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IIR 06-091 – HSR Study

IIR 06-091
Summary Measures of Quality for Diabetes Care
Leonard M Pogach, MD MBA
East Orange Campus of the VA New Jersey Health Care System, East Orange, NJ
East Orange, NJ
Funding Period: October 2007 - September 2010
To enhance assessments of healthcare quality, the Institute of Medicine (IOM), private sector organizations, the Agency for Healthcare Quality and Research (AHRQ), and health services researchers have proposed the use of quality measures that summarize care for a condition in multiple dimensions, using approaches that account for the relative contribution of each component to mortality and morbidity.

Aim 1: Compare rankings of between- and within-facility performance on diabetes quality of care, determined by a threshold-based approach that uses two different data sources -- medical abstraction from VA External Peer Review Program databases (EPRP), and the administrative Diabetes Epidemiologic Cohort database (DEpiC).
Aim 2: Compare rankings of between- and within-facility performance on diabetes quality of care determined by a threshold- and continuum-based approach, using data from DEpiC only.
Aim 3: Compare rankings of between-facility performance on diabetes quality of care determined by a simulation- and continuum-based approach, using data from DEpiC only.
Aim 4: Compare rankings of between-facility performance on projected outcomes and quality-adjusted life years (QALYs) determined using the CDC Diabetes Model in addition to the United Kingdom Prospective Diabetes Study (UKPDS) model.
Aim 5: Compare projected costs a) using CDC model base case cost assumptions, and b) using CDC model incorporating HERC (Health Economics Resource Center) costs.

This study compared administrative data from 2003-2004 and medical record abstracted data from EPRP databases from the Office of Quality and Performance (OQP). We created measures to assess facility-level performance using three specific approaches (i.e., threshold-, continuum-, and simulation-based).

1. We used the UKPDS model to calculate new measures reflecting 1) the proportion of veterans' QALY "captured" (observed control of these three values/values resulting from guideline-recommended control), and 2) the QALYs "missed" (i.e., the sum of differences in QALY [guideline-recommended vs. observed control] in a10% random sample of VA facilities. Rankings changed by >= 2 in either direction at 4/12 (33%) facilities when based on the current approach vs. one combining information from all three approaches. Although only about one in five subjects met a composite threshold at the facility level, this translated into capturing 95% of QALYs. These results suggest a relatively narrow distribution of the outcome (simulated QALYs), despite a broad distribution of the intermediate outcomes used as inputs for the UKPDS model.

2. Findings show that the assessment of the quality of good glycemic control among VA facilities differs using the National Committee for Quality Assurance (NCQA) - Healthcare Effectiveness Data and Information Set (HEDIS) measure for the overall study population compared to a subset of patients receiving complex glycemic regimens (largely insulin). The use of a continuous measure provided comparable overall rankings (Spearman Rank Correlation test) to the dichotomous measure, but about 15% of the best performing facilities (statistically different) changed to average (not statistically different).

3. We proposed that assessment of efficiency in the treatment of glucose, blood pressure, and cholesterol in persons with diabetes should incorporate evaluation of the future health care benefit that is "purchased" by direct pharmaceutical costs. This will require paradigm shifts in conceptualizing quality measures as being continuous rather than dichotomous, and evaluating benefit in multiple populations that may differ by age and co-morbid conditions. Using a simulation model, we found that using newer classes of medication, even accounting for QALYs related to putative benefits such as less weight gain, come at considerable costs.

We have concluded that despite its conceptual attractiveness, the use of QALYs for evaluating "rankings" probably is of marginal utility to decision makers. Based upon our findings, we have proposed the combined use of continuous rather than threshold measures using risk stratified samples as a more epidemiological (e.g., evidence-based) approach for next generation measures.

In addition to our published impacts, we have also disseminated key findings to VHA and other federal agencies via internal and external operations meetings. Our research on risk stratification by age and chronic complex illness was presented on national Primary Care calls, and our work on glycemic management has led to the development of technical specifications for possible next generation glycemic measures in the Office of Quality and Performance.

External Links for this Project

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Journal Articles

  1. Sinha A, Rajan M, Hoerger T, Pogach L. Costs and consequences associated with newer medications for glycemic control in type 2 diabetes. Diabetes Care. 2010 Apr 1; 33(4):695-700. [view]
  2. Vimalananda VG, Miller DR, Palnati M, Christiansen CL, Fincke BG. Gender disparities in lipid-lowering therapy among veterans with diabetes. Women's health issues : official publication of the Jacobs Institute of Women's Health. 2011 Jul 1; 21(4 Suppl):S176-81. [view]
  3. Pogach LM, Rajan M, Maney M, Tseng CL, Aron DC. Hidden complexities in assessment of glycemic outcomes: are quality rankings aligned with treatment? Diabetes Care. 2010 Oct 1; 33(10):2133-9. [view]
Conference Presentations

  1. Rajan M, Pogach LM, Tseng C, Litaker D, Aron DC. Assessing Progress in Glycemic Improvement for Younger Veterans: A Quality Improvement Measure Proposal. Paper presented at: VA HSR&D National Meeting; 2009 Feb 12; Baltimore, MD. [view]
  2. Litaker D, Rajan M, Pogach LM. Balancing Utility and Data Requirements in the Measurement of Quality in Diabetes Care. Poster session presented at: American College of Medical Quality Annual Conference; 2010 Feb 19; Crystal City, VA. [view]
  3. Pogach LM, Miller ScD D, Christiansen CL, Fincke BG, Tseng C. Do Changes in Population Subgroups Distort Cross Sectional Trends in A1c as a "Quality Improvement" Measure? Paper presented at: VA HSR&D National Meeting; 2009 Feb 12; Baltimore, MD. [view]
  4. Pogach LM. Does Variation in Population Subgroups Distort Cross Sectional and Longitudinal Trends in Glycemic Control? Implications for Public Health, Performance Measurement and Policy for Diabetes. Paper presented at: AcademyHealth Annual Research Meeting; 2009 Jun 29; Chicago, IL. [view]

DRA: Health Systems
DRE: none
Keywords: Diabetes, Quality assessment, Research measure
MeSH Terms: none

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