The impact of morbidity trajectories on identifying high-cost cases: using Taiwan’s National Health Insurance as an example

Published: June 5, 2013
Category: Bibliography > Papers
Authors: Chang HY
Countries: Taiwan
Language: null
Types: Performance Analysis
Settings: Health Plan, Hospital

J Public Health 36:300-307.

Department of Health Policy & Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

BACKGROUND: Incorporating longitudinal information into risk-adjustment models has been considered important. This study aimed to evaluate how morbidity trajectories impact risk-adjustment models in identifying high-cost cases.

METHODS: Claims-based risk adjusters, with or without morbidity trajectories derived from 3-year claims from Taiwan’s National Insurance System, were used to predict being a prospective high-cost user. A random sample of Taiwanese National Health Insurance enrollees continuously enrolled from 2002 to 2005 (n = 147,892) was the study sample. A logistic regression model was employed. The performance measures, based on the split analysis, included statistical indicators (c-statistics, sensitivity and predictive positive value), proportions of true cases identified by models and medical utilization of predicted cases.

RESULTS: As the comprehensiveness of risk adjustment models increased, the performance of the models generally increased. The effect of adding trajectories on the model performance decreased as the comprehensiveness of the model increased. Such impact was most apparent in statistical indicators and medical utilization of the predicted groups.

CONCLUSIONS: In identifying high-cost cases, adding morbidity trajectories might be necessary only for less comprehensive risk adjustment models, and its contributions came from higher c-statistics and increasing medical utilization of predicted groups.

PMID: 23740662

United States,Positive Predictive Model,Resource Use,Predictive Risk Models,Cost Burden Evaluation,Adolescent,Adult,Aged,Child,Preschool,Demography,Gender,Health Services Research,Infant,Newborn,Longitudinal Studies,Middle Aged,Models,Economic,Predictive Value of Tests

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