Combining zip code-based population data and pharmacy administrative claims data to create measures of social determinants of health.

Background: Social determinants of health (SDoH) are key factors that impact health outcomes. However, there are many barriers to collecting SDoH data (eg, cost of data collection, technological barriers, and lack of standardized measures). Population data may provide an accessible alternative to collecting SDoH data for patients.

Objective: To explain how population data can be leveraged to create SDoH measures, assess the association of population SDoH measures with diabetic medication adherence, and discuss how understanding a patient's SDoH can inform care plans and patient engagement.

Methods: A nationally representative commercial sample of patients who were aged 18 years and older and met Pharmacy Quality Alliance inclusion criteria for diabetes mellitus were analyzed (N = 37,789). US Census and North American Industry Classification System data were combined with pharmacy administrative claims data to create SDoH measures. Derived measures represent 2 SDoH domains: (1) economic stability (housing density, housing relocation, jobs per resident, and average salary) and (2) health care access and quality (urban/rural classification, distance traveled to prescriber and pharmacy, use of a primary care provider [PCP], and residents per PCP). The association of population SDoH measures with diabetic medication adherence (proportion of days covered) was assessed via logistic regression, which included covariates (eg, sex, age, comorbidities, and prescription plan attributes).

Results: As housing density (houses per resident) increased, so did the likelihood of adherence (odds ratio = 1.54, 95% CI = 1.21-1.97, = 0.001). Relative to patients who did not move, patients who moved once had 0.87 (95% CI = 0.81-0.93, < 0.001) the odds of being adherent, and patients who moved 2 or more times had 0.82 (95% CI = 0.71-0.95, = 0.008) the odds of being adherent. Compared with areas with fewer jobs per resident, patients living within a zip code with 0.16 to 0.26 jobs per resident were 1.12 (95% CI = 1.04-1.20, = 0.002) times more likely to be adherent. Patients who lived in an urban cluster were 1.11 (95% CI = 1.01-1.22, = 0.037) times more likely to be adherent than patients living in a rural area. Patients who travel at least 25 miles to their prescriber had 0.82 (95% CI = 0.77-0.86, < 0.001) the odds of being adherent. Community pharmacy users had 0.65 (95% CI = 0.59-0.71, < 0.001) the odds of being adherent compared with mail order pharmacy users. Patients who had a PCP were 1.26 (95% CI = 1.18-1.34, < 0.001) times more likely to be adherent to their medication.

Conclusions: Leveraging publicly available population data to create SDoH measures is an accessible option to overcome barriers to SDoH data collection. Derived measures can be used to increase equity in care received by identifying patients who could benefit from assistance with medication adherence.