Article Update

Friday, October 2, 2020



Cardiovascular epidemiology studies the determinants and distribution of cardiovascular disease (CVD). The overarching goal of CVD epidemiology is to reduce the incidence and prevalence of CVD within the population. Cardiovascular epidemiology has provided vital bidirectional connections between basic mechanistic science and clinical research. Through these types of investigations, our understanding of the extent of CVD and its natural history, mechanisms, and underlying pathophysiology is expanding greatly, which provides opportunities for individual-level therapeutic strategies, as well as population-level approaches to reduce the incidence and burden of CVD.



CVD remains the leading cause of death for men and women in the United States, more than cancer and respiratory disease combined. Within the United States, most individuals have an approximately 30% chance of dying of CVD and a 66% lifetime risk of CVD. Fortunately, over the past 40 years, there have been significant and substantial reductions in CVD risk and age-adjusted mortality in the United States by approximately 40%. However, the burden of CVD in the United States and globally still remains massive.

Worldwide, CVD is the most common cause of death, accounting for an estimated 31% of all deaths. Although 22% of reductions in the CVD death rate have been observed, the total number of CVD deaths continues to increase because of population growth and aging of the population. The death rate varies widely across countries, with the highest death rates attributable to CVD in Russia and the lowest rates seen in Western Europe, North America, and Central America.

Similarly, there are substantial differences in CVD incidence, prevalence, and mortality rates seen across different geographic regions, race, ethnic, and socioeconomic groups within the United States. For example, the southeastern United States has a substantially higher incidence rate and mortality associated with stroke. African Americans have long had substantially higher age-adjusted rates of hypertension, stroke, heart failure, and coronary heart disease than age-matched whites in the United States.



Epidemiological research has not only provided critical insights into the prevalence of CVD, but has also identified risk factors and patient characteristics that predict the presence and development of CVD. Several risk factors like sex, race, and age are nonmodifiable and enhance our understanding of the risk of an individual, but these factors are not useful as targets of therapy. In contrast, reducing the incidence and optimizing levels of modifiable risk factors is the mainstay of primary and secondary prevention efforts. The identification of modifiable risk factors led to multiple randomized controlled clinical trials that demonstrated the primacy of risk factor prevention and their management of CVD risk reduction.


The American Heart Association (AHA) defines ideal blood pressure as <120/80 mm Hg. Observational cohort studies have consistently demonstrated increased risk for stroke, heart attack, heart failure, and cardiovascular mortality across all age groups at blood pressures above this level. On average, every increase in the systolic blood pressure by 20 mm Hg or diastolic blood pressure by 10 mm Hg is associated with a doubling in the risk of death caused by stroke, coronary heart disease, or other vascular disease. Using JNC 7 guidelines, hypertension was defined as a systolic blood pressure greater than 140 mm Hg, a diastolic blood pressure greater than 90 mm Hg, or the use of blood pressure–lowering medications. In the fall of 2017, the AHA and ACC redefined hypertension as blood pressure greater than 130/80 mm Hg.

Using statistics that reflect JNC 7 guidelines, there are an estimated 85.7 million adults in the United States with hypertension. The prevalence of hypertension increases with age and varies by race. African Americans have substantially higher rates of hypertension than age- matched white Americans. Unfortunately, only 76% of those with hypertension are on antihypertensive medication, and only 54% have their blood pressure under adequate control.

Hyperlipidemia or Dyslipidemia

Higher levels of total cholesterol, low-density lipoprotein (LDL) cholesterol, and non–high-density lipoprotein (HDL) cholesterol are associated with increased risks for atherosclerotic cardiovascular disease (ASCVD). There is approximately a 50% higher ASCVD risk for every 40 mg/dL increase in total cholesterol. The association appears log-linear across higher levels of total cholesterol. Thus, total cholesterol and the atherogenic fractions LDL cholesterol and non-HDL cholesterol have become targets for therapy with lifestyle modification and pharmaco- therapy. Conversely, across usual levels of HDL cholesterol, there is an inverse association with ASCVD risk.

Hyperlipidemia is commonly defined as total cholesterol levels >230 mg/dL. Within the United States, an estimated 28.5 million adults, or 11.9% of the population, are considered to have hyperlipidemia. Over the last 14 years, the prevalence of hyperlipidemia has decreased by approximately 7%. The most recent estimates of mean total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels in the United States are 196, 113, 53, and 103.5 mg/dL, respectively.

Tobacco Use

Cigarette smoking increases ASCVD risk twofold to fourfold. Cessation of tobacco products is associated with rapid changes in physiology and substantial reductions in CVD risk. Therefore, tobacco use has been a major target of public health campaigns. Because of these large efforts, tobacco use has been declining over the last 50 years from prevalence rates of 51% in men and 34% in women in 1965 to 16.7% in men and 13.7% in women in 2017.

Although the overall trend in tobacco use is promising, certain minority groups, including sexual and gender minorities, individuals with low socioeconomic status, disabled persons, and individuals with psychiatric illness, have not experienced the same decrease in prevalence rates that are seen in the overall population. Furthermore, recent increases in electronic cigarette use, particularly in adolescent populations, could result in increased tobacco use in younger age groups.


The AHA d <100 mg/dL. Currently, only 56% of adults in the United States meet this criterion. Diabetes is associated with a twofold to threefold increase in risk for coronary heart disease, stroke, peripheral artery disease, heart failure, and atrial fibrillation. It is also associated with a 6- to 8-year shorter life expectancy than is seen in nondiabetics.

The National Heart, Blood, and Lung Institute defines diabetes as a fasting blood sugar >125 mg/dL, and it defines prediabetes as a fasting blood sugar between 100 and 125 mg/dL. In a recent representative survey of the United States population, 23.4 million adults have diabetes and 7.6 million are not aware of having diabetes. Furthermore, 81.6 million have prediabetes. Of the cases of diabetes identified, 90% to 95% are classified as type 2 diabetes. In part because of the obesity epidemic, the prevalence of diabetes has been increasing over the last 10 to 15 years. African Americans and Hispanic Americans have substantially higher rates of diabetes than white Americans.

Obesity, Diet, and Physical Activity

The AHA defines an ideal body weight as a body mass index (BMI) of 18.5 to 25 kg/m2. Obesity is defined as a BMI >30 kg/m2, and overweight, which also confers an increased risk for CVD, is defined as a BMI between 25 and 30 kg/m2. Obesity is a risk factor for CVD, including ASCVD, heart failure, stroke, venous thromboembolism, and atrial fibrillation, and a risk factor for other CVD risk factors, including dyslipidemia, hypertension, and diabetes. Obesity rates have slowly increased over the past several decades, with most recent prevalence rates reported in 2013 to 2014 as 37.7%. Women have a higher prevalence of both obesity and class III obesity, defined as BMI >40 kg/m2, with prevalence rates of 40.4% and 9.9%, respectively. There is substantial regional variation in obesity prevalence rates, with higher levels observed in the Midwest and southeastern United States.

A central determinant of the obesity epidemic is caloric excess and physical inactivity. The AHA defines a healthy diet as one that is rich in fresh fruits, vegetables, whole grains, low-fat dairy, seafood, legumes, and nuts. This diet has consistently been found to reduce blood pressure, improve lipid fractions, and reduce risks for heart attack and stroke. Conversely, diets that are high in saturated fats and salt, and low in fruits and vegetables are associated with adverse changes in blood cholesterol and blood pressure, and likely increase ASCVD risk. Current estimates suggest that only 1.5% of adults consume an ideal healthy diet and that 678,000 deaths per year are attributable to a sub- optimal diet.

Like obesity and dietary indiscretion, physical inactivity increases risks for CVD and CVD risk factors. Currently, the AHA recommends that adults perform at least 150 minutes of moderate intensity exercise per week or 75 minutes of vigorous activity a week plus 2 days of muscle strengthening. Individuals who meet these recommendations have been found to have a 30% to 40% lower risk for diabetes, a 30% to 40% lower mortality risk, and a 20% to 30% lower risk for coronary heart disease. Forty-four percent of adults meet the criteria specified by the AHA; however, 30% of adults do not engage in any physical activity. Women, older adults, African Americans, and Hispanics meet these requirements less frequently than other sex, race, and ethnic groups.



Population and High-Risk Approaches to Cardiovascular Disease Risk Reduction

Cardiovascular risk has a bell-shaped distribution within the population. A high-risk approach targets individuals at the highest risk with aggressive risk reduction. This type of approach is currently recommended to determine which patients should receive statin therapy for primary prevention of ASCVD events. Because the relative benefits of statin medications are consistent across absolute levels of risk, the high-risk approach will result in the greatest absolute benefit in individuals at highest absolute risk.

In contrast, a population-level approach attempts to reduce risk factor levels in the population as a whole through optimization of population mean risk factor levels. Although this may seem counter- intuitive, this approach often results in greater numbers of prevented events because there are much larger numbers of individuals around the mean value in a bell-shaped distribution. Therefore, most events occur within this portion of the population. To continue with the example of impact of cholesterol on ASCVD events, the risk of an individual for ASCVD with mean cholesterol levels is low because of the large denominator of people close to the mean. However, the risk for this segment of the population is still high. Therefore, even a modest decrease in the mean cholesterol concentrations would translate into a large number of prevented CVD events (Fig. 5.1).

FIG 5.1 (A) A high-risk approach targets individuals at the high end of risk for intervention. This approach results in the greatest reduction of risk for those individuals, but does little to reduce the risk in the population as a whole. (B) A population-based approach aims to reduce the mean risk, albeit often by a modest amount, in the overall population. This approach can lead to large reductions in risk of coronary heart disease in a population because events actually occur in individuals near the mean level of risk in the population.

High-risk and population-level approaches are not mutually exclusive: in fact, they are complimentary. Thus, CVD epidemiological efforts are aimed at reducing population-level burdens of risk factors and at identifying individuals at higher risk for aggressive primary prevention interventions.


Individual risk factors are probabilistic and typically do not capture the complete ASCVD risk of an individual. Risk estimator equations have been developed to help clinicians determine which patients are in the highest risk groups and, therefore, who would benefit most from primary prevention interventions. The currently recommended use of 10-year and lifetime risk estimator for the management of dyslipidemia with statin medications provides an excellent example of the use of risk scores in clinical practice.

Defining Risk

It is important to understand the term risk is a construct and not a reality. For example, when risk factors of a patient confer a 50% 10-year risk of developing an ASCVD event, the patient in reality will either have the clinical event or not have the clinical event. Because of the inherent limitations in predicting the future, risk estimation is only a starting point when making decisions about initiation of primary prevention measures. Clinicians may need to consider other factors, including family history of CVD, subclinical CVD, and comorbid conditions, among others, to individualize risk estimation for patients and make decisions about the initiation of statin medications.

Currently recommended CVD risk assessment tools generate absolute risk estimates. Absolute risk represents the likelihood of an event occur- ring over a given unit of time. In contrast, relative risk is a metric of how much higher or lower the risk of an individual is compared to some referent level of risk (e.g., relative risk, hazard ratio, and odds ratio).

Absolute risk is chosen over relative risk for clinical risk estimation for multiple reasons. First, estimates of relative risk are dependent on the risk in the referent group, which can be inherently misleading. Second, studies have demonstrated that both patients and clinicians are poor at predicting true risk. Third, in terms of guideline-based recommendations for statin therapy in patients with dyslipidemia, the calculation of absolute risk for ASCVD events allows clinicians to directly compare the average expected benefits of statin therapy to the average expected harms of statin therapy. Therefore, quantifying absolute risk allows for a quantitative risk–benefit decision and improves communication between clinicians and patients.

An alternative approach to a risk-based approach, albeit a cumber-some and impractical approach, would be for a clinician to gather all of the clinical trials that examined the effect of statins as primary prevention for ASCVD and identify which trial identified their specific patient by its inclusion criteria. However, because of the nature of randomized clinical trials, which typically have strict inclusion and exclusion criteria, these studies are unlikely to ever include the complete spectrum of patients encountered in clinical practice.

Risk Score Generation

To apply risk scores appropriately in clinical practice, it is important to have an understanding of their development. Typically, risk scores are derived from cohort study datasets. When developing and using risk estimation equations, it is important that the individuals in the cohort(s) from which the score is derived are similar to the patients to whom the risk score is applied. If the baseline rates of risk factors or disease are substantially higher or lower than the population in which the risk score is applied, the risk score may underestimate or overestimate risk in such a population.

The accuracy and clinical usefulness of absolute risk estimates are sensitive to the time horizon over which they are intended to predict. Most CVD risk estimation scores use 10-year time horizons, because it is a convenient time interval that is easy to remember and understand. Furthermore, it is also easier to develop robust estimates of risk over this time interval. Although 10-year time horizons are clinically convenient and provide robust estimates of risk, they may lead to an incomplete understanding of CVD risk burden in some patients. This is best exemplified in a young woman with multiple risk factors for ASCVD. Although a risk score predicts a low 10-year risk of ASCVD events, this patient has a high lifetime risk for CVD. Thus, if only a 10-year risk estimate were communicated to such a patient, she may overestimate her cardiovascular health and not institute much needed lifestyle change. However, if she is informed of how her modifiable risk factors are increasing her risk well beyond the 10-year time horizon, she may improve adherence to healthy lifestyle modifications. The 2013 AHA risk calculator not only includes 10-year risk estimates of ASCVD events, but estimates lifetime risk for individuals younger than 64 years old, as well.

It is vital that risk calculators use outcomes that are important to both patients and clinicians. The ATP-III risk calculator estimated the risk of nonfatal and fatal myocardial infarction. Unquestionably, these are important outcomes. However, contemporary CVD epidemiological data have demonstrated that women, particularly young African Ameri- can women, are at substantial risks for acute stroke, which was not accounted for by earlier versions of the risk calculator. In response, the 2013 AHA risk calculator was designed to estimate the risk for both fatal and nonfatal stroke, as well as myocardial infarction.

When choosing inputs for risk estimation calculators, researchers choose risk factors that are readily available in clinical practice and that enhance the ability of the statistical model to accurately predict risk. Because of the mathematics of statistical models, variables that are highly correlated tend to be redundant and rarely add substantially to the ability of a statistical model to accurately predict risk. However, it is critical to understand that a variable excluded from the risk estimation model may be of significant clinical importance. For example, obesity is not included in many ASCVD risk calculators despite its central role in CVD risk. Obesity is a major risk factor for the development of hypertension, diabetes, and dyslipidemia, and it is strongly associated with poor diet and a lack of exercise. Because of these associations, it does not add to risk prediction above and beyond blood pressure, the presence of diabetes, levels of HDL cholesterol and total cholesterol. However, when counseling a patient about ways to reduce their CVD risk, a clinician would be remiss to not discuss the importance of optimal body weight with a patient.



Discrimination and calibration are used to evaluate risk score performance. A rudimentary understanding of these concepts allows clinicians to identify risk scores that are most appropriate for their clinical practice and to integrate risk scores into the care of individual patients.

FIG 5.2 Receiving-Operating Characteristic Curve. Movement of the curve to the upper left of the figure represents improved discrimination, movement toward the line of unity represents worsened discrimination. The area under the curve (shaded region) is the c-statistic, which represents the ability of a prediction model to rank order individuals at high risk above individuals at lower risk.


Discrimination refers to a risk estimator’s ability to rank order individuals who will develop disease at a higher level of risk than those who will not develop disease. It is important to note that discrimination does not capture the accuracy of the absolute risk estimate. Statistically, the degree of correct discrimination is described by the c-statistic. The c-statistic is the area under the receiver-operating characteristic curve, or the plot of the sensitivity and 1-specificity of the outputs of the statistical model. Generally, c-statistics of <0.7 are considered poor discrimination, whereas c-statistics between 0.7 and 0.8 are considered adequate discrimination, and c-statistics >0.8 are excellent discrimination (Fig. 5.2). To describe discrimination in common language, a c-statistic of 0.8 means that 80% of the time the risk model ranked individuals who were more likely to have an event as being at higher risk than those who did not have an event and were therefore at lower risk. This metric partially describes the performance of the risk estimation equation when used as a population-wide screening test. It is not sensitive to risks that may be present in certain niche groups within the population (e.g., individuals with HIV, chronic inflammatory conditions, or those in the extreme distributions of CVD risk factors).

FIG 5.3 Cali (A and B) Plots of the risk predicted by a hypothetical risk estimation equation (purple bars) and the observed risk (gray bars) in deciles of coronary heart disease risk in a population sample. (A) The model (purple bars) appears to have excellent discrimination, as individuals at low risk appear to be consistently ranked below individuals at higher risk. However, the estimated absolute risk is significantly lower than the observed (presumably true) risk. Thus, (A) is an example of good discrimination, but poor calibration. (B) However, model B appears to have excellent discrimination and calibration because the absolute risk estimated and observed appear similar across all deciles of risk.


Risk estimation is dependent on accurate absolute risk estimates, not just rank ordering. Calibration compares the estimated absolute risk for an event with the observed event rate in a population sample. When risk estimators are applied to populations with different event rates, the risk calculators often require recalibration to avoid overestimating or underestimating risk. Thus, it is quite possible that a risk estimator may have a high c-statistic but is poorly calibrated for a given population. In many instances, risk estimation equations can be recalibrated to different populations, although this is not routinely done in clinical practice (Fig. 5.3).

Risk Reclassification

When patients have an intermediate estimate of CVD risk, or a 10-year CVD risk of 4% to 7.5%, clinicians commonly use testing above and beyond what is included in the risk estimation equation to individualize the risk of CVD in the patient. For example, high coronary artery calcium scores identify individuals with high atherosclerotic burden. When high coronary artery calcium scores are added to an estimated intermediate 10-year risk of CVD, patients can appropriately be reclassified to a higher level of risk. Therefore, this test can help clinicians determine which patients may benefit from statin therapy and those who may not benefit from statin therapy in the near term.



Although great advances in CVD epidemiology have been achieved over the last 70 years, significant challenges remain. The burden of CVD and CVD risk factors remains high in the US population and worldwide. Thus, despite having identified the determinants of most CVD events, further improvements need to be made to reduce the prevalence of these risk factors. This will require advancements in population-level interventions (changes in food policy, smoking bans, and so on) and continued efforts to understand the underlying physiology that contributes to the development of incident risk factors. Collaborative efforts that compile large cohort, genomic, proteomic, and metabolomic data with the aim of understanding the physiological underpinnings of CVD are currently underway and will certainly be a continued focus of epidemiological research in the future. Similarly, the development of digital and remote sensing technologies and wear-able devices will allow researchers opportunities for surveillance of physiological characteristics and health-related behaviors that may contribute to CVD risk.

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