David Aron1, Mangala Rajan, Leonard M Pogach. 1. VA HSR&D Center for Healthcare Knowledge Management Research, VA New Jersey Healthcare System, 385 Tremont Avenue, East Orange, NJ, USA.
Abstract
BACKGROUND: The Institute of Medicine has suggested that related individual measures of quality be combined into summary measures. Averages of adherence on dichotomous measures for intermediate outcomes have shortcomings because control of individual risk factors differs in their health benefit. Therefore, a common metric is necessary to weight measures appropriately. OBJECTIVE: Compare health care system performance using continuous measures weighted based on quality adjusted life years saved (QALYsS) versus dichotomous threshold measures. RESEARCH DESIGN: Retrospective cross-sectional analysis of 2000-01 chart abstraction data of diabetic patients from 141 Veterans Health Administration medical centers. Outcome variables included correlation of individual level and facility level adherence to and rankings by continuous weighted individual and summary dichotomous measures for glycemic control (<8% A1c), blood pressure (<140/80 mm/Hg), and low-density lipoprotein-cholesterol (LDL-C) <130 mg/dl. RESULTS: The 141 facilities had a range of 163-740 (mean 263) subjects. The population (n = 37 142) was largely male (86.1%) and older (mean age 65.9 years, SD +/-11.4 years), with mean overall A1c of 7.58%, systolic blood pressure of 137.2 mm/Hg, and LDL-C 104.8 mg/dl. There was excellent correlation between QALYsS and dichotomous outcomes for A1c (r = 0.86), blood pressure (r = 0.94), LDL-C (r = 0.95), and the summary measure (r = 0.92), but poor correlation among the risk factors (r = 0.19-0.36). There was considerable difference in rankings between the dichotomous and the continuous weighted measures; only 46% of facilities remained within the same or within 1 decile. CONCLUSION: Continuous weighted measures for the major risk factors for diabetes-related complications have high correlation with dichotomous measures. We propose that a continuous QALYs-weighted summary measure could function as a global measure for the quality of diabetes care.
BACKGROUND: The Institute of Medicine has suggested that related individual measures of quality be combined into summary measures. Averages of adherence on dichotomous measures for intermediate outcomes have shortcomings because control of individual risk factors differs in their health benefit. Therefore, a common metric is necessary to weight measures appropriately. OBJECTIVE: Compare health care system performance using continuous measures weighted based on quality adjusted life years saved (QALYsS) versus dichotomous threshold measures. RESEARCH DESIGN: Retrospective cross-sectional analysis of 2000-01 chart abstraction data of diabeticpatients from 141 Veterans Health Administration medical centers. Outcome variables included correlation of individual level and facility level adherence to and rankings by continuous weighted individual and summary dichotomous measures for glycemic control (<8% A1c), blood pressure (<140/80 mm/Hg), and low-density lipoprotein-cholesterol (LDL-C) <130 mg/dl. RESULTS: The 141 facilities had a range of 163-740 (mean 263) subjects. The population (n = 37 142) was largely male (86.1%) and older (mean age 65.9 years, SD +/-11.4 years), with mean overall A1c of 7.58%, systolic blood pressure of 137.2 mm/Hg, and LDL-C 104.8 mg/dl. There was excellent correlation between QALYsS and dichotomous outcomes for A1c (r = 0.86), blood pressure (r = 0.94), LDL-C (r = 0.95), and the summary measure (r = 0.92), but poor correlation among the risk factors (r = 0.19-0.36). There was considerable difference in rankings between the dichotomous and the continuous weighted measures; only 46% of facilities remained within the same or within 1 decile. CONCLUSION: Continuous weighted measures for the major risk factors for diabetes-related complications have high correlation with dichotomous measures. We propose that a continuous QALYs-weighted summary measure could function as a global measure for the quality of diabetes care.
Authors: Andrew I Geller; Nadine Shehab; Maribeth C Lovegrove; Scott R Kegler; Kelly N Weidenbach; Gina J Ryan; Daniel S Budnitz Journal: JAMA Intern Med Date: 2014-05 Impact factor: 21.873
Authors: Astrid Lavens; Kris Doggen; Chantal Mathieu; Frank Nobels; Evy Vandemeulebroucke; Michel Vandenbroucke; Ann Verhaegen; Viviane Van Casteren Journal: BMC Health Serv Res Date: 2016-08-23 Impact factor: 2.655