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by claude@2026-07, 2026-07-05
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The paper develops a Bayesian distributed lag modeling framework to estimate acute effects of mixed ambient environmental exposures on suicide risk using a case-crossover design. It uses sparsity-enforcing spike-and-slab priors for exposure selection, random effects to address clustered observations, and cubic polynomial reduction to decrease dimensionality of the distributed lag surface, with two referent schemes (unidirectional and bidirectional) evaluated in simulations. In simulated comparisons with a full-parameter approach without dimension reduction, the dimension-reduction strategy showed better false discovery rate, power, and mean squared error. The authors then applied the method to Utah real-world data on exposure mixtures and suicide risk, while the abstract’s limitation is that evidence of performance is primarily established through simulations and the presented real-data application. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
We present a Bayesian modeling framework designed to estimate the immediate effects of combined environmental exposures on suicide risk within a case-crossover design. Our method addresses a limitation observed in current distributed lag modeling approaches for multiple environmental exposures, which primarily focus on cohort or case-control data rather than casecrossover design. We utilize sparsity-enforcing spike-and-slab priors for variable selection, allowing the identification of significant exposures linked to the health outcome. To address clustered observations, we integrate random effects into the model. Additionally, we enhance computational efficiency and reduce dimensionality by implementing cubic polynomial reduction on the distributed lag surface. In a simulation study comparing our dimension reduction approach with a method estimating full model parameters without dimension reduction, we evaluated two referent schemes (unidirectional and bidirectional). The results demonstrate that our strategy, incorporating dimension reduction, outperforms full model parameter estimation in terms of false discovery rate, power, and mean squared error. We applied our framework to real-world data examining the association between a mixture of ambient environmental exposures and suicide risk in Utah.
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Abstract
We present a Bayesian modeling framework designed to estimate the immediate effects of combined environmental exposures on suicide risk within a case-crossover design. Our method addresses a limitation observed in current distributed lag modeling approaches for multiple environmental exposures, which primarily focus on cohort or case-control data rather than casecrossover design. We utilize sparsity-enforcing spike-and-slab priors for variable selection, allowing the identification of significant exposures linked to the health outcome. To address clustered observations, we integrate random effects into the model. Additionally, we enhance computational efficiency and reduce dimensionality by implementing cubic polynomial reduction on the distributed lag surface. In a simulation study comparing our dimension reduction approach with a method estimating full model parameters without dimension reduction, we evaluated two referent schemes (unidirectional and bidirectional). The results demonstrate that our strategy, incorporating dimension reduction, outperforms full model parameter estimation in terms of false discovery rate, power, and mean squared error. We applied our framework to real-world data examining the association between a mixture of ambient environmental exposures and suicide risk in Utah.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was funded by R01 ES033191-01
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Ethics committee/IRB of the University of Utah gave ethical approval for this work
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Footnotes
Data Availability
All data produced in the present study are available upon reasonable request to the authors
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