Key Points Question Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries? Findings In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting machine models outperformed other methods for identifying risk, although positive predictive values were low given the low prevalence of overdose episodes. Meaning Machine-learning algorithms using administrative data appear to be a valuable and feasible tool for more accurate identification of opioid overdose risk.

Aspirin use was associated with a reduced risk of ovarian cancer, especially among daily users of low-dose aspirin. These findings suggest that the same aspirin regimen proven to protect against cardiovascular events and several cancers could reduce the risk of ovarian cancer 20% to 34% depending on frequency and dose of use.

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Six distinct buprenorphine treatment trajectories were identified in this population-based low-income Medicaid cohort in Pennsylvania, USA. There appears to be an association between persistent use of buprenorphine for 12 months and lower risk of all-cause hospitalizations/emergency department visits.