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Cloud‐to‐ground lightning with minimal rainfall ("dry" lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily‐accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning‐ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. "Holdover" fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly‐detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince‐specific precipitation amounts would better inform WUS wildfire prediction and management. Plain Language Summary: Cloud‐to‐ground lightning with minimal rainfall, also known as "dry lightning," is a major wildfire ignition source in the western United States (WUS). Typically, daily‐accumulated precipitation of less than 2.5 mm is used to identify dry lightning occurrence. However, there is limited knowledge of (a) the true precipitation amounts that occur with lightning‐ignited wildfires (LIWs), and (b) how these amounts vary across different landscapes and vegetation types. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across different regions of the WUS. Although we find a 2.8 mm median ignition precipitation for all LIWs, we show that "holdover" fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm). Holdover fires also occur with cooler and wetter environmental conditions. Further, ignition precipitation amounts associated with promptly‐detected and holdover fires vary substantially across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk. WUS wildfire prediction and management could be improved through incorporating ecoprovince‐specific precipitation amounts and accounting for differing characteristics of holdover fires. Key Points: The widely‐used 2.5 mm daily precipitation threshold to define "dry" lightning does not fully capture fire ignition risk across the WUSIgnition precipitation amounts range from 1.7 to 7.7 mm depending on ecoprovince and whether the fire was promptly‐detected or a holdoverHoldover fires occur when conditions are cooler and wetter compared to promptly‐detected fires [ABSTRACT FROM AUTHOR]

Background: Existing research and national surveillance data suggest an increase of the prevalence of mental disorders during the COVID-19 pandemic. Social media platforms, such as Twitter, could be a source of data for estimation owing to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of the prevalence of mental disorders on Twitter compared to that reported by the Centers for Disease Control and Prevention (CDC). Objective: This study aims to verify the feasibility of Twitter-based prevalence of mental disorders symptoms being an instrument for prevalence estimation, where feasibility is gauged via correlations between Twitter-based prevalence of mental disorder symptoms (ie, anxiety and depressive symptoms) and that based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (ie, the temporal trend). Methods: State-level prevalence of anxiety and depressive symptoms was retrieved from the national Household Pulse Survey (HPS) of the CDC from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming application programming interface during the same period and were used to estimate the prevalence of symptoms of mental disorders for each state using keyword analysis. Stratified linear mixed models were used to evaluate the correlations between the Twitter-based prevalence of symptoms of mental disorders and those reported by the CDC. The magnitude and significance of model parameters were considered to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared on the basis of random effects. Results: Pearson correlation coefficients between the overall prevalence reported by the CDC and that on Twitter for anxiety and depressive symptoms were 0.587 (P<.001) and 0.368 (P<.001), respectively. Stratified by 4 phases (ie, April 2020, August 2020, October 2020, and April 2021) defined by the HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3, while a significant correlation for depressive symptoms was identified in phases 1 and 3. Conclusions: Positive correlations were identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of symptoms of mental disorders were found between the northern and southern United States. Findings from this study could inform future investigation on leveraging social media platforms to estimate symptoms of mental disorders and the provision of immediate prevention measures to improve health outcomes. [ABSTRACT FROM AUTHOR]

Previous work has shown socioenvironmental factors can influence firearm injury. Milwaukee County, Wisconsin is a diverse midwestern county with historic disinvestment in marginalized communities yielding stark segregation along racial and ethnic lines. It is also one of the many U.S. counties burdened by surging firearm injuries. The differences among communities within Milwaukee County provides a unique opportunity to explore the intersection of socioenvironmental factors that may affect clinical outcomes and geospatial patterns of firearm injury. The trauma registry from the regional adult level 1 trauma center was queried for patients who sustained a firearm-related injury from 2015 to 2022 (N = 2402). The Social Vulnerability Index (SVI) ranking was derived using patient residence addresses to evaluate its association with traumatic injury clinical outcomes (i.e., in-hospital mortality, length of hospital stay, ICU or ventilator treatment, or injury severity score) and risk screening results for alcohol use disorder (AUD), posttraumatic stress disorder (PTSD), and depression. We evaluated hotspots of firearm injury density over time for patient residences and injury locations and distances between locations. A spatially lagged regression model tested the association between firearm injury density and SVI domains, alcohol outlet types, and park coverage. Most firearm injury patients were younger, male, racial or ethnic minorities from disadvantaged neighborhoods (SVI total; M = 0.86, SD = 0.15). SVI was not associated with any clinical outcomes. Of those screened, 12.9% screened positive for AUD and 44.5% screened at risk for PTSD, depression, or both. Hotspot analysis indicated consistent concentrations of firearm injury density. There were no differences in clinical outcomes between those injured inside or outside the home. Census tracts with lower socioeconomic status, greater off-premises and lower on-premises alcohol outlet density were associated with greater firearm injury density. In Milwaukee County, firearm injury patients are injured in and often return to the same disadvantaged neighborhoods that may hamper recovery. Results replicate and expand previous work and implicate specific socioenvironmental factors for intervention and prevention of firearm injury. • Highlights (3–5 points: maximum 85 characters, including spaces, per bullet point). • 80% of a Milwaukee firearm injury sample live in disadvantaged neighborhoods. • Neighborhood disadvantage was not related to hospital outcomes, possibly due to skew. • Socioeconomic status and alcohol outlet density related to firearm injury density. • Consistent hotspots show need to consider community context within recovery. [ABSTRACT FROM AUTHOR]

Introduction: West Virginia (WV) had the ninth highest rate of firearm mortality of all states in the United States according to the CDC in 2018. Gun violence in WV has been a steady problem over the last decade. The rural population is more vulnerable to unintentional firearm injuries and suicides. Previously published literature from urban settings has demonstrated a link between firearm injuries and modifiable situational variables such as crime, unemployment, low income, and low education. There are very few studies that have utilized geospatial analytic techniques as a tool for injury mapping, surveillance, and primary prevention in rural and frontier zones of the United States.Methods: We performed a 10-year retrospective single-institution review of firearm injuries at a rural WV level 1 trauma center between January 2010 and December 2019. The AIS World Geocoding Service was then used to identify specific areas of emerging firearm-related injuries within the service area.Results: Specific hot spots of emerging firearm injury were identified in both intentional and unintentional populations. These were located in geographically distinct areas of the WV unincorporated rural and frontier population. These rural WV hotspots were associated with the modifiable variables of crime, unemployment, lower income, and lower education level.Conclusions: Emerging hot spots of firearm injury in rural and frontier locations were associated with modifiable social determinants. These areas represent an opportunity for targeted injury prevention efforts addressing these disparities. Further prospective study of these findings is warranted. [ABSTRACT FROM AUTHOR]

Suicide frequency has tripled for some pediatric age groups over the last decade, of which, serious attempts result in pediatric intensive care unit (PICU) admissions. We paired clinical, aggregate geospatial, and temporal demographics to understand local community variables to determine if epidemiological patterns emerge that associate with risk for PICU admission. Data were extracted at an urban, high-volume, quaternary care facility from January 2011 to December 2017 via ICD 10 codes associated with suicide. Clinical, socioeconomic, geographical, and temporal variables were reviewed. In total, 1036 patients over the age of 9 were included, of which n = 161 were PICU admissions. Females represented higher proportions of all suicide-related hospital admissions (67.9%). Looking at race/ethnicity, PICU admissions were largely Caucasian (83.2%); Blacks and Hispanics had lower odds of PICU admissions (OR: 0.49; 0.17, respectively). PICU-admitted patients were older (16.0 vs. 15.5; p = 0.0001), with lower basal metabolic index (23.0 vs. 22.0; p = 0.0013), and presented in summer months (OR: 1.51, p = 0.044). Time-series decomposition showed seasonal peaks in June and August. Local regions outside the city limits identified higher numbers of PICU admissions. PICUs serve discrete geographical regions and are a source of information, when paired with clinical geospatial/seasonal analyses, highlighting clinical and societal risk factors associated with PICU admissions. [ABSTRACT FROM AUTHOR]

Key Points: Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutionsVarious multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawalsThe results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R2 ≈ 0.99 and R2 ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary: Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water‐food‐energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues. [ABSTRACT FROM AUTHOR]

Objective Inappropriate opioid prescribing after surgery contributes to opioid use disorder and risk of opioid overdose. In this cross-sectional analysis of orthopedic surgical patients, we examined the role of patient location on postoperative pain intensity and opioids prescribed on hospital discharge. Methods We used geospatial analyses to characterize spatial patterns of mean pain intensity on the day of discharge (PiDoD) and opioid units prescribed on the day of discharge (OuPoD), as well as the effect of regional social deprivation on these outcomes. Results At a 500-km radius from the surgery site, the Global Moran's I for PiDoD (2.71 × 10−3, variance = 1.67 × 10−6, P = 0.012) and OuPoD (2.19 × 10−3, SD = 1.87, variance = 1.66 × 10−6, P = 0.03) suggested significant spatial autocorrelation within each outcome. Local indicators of spatial autocorrelation, including local Moran's I, Local Indicator of Spatial Autocorrelation cluster maps, and Getis-Ord Gi* statistics, further demonstrated significant, specific regions of clustering both OuPoD and PiDoD. These spatial patterns were associated with spatial regions of area deprivation. Conclusions Our results suggest that the outcomes of pain intensity and opioid doses prescribed exhibit varying degrees of clustering of patient locations of residence, at both global and local levels. This indicates that a given patient's pain intensity on discharge is related to the pain intensity of nearby individuals. Similar interpretations exist for OuPoD, although the relative locations of hot spots of opioids dispensed in a geographic area appear to differ from those of hot spots of pain intensity on discharge. [ABSTRACT FROM AUTHOR]

Cultivated fecal indicator bacteria such as Escherichia coli and enterococci are typically used to assess the sanitary quality of recreational waters. However, these indicators suffer from several limitations, such as the length of time needed to obtain results and the fact that they are commensal inhabitants of the gastrointestinal tract of many animals and have fate and transport characteristics dissimilar to pathogenic viruses. Numerous emerging technologies that offer same-day water quality results or pollution source information or that more closely mimic persistence patterns of disease-causing pathogens that may improve water quality management are now available, but data detailing geospatial trends in wastewater across the United States are sparse. We report geospatial trends of cultivated bacteriophage (somatic, F, and total coliphages and GB-124 phage), as well as genetic markers targeting polyomavirus, enterococci, E. coli, Bacteroidetes, and human-associated Bacteroides spp. (HF183/BacR287 and HumM2) in 49 primary influent sewage samples collected from facilities across the contiguous United States. Samples were selected from rural and urban facilities spanning broad latitude, longitude, elevation, and air temperature gradients by using a geographic information system stratified random site selection procedure. Most indicators in sewage demonstrated a remarkable similarity in concentration regardless of location. However, some exhibited predictable shifts in concentration based on either facility elevation or local air temperature. Geospatial patterns identified in this study, or the absence of such patterns, may have several impacts on the direction of future water quality management research, as well as the selection of alternative metrics to estimate sewage pollution on a national scale. [ABSTRACT FROM AUTHOR]

Introduction: Access to timely, quality, and affordable surgical services is an important component of health care systems. A better understanding of the geographic distribution of surgical services in the USA may help identify disparities in access to surgery.Methods: Using the 2015 American Hospital Association Annual Survey, the 2010 Census Bureau Data, and the American Community Survey 5-year estimates from 2011 to 2016, all hospitals with surgical capabilities were geocoded with 30 straight-line mile service areas around each hospital using geospatial analysis. Major surgical hospitals were defined as meeting three of the four following criteria: bed size ≥ 45, ≥ 8600 operations per year, ≥ 12 operating rooms, and academic medical center. The distribution of the US population based on proximity to a hospital capable of performing adult inpatient surgery and a major surgical hospital was then analyzed and compared.Results: Overall, 3409 hospitals were identified that had the capacity to perform adult inpatient surgery of which 1373 were defined as major surgical hospitals. Based on geospatial analysis, 10% of the US population was found to reside outside of a linear 30-mile radius of a surgical hospital. Younger age (OR 0.97, CI 0.96-0.97), female sex (OR 4.6, CI 4.3-5), African-American race (OR = 5.4, CI 4.7-6.2), Hispanic/Latino race (OR 5.5, CI 4.8-6.3), having completed high school or greater (OR = 3.6, CI 3-4.2), being employed (OR 4.8, CI 4.6-4.9), and having any type of health insurance were significantly associated with living in a service area.Conclusion: A significant proportion of the US population lives greater than 30 straight-line miles from a major surgical hospital. Common demographic and socioeconomic factors highlight disparities in access to surgical care. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers. Agent-based models describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. They are increasingly used to study healthcare-associated infections, including Clostridioides difficile infection. The authors describe their methods of creating a geospatially explicit model of a major regional healthcare network using a synthetic population as microdata input. [ABSTRACT FROM AUTHOR]

From 1998 to 2008, produce-related illness outbreaks accounted for roughly one-half of reported foodborne outbreaks in the United States. In 2013, Mexico accounted for approximately 50 and 30% of the monetary value of all vegetables and fruits, respectively, imported into the United States. We used historical import data to examine the correlation between the port of entry for five implicated produce vehicles from five multistate outbreaks and the geospatial and temporal distribution of illnesses in the corresponding outbreaks in the United States. For comparison, we analyzed the geospatial and temporal distribution of cases from two U.S. multistate outbreaks associated with domestically grown produce. The geospatial distribution of illnesses in the two outbreaks linked to domestic produce differed from that of the import-related produce outbreaks. The results of our pilot study suggest that geospatial distribution of early-onset cases may be used to identify ports of entry for produce likely to be responsible for causing multistate outbreaks in the United States and that targeted sampling of produce items from these ports of entry may expedite identification of an outbreak vehicle. [ABSTRACT FROM AUTHOR]

Objective: To understand the sociodemographic, geographical, and clinical characteristics of rural veterans utilizing home modification (HM) healthcare services under the Home Improvement Structural Alterations (HISA) program, to compare these characteristics between rural and urban veteran users, to estimate the costs of HMs performed, and to present distance that users traveled to HISA-prescribing medical facilities within the Veterans Health Administration (VHA)., Background: Accessible housing is in short supply. HMs allow veterans with disabilities (VWDs) to remain living at home rather than enter institutional-type settings. HISA is associated with decreased inpatient hospitalization rates and increased use of preventative healthcare via outpatient clinic visits. Home accessibility provides psychological benefits improving social interactions and interaction with the physical environment., Methods: This retrospective database study analyzes data from the National Prosthetics Patient Database and other medical datasets within the VHA., Results: Results provide a profile of and comparison between rural and urban veteran users. HISA users are substantially older compared to younger VWDs. The frequency of bathroom, railing, and wooden ramp HMs differed significantly between rural and urban users ( p values < .001). Rural users traveled more miles than urban users to reach a prescribing facility., Conclusions: Older adults and individuals with disabilities have unmet housing needs since accessible housing is in short supply. This HM healthcare service is helping to meet the housing accessibility needs of older veterans, VWDs, older adults, and people with disabilities, in general.

Background The risk of end stage renal disease (ESRD) is increased among individuals with low income and in low income communities. However, few studies have examined the relation of both individual and community socioeconomic status (SES) with incident ESRD. Methods Among 23,314 U.S. adults in the population-based Reasons for Geographic and Racial Differences in Stroke study, we assessed participant differences across geospatially-linked categories of county poverty [outlier poverty, extremely high poverty, very high poverty, high poverty, neither (reference), high affluence and outlier affluence]. Multivariable Cox proportional hazards models were used to examine associations of annual household income and geospatially-linked county poverty measures with incident ESRD, while accounting for death as a competing event using the Fine and Gray method. Results There were 158 ESRD cases during follow-up. Incident ESRD rates were 178.8 per 100,000 person-years (105 py) in high poverty outlier counties and were 76.3 /105 py in affluent outlier counties, p trend = 0.06. In unadjusted competing risk models, persons residing in high poverty outlier counties had higher incidence of ESRD (which was not statistically significant) when compared to those persons residing in counties with neither high poverty nor affluence [hazard ratio (HR) 1.54, 95% Confidence Interval (CI) 0.75-3.20]. This association was markedly attenuated following adjustment for socio-demographic factors (age, sex, race, education, and income); HR 0.96, 95% CI 0.46-2.00. However, in the same adjusted model, income was independently associated with risk of ESRD [HR 3.75, 95% CI 1.62-8.64, comparing the < $20,000 income group to the > $75,000 group]. There were no statistically significant associations of county measures of poverty with incident ESRD, and no evidence of effect modification. Conclusions In contrast to annual family income, geospatially-linked measures of county poverty have little relation with risk of ESRD. Efforts to mitigate socioeconomic disparities in kidney disease may be best appropriated at the individual level. [ABSTRACT FROM AUTHOR]

Abstract: Although prior work has investigated the interplay between demographic and intrasurvey correlations of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores, these prior studies have not included geospatial analyses, or analyses that take into account location effects. Here, we report the results of a geospatial analysis (not equivalent to simple geographical analysis) of patient experience scores pertaining to pain. HCAHPS data collected in 2011 were examined to test the hypothesis that HCAHPS patient experience with pain management (PEPM) scores were geospatially distributed throughout the United States using Moran’s Index, which measures the association between PEPM scores and hospital location. After limiting the dataset to hospitals in the continental United States with nonzero HCAHPS response rates, 3645 hospitals were included in the analyses. “Always” responses were geospatially clustered amongst the analyzed hospitals. Clustering was significant in all distances tested from 10 to 5000km (P <0.0001). We identified 6 demarcated groups of hospitals. Taken together, these results strongly suggest a regional geographic effect on PEPM scores. These results may carry policy implications for U.S. hospitals with regard to acute pain outcomes. Further analyses will be necessary to evaluate policy explanations and implications of the regional geographic differences in PEPM results. [Copyright &y& Elsevier]

In the United States, legal steps have been taken to protect personal privacy, which is viewed as an individual's fundamental right. Location privacy (i.e., protection of personal and location information), however, has not received equal attention in the legal system. Given the recent popularity of location-based services (e.g., cell phones), it is possible to obtain an individual's location information easily and precisely. An investigation of public perception of privacy in the United States indicated that survey responders (1) were unaware of legal implications of location privacy violation and (2) believed that there should be location privacy protection similar to that provided by the Health Insurance Portability and Accountability Act and the Family Educational Rights and Privacy Act. [ABSTRACT FROM AUTHOR]

Firearm violence is a major health problem in the United States that clusters asymmetrically across geographic and demographic lines, and the persistence and unequal distribution of firearm violence suggests that novel causal explanations and theoretical frameworks may be warranted to guide preventive strategies. Thus, this study explores the following three hypotheses that are grounded in complex systems theory: 1) trends in firearm homicides risks have shifted heterogeneously in Harris County across endemic degree of risk; 2) firearm homicides clusters have remained resilient in Harris County across the study time period; and 3), the associations between known contextual correlates of firearm homicides and the distribution of firearm homicides risks in Harris County have manifested as nonlinear. Using a retrospective study design (n = 4,397) from January 1, 2009-June 31, 2021, medicolegal death investigation data from the Harris County Institute of Forensic Sciences and estimates of community characteristics from the American Community Survey were analyzed using Joinpoint trend analysis, kernel density geospatial analysis, and proportion tests. Trend analyses revealed that firearm homicides risks shifted heterogeneously across endemic degree of risk, with geographical areas with lower initial firearm homicides risks experiencing more profound upward shifts across the time period of the study. Geospatial analyses identified the resiliency of firearm homicides clusters across the study period, particularly in central, southern, and south-western districts of the city. Finally, the relationships between known contextual correlates and the distribution of firearm homicides risks in Harris County appeared to be nonlinear, particularly regarding ethnicity. This study provides data-driven results that suggest the plausibility of complex systems theory in advancing the understanding of causality in firearm homicides. Further, these findings support the urgent need for complex systems-informed preventive efforts that account for spatiotemporal heterogeneity, key interactions that generate nonlinearity, and latent feedback loops that underlie resiliency in firearm homicides., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Six percent of practicing otolaryngologists identified by the American Academy of Otolaryngology - Head and Neck Surgery (AAO-HNS) are rhinologists . This is the first study to investigate both the distribution of rhinologists in the United States and the sociodemographic characteristics that may predict their practice locations., Objective: We aim to describe the geospatial distribution of the rhinology workforce and analyze sociodemographic characteristics associated with practice distribution., Methods: We conducted a cross-sectional study of 662 rhinologists queried from the 2020 American Rhinologic Society (ARS) database. Rhinologist practice addresses were compared with ZIP code tabulation area (ZCTA) sociodemographic data from the 2010 US Census Bureau and from the 2014 to 2018 American Community Surveys. Geospatial mapping and multivariate statistics were employed to visualize rhinologist practice locations and analyze which community characteristics were associated with greater densities of rhinologists in ZCTAs., Results: The largest and smallest densities of rhinologists were in coastal areas and in the Central and Midwestern US, respectively. Population characteristics that significantly predicted a higher number of practicing rhinologists included: greater percentage of non-citizens and greater educational attainment (p < 0.001). Population characteristics that significantly predicted a lower number of practicing rhinologists included: greater percentage of self-identified white/Caucasians, median household income, and greater percentage of population aged 65 or older (p < 0.001)., Conclusion: Disparities in healthcare access in the US is evident and applies to rhinologic subspecialty care. Through visual geospatial analysis, we demonstrate the distribution of rhinologists and the population characteristics that may be predictive of whether patients have access to rhinological care., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Previous studies of correlation coefficients between paired observations using census, hydrologic, and remote sensing data abound. It is well established that bivariate relationships at coarser spatial resolutions are often stronger than at finer resolutions. No assessment as yet, however, corroborates this tendency with water resources variables. In this study, multiscale correlations between water use or water availability and population are presented in three river basins—the Missouri (United States), Danube (Europe), and Ganges (South Asia). High-resolution gridded data sets were obtained at 0.5° and resampled to fourteen different geographic scales to examine the effects of scale on the strength and trends of correlations. Correlation coefficients between most variable pairs increased at coarser scales. Smoothing fine-scale spatial patterns in the data at coarser scales is posited as a possible explanation. The increase was not often linear, however, nor was there always an increase. The Missouri Basin did not show a significant increase in correlations between water use and population with grid-cell size and nonlinear increases are evident in the Ganges Basin. [ABSTRACT FROM AUTHOR]

The USDA Farm Bill conservation programs provide landowner incentives to remove less productive and environmentally sensitive lands from agricultural production and reestablish them in natural vegetation (e.g., native grasses, trees, etc.) to achieve conservation objectives. However, removal of arable land from production imposes an opportunity cost associated with loss in revenue from commodities that otherwise would have been produced. Recent Farm Bills have increasingly emphasized targeted practices to achieve specific environmental outcomes that maximize environmental benefits relative to cost. The Habitat Buffers for Upland Birds practice (CP-33) under the continuous Conservation Reserve Program is an example of a targeted conservation practice that has produced measureable outcomes (increased bobwhite and grassland bird populations) with relatively minor changes in primary land use. However, establishing conservation buffers on profitable farmland may be incompatible with the economic objectives of landowners/producers. Precision agriculture technologies provide a powerful conservation planning tool for identifying environmental and economic opportunities in agricultural systems. Precision implementation of conservation practices, such as CP-33, is the foundation of strategic conservation planning and is essential for optimization of environmental and economic benefits. Toward this end, we developed a geospatial decision support tool (Arc GIS tool) to inform this decision-making process. We illustrate the geoprocessing workflow of the tool and demonstrate the conditions under which precision implementation of conservation practices can concomitantly increase whole-field profitability and environmental services for an example farm in Mississippi. [ABSTRACT FROM AUTHOR]

Geomatics educational programs in the United States and Canada are similar in many ways. Differences, however, exist in the relative success of each of them. Healthier Canadian geomatics education is demonstrated through a number of metrics including: the number and diversity of geomatics faculty as well as the enrollment of undergraduate and graduate students. By contrast, geomatics educational programs in the U.S. have struggled; several have closed (e.g., University of Wisconsin) or been restructured (e.g., University of Maine and University of Florida). Getting ample numbers of students into the classroom has posed another challenge for American programs. The evolution and current status of geomatics education in the two countries are explored. Highlights of the gap in geomatics education between the two nations are presented. Lessons learned from this research are outlined in the form of recommendations at the end of the paper for decision and policy makers to take into account. [ABSTRACT FROM AUTHOR]

Wildfires burn several million hectares in the United States annually. Time is critical in gathering information from burned landscapes for post-fire recovery planning. A technology to obtain spatial vegetation information across landscapes is Light Detecting and Ranging (LiDAR). We compared tree positional and height measurements, primarily from Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa) , between field-based and LiDAR-derived measurements at three south-western Oregon (USA) sites. The sites represented a range of tree mortality from minimal to extensive. Our primary objective was to determine whether significant differences existed between field and LiDAR tree measurements in burned landscapes. Secondary objectives were to examine whether LiDAR pulse intensities in burned landscapes could differentiate coniferous from deciduous trees, discern fire-killed from live trees, and whether other tree measurement parameters were related to pulse intensities. No significant differences were detected between field-based and LiDAR-derived horizontal positions. Tree height differences between field-based and LiDAR measurements were significant at one site likely owing to dense canopy and measurement biases. Mean and maximum LiDAR intensities were significantly different between live and dead (fire-killed) trees in two of three sites. Additionally, crown diameter and tree sweep were significant in explaining variation in maximum LiDAR intensities at all sites. [ABSTRACT FROM AUTHOR]

The Geospatial Multistate Archive and Preservation Partnership, GeoMAPP, is a collaborative effort addressing the preservation of at-risk and temporally significant digital geospatial content in state and local governments. The project is one of four initial state government partnerships funded by the Library of Congress's National Digital Information Infrastructure and Preservation Program. The GeoMAPP partnership includes representatives from the geospatial and archive staffs of Kentucky, North Carolina, and Utah. GeoMAPP is exploring digital preservation issues in a number of topic areas, including business planning, data inventory and metadata, appraisal and access, content life cycle and transfer demonstration, and communications and industry outreach. [ABSTRACT FROM AUTHOR]

Abstract: The US Department of Energy’s Regional Carbon Sequestration Partnerships (RCSPs) are responsible for generating geospatial data for the maps displayed in the Carbon Sequestration Atlas of the United States and Canada. Key geospatial data (carbon sources, potential storage sites, transportation, land use, etc.) are required for the Atlas, and for efficient implementation of carbon sequestration on a national and regional scale. The National Carbon Sequestration Database and Geographical Information System (NatCarb) is a relational database and geographic information system (GIS) that integrates carbon storage data generated and maintained by the RCSPs and various other sources. The purpose of NatCarb is to provide a national view of the carbon capture and storage potential in the U.S. and Canada. The digital spatial database allows users to estimate the amount of CO2 emitted by sources (such as power plants, refineries and other fossil-fuel-consuming industries) in relation to geologic formations that can provide safe, secure storage sites over long periods of time. The NatCarb project is working to provide all stakeholders with improved online tools for the display and analysis of CO2 carbon capture and storage data. NatCarb is organizing and enhancing the critical information about CO2 sources and developing the technology needed to access, query, model, analyze, display, and distribute natural resource data related to carbon management. Data are generated, maintained and enhanced locally at the RCSP level, or at specialized data warehouses, and assembled, accessed, and analyzed in real-time through a single geoportal. NatCarb is a functional demonstration of distributed data-management systems that cross the boundaries between institutions and geographic areas. It forms the first step toward a functioning National Carbon Cyberinfrastructure (NCCI). NatCarb provides access to first-order information to evaluate the costs, economic potential and societal issues of CO2 capture and storage, including public perception and regulatory aspects. NatCarb online access has been modified to address the broad needs of a spectrum of users. NatCarb includes not only GIS and database query tools for high-end user, but simplified display for the general public using readily available web tools such as Google Earth™and Google Maps™. Not only is NatCarb connected to all the RCSPs, but data are also pulled from public servers including the U.S. Geological Survey-EROS Data Center and from the Geography Network. Data for major CO2 sources have been obtained from U.S. Environmental Protection Agency (EPA) databases, and data on major coal basins and coalbed methane wells were obtained from the Energy Information Administration (EIA). [Copyright &y& Elsevier]

In this paper, we assume that learning to comprehend the geospatial environment would be significantly facilitated by developing a multi-level task ontology that identifies various levels and complexities of geospatial concepts. We suggest that, apart from four spatial ‘primitives’– identity, location, magnitude, and space-time – all geospatial concepts involve ‘inheritance’ characteristics. The more complex and abstract the concept, the larger the inheritance links that need to be appreciated to enhance concept understanding. For example, many basic geospatial concepts – such as direction and distance – are first-order derivatives from the ‘location’ primitive, whereas concepts such as spatial association, map projection or interpolation are high-order concepts that require several layers of geospatial concepts in their derivation. Having offered a five-level ontology for concept organisation, we suggest sets of tasks that could establish an understanding of concepts, thus directly making the environment more legible in a spatial sense. We develop this framework in the context of the teaching of geography in grades from kindergarten to the final years of high school (grade 12 in the United States system). Our conceptualisation is grounded in the US school system – in which geography is usually absent in the curriculum. [ABSTRACT FROM AUTHOR]

Racial/ethnic minority groups in the United States have high renal cell carcinoma (RCC) mortality rates. This study assessed surgical treatment disparities across racial/ethnic groups and impacts of neighborhood socioeconomic characteristics on surgical treatments and overall mortality. Stage I RCC patients diagnosed between 2004 and 2016 from National Cancer Database were included (n = 238,141). We assessed differences in associations between race/ethnicity and treatment patterns using logistic regression and between race/ethnicity and overall mortality using Cox regression with and without neighborhood characteristics in the regression models. When compared to non-Hispanic Whites (NHWs), American Indians/Alaska Natives and non-Hispanic Blacks (NHBs) were more likely not to receive surgical care and all racial/ethnic minority groups had significantly increased odds of undergoing radical rather than partial nephrectomy, even after adjusting for neighborhood characteristics. Including surgical treatment and neighborhood factors in the models slightly attenuated the association, but NHBs had a significantly increased risk of overall mortality. NHBs who underwent radical nephrectomy had an increased risk of mortality (HR 1.15, 95% CI: 1.08-1.23), but not for NHBs who underwent partial nephrectomy (HR 0.92, 95% CI: 0.84-1.02). Neighborhood factors were associated with surgical treatment patterns and overall mortality in both NHBs and NHWs. Neighborhood socioeconomic factors may only partly explain RCC disparities.

Objective: To analyze the temporal trends and state-wide geospatial variations in Vascular and Interventional Radiology (VIR) workforce in the United States., Methods: The State Physician Workforce Data from the AAMC website was accessed for years 2015, 2017, and 2019. The variables collected for each state included total number of active physicians, total number of physicians per specialty and total number of female physicians in VIR. Comparative data was obtained for vascular surgery (VS), diagnostic radiology (DR), and radiation oncology (RO). The annual growth rate for total physicians and sub-analysis of female physicians in each state was computed for each specialty., Results: From 2015 to 2019, the total number of active physicians in the United States grew by 1.8% per year. Growth of active physicians in VIR grew by 8.3%, DR 0.06%, VS 4.4%, and RO 1.9% per year. Colorado and Minnesota had the highest growth rate for VIR physicians (15%). VIR physicians per 100,000 people increased from 0.84 (2015) to 1.10 (2019) in the US. In comparison, VS physicians increased from 0.99 (2015) to 1.14 (2019), DR physicians decreased from 8.61 (2015) to 8.43 (2019), and RO physicians grew from 1.48 (2015) to 1.56 (2019). Women represented 6.8% of the VIR workforce in the US in 2019 and increased by a rate of 16% annually in the US from 2015 to 2019. In comparison, the number of women in VS has grown by 21%, DR by 2%, and RO by 2.4% during the same period. The state of Maryland has the highest proportion of women in VIR at 18%., Conclusion: The number of VIR physicians is increasing at a higher rate than the national overall physician growth, and while female VIR physicians makeup a small fraction of the VIR workforce, their numbers have increased at a faster rate than overall VIR physicians., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios. [ABSTRACT FROM AUTHOR]

Background: The assessment of geographical heterogeneity of HIV among men who have sex with men (MSM) and people who inject drugs (PWID) can usefully inform targeted HIV prevention and care strategies., Objective: We aimed to measure HIV seroprevalence and identify hotspots of HIV infection among MSM and PWID in Nigeria., Methods: We included all MSM and PWID accessing HIV testing services across 7 prioritized states (Lagos, Nasarawa, Akwa Ibom, Cross Rivers, Rivers, Benue, and the Federal Capital Territory) in 3 geographic regions (North Central, South South, and South West) between October 1, 2016, and September 30, 2017. We extracted data from national testing registers, georeferenced all HIV test results aggregated at the local government area level, and calculated HIV seroprevalence. We calculated and compared HIV seroprevalence from our study to the 2014 integrated biological and behavioural surveillance survey and used global spatial autocorrelation and hotspot analysis to highlight patterns of HIV infection and identify areas of significant clustering of HIV cases., Results: MSM and PWID had HIV seroprevalence rates of 12.14% (3209/26,423) and 11.88% (1126/9474), respectively. Global spatial autocorrelation Moran I statistics revealed a clustered distribution of HIV infection among MSM and PWID with a <5% and <1% likelihood that this clustered pattern could be due to chance, respectively. Significant clusters of HIV infection (Getis-Ord-Gi* statistics) confined to the North Central and South South regions were identified among MSM and PWID. Compared to the 2014 integrated biological and behavioural surveillance survey, our results suggest an increased HIV seroprevalence among PWID and a substantial decrease among MSM., Conclusions: This study identified geographical areas to prioritize for control of HIV infection among MSM and PWID, thus demonstrating that geographical information system technology is a useful tool to inform public health planning for interventions targeting epidemic control of HIV infection., (©Amobi Onovo, Abiye Kalaiwo, Moses Katbi, Otse Ogorry, Antoine Jaquet, Olivia Keiser. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 24.05.2021.)

Introduction: The relationship and trends of geography and travel distance to access surgical cancer care has been poorly characterized. The objective of the study was to define the geographic distribution of access to hospital-based operative cancer care across the USA., Methods: A cohort analysis was performed using the 2005 and 2015 American Hospital Association Annual Survey, Census Bureau Data for 2010, and the American Community Survey 5-year estimates for 2011 to 2016., Results: The number of hospitals that provided surgical services with an approved American College of Surgeons (ACS) cancer program slightly increased over the time periods examined (2005, n = 1203 vs. 2015, n = 1284; p = 0.7210). Based on geospatial analysis, 18,214,994 (5.9%) people lived more than 60 min from a hospital with a cancer program in 2005 compared with 34,630,516 (11.2%) by 2015. Communities within a 60-min drive time were more likely to be composed of individuals who completed high school (85.9% vs. 84.2%), were employed (62.7% vs. 57.1%), had a higher median household income ($67.4 k vs. $53.2 k), and lived within states that had expanded Medicaid (62.5% vs. 48.9%) (all p < 0.0001). In contrast, communities outside of a 60-min drive time had a greater proportion of individuals below the federal poverty level (18.3% vs. 16.5%; p < 0.0001)., Conclusions: While the number of hospitals with ACS approved cancer program designation increased over the last decade, the number of people living greater than 60 min from an approved cancer programs nearly doubled. These data highlight worrisome geospatial trends that may make access to cancer care for certain patient populations increasingly challenging.

Background: School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness., Methods: We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors., Results: At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures., Conclusions: School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Objective: To advance public health support for the U.S. Department of Housing and Urban Development's smoke-free rule, the Centers for Disease Control and Prevention collaborated with the Georgia Institute of Technology to develop a geospatial mapping tool. The objective was to create a tool state and local public health agencies could use to tailor smoke-free educational materials and cessation interventions for specific public housing development resident populations., Results: The resulting "Extinguish Tool" includes an interactive map of U.S. public housing developments (PHDs) and healthcare facilities that provides detailed information on individual PHDs, their proximity to existing healthcare facilities, and the demographic characteristics of residents. The tool also estimates the number of PHD residents who smoke cigarettes and calculates crude estimates of the potential economic benefits of providing cessation interventions to these residents. The geospatial mapping tool project serves as an example of a collaborative and innovative public health approach to protecting the health and well-being of the nation's two million public housing residents, including 760,000 children, from the harms of tobacco smoking and secondhand smoke exposure in the places where they live, play, and gather.

Human health is inextricably tied to ecosystem services (ES), including those associated with greenspace in urban communities. EnviroAtlas provides close to 100 maps of ES metrics based on high-resolution land cover data in featured communities across the contiguous United States. Using selected EnviroAtlas ES metrics, a Community EcoHealth Index (CEHI) was created based on an ecohealth framework including health promotion and hazard buffering domains. Aggregation of eight selected ES metrics in these domains entailed a weighted distance measure, where objective, data-driven weights were generated. CEHI was calculated by Census Block Group (CBG) at both the local level and the national level for 22 EnviroAtlas communities. Results were mapped to show one- to five-star CBGs or neighborhoods within and across all 22 featured communities. At the national level, CEHI favors communities in forested ecoregions. The local version of CEHI is more appropriate to inform social, economic, and environmental decision-making for improving community ES associated with human health.

Precision health can provide an avenue to bridge and integrate ways of knowing for research and practice. Nurse scientists have a long-standing interest in using multiple sources of information to address research questions of significance to the profession and discipline of nursing, which can lead to much needed contributions to precision health care. In this paper, nursing scientists discuss emerging research methods including omics, electronic sensors, and geospatial data, and mixed methods that further develop nursing science and contribute to precision health initiatives. The authors provide exemplars of the types of knowledge and ways of knowing that, using these and other advanced data and analytic strategies, may advance precision health within the context of nursing science., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Perinatal services exist today as a dyad of maternal and neonatal care. When perinatal care is fragmented or unavailable, excess morbidity and mortality may occur in pregnant women and newborns.Objective: The objective of the study was to describe spatial relationships between women of reproductive age, individual perinatal subspecialists (maternal-fetal medicine and neonatology), and obstetric and neonatal critical care facilities in the United States to identify gaps in health care access.Study Design: We used geographic visualization and conducted surface interpolation, nearest neighbor, and proximity analyses. Source data included 2010 US Census, October 2013 National Provider Index, 2012 American Hospital Association, 2012 National Center for Health Statistics Natality File, and the 2011 American Academy of Pediatrics directory.Results: In October 2013, there were 2.5 neonatologists for every maternal-fetal medicine specialist in the United States. In 2012 there were 1.4 level III or higher neonatal intensive care units for every level III obstetric unit (hereafter, obstetric critical care unit). Nationally, 87% of women of reproductive age live within 50 miles of both an obstetric critical care unit and a neonatal intensive care unit. However, 18% of obstetric critical care units had no neonatal intensive care unit, and 20% of neonatal intensive care units had no obstetric critical care unit within a 10 mile radius. Additionally, 26% of obstetric critical care units had no maternal-fetal medicine specialist practicing within 10 miles of the facility, and 4% of neonatal intensive care units had no neonatologist practicing within 10 miles.Conclusion: Gaps in access and discordance between the availability of level III or higher obstetric and neonatal care may affect the delivery of risk-appropriate care for high-risk maternal fetal dyads. Further study is needed to understand the importance of these gaps and discordance on maternal and neonatal outcomes. [ABSTRACT FROM AUTHOR]

Greater community participation among individuals with serious mental illnesses is associated with better psychosocial and health outcomes. Typically, studies examining community participation have utilized self-report measures and been conducted in limited settings. The introduction of methodological advances to examining community participation of individuals with serious mental illnesses has the potential to advance the science of community mental health research and invigorate the work of community psychologists in this area. This study employed an innovative geospatial approach to examine the relationship between community participation and resource accessibility (i.e., proximity) and availability (i.e., concentration) among 294 individuals utilizing community mental health services throughout the United States. Findings suggest small but significant associations between community participation and the accessibility and availability of resources needed for participation. Furthermore, findings demonstrate the importance of car access for individuals residing in both urban and non-urban settings. The methods and results presented in this study have implications for community mental health research and services and provide an illustration of ways that geospatial methodologies can be used to investigate environmental factors that impact community inclusion and participation of individuals with serious mental illnesses., (© Society for Community Research and Action 2017.)

Purpose: Geospatial, contextual, and multilevel research is integral to cancer prevention and control. NCI-designated Cancer Centers are at the forefront of cancer research; therefore, this paper sought to review the geospatial, contextual, and multilevel research at these cancer centers., Methods: Investigators used PubMed and Web of Science to compile geospatial publications from 1971 to February 2016 with cancer center-affiliated authors. Relevant abstracts were pulled and classified by six geospatial approaches, eight geospatial scales, and eight cancer sites., Results: The searches identified 802 geospatial, contextual, and multilevel publications with authors affiliated at 60 of the 68 NCI-designated Cancer Centers. Over 90% were published after 2000. Five cancer centers accounted for approximately 50% of total publications, and 30 cancer centers accounted for over 85% of total publications. Publications covered all geospatial approaches and scales to varying degrees, and 90% dealt with cancer., Conclusions: The NCI-designated Cancer Center network is increasingly pursuing geospatial, contextual, and multilevel cancer research, although many cancer centers still conduct limited to no research in this area. Expanding geospatial efforts to research programs across all cancer centers will further enrich cancer prevention and control. Similar reviews may benefit other domestic and international cancer research institutions.