Diabetes Care and Glycemic Control During the COVID-19 Pandemic in the United States | Diabetes | JAMA Internal Medicine | JAMA Network
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Figure.  Proportion of Patients Receiving Diabetes-Related Care and HbA1c Levels in a Given Week (January 1, 2020, through December 1, 2020) Compared With the Same Weeks in 2019
Proportion of Patients Receiving Diabetes-Related Care and HbA1c Levels in a Given Week (January 1, 2020, through December 1, 2020) Compared With the Same Weeks in 2019

Among patients in the cohorts, 42% (n = 570 492) and 41% (n = 554 275) had at least 1 HbA1c laboratory result in 2019 and 2020 cohorts, respectively.

Table.  Characteristics of Patients With Type 2 Diabetes in the Study Cohorts (2019, 2020)
Characteristics of Patients With Type 2 Diabetes in the Study Cohorts (2019, 2020)
1.
Patel  SY, Mehrotra  A, Huskamp  HA, Uscher-Pines  L, Ganguli  I, Barnett  ML.  Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States.   Health Aff (Millwood). 2021;40(2):349-358. doi:10.1377/hlthaff.2020.01786PubMedGoogle ScholarCrossref
2.
Patel  SY, Mehrotra  A, Huskamp  HA, Uscher-Pines  L, Ganguli  I, Barnett  ML.  Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US.   JAMA Intern Med. 2021;181(3):388-391. doi:10.1001/jamainternmed.2020.5928PubMedGoogle ScholarCrossref
3.
Lu  D. 2020 was especially deadly. Covid wasn’t the only culprit. New York Times. December 13, 2020. Accessed March 1, 2021. https://www.nytimes.com/interactive/2020/12/13/us/deaths-covid-other-causes.html
4.
OptumLabs. OptumLabs and OptumLabs Data Warehouse Descriptions and Citation. July 2020. Reproduced with permission from OptumLabs.
5.
National Committee for Quality Assurance (NCQA). HEDIS 2018 Volume 2 technical update. Accessed March 1, 2021. https://www.ncqa.org/wp-content/uploads/2018/10/20171002_HEDIS_2017_OctoberUpdate.pdf
6.
Allweiss  P.  Diabetes and disasters: recent studies and resources for preparedness.   Curr Diab Rep. 2019;19(11):131. doi:10.1007/s11892-019-1258-7PubMedGoogle ScholarCrossref
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    Research Letter
    July 6, 2021

    Diabetes Care and Glycemic Control During the COVID-19 Pandemic in the United States

    Author Affiliations
    • 1Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
    • 2Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
    • 3Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota
    • 4Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
    • 5Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 6Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
    • 7OptumLabs, Eden Prairie, Minnesota
    JAMA Intern Med. Published online July 6, 2021. doi:10.1001/jamainternmed.2021.3047

    Dramatic reductions in outpatient visits and laboratory testing early in the COVID-19 pandemic1,2 raised concerns about gaps in diabetes management and glycemic control.3 We therefore compared weekly rates of diabetes-related outpatient visits, screening tests, medication fills, and patients’ hemoglobin A1c (HbA1c) levels in 2020 vs 2019 in a national cohort of adults with type 2 diabetes.

    Methods

    Using deidentified claims from OptumLabs Data Warehouse,4 we created 2 cohorts of adults (age ≥18 years) with type 2 diabetes and continuous enrollment in commercial or Medicare Advantage health plans (criteria are listed in the Supplement).5 The 2019 cohort was identified using 2018 data, and their outcomes were assessed during 2019. The 2020 cohort was identified using 2019 data and their outcomes were assessed during 2020.

    We measured the proportion of patients in each cohort who received 1 or more of a given service in a week (services are defined in the Supplement). Outpatient visits included in-person or telemedicine visits. Glycemic control was assessed as the mean weekly HbA1c level for the 41% of patients with available test results. Our study periods were the first 48 weeks of 2019 and 2020; we classified weeks 1 through 10 of 2020 as the prepandemic period and weeks 11 through 48 as the pandemic period.

    To assess for differences in 2020 vs 2019, we calculated the average marginal effects from logistic and linear regression models adjusting for patient characteristics comparing weeks 1 through 10 and 11 through 48 of 2020 vs the same periods of 2019. Standard errors were clustered at the patient level. To address potential selection bias in which patients received a HbA1c test result, we conducted sensitivity analyses testing HbA1c levels within individual patients (detailed in the Supplement), with similar findings.

    Statistical analyses were performed in SAS, version 9.4. The Harvard Medical School institutional review board exempted this study from review owing to the use of deidentified data.

    Results

    There were 1 357 029 and 1 364 522 adults with diabetes in the 2019 and 2020 cohorts, respectively, with similar baseline characteristics (Table). In 2019, 0.3% of cohort had 1 or more telemedicine visit, compared with 29.1% of the 2020 cohort during the pandemic period.

    During the prepandemic period, there was no clinically meaningful difference between the cohorts across all 6 outcome measures (Figure). Early in the pandemic period, there were large reductions in visits and testing that rebounded to near-baseline levels by week 48. Across the entire pandemic period, adjusted use was lower in 2020 compared with 2019 for outpatient visits (85.0% vs 87.3% of patients in 2020 and 2019 cohorts with ≥1 outpatient visit during pandemic period; relative percent change −2.6%), HbA1c testing (76.5% vs 81.8%; −6.5%), retinopathy testing (5.6% vs 6.9%; −18.8%), and nephropathy testing (40.1% vs 43.9%; −8.5%). In contrast, medication fill rates were similar during the pandemic period of 2020 as compared with 2019 (64.2% vs 62.2%; 3.6%). Levels of HbA1c were nearly identical during the pandemic period of 2020 compared with 2019 (7.16% vs 7.14%; 0.3%). Due to sample size, all comparisons between 2020 and 2019 were statistically significant at P < .001.

    Discussion

    While diabetes-related outpatient visits and testing fell during the pandemic, we observed no evidence of a negative association with medication fills or glycemic control. One explanation for these patterns could be the small increase in medication fill rates during the pandemic that may have protected against any disruptions in diabetes self-management during the pandemic and hence staved off detrimental effects on glycemic control. Mail-order pharmacies and pharmacy delivery services may have been key during the pandemic in ensuring patients receive their medications. Together, these would be consistent with diabetes disaster preparedness guidelines, which emphasize prioritizing access to medications over access to health care professionals during an emergency.6

    The unprecedented increase in telemedicine visits we observe during the pandemic, although unable to overcome the overall decrease in visits, may have prevented substantive disruptions in medication prescribing. Our findings also emphasize that there is not a direct relationship between visit frequency and glycemic control. Limitations of our findings include that the results may not generalize to other populations such as those with Medicaid insurance or the uninsured.

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    Article Information

    Accepted for Publication: May 5, 2021.

    Published Online: July 6, 2021. doi:10.1001/jamainternmed.2021.3047

    Corresponding Author: Ateev Mehrotra, MD, MPH, Department of Health Care Policy, Harvard Medical School, 180A Longwood Ave, Boston, MA 02115 (mehrotra@hcp.med.harvard.edu).

    Author Contributions: Dr Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Patel, Barnett, Mehrotra.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Patel.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Patel, Barnett.

    Obtained funding: Mehrotra.

    Administrative, technical, or material support: McCoy, Mehrotra.

    Supervision: Mehrotra.

    Conflict of Interest Disclosures: Dr McCoy reported grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) during the conduct of the study; grants from AARP and grants from NIDDK outside the submitted work. Dr Shah reported research support through Mayo Clinic from the US Food and Drug Administration to establish the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI) program (U01FD005938); the Centers of Medicare and Medicaid Innovation under the Transforming Clinical Practice Initiative (TCPI); the Agency for Healthcare Research and Quality (R01HS025164; R01HS025402; R03HS025517; K12HS026379); the National Heart, Lung and Blood Institute of the National Institutes of Health (NIH) (R56HL130496; R01HL131535; R01HL151662); the National Science Foundation; from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology (NEST) and the Patient Centered Outcomes Research Institute (PCORI) to develop a Clinical Data Research Network (LHSNet). Dr Mehrotra reported grants from Commonwealth Fund during the conduct of the study. No other disclosures were reported.

    Funding/Support: This project was supported by the Commonwealth Fund. There was also support from the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK114497; RGM), National Institute on Aging of the National Institutes of Health (K23 AG058806-01; MLB), and National Institute of Mental Health (T32MH019733; SYP).

    Role of the Funder/Sponsor: The Commonwealth Fund and the National Institutes of Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank Rebecca Shyu, University of Missouri, for contributing to manuscript preparation efforts. She was not compensated.

    References
    1.
    Patel  SY, Mehrotra  A, Huskamp  HA, Uscher-Pines  L, Ganguli  I, Barnett  ML.  Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States.   Health Aff (Millwood). 2021;40(2):349-358. doi:10.1377/hlthaff.2020.01786PubMedGoogle ScholarCrossref
    2.
    Patel  SY, Mehrotra  A, Huskamp  HA, Uscher-Pines  L, Ganguli  I, Barnett  ML.  Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US.   JAMA Intern Med. 2021;181(3):388-391. doi:10.1001/jamainternmed.2020.5928PubMedGoogle ScholarCrossref
    3.
    Lu  D. 2020 was especially deadly. Covid wasn’t the only culprit. New York Times. December 13, 2020. Accessed March 1, 2021. https://www.nytimes.com/interactive/2020/12/13/us/deaths-covid-other-causes.html
    4.
    OptumLabs. OptumLabs and OptumLabs Data Warehouse Descriptions and Citation. July 2020. Reproduced with permission from OptumLabs.
    5.
    National Committee for Quality Assurance (NCQA). HEDIS 2018 Volume 2 technical update. Accessed March 1, 2021. https://www.ncqa.org/wp-content/uploads/2018/10/20171002_HEDIS_2017_OctoberUpdate.pdf
    6.
    Allweiss  P.  Diabetes and disasters: recent studies and resources for preparedness.   Curr Diab Rep. 2019;19(11):131. doi:10.1007/s11892-019-1258-7PubMedGoogle ScholarCrossref
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