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Research ArticleOriginal Research

Adherence to Diabetes Medications and Health Care Use During the COVID-19 Pandemic Among High-Risk Patients

Jean Yoon, Cheng Chen, Shirley Chao, Emily Wong and Ann-Marie Rosland
The Journal of the American Board of Family Medicine April 2023, 36 (2) 289-302; DOI: https://doi.org/10.3122/jabfm.2022.220319R1
Jean Yoon
From the Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California (JY, EW); Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California (JY, CC); University of California, San Francisco, School of Medicine, Department of General Internal Medicine, San Francisco, CA (JY); Department of Pharmacy, VA San Francisco, San Francisco, California (SC); University of California, San Francisco, School of Pharmacy, San Francisco, CA (SC); Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania (AMR); Department of Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (AMR).
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Cheng Chen
From the Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California (JY, EW); Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California (JY, CC); University of California, San Francisco, School of Medicine, Department of General Internal Medicine, San Francisco, CA (JY); Department of Pharmacy, VA San Francisco, San Francisco, California (SC); University of California, San Francisco, School of Pharmacy, San Francisco, CA (SC); Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania (AMR); Department of Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (AMR).
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Shirley Chao
From the Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California (JY, EW); Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California (JY, CC); University of California, San Francisco, School of Medicine, Department of General Internal Medicine, San Francisco, CA (JY); Department of Pharmacy, VA San Francisco, San Francisco, California (SC); University of California, San Francisco, School of Pharmacy, San Francisco, CA (SC); Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania (AMR); Department of Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (AMR).
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Emily Wong
From the Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California (JY, EW); Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California (JY, CC); University of California, San Francisco, School of Medicine, Department of General Internal Medicine, San Francisco, CA (JY); Department of Pharmacy, VA San Francisco, San Francisco, California (SC); University of California, San Francisco, School of Pharmacy, San Francisco, CA (SC); Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania (AMR); Department of Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (AMR).
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Ann-Marie Rosland
From the Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California (JY, EW); Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California (JY, CC); University of California, San Francisco, School of Medicine, Department of General Internal Medicine, San Francisco, CA (JY); Department of Pharmacy, VA San Francisco, San Francisco, California (SC); University of California, San Francisco, School of Pharmacy, San Francisco, CA (SC); Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania (AMR); Department of Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (AMR).
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    Figure 1.

    Mean adjusted primary care visits (A) and medication adherence (B) in high-risk patients by race/ethnicity. In A, blue/orange bars show mean number of primary care visits per patient per quarter estimated from regression models. In B, adherence was measured by proportion of days covered, ranging from 0 to 1.0, per patient per quarter and estimated from regression models. Mean visits and adherence per quarter were estimated from linear regression models adjusting for age-group and pandemic phase and their interaction terms, quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, rurality, clinic factors, and community rate of COVID infections.

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    Figure 2.

    Mean adjusted primary care visits (A) and medication adherence (B) in high-risk patients by age group. In A, blue/orange bars show mean number of primary care visits per patient per quarter estimated from regression models. In B, adherence was measured by proportion of days covered, ranging from 0 to 1.0, per patient per quarter and estimated from regression models. Mean visits and adherence per quarter were estimated from linear regression models adjusting for age-group and pandemic phase and their interaction terms, quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, rurality, clinic factors, and community rate of COVID infections.

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    Appendix 3.

    Mean Adjusted Primary Care Visits (A) and Medication Adherence (B) in High-Risk Patients by Rurality. Mean visits and adherence per quarter were estimated from linear regression models adjusting for rurality and pandemic phase and their interaction terms, quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, rurality, clinic factors, and community rate of COVID infections.

Tables

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    Table 1.

    Patient and Clinic Characteristics for High-Risk VA Patients with Diabetes, Fiscal Year 2019

    Patient Characteristics, n = 188,569N (%)/Mean (SD)
    Age Group 
    <65 61,097 (32)
    65 to 70 52,453 (28)
    71+ 75,019 (40)
    Gender 
    Male 179,134 (95)
    Female 9,435 (5)
    Race/ethnicity 
    White123,126 (65)
    Black41,508 (22) 
    Hispanic13,016 (7) 
    Other* 10,919 (6)
    Marital status 
    Married 93,391 (50)
    Single/never married 19,380 (10)
    Divorced/separated 61,932 (33)
    Widowed 13,866 (7)
    Priority status 
    1 to 2: service-connected disability 30%+ 101,733 (54)
    3 to 4: service-connected disability 10% to 20%/housebound 23,546 (12)
    5 to 6: below VA means test/5 years postdischarge 57,033 (30)
    7 to 8: above VA means test 6,257 (3)
    Drive time to nearest primary care site (in minutes)21 (16)
    Rurality
    Rural64,997 (34)
    Urban123,572 (66)
    Comorbidities
    Sum of Elixhauser comorbid conditions6.4 (3.0)
    Hypertension176,426 (94)
    Hyperlipidemia157,115 (83)
    Depression72,273 (38)
    Diabetes medication drug class
    Alpha-glucosidase1,656 (1)
    Biguanides84,732 (45)
    Dipeptidyl-peptidase-4 inhibitors19,686 (10)
    Glucagon-like peptide-1 agonist14,874 (8)
    Meglitinides41 (<1)
    Sodium-glucose co-transporter-2 (SGLT2) inhibitors9,825 (5)
    Sulfonylureas59,727 (32)
    Thiazolidinediones6,828 (4)
    Clinic characteristics, n = 930
    Average wait time for appointment (in days)4.3 (2.8)
    Proportion of encounters provided by telephone27% (10)
    Percent of patients with prior telehealth use17% (6.8)
    • ↵* Other includes Asian, Pacific Islander, American Indian, Alaska Native, and unknown race/ethnicity.

    • Abbreviations: VA, Veterans Affairs; SD, standard deviation.

    • View popup
    Table 2.

    Mean Unadjusted VA Use by Pandemic Phase in High-Risk VA Patients with Diabetes, FY2019-2021, n = 188,569

    Use TypeMean per Patient per Quarter (SD)
    PrepandemicEarly PandemicMidpandemic
    In-person primary care visits1.48 (2.09)0.66 (1.77)1.27 (1.99)
    Virtual primary care visits1.34 (2.03)2.05 (2.72)1.58 (2.30)
    Adherence to diabetes medications*0.82 (0.25)0.81 (0.27)0.82 (0.26)
    Emergency department visits0.22 (0.42)0.16 (0.36)0.17 (0.38)
    All-cause medical/surgical hospitalizations0.10 (0.37)0.07 (0.33)0.08 (0.35)
    Hospitalizations for ambulatory care sensitive conditions0.022 (0.15)0.015 (0.12)0.017 (0.13)
    Hospitalizations for diabetes complications0.010 (0.10)0.007 (0.08)0.007 (0.09)
    • ↵* Measured by the proportion of days covered.

    • Abbreviations: VA, Veterans Affairs; SD, standard deviation.

    • View popup
    Table 3.

    Adjusted Differences in Use During Early and Midpandemic Phases Compared to Prepandemic Phase Among High-Risk VA Patients with Diabetes, n = 188,569

    Use TypeMean Difference per Patient per Quarter (95% CI)*
    Early PandemicMidpandemic
    In-person primary care visits−0.68 (−0.70, −0.66)0.08 (0.05, 0.11)
    Virtual primary care visits0.88 (0.84, 0.92)0.49 (0.45, 0.53)
    Adherence to diabetes medications†0.00 (−0.00, 0.00)−0.00 (−0.01, 0.00)
    Emergency department visits−0.22 (−0.24, −0.21)−0.03 (−0.06, 0.001)
    All-cause medical/surgical hospitalizations−0.23 (−0.28, −0.19)−0.02 (−0.06, 0.02)
    Hospitalizations for ambulatory care sensitive conditions−0.26 (−0.32, −0.20)−0.05 (−0.12, 0.02)
    Hospitalization for diabetes complications−0.002 (−0.002, −0.001)−0.000 (−0.000, 0.000)
    • ↵* Mean difference was estimated from linear regression models adjusting for quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, rurality, clinic factors, and community rate of COVID infections. A Poisson model was used to estimate emergency department visits, all-cause, and ACSC hospitalizations, whereas a logistic model was used to estimate hospitalization for diabetes complications.

    • † Measured by the proportion of days covered.

    • Abbreviations: CI, confidence interval; VA, veterans affairs; ACSC, ambulatory care-sensitive condition.

    • View popup
    Appendix 1.

    Regression Results for All Outcomes in Full Cohort, FY2019-2021

    In-Person Primary Care VisitsVirtual Primary Care VisitsAdherence (Proportion of days covered) to Diabetes MedicationsAll-Cause Emergency Department VisitsAll-Cause Medical/ Surgical HospitalizationHospitalization for Ambulatory Care Sensitive ConditionHospitalization for Diabetes Domplications
    CoefficientCoefficientCoefficientIRRIRRIRROR
    Quarter−0.05*−0.02*0.003*0.97*0.990.990.98
    (0.002)(0.002)(0.000)(0.002)(0.011)(0.029)(0.006)
    Pandemic phase
     Prepandemic phaseRefRefRefRefRefRefRef
     Early pandemic phase−0.68*0.88*0.0010.80*0.79*0.77*0.77*
    (0.011)(0.021)(0.0001)(0.006)(0.018)(0.023)(0.027)
     Midpandemic phase0.08*0.49*−0.0030.97*0.980.950.99
    (0.015)(0.019)(0.002)(0.014)(0.019)(0.034)(0.048)
    Patient factors
     Age group
      <65 yearsRefRefRefRefRefRefRef
      65 to 70 years0.07*0.06*0.02*0.85*0.85*0.790.62*
    (0.007)(0.008)(0.001)(0.034)(0.021)(0.332)(0.016)
      71+ years0.19*0.10*0.03*0.83*0.82*0.750.49*
    (0.008)(0.009)(0.001)(0.013)(0.025)(0.372)(0.013)
     Gender
      FemaleRefRefRefRefRefRefRef
      Male0.23*−0.24*0.01*0.921.171.361.53*
    (0.016)(0.022)(0.002)(0.033)(0.083)(0.314)(0.081)
     Race/ethnicity
      WhiteRefRefRefRefRefRefRef
      Black−0.07*−0.15*−0.05*1.20*0.951.131.07
    (0.019)(0.022)(0.002)(0.066)(0.048)(0.079)(0.032)
      Hispanic0.01−0.10*−0.03*1.26*1.091.041.10
    (0.028)(0.025)(0.004)(0.077)(0.050)(0.279)(0.047)
      Other0.01−0.004−0.02*1.011.001.031.02
    (0.014)(0.020)(0.002)(0.024)(0.056)(0.107)(0.049)
    Drive time to primary care (in miles)−0.004*−0.002*<0.0011.001.001.001.00
    (0.0003(0.0005)<0.001(0.001)(0.001)(0.002)(0.001)
     Rural/urban residence
      UrbanRefRefRefRefRefRefRef
      Rural0.051*0.10*0.01*0.77*0.90*0.890.83*
    (0.019)(0.026)(0.002)(0.041)(0.041)(0.142)(0.026)
     Marital status
      Currently marriedRefRefRefRefRefRefRef
      Divorced/separated−0.001−0.05*−0.02*0.971.031.13*1.22*
    (0.007)(0.008)(0.001)(0.020)(0.031)(0.037)(0.028)
      Single, never married0.020−0.10*−0.02*0.991.061.171.39*
    (0.011)(0.013)(0.002)(0.019)(0.028)(0.252)(0.044)
     Widowed0.12*0.02−0.01*1.001.051.121.25*
    (0.015)(0.016)(0.002)(0.039)(0.040)(0.083)(0.048)
     VA enrollment priority
      Group 1 to 2RefRefRefRefRefRefRef
      Group 3 to 40.12*0.10*−0.01*1.051.121.161.20*
    (0.012)(0.013)(0.002)(0.021)(0.089)(0.139)(0.039)
      Group 5 to 6−0.005−0.09*−0.02*1.071.171.311.33*
    (0.007)(0.009)(0.001)(0.027)(0.109)(0.459)(0.033)
      Group 7 to 8−0.04−0.05−0.01*1.011.101.291.23*
    (0.016)(0.020)(0.003)(0.028)(0.072)(0.246)(0.072)
    Elixhauser comorbidity score0.11*0.10*−0.001*1.16*1.361.421.35*
    (0.005)(0.003)(<0.001)(0.028)(0.179)(0.751)(0.005)
    Comorbid depression0.09*0.10*−0.02*0.960.750.690.81*
    (0.008)(0.009)(0.001)(0.020)(0.139)(0.339)(0.019)
    Clinic factors
     Clinic appointment wait time−0.0040.013<−0.0011.010.98*0.980.99
    (0.014)(0.016)(0.001)(0.002)(0.007)(0.034)(0.020)
     Clinic ratio of telephone encounters−0.03*0.28*0.0011.041.011.031.04*
    (0.011)(0.018)(0.001)(0.016)(0.026)(0.075)(0.017)
     Clinic use of telehealth0.014−0.042*<−0.0010.980.99*0.990.98
    (0.013)(0.015)(0.001)(0.009)(0.004)(0.019)(0.018)
     County rate of COVID-190.06*−0.05*−0.003*1.02*1.021.001.01
    (0.003)(0.005)(0.0003)(0.005)(0.013)(0.041)(0.009)
    • Outcomes for primary care use and adherence were estimated from linear regression models adjusting for quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, rurality, clinic factors, and community rate of COVID infections. A Poisson model was used to estimate emergency department visits, all-cause, and ambulatory care-sensitive condition hospitalizations, whereas a logistic model was used to estimate hospitalization for diabetes complications. All results have standard errors reported in parentheses.

    • *P < .01.

    • Abbreviations: IRR, incidence rate ratio; VA, veterans affairs; OR, odds ratio.

    • View popup
    Appendix 2.

    Mean Adjusted Number of Emergency Department Visits/Hospitalizations per Quarter in High-Risk Patients by Race/Ethnicity, Age Group, and Rurality

    Acute Care TypeMean per Patient per Quarter (SD)
    PrepandemicEarly PandemicMidpandemic
    Emergency department visits
     Race/ethnicity
      White0.190.160.19
      Black0.230.180.23
      Hispanic0.240.190.24
      Other0.200.160.19
     Age group
      <650.230.180.21
      65 to 700.200.160.20
      71+0.190.160.20
     Rurality
      Rural0.170.140.18
      Urban0.220.180.22
    All-cause medical/surgical hospitalizations0.100.070.08
     Race/ethnicity
      White0.100.080.10
      Black0.090.080.10
      Hispanic0.110.090.10
      Other0.100.080.10
     Age group
      <650.110.090.10
      65 to 700.090.080.10
      71+0.090.070.09
     Rurality
      Rural0.090.070.09
      Urban0.100.080.10
    Hospitalizations for ambulatory care sensitive conditions
     Race/ethnicity
      White0.020.020.02
      Black0.020.020.02
      Hispanic0.020.020.02
      Other0.020.020.02
     Age group
      <650.030.020.02
      65 to 700.020.020.02
      71+0.020.010.02
     Rurality
      Rural0.020.020.02
      Urban0.020.020.02
    Hospitalization for diabetes complications0.0100.0070.007
     Race/ethnicity
      White0.0090.0070.009
      Black0.0100.0070.009
      Hispanic0.0100.0080.009
      Other0.0090.0070.009
     Age group
      <650.0140.0100.012
      65 to 700.0080.0070.009
      71+0.0070.0060.007
     Rurality
      Rural0.0080.0070.010
      Urban0.0100.0070.010
    • Mean number of hospitalizations and ED visits were estimated from Poisson regression models adjusting for race/ethnicity, age group, and rurality and pandemic phase and their interaction terms, quarter, patients’ sociodemographic characteristics, Elixhauser comorbidity, comorbid depression, distance to primary care site, clinic factors, and community rate of COVID infections. Hospitalizations for diabetes complications were estimated using a logistic model.

    • Abbreviations: ED, emergency department; SD, standard deviation.

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The Journal of the American Board of Family     Medicine: 36 (2)
The Journal of the American Board of Family Medicine
Vol. 36, Issue 2
March/April 2023
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Adherence to Diabetes Medications and Health Care Use During the COVID-19 Pandemic Among High-Risk Patients
Jean Yoon, Cheng Chen, Shirley Chao, Emily Wong, Ann-Marie Rosland
The Journal of the American Board of Family Medicine Apr 2023, 36 (2) 289-302; DOI: 10.3122/jabfm.2022.220319R1

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Adherence to Diabetes Medications and Health Care Use During the COVID-19 Pandemic Among High-Risk Patients
Jean Yoon, Cheng Chen, Shirley Chao, Emily Wong, Ann-Marie Rosland
The Journal of the American Board of Family Medicine Apr 2023, 36 (2) 289-302; DOI: 10.3122/jabfm.2022.220319R1
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