Elizabeth Savoca

Psychiatric co-morbidity and hospital utilization in the general medical sector

BACKGROUND This article provides evidence about the relationship between psychiatric disorders, physical disorders and hospital use in the general medical sector using a broadly based survey of the US population. METHODS The data are from the 1989 National Health Interview Survey. This survey contains medical and mental health evaluations for the entire sample. In a multivariate framework, the author estimates the effect of mental illness on the probability of being admitted to a general hospital, the number of admissions and the length of stay. RESULTS Hospital use in the general medical sector is significantly higher for persons with coexisting physical and psychiatric conditions than for those with no psychiatric disorders. For a wide range of medical conditions, the predicted number of hospital admissions and the length of a hospital stay increase substantially when the physical illness is accompanied by a psychiatric condition. CONCLUSIONS One implication of this finding is that economic evaluations of alternative psychiatric treatments should consider any differences in hospital costs related to the treatment of coexisting medical conditions. Another implication pertains to health care systems where insurers have some discretion over which individuals to insure. In the absence of adequate adjustments in insurance payments for high-risk potential enrollees, psychiatrically disabled persons may have more limited access to health insurance.

Gender differences in learning and earning in nineteenth-century America: The role of expected job and career attachment

Abstract Quick, short-lived payoffs to experience in womens occupations about the turn of the century have led to the suggestion that gender differences in occupational distribution and wage rates were the result of profit- and income-maximizing choices in the context of differences in expected firm and occupational tenure. Using micro-level data on California workers in 1892 we develop the first estimates of completed firm and occupational tenure by gender. We show that, while womens tenure was indeed briefer than mens, the differences were too small to have warranted differences in training.

Measurement Errors in Binary Regressors: An Application to Measuring the Effects of Specific Psychiatric Diseases on Earnings

This paper presents an overview of the theory of measurement error bias in ordinary regression estimators when several binary explanatory variables are mismeasured. This situation commonly occurs in health-related applications where the effects of illness are modeled in a multivariate framework and where health conditions are usually 0–1 survey responses indicating the absence or presence of diseases. An analysis of the effect of psychiatric diseases on male earnings provides an empirical example that indicates extensive measurement error bias even in sophisticated survey measures that are designed to simulate clinical diagnoses. A corrected covariance matrix is constructed from a validity study of the survey mental health indicators. When ordinary least squares estimators are adjusted by this correction matrix, the estimated earnings effects drop for certain diseases (drug abuse, general phobic disorders) and rise for others (anti-social personality).

ACCOUNTING FOR MISCLASSIFICATION BIAS IN BINARY OUTCOME MEASURES OF ILLNESS: THE CASE OF POST‐TRAUMATIC STRESS DISORDER IN MALE VETERANS

The theoretical consequences of measurement error in outcome variables that are continuous are widely known by practitioners, at least for the classical model: purely random errors will lead to a loss of efficiency but not to bias in regression coefficients. When the outcome variable is binary, however, regression coefficients, both linear and nonlinear, will contain bias, even if the measurement error (in this setting more commonly referred to as classification error) is purely random. This paper illustrates a method of correcting for misclassification bias that relies solely on the primary survey data. It is particularly suited to analyses of surveys where external validation of survey responses is unavailable but where there is strong reason to suspect contaminated data. This situation is common in observational studies of the health of populations. The technique is applied to a model of the antecedents of post-traumatic stress disorder (PTSD) using data from a large-scale cross-sectional survey of Vietnam-era veterans. Results show that when adjusted for errors in diagnoses, the sample PTSD prevalence estimate falls significantly; that failure to correct for misclassification in PTSD dramatically understates the effects of risk factors; and that this downward bias remains even when the model incorporates differential classification errors—that is, errors that are correlated with some of the explanatory variables in the model.

Sociodemographic correlates of psychiatric diseases: accounting for misclassification in survey diagnoses of major depression, alcohol and drug use disorders

This paper illustrates how validation data can be used to correct for errors in survey indicators of psychiatric disorders in models where the outcome of interest is the probability of a positive diagnosis. Nonlinear models of the risks associated with a broad range of sociodemographic factors for three disorders (major depression, alcohol and drug use disorders) are estimated with adjustments for classification errors in the survey diagnoses. Estimates show that inferences drawn from the unadjusted models may seriously understate gender and regional differences in the prevalence rates of all three disorders, the effects of education and ethnicity on the development of alcohol use disorders, and the relationship between marital status and the risk of major depression.

The "teaching procession"? another look at teacher tenure, 1845-1925

Abstract Prior to the Great Depression the turnover rates of teachers, especially female and rural teachers, were very high. These high turnover rates have led historians to conclude that the average teacher was inexperienced and had little professional commitment. Here we examine direct evidence on teacher tenure. We find that the teacher the typical student was likely to encounter taught for more than five years in rural schools and practically a lifetime in the urban schools. Personal characteristics associated with stable job attachment in the modern era exerted a similar influence on the tenure of nineteenth-century teachers.