Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis.

OBJECTIVES: Although the link between poverty and tuberculosis (TB) is widely recognised, limited studies have investigated the association between neighbourhood factors and TB incidence. Since the factors influencing different episodes of TB might be different, this study focused on the first episode of TB disease (first-episode TB). METHODS: All first episodes in previously linked and geocoded TB notification data from 2007 to 2015 in Cape Town, South Africa, were aggregated at the neighbourhood level and merged with the 2011 census data. We conducted an ecological study to assess the association between neighbourhood incidence of first-episode TB and neighbourhood factors (total TB burden [all episodes] in the previous year, socioeconomic index, mean household size, mean age, and percentage males) using a negative binomial regression. We also examined the presence of hotspots in neighbourhood TB incidence with the Global Moran's I statistic and assessed spatial dependency in the association between neighbourhood factors and TB incidence using a spatial lag model. RESULTS: The study included 684 neighbourhoods with a median first-episode TB incidence rate of 114 (IQR: 0-345) per 100,000 people. We found lower neighbourhood socioeconomic index (SEI), higher neighbourhood total TB burden, lower neighbourhood mean household size, and lower neighbourhood mean age were associated with increased neighbourhood first-episode TB incidence. Our findings revealed a hotspot of first-episode TB incidence in Cape Town and evidence of spatial dependency in the association between neighbourhood factors and TB incidence. CONCLUSION: Neighbourhood TB burden and SEI were associated with first-episode TB incidence, and there was spatial dependency in this association. Our findings can inform targeted interventions to reduce TB in high-risk neighbourhoods, thereby reducing health disparities and promoting health equity.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9 months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

SARS-CoV-2 Pandemic in a Small-Sized Municipality in Ceará State, Brazil: Temporal and Spatial Evolution.

Data on the temporal and spatial evolution of SARS-CoV-2 and local control measures and their effects on morbidity and mortality patterns in rural Brazil are scarce. We analyzed the data from case notification systems, epidemiological investigation reports, and municipal decrees in Itapajé, a small municipality in Ceará State in northeast Brazil. For spatial and spatio-temporal analyses, cases and deaths were mapped. There were a total of 3020 cases of COVID-19, recorded between April 2020 and December 2021; 135 (4.5%) died. The cumulative incidence and mortality rates were 5650.3 cases and 252.6 deaths per 100,000 people, respectively. The index case of SARS-CoV-2 in Itapajé was diagnosed in March 2020. The first peak of cases and deaths occurred in May 2020. The second wave peaked in May 2021, with the highest number of deaths in March 2021. According to the spatial analysis, the highest density of cases and deaths occurred in the central urban areas. In these areas, there were also the clusters of highest risk according to the spatio-temporal analyses. The municipal government issued 69 decrees on restriction measures, surveillance, and the maintenance of social isolation as a response to the pandemic. The spread of the SARS-CoV-2 pandemic in Itapajé mirrored the dynamics in large metropolitan regions, going from central neighborhoods of low socio-economic status to the wealthier peripheries.

High-Risk Areas for Congenital Zika Syndrome in Rio de Janeiro: Spatial Cluster Detection.

Brazil reported 18,282 suspected congenital Zika syndrome (CZS) cases up to 2018 and accounts for 61.4% of the total reported Zika cases in the Americas in the period. To detect high-risk areas for children with CZS in the city of Rio de Janeiro, we used cluster detection and thematic maps. We analyzed data using a Poisson model in Satscan 10.1.3 software. We also analyzed the records of children with CZS from 2015 to 2016 to describe the clinical and epidemiological maternal and child profile, as well as live births in 2016 and the social development index (SDI) by neighborhood. In 2015 and 2016, the incidence rates of CZS were 8.84 and 46.96 per 100,000 live births in the city, respectively. Severe congenital findings such as microcephaly and brain damage, osteoarticular impairment, ocular abnormalities, and hearing loss were observed in 47 children. The spatial distribution of CZS was concentrated in the north and west zones in heterogeneous neighborhoods. The neighborhoods with the highest occurrence of CZS cases were found to have the worst SDIs. Stascan detected three spatial clusters in the north zone, where the SDI is lower. The clusters presented high relative risks for CZS (7.86, 1.46, and 2.08), although they were not statistically significant. Our findings highlight a higher occurrence of CZS in areas with less favorable socioeconomic conditions.

Finding Priority Areas in the Evaluation of Strategies for the Prevention of Leishmaniasis in an Endemic Municipality of Brazil.

Visceral leishmaniasis is a zoonotic disease that affects humans and dogs. The infection is endemic in the municipality of Araçatuba, São Paulo, Brazil. Given the role of dogs in the epidemiology of leishmaniasis, strategies to enhance surveillance and reduce transmission are focused on dogs. In this study, we retrospectively analyzed records of canine visceral leishmaniasis from 2013 to 2022. According to this database, the prevalence of dogs testing positive for leishmaniasis fluctuated, with an average of 65.04% (6590/10,133). Cases were clustered in 10 statistically significant areas. Environmental analyses identified a significant geographical association between animals testing positive and higher vegetation density rates compared with animals testing negative. The period from sample collection to diagnosis and euthanasia, as recommended by the Brazilian Ministry, correlated with disease prevalence and decreased over time. These findings serve to implement different action plans against leishmaniasis for each geographic region and to understand the impact and efforts of strategies in an endemic area.

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology.

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Despite extensive research efforts dedicated to characterizing this complex and heterogeneous environment, considerable challenges persist. In this study, we introduce a data-driven approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients, where the identified tumor microenvironment patterns result in a risk stratification system that provides complementary, new information with respect to alternative standards. Our results, which are validated in a completely independent cohort, allow for new insights into the prognostic implications of the breast tumor microenvironment, and this methodology could be applied to other cancer types more generally.

Geography and health: role of human translocation and access to care.

Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today's interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled "Geography and health: role of human translocation and access to care" document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era.

Physical environmental roadway interventions and injury and death for vulnerable road users: a natural experiment in New York City.

INTRODUCTION: This study examined the effectiveness of three physical environmental roadway interventions (enhanced crossings, speed humps, and turn traffic calming) in preventing crashes involving pedestrian and cyclist injury and mortality in New York City. METHODS: We examined crashes that occurred within a 100-foot radius of intervention and control sites from 2015 to 2019. We used a staggered difference-in-difference design to estimate the association between each intervention type and pedestrian and cyclist crash outcomes. RESULTS: Estimates for enhanced crossings and speed humps included the possibility of no association with crashes, but estimates for turn traffic calming interventions showed reduced odds of crashes involving pedestrian injury by 16% (OR 0.84, 95% CI 0.74 to 0.95) and crashes involving pedestrian fatality by 80% (OR 0.20, 95% CI 0.08 to 0.47). When stratifying by street segment length as a proxy for areas with high speeding risk, turn traffic calming treatments appeared to be most effective at intersections connected to long street segments. DISCUSSION: Turn traffic calming may substantially reduce crash risks for pedestrians. Municipalities can prioritise this physical environmental intervention, especially at turns near long street segments, as a low-cost intervention with substantial public health impact.

Geographic clustering of cannabis stores in Canadian cities: A spatial analysis of the legal cannabis market 4 years post-legalisation.

INTRODUCTION: Following the legalisation of non-medical cannabis in 2018, the number of cannabis stores in Canada has rapidly expanded with limited regulation on their geographic placement. This study characterised the clustering of cannabis stores in Canadian cities and evaluated the association of clustering with provincial policy and sociodemographic variables. METHODS: Cross-sectional spatial analysis of cannabis store density in dissemination areas ('neighbourhoods', n = 39,226) in Canadian cities in September 2022. Cannabis store density was defined as the count of stores within 1000 m of a neighbourhood centre. Clusters of high-density cannabis retail were identified using Local Indicators of Spatial Autocorrelation. Associations between provincial policy (privatised vs. public market), sociodemographic variables and cannabis store density were evaluated using multivariable regression. RESULTS: Clusters of high-density cannabis retail were identified in 86% of Canadian cities, and neighbourhoods in clusters had a median of 5 stores within 1000 m. Toronto, Canada's most populous city, had the most extreme clustering where neighbourhoods in clusters had a median of 10 stores (and a maximum of 25 stores) within 1000 m. Neighbourhoods in private versus public retail markets had a significantly higher neighbourhood-level density of cannabis stores (adjusted rate ratio [aRR] 63.37, 95% confidence interval [CI] 25.66-156.33). Lower neighbourhood income quintile was also associated with a higher neighbourhood-level density of cannabis stores (Q5 vs. Q1, aRR 1.28, 95% CI 1.17-1.40). DISCUSSION AND CONCLUSIONS: Since cannabis was legalised, clusters of high-density cannabis retail have emerged in most Canadian cities and were more likely to form lower income neighbourhoods and in private retail markets.

Assessing the spillover effects of various forms of energy on CO2 emissions - An empirical study based on dynamic spatial Durbin model.

Previous studies ignored the geospatial dynamics spillover effects of energy consumption on CO2 emissions while assessing such impacts in developed and developing countries. Moreover, most studies wrongfully assess spillover effects in its aggregated format rather than decomposing by its components. This is important as not all energy sources share the same characteristics. We fill these gaps in the literature by investigating the spillover effects of various forms of energy, including fossil fuels, renewable energy, and nuclear power, on CO2 emissions in 135 developed and developing countries from 2000 to 2019. We used the Dynamic Spatial Durbin Model (DSDM) to better understand the results. A series of indicative tests confirmed using the DSDM model and including spatial interaction of CO2 emissions in the analysis. Our findings show evidence of indirect spillover effects of the various energy sources on CO2 emissions. Further considering the spillover effects of the energy sources of neighbouring countries, the paper finds that the driving increase in CO2 emissions mainly came from the energy consumption of the country itself and neighbouring countries' energy consumption. Nevertheless, the results indicate that the direct effects of energy consumption often exceed its indirect effects. The results also confirm that total and fossil energy consumption harms the environment, whereas adopting renewable and nuclear energy sources reduces CO2 emissions. Lastly, we find nuclear energy is the most environmentally sustainable energy source. The study concludes that the Dynamic Spatial Durbin Model is paramount in estimating the environmental impact of energy consumption in our sample. The practical policy implications drawn from this study could be used to promote increased collaboration to hasten the energy transition process and address global warming and climate change.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Genomic and spatial analysis reveal the transmission dynamics of tuberculosis in areas with high incidence of Zhejiang, China: A prospective cohort study.

In the mountainous, rural regions of eastern China, tuberculosis (TB) remains a formidable challenge; however, the long-term molecular epidemiological surveillance in these regions is limited. This study aimed to investigate molecular and spatial epidemiology of TB in two mountainous, rural counties of Zhejiang Province, China, from 2015 to 2021, to elucidate the recent transmission and drug-resistance profiles. The predominant Lineage 2 (L2) Beijing family accounted for 80.1% of total 532 sequenced Mycobacterium tuberculosis (Mtb) strains, showing consistent prevalence over seven years. Gene mutations associated with drug resistance were identified in 19.4% (103/532) of strains, including 47 rifampicin or isoniazid-resistant strains, eight multi-drug-resistant (MDR) strains, and five pre-extensively drug-resistant (pre-XDR) strains. Genomic clustering revealed 53 distinct clusters with an overall transmission clustering rate of 23.9% (127/532). Patients with a history of retreatment and those infected with L2 strains had a higher risk of recent transmission. Spatial and epidemiological analysis unveiled significant transmission hotspots, especially in densely populated urban areas, involving various public places such as medical institutions, farmlands, markets, and cardrooms. The study emphasizes the pivotal role of Beijing strains and urban-based TB transmission in the western mountainous regions in Zhejiang, highlighting the urgent requirement for specific interventions to mitigate the impact of TB in these unique communities.

Multiplex imaging reveals spatially resolved DNA-damage response neighborhoods in TP53-mutated myelodysplastic neoplasms.

While increased DNA damage is a well-described feature of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), it is unclear whether all lineages and all regions of the marrow are homogeneously affected. In this study, we performed immunohistochemistry on formalin-fixed, paraffin-embedded whole-section bone marrow biopsies using a well-established antibody to detect pH2A.X (phosphorylated histone variant H2A.X) that recognizes DNA double-strand breaks. Focusing on TP53-mutated and complex karyotype MDS/AML, we find a greater pH2A.X+ DNA damage burden compared to TP53 wild-type neoplastic cases and non-neoplastic controls. To understand how double-strand breaks vary between lineages and spatially in TP53-mutated specimens, we applied a low-multiplex immunofluorescence staining and spatial analysis protocol to visualize pH2A.X+ cells with p53 protein staining and lineage markers. pH2A.X marked predominantly mid- to late-stage erythroids, whereas early erythroids and CD34+ blasts were relatively spared. In a prototypical example, these pH2A.X+ erythroids were organized locally as distinct colonies, and each colony displayed pH2A.X+ puncta at a synchronous level. This highly coordinated immunophenotypic expression was also seen for p53 protein staining and among presumed early myeloid colonies. Neighborhood clustering analysis showed distinct marrow regions differentially enriched in pH2A.X+/p53+ erythroid or myeloid colonies, indicating spatial heterogeneity of DNA-damage response and p53 protein expression. The lineage and architectural context within which DNA damage phenotype and oncogenic protein are expressed is relevant to current therapeutic developments that leverage macrophage phagocytosis to remove leukemic cells in part due to irreparable DNA damage. © 2024 The Pathological Society of Great Britain and Ireland.

The Impact of Sunshine Duration on Myopia in Central China: Insights from Populational and Spatial Analysis in Hubei.

Purpose: This study aimed to analyze myopia distribution in Hubei and the impact of regional Sunshine Duration on myopia in children and adolescents. Patients and Methods: The Cross-sectional study included students (kindergarten to grade 12) through multistage cluster stratified sampling in 17 cities (103 areas) of Hubei, China, who underwent ophthalmic examinations from September 2021 to November 2021. The association of sunshine duration with the prevalence and distribution of myopia was analyzed. Using Moran's index to quantify the distribution relationship, a spatial analysis was constructed. Results: A total of 435,996 students (53.33% male; mean age, 12.16±3.74 years) were included in the study. A negative association was identified between myopia prevalence and sunshine duration in the region, especially in population of primary students (r=-0.316, p<0.001). Each 1-unit increment in the sunshine duration was associated with a decreased risk of myopia prevalence (OR=0.996; 95% CI, 0.995-0.998; P <0.001). Regression showed a linear relationship between sunshine duration and myopia rates of primary school students [Prevalence%= (-0.1331*sunshine duration+47.73)%, p = 0.02]. Sunshine duration influenced the distribution of myopia rates among primary (Moran's I=-0.206, p<0.001) and junior high school (Moran's I=-0.183, p=0.002). Local spatial analysis showed that areas with low sunshine duration had high myopia prevalence concentration. Conclusion: This study revealed sunshine duration associations with myopia prevalence at the regional and population levels. The results may emphasize the significance of promptly implementing myopia control in regions with poor sunshine. The effect of sunshine on myopia is pronounced in the early years of education, especially in primary students.

Spatial Analysis of Dengue Fever Incidence and Serotype Distribution in Vientiane Capital, Laos: A Multi-year Study.

Laos is a hyperendemic country of all 4 dengue serotypes. Various factors contribute to the spread of the disease including viral itself, vectors, and environment. This study aims to analyze dengue data and its incidence in nine districts of Vientiane Capital, Laos spanning from 2019 to 2021 by data collected from Mittaphab Hospital. The Maximum Entropy algorithm (MaxEnt) was applied to assess spatial distribution and identify high-probability locations for dengue occurrence by analyzing crucial environmental and climatic conditions. Dengue cases were more prominent in female (54.88%) and highest case number was found in worker group (29.02%) followed by student (28.47%) and officer (16.92%). In this study, the age group 21-30 years old had the highest infection rate (42.23%), followed by 10-20 years old (24.21%). Most of dengue cases was primary infection (91.61%). Dengue serotype 2 predominated in 2019 and 2020 and substitute by serotype 1 in 2021. Across the nine districts of Vientiane Capital, the highest incidence of dengue was found in Xaythany district population in 2019, shifting to Chanthabouly district in 2020 and 2021. The MaxEnt revealed potentially most suitable areas for dengue were widely distributed central south part of Vientiane, Laos. Additionally, the best predictive variable for dengue occurrence was normalized difference vegetation index. Understanding of case characteristics and spatial distribution features of dengue will be helpful in effective surveillance and disease control in the future.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

BACKGROUND: Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. METHODS: The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. RESULTS: Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. CONCLUSIONS: Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Spatial analysis of leachate penetration at Lemna dumpsite, Calabar: Implications for sustainable waste management in Cross River State.

This study rigorously investigated the spatial analysis of leachate penetration at Lemna dumpsite, located in Calabar, Cross River State, Nigeria. Purposeful soil sampling, performed at specific intervals (5 m, 25 m, and 50 m) along the Electrical Resistivity profile line within the dumpsite, was augmented by water sample collection from five boreholes near Lemna dumpsite. Utilizing Electrical Resistivity Tomography (ERT) and Vertical Electric Sounding (VES) survey techniques, resistivity data were systematically gathered to comprehensively analyze the Leachate Penetration in the Lemna dumpsite. Laboratory analysis of soil and borehole water quality focused on Benzene, Toluene, Ethylbenzene, and Xylene (BTEX), with paired sample t-tests applied for statistical scrutiny. Analyzing the ERT and VES data employed sophisticated techniques embedded in Resistivity Two Dimension Invasion software and Advanced Geosciences Incorporation Earth Imager software. Substantial disparities (p < 0.05) emerged in the paired sample t-tests for BTEX in soil compared to National Environmental Standard Regulation and Enforcement Agency (NESREA) limits. Similarly, BTEX in borehole water displayed significant differences (p < 0.05) when compared to World Health Organization (WHO) standards, raising alarming concerns about the safety and portability of groundwater in the area. The examination of dumpsite leachate penetration revealed a resistivity anomaly of 8.01 Ωm and an inverse depth of 12.4 m, underscoring profound environmental implications and necessitating immediate remediation efforts. Additionally, Vulnerability and Aquifer Protective Capacity Index (VES) results, with a rating of <0.1, indicated severely compromised aquifer protective capacity, emphasizing the vulnerability of groundwater resources to further contamination. Our study advocates for strategic management, remediation, and monitoring measures to prevent contamination and safeguard water quality in the region.

Navigating geographical disparities: access to obstetric hospitals in maternity care deserts and across the United States.

BACKGROUND: Access to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care "deserts." Living in these areas, exacerbated by hospital closures and workforce shortages, heightens the risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities. METHODS: The research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017-2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances. RESULTS: The mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities. CONCLUSIONS: Our study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice.

Measuring access to food banks and food pantries: A scoping review of spatial analysis approaches.

Food banks and food pantries are crucial in supporting access to food and addressing food insecurity for millions of people. This scoping review identified eighteen articles that applied spatial analysis approaches to measure access to food banks and food pantries. The review summarizes the methods and primary findings of these studies, and examines how these address different dimensions of food access. Findings suggest that the majority of studies measured the accessibility and availability dimensions of food access, and two addressed accommodation. Through a discussion of these studies' methods and broader literature on food environments, we highlight opportunities to integrate advanced geospatial and mixed methods to support an empirically grounded and broader understanding of food bank and pantry access in future research. This will yield a more holistic picture of food environments and provide practical implications for site selection, resource allocation, and food assistance operations.

Temporal trends and space-time distribution of leprosy relapse in Brazil from 2001 to 2021.

BACKGROUND: To analyse the temporal trends and spatiotemporal distribution of leprosy relapse in Brazil from 2001 to 2021. METHODS: An ecological study with a temporal trend approach and space-time analysis of leprosy relapse in Brazil was carried out with data from the Notifiable Diseases Information System. RESULTS: A total of 31 334 patients who experienced leprosy relapse were identified. The number of recurrent cases tended to increase throughout the study period, and this increase was significant among females and in almost all age groups, except for those <15, 50-59 and ≥70 y. Several clusters of high- and low-risk patients were identified across all regions with a heterogeneous distribution. CONCLUSIONS: The burden of relapse showed an increasing trend in some groups and was distributed in all regions.