Andrew M. Ryan

Objective: To evaluate the impact of hospital value‐based purchasing (HVBP) on clinical quality and patient experience during its initial implementation period (July 2011–March 2012).Data Sources: Hospital‐level clinical quality and patient experience data from Hospital Compare from up to 5 years before and three quarters after HVBP was initiated.Study Design: Acute care hospitals were exposed to HVBP by mandate while critical access hospitals and hospitals located in Maryland were not exposed. We performed a difference‐in‐differences analysis, comparing performance on 12 incentivized clinical process and 8 incentivized patient experience measures between hospitals exposed to the program and a matched comparison group of nonexposed hospitals. We also evaluated whether hospitals that were ultimately exposed to HVBP may have anticipated the program by improving quality in advance of its introduction.Principal Findings: Difference‐in‐differences estimates indicated that hospitals that were exposed to HVBP did not show greater improvement for either the clinical process or patient experience measures during the program's first implementation period. Estimates from our preferred specification showed that HVBP was associated with a 0.51 percentage point reduction in composite quality for the clinical process measures (p > .10, 95 percent CI: −1.37, 0.34) and a 0.30 percentage point reduction in composite quality for the patient experience measures (p > .10, 95 percent CI: −0.79, 0.19). We found some evidence that hospitals improved performance on clinical process measures prior to the start of HVBP, but no evidence of this phenomenon for the patient experience measures.Conclusions: The timing of the financial incentives in HVBP was not associated with improved quality of care. It is unclear whether improvement for the clinical process measures prior to the start of HVBP was driven by the expectation of the program or was the result of other factors.

Objective: To test whether receiving a financial bonus for quality in the Premier Hospital Quality Incentive Demonstration (HQID) stimulated subsequent quality improvement.Data: Hospital‐level data on process‐of‐care quality from Hospital Compare for the treatment of acute myocardial infarction (AMI), heart failure, and pneumonia for 260 hospitals participating in the HQID from 2004 to 2006; receipt of quality bonuses in the first 3 years of HQID from the Premier Inc. website; and hospital characteristics from the 2005 American Hospital Association Annual Survey.Study Design: Under the HQID, hospitals received a 1 percent bonus on Medicare payments for scoring between the 80th and 90th percentiles on a composite quality measure, and a 2 percent bonus for scoring at the 90th percentile or above. We used a regression discontinuity design to evaluate whether hospitals with quality scores just above these payment thresholds improved more in the subsequent year than hospitals with quality scores just below the thresholds. In alternative specifications, we examined samples of hospitals scoring within 3, 5, and 10 percentage point “bandwidths” of the thresholds. We used a Generalized Linear Model to estimate whether the relationship between quality and lagged quality was discontinuous at the lagged thresholds required for quality bonuses.Principal Findings: There were no statistically significant associations between receipt of a bonus and subsequent quality performance, with the exception of the 2 percent bonus for AMI in 2006 using the 5 percentage point bandwidth (0.8 percentage point increase, p < .01), and the 1 percent bonus for pneumonia in 2005 using all bandwidths (3.7 percentage point increase using the 3 percentage point bandwidth, p < .05).Conclusions: We found little evidence that hospitals' receipt of quality bonuses was associated with subsequent improvement in performance. This raises questions about whether winning in pay‐for‐performance programs, such as Hospital Value‐Based Purchasing, will lead to subsequent quality improvement.

Objectives: To assess associations of caesarean section with body mass from birth through adolescence. Design: Longitudinal birth cohort study, following subjects up to 15 years of age. Setting and participants: Children born in 1991–1992 in Avon, UK who participated in the Avon Longitudinal Study of Parents and Children (ALSPAC) (n=10 219). Outcome measures: Primary outcome: standardized measures of body mass (weight-for length z-scores at 6 weeks, 10 and 20 months; and body mass index (BMI) z-scores at 38 months, 7, 9, 11 and 15 years). Secondary outcome: categorical overweight or obese (BMI ⩾85th percentile) for age and gender, at 38 months, 7, 9, 11 and 15 years. Results: Of the 10 219 children, 926 (9.06%) were delivered by caesarean section. Those born by caesarean had lower-birth weights than those born vaginally (−46.1 g, 95% confidence interval(CI): 14.6–77.6 g; P=0.004). In mixed multivariable models adjusting for birth weight, gender, parental body mass, family sociodemographics, gestational factors and infant feeding patterns, caesarean delivery was consistently associated with increased adiposity, starting at 6 weeks (+0.11 s.d. units, 95% CI: 0.03–0.18; P=0.005), through age 15 (BMI z-score increment+0.10 s.d. units, 95% CI: 0.001–0.198; P=0.042). By age 11 caesarean-delivered children had 1.83 times the odds of overweight or obesity (95% CI: 1.24–2.70; P=0.002). When the sample was stratified by maternal pre-pregnancy weight, the association among children born of overweight/obese mothers was strong and long-lasting. In contrast, evidence of an association among children born of normal-weight mothers was weak. Conclusion: Caesarean delivery is associated with increased body mass in childhood and adolescence. Research is needed to further characterize the association in children of normal weight women. Additional work is also needed to understand the mechanism underlying the association, which may involve relatively enduring changes in the intestinal microbiome.

Provider profiling of outcome performance has become increasingly common in pay-for-performance programs. For chronic conditions, a substantial proportion of patients eligible for outcome measures may be lost to follow-up, potentially compromising outcome profiling. In the context of primary care depression treatment, we assess the implications of missing data for the accuracy of alternative approaches to provider outcome profiling. We used data from the Improving Mood-Promoting Access to Collaborative Treatment trial and the Depression Improvement across Minnesota, Offering a New Direction initiative to generate parameters for a Monte Carlo simulation experiment. The patient outcome of interest is the rate of remission of depressive symptoms at 6 months among a panel of patients with major depression at baseline. We considered two alternative approaches to profiling this outcome: (1) a relative, or tournament style threshold, set at the 80th percentile of remission rate among all providers, and (2) an absolute threshold, evaluating whether providers exceed a specified remission rate (30 percent). We performed a Monte Carlo simulation experiment to evaluate the total error rate (proportion of providers who were incorrectly classified) under each profiling approach. The total error rate was partitioned into error from random sampling variability and error resulting from missing data. We then evaluated the accuracy of alternative profiling approaches under different assumptions about the relationship between missing data and depression remission. Over a range of scenarios, relative profiling approaches had total error rates that were approximately 20 percent lower than absolute profiling approaches, and error due to missing data was approximately 50 percent lower for relative profiling. Most of the profiling error in the simulations was a result of random sampling variability, not missing data: between 11 and 21 percent of total error was attributable to missing data for relative profiling, while between 16 and 33 percent of total error was attributable to missing data for absolute profiling. Finally, compared with relative profiling, absolute profiling was much more sensitive to missing data that was correlated with the remission outcome. Relative profiling approaches for pay-for-performance were more accurate and more robust to missing data than absolute profiling approaches.

The 2009 American Recovery and Reinvestment Act spurred adoption of electronic health records (EHRs) in the United States, through such measures as financial incentives to providers through Medicare and Medicaid and regional extension centers, which provide ongoing technical assistance to practices. Yet the relationship between EHR adoption and quality of care remains poorly understood. We evaluated the early effects on quality of the Primary Care Information Project, which provides subsidized EHRs and technical assistance to primary care practices in underserved neighborhoods in New York City, using the regional extension center model. We found that just general participation in, or exposure to, the project was not enough to improve quality of care. It took sustained exposure on the part of these practices and technical assistance to them before they demonstrated improvement on measures of care most likely to be affected by the use of electronic health records, such as cancer screenings and care for patients with diabetes. Participating in the Primary Care Information Project for nine or more months was associated with significantly improved quality, but only for this limited group of quality measures and only for physicians receiving extensive technical assistance.