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Medicaid enrollees residing in counties with greater food affordability had lower odds of preventable hospitalization related to diabetes.
Objectives: This study aims to estimate the burden of food affordability on diabetes-related preventable hospitalizations among Medicaid enrollees in the United States.
Study Design: This study used a retrospective observational design with Medicaid administrative claims data from 17 states from 2014.
Methods: Data were linked with county-level social determinants of health (SDOH) from the American Community Survey. The rate of diabetes-related preventable hospitalizations was measured using the Agency for Healthcare Research and Quality’s Prevention Quality Diabetes Composite, which includes hospitalization for short-term complications, long-term complications, lower extremity amputations, and uncontrolled diabetes. Multivariable logistic regression was used to predict the occurrence of diabetes-related preventable hospitalization.
Results: Among the 16 million eligible individuals, diabetes-related preventable hospitalizations were identified at the rate of 1.91 per 1000 individuals and contributed to more than $160 million in charges. Rates were higher among men compared with women (0.25% vs 0.15%; P < .001) and among Black adults compared with White adults (0.29% vs 0.18%; P < .001). Compared with individuals residing in counties with low food affordability, those residing in counties with high (odds ratio [OR], 0.84; 95% CI, 0.78-0.91; P < .001) or medium (OR, 0.85; 95% CI, 0.81-0.90; P < .001) food affordability had lower odds of hospitalization.
Conclusions: This study provides real-world evidence about the impact of SDOH on diabetes-related preventable hospitalizations. Federal and state policies that can help improve accessibility of healthy foods are needed to ameliorate the burden of diabetes on society.
Am J Manag Care. 2022;28(11):In Press
Nearly 10% of the US population, or 30 million individuals, have type 2 diabetes (T2D), which contributed to 7.2 million hospital discharges and 14.2 million emergency department visits in 2014, resulting in a significant health system burden. Diabetes is the seventh leading cause of death in the United States, accounting for more than $245 billion in total costs in 2015.1 Diabetes management depends on appropriate provision of primary care, and poor quality of care can result in preventable complications such as hospitalization.2 This preventable burden shows considerable variation as a result of various social determinants of health (SDOH), such as food insecurity.1,3-6 It is estimated that nearly 15% of all US households experience food insecurity.7 Although food insecurity incorporates availability of and access to nutritional food sources, the importance of food affordability cannot be understated.8 The cost of food is an important factor driving quality and quantity of food purchased, particularly in low-income households. It is estimated that an average household spends nearly 18% of its income on purchasing food. However, such estimates are significantly higher in rural and low-income neighborhoods.9
Previous research has demonstrated that neighborhood characteristics and SDOH are key predictors of preventable hospitalizations. However, these relationships have not been explored specifically with respect to food affordability. This study aims to estimate the burden of food affordability on diabetes-related preventable hospitalization in the United States. The study objectives are to (1) estimate the rate of diabetes-related preventable hospitalizations among patients enrolled in Medicaid and (2) evaluate the impact of food affordability on these diabetes-related preventable hospitalizations. We hypothesize that counties with low food affordability will experience higher rates of diabetes-related preventable hospitalizations.
This retrospective, observational study utilized Medicaid administrative claims data from 17 states (California, Georgia, Idaho, Iowa, Louisiana, Michigan, Minnesota, Mississippi, Missouri, New Jersey, Pennsylvania, South Dakota, Tennessee, Utah, Vermont, West Virginia, and Wyoming) for 2014. These were the states for which there were complete data for the purposes of this analysis. These data contain deidentified demographic and health information pertaining to more than 25 million Medicaid beneficiaries. The data include an inpatient claims database, an outpatient claims database, a pharmacy claims database, and a beneficiary master file that provides information about demographics, eligibility, and zip code of residence. All files were linked using an encrypted beneficiary identifier. All study procedures were approved by the University of Mississippi institutional review board, and the use of Medicaid data was approved under a data use agreement (DUA#RSCH-2017-51606).
The primary dependent variable of this study was the measurement of preventable hospitalizations related to T2D. The rate of preventable hospitalizations was measured using a standardized measure, the Prevention Quality Diabetes Composite (PQI-93) endorsed by the Agency for Healthcare Research and Quality (AHRQ).10 The exact specifications for PQI-93 are provided by AHRQ and include hospitalizations due to short-term complications, long-term complications, uncontrolled diabetes, and lower extremity amputations.10 The eligible population for this study was also obtained from AHRQ specifications and included all individuals 18 years or older as long as they were Medicaid eligible for at least 1 month of the calendar year. Beneficiary’s location of residence from the Medicaid files was used to estimate state-specific measure performance. Other variables obtained from Medicaid administrative claims included identification of prescription fills for any oral antidiabetic medication or insulin, as well as a measure of the severity of comorbidities as captured by the Charlson Comorbidity Index (CCI).11,12
The selection of the SDOH variables was driven by the theoretical framework proposed by Brown et al and included the county’s median household income, education, and violent crime rates, all obtained from the American Community Survey (ACS) 2014 1-year estimates.4 Access to care was operationalized based on the Brown et al definition as the total number of physician office visits during the study year, along with the number of primary care providers per 100,000 individuals.4 All SDOH predictors were categorized depending on whether the individual resided in a county in the top 25th percentile, the bottom 25th percentile, or the middle. Because SDOH variables were available at the county level, each study participant was assigned the value corresponding to their county of residence.
The county’s median food affordability was calculated using a methodology previously developed and applied in literature.8,9 This methodology estimates food affordability in a given county by multiplying national mean meal costs by 21 (to estimate weekly food expenditure for 3 meals a day), then dividing it by the median weekly income per household member for each county. Variables used for calculating median food affordability were identified from Feeding America’s Map the Meal Gap report, ACS, and US Census Quick Facts.13-15 The percentage of each county’s weekly median income spent on food was then estimated and linked to the eligible study population based on the individual’s county of residence.8 By combining mean costs of meals with information about median income in a neighborhood, this methodology is able to account for the impact of income in estimating the impact of food cost, thereby providing findings that are easier to evaluate and interpret.
The PQI-93 measure performance was first estimated for all available individuals from 17 states individually and then as an overall rate. Bivariate analyses were conducted using t tests and χ2 tests of significance, as appropriate, to identify the relationship between occurrence of preventable hospitalization and other covariates measured in this study. Multivariable logistic regression models were used to predict the occurrence of preventable hospitalization based on hypothesized SDOH predictors as independent variables while adjusting for covariates and demographic characteristics including age, sex, race, and geographic region. All data management and analysis were conducted in SAS version 9.4 (SAS Institute).
The final study sample consisted of 16,008,620 individuals, of whom 61.3% were women and 43.3% were non-Hispanic White. The mean (SD) age was 43.6 (19.2) years. Among eligible individuals, 30,651 individuals (0.19%) had a diabetes-related preventable hospitalization during the study year. The demographic characteristics and clinical factors of the study population across study groups are outlined in Table 1. Individuals with at least 1 diabetes-related preventable hospitalization during the study period were likely to be older (mean age, 48.5 vs 43.6 years; P < .001) than those who did not have such hospitalizations. Rates of hospitalization were higher among men compared with women (0.25% vs 0.15%; P < .001) and among Black non-Hispanic participants (0.29%) compared with Native American (0.14%), Hispanic (0.16%), Asian (0.06%), and White (0.18%; P < .001) participants. Individuals residing in the South and the Northeast had the highest rate of hospitalization (0.24% for both), followed by the Midwest (0.2%) and the West (0.15%; P < .001). Preventable hospitalization rates were significantly higher among patients with a diagnosis of diabetes (2.7% vs 0.02%; P < .001), those taking insulin (5.85% vs 0.02%; P < .001), those taking oral antidiabetic agents (1.32% vs 0.14%; P < .001), and patients with more comorbidities (CCI score of 0, 0.03%; CCI score of 1, 0.94%; CCI score ≥ 2, 1.4%; P < .001).
A total of 47,610 preventable hospitalizations were identified in 30,651 unique individuals in 2014, accounting for a rate of 1.91 per 1000 individuals. When this rate was broken down into the components that make up PQI-93, the rate of long-term complications was found to be 11.61 per 1000 individuals, and the rates of short-term complications, lower extremity amputations, and uncontrolled diabetes were 9.36, 0.30, and 0.17 per 1000 individuals, respectively. The component rates of PQI-93 do not necessarily add up to the total rate of preventable hospitalizations because the eligible populations for each component measure are different and cannot be directly summated. The total direct charged amount for all preventable hospitalizations was found to be $162,431,493, at a mean of $3411 per hospitalization.
The rates of diabetes-related preventable hospitalizations broken down by the 17 states included in the analysis are reported in Table 2. The state with the highest rate of diabetes-related preventable hospitalizations was Mississippi (4.25 per 1000 individuals), followed by South Dakota (3.40 per 1000 individuals) and Georgia (2.90 per 1000 individuals). The state with the lowest rate of diabetes-related preventable hospitalizations was Vermont (1.43 per 1000 individuals), followed by California (1.47 per 1000 individuals) and Tennessee (1.58 per 1000 individuals).
The multivariable logistic regression model predicting preventable hospitalizations is shown in Table 3. Women had 40% lower odds of hospitalization compared with men (odds ratio [OR], 0.60; 95% CI, 0.59-0.62; P < .001) and those with more comorbidities had significantly higher odds of hospitalization (CCI score of 1: OR, 15.12; 95% CI, 14.51-15.75; P < .001; CCI score ≥ 2: OR, 22.42; 95% CI, 21.52-23.36; P < .001) compared with those without any comorbidities. Odds of hospitalization were also significantly higher among users of insulin (OR, 20.37; 95% CI, 19.78-20.97; P < .001). Compared with White non-Hispanic participants, Black individuals had significantly higher odds of experiencing a preventable hospitalization (OR, 1.35; 95% CI, 1.31-1.39; P < .001), and Hispanic (OR, 0.94; 95% CI, 0.90-0.98; P = .002), Asian (OR, 0.50; 95% CI, 0.46-0.55; P < .001), and Native American (OR, 0.85; 95% CI, 0.79-0.92; P < .001) individuals had significantly lower odds of hospitalization. Each additional physician office visit for diabetes was associated with 2% greater odds of preventable hospitalization (OR, 1.02; 95% CI, 1.02-1.02; P < .001).
The hypothesized relationship between the key independent variable—food affordability—and the study outcome was confirmed in this study. Compared with individuals residing in counties with low food affordability, those residing in counties with the most affordable food options had 16% lower odds of hospitalization (OR, 0.84; 95% CI, 0.78-0.91; P < .001) and those residing in counties with medium food affordability had 15% lower odds of hospitalization (OR, 0.85; 95% CI, 0.81-0.90; P < .001). Several SDOH variables were also found to be significantly associated with preventable hospitalizations in this study. Individuals residing in counties with high education levels (OR, 0.85; 95% CI, 0.81-0.90) had lower odds of hospitalizations, but individuals residing in counties with greater access to primary care providers (OR, 1.03; 95% CI, 1.00-1.07; P = .037) had higher odds of hospitalization compared with their counterparts residing in more disadvantaged counties.
Using data from 17 states, our study found that 1.91 of every 1000 Medicaid enrollees had a diabetes-related preventable hospitalization in 2014. The findings of this study exclusively highlight a population of Medicaid enrollees that is primarily composed of racial/ethnic minority individuals, individuals with disabilities, low-income individuals, and those with disadvantaged backgrounds.16,17 These individuals face greater challenges in obtaining appropriate care to meet their chronic care needs.18-20 Previous studies examining rates of diabetes-related preventable hospitalizations usually included different populations. For example, Shrestha et al used National Inpatient Sample (NIS) data and found that 2.42% of a nationally representative sample of patients with diabetes had preventable hospitalizations in 2014.21 Fingar and colleagues also used data from NIS and found that the age- and sex-adjusted rates of long-term complications, short-term complications, lower extremity amputations, and uncontrolled diabetes were 1.16, 0.73, 0.17, and 0.17 per 1000 adults, respectively, in 2012.22 Gounder and colleagues analyzed all discharge data from Alaska between 2010 and 2012 and found that the rate of diabetes-related preventable hospitalizations was 0.9%.23 Our study chose to estimate unadjusted rates from all adults, not just those with diabetes.22 Although we included all adults in our population, the departure of our estimates from those in the literature may be because the Medicaid population does not present a representative snapshot of the older population, who are often at highest risk of hospitalization.
The geographic disparities found in this study are consistent with previous literature showing significant variation in rates of all preventable hospitalizations based on individual location of residence.3,24 Mississippi, which was found to have the highest rate in this study, has a very high prevalence of diabetes along with high rates of poverty, low education, and poor health care access.25,26 In 2016, more than 13% of adults in Mississippi had a diagnosis of diabetes.25-27 The study findings regarding race/ethnicity were also consistent with previous literature. Both adjusted and unadjusted analyses showed that the risk of preventable hospitalizations was significantly higher among Black non-Hispanic individuals compared with other groups. This racial disparity in diabetes burden is well documented and should serve as a further call to action for policy makers and health care providers to identify approaches that can focus on addressing health disparities.28
This study confirmed its principal hypothesis that counties with greater food affordability had lower rates of preventable hospitalization. The importance of a nutritious and healthy diet for the management of diabetes is well known and cannot be understated. When such healthy food options are not accessible, poor control of blood sugar levels and subsequent complications may follow, as this study demonstrates. Previous research also demonstrates that food insecurity and food deserts lead to greater risk of diabetes, poor metabolic control, and worse outcomes among patients with diabetes.29-31 It is important to note that affordability of food is not a substitute for other forms of food security, such as the availability of grocery stores in the neighborhood or the accessibility of healthy foods. However, these findings lend further credibility to the argument that the operationalization of food affordability used in this study, which uses estimates of median income in the neighborhood, can lead to easily interpretable and valid findings.
As defined by AHRQ, diabetes-related preventable hospitalizations are an ambulatory care–sensitive condition, which indicates that appropriate management of diabetes through primary care can prevent this burden.32 Appropriate management of diabetes involves careful meal planning and purchasing (including the ability to maintain a healthy diet), exercising regularly, self-glucose monitoring, blood pressure monitoring, cholesterol monitoring, and foot examinations.33 However, individuals living in neighborhoods with low food affordability may rely on low-cost, calorie-dense food options, such as processed or canned foods, and other fast foods that may be accessible to them, leading to poor outcomes. When food prices are unaffordable, primary care management may not be adequate for preventing diabetes-related hospitalizations. This study makes the case for holistic management of patients with diabetes, including appropriate screening for food insecurity and other SDOH that may increase disease burden.
This study found that SDOH captured through geographic proxies were related to rates of preventable hospitalization, as shown by previous studies.5,24,34,35 Previous studies have reported that SDOH variables play an important role in influencing mortality and the development of many chronic conditions including diabetes.4,36-38 The results of this study show a negative effect of education level on the rate of diabetes-related preventable hospitalizations. Our results are consistent with the findings of previous studies.3,38-40 The effect of education on improving diabetes-related outcomes is likely mediated by differences in access to health care services, diabetes knowledge, the process of care, and health literacy.4 Chen and colleagues also found that lower education levels were associated with preventable hospitalization among patients with T2D in Taiwan.39 Similar relationships have been demonstrated in the literature with respect to household income.3,41 However, this study did not find an association between household income and preventable hospitalization, most likely because the impact of household income was already captured through food affordability. Although this study did not conduct a mediation analysis, this finding suggests that the impact of household income on diabetes outcomes is primarily through the affordability of healthy dietary options.
Finally, this study found that adjusted rates of preventable hospitalization were not significantly different among those living in counties with the lowest access to primary care providers compared with those living in counties with high access to primary care providers. Although this finding is surprising, previous literature shows that the relationship between access to care and preventable hospitalizations is not necessarily simple. For example, improving access by providing free clinics and enrollment in home-based primary care was found to improve rates of preventable hospitalization, but other access improvements such as Medicaid expansion were not found to be associated with preventable hospitalizations.34,42,43 It is possible that the alternative operationalization of access to primary care providers could have been more effective at capturing the relationship seen in other studies.5,24
The findings of this study must be interpreted in the context of a few limitations. First, the SDOH used in this study were captured using geographic proxies and were not available at the individual level in Medicaid claims data. Such geographic proxies of SDOH have been successfully used in combination with claims data before, so we do not anticipate that this would bias our findings.44 Second, the results of this study may be representative only of Medicaid populations from the 17 states included in the analysis, and further generalizations have to be made with caution. Third, although AHRQ’s preventable hospitalizations indicators have been used in several studies before, there is some discussion about whether they truly capture hospitalizations that could have been prevented through better management.45 Future research should study the impact of food affordability using alternative outcomes to confirm these findings. Fourth, we chose to include individuals in the analysis even if they were not Medicaid-eligible throughout the year in order to maximize representativeness of the results. However, because of missing data during periods without eligibility, we may have undercounted or misclassified some variables in the analysis. Future research should consider replicating these analyses with other data sources where loss of eligibility is not as commonly present as in a Medicaid population. Finally, although this study was specifically focused on food affordability, other indicators of food insecurity such as access and availability are also important in the context of diabetes outcomes. In addition, there may be other variables that may confound the relationship between food affordability and preventable hospitalizations that were not included in this study because of the lack of availability of these variables in our data. Future studies should expand upon the scope of this study to evaluate such relationships.
This study found that 1.91 of every 1000 Medicaid enrollees experienced a diabetes-related preventable hospitalization, accounting for more than $160 million in expenses during 2014 in 17 states. A disproportionate share of the burden of these hospitalizations falls on some geographic and racial subgroups and requires close attention to help reduce disparities in society. Our study provides real-world evidence about the impact of SDOH variables on diabetes-related preventable hospitalizations and calls for the development of tailored interventions that involve improving the social, economic, and physical environments for patients who are at risk of such outcomes. Federal and state policies that can help improve affordability of healthy foods are also needed to ameliorate the burden of diabetes on society. Future studies should continue to explore the impact of food insecurity on diabetes-related outcomes using both individual-level and community-level SDOH variables.
Author Affiliations: Department of Pharmacy Administration (SR, YZ, TJD, SG, EP) and Center for Pharmaceutical Marketing and Management (SR, EP), University of Mississippi School of Pharmacy, University, MS; Department of Nutrition and Hospitality Management (GM) and Department of Sociology and Anthropology (AC), University of Mississippi, University, MS; University of Mississippi Community First Research Center for Wellbeing and Creative Achievement (AC), University, MS.
Source of Funding: This study was funded internally by the University of Mississippi. The funding body was not involved in study design, data analysis or interpretation of results, or the decision to submit a manuscript.
Author Disclosures: Dr Cafer reports receiving grant funding for another project from Kroger and Anthem. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (SR, YZ, TJD, EP, GM, AC); acquisition of data (SR); analysis and interpretation of data (SR, YZ, TJD, SG, GM, AC); drafting of the manuscript (SR, YZ, TJD, EP, GM, AC); critical revision of the manuscript for important intellectual content (SR, YZ, TJD, SG, GM, AC); statistical analysis (SR, YZ, TJD); provision of patients or study materials (SG); obtaining funding (SR); administrative, technical, or logistic support (EP); and supervision (SR).
Address Correspondence to: Sujith Ramachandran, PhD, University of Mississippi School of Pharmacy, 232 Faser Hall, University, MS 38677. Email: firstname.lastname@example.org.
1. National Diabetes Statistics Report 2020: estimates of diabetes and its burden in the United States. CDC. September 28, 2020. Accessed December 24, 2020. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
2. Yaqoob M, Wang J, Sweeney AT, Wells C, Rego V, Jaber BL. Trends in avoidable hospitalizations for diabetes: experience of a large clinically integrated health care system. J Healthc Qual. 2019;41(3):125-133.
3. Bocour A, Tria M. Preventable hospitalization rates and neighborhood poverty among New York City residents, 2008-2013. J Urban Health. 2016;93(6):974-983. doi:10.1007/s11524-016-0090-5
4. Brown AF, Ettner SL, Piette J, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev. 2004;26(1):63-77.
5. Evans CS, Smith S, Kobayashi L, Chang DC. The effect of community health center (CHC) density on preventable hospital admissions in Medicaid and uninsured patients. J Health Care Poor Underserved. 2015;26(3):839-851. doi:10.1353/hpu.2015.0081
6. Schulz AJ, Zenk S, Odoms-Young A, et al. Healthy eating and exercising to reduce diabetes: exploring the potential of social determinants of health frameworks within the context of community-based participatory diabetes prevention. Am J Public Health. 2005;95(4):645-651. doi:10.2105/AJPH.2004.048256
7. Coleman-Jensen A, Nord M, Andrews M, Carlson S. Household food security in the United States in 2010. US Department of Agriculture Economic Research Service. September 2011. Accessed December 24, 2020. https://www.ers.usda.gov/webdocs/publications/44906/6893_err125_2_.pdf
8. Cafer AM, Kaiser ML. An analysis of differences in predictors of food affordability between rural and urban counties. J Poverty. 2016;20(1):34-55. doi:10.1080/10875549.2015.1094760
9. Cafer A, Mann G, Ramachandran S, Kaiser M. National food affordability: a county-level analysis. Prev Chronic Dis. 2018;15:E115. doi:10.5888/pcd15.180079
10. Prevention Quality Indicator 93 (PQI 93): prevention quality diabetes composite. Agency for Healthcare Research and Quality. July 2020. Accessed December 24, 2020. https://qualityindicators.ahrq.gov/Downloads/Modules/PQI/V2020/TechSpecs/PQI_93_Prevention_Quality_Diabetes_Composite.pdf
11. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619. doi:10.1016/0895-4356(92)90133-8
12. Legler A, Bradley EH, Carlson MD. The effect of comorbidity burden on health care utilization for patients with cancer using hospice. J Palliat Med. 2011;14(6):751-756. doi:10.1089/jpm.2010.0504
13. American Community Survey (ACS). Accessed September 25, 2020. https://data.census.gov/cedsci/
14. QuickFacts: United States. US Census Bureau. Accessed December 24, 2020. https://www.census.gov/quickfacts/fact/table/US/PST045219
15. Food insecurity among overall (all ages) population in the United States. Feeding America. Accessed December 24, 2020. https://map.feedingamerica.org/
16. Gruber J. Medicaid. In: Moffitt RA, ed. Means-Tested Transfer Programs in the United States. University of Chicago Press; 2003:15-78.
17. Rowland D, Garfield R. Health care for the poor: Medicaid at 35. Health Care Financ Rev. 2000;22(1):23-34.
18. Long SK, Coughlin TA, Kendall SJ. Access to care among disabled adults on Medicaid. Health Care Financ Rev. 2002;23(4):159-173.
19. Miller S, Wherry LR. Health and access to care during the first 2 years of the ACA Medicaid expansions. N Engl J Med. 2017;376(10):947-956. doi:10.1056/NEJMsa1612890
20. Thomas LV, Wedel KR, Christopher JE. Access to transportation and health care visits for Medicaid enrollees with diabetes. J Rural Health. 2018;34(2):162-172. doi:10.1111/jrh.12239
21. Shrestha SS, Zhang P, Hora I, Geiss LS, Luman ET, Gregg EW. Factors contributing to increases in diabetes-related preventable hospitalization costs among U.S. adults during 2001-2014. Diabetes Care. 2019;42(1):77-84. doi:10.2337/dc18-1078
22. Fingar KR, Barrett ML, Elixhauser A, Stocks C, Steiner CA. Trends in potentially preventable inpatient hospital admissions and emergency department visits. Healthcare Cost and Utilization Project statistical brief No. 195. Agency for Healthcare Research and Quality. November 2015. Accessed October 4, 2022. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb195-Potentially-Preventable-Hospitalizations.jsp
23. Gounder PP, Seeman SM, Holman RC, et al. Potentially preventable hospitalizations for acute and chronic conditions in Alaska, 2010-2012. Prev Med Rep. 2016;4:614-621. doi:10.1016/j.pmedr.2016.03.017
24. Huang Y, Meyer P, Jin L. Neighborhood socioeconomic characteristics, healthcare spatial access, and emergency department visits for ambulatory care sensitive conditions for elderly. Prev Med Rep. 2018;12:101-105. doi:10.1016/j.pmedr.2018.08.015
25. Hart-Hester S, Thomas C. Access to health care professionals in rural Mississippi. South Med J. 2003;96(2):149-154. doi:10.1097/01.SMJ.0000051180.92828.DD
26. Mississippi primary care needs assessment. Mississippi State Department of Health. March 2021. Accessed October 4, 2022. https://msdh.ms.gov/msdhsite/_static/resources/7357.pdf
27. Diabetes prevention and control. Mississippi State Department of Health. March 28, 2018. Accessed October 5, 2020. https://msdh.ms.gov/msdhsite/_static/43,0,296.html
28. Diabetes and African Americans. HHS Office of Minority Health. Accessed January 26, 2022.
29. Berkowitz SA, Baggett TP, Wexler DJ, Huskey KW, Wee CC. Food insecurity and metabolic control among US adults with diabetes. Diabetes Care. 2013;36(10):3093-3099. doi:10.2337/dc13-0570
30. Gucciardi E, Vahabi M, Norris N, Del Monte JP, Farnum C. The intersection between food insecurity and diabetes: a review. Curr Nutr Rep. 2014;3(4):324-332. doi:10.1007/s13668-014-0104-4
31. Seligman HK, Jacobs EA, López A, Tschann J, Fernandez A. Food insecurity and glycemic control among low-income patients with type 2 diabetes. Diabetes Care. 2012;35(2):233-238. doi:10.2337/dc11-1627
32. Guide to Prevention Quality Indicators. Agency for Healthcare Research and Quality. March 12, 2007. Accessed December 24, 2020. https://www.qualityindicators.ahrq.gov/Downloads/Modules/PQI/V31/pqi_guide_v31.pdf
33. Glasgow RE, Strycker LA. Preventive care practices for diabetes management in two primary care samples. Am J Prev Med. 2000;19(1):9-14. doi:10.1016/s0749-3797(00)00157-4
34. Edwards ST, Saha S, Prentice JC, Pizer SD. Preventing hospitalization with veterans affairs home-based primary care: which individuals benefit most? J Am Geriatr Soc. 2017;65(8):1676-1683. doi:10.1111/jgs.14843
35. Hutchison J, Thompson ME, Troyer J, Elnitsky C, Coffman MJ, Thomas ML. The effect of North Carolina free clinics on hospitalizations for ambulatory care sensitive conditions among the uninsured. BMC Health Serv Res. 2018;18(1):280. doi:10.1186/s12913-018-3082-1
36. Blackwell DL, Hayward MD, Crimmins EM. Does childhood health affect chronic morbidity in later life? Soc Sci Med. 2001;52(8):1269-1284. doi:10.1016/s0277-9536(00)00230-6
37. Diez Roux AV, Merkin SS, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med. 2001;345(2):99-106. doi:10.1056/NEJM200107123450205
38. Smith GD, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ. 1998;316(7145):1631-1635. doi:10.1136/bmj.316.7145.1631
39. Chen PC, Tsai CY, Woung LC, Lee YC. Socioeconomic disparities in preventable hospitalization
among adults with diabetes in Taiwan: a multilevel modelling approach. Int J Equity Health. 2015;14:31. doi:10.1186/s12939-015-0160-4
40. Gupta N, Crouse DL. Social disparities in the risk of potentially avoidable hospitalization for diabetes mellitus: an analysis with linked census and hospital data. Can Stud Popul. 2019;46(2):145-159.
41. Jiang HJ, Andrews R, Stryer D, Friedman B. Racial/ethnic disparities in potentially preventable readmissions: the case of diabetes. Am J Public Health. 2005;95(9):1561-1567. doi:10.2105/AJPH.2004.044222
42. Dresden SM, Feinglass JM, Kang R, Adams JG. Ambulatory care sensitive hospitalizations through the emergency department by payer: comparing 2003 and 2009. J Emerg Med. 2016;50(1):135-142. doi:10.1016/j.jemermed.2015.02.047
43. Sharma AI, Dresden SM, Powell ES, Kang R, Feinglass J. Emergency department visits and hospitalizations for the uninsured in Illinois before and after Affordable Care Act insurance expansion. J Community Health. 2017;42(3):591-597. doi:10.1007/s10900-016-0293-4
44. Gatwood J, Shuvo S, Hohmeier KC, et al. Pneumococcal vaccination in older adults: an initial analysis of social determinants of health and vaccine uptake. Vaccine. 2020;38(35):5607-5617. doi:10.1016/j.vaccine.2020.06.077
45. Longman JM, Passey ME, Ewald DP, Rix E, Morgan GG. Admissions for chronic ambulatory care sensitive conditions – a useful measure of potentially preventable admission? BMC Health Serv Res. 2015;15:472. doi:10.1186/s12913-015-1137-0