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Journal of Urban Health
Published in: Journal of Urban Health 3/2008

01-05-2008

Estimating the Prevalence of Injection Drug Users in the U.S. and in Large U.S. Metropolitan Areas from 1992 to 2002

Authors: Joanne E. Brady, Samuel R. Friedman, Hannah L. F. Cooper, Peter L. Flom, Barbara Tempalski, Karla Gostnell

Published in: Journal of Urban Health | Issue 3/2008

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Abstract

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.
Appendix
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Footnotes
1
Holmberg does not explicitly state the year to which his estimates apply, although data used to calculate these estimates are from 1990 to 1993. In previous papers, Holmberg’s estimates have been referred to as applying to 1992 and 1993. In this paper and henceforth, we will refer to these estimates as applying to 1992.
 
2
Treatment providers receiving state agency funding, including the federal block grant monies, are obligated to provide data on all clients admitted to treatment, regardless of the source of funding for individual clients. In 1997, TEDS was estimated to include 83% of admissions receiving state funding and 67% of all known admissions.64
 
3
Annual data on the national number of tests for the IDU risk exposure group for 1992–1994 were not feasibly available. We estimated the number of IDUs tested for HIV nationally for 1992–1994 by multiplying the number of IDUs tested in the 96 MSAs by the ratio of the number of IDUs tested nationally to those tested in the largest 96 MSAs averaged over 1995–2002. This ratio was essentially constant at 1.70 from 1995 to 2002. Inflating the IDUs tested in the 96 metros to create an estimate of the IDUs tested nationally in 1992–1994 assumes that the ratio number of IDUs tested for HIV in the nation to the 96 metros remains constant over our study period.
 
4
Component count refers to the number of IDUs ascertained for a data source in each MSA and year. The population aged 15–64 years was used because this age group is the population at risk.
 
5
The component series count based on Holmberg and Friedman data is per capita, not per 10,000. A linear trend for interpolation and extrapolation would allow the number of IDUs to be less than 0. IDU per capita values fall between 0 and 1 and were log transformed. Formula 1 was applied to the log of the per capita component series count. The final component estimates for this series were exponentiated and are shown per 10,000 and are on the same scale as our other component estimates.
 
6
In the Sarasota FL, Scranton PA, Seattle WA and Springfield MA MSAs CTS data were acquired from state health departments rather than the CDC. These data were requested when a separate analysis found substantial missing data for IDUs testing HIV positive. State-level data were used when they were judged to have more complete reporting.
 
7
Holmberg estimated the prevalence of injecting in 1992 for each of the 96 largest MSAs using a components model, which divides the population into risk groups and then calculates risk group size and seroprevalence.80 MSA-level data from a literature review of estimates created by researchers at federal, state, and local agencies, universities, and drug treatment programs were used to estimate IDU risk group size. Estimates that fell outside the prespecified range of plausible values were omitted. Final estimates for MSAs were calculated by averaging data series estimates.15
 
8
Friedman and colleagues estimated the number of current IDUs in the U.S. in 1998 based on Holmberg’s IDU estimates for 1992 and the National Household Survey on Drug Abuse (NHSDA). Then, for each of the 96 largest MSAs, IDU estimates that reflected service coverage were created based on drug treatment data, CTS data, AIDS case data adjusted for HIV prevalence, and Holmberg’s IDU estimates. These four component estimates were calculated based on multiplier methods and then averaged to create the final estimate for each MSA in 1998.16
 
9
The U.S. Census Bureau revised the estimate of population size for 1992 and 1998 based on Census 2000 data, which were not available when the previous papers were submitted for publication.
 
10
This formula uses seroprevalence estimates for 1992 as calculated by Holmberg: 14%, which refers to the HIV seroprevalence of IDUs in 1992 for the largest 96 MSAs. This 1992 seroprevalence was used as a proxy for the country as a whole.
 
11
HIV prevalence estimates for IDUs at the MSA-level for use in Formula 2a could have been created using the CTS data. However, we chose not to because we only had data for 1992–2002, and lack of prevalence estimates before 1992 would not have allowed for any lag time between HIV infection and the development of AIDS. Alternatively, we could have used the HIV prevalence estimates for 1998 put forth by Friedman and colleagues, but we decided that a longer lag time would allow more AIDS cases to develop and would more accurately describe the relationship between HIV and AIDS.16 In the absence of a better HIV seroprevalence proxy, we used Holmberg’s 1992 HIV seroprevalence estimates for IDUs.15
 
12
Data values were determined to be outliers if they differed from the previous and subsequent year by a factor of 2 or more. Component estimate values for initial and terminal years were considered outliers when the following conditions were true: for initial year values when the value in the subsequent year differed by a factor of 3 and for terminal year values when the preceding year value differed by a factor of 3 or more. Data points that met these criteria were set to missing. Final estimates were examined with and without removing outliers. (When outliers were removed, values set to missing were replaced with imputed values, which were computed using single regression imputation.)
 
13
The data were smoothed and then averaged, rather than averaged and then smoothed, because the former method is more intuitive. When we smooth each component estimate, we know what we are smoothing, as opposed to the average of the estimates, where it is not clear what noise is being smoothed. Also, in future analyses, we may wish to omit a component estimate from our final estimate and smoothing and then averaging allows this to be done more easily. Further, we compared estimates prepared using both the former and the latter methods and there was not much difference.
 
14
We did not impute UFDS/N-SSATS for the years 1992, 1994, 1999, and 2001 because we did not believe there was a need to estimate the variance of the individual series, as the treatment component estimate would be smoothed and averaged with the other component estimates. Results are shown for our final estimates calculate without using UFDS/N-SSATS data in the above years and using UFDS/N-SSATS, respectively: in 1992—126.9 and 121.8; in 1994—123.3 and 118.3; in 1999—118.0 and 113.9; in 2001—116.2 and 111.4.
 
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Metadata
Title
Estimating the Prevalence of Injection Drug Users in the U.S. and in Large U.S. Metropolitan Areas from 1992 to 2002
Authors
Joanne E. Brady
Samuel R. Friedman
Hannah L. F. Cooper
Peter L. Flom
Barbara Tempalski
Karla Gostnell
Publication date
01-05-2008
Publisher
Springer US
Published in
Journal of Urban Health / Issue 3/2008
Print ISSN: 1099-3460
Electronic ISSN: 1468-2869
DOI
https://doi.org/10.1007/s11524-007-9248-5

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