Geospatial joint modeling of vector and parasite serology to microstratify malaria transmission.

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.

Socio-ecological gap analysis to forecast species range contractions for conservation.

Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species' range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species' ranges that are increasingly vital in an era of great socio-ecological change to mitigate human-wildlife conflict and promote inclusive carnivore conservation across geographies.

High-Resolution Geospatial Mapping of Zero-Dose and Underimmunized Children Following Nigeria's 2021 Multiple Indicator Cluster Survey/National Immunization Coverage Survey.

BACKGROUND: "Zero-dose" children are those who are without any routine vaccination or are lacking the first dose of the diphtheria, tetanus, and pertussis-containing vaccine. Based on global estimates from the World Health Organization/United Nations Children's Fund in 2022, Nigeria has the highest number of zero-dose children, with >2.3 million unvaccinated. METHODS: We used data from the 2021 Nigeria Multiple Indicator Cluster Survey/National Immunization Coverage Survey to identify zero-dose and underimmunized children. Geospatial modeling techniques were employed to determine the prevalence of zero-dose children and predict risk areas with underimmunized children at a high resolution (1 × 1 km). RESULTS: Zero-dose and underimmunized children are more prevalent in socially deprived groups. Univariate and multivariate bayesian analyses showed positive correlations between the prevalence of zero-dose and underimmunized children and factors such as stunting, contraceptive prevalence, and literacy. The prevalence of zero-dose and underimmunized children varies significantly by region and ethnicity, with higher rates observed in the country's northern parts. Significant heterogeneity in the distribution of undervaccinated children was observed. CONCLUSIONS: Nigeria needs to enhance its immunization system and coverage. Geospatial modeling can help deliver vaccines effectively to underserved communities. By adopting this approach, countries can ensure equitable vaccine access and contribute to global vaccination objectives.

Subnational Projections of Lymphatic Filariasis Elimination Targets in Ethiopia to Support National Level Policy.

BACKGROUND: Lymphatic filariasis (LF) is a debilitating, poverty-promoting, neglected tropical disease (NTD) targeted for worldwide elimination as a public health problem (EPHP) by 2030. Evaluating progress towards this target for national programmes is challenging, due to differences in disease transmission and interventions at the subnational level. Mathematical models can help address these challenges by capturing spatial heterogeneities and evaluating progress towards LF elimination and how different interventions could be leveraged to achieve elimination by 2030. METHODS: Here we used a novel approach to combine historical geo-spatial disease prevalence maps of LF in Ethiopia with 3 contemporary disease transmission models to project trends in infection under different intervention scenarios at subnational level. RESULTS: Our findings show that local context, particularly the coverage of interventions, is an important determinant for the success of control and elimination programmes. Furthermore, although current strategies seem sufficient to achieve LF elimination by 2030, some areas may benefit from the implementation of alternative strategies, such as using enhanced coverage or increased frequency, to accelerate progress towards the 2030 targets. CONCLUSIONS: The combination of geospatial disease prevalence maps of LF with transmission models and intervention histories enables the projection of trends in infection at the subnational level under different control scenarios in Ethiopia. This approach, which adapts transmission models to local settings, may be useful to inform the design of optimal interventions at the subnational level in other LF endemic regions.

Geospatial analysis of Plasmodium falciparum serological indicators: school versus community sampling in a low-transmission malaria setting.

BACKGROUND: Due to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling. METHODS: This study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti. RESULTS: Consistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman's rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09-0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35-0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47-0.49). CONCLUSIONS: In settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Association between the social vulnerability index and breast, cervical, and colorectal cancer screening rates in Rhode Island.

BACKGROUND: Cancer screening is effective in reducing the burden of breast, cervical, and colorectal cancers, but not all communities have appropriate access to these services. In this study, we aimed to identify under-resourced communities by assessing the association between the Social Vulnerability Index (SVI) with screening rates for breast, cervical, and colorectal cancers in ZIP-code tabulation areas (ZCTAs) in Rhode Island. METHODS: This study leveraged deidentified health insurance claims data from HealthFacts RI, the state's all-payer claims database, to calculate screening rates for breast, cervical, and colorectal cancers using Healthcare Effectiveness Data and Information Set measures. We used spatial autoregressive Tobit models to assess the association between the SVI, its four domains, and its 15 component variables with screening rates in 2019, accounting for spatial dependencies. RESULTS: In 2019, 73.2, 65.0, and 66.1% of eligible individuals were screened for breast, cervical, and colorectal cancer, respectively. For every 1-unit increase in the SVI, screening rates for breast and colorectal cancer were lower by 0.07% (95% CI 0.01-0.08%) and 0.08% (95% CI 0.02-0.15%), respectively. With higher scores on the SVI's socioeconomic domain, screening rates for all three types of cancers were lower. CONCLUSION: The SVI, especially its socioeconomic domain, is a useful tool for identifying areas that are under-served by current efforts to expand access to screening for breast, cervical, and colorectal cancer. These areas should be prioritized for new place-based partnerships that address barriers to screening at the individual and community level.

Geospatial Distribution of Prenatally and Postnatally Diagnosed Congenital Heart Disease: Implications for Equitable Care from a Fetal Heart Society Research Collaborative Study.

OBJECTIVE: To characterize patterns in the geospatial distribution of pre- and postnatally diagnosed congenital heart disease (CHD) across 6 surgical centers. STUDY DESIGN: A retrospective, multicenter case series from the Fetal Heart Society identified patients at 6 centers from 2012 through 2016 with prenatally (PrND) or postnatally (PoND) diagnosed hypoplastic left heart syndrome (HLHS) or d-transposition of the great arteries (TGA). Geospatial analysis for clustering was done by the average nearest neighbor (ANN) tool or optimized hot spot tool, depending on spatial unit and data type. Both point location and county case rate per 10 000 live births were assessed for geographic clustering or dispersion. RESULTS: Of the 453 CHD cases, 26% were PoND (n = 117), and 74% were PrND (n = 336). PrND cases, in all but one center, displayed significant geographic clustering by the ANN. Conversely, PoND cases tended toward geographic dispersion. Dispersion of PoND HLHS occurred in 2 centers (ANN = 1.59, P < .001; and 1.47, P = .016), and PoND TGA occurred in 2 centers (ANN = 1.22, P < .05; and ANN = 1.73, P < .001). Hot spot analysis of all CHD cases (TGA and HLHS combined) revealed clustering near areas of high population density and the tertiary surgical center. Hot spot analysis of county-level case rate, accounting for population density, found variable clustering patterns. CONCLUSION: Geographic dispersion among postnatally detected CHD highlights the need for a wider reach of prenatal cardiac diagnosis tailored to the specific needs of a community. Geospatial analysis can support centers in improving the equitable delivery of prenatal care.

Application of multivariate binary logistic regression grouped outlier statistics and geospatial logistic model to identify villages having unusual health-seeking habits for childhood malaria in Malawi.

BACKGROUND: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation. METHODS: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning. RESULTS: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka. CONCLUSIONS: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed.

Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.

BACKGROUND: Ghana is among the top 10 highest malaria burden countries, with about 20,000 children dying annually, 25% of which were under five years. This study aimed to produce interactive web-based disease spatial maps and identify the high-burden malaria districts in Ghana. METHODS: The study used 2016-2021 data extracted from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modelling and interactive web-based spatial disease mapping methods were employed to quantify spatial variations and clustering in malaria risk across 260 districts. For each district, the study simultaneously mapped the observed malaria counts, district name, standardized incidence rate, and predicted relative risk and their associated standard errors using interactive web-based visualization methods. RESULTS: A total of 32,659,240 malaria cases were reported among children < 5 years from 2016 to 2021. For every 10% increase in the number of children, malaria risk increased by 0.039 (log-mean 0.95, 95% credible interval = - 13.82-15.73) and for every 10% increase in the number of males, malaria risk decreased by 0.075, albeit not statistically significant (log-mean - 1.82, 95% credible interval = - 16.59-12.95). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.25 (95% credible interval = 1.23, 1.27). The malaria risk is relatively the same over the entire year. However, a slightly higher relative risk was recorded in 2019 while in 2021, residing in Keta, Abuakwa South, Jomoro, Ahafo Ano South East, Tain, Nanumba North, and Tatale Sanguli districts was associated with the highest malaria risk ranging from a relative risk of 3.00 to 4.83. The district-level spatial patterns of malaria risks changed over time. CONCLUSION: This study identified high malaria risk districts in Ghana where urgent and targeted control efforts are required. Noticeable changes were also observed in malaria risk for certain districts over some periods in the study. The findings provide an effective, actionable tool to arm policymakers and programme managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 for limited public health resource settings, where universal intervention across all districts is practically impossible.

Geospatial Analysis of Social Vulnerability, Race, and Firearm Violence in Chicago.

INTRODUCTION: Urban firearm violence (UFV) is associated with inequities rooted in structural racism and socioeconomic disparities. Social vulnerability index (SVI) is a composite measure that encompasses both. We sought to understand the relationship between SVI and the incidence of UFV in Chicago using geospatial analysis for the first time. MATERIALS AND METHODS: Firearm assaults in Chicago 2001-2019 were obtained from the Trace. Locations of incidents were geocoded using ArcGIS and overlaid with census tract vector files. These data were linked to 2018 SVI measures obtained from the Center for Disease Control and Prevention. Shooting rates were calculated by tabulating the total number of shootings per capita in each census tract. We used Poisson regression with robust error variance to estimate the incident rate of UFV in different levels of social vulnerability and Local Moran's I to evaluate spatial autocorrelation. RESULTS: In total, 642 census tracts were analyzed. The median shooting rate was 2.6 per 1000 people (interquartile 0.77, 7.0). When compared to those census tracts with very low SVI, census tracts with low SVI had a 1.7-time increased incident rate of shootings (incidence rate ratio [IRR] 1.74, 95% CI 1.08, 2.81), tracts with moderate SVI had a 3.1-time increased incident rate (IRR 3.07, 95% CI 2.31, 4.10), and tracts with high SVI had a 7-time increased incident rate (IRR 7.03, 95% CI 5.45, 9.07). CONCLUSIONS: In Chicago, social vulnerability has a significant association with rates of firearm violence, providing a focus point for policy intervention to address high rates of interpersonal violence in similar cities.

The Scalding Truth: Geospatial Analysis Identifies Communities at Risk for Pediatric Scald Burns.

INTRODUCTION: Pediatric scald burns account for 12% of all U.S. burn center admissions and are the most common type of burn in children. We hypothesized that geospatial analysis of burn registry data could identify specific geographic areas and risk factors to focus injury prevention efforts. METHODS: The burn registry of a U.S. regional burn center was used to retrospectively identify pediatric scald burn patients ages 0-17, from January 2018 to June 2023. Geocoding of patient home addresses with census tract data was performed. Area Deprivation Index (ADI) was assigned to patients at the census block group level. Burn incident hot spot analysis to identify statistically significant burn incident clusters was done using the Getis Ord Gi∗ statistic. RESULTS: There were 950 pediatric scald burn patients meeting study criteria. The cohort was 52% male and 36% White, with median age of 3 y and median total body surface area of 1.5%; 23.8% required hospital admission. On multivariable logistic regression, increased child poverty levels (P = 0.004) and children living in single-parent households (P = 0.009) were associated with increased scald burn incidence. Geospatial analysis identified burn hot spots, which were associated with higher ADI (P < 0.001). Black patients were more likely to undergo admission compared to White patients. CONCLUSIONS: Geospatial analysis of burn registry data identified geographic areas at high risk of pediatric scald burn. ADI, poverty, and children in single-parent households were the greatest predictors of injury. Addressing these inequalities requires targeted injury prevention education, enhanced outpatient support systems and more robust community resources.

Capacity and capability of remote sensing to inform invasive plant species management in the Pacific Islands region.

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

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

The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis.

Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.

Achieving Decentralized, Electrified, and Decarbonized Ammonia Production.

The rapid reduction in the cost of renewable energy has motivated the transition from carbon-intensive chemical manufacturing to renewable, electrified, and decarbonized technologies. Although electrified chemical manufacturing technologies differ greatly, the feasibility of each electrified approach is largely related to the energy efficiency and capital cost of the system. Here, we examine the feasibility of ammonia production systems driven by wind and photovoltaic energy. We identify the optimal regions where wind and photovoltaic electricity production may be able to meet the local demand for ammonia-based fertilizers and set technology targets for electrified ammonia production. To compete with the methane-fed Haber-Bosch process, electrified ammonia production must reach energy efficiencies of above 20% for high natural gas prices and 70% for low natural gas prices. To account for growing concerns regarding access to water, geospatial optimization considers water stress caused by new ammonia facilities, and recommendations ensure that the identified regions do not experience an increase in water stress. Reducing water stress by 99% increases costs by only 1.4%. Furthermore, a movement toward a more decentralized ammonia supply chain driven by wind and photovoltaic electricity can reduce the transportation distance for ammonia by up to 76% while increasing production costs by 18%.

Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research.

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

Utilization of geospatial distribution in the measurement of study cohort representativeness.

OBJECTIVE: The ability to apply results from a study to a broader population remains a primary objective in translational science. Distinct from intrinsic elements of scientific rigor, the extrinsic concept of generalization requires there be alignment between a study cohort and population in which results are expected to be applied. Widespread efforts have been made to quantify representativeness of study cohorts. These techniques, however, often consider the study and target cohorts as monolithic collections that can be directly compared. Overlooking known impacts to health from socio-demographic and environmental factors tied to individual's geographical location, and potentially obfuscating misalignment in underrepresented population subgroups. This manuscript introduces several measures to account for geographic information in the assessment of cohort representation. METHODS: Metrics were defined across two themes: First, measures of recruitment, to assess if a study cohort is drawn at an expected rate and in an expected geographical pattern with respect to individuals in a reference cohort. Second, measures of individual characteristics, to assess if the individuals in the study cohort accurately reflect the sociodemographic, clinical, and geographic diversity observed across a reference cohort while accounting for the geospatial proximity of individuals. RESULTS: As an empirical demonstration, methods are applied to an active clinical study examining asthma in Black/African American patients at a US Midwestern pediatric hospital. Results illustrate how areas of over- and under-recruitment can be identified and contextualized in light of study recruitment patterns at an individual-level, highlighting the ability to identify a subset of features for which the study cohort closely resembled the broader population. In addition they provide an opportunity to dive deeper into misalignments, to identify study cohort members that are in some way distinct from the communities for which they are expected to represent. CONCLUSION: Together, these metrics provide a comprehensive spatial assessment of a study cohort with respect to a broader target population. Such an approach offers researchers a toolset by which to target expected generalization of results derived from a given study.

Impact of Geospatial Food Access on Acute Pancreatitis Outcomes.

BACKGROUND AND AIM: Food access is an important social determinant of health and refers to geographical and infrastructural aspects of food availability. Using publicly available data on food access from the United States Department of Agriculture (USDA), geospatial analyses can identify regions with variable food access, which may impact acute pancreatitis (AP), an acute inflammatory condition characterized by unpredictable outcomes and substantial mortality. This study aimed to investigate the association of clinical outcomes in patients with AP with geospatial food access. METHODS: We examined AP-related hospitalizations at a tertiary center from January 2008 to December 2018. The physical addresses were geocoded through ArcGIS Pro2.7.0 (ESRI, Redlands, CA). USDA Food Access Research Atlas defined low food access as urban areas with 33% or more of the population residing over one mile from the nearest food source. Regression analyses enabled assessment of the association between AP outcomes and food access. RESULTS: The study included 772 unique patients with AP residing in Massachusetts with 931 AP-related hospitalizations. One hundred and ninety-eight (25.6%) patients resided in census tracts with normal urban food access and 574 (74.4%) patients resided in tracts with low food access. AP severity per revised Atlanta classification [OR 1.88 (95%CI 1.21-2.92); p = 0.005], and 30-day AP-related readmission [OR 1.78(95%CI 1.11-2.86); p = 0.02] had significant association with food access, despite adjustment for demographics, healthcare behaviors, and comorbidities (Charlson Comorbidity Index). However, food access lacked significant association with AP-related mortality (p = 0.40) and length of stay (LOS: p = 0.99). CONCLUSION: Low food access had a significant association with 30-day AP-related readmissions and AP severity. However, mortality and LOS lacked significant association with food access. The association between nutrition, lifestyle, and AP outcomes warrants further prospective investigation.

Geospatial Analysis for Prehospital Extracorporeal Cardiopulmonary Resuscitation in Houston, Texas.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Intersecting social determinants of health among patients with childcare needs: a cross-sectional analysis of social vulnerability.

INTRODUCTION: Access to childcare is an understudied social determinant of health (SDOH). Our health system established a childcare facility for patients to address childcare barriers to healthcare. Recognizing that social risk factors often co-exist, we sought to understand intersecting social risk factors among patients with childcare needs who utilized and did not utilize the childcare facility and identify residual unmet social needs alongside childcare needs. METHODS: We conducted a cross-sectional analysis of patients who enrolled in the childcare facility from November 2020 to October 2022 to compare parameters of the Social Vulnerability Index (SVI) associated with the census tract extracted from electronic medical record (EMR) data among utilizers and non-utilizers of the facility. Overall SVI and segmentation into four themes of vulnerability (socioeconomic status, household characteristics, racial/ethnic minority status, and housing type/transportation) were compared across utilizers and utilizers. Number of 90th percentile indicators were also compared to assess extreme levels of vulnerability. A sample of utilizers additionally received a patient-reported social needs screening questionnaire administered at the childcare facility. RESULTS: Among 400 enrollees in the childcare facility, 70% utilized childcare services and 30% did not. Utilizers and non-utilizers were demographically similar, though utilizers were more likely to speak Spanish (34%) compared to non-utilizers (22%). Mean SVI was similar among utilizers and non-utilizers, but the mean number of 90th percentile indicators were higher for non-utilizers compared to utilizers (4.3 ± 2.7 vs 3.7 ± 2.7, p = 0.03), primarily driven by differences in the housing type/transportation theme (p = 0.01). Non-utilizers had a lower rate of healthcare utilization compared to utilizers (p = 0.02). Among utilizers who received patient-reported screening, 84% had one unmet social need identified, of whom 62% agreed for additional assistance. Among social work referrals, 44% were linked to social workers in their medical clinics, while 56% were supported by social work integrated in the childcare facility. CONCLUSIONS: This analysis of SDOH approximated by SVI showed actionable differences, potentially transportation barriers, among patients with childcare needs who utilized a health system-integrated childcare facility and patients who did not utilize services. Furthermore, residual unmet social needs among patients who utilized the facility demonstrate the multifactorial nature of social risk factors experienced by patients with childcare needs and opportunities to address intersecting social needs within an integrated intervention. Intersecting social needs require holistic examination and multifaceted interventions.

Effectiveness of human immunodeficiency virus prevention strategies by mapping the geographic dispersion pattern of human immunodeficiency virus prevalence in Nanning, China.

BACKGROUND: The Guangxi government initiated two rounds of the Guangxi AIDS Conquering Project (GACP) in 2010 (Phase I) and 2015 (Phase II) to control human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemics. However, the effectiveness of GACP in HIV prevention and treatment has rarely been reported. This study aimed to assess the effectiveness of the GACP implemented in Guangxi, China and provide data for strategy and praxis improvements to achieve Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95 targets. METHODS: We used spatial approaches to trace the spatiotemporal distribution properties, epidemic trends, and correlation between macroscopic factors and HIV incidence using data from the Chinese HIV/AIDS case reporting system to explore the effects of the GACP. RESULTS: During the GACP era, the HIV epidemic stabilized in urban centers, showing a downward trend in the Hengzhou and Binyang Counties in the eastern region, whereas it continued to increase in rural areas of the northwest region, such as the Long'an, Mashan, Shanglin, and Wuming Districts. The linear directional mean (LDM) of HIV infection reported cases displayed a southeast-northwest direction, with an LDM value of 12.52°. Compared with that in Phase I, Hengzhou withdrew from the high-high clustering area, and the west-north suburban counties pulled out the low-low clustering area during Phase II. Significant HIV clusters were identified in the eastern region during Phase I, whereas these clusters emerged in the northwestern areas during Phase II. Regarding HIV, socioeconomic status, population mobility, and medical care levels were the key social drivers of heterogeneous spatial distribution. CONCLUSIONS: The GACP assisted in effectively managing the HIV epidemic in urban and eastern areas of Nanning City. However, prevention and control efforts in rural regions, particularly those located in the northwest, may not have yielded comparable outcomes. To address this disparity, allocating additional resources and implementing tailored intervention measures for these rural areas are imperative.