GeoHealth

Tick-borne disease risk reflects the combined influence of vector abundance, pathogen prevalence, human exposure, and seasonal activity, yet these components are rarely integrated at the county level. We developed species-specific ecological risk scores for three medically important tick species (Acari: Ixodidae): Amblyomma americanum (L.), Dermacentor variabilis (Say), and Ixodes scapularis Say, across all 102 Illinois counties using data from 2019 to 2022. County-level predicted tick abundance and pathogen prevalence were scaled and combined using equal weights to generate primary vector-level risk scores. Robustness to the equal-weight assumption was evaluated by systematically varying the relative weights assigned to abundance and pathogen prevalence, while a separate sensitivity analysis incorporated reported tick-bite incidence where available. Associations with human disease were evaluated using negative binomial regression of tick-borne disease case counts on the corresponding risk scores, with county population included as an offset. Monthly phenology indicators were constructed by integrating human disease cases, reported tick bites, and sampled tick abundance and were evaluated in relation to temperature and precipitation. Seasonal risk was estimated by combining county-level vector risk with species-specific monthly phenology, generating monthly risk surfaces that were deployed through an interactive R Shiny application. Higher risk scores were significantly associated with greater disease case rates for each tick species. County rankings were highly stable under moderate changes in abundance and prevalence weighting. Seasonal activity differed among species, with A. americanum and D. variabilis showing concentrated warm-season risk and I. scapularis exhibiting a broader seasonal profile. These county-level seasonal risk maps identify where and when relative tick-borne disease risk is elevated in Illinois, supporting targeted prevention, surveillance, and clinical awareness.

Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.

Understanding the relationship between weather conditions and infectious disease transmission has important public health implications; nevertheless, the functional form of this relationship often remains poorly understood. Humidity has previously been suggested to have a U-shaped effect on the transmission of respiratory pathogens, with optimal transmission occurring at both low and high levels. Here, to assess the impact of this functional form and of its potential misspecification, we first simulate transmission rates based on the U-shaped relationship and then test whether an exponential function accurately estimates these rates across a range of climates. We further use a transmission model to simulate case count data for a range of hypothetical pathogens, comparing outbreak dynamics under the U-shaped forcing function to those under the best-fit exponential approximation. In our simulations, the model fit for the exponential function is climate-dependent, performing best in very dry and very humid environments. In locations where specific humidity values centre around the vertex of the U-shaped function, the exponential model underestimates the amplitude and frequency of incidence peaks. These findings demonstrate the potential consequences of assuming an incorrect functional form for weather-infectious disease relationships and highlight the need for more careful consideration of this functional form in modelling practice.

Vegetation plays a critical role in shaping fine-scale tick density by structuring local microclimates, habitat resources, and host interactions that largely determine tick survival and activity, with implications for tick-borne disease transmission. Tick burden in the United States continues to increase, with annual reported tick-borne disease cases more than doubling from 2004 through 2019. These trends call for a broader scope of tick-mitigation strategies that go beyond traditional prevention measures that focus on personal protection; and there is an opportunity to inform habitat-level interventions. Here, we quantify fine-scale vegetation structure across 48 subplots at tick surveillance sites in Western Massachusetts to assess local habitat features associated with Ixodes scapularis density and potential for human-tick contact risk. In assessing understory and overstory vegetation attributes, this study identified life stage-specific differences in predicted tick density, with higher adult densities in areas with deeper leaf litter and presence of invasive plant species and nymphs associated with high presence of ferns. Both adult and nymphal Ix. scapularis were predicted at higher densities in relatively low basal area forests (18.9 to 26.9 m2/hectare). This study design provides a simple, low-cost framework for organizations looking to incorporate tick-informed forest measurement ecology into their public health tick surveillance protocols with minimal training.

Brazil has one of the highest dengue burdens globally, with transmission influenced by climate events. Yet, the effects of compound climate extremes on dengue risk and prediction have not been quantified to date. Using a Bayesian spatiotemporal model of monthly dengue cases across 435 health regions from 2010 to 2025, we estimated relative risk associated with interacting long-lag and short-lag drought indicators and temperature anomalies across nine Koppen-Geiger climatic zones, adjusting for absolute temperature and El Nino-Southern Oscillation. Long-lag dry, mid-lag warm and short-lag wet conditions were associated with the highest relative risk in five climatic zones, with temperature anomalies modifying hydrometeorological dynamics more strongly in temperate than tropical areas. Climate covariates improved the prediction of case counts and outbreaks compared to baseline models at lead times of 1, 3, 6 and 12 months, and for the upcoming season 5 months ahead. Improvements were greatest in the Northeast, Southeast, and Midwest. Recent dengue incidence provided the greatest improvement at the 1-month lead, whereas combining incidence with climate information performed best at 3- and 6-month leads. We extended our framework to weekly state-level predictions of the season ahead to align with national public health planning. We proposed a multi-staged dengue forecasting approach to enhance early warning and response across Brazil.

Coal-fired power plants remain a major source of fine particulate matter (PM2.5) exposure and mortality in the United States. Announced retirement dates mark when those burdens are expected to end, but rising electricity demand, partly from data centers supporting cloud computing and artificial intelligence, has led utilities and regulators to revisit them. Of 50 coal plants scheduled in January 2024 to retire before 2035, 20 had later or removed dates by January 2026. Projecting coal PM2.5 and mortality among Medicare beneficiaries from the 20 plants for 2026-2035, we estimate the revised schedule would result in 1,256 additional deaths (1,136 to 1,376), raising mortality to 3.1 times the original burden. Eighteen plants operate in balancing authorities with hyperscale data-center demand, and federal orders and utility filings supporting several plants' operation cited data-center growth. Retirement schedules are revised faster than health consequences are assessed, and those delays carry a measurable mortality cost.

Long-term PM2.5 exposure is associated with increased mortality and is usually represented by annual mean concentration, which does not capture how concentrations are distributed across days. We tested whether the prespecified PM2.5 magnitude-rank index (PMRI), a summary of within-year daily concentration structure, improves mortality prediction beyond the annual mean. Daily modeled PM2.5 estimates were linked to age-adjusted mortality rates for 27,289 county-years from 3,064 U.S. counties during 2003-2011. PMRI is the largest k for which at least k days reached k micrograms per cubic meter. County-grouped 10-fold cross-validation compared models with annual mean alone versus annual mean plus PMRI, with additional geographic, temporal, cause-specific, and specification analyses. Annual mean PM2.5 had the highest standalone predictive performance. Adding PMRI increased county-held-out R-squared from 0.0802 to 0.1391 (increase in R-squared = 0.0589; 95% CI, 0.0499-0.0677) and reduced RMSE and MAE. Improvement persisted across geographic and temporal validations and all six cause-specific mortality outcomes. Within-year PM2.5 exposure structure therefore contains reproducible mortality-predictive information not captured by annual mean concentration.

Wastewater-based epidemiology (WBE) has become a cornerstone of public health surveillance, yet routine regulatory monitoring remains largely focused on respiratory viruses and selecting antimicrobial resistant bacteria. Emerging foodborne parasites, such as Cyclospora cayetanensis, remain excluded from standard environmental surveillance panels despite increasing domestic outbreaks. This study presents a laboratory-based pilot protocol evaluating the feasibility of extracting and quantifying C. cayetanensis from urban wastewater across the Chicago metropolitan area. Using sample concentration via centrifugation and mechanical bead-beating, automated magnetic bead nucleic acid extraction, and digital PCR (dPCR), we demonstrate high-sensitivity detection in municipal influents and sewer nodes. These findings establish a proof-of-concept laboratory methodology to support the expansion of regulatory public health surveillance frameworks to include non-routine protozoan pathogens.

PurposeRural Alaska relies on air ambulances ("medevacs") to connect remote villages off the road system to higher-level care. Yet medevac decision-making is complicated by limited guidelines, high costs, and delayed transport. We aimed to quantify variability in physician medevac decisions and identify contributing factors. MethodsWe conducted a cross-sectional survey of physicians (n=20) within the Alaska Tribal Health System using 20 clinical vignettes representing varying levels of transport complexity. Participants selected transport modality (medevac, commercial, or remain in village) and rated confidence. Interrater agreement was assessed using Gwets AC1. A generalized linear mixed model evaluated predictors of medevac use. FindingsAgreement was "almost perfect" for clearly critical cases ("always medevac," AC1=0.97) but declined substantially for intermediate scenarios ("possible medevac" AC1=0.39; "never medevac" AC1=0.26; "special considerations" AC1=0.12). Confidence strongly correlated with agreement (r=0.83, p<0.001), forming distinct clusters of high-, medium-, and low-certainty decisions. Vignette type was the dominant driver of medevac use (OR 455.5 for "always medevac"), but decisions did not change with experience (OR=0.99, 0.94-1.04). Between-physician variability persisted after adjustment (ICC 3.8%; MOR 1.67), consistent with differences in risk tolerance. ConclusionsMedevac decision-making is highly consistent in certain clinical cases but interrater agreement is limited for intermediate cases. Without a shared gold standard of "correct" medevac decisions, future clinical decision support cannot rely on automated medevac decision making. Efforts to improve care should focus on deconstructing decisions, enhancing outcome feedback, and developing targeted decision support rather than prescriptive decision models.

This study developed a streamlined, sensitive and rapid method for co-detection of low concentrations of Salmonella enterica, Shigella flexneri, Listeria monocytogenes, and pathogenic Escherichia coli from soil. First, autoclaved soil from Iowa with serial dilutions of bacteria to confirm individual species recovery rates on selective agar by qPCR under varying primary enrichment times was used. All pathogens were detected by qPCR of colonies recovered from replicate sterile soils spiked with 10^1 or greater bacteria within 5 hours of enrichment. Shiga toxin expressing E. coli (STEC) and L. monocytogenes were consistently co-recovered in autoclaved and natural soil, pathogen spiking concentration, and secondary enrichment time. S.enterica was consistently recovered at autoclaved and natural concentrations, while S. flexneri detection rates fluctuated. Twenty-three soil samples from Kenyan neighborhoods, with prior evidence of contamination, was compared to qPCR detection at each protocol stage: processing, primary enrichment, secondary enrichment, and selective colony isolation. Detection rates substantially increased for S. enterica, STEC and other pathogenic E. coli after enrichment, with optimal detection rates occurring after secondary enrichment. Testing colonies from selective agar resulted in high false negative rates, possibly reflecting competition from native soil microbiota that was absent in spiked sterile soil experiment. Evaluation of the method with other environmental sample types yielded consistent detection patterns. Evidence suggests a protocol including 24-hour primary enrichment, followed by 24-hour secondary enrichment, and qPCR testing of soil DNA at primary and secondary steps is a reproducible and more expedient method for co-detecting common Enterobacteriaceae and L. monocytogenes pathogens in soil.

BackgroundIn rural Alaska, where most communities lack road access, air ambulances serve as a critical link between remote communities and hospital services. ObjectiveTo examine how air ambulance utilization decisions are made within the rural Alaskan health organization ecosystem. MethodsWe conducted semi-structured interviews with five key stakeholders. Transcripts were analyzed thematically using the Practical, Robust Implementation and Sustainability Model (PRISM). ResultsFour themes emerged that collectively define the structure of the air ambulance decision-making system, each reflecting interactions across multiple PRISM domains: limited formal guidance, logistical constraints affecting transport options, variability driven by communication and clinician risk tolerance, and reliance on unwritten rules. Together, these describe decision-making in a limited information state with incomplete feedback, leading to experiential and locally evolved practices. ConclusionAir ambulance use reflects clinical-logistical constraints rather than critical illness alone. Linking transport decisions to outcomes and developing structured decision support may improve consistency and optimize care delivery.

Palm oil is the worlds leading vegetable oil, with production continuing to expand, particularly across Southeast Asia. Within established plantations, conserving and restoring riparian margins can reduce erosion, improve connectivity, and support biodiversity. Simultaneously, climate change is increasing thermal stress within tropical agricultural landscapes, raising the potential importance of riparian buffers as microclimatic refugia, although this has not previously been evaluated. The Riparian Ecosystem Restoration in Tropical Agriculture (RERTA) Project is a long-term Before-After Control-Impact experiment established within oil palm plantations in Riau, Indonesia, to evaluate alternative riparian restoration strategies following plantation replanting. Treatments included passive restoration (retaining mature oil palms and allowing natural regeneration), active restoration (enrichment planting with six native tree species), and a control (replanting immature oil palms to the river edge). Six years after restoration, we assessed vegetation structure using terrestrial laser scanning and thermal stability using temperature loggers and thermal imaging. We also evaluated the long-term temperature buffering capacity of each treatment under current and projected climates using CHELSA climate data and downscaled outputs from multiple General Circulation Models under Shared Socioeconomic Pathway scenarios for 2045 and 2085. Active restoration produced greater structural complexity, a more closed canopy, and cooler, more thermally stable conditions than passive restoration and control treatments. Restored margins also contained significantly more cool surface microhabitats, reducing thermal extremes under both current and future climate scenarios. Our findings demonstrate that active restoration of riparian margins can enhance thermal buffering and create microclimatic refugia for biodiversity within tropical oil palm landscapes. As agricultural intensification and climate change continue to reshape tropical ecosystems, restoring structurally complex riparian vegetation will be critical for sustaining biodiversity and the ecosystem services upon which agricultural production depends.

Wildfire smoke exposure is increasing across the U.S., but the long-term neurological effects of smoke-specific fine particulate matter (PM2.5) and its carbonaceous components remain uncertain. Using Medicare data from 2002 through 2019, we examined incident Alzheimer's disease (AD) and Alzheimer's disease and related dementias (ADRD) among 39,678,630 fee-for-service Medicare beneficiaries followed from 2005 through 2019. We identified 3,501,414 incident AD cases over 258.5 million person-years and 8,377,416 incident ADRD cases over 248.6 million person-years. We linked the cohort with annual, high-resolution estimates of smoke and non-smoke PM2.5 mass and carbonaceous components (elemental and organic carbon) and fitted stratified Cox proportional hazards models. Per 1 g/m3 increase, smoke PM2.5 was associated with incident AD (hazard ratio [HR], 1.015, 95% confidence interval [CI], 1.005-1.026) and ADRD (1.029 [1.024-1.035]). Smoke carbonaceous fraction was also associated with AD (1.063 [1.042-1.085]) and ADRD (1.090 [1.077-1.103]). Within the ADRD endpoint, smoke-related associations exceeded their corresponding non-smoke associations on a per-unit-mass basis. Per interquartile range, associations were smaller for smoke PM2.5 (AD, 1.005 [1.002-1.008]; ADRD, 1.009 [1.008-1.011]) than for non-smoke PM2.5 (AD, 1.086 [1.079-1.094]; ADRD, 1.048 [1.043-1.053]). Associations varied across geographic, racial, socioeconomic, and clinical subgroups. Long-term wildfire smoke exposure, particularly its carbonaceous fraction, was associated with higher incidence of AD and ADRD among older U.S. adults. The associations correspond to 2,284 and 11,142 incident cases per year of AD and ADRD attributable to smoke PM2.5 among older Medicare beneficiaries, with the carbonaceous fraction accounting for most of this burden. These findings support the use of source-resolved and composition-specific smoke metrics in dementia research and wildfire-smoke health-impact assessments.

The 2026 Andes virus (ANDV) outbreak aboard the MV Hondius cruise ship resulted in 11 confirmed cases and 3 deaths and raised questions regarding the origin of infection, with much media focus resting on a landfill in Tierra del Fuego. We evaluated competing exposure hypotheses by assembling open data on reservoir host occurrences, rodent serosurveys, and historical ANDV outbreaks along with the index case's self-documented, publicly-posted citizen-science digital record of their movements. Historical outbreaks associated with human-to-human transmission were concentrated within a restricted region spanning southern Chile and adjacent western Argentina, coinciding with the distribution of the principal reservoir host, Oligoryzomys longicaudatus, and areas identified as elevated risk by multiple ecological and genetic models. The index case was documented within this transmission focus shortly before the putative exposure period. Together, our open data approach was able to rapidly locate initial exposure within the established ANDV transmission focus and develop scenarios to prioritise further epidemiological investigation.

Nitrogen dioxide (NO2) is a widespread air pollutant associated with the risk and prevalence of various respiratory infectious diseases. Epidemiological studies have reported associations between NO2 exposure and COVID-19 incidence, and biological evidence indicates that NO2 exposure increases vulnerability to infection. Motivated by these findings and by the scarcity of mechanistic epidemic models that incorporate air pollutant data, here we introduce an NO2- and mobility-informed metapopulation model and study the first COVID-19 wave in Oklahoma. County-level NO2 concentrations enter the infection rates through a first-order parameterization, alongside high-resolution mobility patterns capturing heterogeneous inter-county connectivity. Using numerical simulations, we demonstrate that higher environmental sensitivity produces earlier and more synchronized outbreaks across counties, while lower sensitivity yields delayed, asynchronous trajectories. When fitted to county-level COVID-19 incidence data, our model outperformed the classic SIR model (fitted independently in each county) in over 75% of counties in Oklahoma across all standard error metrics. The improvements were even more pronounced in forecasting tasks across epidemic stages, with county-level win rates against the SIR model of 59.74% (surge), 87.01% (peak), 80.52% (post-peak), and 88.31% (decline) by nRMSE. To isolate the contribution of NO2, we compared our model against a mobility-only model with NO2 removed and homogeneous infection rates across counties. The NO2 term improved county-level forecasts during the surge (94.81% of counties by nRMSE), post-peak (88.31%), and peak (62.34%) stages, though not during the early and decline stages, suggesting that NO2 information can generate better forecasts than mobility alone during periods of high incidence. Our results demonstrate that environmental exposure can enter the transmission structure of a mechanistic epidemic model directly and that doing so yields measurable forecasting gains.

Antimicrobial resistance (AMR) is a major threat to modern medicine and an emerging environmental pollutant. Wastewater-based monitoring offers a scalable alternative to traditional surveillance by enabling population-level monitoring across One Health compartments. However, it remains unclear how well bacterial variants detected in wastewater reflect those causing clinical infections. Here, we assessed the prevalence of carbapenem-resistant Escherichia coli (CREC) in wastewater from Uppsala, Sweden, and compared wastewater isolates with clinical isolates from the same region and period. We observed partial overlap between wastewater and clinical CREC populations, but several common clinical variants were not detected in wastewater and vice versa. CREC prevalence was an order of magnitude higher in wastewater receiving hospital effluent, yet this did not improve concordance with clinical isolates, suggesting that hospital wastewater may artificially inflate CREC prevalence estimates. These findings indicate that wastewater CREC prevalence does not necessarily reflect the regional clinical burden. Furthermore, metagenomic sequencing of wastewater-derived E. coli populations rarely detected CREC resistance genes, suggesting that CREC abundance is below the detection limit of this approach and that targeted enrichment is required for reliable monitoring. Together, our results show that while wastewater-based monitoring can complement AMR surveillance, it should be interpreted cautiously when used as an indicator of clinical risk.

Japanese encephalitis virus (JEV) is a leading cause of viral encephalitis in humans in Asia. Prior to 2021, only sporadic outbreaks had been detected in Australia's tropical northern extremes. However, during 2021-23, JEV infection was identified in 45 people and in more than 85 piggeries, together with many detections in feral pigs, mosquitoes, and other livestock across Australia, including in the temperate far south. Here we estimated the spatial extent of virus transmission during the outbreak (the realised niche) and the area environmentally suitable for transmission (the potential niche). We collated a national dataset of JEV surveillance and detection in humans, mosquitoes, domestic pigs, feral pigs, and sentinel chickens from 2021-23. We combined these data with (1) previously published predictions of habitat suitability for vertebrate host and vector species, and (2) estimated densities of humans, domestic pigs, and feral pigs, in a multipart assessment of spatial risk. Our model projected broad potential environmental suitability over much of northern and eastern Australia during the outbreak period, but floodplain wetland areas in the north and the inland east of Australia had both the highest estimated environmental suitability for transmission, and the strongest evidence for virus circulation.

IntroductionUsing an open enrollment process in 2023, California funded 28 emergency departments (EDs) to implement or expand HIV and syphilis screening programs. Californias 2026 state budget now commits an additional $60 million, over six years, to renew support for this screening. How renewed funds are allocated will determine which communities they reach. This analysis establishes a framework derived from public information to simulate and compare several funding strategies, highlighting the tradeoffs across disease burden, geographic reach, and social vulnerability. MethodsPublicly available data were used to construct ED-level profiles for the 282 EDs in the 35 California counties with populations exceeding 100,000. Profiles were then linked to corresponding HIV and syphilis burden (z-score), measures of social vulnerability for the surrounding service area , and socioeconomic and demographic characteristics of the population each ED serves. At a 28 ED award scale, allocation strategies (random selection, prior 2023 awardees, federal priority-county targeting, ranking on burden alone, and a composite of burden, vulnerability, and visit volume) were then compared, simulating ED participation across 100,000 Monte Carlo draws at 70% participation. Sensitivity analyses included varied (50% and 90%) participation and a larger (n=64) ED award scale. Analyses were conducted in 2026. ResultsNo single strategy performed best. Ranking on burden alone reached the highest mean county burden but only 4 of 35 counties, whereas a composite approach reached 8 counties, the highest social vulnerability, and the largest share of patients who identified as Hispanic. Every targeted strategy performed better than both random selection and the prior 2023 awardees on burden but reached fewer counties than either. In sensitivity analyses, the tradeoffs between burden and reach persisted across participation rates and at the 64-ED scale.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [&ge;]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

Background Italy experiences a substantial heat-related mortality burden, but evidence on cause-specific mortality across its heterogeneous climatic settings remains limited. We aimed to quantify cause-specific mortality attributable to summer heat in Italy at national and ecological region levels and to map its distribution across municipalities. Methods We analysed daily deaths classified by underlying cause in eight ICD-10 groups among individuals aged 18-64 years and 65 years or older across 7,864 municipalities during June-August, 2004-2023. Mortality data were linked to area-weighted ERA5-Land daily maximum temperature. Nonlinear temperature-mortality associations were estimated with Bayesian hierarchical Poisson models, fitted separately for each cause, age group, and ecological region, with municipality-level attributable fractions. Findings High temperatures were associated with mortality from a broad range of causes, particularly among individuals aged 65 years or older. Among the study population, heat-attributable fractions were highest for nervous system diseases (14.93%, 95% credible interval 14.22-15.69) and mental and behavioural disorders (14.47%, 13.55-15.39). Circulatory diseases accounted for the largest absolute burden, with 85,524 heat-attributable deaths (82,762-88,271) across the summers of the 20 years considered. Interpretation The mortality burden associated with summer heat in Italy extended well beyond circulatory and respiratory diseases, highlighting the relevance of a broader range of health outcomes. These findings support heat-health strategies that account for cause-specific and geographical differences in heat-related risk, with greater consideration of neurological and mental health outcomes alongside established cardiorespiratory risks. Funding NextGenerationEU (CUP: B53C23002660006); Imperial College Research Fellowship; NIHR Imperial Biomedical Research Center (BRC: NIHR203323).