NEW YORK (Reuters Health) – A widely used tool to predict cardiovascular risk, the Pooled Cohort Equations Risk Model (PCERM), underestimates the heart-related effects of living in a disadvantaged neighborhood, researchers suggest.
“The goal of cardiovascular prevention is to minimize patients’ risk of future heart disease-related events, such as stroke and heart attack, without imposing harms like side effects. Accurately measuring someone’s risk is critical, in that it helps provide targeted interventions such as cholesterol-lowering medications for high-risk patients,” Dr. Jarrod Dalton of Cleveland Clinic, in Ohio, told Reuters Health.
“The standard tool for measuring cardiovascular risk (PCERM), jointly developed by the American College of Cardiology and the American Heart Association, is widely used in primary care to determine risk and deliver targeted prevention,” he said by email. “It relies on well-established clinical risk factors, including sex, race, age, smoking, blood pressure, and whether or not someone has diabetes.”
“However,” he added, “there is a growing body of research suggesting that non-clinical risk factors like socioeconomic status may also contribute to an individual’s cardiovascular risk.”
Dr. Dalton and colleagues sought to investigate the relationship between neighborhood disadvantage and major atherosclerotic cardiovascular disease (ASCVD), including the PCERM’s predictive accuracy with respect to neighborhood socioeconomic position. They reviewed records of nearly 110,000 Cleveland Clinic patients (mean age, 56) who had an outpatient lipid panel drawn from 2007 through 2010.
They also created a neighborhood disadvantage index (NDI) as a single-factor representation of several variables that reflect neighborhood socioeconomic position including, among others, the percentages of white, non-Hispanic residents; people with a high school degree; Medicaid-insured residents; uninsured people; households below the federal poverty level; and households headed by an unmarried mother.
Patients in the top 5% of the NDI (close to 2,000) were most disadvantaged; those in the lowest 5% (close to 20,000) were the least disadvantaged.
Relative to those in low-NDI neighborhoods, those living in higher-NDI neighborhoods at baseline were more likely to have these characteristics: female, black, slightly higher average blood pressure, diabetes, coronary artery or peripheral vascular disease, and prescriptions for antihypertensive medication or statins. The higher-NDI group also had a higher 5-year predicted ASCVD-event risk, according to the PCERM.
Nonetheless, the PCERM systematically underpredicted ASCVD-event risk among patients from disadvantaged communities compared with those from the most affluent communities, according to the August 28 online report in Annals of Internal Medicine.
The NDI alone accounted for 32% of the geographic variability in major ASCVD-event rates, compared with 10% accounted for by the PCERM.
The authors conclude that “neighborhood disadvantage may be a powerful regulator of ASCVD event risk,” and call for efforts to enhance risk prediction “by incorporating aspects of neighborhood socioeconomic position and discerning its systemic effects on individuals.”
Dr. Dalton said, “Our study only identified a lack of predictive accuracy of (PCERM) among patients from disadvantaged neighborhoods. The reasons behind this disparity are complex and haven’t been fully studied to date.”
“It could be that relationships between clinical risk factors and cardiovascular events differ among these patients. It could also be that environmental exposures such as lead contamination, air pollution, less healthy food options, and lack of places to exercise increase cardiovascular risk,” he suggested.
“We are learning more about how different kinds of stress increase inflammation within the body, which influences cardiovascular disease,” he noted. “Going forward, we intend to look into questions like these in order to get a better understanding of the non-clinical mechanisms that determine cardiovascular risk.”
“Population-based grass roots and policy solutions are needed to help reduce the negative effects of resource-challenged neighborhood conditions on health outcomes,” Dr. Dalton added. “We are currently exploring the development of more-comprehensive risk models that incorporate surrogate risk factors associated with the patient. . . . In particular, our early efforts are to incorporate socioeconomic data on patients’ neighborhood of residence into the development of risk models.”
Dr. Sandro Galea of Boston University, coauthor of a related editorial, told Reuters Health by email, “Traditional measures of risk prediction that consider only individual behavior are likely always going to underperform. We need to consider neighborhood and other group-level factors as drivers of health and as predictors of disease, something we do infrequently.”
SOURCES: http://bit.ly/2iBy9ZD and http://bit.ly/2glNNr9
Ann Intern Med 2017.
Tidak ada komentar:
Posting Komentar