Longitudinal Analysis of Changes in Illicit Drug Use and Health Services Utilization

Authors

  • Michael T. French,

    1. Professor of Health Economics, Health Economics Research Group, Department of Sociology, Department of Epidemiology and Public Health, and Department of Economics, University of Miami, 5202 University Drive, Merrick Building, Room 121F, PO Box 248162, Coral Gables, FL 33124-2030
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    • Address correspondence to Michael T. French, Ph.D., Professor of Health Economics, Health Economics Research Group, Department of Sociology, Department of Epidemiology and Public Health, and Department of Economics, University of Miami, 5202 University Drive, Merrick Building, Room 121F, PO Box 248162, Coral Gables, FL 33124-2030; e-mail: mfrench@miami.edu. Hai Fang, Ph.D., Assistant Professor, is with the Department of Health Systems, Management, and Policy, Colorado School of Public Health, University of Colorado Denver, Aurora, CO. Ana I. Balsa, Ph.D., is with the Center for Applied Research on Poverty, Family, and Education, University of Montevideo, Montevideo, Uruguay.

  • Hai Fang,

    1. Assistant Professor, is with the Department of Health Systems, Management, and Policy, Colorado School of Public Health, University of Colorado Denver, Aurora, CO
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  • Ana I. Balsa

    1. Center for Applied Research on Poverty, Family, and Education, University of Montevideo, Montevideo, Uruguay
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Abstract

Objective. To analyze the relationships between illicit drug use and three types of health services utilization: emergency room utilization, hospitalization, and medical attention required due to injury(s).

Data. Waves 1 and 2 (11,253 males and 13,059 females) from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

Study Design. We derive benchmark estimates by employing standard cross-sectional data models to pooled waves of NESARC data. To control for potential bias due to time-invariant unobserved individual heterogeneity, we reestimate the relationships with fixed-effects models.

Principal Findings. The cross-sectional data models suggest that illicit drug use is positively and significantly related to health services utilization in almost all specifications. Conversely, the only significant (p<.05) relationships in the fixed-effects models are the odds of receiving medical attention for an injury and the number of injuries requiring medical attention for men, and the number of times hospitalized for men and women.

Conclusions. Failing to control for time-invariant individual heterogeneity could lead to biased coefficients when estimating the effects of illicit drug use on health services utilization. Moreover, it is important to distinguish between types of drug user (casual versus heavy) and estimate gender-specific models.

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