Spatial and Spatio-temporal Epidemiology

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Introduction Louisiana ranks last in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of some social determinants of health on the incidence of adverse birth outcomes. These works have subjectively selected specific social determinants of health from larger datasets. Here, we attempt to replicate their results with objective variable selection techniques. Methods By deriving principal components from the Agency of Healthcare Research and Quality's parish-level social determinants of health dataset, we were able to objectively find social determinants of health associations instead of the conventional subjective variable selection approach. Then, we applied Bayesian linear mixed-effects models to calculate more conservative parameter estimates about the effects of social determinants of health on adverse birth outcome incidence. Then, we used local Moran's I to identify clusters of spatially autocorrelated parishes. Finally, we combined the results of these two methods and inspected the relationship between important predictors and clusters of spatial autocorrelation. Results We identified several significant effects on the incidence of adverse birth outcomes, including populational composition and economic attainment, and several clusters of high and low incidences of adverse birth outcomes in Louisiana. There was also a concordant relationship between important predictors from our predictive models and the cluster assignments of Local Moran's I. Conclusion Our results validate previous works in the subject area and hold implications for precision development of maternal health interventions in Louisiana.

Background Childhood overweight and obesity represent major public health challenges, shaped by socio-economic and environmental factors. This study investigates the mediating and moderating role of urban environmental exposures in socio-economic disparities in childhood excess weight. Methods Data was drawn from a population-based sample of children (2-9 years) and adolescents (10-17 years) living in Geneva, Switzerland. Parents reported household financial situation and children's height and weight, from which excess weight (i.e. overweight or obesity) was derived. Residential exposures to air pollution (PM2.5, NO2), noise (daytime, nighttime), and neighborhood greenness (green areas, canopy coverage) were estimated based on geocoded residential addresses. The association between household financial situation and excess weight was evaluated, as well as the mediating and moderating roles of urban environmental exposures. Results The analysis included 1006 children and 1154 adolescents. Among children, an average-to-poor household financial situation was associated with higher odds of excess weight in children (adjusted odds ratio [aOR]: 1.79, 95% confidence interval [CI]: 1.13; 2.84). Higher noise exposure was associated with excess weight (daytime: aOR: 1.40, 95% CI: 1.10; 1.77, nighttime: aOR: 1.37, 95% CI: 1.08; 1.74), while the association with PM2.5 appeared stronger among socio-economically disadvantaged children, though the interaction did not reach statistical significance (financial situation x PM2.5 interaction: aOR: 1.59, 95% CI: 0.98; 2.59). No significant associations were observed among adolescents. Conclusion These findings highlight the joint influence of social and environmental inequalities on childhood excess weight and stress the need to address these interconnected determinants to design equitable, targeted public health interventions.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

BackgroundImplementing proven vehicle safety standards recommended by the UN World Forum for Harmonization of Vehicle Regulations is among the most cost-effective strategies to reduce road traffic deaths. In 2022, Mexico approved updated vehicle safety standards, including side pole testing, electronic stability control, seatbelts, airbags, side structures, and anchorage child restraint systems. However, pedestrian protection and advanced driver-assistance technologies, such as autonomous emergency braking systems (AEBS), were excluded. These exclusions are critical, given that more than half of road traffic deaths involve vulnerable road users. Local evidence on the expected benefits of implementing comprehensive vehicle safety standards is needed to guide policy decision-making. ObjectiveTo estimate the potential public health impact of increasing the availability of recommended vehicle safety technologies in Mexico. MethodsWe conducted a comparative risk assessment analysis to estimate the impact of improving vehicle safety standards on road traffic deaths, injuries, and disability-adjusted life years. Counterfactual analyses were defined using traffic statistics for 2019 as baseline, relative risk estimates associated with each safety technology, and technology penetration within Mexicos vehicle fleet. Three scenarios were modeled: (1) full implementation of Mexicos 2022 standards; (2) addition of crashworthiness, AEBS, and motorcycle ABS/ESC; and (3) inclusion of expanded AEBS crash configurations, lane departure warning (LDW), and lane keeping assistance (LKA) systems. ResultsScenario 1 reduced deaths by 18%, injuries by 16%, and DALYs by 18%, with the greatest benefits for car occupants. Scenario 2 reduced deaths by 29%, injuries by 27%, and DALYs by 30%, benefiting motorcyclists and pedestrians the most. Scenario 3 reduced deaths, injuries, and DALYs by 41%, 38%, and 41%, respectively, benefiting car occupants and motorcyclists. ConclusionsCurrent vehicle safety standards in Mexico are expected to reduce deaths, injuries, and disabilities, yet existing guidelines focus largely on protecting car occupants. Mexico should strive to update and strengthen its current legislation by adding technologies that protect vulnerable road users, such as pedestrians and cyclists, and to focus on technologies for motorcycle users to further reduce the burden of road traffic injuries.

Recognizing the challenges faced by primary caregivers regarding the health of children with congenital craniofacial anomalies (CCAs) contributes to strengthening healthcare programs according to patient[s] and families differential needs. This qualitative study presents the experiences of 25 caregivers of children with CCAs from Bogota and Cali, Colombia, identified from care registries and consultation statistics provideed from public high-complexity healthcare institutions. Grounded in Giorgis descriptive phenomenology and employing thematic analysis, this research utilized semi-structured interviews and focus groups to explore the diagnostic process and its impact, experiences with healthcare services, and the caregivers role and daily care activities. Data were analyzed using MAXQDA(R) qualitative software. Findings highlighted the emotional complexity of caring for childre[n]s health. Challenges included late diagnoses, pessimistic views of the children with CCAs condition by healthcare team members; lack of effective support, information, and guidance from health staff; absence of clear care and referral protocols, and limited access to specific adaptations and timely specialized care for children with CCAs. There were also reduced therapeutic services, and a pronounced gendered caregiving burden when responsibilities fall almost exclusively on mothers. System fragmentation, reflected in deficiencies in communication and a lack of clear, coordinated, and timely pathways of care, as well as the absence of adequate psychosocial support for families, emerged as common structural problems in healthcare services in both geographic settings where this research has been conducted. Gender-sensitive strategies focused on alleviating emotional concerns and the burden of caregiving from diagnosis onward within a patient and family-centered care model are decisive. Improving comprehensive CCAs training for healthcare personnel and making adjustments to care pathways are suggested to contribute to the implementation of inclusive health programs that address the diverse needs of children and their families.

BackgroundYoung sexual and gender minorities of color face compound health risks shaped by interlocking systems of racism, cisgenderism, and class inequality. Spatial health research documents that place shapes health, but existing methods cannot specify the mechanisms through which spatial configurations produce different health outcomes for differently positioned people. This gap prevents targeted intervention. ObjectiveTo develop and pilot test the Spatial Intersectionality Health Framework (SIHF), which specifies three mechanisms through which space produces intersectional health inequities: Layered (multiple oppressive systems activating simultaneously), Positional (the same space producing different health pathways by intersectional position), and Conditional (nominally protective spaces carrying hidden costs for specific positions). We also introduce and validate Intersectional Geographically-Explicit Ecological Momentary Assessment (IGEMA) as the methodology operationalizing SIHF across three data levels. MethodsThe GeoSense study enrolled 32 young sexual and gender minorities of color (ages 18-29) in New York City. IGEMA was implemented across three integrated levels: (1) GPS mobility tracking via participants personal smartphones, linked to census tract structural exposure indices across n=19 participants; (2) ecological momentary assessment of intersectional discrimination with multilevel modeling of mood, stress, and sleep outcomes; and (3) map-guided qualitative interviews with SIHF mechanism coding and intercoder reliability assessment across 92 coded records from 18 participants. This study was conducted as the pilot for NIH R01HL169503. ResultsAll three SIHF mechanisms were empirically detectable. A compound structural gendered racism index outperformed every single-axis alternative in predicting daily mood (b=-0.048, p=.001) and stress (b=0.121, p<.001). The Positional mechanism accounted for 71% of coded harm experiences. Intercoder reliability for mechanism assignment reached kappa=0.824 at Stage 2 reconciliation. Daily intersectional discrimination predicted greater sleep disturbance (b=1.308, p=.004). ConclusionsSIHF and IGEMA together provide an empirically testable framework for specifying how space produces intersectional health inequities. Mechanism specification, not spatial location alone, is the condition for designing research and intervention that reaches the source of harm for multiply marginalized populations.

Background: Dengue is a major public health problem in Brazil, and Minas Gerais is one of the states with the highest burden. In January 2019, the Brumadinho dam collapse released about 12 million cubic meters of iron ore tailings into the Paraopeba River basin, causing environmental disturbance that could plausibly affect vector habitats and dengue transmission. We evaluated the spatiotemporal dynamics of dengue in Minas Gerais from 2014 to 2023 and tested whether the disaster was associated with changes in affected municipalities. Methods: We performed an ecological spatiotemporal analysis using dengue notifications from SINAN for all municipalities in Minas Gerais (2014-2023). Municipalities were classified as Paraopeba basin, regional controls, or state controls. Temporal similarity was assessed using Pearson correlation-based hierarchical clustering and non-metric multidimensional scaling (NMDS). Sources of variation were examined with PERMANOVA and principal component analysis (PCA). A linear mixed-effects model with municipality as a random effect was used to test changes after 2019, with pre/post contrasts estimated from marginal means. Results: Dengue showed strong temporal synchrony across the state, with major epidemic peaks in 2015-2016, 2019, and 2023. Health region explained 31.5% of the variation in temporal incidence profiles (p = 0.001), whereas Paraopeba basin status explained no significant variation (p = 0.998). No temporal cluster was enriched for municipalities in the Paraopeba basin. PCA identified 2023, 2019, and 2016 as the main years driving variability. In the mixed model, year was significant (p < 0.001), but Paraopeba basin status and its interaction with time were not. Incidence increased significantly after 2019 in non-exposed municipalities (p < 0.001), but not in basin municipalities (p = 0.088). Conclusions: Dengue dynamics in Minas Gerais were driven mainly by regional and state-wide epidemic processes, with no significant independent effect of the Brumadinho dam collapse on notified dengue patterns.

Importance: Foot drop impairs mobility for many children globally, causing life-long health issues. Existing treatments are costly, custom-made, and require frequent clinical visits. A new, low-cost, off-the-shelf splint (OrthoPed) could improve access and user experience. Objective: To determine the feasibility of recruiting children (4-17 years) with moderate foot drop and collecting biomechanical, clinical, and patient-reported outcomes to compare OrthoPed with existing treatments. Design: Single-centre cross-sectional feasibility and pilot study informing a future randomised clinical trial. Participants: Twelve children (target=20; mean age=10.6 {+/-} 3.5 years; 2 females) with moderate foot drop and prescribed orthotic support were recruited via physiotherapy. Intervention: The new OrthoPed splint was compared against existing treatments: ankle foot orthoses (AFOs) and Lycra socks. Main outcome measures: Primary outcome: recruitment and retention rates. Secondary outcomes: biomechanical and clinical gait measures, alongside useability and performance questionnaires. Results: Recruitment reached 22% of eligible participants (an "amber" rating for future trials). Despite four dropouts due to treatment burden, all outcome measures were successfully collected. Preliminarily, OrthoPed supported more natural gait mechanics than AFOs and offered better usability and comfort than AFOs and Lycra socks, potentially enhancing adherence. Conclusions: Recruiting children for orthotic trials is feasible, though coordinating gait testing with routine clinical appointments could improve future recruitment. Importantly, low-cost orthotic devices may provide better usability, accessibility and adherence than existing prescribed options.

Stroke, as an acute cerebrovascular disease with significant public health implications, is influenced by a complex interplay of meteorological conditions, air quality, and socioeconomic factors. However, the inherent challenges of mixed-frequency data from diverse sources and high-dimensional variable spaces limit the effectiveness of traditional regression models. This study develops a Lasso-MIDAS model framework to identify the key multidimensional drivers of stroke admissions. Using this approach, 21 candidate variables encompassing meteorological, environmental, and economic indicators were screened. The empirical results identified 11 core influencing factors. In the meteorological and environmental dimensions, Wind Speed, Carbon Monoxide (CO), and Sulfur Dioxide (SO2) were identified as significant positive drivers, with Temperature Difference also positively correlating with admission risks. Conversely, Nitrogen Dioxide (NO2) exhibited a negative correlation, potentially reflecting behavioral adaptation and exposure reduction during peak pollution periods. In the socioeconomic dimension, the Consumer Price Index (CPI) for Food, Tobacco, and Alcohol emerged as a major risk factor, highlighting the impact of living cost pressures on public health. The findings demonstrate the superiority of the Lasso-MIDAS model in handling large-scale healthcare data. It effectively addresses the frequency mismatch problem while enhancing the robustness of causal identification through variable shrinkage. These conclusions provide a scientific basis for health authorities to establish early warning systems and optimize public health policy interventions.

BackgroundDisparities in injury and death indicate that Latinos and Hispanics are disproportionately affected by firearm violence. Understanding the factors that impact Latino and Hispanic engagement with firearm safety is integral to developing nuanced interventions, yet these factors remain largely understudied. This study explores the social ecological factors related to firearm safety engagement among Latino and Hispanic adults residing in New Mexico. MethodsThe study used a convergent mixed-methods design with quantitative and qualitative components. Data were collected from a predominantly Latino-Hispanic community experiencing high rates of firearm violence near Albuquerque, New Mexico. Quantitative data (n=303) were collected using a community-based survey with a non-random sample on firearm safety engagement, collective efficacy, and sociodemographic characteristics. Qualitative data (n=18) included semi-structured interviews from a subset of the survey population who expressed interest in participating. Quantitative data was used to explore descriptive statistics and correlations between reported levels of collective efficacy and firearm safety engagement. Qualitative data were used to explore the firearm safety experiences of Latino and Hispanic participants. AnalysesMultivariate regression analyses examined associations between collective efficacy (exposure) and engagement with firearm safety (outcome). I also explored associations across key domains: collective efficacy, neighborhood characteristics, individual characteristics, and sociodemographic factors. Interviews were analyzed using framework analysis to generate a cohesive thematic structure informed by a social ecological model. The results from the quantitative and qualitative data were then integrated to develop a robust understanding of social ecological factors related to firearm safety engagement using a mixed methods joint display. ResultsThere were 303 survey participants (40.6% male; 55.1% female; 4.3% other gender identity) and 18 interview participants in this study. 57.1% of survey participants reported engaging with at least one firearm safety practice or initiative. Results from multivariate regression indicated that higher collective efficacy ({beta} = 0.082, p = 0.002), higher informal social control ({beta} = 0.174, p = 0.001), stronger endorsement of gun safety principles ({beta} = 0.079, p < 0.001), being married vs. unmarried ({beta} = -0.334, p < 0.001), speaking Spanish in the home vs. English ({beta} = 1.048, p < 0.001), and not owning a gun ({beta} = - 0.638, p = 0.006) were significantly and positively associated with firearm safety engagement. Themes from the qualitative data included barriers (insecure environment; lack of meaningful engagement) and facilitators (location-specific contributors to safety; collective identity and pride) to firearm safety engagement, organized by social ecological domain. Mixed methods findings indicate factors associated with participants individual firearm safety engagement, while providing insights into the perceived barriers and facilitators across social ecological domains. DiscussionFindings from this mixed-methods study suggest that processes of empowerment and collective efficacy may contribute to greater firearm safety engagement within Latino and Hispanic communities. Findings expand injury prevention research by exploring the factors influencing firearm safety engagement among a marginalized and hard-to-reach population who have disproportionate experiences with firearm victimization, perpetration, and injury. ConclusionThis study offers unique methodological approaches by using concurrent mixed methods and collecting complementary data sources to understand firearm safety engagement among Latinos and Hispanics. Findings highlight the need for culturally specific and community-engaged interventions that address social ecological disparities to strengthen safety practices and reduce firearm-related harms.

Background: Despite the significant risks associated with online substance procurement (SP), few researchers have examined this practice in U.S. youth. The studies that do exist are cross-sectional and cannot temporally connect specific digital behaviors to online SP. This longitudinal cohort study examined youth SP and digital media habits to determine whether use of certain smartphone applications correlated with increased odds of online SP or being contacted online about procuring drugs or alcohol. Methods: A cohort of U.S. youth (aged 15-20) with a history of non-daily substance use in the 3 months prior to enrollment was recruited to use the digital phenotyping smartphone application EARS for 90 days. On a nightly basis, participants were asked to complete surveys about online experiences related to SP and instances of substance use. Smartphone-generated screen use data were also collected passively each day. Results: Out of 112 enrolled participants, 106 were able to be included in analyses. Over approximately 3 months, 28.3% of participants (n=30) reported a collective 91 instances where they used social media to acquire drugs or alcohol. Screen use data demonstrated temporal relationships between social media SP and applications previously connected to the social media drug-purchasing process (e.g., TikTok, encrypted apps), as well as other school-specific social media. Discussion: Our results provide critically needed research evidence to support a body of literature composed predominantly of anecdotal reports. Despite measures taken by social media companies to prevent use of their platforms for drug procurement, underage youth continue to engage in this practice.

Occupational activities play a critical role in shaping patterns of infectious disease transmission, as work-related contact, exposure duration, and environmental conditions vary substantially across jobs. Evidence from infectious disease outbreaks, including tuberculosis and other respiratory infections, shows that occupations involving frequent close contact and crowded settings face elevated exposure risk, highlighting the need for systematic occupational risk assessment to inform public health action. This study adopts a data-driven approach to characterize occupational risk for infectious disease transmission in the Philippine workforce. Using job-specific indicators such as encounter frequency, work shift duration, and crowd density, unsupervised clustering methods were applied to group occupations into distinct risk profiles. Unlike predefined sector-based classifications, this approach identifies latent groupings that capture shared exposure characteristics and socioeconomic context. The resulting clusters reveal a clear gradient in exposure risk across occupations, with high-risk roles often concentrated among jobs with lower to moderate income levels, while lower-risk occupations tend to be associated with higher income and greater structural capacity for risk mitigation. These findings provide a framework for occupational risk stratification that is directly relevant for public health planning. Overall, this study demonstrates the value of integrating occupational and socioeconomic data to support targeted workplace interventions, risk-informed surveillance, and more equitable allocation of public health resources for infectious disease prevention and control in the Philippine context.

As climate change intensifies, health risks from extreme heat are rising. Accurate assessment of heat vulnerability at high spatial resolution is crucial for developing effective adaptation strategies, particularly in socioeconomically heterogeneous urban settings. However, the identification of key indicators underlying heat vulnerability remains challenging. Using Chicago, Illinois (USA) as a case study, we systematically compare different variable selection strategies in community-level heat vulnerability assessments. We take the conventional unsupervised principal component analysis (PCA)-based Heat Vulnerability Index (HVI) as a baseline, and compare it with supervised approaches that incorporate variable selection, including machine learning algorithms (Lasso regression, Random Forest, and XGBoost) as well as traditional statistical methods (simple linear regression and polynomial regression). Using the vulnerability indicator subsets identified by each variable selection method, we construct multiple HVIs and evaluate their performance against heat-related excess mortality. Our work indicates that supervised variable selection improves the performance of HVIs in capturing heat-related health risks. Among all methods, the Random Forest-based variable selection algorithm achieves the best overall results, highlighting the potential of machine learning to enhance heat vulnerability assessment tools. Our results demonstrate that poverty rate, lack of air conditioning, and proportion of residents aged 65 and above are robust determinants of heat vulnerability in Chicago.

Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.

Background Heatwaves increasingly threaten public health in the Mediterranean region, and Greece is among the hardest hit countries. Yet evidence on long-term adaptation, spatial vulnerability, and the contribution of human-induced climate change to heatwave-related mortality in Greece remains limited. Methods We analysed 2,144,957 all cause deaths in Greece during 2000 and 2019 using a time stratified case crossover design. We derived population weighted daily maximum temperatures at NUTS3 level from ERA5 reanalysis and WorldPop. We applied six heatwave definitions (HD1-HD6) varying by duration (2 or 3 consecutive days or more) and thresholds (90th, 95th, 99th percentiles). We fitted Bayesian hierarchical Poisson models to estimate heatwave-mortality associations varying by space and time. We additionally adjusted for relative humidity and national. We then combined these estimates with probabilistic climate attribution methods to quantify the number and proportion of heatwave-related deaths attributable to human induced climate change. Results Heatwaves raised mortality consistently, with relative risks from 1.08 (95% CrI (Credible Interval): 1.07- 1.09; HD1) to 1.15 (1.11- 1.20; HD6). Risks increased with heatwave intensity and duration and peaked among females and adults aged 85 years and older. We did not detect a consistent temporal decline in risk or marked spatial heterogeneity. Human induced climate accounted for 51-94% of heatwave related deaths across definitions. The proportion attributable to climate change rose over time. Conclusions Heatwaves already impose a major mortality burden in Greece, with more than half driven by anthropogenic climate change and little evidence of population level adaptation. These findings call for rapid emissions reductions and targeted adaptation, including stronger heat health warning systems and protection of vulnerable groups.

Background Despite existing road safety regulations, commercial motorcycle riders commonly referred to as "Boda Bodas" in Uganda continue to experience high rates of injuries due to road traffic accidents resulting from unsafe riding behaviours, contributing significantly to morbidity and mortality among both riders and passengers. Safe riding behaviours are less well documented, as well as factors associated with the observance of those behaviours. This study aimed to determine factors associated with safe riding behaviors for both boda-boda riders and their passengers in Kampala Central Division. Methods A cross-sectional survey study design was conducted using a convergent parallel mixed-methods design guided by the PRECEDE model. Quantitative data were collected from 424 riders through structured questionnaires administered by trained research assistants. Binary Logistic regression was used to determine the independent predictors of safe road riding behaviors, and Adjusted Odds ratios (AORs) have been reported. Data were analyzed using descriptive and inferential statistics, with a p-value <0.05 considered statistically significant. Qualitative data were collected simultaneously with quantitative data through in-depth semi-structured interviews with 10 passengers to capture perceptions of rider behaviors and safety practices. Thematic analysis was applied, and results were triangulated to highlight convergences and divergences between quantitative and qualitative findings, providing a comprehensive understanding of safety determinants for both riders and passengers. Results Of the 424 riders (mean rider age was 29.56 {+/-} 5.71), overall, 276 (65.1%) of riders exhibited unsafe riding behaviors. In the bivariate analysis with Logistic regression, predisposing factors (education, marital status, religion, and willingness to obey traffic regulations), and reinforcing factors (family encouragement) were significantly associated with safe riding behaviors. However, in the adjusted model, secondary (AOR=0.50; 95% CI:0.30-0.85) and post-secondary education (AOR=0.57; 95% CI:0.33-0.98), being married (AOR=0.56; 95% CI:0.34-0.91), Christian religion (AOR=2.98; 95% CI:1.63-5.47), willingness to obey traffic regulations (AOR=0.41; 95% CI:0.24-0.70), union advocacy (AOR=1.76; 95% CI:1.03-3.01), and well-maintained roads (AOR=1.65; 95% CI:1.07-2.55) were significant predictors of safe riding behaviors. Qualitative interviews further highlighted barriers to safety, including a lack of helmets, over-speeding, disregard for traffic regulations, and poor road infrastructure. Conclusions Rider and passenger safety is still low, interdependent, and influenced by multiple factors. Integrated interventions focusing on education, stronger families, religious affiliations, union safety advocacy, and stricter enforcement of traffic regulations are vital for enhancing safety for both riders and passengers.

Background: Nitrogen dioxide (NO2) is a surrogate for traffic and industrial air pollution associated with adverse respiratory outcomes. Whether elevated NO2 and temperature jointly influence adult-onset asthma (AOA) risk is unclear, especially among subgroups with varying lifestyle and exposure profiles. We investigated further in the prospective All of Us research program. Methods: Among 596,926 U.S. participants who consented to electronic health record release, annual average NO2 concentrations from satellite data were linked to residential locations for 376,535 individuals. We used multivariable Cox regression to estimate associations between NO2, temperature, and incident AOA, adjusting for co-pollutants and potential confounders. We analyzed 4-category cross-classification variables between NO2 (high>75th percentile vs. low<=75th percentile) and maximum or average temperature (high>median vs. low<=median). We also stratified by sex, age, income, and smoking status. Additive interactions were estimated using Relative Excess Risk due to Interaction, Attributable Proportion, and Synergy Index. Results: We identified 10,413 incident AOA cases over an average 4-year follow-up. Participants with the highest categories of NO2 and temperature exposure had significantly higher risk compared to those with the lowest (HRHigh NO2 x High Max. Temp.=1.37, 95%CI:1.26-1.49; HRHigh NO2 x High Average Temp.=1.49, 95%CI:1.38-1.61). The joint association of high NO2 and high maximum temperature was more pronounced among ever-smokers (HR=1.59, 95%CI:1.40-1.81) than never-smokers (HR=1.26, 95%CI:1.13-1.41). Interaction analyses supported super-additive interactions of high NO2 and high average temperature on AOA risk, particularly among ever smokers, lower-income participants, and younger adults. Conclusion: Our findings highlight the respiratory health threat of long-term joint exposure to elevated NO2 and average temperature, particularly among vulnerable subgroups.

BackgroundAlthough the number of new HIV diagnoses among men who have sex with men (MSM) in the Netherlands has declined considerably, the recent plateau suggests ongoing transmission. In 2024, 29% of new diagnoses among MSM were in a late HIV stage, showing that the time between infection and diagnosis can still be substantially reduced. In low-incidence settings, infections introduced through immigration are increasingly important in sustaining transmission, highlighting the need to re-evaluate current testing guidelines. We assess targeted testing strategies among MSM in the Netherlands addressing these considerations. MethodsWe used an agent-based model of HIV transmission among MSM in the Netherlands, incorporating infections acquired domestically and abroad. For 2024 - 2040, we simulated testing interventions targeting different subgroups, including offering an HIV test to immigrants upon entry, increasing testing rates among MSM residing in the Netherlands, and combinations of these approaches. ResultsOffering HIV testing to immigrating MSM at the entry averted up to 94 (95-th % quantile interval, 95% QI -128 - 328) new infections over 15 years if at least 50% take the test. Increasing testing to every 7 months in the general MSM population achieved the largest reduction, with up to 508 (95% QI 292 - 900) infections averted. The same testing rate in MSM with more than 5 partners within the previous six months resulted in 340 (95% QI 132-592) infections averted. Combining testing at entry with 7-months testing among general resident MSM averted the most infections, 534 (95% QI 308 - 884). ConclusionsCombination of offering HIV test to immigrating MSM at the entry with 7-month testing frequency in the general resident MSM population can substantially reduce HIV infections. The difference in impact between targeting general MSM and those with relatively high recent partner numbers suggests that criteria for being at risk of having HIV need to expand. 1 Author summaryWhile HIV transmission among MSM in the Netherlands has decreased substantially over the last decade, it is still ongoing. In 2024, 29% of new HIV diagnoses in MSM were in individuals in late-stage of HIV infection, suggesting that the time between HIV acquisition and diagnosis should be shortened further. Additionally, in a low-incidence setting such as MSM in the Netherlands, introduction of HIV infections through immigration becomes more important. We evaluated several HIV testing strategies for this context, considering both immigrating MSM and resident MSM. While offering HIV test at entry point can avert many HIV infections, increasing testing rate in resident MSM to on average every seven months can avert substantially more HIV infections. The greatest impact is achieved when these approaches are combined: targeting both immigrating MSM and those already living in the country. This combined strategy requires the fewest additional tests per infection averted. Importantly, our simulations show that there are MSM living with undiagnosed HIV who do not necessarily meet the traditional criteria for being at risk. Improved testing strategies can help reach these individuals earlier, benefiting both public and their personal health.