biostudies-literatureKondo MCU.S. Forest ServiceNICHD NIH HHS150564https://www.ebi.ac.uk/biostudies/studies/S-EPMC9063456biostudies-literatureUnknown806(Pt 2)Prescribed fire is an increasingly important tool in restoring ecological conditions and reducing uncontrolled wildfire. Prescribed burn techniques could reduce public health impacts associated with wildfire smoke exposure. However, there have been few assessments of the health impacts of prescribed burning, and potential vulnerabilities among populations exposed to smoke from prescribed fires. Our study area focused on counties in and near U.S. National Forests - a set of lands distributed across the U.S. In county-level analyses, we compared the sociodemographic and health characteristics of areas that were exposed with those that were not exposed to prescribe burns during the years 2010-2019 on a national level and within three regions. In addition, using spatial error regression modelsThe Science of the total environmentSocio-demographic and health vulnerability in prescribed-burn exposed versus unexposed counties near the National Forest System.PMC9063456P2C HD066613Long DKondo MCHeilman WEReid CEMockrin MHfalseSocio-demographic and health vulnerability in prescribed-burn exposed versus unexposed counties near the National Forest System.Prescribed fire is an increasingly important tool in restoring ecological conditions and reducing uncontrolled wildfire. Prescribed burn techniques could reduce public health impacts associated with wildfire smoke exposure. However, there have been few assessments of the health impacts of prescribed burning, and potential vulnerabilities among populations exposed to smoke from prescribed fires. Our study area focused on counties in and near U.S. National Forests - a set of lands distributed across the U.S. In county-level analyses, we compared the sociodemographic and health characteristics of areas that were exposed with those that were not exposed to prescribe burns during the years 2010-2019 on a national level and within three regions. In addition, using spatial error regression models2022-01-01T00:00:00Z2022 Feb2025-04-19T17:58:15.59Z2025-04-19T17:58:15.59ZS-EPMC90634563458285910.1016/j.scitotenv.2021.150564