A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914176042008576 |
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| author | Briz-Redón, Álvaro Corberán-Vallet, Ana Iftimi, Adina Íñiguez, Carmen |
| author_facet | Briz-Redón, Álvaro Corberán-Vallet, Ana Iftimi, Adina Íñiguez, Carmen |
| contents | Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non-linear exposure-lag-response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non-Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure-lag-response association across the study region, overlooking potential spatial heterogeneity, which can lead to biased risk estimates. To address this limitation, we introduce DLNM-Clust: a novel mixture of DLNMs that extends the traditional DLNM. Within a Bayesian framework, DLNM-Clust probabilistically assigns each geographic unit to one of $C$ latent spatial clusters, each of which is defined by a distinct DLNM specification. This approach allows capturing both common patterns and singular deviations in the exposure-lag-response surface. We demonstrate the method using municipality-level time-series data on the relationship between air pollution and the incidence of COVID-19 in Belgium. Our results emphasize the importance of spatially aware modeling strategies in environmental epidemiology, facilitating region-specific risk assessment and supporting the development of targeted public health initiatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations Briz-Redón, Álvaro Corberán-Vallet, Ana Iftimi, Adina Íñiguez, Carmen Methodology Applications Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non-linear exposure-lag-response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non-Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure-lag-response association across the study region, overlooking potential spatial heterogeneity, which can lead to biased risk estimates. To address this limitation, we introduce DLNM-Clust: a novel mixture of DLNMs that extends the traditional DLNM. Within a Bayesian framework, DLNM-Clust probabilistically assigns each geographic unit to one of $C$ latent spatial clusters, each of which is defined by a distinct DLNM specification. This approach allows capturing both common patterns and singular deviations in the exposure-lag-response surface. We demonstrate the method using municipality-level time-series data on the relationship between air pollution and the incidence of COVID-19 in Belgium. Our results emphasize the importance of spatially aware modeling strategies in environmental epidemiology, facilitating region-specific risk assessment and supporting the development of targeted public health initiatives. |
| title | A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2512.01508 |