{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:35:55Z","timestamp":1790386555438,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"15-17","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non\u2010linear exposure\u2010lag\u2010response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non\u2010Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure\u2010lag\u2010response association across the study region, overlooking potential spatial heterogeneity, which can lead to biased risk estimates. To address this limitation, we introduce DLNM\u2010Clust: a novel mixture of DLNMs that extends the traditional DLNM. Within a Bayesian framework, DLNM\u2010Clust probabilistically assigns each geographic unit to one of  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\u2010lag\u2010response surface. We demonstrate the method using municipality\u2010level time\u2010series data on the relationship between air pollution and the incidence of COVID\u201019 in Belgium. Our results emphasize the importance of spatially aware modeling strategies in environmental epidemiology, facilitating region\u2010specific risk assessment and supporting the development of targeted public health initiatives.<\/jats:p>","DOI":"10.1002\/sim.70661","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:41:05Z","timestamp":1782924065000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Mixture of Distributed Lag Non\u2010Linear Models to Account for Spatially Heterogeneous Exposure\u2010Lag\u2010Response Associations"],"prefix":"10.1002","volume":"45","author":[{"given":"\u00c1lvaro","family":"Briz\u2010Red\u00f3n","sequence":"first","affiliation":[{"name":"Department of Statistics and Operations Research University of Valencia  Valencia 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