Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning
In recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rates, addressing this emerging public health concern globally. We compiled a range of widely recognized factors to examine spatial heterogeneity and its...
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| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-23375-y |
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| author | Amir Hossein Ebrahimi Ali Asghar Alesheikh Aynaz Lotfata |
| author_facet | Amir Hossein Ebrahimi Ali Asghar Alesheikh Aynaz Lotfata |
| author_sort | Amir Hossein Ebrahimi |
| collection | DOAJ |
| description | In recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rates, addressing this emerging public health concern globally. We compiled a range of widely recognized factors to examine spatial heterogeneity and its associations with overdose mortality using a non-linear geographically weighted random forest approach. The model outperformed conventional ones with (R2 = 0.83 and MAE = 0.26). We found that, on average, the population rate of Asians (12.8%) is the most important determinant of the model, followed by the population rate of African Americans (10.1%) and the rate of cost-burdened housing units (9.9%). Although the results indicate that climatic determinants have had a lesser impact on overdose mortality rates, locally, their importance is greater in central and eastern counties. The spatial analysis revealed that the significance of determinants varies greatly by location. These findings could inform the development of localized spatial models, enabling more efficient allocation of resources to control overdose mortality rates at the community level. |
| format | Article |
| id | doaj-art-71c9bc88f3a742e8bd2427a18e560ec0 |
| institution | DOAJ |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-71c9bc88f3a742e8bd2427a18e560ec02025-08-20T03:04:07ZengBMCBMC Public Health1471-24582025-07-0125111610.1186/s12889-025-23375-ySpatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learningAmir Hossein Ebrahimi0Ali Asghar Alesheikh1Aynaz Lotfata2Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of TechnologyDepartment of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of TechnologyDepartment of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of CaliforniaIn recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rates, addressing this emerging public health concern globally. We compiled a range of widely recognized factors to examine spatial heterogeneity and its associations with overdose mortality using a non-linear geographically weighted random forest approach. The model outperformed conventional ones with (R2 = 0.83 and MAE = 0.26). We found that, on average, the population rate of Asians (12.8%) is the most important determinant of the model, followed by the population rate of African Americans (10.1%) and the rate of cost-burdened housing units (9.9%). Although the results indicate that climatic determinants have had a lesser impact on overdose mortality rates, locally, their importance is greater in central and eastern counties. The spatial analysis revealed that the significance of determinants varies greatly by location. These findings could inform the development of localized spatial models, enabling more efficient allocation of resources to control overdose mortality rates at the community level.https://doi.org/10.1186/s12889-025-23375-yOverdose mortalitySpatial analysisSpatial machine learning model |
| spellingShingle | Amir Hossein Ebrahimi Ali Asghar Alesheikh Aynaz Lotfata Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning BMC Public Health Overdose mortality Spatial analysis Spatial machine learning model |
| title | Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning |
| title_full | Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning |
| title_fullStr | Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning |
| title_full_unstemmed | Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning |
| title_short | Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning |
| title_sort | spatial analysis of county level determinants of overdose mortality in the united states using spatial machine learning |
| topic | Overdose mortality Spatial analysis Spatial machine learning model |
| url | https://doi.org/10.1186/s12889-025-23375-y |
| work_keys_str_mv | AT amirhosseinebrahimi spatialanalysisofcountyleveldeterminantsofoverdosemortalityintheunitedstatesusingspatialmachinelearning AT aliasgharalesheikh spatialanalysisofcountyleveldeterminantsofoverdosemortalityintheunitedstatesusingspatialmachinelearning AT aynazlotfata spatialanalysisofcountyleveldeterminantsofoverdosemortalityintheunitedstatesusingspatialmachinelearning |