The American Journal of Managed Care February 2011
Behavioral Health Disorders and Adherence to Measures of Diabetes Care Quality
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).
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.
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.
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: email@example.com.
1. Druss BG, Rosenheck RA, Desai MM, Perlin JB. Quality of preventive medical care for patients with mental disorders. Med Care. 2002;40(2): 129-136.
2. Druss BG, Bradford DW, Rosenheck RA, Radford MJ, Krumholz HM. Mental disorders and use of cardiovascular procedures after myocardial infarction. JAMA. 2000;283(4):506-511.
3. Desai MM, Rosenheck RA, Druss BG, Perlin JB. Mental disorders and quality of diabetes care in the Veterans Health Administration. Am J Psychiatry. 2002;159(9):1584-1590.
4. Petersen LA, Normand S-T, Druss BG, Rosenheck RA. Process of care and outcome after acute myocardial infarction for patients with mental illness in the VA health care system: are there disparities? Health Serv Res. 2003;38(1 pt 1):41-63.
5. Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med. 1998;338(21):1516-1520.
6. Druss B, Rosenheck R. Use of medical services by veterans with mental disorders. Psychosomatics. 1997;38(5):451-458.
7. Druss BG, Rosenheck RA. Mental disorders and access to medical care in the United States. Am J Psychiatry. 1998;155(12):1775-1777.
8. Jackson JL, Kroenke K. Difficult patient encounters in the ambulatory clinic: clinical predictors and outcomes. Arch Intern Med. 1999;159(10):1069-1075.
9. Krebs EE, Garrett JM, Konrad TR. The difficult doctor? Characteristics of physicians who report frustration with patients: an analysis of survey data. BMC Health Serv Res. 2006;6:128.
10. Clark RE, Samnaliev M, McGovern MP. Treatment for co-occurring mental and substance use disorders in five state Medicaid programs. Psychiatr Serv. 2007;58(7):942-948.
11. Clark RE, Samnaliev M, McGovern MP. Impact of substance disorders on medical expenditures for medicaid beneficiaries with behavioral health disorders. Psychiatr Serv. 2009;60(1):35-42.
12. Santora PB, Hutton HE. Longitudinal trends in hospital admissions with co-occurring alcohol/drug diagnoses, 1994-2002. J Subst Abuse Treat. 2008;35(1):1-12.
13. Jones LE, Clarke W, Carney CP. Receipt of diabetes services by insured adults with and without claims for mental disorders. Med Care. 2004;42(12):1167-1175.
14. Goldberg RW, Kreyenbuhl JA, Medoff DR, et al. Quality of diabetes care among adults with serious mental illness. Psychiatr Serv. 2007; 58(4):536-543.
15. Frayne SM, Halanych JH, Miller DR, et al. Disparities in diabetes care: impact of mental illness. Arch Intern Med. 2005;165(22): 2631-2638.
16. National Committee for Quality Assurance. HEDIS 2008: List of measures. http://www.ncqa.org/Portals/0/HEDISQM/HEDIS2008/ 2008_Measures.pdf. Accessed February 5, 2011.
17. Kaiser Commission on Medicaid and the Uninsured. Dual Eligibles: Medicaid's Role for Low-Income Medicare Beneficiaries. http://www.kff. org/medicaid/upload/Dual-Eligibles-Medicaid-s-Role-for-Low-Income- Medicare-Beneficiaries-Feb-2006.pdf. Accessed April 11, 2008.
18. Adelmann PK. Mental and substance use disorders among Medicaid recipients: prevalence estimates from two national surveys. Adm Policy Ment Health. 2003;31(2):111-129.
19. Kaiser Commission on Medicaid and the Uninsured. Medicaid and Managed Care: Key Data, Trends, and Issues. February 2010. http:// www.kff.org/medicaid/upload/8046.pdf. Accessed October 21, 2010.
20. Kaiser Family Foundation. Medicare Advantage Fact Sheet. September 2010. http://www.kff.org/medicare/upload/2052-14.pdf. Accessed October 21, 2010.
21. Donaldson MS. Accountability for quality in managed care. Jt Comm J Qual Improv. 1998;24(12):711-725.
22. Dixon LB, Kreyenbuhl JA, Dickerson FB, et al. A comparison of type 2 diabetes outcomes among persons with and without severe mental illnesses. Psychiatr Serv. 2004;55(8):892-900.
23. Kreyenbuhl J, Dickerson FB, Medoff DR, et al. Extent and management of cardiovascular risk factors in patients with type 2 diabetes and serious mental illness. J Nerv Ment Dis. 2006;194(6):404-410.
24. Kronick R, Gilmer T, Dreyfus T, Lee L. Improving Health-Based Payment for Medicaid Beneficiaries: CDPS. http://cdps.ucsd.edu/cdps_hcfr.pdf. Accessed April 13, 2008.
25. The Dartmouth Atlas of Health Care. 2004 ZIP Code Crosswalk. http://www.dartmouthatlas.org/data/download.shtm. Accessed September 22, 2008.
26. The Dartmouth Atlas of Health Care. Appendix on the Geography of Health Care in the United States. http://www.dartmouthatlas.org/ downloads/methods/geogappdx.pdf. Accessed February 5, 2011.
27. Gilmer T, Kronick R, Fishman P, Ganiats TG. The Medicaid Rx model: pharmacy-based risk adjustment for public programs. Med Care. 2001; 39(11):1188-1202.
28. Regents of the University of California. University of California, San Diego. Medicaid Rx. http://medicaidrx.ucsd.edu/. Accessed May 9, 2008.
29. Llorente M, Urrutia V. Diabetes, psychiatric disorders, and the metabolic effects of antipsychotic medications. Clin Diabetes. 2006;24(1): 18-24.
30. Krosnick A, Wilson MG. A retrospective chart review of the clinical effects of atypical antipsychotic drugs on glycemic control in institutionalized patients with schizophrenia and comorbid diabetes mellitus. Clin Ther. 2005;27(3):320-326.
31. American Diabetes Association; American Psychiatric Association; American Association of Clinical Endocrinologists; North American Association for the Study of Obesity. Consensus development conference on antipsychotic drugs and obesity and diabetes. Diabetes Care. 2004;27(2):596-601.
32. Berlin I, Bisserbe JC, Eiber R, et al. Phobic symptoms, particularly the fear of blood and injury, are associated with poor glycemic control in type I diabetic adults. Diabetes Care. 1997;20(2):176-178.
33. Metsch J, Tillil H, Kobberling J, Sartory G. On the relation among psychological distress, diabetes-related health behavior, and level of glycosylated hemoglobin in type I diabetes. Int J Behav Med. 1995; 2(2):104-117.
34. Van Tilburg MA, McCaskill CC, Lane JD, et al. Depressed mood is a factor in glycemic control in type 1 diabetes. Psychosom Med. 2001;63(4):551-555.
35. Ahmed AT, Karter AJ, Liu J. Alcohol consumption is inversely associated with adherence to diabetes self-care behaviours. Diabet Med. 2006;23(7):795-802.
36. Jackson JL, Kroenke K. The effect of unmet expectations among adults presenting with physical symptoms. Ann Intern Med. 2001; 134(9 pt 2):889-897.
37. Keating NL, Landrum MB, Landon BE, Ayanian JZ, Borbas C, Guadagnoli E. Measuring the quality of diabetes care using administrative data: is there bias? Health Serv Res. 2003;38(6 pt 1):1529-1545.
38. Maclean JR, Fick DM, Hoffman WK, King CT, Lough ER, Waller JL. Comparison of 2 systems for clinical practice profiling in diabetic care: medical records versus claims and administrative data. Am J Manag Care. 2002;8(2):175-179.