Some perspectives on the role of biostatistics and epidemiology in the prevention and control of mental disorders.
TL;DR: Progress in the past 30 years in the development of statistical and epidemiological methods in the mental health field has included determinations of need for psychiatric care and supporting personnel; interpretation of morbidity indices, and cross-national comparisons of diagnoses of mental disorders.
Abstract: The paper reviews progress made in the past 30 years in the development of statistical and epidemiological methods in the mental health field. Applications have included determinations of need for psychiatric care and supporting personnel; interpretation of morbidity indices, and cross-national comparisons of diagnoses of mental disorders. Much remains to be done. Progress would include better measurement of incidence, duration, and prevalence of mental disorders; more precise estimates of service needs; more effective programs to prevent or reduce disability. Particularly needed are field-research units under long-term funding with the task of assessing effectiveness of mental health programs at the catchment-area level.
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TL;DR: The National Institute of Mental Health multisite Epidemiologic Catchment Area (ECA) program is described in the context of four previous psychiatric epidemiologic surveys that included a combined total of 4,000 subjects from Stirling County, the Baltimore Morbidity Study, Midtown Manhattan, and the New Haven third-wave survey.
Abstract: The National Institute of Mental Health multisite Epidemiologic Catchment Area (ECA) program is described in the context of four previous psychiatric epidemiologic surveys that included a combined total of 4,000 subjects from Stirling County, the Baltimore Morbidity Study, Midtown Manhattan, and the New Haven third-wave survey. The ECA program is distinguished by its sample size of at least 3,500 subjects per site (about 20,000 total); the focus on Diagnostic Interview Schedule--defined DSM-III mental disorders; the one-year reinterview-based longitudinal design to obtain incidence and service use data; the linkage of epidemiologic and health service use data; and the replication of design and method in multiple sites. Demographic characteristics of community and sample populations are provided for New Haven, Conn, Baltimore, and St Louis.
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Journal Article•
TL;DR: Its two-wave design enhances the study of incidence, etiology, and the natural history of disorders and also allows study of the social behavior of persons entering treatment for mental disorders--a subject important to health planners.
Abstract: We hope that the ECA Program can make a significant, and perhaps unique, contribution to the field of psychiatric epidemiology and to mental health services research. If the Program provides total true prevalence data on mental disorders according to the latest diagnostic criteria, that in itself will be a significant contribution. Such data should be of enormous benefit to those interested in etiology as well as those interested in health services research. For researchers interested in etiology, the data can be used to identify, by comparison, high-risk groups; for those interested in health services research, the results can serve as a health planning guide that does not depend on the presence or absence of treatment facilities in a given area. Incidence data will be the second major contribution of the ECA Program. Its two-wave design enhances the study of incidence, etiology, and the natural history of disorders and also allows study of the social behavior of persons entering treatment for mental disorders--a subject important to health planners. Finally, a significant result of the ECA Program may be the establishment of a viable standardized methodology for the epidemiologic study of mental disorders by means of which demonstrably replicable results can be produced. Once we demonstrate the equivalence of method and results, then the stage is set for comparative studies of all sorts.
195 citations
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TL;DR: A wide variety of clinically meaningful scales drawn from the PSS are found reliable for use with patients, in striking contrast, however, most of these proved unreliable in the general population sample.
Abstract: • The Psychiatric Status Schedule (PSS) is a widely used interview that was designed to improve the research value of clinical judgments. Although it was developed with psychiatric patients, its authors hoped it could be used to evaluate nonpatients, a capability that would make it a much needed tool for epidemiologic research. The present study tests the internal consistency reliability of scales drawn from the PSS in both a general population sample (n. = 133) and a patient sample (n. = 100). Like the PSS's authors, we found a wide variety of clinically meaningful scales reliable for use with patients. In striking contrast, however, most of these proved unreliable in the general population sample. Speculative explanations are offered for the failure of most of the PSS scales in the general population sample and for the success of a few.
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TL;DR: In this article, an approximate procedure based on classical analysis of variance is presented, including an adjustment to the degrees of freedom resulting in conservative F tests, which can be applied to the case where the variance covariance matrices differ from group to group.
Abstract: This paper is concerned with methods for analyzing quantitative, non-categorical profile data, e.g., a battery of tests given to individuals in one or more groups. It is assumed that the variables have a multinormal distribution with an arbitrary variance-covariance matrix. Approximate procedures based on classical analysis of variance are presented, including an adjustment to the degrees of freedom resulting in conservativeF tests. These can be applied to the case where the variance-covariance matrices differ from group to group. In addition, exact generalized multivariate analysis methods are discussed. Examples are given illustrating both techniques.
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TL;DR: In this article, the authors show that the treatment mean square and the treatment group interaction can be tested in the same approximate fashion by using the Box procedure, and that the conservative test would be $F(1, n - 1).
Abstract: The mixed model in a 2-way analysis of variance is characterized by a fixed classification, e.g., treatments, and a random classification, e.g., plots or individuals. If we consider $k$ different treatments each applied to everyone of $n$ individuals, and assume the usual analysis of variance assumptions of uncorrelated errors, equal variances and normality, an appropriate analysis for the set of $nk$ observations $x_{ij}, i = 1, 2, \cdots n, j = 1, 2, \cdots k$, is ???? where the $F$ ratio under the null hypothesis has the $F$ distribution with $(k - 1)$ and $(k - 1)(n - 1)$ degrees of freedom. As is well known, if we extend the situation so that the errors have equal correlations instead of being uncorrelated, the $F$ ratio has the same distribution. Under the null hypothesis, the numerator estimates the same quantity as the denominator, namely, $(1 - \rho)\sigma^2$, where $\rho$ is the constant correlation coefficient among the treatments. This case can also be considered as a sampling of $n$ vectors (individuals) from a $k$-variate normal population with variance-covariance matrix $$V = \sigma^2 \begin{pmatrix} 1 & \rho & \cdots & \rho \\ \rho & & & \vdots \\ \vdots & & & \rho \\ \rho & \cdots & \rho & 1\end{pmatrix}.$$ If we consider this type of formulation and suppose the $k$ treatment errors to have a multivariate normal distribution with unknown variance-covariance matrix (the same for each individual), then the usual test described above is valid for $k = 2$. For $k > 2$, and $n \geqq k$, Hotelling's $T^2$ is the appropriate test for the homogeneity of the treatment means. However, the working statistician is sometimes confronted with the case where $k > n$, or he does not have the adequate means for computing large order inverse matrices and would therefore like to use the original test ratio which in general does not have the requisite $F$ distribution. Box [1] and [2] has given an approximate distribution of the test ratio to be $F\lbrack(k - 1)\epsilon, (k - 1)(n - 1)\epsilon\rbrack$ where $\epsilon$ is a function of the population variances and covariances and may further be approximated by the sample variances and covariances. We show in Section 3 that $\epsilon \geqq (k - 1)^{-1}$, and therefore a conservative test would be $F(1, n - 1)$. Box referred only to one group of $n$ individuals. We shall extend his results to a frequently occurring case, namely, the analysis of $g$ groups where the $\alpha$th group has $n_\alpha$ individuals, $\alpha = 1, 2, \cdots g$, and $\Sigma^g_{\alpha = 1} n_\alpha = N$. We will show that the treatment mean square and the treatment $\times$ group interaction can be tested in the same approximate fashion by using the Box procedure.
1,102 citations