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.

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.

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.

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.

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.

A Geospatial Analysis of Social and Structural Determinants of Health and High HIV Prevalence in Alabama, USA.

OBJECTIVE: This study utilizes geospatial analytic techniques to examine HIV hotspots in Alabama leveraging Medicaid utilization data. METHODS: This cross-sectional study leveraged Medicaid utilization data from Alabama's 67 counties, averaging 9,861 Medicaid recipients aged > 18 years old per county. We used Alabama Medicaid administrative claims data from January 1, 2016, to December 31, 2020, to identify individuals with HIV. Using Microsoft SQL Server, we obtained the average annual count of HIV Medicaid claims in each of the 67 Alabama counties (numerator) and the number of adult Medicaid recipients in each county (denominator), and standardized with a multiplier of 100,000. We also examined several other area-level summary variables (e.g., non-high school completion, income greater than four times the federal poverty level, social associations, urbanicity/rurality) as social and structural determinants of health. County-boundary choropleth maps were created representing the geographic distribution of HIV rates per 100,000 adult Medicaid recipients in Alabama. Leveraging ESRI ArcGIS and local indicators of spatial association (LISA), results were examined using local Moran's I to identify geographic hotspots. RESULTS: Eleven counties had HIV rates higher than 100 per 100,000. Three were hotspots. Being an HIV hotspot was significantly associated with relatively low educational attainment and less severe poverty than other areas in the state. CONCLUSIONS: Findings suggesting that the HIV clusters in Alabama were categorized by significantly less severe poverty and lower educational attainment can aid ongoing efforts to strategically target resources and end the HIV epidemic in U.S.' Deep South.

Applying XGBoost and SHAP to Open Source Data to Identify Key Drivers and Predict Likelihood of Wolf Pair Presence.

Wolves have returned to Germany since 2000. Numbers have grown to 209 territorial pairs in 2021. XGBoost machine learning, combined with SHAP analysis is applied to predict German wolf pair presence in 2022 for 10 × 10 km grid cells. Model input consisted of 38 variables from open sources, covering the period 2000 to 2021. The XGBoost model predicted well, with 0.91 as the AUC. SHAP analysis ranked the variables: distance to the closest neighboring wolf pair was the main driver for a grid cell to become occupied by a wolf pair. The clustering tendency of related wolves seems to be an important explanatory factor here. Second was the percentage of wooded area. The next eight variables related to wolf presence in the preceding year, except at fifth, eighth and tenth position in the total order: human density (square root) in the grid, percentage arable land and road density respectively. Other variables including the occurrence of wild prey were the weakest predictors. The SHAP analysis also provided crucial added value in identifying a variable that had threshold values where its contribution to the prediction changed from positive to negative or vice versa. For instance, low density of people increased the probability of wolf pair presence, whereas a high density decreased this probability. Cumulative lift techniques showed that the model performed almost four times better than random prediction. The combination of XGBoost, SHAP and cumulative lift techniques is new in wolf management and conservation, allowing for the focusing of educational and financial resources.

Breast Cancer Champions: a peer-to-peer education and mobile mammography program improving breast cancer screening rates for women of African heritage.

INTRODUCTION: Nationally, women of African heritage die at higher rates from breast cancer than women of other races or ethnicities. We developed Breast Cancer Champions (BCC) a peer-to-peer education program, which recruited 12 women and deployed them into the community in August 2020 during the height of the COVID pandemic. BCC aims to improve breast cancer screening rates for women of African heritage through peer-to-peer education, which has proven successful for addressing cancer-related health disparities. METHODS: BCC community experts, or "Champions," are peer-to-peer educators that conduct awareness and screening events in their communities. Champion's education activities were tracked by bi-weekly check-in calls, which recorded the activity type, location, and the number of participants for each event. We used spatial and statistical analyses to determine the efficacy of the program at increasing screening rates for women within the area of Champion activity versus women outside of their activity area. RESULTS: Over 15 months, Champions conducted 245 in-person or online events to engage women in their community for screening. More women of African heritage were screened in areas Champions were active during the intervention compared to historical data comparing areas outside of the Champion activity in the prior 15 months (X 2 = 3.0845, p = 0.079). CONCLUSION: BCC successes could be attributed to pivoting to online community building when in-person events were restricted and enabling Champions to design and conduct their own events, which increased outreach possibilities. We demonstrate improved screening outcomes associated with an updated peer-to-peer education program.

Spatial Analysis of the Alcohol, Intimate Partner Violence, and HIV Syndemic Among Women in South Africa.

The co-occurrence of and synergistic interactions between substance abuse, violence, and HIV, known as the "SAVA syndemic," is thought to be a driver of women's elevated risk for HIV in sub-Saharan Africa. This study uses data from the 2016 South African Demographic and Health Survey (DHS) and the 2016 South African Census to examine geospatial associations between alcohol use, intimate partner violence (IPV), unprotected sex, and HIV status among a population-based sample of 8528 South African women (age 15-49). Results support the geographic clustering of alcohol use, unprotected sex, and IPV, but not HIV, and geospatial clustering of HIV alone. This study highlights the need for geographically-tailored interventions to address syndemics through integrated interventions, such as those simultaneously focused on alcohol, IPV, and sexual risk reduction, and points to the need for more targeted research to link these factors to HIV from a place-based risk perspective.

HIV geospatially clusters with firearm trauma in 35 Atlanta zip codes.

The purpose of this study was to assess if rates of firearm trauma within Atlanta geospatially clustered with HIV prevalence and new HIV diagnosis rates. We retrospectively reviewed our Atlanta trauma center's registry for patients sustaining a ballistic firearm trauma from 2014 through 2018. Using the patient's zip code of home residence, we determined the rate of firearm trauma for that zip code. We obtained publicly available rates for HIV that corresponded with these select zip codes to perform a geospatial cluster analysis. The cohort was comprised of 1495 patients and represented 35 zip codes in Atlanta. The mean rate of firearm trauma for the 35 zip codes was 171.1 (±296.4) per 100,000 people. Compared to all Atlanta, the 35 zip codes' mean HIV prevalence (1863.9 vs 924.1, p < .0001) and new HIV diagnosis rate (396.9 vs 199.7, p < .0001) were significantly higher. Rates of firearm trauma and HIV prevalence demonstrated significant geospatial clustering (ß 0.38, 95% CI 0.22-0.53, p < .0001) as did rates of firearm trauma and new HIV diagnoses (ß 0.36, 95% CI 0.18-0.54, p = 0.0002). Our findings provide granular geographic data that could guide targeted HIV screening efforts in communities where our firearm-injured patients live.

A novel GIS-based multicriteria analysis approach for ascertaining the catchment-scale degradation of a Himalayan wetland.

Wetland degradation through a diverse spectrum of anthropogenic stressors worldwide has taken a heavy toll on the health of wetlands. This study examined the health of a semi-urban wetland Bodsar, located in the Kashmir Himalaya using multicriteria analysis approach assimilating data on land use land cover (LULC), landscape fragmentation, soil loss, and demography. Wetland and catchment-scale land system changes from 1980 to 2022 were assessed using high-resolution imagery. Fragmentation of the natural landscape was assessed using the Landscape Fragmentation Tool (LFT) and soil loss was assessed using the Revised Universal Soil Loss Equation (RUSLE). In addition, the water quality was examined at 12 sites distributed across the wetland using standard methods. Satellite data revealed 12 categories of land use with areas under exposed rock, orchards, built-up and sparse forest having increased by 1005%, 623%, 274%, and 37% respectively. LFT indicated that the core (>500 acres) and core (<250 acres) zones decreased by approximately 16% and 64%, respectively, whereas the areas under the perforated, edge and patch classes increased significantly. RUSLE estimates show a ∼77% increase in soil erosion from 116.26 Mg a-1 in 1980 to 205.68 Mg a-1 in 2022, mostly due to changes in LULC. Total phosphorus (0.195-2.04 mg L -1), nitrate nitrogen (0.306-2.79 mg L -1), and total dissolved solids (543-774 mg L-1) indicated nutrient enrichment of the wetland influenced by anthropogenically-driven land system changes. The wetland degradation index revealed that 21% of the wetland experienced high-to-severe degradation, 62% experienced moderate degradation, and 17% did not face any significant degradation pressure. The novel GIS-based approach adopted in this study can act as a prototype for ascertaining the catchment-scale degradation of wetlands worldwide.

Non-adherence to WHO's recommended 8-contact model: geospatial analysis of the 2017 Maternal Health Survey.

INTRODUCTION: Evidence shows that most women in Ghana do not meet the minimum 8-contact model for antenatal care as recommended by WHO with only 31.2%-41.9% of them meeting the recommendation. To the best of our knowledge, no study in Ghana has examined women's noncompliance with the WHO's recommended 8-contact model for antenatal care using geospatial analysis, as this study sets out to do. METHODS: We sourced data from the recent version of the Ghana Maternal Health Survey which was executed in 2017. A sample of 10,077 women with complete data participated in this study. The link between the explanatory variables and the outcome variable was investigated using binary and multivariate logistic regression models and Spatial analyses such as spatial autocorrelation (Moran's I), hotspot, cluster and outlier analysis, and geographically weighted regression were conducted using ArcMap version 10.7. RESULTS: Districts found in the north-eastern and south-western parts of the country were more likely to experience noncompliance with ANC. Women staying within the middle belt without health insurance were more likely (17-29%) to be noncompliant with ANC. Women with low community socioeconomic status were found to be more likely (17-34%) to be noncompliant with ANC in the eastern parts of Ghana. CONCLUSION: The study has shown that in order to achieve targets one and three of Sustainable Development Goal 3, the government of Ghana, the Ministry of Health, together with the Ghana Health Service may have to intensify health education in the identified areas to highlight the importance of adherence to the WHO recommendations on ANC 8-contact model.

County incidence and geospatial trends of early-onset hypertensive disorders of pregnancy in Kentucky, 2008-2017.

BACKGROUND: Early-onset hypertensive disorders of pregnancy (eHDP) are associated with more severe maternal and infant outcomes than later-onset disease. However, little has been done to evaluate population-level trends. Therefore, in this paper, we seek to address this understudied area by describing the geospatial and temporal patterns of county-level incidence of eHDP and assessing county-level demographics that may be associated with an increased incidence of eHDP. METHODS: Employing Kentucky certificates of live and stillbirth from 2008-2017, this ecological study detected county-level clusters of early-onset hypertensive disorders of pregnancy using SaTScan, calculated average annual percent change (AAPC) with a join point analysis, and identified county-level covariates (% of births to women ≥ 35 years of age, % with BMI ≥ 30 kg/m2, % currently smoking, % married, and % experienced eHDP) with a fixed-effects negative binomial regression model for longitudinal data with an autoregressive (AR) correlation structure offset with the natural log of the number of births in each county and year. RESULTS: County-level incidence of eHDP had a non-statistically significant increase of almost 3% (AAPC: 2.84, 95% CI: -4.26, 10.46), while maternal smoking decreased by almost 6% over the study period (AAPC:-5.8%, 95%CI: -7.5, -4.1), Risk factors for eHDP such as pre-pregnancy BMI ≥ 30 and proportion of births to women ≥ 35 years of age increased by 2.3% and 3.4% respectively (BMI AAPC:2.3, 95% CI: 0.94, 3.7; ≥ 35 years AAPC:3.4, 95% CI: 0.66, 6.3). After adjusting for race, county-level proportions of college attainment, and maternal smoking throughout pregnancy, counties with the highest proportion of births to women with BMI ≥ 30 kg/m2 reported an eHDP incidence 20% higher than counties with a lower proportion of births to mothers with a BMI ≥ 30 kg/m2 and a 20% increase in eHDP incidence (aRR = 1.20, 95% CI: 1.00, 1.44). We also observed that counties with the highest proportion vs. the lowest of mothers ≥ 35 years old (> 6.1%) had a 26% higher incidence of eHDP (RR = 1.26, 95%CI: 1.04, 1.50) compared to counties with the lowest incidence (< 2.5%). We further identified two county-level clusters of elevated eHDP rates. We also observed that counties with the highest vs. lowest proportion of mothers ≥ 34 years old (> 6.1% vs. < 2.5%) had a 26% increase in the incidence of eHDP (RR = 1.26, 95% CI: 1.04, 1.50). We further identified two county-level clusters of elevated incidence of eHDP. CONCLUSIONS: This study identified two county-level clusters of eHDP, county-level covariates associated with eHDP, and that while increasing, the average rate of increase for eHDP was not statistically significant. This study also identified the reduction in maternal smoking over the study period and the concerning increase in rates of elevated pre-pregnancy BMI among mothers. Further work to explore the population-level trends in this understudied pregnancy complication is needed to identify community factors that may contribute to disease and inform prevention strategies.

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis.

INTRODUCTION: Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention. METHODS: A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data. RESULTS: PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis. CONCLUSIONS: The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities.

BACKGROUND: Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS: This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS: In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS: Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.

Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria.

BACKGROUND: Sub-Saharan African countries, Nigeria inclusive, are constrained by grossly limited access to quality pre-hospital trauma care services (PTCS). Findings from pragmatic approaches that explore spatial and temporal trends of past road crashes can inform novel interventions. To improve access to PTCS and reduce burden of road traffic injuries we explored geospatial trends of past emergency responses to road traffic crashes (RTCs) by Lagos State Ambulance Service (LASAMBUS), assessed efficiency of responses, and outcomes of interventions by local government areas (LGAs) of crash. METHODS: Using descriptive cross-sectional design and REDcap we explored pre-hospital care data of 1220 crash victims documented on LASAMBUS intervention forms from December 2017 to May 2018. We analyzed trends in days and times of calls, demographics of victims, locations of crashes and causes of delayed emergency responses. Assisted with STATA 16 and ArcGIS pro we conducted descriptive statistics and mapping of crash metrics including spatial and temporal relationships between times of the day, seasons of year, and crash LGA population density versus RTCs incidence. Descriptive analysis and mapping were used to assess relationships between 'Causes of Delayed response' and respective crash LGAs, and between Response Times and crash LGAs. RESULTS: Incidences of RTCs were highest across peak commuting hours (07:00-12:59 and 13:00-18:59), rainy season and harmattan (foggy) months, and densely populated LGAs. Five urban LGAs accounted for over half of RTCs distributions: Eti-Osa (14.7%), Ikeja (14.4%), Kosofe (9.9%), Ikorodu (9.7%), and Alimosho (6.6%). On intervention forms with a Cause of Delay, Traffic Congestion (60%), and Poor Description (17.8%), had associations with LGA distribution. Two densely populated urban LGAs, Agege and Apapa were significantly associated with Traffic Congestion as a Cause of Delay. LASAMBUS was able to address crash in only 502 (36.8%) of the 1220 interventions. Other notable outcomes include: No Crash (false calls) (26.6%), and Crash Already Addressed (22.17%). CONCLUSIONS: Geospatial analysis of past road crashes in Lagos state offered key insights into spatial and temporal trends of RTCs across LGAs, and identified operational constraints of state-organized PTCS and factors associated with delayed emergency responses. Findings can inform programmatic interventions to improve trauma care outcomes.

Declining Geographic Access to High-Volume Revision Total Hip Arthroplasty Surgeons: A National Medicare Analysis.

BACKGROUND: While trends in the economics of revision THA (revTHA) procedures have been well-described from the standpoint of both hospitals and surgeons, their population-level effects of these trends on patient access are not well-understood. METHODS: The Medicare fee-for-service provider utilization and payment public use files were used to extract data for primary and revTHA for beneficiaries between 2013 and 2019. Primary and revTHA procedures were identified using the Healthcare Common Procedure Coding System code; 27130 for primaries and 27132, 27134, 27137, or 27138 for revisions. Geospatial analyses were performed by aggregating surgeon practice locations at the level of individual counties, hospital service areas, and hospital referral regions. RESULTS: The number of high-volume primary THA surgeons within the Medicare population increased by 17.6% over the study period (3,838 in 2013 to 4,515 in 2019). Conversely, the number of high-volume revTHA surgeons decreased by 36.1% (178 in 2013 to 129 in 2019). Linear regression revealed a significant increase and decrease in high-volume primary (ß = 109.07, P ≤ .001) and revision (ß = -13.04, P = .011) THA surgeons, respectively. Over the study period, the number of counties with at least 1 high-volume primary THA surgeon increased by 6.1% (1,194 to 1,267), while the number of counties with at least 1 high-volume revTHA surgeon decreased by 30.2% (159 to 111). CONCLUSION: The present findings of declining geographic access may represent a consequence of shifting economic incentives and declining reimbursements for the care of complicated revTHA patients.

GEospatial aNalysis of ExtRacorporeal membrane oxygenATion in Europe (GENERATE).

INTRODUCTION: A cross-sectional survey GENERATE (GEospatial aNalysis of ExtRacorporeal membrane oxygenATion in Europe) initiated on behalf of the European chapter of the Extracorporeal Life Support Organization (EuroELSO), aims to provide a systematic, detailed description of contemporary Extracorporeal Life Support (ECLS) provision in Europe, map the spatial distribution of ECLS centers, and the accessibility of ECLS. METHODS: Structured data collection forms were used to create a narrative description of ECLS provision in EuroELSO affiliated countries. This consisted of both center-specific data and relevant national infrastructure. Data was provided by a network of local and national representatives. Spatial accessibility analysis was conducted where appropriate geographical data were available. RESULTS: 281 centers from 37 countries affiliated to EuroELSO were included in the geospatial analysis and demonstrate heterogeneous patterns of ECLS provision. Accessibility of ECLS services within 1 hour of drive-time is available for 50% of the adult population in 8 of 37 countries (21.6%). This proportion is reached within 2 hours in 21 of 37 countries (56.8%) and within 3 hours in 24 of 37 countries (64.9%). For pediatric centers, accessibility is similar with 9 of 37 countries (24.3%) reached the covering of 50% of the population aged 0-14 within 1 hour and 23 of 37 countries (62.2%) within 2 hours and 3 hours. CONCLUSIONS: ECLS services are accessible in most of the European countries, but their provision differs across the continent. There is still no solid evidence given regarding the optimal ECLS provision model. The spatial disparity in ECLS provision demonstrated in our analysis requires governments, healthcare professionals and policy makers to consider how to develop existing provision to accommodate the anticipated increase in need for time critical access to this advanced support modality.