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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
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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
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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
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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
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.
Table 2 illustrates the rates of adherence to measures of diabetes care quality across mental health disorders in 2005. Beneficiaries with mental health disorders were less likely to achieve full adherence than those without mental health disorders (19%-22% vs 25%, respectively; P <.001). The rates of adherence to A1C testing were lower among beneficiaries with mental health disorders (64%-72%) than among those with no mental health disorders (76%). Lower proportions of beneficiaries with mental health disorders (61%-68%) had at least 1 LDL-C test in 2005 compared with beneficiaries who had no mental health disorders (73%). Lower rates of adherence to eye examinations were found among beneficiaries with mental health disorders (42%-54%) than among those with no mental health disorders (61%). However, a higher proportion of beneficiaries with mental health disorders had a nephropathy test (49%-51% vs 47%).

In adjusted analyses, beneficiaries with schizophrenia/paranoid states had higher odds of adherence to quality measures, whereas beneficiaries with depression/anxiety and other mental health disorders had lower odds of adherence. For example, the likelihood of full adherence to quality measures in 2005 was lower in beneficiaries with depression/anxiety (odds ratio [OR] = 0.95; 95% confidence interval [CI], 0.90-1.00) and other mental health disorders (OR = 0.88; 95% CI, 0.79-0.98) than it was among those who had no mental health disorders. On the other hand, the likelihood of full adherence to quality measures in 2005 was higher among individuals with schizo-phrenia/paranoid states (OR = 1.22; 95%  CI, 1.08-1.37) than it was among those who had no mental health disorders.

Table 3 shows some variation across specific measures and diagnoses. Beneficiaries with alcohol and drug use disorders had lower odds of adherence for LDL-C testing (OR for alcohol abuse/dependence = 0.82, 95% CI, 0.76-0.89; OR for drug abuse/dependence = 0.84, 95% CI, 0.74-0.95), eye examination (OR for alcohol abuse/dependence = 0.80, 95% CI, 0.74-0.86; OR for drug abuse/dependence = 0.71, 95% CI, 0.65-0.78) and full adherence (OR for alcohol abuse/dependence = 0.79, 95% CI, 0.71-0.86; OR for drug  abuse/dependence = 0.67, 95% CI, 0.59-0.76).

Sensitivity Analysis

Besides diagnostic codes, medication data can be used to identify mental health disorders. An example is the Medicaid Rx model by Gilmer and colleagues.27,28 An exploratory analysis on individuals receiving Medicaid (ie, Medicaid alone and dual eligibles) was performed to compare the estimates from the Medicaid Rx models28 for the association between mental health disorders and full adherence to quality measures with the estimates obtained by using ICD-9-CM codes. The results showed no significant differences using the 2 case identification methods. The data were analyzed with and without variables derived from census data to determine the impact of excluding cases that could not be linked to census areas. Results were similar for the 2 models.

DISCUSSION

The analyses showed that the relationships between mental health disorders and adherence to quality measures varied depending on the type of BHD as well as the measure of interest. While schizophrenia/paranoid states were associated with increased odds of adherence to all quality measures, depression/ anxiety or other mental health disorders were correlated with lower odds of LDL-C tests and eye examinations. Bipolar disorder was not significantly associated with adherence to most quality measures, except nephropathy tests. Similar to those with depression/anxiety, beneficiaries with SUD had lower odds of having LDL-C tests and eye examinations. Similar associations between quality measures (eg, LDL-C tests, eye examinations) and BHDs (eg, SUD,3,13,15 depression/ anxiety,13,15 other mental health disorders13) have also been reported in previous studies.

Contrary to some previous research, this study did not show that beneficiaries with schizophrenia/paranoid states had lower odds of adherence to measures of diabetes care quality. A possible explanation is that there was a change in practice among clinicians regarding the treatment of patients with co-occurring mental health disorders. Previous studies showed that the use of psychotropic medications, especially antipsychotics, was associated with increased risk of metabolic dysfunction, such as hyperglycemia and hyperlipidemia, particularly among people with diabetes.29,30 New clinical practice guidelines for managing physical health among people with schizophrenia were adopted in 2004 by the American Diabetes Association and the American Psychiatric Association, largely due to increased risk associated with psychotropic medications.31 Clinicians may have become more vigilant about monitoring diabetes in their patients with schizophrenia after these guidelines were adopted.

Consistent with previous research, beneficiaries with SUDs were less likely to achieve adherence to quality measures.31 It is crucial to realize that diabetes care quality is affected  by both patient compliance and physician behavior. A previous study by Frayne and colleagues showed that individuals with SUD had poorer control of A1C levels.15 Other studies have shown that diabetes self-care is essential in maintaining proper A1C levels.32-34 Ahmed and colleagues observed that increased alcohol consumption in people with diabetes was associated with poorer self-care behaviors, such as lower rates of adherence to oral hypoglycemics and self-monitoring of blood glucose.35 However, this study assessed whether beneficiaries received the tests or examinations for diabetes monitoring, which need to be ordered or performed by physicians and are not related to self-care. Therefore, physician attitudes may influence how diabetes is managed in people with SUD. In a study by Krebs et al, physicians were 3 times more likely to regard a patient visit as difficult if the patient had SUD.9 Other studies suggested that physician attitudes might affect patient care. Jackson and Kroenke observed that patients were more likely to have  unmet expectations (eg, about having tests or getting diagnoses) after a difficult encounter with their physician.36 Thus, SUD may affect both patients’ and physicians’ adherence to best practice.

However, our findings showed that beneficiaries with drug abuse/dependence had higher odds of receiving nephropathy tests. Because nephropathy tests are performed on urine samples, they may be ordered along with routine drug screens. Further studies should investigate whether quality of diabetes care improves after individuals with SUD receive addiction treatment.

One limitation of this study was its reliance on administrative data. It is possible that the study underestimated the adherence rates to quality measures among beneficiaries  because some of the procedures or tests performed did not appear on claims. In previous studies comparing the rates of patient adherence to quality measures indicated by claims data with the rates indicated by medical records, some underdetection of diabetes care services was observed when only claims data were used.37,38 Another limitation is the lack of laboratory data on A1C or LDL-C control because proper control of A1C and LDL-C levels is part of diabetes care quality. Further investigation of data on A1C and LDL-C control is necessary to assess diabetes care quality in this population. Another weakness was that the study was not able to assess the socioeconomic status of individual beneficiaries. Data from Census 2000 were used as proxy for community socioeconomic conditions.

The major strength of the current study is the use of population-based data; hence, the results may be applicable to other Medicare and Medicaid populations with demographic composition and Medicaid programs similar to those in Massachusetts. In addition, the size of the study population provided sufficient confidence in detecting any significant relationships between the BHDs and adherence to quality measures.

CONCLUSION

Evidence suggests that Medicare/Medicaid beneficiaries with SUDs or mental health disorders other than schizophrenia/ paranoid states were less likely to receive laboratory tests and/or clinical examinations for monitoring diabetes. Efforts to improve diabetes care quality should focus on these populations. These efforts should also include targeted interventions such as integrated treatment programs to improve diabetes care.

Future studies using multistate data should be performed to estimate the rates of adherence to measures of diabetes care quality over a broader population. Medical records  should be used to evaluate the sensitivity of administrative data in assessing healthcare utilization. Measures of glycemic and lipid control (ie, laboratory values) should also be included. Future studies should focus on examining possible factors that contribute to the disparities in diabetes care quality among individuals with diabetes and comorbid SUD, as well as the reasons for differences in adherence among mental health disorder groups.

Author Affiliations: From Center for Health Policy & Research (GYL, JZ, W-CL, REC), University of Massachusetts Medical School, Shrewsbury, MA.

 

Funding Source: No external funding was received for the study.

 

Author Disclosures: The authors (GYL, JZ, W-CL, REC) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 

Authorship Information: Concept and design (GYL, W-CL, REC); acquisition of data (W-CL, REC); analysis and interpretation of data (GYL, JZ, W-CL, REC); drafting of the manuscript (GYL, REC); critical revision of the manuscript for important intellectual content (W-CL, REC); statistical analysis (GYL, JZ); administrative, technical, or logistic support (JZ); and supervision (REC).

 

Address correspondence to: Robin E. Clark, PhD, Center for Health Policy & Research, University of Massachusetts Medical School, 333 South Street, Shrewsbury, MA 01545. E-mail: robin.clark@umassmed.edu.

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