Hospital readmission rates are increasingly used for both quality improvement and cost control. However, the validity of readmission rates as a measure of quality of hospital care is not evident. We aimed to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of care. We conducted a systematic literature review, using the bibliographic databases Embase, Medline OvidSP, Web-of-Science, Cochrane central and PubMed for the period of January 2001 to May 2013. The search resulted in 102 included papers. We found that definition of the context in which readmissions are used as a quality indicator is crucial. This context includes the patient group and the specific aspects of care of which the quality is aimed to be assessed. Methodological flaws like unreliable data and insufficient case-mix correction may confound the comparison of readmission rates between hospitals. Another problem occurs when the basic distinction between planned and unplanned readmissions cannot be made. Finally, the multi-faceted nature of quality of care and the correlation between readmissions and other outcomes limit the indicator's validity. Although readmission rates are a promising quality indicator, several methodological concerns identified in this study need to be addressed, especially when the indicator is intended for accountability or pay for performance. We recommend investing resources in accurate data registration, improved indicator description, and bundling outcome measures to provide a more complete picture of hospital care.

Background: Quality indicators are increasingly being implemented in Europe for policy and management purposes. Many of these indicators were initially developed and implemented in the USA. However, the suitability of directly adopting indicators that have been developed in a different health care system can be questioned. Therefore, we investigate the validity behind the readmission rate indicator in the European setting. Methods: A systematic literature study was conducted to identify the status of scientific research on the validity of this indicator (January 1999 and April 2010). Descriptive information as well as information on the data source, indicator definition, risk adjustment factors, and conclusions was assessed. Results: The majority of the 486 included studies focused on the actual use of the indicator as an outcome measure in European countries. Only 21 studies specifically addressed its validity, or important prerequisites of validity. There is little consensus over the time-frame used to calculate the indicator, the type of readmission that is included, and the case-mix adjustment applied. Conclusions: Despite the increase in Europe of the use of the readmission rate as a measure of quality of care, the amount of research performed on its validity is scarce. Those studies that report on validity replicate earlier, mainly US findings (<1999) of methodological problems and express reservations on its large-scale use. The readmission rate as an indicator should be used with care. Users should address issues related to definition, time-frame and case-mix adjustment as part of the process to enhance validity in the European settings.

Objective: Measuring quality of care through performance indicators and subsequently using these to compare, learn, and improve (benchmarking) has become a central component of health care policy. This paper aims to identify the main themes of health services research in this area and focuses on opportunities for improving the evidence underpinning performance indicators. Methods: A literature survey was carried out to identify research activities and main research themes in Europe in the years 2000-09. Identified literature was categorized into sub-topics and for each topic the main methodological issues were identified and discussed. Experts validated the findings and explored the potential for related further European research. Results: The distribution of research on performance and benchmarking across EU member states varies in time, scope and settings with a large amount of studies focusing on hospitals. Eight specific fields of research were identified (research on concepts and performance frameworks; performance indicators and benchmarking using mortality data; performance indicators and benchmarking related to cancer care; performance indicators and benchmarking on care delivered in hospitals; patient safety indicators; performance indicators in primary care; patient experience; research on the practice of benchmarking and performance improvement). Expert discussions confirmed that research on performance indicators and benchmarking should focus on the development of indicators, as well as their use. The research should involve the potential users and incorporate scientific approaches from biomedicine and epidemiology as well as the social sciences. Further progress is hampered by data availability. Issues which need to be addressed include the use of unique patient identifiers (UPIs) to facilitate linkages between separate databases; standardized measurement of the experiences of patients and others; and deepening collaboration between Eurostat, the World Health Organization (WHO), and the Organization for Economic Co-operation and Development (OECD) to facilitate the availability of internationally comparable performance information. Conclusions: This study suggests a number of themes for future research. These include testing and improving: the validity and reliability of performance indicators, especially related to avoidable mortality and other outcome indicators; the effectiveness and efficiency of embedding performance indicators in the various governance, monitoring and management models, and their effect on health systems, services and professionals; and the effectiveness and efficiency of linking performance indicators to other national and international strategies and policies such as accreditation and certification, practice guidelines, audits, quality systems, patient safety strategies, national standards on volume and/or quality, public reporting, pay-for-performance and patient/consumer involvement. The field would benefit from strengthening the clearinghouse function for research findings, training of researchers and appropriate scientific publication media. Results should be systematically shared with policy-makers and managers, and networking stimulated between the growing number of regional and national institutes involved in quality measurement and reporting.

Most studies showing a volume outcome effect in resection surgery for oesophago-gastric cancer were conducted before the centralisation of clinical services. This study evaluated the relation between hospital- and surgeon volume and different risk-adjusted outcomes after oesophago-gastric (OG) cancer surgery in England between 2011 and 2013. In data from the National Oesophago-Gastric Cancer Audit from the UK, multivariable random-effects logistic regression models were used to quantify the effect of surgeon and hospital volume on three outcomes: 30-day and 90-day mortality and anastomotic leakage. The models included patient risk factors to adjust for differences in case-mix among hospitals and surgeons. The between-cluster heterogeneity was estimated with the median odds ratio (MOR). The study included patients treated at 42 hospitals and 329 surgeons. The median (interquartile range) of the annual hospital and surgeon volumes were 110 patients (82 to 137) and 13 patients (8 to 19), respectively. The overall rates for 30-day and 90-day mortality were 2.3% and 4.4% respectively, and the anastomotic leakage was 6.3%. Higher hospital volume was associated with lower 30-day mortality (OR: 0.94; 95% CI: 0.91–0.98) and lower anastomotic leakage rates (OR: 0.96; 95% CI: 0.93–0.98) but not 90-day mortality. Higher surgeon volume was only associated with lower anastomotic leakage rates (OR: 0.81; 95% CI: 0.72–0.92). Hospital volume explained a part of the between-hospital variation in 30-day mortality whereas surgeon volume explained part of the between-hospital variation in anastomotic leakage. In the setting of centralized O-G cancer surgery in England, we could still observe an effect of volume on short-term outcomes. However, the effect is inconsistent, depending on the type of outcome measure under consideration, and much smaller than in previous studies. Efforts to centralise O-G cancer services further should carefully address the effects of both hospital and surgeon volume on the range of outcome measures that are relevant to patients.

Background: This study aimed to provide an extensive and up-to-date analysis of running-related injuries (RRI) and analyze a broad range of contributing factors for a large heterogeneous and non-selected running population from Central Europe. Methods: Anthropometric, training, footwear, anatomic malalignment, and injury data from 196 injured runners were assessed case-controlled and retrospectively. Univariate and multivariate regression models were developed to identify associated factors for specific injury locations and diagnoses. Results: The majority of patients were female (56%). Three most frequently observed malalignments included varus knee alignment, pelvic obliquity, and patellar squinting. The most common injuries were the patellofemoral pain syndrome (PFPS), the iliotibial band friction syndrome (ITBFS), patellar tendinopathy, spinal overload, and ankle instability. A number of contributing factors were identified. Previous injury history was a contributing factor for knee injuries and ITBFS. Lower training load was reported with a higher incidence of PFPS, while a higher training load was positively associated with injuries of the lower leg. Runners with a higher body mass index (BMI) were at a significantly higher risk for lower back injuries. Conclusions: Running-related injuries are multifactorial associated with a combination of variables including personal data, training load, anatomic malalignments, and injury history. They can furthermore result from a lack of experience/training as well as from overuse. Suffering a specific RRI of high risk could be defined based on individual predispositions and help to induce appropriate training balance.