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Quantifying and ameliorating socioeconomic inequity in first cardiovascular events through improved risk prediction and treatment


Cardiovascular disease (CVD) risk is negatively associated with socioeconomic status (SES) independent of traditional CVD risk factors but clinicians lack the tools to incorporate this SES gradient into their calculation of CVD risk; and policymakers infrequently evaluate the impact of CVD interventions on summary measures of health inequity. Current CVD risk calculators in the United States under-estimate CVD risk among low SES populations and may exacerbate SES inequity in CVD outcomes. The goals of this dissertation were to develop and evaluate a clinically practical equation that improves CVD risk prediction among middle age low SES groups, and to estimate the potential impact of using the equation on the income-related inequity in CVD in a community-based middle-age cohort.

Primary analyses were conducted 12,218 participants in the Atherosclerosis Risk in Communities (ARIC) who were free vascular disease and diabetes at baseline and followed for up to 22 years. Geocoded observations were linked to a neighborhood deprivation index calculated from six census tract-level variables of the U.S. Census. Cox proportional hazards and competing risk regression models were used to predict 10 and 20 year risk of coronary heart disease or ischemic stroke (CVD), respectively. Applying methods recommended by the American Heart Association for the evaluation of novel CVD risk factors, the dissertation demonstrates that the addition of an SES-5 variable comprised of education and neighborhood SES is associated with CVD, predicts development of future outcomes, adds incremental value to established risk factors and has demonstrated clinical utility by changing predicted risk sufficiently to modify treatment decisions, in a middle-age cohort free of CVD at baseline in the late 1980s.

The analyses also demonstrate how the Framingham risk function systematically under-estimates the socioeconomic gradient in CVD at 10 and 20 years, for multiple CVD outcomes; and that inclusion of an SES-5 variable attenuates this bias. We report results specific to low SES participants with low income or educational attainment, and demonstrate that the SES-5 variable improves reclassification to a greater extent in this low SES group than in the cohort as a whole. We further demonstrated that additional improvement in net reclassification is possible when single risk equations are replaced by a hybrid approach that uses the higher of two predicted risks to classify individuals into treatment categories. The individuals reclassified into higher risk categories experience elevated risk of CVD, and the expected numbers needed to treat with a statin for ten years (NNT) compares favorably with the NNT of traditional approaches. The SES-5 and hybrid approaches would begin to ameliorate CVD inequity by minimizing under-treatment among low SES groups and increasing the proportion of CVD events potentially averted by approximately 7.6% percentage points, representing a relative increase in events averted of 15% to 50%.

We further explore the population-wide impact of adding the SES-5 variable to traditional CVD risk equations by calculating observed and predicted values for the concentration index of incident CVD, a summary measure of relative CVD inequity that accounts for differences across the entire spectrum of household income. We demonstrate that the traditional FRS risk function underestimates the observed CI in incident CVD at 10 and 20 years by nearly 50%. In contrast, a risk function that includes the SES-5 variable attenuates this under-estimation, which is no longer significantly different than zero.

We also model the impact on health equity and achievement of competing treatment approaches based on risk equations that include or exclude the SES-5 variable. Although all CVD risk-based treatment approaches would improve CVD inequity and inequity-weighted CVD achievement, as measured by the concentration (CI) and achievement (AI) indices, respectively, the SES-5 treatment approach would do so to a greater extent than and approach based exclusively on traditional FRS risk factors.

In order to illustrate how social attitudes towards inequity can be incorporated in the evaluation of CVD interventions, we add a parameter reflecting society's aversion to inequity to the equation used to calculate the CI and AI. We calculate the inequity-weighted absolute risk reduction (iARR) for each treatment approach and find that if inequity does not matter then society will be indifferent to the SES-5 and FRS treatment strategies that prevent a similar number of CVD events overall. However, if inequality does matter and if the treatment threshold falls in the range of 5-20% then a risk equation that includes SES-5 should allocate treatment in a manner that is preferable to a strategy based on traditional risk factors alone. A hybrid strategy will avert more CVD events than either the SES-5 and or treatment strategies alone, irrespective of society's aversion to inequity or choice of treatment threshold. Implications for clinical practice and policy are discussed.

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