Exposure to Potentially Harmful Drug–Disease Interactions in Older Community‐Dwelling Veterans Based on the Healthcare Effectiveness Data and Information Set Quality Measure: Who Is at Risk?

OBJECTIVES: To identify prevalence and risk factors for exposure to drug–disease interactions included in the Healthcare Effectiveness Data and Information Set (HEDIS) Drug–Disease Interaction (Rx‐DIS) measure. DESIGN: Cross‐sectional retrospective database analysis. SETTING: Outpatient clinics within the Department of Veterans Affairs (VA). PARTICIPANTS: Individuals aged 65 and older who received VA outpatient care between October 1, 2003, and September 30, 2006. MEASUREMENTS: Rx‐DIS exposure based on the HEDIS measure was identified in VA patients with dementia, falls, and chronic renal failure using VA pharmacy and administrative databases. Factors associated with Rx‐DIS exposure were examined, including demographic, health status, and access‐to‐care factors, including VA outpatient health services use and copayment status. RESULTS: Of the 305,041 older veterans who met criteria for inclusion, the 1‐year prevalence of Rx‐DIS exposure was 15.2%; prevalence was 20.2% for dementia, 16.2% for falls, and 8.5% for chronic renal failure. Patients with high disease burden (physical, psychiatric, number of medications) were significantly more likely to have Rx‐DIS exposure, regardless of condition. Hispanics and individuals with no copayments were more likely to have Rx‐DIS exposure than whites or those with required copayments. There was variation in other predictors based on the type of Rx‐DIS. CONCLUSION: The prevalence of Rx‐DIS was common in older VA outpatients. Future studies should examine the risk of Rx‐DIS exposure on health outcomes using separate analyses for each type of Rx‐DIS separately before combining all Rx‐DIS into a single measure of exposure. Studies that examine the effectiveness of interventions to reduce Rx‐DIS exposure will also be helpful in improving the quality of care for older adults.     

Identifying physician-recognized depression from administrative data: consequences for quality measurement

BACKGROUND: Multiple factors limit identification of patients with depression from administrative data. However, administrative data drives many quality measurement systems, including the Health Plan Employer Data and Information Set (HEDIS). METHODS: We investigated two algorithms for identification of physician-recognized depression. The study sample was drawn from primary care physician member panels of a large managed care organization. All members were continuously enrolled between January 1 and December 31, 1997. Algorithm 1 required at least two criteria in any combination: (1) an outpatient diagnosis of depression or (2) a pharmacy claim for an antidepressant Algorithm 2 included the same criteria as algorithm 1, but required a diagnosis of depression for all patients. With algorithm 1, we identified the medical records of a stratified, random subset of patients with and without depression (n = 465). We also identified patients of primary care physicians with a minimum of 10 depressed members by algorithm 1 (n = 32,819) and algorithm 2 (n = 6,837). RESULTS: The sensitivity, specificity, and positive predictive values were: Algorithm 1: 95 percent, 65 percent, 49 percent; Algorithm 2: 52 percent, 88 percent, 60 percent. Compared to algorithm 1, profiles from algorithm 2 revealed higher rates of follow-up visits (43 percent, 55 percent) and appropriate antidepressant dosage acutely (82 percent, 90 percent) and chronically (83 percent, 91 percent) (p < 0.05 for all). CONCLUSIONS: Both algorithms had high false positive rates. Denominator construction (algorithm 1 versus 2) contributed significantly to variability in measured quality. Our findings raise concern about interpreting depression quality reports based upon administrative data.     

Modifications to HEDIS and CSTE algorithms improve case recognition of pediatric asthma

Our objective was to validate the Council of State and Territorial Epidemiologists (CSTE) definition of "probable" asthma and the Health Plan Employer Data and Information Set (HEDIS) definition of persistent asthma for diagnosis of pediatric asthma, and examine modifications that improve case recognition. CSTE and HEDIS criteria were applied to a cross-sectional study of 3,905 Medicaid children with physician-confirmed diagnosis of asthma/no asthma using a validated survey instrument based upon National Asthma Education and Prevention Program (NAEPP) Guidelines. Modified criteria were applied to another group of 1,458 non-Medicaid children from a managed care organization (MCO). Of 1,852 Medicaid children with physician-confirmed asthma, 906 had persistent asthma. CSTE identified 61% of children with "probable" asthma; HEDIS identified 44% of children with persistent asthma. Correct identification increased with greater disease severity. A modified CSTE increased sensitivity from 0.61 to 0.90, while maintaining high specificity. Three new HEDIS algorithms increased sensitivity from 0.44 to >0.84, with specificity >0.89. When applied prospectively to MCO children, these new algorithms demonstrated improved sensitivity. In conclusion, studies using current CSTE or HEDIS algorithms for case recognition underestimate asthma prevalence and overestimate asthma severity in children. Modified algorithms improve the identification of "probable" and persistent asthma.     

Adherence to the American Diabetes Association retinal screening guidelines for population with diabetes in the United States

Purpose: (1) To assess long-term adherence to American Diabetes Association guideline-recommended retinal screening among population with diabetes in the United States. (2) To determine factors associated with long-term adherence to routine eye screening exams.

Methods: A retrospective cohort study was conducted in adult patients with diabetes identified from January 2009 to December 2010. Patients were followed until disenrollment, death, or study end date (December 2013). A patient was defined as adherent when having at least one exam in each 12-month period if there was evidence of retinopathy, or at least one exam in each 24-month period if there was no evidence of retinopathy. Multivariate logistic regressions were used to investigate patient demographics and other baseline characteristics associated with adherence to guidelines.

Results: A total of 204,073 patients were identified; the mean age (SD) was 61 (13) years and 48% were female. Overall, 71.1% were adherent to the retinal screening guidelines during a median of 4.8 years of follow-up including 27.7% who received an eye exam every year. Patient socioeconomic status (younger age, black race, lower income/education), less comorbidity, insulin use, higher specialist copayment plans, and proxies for poor patient behavior (lower adherence to the oral hypoglycemic agents, less diabetes education, hemoglobin A1C >9%) were associated with nonadherence to routine eye screening exams.

Conclusion: During nearly 5 years of follow-up, 28.9% of patients with diabetes were nonadherent to the retinal screening guidelines. Future research should focus on the development of interventions to address modifiable factors associated with nonadherence.