League tables and acute myocardial infarction

Leyland, Alastair H.; Boddy, F A

doi:10.1016/s0140-6736(97)09362-8

Cited by 82 publications

(48 citation statements)

References 21 publications

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“…Moreover, some degree of risk adjustment may be obtained by including patient-level measures of risk or severity in a multilevel model. Several investigators, therefore, have used multilevel models of the type given in Equations 4a through 4c, with patients at level 1 and providers at level 2, to obtain stabilized, risk-adjusted indices of provider performance (15,72,(81)(82)(83). In these applications, X i j represents some measure of risk for the ith patient being treated by the jth health care provider.…”

Section: Questions About the Values Of Microparametersmentioning

confidence: 99%

Statistical and Substantive Inferences in Public Health: Issues in the Application of Multilevel Models

Bingenheimer

Raudenbush

2004

Annu. Rev. Public Health

145

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s Abstract Multilevel statistical models have become increasingly popular among public health researchers over the past decade. Yet the enthusiasm with which these models are being adopted may obscure rather than solve some problems of statistical and substantive inference. We discuss the three most common applications of multilevel models in public health: (a) cluster-randomized trials, (b) observational studies of the multilevel etiology of health and disease, and (c) assessments of health care provider performance. In each area of investigation, we describe how multilevel models are being applied, comment on the validity of the statistical and substantive inferences being drawn, and suggest ways in which the strengths of multilevel models might be more fully exploited. We conclude with a call for more careful thinking about multilevel causal inference.

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Section: Questions About the Values Of Microparametersmentioning

confidence: 99%

Statistical and Substantive Inferences in Public Health: Issues in the Application of Multilevel Models

Bingenheimer

Raudenbush

2004

Annu. Rev. Public Health

145

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“…This question has not been posed regarding children's hospitals. Research on adult hospitals in the United Kingdom showed that performance rankings are statistically imprecise, even after adjustment for case mixtures, [14][15][16][17] which suggests that where a hospital appears in a ranking from best and worst may have as much to do with random chance as with any underlying quality of care. Furthermore, smaller hospitals have been shown to have greater statistical instability in their rankings, which creates a potential bias for comparisons of hospitals of different sizes 18 and which may be quite relevant in comparisons of children's hospitals.…”

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confidence: 99%

Statistical Uncertainty of Mortality Rates and Rankings for Children's Hospitals

et al. 2011

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WHAT'S KNOWN ON THIS SUBJECT:Many hospital quality-of-care stakeholders wish to identify hospitals with leading or lagging performance on the basis of mortality rates, but the degree of statistical imprecision of comparative rates or rankings may not be fully appreciated. WHAT THIS STUDY ADDS:Children's hospitals' mortality rates have substantial statistical imprecision, and hospital rankings based on those rates have even more. Stakeholders who seek to improve quality of care and patient outcomes may want to find more informative measures to guide action. abstract OBJECTIVES: Hospitals are being required to report publically their adjusted mortality rates, which are then being used to rank hospitals. Our objectives were to assess the statistical reliability of the determination of a hospital's adjusted mortality rate, of comparisons of that rate with the rates of other hospitals, and of the use of those rates to rank the hospitals. METHODS:A cross-sectional study of 473 383 patients discharged from 42 US children's hospitals in 2008 was performed. Hospitalspecific observed/expected (O/E) mortality rate ratios and corresponding hospital rankings, with 95% confidence intervals (CIs), were examined. RESULTS:Hospitals' O/E mortality rate ratios exhibited wide 95% CIs, and no hospital was clearly distinguishable from the other hospitals' aggregated mean mortality performance. Only 2 hospitals' mortality performance fell outside the comparator hospitals' 95% CI. Those hospitals' 95% CIs overlapped with the overall comparator set's 95% CI, which suggests that there were no statistically significant hospital outliers. Fourteen (33.3%) of the 42 hospitals had O/E ratios that were not statistically different from being in the 95% CI of the top 10% of hospitals. Hospital-specific mortality rate rankings displayed even broader 95% CIs; the typical hospital had a 95% CI range that spanned 22 rank-order positions. CONCLUSION:Children's hospital-specific measures of adjusted mortality rate ratios and rankings have substantial amounts of statistical imprecision, which limits the usefulness of such measures for comparisons of quality of care.

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“…Unless variation in the characteristics of patients admitted (case mix) is taken into account, differences in outcome between hospitals are more likely to reflect differences in referral patterns, admission thresholds, and the nature of the local population than differences in the process of care or its quality. 12,13 However, hospital stroke outcomes published in the United Kingdom take into account only variation in age, sex, and socioeconomic circumstance (Scotland) or age and hospital type (England and Wales) and therefore fail to adjust for more important prognostic variables such as prestroke functional status and the severity of the stroke itself. In their current format, therefore, these data may provide misleading indications of the quality of stroke care.…”

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confidence: 99%

Towards a National System for Monitoring the Quality of Hospital-Based Stroke Services

Weir¹,

Dennis²

2001

Stroke

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, MB ChB, MRCP(UK); Martin S. Dennis, MB ChB, FRCP; on behalf of the Scottish Stroke Outcomes Study Group Background and Purpose-We sought to evaluate a system for monitoring the quality of hospital-based stroke services that uses routinely collected case fatality data, adjusted for case mix, as well as simple measures of the process of stroke care. Methods-We compared the process of care and case fatality after stroke between 5 Scottish hospitals (A through E) during 1995-1997. We retrospectively identified 2724 patients with acute stroke using routine discharge data and obtained case mix and process of care data from the medical record. We ascertained case fatality by record linkage and adjusted for case mix using a simple, externally validated regression model. Results-Crude case fatality varied by 21 deaths per 100 admissions between the 5 hospitals. After adjustment, case fatality still differed significantly (Pϭ0.047), with 5 to 7 more deaths per 100 admissions at Hospital A than at Hospitals B through E. There were major shortcomings in the specialization and organization of care, the use of CT scanning, and the completeness of documentation at Hospital A compared with the other hospitals. There were smaller, but clinically important, differences in care between Hospitals B through E but no significant differences in adjusted case fatality. Conclusions-Once adjusted for important prognostic variables, routinely collected case fatality data might identify hospitals with major shortcomings in the processes of stroke care. More moderate, but still clinically important, variations in stroke care can only be identified by monitoring the process of care directly.

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League tables and acute myocardial infarction

Cited by 82 publications

References 21 publications

Statistical and Substantive Inferences in Public Health: Issues in the Application of Multilevel Models

Statistical and Substantive Inferences in Public Health: Issues in the Application of Multilevel Models

Statistical Uncertainty of Mortality Rates and Rankings for Children's Hospitals

Towards a National System for Monitoring the Quality of Hospital-Based Stroke Services

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