The Science of Diabetes Self-Management and Care2024, Vol. 50(4) 263–274© The Author(s) 2024Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106241256324journals.sagepub.com/home/tde
AbstractPurpose: The purpose of the study was to examine financial well-being among a diverse population of individuals with and without diabetes.
Methods: Data from the Understanding America Survey, a nationally representative, longitudinal panel, were utilized to identify adults with self-reported diabetes diagnoses between 2014 and 2020. We used longitudinal mixed effects regression models to assess the association between diabetes and financial well-being score (FWBS) among racial and ethnic population subgroups. Models included sex, age, marital status, household size, income, education, race/ethnicity, insurance, body mass index, employment, and health insurance, incorporating individual- and household-level fixed effects. Racial and ethnic differentials were captured using group-condition interactions.
Results: Black participants (17.06%) had the highest prevalence of diabetes, followed by White participants (12.2%), “other” racial groups (10.7%), and Hispanic participants (10.0%). In contrast, White participants (M = 67.66, SD = 22.63) and other racial groups (M = 67.99, SD = 18.45) had the highest FWBSs, followed by Hispanic participants (M = 59.31, SD = 22.78) and Black participants (M = 55.86, SD = 25.67). Compared to White participants, Black participants (β = −5.49, SE = 0.71) and Hispanic participants (β = −2.06, SE = 0.63) have significantly lower FWBSs. Compared to males, females (β = −3.25, SE = 0.41) had lower FWBSs among individuals with diabetes. FWBSs of individuals with diabetes was 2.71 points lower (SE = 0.52), on average, than those without diabetes. Education, household size, age, marital status, and income were also significantly associated with FWBSs.
Conclusions: Findings suggest potential disparities in the financial ramifications of diabetes among socially marginalized populations.
The year 2021 marked the 100th anniversary of the discovery of insulin, a key component in the management of type 2 diabetes mellitus (diabetes) for many individuals. Insulin changed diabetes from a fatal condition to one requiring lifelong management, and individuals with diabetes were able to live longer, higher quality lives. The discovery of insulin coupled with other medical breakthroughs has resulted in a record number of individuals living with diabetes: over 37 million, or 1 in 10 (in 2020).1
The daily management of diabetes is burdensome and costly. Estimates suggest the annual medical cost and lost wages total is $327 billion. Additionally, individuals with diabetes have approximately 2 times higher health care expenditures compared to those without diabetes.2 Proper management of diabetes helps to avoid severe, longterm microvascular and macrovascular complications, such as cardiovascular disease, stroke, eye disorders, foot ulcers, and chronic kidney disease. However, the high cost of A1C control can result in poor self-management3,4 and irregular monitoring of the condition.5 Individuals with diabetes experience higher financial hardship, impacting not only management of diabetes but also additional factors, such as food security.6 Additionally, individuals with uncontrolled diabetes report reduced financial well-being factors such as saving.7 These increased financial hardships presented through financial stress and poor financial well-being (FWB) are associated with higher levels of diabetes distress8 and reduced physical and mental health.4
A concurrent issue among individuals with diabetes are racial-ethnic differences in diabetes prevalence and outcomes. Diabetes disproportionately affects minority populations in the United States. High rates of diabetes are found among people who identify as non-Hispanic (NH) American Indian/Alaska Native (14.7%), Hispanic (12.5%), and NH Black (11.7%), while lower rates are seen among those who identify as NH Asian (9.2%) and NH White (7.5%).9 NH Black populations also experience a higher burden of diabetes-related complications. NH Black and Hispanic adults with diabetes have higher rates of albuminuria and retinopathy and worse glycemic control compared to NH White adults.10 Diabetes is estimated to contribute to 1 in 9 deaths among adults age 20 to 79 years,11 but mortality risks are higher among minority population groups relative to their NH White counterparts.12 Lastly, although more men are diagnosed with type 2 diabetes,13 women with diabetes have higher health care expenditures.14 Additionally, women experience overall higher burden associated with diabetes due to diagnosis at older ages and higher likelihood of comorbidites.15 This emphasizes the need to explore FWB not only across racial-ethnic groups but also between sexes.
Currently, 11.6% of US adults have diabetes.16 Not only is the prevalence of the condition expected to increase,1 the cost of diabetes management is also projected to increase over the next decade, further exacerbating the financial burden of the condition.17 Although studies have explored the costs associated with diabetes care and management,2,18 less is known about the overall financial health of individuals and population subgroups diagnosed with the condition. Using the Consumer Financial Protection Bureau (CFPB) FWB Scale as an indicator of the ability to meet current and ongoing financial obligations, this study explored relative differences in FWB among a diverse population of individuals with and without diabetes.
The goal of this study was to evaluate the association between diabetes and the trajectory of FWB and determine if the association varied between racial and ethnic population subgroups. Data from the Understanding America Survey (UAS) tracked individuals’ health conditions, demographic characteristics, and FWB between 2014 and 2020. Analysis used linear mixed effects (LME) regression models,19 a flexible method enabling correct modeling of both longitudinal and crossed or nested correlations, to assess these associations.
The current study used data from the UAS, a nationally representative, probability-based longitudinal study of recruited individuals using address-based sampling from the US Postal Service Computerized Delivery Sequence (CDS) file, covering almost 100% of US households.
The UAS is an ongoing nationally representative internet panel of respondents age 18 and over supported by the Social Security Administration and the National Institute on Aging and administered by the Center for Economic and Social Research (CESR) at the University of Southern California (USC). Participants complete surveys on their own time with their internet devices, such as a computer, tablet, or smartphone, and they are provided with a tablet and internet access if needed.20 Participants in the survey were UAS panel members who are randomly selected based on US addresses in the (CDS) file, which covers almost all or 100% of US households. The UAS oversamples Native Americans and residents of Los Angeles County and California; however, the data are weighted to produce a national sample. The UAS data were collected every 2 years by the USC CESR and includes information from 10 topics in the core Health and Retirement Study questionnaire. Currently, UAS data from 2014 (Wave 12), 2016, (Wave 13), 2018 (Wave 14), and 2020 (Wave 15) are available for research purposes, containing response information from 15 345 individuals. Utilization of the UAS sensitive data panel discussed herein was obtained through the deployment of an institutionally signed data use agreement and supervised by the institutional review board.
To provide practitioners and researchers with a standard, reliable, and broadly available way to measure individual FWB, the CFPB led a rigorous research effort to develop a consumer-driven definition of FWB and tested a mechanism to measure FWB. The scale was designed to allow practitioners and researchers to accurately and consistently quantify the extent to which one’s financial situation and capability have provided them with security and freedom of choice. The CFPB FWB Scale, a consumer-driven measurement tool, was developed through cognitive interviewing and testing to ensure accurate comprehension of questions. The scale is a reliable and valid measure of the FWB construct based on multiple waves of quantitative testing.21 It provides a common metric for comparison of scores across consumers and can be used to assess the state of FWB, financial progress over time, and how other factors affect FWB.
The FWB scale consists of 10 items and 5 responses for each item (4 = completely, 3 = very well, 2 = somewhat, 1 = very little, 0 = not at all). To determine an individual’s FWB score, responses are summed to derive a raw response value, and then the response values are converted to scores using a provided lookup table. Scores are based on an item response theory analysis, a statistical method that provides a more precise measure than a simple summary score by allowing different items and responses in a scale to these items to contribute differently to the final score. Different sets of scores were used for adults age 18 to 61 and those 62 and above. FWB scale scores range from 0 to 100, with higher scores representing higher levels of financial well-being. For clarity in terminology throughout the rest of this article we utilize the term “FWB” when discussing any analytic findings using the scale.
Demographic information for each respondent included age, marital status (married, not married*), sex (male,* female), insurance status (insured, not insured), number of household residents, body mass category (obese, not obese*), employment status (currently working for pay, not working for pay*), educational attainment (less than a high school degree, high school degree/some college, college degree or above*), race (NH White*, NH Black, NH Native American/Asian/Pacific Islander/other/mixed, Hispanic), and annual household income (greater than $75 000, less than or equal to $75 000*). Categories subsumed in the reference group are indicated with an asterisk. The reference group indicates the group in which all other groups FWB scores are compared to in that category. For example, regarding race, FWB among NH Black and NH Native American/Asian/Pacific Islander/other/mixed respondents is compared to FWB among NH White respondents. Income was divided into 2 groups, above and below $75 000, based on the median income level reported in the sample.
Additionally, UAS reported current and previous diagnoses made by “nurses, nurse practitioners, physicians’ assistants and internists” and “medical doctors includ[ing] specialists such as cardiologists, osteopaths, dermatologists, ophthalmologists, psychiatrists, as well as family doctors and general practitioners.” Thus, respondents were instructed to report only a clinical diagnosis of diabetes, excluding gestational diabetes or prediabetes.
Longitudinal mixed effects regression models assessed the association between FWB and diabetes. Models included individual- and household-level fixed effects and account for individual-level repeated measures. Estimates were weighted using sampling weights so that parameter estimates reflect a nationally representative, US-based population. Model covariates included sex, age, marital status, employment status, insurance status, household size, employment status, household income, education, race/ethnicity, body mass index category, and an indicator of diabetes diagnosis. Racial/ethnic and socioeconomic differences in FWB were reflected in interactions between these characteristics and a condition indicator. To contextualize the relative differences in FWB between racial, ethnic, and condition-related groups, fitted values from the regression model were used to calculate adjusted subgroup FWB levels. To test the sensitivity to differences between males and females, sex-specific estimates were also evaluated and compared to the full sample regression model.
Table 1 provides characteristics for the full sample and racial and ethnic subgroups. Of the 13 524 respondents in the sample, 64.3% were NH White, 11.7% were NH Black, 8.2% were NH other racial groups, and 15.8% were Hispanic. The average age of respondents was 48.1 (SD = 16.62) years old, and the average household consisted of between 2 and 3 individuals (SD = 1.31). Just over half (51.8%) of respondents were female, and most were married (53.3%). Only 9.5% of the sample had not graduated from high school, and over half had a high school diploma (54.5%). Only 36.9% were obese, 37.1% had no insurance, and 43.7% had a household income above $75 000.
The sample prevalence of diabetes was 12.4%, but rates differed between racial and ethnic groups, with 17.1%, 12.2%, 10.7%, and 10.0% of NH Black, NH White, NH other racial groups, and Hispanic respondents, respectively, having reportedly been diagnosed with diabetes. On average, FWB was 65.2 (SD = 23) with a Cronbach coefficient alpha between 0.93 and 0.94, indicating high internal consistency throughout the panel. NH other racial groups (68, SD = 18.5) and NH White respondents (67.7, SD = 22.6) had the highest FWB, followed by Hispanic (59.3, SD = 22.8) and NH Black (55.9, SD = 25.7) respondents.
Table 2 lists estimates from the longitudinal mixed effects regression model. A 95% confidence interval was used to determine statistical significance. Results discussed in the following met the criteria for statistical significance with a probability value below .05. Age (β = 0.29, SE = 0.02) was significantly associated with FWB, and household size was negatively associated (β = −0.87, SE = 0.14). On average, females had FWB 4.47 (SE = 0.41) points below men. Individuals who were married (β = 4.18, SE = 0.39), employed (β = 0.90, SE = 0.32), and earning over $75 000 (β = 2.86, SE = 0.24) also had significantly higher FWB. Relative to NH White respondents, NH Black and Hispanic respondents had 5.49 (SE = 0.71) and 2.06 (SE = 0.63) points lower FWB, respectively, but other racial groups respondents showed no statistically significant difference in FWB. Compared to respondents with health insurance, uninsured (β = −1.48, SE = 0.24) individuals had lower FWB, and relative to nonobese respondents, FWB was lower among those with obesity (β = −2.49, SE = 0.30). Similarly, relative to college graduates, respondents with no high school degree (β = −17.37, SE = 0.75) or only a high school degree (β = −8.99, SE = 0.43) had significantly lower FWB.
Diabetes was significantly associated with 2.71 (SE = 0.52) points lower FWB after adjusting for age, income, and education. Interaction terms comparing the FWB of racial and ethnic groups with diabetes showed that NH Black respondents with diabetes had 3.40 (SE = 1.21) lower FWB relative to NH Black respondents without diabetes. These differences are depicted in Figure 1, which shows the adjusted score differentials between groups.
This sensitivity test assessed whether the associations between FWB and diabetes were different between males and females. Results (Table 3) showed that the diabetes had less of a negative association with FWB among females (β = −3.25, SE = 0.76) than males (β = −2.32, SE = 0.72). Among females, the NH Black-NH White differential in FWB (β = −4.65, SE = 0.87) was smaller than among males (β = −6.39, SE = 1.19). Finally, NH Black females with diabetes showed no statistically significant difference in FWB from NH Black females without diabetes. Black males with diabetes (β = −5.68, SE = 1.86), however, had significantly lower FWB compared to those without diabetes.
In this study exploring FWB differences in a diverse population with and without diabetes, we found lower FWB in those diagnosed with diabetes compared to those without diabetes. However, the influence of diabetes on FWB differed among sociodemographic groups. Although NH Black respondents, Hispanic respondents, and females all had significantly less FWB compared to their White, NH, and male counterparts, respectively, the interaction of diabetes was not significant for Hispanic respondents. Additionally, those with less education, less income, and not employed or married had significantly lower FWB.
Interestingly, the relationship between diabetes and FWB differed between males and females. In females, diabetes demonstrated reduced negative association with FWB compared to males. Furthermore, NH Black females with diabetes showed no significant differences in FWB compared to those without diabetes, whereas NH Black males with diabetes demonstrated significantly lower FWB. Evidence suggests that there is a strong correlation between measures of financial hardship and glycemic control such that each additional hardship associated with a 0.1% increase in A1C.22 Therefore, this discussion considers the intersectional relationship between FWB, demographic characteristics, and human capital factors.
Previous findings noted a bidirectional relationship between physical health and FWB showing that poor financial status is not only a known risk factor for the development of chronic disease but that chronic disease is also costly to manage.23-25 Individuals with more medical debt forgo medical care at higher rates26 and have higher rates of poor physical health, psychiatric disorders, and all-cause mortality.8,24,27 This has been shown to be particularly true among individuals with diabetes, who incur high direct costs of medical expenditures and high indirect costs (ie, reduced work hours, loss of employment, etc), resulting in increased financial burden and financial loss.2,28
The association between race/ethnicity and diabetes is not fully understood. This may in part be due to differential relationships between race/ethnicity and diabetes. For example, whereas NH Black respondents demonstrated an interaction between diabetes and FWB, this same pattern was not observed in Hispanic respondents. Lower FWB in Hispanic respondents but observed lack of diabetes interaction may be explained by overall lower FWB in minority populations, and specifically in Hispanic populations, that is not explained by diabetes but by other social determinants of FWB. Social determinants of FWB29 may consist of prior to disease diagnosis economic stability, education, or neighborhood environment. For example, a review by Clark and Utz30 found the built environment was a stronger predictor of diabetes outcomes than race.
Regarding the findings among NH Black respondents, Simmons et al31 found decreased out-of-pocket expenses for minorities with diabetes compared to their NH White counterparts. Furthermore, when examining the interactive role of gender, Chlebowy et al32 identified costs as a partial cause for reduced medication adherence among NH Black women with diabetes. Observed decreased out-of-pocket expenses in minorities are most likely partially explained by cost barriers to access and not reduced need for diabetes management medications and tools. Regardless, these findings suggest that day-to-day expenditures for diabetes management do not explain decreased FWB in NH Black populations. More specifically, the differences observed may be due to an association between FWB and other social determinants of health and not an association between FWB and diabetes like patterns seen in Hispanic respondents. Further exploration is needed to distinguish racial/ethnic differences in FWB among persons with diabetes from social determinants influencing FWB that disproportionality affect marginalized racial/ethnic groups.
Regarding sex, the findings align with previous findings identifying higher financial burden among women with diabetes.4,8 Additionally, this study demonstrating a significant difference in FWB between NH Black males with diabetes and NH Black adults without diabetes but not among NH Black females also suggests that the relationship between FWB and diabetes is influenced by sex. This suggests that in contrast to differences in FWB among males with and without diabetes, differences in FWB among women may be associated with gender-related factors outside of diabetes diagnosis. For example, proxies for gender-related differences, such as status as primary earner and a higher need for social support, have been associated with diabetes risk.33 However, future work is needed examining the influence of societal gender roles on diabetes risk and outcomes.
Low income, less education, and unemployment, which was found to all be associated with reduced FWB, are often interrelated. Although socioeconomic status including income and employment has been associated with diabetes outcomes, the pathways of this influence are less understood.34 The explanation for the role of employment status on FWB in persons with diabetes may rest in the intersection between insurance status, diabetes management, and FWB. Employment status is associated with quality of health insurance and subsequently, access to quality health care.35 Additionally, the type of job matters. Employees at medium or large size work establishments with more stable incomes are more likely to have higher quality health insurance or health insurance at all.36,37 Lastly, different work environments may be more conducive to effective diabetes management.
Regarding education, the finding of an association between educational attainment and FWB aligns with previous findings demonstrating increased risk of diabetes with less education.38,39 The pathway of education to diabetes outcomes may be partially explained through health literacy. Higher educational attainment is associated with better health literacy,40 and health literacy and numeracy are associated with diabetes-related outcomes such as glycemic control.41,42
Due to the high probability of significant interaction between income, education, and employment status, further research should explore the pathways in which these determinants influence FWB. It is important to note that the association between educational attainment and diabetes risk is not consistent among race/ethnicities,38 suggesting a need to further explore the interaction of race/ethnicity on the relationship between FWB, educational attainment, and diabetes.
Differences in FWB among individuals with diabetes suggest interventions targeting FWB may be one such area to reduce disparities in diabetes outcomes. Methods such as explicit discussions of cost concerns or financial strain among individuals with diabetes have been shown to improve financial management and medication adherence.43 However, these findings suggest FWB intervention may vary across individuals. Therefore, exploration into the interrelationship between sociodemographic variables and the multiple dimensions of FWB among persons with diabetes may provide clarity on effective individualized interventions.
Although important, these findings should be interpreted within the context that they were derived. First, UAS is a longitudinal panel of households at USC of approximately 13 000 respondents representing the entire United States. The study is an “internet panel,” which means that respondents answer surveys on a computer, tablet, or smartphone wherever they are and whenever they wish to participate. These methods may differ from other standard survey collection procedures. Therefore, difference in the deployment and data collection mechanisms between UAS and benchmark surveys (ie, National Health Interview Survey, Behavioral Risk Factor Surveillance System) may result in nonparallel trends.44-46 Second, this study was not able to identify timing of diabetes diagnosis, time after condition onset, or type of diabetes. Timing after onset may influence self-management and subsequently, FWB. Third, the UAS did not provide any information about diabetes type, diabetes-related care, medication, or treatment, and respondents did not indicate their mechanism of glycemic index control. Fourth, diabetes can co-occur with other chronic conditions that may significantly impact the condition severity and manageability. Fourth, all survey data are self-reported and as such, is subject to recall, recency, prestige, and social desirability bias, which may influence which events respondents recall or report at the time of response. Lastly, due to sample size limitations, the statistical analysis of race and ethnicity was limited to NH White, NH Black, Hispanic, and NH other racial and ethnic groups. Additionally, the sample rates of diabetes among NH White and NH Black respondents were higher than previously reported rates of diabetes.12 Further research should examine possible variability in the relationship between FWB and diabetes in additional racial and ethnic groups.
As the prevalence of diabetes continues to increase, it is important to explore all the health and lifestyle consequences of this condition and how these consequences manifest differently among diverse populations. Focusing on a single consequence of diabetes, FWB, this study found lower levels of FWB among individuals with diabetes compared to those without this condition. Additionally, low-income earners, women, and some minoritized populations not only have a higher prevalence of diabetes but also experience disparate financial impacts. Even though the causal relationship between FWB and diabetes is likely multidirectional, this study elucidated the demographic and social characteristics associated with differences in FWB among individuals with diabetes.
Although the causal mechanism underlying these observed financial differences merits further exploration, these findings suggest the need for interventions aimed at easing financial strain among individuals with this condition. Additional exploration of the multifactorial concept of FWB may provide critical insight into observed differences in the disease progression or treatment among individuals with chronic illnesses by suggesting potential targets for interventions aimed at increasing equity in diabetes outcomes.
The project described in this article relies on data from survey(s) administered by the Understanding America Study (UAS), which is maintained by the Center for Economic and Social Research at the University of Southern California (USC). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of USC or UAS.
The authors have no relevant financial or nonfinancial interests to disclose.
Elizabeth Evans, https://orcid.org/0009-0000-8534-2891
Williams R, Karuranga S, Malanda B, et al. Global and regional estimates and projections of diabetes-related health expenditure: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2020;162:108072. doi:10.1016/j.diabres.2020.108072
American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917-928. doi:10.2337/dci18-0007
Patel MR, Piette JD, Resnicow K, Kowalski-Dobson T, Heisler M. Social determinants of health, cost-related nonadherence, and cost-reducing behaviors among adults with diabetes: findings from the National Health Interview Survey. Med Care. 2016;54(8):796-803. doi:10.1097/mlr.0000000000000565
Morris JL, Chasens ER. Financial difficulty: a barrier to self-care in patients with diabetes. Diabetes Educ. 2017;43(3):247-248. doi:10.1177/0145721717703486
Patel S, Abreu M, Tumyan A, Adams-Huet B, Li X, Lingvay I. Effect of medication adherence on clinical outcomes in type 2 diabetes: analysis of the SIMPLE study. BMJ Open Diabetes Res Care. 2019;7(1):e000761. doi:10.1136/bmjdrc-2019-000761
Caraballo C, Valero-Elizondo J, Khera R, et al. Burden and consequences of financial hardship from medical bills among nonelderly adults with diabetes mellitus in the United States. Circ Cardiovasc Qual Outcomes. 2020;13(2):e006139. doi:10.1161/CIRCOUTCOMES.119.006139
Patel MR, Anthony Tolentino D, Smith A, Heisler M. Economic burden, financial stress, and cost-related coping among people with uncontrolled diabetes in the U.S. Prev Med Rep. 2023;34:102246. doi:10.1016/j.pmedr.2023.102246
Patel MR, Zhang G, Heisler M, et al. Measurement and validation of the comprehensive score for financial toxicity (COST) in a population with diabetes. Diabetes Care. 2022;45(11):2535-2543. doi:10.2337/dc22-0494
Centers for Disease Control and Prevention. Measuring healthy days: population assessment of health-related quality of life. Accessed June 13, 2023. https://www.cdc.gov/hrqol/pdfs/mhd.pdf
Canedo JR, Miller ST, Schlundt D, Fadden MK, Sanderson M. Racial/ethnic disparities in diabetes quality of care: the role of healthcare access and socioeconomic status. J Racial Ethn Health Disparities. 2018;5(1):7-14. doi:10.1007/s40615-016-0335-8
Saeedi P, Salpea P, Karuranga S, et al. Mortality attributable to diabetes in 20-79 years old adults, 2019 estimates: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2020;162:108086. doi:10.1016/j.diabres.2020.108086
Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13(6):814-823. doi:10.1007/s11892-013-0421-9
Magliano DJ, Boyko EJ, Committee IDFDAtes. IDF Diabetes Atlas. International Diabetes Federation; 2021.
Williams JS, Bishu K, Dismuke CE, Egede LE. Sex differences in healthcare expenditures among adults with diabetes: evidence from the medical expenditure panel survey, 2002-2011. BMC Health Serv Res. 2017;17(1):259. doi:10.1186/s12913-017-2178-3
Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66(6):986-1002. doi:10.1007/s00125-023-05891-x
American Diabetes Association. Statistics about diabetes. Updated November 2, 2023. Accessed May 22, 2024. https://diabetes.org/about-diabetes/statistics/about-diabetes#:~:text=Prevalence%3A%20In%202021%2C%2038.4%20million,of%20the%20population%2C%20had%20diabetes.&text=Diagnosed%20and%20undiagnosed%3A%20Of%20the,and%208.7%20million%20were%20undiagnosed.
Parker ED, Lin J, Mahoney T, et al. Economic costs of diabetes in the U.S. in 2022. Diabetes Care. 2023;47(1):26-43. doi:10.2337/dci23-0085
Shrestha SS, Honeycutt AA, Yang W, et al. Economic costs attributable to diabetes in each U.S. state. Diabetes Care. 2018;41(12):2526-2534. doi:10.2337/dc18-1179
Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer Publishing Co.; 2005.
Alattar L, Messel M, Rogofsky D. An introduction to the understanding America study internet panel. Social Security Bulletin. 2018;78(2). https://www.ssa.gov/policy/docs/ssb/v78n2/v78n2p13.html
Consumer Financial Protection Bureau. CFPB Financial Well-Being Scale: scale development technical report. Development of the CFPB Financial Well-Being Scale 2017. Published May 26, 2017. Accessed May 22, 2024. https://www.consumerfinance.gov/data-research/research-reports/financial-well-being-technical-report/
Walker RJ, Garacci E, Campbell JA, Harris M, Mosley-Johnson E, Egede LE. Relationship between multiple measures of financial hardship and glycemic control in older adults with diabetes. J Appl Gerontol. 2021;40(2):162-169. doi:10.1177/0733464820911545
Khullar D, Chokshi DA. Health, income, & poverty: where we are & what could help. Health Affairs. 2018;10. doi:10.1377/hpb20180817.901935
Pool LR, Burgard SA, Needham BL, Elliott MR, Langa KM, Mendes de Leon CF. Association of a negative wealth shock with all-cause mortality in middle-aged and older adults in the United States. JAMA. 2018;319(13):1341-1350. doi:10.1001/jama.2018.2055
Sweet E, Nandi A, Adam EK, McDade TW. The high price of debt: household financial debt and its impact on mental and physical health. Soc Sci Med. 2013;91:94-100. doi:10.1016/j.socscimed.2013.05.009
Kalousova L, Burgard SA. Debt and foregone medical care. J Health Soc Behav. 2013;54(2):204-220. doi:10.1177/0022146513483772
Jenkins R, Bhugra D, Bebbington P, et al. Debt, income and mental disorder in the general population. Psychol Med. 2008;38(10):1485-1493. doi:10.1017/s0033291707002516
Schofield DJ, Percival R, Passey ME, Shrestha RN, Callander EJ, Kelly SJ. The financial vulnerability of individuals with diabetes. Br J Diabetes & Vasc Dis. 2010;10(6):300-304. doi:10.1177/1474651410385864
Office of Disease Prevention and Health Promotion. Social determinants of health. U. S. Department of Health and Human Services. Accessed May 22, 2024. https://health.gov/healthypeople/priority-areas/social-determinants-health
Clark ML, Utz SW. Social determinants of type 2 diabetes and health in the United States. World J Diabetes. 2014;5(3):296-304. doi:10.4239/wjd.v5.i3.296
Simmons M, Bishu KG, Williams JS, Walker RJ, Dawson AZ, Egede LE. Racial and ethnic differences in out-of-pocket expenses among adults with diabetes. J Natl Med Assoc. 2019;111(1):28-36. doi:10.1016/j.jnma.2018.04.004
Chlebowy DO, Hood S, LaJoie AS. Gender differences in diabetes self-management among African American adults. West J Nurs Res. 2013;35(6):703-721. doi:10.1177/0193945912473370
Muilwijk M, Bolijn R, Galenkamp H, Stronks K, Moll van Charante E, van Valkengoed IGM. The association between gender-related characteristics and type 2 diabetes risk in a multi-ethnic population: the HELIUS study. Nutr Metab Cardiovasc Dis. 2022;32(1):142-150. doi:10.1016/j.numecd.2021.09.015
Tatulashvili S, Fagherazzi G, Dow C, Cohen R, Fosse S, Bihan H. Socioeconomic inequalities and type 2 diabetes complications: a systematic review. Diabetes Metab. 2020;46(2):89-99. doi:10.1016/j.diabet.2019.11.001
Silver SR, Li J, Quay B. Employment status, unemployment duration, and health-related metrics among US adults of prime working age: Behavioral Risk Factor Surveillance System, 2018–2019. Am J Ind Med. 2022;65(1):59-71. doi:10.1002/ajim.23308
Su CP, Asfaw A, Tamers SL, Luckhaupt SE. Health insurance coverage among U.S. workers: differences by work arrangements in 2010 and 2015. Am J Prev Med. 2019;56(5):673-679. doi:10.1016/j.amepre.2018.12.010
Lin KH, Aragão C, Dominguez G. Firm size and employment during the pandemic. Socius. 2021;7. doi:10.1177/2378023121992601
Whitaker SM, Bowie JV, McCleary R, Gaskin DJ, LaVeist TA, Thorpe RJ Jr. The association between educational attainment and diabetes among men in the United States. Am J Mens Health. 2014;8(4):349-356. doi:10.1177/1557988313520034
Kanjilal S, Gregg EW, Cheng YJ, et al. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971-2002. Arch Intern Med. 2006;166(21):2348-2355. doi:10.1001/archinte.166.21.2348
van der Heide I, Wang J, Droomers M, Spreeuwenberg P, Rademakers J, Uiters E. The relationship between health, education, and health literacy: results from the Dutch Adult Literacy and Life Skills Survey. J Health Commun. 2013;18(suppl 1):172-184. doi:10.1080/10810730.2013.825668
Schillinger D, Grumbach K, Piette J, et al. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4):475-482. doi:10.1001/jama.288.4.475
Cavanaugh KL. Health literacy in diabetes care: explanation, evidence and equipment. Diabetes Manag. 2011;1(2):191-199. doi:10.2217/dmt.11.5
Patel MR, Resnicow K, Lang I, Kraus K, Heisler M. Solutions to address diabetes-related financial burden and cost-related nonadherence: results from a pilot study. Health Educ Behav. 2018;45(1):101-111. doi:10.1177/1090198117704683
Chan M, Kwok S. The PCDID approach: difference-in-differences when trends are potentially unparallel and stochastic. J Bus Econ Stat. 2021;40(3):1216-1233. doi:10.1080/07350015.2021.1914636?journalCode=ubes20
Marcus M, Sant’Anna PHC. The role of parallel trends in event study settings: an application to environmental economics. J Assoc Environ Resour Econ. 2021;8(2):235-275.
Rambachan A, Roth J. A more credible approach to parallel trends. Rev Econ Stud. 2023;90(5):2555-2591. doi:10.1093/restud/rdad018
From Communication Equity and Outcomes Laboratory, Department of Speech, Language and Hearing Sciences, College of Public Health and Health Professions, University of Florida, Gainesville, Florida (Ms. Evans); and Department of Health Services Research, Management and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, Florida (Dr Jacobs).
Corresponding Author:Elizabeth Evans, Communication Equity and Outcomes Laboratory, Department of Speech, Language, and Hearing Sciences, College of Public Health and Health Professions, University of Florida, 1225 Center Drive, PO Box 100174, Gainesville, FL 32610-0174, USA.Email: evans.e@phhp.ufl.edu
