title: Longitudinal analysis of changes in illicit drug use and health services utilization.
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).
  
  
  METHODS
  Waves 1 and 2 (11,253 males and 13,059 females) from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: Health Economics Research Group, Department of Sociology, University of Miami, 5202 University Drive, Coral Gables, FL 33124-2030, USA. mfrench@miami.edu
authors.forename: Michael T
authors.initials: MT
authors.lastname: French
authors.forename: Hai
authors.initials: H
authors.lastname: Fang
authors.forename: Ana I
authors.initials: AI
authors.lastname: Balsa
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000437:Alcoholism / Q000453:epidemiology; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D006304:Health Status; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D019458:Insurance Coverage; D016013:Likelihood Functions; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D015999:Multivariate Analysis; D017678:Sex Distribution; D019966:Substance-Related Disorders / Q000191:economics / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000191:economics / Q000453:epidemiology*