ArticleJournal of data science : JDS2018
A BAYESIAN SPATIAL AND TEMPORAL MODELING APPROACH TO MAPPING GEOGRAPHIC VARIATION IN MORTALITY RATES FOR SUBNATIONAL AREAS WITH R-INLA.
Article in Journal of data science : JDS, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach.Infectious diseases of poverty · 2026Article
- Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA.Spatial and spatio-temporal epidemiology · 2026Article
- Trends in maternal mortality rates by state, United States, 2018 to 2023.American journal of obstetrics and gynecology · 2026Article
- Dynamics of social inequalities in severe COVID-19 outcomes in metropolitan France from 2020 to 2022.Communications medicine · 2025Article
- Unveiling Mexico's Demographic Transitions.Studies in family planning · 2025Article
- Exploring social, economic, and environmental correlates of suicide in Puerto Rico, 2017-2022: an ecological cross-sectional study.Injury epidemiology · 2025Article
- Routine immunisation coverage in the context of seasonal influenza vaccine introduction for children in Nakuru and Mombasa counties in Kenya, 2019-21: an observational study.The Lancet. Global health · 2025Observational
- Distribution and Risk Factors of Scrub Typhus in South Korea, From 2013 to 2019: Bayesian Spatiotemporal Analysis.JMIR public health and surveillance · 2025Article
- Changes in the association between county industrial composition and working-age mortality from 2000 to 2022.SSM - population health · 2025Article
- Bayesian spatio-temporal modeling and prediction of malaria cases in Tanzania mainland (2016-2023): unveiling associations with climate and intervention factors.International journal of health geographics · 2025Article
- Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: A Bayesian spatiotemporal modelling study.PLOS global public health · 2025Article
- Assessing access: Texting hotline app provides mental health crisis care for economically deprived youth.Social science & medicine (1982) · 2024Article
- Factors associated with the spatiotemporal distribution of dog rabies in Tunisia.PLoS neglected tropical diseases · 2024Article
- An introduction to bayesian spatial smoothing methods for disease mapping: modeling county firearm suicide mortality rates.American journal of epidemiology · 2024Article
- Reliable event rates for disease mapping.Journal of official statistics · 2024Article
- Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.Malaria journal · 2024Article
- Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs.Journal of environmental management · 2024Article
- Twenty-Year Disparity Trends in United States Stroke Death Rate by Age, Race/Ethnicity, Geography, and Socioeconomic Status.Neurology · 2023Article
- Geography, rurality, and community distress: deaths due to suicide, alcohol-use, and drug-use among Colorado Veterans.Injury epidemiology · 2023Article
- Spatiotemporal trends in neonatal, infant, and child mortality (1990-2019) based on Bayesian spatiotemporal modeling.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.
Indexed as
Identifiers
29520299PMC5839164What OpenQuestion holds
Registered trials
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