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Theoretical Perspectives on Mental Health and Illness: Introduction to Part I
Jerome C. Wakefield, University Professor, Silver School of Social Work and Department of Psychiatry, School of Medicine, New York University,
Mark F. Schmitz, Associate Professor, School of Social Administration, Temple University, Philadelphia, PA
This chapter examines the assessment and measurement of mental disorders. Researchers must distinguish between clinical prevalence (people who are treated for mental disorder) and true prevalence (the actual rate of disorder in a community, including those not in treatment). The measurement of mental illness must be conceptually valid; that is, there must be criteria that successfully distinguish cases of disorder from cases of non-disorder. In the past, researchers relied upon general symptom checklists, which identify a threshold above which an individual is considered disordered, but without specifying a particular disorder. An alternative to checklists is provided by the American Psychiatric Association's Diagnostic and Statistical Manual (DSM) of mental disorders, which provides sets of diagnostic criteria for specific disorders. The assumption behind the DSM is that mental disorders result from internal psychological dysfunctions (i.e., failures of proper functioning of mental processes), a presumption that Wakefield and Schmitz accept but demonstrate is often violated by the DSM's own criteria for mental disorder. Their critique of the DSM's approach to measurement is illustrated with several DSM diagnoses. In addition to thoroughly discussing the conceptual basis of the DSM, Wakefield and Schmitz provide examples of the attempts to use DSM-derived criteria to measure prevalence of mental disorder in the community. These examples demonstrate the recurrent problems with creating conceptually valid measures for use in psychiatric epidemiology. It is unclear whether these problems can be overcome or circumvented with methodological innovations. The student should consider why it is so difficult to determine who is mentally disordered, and to distinguish mental disorder from intense normal distress. Is a conceptually valid resolution of these problems possible?
How many people in the United States suffer from mental disorder in general and from each specific mental disorder, and what characteristics are correlated with each disorder? The answers to such questions are important in formulating mental health policy, in evaluating theories of the causes of disorder, in planning efficient distribution of mental health care, and in justifying funding for mental health services and research. Thus, there have long been efforts to measure the rate, or prevalence, of mental disorder both in the population as a whole and in various segments of the population. Psychiatric epidemiology, the discipline that pursues such studies, is logically part of medical epidemiology, the study of the occurrence and correlates of medical disorders in various populations.
This chapter examines the assessment and measurement of mental disorders. It explores the special problems that have arisen in epidemiologists' attempts to transfer diagnostic criteria from the domain of clinical evaluation to the much different epidemiological arena in which disorder is measured in the general population by survey. Some early psychiatric epidemiological studies were based on the assumption that clinical prevalence could be used to validly infer the community population's true prevalence. The most notable attempt in Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) to make general progress on the false positives problem has been the development of a clinical significance (CS) criterion for use in evaluating mental disorder. The chapter also presents an example of a recent attempt to use DSM derived criteria in an epidemiological study to measure the prevalence of a particular mental disorder, namely, major depressive disorder (MDD).
Three reservations about Keller & Miller's (K&M's) argument are explored: Serious validity problems afflict epidemiological criteria discriminating disorders from non-disorders, so high rates may be misleading. Normal variation need not be mild disorder, contrary to a possible interpretation of K&M's article. And, rather than mutation-selection balance, true disorders may result from unselected combinations of normal variants over many loci.
One goal of developmental psychopathology is to understand the origins and course of mental disorders. I argue that pursuit of this goal requires a valid conceptual understanding of disorder and that this understanding can be provided by the “harmful dysfunction” analysis of the concept of disorder. The harmful dysfunction analysis holds that a disorder is a condition that is both harmful according to social values and caused by an internal dysfunction, that is, by a failure of an internal mechanism to perform a function for which it was naturally selected. This analysis explains why many of the distinctive features of developmental psychopathology are appropriate to the study of disorder. It is argued that the harmful dysfunction analysis is a necessary supplement to other proposed criteria for disorder, such as developmental deviation or predictive validity.
Andrews et al.'s analysis suffers from a series of conceptual confusions they inherit from Gould's work. Their proposal that adaptations can be distinguished from exaptations essentially by specific design criteria fails because exaptations are often maintained and secondarily adapted by natural selection and therefore, over evolutionary time, can come to have similar levels of design specificity to adaptations.
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