Buprenorphine & methadone dosing strategies to reduce risk of relapse in the treatment of opioid use disorder.

BACKGROUND: Although there is consensus that having a "high-enough" dose of buprenorphine (BUP-NX) or methadone is important for reducing relapse to opioid use, there is debate about what this dose is and how it should be attained. We estimated the extent to which different dosing strategies would affect risk of relapse over 12 weeks of treatment, separately for BUP-NX and methadone. METHODS: This was a secondary analysis of three comparative effectiveness trials. We examined four dosing strategies: 1) increasing dose in response to participant-specific opioid use, 2) increasing dose weekly until some minimum dose (16 mg BUP, 100 mg methadone) was reached, 3) increasing dose weekly until some minimum and increasing dose in response to opioid use thereafter (referred to as the "hybrid strategy"), and 4) keeping dose constant after the first 2 weeks of treatment. We used a longitudinal sequentially doubly robust estimator to estimate contrasts between dosing strategies on risk of relapse. RESULTS: For BUP-NX, increasing dose following the hybrid strategy resulted in the lowest risk of relapse. For methadone, holding dose constant resulted in greatest risk of relapse; the other three strategies performed similarly. For example, the hybrid strategy reduced week 12 relapse risk by 13 % (RR: 0.87, 95 %CI: 0.83-0.95) and by 20 % (RR: 0.80, 95 %CI: 0.71-0.90) for BUP-NX and methadone respectively, as compared to holding dose constant. CONCLUSIONS: Doses should be targeted toward minimum thresholds and, in the case of BUP-NX, raised when patients continue to use opioids.

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing, case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of neighborhood characteristics were independently associated with COVID-19 testing, case, and death rates. For example, the proportions of Blacks and Hispanics in a ZCTA were positively associated with COVID-19 case rate. Contrary to the conventional hypothesis, neighborhoods with low-density housing experienced higher COVID-19 case rates. In addition, demographic changes (e.g. out-migration) during the pandemic may bias the estimates of COVID-19 rates. Future research should further investigate these neighborhood-level factors and their interactions over time to better understand the mechanisms by which they affect COVID-19.