Journal article
A Bayesian spatial measurement error approach to incorporate heterogeneous population-at-risk uncertainty in estimating small-area opioid mortality rates
Spatial and spatio-temporal epidemiology, v 53, 100719
Jun 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Monitoring small-area geographical population trends in opioid mortality has significant implications for informing preventative resource allocation. A common approach to estimating small-area opioid mortality uses a standard disease mapping method where population-at-risk estimates (denominators) are treated as fixed. This assumption ignores the uncertainty in small-area population estimates, potentially biasing risk estimates and underestimating their uncertainties. We compare a Bayesian Spatial Berkson Error model and a Bayesian Spatial Classical Error model to a naive approach that treats denominators as fixed. Using simulations, we illustrate potential bias from ignored population-at-risk uncertainty. We apply these methods to obtain 2020 opioid mortality risk estimates for 159 counties in Georgia. Assessing differences in bias and uncertainty across approaches can improve the accuracy of small-area opioid risk estimates, guiding public health interventions, policies, and resource allocation.
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Details
- Title
- A Bayesian spatial measurement error approach to incorporate heterogeneous population-at-risk uncertainty in estimating small-area opioid mortality rates
- Creators
- Emily N. Peterson - Emory and Henry CollegeRachel C. Nethery - Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USAJarvis T. Chen - Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USALoni P. Tabb - Drexel UniversityBrent A. Coull - Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USAFrederic B. Piel - Imperial College LondonLance A. Waller - Emory and Henry College
- Publication Details
- Spatial and spatio-temporal epidemiology, v 53, 100719
- Publisher
- Elsevier
- Number of pages
- 17
- Grant note
- US National Institutes for Health (NIH): R01HD092580 National Institute of Health: P30ES000002 Health Data Research UKUK National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre: NIHR200922, NIHR200880 Imperial College London - UK NIHR
This work, led by Lance A. Waller, was funded by the US National Institutes for Health (NIH) (#R01HD092580) . BAC acknowledges support from National Institute of Health (#P30ES000002) . FBP acknowledges support from Health Data Research UK (HDR UK) and the UK National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre. FBP is a member of the NIHR Health Protection Research Units in Chemical and Radiation Threats and Hazards (#NIHR200922) , and in Environmental Exposures and Health (#NIHR200880) , which are partnerships between UK Health Security Agency and Imperial College London funded by the UK NIHR.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics
- Web of Science ID
- WOS:001466974000001
- Scopus ID
- 2-s2.0-105002040870
- Other Identifier
- 991022048307004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health