Abstract
Purpose:
The purpose of the study is to determine the rate of Diabetes Self-Management Education and Support (DSMES) utilization among Medicare fee-for-service (FFS) and Medicare Advantage (MA) populations with type 2 diabetes in Arkansas.
Methods:
The Arkansas All-Payer Claims Database was used to identify Medicare FFS and MA beneficiaries diagnosed with type 2 diabetes from 2015 to 2018. Claims from 2013 to 2020 were analyzed to determine newly diagnosed individuals from 2015 to 2018. The criteria included one outpatient diabetes claim in the index year and at least one inpatient or outpatient claim in the two years following the initial claim. A total of 15,648 Medicare FFS individuals and 7,520 MA individuals with newly diagnosed type 2 diabetes were identified. The use of DSMES one year following the diagnosis dates for both Medicare FFS and MA populations was assessed. Descriptive statistics and multiple logistic regression analyses were conducted to understand the factors associated with DSMES utilization.
Results:
DSMES utilization consistently remained lower within the MA population compared to Medicare FFS (2.3% vs. 4.9%, p<0.001). The adjusted analysis indicated that factors such as older age, living in a rural area, belonging to a racial group other than White, and MA enrollment were associated with a lower likelihood of receiving DSMES.
Conclusions:
DSMES utilization in Arkansas, where the prevalence of diabetes is higher than the national average, is notably low. There is a need for coordinated efforts at various levels to enhance access to DSMES.
Keywords: diabetes education, Medicare, administrative claims data
Type 2 diabetes is a complex disease and requires a significant amount of self-management to navigate the day-to-day challenges and avoid glycemic excursions.1 Diabetes Self-Management Education and Support (DSMES) provides clinical, educational, psychological, and behavioral care needed for individuals to manage their diabetes effectively and confidently, ultimately leading to enhanced health outcomes.2,3 The current evidence suggests that DSMES reduces all-cause mortality risk 4 and significantly decreases the A1C levels in adults with type 2 diabetes.5 Despite the proven benefits of DSMES, the utilization of DSMES is low.6
The highest incidence of both known and unknown diabetes cases occurs in individuals over the age of 65.7 However, research reveals that only 5% of Medicare participants receive DSMES during the first year after diagnosis.8 Older adults with type 2 diabetes are at higher risk of cardiovascular complications and mortality.9 Despite the increased risk of severe complications, there is a discernible gap in disease awareness and management among older adults, underscoring a critical need for enhanced DSMES provision.7
Insurance structure could play a crucial role in determining the accessibility of diabetes treatments including DSMES access and how often these are offered and utilized. This study aims to examine how DSMES services are utilized by both Medicare fee-for-service (FFS) and Medicare Advantage (MA) enrollees with type 2 diabetes in Arkansas. This is important for several reasons. First, the age-adjusted percentage of diagnosed diabetes among adults in Arkansas was 29% higher than the national age-adjusted percentage in 2021.10 Second, MA is quickly expanding but it is not clear whether MA is improving the utilization of DSMES. MA and FFS differ in several aspects. MA plans utilize incentive-based structures to maintain care while limiting excessive healthcare utilization.11 Additionally, MA plans have a distinctive feature of capping the out-of-pocket spendings for beneficiaries, a provision not present in Medicare FFS.12 Evidence from prior studies indicates that MA is associated with more preventive care visits,13,14 a reduction in healthcare spendings14 and lower healthcare utilization.15-18 Research has also shown that diabetes care, including A1C testing, retinal exam and cholesterol testing is consistently higher among enrollees of MA health maintenance organizations than among those with traditional Medicare.19 Additionally, other studies found that relative resource use for individuals with diabetes is lower in MA, while the quality of care is higher.18,20 Building on the existing literature, this research aims to expand the body of knowledge by comparing the use of DSMES between MA and FFS beneficiaries.
METHODS
Research Design
This study is a retrospective claims-based analysis using data from the Arkansas All-Payer Claims Database (APCD) to identify Medicare-enrolled beneficiaries in Arkansas with type 2 diabetes and DSMES claims. The Arkansas APCD is a large administrative database that includes medical, pharmacy, and dental claims, as well as enrollment and provider files, covering approximately 4 million privately insured individuals, 2 million Medicaid enrollees for the years 2013-2023, and medical and pharmacy claims for approximately 965,000 Medicare beneficiaries for the years 2013 through 2021.21
Study Population
All individuals 65 years and above who were newly diagnosed with type 2 diabetes between 2015 to 2018 were identified. To determine newly diagnosed individuals in each index year from 2015 to 2018, claims for up to 2 years before and 2 years after each index year were obtained. The analysis was limited to individuals continuously enrolled in their insurance plan for 5 years (Figure 1). Data were limited to individuals meeting the criteria based on the Center for Disease Control and Prevention (CDC) report.22 The sample included individuals who had at least 1 outpatient diabetes claim within the index year and at least 1 inpatient or outpatient claim within the 2 years following the initial claim.22 A case was identified as newly diagnosed if there was a 2-year gap without any diabetes-related diagnosis codes at the beginning of the 5-year window.22 Diagnosis codes (International Classification of Diseases, Ninth Revision [ICD-9] 250.x or ICD-10 codes E11–E11.9) were used to restrict claims to individuals with type 2 diabetes during the study period.
Figure 1. Study Timeline.
DSMES: Diabetes Self-Management Education and Support
Each index year encompassed Medicare FFS and MA beneficiaries who met the CDC criteria for a new case of type 2 diabetes for the first time. Furthermore, the samples of new type 2 diabetes cases in each index year were mutually exclusive. Figure 1 shows the study period and the cohort identification period. Enrollees were required to be continuously enrolled in their insurance plan for a given year. However, switching between Medicare FFS and MA was allowed during the study period, although this was rare (<5%). For instance, an enrollee could have been enrolled in Medicare FFS in 2016 but in MA in years 2015 and 2017. The index date of type 2 diabetes diagnosis was used to categorize the enrollee in either Medicare or MA group. For example, if the beneficiary was first diagnosed in 2015 while enrolled in MA, then the beneficiary was included under newly diagnosed type 2 diabetes MA beneficiaries in 2015.
Between January 1, 2013, and December 31, 2020, a total of 747,117 individuals were enrolled in Medicare in Arkansas. Out of this population, 306,414 individuals maintained continuous enrollment for a span of five years, being covered by Medicare FFS during the index year and by Medicare or MA for the remaining four years. Excluding this cohort to those who were 68 years old in the index year and not older than 99 resulted in a sample size of 288,008 individuals. This sample was further refined to include only those enrollees who had at least one claim related to type 2 diabetes between 2015 and 2018, restricting the sample to 93,254 enrollees. In the final analysis, 15,648 enrollees who met the CDC criteria for a new type 2 diabetes diagnosis were selected.22
A total of 274,119 individuals enrolled in MA, with 106,442 of these individuals having MA during the index year and being covered by either Medicare or MA in the remining 4 years were identified. This cohort was subsequently narrowed down to 46,571 enrollees who had at least one diabetes diagnosis. Within this group, there were 7,520 MA enrollees who fulfilled the CDC criteria for a new type 2 diabetes diagnosis and were aged between 68 and 99 during the index year.
DSMES Utilization
The American Diabetes Association 2024 Standards of Care for Diabetes recommend providers assess a patient’s need for DSMES at the time of diagnosis, during annual condition assessment, at the onset of complicating factors, and during the transition of care.6 Although DSMES is available to all people with diabetes and across all stages of diabetes, this study explores the utilization rates of DSMES only among individuals newly diagnosed with type 2 diabetes. The rationale is that those with pre-existing diagnosis of type 2 diabetes may have utilized DSMES outside the study period.
The DSMES services can be identified with Healthcare Common Procedure Coding System (HCPCS) codes G0108 and G0109 (available on the Revenue Center files for institutional claims and Carrier file for non-institutional claims for Medicare FFS beneficiaries and medical claims file for MA beneficiaries). Medicare Part B covers 10 hours of initial education for beneficiaries with a diagnosis of diabetes; education can be completed in any combination of half-hour increments.23 Follow-up training is covered by Medicare Part B if completed in the calendar year following the initial training.23 Initial and follow-up DSMES training was not differentiated in this study. The HCPCS codes G0108 and G0109 were looked up one year after the diagnosis date. This approach aligns with the timeline used in a previous study to identify DSMES services among the Medicare FFS population.8 The initial training for DSMES has a 12-month period following the initial date (10 hours), hence 12 months after the diagnosis of type 2 diabetes is appropriate to look for DSMES claims.8 To enhance the accuracy of measuring DSMES utilization, a sensitivity analysis is performed extending the time period to 2 years after the diagnosis of type 2 diabetes. Each individual was counted only once for DSMES utilization, regardless of the number of DSMES claims they had. This means if an individual had multiple DSMES claims, they were still recorded as a single instance in the DSMES count.
Key Predictor Variables
A variety of factors that are known to influence health care utilization were chosen to investigate their association with the use of DSMES. These factors include age in the index year, gender, race/ethnicity (non-Hispanic White, non-Hispanic Black, Other), rural-urban residence, and whether the individual has Medicare FFS or MA insurance. The Master Beneficiary File was utilized to gather these details. The race/ethnicity variable in this file is based on data collected from the Social Security Administration and encompasses categories including White, Black, Other, Asian, Hispanic, North American Native, and Unknown. However, due to the small number of enrollees utilizing DSMES program from the Other, Asian, Hispanic, North American Native, and Unknown categories, these were combined for the purposes of the study. Rural-urban residence status is defined through linkage of zip codes to Rural-Urban Commuting Area Codes (RUCA), with codes 4-9 representing rural areas.24 This study was determined as non-human subjects research by the first author’s institution.
Statistical Analysis
To examine the factors associated with the use of DSMES, a multiple logistic regression model was employed with the binary outcome of DSMES utilization (yes/no) within the 12-months post-diagnosis of diabetes, regardless of the frequency with which the individual utilized DSMES during that time. The level of statistical significance used in this study was 0.05. Statistical analyses were conducted using SAS version 9.
Data and Resource Availability
The datasets generated during and/or analyzed during the current study are not publicly available due to Data Use Agreement with the Arkansas Insurance Department (AID) and the Arkansas Center for Health Improvement (ACHI). Access to the data is contingent upon approval from AID/ACHI, and it follows the procedures outlined in accordance with Arkansas General Assembly Act 948 of 2017. Individuals seeking access to the data will be directed to ACHI's website (https://www.arkansasapcd.net/Home/) to initiate the process of obtaining their own data use agreement, which is a prerequisite for accessing the data.
RESULTS
To precisely determine the utilization of DSMES among individuals newly diagnosed with type 2 diabetes within a particular timeframe after diagnosis, exclusive annual categories were established. Table 1 shows the breakdown of type 2 diabetes individuals and DSMES utilization by insurance type within each year of the study. The numbers in Table 1 are determined by the year individuals were first classified as a new type 2 diabetes case. Sensitivity analysis was performed extending the lookback period and follow up to two years. Overall, 23,168 cases of new type 2 diabetes between 2015 to 2018 were identified. Among these cases, the utilization of DSMES was 4% (n=930) within a timeframe of 1 year following the diagnosis date. The utilization increased to 4.6% (n=1,066) when the timeframe extended to 2 years after the diagnosis date.
Table 1.
Number of Individuals Newly Diagnosed with Type 2 Diabetes and Utilization of DSMES per Index Year
| Incident Cases Type 2 Diabetesa |
DSMES Utilizationb (1 year after diagnosis) |
DSMES Utilizationb (2 years after diagnosis) |
||||
|---|---|---|---|---|---|---|
| n= 23,168 | n=930 | n=1,066 | ||||
| Index Year | FFS (n=15,648) |
MA (n=7,520) |
FFS (n=760) |
MA (n=170) |
FFS (n=855) |
MA (n=211) |
| 2015 | 4,145 (26.5%) |
889 (11.8%) |
189 (4.6%) |
34 (3.8%) |
210 (5.1%) |
36 (4.1%) |
| 2016 | 4,254 (27.2%) |
4,153 (55.2%) |
207 (4.9%) |
60 (1.4%) |
235 (5.5%) |
84 (2.0%) |
| 2017 | 3,605 (23%) |
1,290 (17.2%) |
179 (5%) |
43 (3.3%) |
201 (5.6%) |
50 (3.9%) |
| 2018 | 3,644 (23.3%) |
1,188 (15.8%) |
185 (5.1%) |
33 (2.8%) |
209 (5.7%) |
41 (3.5%) |
DSMES: Diabetes Self-Management Education and Support
The percentage is calculated based on the total number of new type 2 diabetes cases from 2015 to 2018
The percentage is calculated based on the number of new type 2 diabetes individuals per year
Throughout the study period, the DSMES utilization was consistently higher among Medicare FFS enrollees, with utilization rates of 4.6%, 4.9%, 5% and 5.1% for each respective year. In comparison, the utilization rates for MA enrollees were lower at 3.8%, 1.4%, 3.3% and 2.8% for each year.
There was a significant increase in the number of incident cases of type 2 diabetes among the MA population in 2016. As MA plans operate under capitated or risk-based payment systems, the rise in type 2 diabetes cases among the MA population in this study may be linked to increased coding intensity.25,26 Therefore, type 2 diabetes diagnoses over the years within the study, covering both outpatient and inpatient claims among the MA population, regardless of whether individuals met the CDC criteria for type 2 diabetes were examined. There were 46,571 MA beneficiaries who had at least one type 2 diabetes claim during the study period, considering both inpatient and outpatient claims. There was a notable increase in enrollees with at least one type 2 diabetes claim in 2016 (33% increase in outpatient and 53% in inpatient claims) which persisted in the following years (Table 2). This study identified new cases of diabetes as mutually exclusive for each index year (e.g., a new case of type 2 diabetes in 2016 was excluded from 2017 and 2018). A significant portion of these cases first appeared in 2016, which correlates with a higher number of type 2 diabetes claims in that year. Subsequently, there was a decline in the number of new cases in 2017 and 2018.
Table 2.
Medicare Advantage patients with at least one type 2 diabetes claim in 2015-2018
| No. of Patients with Type 2 Diabetes Using Inpatient Claims (%) (n=46,571) |
No. of Patients with Type 2 Diabetes Using Outpatient Claims (%) (n=46,571) |
Incident Cases Type 2 Diabetes (n=7,520) |
|
|---|---|---|---|
| 2015 | 3,101 (6.66%) | 21,854 (46.93%) | 889 |
| 2016 | 4,740 (10.18%) | 29,117 (62.52%) | 4,153 |
| 2017 | 5,506 (11.82%) | 29,333 (62.99%) | 1,290 |
| 2018 | 6,001 (12.89%) | 29,383 (63.09%) | 1,188 |
Tables 3-4 examine DSMES utilization during the first year and over two years post-diagnosis, respectively. The descriptive results (Table 3) showed no significant difference in utilization of DSMES by gender, with males having slightly higher utilization than females (1 year: 4.2% vs. 3.8% and 2 years: 4.8% vs 4.4%, p>0.10). However, there were differences in utilization of DSMES by age, race and ethnicity, rural/urban residence, and insurance type (all p<0.001). The DSMES utilization was highest among the 65-69 age group in both 1 and 2 follow-up years (1 year: 5.2% and 2 years: 6.1%) compared to older age groups (1 year, 70-74 years: 4.7%; 75-79 years: 3.6%; 80-84 years: 2.5% and over 85 years: 1.6% and 2 years, 70-74 years: 5.3%; 75-79 years: 4.1%; 80-84 years: 2.9% and over 85 years: 1.7%). Non-Hispanic White individuals had higher DSMES utilization compared to Non-Hispanic Black individuals (1 year: 4.4% vs. 2.3% and 2 years: 5.0% vs. 2.6%) and other race and ethnicity groups (1 year: 4.4% vs. 2.6% and 2 years: 5.0% vs. 4.2%). DSMES utilization was lower in rural areas compared to urban areas (1 year: 3.0% vs. 4.9% and 2 years: 3.5% vs. 5.6%). MA beneficiaries had lower DSMES utilization than Medicare FFS beneficiaries (1 year: 2.3% vs. 4.9% and 2 years: 2.8% vs. 5.5%).
Table 3.
Rates of DSMES Utilization 1-year and 2-year Post-diagnosis of Diabetes by Demographics and Insurance Type
| Characteristics | Overall N (%) |
DSMES Utilization (during 1 year post- diagnosis) |
P-
Value |
DSMES Utilization (during 2 years post- diagnosis) |
P-
Value |
|---|---|---|---|---|---|
| 23168 | 930 (4%) | 1066 (4.6%) | |||
| Age | <0.001 | <0.001 | |||
| 65-69 (Ref.) | 5124 (22.1%) | 268 (5.2%) | 313 (6.1%) | ||
| 70-74 | 7916 (34.2%) | 369 (4.7%) | 422 (5.3%) | ||
| 75-79 | 5177 (22.4%) | 186 (3.6%) | 213 (4.1%) | ||
| 80-84 | 2975 (12.8%) | 75 (2.5%) | 85 (2.9%) | ||
| 85+ | 1976 (8.5%) | 32 (1.6%) | 33 (1.7%) | ||
| Gender | 0.11 | 0.15 | |||
| Male (Ref.) | 10713 (46.2%) | 454 (4.2%) | 516 (4.8%) | ||
| Female | 12455 (53.8%) | 476 (3.8%) | 550 (4.4%) | ||
| Race and ethnicity | <0.001 | <0.001 | |||
| Non-Hispanic White (Ref.) | 19331 (83.4%) | 842 (4.4%) | 957 (5.0%) | ||
| Non-Hispanic Black | 3334 (14.4%) | 75 (2.3%) | 88 (2.6%) | ||
| Other | 503 (2.2%) | 13 (2.6%) | 21 (4.2%) | ||
| Residence | <0.001 | <0.001 | |||
| Urban (Ref.) | 11842 (51.8%) | 584 (4.9%) | 662 (5.6%) | ||
| Rural | 11012 (48.2%) | 335 (3.0%) | 388 (3.5%) | ||
| Missing | 314 | - | - | ||
| Insurance type | <0.001 | <0.001 | |||
| Medicare (Ref.) | 15648 (67.5%) | 760 (4.9%) | 855 (5.5%) | ||
| Medicare Advantage | 7520 (32.5%) | 170 (2.3%) | 211 (2.8%) |
DSMES: Diabetes Self-Management Education and Support
Table 4.
Logistic Regression Adjusted Odds Ratios of DSMES Utilization
| Model 1: DSMES Utilization (1 year after diagnosis) |
Model 2: DSMES Utilization (2 years after diagnosis) |
|||
|---|---|---|---|---|
| Characteristics | Odds Ratioa (95% CI) | P-Value | Odds Ratioa (95% CI) | P-Value |
| Age | <0.001 | <0.001 | ||
| 65-69 (Ref.) | ||||
| 70-74 | 0.9 (0.8-1.1) | 0.62 | 0.9 (0.8-1.1) | 0.30 |
| 75-79 | 0.7 (0.6-0.9) | <0.01 | 0.7 (0.6-0.9) | <0.001 |
| 80-84 | 0.5 (0.4-0.6) | <0.001 | 0.5 (0.4-0.60) | <0.001 |
| 85+ | 0.3 (0.2-0.5) | <0.001 | 0.3 (0.2-0.4) | <0.001 |
| Gender | 0.73 | 0.9 | ||
| Male (Ref.) | ||||
| Female | 1.0 (0.9-1.1) | 1.0 (0.9-1.1) | ||
| Race and ethnicity | <0.001 | <0.001 | ||
| Non-Hispanic White (Ref.) | ||||
| Non-Hispanic Black | 0.6 (0.5-0.8) | <0.001 | 0.6 (0.50-0.8) | <0.001 |
| Other | 0.4 (0.2-0.8) | <0.01 | 0.5 (0.3-0.9) | 0.02 |
| Residence | <0.001 | <0.001 | ||
| Urban (Ref.) | ||||
| Rural | 0.6 (0.5-0.7) | 0.60 (0.5-0.7) | ||
| Insurance type | <0.001 | <0.001 | ||
| Medicare (Ref.) | ||||
| Medicare Advantage | 0.5 (0.4-0.6) | 0.5 (0.4-0.6) | ||
DSMES: Diabetes Self-Management Education and Support
Adjusted for index year
The adjusted analysis results (Table 4) reveal the same patterns as the descriptive results, suggesting minimal confounding between the predictors analyzed. Increasing age was associated with less DSMES utilization, with age group 85 and over having the lowest odds of DSMES utilization for both one year (adjusted odds ratio [AOR]=0.3, 95% confidence intervals [CI]=0.2-0.5) and two years (AOR=0.3, CI=0.2-0.4) following diagnosis. Non-Hispanic Black individuals (Model 1 AOR=0.6, CI=0.5-0.8 and Model 2 AOR=0.6, CI=0.50-0.8) and other racial ethnic groups (Model 1 AOR=0.4, CI=0.2-0.8 and Model 2 AOR=0.5, CI=0.3-0.9) had lower odds of DSMES utilization compared to Non-Hispanic White individuals. The MA population had significantly lower DSMES utilization compared to the Medicare FFS population in both timeframes (Model 1 AOR=0.5, CI=0.40-0.6 and Model 2 AOR=0.5, CI=0.4-0.6). Individuals living in rural areas had significantly lower odds for DSMES utilization compared to urban areas (Model 1 AOR=0.6, CI=0.5-0.7 and Model 2 AOR=0.60, CI=0.5-0.7). No significant difference was observed between males and females.
DISCUSSION
This study of DSMES utilization by individuals newly diagnosed with type 2 diabetes among Medicare FFS and MA populations in Arkansas revealed only 4.9% of enrollees in FFS Medicare and 2.3% in MA utilize DSMES within 1 year of diagnosis. This study is innovative as it evaluates and compares DSMES utilization between the MA and Medicare FFS, particularly considering the growing number of Medicare-eligible individuals opting for Medicare Advantage plans.
The finding of low DSMES utilization among Medicare eligible beneficiaries in this study aligns with prior research.8 Similar to other studies,27,28 results of this study suggest that older adults are less likely to utilize DSMES. However, findings regarding race differ from those in previous literature. Whereas existing studies, which do no not rely on insurance claims, indicate that Black individuals are more likely to utilize DSMES services or show no difference in referral to lifestyle counseling between Black and White individuals,29 this study found that Black and other minority populations had lower rates of DSMES utilization. Although the reason for differential DSMES utilization has not been clearly delineated, there are several potential obstacles to patients engaging in DSMES. These barriers fall into several categories: health system barriers, including limited number of diabetes care and education specialists; restricted or lack of access to services; limited reimbursement rates; provider-related barriers, including lack of awareness about DSMES services and misunderstandings among providers regarding the necessity of DSMES; and patient-related barrier including lack of knowledge about the benefits, timing, transportation, and medical status.1,30-32 Black individuals with diagnosed diabetes are significantly less likely than their White counterparts to achieve clinical and quality targets.33 Substantial evidence links social determinants of health, such as socioeconomic status, neighborhood and physical environment, food environment, and healthcare, along with social factors including racism, to both the risk of diabetes and its outcomes, as well as the noticeable disparities in diabetes prevalence and management across racial groups.34
There is limited evidence examining how variation in Medicare plans impact utilization of DSMES. One study found that MA plans enable greater access to preventive care including receipt of ACE inhibitors or angiotensin receptor blockers for coronary artery disease, screening for retinopathy, foot care, and nephropathy, and tobacco cessation counseling but it did not investigate DSMES utilization among MA and Medicare FFS populations.11 MA enrollees had significantly lower rates of DSMES utilization compared to traditional Medicare populations. This difference can be attributed to several factors. First, because MA plans often provide enhanced preventive services, beneficiaries with type 2 diabetes likely have access to nurses or diabetes care and education specialists who can offer guidance on managing the condition, perhaps reducing the need for formal DSMES services. Furthermore, research indicates that FFS beneficiaries with lower health care expenditures are more inclined to switch to MA plans, whereas those with higher expenditures tend to remain with FFS. Additionally, the significant investment MA plans make in primary care, both to enhance care quality and to realize cost savings, tends to attract relatively healthier beneficiaries. This phenomenon, known as 'favorable selection,' may be driven largely by individual preferences or by strategic actions of the plans aimed at improving care and lowering costs.35 As a result, MA plan members may not be referred as frequently to formal DSMES programs.35
Consistent with previous research, the findings of this study show that individuals residing in rural areas exhibit lower participation rates in DSMES programs.28,36 These results align with a study utilizing the Behavioral Risk Factor Surveillance System (BRFSS) survey, which reported significantly reduced DSMES participation among rural residents of North Carolina compared to urban residents.37 Reduced participation in rural areas may stem from a scarcity of DSMES programs, challenges related to recruiting health professionals, particularly registered dietitians, and transportation barriers.37,38 This study has several strengths. First, it provides accurate estimates of DSMES utilization as it is only limited to individuals newly diagnosed with type 2 diabetes. This specific approach significantly reduces the likelihood of participants having received DSMES before the study period. Second, to our knowledge, this is the first study to investigate DSMES utilization among both traditional Medicare and MA populations.
Limitations
This study has several limitations. This is a retrospective claims-based study, and although several control variables are included in the analysis, selection bias is likely present. The study did not adjust for the comorbid conditions and education level. It is possible that individuals who lived in rural areas had more comorbid conditions and had lower health literacy in addition to lower access to diabetes education programs. Additionally, A1C level is not adjusted for in this study, as it is not available in insurance claims data. Finally, the frequency of DSMES utilization per individual is not accounted for in this study, which limits the ability to identify variation in the intensity of DSMES utilization.
CONCLUSIONS
In summary, this retrospective claims-based study found low utilization of DSMES within the Medicare-eligible population, noting varying levels of DSMES use between Medicare FFS and MA beneficiaries. Specifically, MA beneficiaries demonstrated lower utilization rates. The study identified significant disparities in DSMES access influenced by several factors, such as race and ethnicity, older age, and living in rural areas. Notably, non-Hispanic Black beneficiaries were significantly less likely to utilize DSMES, possibly exacerbating racial disparities in diabetes outcomes. These findings suggest a need for policymakers to focus on improving education, outreach, and overall access to these services, aiming to enhance health outcomes.
Acknowledgments
Access to the Arkansas All-Payer Claims Database for this study was provided by the support from the Arkansas Biosciences Institute/Arkansas Insurance Department/Arkansas Healthcare Transparency Initiative Collaboration.
During the course of preparing this work, the author(s) used ChatGPT4 from OpenAI for the purpose of linguistic and grammatical proofreading. Following the use of this tool/service, the author(s) formally reviewed the content for its accuracy and edited it as necessary. The author(s) take full responsibility for all the content of this publication.
MR attests that all individuals who contributed to the manuscript have been appropriately acknowledged, and that all contributors who are not authors have agreed in writing to be named in the Acknowledgement section of the article.
Funding:
MR was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institute of Health [1K25DK136966]. Access to the Arkansas All-Payer Claims Database for this study was provided by the support from the Arkansas Biosciences Institute/Arkansas Insurance Department/Arkansas Healthcare Transparency Initiative Collaboration.
Contributor Information
Mandana Rezaeiahari, Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Mahip Acharya, Institute for Digital Health and Innovation, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Joseph Henske, Division of Endocrinology and Metabolism, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Kelsey Owsley, Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Jodi Bodenhamer, Baxter Regional Medical Center, Mountain Home, AR.
References
- 1.Powers MA, Bardsley JK, Cypress M, et al. Diabetes Self-management Education and Support in Adults With Type 2 Diabetes: A Consensus Report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American Pharmacists Association. The Diabetes Educator. 2020;46(4):350–369. doi: 10.1177/0145721720930959. [DOI] [PubMed] [Google Scholar]
- 2.Davies MJ, D’Alessio DA, Fradkin J, et al. Management of hyperglycemia in type 2 diabetes, 2018. a consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;41(12):2669–2701. doi: 10.2337/DCI18-0033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Committee ADAPP ElSayed NA, Aleppo G, et al. 5. Facilitating positive health behaviors and well-being to improve health outcomes: standards of care in diabetes—2024. Diabetes Care. 2024;47(Supplement_1):S77–S110. doi: 10.2337/DC24-S005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.He X, Li J, Wang B, et al. Diabetes self-management education reduces risk of all-cause mortality in type 2 diabetes patients: a systematic review and meta-analysis. Endocrine. 2016 55:3. 2016;55(3):712–731. doi: 10.1007/S12020-016-1168-2 [DOI] [PubMed] [Google Scholar]
- 5.Chrvala CA, Sherr D, Lipman RD. Diabetes self-management education for adults with type 2 diabetes mellitus: a systematic review of the effect on glycemic control. Patient Educ Couns. 2016;99(6):926–943. doi: 10.1016/J.PEC.2015.11.003 [DOI] [PubMed] [Google Scholar]
- 6.Fischl AH, Beck J et al. 2022 National standards for diabetes self-management education and support. Sci Diabetes Self Manag Care. 2022;48(1):44- doi: 10:1177/2635010621107223 [DOI] [PubMed] [Google Scholar]
- 7.Nguyen AT, Pham HQ, Nguyen TX, et al. Knowledge, Attitude and practice of elderly outpatients with type 2 diabetes mellitus in national geriatric hospital, Vietnam. Diabetes Metab Syndr Obes. 2020;13:3909. doi: 10.2147/DMSO.S267866 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Strawbridge LM, Lloyd JT, Meadow A, Riley GF, Howell BL. Use of Medicare’s diabetes self-management training benefit. Health Educ Behav. 2015;42(4):530–538. doi: 10.1177/1090198114566271 [DOI] [PubMed] [Google Scholar]
- 9.Huang ES, Laiteerapong N, Liu JY, John PM, Moffet HH, Karter AJ. Rates of complications and mortality in older patients with diabetes mellitus: the diabetes and aging study. JAMA Intern Med. 2014;174(2):251–258. doi: 10.1001/JAMAINTERNMED.2013.12956 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Surveillance - United States Diabetes Surveillance System. Accessed January 1, 2024. https://gis.cdc.gov/grasp/diabetes/diabetesatlas-surveillance.html#
- 11.Essien UR, Tang Y, Figueroa JF, et al. Diabetes care among older adults enrolled in Medicare Advantage versus traditional Medicare fee-for-service plans: the diabetes collaborative registry. Diabetes Care. 2022;45(7):1549–1557. doi: 10.2337/DC21-1178 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Neuman P, Jacobson GA. Medicare Advantage checkup. N Engl J Med. 2018;379(22):2163–2172. doi: 10.1056/NEJMhpr1804089 [DOI] [PubMed] [Google Scholar]
- 13.Park S, Figueroa JF, Fishman P, Coe NB. Primary care utilization and expenditures in traditional Medicare and Medicare advantage, 2007-2016. J Gen Intern Med. 2020;35(8):2480–2481. doi: 10.1007/S11606-020-05826-X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Agarwal R, Connolly J, Gupta S, Navathe AS. Comparing Medicare advantage and traditional Medicare: a systematic review. Health Aff (Millwood). 2021;40(6):937–944. doi: 10.1377/HLTHAFF.2020.02149 [DOI] [PubMed] [Google Scholar]
- 15.Curto V, Einav L, Finkelstein A, Levin J, Bhattacharya J. Health care spending and utilization in public and private Medicare. Am Econ J Appl Econ. 2019;11(2):302. doi: 10.1257/APP.20170295 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kumar A, Rahman M, Trivedi AN, Resnik L, Gozalo P, Mor V. Comparing post-acute rehabilitation use, length of stay, and outcomes experienced by Medicare fee-for-service and Medicare Advantage beneficiaries with hip fracture in the United States: A secondary analysis of administrative data. PLoS Med. 2018;15(6). doi: 10.1371/JOURNAL.PMED.1002592 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Landon BE, Zaslavsky AM, Saunders RC, Gregory Pawlson L, Newhouse JP, Ayanian JZ. Analysis of Medicare Advantage HMOs compared with traditional Medicare shows lower use of many services during 2003-09. Health Aff (Millwood). 2012;31(12):2609–2617. doi: 10.1377/hlthaff.2012.0179 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Landon BE, Zaslavsky AM, Saunders R, Gregory Pawlson L, Newhouse JP, Ayanian JZ. A comparison of relative resource use and quality in Medicare advantage health plans versus traditional Medicare. Am J Manag Care. 2015;21(8):559–66. [PMC free article] [PubMed] [Google Scholar]
- 19.Ayanian JZ, Landon BE, Zaslavsky AM, Saunders RC, Gregory Pawlson L, Newhouse JP. The care span: Medicare beneficiaries more likely to receive appropriate ambulatory services in HMOs than in traditional Medicare. Health Aff (Millwood). 2013;32(7):1228–1235. doi: 10.1377/hlthaff.2012.0773 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Park S, Larson EB, Fishman P, White L, Coe NB. Differences in health care utilization, process of diabetes care, care satisfaction, and health status in patients with diabetes in Medicare Advantage vs traditional Medicare. Med Care. 2020;58(11):1004. doi: 10.1097/MLR.0000000000001390 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Arkansas All-Payer Claims Database. Accessed November 1, 2023. https://www.arkansasapcd.net/Home/
- 22.Andes LJ, Li Y, Srinivasan M, Benoit SR, Gregg E, Rolka DB. Diabetes prevalence and incidence among Medicare beneficiaries — United States, 2001–2015. MMWR Morb Mortal Wkly Rep. 2019;68(43):961–966. doi: 10.15585/MMWR.MM6843A2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Medicare Benefit Policy Manual Transmittals for Chapter 15 Payment for Medicare Part B Services Furnished by Certain IHS Hospitals and Clinics. Accessed February 22, 2022. https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/bp102c15.pdf
- 24.USDA ERS - Rural-Urban Commuting Area Codes. Accessed March 16, 2023. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/
- 25.McGuire TG, Newhouse JP, Sinaiko AD. An Economic History of Medicare Part C. Milbank Q. 2011;89(2):289. doi: 10.1111/J.1468-0009.2011.00629.X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kronick R, Welch WP. Measuring Coding Intensity in The Medicare Advantage Program. Medicare Medicaid Res Rev. 2014;4(2). doi: 10.5600/mmrr.004.02.a06 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Boakye EA, Varble A, Rojek R, et al. Sociodemographic factors associated with engagement in Diabetes Self-anagement Education among people with diabetes in the United States. Public Health Rep. 2018;133(6):685–691. doi: 10.1177/0033354918794935 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Brown-Guion SY, Youngerman SM, Hernandez-Tejada MA, Dismuke CE, Egede LE. Racial/ethnic, regional, and rural/urban differences in receipt of diabetes education. Diabetes Educ. 2013;39(3):327–334. doi: 10.1177/0145721713480002 [DOI] [PubMed] [Google Scholar]
- 29.Peek ME, Tang H, Alexander GC, Chin MH. National prevalence of lifestyle counseling or referral among African-Americans and whites with diabetes. J Gen Intern Med. 2008;23(11):1858–1864. doi: 10.1007/S11606-008-0737-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Peyrot M, Rubin RR, Funnell MM, Siminerio LM. Access to diabetes self-management education: results of national surveys of patients, educators, and physicians. Diabetes Educ. 2009;35(2):246–263. doi: 10.1177/0145721708329546 [DOI] [PubMed] [Google Scholar]
- 31.Centers for Disease Control and Prevention (CDC). Diabetes self-management education and support (DSMES) toolkit. Accessed September 6, 2022. https://www.cdc.gov/diabetes/dsmes-toolkit/index.html
- 32.Horigan G, Davies M, Findlay-White F, Chaney D, Coates V. Reasons why patients referred to diabetes education programmes choose not to attend: a systematic review. Diabet Med. 2017;34(1):14–26. doi: 10.1111/DME.13120 [DOI] [PubMed] [Google Scholar]
- 33.Wang L, Li X, Wang Z, et al. Trends in prevalence of diabetes and control of risk factors in diabetes among us adults, 1999-2018. JAMA. 2021;326(8):704–716. doi: 10.1001/JAMA.2021.9883 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Hill-Briggs F, Ephraim PL, Vrany EA, et al. Social determinants of health, race, and diabetes population health improvement: Black/African Americans as a population exemplar. Curr Diab Rep. 2022;22(3):117. doi: 10.1007/S11892-022-01454-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lieberman SM, Ginsburg PB, Valdez S. Favorable selection ups the ante on Medicare Advantage payment reform. Health Affairs Forefront. doi: 10.1377/FOREFRONT.20230606.520135 [DOI] [Google Scholar]
- 36.Ruppert K, Uhler A, Siminerio L. Examining patient risk factors, comorbid conditions, participation, and physician referrals to a rural diabetes self-management education program. Diabetes Educ. 2010;36(4):603–612. doi: 10.1177/0145721710369705 [DOI] [PubMed] [Google Scholar]
- 37.Luo H, Bell RA, Winterbauer NL, et al. Trends and rural-urban differences in participation in diabetes self-management education among adults in North Carolina: 2012-2017. J Public Health Manag Pract. 2022;28(1):E178–E184. doi: 10.1097/PHH.0000000000001226 [DOI] [PubMed] [Google Scholar]
- 38.Balamurugan A, Rivera M, Jack L, Allen K, Morris S. Barriers to diabetes self-management education programs in underserved rural Arkansas: implications for program evaluation. Prev Chronic Dis. 2006;3(1):A15. [PMC free article] [PubMed] [Google Scholar]