Mirror, mirror on the wall: reflections from failure to establish a truly uniform national health care policy

When the author gazed into the proverbial mirror and asked if the U.S. health care system was the fairest of them all, it shattered. In this article, Thompson tells why the system is broken and what failure to fix it means to physician executives. He suggests that we, as Americans, must reinvent ourselves by realigning our value systems and and stifling our obsession with profit before trying to reinvent health care.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme

When biological or physiological variables change over time, we are often interested in making predictions either of future measurements or of the time taken to reach some threshold value. On the basis of longitudinal data for multiple individuals, we develop Bayesian hierarchical models for making these predictions together with their associated uncertainty. Particular aspects addressed, which include some novel components, are handling curvature in individuals' trends over time, making predictions for both underlying and measured levels, making predictions from a single baseline measurement, making predictions from a series of measurements, allowing flexibility in the error and random-effects distributions, and including covariates. In the context of data on the expansion of abdominal aortic aneurysms over time, where reaching a certain threshold leads to referral for surgery, we discuss the practical application of these models to the planning of monitoring intervals in a national screening programme. Prediction of the time to reach a threshold was too imprecise to be practically useful, and we focus instead on limiting the probability of exceeding the threshold after given time intervals. Although more complex models can be shown to fit the data better, we find that relatively simple models seem to be adequate for planning monitoring intervals.     

Integration Between Business Planning and Information Systems Planning: Validating a Stage Hypothesis

This paper proposes and empirically validates a stages of growth model for the evolution of Information Systems Planning (ISP). A questionnaire survey of senior IS executives is used to gather information pertaining to the stages of growth model, which includes measurement of the nature and level of integration between business planning (BP) and ISP. The del test is used to validate empirically benchmark variables for each stage of BP-ISP integration. The results support the stages of growth model of BP-ISP integration and the benchmark variables are generally found to be successful in predicting the stage of integration.     

Sexual Abuse by Design: an examination of the issues in learning disability services

This paper is based upon ideas developed by the authors during their past 6 years work on issues of sexuality and sexual abuse in a wide variety of learning disability settings in Britain. The paper argues that some features of service design in themselves make sexual abuse more likely to occur. It suggests ways of reducing risk, which should work alongside the more usually suggested educative approaches to individuals and groups of vulnerable people.     

The NFC buy-out—A new form of industrial enterprise

When the Conservative Government's plans for floating the National Freight Company, Britain's biggest road freight business, on the Stock Exchange were delayed by economic factors, senior management in the business put together a scheme to buy NFC. In what became Britain's largest employee buy-out, with over 10,000 employees and pensioners putting up £6·187m of the purchase price, a new form of industrial enterprise was created. In this article Sir Peter Thompson, leader of the buy-out team and now Chairman of the National Freight Consortium plc, describes the background to the buy-out, the complex financial, legal and communications activities involved, and the way in which employee-involvement has become an integral part of the operation of what has proved to be a commercially successful business providing exceptional returns on investment.     

The advantages and disadvantages of different social welfare strategies

This paper presents a framework for consideration of change in or reform of social welfare systems. The author describes characteristics of the approaches to providing social welfare most commonly used in North America and western Europe. These include social insurance, employer mandates, individual mandates, voluntary arrangements (tax expenditures), means-tested programmes, and universal programmes (demogrants). He then evaluates each of the approaches in terms of its ability to achieve a set of social and economic objectives. The social protection objectives include treating people with dignity and respect, assuring complete coverage, equitably distributing costs and benefits, and operating efficiently to lower overhead. The objectives important to promoting a healthy economic environment include encouraging individual saving and work effort, fostering government fiscal responsibility, and facilitating smooth market functioning. The author concludes that there is no one correct way to organize social welfare systems, that each approach has strengths and weaknesses, and that different societies will attach different priorities to different objectives. Thus, as social goals change, we may expect that the particular mix of approaches used will also evolve and change over time.