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.

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.

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.

Individual and Community level Developmental Adversities: Associations with Marijuana and Alcohol Use in Late-Adolescents and Young Adults

Exposure to community and individual level stressors during adolescence has been reported to be associated with increased substance use. However, it remains unclear what the relative contribution of different community- and individual-level factors play when alcohol and marijuana use become more prevalent during late adolescence. The present study uses a large longitudinal sample of adolescents (Wave 1: N = 2017; 55% Female; 54.5% White, 22.3% Black, 8% Hispanic, 15% other) to evaluate the association and potential interactions between community- and individual-level factors and substance use from adolescence to young adulthood (Wave 1 to Wave 3 Age Mean [SD]: 16.7 [1.1], 18.3 [1.2], 19.3 [1.2]). Across three waves of data, multilevel modeling (MLM) is used to evaluate the association between community affluence and disadvantage, individual household socioeconomic status (SES, measured as parental level of education and self-reported public assistance) and self-reported childhood maltreatment with self-reported 12-month alcohol and 12-month marijuana use occasions. Sample-selection weights and attrition-adjusted weights are accounted for in the models to evaluate the robustness of the estimated effects. Across the MLMs, there is a significant positive association between community affluence and parental education with self-reported alcohol use but not self-reported marijuana use. In post hoc analyses, higher neighborhood affluence in older adolescents is associated with higher alcohol use and lower use in younger adolescents; the opposite association is found for neighborhood disadvantage. Consistent with past literature, there is a significant positive association between self-reported childhood maltreatment and self-reported 12-month alcohol and 12-month marijuana use. Results are largely consistent across weighted and unweighted analyses, however, in weighted analyses there is a significant negative association between community disadvantage and self-reported 12-month alcohol use. This study demonstrates a nuanced relationship between community- and individual-level factors and substance use during the transitional window of adolescence which should be considered when contextualizing and interpreting normative substance use during adolescence.

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.

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9-12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

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.

The offshore wind energy potential of Morocco: Optimal locations, cost analysis, and socioenvironmental examination

Morocco, a country without substantial fossil fuels, is emerging as a leader in renewable energy. This article addresses an untapped natural resource in Morocco: offshore wind. Methodologically, we use geographic information systems to assess optimal locations for offshore wind energy plants based on wind speed and power density from the Global Wind Atlas. We incorporate geological, environmental, and economic parameters and calculate capital expenses, operating expenses, development costs, and decommissioning costs to determine if the wind plant will provide a return on investment during its lifetime. The results identify three locations near the cities of Dakhla, Boujdour, and Essaouira that would recoup their investment in 14, 13, and 18 years, respectively, along with supplying 21.8% of Morocco's current energy needs for 2022. We conclude by considering the implications that the new plants would have for Morocco in the social, political, environmental, and economic dimensions. Integr Environ Assess Manag 2024;20:201-210. © 2023 SETAC.

Urban green and blue spaces and general and mental health among older adults in Washington state: Analysis of BRFSS data between 2011-2019

A growing body of evidence highlights the significant effect of built environment features on mental and general health. This study examined the association between distance to and percentage of green and blue space measures and serious psychological distress, general health, and frequent mental distress among older adults living in urban ZIP codes in Washington state. Percentage of green space, particularly tree canopy and forest space, was significantly associated with better self-rated general health and reduced odds of serious psychological distress. Closer distance to blue space was associated with better self-rated general health. Programs which incentivize exposure to green and blue spaces for urban-dwelling, older adults may improve health outcomes.

Spatial methods for measuring access to health care

Access to health care is a universal human right and key indicator of health system performance. Spatial access encompasses geographic factors mediating with the accessibility and availability of health services. Equity of health service access is a global issue, which includes access to the specialized nursing workforce. Nursing research applying spatial methods is in its infancy. Given the use of spatial methods in health research is a rapidly developing field, it is timely to provide guidance to inspire greater application in cardiovascular research. Therefore, the objective of this methods paper is to provide an overview of spatial analysis methods to measure the accessibility and availability of health services, when to consider applying spatial methods, and steps to consider for application in cardiovascular nursing research.

GIS based method for mapping actual LULC by combining seasonal LULCs

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Pluvial flood adaptation using nature-based solutions: An integrated biophysical-economic assessment

Globally, flood events are considered the costliest natural hazard. Changes in precipitation patterns and large areas of impervious surfaces in urban environments are increasing the sensitivity of these systems to runoff production. At the same time, projected global sea-level rise may further increase the frequency of compound flooding due to simultaneous storm surge, sea-level rise and pluvial runoff that cause vast socio-economic and ecological impacts to coastal cities. In this context, over the last decade, the role of Nature-Based Solutions (NBS) has been recognised to support climate change adaptation by addressing ideas of multi-functionality, non-linearity and heterogeneity in urban design. Thus, increasing awareness about NBS benefits increases the willingness to accept these solutions. However, empirical evidence of NBS effectiveness at the urban catchment scale is still subject to debate. This study develops a spatial biophysical-economic framework that allows for the integrated assessment of NBS flood risk mitigation impacts, costs and benefits in the face of climate change, combining the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, benefit transfer methods and Geographic Information System (GIS) tools. Specifically, the InVEST Urban Flood Risk Mitigation model was used to assess the biophysical impacts of NBS on urban pluvial flood risk, benefit-transfer methods were used to evaluate the economic implications of such solutions, and GIS was used to integrate and map biophysical impacts and economic implications. For the case of the coastal lagoon city of Aveiro (Portugal), NBS scenarios of green roofs and bioswales under current and future climate conditions were assessed. The main findings of this study show that green roofs scenarios would save 32 % of the flood damages to buildings and infrastructures every year, while bioswales help save only 0.1 %. Moreover, green roofs implementation provides larger benefits in the future climate scenario (representative concentration pathway - RCP - 4.5). The findings confirm the extent to which knowledge on NBS benefits and costs is partial and uncertain, thus requiring constant progress through biophysical-economic assessment to support an evolutive decision making process in climate adaptation planning.

'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.

Multi-hazard susceptibility model based on high spatial resolution data-a case study of Sali settlement (Dugi otok, Croatia)

The world has been facing an increase in various natural hazards. The coastal regions are recognized as one of the most vulnerable due to high population pressure and climate change intensity. Mediterranean countries have one of the most burnable ecosystems in the world, one of the most exposed to pluvial floods, and have the highest erosion rates within the EU. Therefore, the aim of this study was to develop the first multihazard susceptibility model in Croatia for the Sali settlement (island of Dugi otok). The creation of a multi-hazard susceptibility model (MHSM) combined the application of geospatial technology (GST) with a local perception survey. The methodology consisted of two main steps: (1) creating individual hazard susceptibility models (soil erosion, wildfires, pluvial floods), and (2) overall hazard susceptibility modeling. Multicriterial GIS analyses and the Analytical Hierarchy Process were used to create individual hazard models. Criteria used (32) to create models are derived from very-high-resolution (VHR) models. Two versions of MHSM are created: 1) all criteria with equal weighting coefficients and 2) weight coefficients determined based on public perception. According to MHSM 1, most of the research (58%) area is moderately susceptible to multiple hazards. Highly and very highly susceptible areas are 27% of the drainage basin and are mostly located near roads and houses. MHSM 2 reveals similar results to MHSM 1. The public perceives that the research area is the most susceptible to wildfires. The wildfire ignition risk is ranked as moderate (3.00) with a standard deviation of 1.16. Pluvial flood risk is ranked low (2.78), with a standard deviation of 1.15. The risk of soil is most inferior (2.24) with a standard deviation of 0.91. The the most significant difference between public perception and the GIS-MCDA model of hazard susceptibility is related to soil erosion. However, the accuracy of the soil erosion model was confirmed by ROC curves based on recent traces of soil erosion in the research area. The proposed methodological framework of multi-hazard susceptibility modeling can be applied, with minor modifications, to other Mediterranean countries.

Residential green and blue spaces and working memory in children aged 6-12 years old. Results from the INMA cohort

Availability of green and blue spaces in the area of residence has been related to various health outcomes during childhood, including neurodevelopment. Some studies have shown that children living in greener and/or bluer areas score better on cognitive tasks although the evidence is inconsistent. These protective effects are hypothesized to occur in part through reductions in air pollution exposure and odds of attention-deficit/hyperactivity disorder (ADHD). This study analysed the effects of residential green and blue spaces on working memory of children in the Spanish INfancia y Medio Ambiente (INMA) birth cohort and the potential joint mediating role of air pollution and ADHD. The study samples were composed of 1738 six-to eight-year-olds (M = 7.53, SD = 0.68, 49% female) and 1449 ten-to twelve-year-olds (M = 11.18, SD = 0.69, 50% female) living in Asturias, Gipuzkoa, Sabadell or Valencia, Spain. Individual Normalized Difference Vegetation Index (NDVI) values in 100-, 300- and 500-m buffers and availability of green and blue spaces >5000 m2 in 300-m buffers were calculated using Geographic Information Systems software. Individual NO2 values for the home environment were estimated using ESCAPE's land use regression models. ADHD diagnosis was reported by participants' parents via a questionnaire. Working memory was measured with numbers and colours (in the younger group only) N-back tests (2- and 3-back d'). Mixed-effects models informed of the beneficial effects of NDVI in a 300-m buffer on numerical working memory in the younger sample although the results were not consistent for all d' scores considered and failed to detect significant effects through the candidate mediators. Availability of major blue spaces did not predict working memory performance. Provision of green spaces may play a role in children's working memory but further research is required.

Habitat suitability mapping using logistic regression analysis of long-term bioacoustic bat survey dataset in the Cassadaga Creek watershed (USA)

Bat species show global ecological importance, yet their numbers are declining worldwide. Understanding bat-habitat interactions is crucial in terms of developing effective conservation plans. In an effort to model bat habitat suitability in the Cassadaga Creek watershed, long-term bioacoustic bat data (spanning 2009-2020) was compiled, georeferenced and statistically analyzed using logistic regression techniques. In total, 1600 bat occurrence records from five species of bat (559 Eptesicus fuscus, 560 Lasionycteris noctivagans, 143 Lasiurus borealis, 260 Lasiurus cinereus, and 78 Myotis lucifugus) were paired with pseudo-absence points to study the relationship between bat calling behavior and land cover. All bats but Myotis lucifugus had a statistically significant relationship with forested land cover, and all bats had negative interactions with agricultural habitats. Geospatial data was coupled with the statistical output to create maps of habitat suitability and echolocation calling density. This work provides a model that can be employed worldwide to evaluate bat habitat needs or patterns in echolocation behavior. Future research will incorporate a more recently collected dataset that is of greater geographic diversity with a larger number of environmental variables in the species distribution model.

Geospatial patterns of progress towards UNAIDS '95-95-95' targets and community vulnerability in Zambia: insights from population-based HIV impact assessments

INTRODUCTION

In sub-Saharan Africa, HIV/AIDS remains a leading cause of death. The UNAIDS established the '95-95-95' targets to improve HIV care continuum outcomes. Using geospatial data from the Zambia Population-based HIV Impact Assessment (ZAMPHIA), this study aims to investigate geospatial patterns in the '95-95-95' indicators and individual-level determinants that impede HIV care continuum in vulnerable communities, providing insights into the factors associated with gaps.

METHODS

This study used data from the 2016 ZAMPHIA to investigate the geospatial distribution and individual-level determinants of engagement across the HIV care continuum in Zambia. Gaussian kernel interpolation and optimised hotspot analysis were used to identify geospatial patterns in the HIV care continuum, while geospatial k-means clustering was used to partition areas into clusters. The study also assessed healthcare availability, access and social determinants of healthcare utilisation. Multiple logistic regression models were used to examine the association between selected sociodemographic and behavioural covariates and the three main outcomes of study.

RESULTS

Varied progress towards the '95-95-95' targets were observed in different regions of Zambia. Each '95' displayed a unique geographical pattern, independent of HIV prevalence, resulting in four distinct geographical clusters. Factors associated with gaps in the '95s' include younger age, male sex, and low wealth, with younger individuals having higher odds of not being on antiretroviral therapy and having detectable viral loads.

CONCLUSIONS

Our study revealed significant spatial heterogeneity in the HIV care continuum in Zambia, with different regions exhibiting unique geographical patterns and levels of performance in the '95-95-95' targets, highlighting the need for geospatial tailored interventions to address the specific needs of different subnational regions. These findings underscore the importance of addressing differential regional gaps in HIV diagnosis, enhancing community-level factors and developing innovative strategies to improve local HIV care continuum outcomes.

Examining spatial variation for immunisation coverage in pregnant women: A nationwide and geospatial retrospective cohort study in Aotearoa New Zealand

BACKGROUND

Maternal influenza and pertussis immunisation is crucial for protecting mothers during pregnancy and their babies in the first weeks of life against severe disease. We examined geospatial variation in maternal immunisation coverage among pregnant women in Aotearoa New Zealand and its health equity implications.

METHOD

We constructed a retrospective cohort including all pregnant women who delivered between 01 January 2013 and 31 December 2020 using administrative health datasets. Our outcomes were receipt of influenza or pertussis vaccine in any one of three relevant national databases (e.g. National Immunisation Register, Proclaims, or Pharmaceutical collection) during the eligible pregnancy.

RESULTS

Data from our retrospective cohort study show significant regional variation in maternal immunisation coverage for both influenza and pertussis from 2013 to 2020. Maximal coverage was around 50% in the best performing regions, which means that half of the women who were pregnant (183,737 women) were not protected. In addition, we found significant spatio-temporal variation and clustering of immunisation coverage. Our findings are interactively available to explore here: https://geohealthlab.shinyapps.io/hapumama/ CONCLUSION: Our study is one of the first to examine spatial variation in maternal vaccination coverage in pregnant women at a national level over space and time. This provides powerful tools to measure the impact of interventions to improve coverage at national and regional levels, with specific reference to inequities between ethnic groups, likely applicable to similar settings internationally.

Social inequalities, green and blue spaces and mental health in 6-12 years old children participating in the INMA cohort

Availability of green and blue spaces in the area of residence has been related to various health outcomes during childhood, including mental health. These environmental exposures are not evenly distributed among socioeconomic groups, which may increase social inequalities in mental health. The mechanisms through which natural environments may promote mental health are numerous and diverse. This study aimed to explore 1) the potential associations of socioeconomic variables (SES and maternal education attainment) with mental health scores and residential greenness, blueness and NO2 metrics, and, 2) the association between greenness and blueness metrics and mental health scores of children in the Spanish INfancia y Medio Ambiente (INMA) birth cohort at two different time points. The study samples were composed of 1738 six-to eight-year-olds (49% female) and 1449 ten-to twelve-year-olds (living in Asturias, Gipuzkoa, Sabadell and Valencia, Spain. Individual Normalized Difference Vegetation Index (NDVI) values in 100-, 300- and 500-m buffers and availability of green and blue spaces >5000 m2 in 300-m buffers were calculated using Geographic Information Systems software. Residential NO2 values were estimated using land use regression models. Internalizing, externalizing and total problems scores were obtained with the Strengths and Difficulties Questionnaire (SDQ). Linear and logistic mixed-effects models revealed unequal distribution of environmental exposures by SES and maternal education but did not show statistically significant associations between greenness and blueness metrics and mental health indicators. The protective effect of green and blue spaces on children's mental health could not be confirmed in this study and therefore further research is required.

Assessment of roadside pollution by heavy metals: A case study from the District of Bani Kinanah, Irbid, Northern Jordan

The District of Bani Kenanah is home to natural reserves, forests, local agricultural practices, and a multitude of historical and cultural sites. The majority of the population in the study area is employed in the agricultural sector, which is dominated by olive tree cultivation and the production of premium olive oil. The current study aimed to assess the degree of roadside soil pollution and predict the potential ecological impacts of Pb, Cd, Zn, Cr, Ni, Mn, Cu, Fe, and Co. Therefore, 30 soil samples were collected and analyzed with a Flame atomic absorption spectrophotometer for the aforementioned metals. The spatial distribution maps of these metals were generated by inverse distance weighted (IDW) interpolation in ArcGIS 10.3. The values of heavy metals (HMs) in the study were lower than the international standards for soil quality. The soil was classified as moderately to substantially enriched with Cd, Pb, and Zn based on the EF values. However, the indices of geo-accumulation (Igeo), contamination factor (CF), and potential ecological risk (RI) generally revealed minor metallic contamination. The traffic and agricultural activities were the potential sources of Cd, Pb, and Zn in the study area. Incorporating HMs analysis with GIS maps was beneficial for assessing soil pollution.

The development and validation of a nationwide dataset of water distribution zones in Aotearoa New Zealand: A cross-sectional geospatial study

The reliable supply of safe drinking water is vital for the health of human populations. Despite this, there is no consistent nationwide spatial dataset of water distribution zones (WDZ) for Aotearoa New Zealand (A-NZ). The purpose of this data article is to describe the development and validation of a consistent nationwide dataset of WDZ across A-NZ. We obtained spatial data from all 67 district and city councils through: 1) information requests between 2021 and 2023; 2) the Ministry of Health and; 3) the Institute of Environmental Science and Research. Data were modified to improve the spatial accuracy of the WDZ using auxiliary data on the building footprints (Land Information New Zealand) and the drinking water reticulation (WSP & councils). We estimated the population served by each WDZ through spatial linking to meshblock-level data provided by Statistics New Zealand (meshblocks are the smallest administrative geographic unit in A-NZ). The dataset will be useful to provide insights into the extent of the publicly-owned drinking water assets in A-NZ and is essential for the accurate exposure assessment in epidemiological research investigating the impact of drinking water quality on human health.

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200 mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Arbitrary absolute vs. individualized running speed thresholds in team sports: A scoping review with evidence gap map

The aims of this scoping review were (i) to characterize the main methodological approaches to assessing individualized running speed thresholds in team sports players; (ii) to assess the use of traditional arbitrary (absolute) thresholds compared to individualized running speed thresholds in team sports players; (iii) to provide an evidence gap map (EGM) about the approaches and study designs employed in investigations in team sports and (iv) to provide directions for future research and practical applications for the strength and conditioning field. Methods studies were searched for in the following databases: (i) PubMed; (ii) Scopus; (iii) SPORTDiscus and (iv) Web of Science. The search was conducted on 15/07/2022. Risk of bias was assessed using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). From 3,195 potentially relevant articles, 36 were eligible for inclusion in this review. Of the 36 included articles, 27 (75%) focused on the use of arbitrary and individualized running speed thresholds to describe the locomotor demands (e.g., high intensity running) of players. Thirty-four articles used individualized speed running thresholds based on physical fitness assessments (e.g., 40-m linear sprint) or physical performance (e.g., maximal acceleration). This scoping review supported the need for a greater focus to be placed on improving the methodological aspects of using individualized speed running thresholds in team sports. More than just creating alternatives to arbitrary thresholds, it is essential to increase the replicability of methodological conditions whilst ensuring that research comparing the most adequate measures and approaches to individualization takes into consideration the population and context of each study.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination

BACKGROUND

Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence.

METHODS

In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data.

RESULTS

The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways.

CONCLUSION

Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Examining Increasing Racial Inequities in Opioid Overdose Deaths: a Spatiotemporal Analysis of Black and White Decedents in St. Louis, Missouri, 2011-2021

The third wave of the opioid overdose crisis-defined by the proliferation of illicit fentanyl and its analogs-has not only led to record numbers of overdose deaths but also to unprecedented racial inequities in overdose deaths impacting Black Americans. Despite this racialized shift in opioid availability, little research has examined how the spatial epidemiology of opioid overdose death has also shifted. The current study examines the differential geography of OOD by race and time (i.e., pre-fentanyl versus fentanyl era) in St. Louis, Missouri. Data included decedent records from the local medical examiners suspected to involve opioid overdose (N = 4420). Analyses included calculating spatial descriptive analyses and conducting hotspot analyses (i.e., Gettis-Ord Gi*) stratified by race (Black versus White) and time (2011-2015 versus 2016-2021). Results indicated that fentanyl era overdose deaths were more densely clustered than pre-fentanyl era deaths, particularly those among Black decedents. Although hotspots of overdose death were racially distinct pre-fentanyl, they substantially overlapped in the fentanyl era, with both Black and White deaths clustering in predominantly Black neighborhoods. Racial differences were observed in substances involved in cause of death and other overdose characteristics. The third wave of the opioid crisis appears to involve a geographic shift from areas where White individuals live to those where Black individuals live. Findings demonstrate racial differences in the epidemiology of overdose deaths that point to built environment determinants for future examination. Policy interventions targeting high-deprivation communities are needed to reduce the burden of opioid overdose on Black communities.

A systematic review of geographic information systems based methods and criteria used for electric vehicle charging station site selection

Many studies have incorporated particular models with various methods and algorithms to resolve the site selection problem for electric vehicle charging stations (EVCS). This paper systematically reviews research that evaluates geographic information systems (GIS) based EVCS location techniques and the variables used for decision making. We classify and characterize those techniques and variables to determine important linkages within the literature. A variety of databases were referenced to extract research published from 2010 to March 2023 pertinent to this specific location optimization problem, and 74 papers were selected after thorough evaluation. The models used in each paper were examined along with the methods for selecting variables and ranking alternate locations. Site selection for EVCS requires a multi-criteria decision making approach to meet the sustainability, efficiency, and performance goals of communities adopting electric vehicle mobility. Our results indicate that map algebra and data overlay methods have been used more frequently with GIS-based analysis than other techniques, while geographic and demographic variables are commonly the most significant site selection characteristics. The reviewed methods have most often been applied to urban locations; however, the transfer of these techniques to a rural EVCS site selection problem has been rarely explored in the current literature. This research assessment contributes relevant guidance for the application of methodologies useful in policymaking and provides recommendations for future research based on these findings.

Distribution of dental practices in Jazan of Saudi Arabia: a GIS-based approach

BACKGROUND

Jazan region in the Kingdom of Saudi Arabia (KSA) has been extensively studied regarding access to dental care services, but there is currently no specific study on the distribution of public (primary healthcare centres (PHCs) and hospitals) and private dental healthcare facilities in the area. This study aimed to evaluate the spatial distribution of public and private dental care facilities in the Jazan region in relation to the population distribution in each governorate of the region.

METHODS

The most up-to-date, easily accessible, and anonymous data and information were used for this investigation. The Ministry of Health's (MOH) Statistical Yearbook 2020 and interactive map were used to identify the locations of healthcare facilities. These locations were plotted on a map using Google Maps, and the data was converted to longitude and latitude with 90% level building accuracy. QGIS's integrated database was used to develop buffer zones and perform attribute analysis. The data was then exported for analysis in Microsoft Excel, where healthcare facility-to-population ratios were determined.

RESULTS

In Jazan region, consisting of 17 governorates and a population of 1,726,739, there were 275 public and private dental clinics, with a ratio of one dental clinic per 6,279 people in terms of general health services. Only 12.4% of these clinics were located beyond 20 km of the city centre, serving approximately 70% of the region's population.

CONCLUSION

The uneven distribution of dental clinics in the Jazan region has hampered access to dental treatments and has led to a significant burden on dental health facilities, reducing the quality of care available in the region. Mapping the distribution of MOH, private, and other health facilities, as well as the burden of oral disease in the Jazan region, is necessary for further research.

The environment a young person grows up in is associated with their mental health: A nationwide geospatial study using the integrated data infrastructure, New Zealand

BACKGROUND

Mental health conditions often arise during adolescence, are multifaceted in aetiology, and may be related to the type of environment in which young people reside. This study used nationwide population-level data to investigate whether the environment a young person grows up in is associated with their mental health.

METHOD

Data were extracted from the Integrated Data Infrastructure (IDI), a large nationwide research repository, for 917,211 young people (aged 10-24 years) including sociodemographic and mental health data (i.e. emotional, behavioural, substance problems, and self-harm). Environmental data were sourced from the nationwide area-based Healthy Location Index (HLI), which has comprehensive data on the location of several health-constraining (i.e. fast-food outlets) and health-promoting features (i.e. physical activity facilities). Environments were classified as: i) health-promoting, ii) health-constraining, or iii) neither. Associations between the HLI and mental health were investigated using multi-level mixed effects logistic regression modelling.

RESULTS

Overall, there was evidence of an association between the environment a young person resided in and their mental health. Young people residing in health-constraining environments had higher odds of any mental health condition (Adjusted Odds Ratio (AOR) = 1.020 [1.001, 1.040]) and any emotional condition (AOR = 1.037 [1.012, 1.062]). Young people residing in health-promoting environments had lower odds of substance problems (AOR = 0.950 [0.905, 0.997]). There were no significant effects of the environment on behavioural conditions.

CONCLUSION

Our study utilises a large national sample of almost one million young people to confirm the importance of environmental determinants for mental health. It is possible that leverage points for improving the mental health of young people, and reducing the burden to the health system of mental health, can be sought in upstream environmental based interventions.

An efficient region expansion algorithm for regular triangulated meshes

Region expansion-the growth of regions to include all points within a certain distance of their perimeters-is a basic, widely applicable operation, but is expensive to perform exactly. It has been shown that, if the solution is approximated by relaxing the distance metric to the L∞-norm, efficiency can be greatly improved using properties of quadtrees. The method as described, however, requires the quadtrees to be square, both for the metric and the particular details of the algorithm. In some cases, such as spherical surface approximation, it is desirable for the quadtree nodes to be triangular instead. In this work, we thus describe an adaptation of the L∞-norm metric and the previously described algorithm to allow efficient approximation of region expansion in images represented as regular triangulated meshes. Like the original method for square quadtrees, our algorithm achieves sublinear time with respect to expansion radius.

Modelling hydrological factors from DEM using GIS

Hydrological modelling is a precondition for many scientific researches such as species distribution models, ecological models, agricultural suitability models, climatological models, hydrological models, flood and flash flood models, landslide models etc. Even the topographic control over many hydrological factors has also been studied. Over time different hydrological models have been developed and extensively used. Recently, these models have been used to prepare different types of conditional factors that are widely used in hazard modelling such as floods, flash floods, landslides etc. Quantitative analysis of the Digital Elevation Model (DEM) according to different models by engaging Geographic Information Systems (GIS) supports users to extract various types of information about landscapes where hydrological and topographic information are most important. Methods to prepare hydrological factors namely TWI, TRI, SPI, STI, TPI, stream density and distance to stream by processing DEM in GIS are discussed in this paper. These common hydrological factors are extensively used in many scientific research papers either for modelling or to measure their relationship with other environmental factors.•Hydrological factors have great importance in understanding the landscape and are widely used in scientific research, especially geo-environmental hazard mapping.•Physically based hydrological methods are engaged in ArcMap 10.5 software.•Commonly used hydrological factors are processed using freely available DEM and ArcMap 10.5 software.

Trends in Quick-Service Restaurants near Public Schools in the United States: Differences by Community, School, and Student Characteristics

BACKGROUND

More than one-third of children and adolescents consume foods from quick-service restaurants (QSRs) daily, which is associated with an increased risk of diet-related adverse health conditions.

OBJECTIVE

To examine trends in the proximity of top-selling QSR chains to all public schools across the United States between 2006 and 2018 by community-, school-, and student-level characteristics.

DESIGN

This longitudinal study examined changes in the number QSRs between the 2006-2007 and 2017-2018 school years using data from National Center for Education Statistics, Infogroup US Historical Business Data, and the US Department of Agriculture's Economic Research Service.

STATISTICAL ANALYSIS

A mixed-model analysis of variance using census tract as a random effect and accounting for repeated measures by school was used to examine the proximity of QSRs near schools. Models adjusted for demographic characteristics and census tract population density. Data were analyzed in 2021.

RESULTS

During 2006, 9% of schools had QSRs within 400 m, and 25% of schools in the most populated areas had at least one QSR within 400 m. There were more QSRs near schools with a high percentage of poverty (12%), and near schools with high school students with the highest population of Black or African American (16%) and Hispanic or Latino (18%) students. By 2018, the percent of all public schools within 400 m of QSRs increased to 12%. The increase over time was greater near schools with a high percentage of poverty (16%) and near schools with high school students with the highest population of Black or African American students (22%) and Hispanic or Latino (23%) students.

CONCLUSIONS

This is the first nationwide study to examine trends in QSR proximity to all public schools. QSRs were most likely to be located near schools with high school students, near schools with a high percentage of poverty, and near schools with a higher proportion of racial and ethnic minority students. Over time, there were greater increases in QSRs near these schools which may have important implications for children's health and diet-related disparities.

Assessment of the relationship of the salt-covered area and the groundwater storage/drought indicators in the disappearing Lake Tuz in Turkey (1985-2021)

This study aims to reveal the relationship of the expansion of the salt-covered area of Lake Tuz in Turkey with the drought in the region and the change of the groundwater storage. The changes in the salt-covered area in Lake Tuz between 1985 and 2021 (37 years), which have been obtained by August satellite images, are compared with those in groundwater and the 12-month Standardized Precipitation-Evapotranspiration Index (SPEI) variations in the lake. The results show that the salt-covered area on the lake surface was most strongly correlated with the SPEI spring (-0.78, t test; p < 0.01) during the period of August 1985-2021, implying that the drought conditions in spring determines 62% of the salt-covered area on the lake in August. The groundwater storage (GWS) in June and July and the salt-covered area in August in Lake Tuz also depict a moderate correlation of -0.60 at 1% significance level from 1985 to 2021. The results illustrate that the SPEI and GWS values decreased, while the salt-covered area on the lake surface expanded in the Lake Tuz Basin over the past 37 years. The trends of the salt-covered area, SPEI, and GWS became especially pronounced after 1999, during which the SPEI change and similar changing trends appeared in the GWS and salt-covered area. These patterns of increase in drought severity and decreasing groundwater storage are expected to increasingly compromise the future of Lake Tuz and cause ecological and environmental problems in the near future.

Frameworks for mapping lake ecosystem services. An example from Lithuania

Lake ecosystems are essential for human well-being. Due to its transboundary nature, connecting terrestrial and aquatic habitats, its environmental processes and functions support a wide array of ecosystem services (ES). Hence, despite its importance in supporting socio-ecologic systems, these freshwater ecosystems are highly impacted due to anthropogenic influence. ES may shed light on the importance of maintaining and restoring lake ecosystems. Hence, despite the increased effort in the last decades to provide robust spatial information, most scientific studies still follow a qualitative approach that carries a great deal of uncertainty and is not uptake by decision-makers. It is necessary to develop quantitative and robust methodologies for mapping ES, especially in the aquatic realm. This study develops several frameworks to map ES in lake ecosystems. Important to highlight is that this study.•develops a quantitative multi-method (biophysical and statistical) toolbox for lake ES multi-temporal mapping.•covers five ES from Provisioning, Regulating & Maintenance, and Cultural sections, namely: (1) Fibres and other materials for construction; (2) Water used for non-drinking purposes; (3) Maintenance of nursery populations; (4) nutrient regulation; and (5) recreation.•addresses three dimensions of ES: supply, flow; and demand.

Spatial Patterns and Determinants of Diabetes Mellitus in Indian Adult Population: a Secondary Data Analysis from Nationally Representative Surveys

INTRODUCTION

Diabetes mellitus (DM) is a major public health challenge around the world. It is crucial to understand the geographic distribution of the disease in order to pinpoint high-priority locations and focus intervention on the target populations. Hence, this study was carried out to determine the spatial pattern and determinants of type-2 DM in an Indian population using National Family Health Survey-4 (NFHS-4) and Longitudinal Aging Survey in India (LASI).

METHODS

We have adopted an ecological approach, wherein geospatial analysis was performed using aggregated district-level data from NFHS-4 (613 districts) and LASI survey datasets (632 districts). Moran's I statistic was determined and Local Indicators of Spatial Association (LISA) maps were created to understand the spatial clustering pattern of DM. Spatial regression models were run to determine the spatial factors associated with DM.

RESULTS

Prevalence of self-reported DM among males (15-50 years) and females (15-49 years) was 2.1% [95% confidence interval (CI) 2.0-2.3%] and 1.7% (95% CI 1.6-1.8%), respectively. Prevalence of self-reported DM among males and females aged 45 years and above was 12.5% (95% CI 11.5-13.5%) and 10.9% (95% CI 9.8-12%). Positive spatial autocorrelation with significant Moran's I was found for both males and females in both NFHS-4 and LASI data. High-prevalence clustering (hotspots) was maximum among the districts belonging to southern states such as Kerala, Tamil Nadu, Karnataka, and Andhra Pradesh. Northern and central states like Madhya Pradesh, Chhattisgarh, and Haryana mostly had clustering of cold spots (i.e., lower prevalence clustered in the neighboring regions).

CONCLUSION

DM burden in India is spatially clustered. Southern states had the highest level of spatial clustering. Targeted interventions with intersectoral coordination are necessary across the geographically clustered hotspots of DM.

Characterizing the perception of urban spaces from visual analytics of street-level imagery

This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across two points in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession. We hypothesize specific socioeconomic processes can be substantially reflected in the built environment of cities and, thus, render themselves visible at the street level. This paper describes the process of incorporating subjective visual ratings across two datasets of temporally separated street-view images, an algorithm, and a visualization tool. This work serves as a case study for utilizing AI and visualization techniques in a richer characterization of urban spaces that includes both objective metrics such as income (that operates at a broader scale) and subjective metrics such as perception of individuals (that operates at a narrower scale at specific locations). We outline an interdisciplinary methodology to test this hypothesis in streetscape data from Raleigh, NC, from 2008 to 2020. We describe the results of training algorithms that utilized image features with crowdsourced human perception ratings. We provide a comparison of the results with income data. The analysis and interpretation of this comparison provide insight into the challenges and opportunities for using AI technology in characterizing changes in urban environments. One challenge is the ability of human domain experts to interpret the output of algorithms through manipulation and to integrate these results into their workflow. This is addressed with a novel interface designed for interactive analysis and visualization. We conclude with a discussion of some of the benefits and limitations of integrating AI models in the human expert's decision-making process in the presence of both subjective and objective metrics.

ToxPi*GIS Toolkit: creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS

BACKGROUND

Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps.

OBJECTIVE

We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public.

METHODS

The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability.

RESULTS

Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States.

SIGNIFICANCE

The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org .

Geographically regulated designs of incidence surveys can match the precision of classical survey designs whilst requiring smaller sample sizes: the case of snakebite envenoming in Sri Lanka

Background

Snakebite envenoming is a neglected tropical disease. Data from the worst affected countries are limited because conducting epidemiological surveys is challenging. We assessed the utility of inhibitory geostatistical design with close pairs (ICP) to estimate snakebite envenoming incidence.

Methods

The National Snakebite Survey (NSS) in Sri Lanka adopted a multistage cluster sampling design, based on population distribution, targeting 1% of the country’s population. Using a simulation-based study, we assessed predictive efficiency of ICP against a classical survey design at different fractions of the original sample size of the NSS. We also assessed travel distance, time taken to complete the survey, and sensitivity and specificity for detecting high-risk areas for snake envenoming, when using these methods.

Results

A classical survey design with 33% of the original NSS sample size was able to yield a similar predictive efficiency. ICP yielded the same at 25% of the NSS sample size, a 25% reduction in sample size compared with a classical survey design. ICP showed >80% sensitivity and specificity for detecting high-risk areas of envenoming when the sampling fraction was >20%. When ICP was adopted with 25% of the original NSS sample size, travel distance was reduced by >40% and time to conduct the survey was reduced by >75%.

Conclusions

This study showed that snakebite envenoming incidence can be estimated by adopting an ICP design with similar precision at a lower sample size than a classical design. This would substantially save resources and time taken to conduct epidemiological surveys and may be suited for low-resource settings.

The CIVISANO protocol: a mixed-method study about the role of objective and perceived environmental factors on physical activity and eating behavior among socioeconomically disadvantaged adults

BACKGROUND

Overweight and obesity have a strong socioeconomic profile. Unhealthy behaviors like insufficient physical activity and an unbalanced diet, which are causal factors of overweight and obesity, tend to be more pronounced in socioeconomically disadvantaged groups in high income countries. The CIVISANO project aims to identify objective and perceived environmental factors among different socioeconomic population groups that impede or facilitate physical activity and healthy eating behavior in the local context of two peri-urban Flemish municipalities in Belgium. We also aim to identify and discuss possible local interventions and evaluate the participatory processes of the project.

METHODS

This study (2020-2023) will use community-based participatory tools, involving collaborative partnerships with civic and stakeholder members of the community and regular exchanges among all partners to bridge knowledge development and health promotion for socioeconomically disadvantaged citizens. Furthermore, a mixed-methods approach will be used. A population survey and geographic analysis will explore potential associations between the physical activity and eating behaviors of socioeconomically disadvantaged adults (25-65 years old) and both their perceived and objective physical, food and social environments. Profound perceptive context information will be gathered from socioeconomically disadvantaged adults by using participatory methods like photovoice, walk-along, individual map creation and group model building. An evaluation of the participatory process will be conducted simultaneously.

DISCUSSION

The CIVISANO project will identify factors in the local environment that might provoke inequities in adopting a healthy lifestyle. The combination of perceived and objective measures using validated strategies will provide a robust assessment of the municipality environment. Through this analysis, the project will investigate to what extent community engagement can be a useful strategy to reduce health inequities. The strong knowledge exchange and capacity-building in a local setting is expected to contribute to our understanding of how to maximize research impact in this field and generate evidence about potential linkages between a health enhancing lifestyle among socioeconomically disadvantaged groups and their physical, food and social environments.

Validating a geospatial healthfulness index with self-reported chronic disease and health outcomes

Leveraging community engagement from past research may yield frameworks on which to build new inquiries. We previously integrated community voice into the development of a healthfulness index to increase awareness of social determinants of health in the built environment and inform deployment of public health interventions in the Flint (Michigan, USA) Center for Health Equity Solutions. Here we combine the healthfulness index with self-reported chronic disease and health outcomes (n = 12,279) from a community-based healthcare entity, the Genesee Health Plan. The healthfulness index purports to predict how health-promoting a neighborhood is based on many spatially varying characteristics; by linking our health plan data to this index, we validate the effectiveness of the healthfulness index. After geocoding all enrollees and joining their healthfulness scores, we conducted a series of logistic regressions to compare the relationship between self-reported outcomes and healthfulness. Matching the two intervention projects of our center (revolving around healthy eating & physical activity in project 1 and mental health sustainment & substance use prevention in project 2), our analyses also focused on classes of outcomes related to a) cardiovascular disease and b) mental health. In only select cases, higher (better) healthfulness scores from each project were independently associated with better cardiovascular and mental health outcomes, controlling for age, race, and sex. Generally, however, healthfulness did not add predictive strength to the association between health and sociodemographic covariates. Even so, the use of composite healthfulness indices to describe the health-promoting or degrading qualities of a neighborhood could be valuable in identifying differences in health outcomes. Future researchers could further explore healthcare claims datasets to increase understanding of the links between healthfulness and health outcomes. This and future work will be valuable in advocacy toward additional healthfulness indices to aid other communities in enriching understanding between the built environment and health.

Associated factors with patient-reported unmet food needs among emergency department adult patients - A social need perspective

Food insecurity is a complex problem affected by a number of factors from individual to societal. While individual-level demographic information and population-level social determinants of health (SDoH) are commonly used to identify patients at risk of food insecurity and to direct resources, a more comprehensive understanding of food insecurity requires integrating multi-level data. Our goal is to identify factors associated with food insecurity using patient, health system, and population level data. Between January 2019 and April 2020, we screened adult patients visiting an academic health sciences emergency department in Utah using a 10-item social needs screener. Patients' demographic data were linked to their screener responses. ZIP Code-level food-related SDoH such as accessibility to food providers, measured by geographic information systems methods, were assigned to patients. We then applied multilevel logistic regression modeling to identify factors associated with unmet food needs at two different levels-individual and ZIP Code. Unmet food needs were identified by asking patients if they felt there was not enough money for food in the last month, which grossly represents food insecurity. On a sample of 2,290 patients, 21.61% reported unmet food needs. Patient-reported housing, medical care, and utility needs along with Supplemental Nutrition Assistance Program participation and primary care provider utilization were highly associated with unmet food needs. Our efforts to identify the population at risk of food insecurity should be centered around patient-reported social needs. Our results suggest that addressing food insecurity in health care settings should include assessing social needs in primary care.

Retail food environment in a Brazilian metropolis over the course of a decade: evidence of restricted availability of healthy foods

OBJECTIVE

To evaluate changes in the retail food environment profile in a Brazilian metropolis over a 10-year period.

DESIGN

An ecological study was conducted in the city of Belo Horizonte, Minas Gerais, Brazil. The addresses of formal food establishments were geocoded and classified according to their sold-food profile. Density changes were analysed according to neighbourhood, population size, income level and geospatial distribution.

SETTING

Totally, 468 neighbourhoods in the city of Belo Horizonte, Minas Gerais, Brazil.

PARTICIPANTS

Totally, 83 752 formal food establishments registered for operation in any one or more of those years: 2008, 2011, 2015 and 2018.

RESULTS

There was an increase in unhealthy establishments (154 %), followed by mixed (51 %) and healthy establishments (32 %), during the period evaluated, in addition to an increase in density according to income categories. There was a higher proportion of unhealthy establishments in relation to healthy establishments, indicating worsening of the community food environment over time.

CONCLUSIONS

Over the course of 10 years, changes in the neighbourhood's food environment were unfavourable for adequate access to healthy foods in lower-income neighbourhoods. The findings reinforce the need for interventions aimed at increasing the availability of businesses that offer healthy food in the city.

Geographic accessibility to public dental practices in the Jazan region of Saudi Arabia

BACKGROUND

It is impossible to attain good general health without maintaining oral health and this becomes problematic when dental services are located far from the population that needs to utilise them. This study aimed to assess the geographic accessibility of dental clinics located in public primary healthcare clinics (PHCs) and hospitals in the Jazan region of Saudi Arabia and how long it takes to reach them by car and on foot.

METHODS

The location of clinics and hospitals, maps of road systems, and the governorates' borders (administrative areas) within the Jazan region were downloaded using the QGIS mapping tool. The time taken to travel to the clinics and hospitals, either by driving or walking, was assessed. If the time was 30 min or less, residents in the area were classified as 'serviced'. It was more than 30 min, they were 'underserved'.

RESULTS

Only 31% of Jazan residents were found to live in a serviced area if they drove to clinics and hospitals. Residents of Jazan's seven mountainous governorates were more likely to require services. Only 40% could drive less than 30 min to a primary health dental clinic. Only 19% of people could walk to a hospital in less than 30 min. Only two governorates had a majority of residents who lived in serviced areas.

CONCLUSION

The study demonstrates an accessibility issue, as many Jazan inhabitants must drive or walk for an extended period (> 30 min) to reach a healthcare facility, whether a primary health care centre or a hospital. This issue may result in many people not receiving necessary health care, compromising their oral health status. Additional research is needed to identify public, private and other health facilities in the region and the prevalence of oral disease.

Geographic distribution of colonoscopy providers in the United States: An analysis of medicare claims data

INTRODUCTION

Screening colonoscopy is one of the few procedures that can prevent cancer. While the majority of colonoscopies in the USA are performed by gastroenterologists, general surgeons play a key role in at-risk, rural populations. The aim of this study was to examine geographic practice patterns in colonoscopy using a nationwide Medicare claims database.

METHODS AND PROCEDURES

The 2017 Medicare Provider Utilization and Payment database was used to identify physicians performing colonoscopy. Providers were classified as gastroenterologists, surgeons, ambulatory surgical centers (ASCs), or other. Rural-Urban Commuting Area classification at the zip code level was used to determine whether the practice location for an individual provider was in a rural area/small town (< 10,000 people), micropolitan area (10-50,000 people), or metropolitan area (> 50,000 people).

RESULTS

Claims data from 3,861,187 colonoscopy procedures on Medicare patients were included. The majority of procedures were performed by gastroenterologists (57.2%) and ASCs (32.1%). Surgeons performed 6.8% of cases overall. When examined at a zip code level, surgeons performed 51.6% of procedures in small towns/rural areas and 21.7% of procedures in micropolitan areas. Individual surgeons performed fewer annual procedures as compared to gastroenterologists (median 51 vs. 187, p < 0.001).

CONCLUSIONS

Surgeons perform the majority of colonoscopies in rural zip codes on Medicare patients. High-quality, surgical training in endoscopy is essential to ensure access to colonoscopy for patients outside of major metropolitan areas.

Conclusions of the African Regional GIS Summit (2019): using geographic information systems for public health decision-making

The use of geographic information system (GIS) technologies to improve access to health is gaining momentum in Africa. This has become more pertinent with the increasing penetration of mobile-phone technology and internet use, and calls for innovative strategies to support implementation of the World Health Organization Sustainable Development Goals for universal health coverage on the continent. The huge potential benefits of GIS to advance health service delivery in Africa is, however, yet to be fully harnessed due to critical challenges such as proliferation of pilot projects, poor coordination, inadequate preparedness of the health workforce for GIS, lack of interoperability, and inadequate sustainable financing. To discuss these challenges and propose the way forward for rapid, cost-effective, and sustainable deployment of GIS, the African Regional GIS Summit was held in Brazzaville, Republic of the Congo, on 7–10 October 2019 under the umbrella of the AFRO GIS Centre.

Delineating natural catchment health districts with routinely collected health data from women's travel to give birth in Ghana

Background

Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators.

Methods

A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.

Results

Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.

Conclusion

Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.

Mapping agricultural use of pesticides to enable research and environmental health actions in Belgium

Given the many public health and environmental impacts associated with the use of pesticides, comprehensive pesticide application data are a high priority for environmental and health professionals, government agencies, and community groups in Wallonia (Belgium). In that context, geographic information system (GIS) approaches for mapping estimates of agricultural pesticide use were developed in the present study. Data on pesticide application rates and high-resolution annual datasets of the geographic distribution of crops were used to complete this analysis in Wallonia over the period 2015-2017. The method was implemented in Python in order to allow easy update and improvements of maps, or to segment maps by individual pesticides, chemical groups of pesticides (e.g. insecticides, herbicides), etc. Linked databases were created to classify, select, and possibly weight AIs according to specific requests and criteria. The results provide a first map of agricultural pesticide use in Wallonia, which depicts the best picture up to now of their geographic distribution. Maps of fungicides, herbicides, and plant growth regulators showed quite similar spatial patterns as the map of the combination of all pesticides. In contrast, the insecticide map showed a specific pattern related almost exclusively to dwarf-tree orchards in some municipalities in northern Wallonia. This research work is a preliminary result on the spatial characterization of agricultural pesticide use in Wallonia and give a valuable basis for research and environmental health actions in Belgium. Forthcoming developments will focus on exposure characterization to agricultural pesticides using GIS models. Using this information, policymakers will able to detect potential priority zones and take action to check and reduce agricultural pesticide loads in the environment.

Potential risk of colonization of Bulinus globosus in the mainland of China under climate change

Background

Bulinus globosus, the main intermediate snail host of Schistosoma haematobium. The increased contacts between Africa and China could even lead to large-scale dissemination of B. globosus in China. Temperature is the key factor affecting fresh-water snail transmission. This study predicted potential risk of colonization of B. globosus in the mainland of China under climate change.

Methods

We investigated minimum and maximum temperatures for B. globosus eggs, juveniles and adult snails kept under laboratory conditions to find the most suitable range by pinpointing the median effective temperatures (ET50). We also assessed the influence of temperature on spawning and estimated the accumulated temperature (AT). The average air temperatures between 1955 and 2019 in January and July, the coldest and hottest months in China, respectively, were collected from national meteorological monitoring stations and investigated in a geographic information system (GIS) using empirical Bayesian Kriging to evaluate the theoretical possibility for distribution of B. globosus in southern China based on temperature.

Results

The effective minimum temperature (ET50_min) for eggs, juveniles, adult snails and spawning were 8.5, 7.0, 7.0, 14.9 °C, respectively, with the corresponding maximum values (ET50_max) of 36.6, 40.5, 40.2 and 38.1 °C. The AT was calculated at 712.1 ± 64.9 °C·d. In 1955, the potential B. globosus distribution would have had a northern boundary stretching from the coastal areas of Guangdong Province and Guangxi Autonomous Region to southern Yunnan Province. Since then, this line has gradually moved northward.

Conclusions

Annual regeneration of B. globosus can be supported by the current climate conditions in the mainland of China, and a gradual expansion trend from south to north is shown in the study from 2015 to 2019. Thus, there is a potential risk of colonization of B. globosus in the mainland of China under climate change.

Graphical Abstract

The environment an adult resides within is associated with their health behaviours, and their mental and physical health outcomes: a nationwide geospatial study

BACKGROUND

The determinants of health behaviours and health outcomes are multifaceted and the surrounding environment is increasingly considered as an important influence. This pre-registered study investigated the associations between the geospatial environment people live within and their health behaviours as well as their mental and physical health outcomes.

METHOD

We used the newly developed Healthy Location Index (HLI) to identify health-promoting and health-constraining environmental features where people live. We then used Time 10 (2018) data from the New Zealand Attitudes and Values Survey (NZAVS; N = 47,951), a national probability sample of New Zealand adults, to gauge mental health outcomes including depression, anxiety and psychological distress, physical health outcomes including BMI and type II diabetes, and health behaviours such as tobacco smoking and vaping. Linear and logistic multilevel mixed effect regression models with random intercepts of individuals nested within geographical areas (meshblocks) were employed.

RESULTS

The presence of health-constraining environmental features were adversely associated with self-reported mental health outcomes of depression, anxiety and psychological distress, physical health outcomes of BMI and type II diabetes, and negative health behaviours of tobacco smoking and vaping. By contrast, health-promoting environmental features were uniquely associated only with physical health outcomes of BMI and type II diabetes.

CONCLUSION

The current study advances research on environmental determinants of health behaviours by demonstrating that close proximity to health-constraining environmental features is related to a number of self-reported physical and mental health outcomes or behaviours. We provide some evidence to support the notion that preventive population-health interventions should be sought.

Assessing Response Readiness to Health Emergencies: A Spatial Evaluation of Health and Socio-Economic Justice in Pakistan

COVID19 pandemic has put the global health emergency response to the test. Providing health and socio-economic justice across communities/regions helps in resilient response. In this study, a Geographic Information Systems-based framework is proposed and demonstrated in the context of public health-related hazards and pandemic response, such as in the face of COVID19. Indicators relevant to health system (HS) and socio-economic conditions (SC) are utilized to compute a response readiness index (RRI). The frequency histograms and the Analysis of Variance approaches are applied to analyze the distribution of response readiness. We further integrate spatial distributional models to explore the geographically-varying patterns of response readiness pinpointing the priority intervention areas in the context of cross-regional health and socio-economic justice. The framework's application is demonstrated using Pakistan's most developed and populous province, namely Punjab (districts scale, n = 36), as a case study. The results show that ~ 45% indicators achieve below-average scores (value < 0.61) including four from HS and five from SC. The findings ascertain maximum districts lack health facilities, hospital beds, and health insurance from HS and more than 50% lack communication means and literacy-rates, which are essential in times of emergencies. Our cross-regional assessment shows a north-south spatial heterogeneity with southern Punjab being the most vulnerable to COVID-like situations. Dera Ghazi Khan and Muzaffargarh are identified as the statistically significant hotspots of response incompetency (95% confidence), which is critical. This study has policy implications in the context of decision-making, resource allocation, and strategy formulation on health emergency response (i.e., COVID19) to improve community health resilience.

A Cross-Sectional Geographic Information Systems Study of a Pediatric Emergency Department Child Restraint System Distribution Program

INTRODUCTION

A pediatric ED program sought to promote injury prevention through distribution of child restraint systems. Program funds are paid for child passenger safety technician certification of all personnel. Pediatric emergency nurses distributed child restraint systems at hospital discharge and dedicated technicians at fitting stations. Researchers described program characteristics, developed a baseline understanding of program outreach using geographic information systems, and evaluated adherence to manufacturer guidelines with a sensitivity analysis.

METHODS

This retrospective cross-sectional study used distribution forms linked to hospital records from 2013 to 2016. Testing for differences used nonparametric methods. Median values and interquartile ranges for weight and height of children were compared with manufacturer guidelines. Geographic information systems visualized recipients' street addresses and motor vehicle crashes on an underlying base map.

RESULTS

There were 312 child restraint systems distributed: of which 179 (57.4%) at the hospital, 126 (40.4%) at fitting stations, and 7 (2.2%) missing a location. Among those on Medicaid, 64.4% received a child restraint system at the hospital compared with 35.6% at fitting stations (χ² = 5.40, P < .02). Fitting stations had limited outreach to rural residents. Finally, results from the sensitivity analysis showed that devices were issued according to manufacturer guidelines.

DISCUSSION

Despite the workplace pressures of clinical care, pediatric emergency nurses delivered educational information and demonstrated hands-on installation at similar rates to dedicated technicians. Distribution of child restraint systems through the hospital reached a uniquely underserved population. Further research should investigate methods to improve fitting station outreach among Medicaid recipients.