PubMed · 8103899
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J Nicholl. 1993-09-25. Is the study worth doing?. https://doi.org/10.1016/0140-6736(93)91576-8
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A major new study carried out by WHO and the United Nations Children's Fund (UNICEF) indicates that maternal mortality has been substantially underestimated in the past, and that there are nearly 80 000 more pregnancy-related deaths per year than previously reported. According to the study, some 585 000 maternal deaths occur in the world each year, 99% of them in developing countries.
This paper surveys the field that has come to be known as the epidemiology of religion. Epidemiologic study of the impact of religious involvement, broadly defined, has become increasingly popular in recent years, although the existence, meaning and implications of an apparently salutary religious effect on health have not yet been interpreted in an epidemiologic context. This paper attempts to remedy this situation by putting the "epidemiology" into the epidemiology of religion through discussion of existing empirical findings in terms of several substantive epidemiologic concepts. After first providing an overview of key research findings and prior reviews of this field, the summary finding of a protective religious effect on morbidity is examined in terms of three important epidemiologic concepts: the natural history of disease, salutogenesis and host resistance. In addition to describing a theoretical basis for interpreting a religion-health association, this paper provides an enumeration of common misinterpretations of epidemiologic findings for religious involvement, as well as an outline of hypothesized pathways, mediating factors, and salutogenic mechanisms for respective religious dimensions. It is hoped that these reflections will serve both to elevate the status of religion as a construct worthy of social-epidemiologic research and to reinvigorate the field of social epidemiology.
A method of sample size determination for estimation of probabilities based on a test variable is presented. Applications to estimation of sensitivity and specificity of medical tests are the focus of this research, although the methods can be applied to other areas of study such as engineering reliability. Examples are given for determining sample sizes required for the classification of patients with cutaneous lupus erythematosus based on the incidence of several markers. In this example, the test variable is the number of markers present. The methodology employs a weighted average of model-based and non-model-based estimates of the probability with the weights determined by the closeness to or the confidence in the given model. Formulas and charts required for determining sample size are provided for test variables that can be modeled by the binomial, Poisson, or normal distributions, i.e., for the most commonly encountered distributions for counting events (binomial and Poisson) and for measurements (normal). However, the methods given can be applied to any distribution, including multivariate. Especially when relatively small probabilities (the rare events) are being estimated, the techniques provided assistance in safeguarding against undersampling brought on by unwarranted confidence in a test variable distribution and against oversampling required for high accuracy in non-model-based probability estimators.