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The American Journal of Managed Care February 2011
Effect of Multiple Chronic Conditions Among Working-Age Adults
James M. Naessens, ScD; Robert J. Stroebel, MD; Dawn M. Finnie, MPA; Nilay D. Shah, PhD; Amy E. Wagie, BA; William J. Litchy, MD; Patrick J. F. Killinger, MA; Thomas J. D. O'Byrne, BS; Douglas L. Wood, MD; and Robert E. Nesse, MD
Psychological Family Intervention for Poorly Controlled Type 2 Diabetes
Karen M. Keogh, PhD; Susan M. Smith, MD; Patricia White, PhD; Sinead McGilloway, PhD; Alan Kelly, PhD; James Gibney, MD; and Tom O'Dowd, MD
Abolishing Coinsurance for Oral Antihyperglycemic Agents: Effects on Social Insurance Budgets
Kostas Athanasakis, MSc; Anastasis G. Skroumpelos, MSc; Vassiliki Tsiantou, MSc; Katerina Milona, MSc; and John Kyriopoulos, PhD
Effects of Nonadherence With Prescription Drugs Among Older Adults
Richard J. Butler, PhD; Taylor K. Davis, BA; William G. Johnson, PhD; and Harold H. Gardner, MD
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Behavioral Health Disorders and Adherence to Measures of Diabetes Care Quality
Gary Y. Leung, PhD; Jianying Zhang, MD, MPH; Wen-Chieh Lin, PhD; and Robin E. Clark, PhD
Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners
Christopher S. Kim, MD, MBA; Anita L. Hart, MD; Robert F. Paretti, MD; Latoya Kuhn, MPH; Ann E. Dowling, BSN, RN; Judy L. Benkeser, BSN, RN; and David A. Spahlinger, MD
Health Insurance in India: Need for Managed Care Expertise
Thomas K. Thomas, MBA
Outpatient Wait Time and Diabetes Care Quality Improvement
Julia C. Prentice, PhD; B. Graeme Fincke, MD; Donald R. Miller, ScD; and Steven D. Pizer, PhD

Behavioral Health Disorders and Adherence to Measures of Diabetes Care Quality

Gary Y. Leung, PhD; Jianying Zhang, MD, MPH; Wen-Chieh Lin, PhD; and Robin E. Clark, PhD
Persons with substance use disorders were less likely and persons with schizophrenia/paranoia were more likely to be adherent to measures of diabetes care quality.

Objective: To investigate whether Medicare and/or Medicaid beneficiaries with behavioral health disorders (BHDs) receive lower quality diabetes care.


Study Design: Retrospective observational study using merged Medicare and Medicaid claims data from Massachusetts in calendar years 2004 and 2005.


Methods: The study included beneficiaries who had type 2 diabetes, stayed at nursing homes for fewer than 90 days, and were enrolled in Medicare and/or Medicaid for at least 10 months during the study period. We used Current Procedural Terminology (CPT) codes to identify the receipt of 4 measures of diabetes care quality (ie, glycated hemoglobin tests, low-density lipoprotein cholesterol tests, nephropathy tests, eye examinations). The rates of adherence (defined by proportions of beneficiaries receiving appropriate services for each measure) were compared across different types of BHDs as identified by International Classification of Diseases, Ninth Revision, Clinical Modification diagnoses. Multivariate logistic regression was used to compare the odds of adherence among beneficiaries who had BHDs with the odds among beneficiaries who had no BHDs, while adjusting for case mix.


Results: A total of 106,174 individuals met inclusion criteria. Results from adjusted analysis showed a mixed picture of the relationships between BHDs and adherence to quality measures. While substance use disorders were associated with lower adherence to quality measures, beneficiaries with diagnoses of schizophrenia or paranoid states had higher odds for adherence to quality measures.


Conclusions: Individuals with diabetes and substance use disorders receive lower quality diabetes care. Further studies to examine the factors associated with this disparity are needed.


(Am J Manag Care. 2011;17(2):144-150)

The study examined the adherence to measures of diabetes care quality among Medicare and/or Medicaid beneficiaries with type 2 diabetes using an integrated data set of Medicare and Medicaid claims from Massachusetts in 2005.


  • The presence of alcohol or drug abuse/dependence was associated with lower odds for full adherence to quality measures.


  • Compared with people with no mental health disorders, those with schizophrenia or paranoid states were more likely to achieve adherence.
Evidence suggests that individuals with behavioral health disorders (BHDs) may receive substandard care for chronic physical illnesses.1-5 Several studies indicate that BHDs, which include depression, schizophrenia, bipolar disorder, anxiety disorders, and substance use disorders (SUDs), are associated with difficulties in accessing care2,6,7 and in communicating with primary care physicians. 8,9 The barriers that people with BHDs encounter may contribute to (1) their low rates of receiving community-based care and (2) heavy utilization of inpatient care and frequent visits to emergency departments.10 Consequently, healthcare expenditures for people with BHDs, especially those with SUD, are high11 and have been rising in recent years.12

Some studies show that individuals with BHDs receive poorer quality diabetes care, as indicated by lower rates of adherence to Health Effectiveness Data and Information Set (HEDIS) measures of diabetes care quality.13-15 These measures recommended by the National Committee for Quality Assurance include receiving each of the 6 services or tests: glycated hemoglobin (A1C) tests, low-density lipoprotein cholesterol (LDL-C) tests, nephropathy screening/urine profile, blood pressure check, and eye and foot examinations.16 For example, Jones and colleagues found that people with diabetes and co-occurring BHDs were less likely to receive A1C tests and LDL-C tests than those with diabetes alone.13 Goldberg and colleagues reviewed medical charts in a community sample and showed that people with diabetes and schizophrenia were less likely to receive all 6 services than  people with diabetes alone.14 Frayne and colleagues analyzed administrative data and observed that people with schizophrenia were less likely to achieve adherence to all measures.15 These studies suggest that individuals with BHDs face obstacles in receiving proper diabetes care and may require targeted interventions to improve diabetes care quality.

No studies to date have examined the associations between BHDs and diabetes care quality in Medicare or Medicaid populations. This is of serious concern because these are vulnerable populations with high rates of mental health disorders,17,18 and they may be at higher risk of worse health outcomes. Further, large portions of Medicaid populations in most states are enrolled in some form of managed care,19 and participation in Medicare Advantage managed care plans is growing.20 Managed care organizations are  increasingly accountable for the quality of care among high-risk beneficiaries.21 Effective management of chronic diseases is crucial in controlling healthcare costs.

However, it is still unclear whether BHDs are associated with poorer quality diabetes care, because other studies found contrary results.22,23 Dixon and colleagues observed that people with diabetes and schizophrenia had better glycemic control, indicated by lower A1C levels, than those with diabetes alone.22 Further, Kreyenbuhl and colleagues showed that glycemic control among people with BHDs was not significantlydifferent from that among people with diabetes alone.23 These different observations may be due to unmeasured confounders.

It is worth noting that none of the previously mentioned studies adjusted for SUD.13-15,22,23 It is possible that SUD confounded the relationship between schizophrenia and adherence to quality measures. Therefore, further research is needed to delineate the relationship between BHDs and diabetes care quality. By identifying the areas of health disparities and the factors associated with them, appropriate interventions can be designed to improve outcomes in diabetes care.

This study investigated the relationship between BHDs and adherence to measures for diabetes care quality in Massachusetts residents with Medicare and/or Medicaid coverage during 2005. Rates of adherence to HEDIS measures of diabetes care quality, as indicated by the proportion of beneficiaries receiving the services or tests for monitoring diabetes,  were compared across different BHD diagnostic groups using multivariate analyses to adjust for potential confounders.


Study Design and Data

This was a retrospective observational study using individual-level administrative data from Medicare and Medicaid populations in Massachusetts during calendar years 2004 and 2005. Medicare claims were obtained from the Centers for Medicare & Medicaid Services. Medicaid claims were obtained from the Massachusetts Medicaid Management Information System. Socioeconomic status data for beneficiaries’ ZIP code–based community (ie, median household income in 1999 and percentage of high school graduates) came from Census 2000.

Selection Criteria

We included beneficiaries who were at least 18 years old as of January 1, 2004. The study population was characterized by their Medicare and Medicaid coverage. The  Medicare-only group included beneficiaries who enrolled in Parts A and B for at least 10 months during both calendar year 2004 and 2005 and did not enroll in Medicaid. Beneficiaries with Medicaid only were enrolled in Medicaid for at least 10 months during both years with no Medicare enrollment. Dually eligible beneficiaries were enrolled in Medicare Part A and Medicaid for at least 10 months during both years. The length of enrollment requirement ensured stable health coverage in the study population. Beneficiaries with Medicare Advantage (managed care) or residing in nursing homes for 90 days or more were excluded because adequate claims were unavailable.

All beneficiaries with type 2 diabetes were identified by either 1 diagnosis on an inpatient claim or 2 diagnoses on outpatient claims (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 250.xx, 357.2, 362.0, 362.01, 362.02, 366.41, and 648.0). The choice of diagnostic codes is consistent with previously publishedstudies.13,15 We could not link 2263 individuals to a census area and they were excluded from the study. As a result, the study included 106,174 individuals.

Identification and Classification of Behavioral Health Disorders

Behavioral health disorders were also identified by any 1 inpatient ICD-9-CM diagnosis or any 2 outpatient diagnoses. Mental health disorders included schizophrenia/paranoid states (ICD-9-CM 295.x, 297.x), bipolar disorder (ICD-9-CM 296.0, 296.1, 296.4-296.7), depression/anxiety (ICD-9-CM 296.2, 296.3, 298.0, 300.01, 300.02, 300.4, 309.0, 309.1, 309.81, 311.x), and other mental health disorders (ICD-9-CM 298.1- 298.4, 298.8-298.9, 300.1, 300.2, 300.3, 300.5-300.9, 301.x, 302.x, 306.x-308.x, 309.2-309.4, 309.82, 309.83, 309.89, 309.9, 312.x-316.x). They were categorized hierarchically: schizophrenia/ paranoid > bipolar disorder > depression/anxiety > other mental health disorders. Individuals with no mental health disorders were the reference group. Substance use disorders included alcohol abuse/dependence (ICD-9-CM 291.x, 303.x, 305.0, 571.0-571.3) (reference group: no alcohol abuse/dependence) and drug abuse/dependence (ICD-9-CM 292.x, 304.x, 305.2- 305.9, 648.3) (reference group: no drug abuse/dependence).

A subanalysis was performed using medication data to identify mental health disorders and to assess any differences in estimates of outcomes based on different case identification methods.

Identification of Quality Measures for Diabetes Care

Using Current Procedural Terminology (CPT) codes, we assessed adherence to 4 measures for diabetes care quality: having an A1C test, an LDL-C test, a nephropathy test, and an eye examination (no appropriate CPT or ICD-9-CM codes available for foot examinations or blood pressure checks in 2005 Medicare and Medicaid data for Massachusetts). Beneficiaries who had at least 1 claim for a test or examination during 2005 were considered to be adherent to that measure during 2005. A summary measure was created to indicate full adherence to quality measures, defined as having all 4 procedures during calendar year 2005.


The following covariates were obtained from Medicare or Medicaid data: sex; race/ethnicity (white, African American, Hispanic, and other); age; physical illness burden (represented by the Chronic Illness and Disability Payment System [CDPS] and computed from claims24); preexisting diabetesrelated complications (eye complications, nephropathy, neuropathy, lower-limb amputation, ischemic heart disease, and cerebrovascular disease) identified by ICD-9-CM codes; types of health coverage; and frequency of physician visits. See the eAppendix at for a list of codes used to identify diabetes complications. In computing the CDPS scores, the diagnoses of BHDs were omitted so that the  analyses could investigate the effects of BHDs on outcomes separate from the effects of other illnesses. Treatment is likely to vary by practice or by provider. Because we were unable to identify specific providers across Medicaid and Medicare claims, we used Hospital Service Areas (HSAs) as a clustering variable to approximate small area variations in care.25 Median household income and percentage of high school graduates in beneficiaries’ ZIP code–based communities were included as a proxy measure of the beneficiaries’ socioeconomic status.

Statistical Analysis

Unadjusted analyses were performed to compare the distributions of covariates and outcomes across mental disorder categories (analysis of variance, X2, or Kruskal-Wallis tests), as well as between mental health disorders and no mental health disorders (t tests, X2 tests, or Kruskal-Wallis tests). We used logistic regression to examine the effects of BHDs on the individual outcomes as well as the summary measure, adjusting for covariates. We included robust variance estimates in the model to account for the correlations in the  patterns of care among patients within HSAs.25 Hospital Service Areas are defined by the areas where local residents received most of their hospital care.26 In adjusted analyses, age was categorized into 4 groups: <55, 55 to 64, 65 to 74, and >75 years, based on classifications used in other studies such as that by Frayne and colleagues.15 Chronic Illness and Disability Payment System scores were divided into quartiles (lowest burden: CDPS <0.8). All statistical analyses were performed using STATA version 10 (StataCorp LP, College Station, TX).


Of the 106,174 Medicare and Medicaid beneficiaries with diabetes, 28.0% (n = 29,772) had mental health disorders and 5.1% (n = 5414) had SUDs. The most prevalent mental health disorder group was depression/anxiety (19%, n = 19,690; see Table 1). The rate of comorbid SUD was highest among beneficiaries with bipolar disorder (26%). Beneficiaries with diabetes and mental health disorders were younger (average age ranged from 52 to 65 years) than those with diabetes alone (average age was 70 ± 13 years).

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