Space-time cluster detection techniques for infectious diseases: A systematic review.

BACKGROUND: Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS: We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS: Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION: This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis.

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil.

BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

Detecting space-time patterns of disease risk under dynamic background population.

We are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space-time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space-time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010-2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.

The association of crash response times and deaths at the crash scene: A cross-sectional analysis using the 2019 National Emergency Medical Service Information System.

BACKGROUND: Deaths at the crash scene (DAS) are crash deaths that occur within minutes after a crash. Rapid crash responses may reduce the occurrence of DAS. OBJECTIVES: This study aims to assess the association of crash response time and DAS during the rush and nonrush hour periods by rurality/urbanicity. METHOD: This single-year cross-sectional study used the 2019 National Emergency Medical Services (EMS) Information System. The outcome variable was DAS. The predictor variables were crash response measures: EMS Chute Initiation Time (ECIT) and EMS Travel Time (ETT). Age, gender, substance use, region of the body injured, and the revised trauma score were used as potential confounders. Logistic regression was used to assess the unadjusted and adjusted odds of DAS. RESULTS: A total of 654,675 persons were involved in EMS-activated road crash events, with 49.6% of the population exposed to crash events during the rush hour period. A total of 2,051 persons died at the crash scene. Compared to the baseline of less than 1 minute, ECIT ranging from 1 to 5 minutes was significantly associated with 58% (95% CI: 1.45-1.73) increased odds of DAS. Also, when compared to the baseline of less than 9 minutes, ETT ranging between 9 and 18 minutes was associated with 34% (95% CI: 1.22-1.47) increased odds of DAS. These patterns were consistent during the rush and nonrush hour periods and across rural and urban regions. CONCLUSION: Reducing crash response times may reduce the occurrence of DAS.

An assessment of the non-fatal crash risks associated with substance use during rush and non-rush hour periods in the United States.

BACKGROUND: Understanding how substance use is associated with severe crash injuries may inform emergency care preparedness. OBJECTIVES: This study aims to assess the association of substance use and crash injury severity at all times of the day and during rush (6-9 AM; 3-7 PM) and non-rush-hours. Further, this study assesses the probabilities of occurrence of low acuity, emergent, and critical injuries associated with substance use. METHODS: Crash data were extracted from the 2019 National Emergency Medical Services Information System. The outcome variable was non-fatal crash injury, assessed on an ordinal scale: critical, emergent, low acuity. The predictor variable was the presence of substance use (alcohol or illicit drugs). Age, gender, injured part, revised trauma score, the location of the crash, the road user type, and the geographical region were included as potential confounders. Partially proportional ordinal logistic regression was used to assess the unadjusted and adjusted odds of critical and emergent injuries compared to low acuity injury. RESULTS: Substance use was associated with approximately two-fold adjusted odds of critical and emergent injuries compared to low acuity injury at all times of the day and during the rush and non-rush hours. Although the proportion of substance use was higher during the non-rush hour period, the interaction effect of rush hour and substance use resulted in higher odds of critical and emergent injuries compared to low acuity injury. CONCLUSION: Substance use is associated with increased odds of critical and emergent injury severity. Reducing substance use-related crash injuries may reduce adverse crash injuries.

Uncertainty in geospatial health: challenges and opportunities ahead.

PURPOSE: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil

ABSTRACT Background: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. Methods: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. Results: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. Conclusions: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

Patient-specific and healthcare real-world costs of atrial fibrillation in individuals treated with direct oral anticoagulant agents or warfarin.

BACKGROUND: Anticoagulant therapies are used to prevent atrial fibrillation-related strokes, with warfarin and direct oral anticoagulant (DOAC) the most common. In this study, we incorporate direct health care costs, drug costs, travel costs, and lost working and leisure time costs to estimate the total costs of the two therapies. METHODS: This retrospective study used individual-level patient data from 4000 atrial fibrillation (AF) patients from North Karelia, Finland. Real-world data on healthcare use was obtained from the regional patient information system and data on reimbursed travel costs from the database of the Social Insurance Institution of Finland. The costs of the therapies were estimated between June 2017 and May 2018. Using a Geographical Information System (GIS), we estimated travel time and costs for each journey related to anticoagulant therapies. We ultimately applied therapy and travel costs to a cost model to reflect real-world expenditures. RESULTS: The costs of anticoagulant therapies were calculated from the standpoint of patient and the healthcare service when considering all costs from AF-related healthcare visits, including major complications arising from atrial fibrillation. On average, the annual cost per patient for healthcare in the form of public expenditure was higher when using DOAC therapy than warfarin therapy (average cost = € 927 vs. € 805). Additionally, the average annual cost for patients was also higher with DOAC therapy (average cost = € 406.5 vs. € 296.7). In warfarin therapy, patients had considerable more travel and time costs due the different implementation practices of therapies. CONCLUSION: The results indicated that DOAC therapy had higher costs over warfarin from the perspectives of the patient and healthcare service in the study area on average. Currently, the cost of the DOAC drug is the largest determinator of total therapy costs from both perspectives. Despite slightly higher costs, the patients on DOAC therapy experienced less AF-related complications during the study period.

Examining Wing Length-Abundance Relationships and Pyrethroid Resistance Mutations among Aedes albopictus in a Rapidly Growing Urban Area with Implications for Mosquito Surveillance and Control.

Aedes albopictus is a cosmopolitan mosquito species capable of transmitting arboviruses such as dengue, chikungunya, and Zika. To control this and similar species, public and private entities often rely on pyrethroid insecticides. In this study, we screened Ae. albopictus collected from June to August 2017 in Mecklenburg County, a rapidly growing urban area of North Carolina, for mutations conferring pyrethroid resistance and examined spatiotemporal patterns of specimen size as measured by wing length, hypothesizing that size variation could be closely linked to local abundance, making this easily measured trait a useful surveillance proxy. The genetic screening results indicated that pyrethroid resistance alleles are not present in this population, meaning that this population is likely to be susceptible to this commonly used insecticide class. We detected no significant associations between size and abundance-related factors, indicating that wing-size is not a useful proxy for abundance, and thus not useful to surveillance in this capacity. However, mosquitoes collected in June were significantly larger than July or August, which may result from meteorological conditions, suggesting that short-term weather cues may modulate morphological traits, which could then affect local fecundity and virus transmission dynamics, as previously reported.

What is the place for space in epidemiology?

At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on "Spatial Analysis and GIS in Epidemiology," we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.

Predicting coliform presence in private wells as a function of well characteristics, parcel size and leachfield soil rating.

Public water systems must be tested frequently for coliform bacteria to determine whether other pathogens may be present, yet no testing or disinfection is required for private wells. In this paper, we identify whether well age, type of well, well depth, parcel size, and soil ratings for a leachfield can predict the probability of detecting coliform bacteria in private wells using a multivariate logistic regression model. Samples from 1163 wells were analyzed for the presence of coliform bacteria between October 2017 and October 2019 across Gaston County, North Carolina, USA. The maximum well age was 30 years, and bored wells (median age = 24 years) were older than drilled wells (median age = 19 years). Bored wells were shallower (mean depth = 18 m) compared to drilled wells (mean depth = 79 m). We found coliform bacteria in 329 samples, including 290 of 1091 drilled wells and 39 of 72 bored wells. The model results showed bored wells were 4.76 times more likely to contain bacteria compared to drilled wells. We found that the likelihood of coliform bacteria significantly increased with well age, suggesting that those constructed before well standards were enforced in 1989 may be at a higher risk. We found no significant association between poorly rated soils for a leachfield, well depth, parcel size and the likelihood of having coliform in wells. These findings can be leveraged to determine areas of concern to encourage well users to take action to reduce their risk of drinking possible pathogens in well water.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia.

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level-where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space-time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.

Estimating the prevalence and spatial clusters of coal workers' pneumoconiosis cases using medicare claims data, 2011-2014.

BACKGROUND: Workers employed in the coal mining sector are at increased risk of respiratory diseases, including coal workers' pneumoconiosis (CWP). We investigated the prevalence of CWP and its association with sociodemographic factors among Medicare beneficiaries. METHODS: We used 5% Medicare Limited Data Set claims data from 2011 to 2014 to select Medicare beneficiaries with a diagnosis of ICD-9-CM 500 (CWP). We aggregated the data by county and limited our analysis to seven contiguous states: Illinois, Indiana, Kentucky, Ohio, Pennsylvania, Virginia, and West Virginia. We estimated county-level prevalence rates using total Medicare beneficiaries and miners as denominators and performed spatial hotspot analysis. We used negative binomial regression analysis to determine the association of county-wise sociodemographic factors with CWP. RESULTS: There was significant spatial clustering of CWP cases in Kentucky, Virginia, and West Virginia. Spatial clusters of 210 and 605 CWP cases representing an estimated 4200 to 12 100 cases of Medicare beneficiaries with CWP were identified in the three states. Counties with higher poverty levels had a significantly elevated rate of CWP (adjusted rate ratios [RR]: 1.15; 95% CI, 1.12-1.18). There was a small but significant association of CWP with the county-wise catchment area. Rurality was associated with a more than three-fold elevated rate of CWP in the unadjusted analysis (RR: 3.28, 95% CI, 2.22-4.84). However, the rate declined to 1.45 (95% CI, 1.04-2.01) after adjusting for other factors in the analysis. CONCLUSIONS: We found evidence of significant spatial clustering of CWP among Medicare beneficiaries living in the seven states of the USA.

Do socioeconomic factors drive Aedes mosquito vectors and their arboviral diseases? A systematic review of dengue, chikungunya, yellow fever, and Zika Virus.

As the threat of arboviral diseases continues to escalate worldwide, the question of, "What types of human communities are at the greatest risk of infection?" persists as a key gap in the existing knowledge of arboviral diseases transmission dynamics. Here, we comprehensively review the existing literature on the socioeconomic drivers of the most common Aedes mosquito-borne diseases and Aedes mosquito presence/abundance. We reviewed a total of 182 studies on dengue viruses (DENV), chikungunya virus (CHIKV), yellow fever virus (YFVV), Zika virus (ZIKV), and presence of Aedes mosquito vectors. In general, associations between socioeconomic conditions and both Aedes-borne diseases and Aedes mosquitoes are highly variable and often location-specific. Although 50% to 60% of studies found greater presence or prevalence of disease or vectors in areas with lower socioeconomic status, approximately half of the remaining studies found either positive or null associations. We discuss the possible causes of this lack of conclusiveness as well as the implications it holds for future research and prevention efforts.

Residential mobility impacts relative risk estimates of space-time clusters of chlamydia in Kalamazoo County, Michigan.

We determine the impact of residential mobility in the prevalence and transmission dynamics of sexually transmitted infections. We illustrate our approach on reported chlamydia infections obtained from the Michigan Disease Surveillance System for Kalamazoo County, USA, from 2006 to 2014. We develop two scenarios, one with fixed residential addresses and one considering residential mobility. We then compare the resulting space-time clusters and relative risk (RR) of infection. The space-time scan statistics showed increased RR in an area with previously low risk of sexually transmitted infections. In addition, even though the spatial extent of the three clusters identified did not change significantly at the scale we conducted our analysis at, the temporal extent (duration) did exhibit significant changes and could be considered for unique interventions. The results indicate that residential mobility has some dependency on the prevalence and transmission dynamics of sexually transmitted infections to new areas. We suggest that strategies adopted to reduce the burden of sexually transmitted infections take into consideration the relatively high residential mobility of at-risk populations to reduce spreading the infections to new areas.

Landscapes of healthcare utilization during a dengue fever outbreak in an urban environment of Colombia.

The well-being of a population and its health are influenced by a myriad of socioeconomic and environmental factors that interact across a wide range of scales, from the individual to the national and global levels. One of these factors is the provision of health services, which is regulated by both demand and supply. Although an adequate provision can significantly improve health outcomes of a population, lopsided flow of patients to specific health centers can result in serious disparities and potentially delay the timeliness of a diagnosis. In this paper, utilization patterns during an epidemic of dengue fever in the city of Cali, Colombia for the year 2010 are investigated. Specifically, the objectives are to (1) identify health facilities that exhibit patterns of over- and underutilization, (2) determine where patients who are being diagnosed at a particular facility originate from, and (3) whether patients are traveling to their closest facility and hence (4) estimate how far patients are willing to travel to be diagnosed and treated for dengue fever. Analysis is further decomposed by age group and by gender, in an attempt to test whether utilization patterns drastically change according to these variables. Answers to these questions can help health authorities plan for future epidemics, for instance, by providing guidelines as to which facilities require more resources and by improving the organization of health prevention campaigns to direct population seeking health assistance to use facilities that are underutilized.

A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina.

Climate change, urbanization, and globalization have facilitated the spread of Aedes mosquitoes into regions that were previously unsuitable, causing an increased threat of arbovirus transmission on a global scale. While numerous studies have addressed the urban ecology of Ae. albopictus, few have accounted for socioeconomic factors that affect their range in urban regions. Here we introduce an original sampling design for Ae. albopictus, that uses a spatial optimization process to identify urban collection sites based on both geographic parameters as well as the gradient of socioeconomic variables present in Mecklenburg County, North Carolina, encompassing the city of Charlotte, a rapidly growing urban environment. We collected 3,645 specimens of Ae. albopictus (87% of total samples) across 12 weeks at the 90 optimized site locations and modelled the relationships between the abundance of gravid Ae. albopictus and a variety of neighborhood socioeconomic attributes as well as land cover characteristics. Our results demonstrate that the abundance of gravid Ae. albopictus is inversely related to the socioeconomic status of the neighborhood and directly related to both landscape heterogeneity as well as proportions of particular resident races/ethnicities. We present our results alongside a description of our novel sampling scheme and its usefulness as an approach to urban vector epidemiology. Additionally, we supply recommendations for future investigations into the socioeconomic determinants of vector-borne disease risk.

Accessibility Landscapes of Supplemental Nutrition Assistance Program-Authorized Stores.

BACKGROUND: The Supplemental Nutrition Assistance Program (SNAP) is the largest food assistance program in the United States. Participants receive electronic benefits that are redeemable at a variety of food stores. Previous research notes that low-income neighborhoods often lack supermarkets with high-quality, affordable food. OBJECTIVE: The first aim of this study was to explore the number and spatial distribution of SNAP stores by type and to assess how SNAP benefit redemption is linked to store type in North Carolina in 2015. The second aim was to compare the demographics of populations living in areas with a high concentration of SNAP participants vs areas with a lower concentration of SNAP participants. The third aim was to test for disparities in the availability of and access to SNAP-authorized stores in areas with high vs low concentration of SNAP participants stratified by rural/urban status. DESIGN: US Department of Agriculture and US Census data were used to explore the spatial distribution of SNAP stores at the census block group level utilizing a Geographic Information System. PARTICIPANTS: The 9,556 North Carolina SNAP stores in 2015 categorized into full-variety and limited-variety stores. OUTCOME MEASURES: Proximity to limited-variety SNAP food stores and full-variety SNAP food stores within access range (1 mile in urban areas and 10 miles in rural areas). STATISTICAL ANALYSES: Wilcoxon rank sum and χ2 tests are used to compare the distance to and concentration of SNAP stores by rurality and SNAP participant concentration at census block group scale. RESULTS: Among the SNAP stores in North Carolina, 83% are limited-variety stores and 17% are full-variety stores. There are disparities in the demographics of individuals living in census block groups with a high proportion of SNAP participants compared to census block groups with a lower proportion of SNAP participants. More households in higher SNAP participant census block groups were non-white, did not have a car, and had children compared to census block groups with lower SNAP participation. Residents in high SNAP participant census block groups typically had access to 0 full-variety stores and 4 limited-variety stores in urban areas and 3 full-variety stores and 17 limited-variety stores in rural areas. CONCLUSIONS: SNAP participant access to a variety of stores should be considered when approving food stores for SNAP authorization. More research is essential to disentangle the relationship between access to SNAP store type and SNAP food choice and to estimate geographical disparities.