ArticleInternational journal of epidemiology2024
Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.
Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Building the model: a review of input structures in extreme temperature-health.Environmental health : a global access science source · 2026Pooled it
- District-level analysis of temperature-related excess mortality in the Netherlands.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.Statistics in medicine · 2026Article
- Eligibility criteria for the UK Winter Fuel Payment: are we targeting the right people?Journal of epidemiology and community health · 2026Article
- Exploring the local impacts of heat on mortality: A small-area spatial analysis of Montreal, Toronto, and Vancouver, Canada.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- Prenatal exposure to extreme heat and autism in children.The Science of the total environment · 2026Article
- Too Hot, Too Wet: Bayesian Spatial Modeling of Climate-Driven Salmonella Risk in New South Wales, Australia, 1991-2022.GeoHealth · 2026Article
- Heat Exposure and the Risk of Emergency Hospitalization in Germany: Stratified Analyses by Age, Sex, and Diagnostic Group.Deutsches Arzteblatt international · 2025Observational
- Broad Applications of Distributed Lag Non-Linear Model in Public Health: A Comprehensive Review.GeoHealth · 2025Review
- Heat-related mortality in Europe during 2024 and health emergency forecasting to reduce preventable deaths.Nature medicine · 2025Article
- Modelling extensions for multi-location studies in environmental epidemiology.Statistical methods in medical research · 2025Article
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4 authors.
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Abstract
backgroundDistributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power.
methodsHere we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks.
resultsThe results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies.
conclusionsSB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.
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