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

UNASSIGNED: Extracorporeal cardiopulmonary resuscitation (eCPR) is a promising treatment that could improve survival for refractory out-of-hospital (OHCA) patients. Healthcare systems may choose to start eCPR in the prehospital setting to optimize time to eCPR initiation and decrease low-flow time. We used geospatial modeling to evaluate different eCPR catchment strategies for a forthcoming prehospital eCPR program in Houston, Texas.
UNASSIGNED: We studied OHCAs treated by the Houston Fire Department from 2013-2021. We included OHCA patients aged 18-65 years old with an initial shockable rhythm that did not have prehospital return of spontaneous circulation (ROSC). Based on the geolocation that each OHCA occurred, we used geospatial modeling to identify eCPR candidates using four mapping strategies based on distance/drive time from the eCPR center: 1) 15-minute drive time, 20-minute drive time, 10-mile drive distance, and 15-mile drive distance.
UNASSIGNED: Of 18,501 OHCAs during the study period, 881 met the eCPR inclusion criteria. Compared to non-eCPR candidates, eCPR candidates were younger (median age 52.3 years vs 62.7 years, p < 0.01) and had a higher proportion of males (76.6% v 59.8%, p < 0.01). Of eCPR candidate OHCAs, OHCAs occurred more frequently during the weekdays and the daytime, with 5:00 PM being the most common time. Using geospatial modeling and based on drive time, 219 OHCAs (24.9% of 881) were within a 15-minute drive, and 454 (51.5%) were within a 20-minute drive. Using drive distance, 383 eCPR candidates (43.5%) were within 10 miles, and 703 (79.8%) were within 15 miles.
UNASSIGNED: Using geospatial modeling, we demonstrated a process to estimate potential eCPR patient volumes for a geographic region. Geospatial modeling represents a viable strategy for healthcare systems to delineate eCPR catchment areas.

BACKGROUND: The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system.
OBJECTIVE: This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data.
METHODS: A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version.
RESULTS: The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13).
CONCLUSIONS: The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

The main objective of the current study was to use seven lots in Hartford, CT that are planned for community reuse to determine the optimal sampling density that allows for the detection of hotspots of lead pollution while limiting the labor of the sampling process. The sampling density was investigated using soil Pb measured by in situ X-ray Fluorescence as the indicator to evaluate soil health, with a new threshold of 200-mg/kg proposed by the USEPA in January of 2024. Even though this study takes place in an urban setting, where the new USEPA policy requires the use of a 100-mg/kg threshold for Pb due to the fact that there are other identifiable sources of the contaminant, only the 200-mg/kg threshold is discussed because it is evident from the analysis that compliance of a 100 mg/kg threshold in urban plots is highly unlikely (five out of seven sites would require complete site excavation prior to reuse). Using the inverse distance weighted geospatial interpolation of in situ pXRF determined lead measurements, grid sampling resolutions of 3-m, 4-m, 5-m, 6-m, 8-m, 10-m, and 12-m were compared. Ultimately, the case study finds that the largest grid resolution that can be implemented for soil screening to maintain hotspots of pollution to properly inform soil management decisions is a 6-m grid, or a density of approximately 1/36-m2.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

BACKGROUND: Previous work has shown socioenvironmental factors can influence firearm injury. Milwaukee County, Wisconsin is a diverse midwestern county with historic disinvestment in marginalized communities yielding stark segregation along racial and ethnic lines. It is also one of the many U.S. counties burdened by surging firearm injuries. The differences among communities within Milwaukee County provides a unique opportunity to explore the intersection of socioenvironmental factors that may affect clinical outcomes and geospatial patterns of firearm injury.
METHODS: The trauma registry from the regional adult level 1 trauma center was queried for patients who sustained a firearm-related injury from 2015 to 2022 (N = 2402). The Social Vulnerability Index (SVI) ranking was derived using patient residence addresses to evaluate its association with traumatic injury clinical outcomes (i.e., in-hospital mortality, length of hospital stay, ICU or ventilator treatment, or injury severity score) and risk screening results for alcohol use disorder (AUD), posttraumatic stress disorder (PTSD), and depression. We evaluated hotspots of firearm injury density over time for patient residences and injury locations and distances between locations. A spatially lagged regression model tested the association between firearm injury density and SVI domains, alcohol outlet types, and park coverage.
RESULTS: Most firearm injury patients were younger, male, racial or ethnic minorities from disadvantaged neighborhoods (SVI total; M = 0.86, SD = 0.15). SVI was not associated with any clinical outcomes. Of those screened, 12.9% screened positive for AUD and 44.5% screened at risk for PTSD, depression, or both. Hotspot analysis indicated consistent concentrations of firearm injury density. There were no differences in clinical outcomes between those injured inside or outside the home. Census tracts with lower socioeconomic status, greater off-premises and lower on-premises alcohol outlet density were associated with greater firearm injury density.
CONCLUSIONS: In Milwaukee County, firearm injury patients are injured in and often return to the same disadvantaged neighborhoods that may hamper recovery. Results replicate and expand previous work and implicate specific socioenvironmental factors for intervention and prevention of firearm injury.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination.

BACKGROUND: Location and environmental social determinants of health are increasingly important factors in both an individual\'s health and the monitoring of community-level public health issues.
OBJECTIVE: We aimed to measure the extent to which location obfuscation techniques, designed to protect an individual\'s privacy, can unintentionally shift geographical coordinates into neighborhoods with significantly different socioeconomic demographics, which limits the precision of findings for public health stakeholders.
METHODS: Point obfuscation techniques intentionally blur geographic coordinates to conceal the original location. The pinwheel obfuscation method is an existing technique in which a point is moved along a pinwheel-like path given a randomly chosen angle and a maximum radius; we evaluate the impact of this technique using 2 data sets by comparing the demographics of the original point and the resulting shifted point by cross-referencing data from the United States Census Bureau.
RESULTS: Using poverty measures showed that points from regions of low poverty may be shifted to regions of high poverty; similarly, points in regions with high poverty may be shifted into regions of low poverty. We varied the maximum allowable obfuscation radius; the mean difference in poverty rate before and after obfuscation ranged from 6.5% to 11.7%. Additionally, obfuscation inadvertently caused false hot spots for deaths by suicide in Cook County, Illinois.
CONCLUSIONS: Privacy concerns require patient locations to be imprecise to protect against risk of identification; precision public health requires accuracy. We propose a modified obfuscation technique that is constrained to generate a new point within a specified census-designated region to preserve both privacy and analytical accuracy by avoiding demographic shifts.

Discerning the impact of anthropogenic impacts requires the implementation of bioindicators that quantify the susceptibilities and vulnerabilities of natural terrestrial and aquatic ecosystems to perturbation and transformation. Although legal regulations in Brazil recognize the value of bioindicators in monitoring water quality, the depreciation of soil conditions has yet to receive adequate attention. Thus, our study aimed to evaluate the potential of odonates (dragonflies and damselflies) as amphibiotic bioindicators to reflect the correlation between the degradation of aquatic and terrestrial habitats in pasture-dominated landscapes. We assessed the relationship between the biotic indices of Odonata and the conservation status of preserved riparian landscapes adjacent to anthropogenically altered pastures in 40 streams in the Brazilian savannah. Our results support the hypothesis that Odonata species composition may be a surrogate indicator for soil and water integrity, making them promising sentinels for detecting environmental degradation and guiding conservation strategies in human-altered landscapes. Importantly, while the Zygoptera/Anisoptera species ratio is a useful bioindicator tool in Brazilian forest, it is less effective in the open savannah here, and so an alternative index is required. Importantly, while the Zygoptera/Anisoptera species ratio is a useful bioindicator tool in Brazilian forest, it is less effective in the open savannah here, and so an alternative index is required. On the other hand, our results showed the Dragonfly Biotic Index to be a suitable tool for assessing freshwater habitats in Brazilian savannah. We also identified certain bioindicator species at both ends of the environment intactness spectrum.

BACKGROUND: Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation.
OBJECTIVE: This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies.
METHODS: We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health.
RESULTS: While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health.
CONCLUSIONS: The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.