Spatial and Spatio-temporal Epidemiology

Since the onset of the COVID-19 pandemic, researchers mainly examined how socio-economic, demographic, and environmental factors are related to disparities in SARS-CoV-2 infection rates. However, we dont know the extent to which racial disparities in environmental exposure are related to racial disparities in SARS-CoV-2 infection rates. To address this critical issue, we gathered black vs. white infection records from 1416 counties in the contiguous United States. For these counties, we used 30m-spatial resolution land cover data and racial mappings to quantify the racial disparity between black and white peoples two types of environmental exposure, including exposures to various types of landscape settings and urban development intensities. We found that racial disparities in SARS-CoV-2 infection rates and racial disparities in exposure to various types of landscapes and urban development intensities were significant and showed similar patterns. Specifically, less racial disparity in exposure to forests outside park, pasture/hay, and urban areas with low and medium development intensities were significantly associated with lower racial disparities in SARS-CoV-2 infection rates. Distance was also critical. The positive association between racial disparities in environmental exposures and racial disparity in SARS-CoV-2 infection rates was strongest within a comfortable walking distance (approximately 400m). HighlightsO_LIRacial dot map and landcover map were used for population-weighted analysis. C_LIO_LIRacial disparity in environmental exposures and SARS-CoV-2 infection were linked. C_LIO_LIForests outside park are the most beneficial landscape settings. C_LIO_LIUrban areas with low development intensity are the most beneficial urban areas. C_LIO_LILandscape and urban exposures within the 400m buffer distances are most beneficial. C_LI

Introduction Nontyphoidal Salmonella enterica (NTS) is a major zoonotic enteric pathogen. Animal contact-related NTS outbreaks have increased in the United States of America (U.S.) over the last decade. Geospatial analysis can identify locations with elevated risk of NTS outbreaks where public health authorities can focus their NTS prevention and intervention efforts. Methods We analyzed NTS outbreak data reported from individual states to the Centers for Disease Control via the National Outbreak Reporting System between 2009 and 2022 across the continental contiguous U.S. A geospatial analytical framework that included disease mapping, spatial interpolation, and global and local clustering methods was applied to identify regions with high NTS outbreak rates. Results A total of 104 NTS single-state outbreaks were reported to the National Outbreak Reporting System (NORS) during the study period. The mean annual incidence rate was 0.02 NTS outbreaks per million person-years. The primary animal contact categories associated with these outbreaks were mammals (cattle, pigs, sheep, and horses), birds (backyard chickens, ducklings, and turkeys), and reptiles (turtles and lizards). Exposure settings included farms, fairgrounds, agricultural feed stores, veterinary clinics, dairy/agricultural settings, and residential settings. The local cluster detection methods consistently identified areas with significantly high NTS animal contact-related outbreak rates in the Mountain West, Midwest, and Northeast of the US. Conclusion NTS animal contact-related single-state outbreaks revealed distinct spatial clustering across the United States, with potentially higher risks in the Mountain West, Midwest, and Northeast. Diversity of animal-contact sources and exposure settings depicted complex transmission dynamics of NTS. Focused prevention and control programs in these areas are needed to mitigate the burden of NTS outbreaks.

The COVID-19 pandemic in Toronto, Canada was unequal for its 2.7 million residents. As a dynamic pandemic, COVID-19 trends might have also varied over space and time. We conducted a spatiotemporal hotspot analysis of COVID-19 over the first four major waves of COVID-19 using three different applications of Morans I to highlight the variable experience of COVID-19 infections in Toronto, while describing the potential impact of socioeconomic and sociodemographic factors on increased risk of COVID-19 exposure and infection. Results highlight potential clustering of COVID-19 case rate hot spots in areas with higher concentrations of immigrant and low-income residents and cold spots in areas with more affluent and non-immigrant residents during the first three waves. By the fourth wave, case rate clustering patterns were more dynamic. In all, a better understanding of the unequal COVID-19 pandemic experience in Toronto needs to also consider the dynamic nature of the pandemic. HIGHLIGHTS- The COVID-19 pandemic was spatially and temporarily dynamic in the City of Toronto. - At first, hotspots were concentrated in areas with more marginalized residents. - Later, COVID-19 spatial trends diverged from initially identified patterns. - East Asian enclaves in the city disproportionally had lower COVID-19 case counts. - COVID-19 studies need to consider the dynamic nature of the pandemic.

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.

Distributed lag non-linear models (DLNMs) offer a flexible approach towards modelling time-delayed exposures. They are popular to study the effect of environmental exposure on health outcomes, such as the effect of temperature on mortality. Conventional distributed lag non-linear models typically focus on a single exposure variable, potentially overlooking complex interactions between multiple predictors. In this paper, we propose a distributed lag non-linear model that captures the joint delayed impact of two exposure variables by incorporating their interaction through a tensor basis constructed from univariate P-splines. This model is compared to a model assuming an additive effect of delayed exposures. Our model is used to examine the joint impacts of maximum and minimum temperatures on all-cause mortality in Flanders during summer. The results show that our model provides a flexible strategy towards the analysis of two predictors with interacting time-delayed effects on an outcome of interest. The importance of both maximum and minimum temperatures in explaining variability in mortality is illustrated, and we show that the interaction effect varies across age and gender groups. A spatial risk analysis at the municipality level reveals that mortality is attributed differently to temperature exposure across different areas, due to temperature variations as well as spatial trends in age and gender.

Cancer poses a significant public health challenge, and accurate tools are crucial for effective intervention, especially in high-risk areas. The North West of England, historically identified as a region with high cancer incidence, has become a focus for public health initiatives. This study aims to analyse cancer risk factors, demographic trends and spatial patterns in this region by employing a novel spatial joint modelling framework designed to account for large frequencies of left-censored data. Cancer diagnoses were collected at the postcode sector level. The dataset was left-censored due to confidentiality issues, and categorised as interval censored. Demographic and behavioural factors, alongside socio-economic variables, both at individual and geographic unit levels, were obtained from the linkage of primary and secondary health data and various open source datasets. An ecological investigation was conducted using joint spatial modelling on nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper GI, urology), for which explanatory factors were selected by employing an accelerated failure model with lognormal distribution. Post-processing included principal components analysis and hierarchical clustering to delineate geographic areas with similar spatial patterns of different cancer types. The study included 15,506 cancer diagnoses from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Preliminary censoring adjustments reduced censored records from 86% to 60%. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk. The analysis identified 22 relevant variables, with comorbidities and ethnicity being prominent. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified. Rural areas were the least affected by cancer and Barrow-in-Furness was the area with the highest cancer risk. This study emphasizes the need for targeted interventions addressing health inequalities in different geographical regions. The findings suggest the need for tailored public health interventions, considering specific risk factors and socio-economic disparities. Policymakers can utilize the spatial patterns identified to allocate resources effectively and implement targeted cancer prevention programmes.

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.

Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus (WNV) has shifted from being a novel to an established arbovirus in the United States. Subsequently, more than 25,000 cases of West Nile Neuro-invasive Disease (WNND) have been diagnosed, cementing WNV as an arbovirus of public health importance. Given its novelty in the United States, high-risk ecologies are largely underdefined making targeted population-level public health interventions challenging. Using the Centers for Disease Control and Prevention ArboNET WNV data from 2000 - 2021, this study aimed to predict WNND human cases at the county level for the contiguous US states using a spatio-temporal Bayesian negative binomial regression model. The model includes environmental, climatic, and demographic factors, as well as the distribution of host species. An integrated nested LaPlace approximation (INLA) approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. The validated models were then fit to the entire dataset for 2022 predictions. This proof-of-concept mathematical, geospatial modelling approach has proven utility for national health agencies seeking to allocate funding and other resources for local vector control agencies tackling WNV and other notifiable arboviral agents.

ObjectiveThe aim of this study was to describe differences and trends in ATV-related hospitalizations for urban and rural-dwelling youth in Canada. MethodsWe conducted a cross-sectional study using administrative hospital abstract data all patients admitted for an ATV-related injury to hospitals in 9 provinces in Canada between 2002 and 2019. The primary exposure was rural residence, defined by postal code. Rural-urban comparisons were stratified by age group: children (<16 years), adolescents (16-20 years) and adults (>21 years). The primary outcome was the incidence of any hospitalization, secondary outcomes were head injury, fractures, crush injury and spinal cord injury.. ResultsAmong 34,390 patients with complete data, 17% were children younger than 16 yrs and 14% were adolescents 16-20 yrs; 78% of children and 85% of adolescents were male, and 47% lived rurally. The incident rate ratio (IRR) for being hospitalized for an ATV-related injury was 5-fold higher for rural children (5.59; 95% CI: 5.30-5.88) and adolescents (5.16; 95% CI: 4.88, 5.47) compared to urban children and adolescents, respectively. The 5-fold higher IRR was also evident for ATV-related fractures among rural children and adolescents. Adolescents had a particularly higher risk for ATV-related crush injuries (IRR: 10.43; 95% CI: 5.74-18.96) and spinal cord injuries (IRR: 5.21; 95% CI: 3.33-8.15) while children were at higher risk of ATV-related head injuries (IRR: 6.55; 95% CI: 5.76-7.46) compared to urban dwelling youth. ConclusionsIn Canada, rural children and adolescents were at a very elevated risk of ATV injuries compared to those living in urban centres.

Preeclampsia is a dangerous pregnancy disorder, with evidence suggesting that high ambient temperatures may increase its risk, making future incidence projections crucial for health planning. While temperature-related projections for all-cause mortality exist, disease-specific projections, especially for pregnancy complications, are limited due to data and methodological challenges. Vicedo-Cabrera et al. (2019) pioneered a time-series approach to project health impacts using the attributable fraction (AF) of cases due to climate change. We adjusted this method for preeclampsia, whose risk involves long-term exposures, with delivery as a competing event. We based our analysis on the exposure-response relationship estimated in our previous study in southern Israel using cause-specific hazard and distributed lag nonlinear models. In the current study, we modeled several demographic and climate scenarios in the region for 2020-2039 and 2040-2059. Scenario-specific AFs were calculated by comparing cumulative preeclampsia incidence with and without corresponding climate change. Finally, annual cases were estimated by applying climate scenario-specific AFs to cases projected under each demographic scenario. Our models show that climate change alone may increase preeclampsia by 3.2% to 4.3% in 2040-2059 relative to 2000-2019. Fertility trends are modeled to have a larger impact, with a 30% increase in cases by 2020-2039 under a low-fertility scenario. Extreme high-fertility and climate scenarios could result in a 2.3-fold rise in incidence, from 486 cases annually in 2000-2019 to 1,118 by 2040-2059.

AimThis study aims to investigate the spatial patterns of COVID-19 mortality across U.S. counties and identify the socioeconomic determinants that influence these mortality trends, using spatial epidemiological methods. Subject and MethodsWe conducted a spatial analysis of COVID-19 mortality data from over 3,000 U.S. counties, applying cluster detection techniques, including SatScan, to identify areas with significant mortality trends. Spatial regression models, including spatial lag and spatial error models, were employed to examine the impact of socioeconomic variables, such as race, income inequality, and insurance rates, on COVID-19 mortality. The analysis controlled for multicollinearity and spatial autocorrelation in the data. ResultsCounties with higher proportions of Black populations and higher uninsured rates exhibited significantly lower COVID-19 trends over the study period. Spatial clustering revealed regions in the northwestern and eastern/northeastern United States with a mix of positive and negative mortality rate trends. The spatial lag model showed the strongest fit, confirming the importance of spatial dependency in explaining mortality patterns. ConclusionThis study highlights the significant spatial disparities in COVID-19 mortality across U.S. counties. The findings emphasize the need for targeted public health interventions in vulnerable regions to address these disparities.

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.

The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading. HighlightsO_ST_ABSBackgroundC_ST_ABSResearch on viral respiratory infections has found that infection risks increase following exposure to high concentrations of particulate matter. Several hot-spots of Severe Acute Respiratory Syndrome Coronavirus 2 infections are in areas associated with high levels of air pollution. ApproachThis study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands using data on confirmed cases and hospital admissions coded by residence, along with local PM2.5, PM10, population density, demographics and health-related pre-conditions. The analysis utilizes different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment. ResultsPM2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. Taking the WHO guideline of 10mcg/m3 as a baseline, the estimates suggest that expected COVID-19 cases increase by nearly 100% when pollution concentrations increase by 20%. ConclusionThe findings call for further investigation into the association between air pollution on SARS-CoV-2 infection risk. If particulate matter plays a significant role in the incidence of COVID-19 disease, it has strong implications for the mitigation strategies required to prevent spreading, particularly in areas that have high levels of pollution.

We highlight a negative association between the severity of the first-wave of SARS-Cov-2 and the spread of the virus during the second-wave. Analyzing data of a sample of municipalities from the Italian region of Lombardy, we find that a one standard deviation increase in excess of mortality during the first-wave is associated with a reduction of approximately 30% in the number of detected infected individuals in the initial phase of the second-wave. Our findings may reflect a behavioral response in more severely hit areas as well as a cross-protection between successive waves. JEL ClassificationI10; I18

PurposeMost spatio-temporal models identify COVID-19 sociodemographic and socioeconomic risk factors using methods that assume a single spatial dependency pattern across the city, which may not reflect reality. The purpose of this study is to apply a spatially and temporally localized Bayesian model to identify COVID-19 risk factors that account for localized context. MethodsFor this study, a spatio-temporal localized Bayesian Hierarchical Model (ST-LCAR) was used to assess the relationships between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk. The ST-LCAR model accounts for spatial and temporal autocorrelation through spatio-temporal random effects along with piecewise intercepts to capture step changes in relative risk patterns that might be reflective of underlying local contexts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. ResultsA 10-percentage-point increase in the proportion of residents who identify as visible minorities was associated with a 3% increase in COVID-19 relative risk; however, this association varied across different social contexts. On the other hand, a 10-percentage-point increase in the proportion of residents with post-secondary education was associated with a 22% decrease in relative risk. Beyond quantitative relationships, our model identified 3 times higher COVID-19 relative risk in the northwestern portion of the city, with patterns varying over time. ConclusionThe different COVID-19 patterns in the city of Toronto may have been shaped by the complex and diverse social contexts, products of ingrained systems of structural inequities that influence the living, working, and economic conditions of city residents. Public health interventions and pandemic preparedness should integrate an equity-focused lens that considers the diverse social contexts across the city and how it shapes health outcomes.

Highlights- Nation-wide study with data for nearly all adult Dutch inhabitants. - Ethnic minorities face higher physico-chemical exposures than Dutch-origin inhabitants. - Food & physical activity environments better for ethnic minorities than Dutch-origin. - Socio-economic characteristics less favorable for ethnic minorities than Dutch-origin. IntroductionEthnic minority populations may be disproportionally affected by unhealthy environmental exposures, increasing health inequities. This study aims to identify whether residential neighborhood exposures differ between ethnic groups in the Netherlands. MethodsThis cross-sectional study included all adult residents of the Netherlands registered in the national population register on 01/01/2022 (N=13,926,871). Exposure data (physico-chemical, food and physical environment, socio-economic characteristics, health and social well-being) were obtained from Statistics Netherlands, GECCO and the Dutch Health Monitor, and linked to individuals based on geocoded home addresses. Ethnicity was based on country of birth of individuals and their parents. Estimated marginal means were calculated and ethnic differences in exposure determined using multiple linear and logistic regression, adjusted for age and sex, stratified by socio-economic status (SES) and population density. ResultsCompared to Dutch-origin, ethnic minority populations had less favorable physico-chemical exposures (e.g. 0.87{micro}g/m3 [95%-CI: 0.86;0.88] higher PM2.5 exposure for Moroccans in "high SES-high population density"). Conversely, the food and physical activity environment was more favorable for ethnic minorities (e.g. 1.82km/ha [95%-CI 1.80;1.83] higher bike path density among Turks in the "low SES-low population" density category). Socio-economic characteristics of the environment were generally less favorable for ethnic minorities (E.g. difference between Dutch Caribbeans and Dutch-origin -4.23% [95%-CI -4.35;-4.11] in "high income-high population density". Ethnic differences in health and social well-being varied. Neighborhood-level smoking was most prevalent among ethnic minorities, while excessive drinking was most prevalent among Dutch-origin. Exposure to vandalism and (sexual)violence was lowest among Dutch-origin and highest among Dutch Caribbean, Moroccans, Turks and Surinamese. ConclusionPhysico-chemical exposure, socio-economic characteristics of the environment and safety from crime were less favorable among ethnic minority populations compared to Dutch-origin. The food and physical activity environment was more favorable for ethnic minorities. Ethnic inequalities were most pronounced among Moroccans, Turks, Surinamese and Dutch Caribbeans compared to Dutch-origin.

BackgroundSexual violence is one of the most severe forms of civilian victimisation in the Colombian armed conflict. Despite its importance, population-scale analysis of conflict-related sexual violence patterns is limited, something essential for informing public health responses and prevention strategies. MethodsWe analysed two national databases of armed-conflict-related data from Colombia: the statutory Register of Victims and the National Centre for Historical Memory. We profiled victim demographics and used i) log-Gaussian Cox process modelling to identify geospatial clustering and ii) Hawkes process modelling to examine temporal contagion. Analyses covered the entire conflict period (1964-2024) and a recent period (2014-2024). ResultsVictims were predominantly female ({approx}90%) and disproportionately from ethnic minorities. Sexual violence showed geographic clustering, with a spatial range of up to 2.4km for the entire conflict period and reducing to 1.7km after adjustment for population density. In 2014-2024, baseline intensity decreased but spatial clustering extended over wider areas (range = 2.5km unadjusted; 1.9km adjusted). For the entire conflict, the unadjusted Hawkes model indicated near-critical temporal dependency (branching ratio = 0.99) and multi-day temporal contagion (half-life = 3.7 days). The model, adjusted for changes in background rates, revealed little evidence for temporal contagion independent of changes in background rates. For 2014-2024, the unadjusted model similarly showed near-critical branching (branching ratio = 1.00) with extended decay (half-life = 10.04 days), whereas background rate-adjusted models indicated little evidence for temporal contagion beyond background rate changes. ConclusionsSexual violence in the Colombian armed conflict may be more widely present than is apparent from raw event counts and has shifted from concentrated, high-intensity incidents to more dispersed patterns. Areas of historically high prevalence remain critical for victim support. The temporal dynamics suggest that prevention efforts may benefit from proactively addressing long-term structural and contextual determinants.

This study analyzes the link between temperatures and COVID-19 contagions in a sample of Italian regions during the period ranging from February 24 to April 15. To that end, Bayesian Model Averaging techniques are used to analyze the relevance of the temperatures together with a set of additional climate, environmental, demographic, social and policy factors. The robustness of individual covariates is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by the temperatures given that it appears as the most relevant determinant of contagions. This finding is robust to (i) the prior distribution elicitation, (ii) the procedure to assign weights to the regressors, (iii) the presence of measurement errors in official data due to under-reporting, (iv) the employment of different metrics of temperatures or (v) the inclusion of additional correlates. In a second step, relative importance metrics that perform an accurate partitioning of the R2 of the model are calculated. The results of this approach support the evidence of the model averaging analysis, given that temperature is the top driver explaining 45% of regional contagion disparities. The set of policy-related factors appear in a second level of importance, whereas factors related to the degree of social connectedness or the demographic characteristics are less relevant.

BackgroundLeprosy remains a significant public health challenge in Brazil, with distinct spatial and temporal patterns reflecting underlying socioeconomic inequalities. This study investigated the association between municipal socio-territorial indicators and the geographic distribution of leprosy in Sao Paulo State. MethodsA total of 4,562 new leprosy cases reported across 645 municipalities from 2017-2020 were analyzed using Bayesian spatial modeling. The Besag-York-Mollie 2 (BYM2) model was implemented to estimate relative risk while accounting for spatial dependence, incorporating municipal wealth as a covariate. Expected case counts were calculated based on overall state incidence rates and local population sizes. Exceedance probabilities were computed to identify high-risk clusters using a threshold of relative risk >2.0. Results and DiscussionsRelative risk estimates ranged from 0.06 to 31.52 (median: 0.96), with substantial spatial heterogeneity across municipalities. Municipal wealth demonstrated a significant protective effect (posterior mean: -0.059, 95% CrI: -0.081 to -0.037), indicating lower leprosy incidence in economically advantaged areas. The mixing parameter ({varphi} = 0.517) revealed approximately equal contributions of structured spatial and unstructured random effects. High-risk clusters were predominantly concentrated in western and northern regions of the state. ConclusionsLeprosy distribution in Sao Paulo State exhibits significant spatial clustering associated with municipal wealth disparities. The persistent geographic patterns suggest sustained socioeconomic determinants affecting disease transmission. These findings support targeted public health interventions in identified high-risk areas and emphasize the importance of addressing structural inequalities in leprosy control strategies.

Pulmonary embolism (PE) is a sudden blockage of lung arteries, usually caused by a blood clot that travels from the deep veins of the legs. As the world becomes more sedentary and lifestyle diseases emerge, deaths from PE are expected to rise in the next 20 years. For instance, the United States records annual deaths of 60 per 100,000 people. The degree to which these deaths are affected by demographic, socioeconomic and environmental predisposing factors as well as how they vary across time and space remains an open science question. In this paper, we conduct a detailed statistical and spatial-temporal study PE mortality counts across US counties from 2005 to 2022. Our study shows that study shows that PE mortality is not randomly distributed in space and time but concentrated in most counties in Arkansas, Mississippi, Kansas, Missouri, Oklahoma, Louisiana, Nebraska, Tennessee, and Texas. We also established that age is a statistically significant predictor (mean coefficient of 0.52) of PE mortality especially in counties of Mississippi, Kansas, Missouri, Tennessee, Illinois, Kentucky, Texas and Virginia. Our results thus provide empirical support for prioritizing regionally targeted PE prevention policies. Furthermore, the adopted county-level analysis uncovered granular geographic patterns that are usually obscured in state or national level analysis. Our study thus provides actionable evidence to support geographically tailored strategies aimed at reducing mortality by pinpointing counties with consistently elevated PE mortality risk at different timescales.