Spatial and Spatio-temporal Epidemiology

Reports of major limb defects after prenatal cannabis exposure (PCE) in animals and of human populations in Hawaii, Europe and Australia raise the question of whether the increasing use of cannabis in USA might be spatiotemporally associated with limb reduction rates (LRR) across USA. Geotemporospatial analysis conducted in R. LRR was significantly associated with cannabis use and THC potency and demonstrated prominent cannabis-use quintile effects. In final lagged geospatial models interactive terms including cannabinoids were highly significant and robust to adjustment. States in which cannabis was not legalized had a lower LRR (4.28 v 5.01 /10,000 live births, relative risk reduction = -0.15, (95%C.I. -0.25, -0.02), P=0.021). 37-63% of cases are estimated to not be born alive; their inclusion strengthened these associations. Causal inference studies using inverse probabilty-weighted robust regression and e-values supported causal epidemiological pathways. Findings apply to several cannabinoids, are consistent with pathophysiological and causal mechanisms, are exacerbated by cannabis legalization and demonstrate dose-related intergenerational sequaelae. HighlightsO_LILimb reduction rates (LRR) were associated with cannabis use, and THC potency C_LIO_LIThese relationships were robust to adjustment for ethic and economic covariates C_LIO_LIThey were maintained at geospatiotemporal regression C_LIO_LILRR elevated as stillborn and aborted cases were considered C_LIO_LICriteria of causality was fulfilled C_LI

BackgroundDepressive symptoms lead to a serious public health burden and are considerably affected by the environment. Land use, describing the urban living environment, has an impact on mental health, but complex relationship assessment is rare. ObjectivesWe aimed to examine the complicated association between urban land use and depressive symptoms among young adults with differential land use environments, by applying multiple models, as an exposome study. MethodsWe included 1804 individual twins from the FinnTwin12 cohort, living in urban areas in 2012. There were 8 types of land use exposures in 3 buffer radii. The depressive symptoms were assessed through General Behavior Inventory (GBI) in young adulthood (mean age: 24.1). First, K- means clustering was performed to distinguish participants with differential land use environments. Then, linear elastic net penalized regression and eXtreme Gradient Boosting (XGBoost) were used to reduce dimensions or prioritize for importance and examine the linear and nonlinear relationships. ResultsTwo clusters were identified with notable differences in the percentage of high-density residential, low-density residential, and natural land use. One is more typical of city centers, and another of suburban areas. A heterogeneous pattern in results was detected from the linear elastic net penalized regression model among the overall sample and the two separated clusters. Agricultural residential land use in a 100 m buffer contributed to GBI most (coefficient: 0.097) in the "suburban" cluster among 11 selected exposures. In the "city center" cluster, none of the land use exposures was associated with GBI. From the XGBoost models, we observed that ranks of the importance of land use exposures on GBI and their nonlinear relationships are also heterogeneous in the two clusters. DiscussionAs a hypothesis-generating study, we found heterogeneous linear and nonlinear relationships between urban land use environment and depressive symptoms under different contexts in pluralistic exposome analyses.

High temperature is associated with adverse health outcomes, particularly for vulnerable subpopulations including pregnant individuals and their unborn babies. Several recent studies have investigated the association between temperature and preterm birth at different geographic scales and across different spatial locations. However, there has been less focus on characterizing spatial heterogeneity in risks and identifying modifiable factors that contribute to the observed variation. In this work, we carry out a two-stage modeling approach to (i) estimate county-level short-term associations between heat waves and preterm birth across eight states in the United States and (ii) explore county-level factors that modify these associations using a newly developed hierarchical Bayesian spatial meta-regression approach. Specifically, we extend the traditional meta-regression framework to account for spatial correlation between counties within the same state by modeling the effect estimates using a variant of the conditional autoregressive model. We report several variables that modified the associations between heatwaves and preterm birth, including housing quality, energy affordability, and social vulnerability for minority status and language barriers. An R package, SpMeta, is developed for analyses that aims to synthesize area-level risk estimates while accounting for spatial dependence.

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.

This study shows that the quality of the morbidity data is sufficient to allow for meaningful analyses of spatiotemporal dynamics and provides a significant contribution to understanding the 1918-1920 influenza pandemic in Switzerland by complementing existing mortality- focused research with a morbidity perspective. Previous studies have examined the spatial patterns of mortality during the 1918-1920 influenza pandemic and associated explanatory factors. However, while mortality reflects the severity of a pandemic, a full understanding requires analysis of both morbidity and mortality. For the first time, this study systematically analysed district-level morbidity data for all of Switzerland and their associations with several ecological determinants. The spatial pattern of morbidity with the spatial pattern of mortality were also compared to investigate potential differences. Spatial clustering was assessed using the Getis-Ord Gi* statistic, and geographically weighted regression was employed to evaluate local relationships between incidence and ecological variables. Across all waves, higher incidence rates were positively associated with population density, GDP per capita, the share of industry, and the number of private physicians per km{superscript 2}. Conversely, GDP per capita and industrial activity were associated with lower mortality, while a higher proportion of men in a district correlated with lower incidence and higher mortality. The share of individuals aged 20- 39 years was associated with both higher incidence and higher mortality. These findings highlight that those factors shaping morbidity patterns can differ from those influencing mortality, emphasizing the importance of examining both dimensions for a comprehensive understanding of pandemic dynamics.

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subjects residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

BackgroundThe aetiology of most childhood cancers is largely unknown. Spatially varying environmental factors such as traffic-related air pollution, background radiation and agricultural pesticides might contribute to the development of childhood cancer. We investigated the spatial variation of childhood cancers in Switzerland using exact geocodes of place of residence. MethodsWe included 5,947 children diagnosed with cancer during 1985-2015 at age 0-15 from the Swiss Childhood Cancer Registry. We modelled cancer risk using log-Gaussian Cox processes and indirect standardization to adjust for age and year of diagnosis. We examined whether the modelled spatial variation of risk can be explained by ambient air concentration of NO2, natural background radiation, area-based socio-economic position (SEP), linguistic region, years of existing general cancer registration in the canton or degree of urbanization. ResultsFor all childhood cancers combined, the posterior median relative risk (RR), compared to the national level, varied by location from 0.83 to 1.13 (min to max). Corresponding ranges were 0.96 to 1.09 for leukaemia, 0.90 to 1.13 for lymphoma, and 0.82 to 1.23 for CNS tumours. The covariates considered explained 72% of the observed spatial variation for all cancers, 81% for leukaemia, 82% for lymphoma and 64% for CNS tumours. There was evidence of an association of background radiation and SEP with incidence of CNS tumours, (1.19;0.98-1.40) and (1.6;1-1.13) respectively. ConclusionOf the investigated diagnostic groups, childhood CNS tumours show the largest spatial variation in Switzerland. The selected covariates only partially explained the observed variation of CNS tumours suggesting that other environmental factors also play a role.

Childrens risk of exposure to carbon monoxide (CO) increases after disasters, likely due to improper generator use during power outages. Here, we evaluate the impact of outages on childrens CO-related emergency department (ED) visits in New York State (NYS). We leveraged power outage data spanning 2017-2020 from the NYS Department of Public Service for 1,865 power operating localities (i.e., communities) and defined all-size and large-scale power outage hours. All-size outage hours affected [&ge;]1% of customers, and large-scale outage hours affected [&ge;]20%. We identified CO poisoning using diagnostic codes among those aged <18 between 2017 and 2020 using the Statewide Planning and Research Cooperative System (SPARCS), an all-payer reporting system in NYS. We linked community power outage exposure to patients using the population-weighted centroid of their block group of residence. We estimated the impact of power outages on CO poisoning using a time-stratified case-crossover study design with conditional logistic regression, controlling for daily relative humidity, mean temperature, and total precipitation. Analyses were stratified by urban and rural communities. From 2017-2020, there were 917 pediatric CO poisoning ED visits in NYS. Most cases (83%) occurred in urban region of the state. We observed an association statewide between all-size and large-scale outages and CO ED visits on the index day and the following two days before a return to baseline on lag day 3. Four hours without power increased the odds of a pediatric CO poisoning ED visit by [&ge;]50% for small-scale and [&ge;]150% for large-scale outages, and associations were stronger in urban versus rural areas. While CO poisoning is a relatively rare cause of pediatric ED visits in NYS, it can be deadly and is also preventable. Expanded analyses of the health impacts of outages and advocacy for reliable energy access are needed to support childrens health in a changing climate

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.

1Non-communicable diseases (NCDs) account for [~]71% of all deaths globally, including 15 million premature deaths each year (deaths between 30-69 years of age). Instead of waiting until the disease manifestation, focusing on the origins of NCDs during childhood offers a critical window of disease prevention and control for effective interventions. The CHILDREN_FIRST study aims to investigate how the spatio-temporal evolution of the childrens exposome profiles in the Mediterranean region influences the early-life programming of chronic disease risk during the unique critical window of susceptibility in the primary school years (6-11 years of age). The study protocol adopts the human exposome framework integrated with a personalized prevention approach using multi-omics platforms and advanced machine learning algorithms implemented across five Mediterranean countries, namely Cyprus, Greece, Spain, Israel, and Albania. The cohort will consist of children enrolled in the first grade of primary school, who will undergo annual follow-up assessments until completion of primary education. During the annual assessments, childrens exposome parameters from the three main exposome domains will be evaluated using different assessment types i.e., biospecimen, sensors, questionnaires. Standardized human sample and data collection methods will be employed following harmonized standardized operating procedures. The reference model of Observational Medical Outcomes Partnership - Common Data Model part of the Observational Health Data Sciences and Informatics will be used to conduct federated data analysis. This CHILDREN_FIRST study protocol is a human exposome-based initiative to establish a long-term prospective cohort infrastructure for biomedical research on childrens health within the Mediterranean region. The cohorts exposome-based findings will systematically feed into the evaluation and design of chronic disease prevention programs. Expected results would inform evidence-based policy making and the development of health interventions for reducing the risk of NCDs.

Urban heat islands and climate change create increasingly hot environments that pose a threat to the health of the public in urban areas throughout the planet. In Maricopa County, Arizona, --- the hottest metropolitan area in the United States---we have previously shown that the effects of heat on mortality are greater in the social and built environments of low-income and communities of color (predominantly Hispanic/Latinx and Black neighborhoods). In this analysis of morbidity data from Maricopa County, we examined the relationship between heat-related hospitalization and summertime daily maximum air temperatures in groups defined at the census block group level as being at high, medium, or low vulnerability based on a Heat Vulnerability Index that was derived from socio-economic and built-environment data. For all three categories of census block group heat vulnerability, we identified 26{degrees}C as the daily maximum air temperature threshold beyond which heat-related hospitalization risk increased rapidly with each 1 {degrees}C increase in temperature. Compared to this baseline temperature, the relative risk of hospitalization was greatest in the high vulnerability census block groups and least in the low vulnerability census block groups with intermediate increases in the medium vulnerability census block groups. Specifically, with 26{degrees}C as the referent, the relative risks of heat-related hospitalization increased from 0.97 at 27{degrees}C to 15.71 at 46{degrees}C in the low vulnerability group, from 1.03 at 27{degrees}C to 53.97 at 46{degrees}C in the medium vulnerability group, and from 1.09 at 27{degrees}C to 162.46 at 46{degrees}C in the high vulnerability group. Our research helps identify areas with high heat population sensitivity and exposure that can be targeted for adaptation with policies and investments, which include, for example, improving public health safety nets and outcomes, access to affordable energy-efficient housing and health care, energy justice, and modifications to cool the urban built environment. Our hospitalization risk estimates can be incorporated into quantitative risk assessments of heat-related morbidity in Maricopa County.

ObjectiveThe relationship between specific humidity and influenza/SARS-CoV-2 in the Netherlands is evaluated over time and at regional level. DesignParametric and non-parametric correlation coefficients are calculated to quantify the relationship between humidity and influenza, using five years of weekly data. Bayesian spatio-temporal models--with a Poisson and a Gaussian likelihood--are estimated to find the relationship between regional humidity and the daily cases of SARS-CoV-2 in the municipalities and provinces of the Netherlands. ResultsAn inverse (negative) relationship is observed between specific humidity and the incidence of influenza between 2015 and 2019. The space-time analysis indicates that an increase of specific humidity of one gram of water vapor per kilogram of air (1 g/kg) is related to a reduction of approximately 5% in the risk of COVID-19 infections. ConclusionsThe increase in humidity during the outbreak of the SARS-CoV-2 in the Netherlands helped to reduce the risk of regional COVID-19 infections. Public policies that promote higher levels of specific humidification--above 6 g/Kg--can lead to significant reductions in the spread of respiratory viruses, such as influenza and SARS-CoV-2. Summary BoxO_ST_ABSWhat is already known on this subject?C_ST_ABSO_LIEnvironmental conditions have been related to the airborne transmission of respiratory viruses. C_LIO_LIPrevious observational studies have found an inverse correlation between humidity and the spread of SARS-CoV-2. C_LI What does this study add?O_LIWe analyzed the relation between specific humidity and airborne virus transmission using data with a higher temporal and spatial resolution. C_LIO_LISpatio-temporal risk estimates of SARS-CoV-2 are obtained after controlling for humidity levels at sub-national level in the Netherlands. C_LIO_LIOur results indicate that the increase of specific humidity during the outbreak of the SARS-CoV-2 helped to reduce the risk of regional COVID-19 cases in the Netherlands. Specifically, an increase of specific humidity of one gram of water vapor per kilogram of air (1 g/kg) is related to a reduction of approximately 5% in the risk of COVID-19 cases. C_LI

IntroductionAtmospheric conditions under climate change increase pressure on healthcare systems. Especially, the intensive care units (ICU) are vulnerable due to low buffer capacity and high utilization rates. MethodsDaily ICU cases from 2009 to 2023 were derived from the German statutory health insurance data of eleven regional AOK insurances. Cases were stratified by age and sex. Generalized additive models were used to investigate the associations between daily ICU cases and lagged atmospheric variables. Thirteen intensive care relevant diseases were analyzed using disease-specific predictor sets. Analyses were conducted for regions derived from a human-biometeorological characterization of Germany. Model performance was assessed using (weighted) explained deviance. ResultsOver the 15-year study period, 9,970,548 ICU patients were recorded (44% women), 74.3% aged [&ge;]60 years. Trauma was the most common ICU-related disease, followed by non-ST elevation myocardial infarction (NSTEMI), pneumonia and ischemic stroke. ICU demand was most sensitive (p [&le;] 0.05) to pressure-related factors, thermo-physiological parameters and ozone concentration. In terms of sex-age differences, atmospheric factors affected men more frequently, while women were more impacted by cold weather and particulate matter (PM10). Heat was more relevant for patients aged [&ge;]60 years. The NSTEMI model in Central Eastern Germany performed best (weighted explained deviance of 49.3%). In males [&ge;]60 years, heatwaves were associated with a reduced risk of ICU cases (Relative Risk = 0.94, 95%-Confidence Interval 0.89 to 0.99). ConclusionThe study identified key atmospheric factors for ICU, enabling the German healthcare system to prepare better for short-term impacts of meteorological and air quality factors. KEY MESSAGESWhat is already known on this topic: O_LIThe atmospheric changes have a direct impact on public health and the inpatient care, particularly in intensive care units. C_LIO_LIConsequently, there is a necessity to investigate the influence of atmospheric factors on intensive care in order to prepare the healthcare system for the new circumstances. C_LI What this study adds: O_LIThe study provides evidence that atmospheric factors influence the intensive care in Germany and describes age and sex-specific aspects. C_LIO_LIThe results offer valuable insights into how different atmospheric factors affect the demand for intensive care in hospitals. C_LI How this study might affect research, practice or policy: O_LIThe study enables the German healthcare system to better prepare for short-term effects of atmospheric factors, and structural or resource-related adjustments could be made in hospitals to anticipate for short-term fluctuations in intensive care demand. C_LI

IntroductionHeat significantly impacts human health by causing heat strain or exacerbating pre-existing conditions. Hospitals may suffer a higher healthcare demand during intense heat periods, especially if climate change continues to increase the severity and frequency of heatwaves. Anticipating episodes of higher hospital demand would allow better resource planning and quality of care. MethodsWe developed a real-time forecasting tool of daily hospital demand (specifically, all-cause emergency room visits (ERV)) which accounts for the impact of heat. Our tool is based on a regression model integrating temperature-ERV function with autoregressive terms and other temporal trends. The model can (i) quantify the association between the number of hospital visits and temperature based on historical data and (ii) provide accurate short-term forecasts of the daily ERV based on temperature values expected for the upcoming days. As a case study, we used data from the Bern University Hospital from the summer of 2014 to 2022, and mean temperature per day as an indicator of heat exposure. ResultsTemperature-ERV relationship exhibited a non-linear shape. We found that, with respect to the mean temperature of minimum risk of 15{degrees}C, there were approximately 6 (95% CI 2-10) additional ERVs when mean temperature was around 25 {degrees}C, corresponding to a 3% increase in summer 2022. The estimated variation increased for mean temperature above 25 {degrees}C, but with large uncertainty. We also found that our model showed higher accuracy at forecasting hospital demand during periods with particularly hot days, compared to a model neglecting temperature. Our forecasting tool is implemented in a user-friendly R shiny app, allowing for application to new datasets. ConclusionsWe found a robust association between ambient temperature and visits to the emergency department in a Swiss hospital. Our findings suggest that including temperature can increase the accuracy of predictions for hospital demand during summer.

The ongoing outbreak of COVID-19 challenges health systems and epidemiological responses of all countries worldwide. Although mitigation measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we evidenced that factors contributing to poverty are also risk factors for COVID-19 case-fatality, and unexpectedly, their impact on the case-fatality risk is comparable to that produced by health factors. Additionally, we confirm that both case-fatality risk and multidimensional poverty index have a heterogeneous spatial distribution, where the lastest consists of health, educational, dwelling, and employment dimensions. Spatio-temporal analysis reveals that the spatial heterogeneity in case-fatalities is associated with the percentage contribution of the health (RR 1.89 95%CI=1.43-2.48) and dwelling (RR 2.01 95%CI=1.37-2.63) dimensions to the multidimensional poverty, but also with the educational (RR 1.21 95%CI=1.03-1.49), and employment (RR 1.23 95%CI=1.02-1.47) dimensions. This spatial correlation indicates that the case-fatality risk increase by 189% and 201% in regions with a higher contribution of the health dimension (i.e., lack of health insurance and self-reporting) and dwelling dimension (i.e., lack of access to safe water, inadequate disposal of human feces, poor housing construction, and critical overcrowding), respectively. Furthermore, although a temporal decrease is evident, the relative risk of dying by COVID-19 in Colombia is still 200% higher than the established case-fatality risk based on the COVID-19 dynamics in Italy and China. These findings assist policy-makers in the spatial and temporal planning of strategies focused on mitigating the case-fatality risk in most vulnerable communities and preparing for future pandemics by progressively reducing the factors that generate health inequality.

Under a climate change scenario, extreme heat episodes show an increase in frequency and intensity, with a scaling impact in Latin American cities. Recently, Rio de Janeiro City developed its heat protocol, using the amount of hours spent over specific heat thresholds as its trigger metric. This study gathers mortality data by 17 different causes in Rio, in a 12.5 year period (2012-2024). We use Distributed Lag Non-Linear Models (DLNM) to assess the relationship between different heat exposure metrics to mortality among the young (< 65) and elderly (>=65y), including a novel metric called Heat Area Above a Threshold (HAAT). In the study period, there were 466,121 deaths in the city from natural causes. Deaths due to diabetes, hypertensive diseases, Alzheimers/dementia, renal failure and even undetermined deaths were strongly associated with extreme heat episodes, especially among the elderly. The proposed HAAT metric showed better performance on explaining mortality for most causes (10 out of the 17), when compared to temperature or heat index, or commonly used heat wave definitions. The results dialogue with Rios heat protocol, evaluating the cut-off points defined and proposing simpler definitions using the HAAT metric. An exposure to a HAAT of 64{degrees}C*h increases mortality by natural causes by 50%, and 91.2{degrees}C*h already doubles the mortality risk. Main strengths of the study lie on the comparison of different heat exposure metrics and the investigation of cause-specific mortality in a period when recent and remarkable heat waves occurred. There is still fragility when considering a compound index such as the Heat Index, and social and spatial differences on heat-related mortality should also be considered in future models. The proposed metric, however, appears as a relevant indicator to distinguish unusually warm days that lead to elevated mortality, and could guide definitions for Heat Warning Systems.

BackgroundDisease infection data is usually aggregated and shared as a sum of infections in a given area over time. This data is presented as choropleth maps. The aggregation process protects privacy and simplifies decision-making but introduces visual bias for large areas and sparsely populated places. Moreover, aggregated areas of varying sizes cannot be simply used as the input for complex ecological models, which are based on data retrieved at higher resolution on regular grids. The issue is especially painful for vector-borne diseases, e.g. Lyme Disease, where infection risk is closely related to vector species and their ecological niche. MethodsThe paper presents the method of obtaining high-resolution risk maps using a pipeline with two components: (1) spatial disaggregation component, which transforms incidence rate aggregates into the point-support model using Area-to-Point Poisson Kriging, and (2) species distribution modeling component, which detects areas where ticks bite is more likely using MaxEnt model. The first component disaggregates Lyme Disease incidence rates summed over counties in Poland, Central Europe, in 2015. The second component uses ticks occurrence maps, Leaf Area Index, Normalized Difference Vegetation Index, Land Surface Temperature derived from Earth Observation satellites, and Digital Elevation Model. The final weighted population-at-risk map is a product of both components outputs. The pipeline is built upon open source and open science projects, and it is reusable. ResultsThe presented pipeline creates high-resolution risk maps: vector occurrence probability map, population-at-risk map, and weighted population-at-risk map which includes information about local infections and about vector species. The final maps have much better resolution than aggregated incidence rates. Visual bias for population-at-risk maps is removed, and unpopulated areas are not presented on the map. ConclusionsThe pipeline might be used for other vector-borne diseases. The final weighted population-at-risk map might be used as an input for another analytical model requiring high-resolution data placed over a regular grid. The pipeline removes visual bias and transforms aggregated data into a high-resolution point-support layer.

BackgroundThe role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S. MethodsUsing Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity. ResultsOverall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country. ConclusionThe existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity. KEY POINTSO_ST_ABSQuestionC_ST_ABSWhat are the sociodemographic and environmental drivers of the heterogeneous distribution of the COVID-19 related mortality in the U.S., and what are the vulnerable areas at higher risk of COVID-19 related mortality and low critical healthcare capacity? FindingsHigher proportions of African American and Latino populations, as well as high levels of air pollution and airport connectivity were linked to higher risk of COVID-19 related mortality. Over 68% of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. MeaningIn a time-limited response, the identification and targeting prevention efforts should focus in vulnerable populations located in high risk areas in which sociodemographic and environmental factors are exacerbating the burden of COVID-19 related deaths.

Climate change significantly affects both environmental and human health. A recent, comprehensive global overview of its impacts on mental health and self-harming behaviours is lacking, as well as detailed insights into the effects of potential confounders, including gender, age, socioeconomic factors and their potential interactions. Using a cutting-edge generalized additive model (GAM) framework, we analyzed multiple global datasets covering between 175 to 201 countries from 1990 to 2020. We found robust associations between self-harm incidence rates and major climate change parameters, including greenhouse gas emission and temperature change, as well as air pollutant particulate matter 2.5 (PM2.5) exposure, a critical contributor to climate change. Importantly, detailed analyses suggested that self-harm in young males had stronger links to climate change parameters than in young females, while the opposite gender associations were found later in life. Our global analyses provide important evidence on mental health consequences of climate change, instructive for developing appropriate population-based mental health strategies and climate policies and enhancing mental health services. This will contribute to improving human and planetary health.

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