1 Urban Health Study, Department of Family and Community Medicine, University of California, San Francisco, San Francisco, CA.
2 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA.
3 Blood Centers of the Pacific, San Francisco, CA.
Received for publication March 18, 2002; accepted for publication November 19, 2002.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
adolescence; HIV; homosexuality, male; incidence; risk factors; sex behavior; substance abuse, intravenous
Abbreviations: Abbreviations: CI, confidence interval; EIA, enzyme immunoassay; HIV, human immunodeficiency virus; IDUs, injection drug users; OR, odds ratio.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Recent developments in HIV antibody testing have enabled researchers to estimate HIV incidence from single specimens (9). This is achieved by using a sensitive/less sensitive enzyme immunoassay (EIA) testing algorithm to distinguish "recent" HIV infection from "long-standing" infection (9). Because the duration during which recent seroconverters remain positive on the sensitive assay but negative on the less sensitive assay is known with some precision, it is possible to estimate incidence from single specimens. This obviates the need to carry out cohort studies to assess trends in HIV incidence, thus limiting the costs and methodological problems, such as retention bias, associated with following a difficult-to-reach population (10).
In order to examine trends in HIV incidence from 1987 to 1998, we used the sensitive/less sensitive EIA testing algorithm to test stored serum specimens from the Urban Health Study, the longest-running epidemiologic study of street-recruited IDUs in North America (1113). To obtain better guidance for public health efforts, we also conducted a risk factor analysis to delineate which subpopulations of IDUs were at highest risk for seroconversion.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Study procedures
Study procedures have been outlined in detail previously (1113). Once respondents were determined to be eligible and gave informed consent, they participated in an interview and a counseling session with a trained interviewer/counselor in a private space. Participants were interviewed using a structured questionnaire, with questions being asked about demographic factors (race/ethnicity, age, education, homelessness, and neighborhood), drug-use practices (injection frequency, number of years of injection, injection drug of choice, crack cocaine use, alcohol use, sharing of syringes, sharing of cookers, and sources of syringes), sexual practices (number of sex partners, number of male sex partners, being a man who had sex with men, having sex for money, condom use, having a steady sex partner, and self-reported sexually transmitted diseases, including gonorrhea, chlamydia, syphilis, and genital herpes), and use of services (syringe exchange, substance abuse treatment, contact with outreach workers, and number of previous HIV test results received). The questions captured information for a recent window of time, such as the past 6 months (sexual behaviors) or the past month (injection behaviors), or for the present (homelessness, substance abuse treatment, and steady sex partner).
After the interview, respondents were counseled regarding their risk behaviors as reported during the interview, given HIV pretest counseling, and referred to medical and social services as needed. Blood was then drawn by trained phlebotomists for HIV antibody testing. Study participants were paid US$15 for their time and were asked to return for their HIV test results and counseling 4 weeks (19871994) or 2 weeks (19951998) after the interview. All study methods were approved by the Committee on Human Research at the University of California, San Francisco.
Laboratory methods
For determination of HIV status, blood specimens were drawn immediately following the interview/counseling session. Blood specimens were initially analyzed for HIV antibodies using EIA. Repeatedly EIA-positive specimens were confirmed using Western blot assay. Criteria for a seropositive Western blot result were the presence of reactive bands at two of the following locations: p24, gp41, and gp120/160, as described by the Centers for Disease Control and Prevention (14). In 2000, to ascertain the prevalence of recent HIV infection, we tested all previously known HIV antibody-positive specimens for which we had at least 2.25 ml of stored serum according to the Centers for Disease Control and Prevention 3A11-LS EIA protocol (Investigational New Drug program for serologic testing algorithm for recent HIV seroconversion) using the Abbott 3A11 HIV type 1 EIA kit (Abbott Laboratories, Abbott Park, Illinois). We required at least 2.25 ml to maintain serum resources for future research; the laboratory only required 0.25 ml. All tests were performed at Blood Centers of the Pacific (San Francisco, California) using methods described by Janssen et al. (9).
Briefly, using the sample diluent, three separate 1:20,000 dilutions of the specimen were placed in the 60-well Abbott assay tray. The tray also contained, in three separate replicates: 1) a 1:20 dilution of the Abbott negative control; 2) a 1:20,000 dilution of a calibrator (Boston Biomedica, Inc., West Bridgewater, Massachusetts) used in the calculation of standardized absorbance; and 3) a 1:20,000 dilution of an HIV low-positive control material (Boston Biomedica, Inc.) used to assess run validity. A calibrator was developed to overcome the variability found with the Abbott kit positive control; the calibrator was more stable when diluted 1:20,000, whereas the kit positive control fluctuated from lot to lot and from assay to assay. Next, antigen-coated beads were added to each well, and the plate was incubated at 40°C for 30 minutes. Following five washes in the Abbott Programmed Processing Center, we added 200 µl conjugate and incubated the reaction beads for 30 minutes at 40°C. After five washes in the Programmed Processing Center, we added 300 µl of working orthophenylenediamine solution, incubated the reaction beads at room temperature for 30 minutes, and added 300 µl of 1 N sulfuric acid to stop the reaction. The plates were read in the Programmed Processing Center at 450 nm. Median absorbance values were then applied for calculation of the standardized absorbance ([median specimen absorbance median negative control absorbance]/median calibrator absorbance). Specimens with a calculated standardized absorbance of 0.750 or less were considered negative. This cutoff is generally used for incidence projections because it has a high sensitivity for detecting recent infections (98 percent) and gives a reasonably long window period (4 months), while still yielding an acceptably low rate of misclassification of long-standing infections (0.4 percent) and acquired immunodeficiency syndrome patients (2.4 percent). Specimens that were positive in the sensitive EIA and negative in the less sensitive EIA were considered to indicate recent HIV type 1 infection (defined as seroconversion
129 days (95 percent confidence interval (CI): 109, 149) prior to the sample collection date (9)).
Statistical analysis
To estimate HIV incidence, we divided the number of specimens showing recent infection by the number that were susceptible to recent infectionthose recently infected plus those that were seronegative. Then we divided this number by the estimated time to being 3A11 reactive/3A11-LS nonreactive, which is 129 days (95 percent CI: 109, 149), and annualized the rates by multiplying them by 365 days. We calculated Bonferroni confidence intervals reflecting both the binomial uncertainty in our data and the uncertainty about the window period (15). Fishers exact test was used to determine statistical significance in bivariate analysis, and exact logistic regression was conducted for multivariate analyses using Statistical Analysis System software, release 8.02 (SAS Institute, Inc., Cary, North Carolina). Because events were rare, odds ratios approximated the relative hazards; thus, we present these and adjusted odds ratios with 95 percent confidence intervals. All variables that were associated with recent infection in bivariate analysis (p < 0.10) were considered for inclusion in multivariate analysis. Pairwise interactions among main effects and with time were considered in each model. Only statistically significant (p < 0.05) main effects and interactions were retained in final multivariate models.
Although some individuals contributed observations from multiple waves of data collection, all were treated as independent in the logistic regression models, a method that has been called "pooled logistic regression" (16). This reflects the special structure of the multiple observations in this data setnamely, that only the last observation can be a recent infection. The basis for such use of multiple observations with sensitive/less sensitive EIA data has been discussed elsewhere (15). Methods for analysis of repeated binary data, such as generalized estimating equations, were not necessary for the present data structure, since there was a maximum of one event per subject. The small number of events ensured that one of the assumptions for pooled logistic regression was valida low probability of an event in any one time periodbut it also limited our ability to model the effect of time in detail. As is noted below, time did not appear to be an important predictor in models that controlled for previous testing. Therefore we did not include it, and our models are equivalent to parametric proportional hazards models that assume a constant baseline hazard (i.e., exponential survival regression).
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Participants with observations that were eligible for the study were less likely than those with ineligible observations to be female (30 percent vs. 39 percent; p < 0.001), to have injected "speedballs" (a mixture of heroin and cocaine) in the past year (57 percent vs. 62 percent; p < 0.001), to have injected heroin in the past year (78 percent vs. 85 percent; p < 0.001), and to have been paid cash for sex in the past year (27 percent vs. 31 percent; p = 0.016), and they had a slightly shorter median duration of drug injecting (21 years vs. 23 years; p < 0.001). Persons with eligible observations were more likely than persons with ineligible observations to have injected amphetamine in the past year (32 percent vs. 22 percent; p < 0.001) and to have shared syringes in the past 30 days (32 percent vs. 27 percent; p < 0.001). We used logistic regression models with effects for time and HIV status to examine whether there were any differences in the proportion of HIV-positive and HIV-negative observations that met the eligibility criterion. Although the overall eligibility proportion varied significantly over time, there was no systematic trend, and there was no difference in the time effect by HIV status.
Most observations in the sample were from men, persons aged 3049 years, and African Americans (table ). Thirty-eight percent of participants considered themselves homeless, and 14 percent were in drug treatment at the time of the interview.
|
|
We examined behavioral risk factors for recent HIV infection using both bivariate (table ) and multivariate (table ) statistics. In bivariate analysis, the only injection-related variable that was statistically significantly associated with recent infection was having injected drugs for 20 years or less (OR = 4.2, 95 percent CI: 1.6, 11.2). Several sexual risk behaviors were statistically significantly associated with recent infection, including male-male sexual contact (OR = 2.8, 95 percent CI: 1.2, 6.4) and unprotected receptive anal intercourse (OR = 3.2, 95 percent CI: 1.12, 9.3). IDUs who reported having a steady sex partner who did not inject drugs were less likely to have recently seroconverted (OR = 0.14, 95 percent CI: 0.02, 1.00) than IDUs with no steady sex partner or those with a steady sex partner who injected drugs. IDUs who reported having ever received an HIV test result were less likely to have recently seroconverted than IDUs who had never received an HIV test result (OR = 0.37, 95 percent CI: 0.18, 0.76).
|
|
To assess potential biases introduced because some participants chose to return for multiple observations (retention bias), we first compared HIV incidence rates for once-only participants with the HIV incidence rates using only baseline observations of repeat participants. Incidence rates were the same for once-only participants (incidence = 1.5 percent per person-year; 95 percent CI: 0.7, 2.9) and baseline observations of repeat participants (incidence = 1.5 percent per person-year; 95 percent CI: 0.5, 3.6). Then we assessed three different variables by entering them individually in multivariate models together with the aforementioned statistically significant variables in table . First, we assessed whether the number of previous study visits had an independent effect on being a recent seroconverter (adjusted OR = 0.89, 95 percent CI: 0.73, 1.09). Second, we assessed whether IDUs with repeated visits differed from those who participated only once (adjusted OR = 0.97, 95 percent CI: 0.45, 2.0). Lastly, we assessed whether first observations differed from subsequent observations (adjusted OR = 0.89, 95 percent CI: 0.38, 2.1). None of these three variables had statistically significant independent relations with recent seroconversion. The inclusion of these variables also did not significantly alter the relations between the other variables in the models and recent seroconversion.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Our data identified three subgroups of street-recruited IDUs who are at elevated risk for HIV infection in San Francisco: young IDUs, IDUs who have never received HIV test results, and male IDUs who have sex with men. Several US studies have shown that younger IDUs are more likely than older IDUs to report HIV risk behaviors (2024) and to be at higher risk for HIV infection (25, 26). This study confirms the urgent need for prevention programs that effectively reach younger IDUs. At least four types of programs have been shown to be effective at helping IDUs reduce their risk of HIV transmission: street outreach, client-centered counseling and testing, access to sterile syringes, and substance abuse treatment (13, 2730). Culturally appropriate prevention programs of each type need to be designed specifically to reach young IDUs. Intervention programs established to serve youth may be more successful if youth are involved in program design, decision-making, and implementation (31).
The results of this study suggest that HIV testing and counseling was associated with a lower HIV incidence rate among IDUs in San Francisco. Our multivariate analysis also suggested that the decline in HIV incidence in 1989 was accounted for in part by an increase in the proportion of participants who had received a previous HIV test result. Other studies have found that HIV testing and counseling reduces risk behavior and HIV infection among drug users (13, 30, 32, 33). HIV testing and counseling is easily accessible in San Francisco through numerous anonymous and confidential testing sites, including research studies like our own. It is important to facilitate attendance at HIV test-result sessions, since only 63 percent of persons tested for HIV in publicly funded clinics in the United States return for their results (34). In the Urban Health Study, we use a variety of methods to ensure a high return rate of IDUs to HIV test-result sessions, including a monetary incentive ($15), easily accessible community field sites, a nonjudgmental staff, excellent phlebotomists, and integrated care (e.g., immunizations, abscess care). In 2000, 88 percent of our study participants returned for their HIV test-results session. To be sure that participants are not induced to receive their results before they are ready, monetary incentives are given at the beginning of the post-test counseling session and are not conditional upon whether the person receives his or her HIV test result.
Male IDUs who had sex with men were at higher risk for recent infection than other IDUs. This supports our previously published report on risk factors for HIV seroconversion among IDUs, which used a nested case-control analysis of repeat participants (11). Men who have sex with men have been found to have a higher likelihood of seroconversion in other studies of IDUs as well (25, 26). Interventions targeting this high-risk population, which may not be adequately served by either prevention efforts targeted at gay men or efforts targeted at IDUs, are needed. Formative, qualitative research with gay male IDUs may provide suggestions as to how to appropriately apply lessons learned from research on sexual risk reduction among gay men to men who have sex with men and inject drugs (35, 36).
Use of the sensitive/less sensitive EIA method to ascertain recent HIV seroconversion is a novel development (37). The overall incidence estimate using sensitive/less sensitive EIA in this study was the same as our incidence estimate using a repeater cohort (1.2 percent) (11). The sensitive/less sensitive EIA method has several advantages over traditional cohort methods for ascertaining HIV seroconversion. First, conducting cross-sectional studies is logistically easier and considerably less expensive than conducting cohort studies. Second, the barriers to participation are greater for traditional cohort studies than for cross-sectional studies, since cohort studies require the participant to commit in advance to repeated visits over the long term, whereas cross-sectional studies simply require participation at a single point in time. The less stringent requirements for participation in cross-sectional studies result in less selection bias. Third, cross-sectional studies are not susceptible to retention biases associated with differential follow-up of cohort members. Fourth, cross-sectional studies are also not susceptible to intervention effects that can be observed in studies that require multiple observations on the same individual, in which risk reduction interventions must be provided. Since a subset of our study population contributed multiple observations, we examined whether retention bias influenced our outcome variable. We did not find any evidence of such bias, although confidence intervals were too wide to conclusively rule out any substantial bias.
Using the sensitive/less sensitive EIA method also has disadvantages. First, the sensitive/less sensitive EIA method may misclassify as recent seroconverters a small percentage of people who have late-stage acquired immunodeficiency syndrome with severe immunosuppression or who have been treated with antiretroviral regimens with resulting undetectable viremia and reduced HIV antigen stimulation of the anti-HIV immune response (9). In our study, however, none of the recent seroconverters reported knowing that they were HIV antibody-positive, which suggests that few, if any, of our recent seroconverters had late-stage acquired immunodeficiency syndrome or had received antiretroviral therapy. Second, the sensitive/less sensitive approach requires fairly large denominators or high incidence rates in order to be accurate, since there is a short period of observation (129 days) and error around the estimates. Third, the sensitive/less sensitive EIA window period is not well defined for non-B-clade HIV infections; this currently limits the methods to geographic areas of the world where B-clade infections exist.
This study has limitations that must be considered when interpreting these results. First, any observational study may suffer from selection biases. Our study oversamples IDUs in poor, inner-city neighborhoods, those who spend time on the streets, and those willing to identify themselves to others in their environment as IDUs. Because of these potential selection biases, it is not possible to generalize these findings to all IDUs in San Francisco or to IDUs in suburban or rural areas. Second, information on the exposure variables is self-reported, which means the data are vulnerable to biases resulting from social desirability, poor recall, and intoxication (38). However, multicenter research on IDUs has shown that self-reports are very reliable among drug users not recruited in clinical settings (3941). Third, the questions on injection and sexual risk behaviors had different recall periods (1 month vs. 6 months), which could have led to imprecise risk estimates. We used different recall periods because injection risk behaviors are much more frequent (a median of more than once per day) than sexual risk behaviors (a median of less than once per month) in our population. Fourth, because of the low number of recent infections in our study, we did not have sufficient statistical power to determine whether specific heterosexual risk behaviors were associated with acquisition of HIV infection. It would have been optimal to stratify the analysis by sex, as was done in our case-control analysis (11). Note that differences in results between these two studies are due to this stratification of the sexes, different sample sizes resulting from the different sampling designs (case-control and cross-sectional), and the slightly different time periods.
Despite its limitations, this study found that the sensitive/less sensitive EIA HIV testing method is an effective and cost-efficient tool for assessing trends in recent HIV infection. Our data suggest that HIV incidence among street-recruited IDUs in San Francisco has remained stable and moderate since the late 1980s. This may be a result of San Franciscos harm reduction policy regarding IDUs, including a large citywide effort to conduct HIV testing in this population. Future prevention efforts should focus on young IDUs and gay male IDUs, two subpopulations at highest risk who have culturally specific prevention needs.
![]() |
ACKNOWLEDGMENTS |
---|
The authors thank the following people for their important contributions to this study: Timothy Kellogg, Dr. Willi McFarland, Dr. Kim Page-Shafer, and Dr. John K. Watters.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|