First national mapping of cadmium in cacao beans in Colombia.

Cacao crops in Colombia play a fundamental role in its economy. Cacao bean exports have a rising tendency due to increased area and production. However, cadmium (Cd) in cacao beans has affected market in Colombia. The aim of this study is to assess Cd distribution hotspots for cacao beans, Cd in beans and soil relationships, geological ages and soil chemical parameters, overall Cd dynamics, soil-to-plant translocation, and EU regulation impacts on Cd presence in Colombian cacao production. A hot and cold spot analysis combined with a cluster and outlier analysis was used to build the geographic Cd distribution in cacao beans in Colombia. Cadmium in the subsurface and the relationship with Cd in the soil surface of cacao farms were analysed using the geoaccumulation index (Igeo). The bioconcentration factor (BCF) and soil chemical features in cacao beans were quantified. Positive correlations were observed within all the studied variables except for Mn and Fe. The highest correlation was observed within Ca, pH, and Cd in soil. The Valanginian-Albian and Albian-Maastrichtian geological ages are directly related to Cd in beans, Zn, P, soil Cd, and Fe. Regions with the highest cacao productivity and export registered an average Cd concentration in beans of 2.39_ ± _2.82 mg kg-1. Two municipalities in Santander were 'partially contaminated' according to Igeo. The BCF was not directly proportional to soil and bean Cd level and did not fully coincide with hot spots. This evidence reinforces the idea that even areas with apparently low Cd levels may register a latent problem associated with genetic/ontogenetic, climatic, or management factors. This first distribution map of Cd in cacao beans constitutes a tool to implement actions to minimize risks and face challenges posed by the imposed regulations that may limit raw cacao bean exports in Colombia.

Disparities in Spatial Access to Sleep Health Care in the United States: A Population-Based Geospatial Analysis.

OBJECTIVES: To examine spatial access to sleep health care in the United States and investigate associations with demographic and socioeconomic characteristics, thereby identifying high-risk communities with limited spatial access to sleep health service. DESIGN: A cross-sectional population-based geospatial analysis. SETTINGS AND PARTICIPANTS: Residents in US Census tracts across the 48 contiguous states, Alaska, and Hawaii. METHODS: The 2020 American Community Survey 5-year estimates, 2010 rural-urban commuting area codes, 2020 Area Deprivation Index, and sleep care provider locations from the National Provider Identifier file were used to assess the spatial access and related demographic/socioeconomic characteristics. Spatial access was measured by spatial access ratio using enhanced 2-step floating catchment area methods. The associations were investigated using logistic regression analysis and multivariate linear regression analysis. RESULTS: A total of 45.8 million residents experienced low spatial access to sleep health care. Spatial access decreased in rural and high Area Deprivation Index areas, and in areas characterized by higher population with uninsured status, vehicle unavailability, internet unavailability, cognitive difficulties, and hearing difficulties. With a 10% increase in the percentage of the racial minority (non-white) population, metropolitan census tracts experienced an increase in spatial access (3.268%), whereas micropolitan (-1.526%) and rural (-4.493%) areas experienced a decrease in spatial access. Similar findings were observed within the ethnic minority (Hispanic or Latino) population. CONCLUSIONS AND IMPLICATIONS: Disparities exist in spatial access to sleep health care across the United States, especially for disadvantaged individuals. Racial/ethnic minorities exhibit contrasting spatial access patterns in urban and rural areas, with those in rural areas facing more challenges in spatial access to sleep health care.

The impact of digital inequities on salivary gland cancer disparities in the United States.

INTRODUCTION: Technology and internet access have become increasingly integrated into healthcare as the primary platform for health-related information and provider-patient communication. Disparities in access to digital resources exist in the United States and have been shown to impact health outcomes in various head and neck malignancies. Our objective is to evaluate the associations of digital inequity on health outcomes in patients with salivary gland cancer (SGC). METHODS: The Digital Inequity Index (DII) was developed using 17 census-tract level variables obtained from the American Community Survey and Federal Communications Commission. Variables were categorized as digital infrastructure or sociodemographic (e.g., non-digital) and scored based on relative rankings across all US counties. Scores were assigned to patients from the Surveillance-Epidemiology-End Results (SEER) database diagnosed with SGC between 2013 and 2017 based on county-of-residence. Regressions were performed between DII score and outcomes of surveillance time, survival time, tumor stage at time of diagnosis, and treatment modality. RESULTS: Among 9306 SGC-patients, increased digital inequity was associated with advanced-staging at presentation (OR: 1.04, 95% CI: 1.01-1.07, p = 0.033), increased odds of chemotherapy receipt (OR: 1.05, CI: 1.01-1.10, p = 0.010), and decreased odds of surgical intervention (OR: 0.94, 95% CI: 0.91-0.98, p = 0.003) after accounting for traditional sociodemographic factors. Increased digital inequity was also associated with decreased surveillance time and survival periods. CONCLUSIONS: Digital inequity significantly and independently associates with negative health and treatment outcomes in SGC patients, highlighting the importance of directed efforts to address these seldom-investigated drivers of health disparities.

Geospatial Analysis of Pediatric Burns Reveals Opportunities for Injury Prevention.

BACKGROUND: Geospatial analysis is useful for identifying hot spots for preventable injuries and for informing prevention efforts. We hypothesize that specific populations of children in South Florida are at increased risk of burn injury. METHODS: We used a regional burn center registry to geocode burn cases treated from July 2013 to December 2022 for patients <18 years. Spatial analysis was utilized to identify high-density areas and potential spatial clusters of patients living in Palm Beach, Broward, and Miami-Dade Counties. Sociodemographic factors, burn etiology, and physiologic characteristics were analyzed using geospatial and statistical analyses. RESULTS: 689 patients (58% male, median age 2 [1-8] years) were identified. The annual incidence of burns was 5.5 per 100,000 children. There was no seasonal variation in injury patterns. Most patients were Black (51%) and non-Hispanic (73%). Scald burns (72%) represented the most common etiology, followed by flame (10%) and contact with hot objects (9%). Most patients (58%) required inpatient admission. Scald and contact burns occurred in younger patients compared to other mechanisms (median [IQR] age: 2 [1-6] vs. 8 [4-12] years, p < 0.001). Race, ethnicity, and insurance status were not associated with inpatient admission (all p > 0.05). Overall, there was a higher rate of pediatric burn injuries affecting Black residents, with a paucity of injuries in predominately high-income areas. CONCLUSIONS: A disproportionate amount of pediatric burn injuries occur in low-income and predominantly Black neighborhoods. Additionally, scald and contact burns are a target for injury prevention in South Florida. These data may inform public health implementation to reduce morbidity in vulnerable populations. LEVEL OF EVIDENCE: Level III. TYPE OF STUDY: Retrospective comparative study.

Establishing a Regional Health System and Community-Based Organization Social Care Coordination Network: An Application of Geospatial Analysis.

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

Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic.

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

Comprehensive Clustering Analysis and Profiling of COVID-19 Vaccine Hesitancy and Related Factors across U.S. Counties: Insights for Future Pandemic Responses.

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

Get to know your neighbors with a SNAQ™: A framework for single cell spatial neighborhood analysis in immunohistochemical images.

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

Statistical and geospatial assessment of trace and toxic elements distribution in ground and surface water of northern parts of the Indo-Gangetic plains: Source identification and health risk assessment.

This study focusses on ground and surface water resources in the northern parts of the Indo-Gangetic Plains. The study aims to identify potential contaminants, analyse their distribution, trace their origins, and evaluate associated health risk. Samples from 80 locations; groundwater (n = 69) and surface water (n = 11) were analysed for nineteen trace and toxic elements using ICP-MS. Eight elements in groundwater (Mn, Fe, Ni, Zn, As, Tl, U and Se) and six in surface water (Al, Mn, Fe, Ni, Zn, and Tl) exceeded WHO (2011) and BIS (2012) limits in certain areas. The geospatial analysis reveals hotspots of trace and toxic element concentration, with higher levels detected in the southeast and western regions of the study area. Correlation matrices revealed a robust correlation (∼>0.75-0.99, p < 0.01) among all trace and toxic elements (excluding Li, Be, As, Ag, and U) in surface water samples when compared to groundwater samples. Cluster analysis and principal component analysis (PCA) (explains 70.09 cumulative percent for total 6 of factors) of groundwater chemistry indicates that Zn, Ni and Tl contamination may originate from industrial activities (metallurgical processes and manufacturing). The potential sources of Mn may be both geological and human-induced, while Fe, Se, As and U can originate from specific geological formations or human-related activities like over-extraction and leaching of excess fertilizers into aquifers. For surface water, PCA (explains 92.92 cumulative percent for total 5 of factors) identifies industrial activities as the main source of Mn, Fe, Tl, Ni, and Zn, while Al originates from both geological and anthropogenic sources. The water quality index indicated poor to very poor water quality in the western and central regions, whereas the northern and eastern regions exhibited excellent water quality. Health risk assessment reveals HI values for groundwater water: 3.85 (adults), 7.70 (children); surface water: 1.52 (adults), 3.05 (children), emphasizing the urgent need for remediation measures.

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.

Spatiotemporal analysis of HPV vaccination and associated neighborhood-level disparities in Texas-an ecological study.

Background: HPV is responsible for most cervical, oropharyngeal, anal, vaginal, and vulvar cancers. The HPV vaccine has decreased cervical cancer incidence, but only 49% of Texas adolescents have initiated the vaccine. Texas shows great variation in HPV vaccination rates. We used geospatial analysis to identify areas with high and low vaccination rates and explored differences in neighborhood characteristics. Methods: Using Anselin's Local Moran's I statistic, we conducted an ecological analysis of hot and cold spots of adolescent HPV vaccination coverage in Texas from 2017 to 2021. Next, we utilized a Mann-Whitney U test to compare neighborhood characteristics of vaccination coverage in hot spots versus cold spots, leveraging data from the Child Opportunity Index (COI) and American Community Survey. Results: In Texas, there are 64 persistent vaccination coverage hotspots and 55 persistent vaccination coverage cold spots. The persistent vaccination coverage hot spots are characterized by ZIP codes with lower COI scores, higher percentages of Hispanic residents, higher poverty rates, and smaller populations per square mile compared to vaccine coverage cold spots. We found a more pronounced spatial clustering pattern for male adolescent vaccine coverage than we did for female adolescent vaccine coverage. Conclusion: In Texas, HPV vaccination coverage rates differ depending on the community's income level, with lower-income areas achieving higher success rates. Notably, there are also gender-based discrepancies in vaccination coverage rates, particularly among male adolescents. This knowledge can aid advocates in customizing their outreach initiatives to address these disparities.

Environmental exposure and respiratory health: Unraveling the impact of toxic release inventory facilities on COPD prevalence.

This cross-sectional geospatial analysis explores the prevalence of Chronic Obstructive Pulmonary Disease (COPD) concerning the proximity to toxic release inventory (TRI) facilities in Jefferson County, Alabama. Employing the fuzzy analytical hierarchy process (FAHP), the study evaluates COPD prevalence, comorbidities, healthcare access, and individual health assessments. Given the mounting evidence linking environmental pollutants to COPD exacerbations, the research probes the influence of TRI sites on respiratory health, integrating Geographic Information Systems (GIS) to scrutinize the geospatial vulnerability of communities neighboring TRI sites. Socio-demographic disparities, economic conditions, and air pollution are emphasized in the analysis. The EPA's Toxic Release Inventory serves as the cornerstone for assessing the association between TRI proximity and COPD prevalence. The analysis uncovers a notable inverse correlation between distance from TRI sites and COPD prevalence, signaling potential health risks for populations residing closer to these facilities. Moreover, factors such as minority status, low income, and air pollution are associated with higher COPD prevalence, underscoring the imperative of comprehending the interplay between environmental exposure and respiratory health. This study bridges gaps in the literature by addressing the geographical nexus between COPD prevalence and pollution exposure. By leveraging FAHP, the research furnishes a holistic understanding of the multifaceted factors influencing vulnerability to COPD. The findings underscore the necessity for targeted public health interventions and policy measures to redress environmental disparities and alleviate the repercussions of TRI facilities on respiratory health.

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.

A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi.

BACKGROUND: Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis. METHODS: A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping. RESULTS: A significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected (log risk ratio - 0.83, 95% CrI - 1.57, - 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio - 1.42, 95% CrI - 3.09, 0.10). Analyses of all other environmental data were considered non-significant. CONCLUSIONS: The spatial range in which interpolation of snail distributions is possible appears < 10km owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities.

Anaemia prevalence and socio-demographic factors among women of reproductive age (WRA): A geospatial analysis of empowered action group (EAG) states in India.

Anaemia remains a major nutritional-related health concern for women under reproductive age (WRA) in developing nations like India as well as the Indian EAG states. According to NFHS round-5, EAG states constitute 57% of WRA having any form of anaemia, higher than many other states of India and other developed and developing nations. This study aimed to assess the frequency of anaemia among the WRA in India's eight EAG states. Also, it attempts to analyse the causes associated with anaemia by the women's background characteristics with spatial correlation with its co-variates across 291 districts of the EAG states. One of the most current Demographic and Health Survey's (DHS) cross-sectional data is the NFHS-5th (2019-21) round taken, conducted by the IIPS under the administration of MoHFW, India. This study only included 315,069 women under reproductive age (WRA). The variables related to anaemia among women's (WRA) background socio-demographic characteristics were assessed using bivariate statistics and multinominal logistic regression analysis to comprehend the spatial correlation between women and their determinant factors. Among the EAG states, the overall prevalence of anaemia was 57%, varying from 42.6% in Uttarakhand to 65.3% in Jharkhand. Multinominal logistic regression analyses reveal that the chances of anaemia are remarkably more prevalent in younger women (15-19 years of age), women living in rural areas, no educated and primary level educated women, women belonging to the middle to poorest wealth quintile, women no longer living together, women of the Christian religion, women who are not exposed to reading newspapers, underweight BMI women, and scheduled tribe women. Mainly, the prevalence is observed in the North-eastern and southeastern states of Bihar, Jharkhand, Odisha, Chhattisgarh, some parts of Madhya Pradesh, Uttar Pradesh, and Rajasthan, which is shown by the hotspot map. According to the findings of this study, numerous factors like family, socioeconomic, educational, awareness, and individual characteristics such as caste and domicile all lead to a risk of anaemia. The WRA suffers from anaemia as a result of their socioeconomic background and awareness, which leads to a lack of nourishment, and they seek nutrient deficiencies. To overcome this anaemia, multiple discipline policies and initiatives need to be taken targeting women's wellness and nutritional status by increasing women's education and socioeconomic status.

Spatial and temporal distribution of Scirtothrips dorsalis (Thysanoptera: Thripidae) and their natural enemies in Florida strawberry fields.

Given the recent invasion of Scirtothrips dorsalis Hood in North America, there is limited information regarding their distribution and population dynamics in cultivated small fruit crops. Therefore, we investigated the spatial and temporal distribution of S. dorsalis and their natural enemies in commercially produced strawberry fields in Florida. During 2 consecutive strawberry production seasons, 4 and 6 geographically separated strawberry fields were sampled and were divided into grids with 30-40 sampling points per field. At each sampling point, 4-5 leaf and flower samples were collected, and sticky traps were deployed. We quantified the occurrence of S. dorsalis as well as potential natural enemies, including Orius spp., Geocoris spp., and other predators such as long-legged flies. During both years, most of the S. dorsalis and natural enemies were found on field borders, and counts progressively diminished further into the interiors of plots and away from field edges. Cluster and outlier analysis revealed that S. dorsalis formed statistically significant clusters and that these "hot spots" remained in the same general locations throughout the season. There was a strong relationship between the occurrence of natural enemies and the presence of S. dorsalis, but the number of natural enemies was generally low compared to S. dorsalis. Our results indicate that targeting field borders for chemical control or planting strawberries away from natural areas containing potential alternative hosts for thrips may be an effective strategy for reducing agricultural inputs; however, future field assessments are needed to determine if these methods could replace the treatment of entire fields.

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.

Distribution of catheterisation laboratories in Indonesia 2017-2022: a nationwide survey.

Background: Geographical terrains of Indonesia pose a major hindrance to transportation. The difficulty of transportation affects the provision of acute time-dependent therapy such as percutaneous coronary intervention (PCI). Also, Indonesia's aging population would have a significant impact on the prevalence of acute coronary syndrome in the next decade. Therefore, the analysis and enhancement of cardiovascular care are crucial. The catheterisation laboratory performs PCI procedures. In the current study, we mapped the number and distribution of catheterisation laboratories in Indonesia. Methods: A direct survey was used to collect data related to catheterisation laboratory locations in July 2022. The population data was sourced from the Ministry of Home Affairs. The recent growth of catheterisation laboratories was examined and evaluated based on geographical areas. The main instruments for comparing regions and changes throughout time are the ratio of catheterisation laboratories per 100,000 population and the Gini index (a measure of economic and healthcare inequality. Gini index ranges from 0 to 1, with greater values indicating more significant levels of inequality). Regression analysis was carried out to see how the number of catheterisation laboratories was affected by health demand (prevalence) and economic capacity (Gross Domestic Regional Product [GDRP] per Capita). Findings: The number of catheterisation laboratories in Indonesia significantly increased from 181 to 310 during 2017-2022, with 44 of the 119 new labs built in an area that did not have one. Java has the most catheterisation laboratories (208, 67%). The catheterisation laboratory ratio in the provinces of Indonesia ranges from 0.0 in West Papua and Maluku to 4.46 in Jakarta; the median is 1.09 (IQR 0.71-1.18). The distribution remains a problem, as shown by the high catheterisation laboratory Gini index (0.48). Regression shows that distribution of catheterisation laboratories was significantly affected by GDRP and the prevalence of heart disease. Interpretation: The number of catheterisation laboratories in Indonesia has increased significantly recently, however, maldistribution remains a concern. To improve Indonesia's cardiovascular emergency services, future development of catheterisation laboratories must be better planned considering the facility's accessibility and density. Funding: Airlangga Research Fund - Universitas Airlangga.

A Geospatial Analysis of the Availability, Distribution, and Accessibility of Neurosurgical Facilities, Workforce, and Infrastructure in Nigeria; and Projection Towards 2050.

OBJECTIVE: There has been a modest but progressive increase in the neurosurgical workforce, training, and service delivery in Nigeria in the last 2 decades. However, these resources are unevenly distributed. This study aimed to quantitatively assess the availability and distribution of neurosurgical resources in Nigeria while projecting the needed workforce capacity up to 2050. METHODS: An online survey of Nigerian neurosurgeons and residents assessed the country's neurosurgical infrastructure, workforce, and resources. The results were analyzed descriptively, and geospatial analysis was used to map their distribution. A projection model was fitted to predict workforce targets for 2022-2050. RESULTS: Out of 86 neurosurgery-capable health facilities, 65.1% were public hospitals, with only 17.4% accredited for residency training. Dedicated hospital beds and operating rooms for neurosurgery make up only 4.0% and 15.4% of the total, respectively. The population disease burden is estimated at 50.2 per 100,000, while the operative coverage was 153.2 cases per neurosurgeon. There are currently 132 neurosurgeons and 114 neurosurgery residents for a population of 218 million (ratio 1:1.65 million). There is an annual growth rate of 8.3%, resulting in a projected deficit of 1113 neurosurgeons by 2030 and 1104 by 2050. Timely access to neurosurgical care ranges from 21.6% to 86.7% of the population within different timeframes. CONCLUSIONS: Collaborative interventions are needed to address gaps in Nigeria's neurosurgical capacity. Investments in training, infrastructure, and funding are necessary for sustainable development and optimized outcomes.