Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.     

Clinical supervision: Its role in “Containing” countertransference responses to a filicidal patient

This paper illustrates the role of supervision in processing countertransference responses with might have disrupted the therapist's empathy with a patient who had killed her child. The clinical material demonstrates how the therapist's responses of initial denial and subsequent disgust and fear were dealt with in supervision. As a result, the treatment process led to genuine, albeit limited, therapeutic change in a patient with severe character pathology.This paper is developed from a presentation of the case of Mrs. Jay at Department of Psychiatry Grand Rounds, University of Cincinnati College of Medicine, on May 2, 1989.     

The NTEE classification system: tests of reliability/validity in the field of higher education

An analysis of the field of higher education reveals a surprisingly large number of classification errors within the (US) National Taxonomy of Exempt Entities database. Of the nearly 4,000 non-profit entities coded within the universe of institutions of higher education, we estimate that approximately 60 per cent were incorrectly included (Type II errors). Institutions incorrectly excluded from the higher education universe (Type I errors) represent roughly 10 per cent of the institutions coded correctly. These errors result primarily from assigning codes on the basis of the names of organisations (for example, College Park Towers is classified as a college, when it is in fact a housing complex for senior citizens) and from trying too hard to assign codes in ambiguous situations. The consequences can be significant for the users of these data, and we recommend raising bright warning flags while simultaneously enlisting the help of the entire non-profit sector in improving the classification coding process.The authors are with The Andrew W. Mellon Foundation. In addition to our colleagues on the staff of the Foundation, we wish to thank Helmut Anheier, Ted Bozovich, Harvey Dale, Virginia Hodgkinson, Stephen Noga and Christopher Toppe for helpful suggestions.     

The administrative costs of non-profit health insurers

Interest in the efficiency or inefficiency of non-profit firms has spawned research on the administrative costs of Blue Cross and Blue Shield plans. Relying on plausible implications of the property rights theory of the firm, both Blair, Ginsburg, and Vogel (1975) and Vogel (1977) noted some evidence of inefficiency in these firms. Our analysis, however, utilizing their models and more recent data, reveals little convincing evidence for the view that Blue Shield plans are inefficient.