Revisiting the Modified Retail Food Environment Index (mRFEI): Examining Food Access Inequities Over a Decade in the United States

Revisiting the Modified Retail Food Environment Index (mRFEI): Examining Food Access Inequities Over a Decade in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Revisiting the Modified Retail Food Environment Index (mRFEI): Examining Food Access Inequities Over a Decade in the United States Weixuan Lyu, Xiang Chen, Congcong Miao, Qinyun Lin, Xukun Xiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5975401/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Discover Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract This study recalculates the modified Retail Food Environment Index (mRFEI) using the 2018–2019 Infogroup dataset, addressing a data gap left since the index’s last revision in 2011. The updated mRFEI is employed to assess current food access disparities in the United States. Based on the recalculated mRFEI, we have evaluated the evolution of food access inequities by examining the relationships between the mRFEI and each theme of the community’s social vulnerability index (SVI) over a decade. Our findings reveal that higher social vulnerability remains associated with lower access to healthy food retailers, particularly for socioeconomic status ( b = -0.123, 95% CI [-0.163, -0.082]) and minority status and language ( b = -0.277, 95% CI [-0.312, -0.241]). However, these associations have weakened over time. The mRFEI gap between the most (i.e. bottom 10%) and the least (i.e. top 10%) vulnerable tracts (based on the overall SVI) decreases by 2.176 (95% CI [1.638, 2.714]) over time. Similar reductions are observed for other SVI themes, as well as when comparing the top and the bottom 25%, suggesting a gradual narrowing of food inequities. However, persistent disparities highlight the need for continued policy efforts. We make our data and product publicly available through a web Geographic Information System (GIS) tool. By offering the updated mRFEI, we equip researchers, policymakers, and practitioners with a timely, accessible, and reusable resource that enables a more accurate understanding of current food access disparities. This resource can be directly incorporated into future studies, policy development, and intervention design. mRFEI food environment social vulnerability food access inequities Figures Figure 1 1 Introduction The community food environment plays a crucial role in shaping individuals' food behaviors and, consequently, their dietary health comorbidities, making it a focus area in chronic disease prevention [ 1 – 3 ]. However, equitable food access is not always guaranteed, as socioeconomically disadvantaged neighborhoods often have limited access to healthy and affordable food sources. This spatial isolation can further contribute to poor dietary health outcomes, exacerbating broader community health disparities [ 2 , 4 , 5 ]. National food and health initiatives have employed tools like the modified Retail Food Environment Index (mRFEI) to assess food access disparities [ 6 ]. Developed by the Centers for Disease Control and Prevention (CDC) of the United States, the mRFEI quantifies the percentage of healthy food retailers relative to the total number of food retailers within a census tract and was released in the 2011 Children’s Food Environment State Indicator Report [ 6 ]. The index was calculated using 2008–2009 datasets, including the 2009 InfoUSA, the 2008 Homeland Security Infrastructure Program Database, and the 2009 Navteq [ 6 ]. While this index has limitations, such as overlooking non-spatial factors [ 7 ] and individual characteristics [ 8 ], the mRFEI is recognized as a practical tool for assessing large-scale food access, capturing broad patterns, and conducting long-term longitudinal analyses [ 9 , 10 ]. However, a major gap exists as the current mRFEI has not been updated since its last release in 2011 [ 6 ], failing to account for significant changes in the retail food sector over the past decade, particularly those induced by gentrification in local markets [ 11 , 12 ], the rise of online grocery services [ 13 ], and the disruptions caused by COVID-19 [ 14 ]. To this end, this 2011 mRFEI metric creates a significant gap in understanding current food environments, limiting the ability to track disparities over time and develop effective interventions. This study addresses this gap by updating the mRFEI to reflect the current food environment conditions. We further examine the dynamics of the mRFEI’s relationship with social vulnerability using the existing and the recalculated indices, aiming to assess whether issues of food inequities have been mitigated or exacerbated over the decade. Social vulnerability, as measured by the Social Vulnerability Index (SVI), is a composite metric developed by the CDC to assess a community’s susceptibility to external stressors, including economic instability, access to resources, and public health crises [ 15 ]. Given ongoing policy efforts to improve food access—including federal nutrition assistance programs (e.g., SNAP and WIC) [ 16 ], healthy food financing initiatives [ 17 ], tax incentives for grocery stores in underserved areas [ 18 ], zoning regulations to promote healthy food retail [ 19 ], mobile farmers' markets and food trucks [ 18 ], as well as community gardens and urban farms [ 20 ]—we hypothesize that food inequities have narrowed over the past decade, particularly in communities with higher social vulnerability. By providing a more accurate and timely measure of the food environment, this work supports efforts to address food inequities and informs strategies to prevent diet-related chronic diseases. 2 Methods Our primary data sources for this study included the 2018–2019 Infogroup dataset [ 21 ], the Social Vulnerability Index (SVI) data from 2010 and 2018 [ 15 ], and the 2008–2009 mRFEI data obtained from the CDC's Division of Nutrition, Physical Activity, and Obesity [ 6 ]. Updating mRFEI. The recalculated mRFEI for 2018–2019 was constructed using the Infogroup dataset, which provides detailed information on business establishments nationwide, including business name, address, North American Industry Classification System (NAICS) codes, and number of employees [ 21 ]. The categorization of retailers as "healthy" or "less healthy" conforms to the original mRFEI definition by the CDC [ 6 ]. Healthy food retailers included supermarkets and large grocery stores (NAICS 445110, with > = 50 and 10–49 employees, respectively), fruit and vegetable markets (NAICS 445230), and warehouse clubs (NAICS 452910). Less healthy food retailers included convenience stores (NAICS 445120 or 445110 with ≤ 3 employees) and limited-service restaurants (NAICS 722513, updated from 722211 following a 2012 code revision) [ 22 ]. For each census tract, we calculated the counts of healthy and less healthy food retailers within the tract or a 0.5-mile buffer around its boundary. These values were then used to recalculate the mRFEI for each buffered tract using the formula [ 6 ]: $$\:mRFEI=\:\frac{\#\:Healthy\:Food\:Retailers}{\#\:Healthy\:Food\:Retailers+\#\:Less\:Healthy\:Food\:Retailers}$$ SVI. For each census tract, we obtained the percentile ranking of the SVI in 2018 and in 2010 from the CDC (ranging from 0–1, whereas 1 means the most socially vulnerable) [ 15 ]. We selected these years as they best align with the periods covered by the two mRFEI versions. The SVI ranks each census tract based on 15 sociodemographic factors derived from the 5-year American Community Survey (ACS), which are further categorized into four themes: socioeconomic status (Theme 1), household composition and disability (Theme 2), minority status and language (Theme 3), and housing type and transportation (Theme 4) [ 15 ]. Statistical analysis. To explore the relationship between the mRFEI and social vulnerability, we applied linear regression models [ 23 ] to analyze the association between the mRFEI and the SVI percentile rankings across all census tracts for both 2008–2009 and 2018–2019. To assess how this relationship has changed over time, we derived the descriptive statistics (i.e. mean and standard deviation) of the mRFEI from the least socially vulnerable tracts (e.g., top 10%, top 25%) and those of the most socially vulnerable tracts (e.g., bottom 10%, bottom 25%). By excluding middle-tier tracts, we aimed to sharpen the contrast between these two groups, allowing for a clearer assessment of whether food inequities have worsened or improved over the past decade. Then, we employed difference-in-differences regression analyses [ 24 ] to determine whether the difference in the mRFEI between these selected sets of tracts has significantly changed over time. Statistical analyses were performed in R version 4.3.1. 3 Results Figure 1 The recalculated mRFEI at the census tract level across the United States. Darker shades indicate higher mRFEI values, while white areas represent census tracts with no available data Nationwide, the mRFEI declined slightly over the decade, with some census tracts experiencing decreases, others showing improvements, and some remaining unchanged. Specifically, among the 69,558 census tracts analyzed, 34,329 (50%) showed a decrease in the mRFEI over the decade, 28,026 (40%) saw an increase, and 7,203 (10%) showed no change. On average, the mRFEI declined from 11.66 (SD = 10.77) in 2008–09 to 11.03 (SD = 11.71) in 2018–2019. Socially vulnerable areas had significantly lower access to healthy food retailers compared to less vulnerable areas in 2008–09 ( b = -0.433, 95% CI [-0.462, -0.403], p < 0.001; see Table 1 ). This disparity was most pronounced in socioeconomic status (Theme 1) ( b = -0.283, 95% CI [-0.317, -0.249], p < 0.001) and minority status and language (Theme 3) ( b = -0.468, 95% CI [-0.505 - -0.430], p < 0.001), both of which showed strong negative correlations with the mRFEI. The descriptive statistics in Table 2 further corroborate this pattern, showing that tracts in the bottom 10% and 25% of the overall SVI, Theme 1, and Theme 3 consistently had lower mRFEI scores than those in the top 10% and 25%. By 2018–2019, disparities in access to healthy food retailers persisted, but modest improvements were observed in the socially vulnerable tracts. As shown in Table 1 , overall SVI ( b = -0.151, 95% CI [-0.177 - -0.125], p < 0.001), Theme 1 ( b = -0.123, 95% CI [-0.163 - -0.082], p < 0.001), and Theme 3 ( b = -0.277, 95% CI [-0.312 - -0.241], p < 0.001) remained negatively correlated with the mRFEI, highlighting ongoing inequities in access to healthy food retailers. However, as shown in Table 2 , the average mRFEI for the bottom 10% and bottom 25% of overall SVI increased slightly, while the less vulnerable tracts (top 10% and top 25%) saw small declines. Similar patterns were observed for Theme 1 (socioeconomic status) and Theme 3 (minority status and language) (p-values for all differences < 0.001), suggesting incremental progress toward narrowing food access gaps. Specifically, the difference in the mRFEI between the top and bottom 10% of the census tracts (based on overall SVI) decreased by 2.176 (95% CI [1.638, 2.714]) over time, while the gap between the top and bottom 25% narrowed by 1.963 (95% CI [1.629, 2.298]). For Theme 1, the difference in the mRFEI between the top and bottom 10% decreased by 1.832 (95% CI [1.352, 2.311]) over time, and by 1.173 (95% CI [0.860, 1.486]) between the top and bottom 25%. Similarly, for Theme 3, the mRFEI gap between the top and bottom 10% decreased by 2.478 (95% CI [1.894, 3.063]), and by 1.773 (95% CI [1.419, 2.126]) between the top and bottom 25% over time. To enhance accessibility, a web Geographic Information System (GIS) tool was developed, allowing users to view mRFEI data for single or multiple tracts [ 25 ]. Table 1 Association between the mRFEI and the SVI percentile ranking among all census tracts 2008–09 (N = 69558) 2018–19 (N = 69558) b 95% CI p- value b 95% CI p- value Model 1a (SVI from 2010) SVI overall ranking -0.433 -0.462 - -0.403 * Population density (log-transformed) 0.009 0.006–0.013 * Model 1b (SVI from 2010) Theme 1 (Socioeconomic status) -0.283 -0.317 - -0.249 * Theme 2 (Household composition & disability) 0.241 0.210–0.273 * Theme 3 (Minority status & language) -0.468 -0.505 - -0.430 * Theme 4 (Housing type & transportation) -0.004 -0.034–0.026 0.786 Population density (log-transformed) 0.003 -0.0005–0.006 0.079 Model 2a (SVI from 2018) SVI overall ranking -0.151 -0.177 - -0.125 * Population density (log-transformed) 0.002 -0.001–0.006 0.145 Model 2b (SVI from 2018) Theme 1 (Socioeconomic status) -0.123 -0.163 - -0.082 * Theme 2 (Household composition & disability) 0.159 0.126–0.192 * Theme 3 (Minority status & language) -0.277 -0.312 - -0.241 * Theme 4 (Housing type & transportation) -0.011 -0.042–0.020 0.494 Population density (log-transformed) 0.003 -0.00008–0.007 0.056 Note: All models include all census tracts in the United States with non-missing mRFEI and SVI. SVI percentile rankings are included as the independent variables; state-fixed effects and population density (log-transformed) are included as covariates. mRFEI have been standardized to facilitate coefficient comparison across years. b : regression coefficient; *: p- value < 0.001. Table 2 Comparison of mRFEI across census tract groups by levels of social vulnerability over ten years (2008–09 vs. 2018–19) SVI-based tracts selected for comparison a N 2008–09 mRFEI Mean (SD) b, d 2018–19 mRFEI Mean (SD) c, d Change Direction Bottom 10% SVI (overall theme) 6956 9.90 (8.03) 10.19 (9.26) ↑ Top 10% SVI (overall theme) 13.89 (14.15) 12.01 (13.20) ↓ Bottom 25% SVI (overall theme) 17390 10.14 (8.33) 10.37 (9.67) ↑ Top 25% SVI (overall theme) 13.44 (13.32) 11.70 (12.88) ↓ Bottom 10% SVI (theme 1 - socioeconomic status) 6956 8.72 (8.28) 9.87 (9.85) ↑ Top 10% SVI (theme 1 - socioeconomic status) 12.76 (10.53) 12.09 (11.80) ↓ Bottom 25% SVI (theme 1 - socioeconomic status) 17390 9.71 (8.83) 10.12 (10.30) ↑ Top 25% SVI (theme 1 - socioeconomic status) 12.56 (10.78) 11.80 (11.92) ↓ Bottom 10% SVI (theme 2 - household characteristics) 6956 10.04 (9.35) 10.79 (11.31) ↑ Top 10% SVI (theme 2 - household characteristics) 10.17 (8.83) 10.03 (8.21) ↓ Bottom 25% SVI (theme 2 - household characteristics) 17390 10.58 (9.47) 10.88 (11.63) ↑ Top 25% SVI (theme 2 - household characteristics) 11.39 (10.66) 10.77 (10.24) ↓ Bottom 10% SVI (theme 3 - racial & ethnic minority status) 6956 9.77 (7.67) 10.82 (8.35) ↑ Top 10% SVI (theme 3 - racial & ethnic minority status) 14.40 (14.87) 12.97 (16.38) ↓ Bottom 25% SVI (theme 3 - racial & ethnic minority status) 17390 9.96 (8.02) 10.33 (8.54) ↑ Top 25% SVI (theme 3 - racial & ethnic minority status) 13.93 (13.91) 12.53 (15.31) ↓ Bottom 10% SVI (theme 4 - housing type & transportation) 6956 10.26 (8.50) 10.44 (8.19) ↑ Top 10% SVI (theme 4 - housing type & transportation) 13.18 (13.05) 11.62 (13.22) ↓ Bottom 25% SVI (theme 4 - housing type & transportation) 17390 10.71 (9.10) 10.80 (9.63) ↑ Top 25% SVI (theme 4 - housing type & transportation) 12.81 (12.51) 11.31 (13.10) ↓ Note: a SVI-based tracts mentioned in the results section are in bold. b SVI percentile ranking from 2010 is used for 2008–09 analysis. c SVI percentile ranking from 2018 is used for 2018–19 analysis. d All statistical tests assessing whether the differences in mRFEI between more socially vulnerable and less socially vulnerable tracts change over time are significant at 0.001 level. 4 Discussion The average mRFEI decline of 0.63 reflects a modest nationwide reduction in access to healthy food retailers over the decade, particularly in the least vulnerable tracts. Gentrification may have displaced traditional supermarkets in favor of upscale markets, restaurants, and non-food retail [ 12 ], while store closures due to supply chain disruptions and labor shortages—especially during COVID-19—may have further reduced access [ 26 ]. Unlike vulnerable communities, which have been targeted for food access interventions, socioeconomically affluent areas may have been more susceptible to market-driven closures. These closures may have contributed to the observed decline in healthy food access. Despite this nationwide decline and reductions in less vulnerable tracts, the increase in average mRFEI in more vulnerable tracts suggests a gradual narrowing of food inequities. Compared to the previous mRFEI, the updated mRFEI remains negatively correlated with social vulnerability, particularly socioeconomic status (Theme 1) and minority status and language (Theme 3). However, the weakened strength of these associations further supports the trend of narrowing food inequities over time. Although we cannot directly attribute these shifts to specific policies, our findings suggest that targeted interventions, such as grocery store expansions [ 27 , 28 ], the improvement of transportation structure [ 29 ], and the zoning regulations to promote healthy food retail [ 19 ], may have played a role. However, persistent disparities and the slight nationwide decline highlight the need for continued efforts to fully close the gap. Addressing this gap through evidence-based policies and interventions will be critical for mitigating nutrition-related comorbidities and reducing long-term health inequities [ 30 – 32 ]. This study has several strengths. By recalculating the mRFEI with 2018–2019 data, the study provides a more current assessment of access to healthy food retailers and enables an examination of changes in food inequities. Additionally, the development of a web GIS tool enhances accessibility, allowing researchers and policymakers to interactively explore food access data. Future public health studies can leverage this updated dataset to investigate related topics. Nevertheless, this study has limitations. First, the mRFEI does not account for other dimensions of access, such as affordability and accommodation (e.g., acceptance of food assistance programs), which remains a critical barrier to healthy food access [ 7 ]. Future research should consider refining the mRFEI to better capture regional differences (e.g., urban vs. rural) and incorporate broader food access dimensions. Second, the mRFEI applies a 0.5-mile buffer in summarizing food stores, which is based on the CDC’s definition. The buffer size may be less suitable for rural areas, where travel distances to food retailers are typically greater [ 34 ]. The change of spatial patterns based on different buffer sizes, known as the Modifiable Areal Unit Problem (MAUP) [ 33 ], must be noted and carefully examined in future research. 5 Conclusions This study recalculates the mRFEI using the 2018–2019 Infogroup dataset, addressing a data gap left since the index’s last revision in 2011. The updated mRFEI is employed to assess current food access disparities and examine the dynamics of food inequities in the United States over a decade. To support future research, policy development, and intervention design, we have made our data and product publicly accessible through a web GIS tool. Our findings reveal a dual narrative of progress and ongoing challenges. Improvements in vulnerable tracts show the potential of evidence-based policies, while declines in less vulnerable areas underscore the need for sustained strategies. A comprehensive approach that builds on progress in underserved areas while safeguarding gains in historically better-access communities is critical to achieving nationwide equitable access to healthy food retailers. Declarations Financial support This work was supported by Alan R. Bennett public health policy research funding from College of Liberal Arts and Sciences (CLAS), University of Connecticut and an internal funding for pilot studies addressing U.S. health disparities from the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut. Ethics statement Not applicable Author Contribution W.L. conceptualized the work, visualized, analyzed, and interpreted the data, and drafted the manuscript. R.X. conceptualized the work, interpreted the data, and provided substantive revisions to the manuscript. X.C. conceptualized the work and provided substantive revisions to the manuscript. Q.L. and X.X. acquired and cleaned the data. C.M. and W.L. developed the dashboard. G.Z. provided substantive revisions to the manuscript. All authors read and approved the final manuscript. Data Availability The data supporting the findings of this study have been published on a web Geographic Information System (GIS) platform and can be accessed at the following link: https://experience.arcgis.com/experience/6c29fb6c5e0549c0ba4f239b36a8f546/page/Map/ References Chen X, Yang X. Does Food Environment Influence Food Choices? A Geographical Analysis through Tweets. Appl Geogr. 2014;51:82–9. 10.1016/j.apgeog.2014.04.003 . Suarez JJ, Isakova T, Anderson CAM, Boulware LE, Wolf M, Scialla JJ. Food Access, Chronic Kidney Disease, and Hypertension in the U.S. Am J Prev Med. 2015;49:912–20. 10.1016/j.amepre.2015.07.017 . Haslam A, Nikolaus CJ, Sinclair KA. Association of Food Environment Characteristics with Health Outcomes in Counties with a High Proportion of Native American Residents. 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Cite Share Download PDF Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 24 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Editor assigned by journal 09 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 23 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5975401","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":437446946,"identity":"ca2df8a2-3e8c-4445-a976-fdc73fc48e59","order_by":0,"name":"Weixuan Lyu","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Weixuan","middleName":"","lastName":"Lyu","suffix":""},{"id":437446947,"identity":"f6384fe9-efa8-4c75-a920-5628f01acb99","order_by":1,"name":"Xiang Chen","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Chen","suffix":""},{"id":437446948,"identity":"4eab6666-fb9d-49d3-bfe8-3753d150cdf2","order_by":2,"name":"Congcong Miao","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Congcong","middleName":"","lastName":"Miao","suffix":""},{"id":437446949,"identity":"568caa29-6eed-40ff-9040-d58562300426","order_by":3,"name":"Qinyun Lin","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Qinyun","middleName":"","lastName":"Lin","suffix":""},{"id":437446950,"identity":"f16ec524-8dff-447f-85f1-9b15d9b596d4","order_by":4,"name":"Xukun Xiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xukun","middleName":"","lastName":"Xiang","suffix":""},{"id":437446951,"identity":"8ce7eec9-d395-48b6-8f28-ec2252c28328","order_by":5,"name":"Gaofei Zhang","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Gaofei","middleName":"","lastName":"Zhang","suffix":""},{"id":437446952,"identity":"9b864865-353b-4580-a33f-2f233cabcc93","order_by":6,"name":"Ran Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCCD0DMD+cdIEIH4wwgIdlAihZmHiBhAFdJSIt5+9kDDDwMhxM330h++LhwB4Mc340E/FpkzuQlMEgwpCVuu5FmbDzzDIOxJCEtEgw5BgwGDDZALTls0rxtDIkbCGrhf2PAALQncfMMiJZ6wlokgLYcANqyQQKiJcGAsJY3BgcbDNKMZ5x5ZmzM2yZhOPPMA0IOyzF8/KfisGx/OzDEeNts5PmOE7AFBA4A/Q83grDyUTAKRsEoGAWEAQATnj3MoESRVgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Connecticut","correspondingAuthor":true,"prefix":"","firstName":"Ran","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-02-06 17:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5975401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5975401/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-00704-5","type":"published","date":"2025-06-02T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79836549,"identity":"0f588fe9-c05f-484c-a8ca-33d44106c621","added_by":"auto","created_at":"2025-04-03 11:25:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":435619,"visible":true,"origin":"","legend":"\u003cp\u003eThe recalculated mRFEI at the census tract level across the United States. Darker shades indicate higher mRFEI values, while white areas represent census tracts with no available data\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5975401/v1/fe3aef72a0b70932ecefe3de.jpeg"},{"id":84243059,"identity":"26c07826-e74c-44b9-83f0-fcb0ba5f4398","added_by":"auto","created_at":"2025-06-09 16:12:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1385035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5975401/v1/8f30f463-462a-453a-afed-81f98d336a11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revisiting the Modified Retail Food Environment Index (mRFEI): Examining Food Access Inequities Over a Decade in the United States","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe community food environment plays a crucial role in shaping individuals' food behaviors and, consequently, their dietary health comorbidities, making it a focus area in chronic disease prevention [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, equitable food access is not always guaranteed, as socioeconomically disadvantaged neighborhoods often have limited access to healthy and affordable food sources. This spatial isolation can further contribute to poor dietary health outcomes, exacerbating broader community health disparities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. National food and health initiatives have employed tools like the modified Retail Food Environment Index (mRFEI) to assess food access disparities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Developed by the Centers for Disease Control and Prevention (CDC) of the United States, the mRFEI quantifies the percentage of healthy food retailers relative to the total number of food retailers within a census tract and was released in the 2011 Children\u0026rsquo;s Food Environment State Indicator Report [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The index was calculated using 2008\u0026ndash;2009 datasets, including the 2009 InfoUSA, the 2008 Homeland Security Infrastructure Program Database, and the 2009 Navteq [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While this index has limitations, such as overlooking non-spatial factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and individual characteristics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the mRFEI is recognized as a practical tool for assessing large-scale food access, capturing broad patterns, and conducting long-term longitudinal analyses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, a major gap exists as the current mRFEI has not been updated since its last release in 2011 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], failing to account for significant changes in the retail food sector over the past decade, particularly those induced by gentrification in local markets [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the rise of online grocery services [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the disruptions caused by COVID-19 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo this end, this 2011 mRFEI metric creates a significant gap in understanding current food environments, limiting the ability to track disparities over time and develop effective interventions. This study addresses this gap by updating the mRFEI to reflect the current food environment conditions. We further examine the dynamics of the mRFEI\u0026rsquo;s relationship with social vulnerability using the existing and the recalculated indices, aiming to assess whether issues of food inequities have been mitigated or exacerbated over the decade. Social vulnerability, as measured by the Social Vulnerability Index (SVI), is a composite metric developed by the CDC to assess a community\u0026rsquo;s susceptibility to external stressors, including economic instability, access to resources, and public health crises [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given ongoing policy efforts to improve food access\u0026mdash;including federal nutrition assistance programs (e.g., SNAP and WIC) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], healthy food financing initiatives [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], tax incentives for grocery stores in underserved areas [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], zoning regulations to promote healthy food retail [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], mobile farmers' markets and food trucks [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], as well as community gardens and urban farms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u0026mdash;we hypothesize that food inequities have narrowed over the past decade, particularly in communities with higher social vulnerability. By providing a more accurate and timely measure of the food environment, this work supports efforts to address food inequities and informs strategies to prevent diet-related chronic diseases.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eOur primary data sources for this study included the 2018\u0026ndash;2019 Infogroup dataset [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the Social Vulnerability Index (SVI) data from 2010 and 2018 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and the 2008\u0026ndash;2009 mRFEI data obtained from the CDC's Division of Nutrition, Physical Activity, and Obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eUpdating mRFEI.\u003c/b\u003e The recalculated mRFEI for 2018\u0026ndash;2019 was constructed using the Infogroup dataset, which provides detailed information on business establishments nationwide, including business name, address, North American Industry Classification System (NAICS) codes, and number of employees [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The categorization of retailers as \"healthy\" or \"less healthy\" conforms to the original mRFEI definition by the CDC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Healthy food retailers included supermarkets and large grocery stores (NAICS 445110, with \u0026gt;\u0026thinsp;=\u0026thinsp;50 and 10\u0026ndash;49 employees, respectively), fruit and vegetable markets (NAICS 445230), and warehouse clubs (NAICS 452910). Less healthy food retailers included convenience stores (NAICS 445120 or 445110 with \u0026le;\u0026thinsp;3 employees) and limited-service restaurants (NAICS 722513, updated from 722211 following a 2012 code revision) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For each census tract, we calculated the counts of healthy and less healthy food retailers within the tract or a 0.5-mile buffer around its boundary. These values were then used to recalculate the mRFEI for each buffered tract using the formula [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:mRFEI=\\:\\frac{\\#\\:Healthy\\:Food\\:Retailers}{\\#\\:Healthy\\:Food\\:Retailers+\\#\\:Less\\:Healthy\\:Food\\:Retailers}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eSVI.\u003c/b\u003e For each census tract, we obtained the percentile ranking of the SVI in 2018 and in 2010 from the CDC (ranging from 0\u0026ndash;1, whereas 1 means the most socially vulnerable) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We selected these years as they best align with the periods covered by the two mRFEI versions. The SVI ranks each census tract based on 15 sociodemographic factors derived from the 5-year American Community Survey (ACS), which are further categorized into four themes: socioeconomic status (Theme 1), household composition and disability (Theme 2), minority status and language (Theme 3), and housing type and transportation (Theme 4) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e To explore the relationship between the mRFEI and social vulnerability, we applied linear regression models [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to analyze the association between the mRFEI and the SVI percentile rankings across all census tracts for both 2008\u0026ndash;2009 and 2018\u0026ndash;2019. To assess how this relationship has changed over time, we derived the descriptive statistics (i.e. mean and standard deviation) of the mRFEI from the least socially vulnerable tracts (e.g., top 10%, top 25%) and those of the most socially vulnerable tracts (e.g., bottom 10%, bottom 25%). By excluding middle-tier tracts, we aimed to sharpen the contrast between these two groups, allowing for a clearer assessment of whether food inequities have worsened or improved over the past decade. Then, we employed difference-in-differences regression analyses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to determine whether the difference in the mRFEI between these selected sets of tracts has significantly changed over time. Statistical analyses were performed in R version 4.3.1.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e The recalculated mRFEI at the census tract level across the United States. Darker shades indicate higher mRFEI values, while white areas represent census tracts with no available data\u003c/p\u003e \u003cp\u003eNationwide, the mRFEI declined slightly over the decade, with some census tracts experiencing decreases, others showing improvements, and some remaining unchanged. Specifically, among the 69,558 census tracts analyzed, 34,329 (50%) showed a decrease in the mRFEI over the decade, 28,026 (40%) saw an increase, and 7,203 (10%) showed no change. On average, the mRFEI declined from 11.66 (SD\u0026thinsp;=\u0026thinsp;10.77) in 2008\u0026ndash;09 to 11.03 (SD\u0026thinsp;=\u0026thinsp;11.71) in 2018\u0026ndash;2019. Socially vulnerable areas had significantly lower access to healthy food retailers compared to less vulnerable areas in 2008\u0026ndash;09 (\u003cem\u003eb\u003c/em\u003e = -0.433, 95% CI [-0.462, -0.403], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This disparity was most pronounced in socioeconomic status (Theme 1) (\u003cem\u003eb\u003c/em\u003e = -0.283, 95% CI [-0.317, -0.249], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and minority status and language (Theme 3) (\u003cem\u003eb\u003c/em\u003e = -0.468, 95% CI [-0.505 - -0.430], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), both of which showed strong negative correlations with the mRFEI. The descriptive statistics in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e further corroborate this pattern, showing that tracts in the bottom 10% and 25% of the overall SVI, Theme 1, and Theme 3 consistently had lower mRFEI scores than those in the top 10% and 25%.\u003c/p\u003e \u003cp\u003eBy 2018\u0026ndash;2019, disparities in access to healthy food retailers persisted, but modest improvements were observed in the socially vulnerable tracts. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, overall SVI (\u003cem\u003eb\u003c/em\u003e = -0.151, 95% CI [-0.177 - -0.125], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Theme 1 (\u003cem\u003eb\u003c/em\u003e = -0.123, 95% CI [-0.163 - -0.082], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Theme 3 (\u003cem\u003eb\u003c/em\u003e = -0.277, 95% CI [-0.312 - -0.241], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained negatively correlated with the mRFEI, highlighting ongoing inequities in access to healthy food retailers. However, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the average mRFEI for the bottom 10% and bottom 25% of overall SVI increased slightly, while the less vulnerable tracts (top 10% and top 25%) saw small declines. Similar patterns were observed for Theme 1 (socioeconomic status) and Theme 3 (minority status and language) (p-values for all differences\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting incremental progress toward narrowing food access gaps. Specifically, the difference in the mRFEI between the top and bottom 10% of the census tracts (based on overall SVI) decreased by 2.176 (95% CI [1.638, 2.714]) over time, while the gap between the top and bottom 25% narrowed by 1.963 (95% CI [1.629, 2.298]). For Theme 1, the difference in the mRFEI between the top and bottom 10% decreased by 1.832 (95% CI [1.352, 2.311]) over time, and by 1.173 (95% CI [0.860, 1.486]) between the top and bottom 25%. Similarly, for Theme 3, the mRFEI gap between the top and bottom 10% decreased by 2.478 (95% CI [1.894, 3.063]), and by 1.773 (95% CI [1.419, 2.126]) between the top and bottom 25% over time. To enhance accessibility, a web Geographic Information System (GIS) tool was developed, allowing users to view mRFEI data for single or multiple tracts [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between the mRFEI and the SVI percentile ranking among all census tracts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2008\u0026ndash;09 (N\u0026thinsp;=\u0026thinsp;69558)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2018\u0026ndash;19 (N\u0026thinsp;=\u0026thinsp;69558)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1a (SVI from 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVI overall ranking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.462 - -0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003cp\u003e(log-transformed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u0026ndash;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1b (SVI from 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 1\u003c/p\u003e \u003cp\u003e(Socioeconomic status)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.317 - -0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 2\u003c/p\u003e \u003cp\u003e(Household composition \u0026amp; disability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u0026ndash;0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 3\u003c/p\u003e \u003cp\u003e(Minority status \u0026amp; language)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.505 - -0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 4\u003c/p\u003e \u003cp\u003e(Housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.034\u0026ndash;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003cp\u003e(log-transformed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0005\u0026ndash;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2a (SVI from 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVI overall ranking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.177 - -0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003cp\u003e(log-transformed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u0026ndash;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2b (SVI from 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 1\u003c/p\u003e \u003cp\u003e(Socioeconomic status)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.163 - -0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 2\u003c/p\u003e \u003cp\u003e(Household composition \u0026amp; disability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.126\u0026ndash;0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 3\u003c/p\u003e \u003cp\u003e(Minority status \u0026amp; language)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.312 - -0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme 4\u003c/p\u003e \u003cp\u003e(Housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.042\u0026ndash;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003cp\u003e(log-transformed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00008\u0026ndash;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: All models include all census tracts in the United States with non-missing mRFEI and SVI. SVI percentile rankings are included as the independent variables; state-fixed effects and population density (log-transformed) are included as covariates. mRFEI have been standardized to facilitate coefficient comparison across years. \u003cem\u003eb\u003c/em\u003e: regression coefficient; *: \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of mRFEI across census tract groups by levels of social vulnerability over ten years (2008\u0026ndash;09 vs. 2018\u0026ndash;19)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVI-based tracts selected for comparison\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2008\u0026ndash;09 mRFEI\u003c/p\u003e \u003cp\u003eMean (SD)\u003csup\u003eb, d\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u0026ndash;19 mRFEI\u003c/p\u003e \u003cp\u003eMean (SD)\u003csup\u003ec, d\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange Direction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(overall theme)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.90 (8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.19 (9.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(overall theme)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.89 (14.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.01 (13.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(overall theme)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.14 (8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.37 (9.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(overall theme)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.44 (13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.70 (12.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 1 - socioeconomic status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.72 (8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.87 (9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 1 - socioeconomic status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.76 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.09 (11.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 1 - socioeconomic status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.71 (8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.12 (10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 1 - socioeconomic status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.56 (10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.80 (11.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom 10% SVI\u003c/p\u003e \u003cp\u003e(theme 2 - household characteristics)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.04 (9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.79 (11.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop 10% SVI\u003c/p\u003e \u003cp\u003e(theme 2 - household characteristics)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.17 (8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.03 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom 25% SVI\u003c/p\u003e \u003cp\u003e(theme 2 - household characteristics)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.58 (9.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.88 (11.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop 25% SVI\u003c/p\u003e \u003cp\u003e(theme 2 - household characteristics)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.39 (10.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.77 (10.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 3 - racial \u0026amp; ethnic minority status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.77 (7.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.82 (8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 10% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 3 - racial \u0026amp; ethnic minority status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.40 (14.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.97 (16.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBottom 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 3 - racial \u0026amp; ethnic minority status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.96 (8.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.33 (8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTop 25% SVI\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(theme 3 - racial \u0026amp; ethnic minority status)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.93 (13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.53 (15.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom 10% SVI\u003c/p\u003e \u003cp\u003e(theme 4 - housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.26 (8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.44 (8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop 10% SVI\u003c/p\u003e \u003cp\u003e(theme 4 - housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.18 (13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.62 (13.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom 25% SVI\u003c/p\u003e \u003cp\u003e(theme 4 - housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.71 (9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.80 (9.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop 25% SVI\u003c/p\u003e \u003cp\u003e(theme 4 - housing type \u0026amp; transportation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.81 (12.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.31 (13.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e SVI-based tracts mentioned in the results section are in bold. \u003csup\u003eb\u003c/sup\u003e SVI percentile ranking from 2010 is used for 2008\u0026ndash;09 analysis. \u003csup\u003ec\u003c/sup\u003e SVI percentile ranking from 2018 is used for 2018\u0026ndash;19 analysis. \u003csup\u003ed\u003c/sup\u003e All statistical tests assessing whether the differences in mRFEI between more socially vulnerable and less socially vulnerable tracts change over time are significant at 0.001 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe average mRFEI decline of 0.63 reflects a modest nationwide reduction in access to healthy food retailers over the decade, particularly in the least vulnerable tracts. Gentrification may have displaced traditional supermarkets in favor of upscale markets, restaurants, and non-food retail [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while store closures due to supply chain disruptions and labor shortages\u0026mdash;especially during COVID-19\u0026mdash;may have further reduced access [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Unlike vulnerable communities, which have been targeted for food access interventions, socioeconomically affluent areas may have been more susceptible to market-driven closures. These closures may have contributed to the observed decline in healthy food access.\u003c/p\u003e \u003cp\u003eDespite this nationwide decline and reductions in less vulnerable tracts, the increase in average mRFEI in more vulnerable tracts suggests a gradual narrowing of food inequities. Compared to the previous mRFEI, the updated mRFEI remains negatively correlated with social vulnerability, particularly socioeconomic status (Theme 1) and minority status and language (Theme 3). However, the weakened strength of these associations further supports the trend of narrowing food inequities over time. Although we cannot directly attribute these shifts to specific policies, our findings suggest that targeted interventions, such as grocery store expansions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the improvement of transportation structure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and the zoning regulations to promote healthy food retail [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], may have played a role. However, persistent disparities and the slight nationwide decline highlight the need for continued efforts to fully close the gap. Addressing this gap through evidence-based policies and interventions will be critical for mitigating nutrition-related comorbidities and reducing long-term health inequities [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several strengths. By recalculating the mRFEI with 2018\u0026ndash;2019 data, the study provides a more current assessment of access to healthy food retailers and enables an examination of changes in food inequities. Additionally, the development of a web GIS tool enhances accessibility, allowing researchers and policymakers to interactively explore food access data. Future public health studies can leverage this updated dataset to investigate related topics.\u003c/p\u003e \u003cp\u003eNevertheless, this study has limitations. First, the mRFEI does not account for other dimensions of access, such as affordability and accommodation (e.g., acceptance of food assistance programs), which remains a critical barrier to healthy food access [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Future research should consider refining the mRFEI to better capture regional differences (e.g., urban vs. rural) and incorporate broader food access dimensions. Second, the mRFEI applies a 0.5-mile buffer in summarizing food stores, which is based on the CDC\u0026rsquo;s definition. The buffer size may be less suitable for rural areas, where travel distances to food retailers are typically greater [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The change of spatial patterns based on different buffer sizes, known as the Modifiable Areal Unit Problem (MAUP) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], must be noted and carefully examined in future research.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study recalculates the mRFEI using the 2018\u0026ndash;2019 Infogroup dataset, addressing a data gap left since the index\u0026rsquo;s last revision in 2011. The updated mRFEI is employed to assess current food access disparities and examine the dynamics of food inequities in the United States over a decade. To support future research, policy development, and intervention design, we have made our data and product publicly accessible through a web GIS tool. Our findings reveal a dual narrative of progress and ongoing challenges. Improvements in vulnerable tracts show the potential of evidence-based policies, while declines in less vulnerable areas underscore the need for sustained strategies. A comprehensive approach that builds on progress in underserved areas while safeguarding gains in historically better-access communities is critical to achieving nationwide equitable access to healthy food retailers.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eFinancial support\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis work was supported by Alan R. Bennett public health policy research funding from College of Liberal Arts and Sciences (CLAS), University of Connecticut and an internal funding for pilot studies addressing U.S. health disparities from the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthics statement\u003c/b\u003e \u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.L. conceptualized the work, visualized, analyzed, and interpreted the data, and drafted the manuscript. R.X. conceptualized the work, interpreted the data, and provided substantive revisions to the manuscript. X.C. conceptualized the work and provided substantive revisions to the manuscript. Q.L. and X.X. acquired and cleaned the data. C.M. and W.L. developed the dashboard. G.Z. provided substantive revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study have been published on a web Geographic Information System (GIS) platform and can be accessed at the following link: https://experience.arcgis.com/experience/6c29fb6c5e0549c0ba4f239b36a8f546/page/Map/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen X, Yang X. Does Food Environment Influence Food Choices? A Geographical Analysis through Tweets. Appl Geogr. 2014;51:82\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apgeog.2014.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2014.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuarez JJ, Isakova T, Anderson CAM, Boulware LE, Wolf M, Scialla JJ. 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J Nutr Educ Behav. 2011;43:426\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jneb.2010.07.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jneb.2010.07.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mRFEI, food environment, social vulnerability, food access, inequities","lastPublishedDoi":"10.21203/rs.3.rs-5975401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5975401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study recalculates the modified Retail Food Environment Index (mRFEI) using the 2018\u0026ndash;2019 Infogroup dataset, addressing a data gap left since the index\u0026rsquo;s last revision in 2011. The updated mRFEI is employed to assess current food access disparities in the United States. Based on the recalculated mRFEI, we have evaluated the evolution of food access inequities by examining the relationships between the mRFEI and each theme of the community\u0026rsquo;s social vulnerability index (SVI) over a decade. Our findings reveal that higher social vulnerability remains associated with lower access to healthy food retailers, particularly for socioeconomic status (\u003cem\u003eb\u003c/em\u003e = -0.123, 95% CI [-0.163, -0.082]) and minority status and language (\u003cem\u003eb\u003c/em\u003e = -0.277, 95% CI [-0.312, -0.241]). However, these associations have weakened over time. The mRFEI gap between the most (i.e. bottom 10%) and the least (i.e. top 10%) vulnerable tracts (based on the overall SVI) decreases by 2.176 (95% CI [1.638, 2.714]) over time. Similar reductions are observed for other SVI themes, as well as when comparing the top and the bottom 25%, suggesting a gradual narrowing of food inequities. However, persistent disparities highlight the need for continued policy efforts. We make our data and product publicly available through a web Geographic Information System (GIS) tool. By offering the updated mRFEI, we equip researchers, policymakers, and practitioners with a timely, accessible, and reusable resource that enables a more accurate understanding of current food access disparities. This resource can be directly incorporated into future studies, policy development, and intervention design.\u003c/p\u003e","manuscriptTitle":"Revisiting the Modified Retail Food Environment Index (mRFEI): Examining Food Access Inequities Over a Decade in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 11:25:52","doi":"10.21203/rs.3.rs-5975401/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-24T07:37:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T07:36:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T16:03:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T20:17:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231310627827089787176305079027886175708","date":"2025-04-15T16:53:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262896566898595446933253796017231571964","date":"2025-04-15T12:58:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4260142273456110501801371254614671717","date":"2025-04-10T12:48:41+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T09:39:26+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T12:54:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T10:04:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-03-23T22:25:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f562274-a25d-4453-b4a5-128352fb7065","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:09:14+00:00","versionOfRecord":{"articleIdentity":"rs-5975401","link":"https://doi.org/10.1186/s12982-025-00704-5","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-06-02 15:57:24","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2025-04-03 11:25:52","video":"","vorDoi":"10.1186/s12982-025-00704-5","vorDoiUrl":"https://doi.org/10.1186/s12982-025-00704-5","workflowStages":[]},"version":"v1","identity":"rs-5975401","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5975401","identity":"rs-5975401","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}