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A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

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This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

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BACKGROUND: This paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data. METHODS: National Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods. RESULTS: Our analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns. CONCLUSION: In conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

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AIMS: Suicide prevention strategies have shifted in many countries, from a national approach to one that is regionally tailored and responsive to local community needs. Previous Australian studies support this approach. However, most studies have focused on suicide deaths which may not fully capture a complete understanding of prevention needs, and few have focused on the priority population of youth. This was the first nationwide study to examine regional variability of self-harm prevalence and related factors in Australian young people. METHODS: A random sample of Australian adolescents (12-17-year-olds) were recruited as part of the Young Minds Matter (YMM) survey. Participants completed self-report questions on self-harm (i.e., non-suicidal self-harm and suicide attempts) in the previous 12 months. Using mixed effects regressions, an area-level model was built with YMM and Census data to produce out-of-sample small area predictions for self-harm prevalence. Spatial unit of analysis was Statistical Area Level 1 (average population 400 people), and all prevalence estimates were updated to 2019. RESULTS: Across Australia, there was large variability in youth self-harm prevalence estimates. Northern Territory, Western Australia, and South Australia had the highest estimated state prevalence. Psychological distress and depression were factors which best predicted self-harm at an individual level. At an area-level, the strongest predictor was a high percentage of single unemployed parents, while being in an area where ≥30% of parents were born overseas was associated with reduced odds of self-harm. CONCLUSIONS: This study identified characteristics of regions with lower and higher youth self-harm risk. These findings should assist governments and communities with developing and implementing regionally appropriate youth suicide prevention interventions and initiatives.

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African American (AA) women confront distinct disparities in breast cancer rates, and the impact of their living environment is unclear. This study aimed to examine the association between breast cancer incidence and environmental factors among a high-risk female population. The study recruited 355 AA women ages 20-88 in Memphis from 2016-2018. Their addresses were geocoded and linked to environmental and socioeconomic data. The final dataset contained 50 cases and 157 controls. Associations between breast cancer incidence and social and environmental factors were examined using logistic regression. Spatial analysis in ArcGIS showed that cases clustered in Southwest Memphis. Proximity to traffic and Superfund sites had odds ratios of 1.636 (95% CI: 25 1.046, 2.560) and 12.262 (95% CI: 1.814, 82.864), respectively. Mediating analyses further revealed that environmental inequities contributed significantly to breast cancer inequalities. In conclusion, the built environment plays a role in breast cancer onset among AA females.

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Influence of climate change on the geospatial heterogeneity in agricultural production remains poorly understood. In this study, heterogeneity in climate's impacts on wheat production across the North China Plain (NCP) was explored by integrating APSIM model, process-based factor-control quantitative approach, and geostatistical analyses. The results indicated that increased precipitation and minimum temperature boosted yields, while elevated maximum temperature and reduced radiation exerted adverse effects. The most pronounced negative impact arose from the coupling variation between maximum temperature and radiation, contributing to yields' variations of -5.84% from 2000 to 2010 and -5.22% from 2010 to 2020. In last two decades, climate change has augmented the overall geospatial heterogeneity degree in wheat yields. The chief factor contributing to yields' heterogeneity was the maximum temperature during anthesis-maturation stage, explaining an average of 37.6% of yields' heterogeneity, followed by precipitation throughout the whole growth period and the anthesis-maturation stage, explaining 36.1% and 34.5% respectively. A reciprocal enhancement mechanism exists between factors in driving yields' heterogeneity. Wheat yields in the southwestern NCP benefited more from increased precipitation and minimum temperature. Between 2000 and 2010, yields in the central NCP (junctions of Henan, Hebei, and Shandong) experienced the most pronounced adverse impact from increased maximum temperature. However, by 2010-2020, significant adverse impact shifted to western NCP, expanding spatially. During 2010-2020, the geospatial scope of radiation's significant negative impact expanded compared to the preceding decade, particularly affecting the yields in central and eastern NCP. The identified geospatial heterogeneity pattern of climate's impacts can guide spatially-matched climate-adaptive management adjustments. For instance, intensifying the defense against high-temperature's impacts in northwestern Henan, southern Hebei, and western Shandong, while improving the adaptation to radiation reduction in the central and eastern NCP. The findings are expected to advance regional-scale climate-smart agricultural development.

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Background: The COVID-19 pandemic resulted in a massive disruption in access to care and thus passive, hospital- and clinic-based surveillance programs. In 2020, the reported cases of Lyme disease were the lowest both across the United States and North Carolina in recent years. During this period, human contact patterns began to shift with higher rates of greenspace utilization and outdoor activities, putting more people into contact with potential vectors and associated vector-borne diseases. Lyme disease reporting relies on passive surveillance systems, which were likely disrupted by changes in health care-seeking behavior during the pandemic. Objective: This study aimed to quantify the likely under-ascertainment of cases of Lyme disease during the COVID-19 pandemic in the United States and North Carolina. Methods: We fitted publicly available, reported Lyme disease cases for both the United States and North Carolina prior to the year 2020 to predict the number of anticipated Lyme disease cases in the absence of the pandemic using a Bayesian modeling approach. We then compared the ratio of reported cases divided by the predicted cases to quantify the number of likely under-ascertained cases. We then fitted geospatial models to further quantify the spatial distribution of the likely under-ascertained cases and characterize spatial dynamics at local scales. Results: Reported cases of Lyme Disease were lower in 2020 in both the United States and North Carolina than prior years. Our findings suggest that roughly 14,200 cases may have gone undetected given historical trends prior to the pandemic. Furthermore, we estimate that only 40% to 80% of Lyme diseases cases were detected in North Carolina between August 2020 and February 2021, the peak months of the COVID-19 pandemic in both the United States and North Carolina, with prior ascertainment rates returning to normal levels after this period. Our models suggest both strong temporal effects with higher numbers of cases reported in the summer months as well as strong geographic effects. Conclusions: Ascertainment rates of Lyme disease were highly variable during the pandemic period both at national and subnational scales. Our findings suggest that there may have been a substantial number of unreported Lyme disease cases despite an apparent increase in greenspace utilization. The use of counterfactual modeling using spatial and historical trends can provide insight into the likely numbers of missed cases. Variable ascertainment of cases has implications for passive surveillance programs, especially in the trending of disease morbidity and outbreak detection, suggesting that other methods may be appropriate for outbreak detection during disturbances to these passive surveillance systems.

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Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

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Wildlife and natural resources constitute an integral part of the ecosystem, whereas human interventions dismantled the living conditions of the wildlife. This is testified in the Dalma Wildlife Sanctuary (DWS) where the habitats of Asian elephants have changed due to human intervention and deforestation over the decades. The present study aimed to assess the elephant habitat suitability in the DWS of Jharkhand state (India) using the geospatial parameters such as forest density, degree of slope, proximity to water bodies, land use land cover, proximity to agricultural land, built-up density, and road density. The analytical hierarchical process technique was utilized to determine habitat preference and selection of relevant factors to categorize criteria. The study revealed that about 6.7% (26.74 km2) of the area is very highly suitable for elephant habitat, while 52.26% (208.49 km2) of the forest area was found highly suitable. The most suitable habitat was identified in the core parts of the forest, while the least suitable areas were found in the southern part, where the presence of roads, built-up, and agricultural land was prominent. It was also observed that most human-elephant conflicts were exhibited in the low and very low suitable areas, while 90% of the elephant movement was witnessed in the high and very high suitable areas. Among the four identified corridors, three are inactive, and their location corresponds with low to very low suitable habitats. The study identified the migratory corridor routes inside the sanctuary where effective management is required for the conservation of elephant habitats and minimizing conflicts.

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This study employs comprehensive clustering analysis to examine COVID-19 vaccine hesitancy and related socio-demographic factors across U.S. counties, using the collected and curated data from Johns Hopkins University. Utilizing K-Means and hierarchical clustering, we identify five distinct clusters characterized by varying levels of vaccine hesitancy, MMR vaccination coverage, population demographics, and political affiliations. Principal Component Analysis (PCA) was conducted to reduce dimensionality, and key variables were selected based on their contribution to cumulative explained variance. Our analysis reveals significant geographic and demographic patterns in vaccine hesitancy, providing valuable insights for public health strategies and future pandemic responses. Geospatial analysis highlights the distribution of clusters across the United States, indicating areas with high and low vaccine hesitancy. In addition, multiple regression analyses within each cluster identify key predictors of vaccine hesitancy in corresponding U.S. county clusters, emphasizing the importance of socio-economic and demographic factors. The findings underscore the need for targeted public health interventions and tailored communication strategies to address vaccine hesitancy across the United States and, potentially, across the globe.

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BACKGROUND: Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. OBJECTIVE: Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). METHODS: We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. RESULTS: Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. IMPACT STATEMENT: This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

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BACKGROUND: The highly variable occurrence of primary liver cancers across the United States emphasize the relevance of location-based factors. Social determinants such as income, educational attainment, housing, and other factors may contribute to regional variations in outcomes. To evaluate their impact, this study identified and analyzed clusters of high mortality from primary liver cancers and the association of location-based determinants with mortality across the contiguous United States. METHODS: A geospatial analysis of age-adjusted incidence and standardized mortality rates from primary liver cancers from 2000 to 2020 was performed. Local indicators of spatial association identified hot-spots, clusters of counties with significantly higher mortality. Temporal analysis of locations with persistent poverty, defined as high (>20%) poverty for at least 30 years, was performed. Social determinants were analyzed individually or globally using composite measures such as the social vulnerability index or social deprivation index. Disparities in county level social determinants between hot-spots and non-hot-spots were analyzed by univariate and multivariate logistic regression. RESULTS: There are distinct clusters of liver cancer incidence and mortality, with hotspots in east Texas and Louisiana. The percentage of people living below the poverty line or Hispanics had a significantly higher odds ratio for being in the top quintile for mortality rates in comparison to other quintiles and were highly connected with mortality rates. Current and persistent poverty were both associated with an evolution from non-hotspots to new hotspots of mortality. Hotspots were predominantly associated with locations with significant levels of socioeconomic vulnerability or deprivation. CONCLUSIONS: Poverty at a county level is associated with mortality from primary liver cancer and clusters of higher mortality. These findings emphasize the importance of addressing poverty and related socio-economic determinants as modifiable factors in public health policies and interventions aimed at reducing mortality from primary liver cancers.

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The Pacific Islands region is home to several of the world's biodiversity hotspots, yet its unique flora and fauna are under threat because of biological invasions. These invasions are likely to proliferate as human activity increases and large-scale natural disturbances unfold, exacerbated by climate change. Remote sensing data and techniques provide a feasible method to map and monitor invasive plant species and inform invasive plant species management across the Pacific Islands region. We used case studies taken from literature retrieved from Google Scholar, 3 regional agencies' digital libraries, and 2 online catalogs on invasive plant species management to examine the uptake and challenges faced in the implementation of remote sensing technology in the Pacific region. We synthesized remote sensing techniques and outlined their potential to detect and map invasive plant species based on species phenology, structural characteristics, and image texture algorithms. The application of remote sensing methods to detect invasive plant species was heavily reliant on species ecology, extent of invasion, and available geospatial and remotely sensed image data. However, current mechanisms that support invasive plant species management, including policy frameworks and geospatial data infrastructure, operated in isolation, leading to duplication of efforts and creating unsustainable solutions for the region. For remote sensing to support invasive plant species management in the region, key stakeholders including conservation managers, researchers, and practitioners; funding agencies; and regional organizations must invest, where possible, in the broader geospatial and environmental sector, integrate, and streamline policies and improve capacity and technology access.

Capacidad y potencial de la telemetría para informar la gestión de especies de plantas invasoras en las islas del Pacífico Resumen Las islas del Pacífico albergan varios de los puntos calientes de biodiversidad del planeta; sin embargo, su flora y fauna únicas se encuentran amenazadas por las invasiones biológicas. Es probable que estas invasiones proliferen conforme incrementa la actividad humana y se desarrollan las perturbaciones naturales a gran escala, exacerbadas por el cambio climático. Los datos y las técnicas telemétricas proporcionan un método viable para mapear y monitorear las especies invasoras de plantas y orientar su manejo en la región de las islas del Pacífico. Usamos estudios de caso tomados de la bibliografía de Google Scholar, las bibliotecas digitales de tres agencias regionales y dos catálogos virtuales del manejo de especies invasoras de plantas para analizar la asimilación y retos que enfrenta la implementación de la telemetría en la región del Pacífico. Sintetizamos las técnicas telemétricas y describimos su potencial para detectar y mapear las especies de plantas invasoras con base en la fenología de las especies, características estructurales y algoritmos de textura de imagen. La aplicación de los métodos de telemetría para detectar las especies invasoras de plantas dependió en gran medida de la ecología de la especie, la extensión de la invasión y los datos disponibles de imágenes telemétricas y geoespaciales. Sin embargo, los mecanismos actuales de apoyo para el manejo de especies invasoras de plantas, incluyendo los marcos normativos y la infraestructura para datos geoespaciales, operan de manera aislada, lo que lleva a que se dupliquen los esfuerzos y se creen soluciones insostenibles para la región. Para que la telemetría apoye al manejo de especies invasoras de plantas en la región, los actores clave, incluidos los gestores, investigadores, practicantes, agencias financiadoras y organizaciones regionales, deben invertir, en lo posible, en un sector ambiental y geoespacial más amplio, integrar y simplificar las políticas y mejorar la capacidad y el acceso a la tecnología.

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Motivation: Analyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology. Results: We introduce an R Markdown script called SNAQ™ (Single-cell Spatial Neighborhood Analysis and Quantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQ™ was used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQ™. Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest. Availability and implementation: The R Markdown files that comprise the SNAQ™ algorithm and the input data from this paper are freely available on the web at https://github.com/AryehSilver1/SNAQ.

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BACKGROUND: Helicopter Emergency Medical Services (HEMS) in the United Kingdom (UK) are provided in a mixed funding model, with the majority of services funded by charities alongside a small number of government-funded operations. More socially-deprived communities are known to have greater need for critical care, such as that provided by HEMS in the UK. Equity of access is an important pillar of medical care, describing how resource should be allocated on the basis of need; a concept that is particularly relevant to resource-intensive services such as HEMS. However, the Inverse Care Law describes the tendency of healthcare provision to vary inversely with population need, where healthcare resource does not meet the expected needs in areas of higher deprivation. It is not known to what extent the Inverse Care Law applies to HEMS in the UK. METHODS: Modelled service areas were created with each small unit geography locus in the UK assigned to its closest HEMS operational base. The total population, median decile on index of multiple deprivation, and geographic area for each modelled service area was determined from the most recently available national statistics. Linear regression was used to determine the association between social deprivation, geographic area, and total population served for each modelled service area. RESULTS: The provision of HEMS in the UK varied inversely to expected population need; with HEMS operations in more affluent areas serving smaller populations. The model estimated that population decreases by 18% (95% confidence interval 1-32%) for each more affluent point in median decile of index of multiple deprivation. There was no significant association between geographic area and total population served. CONCLUSION: The provision of HEMS in the UK is consistent with the Inverse Care Law. HEMS operations in more deprived areas serve larger populations, thus providing a healthcare resource inversely proportional with the expected needs of these communities. Funding structures may explain this variation as charities are more highly concentrated in more affluent areas.

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Background: During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations. Methods: The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including "date, test result, population density, area, latitude, longitude, district, and state" to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System. Findings: The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate ß approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed 100. Interpretation: The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas. Funding: This study is Funded by Indian Council of Medical Research (ICMR).

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Asthma, the most common chronic disease in children, affects more than 4 million children in the United States, disproportionately affecting those who are economically disadvantaged and racial and ethnic minorities. Studies have shown that the racial and ethnic disparities in asthma outcomes can be largely explained by environmental, socioeconomic and other social determinants of health (SDoH). Utilizing new approaches to stratify disease severity and risk, which focus on the underlying SDoH that lead to asthma disparity, provides an opportunity to disentangle race and ethnicity from its confounding social determinants. In particular, with the growing use of geospatial information systems, geocoded data can enable researchers and clinicians to quantify social and environmental impacts of structural racism. When these data are systematically collected and tabulated, researchers, and ultimately clinicians at the bedside, can evaluate patients' neighborhood context and create targeted interventions toward those factors most associated with asthma morbidity. To do this, we have designed a view (mPage in the Cerner electronic health record) that centralizes key clinical information and displays it alongside SDoH variables shown to be linked to asthma incidence and severity. Once refined and validated, which is the next step in our project, our goal is for emergency medicine clinicians to use these data in real time while caring for patients with asthma. Our multidisciplinary, patient-centered approach that leverages modern informatics tools will create opportunities to better triage patients with asthma exacerbations, choose the best interventions, and target underlying determinants of disease.

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Established life course approaches suggest that health status in adulthood can be influenced by events that occurred during the prenatal developmental period. Yet, interventions such as diet and lifestyle changes performed during pregnancy have had a small impact on both maternal and offspring health outcomes. Currently, there is a growing body of literature that highlights the importance of maternal health before conception (months or years before pregnancy occurs) for the future health of offspring. While some studies have explored factors such as maternal body composition, nutrition, and lifestyle in this area, location-based environmental and socioeconomic exposures before conception may also contribute to future offspring health. In this line, the study of a patient's geographic history presents a promising avenue. To foster research in this direction, the integration of geospatial health, medical informatics and artificial intelligence techniques offers great potential. Importantly, novel sources of big health data sets such as electronic health records registered at the primary care level provide a unique framework due to its inherent longitudinal nature. Nonetheless, a number of privacy, ethical, and technical challenges need to be overcome for this kind of longitudinal analysis to mature and succeed. In the long-term, we support the vision of incorporating a patient's geographic history into her clinical history to equip clinicians with useful contextual information to explore.

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INTRODUCTION: Adverse social determinants of health have been shown to be associated with a greater chance of developing chronic conditions. Although there has been increased focus on screening for health-related social needs (HRSNs) in health care delivery systems, it is seldom examined if the provision of needed services to address HRSNs is sufficiently available in communities where patients reside. METHODS: The authors used geospatial analysis to determine how well a newly formed health system and community-based organizations (CBOs) social care coordination network covered the areas in which a high number of patients experiencing HRSNs live. Geospatial clusters (hotspots) were constructed for Kaiser Permanente Northwest members experiencing any of the following 4 HRSNs: transportation needs, housing instability, food insecurity, or financial strain. Next, a geospatial polygon was calculated indicating whether a member could reach a social care provider within 30 minutes of travel time. RESULTS: A total of 185,535 Kaiser Permanente Northwest members completed a HRSN screener between April 2022 and April 2023. Overall, the authors found that among Kaiser Permanente Northwest members experiencing any of the 4 HRSNs, 97% to 98% of them were within 30 minutes of a social care provider. A small percentage of members who lived greater than 30 minutes to a social care provider were primarily located in rural areas. DISCUSSION AND CONCLUSION: This study demonstrates the importance of health system and community-based organization partnerships and investment in community resources to develop social care coordination networks, as well as how patient-level HRSN can be used to assess the coverage and representativeness of the network.

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