Biomedical subjects

E Savoca

Publications and source records attributed to E Savoca.

4 recordsLinked to original sources

PubMed1999

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.

Cross-Sectional Studies↗

PubMed1993

Equity, public policy and outpatient specialty mental health services.

This study provides evidence on the role of the public sector in the allocation of ambulatory specialty mental health services across income groups in the adult population. Results suggest that in the early to mid-1980s, the tax and transfer system effectively lowered the price of services to the poor and the rich, thus causing the highest use by persons at the extreme ends of the income distribution. High utilization at the low end of the income scale can be largely attributed to publicly provided insurance. A comparison of demand prior to the Medicaid cuts brought on by the Omnibus Budget and Reconciliation Act (OBRA) of 1981 with post-OBRA estimates reveals the extreme sensitivity of demand to changes in coverage. Among upper income groups the results imply that the implicit price of specialty mental health care falls as income rises. This finding is consistent with the hypothesis that the government's exclusion of health benefits and expenditures from taxation effectively lowers the price of medical services to individuals in high marginal income tax brackets. It also suggests that recent proposals to limit the tax exclusion of employer-paid premiums may lead to a more equitable distribution of resources in the specialty mental health sector.

Adult↗

PubMed1990

Quality and provider choice: a multinomial logit-least-squares model with selectivity.

A Federal Trade Commission survey of contact lens wearers is used to estimate a multinomial logit-least-squares model of the joint determination of provider choice and quality of care in the contact lens industry. The effect of personal and industry characteristics on a consumer's choice among three types of providers--opticians, ophthalmologists, and optometrists--is estimated via multinomial logit. The regression model of the quality of care has two features that distinguish it from previous work in the area. First, it uses an outcome rather than a structural or process measure of quality. Quality is measured as an index of the presence of seven potentially pathological eye conditions caused by poorly fitted lenses. Second, the model controls for possible selection bias that may arise from the fact that the sample observations on quality are generated by consumers' nonrandom choices of providers. The multinomial logit estimates of provider choice indicate that professional regulations limiting the commercial practices of optometrists shift demand for contact lens services away from optometrists toward ophthalmologists. Further, consumers are more likely to have their lenses fitted by opticians in states that require the licensing of opticians. The regression analysis of variations in quality across provider types shows a strong positive selection bias in the estimate of the quality of care received by consumers of ophthalmologists' services. Failure to control for this selection bias results in an overestimate of the quality of care provided by ophthalmologists.

Adolescent↗

PubMedDate not supplied

Controlling for mental health in earnings equations: what do we gain and what do we lose?

This paper examines the biases in estimating wage equations that may arise from measurement errors in various mental health indicators--two subjective proxies and one clinical assessment. The results suggest that a self-reported measure based on whether the individual reported missing school or work for mental-health-related reasons leads to the smallest measurement error bias in the coefficients of the explanatory variables in an earnings equation. Two specific results lead to this conclusion. For one, the variance of the random component of its measurement error is the smallest among the three indicators leading to the least biased estimates of the impact of mental health on wages. Second, systematic reporting biases which vary with the non-health regressors in a wage equation do not appear to exit in this measure. Consequently, this indicator follows the classical measurement error model. This implies that the coefficient estimates of the impact of non-health variables are always improved, in the sense of having a smaller bias, when this mental health proxy is included in the regression. The measurement error in a self-evaluation according to the scale of excellent, good, fair, or poor has the largest variance thus leading to a substantial understatement of the impact of mental health on earnings. This measure also contains significant reporting biases that vary with gender, race, and education--many of the non-health regressors in a wage equation. The measurement error variance in the simulated diagnostic measure analyzed in this paper is also large. Thus this proxy will yield poor estimates of the impact of mental health on earnings.

Absenteeism↗