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Research Letter
October 2015

Adjusting Antidepressant Quality Measures for Race and Ethnicity

Author Affiliations
  • 1Group Health Research Institute, Seattle, Washington
  • 2Kaiser Permanente Southern California Department of Research and Evaluation, Pasadena
  • 3Kaiser Permanente Hawaii Center for Health Research, Honolulu
  • 4Kaiser Permanente Colorado Institute for Health Research, Denver
  • 5HealthPartners Institute for Education and Research, Minneapolis, Minnesota
JAMA Psychiatry. 2015;72(10):1055-1056. doi:10.1001/jamapsychiatry.2015.1437

Increasing awareness of health care disparities has prompted reexamination of the National Quality Forum recommendation that measures of health care quality not be adjusted for patients’ sociodemographic characteristics. Adjustment might appear to endorse poorer-quality care for those traditionally underserved. However, Fiscella and colleagues1 pointed out that failure to adjust for sociodemographic differences might unfairly penalize health systems serving disadvantaged groups. Jha and Zaslavsky2 argued that quality measures should be adjusted for patient characteristics when differences between health systems are confounded by differences between the patients they serve. In those cases, stratified reporting of quality measures would both reveal health disparities and permit fairer comparisons of quality across health systems or facilities.

Given that rates of mental health treatment differ markedly by race and ethnicity,3 this cohort study examined how stratifying by race/ethnicity would affect a specific mental health care quality measure: the proportion of outpatients starting antidepressant treatment who receive adequate or potentially effective acute-phase treatment.4

Methods

The participating health systems (Group Health Cooperative, HealthPartners, Kaiser Permanente Colorado, Kaiser Permanente Hawaii, and Kaiser Permanente Southern California) serve more than 6 million patients, with each system’s membership representing the racial/ethnic distribution of its service area (Table 1). Using methods previously described,5 health system records identified adult outpatients beginning a new episode of antidepressant treatment for a depressive disorder between January 1, 2010, and December 31, 2012. Self-reported race/ethnicity was identified from electronic medical records. Pharmacy dispensing records were used to identify patients receiving more than 90 days’ supply of any antidepressant medication over 180 days, beginning with the index prescription, similar to the National Committee for Quality Assurance/Healthcare Effectiveness Data and Information Set measure of adequate acute-phase treatment.4 The sample was limited to patients continuously enrolled from 270 days before to 180 days after the index prescription. The health systems’ institutional review boards granted waivers of consent for use of deidentified data.

Table 1.  
Racial/Ethnic Composition of Health System Populations
Racial/Ethnic Composition of Health System Populations
Results

The overall proportion of patients meeting this threshold for effective acute-phase treatment varied from 58.2% to 69.9% across the 5 health systems (Table 2). This proportion varied markedly across racial/ethnic groups but showed a similar pattern across health systems (highest in non-Hispanic white individuals, lower in Asian and Hispanic individuals, and lowest in African American individuals). Rates for Native Hawaiian and Pacific Islander individuals were the most variable across sites. When rates for each health system were standardized to replicate the race/ethnicity distribution of the entire sample,6 overall rates of adequate treatment varied from 60.5% to 65.6%.

Table 2.  
Proportion of Adult Outpatients Starting Antidepressant Treatment for Depression Who Received at Least 90 Days of Medication Over the Following 180 Days
Proportion of Adult Outpatients Starting Antidepressant Treatment for Depression Who Received at Least 90 Days of Medication Over the Following 180 Days
Discussion

Most of the observed variation across health systems in overall rates of effective acute-phase antidepressant treatment reflected differences in the racial/ethnic distribution of patient populations. Standardizing across health systems reduced the range in performance from approximately 12% to approximately 5% and significantly altered the overall ranking of these health systems. Rankings also varied widely across racial/ethnic groups, with every health system ranking first or second in at least 1 group.

We could not determine whether lower rates of adequate treatment in some racial or ethnic groups reflect disparities in clinical practice (which should be reduced) or differences in patients’ informed treatment preferences (which should be respected). In either case, use of unadjusted overall rates would bias comparisons between health systems. Unadjusted comparisons could create perverse incentives, punishing a health system for identifying and treating depression in traditionally underserved groups. Stratified rates also revealed important opportunities for improving care in traditionally underserved groups.

In these 5 health systems, most of the variation in overall rates of adequate acute-phase antidepressant treatment was owing to confounding by racial/ethnic differences in the patients they serve. Consistent with recommendations of Fiscella et al1 and Jha and Zaslavsky,2 comparison of depression care across health systems—and incentives to improve health system performance—should be based on stratified performance measures.

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Article Information

Corresponding Author: Gregory E. Simon, MD, MPH, Group Health Research Institute, 1730 Minor Ave, Seattle, WA 98101 (simon.g@ghc.org).

Published Online: September 9, 2015. doi:10.1001/jamapsychiatry.2015.1437.

Author Contributions: Dr Simon had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Simon and Stewart conducted data analyses.

Study concept and design: Simon, Coleman, Beck, Penfold.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Simon, Coleman.

Critical revision of the manuscript for important intellectual content: Simon, Waitzfelder, Beck, Rossom, Stewart, Penfold.

Statistical analysis: Simon, Coleman, Stewart, Penfold.

Obtained funding: Simon, Coleman, Rossom.

Administrative, technical, or material support: Coleman, Waitzfelder, Beck, Rossom, Stewart.

Study supervision: Simon.

Conflict of Interest Disclosures: Drs Simon and Penfold have received salary support from research grants by Bristol-Myers Squibb and Otsuka Pharmaceuticals to Group Health Research Institute. This funding was unrelated to this article. No other disclosures were reported.

Funding/Support: The Mental Health Research Network is supported by National Institute of Mental Health Cooperative Agreement U19MH092201.

Role of the Funder/Sponsor: The National Institute of Mental Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Fiscella  K, Burstin  HR, Nerenz  DR.  Quality measures and sociodemographic risk factors: to adjust or not to adjust. JAMA. 2014;312(24):2615-2616.
PubMedArticle
2.
Jha  AK, Zaslavsky  AM.  Quality reporting that addresses disparities in health care. JAMA. 2014;312(3):225-226.
PubMedArticle
3.
Substance Abuse and Mental Health Services Administration. Racial/Ethnic Differences in Mental Health Service Use Among Adults. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2015.
4.
National Committee for Quality Assurance. HEDIS 2015: Healthcare Effectiveness Data and Information Set. Vol 1, narrative. Washington, DC: National Committee for Quality Assurance; 2014.
5.
Simon  GE, Rossom  RC, Beck  A,  et al.  Antidepressants are not over-prescribed for mild depression. J Clin Psychiatry. In press.
6.
Kalton  G.  Standardization: a technique to control for extraneous variables. Appl Stat. 1968;17:118-136.Article
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