Statistical issues in the prospective monitoring of health outcomes across multiple units

Summary.  Following several recent inquiries in the UK into medical malpractice and failures to deliver appropriate standards of health care, there is pressure to introduce formal monitoring of performance outcomes routinely throughout the National Health Service. Statistical process control (SPC) charts have been widely used to monitor medical outcomes in a variety of contexts and have been specifically advocated for use in clinical governance. However, previous applications of SPC charts in medical monitoring have focused on surveillance of a single process over time. We consider some of the methodological and practical aspects that surround the routine surveillance of health outcomes and, in particular, we focus on two important methodological issues that arise when attempting to extend SPC charts to monitor outcomes at more than one unit simultaneously (where a unit could be, for example, a surgeon, general practitioner or hospital): the need to acknowledge the inevitable between-unit variation in 'acceptable' performance outcomes due to the net effect of many small unmeasured sources of variation (e.g. unmeasured case mix and data errors) and the problem of multiple testing over units as well as time. We address the former by using quasi-likelihood estimates of overdispersion, and the latter by using recently developed methods based on estimation of false discovery rates. We present an application of our approach to annual monitoring 'all-cause' mortality data between 1995 and 2000 from 169 National Health Service hospital trusts in England and Wales.     

Nonparametric analysis of ordinal categorical response data with factorial structure

A simple nonparametric method of analysis for contingency tables with an ordinal response and factorial treatment structure is described. The method involves a partition of Pearson's X ²_P-statistic by using orthogonal polynomials so that location and dispersion effects are estimated for each level of the explanatory variable. Analyses of variance are then performed on these effects to determine the important factors. The methods are applied to two examples, where consumers rate their liking for a product on an ordered categorical scale, one of which highlights the need to look at dispersion as well as location effects.     

THE DISADVANTAGE OF A GOOD REPUTATION: Disney as a Target for Social Problems Claims

Social scientists generally presume that a good reputation has advantages. Yet the Walt Disney Corporation, a firm that has long benefited from a reputation for producing wholesome popular culture, attracts more than its share of efforts to link it to various social problems. In particular, conservative moralists argue that Disney in fact produces morally questionable products, progressive critics claim that Disney's messages help preserve social inequities, and social scientists criticize Disney for fostering inauthentic and alienating entertainment. These claims are a form of blowback—negative reactions to the firm's positive reputation. While blowback makes it easier to construct social problems claims, a good reputation remains a significant resource in deflecting these criticisms.     

A shared component model for detecting joint and selective clustering of two diseases

The study of spatial variations in disease rates is a common epidemiological approach used to describe the geographical clustering of diseases and to generate hypotheses about the possible 'causes' which could explain apparent differences in risk. Recent statistical and computational developments have led to the use of realistically complex models to account for overdispersion and spatial correlation. However, these developments have focused almost exclusively on spatial modelling of a single disease. Many diseases share common risk factors (smoking being an obvious example) and, if similar patterns of geographical variation of related diseases can be identified, this may provide more convincing evidence of real clustering in the underlying risk surface. We propose a shared component model for the joint spatial analysis of two diseases. The key idea is to separate the underlying risk surface for each disease into a shared and a disease-specific component. The various components of this formulation are modelled simultaneously by using spatial cluster models implemented via reversible jump Markov chain Monte Carlo methods. We illustrate the methodology through an analysis of oral and oesophageal cancer mortality in the 544 districts of Germany, 1986–1990.     

Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias

Benzene is classified as a group 1 human carcinogen by the International Agency for Research on Cancer, and it is now accepted that occupational exposure is associated with an increased risk of various leukaemias. However, occupational exposure accounts for less than 1% of all benzene exposures, the major sources being cigarette smoking and vehicle exhaust emissions. Whether such low level exposures to environmental benzene are also associated with the risk of leukaemia is currently not known. In this study, we investigate the relationship between benzene emissions arising from outdoor sources (predominantly road traffic and petrol stations) and the incidence of childhood leukaemia in Greater London. An ecological design was used because of the rarity of the disease, the difficulty of obtaining individual level measurements of benzene exposure and the availability of data. However, some methodological difficulties were encountered, including problems of case registration errors, the choice of geographical areas for analysis, exposure measurement errors and ecological bias. We use a Bayesian hierarchical modelling framework to address these issues, and we investigate the sensitivity of our inference to various modelling assumptions.     

“Status! Yes!”: Tom Wolfe as a sociological thinker

The journalist Tom Wolfe draws heavily upon sociology in his works, yet sociologists have largely ignored his writings. This article argues that Wolfe’s journalism presents a coherent vision of contemporary society which emphasizes the importance of what has become a neglected sociological concept—status. Wolfe argues that economic expansion after World War II fostered the emergence of new “statusspheres,” and encouraged status competition. Wolfe treats issues of religion, character, and politics within the framework of status. His work should remind sociologists of the importance of status and the role of irony as a stance for sociological analysis. The author thanks Anne Bowler, Kathleen Lowney, and Lawrence T. Nichols for their helpful comments on an earlier draft of this paper.