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Objectives. To document state Medicaid pre- and postrelease initiatives for individuals in the criminal legal system with substance use disorder (SUD). Methods. An Internet-based survey was sent in 2021 to Medicaid directors in all 50 US states and the District of Columbia to determine whether they were pursuing initiatives for persons with SUD across 3 criminal legal settings: jails, prisons, and community corrections. A 90% response rate was obtained. Results. In 2021, the majority of states did not report any targeted Medicaid initiatives for persons with SUD residing in criminal legal settings. Eighteen states and the District of Columbia adopted at least 1 Medicaid initiative for persons with SUD across the 3 criminal legal settings. The most commonly adopted initiatives were in the areas of medication for opioid use disorder treatment and Medicaid enrollment. Out of 24 possible initiatives for each state (8 initiatives across 3 criminal legal settings), the 2 most commonly adopted were (1) provision of medication treatment of opioid use disorder before release from criminal legal settings (16 states) and (2) facilitation of Medicaid enrollment through suspension rather than termination of Medicaid enrollment upon entry to a criminal legal setting (14 states). Initiatives pertaining to Medicaid SUD care coordination were adopted by the fewest (9) states. Conclusions. In 2021, states' involvement in Medicaid SUD initiatives for criminal legal populations remained low. Increased adoption of Medicaid SUD initiatives across criminal legal settings is needed, especially knowing the high rate of overdose mortality among this group. (Am J Public Health. 2024;114(5):527–530. https://doi.org/10.2105/AJPH.2024.307604) [ABSTRACT FROM AUTHOR]

Forecasting performance of spatial versus non-spatial Bayesian priors applied to a large vector autoregressive model that includes the 48 lower US states plus and the District of Columbia is explored. Accuracy of one- to six-quarter-ahead personal income forecasts is compared for a model based on the Minnesota prior used in macroeconomic forecasting and a spatial prior proposed by Krivelyova and LeSage (J Reg Sci 39(2):297-317, ). While the Minnesota prior emphasizes time dependence taking the form of a random walk, the spatial prior relies on past values of neighboring state income growth rates while ignoring own-state past income growth. Our findings indicate that forecast accuracy for longer future time horizons is improved by the spatial prior, while that for shorter horizons is better for the non-spatial prior. This motivated a hybrid approach that combines both spatial and time dependence in the prior restrictions placed on the model parameters. [ABSTRACT FROM AUTHOR]