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Research ArticleOriginal Research

The Effect of Depression and Rurality on Diabetes Control

Helen N. C. Fu, Victoria G. Skolnick, Caroline S. Carlin, Leif Solberg, Abagail M. Raiter and Kevin A. Peterson
The Journal of the American Board of Family Medicine November 2020, 33 (6) 913-922; DOI: https://doi.org/10.3122/jabfm.2020.06.200041
Helen N. C. Fu
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
PhD, RN
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Victoria G. Skolnick
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
BA
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Caroline S. Carlin
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
PhD
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Leif Solberg
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
MD
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Abagail M. Raiter
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
BA
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Kevin A. Peterson
From the Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (HF); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (VGS); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis, MN (CC); HealthPartners Institute, Minneapolis MN (LS); College of Liberal Arts, University of Minnesota–Twin Cities, Minneapolis (AMR); Department of Family Medicine and Community Health, University of Minnesota Medical School, Minneapolis (KAP).
MD, MPH
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    Figure 1.

    Predicted change in the probability of achieving HbA1c  < 8% associated with change in depression status by residential location. Note: Predicted changes in probabilities are based on the multivariate logit models. The models are used to compute the average expected change in the probability that HbA1c < 8% when patient depression status changes from “no depression” to “depression,” assuming the residential location is fixed at the level described and all other patient characteristics are held at their true value. The negative changes in probability indicate that glycemic control worsens when a patient is depressed, though the level of this decline may vary by patient location. Error bars indicate the 95% CIs for the estimated changes in probability.

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    Table 1.

    Sample Characteristics of Minnesota Adults with Diabetes Observed 2010 to 2017

    All Residential LocationsUrban Areas†Large Rural Towns†Small Rural Towns†
    Patient-year observations, n (%)1,697,173 (100.0)1,158,921 (68.3)219,677 (12.9)318,575 (18.8)
    Outcome variables
     HbA1c value, mean (SD)7.3 (1.5)7.3 (1.5)7.3 (1.5)7.3 (1.5)
     HbA1c < 8%, n (%)1,291,705 (76.1)843,928 (77.4)167,064 (76.1)242,604 (76.2)
    Patient-Level Characteristics
     Depression, n (%)384,699 (22.7)263,583 (22.7)5,1102 (23.3)7,0014 (22.0)
    Patient age (years), n (%)
     18 to 44178,982 (10.6)129,278 (11.2)2,1731 (9.9)2,7973 (8.8)
     45 to 54286,900 (16.9)206,796 (17.8)3,3661 (15.3)4,6443 (14.6)
     55 to 64504,623 (29.7)347,508 (30.0)6,4905 (29.6)9,2210 (28.9)
     65 to 75726,668 (42.8)475,339 (41.0)9,9380 (45.2)151,949 (47.7)
    Female, n (%)783,237 (46.2)535,938 (46.2)101,793 (46.3)145,506 (45.7)
    Ischemic vascular disease, n (%)291,998 (17.2)188,746 (16.3)4,1128 (18.7)6,2124 (19.5)
    Type 1 diabetes, n (%)100,045 (5.9)6,8620 (5.9)1,3199 (6.0)1,8226 (5.7)
    Insurance, n (%)
    Commercial708,927 (41.8)510,258 (44.0)8,2380 (37.5)116,289 (36.5)
    Medicare623,037 (36.7)393,273 (33.9)9,1842 (41.8)137,922 (43.3)
    Medicaid143,842 (8.5)108,279 (9.3)1,5744 (7.2)1,9819 (6.2)
    Dual Medicare/Medicaid8,0520 (4.7)5,2833 (4.6)1,0471 (4.8)1,7216 (5.4)
    No insurance5,4803 (3.2)3,9285 (3.4)5,382 (2.5)1,0136 (3.2)
    Unknown8,6044 (5.1)5,4993 (4.8)1,3858 (6.3)1,7193 (5.4)
    Practice-Level Characteristics
    Certified as a Patient-Centered Medical Home, n (%)462,728 (27.3)329,007 (28.4)5,4507 (24.8)7,9214 (24.9)
    Ownership*, n (%)
     Single-site medical group5,6155 (3.3)3,6575 (3.2)4,328 (2.0)1,5252 (4.8)
     Small medical group329,268 (19.4)167,391 (14.4)6,8705 (31.3)9,3172 (29.3)
    Large medical group1,311,750 (77.3)954,955 (82.4)146,644 (66.8)210,151 (66.0)
    Neighborhood-Level (Patient ZIP Code) American Community Survey Characteristics
     Percent of population White, non-Hispanic, mean (SD)82.3 (15.8)78.5 (16.7)89.3 (8.6)91.3 (10.1)
    Educational distribution of adult population, mean (SD)
     Percent with no high school degree8.2 (4.7)7.5 (4.9)8.9 (4.2)10.1 (3.5)
     Percent with high school degree or GED but no 4-year college degree60.9 (11.6)56.9 (11.3)66.6 (6.7)71.3 (5.2)
     Percent with 4-year college degree31.0 (13.4)35.6 (13.1)24.5 (7.6)18.6 (5.7)
     Percent of households under the federal poverty level, mean (SD)11.5 (7.4)11.0 (8.1)12.9 (5.9)12.0 (5.2)
    • ↵* Small medical groups were defined as 2-11 primary care sites; large medical groups were defined as 12 or more primary care sites.

    • ↵† Residential locations were mapped from Rural-Urban Commuting Areas.16

    • SD, standard deviation; GED, General educational development tests.

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    Table 2.

    Logit Regression Coefficients for Glycemic Control (HbA1c < 8%) by Depression and Patient Location, Adjusted for Covariates at Patient, Practice, and Neighborhood Levels

    Without Depression-Rural Interactions in ModelWith Depression-Rural Interactions in Model
    CoefficientP ValueCoefficientP Value
    Depression*−0.150<.001−0.162<.001
    Residential location† (ref. group Urban)
     Large rural town0.018.0310.012.289
     Small rural town−0.002.734−0.015.139
    Interactions terms (ref. group Depression* Urban area)
     (Depression)* (Large rural town)0.019.135
     (Depression)* (Small rural town)0.051<.001
    Patient-level control variables
      Annual trend−0.036<.001−0.036<.001
     Patient age, years (ref. group aged 65 to 75)
      18 to 44−0.840<.001−0.840<.001
      45 to 54−0.609<.001−0.609<.001
      55 to 64−0.273<.001−0.273<.001
     Female0.122<.0010.122<.001
     Ischemic vascular disease*−0.210<.001−0.210<.001
     Type 1 diabetes−0.844<.001−0.844<.001
     Insurance (ref. group Commercial)
      Medicare0.195<.0010.195<.001
      Medicaid−0.224<.001−0.224<.001
      Dual Medicare/Medicaid−0.181<.001−0.181<.001
      No insurance−0.401<.001−0.401<.001
      Unknown−0.018.038−0.018.039
    Practice-level control variables
     Certified as a patient-centered medical home0.049<.0010.049<.001
     Practice ownership‡ (ref. group Large medical group)
      Single-site medical group−0.104.006−0.104.006
      Small medical group−0.087<.001−0.087<.001
    Neighborhood-level (Patient ZIP Code) ACS control variables§
     Percent of population White, non-Hispanic0.0027<.0010.0027<.001
     Percent of adults with high school degree only−0.0039<.001−0.0039<.001
     Percent of adults with 4-year college degree0.0008.3060.0008.319
     Percent of households under the federal poverty level−0.0031<.001−0.0030<.001
     Intercept1.680<.0011.684<.001
    • ACS, American Community Survey.

    • ↵* Depression and ischemic vascular disease were reported by the primary care practice to MN Community Measurement (MNCM). MNCM suggested the use of the Major Depression or Dysthymia (DEP-01) Value Set and Ischemic Vascular Disease Value Set, but stated that “Any documentation of a new or existing diagnosis of depression [IV.D] during the measurement period [for IV.D: or year prior] is accepted.”

    • ↵† Rurality was determined by mapping patient ZIP code to Rural-Urban Commuting Areas, and summarizing to the WWAMI Rural Health Research Center’s Categorization B. (https://depts.washington.edu/uwruca/index.php).

    • ↵‡ Small medical groups were defined as 2 to 11 primary care sites; large medical groups were defined as 12 or more primary care sites.

    • ↵§ Coefficients represent the impact of a one percentage-point increase in the ACS-based statistic (e.g., 50% to 51%). Educational distribution was represented by percentage of adults with a high school degree but no 4-year college degree, and percentage with a 4-year college degree. We omitted the percentage without a high school degree to avoid collinearity in the estimation. A U-shaped impact of educational distribution in the patient’s neighborhood was evident, with neighborhoods more heavily weighted toward the high-school-only category showing poorer glycemic control.

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    Table 3.

    Annualized Rates of Office Visit Encounters by Depression Status and Residential Location Observed in Health Plan Administrative Data 2008 to 2014

     Urban PatientsRural Patients
    Average Office Visits per Year%Average Office Visits per Year%
    Patients without depression
     Primary care2.4944.12.2845.3
     Specialty care3.1555.92.7554.7
     Total5.64100.05.03100.0
    Patients with depression
     Primary care3.5839.03.0942.9
     Specialty care5.6061.04.1157.1
     Total9.18100.07.20100.0
    Reduction in primary care fraction−5.2 −2.4
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The Journal of the American Board of Family     Medicine: 33 (6)
The Journal of the American Board of Family Medicine
Vol. 33, Issue 6
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The Effect of Depression and Rurality on Diabetes Control
Helen N. C. Fu, Victoria G. Skolnick, Caroline S. Carlin, Leif Solberg, Abagail M. Raiter, Kevin A. Peterson
The Journal of the American Board of Family Medicine Nov 2020, 33 (6) 913-922; DOI: 10.3122/jabfm.2020.06.200041

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The Effect of Depression and Rurality on Diabetes Control
Helen N. C. Fu, Victoria G. Skolnick, Caroline S. Carlin, Leif Solberg, Abagail M. Raiter, Kevin A. Peterson
The Journal of the American Board of Family Medicine Nov 2020, 33 (6) 913-922; DOI: 10.3122/jabfm.2020.06.200041
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