Prevalence of adults with stimulant misuse and/or stimulant use disorder in Massachusetts, USA, 2014-2021.

Background And Aims: Stimulant-involved overdose deaths are increasing throughout the United States. Because stimulant misuse (i.e.

Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

Purpose: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention.

Methods: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity.

Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

Background: As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g.

Missing Race and Ethnicity Data among COVID-19 Cases in Massachusetts.

Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.

Background: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time.

Methods: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021).

A Novel Imputation Approach for Sharing Protected Public Health Data.

To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing.