Heat-Related Mental Health Hospitalizations in Italy:A Global Sensitivity Analysis Approach to Evaluate Generalized Additive Model Assumptions
AI Summary
High summer temperatures were consistently linked to increased psychiatric hospitalisations, with nonlinear risk rising to nearly twofold at temperatures above 40°C.
Global sensitivity analysis found temperature spline knot number and placement dominated model output variability, with GSA uncertainty exceeding conventional confidence intervals.
Lag structure contributed minimally; including September affected model fit but only modestly altered risk estimates, improving transparency via GSA within the DLNM GAM framework.
High temperatures are increasingly associated with adverse mental health outcomes, yet the influence of structural modeling assumptions on these estimates remains underexplored. This study examined the short-term association between high temperatures and mental health-related-hospitalizations in 21 major Italian cities from 2005 to 2023, using national hospital discharge data. Exposure-lag-response relationships were modeled through a Distributed Lag Nonlinear Model (DLNM) framework estimated within a Generalized Additive Model (GAM). Analyses focused on June-September and included hospitalizations with a primary diagnosis of mental disorders, considering the code 295-316 of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). A Global Sensitivity Analysis (GSA) assessed how structural decisions, such as the specification of temperature and lag splines, and the inclusion of September, affect risk estimates. A total of 210,310 hospitalizations were recorded. The cumulative exposure-response curve showed a marked nonlinear increase in risk, reaching relative risk values close to two at temperatures exceeding 40 ∘C$^circrm{C}$ . The GSA revealed that the number and placement of knots in the temperature dimension were the dominant contributors to output variability, with total-order sensitivity indices approaching one across most of the temperature range. Variations in the lag structure contributed minimally, while including or excluding September influenced model fit but only modestly affected risk estimates. Uncertainty quantified through GSA was substantially larger than that quantified by standard confidence intervals, indicating that structural assumptions meaningfully shape inference. Within the DLNM-GAM framework, high summer temperatures were consistently associated with increased psychiatric hospitalizations. Incorporating GSA clarified which modeling choices influence estimates, improving transparency and robustness in evaluating heat-related mental health impacts.
Neurol Ther. 2026 May 24. doi: 10.1007/s40120-026-00956-6. Online ahead of print.ABSTRACTINTRODUCTION: Sun exposure may influence MS susceptibility, but evidence in pediatric-onset MS (PedMS) is limited....
J Behav Med. 2026 May 24. doi: 10.1007/s10865-026-00666-2. Online ahead of print.ABSTRACTProlonged exposure (PE) is an evidence-based treatment for PTSD. However, the imaginal exposure portion...
Cult Med Psychiatry. 2026 May 24;50(2):34. doi: 10.1007/s11013-026-09993-0.ABSTRACTFollowing the 2011 Great East Japan Earthquake, children in affected regions exhibited everyday behaviors that challenge conventional psychological...
J Trauma Stress. 2026 May 24. doi: 10.1002/jts.70094. Online ahead of print.ABSTRACTIn this commentary on Morland et al. (2026)'s recent State of the Science article...
BMC Psychol. 2026 May 23;14(1):778. doi: 10.1186/s40359-026-04829-3.ABSTRACTBACKGROUND: Child sexual abuse and exploitation, including online material depicting such acts, constitute a major public health concern. Prevention...
Adv Simul (Lond). 2026 May 23. doi: 10.1186/s41077-026-00446-7. Online ahead of print.ABSTRACTOBJECTIVE: Increasingly in psychiatric and psychotherapeutic training, simulated patients (SPs) participate in the teaching...
