Screening Medications for Association with Progression to Wet Age-Related Macular Degeneration.

PURPOSE: There is an urgent need for treatments that prevent or delay development to advanced age-related macular degeneration (AMD). Drugs already on the market for other conditions could affect progression to neovascular AMD (nAMD). If identified, these drugs could provide insights for drug development targets. The objective of this study was to use a novel data mining method that can simultaneously evaluate thousands of correlated hypotheses, while adjusting for multiple testing, to screen for associations between drugs and delayed progression to nAMD. DESIGN: We applied a nested case-control study to administrative insurance claims data to identify cases with nAMD and risk-set sampled controls that were 1:4 variable ratio matched on age, gender, and recent healthcare use. PARTICIPANTS: The study population included cases with nAMD and risk set matched controls. METHODS: We used a tree-based scanning method to evaluate associations between hierarchical classifications of drugs that patients were exposed to within 6 months, 7 to 24 months, or ever before their index date. The index date was the date of first nAMD diagnosis in cases. Risk-set sampled controls were assigned the same index date as the case to which they were matched. The study was implemented using Medicare data from New Jersey and Pennsylvania, and national data from IBM MarketScan Research Database. We set an a priori threshold for statistical alerting at P ≤ 0.01 and focused on associations with large magnitude (relative risks ≥ 2.0). MAIN OUTCOME MEASURES: Progression to nAMD. RESULTS: Of approximately 4000 generic drugs and drug classes evaluated, the method detected 19 distinct drug exposures with statistically significant, large relative risks indicating that cases were less frequently exposed than controls. These included (1) drugs with prior evidence for a causal relationship (e.g., megestrol); (2) drugs without prior evidence for a causal relationship, but potentially worth further exploration (e.g., donepezil, epoetin alfa); (3) drugs with alternative biologic explanations for the association (e.g., sevelamer); and (4) drugs that may have resulted in statistical alerts due to their correlation with drugs that alerted for other reasons. CONCLUSIONS: This exploratory drug-screening study identified several potential targets for follow-up studies to further evaluate and determine if they may prevent or delay progression to advanced AMD.

Association of Geography and Access to Health Care Providers With Long-Term Prescription Opioid Use in Medicare Patients With Severe Osteoarthritis: A Cohort Study.

OBJECTIVE: To evaluate the variation in long-term opioid use in osteoarthritis (OA) patients according to geography and health care access. METHODS: We designed an observational cohort study among OA patients undergoing total joint replacement (TJR) in the Medicare program (2010 through 2014). The independent variables of interest were the state of residence and health care access, which was quantified at the primary care service area (PCSA) level as categories of number of practicing primary care providers (PCPs) and categories of rheumatologists per 1,000 Medicare beneficiaries. The percentage of OA patients taking long-term opioids (≥90 days in the 360-day period immediately preceding TJR) within each PCSA was the outcome variable in a multilevel, generalized linear regression model, adjusting for case-mix at the PCSA level and for policies, including rigor of prescription drug monitoring programs and legalized medical marijuana, at the state level. RESULTS: A total of 358,121 patients with advanced OA, with a mean age of 74 years, were included from 4,080 PCSAs. The unadjusted mean percentage of long-term opioid users varied widely across states, ranging from 8.9% (Minnesota) to 26.4% (Alabama), and this variation persisted in the adjusted models. Access to PCPs was only modestly associated with rates of long-term opioid use between PCSAs with highest (>8.6) versus lowest (<3.6) concentration of PCPs (adjusted mean difference 1.4% [95% confidence interval 0.8%, 2.0%]), while access to rheumatologists was not associated with long-term opioid use. CONCLUSION: We note a substantial statewide variation in rates of long-term treatment with opioids in OA, which is not fully explained by the differences in access to health care providers, varying case-mix, or state-level policies.