Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling

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• dc.contributor.author Quijal-Zamorano, Marcos
• dc.contributor.author Martinez-Beneito, Miguel-Angel
• dc.contributor.author Ballester, Joan
• dc.contributor.author Marí Dell'Olmo, Marc, 1978-
• dc.date.accessioned 2024-07-08T06:03:48Z
• dc.date.available 2024-07-08T06:03:48Z
• dc.date.issued 2024
• dc.description.abstract Background: Distributed 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. Methods: Here 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. Results: The 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. Conclusions: SB-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.
• dc.description.sponsorship M.Q-Z. and J.B. gratefully acknowledge funding from the European Union’s Horizon 2020 and Horizon Europe research and innovation programmes under grant agreement no. 865564 (European Research Council Consolidator Grant EARLY-ADAPT) [https://www.early-adapt.eu/], 101069213 (European Research Council Proof-of-Concept HHS-EWS) and 101123382 (European Research Council Proof-of-Concept FORECAST-AIR). ISGlobal authors acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. M.A.M-B. acknowledges support from Project PID2022-136455NB-I00 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE.
• dc.format.mimetype application/pdf
• dc.identifier.citation Quijal-Zamorano M, Martinez-Beneito MA, Ballester J, Marí-Dell'Olmo M. Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling. Int J Epidemiol. 2024 Apr 11;53(3):dyae061. DOI: 10.1093/ije/dyae061
• dc.identifier.doi http://dx.doi.org/10.1093/ije/dyae061
• dc.identifier.issn 0300-5771
• dc.identifier.uri http://hdl.handle.net/10230/60689
• dc.language.iso eng
• dc.publisher Oxford University Press
• dc.relation.ispartof Int J Epidemiol. 2024 Apr 11;53(3):dyae061
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/865564
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101069213
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101123382
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2022-136455NB-I00
• dc.rights © The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Bayesian models
• dc.subject.keyword DLNM
• dc.subject.keyword Small-area analysis
• dc.subject.keyword Climate change
• dc.subject.keyword Heat-related mortality
• dc.subject.keyword Non-linear dynamics
• dc.subject.keyword Spatial statistics
• dc.title Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion