Objective. Taiwan has instituted a pay-for-performance (P4P) program for diabetes mellitus (DM) patients that rewards doctors based in part on outcomes for their DM patients. Doctors are permitted to choose which of their DM patients are included in the P4P program. We test whether seriously ill DM patients are disproportionately excluded from the P4P program. Data Source/Study Setting. This study utilizes data from the National Health Insurance (NHI) database in Taiwan for the period of January 2007 to December 2007. Our sample includes 146,481 DM-P4P patients (16.56 percent of the total) and 737,971 non-DM-P4P patients. Data Collection/Extraction Methods. We use logistic and multilevel models to estimate the effects of patient and hospital characteristics on P4P selection. Principal Findings. The results show that older patients and patients with more comorbidities or more severe conditions are prone to be excluded from P4P programs. Conclusions. We found that DM patients are disproportionately excluded from P4P programs. Our results point to the importance of mandated participation and risk adjustment measures in P4P programs.

The greater availability of convenience stores near a school is associated with an increased risk of alcohol use among adolescents over the previous 6 months.

A concise and reliable composite quality score would be helpful in judging the quality of a hospital's services, especially for pay-for-performance (P4P) initiatives. This study compared several nonlatent and latent composite quality scores to evaluate the quality of care using diabetes mellitus (DM) P4P data and discusses their characteristics and implications for P4P policy. The authors describe a cross-sectional study of the DM P4P data collected from the claims data of the Bureau of National Health Insurance (NHI) in Taiwan from January 2007 to December 2007. The DM patient outcome data, such as hemoglobin A1C values, were retrieved from the P4P database sponsored by the Bureau of NHI in Taiwan. The composite scores were derived from the following methods: 1) nonlatent scores methods (e.g., the raw sum score and the all-or-none score methods)and 2) latent scores methods (e.g., item-response theory-based Models I and II and the PRIDIT model). These scores are compared in terms of 2 aspects-agreement of hospital rankings (using Spearman's rank correlation) and reliability (using bootstrap methods). The latent methods were superior to the nonlatent methods because they were more reliable and had specific weighting themes. The correlations among the 3 latent methods were moderately high. The use of the PRIDIT approach, which is moderately difficult compared with item response theory-based model, is recommended if the insurer wants to balance convenience and precision.