Shailender Swaminathan1, Michael Chernew, Dennis P Scanlon. 1. Center for Gerontology and Health Care Research, Brown University, 121 South Main Street, Providence, RI 02912, USA. Shailender_Swaminathan@brown.edu
Abstract
OBJECTIVE: To simplify the decision-making process, we propose and implement an approach to assess the stability of health plan performance over time when multiple indicators of performance exist. DATA SOURCE: National Committee for Quality Assurance Health Care Effectiveness Data and Information Set data for childhood immunization for both publicly and non-publicly reporting health plans between 1998 and 2002. DATA/STUDY DESIGN: We use longitudinal data to examine whether plan quality ratings are stable from year to year. We estimate a parametric Multiple Indicator Multiple Cause Model, a model which allows us to aggregate the multiple measures of performance. The model controls for observed characteristics of the plan and market, allowing for unmeasured heterogeneity. PRINCIPAL FINDINGS: We find moderate persistence in plan performance over time. A plan in the upper tier of performance in the year 1999 has only a 0.47 probability of remaining in the upper tier in the year 2001. Multiple years of good performance increase the probability of good performance in the future. For example, from the subset of plans in the upper tier of performance in 1999, 63 percent continued to perform in the upper tier in 2000. However, from the subset of plans in the upper tier in both 1998 and 1999, about three-fourths of the plans continued to perform in the upper tier in the year 2000. Finally, better performance in the more recent past is more indicative of better performance in the future than better performance in the more distant past. CONCLUSIONS: Although there is some persistence in health plan ratings over time, it is not uncommon for ratings of plans to change between when the data are generated and when actions based on that data, such as employers' contracting decisions or consumers' enrollment decisions, may take effect. Decision makers should be cognizant of this issue and methods should be developed to mitigate its consequences.
OBJECTIVE: To simplify the decision-making process, we propose and implement an approach to assess the stability of health plan performance over time when multiple indicators of performance exist. DATA SOURCE: National Committee for Quality Assurance Health Care Effectiveness Data and Information Set data for childhood immunization for both publicly and non-publicly reporting health plans between 1998 and 2002. DATA/STUDY DESIGN: We use longitudinal data to examine whether plan quality ratings are stable from year to year. We estimate a parametric Multiple Indicator Multiple Cause Model, a model which allows us to aggregate the multiple measures of performance. The model controls for observed characteristics of the plan and market, allowing for unmeasured heterogeneity. PRINCIPAL FINDINGS: We find moderate persistence in plan performance over time. A plan in the upper tier of performance in the year 1999 has only a 0.47 probability of remaining in the upper tier in the year 2001. Multiple years of good performance increase the probability of good performance in the future. For example, from the subset of plans in the upper tier of performance in 1999, 63 percent continued to perform in the upper tier in 2000. However, from the subset of plans in the upper tier in both 1998 and 1999, about three-fourths of the plans continued to perform in the upper tier in the year 2000. Finally, better performance in the more recent past is more indicative of better performance in the future than better performance in the more distant past. CONCLUSIONS: Although there is some persistence in health plan ratings over time, it is not uncommon for ratings of plans to change between when the data are generated and when actions based on that data, such as employers' contracting decisions or consumers' enrollment decisions, may take effect. Decision makers should be cognizant of this issue and methods should be developed to mitigate its consequences.
Authors: Nicole A B M Ketelaar; Marjan J Faber; Signe Flottorp; Liv Helen Rygh; Katherine H O Deane; Martin P Eccles Journal: Cochrane Database Syst Rev Date: 2011-11-09