Abstract
BACKGROUND:
In 2023, the Centers for Medicare and Medicaid Services (CMS) expanded continuous glucose monitoring (CGM) coverage in Medicare among individuals with type 2 diabetes and insulin use; however, little is known about current trends in CGM use among Medicare beneficiaries with type 2 diabetes.
OBJECTIVE:
To assess trends in CGM use among Medicare beneficiaries with type 2 diabetes from 2021 to 2023 and to compare demographic and clinical characteristics of beneficiaries who used CGMs to those who did not in 2023.
METHODS:
This was a retrospective, repeated cross-sectional analysis. Using data from a large national Medicare Advantage (MA) plan, we described CGM use among MA beneficiaries with type 2 diabetes and evidence of insulin use to assess monthly trends in use from 2021 to 2023 and, using only 2023 data, examined characteristics of beneficiaries using CGMs.
RESULTS:
We found that the prevalence of CGM use among MA beneficiaries with type 2 diabetes and insulin use increased from 1.4% in January 2021 to 17.2% in December 2023. Among the 2023 cohort, CGM users compared with nonusers had more primary care physician (PCP) visits and a higher likelihood of having a visit with an endocrinologist. CGM users were more clinically complex (ie, exhibiting higher clinical risk scores using the Deyo-Charlson Comorbidity Index and the Diabetes Complications Severity Index), more likely to have visits with both PCPs and endocrinologists, and more likely to be younger, be White, be with a disability, and live in rural areas.
CONCLUSIONS:
CGM utilization in an MA population increased concurrent with the expanded clinical guideline changes and Medicare coverage, though certain types of beneficiaries were more likely to use CGMs than others in 2023. Awareness of the differences in uptake of CGMs among beneficiaries could aid future education and outreach opportunities.
Plain language summary
From 2021 to 2023, more people used continuous glucose monitors (CGMs). In 2023, we saw that people who used CGMs often had more health problems than those who did not. They were also more likely to go to the doctor more often. They were also more likely to have certain characteristics.
Implications for managed care pharmacy
This analysis suggests that CGM use increased concurrent and following expanded Medicare coverage in 2023. For clinicians, this analysis also suggests that beneficiaries who are older, without a disability, or with dementia may be less likely to use CGMs. More research is necessary to understand whether patients with these demographic characteristics were less likely to use the devices because of challenges accessing or using the devices, or whether there is insufficient evidence that CGMs improve outcomes for them.
Recent studies have suggested that use of continuous glucose monitors (CGMs) among individuals with type 2 diabetes is associated with improved glycemic control, reduced acute care use, and better quality of life.1–7 Despite the potential benefits associated with their use, prior evidence suggests that CGM uptake from 2016 to 2021 was concentrated among patients with type 1 diabetes,8 in part because clinical guidelines did not initially recommend use among patients with type 2 diabetes.
In 2021, the American Diabetes Association Standards of Diabetes Care recommended that physicians consider CGMs to improve glycemic outcomes for older adults with type 2 diabetes receiving multiple daily doses of insulin.9 In response, in April 2023, the Centers for Medicare & Medicaid Services (CMS) expanded CGM coverage in Medicare to all beneficiaries with insulin use, regardless of dose or frequency, and to beneficiaries with a history of hypoglycemia without insulin use.10 Further, the National Committee for Quality Assurance has recently endorsed metrics from CGMs as an alternative to hemoglobin A1c in Medicare quality of care measurements.11
Despite these recent policy and guideline changes, little is known about current trends in CGM use among Medicare beneficiaries with type 2 diabetes. Using data from Humana, a large national Medicare Advantage (MA) plan, we pursued 2 objectives in this analysis. First, we assessed trends in CGM use among Medicare beneficiaries with type 2 diabetes from 2021 to 2023, and second, we compared demographic and clinical characteristics of beneficiaries who used CGMs with those who did not in 2023.
Methods
DATA SOURCE
This was a repeated cross-sectional study using medical and pharmacy claims from the Humana Healthcare Research database, which includes enrollment records with demographic information (eg, age, sex, and geographic region); medical claims with detailed information on diagnosed medical conditions and tests and procedures performed; and pharmacy claims with information on prescription fills, quantity dispensed, and days supply.
STUDY POPULATION
For each year in the study (2021, 2022, and 2023), we identified MA beneficiaries with type 2 diabetes who were treated with insulin. Each yearly cohort was identified independently, and beneficiaries could be observed in more than 1 study year. Beneficiaries with claims for at least 1 inpatient or 2 outpatient visits with an associated type 2 diabetes diagnosis (International Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM] code E11.x) in each year that had 1 or more insulin fill in an outpatient medical or pharmacy claim were included (see Supplementary Materials (282.8KB, pdf) , available in online article, for more information on how insulin fills were identified). Patients with type 2 diabetes without evidence of insulin use were excluded from the analysis, as these patients are more likely to be treated with diet, exercise, or an oral hypoglycemic. We also excluded beneficiaries who had claims evidence of both type 1 and type 2 diabetes, as we could not determine their primary diagnosis. Beneficiaries were excluded from the study if their plan was contractually excluded from research or their assigned primary care provider delegated claims to a third party, since the MA plan administrator did not have full access to delegated claims. Beneficiaries were excluded if they were in institutionalized care during the study period or if they entered hospice, as the MA plan administrator did not have access to claims during these months.
MAIN OUTCOME
CGM use was determined using durable medical equipment claims for nonadjunctive CGM devices and associated supplies (Healthcare Common Procedure Coding System codes K0553, K0554, E2103, A4239). During the time period of this study, CGM was not covered under the pharmacy benefit.
BENEFICIARY DEMOGRAPHIC CHARACTERISTICS
Beneficiary demographic characteristics were extracted from 2023 data and included sex, age, race, low-income subsidy (LIS) or dual Medicare-Medicaid eligibility, geographic region, rurality, and disability. All characteristics were measured as of the index date, defined as the first date of a beneficiary’s type 2 diabetes diagnosis in that calendar year. Sex was determined from the enrollment data, and age in years was reported as a continuous and categorical measure. Race was based on CMS beneficiary coding and reported as White, Black, underrepresented race or ethnicity (defined as Hispanic, Asian, American Indian or Alaska Native, Pacific Islander, or Other), or unknown (missing race or ethnicity indicator data in the CMS administrative database). We identified MA beneficiaries who had LIS or dual Medicare-Medicaid eligibility status using flags in the CMS enrollment database. Beneficiary geographic location was defined by region as Northeast, Midwest, South, or West. Rurality was based on mapping zip codes to rural-urban commuting area (RUCA) codes as metropolitan, micropolitan, small town, rural, and unknown using US Department of Agriculture RUCA codes and University of Washington’s Rural Health Research Center RUCA definitions to determine rurality.12 Disability was determined based on the beneficiary’s original reason for Medicare entitlement.
BENEFICIARY CLINICAL CHARACTERISTICS AND OUTPATIENT UTILIZATION
Beneficiary clinical characteristics were extracted from 2023 data and included the Deyo-Charlson Comorbidity (CCI) Score and the Diabetes Complications Severity Index (DCSI), as well as the subcomponent conditions of each measure.13,14 The CCI score is a composite measure of comorbidity comprising 17 clinical conditions that have been weighted and summed based on ICD-10-CM codes. This score ranges from 0 (low comorbidity) to 33 (high comorbidity). The DCSI is a measure of diabetes severity using diabetes-related comorbidities, including retinopathy, nephropathy, and neuropathy; cerebrovascular, cardiovascular, and peripheral vascular disease; and metabolic complications, rated on a 3-level severity scale, with 0 indicating no abnormality, 1 indicating some abnormality, and 2 indicating severe abnormality. The score ranges from 0 (no risk of future complications) to 13 (highest risk of future complications).
Outpatient utilization measures were extracted from 2023 data and included primary care physician (PCP) visits and endocrinology visits. PCP visits were defined as medical claims having place of treatment codes of 05, 06, 11, 19, 22, 49, 50, 71, or 72 or CPT codes of 99201-99205, 99211-99215, 99241-99245. Claims with CPT modifiers of 95, GT, or GQ were excluded as they pertained to telehealth appointments. Endocrinology appointments were a subset of physician office visits in which the associated provider had a taxonomy of 207RE0101X.
STATISTICAL ANALYSIS
First, we used descriptive analyses to characterize monthly trends in the use of CGMs among beneficiaries with type 2 diabetes and using insulin from 2021 through 2023. Second, using the 2023 cohort only, we described clinical characteristics and health care utilization variables among CGM users and nonusers and tested for differences between the groups using standardized mean differences (SMDs). We considered an SMD greater than 0.100 to reflect a meaningful difference. Third, in this same 2023 cohort, we used a logistic regression model (with an α = 0.05 selection threshold) to assess the association between beneficiary characteristics and CGM use in 2023. Beneficiary characteristics included in the model were age, sex, race, LIS and/or dual eligibility, geographic region (Northeast, Midwest, South, and West), rurality, disability, CCI risk score, binary evidence of each of the 7 components of the Diabetes Complications Severity Index, and endocrinology appointments.
This study did not constitute human subjects research and did not require institutional review board oversight, as determined by the Human Subject Protection Office, which uses HHS regulations 45 CFR 46 and the Office for Human Research Protections guidance on Coded Private Information or Specimens Use in Research, Guidance (2008).
Results
For study years 2021, 2022, and 2023, we identified 222 236, 229 093, and 286 951 MA beneficiaries with type 2 diabetes and evidence of insulin use, respectively. From January 2021 through December 2023, monthly CGM use increased from 1.4% of beneficiaries to 17.2% (Figure 1 in the Supplement (282.8KB, pdf) ). The average annual CGM use in the population was 4.8%, 10.2%, and 13.7% for 2021, 2022, and 2023, respectively.
In the 2023 cohort, 17% of the population (n = 49 395) were CGM users and 83% were nonusers (N = 237,556). Compared with nonusers, CGM users were, on average, younger (67.2 vs 69.3, SMD = 0.23), more frequently female (56.0% vs 53.9%, SMD = 0.04), White (67.0% vs 60.6%, SMD = 0.13), and disabled (51.2% vs 44.4%, SMD = 0.14) and lived in the Midwest (24.3% vs 19.1%, SMD = 0.13). Clinically, CGM users had more clinical comorbidities than nonusers, measured using the CCI (4.09 vs 3.79, SMD = 0.12) and scored higher on the diabetes complications severity (3.09 vs 2.65, SMD = 0.20) (Table 1). The mean number of PCP visits per member per year among CGM users was higher than that among nonusers (5.70 per year vs 4.91, SMD = 0.18). A greater proportion of CGM users experienced any endocrinology visit across the year (39.5% vs 12.5%, SMD = 0.65) and had a higher mean number of endocrinology visits compared with nonusers (1.03 vs 0.28, SMD = 0.13).
TABLE 1.
Demographic Characteristics Among Medicare Advantage Beneficiaries Using CGM Users and Nonusers in 2023
| Variables | CGM (n = 49,395 [17%]) | No CGM (n = 237,556 [83%]) | SMD |
|---|---|---|---|
| Sex, n (%) | |||
| Female | 27,648 (56.0) | 127,954 (53.9) | 0.042 |
| Male | 21,747 (44.0) | 109,602 (46.1) | |
| Age | |||
| Mean (SD) | 67.19 (±9.35) | 69.29 (±9.04) | 0.229 |
| Age groups | |||
| <65 years | 15720 (31.8) | 55,486 (23.4) | 0.19 |
| 65-74 years | 23187 (46.9) | 115,023 (48.4) | 0.03 |
| 75-84 years | 9462 (19.2) | 58,632 (24.7) | 0.134 |
| ≥85 years | 1026 (2.1) | 8,415 (3.5) | 0.089 |
| Race | |||
| Black | 10,739 (21.7) | 58,796 (24.8) | 0.071 |
| White | 33,094 (67.0) | 144,029 (60.6) | 0.133 |
| Other | 2,825 (5.7) | 20,325 (8.6) | 0.11 |
| Unknown | 2,737 (5.5) | 14,406 (6.1) | 0.022 |
| Socioeconomic status, n (%) | |||
| LIS only | 3,644 (7.4) | 19,699 (8.3) | 0.034 |
| DE only | 104 (0.2) | 579 (0.2) | 0.007 |
| Both LIS and DE | 21,718 (44.0) | 101,124 (42.6) | 0.028 |
| Neither LIS nor DE | 23,929 (48.4) | 116,154 (48.9) | 0.009 |
| Geographic region, n (%) | |||
| Northeast | 3,755 (7.6) | 14,674 (6.2) | 0.056 |
| Midwest | 12,000 (24.3) | 45,441 (19.1) | 0.126 |
| South | 29,160 (59.0) | 153,240 (64.5) | 0.113 |
| West | 4,480 (9.1) | 24,201 (10.2) | 0.038 |
| Population density, n (%) | |||
| Metropolitan | 37,702 (76.3) | 183,479 (77.2) | 0.022 |
| Micropolitan | 6,502 (13.2) | 30,132 (12.7) | 0.014 |
| Small town | 3,208 (6.5) | 15,186 (6.4) | 0.004 |
| Rural (isolated) | 1980 (4.0) | 8,734 (3.7) | 0.017 |
| Unknown | 3 (0.0) | 25 (0.0) | 0.005 |
| Rural density, n (%) | |||
| Rural | 11,690 (23.7) | 54,052 (22.8) | 0.022 |
| Nonrural | 37,702 (76.3) | 183,479 (77.2) | 0.022 |
| Missing | 3 (0.0) | 25 (0.0) | 0.005 |
| Original reason for Medicare entitlement, n (%) | |||
| Age | 23,059 (46.7) | 128,301 (54.0) | 0.147 |
| Disability | 25,314 (51.2) | 105,523 (44.4) | 0.137 |
| ESRD | 406 (0.8) | 1,464 (0.6) | 0.024 |
| Disability and ESRD | 510 (1.0) | 1,538 (0.6) | 0.042 |
| Missing | 106 (0.2) | 730 (0.3) | 0.018 |
| Deyo-Charlson Comorbidity Index (range: 0-33) | |||
| Mean (SD) | 4.09 (±2.38) | 3.79 (±2.46) | 0.123 |
| Diabetes Complications Severity Index | |||
| Mean (SD) | 3.09 (±2.24) | 2.65 (±2.16) | 0.196 |
| Outpatient health care resource utilization | |||
| Any PCP visits, n (%) | 47,511 (96.2) | 221,713 (93.3) | 0.128 |
| Number of PCP visits, mean (SD) | 5.70 (±4.58) | 4.91 (±4.25) | 0.178 |
| Any endocrinology visits, n (%) | 19,509 (39.5) | 29,637 (12.5) | 0.648 |
| Number of endocrinology visits, mean (SD) | 1.03 (±1.71) | 0.28 (±0.95) | 0.544 |
CGM = continuous glucose monitor; DE = dual-eligible; ESRD = end-stage renal disease; LIS = low-income subsidy; PCP = primary care physician; SMD = standardized mean difference.
Results were consistent in multivariable logistic regression models. Female sex (odds ratio [OR] = 1.10, [95% CI = 1.07-1.12]), Midwest region (OR = 1.35, [95% CI = 1.31-1.38]), rurality (OR = 1.19, [95% CI = 1.16-1.22]), disability (OR = 1.03, [95% CI = 1.01-1.06]), and score on the CCI (OR = 1.01, [95% CI = 1.00-1.01]) were statistically associated with CGM use. Conversely, Black race (OR = 0.78, [95% CI = 0.76-0.80]) and age 85 years or older (OR = 0.44, [95% CI = 0.41, 0.48]) were associated with reductions in CGM use. Other characteristics associated with sizable impacts on CGM use included 3 conditions measured from the DCSI: neuropathy (OR = 1.45, [95% CI = 1.41-1.48]), metabolic conditions (OR = 1.38, [95% CI = 1.33-1.43]), and retinopathy (OR = 1.32, [95% CI = 1.29-1.35]). Beneficiaries with any endocrinology visit during the year were 4.3 times more likely to use a CGM device (OR = 4.35, [95% CI = 4.25-4.45]) (Table 2).
TABLE 2.
Logistic Regression Model Results
| Variables | OR | 95% CI |
|---|---|---|
| Age | ||
| <65 years | ||
| 65-74 years | 0.74 | 0.71-0.76 |
| 75-84 years | 0.57 | 0.55-0.6 |
| 85+ years | 0.44 | 0.41-0.48 |
| Sex | ||
| Female | 1.1 | 1.07-1.12 |
| Male | ||
| Race | ||
| Black | 0.78 | 0.76-0.8 |
| White | ||
| Other | 0.61 | 0.59-0.64 |
| Unknown | 0.81 | 0.77-0.85 |
| LIS/DE | ||
| Any DE and/or LIS | 1.01 | 0.98-1.03 |
| Neither DE nor LIS | ||
| Region | ||
| Northeast | 1.27 | 1.22-1.33 |
| Midwest | 1.35 | 1.31-1.38 |
| South | ||
| West | 1.05 | 1.01-1.09 |
| Population density | ||
| Rural | 1.19 | 1.16-1.22 |
| Nonrural | ||
| Disability | ||
| Yes | 1.03 | 1.01-1.06 |
| No | ||
| Deyo-Charlson Comorbidity Index | 1.01 | 1-1.01 |
| Diabetes Complications Severity Index | ||
| Any cardiovascular | 0.99 | 0.97-1.01 |
| Any cerebrovascular | 1 | 0.97-1.04 |
| Any metabolic | 1.38 | 1.33-1.43 |
| Any nephropathy | 1.13 | 1.1-1.16 |
| Any neuropathy | 1.45 | 1.41-1.48 |
| Any peripheral vascular disease | 1.01 | 0.98-1.03 |
| Any retinopathy | 1.32 | 1.29-1.35 |
| Any PCP appointments | ||
| Yes | 1.68 | 1.6-1.77 |
| No | ||
| Any endocrinology appointments | ||
| Yes | 4.35 | 4.25-4.45 |
| No | ||
CGM = continuous glucose monitor; DE = dual-eligible; LIS = low-income subsidy; PCP = primary care physician.
Discussion
In this analysis, we found that prevalence of CGM use among MA beneficiaries with type 2 diabetes and insulin use increased from 1.4% in January 2021 to 17.2% in December 2023. This study suggests that CGM utilization increased concurrent with the expanded clinical guideline changes and Medicare coverage. Additionally, we found that those who used CGMs in 2023 had more touchpoints with the health care system, including both more PCP visits on average and a higher likelihood of having a visit with an endocrinologist. In our logistic regression model, beneficiaries with any endocrinology visit during the year were 4.3 times more likely to use a CGM device, representing the strongest association across all demographic, clinical, and care utilization measures evaluated. We also evaluated demographic and clinical characteristics of CGM users. Among the 2023 cohort, use of a CGM was more common among MA beneficiaries who were younger, White, and disabled. Clinically, CGM users compared with nonusers were more likely to have diabetes complications and certain comorbid conditions, such as chronic obstructive pulmonary disease and renal disease.
This analysis suggests that there may be opportunities to improve education on CGMs among both beneficiaries and providers, especially for beneficiaries who have less complex disease that are managed in primary care settings, as beneficiaries with these characteristics were less likely to use a CGM in 2023. However, it is not known from this analysis whether increased use of CGMs was because these beneficiaries are more clinically complex or whether use of a CGM causes more touchpoints with the health care system. This analysis also suggests that beneficiaries who are older, without a disability, or with dementia may be less likely to use CGMs. More research is necessary to understand whether patients with these demographic characteristics were less likely to use the devices because of challenges accessing or using the devices, or whether there is insufficient evidence that CGMs improve outcomes among these groups. This may be particularly important among patients with dementia, as there is increasing evidence that there is an association between glycemic control and cognitive impairment.15
LIMITATIONS
There were limitations to this analysis, as well as important areas identified for future research. As a largely descriptive analysis, we cannot draw causal conclusions on the effects of the 2023 policy change from this work. Additionally, more evidence is needed to understand whether the increased use of CGMs is associated with improved health outcomes among Medicare beneficiaries with type 2 diabetes. This analysis measured prevalence of use but did not measure persistence in CGM use over time, and CGMs may be used intermittently for diagnostic purposes.16 Finally, the use of more effective first-line glucose-lowering therapies (such as Glucagon-like peptide-1 Agonists and Sodium-Glucose Cotransporter-2 Inhibitor medications) concomitantly increased over this study period. Recent evidence suggests that CGM in conjunction with GLP1 agonists may enhance diabetes management.17–19 However, additional research is needed to understand if and how CGMs can further augment medication use, as well as the effects of these therapies among Medicare beneficiaries, such as improved adherence or other health outcomes. Despite its limitations, this study adds to the literature as it is the first to assess CGM trends in use in an MA population using recent data that follows the expanded coverage in Medicare for CGM devices.
Conclusions
This analysis showed that CGM use in an MA population went up between 2021 and 2023, a key time period as Medicare coverage of the devices expanded in 2023. This review also shows that certain groups of patients (those with fewer touchpoints with the health care system, older adults, people without disabilities, and those with dementia) used CGMs less often. These differences could represent barriers to getting or using CGMs among certain groups, or that there is not enough proof that CGMs help them manage their disease. Awareness of these differences between CGM users and nonusers may aid clinicians in educating patients with type 2 diabetes about their monitoring options, especially those patients with certain demographic or clinical characteristics.
Disclosures
Dr Boudreau, Mr Rastegar, Dr Swankoski, Dr Hames, and Dr Mugavin are employed by Humana. Dr Boudreau and Dr Hames report stock in Humana Inc. Dr Ross currently receives research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the US Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938), from the Agency for Healthcare Research and Quality (R01HS022882), and from Arnold Ventures; formerly received research support from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology (NEST); and in addition, Dr Ross was an expert witness at the request of Relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc. that was settled September 2022.
Acknowledgments
The research team thanks Suzanne Dixon, PhD, and Mary Constantino, PhD, for their support of this article.
Data Availability
The data that support the findings of this study are available from Humana Healthcare Research, Inc., but restrictions apply to the availability of these data and are not publicly available.
References
- 1.Charleer S, Mathieu C, Nobels F, et al. ; RESCUE Trial Investigators. Effect of continuous glucose monitoring on glycemic control, acute admissions, and quality of life: A real-world study. J Clin Endocrinol Metab. 2018;103(3):1224-1232. doi: 10.1210/jc.2017-02498 [DOI] [PubMed] [Google Scholar]
- 2.Hirsch IB, Kerr MSD, Roberts GJ, Souto D, Nabutovsky Y, Bergenstal RM. 875-P: Utilization of continuous glucose monitors is associated with reduction in inpatient and outpatient emergency acute diabetes events regardless of prior blood test strip usage. Diabetes. 2020;69(Supplement_1). [Google Scholar]
- 3.Dicembrini I, Mannucci E, Monami M, Pala L. Impact of technology on glycaemic control in type 2 diabetes: A meta-analysis of randomized trials on continuous glucose monitoring and continuous subcutaneous insulin infusion. Diabetes Obes Metab. 2019;21(12):2619-25. doi: 10.1111/dom.13845 [DOI] [PubMed] [Google Scholar]
- 4.Martens T, Beck RW, Bailey R, et al. ; MOBILE Study Group. Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: A randomized clinical trial. JAMA. 2021;325(22):2262-72. doi: 10.1001/jama.2021.7444 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Johnston AR, Poll JB, Hays EM, Jones CW. Perceived impact of continuous glucose monitor use on quality of life and self-care for patients with type 2 diabetes. Diabetes Epidemiology and Management. 2022;6:100068. [Google Scholar]
- 6.Hirsch IB, Burugapalli BS, Brandner L, Poon Y, Frazzitta M, Godavarthi L, Virdi N. Impact of continuous glucose monitoring on emergency department visits and all-cause hospitalization rates among Medicaid beneficiaries with type 2 diabetes treated with multiple daily insulin or basal insulin therapy. J Manag Care Spec Pharm. 2024;30(10-b Suppl):S21-9. doi: 10.18553/jmcp.2024.30.10-b.s21 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Norman GJ, Fernandes J, Nemlekar P, Andrade SB, Lupton L, Berk A. Initiating continuous glucose monitoring is associated with improvements in glycemic control and reduced health care resource utilization for people with diabetes in a large US-insured population: A real-world evidence study. J Manag Care Spec Pharm. 2025;31(1):15-24. doi: 10.18553/jmcp.2024.24255 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Abrahami D, Hernández-Díaz S, Munshi MN, Patorno E. Continuous glucose monitoring in adults with diabetes in clinical practice: Increased access and education needed. J Gen Intern Med. 2023;38(8):2011-4. doi: 10.1007/s11606-023-08193-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.American Diabetes Association Professional Practice Committee. 7. Diabetes technology: Standards of medical care in diabetes–2022. Diabetes Care. 2022;45(Suppl. 1):S97-112. doi: 10.2337/dc22-S007 [DOI] [PubMed] [Google Scholar]
- 10.Oser SMM. MPH, Oser TK, MD. Medicare coverage of continuous glucose monitoring — 2023 updates. Fam Pract Manag. 2024;31(1):17-18. [Google Scholar]
- 11.Morris-Murray M, Frazzitta M. Using continuous glucose monitoring to measure and improve quality metrics: Updates on the Healthcare Effectiveness Data and Information Set 2024 Glucose Management Indicator measure. J Manag Care Spec Pharm. 2024;30(10-b Suppl):S30-9. doi: 10.18553/jmcp.2024.30.10-b.s30 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Rural Urban Commuting Area Codes data . General methods. Rural Health Research Center. Accessed March 3, 2025. https://depts.washington.edu/uwruca/ruca-methodology.php
- 13.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
- 14.Young BA, Lin E, Von Korff M, et al. Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization. Am J Manag Care. 2008;14(1):15-23. [PMC free article] [PubMed] [Google Scholar]
- 15.Lin Y, Gong Z, Ma C, Wang Z, Wang K. Relationship between glycemic control and cognitive impairment: A systematic review and meta-analysis. Front Aging Neurosci. 2023;15:1126183. doi: 10.3389/fnagi.2023.1126183 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ziegler R, Heinemann L, Freckmann G, Schnell O, Hinzmann R, Kulzer B. Intermittent use of continuous glucose monitoring: Expanding the clinical value of CGM. J Diabetes Sci Technol. 2021;15(3):684-94. doi: 10.1177/1932296820905577 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wright EE, Roberts GJ, Chuang JS, Nabutovsky Y, Virdi N, Miller E. Initiating GLP-1 therapy in combination with FreeStyle Libre provides greater benefit compared with GLP-1 therapy alone. Diabetes Technol Ther. 2024;26(10):754-62. doi: 10.1089/dia.2024.0015 [DOI] [PubMed] [Google Scholar]
- 18.Miller E, Chuang JS, Roberts GJ, Nabutovsky Y, Virdi N, Wright EE Jr.. Association of changes in A1C following continuous glucose monitoring acquisition in people with sub-optimally treated type 2 diabetes taking GLP-1 RA therapy. Diabetes Ther. 2024;15(9):2027-38. doi: 10.1007/s13300-024-01619-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wright EE, Miller E, Bindal A, Poon Y. Addition of continuous glucose monitoring to glucagon-like peptide 1 receptor agonist treatment for type 2 diabetes mellitus - An economic evaluation. J Manag Care Spec Pharm. 2025;31(2):127-36. doi: 10.18553/jmcp.2025.24253 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from Humana Healthcare Research, Inc., but restrictions apply to the availability of these data and are not publicly available.