Spatial and Spatio-temporal Epidemiology

Background. Current exposure-health models rely on averaged, residential-based environmental exposures, failing to account for human movement. This aggregation can lead to exposure misclassification and biased exposure-response estimates, potentially distorting our understanding of the true health effects of environmental conditions. We developed exposure disaggregation regression models that explicitly account for human movement when linking environmental exposures to health outcomes. Methods. By weighting pixel-level exposures according to distance from home as a simple proxy for human movement, our model linked disaggregated environmental exposures to individual-level health outcomes. Weights were either fixed a priori or derived from a latent distance-decay power parameter learned from the data. We additionally evaluated model performance under a nonlinear exposure-response relationship. Model performance was assessed across multiple sample sizes (N = 1,114; 50,000; and 100,000). A simulation study examined parameter recovery using bias, empirical standard error (EmpSE), and credible interval coverage. As a case study, Demographic and Health Surveys (DHS) data from Albania were used to link acute respiratory infection (ARI) outcomes among children under five to pixel-level NDVI within a 3 km buffer around DHS cluster centroids, and the proposed models were applied to these data. Results. Across all models (fixed-weight, learned-weight, and restricted cubic spline models), parameter recovery improved with increasing sample size. At N = 1,114, estimates were biased and imprecise, with incorrect effect direction for exposure-response parameters (e.g., learned-weight {beta}1 bias = - 0.79; EmpSE = 2.61; coverage = 0.88). In contrast, the models accurately recovered parameters at larger sample sizes, including the latent distance-decay parameter (bias = - 0.02; EmpSE = 0.15; coverage = 0.95 at N = 100,000), demonstrating their ability to reliably learn movement-based exposure weights when sufficient data were available. Conclusion. Instead of relying on arbitrarily-sized buffers, this statistical framework provides a novel method for studying environmental exposure-health relationships whilst accounting for human movement. With sufficiently large sample sizes, it can accurately estimate the influence of disaggregated environmental exposures on individual-level health and help address exposure misclassification arising from residential-only metrics. This methodological framework remains scalable, interpretable, and adaptable to other exposures and outcomes, offering a foundation for future work that integrates richer mobility-informed exposure-health research.

Background: HIV testing is the entry point into the diagnosis, treatment and viral suppression cascade, yet in many low and middle income countries the household surveys used to monitor testing coverage are not powered for estimation below the regional level. We produced calibrated district level estimates of HIV testing uptake among women in Ghana across three Demographic and Health Survey (DHS) rounds and examined the spatial and temporal structure of the heterogeneity that remained once measured covariates were accounted for. Methods: We pooled individual recode and geo referenced cluster data from the 2008, 2014 and 2022 Ghana DHS (n = 4,769, 9,391 and 15,014 women respectively; outcome: ever tested for HIV, variable v781), aggregated to 261 level two administrative districts by survey round, and fitted a Bayesian hierarchical binomial model that combined a BYM2 conditional autoregressive spatial term, a first order random walk (RW1) temporal term, and three standardised covariates: WorldPop population density derived from a machine learning dasymetric algorithm, Malaria Atlas Project travel time to the nearest city, and cluster urban proportion, each extracted within buffers around cluster coordinates that matched the DHS displacement protocol. Inference used integrated nested Laplace approximation (INLA) implemented through R-INLA (Lindgren and Rue, 2015). Residual spatial structure was assessed with global and local Moran's I. Results: National crude testing prevalence rose from 20.7% in 2008 to 46.7% in 2014 and 53.8% in 2022. District sample sizes were small and unevenly distributed (2008 median n = 20 women per district; 91.8% of districts had fewer than 50), which is why model based smoothing rather than direct estimation was required. Urban cluster proportion was independently associated with higher testing odds (odds ratio [OR] 1.10, 95% credible interval [CrI] 1.04 to 1.16 per one standard deviation increase) and travel time to the nearest city with lower odds (OR 0.90, 95% CrI 0.84 to 0.96); population density showed no independent association once these two variables were included (OR 0.95, 95% CrI 0.89 to 1.02). The spatial mixing parameter of the BYM2 term (phi = 0.716, 95% CrI 0.498 to 0.887) indicated that around seven tenths of spatially attributable variance was structured rather than idiosyncratic. Global Moran's I on the fitted spatial effect surface was 0.616 (p = 6.6 x 10 to the power minus 60), and local indicators of spatial association identified a contiguous low uptake cluster across the northern regions together with three compact high uptake clusters in the south central corridor. Conclusions: Combining machine learning derived geospatial covariates with an explicit spatiotemporal Bayesian hierarchy exposes a persistent north to south gradient in HIV testing uptake that measured accessibility and urbanicity do not fully explain and identifies specific district clusters as priorities for targeted testing scale up.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Alpha-gal syndrome (AGS) is a tick bite-associated allergic condition induced by the lone star tick (Amblyomma americanum). Illinois had no confirmed AGS case data as of January 2026, when mandatory reporting began under the state's TICK Act, leaving practitioners without data to guide screening or resource allocation. We constructed a county-level proxy risk score using a Bayesian conditional autoregressive spatial model applied to three Illinois surveillance sources from 2019-2022: tick abundance, ehrlichiosis cases, and tick establishment status, all linked by a shared vector. Ehrlichiosis was modeled as a population-adjusted rate, ticks as a relative-intensity index, and establishment status as a fixed ecological component. The combined risk score identified a high-risk cluster in far southern Illinois that remained stable across alternative weighting scenarios. This approach is transferable to jurisdictions lacking direct AGS surveillance and offers a starting point for clinician education pending validation against confirmed case data.

Abstract Objectives: To estimate surveillance-adjusted county-level residual syphilis risk, quantify posterior support for elevated risk, and identify the geographic distribution of stably high-risk areas across the contiguous United States and the District of Columbia. Methods: County-year primary and secondary syphilis counts from 3,109 counties during 2010-2022 were analyzed using a Bayesian negative-binomial spatial model with county-level covariates capturing social vulnerability and healthcare and surveillance related structure. Residual spatial risk, posterior exceedance probabilities, and stably high-risk counties were estimated. External validation examined whether county-level residual syphilis risk was associated with HIV and gonorrhea burden. Results: A total of 850 stably high-risk counties were identified. These counties were concentrated in the southeastern United States and along the Gulf Coast, with additional clusters in the north-central region and along the Atlantic and Pacific coasts. The social vulnerability index showed the strongest positive association with reported syphilis rates, followed by primary care physician density. External validation and sensitivity analyses showed that higher county-level residual syphilis risk estimates were positively associated with higher HIV diagnosis rates and gonorrhea rates, indicating that these estimates were not merely model-derived numerical outputs but were meaningfully related to the county-level distribution of sexually transmitted infection risk. These findings indicate that surveillance-adjusted residual spatial risk estimates and posterior exceedance probabilities may provide useful county-level evidence for syphilis control prioritization and resource allocation.

Introduction The interaction between air pollution and airborne pollen is emerging as a major public health concern, as it may enhance allergic responses and exacerbate asthma at the population level. Available evidence suggests that urban residents experience a higher burden of respiratory allergies than rural populations, potentially due to the combined effects of chemical air pollutants and pollen exposure. Air pollutants may modify pollen characteristics, increase allergen release and potency, and promote airway inflammation, thereby amplifying allergic sensitization and respiratory symptoms. Aims For the first time, the relationship between air pollution, pollen, and asthma and allergies is investigated by simultaneously factoring in spatiotemporal land cover data (urban, agricultural, and forest spaces). Methods We utilized descriptive statistics, Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and spatial analysis to understand the relationship of birch, grass and all taxons count, NO2 and PM2.5 concentrations (micro grams/m3) and land cover type (urban/rural, agriculture, forest) in Bordeaux, Clermont, Marseille, Nancy, Paris, Poitiers, Reims and Strasbourg using prescribed and over the count sales data for asthma (R03) and allergies (R06), from a representative sample of pharmacies (n=12,000), in 2013. Results In 2013, the 8 cities saw total sales of 9,684,577 R03 and 12,344,102 R06 medications, with sales peaks coinciding with high pollen and air pollution. A spatiotemporal and land cover analysis revealed that relationships between these variables are highly context-dependent. For instance, higher R06 sales clustered in areas with medium pollen (taxon deciles 3 to 4, grass 4 to 5, birch 6 to 7), high pollution ("NO" _2 deciles 8 to 10, "PM" _2.5 5 and 8 to 10, "PM" _10 3, 7, 9 to10), high agricultural land, and low forest/urban space. Conversely, some associations did not vary by location, suggesting external influencing factors. Conclusions Our data raise evidence that pollen and air pollution can act synergistically according to the land cover characteristics. Other investigations are needed to confirm the relationship.

Many urban health outcomes are shaped by environmental stressors that occur together rather than in isolation, yet methods for measuring such co-occurrence at the neighbourhood scale remain underdeveloped. We developed a multi-metric framework for joint co-exposure assessment and applied it to characterise the joint spatial distribution of summer surface heat and fine particulate matter (PM2.5) across 42,304 census tracts in 48 large US metropolitan areas during summers 2015 to 2020, covering approximately 174.6 million residents. The framework combines a composite co-exposure index, a joint exceedance indicator, a conditional exceedance ratio that compares observed joint occurrence to within-group statistical independence, and an upper tail dependence parameter estimated using both the non-parametric Caperaa-Fougeres-Genest estimator and a Gumbel copula, with bias-corrected and accelerated (BCa) confidence intervals obtained from a 5,000-replicate metropolitan-area block bootstrap. Among residents of predominantly Black tracts, 13.21% lived in neighbourhoods that simultaneously exceeded the within-metropolitan-area 80th percentile for both heat and PM2.5, compared with 3.33% of residents of predominantly White tracts; the corresponding heat-only and PM2.5-only ratios were 2.88 and 2.48. Residents of Home Owners Loan Corporation grade D tracts had 3.97 times the odds (95% confidence interval 2.79 to 5.66) of joint hotspot residence compared with grade A residents after adjustment for contemporary tract racial composition, poverty, renter-occupancy, and pre-1960 housing. The within-group conditional exceedance ratio at the 80th percentile was 2.29 in predominantly White tracts (95% BCa CI 1.81 to 2.78), 1.27 in predominantly Black tracts (0.71 to 1.56), and 1.13 in predominantly Hispanic tracts (0.70 to 1.41); the White interval excluded one while the Black and Hispanic intervals included one, which we interpret as power-limited given fewer contributing CBSAs. Magnitudes attenuated under near-surface air temperature surfaces but the direction and statistical significance of the primary findings were preserved. The framework is portable to other compound-exposure questions and supports cumulative-impact assessment.

Urban air pollution remains a significant public health concern, contributing to premature deaths and adverse health outcomes. However, there is little causal research evaluating the effectiveness of policies designed to improve air quality. This study assesses the impact of all three stages of London's Ultra Low Emission Zone (ULEZ) on air pollution, via PM2.5 levels, and respiratory health, via prescription records for bronchodilator and respiratory corticosteroid medications. Analyses are at general practice level, using a generalised synthetic control method to estimate causal impacts. Stage 1 was associated with a statistically significant but negligible 0.77% reduction in PM2.5 levels, with no corresponding change in prescribing. Stage 2 produced a paradoxical 2.69% increase in PM2.5, alongside a 4.44% decrease in inhaled corticosteroid quantity but a 12.51% increase in average daily quantity (ADQ) usage, suggesting a worsening of disease severity among existing patients. Stage 3 yielded a 2.69% PM2.5 reduction and a modest 2.18% decrease in bronchodilator ADQ usage. Spillover effects beyond the ULEZ boundary were statistically significant, but negligible. We find overall that the ULEZ had minimal effects on both air quality and respiratory prescribing across all three stages. These findings provide new insights into the effectiveness of ULEZ policies in reducing air pollution and its associated health impacts, suggesting the zone's effects are considerably smaller than previously reported, and that integration with broader policy measures may be necessary to achieve meaningful public health gains.

Objective: Over the past decades, US policies intended to reduce air pollution emissions from electric generating units (EGUs), mobile sources (e.g., cars and trucks), and port activities have been implemented to improve air quality. This study aimed to estimate and compare counterfactual air pollution concentrations (i.e., concentrations that would have occurred without these policies) to observed concentrations, and then evaluate the health impacts of such policies in New York City, Los Angeles, and Atlanta from 2005 to 2019. Materials and Methods: We obtained data on respiratory emergency department (ED) visits and cardiovascular disease ED visits that result in hospitalizations for the three cities from 2005-2019. Daily concentrations of fine particulate matter (PM2.5), criteria gases [carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3)], and 1-in-3-day measured concentrations of PM2.5 components and PM sources estimated using positive matrix factorization were acquired from six monitoring sites in the three cities. To estimate health impacts of selected EGU, mobile, and port policies we estimated: 1) counterfactual daily pollutant concentrations at each of the 6 city-sites; 2) associations between daily pollutant concentrations and rates of cardiorespiratory visits using city-site specific multi-pollutant Poisson models; and 3) the percent of cardiorespiratory visits prevented by the implementation of the selected policies, through applying observed and counterfactual concentrations to the fitted health models. Results: Air quality policies were estimated to reduce ambient pollutant concentrations across the three cities, with median PM2.5 reductions of 27%-62% due to all policies combined during 2005-2019. Changes in criteria-pollutant concentrations associated with the selected policies were estimated to avert 7.1% (95% UI: 5.4%, 8.9%) of respiratory visits in New York City, 2.4% (95% UI: 1.4%, 3.4%) in Los Angeles, and 4.5% (95% UI: 0.8%, 8.2%) in Atlanta. In addition, 2.6% (95% UI: 0.9%, 4.2%) and 1.2% (95% UI: 0.3%, 2.1%) of cardiovascular visits were averted in New York City and Los Angeles, while the estimate in Atlanta did not indicate cardiovascular visits averted. Conclusion: The selected EGU, mobile-source, and port policies evaluated during 2005-2019 were estimated to reduce ambient pollutant concentrations and avert respiratory visits in all three cities and cardiovascular visits in New York City and Los Angeles.

Background: The effect of extreme temperatures on miscarriage is not well understood. Even less understood is the gestational period most vulnerable to extreme temperature exposure, as early miscarriages are often missed in incident datasets. We employ a birth-rate based approach to infer the risk of miscarriage in response to extreme temperature exposure by gestational week. Methods: We conducted a population-based ecological study using birth registry data from the 7,948 municipalities of Italy between 2013 and 2024 (4.5 million births). The analyses were stratified by five climatically coherent macro-regions (Ecoregions). To infer unreported pregnancy losses, we regressed birth rates dated from the last menstrual period against weekly temperatures across gestational weeks 3-21, accounting for temporal seasonality and spatial heterogeneities. Findings: Exposure to heat (mean weekly temperature of 30.4 degree/C) during gestational weeks 3-4 was associated with a reduction of birth rates of 1.62 (0.71 - 2.51)%, and of 1.91 (0.92 - 2.88)% to mean weekly temperature of 1.6 degree/C. Whilst heat was found to be harmful during gestational weeks 3-4 and 18-21, cold spells were found to be consistently harmful from the 3th up to the 12th week, depending on the Ecoregion. Interpretation: Pregnancies are vulnerable to extreme temperatures during the post-conceptual period and the second trimester. The findings underscore the need for a pre-conceptual cohort to clarify the mechanisms of loss, and urge public health action to protect pregnancies from the beginning of gestation.

Background: Extreme environmental events, such as extreme temperatures and air pollution, have become a global concern due to their detrimental effects on human health. Short-term peak exposure episodes, despite lasting only a few hours, are crucial for exposure-response modelling. The use of time-aggregated exposure data often overlooks the impact of peak exposures on human health. However, studies employing high-temporal resolution exposure data are rare due to the limited availability of high-temporal resolution health outcomes across various scenarios. Therefore, to address the limitations associated with exposure-response modelling using aggregated exposure data, we have developed a model referred to as the mixed-frequency distributed lag non-linear model (mf-DLNM). Methods: In this work, a simulation study was conducted to further validate the mf-DLNM for hourly-daily mixed-frequency data, using data on hourly temperature and daily respiratory mortality for the West Midlands, UK. Given that the focus was on extreme exposures, Relative Risks (RR) at the 5th and 95th temperature quantiles were considered as the estimands of interest. Model performance was evaluated based on the bias, empirical standard error (EmpSE), and coverage of these estimands. Additionally, the model was assessed across various scenarios, considering data size (1, 3, 5, and 11 years with a 24-hour lag), lag length (12 and 24 hours with 11 years), seasonal variation (summer months with 11 years and 24-hour lag) and distribution (Poisson and negative binomial). Results: The mf-DLNM effectively captured the true parameters of the model. The model, fitted to 11 years of simulated data, a 24-hour lag and a Poisson distribution, observed a bias of 0.011 (0.0009) and 0.011 (0.001) for the RR at the 5th and 95th temperature quantiles, respectively, with Monte Carlo SEs (MCSEs) in parentheses. Furthermore, the model exhibited coverage of 0.94 and 0.93 for RR at the 5th and 95th temperature quantiles, respectively. In addition, the mf-DLNM with hourly and daily data demonstrated satisfactory performance across all scenarios except for the RR at 95th temperature quantiles in the seasonal analysis. Conclusions: Researchers are encouraged to adopt mf-DLNM in instances where high-temporal resolution exposure data are available alongside low-resolution health data. It serves as an alternative to traditional approaches that aggregate high-frequency exposure data. By preserving the temporal information of environmental exposures, mf-DLNM enables a more precise assessment of exposure-response relationships, thereby improving the accuracy and reliability of health risk estimates. This approach offers a promising opportunity for informed decision-making and the development of effective interventions for vulnerable populations and healthcare facilities to address short-term environmental episodes.

Objective Sarcomas are rare tumors of connective tissue that can develop in soft-tissue, viscera organs, or bones. Previous occupational studies have mainly focused on men and soft-tissue sarcomas. This study describes associations between occupations and sarcomas, in men and women, for soft-tissue sarcomas (STS), including visceral sarcomas, and bone sarcomas (BS). Methods The ETIOSARC study was a multicenter case-control study conducted across six French geographical areas between 2019 and 2023. Occupational histories were collected by interview and coded according to the International Standard Classification of Occupations (2008). Conditional logistic regression models were applied to estimate odds ratios with 90% confidence intervals for each occupation included in the study. Results A total of 374 male cases (336 STS and 38 BS), 371 female cases (335 STS and 36 BS), and 1,387 controls were included. Positive associations were observed among male STS for painters (OR=5.37, 90% CI=1.83-15.71), waiters (OR=4.54, 90% CI=1.88-10.97), and among male BS for electricians (OR=3.67, 90% CI=0.91-14.86). Among women, increased STS risk was found among real estate agents (OR=3.48, 90% CI=1.03-11.79), food preparation assistants (OR=3.47, 90% CI=1.09-11.04), and administrative and executive secretaries (OR=2.37, 90% CI=1.45-3.86). For BS, a higher risk was observed among sales workers (OR=5.61, 90% CI=1.65-18.99). Conclusions Our study highlights hitherto unreported occupational associations with sarcomas, among both men and women, as well as the importance of sex-stratified analyses. Further research should aim to confirm these results and disentangle the respective roles of occupational exposures, environmental factors, and lifestyle characteristics.

Leptospirosis is a globally important environmentally transmitted disease, with approximately one million cases and 60,000 deaths reported annually. In low-income urban communities, inadequate sanitation, drainage and waste management may increase human exposure to contaminated environments. This study investigated the influence of environmental engineering risk factors on Leptospira exposure in four disadvantaged urban communities (favelas) in Salvador, Brazil. A high-precision georeferenced field survey was developed to identify, map and characterise sanitation, stormwater drainage and solid waste infrastructure. Cross-sectional spatial analyses of baseline data were used to assess associations between environmental risk factors and the residential locations of individuals with anti-Leptospira antibodies. Seropositive individuals tended to reside closer to environmental risk factors and at lower relative elevations. Density analyses indicated that seropositive individuals tended to reside closer to inadequate or partially adequate sewerage components than to sewage-contaminated streams or open sewage points. Inadequate streets showed also showed high density peaks, suggesting that exposure may occur through frequent contact with contaminated runoff and standing water. In contrast, open waste dumping sites and vacant lots showed weaker and more diffuse spatial patterns. The findings highlight the importance of infrastructure quality shaping leptospirosis risk within urban informal settlements. The proposed methodology provides a practical approach for high-resolution characterisation of environmental exposure pathways and may support targeted engineering interventions and epidemiological investigations of leptospirosis and other environmentally transmitted diseases.

Background: Transcranial direct current stimulation (tDCS) is increasingly used as an adjunct to rehabilitation for young people with cerebral palsy (CP), yet considerable variability exists in clinical response. Individualised electric field modelling provides an opportunity to estimate the distribution of electrical fields generated by the stimulation delivered to the brain and explore potential relationships with functional outcomes. Methods: Structural MRI scans from nineteen participants (10-16 years) from a previously published randomised controlled trial (ISRCTN74235136) investigating the effects of tDCS combined with motor training, underwent participant-specific finite element modelling using SimNIBS. Electric field strength was quantified within anatomically defined motor regions of interest, including the primary motor cortex (M1), dorsal premotor cortex (PMd), supplementary motor area (SMA), and a combined motor network. Global grey matter electric field metrics and stimulation focality were also extracted. Results: Estimated electric field strength differed significantly across motor regions (p<0.001), with PMd receiving significantly greater stimulation than both M1 and SMA. Electric field strength within a control region (primary visual cortex) was significantly lower than within M1 (p<0.001). Despite inter-individual variability in regional and global electric field metrics, no significant associations were observed between estimated electric field strength or focality and changes in function following intervention. Conclusion: Individualised electric field modelling demonstrated that an M1-targeted tDCS montage preferentially stimulated PMd rather than M1 in young people with CP. These findings highlight the importance of subject-specific modelling when characterising current distribution and suggest that variability in electric field strength alone does not explain variability in behavioural response.

Determining the appropriate sample size for desired statistical power is crucial for obtaining reliable research outcomes. While methods exist for multiple types of studies, the method for evaluating power of estimating spillover effects in sociometric network-based studies with non randomized interventions remain inadequately explored. We conducted a simulation study to assess how the design parameters (i.e., number of components, number of nodes, node degree, transitivity, and effect size) affects the statistical power for estimating spillover effects in non randomized, network-based studies. Both simulated networks and a real-world network from Transmission Reduction Intervention Project (TRIP) were used in this study. Our simulation results suggests that: (1) power increases with more nodes or a larger effect size, but not necessarily with more components when the number of nodes is fixed; (2) A higher node degree or greater transitivity results in reduced power; (3) Highly unbalanced networks (e.g., most of the nodes are in one component) can drastically reduce power. Furthermore, the power calculated using a closed-form expression developed in this work also shows that power remained the same or even decreases slightly with more components when the number of nodes are fixed, aligning with the simulation findings. All the results were specific to the inverse probability weighting estimator we employed in this study and assumptions it required. An alternative estimator or interference assumption may lead to different results.

Background: Despite the global importance of HIV-1 subtype C, a global-scale GIS characterization of its geographic clustering and the temporal persistence of hotspots is lacking, and systematic cross-method comparisons are scarce. Methods: We assembled 2,220 country-year observations from 111 countries between 2005 and 2024, comprising 161,025 subtype C sequences, and generated internally standardized expected counts. We compared hotspot detection using Getis-Ord Gi-star statistics in ArcGIS, SaTScan space-time scan statistics, and Bayesian hierarchical models with spatiotemporal smoothing, and quantified temporal persistence and cross-model concordance. Results: Documented subtype C sequences showed increasing geographic concentration over time, shifting from relatively widespread detection toward progressively localized clustering, with the strongest and intensifying concentration in Southern Africa. SaTScan and Bayesian models identified fewer hotspots but showed greater temporal stability, whereas Gi-star detected more localized and short-term spatial fluctuations. High-stability hotspots with sustained multi-year detection were predominantly located in Southern Africa. Zimbabwe was the only country classified as a high-stability hotspot across all three frameworks; Eswatini, Botswana, Malawi, and South Africa showed high stability in at least two models, indicating robust, model-consistent persistence. Conclusions: Integrating complementary hotspot methods reveals both convergent and method-specific patterns and provides a quantitative basis to prioritize long-term persistence for targeted surveillance, resource allocation, and precision prevention.

Short-term environmental exposures have been linked to cognitive and behavioral outcomes, although many reported associations may reflect broader geographic and contextual differences. Using longitudinal data from the All of Us Research Program (2018--2024), we linked daily weather and air-pollution exposures to repeated attention-related and subjective cognitive outcomes. Associations were evaluated using pooled, fixed-effects, lagged, and event-study analyses. Additional machine-learning analyses were conducted to explore potential heterogeneity and latent psychosocial structure. Replication analyses were performed using the 2024 Behavioral Risk Factor Surveillance System (BRFSS). Several environmental exposure measures showed small associations with cognitive outcomes in pooled analyses, but most attenuated substantially after accounting for within-location temporal variation. Mediation, sensitivity, and machine-learning analyses yielded similar conclusions. In contrast, mental-health burden, loneliness, and social functioning were consistently associated with subjective cognitive difficulty and exhibited substantially larger effect sizes than environmental exposures. Similar patterns were observed in BRFSS. Exploratory AI-assisted analyses yielded findings broadly consistent with the primary longitudinal analyses. These findings suggest that short-term environmental perturbations may have limited associations with cognitive outcomes after accounting for within-location variation, whereas psychosocial factors appear to be more consistently associated with subjective cognitive burden.

In climate-health impact projection studies, projected impacts from multiple general circulation models (GCMs) are commonly aggregated by reporting the mean of GCM-specific impacts as the point estimate alongside a 95% empirical confidence interval (eCI) constructed from the 2.5th and 97.5th percentiles of the simulated pooled distribution of GCM-specific impacts. This study shows that the eCI generally does not yield the nominal coverage probability for the GCM-ensemble mean and constructs an interval aligned with the estimand. In a simulation study, the coverage of the eCI for the GCM-ensemble mean deviates from the nominal level in both directions, whereas the aligned interval yields coverage near 95% across all considered settings. The exact coverages derived analytically under a location-shift model agree with the simulation results. In a reanalysis of a heat-related mortality projection in London, the eCI is consistently wider. The eCI should be distinguished from confidence intervals for the GCM-ensemble mean; rather, the interval may be better described as a simulation-based approximate prediction interval for a GCM-specific impact under the uniformly randomly selected GCM from the considered GCM set.

Abstract Background. Area-level cancer disparities are routinely estimated from public county data in which rates based on small counts (fewer than 16 cases or deaths) are suppressed. Analysts typically drop suppressed counties (complete-case analysis). Because suppression depends on case counts tied to population size and demographic composition, this missingness may be informative, but its effect on the disparity estimate has not, to our knowledge, been quantified. Methods. In a cross-sectional ecological study of 3,143 U.S. counties (analytic sample 3,018 with computable exposure) using one frozen public release of NCI State Cancer Profiles incidence and mortality data and ACS 2018-2022 5-year data, we estimated the most- versus least-deprived ICE(race+income) quintile rate ratio (RR) and rate difference for female breast, stomach, and cervix cancers under four suppression-handling methods: complete-case, available-case, bounding, and model-based small-area estimation. We characterized which counties were erased, and, following the ADEMP framework, ran a Monte Carlo simulation (1,000 replicates per cell; Monte Carlo standard error of bias approximately 0.0025) calibrated to the release to measure bias against a known truth. Analyses were pre-registered. Results. The suppressed fraction rose with rarity: 7.4% of counties for breast, 61.3% for stomach, and 75.7% for cervix incidence. Suppression was concentrated in the most-deprived quintile (cervix, 81.8% suppressed vs 63.8% least-deprived) and overwhelmingly removed rural rather than minority residents (cervix: 81% of the rural but 9% of the minority population erased). For breast (little suppression) the RR was 0.87 (95% CI 0.85-0.89) and identical across methods; for cervix incidence the complete-case RR (1.56) exceeded the model-based estimate (1.50), and for cervix mortality (91% suppressed) complete-case (1.86) exceeded model-based (1.56) by 16% with a wide bounding interval (1.88-2.62). In calibrated simulation, population-weighted complete-case bias was small (less than 2%) at the observed deprivation-county-size correlation and grew with rarity, threshold, and unweighted aggregation; its direction was conditional, becoming positive (over-estimation) as deprived counties became smaller. Conclusions. Complete-case handling of suppressed counties over-estimates rare-cancer area disparities relative to methods that retain them, while silently erasing most of the rural and most-deprived communities the estimate is meant to represent. The effect is negligible for common cancers and grows with rarity. Public-data disparity analyses should report the suppressed fraction and use bounded or model-based estimates by default. Keywords: cancer disparities; small-count suppression; Index of Concentration at the Extremes; informative missingness; small-area estimation; rural health.

HIV prevention and testing programmes primarily reach people who self-refer or attend routine health services. Higher-risk individuals are missed if they are healthy, under-estimate their risk of infection or under-report sexual risk-behaviours. We assess a new approach to address limitations in existing programmes by targeting HIV services on ''Circumstantial Determinants'' (CDs) of HIV risk - the social circumstances, settings, and norms associated with behaviours that increase risk of HIV acquisition. Data on potential CDs and sexual behaviour were collected in a population survey in Zimbabwe in 2018/19 (N=9141). HIV-negative individuals reporting [&ge;] 1 sexual risk-behaviours were defined as the 'priority population' for HIV prevention. For each sex, six circumstantial determinants were associated with being in the priority population (aOR [&ge;] 1.30; p [&le;] 0.01). Reach and efficiency of CDs (and combinations) were calculated; ROC curve algorithms evaluated their ability to identify priority population membership; and HIV prevention condom cascades were compared between CD-defined priority population subgroups. Example findings include that targeting men at bars and beerhalls could reach 48.5% of the priority population and 25.1% of lower-risk men. These percentages increase to 77.1% and 53.7% if men with poor mental health, no religious affiliation, negative social capital, or living on agricultural estates are also targeted. Targeting women with poor mental health could reach 32.0% of the priority population and 21.3% of lower-risk women. Targeting additional circumstantial determinants increases these percentages to 54.1% and 37.5%, respectively. Cascade barriers to condom use differed between CD-defined subgroups. The Circumstantial Determinants approach demonstrates proof-of-concept potential to strengthen HIV prevention services.