Statistical Methods for Healthcare Regulation: Rating, Screening and Surveillance

Spiegelhalter, David; Sherlaw-Johnson, C; Bardsley, Martin; Blunt, Ian; Wood, Christopher; Grigg, Olivia

doi:10.1111/j.1467-985x.2011.01010.x

Cited by 119 publications

(127 citation statements)

References 104 publications

(99 reference statements)

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“…These are the sort of graphs that could be used to monitor hospital deaths. 17 An outlier is defined as a point above the 3 standard errors line. Table 4⇓ gives the outlying hospital trusts by year.…”

Section: Resultsmentioning

confidence: 99%

Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Campbell

Jacques

Fotheringham

et al. 2012

BMJ

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Objectives To develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.Design Retrospective cross sectional study of admissions to English hospitals. Setting Hospital episode statistics for England from 1 April 2005 to 30September 2010, with linked mortality data from the Office for National Statistics.Participants 36.5 million completed hospital admissions in 146 general and 72 specialist trusts.Main outcome measures Deaths within hospital or within 30 days of discharge from hospital. ResultsThe predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2% for males and 4.5% for females. Emergency admissions comprised 75% of all admissions and 5.5% died, in contrast to 0.8% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2% in contrast with 15% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators. ConclusionThe SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation. IntroductionAbout 60% of deaths occur in hospital.1 Although a large proportion of these are inevitable, avoidance of unnecessary death is an important objective for health services. Several methods are used within the United Kingdom's health service to identify trusts with high in-hospital mortality, the most widely publicised being the standardised mortality ratio (a ratio of observed to expected deaths), which is calculated from a statistical model. The hospital standardised mortality ratio (HSMR)2 produced by Dr Foster, a provider of healthcare information based at Imperial College, London has been used by the Department of Health for several years to identify failing hospitals. 3 Concerns and criticism over the methodology and interpretation of standardised mortality ratios have, however, been raised both i...

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Section: Resultsmentioning

confidence: 99%

Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Campbell

Jacques

Fotheringham

et al. 2012

BMJ

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“…In this light, the empirical null method by Kalbfleisch and Wolfe (2013) reduces FDR and leads to a small percentage of outlying centers for further investigation. Similar techniques for controlling over-dispersion have been applied in Spiegelhalter (2005) and Spiegelhalter et al (2012).…”

Section: Application To Kidney Transplant Datamentioning

confidence: 99%

Semiparametric methods for center effect measures based on the ratio of survival functions

Schaubel

2014

Lifetime Data Anal

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The survival function is often of chief interest in epidemiologic studies of time to an event. We develop methods for evaluating center-specific survival outcomes through a ratio of survival functions. The proposed method assumes a center-stratified additive hazards model, which provides a convenient framework for our purposes. Under the proposed methods, the center effects measure is cast as the ratio of subject-specific survival functions under two scenarios: the scenario in which the subject is treated at center j; and that wherein the subject is treated at a hypothetical center with survival function equal to the population average. The proposed measure reduces to the ratio of baseline survival functions, but is invariant to the choice of baseline covariate level. We derive the asymptotic properties of the proposed estimators, and assess finite-sample characteristics through simulation. The proposed methods are applied to national kidney transplant data.

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“…For this purpose, the 'overall' population of interest consists of treated patients, and the use of an internal standard population (which pools the subpopulations being evaluated) is implied. A frequently used tool for evaluating center-specific mortality relative to a chosen standard population is the standardized mortality ratio (SMR), defined as the ratio of observed to expected number of events (e.g., deaths) at a particular center, where the 'expected' number of events is computed under the scenario wherein the center-specific and standard population mortality rates are equal; e.g., [7,14,19,21,24,26,29,33,34,36].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, such indirect standardization methods are used as a hospital quality measure in various countries including the United States [7], the United Kingdom [29], Canada [26,33], the Netherlands [19], Australia, and New Zealand [21]. In ESRD community, SMR has been used for evaluating dialysis facilities [24,34] and transplant centers [14,36].…”

Section: Introductionmentioning

confidence: 99%

Standardized Mortality Ratio for Evaluating Center-Specific Mortality: Assessment and Alternative

Schaubel

2014

Stat Biosci

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The standardized mortality ratio (SMR) based on a Cox regression model is often used to evaluate center-specific mortality. However, the asymptotic properties and finite-sample behavior of the Cox SMR are not well studied. We describe some strong limitations of the Cox SMR that relate to its underlying assumptions. To address these limitations, we then develop an alternative to the Cox SMR based on stratification. Asymptotic properties of proposed estimators are derived, with finite-sample properties examined through an extensive simulation study. The methods are applied to national kidney transplant data.

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Statistical Methods for Healthcare Regulation: Rating, Screening and Surveillance

Cited by 119 publications

References 104 publications

Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Semiparametric methods for center effect measures based on the ratio of survival functions

Standardized Mortality Ratio for Evaluating Center-Specific Mortality: Assessment and Alternative

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