Health inequities in preventing and controlling chronic disease are evident both in general populations as well as among clinics where targeted activities to reduce health inequity may be implemented. Through the implementation of the Affordable Care Act (ACA), growing focus has been placed on reducing disparities and improving health of the population through clinic-level improvements. This analysis provides a framework for the assessment of clinic-level electronic health record (EHR) data in the evaluation of health equity among populations served in primary care clinics across Colorado.
METHODS:
Data were collected between 2012 and 2015 from 12,537 patient electronic health records in 49 primary care clinics across Colorado assessing breast, cervical and colorectal cancer screening rates. Separately, control rates among patients with diabetes and hypertension were also evaluated. Measures were assessed using Healthcare Effectiveness Data and Information Set (HEDIS) standards established by the National Committee for Quality Assurance (NCQA); briefly, these are the same standards used for national incentive programs such as Meaningful Use. Approximately 2,500 to 5,000 electronic health records were reviewed per measure, and data identifying patient ethnicity and primary payor source were collected. Clinic locations were classified as either rural or urban and as Federally Qualified Health Centers (FQHC), which primarily serve safety-net populations, or Non-FQHC. Crude and adjusted logistic regression analysis was conducted, and odds ratios (OR) were established as measures of effect.
RESULTS:
Significant differences (p < .05) were observed between ethnic and payor groups for adherence to chronic disease and prevention measures. Overall, patients with commercial insurance had higher odds of being adherent than patients with Medicaid or who self-paid. After controlling for payor group Hispanics had higher odds of receiving a cervical cancer screening (OR ≈ 1.47), lower odds of receiving a colorectal cancer screen (OR ≈ .783), and higher odds of having uncontrolled diabetes (OR ≈ 1.34). Interestingly, while geographic location only had a modest impact on measures of effect, significant (p < .05) results stemmed from crude and adjusted analysis corresponding to the effect that FQHC status had on assessed chronic disease and prevention measures.
CONCLUSIONS:
This analysis supports population-based health inequities found through other data sources in Colorado, and demonstrates potential application at the clinic level. Of particular interest is the impact that FQHC status had on assessed measures of effect. This potential association may lend credence to the notion that FQHC requirements and funding structures may be driving factors in increasing health equity.