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

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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.

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Notes

  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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Acknowledgements

The research described in this paper is supported by the National Institute of Drug Abuse grant # R01 DA13336. We would like to thank the Centers for Disease Control and Prevention, specifically Dr. Tonji Durant and Andrew Mitsch at the National Center for HIV, National Center for HIV, Viral Hepatitis, STD, and TB Prevention and the Coordinating Center for Infectious Diseases for their useful comments on the manuscript and for providing data from the national AIDS surveillance and the HIV counseling and testing databases. In addition, we would also like to thank Dr. Scott Holmberg of RTI International for providing the IDU and IDU-HIV estimates, Mr. Spencer Lieb of the Florida Department of Health for his assistance in obtaining the AIDS surveillance database, Dr. Jane Maxwell for her time and feedback on the manuscript and our estimates, Mr. Bob Baxter, Dr. Carl Latkin, Dr. John Newmeyer, Dr. Shruti Mehta, and Dr. Bill Zule for their feedback on our estimates, and Mr. Michael Fanning, Dr. David Hurst, and Dr. Renee R. Stein for their assistance with the HIV counseling and testing data request.

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Authors and Affiliations

  1. Institute for AIDS Research, National Development and Research Institutes, Inc. (NDRI), 71 West 23rd Street, 8th Floor, New York, NY, 10010, USA

    Joanne E. Brady, Samuel R. Friedman, Peter L. Flom, Barbara Tempalski & Karla Gostnell

  2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA

    Samuel R. Friedman

  3. Behavioral Sciences and Health Education, Rollins School of Public Health of Emory University, 1518 Clifton Road NE, Room 568, Atlanta, GA, 30322, USA

    Hannah L. F. Cooper

  4. BrainScope, NYU Brain Research Laboratories, Old Bellevue Administration Building, 8th Floor, 462 First Avenue, New York, NY, 10016, USA

    Peter L. Flom

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  1. Joanne E. Brady
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Corresponding author

Correspondence to Joanne E. Brady.

Electronic supplementary material

Component estimates and final estimates of the number of IDU in each of the 96 large US MSAs.

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Appendix A

Appendix A

Data Series Considerations

While creating national IDU estimates for 1992–2002, we considered implementing the methods and using the data sources utilized by Holmberg and Friedman and colleagues.15,16 Holmberg used data that we did not include, such as data from Ryan White programs, DAWN hospital data and local estimates from ethnographers, and data from treatment providers, law enforcement agencies, and heath departments, whereas Friedman and colleagues also used the National Household Survey on Drug Abuse (NHSDA). In some instances, our approach mirrored the methods used in these prior studies of IDU prevalence. Unfortunately, it was not always possible to use the same data sets because data set availability changed over time.15 In the creation of our longitudinal estimates of IDUs, we also found that information available in data sets changed over time, e.g., UFDS/N-SSATS data were only available: in 1993, 1995, 1996–1998, 2000, and 2002. Injection drug use information was not collected after 1998.

In addition, we considered using the ever- and past-year needle use variables in NHSDA to create our national IDU estimates for our study period. Statisticians at SAMSHA noted the following threats to the validity of the NHDSA data: 1. Underreporting of IDU behavior from in-person interviews, and 2. lack of data on marginalized populations known to inject drugs, such as the homeless and prison populations.15,29 The first threat to validity is inconsistent over time as NHDSA switched to computer-assisted technology to help mitigate known reporting biases of stigmatized behaviors, such as injection drug use. In some instances, adjustments to the data may be made to account for these different sampling strategies. However, when investigating time trends we found different sampling strategies over time made the data noncomparable.69–71 In some instances, the NHDSA proposed adjustments to offset differences caused by varied sampling methodology, incentives, interviewer experience, survey administration methods, imputation procedures, and questionnaires. However, when the proposed adjustments were made very large and inconsistent fluctuations in past-year and ever needle use still persisted. Whether the aforementioned changes in NHDSA methodology, or changing levels of stigmatization or fear of needle use disclosure on the part of those respondents who inject drugs, resulted in these irregularities, these fluctuations made time-series analysis invalid for the needle-use data. Our decision regarding trend analysis of the needle use data is consistent with the NHDSA recommendations against using longitudinal trend analysis for our study period.69–71 In addition, the needle use variables are not core variables in the NHDSA survey, so they are less precise estimates. Therefore, we used alternative data sources in the creation of our national IDU estimates.

In our search to identify data sources for our national estimates, we also investigated alternative data sources and time series. We examined using unintentional poisonings deaths caused by heroin, cocaine, and psychostimulants from the National Center for Vital Statistics Multiple Cause of Death data sets and injection drug-related endocarditis hospitalizations in the National Center for Health Statistics’ National Hospital Discharge Survey from 1992 to 2002.72,73 However, large increases in overdose deaths were observed over our study period. An underlying cause, other than increasing numbers of IDUs, seems to be driving increases in accidental poisoning deaths and injection-related endocarditis hospitalizations over time. Therefore, these indicators are not reflective of national injection drug use trends. The overdose data series is not indicative of trends in IDU drug use because this series includes drug users who have overdosed by other routes of administration (i.e., IDU is not coded in death data). In addition, increases in overdose deaths do not appear to be related to increasing drug injection, but rather changes in coding practice, aging drug users, polydrug mixing, increase in prescription narcotics, and drug strength and availability.50,74–77

Similarly, national injection-related endocarditis trends seem to be inconsistent with national injection drug use patterns over time. Injection-related endocarditis trends may be a function of the increase in methamphetamine use and/or increasing injection frequency among heroin injectors.78

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Brady, J.E., Friedman, S.R., Cooper, H.L.F. et al. Estimating the Prevalence of Injection Drug Users in the U.S. and in Large U.S. Metropolitan Areas from 1992 to 2002. J Urban Health 85, 323–351 (2008). https://doi.org/10.1007/s11524-007-9248-5

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