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, P = 0.001). Relative to patients who did not move, patients who moved once had 0.87 (95% CI = 0.81-0.93, P < 0.001) the odds of being adherent, and patients who moved 2 or more times had 0.82 (95% CI = 0.71-0.95, P = 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, P = 0.002) times more likely to be adherent. Patients who lived in an urban cluster were 1.11 (95% CI = 1.01-1.22, P = 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, P < 0.001) the odds of being adherent. Community pharmacy users had 0.65 (95% CI = 0.59-0.71, P < 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, P < 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.

Retrospective cohort study of medication adherence and risk for 30-day hospital readmission in a Medicare Cost Plan.

PURPOSE: Reducing hospital readmissions requires deploying appropriate interventions to groups at highest risk for readmission. Long-term medication adherence may indicate one's ability to manage recovery and chronic illness after discharge. If so, medication adherence also may be a predictor of hospital readmission. DESIGN: The objective of this study was to test the association of long-term medication adherence with hospital readmission in a cohort of beneficiaries enrolled in a Medicare Cost Plan. METHODOLOGY: The study employed a retrospective cohort design using administrative pharmacy and health care claims for a sample hospitalized in 2009. Medication adherence was measured with the medication possession ratio (MPR) for the 12 months prior to the first hospitalization in 2009. The likelihood of readmission within 30 days from the first hospitalization in 2009 was estimated using the logistic regression model. RESULTS: Long-term medication adherence was not associated with likelihood of 30-day hospital readmission (odds ratio [OR] = 0.82, P = .71). However, older age (OR = 1.07, P = .003) and longer length of hospital stay (OR = 1.2, P < .001) were associated with higher likelihood of 30-day readmission, while having an office visit within 30 days of discharge (OR = 0.38, P = .03) was associated with lower odds of readmission. CONCLUSION: Except for older age, variables associated with likelihood of readmission are difficult for clinical teams to access during a hospital stay to identify those at risk for readmission. Additional work is needed to identify indicators of readmission risk that can be utilized during hospitalization to identify patients needing post-discharge support to help prevent readmission.

Evaluation of health care costs and utilization patterns for patients with gout.

BACKGROUND: The prevalence of gout has been increasing. Serum uric acid (sUA) levels ≥6 mg/dL have been associated with high morbidity and increased health care utilization. OBJECTIVE: To assess the costs and patterns of health care resource utilization for patients with gout, categorized into 3 cohorts based on sUA levels. METHODS: We retrospectively analyzed laboratory, pharmacy, and medical service claims data (January 2005 to June 2010) for patients ≥18 years old. Inclusion criteria were at least 2 sUA levels and at least 1 primary gout diagnosis (International Classification of Disease-9th revision code 274.xx), and/or at least 1 prescription for gout-specific medications. Outcomes including costs, health care resource utilization, and medication adherence and persistence were assessed for the 1-year postindex period and summarized for the 3 cohorts based on sUA levels: <6 mg/dL, 6 to 8.99 mg/dL, and ≥9 mg/dL. Costs were adjusted based on preindex utilization and baseline characteristics. RESULTS: Three hundred fifty-two patients met the inclusion criteria: cohort 1 (sUA <6 mg/dL), n = 38, mean age 59 years; cohort 2 (sUA 6-8.99 mg/dL), n = 231, mean age 61 years; and cohort 3 (sUA ≥9 mg/dL), n = 83, mean age 62 years. Mean adjusted gout-related health care costs were $332, $353, and $663, respectively (P <0.05); mean adjusted all-cause health care costs were $11,365, $11,551, and $14,474, respectively, for the 3 cohorts (P <0.05). sUA levels were positively associated with the percent of patients who had at least 1 hospitalization and the mean number of hospitalizations and emergency department visits (P < 0.05). Significantly more patients with sUA <6 mg/dL achieved adherence rates of ≥80% and had a longer mean duration of continuous treatment compared with patients with sUA between 6 and 8.99 mg/dL or ≥9.0 mg/dL (P <0.05). CONCLUSIONS: Patients with high levels of sUA had higher gout-related and all-cause health care costs, more all-cause hospitalizations, and emergency department visits, but they also exhibited poorer adherence and persistence.

Retrospective evaluation of the impact of copayment increases for specialty medications on adherence and persistence in an integrated health maintenance organization system.

BACKGROUND: Specialty drugs are generally defined as high-cost injectable, infused, oral, or inhaled drugs that require close monitoring. Specialty drugs account for an increasing percentage of total drug expenditures, and management of specialty drugs has become a priority. A Central Texas-based integrated health maintenance organization system implemented a specialty drug benefit to manage expensive specialty drug costs. OBJECTIVES: Our objective was to measure and compare the change in adherence and persistence after implementation of copayment increases for select specialty medications used on a long-term basis (at least 2 years). METHODS: Patients who were long-term users of anti-inflammatory, immunosuppressant, cancer, and multiple sclerosis medications were selected. The intervention group consisted of those whose out-of-pocket payment for specialty medications increased, and the control group consisted of those whose out-of-pocket costs did not change. Adherence, defined by proportion of days covered, was measured every 3 months for 12 months before and after the change. Individual growth model analysis evaluated the changes in adherence. Cox regression analysis determined the difference in persistence between groups. RESULTS: There were 178 and 202 patients in the intervention and control groups, respectively. The growth model showed a small but statistically significant decrease in proportion of days covered of 0.040 after copay changes in the intervention versus control group (P < 0.001) for immunosuppressants. The Cox regression analysis indicated a higher probability of intervention patients on anti-inflammatory drugs (hazard ratio [HR] = 2.53; 95% CI, 1.38-4.62) and immunosuppressants (HR = 3.01; 95% CI, 1.20-7.56) would be nonpersistent compared with those in their control groups. CONCLUSIONS: The move to the specialty formulary allows for closer scrutiny of specialty utilization by pharmacists, who actively monitor utilization and access. Despite the minimal adherence decrease and significant persistence changes with certain drug types, the results indicated relatively more stability with specialty drug use than reported with traditional pharmaceuticals.