Neighborhood Socioeconomic Position Key to Predicting CVD Risk

September 01, 2017 05:14 pm Chris Crawford
[Tenement housing]

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death for most Americans, and different groups' risk for ASCVD varies based on race and socioeconomic position (SEP). However, SEP isn't typically considered in cardiovascular risk assessment. 

A large observational study published Aug. 29( in Annals of Internal Medicine examined relationships among various neighborhood socioeconomic condition levels, clinical assessments of atherosclerotic risk and major ASCVD event occurrences in a patient cohort derived from routinely collected electronic health data.

Story highlights
  • An Annals of Internal Medicine study found that the Pooled Cohort Equations Risk Model (PCERM) systematically underpredicted atherosclerotic cardiovascular disease (ASCVD) event risk among patients from disadvantaged communities.
  • Researchers analyzed electronic health data for 109,793 patients in the Cleveland Clinic health system who had an outpatient lipid panel drawn between 2007 and 2010.
  • Neighborhood disadvantage accounted for more than three times the amount of geographic variability in major ASCVD event rates compared with PCERM, which incorporates only clinically obtained risk factors.  

For the study, researchers from the Cleveland Clinic, Case Western Reserve University and MetroHealth Medical Center -- all from the Cleveland area -- sought to determine the relative accuracy of the Pooled Cohort Equations Risk Model (PCERM)( and that of neighborhood SEP factors for predicting ASCVD event rates. What researchers found was the PCERM systematically underpredicted ASCVD event risk among patients from disadvantaged communities.

In 2014, the American College of Cardiology/American Heart Association Task Force on Practice Guidelines released the PCERM tool( for predicting 10-year ASCVD risk. The tool, which was based on data collected from racially and geographically diverse cohort studies, was intended to establish more demographically representative models for ASCVD events. However, the model failed to account for variations in risk attributable to SEP.

Study Details

Researchers analyzed electronic health data for 109,793 patients in the Cleveland Clinic health system who had an outpatient lipid panel drawn between 2007 and 2010. That lipid panel draw served as a baseline measure.

Time from baseline to first major ASCVD event (myocardial infarction, stroke or cardiovascular death) within five years was modeled as a function of a locally derived neighborhood disadvantage index (NDI) and the predicted five-year ASCVD event rate from the PCERM, respectively, for the study.

Neighborhood disadvantage accounted for more than three times the amount of geographic variability in major ASCVD event rates (32 percent) compared with PCERM (10 percent), which incorporates only clinically obtained risk factors.

Additionally, the study found that compared with patients in low-NDI neighborhoods, those living in higher-NDI neighborhoods at baseline

  • were more likely to be female and black,
  • had slightly higher average blood pressure,
  • were more likely to have diabetes,
  • were more likely to have been prescribed antihypertensive medication or statins,
  • were more likely to have coronary artery or peripheral vascular disease, and
  • had higher five-year predicted ASCVD event risk as defined by the PCERM.

PCERM Tool Limitations

The study authors outline three possible explanations for why the PCERM model failed to accurately predict risk in high NDI areas. First, the relationship between clinical risk factors and ASCVD outcomes may vary across SEPs. Second, the model fails to account for differences in environmental and social risks that affect individuals from disadvantaged communities. Third, certain individual risks might be more prevalent among residents of disadvantaged communities than among those who reside in more affluent areas.

Danielle Jones, M.P.H., manager of the AAFP's Center for Diversity and Health Equity (CDHE) told AAFP News that one of the limitations of the PCERM tool, as the authors suggested, is that using a cumulative risk index fails to accurately map and measure the potential causative pathway of specific measures.

"Further research should aim to strengthen the reliability and validity of the model using additional standardized measures of social and environmental risk factors or by assessing the effect of individual social risk on outcomes separately," Jones said.

Until then, family physicians should consider using complementary tools that provide an assessment of both individual and community-level health risk based on the prevalence of neighborhood exposures, she added.

"The AAFP's CDHE is working on developing these types of assessments to support members in providing comprehensive care to their patients," Jones said.

Helping Reduce Health Disparities

Jones said this study's evidence supports the case that current clinical models of predicting disease risk are limited because they fail to account for the effect of social and environmental risks -- which can determine up to 60 percent of an individual's health status and well-being.

"This study is an excellent example of the confluence of clinical and social applications that can be used to reduce disparities and promote a shift toward achieving health equity in all communities, despite SEP," she explained.

An editorial( that accompanied the study said the challenges associated with using predictive models that the study reveals "warrant substantial humility in the development and use of predictive models and a greater focus on social, economic and geographic assessments in these models."

Jones said communities can improve the health of their neighborhoods by first identifying inequities and then developing targeted solutions that include changing the policies, systems and infrastructures that create these unnatural health landscapes.

"Family physicians are uniquely positioned in this process in that they are often the first to identify disparities in the health of the communities they serve," she said. "They also can act as champions by providing a health equity lens through which nonclinical stakeholders can begin to view and ensure that all policies are healthy policies and strive toward a culture of health equity."

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