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Estimating the Prevalence of Injection Drug Use among Black and White Adults in Large U.S. Metropolitan Areas over Time (1992–2002): Estimation Methods and Prevalence Trends

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An Erratum to this article was published on 15 October 2008

Abstract

No adequate data exist on patterns of injection drug use (IDU) prevalence over time within racial/ethnic groups in U.S. geographic areas. The absence of such prevalence data limits our understanding of the causes and consequences of IDU and hampers planning efforts for IDU-related interventions. Here, we (1) describe a method of estimating IDU prevalence among non-Hispanic Black and non-Hispanic White adult residents of 95 large U.S. metropolitan statistical areas (MSAs) annually over an 11-year period (1992–2002); (2) validate the resulting prevalence estimates; and (3) document temporal trends in these prevalence estimates. IDU prevalence estimates for Black adults were calculated in several steps: we (1) created estimates of the proportion of injectors who were Black in each MSA and year by analyzing databases documenting injectors’ encounters with the healthcare system; (2) multiplied the resulting proportions by previously calculated estimates of the total number of injectors in each MSA and year (Brady et al., 2008); (3) divided the result by the number of Black adults living in each MSA each year; and (4) validated the resulting estimates by correlating them cross-sectionally with theoretically related constructs (Black- and White-specific prevalences of drug-related mortality and of mortality from hepatitis C). We used parallel methods to estimate and validate White IDU prevalence. We analyzed trends in the resulting racial/ethnic-specific IDU prevalence estimates using measures of central tendency and hierarchical linear models (HLM). Black IDU prevalence declined from a median of 279 injectors per 10,000 adults in 1992 to 156 injectors per 10,000 adults in 2002. IDU prevalence for White adults remained relatively flat over time (median values ranged between 86 and 97 injectors per 10,000 adults). HLM analyses described similar trends and suggest that declines in Black IDU prevalence decelerated over time. Both sets of IDU estimates correlated cross-sectionally adequately with validators, suggesting that they have acceptable convergent validity (range for Black IDU prevalence validation: 0.27 < r < 0.61; range for White IDU prevalence: 0.38 < r < 0.80). These data give insight, for the first time, into IDU prevalence trends among Black adults and White adults in large U.S. MSAs. The decline seen here for Black adults may partially explain recent reductions in newly reported cases of IDU-related HIV evident in surveillance data on this population. Declining Black IDU prevalence may have been produced by (1) high AIDS-related mortality rates among Black injectors in the 1990s, rates lowered by the advent of HAART; (2) reduced IDU incidence among Black drug users; and/or (3) MSA-level social processes (e.g., diminishing residential segregation). The stability of IDU prevalence among White adults between 1992 and 2002 may be a function of lower AIDS-related mortality rates in this population; relative stability (and perhaps increases in some MSAs) in initiating IDU among White drug users; and social processes. Future research should investigate the extent to which these racial/ethnic-specific IDU prevalence trends (1) explain, and are explained by, recent trends in IDU-related health outcomes, and (2) are determined by MSA-level social processes.

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Notes

  1. While MSAs are constructed using counties in almost all U.S. regions, in New England, MSAs are based on cities and towns. New England County Metropolitan Areas (NECMAs), however, are county-based areas. To ensure comparability across the sample, we used NECMAs in New England. For brevity’s sake, we refer to NECMAs as MSAs henceforth.

  2. For each database and race/ethnicity, cells were defined by year and MSA (11 years × 95 MSAs = 1,045 cells).

  3. Notably, between 2000 and 2002, PEP counted individuals who identified as belonging to multiple racial/ethnic groups as multiple people (e.g., a single individual who self-identifies as non-Hispanic Black and non-Hispanic White will appear in the PEP database during these years as two distinct people, one of each racial/ethnic group). In contrast, multiracial individuals appear only once in 1992–1999 PEP data, either in the single racial/ethnic category they identify with most closely or in a “more than one race/other race” group. TEDS, APIDS, and CTS all used the latter classification method throughout the time period. Given that only about 1% of residents of the MSAs in our sample identified themselves as belonging to more than one racial/ethnic group in the 2000 Census, this shift should have a negligible impact on our estimates.

  4. These fatalities include those arising from harmful drug use, dependence, poisonings (accidental, intentional, and of undetermined intent), and from drug-related mental and behavioral disorders.

  5. Visual inspection of quantile–quantile plots for each racial/ethnic-specific IDU estimation method indicated deviations from normality for some MSAs. Removing these MSAs did not affect our substantive findings, and so we report results calculated with the full dataset.

  6. Tables reporting index-based estimates of IDU prevalence for Black adults and for White adults for each MSA and year of the study period are available in this paper’s online “Appendix”.

  7. The CDC’s AIDS-related database does not report the date of death. Instead, it records the date of AIDS diagnosis for each case and an indicator of whether the individual was alive in 1999. Deaths occurring after 1999 are not recorded.

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Authors and Affiliations

  1. Department of Behavioral Sciences and Health Education, Rollins School of Public Health at Emory University, Atlanta, GA, USA

    Hannah L. F. Cooper ScD

  2. National Development and Research Institutes, Inc, New York, NY, USA

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

  3. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Samuel R. Friedman

  4. Brainscope, New York, NY, USA

    Peter L. Flom

  5. Rollins School of Public Health, Emory University, 1518 Clifton Road, NE-Room 568, Atlanta, GA, 30322, USA

    Hannah L. F. Cooper ScD

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  1. Hannah L. F. Cooper ScD
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  2. Joanne E. Brady
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Correspondence to Hannah L. F. Cooper ScD.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s11524-008-9324-5

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Web Appendix Figure 1

Median Percent of Injectors who are Black in 95 Large US Metropolitan Statistical Areas Over Time (1992-2002) as Estimated Using Each of Four Methods (The "Index" is the mean of the CTS-, TEDS-, & APIDS-based estimates). (PDF 560 KB)

Web Appendix Figure 2

Median Percent of Injectors Who Are White in 95 Large US Metropolitan Statistical Areas Over Time (1992-2002) As Estimated Using Four Methods (The "Index" is the average of the CTS-, TEDS-, & APIDS-based Estimates). (PDF 88 KB)

Web Appendix Figure 3

Median Number of Injection Drug Users Living in 95 Large US Metropolitan Statistical Areas Over Time (1992-2002), as published in Brady et al, 2008 (see article bibliography for full citation). (PDF 72 KB)

Web Appendix Table 1

Estimated Prevalence of Injection Drug Use per 10,000 Black Adult Residents of 95 Large US Metropolitan Statistical Areas (1992-2002) as Estimated Using the Index. (PDF 560 KB)

Web Appendix Table 2

Estimated Prevalence of Injection Drug Use per 10,000 White Adult Residents of 95 Large US Metropolitan Statistical Areas (1992-2002) as Estimated Using the Index. (PDF 120 KB)

Appendix

Appendix

  1. 1.

    Adjusting Values of P ijk APIDS for HIV Seroprevalence and Determining whether the Advent of HAART Altered the Racial/Ethnic Composition of Injectors in the APIDS Database

We calculated values of P ijk APIDS that were adjusted for HIV seroprevalence as described in Formula 3. HIV seroprevalence values for each racial/ethnic group of injectors, MSA, and year were estimated using CTS data. The resulting values of P ijk APIDS (adjusted for HIV seroprevalence) were smoothed using loess.53

Formula 3: Calculating values of P ijk APIDS that are adjusted for HIV seroprevalence

$$P_{ijk{\text{APIDS}}\_adj} = \frac{{\left( {{{b_{ijk{\text{APIDS}}} } \mathord{\left/ {\vphantom {{b_{ijk{\text{APIDS}}} } {H_{ijk} }}} \right. \kern-\nulldelimiterspace} {H_{ijk} }}} \right)}}{{\left( {{{T_{ij{\text{APIDS}}} } \mathord{\left/ {\vphantom {{T_{ij{\text{APIDS}}} } {H_{ij} }}} \right. \kern-\nulldelimiterspace} {H_{ij} }}} \right)}}$$
(3)

where

H ijk :

the proportion injectors testing positive for HIV in year i, MSA j, racial/ethnic group k

H ij :

the proportion injectors testing positive for HIV in year i and MSA j, regardless of racial/ethnic group

b ijkAPIDS and T ijAPIDS :

as defined in Formula 1.

Calculating the HIV seroprevalence values used in Formula 3 was accomplished as follows: The number of cases in the CTS database testing positive for injection-related HIV reported for each racial/ethnic group, year, and MSA was divided by the corresponding number of injectors tested in the CTS database. The CTS database released to the project suppressed cell values of <5. We classified missing data on the number of seropositive tests as “suppressed” if CTS data indicated that at least one injector was tested in that racial/ethnic group, MSA, and year. This classification system indicated that positive test results were suppressed for 17.42% of cells for Black injectors, 16.46% of cells for White injectors, and 23.44% of cells for all injectors between 1992 and 2002. Where observations were suppressed, we used regression imputation to estimate the number of observations in each racial/ethnic group (and for all injectors, regardless of racial/ethnic group) who tested positive for injection-related HIV in each year and MSA. We imputed suppressed test results for White injectors for each MSA and year as a function of (1) the total number of White injectors tested in that year and MSA; (2) the percent of all injectors (regardless of race/ethnicity) testing positive in that MSA between 1992 and 2002; and (3) the percent of all White MSA residents tested that year who were injectors. Because the outcome was a count and overdispersed, a negative binomial distribution was assumed; the intercept was set to zero to allow predicted values to range between zero and four. Similar methods were used to impute missing suppressed serostatus values for Black injectors and for all injectors in each year and MSA.

HIV seroprevalence values for Black and White injectors, and for all injectors regardless of race/ethnicity, were then calculated for each year and MSA as described above; seroprevalence values were set to missing where the number of injectors tested was <20 because of concern about the stability of these estimates (approximately 26.9% of cells for Black injectors, 32.8% for White injectors, and 25.2% for all injectors between 1992 and 2002).

Because HAART prolongs time to AIDS diagnoses among HIV-positive individuals if the therapy is initiated sufficiently early,148–150 and because access to HAART varies across MSAs and racial/ethnic groups,62–65 we explored whether the advent of the HAART era in our study altered the racial/ethnic composition of injectors in the APIDS database. Specifically, we tested whether the relationship between study year and the APIDS-based estimates of the proportion of injectors who were White (or Black) varied according to whether the study year predated or postdated the advent of HAART (circa 1997 for injectors). The interaction was not statistically significant, and its magnitude was low. We thus concluded that, while HAART reduced the number of injectors diagnosed with AIDS, it had a negligible effect on the proportion of injectors in each racial/ethnic group. No adjustments were made to the APIDS-based estimates to address the onset of the HAART era.

  1. 2.

    Calculating the Number of Injectors (Regardless of Race/Ethnicity) Living in each MSA each Year of the Study Period

We calculated the number of injectors living in each MSA during each year of the study period in a two-stage process: stage 1 consisted of estimating the total number of injectors living in the US each year; stage 2 consisted of allocating this national estimate to each MSA.

Stage 1: Calculating Nationwide IDU Estimates Using CTS data, we first calculated a set of “scores” that describe annual changes in the size of the injecting population in the US by dividing the number of injectors seeking HIV counseling and testing services nationwide each year by the average annual number of injectors seeking such services nationwide across all years of the study period. Through a parallel process, two additional sets of scores were calculated, one based on drug treatment data and another based on data on arrests for heroin or cocaine possession (adjusted for the percent of heroin or cocaine users who inject). These three database-specific sets of scores were averaged to create a single score for each year of the study period.

The total number of injectors living in the US had been calculated previously for 1992 and 1998.69,70 We viewed these two data points as anchors. The proportion of the final annual score to the score in 1992 was then multiplied by the 1992 IDU estimate anchor point. This process was repeated with the 1998 IDU estimate anchor point, creating two strands of nationwide annual IDU estimates (one anchored with 1992 data and the other with 1998 data). The results of these two strands were then averaged to estimate the total number of injectors living in the US during each year of the study period.

Allocating Nationwide IDU Estimates to MSAs The resulting annual national IDU estimates were then allocated to each MSA using ratio methods.66,67 For each of four data series (described below), we calculated the proportion of injectors nationwide who lived in MSA i in year j. We then multiplied these database-specific proportions by our estimate of the number of injectors living in the US for each year of the study period, thereby generating four sets of estimates of the number of injectors living in each MSA each year. These four sets of estimates were smoothed using loess, and then averaged to produce a single estimate for each MSA and year. The four data series analyzed captured information on (1) IDU-related AIDS diagnoses; (2) injectors’ participation in HIV counseling and testing services; (3) drug treatment utilization among injectors; and (4) previously calculated estimates of the number of injectors living in the MSAs studied in 1992 and 1998 (interpolated and extrapolated to cover the remaining years of the study period).

  1. 3.

    Quantifying the Magnitude of AIDS-Related Mortality and of Incarceration among Black Injectors and White Injectors During the Study Period

The combination of AIDS-related mortality and incarceration might have reduced IDU prevalence among Black adults during the study period. Black injectors suffered a heavy burden of AIDS-related mortality during the study period.103 According to CDC surveillance records, 67,314 Black injectors living in the MSAs in our sample died of AIDS-related causes between the date AIDS was first diagnosed in the US and 1999.151 Footnote 7 To begin to capture the toll that AIDS took on the population of Black injectors, we note that, according to our estimates, there were 349,867 Black injectors living in the 95 MSAs in 1992; CDC data indicates that 9,187 (or 2.5%) of these injectors were diagnosed with AIDS in 1992 and died before the year 2000.151 This is a considerable underestimate of the total number of Black injectors alive in 1992 who died of AIDS during our study period because it ignores (1) individuals diagnosed with AIDS in 1992 who died after 1999; and (2) injectors alive in 1992 who were diagnosed with AIDS before or after 1992 and subsequently died. AIDS-related mortality may thus have had powerful effects on IDU prevalence among Black injectors.

Incarceration for drug-related offenses may have further reduced the number of Black injectors living in the MSAs under study between 1992 and 2002. A recent Human Rights Watch report indicates that in the 34 US states for which data are available, 59,535 Black men and women entered prison in 2003 alone to serve time for a drug-related offense; this figure represents a 400% increase since 1986 and 0.26% of the total Black adult population in these states (a percentage that would be substantially higher for the population of Black adult drug users).113 Prisons tend to be located outside of MSA boundaries,116 and so CTS, TEDS, and APIDS will rarely capture imprisoned injectors.

While significant, AIDS-related mortality and incarceration may have had less of an impact on White injectors compared to their Black counterparts. Fewer White injectors than Black injectors died of AIDS during the study period.125–128 According to CDC records, 33,760 White injecting residents of the MSAs in our sample died of AIDS-related causes between the date that AIDS was first diagnosed in the US and 1999.151 Of the 736,100 White injectors residing in the MSAs under study in 1992, 4,791 (0.7%) were diagnosed with AIDS in 1992 and died before 2000.151 Compared to Black adults, White adults have also been less affected by rising incarceration rates for drug-related offenses. In 2003, 37,003 White men and women (or 0.03% of all White adults) entered prison to serve time for a drug-related offense in the 34 states for which data are available.112 The absolute number of White injectors removed from the MSAs during the study period was thus smaller than the absolute number of Black injectors removed; moreover, this removal would have had a smaller impact on White IDU prevalence because there were substantially more White injectors than Black injectors in these MSAs (e.g., in 1992, we estimate that there were 736,100 White injectors and 349,867 Black injectors living in these MSAs).

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Cooper, H.L.F., Brady, J.E., Friedman, S.R. et al. Estimating the Prevalence of Injection Drug Use among Black and White Adults in Large U.S. Metropolitan Areas over Time (1992–2002): Estimation Methods and Prevalence Trends. J Urban Health 85, 826–856 (2008). https://doi.org/10.1007/s11524-008-9304-9

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