<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>6(1)</volume><submitter>Linde S</submitter><pubmed_abstract>&lt;h4>Importance&lt;/h4>Prior research has identified associations between housing insecurity and poor health outcomes.&lt;h4>Objective&lt;/h4>To evaluate the association between US state Medicaid expansions and reductions in eviction; to examine the persistence of these associations and how they vary across US states and counties.&lt;h4>Design, setting, and participants&lt;/h4>This cohort study of 25 398 county-year observations (across 40 states) used US eviction and census data for the years 2002 through 2018 (ie, 17 years). County-level associations were estimated using interactive fixed effects counterfactual estimators, and models were selected using cross validation. Across-county treatment association heterogeneities were assessed using multivariable regression methods. Analyses were performed in J</pubmed_abstract><journal>JAMA network open</journal><pagination>e2249361</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9857591</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Association Between State-Level Medicaid Expansion and Eviction Rates.</pubmed_title><pmcid>PMC9857591</pmcid><pubmed_authors>Linde S</pubmed_authors><pubmed_authors>Egede LE</pubmed_authors></additional><is_claimable>false</is_claimable><name>Association Between State-Level Medicaid Expansion and Eviction Rates.</name><description>&lt;h4>Importance&lt;/h4>Prior research has identified associations between housing insecurity and poor health outcomes.&lt;h4>Objective&lt;/h4>To evaluate the association between US state Medicaid expansions and reductions in eviction; to examine the persistence of these associations and how they vary across US states and counties.&lt;h4>Design, setting, and participants&lt;/h4>This cohort study of 25 398 county-year observations (across 40 states) used US eviction and census data for the years 2002 through 2018 (ie, 17 years). County-level associations were estimated using interactive fixed effects counterfactual estimators, and models were selected using cross validation. Across-county treatment association heterogeneities were assessed using multivariable regression methods. Analyses were performed in J</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jan</publication><modification>2025-04-06T19:14:52.006Z</modification><creation>2025-04-06T19:14:52.006Z</creation></dates><accession>S-EPMC9857591</accession><cross_references><pubmed>36630137</pubmed><doi>10.1001/jamanetworkopen.2022.49361</doi></cross_references></HashMap>