PCD logo

Trends and Predictors of Glycemic Control Among Adults With Type 2 Diabetes Covered by Alabama Medicaid, 2011–2019

Caroline A. Presley, MD, MPH1; Yulia Khodneva, MD, PhD1; Lucia D. Juarez, PhD, MA1; Carrie R. Howell, PhD1; April A. Agne, MPH1; Kevin R. Riggs, MD, MPH1; Lei Huang, PhD2; Maria Pisu, PhD1; Emily B. Levitan, ScD2; Andrea L. Cherrington, MD, MPH1 (View author affiliations)

Suggested citation for this article: Presley CA, Khodneva Y, Juarez LD, Howell CR, Agne AA, Riggs KR, et al. Trends and Predictors of Glycemic Control Among Adults With Type 2 Diabetes Covered by Alabama Medicaid, 2011–2019. Prev Chronic Dis 2023;20:220332. DOI: http://dx.doi.org/10.5888/pcd20.220332.

PEER REVIEWED

Summary

What is already known on this topic?

Alabama has the third highest prevalence of diabetes in the US. Despite advances in diabetes management, only one-quarter of people with diabetes in the US achieve optimal target levels for glycated hemoglobin A1c, blood pressure, and cholesterol, which are key to preventing diabetes complications, illness, and death.

What is added by this report?

In our study, adults covered by Alabama Medicaid had levels of glycemic control that were similar to those of nationally representative samples; we found no differences by race, ethnicity, or geography. More than one-third did not achieve target glycemic control.

What are the implications for public health practice?

More work is needed, including potential Medicaid policy changes, to support people with diabetes covered by Medicaid.

Abstract

Introduction

Despite advances in diabetes management, only one-quarter of people with diabetes in the US achieve optimal targets for glycated hemoglobin A1c (HbA1c), blood pressure, and cholesterol. We sought to evaluate temporal trends and predictors of achieving glycemic control among adults with type 2 diabetes covered by Alabama Medicaid from 2011 through 2019.

Methods

We completed a retrospective analysis of Medicaid claims and laboratory data, using person-years as the unit of analysis. Inclusion criteria were being aged 19 to 64 years, having a diabetes diagnosis, being continuously enrolled in Medicaid for a calendar year and preceding 12 months, and having at least 1 HbA1c result during the study year. Primary outcomes were HbA1c thresholds of <7% and <8%. Primary exposure was study year. We conducted separate multivariable-adjusted logistic regressions to evaluate relationships between study year and HbA1c thresholds.

Results

We included 43,997 person-year observations. Mean (SD) age was 51.0 (9.9) years; 69.4% were women; 48.1% were Black, 42.9% White, and 0.4% Hispanic. Overall, 49.1% had an HbA1c level of <7% and 64.6% <8%. Later study years and poverty-based eligibility were associated with lower probability of reaching target HbA1c levels of <7% or <8%. Sex, race, ethnicity, and geography were not associated with likelihood of reaching HbA1c <7% or <8% in any model.

Conclusion

Later study years were associated with lower likelihood of meeting target HbA1c levels compared with 2011, after adjusting for covariates. With approximately 35% not meeting an HbA1c target of <8%, more work is needed to improve outcomes of low-income adults with type 2 diabetes.

Top

Introduction

The prevalence of diabetes in the US increased during the past 2 decades (1). The prevalence of diabetes also increased during this period in Alabama; in 2019, 14% of adults had diabetes, ranking third among the states, up from 8.4% in 2004 (2). Although the scientific knowledge related to diabetes management, including lifestyle modification and pharmacologic treatments, has advanced considerably, approximately one-quarter of people with diagnosed diabetes in the US achieve optimal target levels for glycated hemoglobin A1c (HbA1c), blood pressure, and cholesterol (1,3–5). Diabetes is the number one cause of kidney failure, new cases of blindness, and nontraumatic lower limb amputation in the US (1). Care for people with diabetes accounts for more than 1 in 4 US health care dollars spent (6).

Prior studies evaluated rates of glycemic control and demographic and clinical factors associated with achieving optimal control among adults with diabetes, defined as an HbA1c level below an individualized target ranging from 7% to 8.5% (4,5). Two recent studies using National Health and Nutrition Examination Survey (NHANES) data reported temporal trends in levels of optimal glycemic control among adults with diabetes. One study demonstrated lower odds of an HbA1c below an individualized target in 2014–2016 compared with 2005–2008, and another reported a lower proportion of people reaching both stringent and less stringent HbA1c targets in 2015–2018 compared with 2007–2010 (4,5). Younger age, racial and ethnic minority status, and lack of health insurance were factors associated with being less likely to achieve optimal glycemic control (4).

Although the aforementioned studies used nationally representative samples, health insurance coverage (ie, commercial, Medicare, Medicaid) was not evaluated as a factor related to glycemic control beyond the presence or absence of coverage, and levels of optimal glycemic control were not described for the different coverage groups. Dall et al showed similar levels of diabetes control by insurance type in a national sample in 2012 (7). Another study found a higher proportion of controlled diabetes among commercially insured adults than among adults covered by Medicare or Medicaid in a national sample from 2011 and 2012. In Alabama, 71% of adults with commercial insurance had controlled diabetes, 55% with Medicare, and 52% with Medicaid (8). These prior studies of diabetes outcomes in populations covered by Medicaid have been limited by the use of diagnosis codes to define “controlled diabetes” instead of HbA1c laboratory results.

Populations with low incomes (<200% of federal poverty level) are at a particularly high risk for poor diabetes outcomes, including diabetes complications (9–12). In Alabama, 36% of the population has a low income; in 2019, one in 8 adults aged 18 to 64 years in Alabama was covered by Medicaid (13). Given these factors, Alabama has a high burden of diabetes complications and death. Given the limitations of prior studies of glycemic control in Medicaid or low-income populations and the lack of data on temporal trends in these populations, we sought to evaluate temporal changes from 2011 through 2019 in achieving glycemic control — using laboratory data and HbA1c targets of <7% and <8% — among adults with type 2 diabetes who are Medicaid beneficiaries in Alabama.

Top

Methods

We conducted a retrospective analysis of Medicaid claims and laboratory data as part of an observational study of the quality of care of adults with diabetes covered by Alabama Medicaid. Medicaid eligibility for adults in Alabama includes parents of minor children with incomes 18% or less of the federal poverty level and adults with disability eligible for the Supplemental Security Income program (13). Enrollment and claims data for people with diabetes were provided for research purposes by the Alabama Medicaid Agency. HbA1c values for Alabama Medicaid beneficiaries were provided for research purposes by LabCorp and Quest Diagnostics. Data use agreements do not allow for sharing of this information. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board at the University of Alabama at Birmingham.

The unit of analysis was person-year observation. In other words, each calendar year that a person met all of the inclusion criteria was considered as a data point, with people potentially contributing multiple data points for each year from 2011 through 2019. Inclusion criteria were 1) being aged 19 to 64 years; 2) having a diagnosis of diabetes (defined by the Chronic Conditions Data Warehouse as having ≥1 inpatient, skilled nursing facility, or home health agency claim or 2 outpatient claims with valid diagnosis codes in the prior 2 years) (14); 3) being continuously enrolled in Alabama Medicaid for a 12-month calendar year and the prior year; and 4) having at least 1 HbA1c result during 2011–2019. For people with more than 1 HbA1c value in a calendar year, we selected the earliest value for analysis. We included 43,997 person-year observations (Figure 1), with 4,022 to 5,471 observations per study year.

Flow diagram of included observations of adults with type 2 diabetes covered by Alabama Medicaid, 2011–2019.

Return to your place in the text
Figure 1.

Flow diagram of included observations of adults with type 2 diabetes covered by Alabama Medicaid, 2011–2019. [A text version of this figure is available.]

Measures

Our primary outcomes were based on the first HbA1c result for a person in the study year; we used HbA1c thresholds of <7% and <8%. HbA1c, which represents the average blood glucose level for a person in the preceding 3 months, provides an estimate of chronic glycemia and the efficacy of a person’s diabetes treatment regimen. An HbA1c target of <7% is a stringent target for the age group included in this study; an HbA1c of <8% is the less stringent target for those in this age group with comorbidities, complications, or risk of hypoglycemia (3). We obtained laboratory results for Alabama Medicaid beneficiaries from 2011 through 2019 from LabCorp and Quest Diagnostics. Medicaid claims data show that these companies provide approximately 45% of all laboratory testing for Medicaid beneficiaries. Laboratory data were matched to Medicaid beneficiaries through linkage of first name, last name, and birth date. Our primary exposure was the study year of the HbA1c result from 2011 through 2019.

Covariates

We adjusted for demographic and clinical factors from Medicaid claims data shown to be associated with glycemic control (15,16). Demographic information included age, sex, race and ethnicity (based on Medicaid enrollment data), reason for Medicaid eligibility, and geography (urban, small town, or rural, based on zip code). Clinical data included insulin use, ambulatory care visits, acute care utilization, and medical comorbidities. Insulin use was based on the National Drug Code recorded on pharmacy claims (17). We determined a participant to be using insulin if their data showed an insulin fill up to the month before the HbA1c result. We included ambulatory care visits in the year before the HbA1c result, including total number of primary care visits and the presence of 1 or more endocrinology visits. We included acute care use based on the presence of 1 or more hospitalizations or the presence of 1 or more emergency department visits. We assessed the presence of 16 medical comorbidities that make up the Charlson Comorbidity Index by examining diagnoses recorded on claims in the year before the HbA1c result (18).

Analysis

We completed descriptive statistics to characterize the study sample. We generated means and SDs for HbA1c levels by study year. We then calculated the sample size and percentage of people with HbA1c <7% and <8% by study year. We conducted separate multivariable logistic regressions to evaluate the relationship between study year and an HbA1c result: <7% and <8%. Model 1 was unadjusted; model 2 was adjusted for demographic factors (age, sex, race and ethnicity, reason for eligibility, geography); model 3 was adjusted for demographic factors plus clinical factors (insulin use, ambulatory care use, acute care utilization, and medical comorbidities). We conducted secondary analyses that repeated the same models above for each HbA1c result with study year included as a continuous variable. The models used the generalized estimating equation approach to account for people contributing multiple calendar year observations.

In an additional analysis, we compared our study sample to the sample of Medicaid beneficiaries with diabetes (N = 262,310 person-years) and the subset of the larger sample with diabetes who had a claim for an HbA1c test but not an HbA1c result (n = 149,647) to assess for potential bias.

We conducted all statistical analyses in SAS version 9.3.1 (SAS Institute Inc).

Top

Results

The mean (SD) age of the Alabama Medicaid beneficiaries with diabetes in our analysis was 51.0 (9.9) years; 69.4% were women; 48.1% were Black and 42.9% were White (Table 1). Based on zip code, 67.8% lived in an urban area, 14.4% in a small town, and 17.9% in a rural area. More than three-fourths were eligible for Medicaid based on disability (77.7%); 22.3% were eligible based on poverty, with an increasing proportion based on poverty in later study years. One-third used insulin, and 28.0% had diabetes with complications. The percentage of beneficiaries who had diabetes with complications increased to 36.7% in 2019.

Mean (SD) HbA1c for the study period was 7.72% (2.24%); it ranged from 7.63% in 2011 to 7.85% in 2017 (Table 1, Figure 2). For the stricter HbA1c target, 49.1% had an HbA1c result <7%, ranging from 46.9% in 2017 to 51.5% in 2011 and 2018. For the higher HbA1c target, 64.6% had an HbA1c result <8%, ranging from 61.8% in 2017 to 67.5% in 2011. Of the 9 study years, the smallest proportion of the sample with an HbA1c at the target level was found in 2017 (46.9% with a result <7% and 61.8% with a result <8%).

Return to your place in the text
Figure 2.
Mean HbA1c by study year and glycemic control among Medicaid beneficiaries with diabetes in Alabama, 2011–2019. A, Mean HbA1c by study year. Error bars indicate SDs. B, Percentage of beneficiaries with an HbA1c <7% and <8%. The unit of analysis was person-years. [A tabular version of this figure is available.]

In models testing the association of study year with an HbA1c result <7% that included demographic and clinical factors as covariates, the study years of 2013–2017 and 2019 were associated with lower odds of an HbA1c result <7% compared with 2011 (Table 2). Poverty-based eligibility was significantly associated with lower odds of having an HbA1c result <7% (odds ratio [OR] = 0.87; 95% CI, 0.84–0.89). We found similar results in the logistic regression models that tested the association of study year with an HbA1c result <8% (Table 3); years 2012–2017 and 2019 were associated with lower odds of an HbA1c result <8% compared with 2011. In these models, older age was associated with higher odds of having an HbA1c result <8%; for every 10-year increment in age, the likelihood of achieving glycemic control increased by 3% (OR = 1.03; 95% CI, 1.02–1.04). Sex, race, ethnicity, and geography were not associated with odds of an HbA1c result <7% or <8% in any of the models. Of the 16 comorbidities assessed as part of the Charlson Comorbidity Index, diabetes with complication was the only covariate associated with lower likelihood of achieving an HbA1c result <7% or <8%. Several comorbidities were associated with higher odds of an HbA1c result <7% or <8%, including cerebrovascular disease, dementia, chronic obstructive pulmonary disease, peptic ulcer disease, chronic renal failure, and AIDS. For the secondary analyses, which included study year as a continuous variable, study year did not have a significant linear association with an HbA1c result <7% or <8% (OR = 0.99; 95% CI, 0.99–1.00 in both models).

In our assessment of potential bias, we found that our study sample was similar for most demographic and clinical factors to the sample of Medicaid beneficiaries with diabetes and the subset of the larger sample with diabetes who had a claim for an HbA1c test but not an HbA1c result (Appendix). We found modest differences: our study sample had a lower proportion of beneficiaries with poverty-based eligibility, a higher proportion in urban areas, a higher proportion who used insulin, and a higher level of primary care use.

Top

Discussion

In our study of adults with type 2 diabetes covered by Alabama Medicaid and represented by 43,997 person-year observations from 2011 through 2019, we found that just under half met an HbA1c target of <7% and approximately 6 in 10 met an HbA1c target of <8%. After adjusting for important covariates, later study years, with the exception of 2018, were associated with lower odds of meeting target HbA1c levels, compared with the reference year of 2011. We found that Medicaid eligibility status was related to achieving optimal glycemic control — poverty-based eligibility, compared with disability-based eligibility, was associated with lower odds of achieving HbA1c targets. We did not find differences by race or ethnicity for meeting HbA1c, a finding that differed from findings in prior studies that used NHANES data or primary care data (4,5,19). Additionally, clinical factors related to the severity of diabetes, including insulin use, endocrinology care, and diagnosis of diabetes complications were significantly associated with lower likelihood of achieving glycemic control. Several medical comorbidities were associated with a higher likelihood of achieving glycemic control, and various factors influenced these associations. Disease-specific factors, including altered glucose and insulin metabolism, may apply to chronic renal failure, while other changes to patient or provider behaviors related to control or increased overall contact with a health care system may apply across different comorbidities (20).

Overall, the proportion of adults meeting HbA1c targets of <7% (49.1%) and <8% (64.6%) in our study were similar to the proportion reaching optimal glycemic control in studies of nationally representative samples with all types of health insurance coverage. For example, Kazemian et al found that 64% met an individualized HbA1c target, while Fang et al found that 50.5% had an HbA1c result <7% and 75.4% were <8% (4,5). Furthermore, the proportion meeting an HbA1c target of <8% (64.6%) was higher in our study than in a prior study, which used diagnosis codes and found that 52% of Medicaid beneficiaries had controlled diabetes (vs 71% with commercial insurance) (7,8). A strength of our study was the use of HbA1c laboratory results, instead of diagnosis codes, to assess glycemic control, which is in line with guideline recommendations to use HbA1c as a measure of glycemic control (3).

We did not find differences in achieving optimal glycemic control between racial or ethnic groups or between those living in rural and urban areas. In prior studies, Black adults with diabetes, compared with non-Hispanic White adults, were less likely to receive recommended HbA1c testing or other screening measures, and had higher average HbA1c levels and higher rates of death (19,21–23). Similar disparities have been seen in comparisons of rural and urban areas: death rates were higher among adults with diabetes living in rural areas (23). Given the limited eligibility for Alabama Medicaid among adults, the lack of disparities by racial or ethnic group or geography may have resulted from similar resource constraints among the low-income population across these groups. However, we found that those with poverty-based eligibility had lower odds of reaching target HbA1c levels than those with disability-based eligibility, suggesting that people in the Medicaid population with greater financial need may need additional resources to achieve glycemic control and improve diabetes-related clinical outcomes. These results are in line with those of other studies, which demonstrated that low socioeconomic status and other social determinants of health, including food insecurity, were linked to poor outcomes among people with diabetes, including increased mortality risk and worse self-management and glycemic control (24–26).

Our study results are consistent with the results of other studies that showed a lower likelihood of achieving HbA1c targets in years 2014–2018 compared with years 2005–2010 (4,5). This trend occurred when guidelines shifted to recommending individualized HbA1c targets based on age, presence of diabetes complications or comorbid conditions, and risk of hypoglycemia, which may be less stringent than previously recommended targets. The shift in guidelines was based on major trials showing the risks and benefits of uniform intensive glycemic control (27,28). Along with the trend in lower likelihood of achieving HbA1c targets, the incidence of diabetes complications since 2010 among younger and middle-aged adults has been increasing, following a period of sustained decline (29). A cost-effectiveness study accounting for rates of diabetes complications showed that individualized HbA1c targets are cost-effective and increase quality of life compared with uniform intensive glycemic control (30). Our study and others demonstrated that more than one-third of adults with diabetes continued to have suboptimal glycemic control even when accounting for less stringent individualized targets (4,5).

Alabama has not expanded Medicaid eligibility under the Affordable Care Act; an estimated 200,000 adults in Alabama, who would be eligible under expansion, remain uninsured (31). Forty states and Washington, DC, have extended Medicaid eligibility to nearly all adults with incomes up to 138% of the federal poverty level (13). Given our results, which show similar glycemic control among adults covered by Alabama Medicaid and nationally representative populations with various types of health insurance, Medicaid expansion in Alabama or other Medicaid policies may be an important route to improve diabetes care and outcomes in the state. In other states, Medicaid expansion or other state policies have influenced diabetes care, self-management, and outcomes. Medicaid-eligible people with diabetes in Medicaid expansion states were more likely than their counterparts in nonexpansion states to afford a physician visit, be seen by a physician, or receive diabetes medications and supplies within the previous year; no significant differences were seen between these groups for self-management of diabetes (32–35). Furthermore, Medicaid expansion has influenced the uptake of newer diabetes medications; increases in rates of use of noninsulin diabetes medications, including glucagon-like peptide 1 receptor agonists and sodium-glucose cotransporter-2 inhibitors, were significantly greater in expansion states than in nonexpansion states (36). Finally, in a study of Wisconsin Medicaid beneficiaries, a reduction in drug copayment amounts was associated with significantly increased medication adherence among adults with diabetes (37).

Limitations

Our study has several limitations. Participant demographic characteristics, including race and ethnicity, were based on Medicaid enrollment data, not participant self-report. We were unable to account for diabetes self-management behaviors or other psychosocial factors that influence glycemic control. Our outcome was based on a single HbA1c result, a snapshot of glycemic control for the preceding 3 months. We were unable to assess an individualized HbA1c target, but we did evaluate 2 HbA1c targets (<7% and <8%). For the age group included in this study (19–64 y), an HbA1c result <8% is the recommended target for those with comorbidities, complications, or risk of hypoglycemia (3). We did not assess clinical outcomes or other recommended risk factor controls for adults with diabetes (ie, blood pressure and lipid control). People in our study sample were connected to the health care system; the proportion achieving glycemic control in our sample may be larger than the proportion of the Alabama Medicaid population with diabetes who did not receive HbA1c testing or did not have HbA1c values from Quest Diagnostics or LabCorp. We were able to obtain HbA1c values only from LabCorp and Quest Diagnostics; we did not have HbA1c values for tests performed in other laboratories. People with available HbA1c values were similar to those with claims for HbA1c testing but no available HbA1c results, except that people with results were more likely to live in urban areas, to have disability-based eligibility, to use insulin, and to use primary care. Some of these factors were associated with higher odds of achieving glycemic control (disability-based eligibility, primary care use) and some were associated with lower odds (insulin use, urban residence). Because the magnitude of the differences was not large, we would not expect a substantial effect on the observed associations. Finally, because of restrictive Medicaid eligibility in Alabama, our study population was very low-income or had a disability; the proportion achieving glycemic control or the relationships between factors associated with glycemic control may not be generalizable to states that expanded Medicaid eligibility to include households up to 138% of the federal poverty level.

Conclusions

Among adults covered by Alabama Medicaid, 49.1% met an HbA1c target <7% and 64.6% met an HbA1c target <8%. Later study years were associated with lower likelihood of meeting HbA1c targets compared with 2011. These results were similar to studies of nationally representative samples that showed no differences across racial or ethnic groups or geography. Medicaid coverage is an important factor in allowing low-income adults with diabetes to access health care and achieve optimal glycemic control. With more than 35% of Alabama Medicaid–eligible adults with diabetes not meeting the less stringent HbA1c target of <8%, more work is needed to translate scientific advances to improve the care and outcomes of low-income adults with type 2 diabetes.

Top

Acknowledgments

The project described was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R18DK109501, Cherrington). Support was also provided by UAB Diabetes Research Center (P30 DK079626, Cherrington).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institutes of Health, the Agency for Healthcare Research and Quality, or the Alabama Medicaid Agency. Representatives for the Alabama Medicaid Agency were provided an opportunity to review and provide comments on the manuscript prior to submission. This article was not prepared in collaboration with the Alabama Medicaid Agency and does not necessarily reflect the opinions or represent the official views of the Alabama Medicaid Agency. The authors do not have any potential conflicts of interest to disclose. No copyrighted tools or materials were used in this research or article.

Top

Author Information

Corresponding Author: Caroline A. Presley, MD, MPH, Division of Preventive Medicine, University of Alabama at Birmingham, 1717 11th Ave South, MT-616, Birmingham, AL 35205 (capresley@uabmc.edu).

Author Affiliations: 1Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham Heersink School of Medicine. 2Department of Epidemiology, School of Public Health, University of Alabama at Birmingham.

Top

References

  1. Centers for Disease Control and Prevention. National diabetes statistics report: estimates of diabetes and its burden in the United States, 2020. Accessed May 4, 2023. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
  2. Robert Wood Johnson Foundation. Diabetes rate by state. 2019. Accessed January 13, 2021. https://stateofchildhoodobesity.org/diabetes
  3. American Diabetes Association. Standards of medical care in diabetes, 2021. Diabetes Care 2021;44(Supplement 1):1–232.
  4. Kazemian P, Shebl FM, McCann N, Walensky RP, Wexler DJ. Evaluation of the cascade of diabetes care in the United States, 2005–2016. JAMA Intern Med 2019;179(10):1376–85. PubMed doi:10.1001/jamainternmed.2019.2396
  5. Fang M, Wang D, Coresh J, Selvin E. Trends in diabetes treatment and control in U.S. adults, 1999–2018. N Engl J Med 2021;384(23):2219–28. PubMed doi:10.1056/NEJMsa2032271
  6. American Diabetes Association. Economic costs of diabetes in the U.S. in 2017. Diabetes Care 2018;41(5):917–28. PubMed doi:10.2337/dci18-0007
  7. Dall TM, Yang W, Halder P, Franz J, Byrne E, Semilla AP, et al. Type 2 diabetes detection and management among insured adults. Popul Health Metr 2016;14(1):43. PubMed doi:10.1186/s12963-016-0110-4
  8. Tan E, Yang W, Pang B, Dai M, Loh FE, Hogan P. Geographic variation in antidiabetic agent adherence and glycemic control among patients with type 2 diabetes. J Manag Care Spec Pharm 2015;21(12):1195–202. PubMed doi:10.18553/jmcp.2015.21.12.1195
  9. Pandit AU, Bailey SC, Curtis LM, Seligman HK, Davis TC, Parker RM, et al. Disease-related distress, self-care and clinical outcomes among low-income patients with diabetes. J Epidemiol Community Health;68(6):557–64.
  10. Silverman J, Krieger J, Kiefer M, Hebert P, Robinson J, Nelson K. The relationship between food insecurity and depression, diabetes distress and medication adherence among low-income patients with poorly-controlled diabetes. J Gen Intern Med 2015;30(10):1476–80. PubMed doi:10.1007/s11606-015-3351-1
  11. Nelson LA, Ackerman MT, Greevy RA Jr, Wallston KA, Mayberry LS. Beyond race disparities: accounting for socioeconomic status in diabetes self-care. Am J Prev Med 2019;57(1):111–6. PubMed doi:10.1016/j.amepre.2019.02.013
  12. Osborn CY, de Groot M, Wagner JA. Racial and ethnic disparities in diabetes complications in the northeastern United States: the role of socioeconomic status. J Natl Med Assoc 2013;105(1):51–8. PubMed doi:10.1016/S0027-9684(15)30085-7
  13. Kaiser Family Foundation. Medicaid in Alabama. Published October 2018. Updated October 2022. Accessed November 26, 2018. http://files.kff.org/attachment/fact-sheet-medicaid-state-AL
  14. Centers for Medicare & Medicaid Services. Chronic Conditions Data Warehouse. Published 2021. Accessed July 6, 2021. https://www2.ccwdata.org/web/guest/home
  15. Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE. Independent effects of socioeconomic and psychological social determinants of health on self-care and outcomes in type 2 diabetes. Gen Hosp Psychiatry 2014;36(6):662–8. PubMed doi:10.1016/j.genhosppsych.2014.06.011
  16. Comellas M, Marrero Y, George F, Matthews L. Age and glycemic control among adults with type 2 diabetes in the United States: an assessment from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. Diabetes Metab Syndr 2019;13(6):3069–73. PubMed doi:10.1016/j.dsx.2019.11.004
  17. US Food and Drug Administration. National drug code directory. Published 2021. Accessed July 6, 2021. https://www.accessdata.fda.gov/scripts/cder/ndc/index.cfm
  18. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004;57(12):1288–94. PubMed doi:10.1016/j.jclinepi.2004.03.012
  19. Heidemann DL, Joseph NA, Kuchipudi A, Perkins DW, Drake S. Racial and economic disparities in diabetes in a large primary care patient population. Ethn Dis 2016;26(1):85–90. PubMed doi:10.18865/ed.26.1.85
  20. Galindo RJ, Beck RW, Scioscia MF, Umpierrez GE, Tuttle KR. Glycemic monitoring and management in advanced chronic kidney disease. Endocr Rev 2020;41(5):756–74. PubMed doi:10.1210/endrev/bnaa017
  21. Brown AF, Gregg EW, Stevens MR, Karter AJ, Weinberger M, Safford MM, et al. Race, ethnicity, socioeconomic position, and quality of care for adults with diabetes enrolled in managed care: the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 2005;28(12):2864–70. PubMed doi:10.2337/diacare.28.12.2864
  22. Peterson K, Anderson J, Boundy E, Ferguson L, McCleery E, Waldrip K. Mortality disparities in racial/ethnic minority groups in the Veterans Health Administration: an evidence review and map. Am J Public Health 2018;108(3):e1–11. PubMed doi:10.2105/AJPH.2017.304246
  23. Aggarwal R, Chiu N, Loccoh EC, Kazi DS, Yeh RW, Wadhera RK. Rural-urban disparities: diabetes, hypertension, heart disease, and stroke mortality among Black and White adults, 1999–2018. J Am Coll Cardiol 2021;77(11):1480–1. PubMed doi:10.1016/j.jacc.2021.01.032
  24. Haire-Joshu D, Hill-Briggs F. The next generation of diabetes translation: a path to health equity. Annu Rev Public Health 2019;40(1):391–410. PubMed doi:10.1146/annurev-publhealth-040218-044158
  25. Saydah S, Lochner K. Socioeconomic status and risk of diabetes-related mortality in the U.S. Public Health Rep 2010;125(3):377–88. PubMed doi:10.1177/003335491012500306
  26. Barnard LS, Wexler DJ, DeWalt D, Berkowitz SA. Material need support interventions for diabetes prevention and control: a systematic review. Curr Diab Rep 2015;15(2):574. PubMed doi:10.1007/s11892-014-0574-1
  27. Qaseem A, Wilt TJ, Kansagara D, Horwitch C, Barry MJ, Forciea MA; Clinical Guidelines Committee of the American College of Physicians. Hemoglobin A1c targets for glycemic control with pharmacologic therapy for nonpregnant adults with type 2 diabetes mellitus: a guidance statement update from the American College of Physicians. Ann Intern Med 2018;168(8):569–76. PubMed doi:10.7326/M17-0939
  28. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. ; American Diabetes Association (ADA); European Association for the Study of Diabetes (EASD). Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2012;35(6):1364–79. PubMed doi:10.2337/dc12-0413
  29. Gregg EW, Hora I, Benoit SR. Resurgence in diabetes-related complications. JAMA 2019;321(19):1867–8. PubMed doi:10.1001/jama.2019.3471
  30. Laiteerapong N, Cooper JM, Skandari MR, Clarke PM, Winn AN, Naylor RN, et al. Individualized glycemic control for U.S. adults with type 2 diabetes: a cost-effectiveness analysis. Ann Intern Med 2018;168(3):170–8. PubMed doi:10.7326/M17-0537
  31. Garfield R, Orgera K, Damico A. The coverage gap: uninsured poor adults in states that do not expand Medicaid. Kaiser Family Foundation. January 2020. Accessed July 6, 2021. https://files.kff.org/attachment/Issue-Brief-The-Coverage-Gap-Uninsured-Poor-Adults-in-States-that-Do-Not-Expand-Medicaid
  32. Sommers BD, Blendon RJ, Orav EJ, Epstein AM. Changes in utilization and health among low-income adults after Medicaid expansion or expanded private insurance. JAMA Intern Med 2016;176(10):1501–9. PubMed doi:10.1001/jamainternmed.2016.4419
  33. Yan LD, Ali MK, Strombotne KL. Impact of expanded Medicaid eligibility on the diabetes continuum of care among low-income adults: a difference-in-differences analysis. Am J Prev Med 2021;60(2):189–97. PubMed doi:10.1016/j.amepre.2020.08.013
  34. Luo H, Chen ZA, Xu L, Bell RA. Health care access and receipt of clinical diabetes preventive care for working-age adults with diabetes in states with and without Medicaid expansion: results from the 2013 and 2015 BRFSS. J Public Health Manag Pract 2019;25(4):E34–43. PubMed doi:10.1097/PHH.0000000000000832
  35. Weinstock RS, Braffett BH, Songer TJ, Herman WH, Kuo S, Gubitosi-Klug RA, et al. ; TODAY Study Group. Health care coverage and glycemic control in young adults with youth-onset type 2 diabetes: results from the TODAY2 study. Diabetes Care 2020;43(10):2469–77. PubMed doi:10.2337/dc20-0760
  36. Sumarsono A, Buckley LF, Machado SR, Wadhera RK, Warraich HJ, Desai RJ, et al. Medicaid expansion and utilization of antihyperglycemic therapies. Diabetes Care 2020;43(11):2684–90. PubMed doi:10.2337/dc20-0735
  37. Kim NH, Look KA. The effect of reduced drug copayments on adherence to oral diabetes medications among childless adults in Wisconsin Medicaid. J Manag Care Spec Pharm 2019;25(12):1432–41. PubMed doi:10.18553/jmcp.2019.25.12.1432

Top

Tables

Return to your place in the text

Table 1. Characteristics of Medicaid Beneficiaries With Diabetes in Alabama, by Study Year, 2011–2019a
Characteristics Overall 2011 2012 2013 2014 2015 2016 2017 2018 2019
No. 43,997 4,022 4,764 5,252 5,031 4,725 5,014 5,294 5,471 4,424
HbA1c, mean (SD), % 7.72 (2.24) 7.63 (2.18) 7.72 (2.26) 7.75 (2.23) 7.75 (2.24) 7.79 (2.23) 7.83 (2.22) 7.85 (2.27) 7.48 (2.37) 7.64 (2.09)
Age, mean (SD), y 51.0 (9.9) 50.9 (9.9) 50.6 (10.0) 51.0 (9.9) 51.0 (9.8) 51.1 (9.8) 51.2 (9.8) 51.4 (9.8) 51.1 (10.1) 51.0 (10.0)
Sex, no. (%)
Male 13,485 (30.6) 1,097 (27.3) 1,431 (30.0) 1,582 (30.1) 1,565 (31.1) 1,471 (31.1) 1,586 (31.6) 1,646 (31.1) 1,735 (31.7) 1,372 (31.0)
Female 30,512 (69.4) 2,925 (72.7) 3,333 (70.0) 3,670 (69.9) 3,466 (68.9) 3,254 (68.9) 3,428 (68.4) 3,648 (68.9) 3,736 (68.3) 3,052 (69.0)
Race and ethnicity, no. (%)
Black 21,141 (48.1) 2,049 (50.9) 2,418 (50.8) 2,617 (49.8) 2,460 (48.9) 2,267 (48.0) 2,398 (47.8) 2,414 (45.6) 2,481 (45.3) 2,037 (46.0)
Hispanic 184 (0.4) 15 (0.4) 21 (0.4) 23 (0.4) 11 (0.2) 15 (0.3) 17 (0.3) 23 (0.4) 35 (0.6) 24 (0.5)
White 18,892 (42.9) 1,693 (42.1) 1,999 (42.0) 2,225 (42.4) 2,170 (43.1) 2,044 (43.3) 2,188 (43.6) 2,341 (44.2) 2,371 (43.3) 1,861 (42.1)
Otherb 3,780 (8.6) 265 (6.6) 326 (6.8) 387 (7.4) 390 (7.8) 399 (8.4) 411 (8.2) 516 (9.7) 584 (10.7) 502 (11.3)
Geography, no. (%)
Urban 29,816 (67.8) 2,702 (67.2) 3,165 (66.4) 3,537 (67.3) 3,412 (67.8) 3,249 (68.8) 3,472 (69.2) 3,629 (68.5) 3,719 (68.0) 2,931 (66.3)
Small town 6,318 (14.4) 647 (16.1) 775 (16.3) 801 (15.3) 782 (15.5) 711 (15.0) 677 (13.5) 695 (13.1) 676 (12.4) 554 (12.5)
Rural 7,863 (17.9) 673 (16.7) 824 (17.3) 914 (17.4) 837 (16.6) 765 (16.2) 865 (17.3) 970 (18.3) 1,076 (19.7) 939 (21.2)
Eligibility reason, no. (%)
Disability 34,197 (77.7) 3,511 (87.3) 3,986 (83.7) 4,324 (82.3) 4,038 (80.3) 3,750 (79.4) 3,805 (75.9) 3,858 (72.9) 3,888 (71.1) 3,037 (68.6)
Poverty 9,800 (22.3) 511 (12.7) 778 (16.3) 928 (17.7) 993 (19.7) 975 (20.6) 1,209 (24.1) 1,436 (27.1) 1,583 (28.9) 1,387 (31.4)
Insulin use in prior year, no. (%) 14,654 (33.3) 1,403 (34.9) 1,649 (34.6) 1,860 (35.4) 1,722 (34.2) 1,569 (33.2) 1,597 (31.9) 1,672 (31.6) 1,753 (32.0) 1,429 (32.3)
Ambulatory care use
No. of primary care visits, mean (SD) 4.96 (2.95) 5.14 (2.87) 4.81 (2.88) 4.85 (2.96) 4.91 (2.85) 4.88 (2.97) 5.01 (3.06) 5.11 (3.00) 4.99 (3.01) 4.94 (2.94)
≥1 Primary care visit, no. (%) of beneficiaries 41,363 (94.0) 3,786 (94.1) 4,457 (93.6) 4,909 (93.5) 4,771 (94.8) 4,432 (93.8) 4,728 (94.3) 4,988 (94.2) 5,133 (93.8) 4,159 (94.0)
No. of endocrinology visits, mean (SD) 0.10 (0.56) 0.07 (0.41) 0.08 (0.48) 0.10 (0.56) 0.10 (0.55) 0.10 (0.55) 0.12 (0.62) 0.11 (0.61) 0.11 (0.58) 0.11 (0.58)
≥1 Endocrinology visit, no. (%) of beneficiaries 2,025 (4.6) 142 (3.5) 185 (3.9) 238 (4.5) 226 (4.5) 217 (4.6) 257 (5.1) 250 (4.7) 289 (5.3) 221 (5.0)
Acute care use, no. (%)
≥1 Emergency department visit 25,991 (59.1) 2,305 (57.3) 2,719 (57.1) 3,014 (57.4) 2,960 (58.8) 2,800 (59.3) 2,969 (59.2) 3,223 (60.9) 3,323 (60.7) 2,678 (60.5)
≥1 Hospitalization 13,141 (29.9) 1,297 (32.2) 1,387 (29.1) 1,584 (30.2) 1,490 (29.6) 1,433 (30.3) 1,465 (29.2) 1,571 (29.7) 1,611 (29.4) 1,303 (29.5)
Medical comorbidities, no. (%)
Myocardial infarction 1,158 (2.6) 89 (2.2) 110 (2.3) 150 (2.9) 121 (2.4) 123 (2.6) 147 (2.9) 158 (3.0) 159 (2.9) 101 (2.3)
Congestive heart failure 5,929 (13.5) 562 (14.0) 605 (12.7) 703 (13.4) 657 (13.1) 658 (13.9) 696 (13.9) 695 (13.1) 747 (13.7) 606 (13.7)
Peripheral vascular disease 4,022 (9.1) 417 (10.4) 443 (9.3) 484 (9.2) 448 (8.9) 445 (9.4) 453 (9.0) 434 (8.2) 490 (9.0) 408 (9.2)
Cerebrovascular disease 4,467 (10.2) 412 (10.2) 449 (9.4) 500 (9.5) 535 (10.6) 504 (10.7) 539 (10.7) 526 (9.9) 566 (10.3) 436 (9.9)
Dementia 449 (1.0) 36 (0.9) 36 (0.8) 41 (0.8) 40 (0.8) 50 (1.1) 53 (1.1) 67 (1.3) 68 (1.2) 58 (1.3)
Chronic pulmonary disease 14,343 (32.6) 1,344 (33.4) 1,431 (30.0) 1,744 (33.2) 1,648 (32.8) 1,585 (33.5) 1,730 (34.5) 1,723 (32.5) 1,772 (32.4) 1,366 (30.9)
Rheumatic disease 1,261 (2.9) 116 (2.9) 120 (2.5) 141 (2.7) 147 (2.9) 144 (3.0) 142 (2.8) 159 (3.0) 173 (3.2) 119 (2.7)
Peptic ulcers 781 (1.8) 95 (2.4) 77 (1.6) 85 (1.6) 91 (1.8) 99 (2.1) 87 (1.7) 89 (1.7) 78 (1.4) 80 (1.8)
Diabetes with complication 12,316 (28.0) 922 (22.9) 1,088 (22.8) 1,145 (21.8) 1,184 (23.5) 1,190 (25.2) 1,486 (29.6) 1,782 (33.7) 1,894 (34.6) 1,625 (36.7)
Paraplegia and hemiplegia 788 (1.8) 64 (1.6) 80 (1.7) 91 (1.7) 90 (1.8) 90 (1.9) 99 (2.0) 97 (1.8) 93 (1.7) 84 (1.9)
Chronic renal failure 4,563 (10.4) 358 (8.9) 424 (8.9) 475 (9.0) 466 (9.3) 465 (9.8) 570 (11.4) 610 (11.5) 662 (12.1) 533 (12.0)
Mild liver disease 2,454 (5.6) 123 (3.1) 142 (3.0) 169 (3.2) 176 (3.5) 169 (3.6) 264 (5.3) 472 (8.9) 523 (9.6) 416 (9.4)
Moderate-severe liver disease 299 (0.7) 34 (0.8) 32 (0.7) 38 (0.7) 39 (0.8) 26 (0.6) 24 (0.5) 46 (0.9) 38 (0.7) 22 (0.5)
Any malignancy 1,892 (4.3) 180 (4.5) 223 (4.7) 237 (4.5) 202 (4.0) 217 (4.6) 219 (4.4) 205 (3.9) 227 (4.1) 182 (4.1)
Metastatic solid tumor 203 (0.5) 19 (0.5) 22 (0.5) 22 (0.4) 21 (0.4) 25 (0.5) 21 (0.4) 22 (0.4) 27 (0.5) 24 (0.5)
AIDS 420 (1.0) 34 (0.8) 32 (0.7) 45 (0.9) 48 (1.0) 49 (1.0) 38 (0.8) 62 (1.2) 62 (1.1) 50 (1.1)
HbA1c target, %
<7% 21,581 (49.1) 2,073 (51.5) 2,400 (50.4) 2,558 (48.7) 2,478 (49.3) 2,239 (47.4) 2,360 (47.1) 2,484 (46.9) 2,816 (51.5) 2,173 (49.1)
<8% 28,386 (64.6) 2,715 (67.5) 3,130 (65.7) 3,382 (64.4) 3,213 (63.9) 2,980 (63.1) 3,172 (63.3) 3,274 (61.8) 3,652 (66.8) 2,868 (64.8)

Abbreviation: HbA1c, glycated hemoglobin A1c.
a Data source: Enrollment and claims data for people with diabetes were provided for research purposes by the Alabama Medicaid Agency. HbA1c values for Alabama Medicaid beneficiaries were provided for research purposes by LabCorp and Quest Diagnostics. Unit of analysis was person-years.
b Includes Asian or Pacific Islander, American Indian or Alaskan Native, Other Race or Ethnicity, or Unknown/Not Provided.

Return to your place in the text

Table 2. Characteristics Associated With Having an HbA1c <7% Among Medicaid Beneficiaries With Diabetes in Alabama, 2011–2019a
Characteristic Unadjusted OR (95% CI) OR (95% CI), adjusted for demographic factorsb OR (95% CI), adjusted for demographic and clinical factorsc
Year
2011 1 [Reference] 1 [Reference] 1 [Reference]
2012 0.98 (0.96–1.01) 0.98 (0.96–1.01) 0.99 (0.97–1.02)
2013 0.93 (0.90–0.96) 0.93 (0.90–0.96) 0.95 (0.92–0.98)
2014 0.94 (0.91–0.97) 0.94 (0.91–0.96) 0.96 (0.93–0.99)
2015 0.90 (0.87–0.93) 0.89 (0.87–0.92) 0.91 (0.89–0.95)
2016 0.88 (0.85–0.91) 0.88 (0.85–0.90) 0.90 (0.87–0.93)
2017 0.87 (0.84–0.90) 0.86 (0.84–0.89) 0.90 (0.87–0.93)
2018 0.94 (0.91–0.97) 0.93 (0.90–0.96) 0.99 (0.95–1.02)
2019 0.90 (0.86–0.93) 0.89 (0.86–0.92) 0.95 (0.92–0.99)
Age (per 10 year) 1.02 (1.00–1.03) 1.03 (1.01–1.04) 1.00 (0.99–1.02)
Sex
Male 1 [Reference] 1 [Reference] 1 [Reference]
Female 0.97 (0.95–1.00) 0.98 (0.95–1.01) 0.99 (0.97–1.02)
Race and ethnicity
Black 0.95 (0.93–0.98) 0.95 (0.93–0.98) 0.99 (0.96–1.01)
Hispanic 0.77 (0.61–0.96) 0.77 (0.62–0.97) 0.85 (0.68–1.06)
White 1 [Reference] 1 [Reference] 1 [Reference]
Otherd 0.96 (0.92–1.01) 0.96 (0.91–1.00) 0.97 (0.93–1.02)
Geography
Urban 1 [Reference] 1 [Reference] 1 [Reference]
Small town 0.98 (0.95–1.01) 0.98 (0.95–1.01) 0.97 (0.94–1.00)
Rural 0.99 (0.96–1.03) 0.99 (0.95–1.02) 0.98 (0.95–1.01)
Reason for eligibility
Disability 1 [Reference] 1 [Reference] 1 [Reference]
Poverty 0.95 (0.93–0.98) 0.97 (0.95–1.00) 0.87 (0.84–0.89)
Insulin use 0.44 (0.43–0.46) 0.44 (0.42–0.45)
Ambulatory care use in prior year
Internist visits (per visit) 1.01 (1.00–1.01) 1.01 (1.01–1.01)
Endocrinology visits (yes/no) 0.84 (0.80–0.88) 0.92 (0.87–0.97)
Acute care use in prior year
Any ED visit (yes/no) 0.99 (0.97–1.00) 0.99 (0.98–1.01)
Any hospitalization (yes/no) 1.00 (0.98–1.01) 1.02 (1.01–1.04)
Medical comorbidities in prior year
Myocardial infarction 0.98 (0.93–1.03) 0.99 (0.94–1.05)
Congestive heart failure 0.99 (0.96–1.02) 1.00 (0.97–1.03)
Peripheral vascular disease 1.01 (0.98–1.04) 1.02 (0.99–1.05)
Cerebrovascular disease 1.03 (1.01–1.06) 1.04 (1.01–1.07)
Dementia 1.11 (1.02–1.21) 1.16 (1.06–1.27)
Chronic pulmonary disease 1.07 (1.05–1.09) 1.06 (1.04–1.08)
Rheumatic disease 1.02 (0.97–1.08) 1.03 (0.98–1.08)
Peptic ulcers 1.09 (1.03–1.15) 1.09 (1.04–1.15)
Diabetes with complication 0.87 (0.85–0.89) 0.90 (0.88–0.92)
Paraplegia and hemiplegia 1.03 (0.97–1.09) 1.01 (0.95–1.08)
Chronic renal failure 0.99 (0.97–1.03) 1.07 (1.03–1.10)
Mild liver disease 1.01 (0.97–1.04) 1.03 (0.99–1.07)
Moderate-severe liver disease 1.03 (0.94–1.14) 1.06 (0.95–1.17)
Any malignancy 1.09 (0.97–1.22) 1.06 (0.94–1.19)
Metastatic solid tumor 1.01 (0.97–1.06) 0.99 (0.95–1.04)
AIDS 1.15 (1.04–1.28) 1.12 (1.02–1.23)

Abbreviations: — , does not apply; HbA1c, glycated hemoglobin A1c.
a Data source: Enrollment and claims data for people with diabetes were provided for research purposes by the Alabama Medicaid Agency. HbA1c values for Alabama Medicaid beneficiaries were provided for research purposes by LabCorp and Quest Diagnostics.
b Adjusted for demographic factors (age, sex, race and ethnicity, reason for eligibility, geography).
c Adjusted for demographic and clinical factors (insulin use, ambulatory care use, acute care utilization, and medical comorbidities). Models used generalized estimating equations to account for people contributing multiple calendar year observations.
d Includes Asian or Pacific Islander, American Indian or Alaskan Native, other race or ethnicity, or unknown/not provided.

Return to your place in the text

Table 3. Characteristics Associated With Having an HbA1c <8% Among Medicaid Beneficiaries With Diabetes in Alabama, 2011–2019a
Characteristic Unadjusted OR (95% CI) OR (95% CI), adjusted for demographic factorsb OR (95% CI), adjusted for demographic and clinical factorsc
Year
2011 1 [Reference] 1 [Reference] 1 [Reference]
2012 0.97 (0.95–0.99) 0.97 (0.95–0.99) 0.98 (0.96–1.00)
2013 0.95 (0.92–0.97) 0.94 (0.92–0.96) 0.96 (0.94–0.98)
2014 0.94 (0.92–0.96) 0.93 (0.91–0.95) 0.95 (0.93–0.97)
2015 0.92 (0.90–0.95) 0.91 (0.89–0.94) 0.93 (0.91–0.96)
2016 0.92 (0.89–0.94) 0.90 (0.88–0.93) 0.93 (0.90–0.95)
2017 0.89 (0.87–0.91) 0.88 (0.85–0.90) 0.90 (0.88–0.93)
2018 0.95 (0.93–0.97) 0.93 (0.91–0.96) 0.97 (0.95–1.00)
2019 0.92 (0.90–0.95) 0.91 (0.88–0.93) 0.95 (0.93–0.98)
Age (per 10 year) 1.05 (1.04–1.06) 1.05 (1.04–1.06) 1.03 (1.02–1.04)
Sex
Male 1 [Reference] 1 [Reference] 1 [Reference]
Female 0.98 (0.96–1.00) 0.98 (0.96–1.00) 0.99 (0.97–1.01)
Race and ethnicity
Black 0.97 (0.95–0.99) 0.97 (0.95–0.99) 1.00 (0.98–1.02)
Hispanic 0.89 (0.76–1.04) 0.90 (0.77–1.04) 0.96 (0.84–1.11)
White 1 [Reference] 1 [Reference] 1 [Reference]
Otherd 0.98 (0.95–1.01) 0.97 (0.94–1.01) 0.99 (0.96–1.02)
Geography
Urban 1 [Reference] 1 [Reference] 1 [Reference]
Small town 1.00 (0.97–1.02) 0.99 (0.97–1.02) 0.98 (0.96–1.01)
Rural 1.00 (0.98–1.03) 0.99 (0.97–1.02) 0.99 (0.97–1.01)
Reason for eligibility
Disability 1 [Reference] 1 [Reference] 1 [Reference]
Poverty 0.98 (0.96–1.00) 1.00 (0.98–1.02) 0.91 (0.89–0.93)
Insulin use 0.56 (0.55–0.57) 0.55 (0.54–0.57)
Ambulatory care use in prior year
Internist visits (per visit) 1.01 (1.01–1.01) 1.01 (1.01–1.01)
Endocrinology visits (yes/no) 0.86 (0.83–0.90) 0.94 (0.90–0.98)
Acute care use in prior year
Any emergency department visit (yes/no) 0.98 (0.97–0.99) 0.99 (0.98–1.00)
Any hospitalization (yes/no) 0.99 (0.97–1.00) 1.01 (0.99–1.02)
Medical comorbidities in prior year
Myocardial infarction 0.99 (0.95–1.03) 1.00 (0.96–1.04)
Congestive heart failure 1.00 (0.98–1.03) 1.01 (0.99–1.03)
Peripheral vascular disease 1.02 (1.00–1.04) 1.02 (0.99–1.04)
Cerebrovascular disease 1.02 (1.00–1.04) 1.02 (1.00–1.04)
Dementia 1.10 (1.04–1.17) 1.13 (1.06–1.20)
Chronic pulmonary disease 1.05 (1.04–1.07) 1.04 (1.02–1.05)
Rheumatic disease 1.01 (0.97–1.05) 1.00 (0.96–1.04)
Peptic ulcers 1.02 (0.98–1.06) 1.01 (0.97–1.05)
Diabetes with complication 0.91 (0.90–0.93) 0.94 (0.92–0.95)
Paraplegia and hemiplegia 1.05 (1.00–1.10) 1.05 (1.00–1.10)
Chronic renal failure 1.02 (0.99–1.04) 1.07 (1.04–1.09)
Mild liver disease 1.02 (0.99–1.05) 1.04 (1.01–1.07)
Moderate-severe liver disease 1.07 (0.99–1.14) 1.07 (0.99–1.14)
Any malignancy 1.05 (1.02–1.09) 1.03 (0.99–1.06)
Metastatic solid tumor 1.08 (1.00–1.17) 1.03 (0.95–1.12)
AIDS 1.10 (1.02–1.19) 1.07 (1.00–1.14)

Abbreviations: — , does not apply; HbA1c, glycated hemoglobin A1c.
a Data source: Enrollment and claims data for people with diabetes were provided for research purposes by the Alabama Medicaid Agency. HbA1c values for Alabama Medicaid beneficiaries were provided for research purposes by LabCorp and Quest Diagnostics.
b Adjusted for demographic factors (age, sex, race/ethnicity, reason for eligibility, geography).
c Adjusted for demographic and clinical factors (insulin use, ambulatory care use, acute care utilization, and medical comorbidities). Models used generalized estimating equations to account for people contributing multiple calendar year observations.
d Includes Asian or Pacific Islander, American Indian or Alaskan Native, other race or ethnicity, or unknown/not provided.

Top

Return to your place in the text

Appendix. Table. Characteristics of Medicaid Beneficiaries With Diabetes in Alabama Overall, With ≥1 HbA1c Claim, and With ≥1 HbA1c Value, 2011–2019a

Table. Characteristics of Medicaid Beneficiaries With Diabetes in Alabama Overall, With ≥1 HbA1c Claim, and With ≥1 HbA1c Value, 2011–2019a
Characteristic Overall (N = 262,310) ≥1 HbA1c claim (n = 149,647) ≥1 HbA1c value (n = 43,997)
Age, mean (SD), y 50.5 (10.3) 50.5 (10.3) 51.0 (9.9)
Sex, no. (%)
Male 79,976 (30.5) 43,944 (29.4) 13,485 (30.6)
Female 182,334 (69.5) 105,703 (70.6) 30,512 (69.4)
Race and ethnicity, no. (%)
Black 130,031 (49.6) 73,031 (48.8) 21,141 (48.1)
Hispanic 1,151 (0.4) 707 (0.5) 184 (0.4)
White 113,162 (43.1) 64,173 (42.9) 18,892 (42.9)
Otherb 17,966 (6.8) 11,736 (7.8) 3,780 (8.6)
Geography, no. (%)
Urban 166,832 (63.6) 94,855 (63.4) 29,816 (67.8)
Small town 39,090 (14.9) 23,265 (15.5) 6,318 (14.4)
Rural 56,388 (21.5) 31,527 (21.1) 7,863 (17.9)
Reason for eligibility, no. (%)
Disability 133,398 (50.9) 100,728 (67.3) 34,197 (77.7)
Poverty 128,912 (49.1) 48,919 (32.7) 9,800 (22.3)
Insulin use in prior year, no. (%) 61,708 (23.5) 48,035 (32.1) 14,654 (33.3)
Ambulatory care use
No. of primary care visits, mean (SD) 4.47 (3.17) 4.77 (3.04) 4.96 (2.95)
≥1 Primary care visit, no. (%) 232,234 (88.5) 137,649 (92.0) 41,363 (94.0)
No. of endocrinology visits, mean (SD) 0.11 (0.56) 0.14 (0.64) 0.10 (0.56)
>1 Endocrinology visit, no. (%) 13,056 (5.0) 9,301 (6.2) 2,025 (4.6)
Acute care use, no. (%)
≥1 Emergency department visit 152,819 (58.3) 89,833 (60.0) 25,991 (59.1)
≥1 Hospitalization 84,559 (32.2) 48,145 (32.2) 13,141 (29.9)
Medical comorbidities, no. (%)
Myocardial infarction 7,823 (3.0) 4,504 (3.0) 1,158 (2.6)
Congestive heart failure 36,749 (14.0) 21,480 (14.4) 5,929 (13.5)
Peripheral vascular disease 23,953 (9.1) 13,377 (8.9) 4,022 (9.1)
Cerebrovascular disease 29,609 (11.3) 16,591 (11.1) 4,467 (10.2)
Dementia 4,616 (1.8) 2,069 (1.4) 449 (1.0)
Chronic pulmonary disease 84,459 (32.2) 48,480 (32.4) 14,343 (32.6)
Rheumatic disease 8,822 (3.4) 4,858 (3.2) 1,261 (2.9)
Peptic ulcers 4,858 (1.9) 2,771 (1.9) 781 (1.8)
Diabetes with complication 71,154 (27.1) 44,162 (29.5) 12,316 (28.0)
Paraplegia and hemiplegia 6,822 (2.6) 3,247 (2.2) 788 (1.8)
Chronic renal failure 29,352 (11.2) 17,164 (11.5) 4,563 (10.4)
Mild liver disease 13,107 (5.0) 8,216 (5.5) 2,454 (5.6)
Moderate-severe liver disease 1,738 (0.7) 1,049 (0.7) 299 (0.7)
Any malignancy 11,589 (4.4) 6,590 (4.4) 1,892 (4.3)
Metastatic solid tumor 1,557 (0.6) 785 (0.5) 203 (0.5)
AIDS 2,789 (1.1) 1,561 (1.0) 420 (1.0)

Abbreviation: HbA1c, glycated hemoglobin A1c.
a Data source: Enrollment and claims data for people with diabetes were provided for research purposes by the Alabama Medicaid Agency. HbA1c values for Alabama Medicaid beneficiaries were provided for research purposes by LabCorp and Quest Diagnostics.
b Includes Asian or Pacific Islander, American Indian or Alaskan Native, other race or ethnicity, or unknown/not provided.

Top

Error processing SSI file

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.