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. 2015 Dec;21(12):10.18553/jmcp.2015.21.12.1195. doi: 10.18553/jmcp.2015.21.12.1195

Geographic Variation in Antidiabetic Agent Adherence and Glycemic Control Among Patients with Type 2 Diabetes

Eleonora Tan 1,✉, Wenya Yang 2, Bo Pang 3, Mingliang Dai 4, F Ellen Loh 5, Paul Hogan 6
PMCID: PMC10398093  PMID: 26679968

Abstract

BACKGROUND:

Medication nonadherence is an imperative public health concern. Among patients with type 2 diabetes mellitus (T2DM), poor adherence to antidiabetic agents is strongly associated with suboptimal glycemic control. Poor adherence and hyperglycemia greatly increase diabetes-related morbidity and mortality. At a national level, diabetes drug adherence using average proportion of days covered (PDC) is estimated to range between 36% and 81%, with an estimated range for diabetes control between 38% and 47%. At a state level no such studies exist.

OBJECTIVE:

To estimate the level of medication adherence to antidiabetic agents and of diabetes control, and their association among patients with T2DM receiving medication treatment at the state and the Metropolitan Statistical Area (MSA) levels among the populations covered by commercial insurance, Medicare, or Medicaid.

METHODS:

The study population included adults with T2DM aged ≥ 18 years who were identified using ICD-9-CM code 250.xx, who received diabetes medication, and who were covered by private insurance, Medicare, or Medicaid in each state, the District of Columbia, and the top 50 MSAs. Medication adherence was measured by average PDC and the percentage of population that had a PDC ≥ 80%. Diabetes control was identified using ICD-9-CM diagnosis codes. Patients who were not diagnosed with uncontrolled diabetes (250.x2 and 250.x3) were identified as being under control. The administrative claims databases used for this study included the 2012 medical and pharmacy claims from a large U.S. health plan, the complete 2011 Medicare Standard Analytical File linked with Part D claims, and the 2008 Mini-Medicaid Analytic eXtract (Mini-Max). Medication adherence and diabetes control were adjusted for age and sex to allow comparison across insurance coverage, states, and MSAs.

RESULTS:

For an insured patient population with T2DM that received diabetic drug treatment, average PDC was 79%. However, 35% of patients did not achieve an adherence of at least 80% of PDC. In addition, at least 40% of patients did not have their diabetes under control. Across insurance types, we found that patients insured with Medicare had relatively high average PDC and adherence levels (83% and 71%) in comparison with the commercially insured population (77% and 60%) and Medicaid patients (75% and 57%). In contrast, commercially insured patients had relatively better diabetes control (69%) than those insured with Medicare and Medicaid (54% and 53%, respectively). At a state level, we found that commercially insured and Medicare populations have relatively smaller geographic variation in drug adherence than the Medicaid population.

CONCLUSIONS:

This study identified gaps in T2DM drug adherence and pinpointed geographic areas that lag in terms of diabetes drug adherence or diabetes control and would benefit from implementing strategies to increase drug adherence.

What is already known about this subject

  • Diabetes drug adherence is strongly related to diabetes control and health outcomes.

  • There is a need to improve drug adherence and glycemic control.

What this study adds

  • This study identifies gaps in drug adherence and diabetes control across insurance types, states, and Metropolitan Statistical Areas (MSAs).

  • Adherence to oral and injectable antidiabetic medications varied significantly across states and MSAs, as well as insurance types. The Medicare population had the highest adherence, while the commercial population had the highest level of diabetes control, partially because of younger age and shorter disease duration.

  • States in the Northeast and Midwest regions were identified as doing better than the national averages in drug adherence and diabetes control, while southern states were found to have larger gaps in these care measures.

Medication nonadherence, particularly among patients with type 2 diabetes mellitus (T2DM), is a vital public health concern. Nonadherence is associated with morbidity and mortality and results in higher health care use and expenditures.1 In 2012, 29.1 million adults in the United States had diabetes, 90% of whom suffered from T2DM.2 Among patients with T2DM, poor adherence to antidiabetic agents is strongly associated with suboptimal glycemic control and greatly increases the incidence of diabetes-related morbidity and mortality and costs.3-5 Therefore, while the American Diabetes Association recommends individualized treatment targets, it does advise that for many nonpregnant adults, lowering hemoglobin A1c (A1c) to below 7% is a reasonable goal.6

Existing research has highlighted gaps in medication adherence and diabetes control at the national level. A systematic review found that drug adherence among patients with T2DM in 4 nationally representative studies ranged between 36% and 81%.7,8 Drug adherence, measured as the proportion of days covered (PDC), was 79% and 81% in 2 studies representative of pharmacy benefit organizations (PBO).7 The third study, using a large pharmacy claims database, used medication possession ratio as a proxy for drug adherence. This study excluded patients with T2DM who used insulin and found 69% of patients to be adherent.8 The fourth study, using a Medicaid population, concluded that drug adherence measured using PDC ranged between 36% and 49%.7 Diabetes control, defined by A1c below 7%, has been estimated to be 53% for commercially insured patients and 62% for Medicare patients.5 To our knowledge, diabetes control for Medicaid patients is not currently available.

Other studies have documented the wide geographic variations in general access to care, health care use, and expenditures across the United States.9 For example, per capita diabetes-related medical expenditures in Massachusetts are 1.4 times the expenditures in Utah.10 Regional differences in health care use, expenditures, and drug adherence are partially explained by population demographics, which include age, sex, and socioeconomic characteristics. Other important determinants of population health care use are local, such as supply of care, financial incentives, practice patterns, and behavioral determinants of health. Furthermore, the degree of illness, out-of-pocket drug costs, polypharmacy, complexity in drug regimen, and patients’ perceptions toward their illness and drug effectiveness are also known to affect adherence.11,12

Little is known about the level of adherence to antidiabetic agents and its potential geographic variation at the state or Metropolitan Statistical Area (MSA) level. One previous study has found that adherence to oral antidiabetic medications varied significantly across 9 regions in the United States, after controlling for age, gender, socioeconomic status, and yearly out-of-pocket pharmacy expenses.13 This study concluded that diabetic drug adherence, captured by average PDC, among commercially insured patients was highest in the New England, Mid-Atlantic, East North Central, and West North Central states, which had 40% to 60% higher probability of being adherent to their medications than East South Central states. In this study, East North Central states included Wisconsin, Illinois, Indiana, Ohio, and Michigan. West North Central states included North and South Dakota, Nebraska, Kansas, Minnesota, Iowa, and Minnesota. East South Central states included Kentucky, Tennessee, Alabama, and Mississippi. In contrast, among the Medicare population with Part D benefits, New England and East North Central states were 8% to 19% more adherent than East South Central states.

Our purpose in this claims-based retrospective crosssectional study was to examine the average PDC to antidiabetic agents, the percentage of optimal adherence, and the percentage of patients with controlled diabetes among patients diagnosed with T2DM who received antidiabetic medication. The results are presented at the state and MSA level for commercially insured, Medicare, and Medicaid patients separately.

Methods

Data Sources

Data sources included the 2012 medical and pharmacy claims from UnitedHealth Group and non-UnitedHealth Group health plans (the deidentified Normative Health Information [dNHI] database), the 2011 Medicare Standard Analytical File medical claims linked with Part D claims from the 5% Chronic Conditions Data Warehouse files, and the 2008 Mini-Medicaid Analytic eXtract (Mini-Max), which is a one-time extraction of a 5% stratified random sample of the Medicaid population. These 3 databases were statistically deidentified. This study was reviewed and approved by an institutional review board and the Centers for Medicare & Medicaid Services (CMS) Privacy Review Board.

Study Population

The study population included adults with T2DM who received diabetes medication and were covered by private insurance (aged 18 to 64 years), Medicare (aged 18 years and above), or Medicaid (aged 18 to 64 years) in each state, the District of Columbia, and the top 50 MSAs. For Medicare and Medicaid, only fee-for-service insured were included. The Medicare sample was limited to fee-for-service Part D enrollees and did not include Medicare Advantage Plans with Part D coverage. Adults with T2DM who filled an antidiabetic prescription any time during the year of data availability were included in the study. Patients were also required to be continuously covered during the entire calendar year. Patients with diagnosed diabetes were identified by 1 or more hospital stays or emergency department visits or at least 2 separate physician office or hospital outpatient visits during which diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 250.xx) was recorded. T2DM patients were then identified using an algorithm described in Appendix A (available in online article). Using these criteria, we identified 561,034 individuals with T2DM across the 3 insurance categories (Figure 1). Excluded from this study were claims where number of days supplied were missing or zero or prescription fill dates were missing. The number of T2DM patients identified who received diabetes medication was 197,941 for commercially insured, 184,439 for Medicare, and 70,003 for Medicaid (Figure 1).

Figure 1.

Figure 1

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State Average PDC in Comparison to Average PDC Across States by Insurance Type and Overall

Study Measures

Drug adherence was measured using PDC, calculated as the proportion of days with 1 or more drugs available during the study period. This period was defined as the time interval between the index date (the first script fill date during the study year) and the last day of the calendar year. We used an interval-based method to calculate a PDC for each patient using pharmacy claims.14 Upon calculating a PDC for each person, patients with a PDC ≥ 80% were classified as being adherent to their medications.15 We reported average PDC and adherence rates.

Antidiabetic agents covered by the PDC calculations included oral antidiabetic drugs (OAD), including alpha-glucosidase inhibitors, meglitinides, biguanides, sulfonylureas, thiazolidinediones, and dipeptidyl peptidase-4 inhibitors, and anti-diabetic combinations, insulin mixes, long-acting insulins, and noninsulin injectable drugs (GLP-1 receptor agonists). Rapid-acting insulin was excluded because of the uncertainty of the real days of supply. Days of supply for insulin and noninsulin injectable drugs were adjusted because of titration using existing method.16,17 Drug lists were constructed using Healthcare Effectiveness Data and Information Set-approved diabetes drugs by 2011 (the National Drug Code list available upon request).

Since our purpose was to examine general medication adherence to antidiabetic therapy, all drugs within antidiabetic agent drug classes were considered interchangeable. Days during which concurrent drugs were supplied were only counted once. We adjusted fill dates and excluded inpatient days for PDC calculations following CMS Technical Notes.18

The optimal way to measure diabetes control is through A1c values. While laboratory results are available for a subset of the commercially insured T2DM patients, Medicare and Medicaid files do not contain laboratory results. In order to create a consistent definition of diabetes control across insurance types, we identified patients with controlled diabetes as those who were not diagnosed with uncontrolled diabetes (ICD-9-CM code 250.x2 or 250.x3) during the study year. For the subset of commercially insured T2DM patients for which we had both ICD9-CM and A1c information, we performed additional correlation analyses to assert the strength of the association between the 2 measures of diabetes control. We found a strongly significant and positive correlation between ICD-9-CM- and A1cbased case identification measures (Spearman rank correlation coefficients of 0.22 [ P < 0.001] for A1c > 9% as uncontrolled). Chi-square statistics were also significant.

Statistical Analysis

Age group and sex specific average PDC and rates of adherence and control were calculated for each state/MSA and insurance type. State and MSA representative outcomes were generated by applying the T2DM rate and adherence and control measures to a representative T2DM population residing in each state/MSA. This population file was constructed combining demographics, medical insurance, and type of living arrangement information from the 2012 American Community Survey, diabetes prevalence for a community-based population from the 2011 and 2012 Behavioral Risk Factor Surveillance System, and diabetes prevalence for a nursing home population from the 2004 National Nursing Home Survey. (Appendix B describes the construction of the MSA population files, available in online article.)

A Z-score was calculated to evaluate the standard deviation of each state’s/MSA’s adherence from the average PDC across states/MSAs. Spearman’s correlation coefficients were calculated between percentage adherent and percentage controlled. Statistical significance was determined using a P value below 0.050. All analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC).

Results

Across the 3 insurance types, 80% of patients received anti-diabetic medication and constituted our study population. Respectively, 88%, 73%, and 82% of commercially insured patients, Medicare patients receiving Part D benefits, and Medicaid insured patients with T2DM received antidiabetic medication (Table 1). Among the commercially insured, 45% were female, while 61% and 67% were female among the Medicare and Medicaid insured, respectively. The majority used OAD drugs only: 73% for commercially insured, 64% for Medicare, and 68% for Medicaid.

TABLE 1.

Sample Statistics of Patients with T2DM Receiving Antidiabetic Medication by Age and Gender

  Commercial Medicare Medicaid
Sample Size % Medication Sample Size % Medication Sample Size % Medication
T2DM patients receiving medication 197,941 88 184,439 73 70,003 82
Age group
  18-34 7,177 89     4,953 74
  35-44 26,505 90     9,794 82
  45-54 65,419 89     23,500 84
  55-59 48,269 87     16,373 86
  60-64 50,571 85     15,383 79
  < 65     39,238 76    
  65-69     32,100 76    
  ≥ 70     113,101 70    
Gender
  Female 88,770 88 112,335 72 46,727 83
  Male 109,171 89 72,104 73 23,276 79
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T2DM = type 2 diabetes mellitus

At the national level, average PDC was 79% for our study population, and 65% of the population had an average PDC of 80% or above. Average PDC and drug adherence increased by age: among commercially insured adults aged 18-34 years, the average PDC was 63%, while the average PDC for people aged 70 years and above insured by Medicare was 84% (Table 2). On average, men had higher adherence than women using average PDC and level of adherence. While the difference was smallest among Medicare patients, the difference was statistically significant: men had an average PDC of 83.2% (95% confidence interval [CI] =83.0-83.3), while women had an average PDC of 82.7% (95% CI = 82.6-82.9). Average PDC and drug adherence were higher for those patients insured by Medicare (83%, 95% CI = 82.9-83.1) than for those patients who were commercially insured (76.6%, 95% CI = 76.5-76.8) and Medicaid insured (74.4%, 95% CI = 74.2-74.6).

TABLE 2.

Average PDC, Drug Adherence Status, and Diabetes Control Status for Patients with T2DM Receiving Antidiabetic Medication by Insurance Type

  Commercial Medicare Medicaid
Mean (%) Standard Error Mean (%) Standard Error Mean (%) Standard Error
Average PDC
Age group
  18-34 63 0.003     64 0.004
  35-44 70 0.002     70 0.003
  45-54 76 0.001     75 0.002
  55-59 79 0.001     79 0.002
  60-64 82 0.001     81 0.002
  <65     79 0.001    
  65-69     84 0.001    
  ≥ 70     84 0.001    
Gender
  Female 75 0.001 83 0.001 73 0.001
  Male 78 0.001 83 0.001 76 0.002
Total 77 0.001 83 0.001 75 0.001
Percentage with PDC ≥ 80%
Age group
  18-34 38 0.006     41 0.005
  35-44 44 0.003     48 0.005
  45-54 47 0.002     49 0.003
  55-59 53 0.003     56 0.004
  60-64 57 0.003     57 0.004
  <65     63 0.002    
  65-69     71 0.003    
  ≥70     73 0.001    
Gender
  Female 57 0.002 70 0.001 55 0.002
  Male 61 0.001 71 0.002 58 0.003
Total 60 0.001 71 0.001 57 0.002
Diabetes Control
Age group
  18-34 67 0.006     54 0.007
  35-44 68 0.003     52 0.005
  45-54 67 0.002     52 0.003
  55-59 68 0.002     52 0.004
  60-64 68 0.002     52 0.004
  <65     47 0.003    
  65-69     54 0.003    
  ≥70     56 0.001    
Gender
  Female 69 0.002 53 0.001 53 0.002
  Male 68 0.001 54 0.002 53 0.003
Total 69 0.001 54 0.001 53 0.002
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PDC = proportion of days covered; T2DM = type 2 diabetes mellitus.

Across the 3 insurance types, 60% of patients with T2DM receiving medication had their diabetes under control (Table 2). Between ages 18 and 64 years, the proportion of patients with their diabetes under control remained stable. However, for Medicare patients, diabetes control increased with age. Below aged 65 years, 46.6% (95% CI = 46.1-47.1) of the patients had their diabetes under control compared with 56.4% (95% CI = 56.1-56.6) of patients aged 70 years and above. Diabetes control was highest for commercially insured patients (68.9%, 95% CI = 69.1-68.7) than for Medicare (53.7%, 95% CI = 53.5-54.0) and Medicaid patients (52.7%, 95% CI = 52.3-53.0).

Figure 2 displays the average PDC across states and insurance types. Across the states, Medicaid patients experienced more variation in average PDC than Medicare or commercially insured patients. Among commercially insured patients, Minnesota had the highest average PDC (85%), while Florida, Georgia, and Mississippi had the lowest average PDC (73%). For Medicare patients, Maine, North Dakota, and Wyoming had the highest average PDC (87%), while the District of Columbia had the lowest average PDC (79%). For the Medicaid patients, Idaho, Montana, and Nebraska had the highest average PDC (82%), while Michigan had the lowest average PDC (61%).

Figure 2.

Figure 2

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State Average PDC in Comparison to Average PDC Across States by Insurance Type and Overall

Among Medicare patients, the variation in percent adherent across states is smaller than the variation in percent adherent across states for Medicaid patients (Table 3). Percent adherent varied between 51% (Mississippi) and 74% (Minnesota) among the commercially insured; between 61% (District of Columbia) and 79% (Maine and North Dakota) among the Medicare patients; and between 33% (Kentucky) and 71% (Montana) among the Medicaid patients. Furthermore, the variation in percent adherent across states was larger than the variation in average PDC, suggesting that while average PDC may be similar, there is a larger variation in adherence if an adherence cutoff of 80% were used.

TABLE 3.

Proportion of the Population Adherent to Medication and with Diabetes Control

State Drug Adherence Diabetes Control
Commercial % Medicare % Medicaid % All Three % Commercial % Medicare % Medicaid % All Three %
Alabama 56 65 52 60 71 55 52 61
Alaska 59 74 59 66 68 67 55 66
Arizona 57 66 50 60 62 50 60 56
Arkansas 57 70 48 62 79 62 51 67
California 61 70 57 65 74 48 51 58
Colorado 61 70 59 65 66 56 52 59
Connecticut 61 72 61 66 70 54 53 61
Delaware 62 75 56 68 70 52 53 59
District of Columbia 56 61 33 56 68 44 53 55
Florida 54 70 55 62 65 50 51 56
Georgia 54 67 58 61 68 51 51 58
Hawaii 59 73 51 65 72 60 52 64
Idaho 61 71 67 66 83 66 55 71
Illinois 61 72 55 66 68 54 52 60
Indiana 60 69 57 65 73 56 51 62
Iowa 70 77 61 73 79 71 53 72
Kansas 65 76 50 69 73 62 52 65
Kentucky 60 70 33 62 76 58 54 65
Louisiana 55 67 59 61 72 53 52 60
Maine 62 79 67 71 77 62 55 67
Maryland 62 72 57 66 62 49 52 55
Massachusetts 68 72 60 69 66 54 52 59
Michigan 58 71 42 63 70 55 53 61
Minnesota 74 78 56 74 81 69 52 72
Mississippi 51 64 37 56 71 54 53 60
Missouri 60 72 64 66 63 54 54 58
Montana 66 75 71 71 81 62 57 69
Nebraska 65 72 67 69 70 70 54 68
Nevada 58 67 63 63 66 52 53 57
New Hampshire 67 73 59 69 72 63 55 66
New Jersey 62 74 59 67 67 49 53 57
New Mexico 59 64 49 60 62 44 51 52
New York 59 75 62 67 70 53 53 59
North Carolina 58 69 53 63 70 54 51 60
North Dakota 71 79 66 75 77 71 56 72
Ohio 61 71 56 66 68 54 60 60
Oklahoma 57 68 56 62 71 55 51 61
Oregon 63 72 69 69 67 55 58 60
Pennsylvania 64 74 67 69 68 58 54 61
Rhode Island 69 68 60 68 71 54 55 61
South Carolina 57 67 49 61 74 60 51 65
South Dakota 68 78 61 72 81 66 55 71
Tennessee 57 67 53 62 68 52 52 58
Texas 55 66 51 60 60 45 50 51
Utah 61 69 66 65 79 67 56 71
Vermont 59 73 57 66 68 67 58 66
Virginia 63 70 66 67 68 54 53 59
Washington 64 73 55 67 70 54 53 60
West Virginia 62 72 66 67 72 55 54 61
Wisconsin 68 74 57 70 72 62 53 65
Wyoming 66 77 60 71 61 63 57 62
Average across states 61 71 57 66 71 57 53 62
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Note: The full table including standard errors for drug adherence and diabetes control and a map for diabetes control are presented in Appendices D and E (available in online article).

At the state level, variation in diabetes control across states and insurance types are more apparent. Diabetes control among the commercially insured patients was higher than among the Medicare and Medicaid populations (Table 3). Average diabetes control ranged between 60% in Texas and Wyoming and 83% in Idaho among the commercially insured; between 44% in District of Columbia and New Mexico and 71% in Iowa and North Dakota among the Medicare patients; and between 50% in Texas and 60% in Arizona and Ohio among the Medicaid patients.

The correlation between the state adherence rate and diabetes control was positive and significant for all insurance types. The correlation coefficient was smallest for the commercially insured patients (0.28, P = 0.043) and somewhat higher for the Medicare and Medicaid patients (0.49 and 0.56, P < 0.001, respectively).

MSA variation in diabetes drug adherence and diabetes control followed a pattern similar to the state variation previously presented. Results for average PDC and diabetes control are available in Appendix F (available in online article). Average PDC is higher for the Medicare patients than for the Medicaid and commercially insured patients and experiences less variation in average PDC across MSAs. In contrast, diabetes control was highest among the commercially insured patients and lowest among the Medicare patients. Variation in average PDC across MSAs was similar to the variation in average PDC across states. However, variation in diabetes control was larger at the MSA level than the state level.

Discussion

This study identifies gaps in drug adherence and diabetes control at the national, state, and MSA levels. Average PDC for an insured patient population with T2DM that received diabetic medication was 79%. However, for the same population, 1 in 3 patients (35%) did not achieve at least 80% PDC. Furthermore, at least 40% of the insured patient population with T2DM that received diabetic medication did not have their diabetes under control. Average PDC for our 3 insurance populations ranged between 75% and 83%, suggesting little difference in adherence across insurance categories. Our drug adherence results are comparable with 2 other nationally representative PBO populations.7 However, our Medicaid adherence results are higher than the 39% and 46% adherence for a Medi-Cal dataset using 1996-1998 data.

Compared with the Medicaid (75%) and commercially insured population (77%), the Medicare insured patients had a significantly higher average PDC (83%). However, commercially insured patients had better diabetes control (71%) than Medicare and Medicaid insured patients (57% and 53%, respectively). The relationship between drug adherence and diabetes control is confounded by disease duration, which could explain why Medicare patients on average have high levels of drug adherence but low levels of diabetes control.19 At the same time, disease complications are more likely to arise as the disease progresses.20 As a result, elderly patients are more likely to have lower diabetes control despite higher levels of drug adherence, although for elderly patients, less stringent A1c levels may be more appropriate depending on their disease history.

State- and MSA-level variation in drug adherence and diabetes control is relatively small for Medicare and commercially insured populations. For example, among the commercially insured population, average PDC ranged between 73% and 85%, while diabetes control ranged between 60% and 83%. However, the Medicaid population experienced substantial variation in drug adherence across states (ranging between 61% and 82%) but little variation in diabetes control (50%-60%). Regional variations in drug adherence that we describe correspond to findings in earlier literature. Similar to the study by Couto et al. (2014),13 the states that correspond to the New England, Middle Atlantic, East North Central, and West North Central regions have higher antidiabetic medication adherence than East South Central states in the Medicare population. Our results for the commercially insured population also largely correspond to the results for the commercial population used in that study.

Limitations

This study has several limitations. First, the definition of diabetes control is based on ICD-9-CM codes and captures lack of control only when physicians identify a patient as such. We therefore compared the prevalence rate when using ICD-9-CM with the prevalence rate when using various A1c cutoffs. The ICD-9-CM-based prevalence rate overestimated controlled status relative to the ADA’s recommended A1c level of below 7% for tight control (51%) but underestimated controlled status when using the Health Resources and Services Administration’s diabetes measure of poor control, which defines poor control with an A1c above 9%. While we do not have laboratory values available for the Medicare and Medicaid patients, we have no reason to suspect that coding would be different across insurance population or across states. Future research should focus on validating the use of ICD-9-CM codes to identify diabetes control across these dimensions.

Second, we used claims from 1 large commercial plan only, which may not be representative of the insured population at subnational levels. These concerns are mitigated partially by reweighting the outcomes by age group and gender. Third, because of data limitations, only Medicare and Medicaid fee-for-service beneficiaries were included in this study. States that have largely transitioned their Medicaid beneficiaries to managed care settings are often more complex. Fourth, some states suffered from small sample size problems including the commercial population in Alaska, Hawaii, and Vermont and the Medicaid population in Arizona. Ohio Medicaid claims did not include number of days supplied and therefore did not meet our selection criteria. Furthermore, our results were not adjusted for individual determinants of drug adherence other than age and gender. Income, race/ethnicity, health status, access to care, and provider treatment patterns could explain some of the geographic variation in outcomes that we highlight in this study. Understanding the significant drivers of geographic variation beyond those that can be explained by population demographics could shed light on creating more effective public health initiatives.

Finally, it is important to note that the Affordable Care Act may have changed the landscape in diabetes care. Expanded coverage such as the further closing of the coverage gap in the Medicare “donut hole” and Medicaid expansion improves access to care and potentially increases the incentives for preventive care and medication adherence. Other factors, such as the emergence of high-deductible employer plans and the large deductibles and cost sharing featured by many individual plans purchased through health exchanges, should also be considered. The interplay of these new trends in the age of health care reform merits additional research.

Conclusions

This study provides a detailed view of the adequacy of diabetes management among the insured population across states and MSAs. The significant and positive correlations between percentage of patients with optimal adherence and percentage with diabetes under control show that those states and MSAs with higher levels of adherence tend to have higher percentages of T2DM patients with control. The findings of this study highlight the need to develop localized efforts in increasing diabetes drug adherence awareness and improving care. Physicians and other prescribers, insurers, and employers should identify and acknowledge potential barriers to adherence. They should strive to educate patients on why they need to fill their prescriptions, even when patients are asymptomatic, and communicate the consequences of lack of adherence on their health on an ongoing basis. A systematic review concluded that continued multiple elements such as self-management plans, reinforcement, and occasionally rewards over time is a key element of success.21 In particular, these efforts should concentrate on the states of Arizona, Georgia, New Mexico, and Texas, where diabetes drug adherence and diabetes control remain the lowest in the country.

Acknowledgments

The authors would like to thank Erin Byrne, Jerry Franz, and Alisa Schiffman for their valuable insights on diabetes care and treatment.

APPENDIX A. Type 2 Diabetes Sample Inclusion Criteria and Identification Algorithm

This appendix describes the inclusion and exclusion criteria that make up the sample of this study and describes the type 2 diabetes (T2DM) identification algorithm that was applied to the sample.

Inclusion criteria

  • Evidence of T2DM (see Type 2 Diabetes Identification Algorithm below).

  • Continuously enrolled in the fee-for-service coverage type of health plan (UnitedHealth Care, Medicare, Medicaid) during the measurement year.

  • For diabetes patients with pharmacy claims or physician orders of prescriptions, ≥ 1 pharmacy claim for an antidiabetic medication.

Exclusion criteria

  • Patients aged < 18 years.

  • Evidence of type 1 diabetes (T1DM; identified with ICD-9-CM diagnosis codes 250.x1 or 250.x3).

  • Evidence of gestational diabetes and/or pregnancy (if longitudinal approach is used during the 6-month baseline and follow-up periods).

Type 2 Diabetes Identification Algorithm

T2DM is defined as a patient who meets the following criteria using data during the measurement period:

  • ≥ 1 medical claim for T2DM (ICD-9-CM diagnosis codes 250.x0 or 250.x2) and no claims for T1DM, identified with ICD-9-CM diagnosis codes 250.x1 or 250.x3. Diagnosis codes in the primary or secondary positions will be used.

OR

  • If medical claims for both T1DM and T2DM, the patient must meet 1 of the following:

    1. ≥ 1 claim for an oral antidiabetic drug (OAD) including sulfonylureas, metformin, thiazolidinediones, α-glucosidase inhibitors, meglitinide derivatives, DPP-4 inhibitors, or combination of OADs with insulin or noninsulin injectable.
      OR
    2. If no claims for OADs, the patient must have 4 or more claims for 250.xx with a valid fifth digit AND the number of claims for T2DM (250.x0, 250.x2) must exceed the number of claims for T1DM (250.x1, 250.x3).

OR

  • If no medical claims for 250.xx with a valid fifth digit, then the patient must have ≥ 1 claim for an OAD AND a claim for an injectable antidiabetic medication (GLP-1, pramlintide, or insulin) and no evidence of medical claims identifying T1DM patient in the previous year.

OR

  • If no medical claims for 250.xx with a valid fifth digit and no claims for injectable antidiabetic medications, then the patient must have ≥ 1 claim for an OAD AND no medical claims with any of the following ICD-9-CM codes: 256.4, 272.6, 277.7, 648.8x, and 790.2x. These diagnosis codes are associated with diseases that require treatment similar to T2DM. As a result, we would be unable to ascertain that a patient was taking the drug for T2DM or another condition. Codes in any position will be used.

APPENDIX B. Methods for Metropolitan Statistical Area Analysis

This appendix details the multiple steps that were undertaken to create representative results at the Metropolitan Statistical Area (MSA) level.

The first step involved creating a population file with a representative sample of the population residing in each county. The county files combined demographics, household income, medical insurance, and type of living arrangement information from the 2012 American Community Survey (ACS; n = 2,375,715); disease prevalence and health risk factors for a community-based population from the 2011 and 2012 Behavioral Risk Factor Surveillance System (BRFSS; n = 982,154); and disease prevalence and health risk factors for a nursing home population from the 2004 National Nursing Home Survey (NNHS; n = 14,017).

Using information on residence type, we divided the ACS population into those in nursing facilities to be matched to people in the NNHS and those not in nursing facilities to be matched to the BRFSS. For the noninstitutionalized population, each ACS individual was randomly matched with someone in the BRFSS from the same state, sex, age group (15 groups); race/ethnicity (non-Hispanic white, non-Hispanic black, non-Hispanic other, Hispanic); insured/ uninsured status; and household income level (8 levels). Individuals categorized as residing in a nursing home were randomly matched to a person in the NNHS in the same age group, sex, and race/ethnicity strata. The final matched ACS-BRFSS-NNHS database included a sample weight for each person. This weight reflected the number of people he or she represents among the general population.

Using U.S. Census Bureau 2012 data, we identified the current size of the population in each county by age, sex, and race/ethnicity. This county population database was merged with the Current Statistical Area Delineation file to aggregate counties to census-defined metropolitan areas. The county population files with the MSA definitions were then merged with the ACS-BRFSS-NNHS matched national population file to create a health and socioeconomic profile for a representative sample of adults in each of the selected 50 metropolitan areas. Finally, for each metropolitan area, the sample weights for the individuals in the merged file were re-weighted so that the weighted statistics matched the U.S. Census-published MSA demographic composition. Note that not all metropolitan areas correspond with federal designations. The New York-Newark-Jersey City, NY-NJ-PA, was split such that the NY numbers corresponded solely to the population in NY, with the NJ population placed in a constructed Northern NJ metropolitan designation. Likewise, Orange County, California, was carved out of the Los Angeles-Long Beach-Anaheim metropolitan area and reported separately. Similarly, West Palm Beach was reported separately from Miami-Fort Lauderdale, Florida (whereas the official designation of this metropolitan statistical area is Miami-Fort Lauderdale-West Palm Beach).

APPENDIX C. Sample Size T2DM Receiving Antidiabetic Medication and Average PDC by State and Insurance Type

State Sample Size Commercial Medicare Medicaid All Three
Commercial Medicare Medicaid Mean StdErr Mean StdErr Mean StdErr Mean StdErr
Alaska 42 209 156 76 0.04 84 0.01 75 0.02 80 0.00
Alabama 1,220 3,514 2,169 75 0.01 80 0.00 70 0.01 77 0.00
Arkansas 1,222 2,516 835 75 0.01 82 0.00 70 0.01 78 0.00
Arizona 5,528 1,971 43 75 0.00 81 0.01 70 0.04 77 0.00
California 14,169 16,093 7,440 78 0.00 83 0.00 76 0.00 80 0.00
Colorado 4,120 1,317 742 77 0.00 83 0.01 75 0.01 80 0.00
Connecticut 2,659 2,177 1,023 77 0.00 84 0.00 79 0.01 80 0.00
District of Columbia 565 317 570 76 0.01 79 0.01 64 0.01 76 0.00
Delaware 238 728 522 77 0.02 85 0.01 73 0.01 81 0.00
Florida 13,351 10,621 1,789 73 0.00 83 0.00 74 0.01 78 0.00
Georgia 27,574 5,474 2,924 73 0.00 81 0.00 76 0.00 77 0.00
Hawaii 47 569 370 77 0.03 84 0.01 71 0.01 80 0.00
Iowa 2,329 2,778 854 82 0.00 86 0.00 78 0.01 84 0.00
Idaho 244 795 534 78 0.02 83 0.01 82 0.01 81 0.00
Illinois 5,255 9,507 2,596 77 0.00 84 0.00 74 0.01 80 0.00
Indiana 2,563 4,880 2,292 76 0.00 83 0.00 74 0.01 79 0.00
Kansas 1,465 2,266 692 79 0.01 85 0.00 72 0.01 81 0.00
Kentucky 1,570 4,438 2,499 76 0.01 82 0.00 66 0.01 78 0.00
Louisiana 3,936 3,183 2,650 74 0.00 81 0.00 76 0.00 78 0.00
Massachusetts 1,467 4,112 1,428 81 0.01 84 0.00 76 0.01 82 0.00
Maryland 6,499 3,276 1,356 79 0.00 84 0.00 75 0.01 81 0.00
Maine 186 1,351 526 78 0.02 87 0.01 80 0.01 83 0.00
Michigan 1,379 6,338 340 75 0.01 83 0.00 61 0.02 77 0.00
Minnesota 5,779 1,877 860 85 0.00 86 0.00 75 0.01 85 0.00
Missouri 4,327 4,449 1,706 76 0.00 83 0.00 79 0.01 80 0.00
Mississippi 1,421 3,467 1,673 73 0.01 80 0.00 63 0.01 75 0.00
Montana 126 616 316 79 0.02 85 0.01 82 0.01 82 0.00
North Carolina 3,675 7,722 2,717 76 0.00 82 0.00 74 0.01 78 0.00
North Dakota 257 571 318 83 0.01 87 0.01 81 0.01 85 0.00
Nebraska 836 1,424 311 79 0.01 84 0.01 82 0.01 82 0.00
New Hampshire 293 832 335 80 0.01 84 0.01 75 0.01 81 0.00
New Jersey 8,176 5,893 1,131 78 0.00 85 0.00 78 0.01 81 0.00
New Mexico 980 1,193 473 76 0.01 81 0.01 72 0.01 78 0.00
Nevada 804 891 411 76 0.01 81 0.01 79 0.01 79 0.00
New York 21,020 9,748 7,506 76 0.00 85 0.00 78 0.00 81 0.00
Ohio 8,345 7,068 — 78 0.00 83 0.00 75 0.00 80 0.00
Oklahoma 3,086 2,979 1,179 75 0.00 82 0.00 75 0.01 78 0.00
Oregon 860 1,549 546 79 0.01 84 0.01 81 0.01 82 0.00
Pennsylvania 2,454 7,635 809 79 0.00 84 0.00 80 0.01 82 0.00
Rhode Island 1,524 568 215 81 0.01 82 0.01 78 0.02 81 0.00
South Carolina 1,330 3,377 1,277 74 0.01 81 0.00 69 0.01 77 0.00
South Dakota 128 731 296 81 0.02 86 0.01 76 0.01 83 0.00
Tennessee 2,819 4,799 2,915 75 0.00 81 0.00 73 0.00 78 0.00
Texas 20,952 13,793 4,923 74 0.00 81 0.00 72 0.00 77 0.00
Utah 861 732 320 77 0.01 82 0.01 80 0.01 80 0.00
Virginia 4,330 5,062 1,050 78 0.00 83 0.00 78 0.01 80 0.00
Vermont 41 537 301 76 0.04 86 0.01 76 0.01 81 0.00
Washington 1,385 3,205 1,246 79 0.01 83 0.00 75 0.01 81 0.00
Wisconsin 3,899 2,897 1,276 81 0.00 85 0.00 76 0.01 82 0.00
West Virginia 477 2,085 1,356 78 0.01 84 0.00 81 0.01 81 0.00
Wyoming 128 309 187 79 0.02 87 0.01 77 0.02 82 0.00
Average PDC across states 197,941 184,439 70,003 77 0.00 83 0.00 75 0.00 80 0.00
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PDC = proportion of days covered; StdErr = standard error; T2DM = type 2 diabetes mellitus

APPENDIX D. Proportion of the Population Adherent to Medication and with Diabetes Control

State Drug Adherence Diabetes Control
Commercial Medicare Medicaid All Three Commercial Medicare Medicaid All Three
Mean StdErr Mean StdErr Mean StdErr Mean StdErr Mean StdErr Mean StdErr Mean StdErr Mean StdErr
Alabama 56 0.01 65 0.01 52 0.01 60 0.01 71 0.01 55 0.01 52 0.01 61 0.01
Alaska 59 0.07 74 0.03 59 0.04 66 0.02 68 0.07 67 0.03 55 0.04 66 0.01
Arizona 57 0.01 66 0.01 50 0.07 60 0.01 62 0.01 50 0.01 60 0.07 56 0.01
Arkansas 57 0.01 70 0.01 48 0.01 62 0.01 79 0.01 62 0.01 51 0.02 67 0.01
California 61 0.00 70 0.00 57 0.00 65 0.00 74 0.00 48 0.00 51 0.01 58 0.01
Colorado 61 0.01 70 0.01 59 0.02 65 0.01 66 0.01 56 0.01 52 0.02 59 0.01
Connecticut 61 0.01 72 0.01 61 0.01 66 0.01 70 0.01 54 0.01 53 0.01 61 0.01
Delaware 62 0.03 75 0.02 56 0.02 68 0.01 70 0.03 52 0.02 53 0.02 59 0.01
District of Columbia 56 0.02 61 0.03 33 0.02 56 0.01 68 0.02 44 0.03 53 0.02 55 0.01
Florida 54 0.00 70 0.00 55 0.01 62 0.00 65 0.00 50 0.00 51 0.01 56 0.02
Georgia 54 0.00 67 0.01 58 0.01 61 0.00 68 0.00 51 0.01 51 0.01 58 0.00
Hawaii 59 0.07 73 0.02 51 0.03 65 0.02 72 0.07 60 0.02 52 0.03 64 0.01
Idaho 61 0.03 71 0.02 67 0.02 66 0.01 83 0.02 66 0.02 55 0.02 71 0.00
Illinois 61 0.01 72 0.00 55 0.01 66 0.00 68 0.01 54 0.01 52 0.01 60 0.00
Indiana 60 0.01 69 0.01 57 0.01 65 0.00 73 0.01 56 0.01 51 0.01 62 0.01
Iowa 70 0.01 77 0.01 61 0.01 73 0.01 79 0.01 71 0.01 53 0.02 72 0.00
Kansas 65 0.01 76 0.01 50 0.01 69 0.01 73 0.01 62 0.01 52 0.02 65 0.00
Kentucky 60 0.01 70 0.01 33 0.01 62 0.01 76 0.01 58 0.01 54 0.01 65 0.00
Louisiana 55 0.01 67 0.01 59 0.01 61 0.00 72 0.01 53 0.01 52 0.01 60 0.00
Maine 62 0.04 79 0.01 67 0.00 71 0.01 77 0.03 62 0.01 55 0.00 67 0.01
Maryland 62 0.01 72 0.01 57 0.01 66 0.00 62 0.01 49 0.01 52 0.01 55 0.01
Massachusetts 68 0.01 72 0.01 60 0.01 69 0.01 66 0.01 54 0.01 52 0.01 59 0.00
Michigan 58 0.01 71 0.01 42 0.02 63 0.01 70 0.01 55 0.01 53 0.02 61 0.02
Minnesota 74 0.01 78 0.01 56 0.01 74 0.00 81 0.01 69 0.01 52 0.02 72 0.00
Mississippi 51 0.01 64 0.01 37 0.01 56 0.01 71 0.01 54 0.01 53 0.01 60 0.01
Missouri 60 0.01 72 0.01 64 0.01 66 0.00 63 0.01 54 0.01 54 0.01 58 0.00
Montana 66 0.04 75 0.02 71 0.02 71 0.01 81 0.03 62 0.02 57 0.03 69 0.00
Nebraska 65 0.02 72 0.01 67 0.02 69 0.01 70 0.02 70 0.01 54 0.03 68 0.01
Nevada 58 0.02 67 0.02 63 0.02 63 0.01 66 0.02 52 0.02 53 0.02 57 0.00
New Hampshire 67 0.03 73 0.02 59 0.02 69 0.01 72 0.03 63 0.02 55 0.03 66 0.02
New Jersey 62 0.01 74 0.01 59 0.01 67 0.00 67 0.01 49 0.01 53 0.01 57 0.01
New Mexico 59 0.02 64 0.01 49 0.02 60 0.01 62 0.02 44 0.01 51 0.02 52 0.00
New York 59 0.00 75 0.00 62 0.00 67 0.00 70 0.00 53 0.01 53 0.01 59 0.01
North Carolina 58 0.01 69 0.01 53 0.01 63 0.00 70 0.01 54 0.01 51 0.01 60 0.01
North Dakota 71 0.03 79 0.02 66 0.02 75 0.01 77 0.03 71 0.02 56 0.03 72 0.02
Ohio 61 0.01 71 0.01 56 0.00 66 0.00 68 0.01 54 0.01 60 0.00 60 0.01
Oklahoma 57 0.01 68 0.01 56 0.01 62 0.01 71 0.01 55 0.01 51 0.01 61 0.01
Oregon 63 0.02 72 0.01 69 0.01 69 0.01 67 0.02 55 0.01 58 0.02 60 0.01
Pennsylvania 64 0.01 74 0.01 67 0.01 69 0.00 68 0.01 58 0.01 54 0.02 61 0.00
Rhode Island 69 0.01 68 0.02 60 0.03 68 0.01 71 0.01 54 0.02 55 0.03 61 0.01
South Carolina 57 0.01 67 0.01 49 0.01 61 0.01 74 0.01 60 0.01 51 0.01 65 0.00
South Dakota 68 0.04 78 0.02 61 0.02 72 0.01 81 0.03 66 0.02 55 0.03 71 0.00
Tennessee 57 0.01 67 0.01 53 0.01 62 0.00 68 0.01 52 0.01 52 0.01 58 0.01
Texas 55 0.00 66 0.00 51 0.01 60 0.00 60 0.00 45 0.00 50 0.01 51 0.01
Utah 61 0.02 69 0.02 66 0.02 65 0.01 79 0.01 67 0.02 56 0.02 71 0.01
Vermont 59 0.08 73 0.02 57 0.03 66 0.02 68 0.07 67 0.02 58 0.03 66 0.00
Virginia 63 0.01 70 0.01 66 0.01 67 0.00 68 0.01 54 0.01 53 0.01 59 0.01
Washington 64 0.01 73 0.01 55 0.01 67 0.01 70 0.01 54 0.01 53 0.01 60 0.01
West Virginia 62 0.02 72 0.01 66 0.01 67 0.01 72 0.02 55 0.01 54 0.01 61 0.01
Wisconsin 68 0.01 74 0.01 57 0.01 70 0.00 72 0.01 62 0.01 53 0.01 65 0.01
Wyoming 66 0.04 77 0.02 60 0.03 71 0.02 61 0.04 63 0.03 57 0.03 62 0.01
Average across states 61 0.00 71 0.00 57 0.00 66 0.00 71 0.00 57 0.00 53 0.00 62 0.00
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StdErr = standard error.

APPENDIX E. Variation in Percentage of Diabetes Control Across Insurance Types and States

graphic file with name jmcp-021-012-1195_g003.jpg

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APPENDIX F. Sample Size of T2DM Receiving Antidiabetic Medication, Average PDC, and Diabetes Control by MSA and Insurance Type

This table presents average proportion of days covered (PDC) and percentage of diabetes control for each of the 50 Metropolitan Stastistical Areas (MSAs) included in this study. Similar to results at the state level, variation in diabetes control at the MSA level is larger than variation in average PDC. Average PDC ranges between 72% (Orlando, FL) and 84% (Minneapolis, MN) for commercially insured patients; 78% (Houston, TX) and 86% (Southern New Jersey, NJ) for Medicare insured patients; and 60% (Detroit, MI) and 82% (Orange County, CA) among Medicaid patients. In contrast, diabetes control varies between 52% (San Antonio, TX) and 82% (Minneapolis, MN) for commercially insured patients; 37% (San Antonio, TX) and 68% (Minneapolis, MN) for Medicare patients; and 42% (Salt Lake City, UT) and 60% (Phoenix, AZ; Pittsburgh, PA; Las Vegas, NV; and Cleveland and Columbus, OH).

For some metropolitan areas, the medical claims sample was small for some demographic groups (in particular the aged 20-34 years population). When the sample size fell below 30 adults for a particular demographic group, we used information for that same demographic group at the state or national level. For example, we did not identify any patients in the metropolitan areas and state level in Ohio. As a result, these statistics are based on the national age group and gender-adjusted estimates.

MSA, State Sample Size of T2DM with Treatment Proportion of Days Covered Diabetes Control
Commercial Medicare Medicaid Commercial Medicare Medicaid All Three Commercial Medicare Medicaid All Three
% Z-score % Z-score % Z-score % Z-score % StdErr % StdErr % StdErr % StdErr
Atlanta, GA 8,817 2,079 561 74 -1.24 80 -1.39 75 0.02 77 -1.33 66 0.01 48 0.01 49 0.02 55 0.01
Austin, TX 1,243 556 125 76 -0.28 82 -0.57 73 -0.41 79 -0.52 55 0.01 41 0.02 47 0.04 47 0.01
Baltimore, MD 1,423 1,683 659 81 1.49 83 0.49 75 0.05 81 1.05 61 0.01 48 0.01 48 0.02 53 0.01
Boston, MA 1,538 2,617 860 81 1.54 84 1.07 77 0.68 82 1.50 64 0.01 55 0.01 48 0.02 58 0.01
Charlotte, NC 1,203 1,524 295 75 -0.76 82 -0.13 72 -0.78 78 -0.65 70 0.01 58 0.01 48 0.03 61 0.01
Chicago, IL 2,303 6,203 1,674 76 -0.40 83 0.31 74 -0.10 79 -0.09 70 0.01 52 0.01 48 0.01 58 0.01
Cincinnati, OH 2,271 1,069 78 78 0.34 82 -0.36 71 -0.98 79 -0.22 77 0.01 58 0.01 54 0.06 65 0.01
Cleveland, OH 831 1,128 —   76 -0.33 82 -0.11 75 0.04 79 -0.22 71 0.02 55 0.01 60 0.00 62 0.01
Columbus, OH 1,208 867 —   79 0.62 84 1.08 75 0.04 81 0.86 63 0.01 44 0.02 60 0.00 53 0.01
Dallas, TX 6,296 2,823 665 74 -1.10 81 -0.97 77 0.44 78 -0.96 62 0.01 47 0.01 48 0.02 53 0.02
Denver, CO 2,041 412 201 78 0.24 81 -0.84 76 0.23 79 -0.22 64 0.01 49 0.02 49 0.03 55 0.00
Detroit, MI 349 2,608 98 75 -0.96 82 -0.10 60 -4.05 77 -1.52 69 0.02 48 0.01 51 0.05 56 0.01
Hartford, CT 1,070 714 365 77 -0.05 84 0.63 75 0.03 80 0.28 71 0.01 54 0.02 50 0.02 60 0.00
Houston, TX 2,637 2,219 901 74 -1.25 78 -2.66 70 -1.36 76 -2.27 60 0.01 41 0.01 50 0.02 49 0.00
Indianapolis, IN 793 1,127 321 77 -0.05 83 0.55 70 -1.18 79 -0.04 72 0.02 53 0.01 49 0.03 60 0.01
Jacksonville, FL 887 749 95 73 -1.62 81 -0.82 74 -0.30 77 -1.34 71 0.02 52 0.02 55 0.05 60 0.00
Kansas City, MO 1,125 1,010 281 79 0.66 82 -0.11 75 0.17 80 0.34 69 0.01 56 0.01 48 0.03 60 0.00
Las Vegas, NV 345 580 —   76 -0.39 81 -1.20 75 0.04 78 -0.78 62 0.03 46 0.02 60 0.00 54 0.00
Los Angeles, CA 2,575 4,539 2,172 77 0.06 83 0.17 75 0.14 80 0.14 77 0.01 45 0.01 49 0.01 58 0.00
Memphis, TN 538 877 378 73 -1.51 79 -2.24 69 -1.53 76 -2.25 62 0.02 46 0.02 49 0.03 52 0.01
Miami, FL 2,342 3,040 78 73 -1.42 83 0.48 74 -0.25 79 -0.59 67 0.01 44 0.01 56 0.05 54 0.01
Milwaukee, WI 1,873 784 428 81 1.47 82 -0.23 69 -1.60 80 0.30 71 0.01 57 0.02 48 0.02 62 0.00
Minneapolis, MN 2,998 830 432 84 2.98 85 1.50 73 -0.53 83 2.20 82 0.01 68 0.02 48 0.02 71 0.02
Nashville, TN 493 762 255 77 -0.03 81 -0.99 72 -0.72 78 -0.66 62 0.02 45 0.02 47 0.03 52 0.00
New York, NY 21,432 10,938 5,121 76 -0.25 85 1.52 79 1.12 81 0.85 70 0.00 50 0.00 47 0.01 57 0.01
Norfolk, VA 275 763 95 75 -0.65 82 -0.55 75 -0.05 78 -0.63 57 0.03 49 0.02 56 0.05 53 0.00
Northern New Jersey, NJ 8,495 4,494 729 78 0.37 84 1.17 78 0.75 81 0.94 66 0.01 49 0.01 47 0.02 55 0.00
Oklahoma City, OK 1,268 713 232 76 -0.48 83 0.05 77 0.69 79 -0.07 70 0.01 55 0.02 48 0.03 60 0.01
Orange County, CA 911 1,092 206 79 0.84 85 1.49 82 1.93 82 1.62 73 0.01 47 0.01 43 0.03 56 0.00
Orlando, FL 1,850 1,046 121 72 -2.01 81 -0.69 77 0.51 77 -1.29 64 0.01 50 0.01 53 0.04 56 0.02
Philadelphia, PA 673 3,282 346 79 0.81 84 0.69 73 -0.44 81 0.66 68 0.02 55 0.01 49 0.02 59 0.01
Phoenix, AZ 3,053 986 15 75 -0.69 82 -0.33 75 0.04 79 -0.52 60 0.01 47 0.01 60 0.13 53 0.00
Pittsburgh, PA 159 714 17 80 1.05 83 0.48 75 0.04 81 0.81 67 0.04 56 0.02 60 0.12 61 0.01
Portland, OR 1,020 523 239 78 0.45 84 0.91 78 0.86 81 0.88 66 0.01 53 0.02 48 0.03 57 0.01
Providence, RI 2,699 1,120 298 81 1.60 84 0.75 78 0.82 82 1.41 70 0.01 57 0.01 46 0.03 61 0.02
Raleigh, NC 565 555 120 76 -0.46 83 0.20 77 0.51 79 -0.03 73 0.02 52 0.02 51 0.04 60 0.01
Richmond, VA 755 832 69 78 0.26 82 -0.47 77 0.60 80 0.06 68 0.02 53 0.02 58 0.06 59 0.01
Riverside, CA 1,255 1,251 659 76 -0.41 82 -0.42 74 -0.12 79 -0.45 74 0.01 43 0.01 49 0.02 56 0.01
Sacramento, CA 596 633 500 80 1.03 83 0.14 77 0.69 81 0.78 68 0.02 45 0.02 47 0.02 54 0.00
Salt Lake City, UT 861 292 130 77 -0.05 81 -0.89 81 1.76 79 -0.04 75 0.01 63 0.03 42 0.03 65 0.01
San Antonio, TX 2,275 796 519 76 -0.56 78 -2.65 75 0.05 77 -1.56 52 0.01 37 0.02 48 0.02 44 0.00
San Diego, CA 1,012 934 436 79 0.63 82 -0.10 78 0.83 80 0.49 74 0.01 55 0.02 48 0.02 61 0.00
San Francisco, CA 1,183 1,548 617 79 0.86 85 1.47 81 1.59 82 1.55 74 0.01 50 0.01 47 0.02 59 0.01
Seattle, WA 1,395 1,314 526 79 1.00 84 0.83 75 0.00 81 0.94 68 0.01 53 0.01 47 0.02 58 0.01
Southern Connecticut, CT 2,373 1,054 464 79 0.88 83 0.50 80 1.36 81 1.04 71 0.01 54 0.01 44 0.02 60 0.01
Southern New Jersey, NJ 354 971 180 77 -0.13 86 1.79 79 1.09 81 1.05 71 0.02 51 0.01 48 0.04 58 0.00
St. Louis, MO 2,867 1,694 442 77 -0.05 83 0.26 73 -0.61 79 -0.05 61 0.01 48 0.01 47 0.02 53 0.01
Tampa, FL 4,089 1,202 203 74 -1.22 83 0.45 73 -0.48 79 -0.56 61 0.01 48 0.01 50 0.03 53 0.01
Washington, DC 3,979 1,972 862 78 0.57 83 0.02 70 -1.42 80 -0.02 66 0.01 48 0.01 49 0.02 55 0.01
West Palm Beach, FL 1,706 721 50 74 -1.39 82 -0.20 74 -0.22 78 -0.90 65 0.01 51 0.02 59 0.07 57 0.01
Average across MSAs 114,299 82,240 24,118 77 N/A 83 N/A 75 N/A 80 N/A 67 0.00 51 0.00 50 0.00 57 0.00
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MSA = Metropolitan Statistical Area; N/A = not applicable; PDC = proportion of days covered; StdErr = standard error; T2DM = type 2 diabetes mellitus.

References

Articles from Journal of Managed Care & Specialty Pharmacy are provided here courtesy of Academy of Managed Care Pharmacy

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