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Observational Study
. 2017 Oct 3;167(7):456-464.
doi: 10.7326/M16-2543. Epub 2017 Aug 29.

Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study

Jarrod E Dalton  1 , Adam T Perzynski  1 , David A Zidar  1 , Michael B Rothberg  1 , Claudia J Coulton  1 , Alex T Milinovich  1 , Douglas Einstadter  1 , James K Karichu  1 , Neal V Dawson  1
Affiliations
Observational Study

Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study

Jarrod E Dalton et al. Ann Intern Med. .

Abstract

Background: Inequality in health outcomes in relation to Americans' socioeconomic position is rising.

Objective: First, to evaluate the spatial relationship between neighborhood disadvantage and major atherosclerotic cardiovascular disease (ASCVD)-related events; second, to evaluate the relative extent to which neighborhood disadvantage and physiologic risk account for neighborhood-level variation in ASCVD event rates.

Design: Observational cohort analysis of geocoded longitudinal electronic health records.

Setting: A single academic health center and surrounding neighborhoods in northeastern Ohio.

Patients: 109 793 patients from the Cleveland Clinic Health System (CCHS) who had an outpatient lipid panel drawn between 2007 and 2010. The date of the first qualifying lipid panel served as the study baseline.

Measurements: Time from baseline to the first occurrence of a major ASCVD event (myocardial infarction, stroke, or cardiovascular death) within 5 years, modeled as a function of a locally derived neighborhood disadvantage index (NDI) and the predicted 5-year ASCVD event rate from the Pooled Cohort Equations Risk Model (PCERM) of the American College of Cardiology and American Heart Association. Outcome data were censored if no CCHS encounters occurred for 2 consecutive years or when state death data were no longer available (that is, from 2014 onward).

Results: The PCERM systematically underpredicted ASCVD event risk among patients from disadvantaged communities. Model discrimination was poorer among these patients (concordance index [C], 0.70 [95% CI, 0.67 to 0.74]) than those from the most affluent communities (C, 0.80 [CI, 0.78 to 0.81]). The NDI alone accounted for 32.0% of census tract-level variation in ASCVD event rates, compared with 10.0% accounted for by the PCERM.

Limitations: Patients from affluent communities were overrepresented. Outcomes of patients who received treatment for cardiovascular disease at Cleveland Clinic were assumed to be independent of whether the patients came from a disadvantaged or an affluent neighborhood.

Conclusion: Neighborhood disadvantage may be a powerful regulator of ASCVD event risk. In addition to supplemental risk models and clinical screening criteria, population-based solutions are needed to ameliorate the deleterious effects of neighborhood disadvantage on health outcomes.

Primary funding source: The Clinical and Translational Science Collaborative of Cleveland and National Institutes of Health.

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Conflict of interest statement

All Authors: No conflicts of interest

Figures

Figure 1:
Figure 1:
Distribution of neighborhood disadvantage index (NDI, defined at the census tract-level) across Northeast Ohio. Higher NDI indicates greater socioeconomic disadvantage.
Figure 2:
Figure 2:
Prognostic accuracy of the PCERM across strata defined according to percentile groups of the neighborhood disadvantage index (highest percentiles correspond to the least affluent communities). Perfect calibration of the PCERM is represented along the line y = x; points above this line indicate under-estimation of risk by the PCERM in relation to observed event rates, and points below this line indicate over-estimation of risk. Concordance indices (C) and corresponding 95% confidence intervals are displayed within each panel. The concordance index ranges from 0.5 to 1.0, where a value of 0.5 represents no discrimination of events from non-events and a value of 1.0 represents complete separation of outcomes. NDI = neighborhood disadvantage index; PCERM = Pooled Cohort Equations Risk Model.
Figure 3:
Figure 3:
Hazard ratios for major atherosclerotic cardiovascular disease events (myocardial infarction, stroke, or cardiovascular death) across Northeast Ohio, from the null model without covariates (Model 1).
Figure 4:
Figure 4:
Hazard ratios for major atherosclerotic cardiovascular disease events (myocardial infarction, stroke, or cardiovascular death) across Northeast Ohio, from the model that adjusted for estimated 5-year risk from the American College of Cardiology/American Heart Association Pooled Cohort Equations Risk Model and our neighborhood socioeconomic status index (Model 4).
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Comment in

References

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