Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality
Abstract Background The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates...
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BMC
2025-05-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-23044-0 |
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| author | Andrew Deas Adam Spannaus Hashan Fernando Heidi A. Hanson Anuj J. Kapadia Jodie Trafton Vasileios Maroulas |
| author_facet | Andrew Deas Adam Spannaus Hashan Fernando Heidi A. Hanson Anuj J. Kapadia Jodie Trafton Vasileios Maroulas |
| author_sort | Andrew Deas |
| collection | DOAJ |
| description | Abstract Background The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates. Methods This study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter’s predicted rates across the nation’s counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally. Results Our spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation’s most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift. Conclusions The initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use. |
| format | Article |
| id | doaj-art-573683c431e14b3aae3cae4e57882d6e |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Public Health |
| spelling | doaj-art-573683c431e14b3aae3cae4e57882d6e2025-08-20T03:54:00ZengBMCBMC Public Health1471-24582025-05-0125111610.1186/s12889-025-23044-0Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortalityAndrew Deas0Adam Spannaus1Hashan Fernando2Heidi A. Hanson3Anuj J. Kapadia4Jodie Trafton5Vasileios Maroulas6Department of Mathematics, University of TennesseeComputational Sciences and Engineering Division, Oak Ridge National LaboratoryThe Bredesen Center for Interdisciplinary Research and Graduate Education, University of TennesseeComputational Sciences and Engineering Division, Oak Ridge National LaboratoryComputational Sciences and Engineering Division, Oak Ridge National LaboratoryOffice of Mental Health and Suicide Prevention, Veterans Health AdministrationDepartment of Mathematics, University of TennesseeAbstract Background The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates. Methods This study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter’s predicted rates across the nation’s counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally. Results Our spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation’s most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift. Conclusions The initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use.https://doi.org/10.1186/s12889-025-23044-0Opioid epidemicOpioid vulnerabilityKalman filterHeat mapsHotspot identification |
| spellingShingle | Andrew Deas Adam Spannaus Hashan Fernando Heidi A. Hanson Anuj J. Kapadia Jodie Trafton Vasileios Maroulas Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality BMC Public Health Opioid epidemic Opioid vulnerability Kalman filter Heat maps Hotspot identification |
| title | Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality |
| title_full | Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality |
| title_fullStr | Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality |
| title_full_unstemmed | Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality |
| title_short | Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality |
| title_sort | identifying spatiotemporal patterns in opioid vulnerability investigating the links between disability prescription opioids and opioid related mortality |
| topic | Opioid epidemic Opioid vulnerability Kalman filter Heat maps Hotspot identification |
| url | https://doi.org/10.1186/s12889-025-23044-0 |
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