HIV Prevalence Rates among Injection Drug Users in 96 Large US Metropolitan Areas, 1992–2002

Article (PDF Available)inJournal of Urban Health 86(1):132-54 · November 2008with50 Reads
DOI: 10.1007/s11524-008-9328-1 · Source: PubMed
Abstract
This research presents estimates of HIV prevalence rates among injection drug users (IDUs) in large US metropolitan statistical areas (MSAs) during 1992-2002. Trend data on HIV prevalence rates in geographic areas over time are important for research on determinants of changes in HIV among IDUs. Such data also provide a foundation for the design and implementation of structural interventions for preventing the spread of HIV among IDUs. Our estimates of HIV prevalence rates among IDUs in 96 US MSAs during 1992-2002 are derived from four independent sets of data: (1) research-based HIV prevalence rate estimates; (2) Centers for Disease Control and Prevention Voluntary HIV Counseling and Testing data (CDC CTS); (3) data on the number of people living with AIDS compiled by the CDC (PLWAs); and (4) estimates of HIV prevalence in the US. From these, we calculated two independent sets of estimates: (1) calculating CTS-based Method (CBM) using regression adjustments to CDC CTS; and (2) calculating the PLWA-based Method (PBM) by taking the ratio of the number of injectors living with HIV to the numbers of injectors living in the MSA. We take the mean of CBM and PBM to calculate over all HIV prevalence rates for 1992-2002. We evaluated trends in IDU HIV prevalence rates by calculating estimated annual percentage changes (EAPCs) for each MSA. During 1992-2002, HIV prevalence rates declined in 85 (88.5%) of the 96 MSAs, with EAPCs ranging from -12.9% to -2.1% (mean EAPC=-6.5%; p<0.01). Across the 96 MSAs, collectively, the annual mean HIV prevalence rate declined from 11.2% in 1992 to 6.2 in 2002 (EAPC, -6.4%; p<0.01). Similarly, the median HIV prevalence rate declined from 8.1% to 4.4% (EAPC, -6.5%; p<0.01). The maximum HIV prevalence rate across the 11 years declined from 43.5% to 22.8% (EAPC, -6.7%; p<0.01). Declining HIV prevalence rates may reflect high continuing mortality among infected IDUs, as well as primary HIV prevention for non-infected IDUs and self-protection efforts by them. These results warrant further research into the population dynamics of disease progression, access to health services, and the effects of HIV prevention interventions for IDUs.

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Journal of Urban Health: Bulletin of the New York Academy of Medicine, Vol. 86, No. 1
doi:10.1007/s11524-008-9328-1
* 2008 The New York Academy of Medicine
HIV Prevalence Rates among Injection Drug Users
in 96 Large US Metropolitan Areas, 19922002
Barbara Tempalski, Spencer Lieb, Charles M. Cleland,
Hannah Cooper, Joanne E. Brady, and Samuel R. Friedman
ABSTRACT This research presents estimates of HIV prevalence rates among injection drug
users (IDUs) in large US metropolitan statistical areas (MSAs) during 19922002. Tr end
data on HIV prevalence rates in geographic areas over time are important for research on
determinants of changes in HIV among IDUs. Such data also provide a foundation for the
design and implementation of structural interventions for preventing the spread of HIV
among IDUs. Our estimates of HIV prevalence rates among IDUs in 96 US MSAs during
19922002 are derived fr om four independent sets of data: (1) research-based HIV
prevalence rate estimates; (2) Centers for Disease Control and Prevention Voluntary HIV
Counseling and Testing data (CDC CTS); (3) data on the number of people living with
AIDS compiled by the CDC (PLWAs); and (4) estimates of HIV prevalence in the US. From
these, we calculated two independent sets of estimates: (1) calculating CTS-based Method
(CBM) using regression adjustments to CDC CTS; and (2) calculating the PLWA-based
Method (PBM) by taking the ratio of the number of injectors living with HIV to the
numbers of injectors living in the MSA. We take the mean of CBM and PBM to calculate
over all HIV prevalence rates for 19922002. We evaluated trends in IDU HIV prevalence
rates by calculating estimated annual percentage changes (EAPCs) for each MSA. During
19922002, HIV prevalence rates declined in 85 (88.5%) of the 96 MSAs, with
EAPCs ranging from 12.9% to 2.1% (mean EAPC=6.5%; pG0.01). Across the
96 MSAs, collectively, the annual mean HIV prevalence rate declined from 11.2% in
1992 to 6.2 in 2002 (EAPC, 6.4%; pG0.01). Similarly, the median HIV prevalence
rate declined from 8.1% to 4.4% (EAPC, 6.5%; pG0.01). The maximum HIV
prevalence rate across the 11 years declined from 43.5% to 22.8% (EAPC, 6.7%;
pG 0.01). Declining HIV prevalence rates may reflect high continuing mortality
among infected IDUs, as well as primary HIV prevention for non-infected IDUs and
self-protection efforts by them. These results warrant further research into the
population dynamics of disease progression, access to health services, and the effects
of HIV prevention interventions for IDUs.
KEYWORDS Injection drug users, HIV prevalence rates, HIV trends over time, PLWA,
Metropolitan statistical areas
Tempalski, Cleland, Brady, and Friedman are with the National Development and Research Institutes,
New York, NY, USA; Lieb is with the Florida Department of Health, Bureau of HIV/AIDS, Tallahassee,
FL, USA; Cooper is with the Rollins School of Public Health at Emory University, Atlanta, GA, USA;
Friedman is with the Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins
University, Baltimore, MD, USA.
Correspondence: Barbara Tempalski, National Development and Research Institutes, 71 West 23rd
Street, 8th Floor, New York, NY 10010, USA. (E-mail: tempalski@ndri.org)
Electronic supplementary material The online version of this article doi:10.1007/s11524-008-9328-1
contains supplementary material, which is available to authorized users.
132
INTRODUCTION
Almost 30 years after the HIV epidemic among injection drug users (IDUs) was
recognized, scientic understanding of the natu re, causes, and consequences of
geographic and temporal variability in HIV prevalence rates among IDUs remains
somewhat limited. Earlier studies, such as the National AIDS Demonstrat ion
Research (NADR) project and the World Health Organizations Multi-Center,
revealed geographic variability in HIV prevalence and incidence rates among IDUs
across cities.
16
More recent studies have also found considerable geographic
variation across US cities,
79
as well as across cities inter nationally.
10
Though many epidemiologists do not completely understand how epidemics
among IDUs start, it is known that outbreaks can arise very quickly. Friedman and
Des Jarlais
11
showed this for a wide range of European cities as well as American
and South Asian cities. Further, support for this is evident from the history of
epidemics in places like New York City in the mid-1970s,
12,13
Southeast Asia in the
late 1980s and early 1990s ,
14
Vancouver in the 1990s,
15,16
and, more recently,
China, Vietnam,
17,18
Iran,
19
and Russia.
20
We have nonetheless been hampered in our ability to understand the spread and
prevalence of HIV among IDUs by the lack of comparable interurban data over time
and spac e. The absence of such data li mits our kn owledge about processes
associated with the rise and fall of epidemics among IDUs a necessary basis for
understanding the origins and paths of large IDU-associated HIV epidemics. The aim
of this re search is to estimate HIV prev alence rates a mong IDUs in 96 US
metropolitan statistical areas (MSAs) from 1992 to 2002, and evaluate trends over
time. Here, we present a method of calculating HIV prevalence rate estimates
annually during an 11-year period. We examine changes in temporal trends in HIV
prevalence rates by computing estimated annual percent change (EAPC) for each
MSA. We also validate the ndings and discuss the limitations of our data.
Geographic-specic data over time on HIV prevalence rates among IDUs are
important as they may help direct policy makers and concerned public in allocating
resources and establishing public policy. Such data can provide a foundation for the
design and implementation of structural interventions for preventing the spread of
HIV epidemics among IDUs.
Historical Data on Differences in HIV Prevalence
among IDUs in MSAs
Holmberg
21
developed a components model to estimate HIV prev alence and
incidence rates in 1992 among IDUs, men who have sex with men (MSM), and
high-risk heterosexuals in 96 MSAs in the US with population 9500,000. Until then,
data on HIVamong IDUs had been limited to a relatively limited number of cities (e.g.,
New York City, San Francisco, Chicago, Miami).
12,13
In addition to providing a
useful description of the number of IDUs and of the HIV epidemic in US metro-
politan areas, data of this kind provide the basis for comparative analysis to study
what metropolitan area characteristics predict (a) the extent to which the local
population injects drugs; (b) HIV prevalence amon g IDUs; and (c) HIV incidence
among IDUs. Thus, Holmbergs estimates allowed for broad geographic compar-
isons across MSAs and risk-populations.
His estimates were derived from a variety of published and unpublished studies,
as well as data from a variety of drug treatment and publicly funded HIV cou nseling
and testing sites. These were validated by comparison with a set of criteria specifying
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 133
interval bounds for reasonable estimates. Estimates that approximately satised
all inclusion criteria were averaged to provide an overall estimate.
Using these methods, Holmberg estimated that HIV prevalence rates among
IDUs ranged from 1% to 41% (with median 5.9% and considerable right-skewing)
across MSAs in 1992, and that incidence rates varied from 0.2 to 4.9 per 100
person-years at risk (Table 1).
Friedman et al.
22
estimated 1998 HIV prevalence rates among IDUs within the
same 96 large MSAs as Holmberg. These estimates were based on (1) adjusting Centers
for Disease Control (CDC) Counseling and Testing data by regression imputation
techniques with research study data, and (2) estimates of the ratio of the number of
IDUs living with HIV to the number of IDUs living in an MSA. The validity of the
resulting estimates was assessed, and mean values were used as best estimates. The
estimates varied from 2.4% to 27.4% across MSAs, with a mean of 7.9% and a
median of 5.9% for IDUs. Results from this study indicate that most MSAs
continued to have HIV prevalence rates among IDUs less than 10%, and
approximately 40% of MSAs had prevalence rates less than 5%.
Because the methods used by Holmberg were no longer feasible, Friedman et al.
worked out modied ways to estimate HIV prevalence rates among IDUs.
Dissimilarity in methods and problems of regression to (and from) the mean,
limited our ab ility to study change in these esti mates and makes interurban
comparisons between Holmbergs 1992
21
estimates and Friedmans 1998
22
HIV
prevalence estimates problematic. Developing our HIV prevalence rates among IDUs
over time will enhance our capacity to study the determinants of changes in HIV
epidemics. Annual data allow us to use longitudinal statistical methods to make
inferences about causes of change in IDU-related HIV transmission and factors
associated with program changes and design.
METHODS
Overview
Our estimates of HIV prevalence rates among IDUs in the 96 MSAs of interest
during 19922002 were derived from four independent sets of data: (1) research-based
HIV prevalence rate estimates compiled from published literature, conference
abstracts, web-based searches and inquiries of researchers to nd HIV prevalence
rate estimates among IDUs; (2) Centers for Disease Control and Prevention Voluntary
HIV Counseling and Testing data (CDC CTS unpublished data 19922002); (3) data
on the number of people living with AIDS (PLWAs unpublished data 19922002),
TABLE 1 HIV prevalence rates among IDUs in large metropolitan areas of the United States,
1992 (n=96) and 1998 (n =95)
1992
a
1998
b
Median (range) 5.9% (1.0%41.0%) 5.9% (2.4%27.4%)
Mean (standard deviation) 9.1% (8.5%) 7.9% (5.5%)
First quartile 3.0% 4.0%
Third quartile 12.5% 10.2%
a
These statistics were compiled from data from Holmberg (1996 and personal communication 4/19/2001).
b
HIV prevalence data on San Juan, PR was not compiled in 1998 analysis.
TEMPALSKI ET AL.134
compiled by the CDC; and (4) estimates of HIV prevalence in the US (Holtgrave
personal communication 3/3/2008).
(23)
Using these data sources, we developed two sets of estimates based on
independent methodologies: (1) the CTS-based method (CBM) and (2) the PLWA-based
method (PBM). The CBM modies the approach of Friedman
22
for lon gitudinal
data. In calculating the CBM estimates, we used a regression model in which research-
based estimates were regressed on CDC CTS estimates to correct for bias in CDC CTS
data due to the fact that people testing positive for HIV tend not to get retested.
2426
In the CBM, we corrected for four types of missing data in the CDC CTS data set:
1. Missing values (suppressed) known to be between 0 and 4 HIV-posit ive tests;
2. Missing data for 13 years (but not all years on number of IDUs tested);
3. Missing data for four or more years, or reporti ng a dramatic drop or increase
*
in the number of IDUs tested, which accounted for nine MSAs
.
. For these
MSAs, prevalence rates could not be determined or imputed based on lack of
trend data. As a result, we treated these MSAs as missing data for all 11 years
4. Missing data for all 11 years which accounted for ve MSAs
-
(i.e., CDC does
not collect data in these areas)
PBM estimates
22,27,28
were based on the annual number of living persons
reported with AIDS in each MSA and the estimated total number of persons living
with HIV or AIDS (PLWHAs) in the US and were adapted for longitudinal data.
CBM and PBM estimated HIV prevalence rates were smoothed and then averaged
to create our nal best estimates. In the following subsections, we describe each
stage in calculating our HIV prevalence rates for all 96 MSAs in greater detail.
Unit of Analysis and Sample
We studied the 96 largest MSAs as dened by the 1993 census boundary le.
29
MSAs are dened by the US Census Bur eau as contiguous counties that contain a
central city of 50,000 people or more and that form a socioeconomic unity as
dened by commuting patterns and social and economic integration among the
constituent counties. The MSA was chosen as the unit of analysis for three reasons.
First, it allows continuity with 1992 HIV prevalence rates from Holmberg
21
and
1998 HIV prevalence rates from Friedm an et al.
22
Second, health data are more
available for the county units that comprise MSAs than for municipalities. Third, the
economic, social, and commuting unity of metropolitan areas makes them a
reasonable unit in which to study drug-related HIV and other epidemi cs.
22
Research-based Estimates for CBM
Stage 1 Compiling research estimates
Our resear ch estimates were based on a review of published literature and
conference abstracts, as well as web-based searches and inquiries of researchers to
*
e.g., number of IDUS tested in 1992 N=673; number tested in 1997 N=88.
.
Akron, OH; Charleston, SC; Gary, IN; Greenville, SC; Kansas City, KS; MinneapolisSt. Paul, MN-
WI; Syracuse, NY; Ventura, CA; Youngstown, PA.
-
Birmingham, AL; Little Rock, AR; Norfolk, VA; San Juan, PR; Wichita, KS.
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 135
nd HIV prevalence rate estimates among IDUs in the 96 MSAs of interest. To
be eligible, a study had to have been conducted during 19922002 and to have
determined HIV serostatus through the testing of blood, urine, or saliva samples
rather than th rough self-report. An additional inclusion criterion was that the
study could not have been part of the CDC CTS system. We identied eligible
research-based estimates from 33 of 96 metropolitan areas totaling 131 data
points over tim e (Electronic Supplementary Material). The annual number of
research-based estimates declined over time (i.e., after 1997), and were concentrated
mainly with in MSAs having substantial research instituti ons with an interest in drug
users and/or tending to have highly populated central cities (i.e., New York City,
Chicago, Baltimore, Seattle and San Francisco).
The research studies were categorized by setting: (1) drug treatment centers and
methadone m aintenanc e treatment prog rams (MMTP); ( 2) syringe exc hange
programs (SEPs); (3) sexually transmitted disease (STD) clinics; (4) prisons; (5)
street outreach/network; and (6) all other settings, e.g., those where homeless
persons are found). Analysis of variance showed there were no large differences in
HIV prevalence rates across study settings. Accordingly, the regression model
included all research study categories without adjustment for study category.
CTS-based Method (CBM)
Stage 2 Correcting for suppressed cell values as reported by the CD C CTS
CDC provided data on numbers of HIV-positive tests among IDUs in publicly
funded counseling and testing sites (duplicate count, not unique individuals) for each
MSA. However, all counts that were G5 were reported as missing by the CDC CTS.
Where this occurred, and where data on HIV prevalence rate trends were sufcient
to determine trends, imputation methods were applied to derive best estimates
within an MSA. This accounted for 28 MSAs with 118 suppressed cells. Imputations
considered both the fact that the number of people positive had to be an integer
under 5 and temporal trends in the proportion of IDUs testing positive in the MSA
could be determined. In some cases, it could not be determined whether data missing
was based on a value G5, or based on the lack of temporal trends in testing. In these
specic cases where this occurred we treated these data in the cell as missing and
dealt with it in Stage 3 of our estimation model.
Stage 3 Regression adjustments to CDC CTS-based on research estimates
After i mputing missing values G5, we then used a regression model to
predict HIV prevalence rates based on our research estimates to correct for bias
in CDC CTS data (i.e., positives tend not to get retested and duplicate
counts).
2426
Included in our regression model was an adjustment for time to the
advent of highly active antiretroviral therapies (HAART) in 1996. Since then,
HAART has become the standard of care for the treatment of H IV-infected
individuals. As HAART has become more widely used, AIDS researchers have seen
an increase in the time from HIV infection to a diagnosis of AIDS, and an increase
in survival time from a diagnosis of AIDS to death.
23
Thus, as s urvival time
increases prevalence rates in the research estimates we needed to adjust for this in
our equation.
TEMPALSKI ET AL.136
During our study period, access to HAART changed over time and took time
to reach IDUsthus, we assume January 1, 1998 as a uniform date for which
IDUs had access to HAART. To adjust for this in our estimation model, we
assume that IDUs in all MSAs have equal access to HAART, starting after 1997.
The pre-HAART period was dened as 19921997, and the post-HAART period
was dened as 19982002. The pre-HAART time variable was dened as equal
to six in 1992 and to decrease by one each year until 1997; from 1998 to 2002,
the pre-HAART time variable was set equal to zero. The post-HAART time
variable was set equal to zero from 1992 to 1997; in 1998 the post-HAART
variable was dened as one and set to increase by one each year until 2002.
The resulting predictor equation for research-based estimates of HIV prevalence
rates (R
2
=0.77) was
1ðÞResearch estimates of HIV prevalence rate 1992 2002ðÞ¼1:023 þ 1:804
CDC CTS 0:039 CDC CTS pre HAART þ 0:025 CDC CTS post HAARTf g
Stage 4 Interpolating and extrapolating where the estimate of HIV prevalence
rate was missing for some years but not all years for a specied MSA
We next interpolated and extrapolated using generalized linear regression
modeling for MSAs where specic years of HIV prevalence rates were missing from
the CDC CTS. The model to correct for missing data included linear and quadratic
effects of time measured as years since 1992. This gave a separate and independent
regression equation for each MSA and year where values of rates were missing.
There were seven MSAs that fell into this category
*
, accounting for 12 missing
values. Four of the seven MSAs each had one missing value, one MSA had two, and
two MSA had three missing values.
After computing stages 13, our CBM provided HIV prevalence rates for 82
MSAs totaling 902 points for years 19922002.
Stage 5 Predicting missing estimates for CBM prevalence rates based on PBM
PBM HIV prevalence rates were available for all 11 years and all 96 MSAs, but
there were only 82 MSAs for which we had CBM estimates based on CDC CTS. In
stage 5 of computing our CBM estimates, we apply a linear regression equation to
the remaining 14 MSAs. Thus, we utilize the PBM estimates to predict missing CDC
CTS values in 14 MSAs that were missing 11 years of data from CDC CTS. Here,
we apply the following generalized linear equation which predicts missing estimates
for CBM prevalence rates based on PBM.
2ðÞCBM ¼ 1:588 þ 0:811 PBM
In the last stages of our estimation model, we smoothed the nal CBM and PBM
estimates for all MSAs and years, and then averaged the two smoothed nal
estimates.
*
Albany-Schenectady, NY; Ann Arbor, MI; Atlanta, GA; Buffalo-Niagara Falls, NY; NassauSuffolk,
NY; ProvidenceFall RiverWarwick, RIMA; Rochester, NY.
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 137
PLWA-based Method (PBM)
Overview
The PBM was derived from an existing model which describes the relationship among
estimates of IDU HIV prevalence rates, IDU HIV prevalence, and numbers of IDUs in a
given population.
27
This model has previously produced plausible estimates of HIV
prevalence rates and HIV prevalence among risk-populations.
22,27,28
In short, the estimated total number of HIV-infected IDUs residing in a given MSA
and year, (i.e., the HIV prevalence estimate, or estimated number of PLWHAs), was
designated as k. The estimated total number of IDUs (a) and the estimated HIV prevalence
rate among IDUs (b) were related by the function, k=ab; thus, b=k/a. Year-specic values
of the estimated number of IDUs, a, for the MSAs were taken from previous research by
Brady et al.
30
Value s of k and b were estimated using two parameters.
Stage 1 Estimating Parameter1an expansion factor equal to the annual ratio
of US total HIV prevalence (PLWHAs) to US total number of persons
living with AIDS (PLWAs).
The purpose of estimating parameter 1 is to extrapolate from the number of
reported PLWAs to the estimated number of PLWHAs through the end of each year.
Thus, the reservoir of all HIV-infe cted persons (PLWHAs, or the HIV prevalence
estimate) includes the (known) number of reported persons living with AIDS
(PLWAs), as well as the (unknown) numbers of persons alive and diagnosed with
AIDS but not reported; the number of persons living with diagnosed HIV; and the
number of undiagnosed HIV-infected persons. To obtain annual estimates of the
number of PLWHAs (variable k), we used a multiplier (expansion factor) for each
year for the US, applying it to the number of reported PLWAs.
We relied on two data sources for this rst parameter: (1) reported annual data
from CDC on PLWAs for the entire US by risk factor, and (2) annual estimates of
HIV prevalence for the entire US, 19922002 (Holtgrave personal communica-
tion).
23
We obtained annual data from CDC on the total number of PLWAs in the
US and 96 MSAs by exposure category. For each year, we estimated the number of
HIV-infected IDUs (HIV prevalence estimate, k) by multiplying the MSA-specic
numbers of IDU PLWAs by the year-specic expansion factor.
(1) Annual Expansion Factor=(Estimated total number of PLWHAs in the US
in that year)/(Total number of reported PLWAs in the US in that year)
(2) Estim ated k=Number of IDU PLWHAs
year i,MSAj
=(Number of IDU
PLWAs
year i, MSA j
)* (Expansion factor
year i
)
This estimates values of k, the numerator of the models equation. Since the
variable of interest, b (HIV prevalence rate
year i, MSA j
), equals k/a, we need values of
the denominator, a (the estimated number of IDUs in
year i, MSA j
), to solve for b.We
rely on research by Brady
30
for estimates of year- and MSA-specic numbers of
IDUs.
Parameter 1 assumes that the year-specic expansion factor does not materially
vary by MSA. It also assumes that the distribution of PLWHAs by HIV risk factor is
similar to the distribution of the known PLWAs. Lastly, PLWA case data is dened as
those injectors who have a history of injection drug use (i.e., any drug injection since
1977) following the CDC convention for classifying HIV exposure category.
25
TEMPALSKI ET AL.138
Stage 2 Estimating Pa rameter 2an adjustment factor to account for differ-
ences in HIV prevalence rates via the CBM and the PBM
For the 85 MSAs where estimates were available by both methods, the
annual MSA-specic PBM HIV prevalence rate estimates were highly correlated
with the CBM estimates, but were systematically greater than t he CBM
estimates. We determined that the PBM estimates were most likely articially
inated because they were p artially based on national estimates of HIV
prevalence and because the number of HIV-infected IDUs may be increasing
over ti me in localities outside of the 96 MSAs i.e., the epidemic is diffusing to
small m etropolitan areas and non-metropolitan areas.
3137
We adjusted for this
systematic difference by computing Parameter 2 for each year, which equals the
ratio of the overall PBM weighted average rates to the overall CBM weighted
average rates. Weighting depended on the estimated size of the IDU popu-
lations,
30
so that, for example, a l arge MSA like New York contributed more to the
weighted average rate t han a smaller MSA like San Francisco. The weighted
average HIV prevalence rates for a given year were computed algebraically,
equaling the sum of the HIV prevalence es timates (i.e., the estimated number of
IDU PLWHAs) across the MSAs (k) divided by the sum of the estimated number of
IDUs across the MSAs (a).
(1) Parameter 2
year i
=(PBM weighted average HIV prevalence rate
year i
)/(CBM
weighted average HIV prevalence rate
year i
)
The adjusted PBM rates then equal the unadjusted PBM rates divided by Parameter 2
(2) PBM adjusted HIV prevalence rate in MSA
j, year i
=(PBM unadjusted rate
year
i, MSA j
)/Parameter 2
year i
Combined Final Best Estimates
Lastly, to minimize random error, we rst smoothed the nal CBM and PBM
estimates for all MSAs and years, and then averaged the two smoothed nal
estimates. The end results are the nal best HIV prevalence rate estimates”—i.e.,
the smoothed average of the CBM and the PBM HIV prevalence rates by MSA for
each year. The data were smoothed using loess regression, which ts curves to noisy
data and smoothes data in a manner similar to computing a weighted moving
average.
38
Sample and its Implications for Statistical Analyses
This is a study of 96 metropolitan areas that were the largest MSAs in the
United States in 1990. Thus, it is a study of a population rather than of a
sample. This has several implications. First, it means that there is no sampling
error (though there is measurement error). There is some debate over whether
issues of statistical inference, (e.g., p-values), have any scientic applicability in
this research. Second, statistical analyses are primarily descriptive rather than
inferential. As a result, p-values generated in this analysis will be used mainly as a
heuristic device.
39
Lastly, there is no universe for which these ndings can be generalized except the
universe being studied. Since these 96 MSAs had a population of 159 million (62%
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 139
of the U.S. population in 1993), and an estimated 1.5 million IDUs in the early
1990s, this universe is of great public health importance in its own right. It will
nonetheless be possible to develop hypotheses about the implications of these
ndings for other localities. With appropriate caution, ndings in this research can
consider the extent to which ndings are relevant to smaller urban areas in the U.S.,
or to metropolitan areas in other countries.
Validating the HIV Prevalence Rate Estimates
Several procedures were used to validate our estimates. First, we used a paired t-test
and exami ned a single measure, absolute agreement intraclass correlation coefcient
(ICC),
40
which counts mean differences as a mismatch, between our 1992 best HIV
prevalence rate estimates and Holmbergs 1992
21
HIV prevalence rate estimates. W e
did the same for our 1998 best HIV prevalence rate estimates and the Friedman 1998
22
HIV prevalence rate estimates. We also use descriptive statistics to provide simple
summaries about the sample and the measures of our HIV prevalence rates over time.
We further validated our estimates by correlating them with measures of
theoretically-related constructs:
1
hard-core drug arrests per capita;
2
police protec-
tion expenditures per capita;
3
corrections expenditures per capita;
4,41
income
inequality;
5
poverty; and
6
anti-over-the-counter (OTC) syringe laws.
7,22,39,42,43
Hard-core drug arrests per capita; police protection expenditures per capita; and
corrections expenditures per capita Theoretically, these variables characterize a
criminal justice approach to social problems, an approach consistent with
hostility toward drug users in general and programs that help reduce harm.
Friedman et al.
41
found that higher rates of legal repressiveness (hard drug arrests;
police employees per capita; and corrections expenditures per capita) are associated
with higher HIV prevalence rates among injec tors. Aggressive police tactics and/or
stigmatization may lead IDUs to engage in hurried injection behaviors, to share
syringes more often, and/or to inject in high-risk environments.
4448
The number of hard drug arrests (per 10,000 population) for possession of
cocaine or heroin comes from the Uniform Crime Reporting Program: County-Level
Detailed Arrest and Offense Data (19922002);
49
the police protection expenditures
per capita and corrections expenditures per capita are taken from United States
Census Bureau data on Government Finances (1992;97;2002).
5052
Poverty and Income inequality Both have been found to be related to a wide range
of morbidity and mortality rates at the neighborhood and MSA level.
5357
Poverty
index comes from the United States Bureau of the Census (2000);
58
and income
inequality is the ratio of income received by the top 10% to that received by the
bottom 10% (Harper unpublished data, 7/18/2005 University of Michigan).
Anti-OTC laws States regulate syringe access through OTC laws; these laws work
against IDUs having access to clean syringes. Previous research has sho wn that OTC
laws are related to HIV prevalence and incidence rates among IDUs.
22,39,43,59
Data
on OTC syringe laws were derived from Burris et al.
60
Trend Analysis
We describe trends in HIV prevalence rates by computing estimated annual percent
change (EAPC) and 95% condence intervals (CIs) for each MSA based on the
TEMPALSKI ET AL.140
Surveillance, Epidemiology, and End Results (SEER) methods established by the
National Cancer Act of 1971. The EAPC was calculated by regres sing the log of the
HIV prevalence rates against the year. We used the MSA- and year-specic averages
of the unsmoothed CBM and PBM HIV prevalence rates as a basis for calculating
the EAPC.
Statistical analyses were conducted using SAS version 9.1.
61
EAPCs and 95%
CIs were calculated using R software version 2.7.1.
62
RESULTS
Descriptive statistics Across the 96 MSAs, collectively, the mean HI V preva-
lence rate declined f rom 11.2% in 1992 to 6.2% in 2002 (Table 2; EAPC,
6.4%; 95% CI, 7.0% to 5.7%; pG 0.001; Table 3). Similarly, the median HIV
prevalence rates declined from 8.1% to 4.4% (EAPC, 6.5%; 95% CI, 7.3% to
5.6%; pG 0.001). The maximum HIV prevalence rate across the 11 years also
showed a signicant decline, ranging from 43.5% (1992) to 22.8% (2002) (EAPC,
6.7%; 95% CI, 7.6% to 5.8%; pG 0.001).
Correlations and Prevalence rates The schedule of unadjusted HIV prevalence
rates by M SA according to the CBM and the PBM were well correlated for
each of the 11 years (Pearson r-squared ranged from 0.42 to 0.79; pG 0.01);
(mean r-squared=0.67; median r-squared=0.71; SD=0.11). The combined best
HIV prevalence rates (i.e., averages of the smoothed adjusted PBM HIV prevalence
rate estimates and the smoothed CBM HI V prevalence rate estimates) for each
MSA and year are shown in Table 4.
Trend Analysis Figure 1 shows point estimates and 95% condence intervals for
MSA-specic EAPCs in HIV prevalence rates over the study period. Most MSAs
(n=85) experienced a signicant decrease in prevalence rate, with EAPCs ranging
TABLE 2 Aggregate-level statistics for estimated HIV prevalence rates among injection drug
users in 96 US MSAs, 19922002
Year 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Mean 11.2 10.6 10.1 9.7 9.2 8.5 7.7 7.0 6.5 6.2 6.2
Median 8.1 7.6 7.1 6.6 6.0 5.5 5.1 4.7 4.5 4.5 4.4
Minimum 2.7 2.5 2.3 2.3 2.4 2.2 2.0 1.9 1.9 2.0 1.7
Maximum 43.5 40.2 37.7 36.1 34.6 33.0 30.6 27.6 24.3 21.8 22.8
SD 8.5 8.2 8.0 7.7 7.4 6.9 6.2 5.5 4.9 4.5 4.4
TABLE 3 Estimated annual percentage change (EAPC) in aggregate-level HIV prevalence rates
among injection drug users in 96 US MSAs, 19922002
Mean Median Maximum 95% CI
6.4% 7.0% to 5.7%
6.5% 7.3% to 5.6%
6.7% 7.6% to 5.8%
pG0.001
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 141
TABLE 4 Estimated HIV prevalence rates (and imputed values) among injection drug users in 96 large metropolitan statistical areas in the USA, 19922002
MSA Name 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Akron, OH
a
3.7 3.3 3.1 3.0 3.0 2.9 2.7 2.5 2.4 2.3 2.4
AlbanySchenectady, NY
a
19.0 17.5 16.2 15.0 13.9 12.5 11.3 10.3 9.4 8.7 8.1
Albuquerque, NM
a
3.2 2.8 2.5 2.4 2.4 2.2 2.0 1.9 1.9 2.0 2.1
AllentownBethlehem, PA 7.0 6.1 5.4 5.1 4.9 4.4 3.9 3.7 3.5 3.6 4.0
Ann Arbor, MI
a
10.2 9.2 8.2 7.3 6.3 5.3 4.8 4.7 4.7 5.0 5.4
Atlanta, GA 21.8 22.2 22.4 22.5 21.8 20.2 18.5 17.2 16.2 15.4 14.9
AustinSan Marcos, TX 7.9 7.2 6.5 5.9 5.3 4.8 4.3 4.1 4.2 4.4 4.8
Bakerseld, CA
a
4.2 3.4 3.0 2.9 3.1 3.0 2.8 2.7 2.8 2.9 3.1
Baltimore, MD 18.2 18.4 18.8 19.6 19.2 16.8 15.0 14.2 13.4 12.6 11.7
BergenPassaic, NJ 26.5 27.0 26.8 26.1 24.1 21.4 19.4 17.6 15.7 14.1 12.7
Birmingham, AL 15.7 12.1 9.7 8.6 8.8 8.5 7.6 6.6 6.0 5.8 6.0
BostonBrocktonNashua, MANH 16.6 14.0 11.9 10.3 8.7 7.3 6.5 5.6 5.0 4.6 4.5
BuffaloNiagara Falls, NY 9.8 8.8 8.0 7.5 7.0 6.3 5.6 5.9 6.4 6.7 6.8
Charleston, SC
a
16.6 17.3 17.5 17.1 16.1 14.4 12.6 11.4 10.6 10.3 10.5
CharlotteRock Hill, NCSC 11.0 10.9 10.7 10.4 9.8 8.8 7.7 6.7 6.0 5.8 5.9
Chicago, IL 14.3 14.6 14.9 15.3 15.6 14.3 12.3 10.6 9.5 8.8 8.4
Cincinnati, OHKYIN
a
3.8 3.9 3.9 3.8 3.8 3.5 3.1 2.8 2.7 2.6 2.7
ClevelandLorainElyria, OH 6.1 6.1 6.1 6.1 5.9 5.1 4.1 3.7 3.7 3.8 4.2
Columbus, OH 3.7 3.7 3.7 3.6 3.5 3.4 3.4 3.5 3.4 3.1 2.8
Dallas, TX 7.7 6.9 6.2 5.7 5.2 4.4 3.5 3.1 2.9 3.0 3.4
DaytonSpringeld, OH
a
4.0 4.0 3.9 3.9 3.9 3.5 3.1 2.8 2.6 2.6 2.6
Denver, CO 6.8 6.3 5.8 5.4 5.0 5.0 5.0 4.5 3.8 3.3 3.1
Detroit, MI 16.3 13.3 10.9 9.2 7.6 6.4 5.8 5.5 5.6 5.8 6.4
El Paso, TX 2.7 2.5 2.4 2.4 2.7 2.7 2.3 1.9 2.1 2.7 3.5
Fort Lauderdale, FL 22.0 20.2 19.7 20.3 23.0 23.9 22.3 18.4 16.3 15.8 16.7
Fort WorthArlington, TX 5.0 5.2 5.3 5.2 5.0 4.4 3.8 3.3 3.1 3.1 3.4
Fresno, CA
a
3.3 3.5 3.8 4.1 4.3 4.0 3.3 2.6 2.2 2.0 2.0
Gary, IN
a
3.9 3.9 3.7 3.5 3.2 3.0 2.8 2.6 2.6 2.6 2.6
Grand RapidsMuskegonHolland, MI 6.0 6.5 6.9 6.9 7.1 6.5 5.3 4.3 3.7 3.6 3.9
TEMPALSKI ET AL.142
GreensboroWinston, NC 7.4 6.3 5.6 5.6 6.0 5.7 5.0 4.4 4.2 4.2 4.3
GreenvilleSpartanburg, SC
a
10.7 11.2 11.3 10.9 10.1 9.2 8.4 7.8 7.3 7.0 6.9
HarrisburgLebanonCarlisle, PA
a
9.1 9.4 9.1 8.0 6.9 6.6 6.2 6.0 5.6 5.3 5.0
Hartford, CT 19.1 19.2 18.7 17.6 16.0 14.2 12.3 10.7 9.7 9.0 8.9
Honolulu, HI 4.0 3.7 3.5 3.4 3.4 3.2 2.8 2.4 2.2 2.4 3.0
Houston, TX 9.8 9.4 9.0 8.6 8.3 7.6 7.0 6.4 6.0 6.0 6.4
Indianapolis, IN
a
4.6 5.4 5.7 5.7 5.2 4.4 3.8 3.5 3.4 3.5 3.9
Jacksonville, FL 19.5 17.8 16.4 15.2 14.3 13.3 12.4 11.3 10.3 9.7 9.6
Jersey City, NJ 31.2 30.7 29.8 28.6 27.7 27.1 26.2 24.7 21.5 17.7 13.2
Kansas City, KS
a
12.3 10.8 9.7 9.0 8.7 8.0 7.2 6.7 6.3 6.4 6.9
Knoxville, TN
a
3.4 2.9 2.7 3.1 3.8 4.1 4.0 3.7 3.2 2.9 2.6
Las Vegas, NVAZ 9.3 8.2 7.2 6.3 5.6 4.9 4.2 3.6 3.4 3.6 4.3
Little Rock, AR
a
5.2 4.8 4.6 4.3 4.1 3.8 3.5 3.2 3.1 3.1 3.3
Los AngelesLong Beach, CA 5.6 5.5 5.3 5.1 4.8 4.7 4.6 4.3 4.0 3.8 3.8
Louisville, KYIN
a
3.7 3.9 4.0 4.1 4.1 3.8 3.6 3.6 3.6 3.6 3.4
Memphis, TNARMS 10.3 10.0 9.7 9.4 8.9 7.9 7.0 6.5 6.4 7.1 8.5
Miami, FL 28.8 29.5 30.0 29.9 30.4 29.5 26.0 21.2 18.9 19.5 22.8
MiddlesexSomersetHunterdon, NJ 19.9 21.4 21.9 21.4 19.5 16.4 13.1 10.6 9.0 8.0 7.6
MilwaukeeWaukesha, WI
a
5.4 5.7 5.8 5.6 5.4 5.1 4.6 4.2 3.8 3.5 3.3
Minneapolis, MI
a
6.2 6.1 6.0 5.9 5.8 5.3 4.6 4.0 3.6 3.4 3.3
MonmouthOcean, NJ 19.8 21.6 22.4 22.0 20.2 17.2 13.9 11.5 9.7 8.1 6.9
New HavenBridgeport, CT 20.8 19.8 18.7 17.5 16.1 14.7 13.2 11.8 10.7 9.9 9.4
Nashville, TN
a
9.6 8.6 7.9 7.7 7.8 7.1 6.5 5.7 5.3 5.4 5.9
NassauSuffolk, NY
a
24.3 23.6 22.7 21.7 19.9 17.7 15.7 14.3 13.3 12.7 12.3
New Orleans, LA 10.2 9.0 8.2 8.0 8.1 7.9 7.4 7.0 6.9 6.9 7.0
New York, NY 43.5 40.2 37.7 36.0 34.2 31.5 29.0 25.9 23.3 21.8 21.2
Newark, NJ 39.9 38.7 37.5 36.1 34.6 33.0 30.6 27.6 24.3 21.1 18.1
Norfolk, VA
a
7.6 7.9 8.1 8.1 8.0 7.4 6.7 6.0 5.5 5.3 5.4
TABLE 4 (continued)
MSA Name 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 143
Oakland, CA 8.3 8.4 8.0 7.3 6.2 5.6 5.1 5.0 4.8 4.6 4.2
Oklahoma City, OK
a
7.9 7.0 6.2 5.6 5.0 4.8 5.5 6.1 5.8 5.0 3.3
Omaha, NEIA
a
5.5 5.2 5.0 4.9 5.0 4.9 4.9 4.6 4.2 3.9 3.7
Orange County, CA 4.2 3.9 3.6 3.6 3.5 3.3 3.2 3.0 2.8 2.6 2.4
Orlando, FL 15.6 15.7 15.4 14.4 12.9 11.9 11.0 10.7 10.2 9.8 9.6
Philadelphia, PANJ 14.9 14.2 13.8 13.6 13.7 13.5 12.6 11.2 10.2 9.3 8.8
PhoenixMesa, AZ 7.5 6.4 5.6 4.9 4.7 5.1 5.1 4.8 4.3 3.9 3.6
Pittsburgh, PA 3.8 3.7 3.6 3.7 3.7 3.2 2.6 2.5 2.8 3.2 3.9
PortlandVancouver, ORWA 6.0 5.1 4.4 4.0 3.9 3.6 3.2 3.0 2.9 3.0 3.1
ProvidenceWarwick, RI
a
8.6 9.0 9.0 8.5 7.2 6.6 6.6 6.8 6.7 6.1 5.3
RaleighDurhamChapel Hill, NC 10.6 10.8 11.0 11.0 10.6 9.2 7.4 7.4 7.9 8.0 7.7
RichmondPetersburg, VA 8.4 7.7 7.0 6.5 5.9 5.7 5.6 5.5 5.2 4.9 4.4
RiversideSan Bernardino, CA 6.0 6.1 6.0 5.6 4.8 4.1 3.8 3.6 3.5 3.4 3.5
Rochester, NY
a
17.0 16.1 15.2 14.3 13.4 11.9 10.3 8.8 7.8 7.4 7.4
Sacramento, CA 4.8 4.4 4.0 3.9 3.7 3.4 2.9 2.6 2.7 3.0 3.7
Salt Lake CityOgden, UT
a
6.8 6.0 5.1 4.3 3.3 2.9 2.9 2.7 2.6 2.8 3.1
San Antonio, TX
a
3.4 3.7 4.0 4.0 4.0 4.0 3.8 3.7 3.7 3.7 3.6
San Diego, CA 6.9 6.3 5.7 5.3 5.0 4.5 4.2 3.8 3.5 3.4 3.4
San Francisco, CA 20.6 18.5 16.4 14.4 12.1 10.6 10.0 9.1 8.9 9.8 11.7
San Jose, CA 3.9 3.7 3.4 3.2 3.3 3.6 3.7 3.5 3.6 3.8 4.4
San Juan, PR
a
41.9 37.9 34.4 31.3 28.6 26.1 23.8 22.0 20.8 20.4 20.6
SarasotaBradenton, FL 9.7 7.2 5.6 4.9 5.0 5.0 4.8 4.2 3.7 3.2 2.8
ScrantonWilkesBarreHazleton, PA 9.3 8.8 8.6 8.5 8.3 7.2 6.0 5.8 6.0 6.1 6.3
SeattleBellevueEverett, WA 5.6 4.5 3.7 3.3 2.9 2.7 2.6 2.5 2.5 2.6 2.9
Springeld, MA 18.8 16.4 14.1 11.8 9.9 9.1 8.5 7.7 7.0 6.3 5.9
St. Louis, MOIL 4.7 4.9 4.8 4.6 4.1 3.8 3.7 3.5 3.4 3.3 3.1
StocktonLodi, CA
a
4.1 3.3 2.7 2.4 2.4 2.3 2.2 2.0 2.0 2.1 2.3
Syracuse, NY
a
15.7 15.4 15.3 15.0 15.0 14.8 13.9 13.0 12.1 11.6 11.2
Tacoma, WA
a
4.3 4.1 3.8 3.5 3.0 2.8 2.8 2.8 2.6 2.4 2.1
TEMPALSKI ET AL.144
TampaSt. PetersburgClearwater, FL 13.7 12.6 11.5 10.5 9.7 8.7 7.3 6.6 6.4 6.2 6.1
Toledo, OH
a
4.1 3.7 3.5 3.3 3.4 3.6 3.6 3.5 3.4 3.4 3.5
Tucson, AZ
a
4.4 4.4 4.4 4.4 4.1 3.8 3.5 3.2 2.8 2.3 1.7
Tulsa, OK
a
7.0 5.9 5.0 4.3 3.9 3.4 2.9 2.8 3.0 3.1 3.2
Ventura, CA
a
2.7 2.5 2.3 2.3 2.4 2.4 2.3 2.3 2.2 2.1 2.0
Washington, DCMDVAWV 13.4 13.8 13.9 13.6 13.0 12.1 11.0 10.2 9.5 9.1 9.0
West Palm BeachBoca Raton, FL 13.8 12.5 11.6 11.0 10.9 10.4 9.9 10.2 10.1 9.6 8.7
Wichita, KS
a
7.5 7.5 7.3 6.7 5.7 4.9 4.6 4.4 4.1 3.8 3.3
WilmingtonNewark, DEMD 15.4 15.8 15.8 15.3 14.5 13.0 10.8 8.7 7.7 7.5 8.2
Youngstown, OH
a
6.4 5.8 5.1 4.4 3.9 3.6 3.5 3.3 3.1 2.9 2.7
a
Imputed values
TABLE 4 (continued)
MSA Name 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 145
from 12.9% to 2.1% annually (mean=6.5; SD=2.1). The remaining eleven
MSAs had either a non-signicant increase (e.g., San Jose, CA; El Paso, TX; and
Knoxville, TN) or a non-signicant decrease (e.g., San Antonio, TX; Toledo, OH;
Louisville, KY; Pittsburgh, PA; Columbus, OH; Bakerseld, CA; Fort Lauderdale,
FL; and Buffalo, NY).
Estimated Annual Percent Change (SEER) in
IDU HIV Prevalence Rates from 1992–2002
EAPC 95% Confidence Intervals
Boston–Brockton–Nashua, MA–NH
Monmouth–Ocean, NJ
Middlesex–Somerset–Hunterdon, NJ
Springfield, MA
Detroit, MI
Dallas, TX
Las Vegas, NV–AZ
Sarasota–Bradenton, FL
Rochester, NY
Hartford, CT
Salt Lake City–Ogden, UT
Wilmington–Newark, DE–MD
Birmingham, AL
Tampa–St. Petersburg–Clearwater, FL
Albany–Schenectady, NY
Wichita, KS
Tucson, AZ
New Haven–Bridgeport, CT
Youngstown, OH
Tulsa, OK
Bergen–Passaic, NJ
Denver, CO
San Francisco, CA
Charlotte–Rock Hill, NC–SC
Ann Arbor, MI
Nassau–Suffolk, NY
San Juan, PR
Newark, NJ
New York, NY
Oakland, CA
Jacksonville, FL
San Diego, CA
Minneapolis, MI
Riverside–San Bernardino, CA
Jersey City, NJ
Grand Rapids–Muskegon–Holland, MI
Tacoma, WA
Harrisburg–Lebanon–Carlisle, PA
Fresno, CA
Seattle–Bellevue–Everett, WA
Portland–Vancouver, OR–WA
Kansas City
Chicago, IL
Allentown–Bethlehem, PA
Charleston, SC
Fort Worth–Arlington, TX
Phoenix–Mesa, AZ
Austin–San Marcos, TX
Cleveland–Lorain–Elyria, OH
Orlando, FL
Greenville–Spartanburg, SC
Milwaukee–Waukesha, WI
Nashville, TN
Richmond–Petersburg, VA
Stockton–Lodi, CA
Houston, TX
Little Rock, AR
Dayton–Springfield, OH
Philadelphia, PA–NJ
Oklahoma City, OK
Greensboro–Winston, NC
Washington, DC–MD–VA–WV
Gary, IN
Honolulu, HI
Providence–Warwick, RI
Indianapolis, IN
St. Louis, MO–IL
Miami, FL
Norfolk, VA
Baltimore, MD
Orange County, CA
Cincinnati, OH–KY–IN
Sacramento, CA
Scranton––Wilkes–Barre––Hazleton, PA
Atlanta, GA
Los Angeles–Long Beach, CA
Raleigh–Durham–Chapel Hill, NC
Akron, OH
Albuquerque, NM
Memphis, TN–AR–MS
Buffalo–Niagara Falls, NY
Syracuse, NY
West Palm Beach–Boca Raton, FL
Omaha, NE–IA
New Orleans, LA
Fort Lauderdale, FL
Bakersfield, CA
Columbus, OH
Ventura, CA
Pittsburgh, PA
Louisville, KY–IN
Toledo, OH
San Antonio, TX
Knoxville, TN
El Paso, TX
San Jose, CA
–15 –10 –5 0 5
p < .01 p < .05 Not Significant
FIGURE 1. Estimated annual percent change (SEER) in IDU HIV prevalence rates from 19922002.
TEMPALSKI ET AL.146
Validating HIV prevalence rates The ICC between Holmbergs 1992
21
HIV
prevalence rate estimates and our 1992 best HIV prevalence rate estimates is
0.941, which indicates very good consistency between the two estimates. The high
ICC shows that the overwhelming majority of the variance is within data sources
rather than between. We also ran a paired t-test, which indicates that our best 1992
estimates are about 1.75 percentage points higher than Holmbergs
21
on average.
The ICC between our 1998 best HIV prevalence rates and Friedman et al. 1998
22
HIV prevalence rates is 0.955. A paired t-test indicates that our 1998 best HIV
estimates are about 0.36 percentage points less on average as compared with
Friedman et al. 1998
22
HIV prevalence rates. These results indicate that our 1992
and 1998 best estimates correlated extremely well with both Holmbergs 1992
21
HIV prevalence rates and Friedman et al. 1998
22
HIV prevalence rates.
The correlations between MSA-specic HIV prevalence rates and hard-core
drug arrests were signicant (at least pG 0.05) for each year, 19922002 (Table 5).
The estimated HIV prevalenc e rates for 2000 and the 2000 income inequality were
signicantly correlated (r=0.467; pG 0.0001). Police expenditures and the estimated
HIV prevalence rates were highly correlated for 1992 (r=0.504); 1997 (r=0.476);
and 2002 (r=0.473) (all pG 0.0001). Correlations between the HIV prevalence rates
and corrections expenditure per capita for 1992 (r=0.285); 1997 (r=0.232); and
2002 (r=0.297) were somewhat lower than those for the police expenditure, but all
were signicant at pG 0.05. The estimated HIV prevalence rates for 2000 and the
2000 poverty were also low (r=0.268) but signicant at pG 0.05. Overall,
correlations between anti-OTC laws and HIV prevalence rates for three years
(19921994) ranged from 0.280 to 0.300 (all pG 0.05).
TABLE 5 Correlation of theoretical constructs and estimated HIV prevalence rates among
injection drug users in 96 US MSAs, 19922002
Year
Hard-core
drug
arrests per
1,000 IDUs
Police
protection
expenditures
per capita
Corrections
expenditures
per capita
Income
inequality Poverty
1993 Anti-OTC
Laws
1992 0.219* 0.504*** 0.285* 0.280*
1993 0.297* 0.296*
1994 0.315* 0.300*
1995 0.393***
1996 0.310*
1997 0.311* 0.476*** 0.232*
1998 0.339**
1999 0.384**
2000 0.423*** 0.467*** 0.268*
2001 0.443***
2002 0.326* 0.473*** 0.297*
Correlations expressed as Pearson rs
*pG0.05
**pG0.001
***pG0.0001
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 147
DISCUSSION
Despite best efforts in computing our estimates of HIV prevalence rates, limitations
exist concerning accuracy. The research-based estimates compiled for 33 MSAs varied
in the duration of injection periods used to classify an individual as an IDU (e.g.,
some people were classied as IDUs if they reported injecting in their lifetimes, while
others were classied as an IDU only if they reported injecting in the past 30 days).
Related biases would affect the accuracy of the regression adjustments made to the
CDC CTS data, as would variations in Counseling and Testing site locations; access to
testing; and other social factors associated with why IDUs do and do not get tested.
As previously noted by Friedman et al.,
22
the CDC CTS data unde restimate HIV
prevalence rates. This bias increases over time, mainly because people who have
tested positive once (or twice, as a conrmation) tend to have no reason to be tested
again. These errors are likely to be greater in MSAs with higher HIV prevalence
rates and those with long-lasting epidemics of stable (or declining) prevalence rates.
Further, the CDC CTS data include duplicate counts, reecting number of HIV-
positive tests, not number of HIV-positive individuals. Thus, it is not known how
many IDUs may get tested more than once in a given year. These data are further
limited by social desirability biases on the part of people being tested when they
report on whether they have ever injected drugs and/or when they last injected.
Consistent with CDCs behavioral risk classication scheme, we thus consider IDUs
to be those that injected drugs at anytime since the beginning of the HIV/AIDS
epidemic (e.g., 1977) and thus capture experimenters and infrequent users.
There are a number of limitations to the PBM that we need to consider. First, this
method relies on PLWA data provided by CDC, which represent estimates of reported
cases, adjusted for reporting delays and redistribution of cases for which no risk is
reported. It is likely that in redistributing such cases, some are misclassied as high-risk
heterosexual or MSM that could actually be IDUs. This would result in undercounts in
the numerators of the HIV prevalence rates, causing underestimates. Secondly, the
proportional distribution of PLWAs was posited to be similar to that of all PLWHAs
with respect to MSA, year and risk factor. The validity of this assumption could not be
determined. Thus, we make an assumption of little proportional variation, and assume
that Parameter 2 in the PBM is a constant for each year, and that all of a given year's
PBM MSA-specic HIV prevalence rates can be adjusted by this constant. The
adjustments could vary by MSA.
Despite limitations, developing HIV prevalence rate estimates among IDUs over
time is useful to: (1) assist in implementing effective social and cultural interventions
and public policy aimed at strengthening drug injectors health; (2) guide discussions
regarding urban policing policy and other social policies that may shape HIV among
IDUs; (3) aid research on how place-based processes (e.g. socio-political or economic
factors) are associated with HIV prevalence rates among IDUs, and (4) serve as
supporting data in seeking funding for harm reduction programs and prevention
and care for IDU-related HIV.
In the past, we have been hampered in our ability to understand the spread and
prevalence of HIV among IDUs by the lack of temporal and spatial data on HIV
prevalence rates. Well-structured HIV public policy along with effective allocation of
prevention resources require valid and current data on trends in HIV prevalence
rates (and incidence rates) in the US and in major metropolitan areas. These current
HIV prevalence rates may help shed light on how future IDU-related HIV epidemics
geographically diffuse, as well as provide a foundation for the design and
TEMPALSKI ET AL.148
implementation of structural interventions for preventing the spread of HIV
among IDUs.
SUMMATION
The high degree of correlation between the unadjusted HIV prevalence rates according
to the two estimation methods suggests a strong association between the two inde-
pendent data sources, which appear to be measuring the same underlying factor. The
results of our validation tests indicate a relatively high correlation between Holmberg
21
and Friedman et al.
22
and our best HIV prevalence rate estimates. In addition, we
examined cross-sectional correlations in other factors that could be associated with
HIV prevalence rate trends, e.g., hard-core drug arrests, income inequality, and police
expenditures, and found signicant correlations. These factors support ndings by
Friedman et al.
41
that suggest that legal repressiveness (i.e., drug arrests; police and
corrections expenditures) may have a high cost with regard to shaping IDU-related
HIV transmission rates and perhaps other disease among injectors and their partners.
Our ndings suggest an overall decreasing trend of HIV prevalence rates among
IDUs in the 96 MSAs during 19922002. During this period, HIV prevalence rate
estimates declined in 85 (88.5%) of the 96 MSAs. Possible reasons for this trend at
the aggregate and individual MSA level include program efforts to increase users
access to clean syringes both through syringe exchange programs and pharma-
cies;
63,64
efforts to promote safer injection practices; effects of antiretroviral therapies
on infectivity of IDUs;
65,66
deaths from HIV not being matched by new infections;
67,68
and possible changes in risk networks and other social mixing patterns which vary
from place to place.
67,69,70
Differences in HIV prevalence rates may also reect
differences in availability, accessibility and effectiveness of HIV prevention and
treatment programs across metropolitan areas.
7173
Recent trends regarding the
effects of HAART in keeping IDUs alive exert an upward pressure on prevalence
rates. Combinations of these other factors seem to outweigh the effects of HAART
producing a downward trend in HIV prevalence rates among IDUs over time.
Thus, declining HIV prevalence rates may reect high continuing mortality
among infected IDUs, as well as primary HIV prevention for non-infected IDUs and
self-protection efforts by them. These results warrant further research into the
population dynamics of disease progression, access to health services, and the effects
of HIV prevention interventions for IDUs.
ACKNOWLEDGMENTS
This project was supported by the National Institute of Drug Abuse (R01 DA13336;
Community Vulnerability and Response to IDU-Related HIV).
We would like to thank Dr. Peter L. Flom, Daniel R. Thompson and Enrique
Pouget for their statistical advice and Dr. David Holtgrave for his insight regarding the
PLWHA data. We further thank the Department of Health and Human Services,
Centers for Disease Control and Prevention, National Center for HIV, STD, and TB
Prevention, specically Michael Fanning, David Hurst and Renee R. Stein for providing
the HIV Counseling & Testing data and Andrew Mitsch from the CDCs HIV Incidence
and Case Surveillance Branch for providing PLWA data for which these analyses are
based. We also thank Ms. Makini Booth for assembling the research on HIV prevalence
studies and related literature reviews for which the research estimates are based.
HIV PREVALENCE RATES AMONG INJECTION DRUG USERS, 19922002 149
We would further like to thank the following State and Local Health Departments
and researchers for their assistance:
Alabama: Anthony Merriweather
Florida: Melinda Waters, Marlene LaLota, Lorene Maddox
Hawaii: Don C. Des Jarlais, Roy Ohye, Peter, M. Whiticar
Kansas: Jennifer VandeVelde, Karl V. Milhon
Massachusetts: Drew Hanchett, Debora h Isenberg, Teresa Anderson
New Mexico: Andrew Gans, Kathleen Rooney, Lily N. Foster, Bruce G. Trigg
New York: Mara Sa n Antonio-Gaddy, Punkin Stevens, Daniel OConnell,
Thomas Chesnut
Pennsylvania: Kenneth McGarvey, Benjamin Muthambi, Brenda Doucette
Puerto Rico: José Toro-Alfonso, Rafaela R. Robles, Sherry Deren
Virginia: Chris Delcher, Jeff Stover, Jennifer Bissette, Theresa Henry
Washington: Frank Chaffee, Hanne Thiede, Leslie Pringle, Keith Okita, Michael
Hanrahan, Mark Doescher
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TEMPALSKI ET AL.154
    • "We correspondingly categorized other agents based on their past-year usage or nonusage of other noninjection drugs (Supplemental material, http://links.lww.com/QAD/A966). At model initialization , 1.9% of agents are classified as PWID, approximately 43% of whom have HIV infection (approximating the highly endemic setting of NYC in 1992) [30,31]. Also, agents are stratified by sex and also by sexual behavior [e.g., heterosexuals, MSM, and women who have sex with women (WSW)]. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: Estimates for the contribution of transmission arising from acute HIV infections (AHI) to overall HIV incidence vary significantly. Furthermore, little is known about AHI-attributable transmission among people who inject drugs (PWID), including the extent to which interventions targeting chronic infections (e.g., highly active antiretroviral therapy [HAART] as prevention) are limited by AHI transmission. Thus, we estimated the proportion of transmission events attributable to AHI within the mature HIV epidemic among PWID in New York City (NYC). Design: Modeling study. Methods: We constructed an interactive sexual and injecting transmission network using an agent-based model simulating the HIV epidemic in NYC between 1996-2012. Using stochastic microsimulations, we catalogued transmission from PWID based on the disease stage of index agents to determine the proportion of infections transmitted during AHI (in primary analyses, assumed to last three months). Results: Our calibrated model approximated the epidemiological features of the mature HIV epidemic in NYC between 1996-2012. Annual HIV incidence among PWID dropped from approximately 1.8% in 1996 to 0.7% in 2012. Over the sixteen-year period, AHI accounted for 4.9% (10/90 percentiles: 0.1%-12.3%) of incident HIV cases among PWID. The annualized contribution of AHI increased over this period from 3.6% in 1996 to 5.9% in 2012. Conclusions: Our results suggest that, in mature epidemics such as NYC, between 3-6% of transmission events are attributable to acute HIV infection among people who inject drugs. Current HIV treatment as prevention strategies are unlikely to be substantially affected by AHI-attributable transmission among PWID populations in mature epidemic settings.
    Article · Aug 2016
    • "HIV epidemics are heterogeneous across populations and places [1,2]. In the United States (US) in 2011, estimated rates of newly diagnosed HIV cases among people who inject drugs (PWID) were eleven times as high among black PWID (230/100,000), and six times as high among Latino PWID (121/100,000), as among white PWID (21/100,000) [1]. "
    [Show abstract] [Hide abstract] ABSTRACT: Introduction: We analyzed relationships between place characteristics and being HIV-negative among black, Latino, and white people who inject drugs (PWID) in the US. Methods: Data on PWID (N = 9077) were from the Centers for Disease Control and Prevention's 2009 National HIV Behavioral Surveillance. Administrative data were analyzed to describe the 968 ZIP codes, 51 counties, and 19 metropolitan statistical areas (MSAs) where they lived. Multilevel multivariable models examined relationships between place characteristics and HIV status. Exploratory population attributable risk percents (e-PAR%s) were estimated. Results: Black and Latino PWID were more likely to be HIV-negative if they lived in less economically disadvantaged counties, or in MSAs with less criminal-justice activity (i.e., lower drug-related arrest rates, lower policing/corrections expenditures). Latino PWID were more likely to be HIV-negative in MSAs with more Latino isolation, less black isolation, and less violent crime. E-PAR%s attributed 8-19% of HIV cases among black PWID and 1-15% of cases among Latino PWID to place characteristics. Discussion: Evaluations of structural interventions to improve economic conditions and reduce drug-related criminal justice activity may show evidence that they protect black and Latino PWID from HIV infection.
    Full-text · Article · Mar 2016
    • "Our findings highlight important factors that may be useful in helping substance use disorder treatment programs increase HIV testing rates, promote awareness of infection status, and prevent HIV infection and transmission . These findings are partly supported by prior studies [2, 14, 24, 26] that identified conditions associated with HIV testing in health care settings. Because client demographics, sociocultural characteristics, and provider conditions play a role in the extent to which clients receive HIV testing and obtain test results, it is critical to build on this preliminary analysis of key factors to develop longitudinal studies to assess risk and likelihood of testing. "
    [Show abstract] [Hide abstract] ABSTRACT: HIV testing and receipt of HIV test results among individuals with substance use disorders is less than optimal. We examined rates and correlates of HIV testing and receipt of test results in one of the largest public addiction health services systems in the United States. The study included 139,516 adult clients in treatment between 2006 and 2011. We used logistic regression models to examine associations between predisposing, enabling, and need factors and two dependent variables, HIV testing rates and receipt of test results. Associations were considered statistically significance at p < .01. We found that 64 % of clients reported being tested for HIV, of whom 85 % reported receiving their test results. Likelihood of being tested was positively associated with being female, a minority, homeless, employed, having prior treatment episodes, comorbidities, injection drug use, or a history of mental illness. It was negatively associated with alcohol or marijuana as primary drug. Receipt of test results was more likely among clients on medication (methadone or buprenorphine) or whose method of drug use was smoking, inhalation, or injecting; it was less likely among older clients and those with more outpatient psychiatric visits. Findings from this study may inform strategies and targeting of population groups to improve HIV testing practices and ultimately increase awareness of infection status among clients of addiction health services.
    Full-text · Article · Aug 2015
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