Measuring the validity of psychiatric diagnoses is still an unsolved
problem. Yet, revisions of the Diagnostic and Statistical
Manual of Mental Disorders and of chapter V of the International Classification of Diseases are now under way, with the
hope of improving the validity of the current systems. This article suggests
data that could be used to assist in this goal. This article has 3 objectives.
(1) To show that although the validity of the interview protocols used in
collecting epidemiologic survey data has not itself been proven, the data
banks they have collected are well suited to raising questions about the validity
of the existing diagnostic nomenclature. This is the case because they faithfully
operationalize the current nomenclature in large interview studies of diverse
general populations. (2) To show the kinds of changes that appropriate analysis
of these data may suggest as ways to improve the validity of the nomenclature.
(3) To show how suggested changes that emerge from such analyses should be
tested to learn whether they actually improve validity before they are implemented.
The data sets from large epidemiologic studies have hardly been tapped for
testing the validity of the current nomenclature. It is feasible to use them
for this purpose because they are in the public domain and because they assess
the presence or absence of each of the criteria in the manuals before applying
the manuals’ algorithms for combining them to make a diagnosis. Thus,
these data banks allow exploration of the effects of combining and splitting
diagnoses, of omitting criteria or reweighting them, and of choosing altered
algorithms with respect to age at onset, number of symptoms, and duration
of episodes. Assessing the consequences of these alterations can be tested
by applying some of the criteria of Robins and Guze and Kendell.
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