Estimating the Prevalence of Injection Drug Users in the U.S. and in Large U.S. Metropolitan Areas from 1992 to 2002
This paper estimates the prevalence of current injection drug users (IDUs) in 96 large U.S. metropolitan statistical areas (MSAs) annually from 1992 to 2002. Multiplier/allocation methods were used to estimate the prevalence of injectors because confidentiality restrictions precluded the use of other commonly used estimation methods, such as capture–recapture. We first estimated the number of IDUs in the U.S. each year from 1992 to 2002 and then apportioned these estimates to MSAs using multiplier methods. Four different types of data indicating drug injection were used to allocate national annual totals to MSAs, creating four distinct series of estimates of the number of injectors in each MSA. Each series was smoothed over time; and the mean value of the four component estimates was taken as the best estimate of IDUs for that MSA and year (with the range of component estimates indicating the degree of uncertainty in the estimates). Annual cross-sectional correlations of the MSA-level IDU estimates with measures of unemployment, hepatitis C mortality prevalence, and poisoning mortality prevalence were used to validate our estimates. MSA-level IDU estimates correlated moderately well with validators, demonstrating adequate convergence validity. Overall, the number of IDUs per 10,000 persons aged 15–64 years varied from 30 to 348 across MSAs (mean 126.9, standard deviation 65.3, median 106.6, interquartile range 78–162) in 1992 and from 37 to 336 across MSAs (mean 110.6, standard deviation 57.7, median 96.1, interquartile range 67–134) in 2002. A multilevel model showed that overall, across the 96 MSAs, the number of injectors declined each year until 2000, after which the IDU prevalence began to increase. Despite the variation in component estimates and methodological and component data set limitations, these local IDU prevalence estimates may be used to assess: (1) predictors of change in IDU prevalence; (2) differing IDU trends between localities; (3) the adequacy of service delivery to IDUs; and (4) infectious disease dynamics among IDUs across time.