in more transportation-related PA and lower all-cause mortality, CVD, and diabetes.53
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.54 Data on participants in NHANES enrolled from 1999 to 2006 indicated that participation in moderate to vigorous walking, bicycling, or running was most beneficial for reducing all-cause and CVD mortality.55
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.56 However, a lower risk of 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. There are several limitations to the literature that demonstrate these seemingly paradoxical results and likely other confounding factors such as fitness, SES, preexisting CVD, type of occupation, and other domains of PA that may modify this relationship.57
A harmonized 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 (HR, 1.27 [95% CI, 1.22–1.32]). For active individuals (top quartile for PA), sitting time was not associated with all-cause mortality (HR, 1.04 [95% CI, 0.98–1.10]), but active people who watched television ≥5 h/d did have higher mortality risk (HR, 1.15 [95% CI, 1.05–1.27]).58
An umbrella review of 24 systematic reviews of older adults concluded that those who are physically active are at a reduced risk of CVD mortality (25%–40% risk reduction), all-cause mortality (22%–35%), breast cancer (12%–17%), prostate cancer (9%–10%), and depression (17%–31%) while experiencing better quality of life, healthier aging trajectories, and improved cognitive functioning.59 Another review indicated that sedentary behavior, specifically transportation-related sitting time, was associated with a lower risk of CVD and less favorable cardiovascular risk factors, whereas less consistent associations were found when the exposure focused on occupational sitting.60
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.61
Device-Measured PA, Sedentary Behavior, and Mortality
In a review of 15 cohort studies, adults in the highest category of total, light, and moderate to vigorous PA had 67%, 40%, and 56% lower risk for mortality compared with adults in the lowest categories, respectively.62
Among individuals 70 years of age who wore an accelerometer for 1 week, both light PA and moderate PA were associated with a lower risk and sedentary behavior was associated with an increased risk of all-cause mortality, stroke, and MI.63
Among participants 40 to 79 years of age in the population-based European Prospective Investigation Into Cancer and Nutrition–Norfolk Study, higher levels of accelerometer-assessed total and moderate to vigorous PA were associated with a lower incident CVD risk; models indicated an initial steep decrease in the HR followed by a flattening of the curve.64
Among females ≥63 years of age who wore an accelerometer for 1 week, those who spent more time standing (quartile 4 versus 1 HR, 0.63 [95% CI, 0.49–0.81]) and more time standing with ambulation (quartile 4 versus 1 HR, 0.50 [95% CI, 0.35–0.71]) had a lower risk of all-cause mortality.65
In a harmonization meta-analysis of 8 prospective studies of adults measured with accelerometry, over a median of 5.8 years of follow-up, the highest 3 quartiles of light (HR, 0.38–0.60 across quartiles) and moderate to vigorous (HR, 0.52–0.64 across quartiles) PA compared with the lowest quartile (least active) were associated with a lower risk of all-cause mortality.66 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). In a follow-up analysis of 9 prospective studies, 30 to 40 min/d of moderate to vigorous PA attenuated the adverse association between sedentary behavior and mortality.67
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.10,68 Results from a systematic review revealed that for every 1000 steps taken at baseline, risk reductions ranged from 6% to 36% for all-cause mortality and 5% to 21% for CVD.69 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.10,68
Cardiorespiratory Fitness and Mortality
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.70 The risk reduction with higher cardiorespiratory fitness was observed for both males and females across ages.
PA and Cardiovascular/Metabolic Risk Factors
In a study of 36 956 Brazilian adolescents, higher self-reported moderate to vigorous PA levels (≥600 min/wk compared with 0 min/wk; adjusted proportional OR, 0.80 [95% CI, 0.6–0.95]) and lower amounts of screen time (≥6 h/d compared with ≤2 h/d; OR, 1.23 [95% CI, 1.10–1.37]) were associated with lower cardiometabolic risk.71
Among the NHANES 2003 to 2006 cohort of youths 6 to 17 years of age assigned to 4 latent classes with the use of accelerometry-assessed PA, those in the highest latent class PA had lower SBP (−4.1 mm Hg [95% CI, −7.7 to −0.6]), lower glucose levels (−4.3 mg/dL [95% CI, −7.8 to −0.7]), and lower insulin levels (−6.8 μU/mL [95% CI, −8.7 to −5.0]) than youths in the lowest latent class PA group.72
An umbrella review of 21 systematic reviews found that greater amounts and higher intensities of PA and limiting sedentary behavior were associated with improved health outcomes (eg, cardiometabolic health, cardiorespiratory fitness, adiposity, and cognition) among youth 5 to 17 years of age.73 However, the evidence base available was insufficient to fully describe the dose-response relationship or whether the association varied by type or domain of PA or sedentary behavior.
A meta-analysis of 37 RCTs of walking interventions in apparently healthy adults indicated favorable effects on cardiovascular risk factors, including body fat, BMI, SBP, DBP, fasting glucose, and maximal cardiorespiratory fitness.74
Multisession behavioral counseling can improve PA among those with elevated lipid levels or BP and reduce LDL, BP, adiposity, and cardiovascular events.75 The US Preventive Services Task Force recommends “offering or referring adults with CVD risk factors to behavioral counseling interventions to promote a healthy diet and PA” (Grade B).76
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).77
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]).78
In an umbrella review of 17 meta-analyses and 1 systematic review, there was a strong inverse dose-response relationship between PA and incident hypertension, and PA reduced the risk of CVD progression among hypertensive adults.79
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).68
Cardiovascular Events Among Adults
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-intensity PA (150–750 min/wk) and high-intensity PA (>750 min/wk) 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.80
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.81
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–step/d increase).68
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]).82
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.83
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.84 In addition, those watching less television had longer life expectancy free of CHD, stroke, and HF of close to 1 year.
According to data from the NHANES-III survey, adults with poor PA (OR, 1.30 [95% CI, 1.10–1.54]) and intermediate PA (OR, 1.19 [95% CI, 1.02–1.38]) had an increased odds of subclinical myocardial injury (based on the ECG) compared with those with ideal PA.85
A meta-analysis summarizing 10 studies found that the pooled fully adjusted risk of venous thromboembolism was 0.87 (95% CI, 0.79–0.95) when the most physically active group was compared with the least physically active group.86
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]).87
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.88
In 2020, the WHO began a review that concluded that services and programs are needed to increase PA and limit sedentary behavior among adults living with chronic conditions, including diabetes and hypertension.89
In a prospective cohort study of 15 486 participants with stable CAD from 39 countries, higher levels of PA were associated with a lower risk of mortality such that doubling the exercise volume was associated with a 10% lower risk of all-cause mortality.90
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.91
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]).92
A study of females in the WHI observational study after MI demonstrated that compared with those who maintained low PA levels, participants 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]) had lower risks of mortality.93
Among males after an MI, those who maintained high PA had a 39% lower risk of all-cause mortality, and those who walked for at least 30 min/d had a 29% lower risk of all-cause mortality.94
Exercise and resistance training are recommended for adults after stroke.95 In a review pooling 499 patients with stroke, exercise programs adhering to these guidelines indicated improved walking speed and endurance, but no differences for PA or other mobility outcomes, compared with usual care.96 An RCT found that higher doses of walking during inpatient rehabilitation 1 to 4 weeks after stroke provided greater walking endurance and gait speed and improved quality of life compared with usual care physical therapy.97
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.98
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.99
Increasing population levels of PA could increase productivity, particularly through presenteeism, and lead to substantial economic gains.100
(See Chart 4-9)
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 the number of males globally.101
The GBD 2020 study produces comprehensive and comparable estimates of disease burden for 370 reported causes and 88 risk factors for 204 countries and territories from 1990 to 2020.
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.80
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