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

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 n...

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Autores: Quijal-Zamorano, Marcos, Martínez Beneito, Miguel Ángel, Ballester, Joan, Marí Dell'Olmo, Marc, 1978-
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/60689
Acceso en línea:http://hdl.handle.net/10230/60689
http://dx.doi.org/10.1093/ije/dyae061
Access Level:acceso abierto
Palabra clave:Bayesian models
DLNM
Small-area analysis
Climate change
Heat-related mortality
Non-linear dynamics
Spatial statistics
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network_acronym_str ES
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dc.title.none.fl_str_mv Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
title Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
spellingShingle Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
Quijal-Zamorano, Marcos
Bayesian models
DLNM
Small-area analysis
Climate change
Heat-related mortality
Non-linear dynamics
Spatial statistics
title_short Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
title_full Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
title_fullStr Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
title_full_unstemmed Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
title_sort Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling
dc.creator.none.fl_str_mv Quijal-Zamorano, Marcos
Martínez Beneito, Miguel Ángel
Ballester, Joan
Marí Dell'Olmo, Marc, 1978-
author Quijal-Zamorano, Marcos
author_facet Quijal-Zamorano, Marcos
Martínez Beneito, Miguel Ángel
Ballester, Joan
Marí Dell'Olmo, Marc, 1978-
author_role author
author2 Martínez Beneito, Miguel Ángel
Ballester, Joan
Marí Dell'Olmo, Marc, 1978-
author2_role author
author
author
dc.subject.none.fl_str_mv Bayesian models
DLNM
Small-area analysis
Climate change
Heat-related mortality
Non-linear dynamics
Spatial statistics
topic Bayesian models
DLNM
Small-area analysis
Climate change
Heat-related mortality
Non-linear dynamics
Spatial statistics
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/60689
http://dx.doi.org/10.1093/ije/dyae061
url http://hdl.handle.net/10230/60689
http://dx.doi.org/10.1093/ije/dyae061
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Int J Epidemiol. 2024 Apr 11;53(3):dyae061
info:eu-repo/grantAgreement/EC/H2020/865564
info:eu-repo/grantAgreement/EC/HE/101069213
info:eu-repo/grantAgreement/EC/HE/101123382
info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-S
info:eu-repo/grantAgreement/ES/3PE/PID2022-136455NB-I00
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
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repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modellingQuijal-Zamorano, MarcosMartínez Beneito, Miguel ÁngelBallester, JoanMarí Dell'Olmo, Marc, 1978-Bayesian modelsDLNMSmall-area analysisClimate changeHeat-related mortalityNon-linear dynamicsSpatial statisticsBackground: 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.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.Oxford University Press202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/60689http://dx.doi.org/10.1093/ije/dyae061reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésInt J Epidemiol. 2024 Apr 11;53(3):dyae061info:eu-repo/grantAgreement/EC/H2020/865564info:eu-repo/grantAgreement/EC/HE/101069213info:eu-repo/grantAgreement/EC/HE/101123382info:eu-repo/grantAgreement/ES/2PE/CEX2018-000806-Sinfo:eu-repo/grantAgreement/ES/3PE/PID2022-136455NB-I00© 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.comhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/606892026-06-12T07:21:37Z
score 15.198674

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