GIS-based intelligent planning approach of child-friendly pedestrian pathway to promote a child-friendly city.

Pedestrian safety, particularly for children, relies on well-designed pathways. Child-friendly pathways play a crucial role in safeguarding young pedestrians. Shared spaces accommodating both vehicles and walkers can bring benefits to pedestrians. However, active children playing near these pathways are prone to accidents. This research aims to develop an efficient method for planning child-friendly pedestrian pathways, taking into account community development and the specific needs of children. A mixed-methods approach was employed, utilizing the Datang community in Guangzhou, China, as a case study. This approach combined drawing techniques with GIS data analysis. Drawing methods were utilized to identify points of interest for children aged 2-6. The qualitative and quantitative fuzzy analytic hierarchy process assessed factors influencing pathway planning, assigning appropriate weights. The weighted superposition analysis method constructed a comprehensive cost grid, considering various community elements. To streamline the planning process, a GIS tool was developed based on the identified factors, resulting in a practical, child-friendly pedestrian pathway network. Results indicate that this method efficiently creates child-friendly pathways, ensuring optimal connectivity within the planned road network.

'Nobody Shops at the Neighborhood Store': Leveraging a Community's Pediatric Fresh Produce Prescription Program to Inform Future Participating Store Redemption Locations.

Research examining the nature of food shopping often considers proximity to the nearest or overall distance travelled to multiple stores. Such studies make up a portion of new work on so-called 'food deserts' and the issues inherent in the term, including that most people do not shop at their nearest store, and mobility challenges vary vastly from one person to the next. Increasing the knowledge base on shopping characteristics could be useful for behavioral interventions and programs aimed at increasing healthy food shopping. In this study, we examined the shopping characteristics of 627 caregivers whose children were enrolled in a pediatric fresh produce prescription program at one of three large pediatric clinics in Flint, Michigan. We compared these characteristics to the potential of a new food cooperative to improve geographic accessibility to healthy food. In particular, we propose the expansion of the prescription program to this new cooperative for health-related as well as local economic development reasons. Our work bridges topics of interest to researchers and practitioners working in nutrition, food access, and economic development.

Analyzing the impact of an MDG-Fund program on childhood malnutrition in Timor-Leste.

BACKGROUND: Timor-Leste's food insecurity, propelled by political conflicts, a fragile economy and biophysical limitations that characterize mountainous Small Island Developing States (SIDS), is expressed in a high incidence (50%) of stunted children. Hence, the Millennium Development Goals Achievement Fund's Joint Program's (MDG-F JP) in 2009 was a timely intervention to reduce prevalence of underweight among under-fives. Since the impact of the program remains largely unclear, the current study investigates the contributions of the MDG-F JP on improving children's nutritional status in Timor-Leste, in order to inform policymakers on how to make future programs more effective. METHODS: Using bivariate analyses and multiple linear regression models we analyzed Demographic and Health Survey (DHS) data from under-fives in 2009-2010 and 2016, combined with spatially explicit data from geographic information systems (GIS). The analyses generated trends and factors associated with undernutrition, which were used in a quasi-experimental setting to compare districts that received the MDG-F JP with similar districts that did not receive MDG-F JP interventions. RESULTS: A comprehensive set of factors dependent on seasons, locations, and individuals determine undernutrition in Timor-Leste. A positive impact of the MDG-F JP was found for the average severity of wasting, but not for stunting and underweight. CONCLUSIONS: The findings reinforce the pressing need for integrated and cross-sectoral programs, aimed especially at agricultural workers, mothers, and children. The agricultural challenge is to sustainably select, produce and conserve higher-yield and nutrient-rich crops, and educational enhancement should be aligned with local practices and research.

Morphometric analysis of Halda River basin, Bangladesh, using GIS and remote sensing techniques.

GIS and remote sensing techniques were effectively used to analyse the morphometric parameters including linear, geometric, basin texture (aerial) and relief aspects of the Halda River Basin, Bangladesh. Along with measuring the morphometric parameters using predetermined formulas, advanced geo-computing tools of spatial analysis, cartography, math, geoprocessing and geometric analysis were employed to carry out the spatial distribution of selected parameters, especially aerial parameters. The linear aspect indicates the basin is six-order and oval-shaped. The bifurcation ratio (4.03) and relevant parameters indicate the moderate effect of geology and structural control is evident. The mean stream length (1.27) and Rho value (ranges between 0.11 and 0.20) indicate high runoff in steep areas and hydrologic storage capacity in flat areas. The stream frequency (0.83), drainage density (1.22), drainage intensity (0.68), infiltration ratio (1.02), length of the overland flow (0.41), and constant of channel maintenance (0.82) indicate the presence of moderate hard rock, less structural disturbances and moderate to high surface runoff in the basin. Basin relief (489 m), relative relief (2.02), ruggedness number (400), Melton's ruggedness number (12.43), and mean slope (9.33%) indicate the potential of high erosion and material transfer. The spatial distribution of selected aerial aspects significantly correlated to elevation and slope. The hierarchical pattern and spatial distribution of the morphometric parameters indicate areas with high slopes and lower-order streams have a high potential to be affected by soil erosion, landslides and flash floods, elsewhere, the areas with low slopes are prone to short-duration riverine floods. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morphohydrological, geological and climatological research will be beneficiary.

Measuring Spatial Access of Vulnerable Population to HIV Testing Facilities in the Baton Rouge Metropolitan Statistical Area, Louisiana.

Ensuring adequate and equitable access to affordable HIV testing is a crucial step toward ending the HIV epidemic (EHE). Using the high-burden Baton Rouge Metropolitan Statistical Area (MSA) as an example, we measure spatial access to HIV testing facilities for vulnerable populations and assess whether their access would improve if eliminating a considerable barrier-costs. Locations and status (free, low-cost, and full cost) of HIV testing facilities are searched on the Internet and confirmed through a field survey. Vulnerable populations include the uninsured and people living with HIV (PLWH), disaggregated from county-level HIV prevalence data. Spatial access is computed by a normalized urban-rural two-step floating catchment area (NUR2SFCA) method. Our survey confirms that only 11% and 37% of the 103 Internet-searched HIV testing facilities are indeed free and low-cost. Making more facilities cheaper or free increases the average access of PLWH, the uninsured, and the entire population but their geographic patterns vary. Free testing facilities, clustered in Baton Rouge city, are highly accessible to 82.6%, 69.4%, and 70.2% of three population groups living in East and West Baton Rouge Parish. In comparison, making all low-cost facilities free increases access in most outlying parishes but at the cost of reducing access in East Baton Rouge Parish, leaving west Livingston, north Iberville, and east Pointe Coupee Parish with the poorest access. Making all full-cost facilities cheaper or free exhibits a similar pattern. The study has important policy implications for where and how to improve access to HIV testing for vulnerable populations.

RESUMEN: Medimos el acceso espacial a las instalaciones de pruebas de VIH para poblaciones vulnerables y evaluamos si su acceso mejoraría si se eliminaran las barreras de costos, utilizando como ejemplo el área estadística metropolitana de Baton Rouge, que tiene una alta carga. Nuestra encuesta confirma que el 11% y el 37% de los 103 centros de pruebas de VIH buscados en Internet son efectivamente gratuitos y de bajo costo. Hacer que más instalaciones sean más baratas o gratuitas aumenta el acceso promedio de las PLWH, las personas sin seguro y toda la población, pero sus patrones geográficos varían. Las instalaciones de pruebas gratuitas, agrupadas en la ciudad de Baton Rouge, son muy accesibles para el 82,6%, el 69,4% y el 70,2% de los tres grupos de población del este y oeste de Baton Rouge. En comparación, hacer que las instalaciones de bajo costo sean gratuitas aumenta el acceso en las parroquias periféricas, pero a costa de reducir el acceso en East Baton Rouge. Hacer que las instalaciones de costo total sean más baratas o gratuitas muestra un patrón similar. El estudio tiene importantes implicaciones políticas para mejorar el acceso a las pruebas del VIH para las poblaciones vulnerables.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

A gender-specific geodatabase of five cancer types with the highest frequency of occurrence in Iran.

OBJECTIVES: Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management. This study aims to create a database containing individual and spatially integrated data on commonly diagnosed cancers in Iran from 2014 to 2017, serving as a valuable resource for spatial-epidemiological approaches. DATA DESCRIPTION: This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules to identify cancers with the highest incidence. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files.

Convenience stores: an obesogenic promoter in a metropolitan area of northern Mexico?

Introduction: The prevalence of obesity in the Mexican school-age (5-11 years old) population increased from 8.9 to 18.1% between 1999 and 2022. Although overweight and obesity (OW + Ob) is a complex and multifactorial phenomenon, alongside its increasing trend, changes in eating patterns as a result of obesogenic environments that promote higher energy intake have been documented. The objective of the present study was to detect possible associations between schools and their proximity to and density of convenience stores in Monterrey, Mexico from 2015 to 2018. Materials and methods: Anthropometric data were obtained from a subset of measurements of the National Registry of Weight and Height (RNPT) performed in the Monterrey Mexico metropolitan area in 2015 and 2018, and obesity prevalence was computed and classified into quintiles at the school level. Convenience store data were obtained from the National Directory of Economic Units (DNUE). The analyses consisted of densities within 400-800 m buffers, distance to the nearest stores, and cartographic visualization of the store's kernel density versus OW + Ob hotspots for both periods. Results: A total of 175,804 children in 2015 and 175,964 in 2018 belonging to 1,552 elementary schools were included in the study; during this period, OW + Ob prevalence increased from 38.7 to 39.3%, and a directly proportional relationship was found between the quintiles with the higher OW + Ob prevalence and the number of stores for both radii. Hotspots of OW + Ob ranged from 63 to 91 between 2015 and 2018, and it was visually confirmed that such spots were associated with areas with a higher density of convenience stores regardless of socioeconomic conditions. Conclusion: Although some relationships between the store's proximity/density and OW + Ob could be identified, more research is needed to gather evidence about this. However, due to the trends and the magnitude of the problem, guidelines aimed at limiting or reducing the availability of junk food and sweetened beverages on the school's periphery must be implemented to control the obesogenic environment.

Data Linkages for Wildfire Exposures and Human Health Studies: A Scoping Review.

Wildfires are increasing in frequency and intensity, with significant consequences that impact human health. A scoping review was conducted to: (a) understand wildfire-related health effects, (b) identify and describe environmental exposure and health outcome data sources used to research the impacts of wildfire exposures on health, and (c) identify gaps and opportunities to leverage exposure and health data to advance research. A literature search was conducted in PubMed and a sample of 83 articles met inclusion criteria. A majority of studies focused on respiratory and cardiovascular outcomes. Hospital administrative data was the most common health data source, followed by government data sources and health surveys. Wildfire smoke, specifically fine particulate matter (PM2.5), was the most common exposure measure and was predominantly estimated from monitoring networks and satellite data. Health data were not available in real-time, and they lacked spatial and temporal coverage to study health outcomes with longer latency periods. Exposure data were often available in real-time and provided better temporal and spatial coverage but did not capture the complex mixture of hazardous wildfire smoke pollutants nor exposures associated with non-air pathways such as soil, household dust, food, and water. This scoping review of the specific health and exposure data sources used to underpin these studies provides a framework for the research community to understand: (a) the use and value of various environmental and health data sources, and (b) the opportunities for improving data collection, integration, and accessibility to help inform our understanding of wildfires and other environmental exposures.

Spatial and temporal distribution of physicochemical parameters of water in Gorgan Bay (the southeast of the Caspian Sea - Iran): Fundamentals for the monitoring of the Caspian Sea coastal areas.

Gorgan Bay as a main part of the Miankaleh (a natural biosphere reserve registered by UNESCO) is one of the richest ecological area in the West Asia and very important internationally recognized refuge for the wildlife. To date, multi physicochemical parameters have not been examined on a large scale. To fill this knowledge gap, the present study aimed to explore the seasonal and spatial variability of water quality parameters of the bay. The results showed that except for depth and transparency, there are significant variations in most parameters across the four seasons. The patterns of these changes in the bay vary, as evidenced by a comparison of the distribution maps of the various factors throughout the year. Notably, alkalinity declined from east to west, reaching its highest levels at important entry points such as the Qarasu River, Bandar-Gaz, and the pier. TDS, on the other hand, increased westward, reaching its highest concentration in the shallow western regions. Maximum depth (310 cm) and transparency (250 cm) were observed in the central bay. While the pH was higher in deeper areas, the distribution of PO4 was more uniform. With lower levels in the east (salinity = 0.40 ‰) and higher levels in the west (salinity = 28.9 ‰), the salinity showed a coherent gradient. Agricultural land use in the basin of the bay and fluxes of nutrients and sediments of the rivers entering the bay has significant contribution to the bay pollution situation. These results will serve as a guide for improving our understanding of the Gorgan Bay ecosystem. They also have implications for informed conservation and management plans adapted to the specifics of this special region within the Caspian Sea.

RocHealthData.org: Development and usage of a publicly available, geographic source of social determinants of health data.

Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center's Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.

Groundwater quality and potential analysis using geospatial techniques: The case of Ashanti Region in Ghana.

The ecosystem and economy's reliance on clean water is influenced by various factors such as geology, topography, soil types, activities, and the presence of plants and animals. The Ghana Water Company is encountering difficulties in delivering water to consumers in the Ashanti Region due to the shortage of surface water resources, leading to water rationing in the area. Furthermore, poor waste disposal practices, illegal mining, use of fertilizers, and industrial activities have resulted in surface and groundwater source damage. Therefore, there is a need to implement a reliable, simple, and timely method to assess groundwater quality. This study aims to employ GIS and RS techniques to evaluate groundwater quality and potential in the Ashanti Region, Ghana. The Water Quality Index (WQI) was estimated using pH, Total Dissolve Solid (TDS), Chloride, Total Hardness (TH), Nitrate, Temperature, Turbidity, Iron, and Electrical Conductivity (EC). The study then used the WQI distribution to conduct a groundwater potential analysis to identify suitable areas for borehole placement. Digital thematic layers and maps were developed to expose the spatial distribution of water quality parameters, enabling the identification of groundwater pollution control and remedial measures. The study estimated the region's groundwater potential using an integrated GIS and Analytical Hierarchical Process (AHP) technique, grouping under excellent, good, fair, and poor potential. The WQI in the Ashanti Region ranged from 5.208 to 134.232, with 32.252% of the study area having an excellent WQI and 60.168% of the study area having a good WQI. Poor water quality covered 7.550% of the study area. The results showed that the GIS-based AHP approach accurately mapped the spatial distribution of WQI and Groundwater Potential Zones (GWPZ). This information is helpful to planners in water resource management in groundwater exploration and future planning. Policymakers and stakeholders must ensure that groundwater sources are protected from pollution.

Soil erodibility mapping of hilly watershed using analytical hierarchy process and geographical information system: A case of Chittagong hill tract, Bangladesh.

Soil erosion across watersheds and river basins is an alarming environmental deterioration process that poses severe risks to hydrological systems, hydrogeochemical processes, agricultural productivity, and the global natural ecosystem. The use of the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) to assess soil erosivity for the watershed is widely known. This study applied the AHP and GIS to understand the degree of erosivity of the hilly Karnaphuli watershed in Chattogram, Bangladesh. The study used topographical maps, soil maps, and satellite imagery datasets. It implemented the GIS-based AHP and weighted overlay technique to derive eight factors (slope, elevation, Stream Power Index (SPI), Land Use and Land Cover (LULC), curvature, soil, Topographic Wetness Index (TWI), and rainfall. The geological stage of erosion potential was also identified using Digital Elevation Model (DEM) data through GIS-based hypsometric analysis. The findings demonstrated that the eastern and north-western parts are particularly vulnerable to erosion compared to other parts of the study area. The most dominant variables identified to influence the process of soil erosion are slope, LULC, elevation, and SPI. According to the AHP analysis, slope was the most influential factor (26%), followed by LULC (23.8%), elevation (20.3%), and SPI (13.9%) in the soil erosion process, and the geological stage of erosion potential was determined from the hypsometric curve (S-shaped) and hypsometric integral (0.49), which revealed that moderately eroded areas characterized the whole research region. The findings are significant as they provide valuable information for researchers and planners to address soil erosion and develop measures to control it effectively.

The MAPSTROKE project: A computational strategy to improve access to acute stroke care.

BACKGROUND: Global access to acute stroke treatment is variable worldwide, with notable gaps in low and middle-income countries (LMIC), especially in rural areas. Ensuring a standardized method for pinpointing the existing regional coverage and proposing potential sites for new stroke centers is essential to change this scenario. AIMS: To create and apply computational strategies (CSs) to determine optimal locations for new acute stroke centers (ASCs), with a pilot application in nine Latin American regions/countries. METHODS: Hospitals treating acute ischemic stroke (AIS) with intravenous thrombolysis (IVT) and meeting the minimum infrastructure requirements per structured protocols were categorized as ASCs. Hospitals with emergency departments, noncontrast computed tomography (NCCT) scanners, and 24/7 laboratories were identified as potential acute stroke centers (PASCs). Hospital geolocation data were collected and mapped using the OpenStreetMap data set. A 45-min drive radius was considered the ideal coverage area for each hospital based on the drive speeds from the OpenRouteService database. Population data, including demographic density, were obtained from the Kontur Population data sets. The proposed CS assessed the population covered by ASCs and proposed new ASCs or artificial points (APs) settled in densely populated areas to achieve a target population coverage (TPC) of 95%. RESULTS: The observed coverage in the region presented significant disparities, ranging from 0% in the Bahamas to 73.92% in Trinidad and Tobago. No country/region reached the 95% TPC using only its current ASCs or PASCs, leading to the proposal of APs. For example, in Rio Grande do Sul, Brazil, the introduction of 132 new centers was suggested. Furthermore, it was observed that most ASCs were in major urban hubs or university hospitals, leaving rural areas largely underserved. CONCLUSIONS: The MAPSTROKE project has the potential to provide a systematic approach to identify areas with limited access to stroke centers and propose solutions for increasing access to AIS treatment. DATA ACCESS STATEMENT: Data used for this publication are available from the authors upon reasonable request.

Geospatial mapping of carbon estimates for forested areas using the InVEST model and Sentinel-2: A case study in Galicia (NW Spain).

CO2 emissions have increased exponentially in recent years, so measuring and quantifying carbon sequestration is a step towards sustainable forest management and combating climate change. The overall goal of this study is to develop an accurate model for estimating carbon storage and sequestration for forest areas of the Atlantic Biogeographic Region. Specifically, the modelling and field sampling are carried out in the municipality of Baiona (Galicia, NW Spain), which was selected as a representative biome of this region. The methodology consists of carrying out two object-based image analysis (OBIA) classifications in spring and autumn to observe possible stocks of seasonal differences. Two carbon storage and sequestration models are built up (model 1 and model 2): model 1 for forest areas only and model 2 including all other land cover in the study area. Sentinel-2 geospatial data for 2021, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools and geographic information systems (GIS) are used. A Kappa index of 0.92 is obtained for both classifications, thus ruling out any notable seasonal differences in the images used. The results from both models indicate that it is land covers associated with forest uses which store the most carbon in the study area, accounting for >50 % more than the other land covers. It is concluded that the methodology and data used are very useful for quantifying ecosystem services, which will help the governance of the region by implementing measures to mitigate some of the effects of climate change and help to create silvicultural models for the sustainable management of the Atlantic Biogeographic Region.

Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

BACKGROUND: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. METHODS: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. RESULTS: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15-2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18-5.93 in males and 1.24-5.38 in females). Furthermore, bladder cancer in males (RR:1.32-2.77) and thyroid cancer in females (RR:1.88-3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23-5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31-3.31 in males and 1.33-4.13 in females), and prostate cancer in males (RR:1.22-2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26-5.25) in central areas. CONCLUSIONS: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA.

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Examining rehabilitation access disparities: an integrated analysis of electronic health record data and population characteristics through bivariate choropleth mapping.

BACKGROUND: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients' social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients' social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. METHODS: This was a retrospective cohort study of patients' access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. RESULTS: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions. CONCLUSIONS: Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care.

Convergence of HIV and non-communicable disease epidemics: geospatial mapping of the unmet health needs in an HIV hyperendemic community in South Africa.

INTRODUCTION: As people living with HIV (PLHIV) are experiencing longer survival, the co-occurrence of HIV and non-communicable diseases has become a public health priority. In response to this emerging challenge, we aimed to characterise the spatial structure of convergence of chronic health conditions in an HIV hyperendemic community in KwaZulu-Natal, South Africa. METHODS: In this cross-sectional study, we used data from a comprehensive population-based disease survey conducted in KwaZulu-Natal, South Africa, which collected data on HIV, diabetes and hypertension. We implemented a novel health needs scale to categorise participants as: diagnosed and well-controlled (Needs Score 1), diagnosed and suboptimally controlled (Score 2), diagnosed but not engaged in care (Score 3) or undiagnosed and uncontrolled (Score 4). Scores 2-4 were indicative of unmet health needs. We explored the geospatial structure of unmet health needs using different spatial clustering methods. RESULTS: The analytical sample comprised 18 041 individuals. We observed a similar spatial structure for HIV among those with combined needs Score 2-3 (diagnosed but uncontrolled) and Score 4 (undiagnosed and uncontrolled), with most PLHIV with unmet needs clustered in the southern urban and peri-urban areas. Conversely, a high prevalence of need Scores 2 and 3 for diabetes and hypertension was mostly distributed in the more rural central and northern part of the surveillance area. A high prevalence of need Score 4 for diabetes and hypertension was mostly distributed in the rural southern part of the surveillance area. Multivariate clustering analysis revealed a significant overlap of all three diseases in individuals with undiagnosed and uncontrolled diseases (unmet needs Score 4) in the southern part of the catchment area. CONCLUSIONS: In an HIV hyperendemic community in South Africa, areas with the highest needs for PLHIV with undiagnosed and uncontrolled disease are also areas with the highest burden of unmet needs for other chronic health conditions, such as diabetes and hypertension. Our study has revealed remarkable differences in the distribution of health needs across the rural to urban continuum even within this relatively small study site. The identification and prioritisation of geographically clustered vulnerable communities with unmet health needs for both HIV and non-communicable diseases provide a basis for policy and implementation strategies to target communities with the highest health needs.