Research Center, University of Miami School of Medicine .... High school/general equivalency. 61.9. 77.6 ..... Conference; July 7-10, 1994; Brighton,. England. 6.
Risk Factors for HIV-1 Seroconversion among Injection Drug Users: A Case-Control Study
Dale D. Chitwood, PhD, David K Griffin, EdD, Mary Comerford, MSPH, J. Bryan Page, PhD, Edward J. Trapido, ScD, Shenghan Lai, PhD, and Clyde B. McCoy, PhD
Introduction The close of the first decade of the acquired immunodeficiency syndrome (AIDS) epidemic produced an increase in the proportion of persons diagnosed with AIDS who were injection drug users.'-7 The first generation of studies of injection drug users involved cross-sectional investigations of human immunodeficiency virus type 1 (HIV-1) prevalence and associated demographic, injection, and sex risk factors.4'8'9 Inferences across these studies are problematic because the date of onset of HIV-1 seropositivity is unknown.20 Seroincident studies establish a temporal sequence between behavioral risk factors and the onset of HIV-1 infection that is not possible in studies of seroprevalence. Seroincident studies are needed to estimate the strength of associations between risk factors and seroconversion. The purpose of this paper is to examine injection and sexual risk factors associated with HIV incidence among parenteral drug users.
Methods A nested case-control study among injection drug users in Miami, Fla, identified incident case and control subjects from two longitudinal cohorts that have been described elsewhere.21'22 The casecontrol study was conducted to obtain identical incident risk factor data from
participants within the two cohorts. One cohort was part of a natural history study of HIV among injection drug users who were recruited from drug treatment facilities (n = 601); the second cohort included injection drug users recruited from the "street" (n = 1022) into a risk reduction
intervention project. HIV status was moni-
tored at 6-month intervals in both cohorts. Seventy-one percent of the individuals in the two cohorts were male, 52.1% were African American, and 26.5% were HIV positive; their mean age was 34.1 years. The prevalence of HIV-1 at baseline for the two cohorts was similar. Among the cohort selected from treatment facilities, the ethnic-specific prevalences were 36.0% for African Americans, 27.2% for Hispanics, and 7.6% for White nonHispanics. The cohort recruited from the street had prevalence rates of 37.2% among African Americans, 30.6% among Hispanics, and 15.0% among White nonHispanics. The combined incidence for the two cohorts at a 24-month follow-up was 2.1 per 100 person-years. Cohort members who previously had been negative and tested positive between December 1988 and August 1991 were invited to enroll as case subjects. Seroconversion date was defined as the date of the first positive HIV test. Four unmatched control subjects for each enrolled case The authors are with the University of Miami School of Medicine, Miami, Fla. Dale D. Chitwood and Clyde B. McCoy are with the Departments of Sociology, EpideiYiology and Public Health, and Psychiatry and the Comprehensive Drug Research Center. David K. Griffin and Mary Comerford are with the Department of Sociology and the Comprehensive Drug Research Center. J. Bryan Page is with the Departments of Anthropology and Psychiatry and the Comprehensive Drug Research Center. Edward J. Trapido is with the Department of Epidemiology and Public Health and the Comprehensive Cancer Center. Shenghan Lai is with the Department of Medicine. Requests for reprints should be sent to Dale D. Chitwood, PhD, Comprehensive Drug Research Center, University of Miami School of Medicine, 1400 NW 10th Ave, Room 210 (D-93), Miami, FL 33136. This paper was accepted May 18, 1995.
November 1995, Vol. 85, No. 11
Drug Users' Risk Factors
subject were randomly selected from a pool of previously negative injection drug users who again tested negative within the 2-week period prior to the date of the first positive test of the case subject. Persons who were available for the pool of control subjects were no different in level of baseline risk behavior than those who were not followed. Participation was voluntary, and each subject received an assurance of confidentiality and was paid a small stipend. If any individual selected to be a control subject was unavailable, unable, or unwilling to participate, no replacement control was selected. No controls had become case subjects by the close of the study. Twenty-one incident case subjects and 76 control subjects were enrolled. Two additional seroconverters were identified but not enrolled; one moved from the area, and the other died of causes unrelated to HIV-1 before the interview could be conducted. Four controls were available for 14 case subjects, 3 controls were available for 6 case subjects, and 2 controls were available for 1 case subject. Four control subjects could not be located after several attempts, 1 refused to participate, 2 were inaccessible, and 1 died (unrelated to HIV-1) prior to the interview. The 8 potential control subjects did not differ in age or gender but were more likely to be African American than were the controls who participated.
Data Collection A questionnaire was developed for study. Each case and case-control the control subject responded to a structured 45- to 60-minute interview administered in strict privacy by a trained interviewer at an assessment center. The interview elic-
ited information concerning drug and needle use and sexual behavior and a brief medical history for the year prior to the last HIV-1 test. This interview occurred 2 weeks after the HIV assessment for most case subjects and was conducted after participants had been informed of their HIV status. Case subjects were interviewed as soon as they were determined to be emotionally prepared to complete the interview. All case and control subjects completed the interview within 2 weeks of their posttest counseling session.
Variable Selection and Definition Variables selected for univariate analysis (Table 1) had been shown to be related to HIV seroprevalence in previous studies.815 All behavioral items measured behavior in the year prior to seroconverNovember 1995, Vol. 85, No. 11
sion (for case subjects) or the last negative test date (for controls). Four variables need explanation. First, "share equipment" was defined as using a needle/ syringe that had been used by another injection drug user. Second, prostitution was defined as trading sex for money or drugs. Third, because of the small number of seroconverters, the variables concerning sexually transmitted diseases in the past year (gonorrhea, syphilis, genital herpes, genital lesions, and "other") were combined into one category for analysis. Finally, male-to-male anal sex was defined as a man having either insertive or receptive anal intercourse with another man. No women reported female-tofemale sex.
Laboratory Methodsfor Classification Serum was determined to be positive for antibodies to HIV-1 after two positive enzyme-linked immunosorbent assays (ELISAs) and confirmatory Western blot (following the Centers for Disease Control and Prevention protocols). Abbot Enzyme Immunoassay was used in determining positive ELISAs; viral lysate obtained from Hillcrest Biologicals was used in Western Blot analysis.
StatisticalAnalysis Variables were dichotomized, and crude odds ratios (ORs), 95% confidence intervals (CIs), and corresponding P values were calculated. Multiple logistic regression analysis, using asymptotic methods, controlled for age, gender, and race, and adjusted odds ratios and 95% confidence intervals were calculated. Variables that exhibited an adjusted odds ratio with a P value less than .10 were entered into stagewise logistic regression models to identify factors that were independently associated with HIV infection. Age, gender, and race were forced into the logistic models to control for possible confounding. As a means of assessing the predictive value of the logistic regression models, cross validation was implemented with the use of cross-validation likelihood. Cross-validation likelihood measures the degree to which the risk for each study subject can be predicted with data from other subjects, and it thus serves as a measure of predictive value23'24 The regression model with the largest decrease in cross-validation likelihood has the best predictive value for future data. In this analysis, the four variables that exhibited an adjusted odds ratio with a level of significance of P < .10 were used to fit all possible models of combined subsets, and
cross-validation likelihood was computed for each regression model. Because of the small sample size and the number of independent variables chosen for analysis, the results were verified by means of the exact logistic regression method. This approach bases the inference of exact permutational distributions of the sufficient statistics corresponding to the regression parameters of interest, conditional on fixing sufficient statistics of the remaining parameters of the observed data.25 All P values were two sided.
Results Between December 1988 and August 1991, the study enrolled 21 injection drug users who had seroconverted. Table 1 presents a univariate analysis for selected demographic characteristics and risk behaviors. HIV seroconversion was associated with being 35 or more years of age, being African American, using crack, using other forms of cocaine, sharing injection equipment, having a sexually transmitted disease, being a male participating in anal intercourse with a male, and spending time in jail. Participation in a methadone treatment program was protective against HIV-1 infection. Since demographic characteristics may confound the identification of risk factors for HIV infection, an analysis was conducted that adjusted for age, gender, and race. Table 1 shows that, after adjustment, heroin use, cocaine use, sharing equipment, and presence of a sexually transmitted disease were associated with an increased risk of HIV infection (P < .10). These behavioral variables subsequently were entered into the regression models. The final logistic regression model (Table 2) indicates that, after adjustment for age, gender, and race, seroconversion was strongly associated with sharing injection equipment in the year prior to conversion and was marginally associated with having a sexually transmitted disease in this same period. There were no significant interactions between sexually transmitted diseases and sharing equipment. Race remained an independent risk factor; African Americans were more likely to seroconvert than other participants. The results of measuring the predictive value of the models by the use of cross-validation likelihood are shown in Table 3. The cross validation confirmed that the final asymptotic model in Table 2 is the best model that can be identified American Journal of Public Health 1539
Chitwood et al.
TABLE 1-Univariate Analysis of Selected Demographic and Risk Behavior Variables among Case and Control Injection Drug Users
Age, y > 35 1
No. injection drug-using sex partners 0
.1
Condom useb Not always
Always Prostitution No Yes Sexually transmitted disease in past year No Yes Spent time in jail No Yes Male-to-male anal sex No Yes
Case Subjects (n = 21), %
Control Subjects (n = 76), %
Crude OR
95% Cl
57.1 42.9
28.9 71.1
1.00 3.27
1.21, 8.86
71.4 28.6
71.1 28.9
1.00 0.98
0.34, 2.86
19.0 81.0
53.9 46.1
1.00 4.98
1.53,16.18
38.1 61.9
22.4 77.6
1.00 0.47
...
...
0.17,1.32
...
...
85.7 14.3
80.3 19.7
1.00 0.68
...
...
0.18, 2.61
...
...
52.4 47.6
67.1 32.9
1.00 1.86
0.70, 4.95
1.00 2.76
0.91, 8.42
28.6 71.4
55.3 44.7
1.00 3.09
1.08, 8.82
1.00 2.04
0.67, 6.26
9.5 90.5
44.7 55.3
1.00 7.69
1.67, 35.36
1.00 4.72
0.95, 23.30
61.9 38.1
80.3 19.7
1.00 2.50
0.88, 7.13
1.00 1.68
0.54, 5.21
81.0 19.0
55.3 44.7
1.00 0.29
0.09, 0.94
1.00 0.70
0.18, 2.81
38.1 61.9
38.2 61.8
1.00 1.00
0.37, 2.71
1.00 0.89
0.31, 2.59
52.4 47.6
80.3 19.7
1.00 3.70
1.33,10.31
1.00 3.45
1.13,10.47
90.5 9.5
94.7 5.3
1.00 1.90
0.32,11.14
1.00 0.91
0.13, 6.53
33.3 66.6
48.7 51.3
1.00 1.90
0.69, 5.22
1.00 0.75
0.22, 2.57
42.9 57.1
55.3 44.7
1.00 1.65
0.62, 4.37
1.00 1.54
0.53, 4.47
100.0 0.0
89.6 10.4
...
81.0 19.0
89.5 10.5
1.00 2.00
0.54, 7.43
1.00 1.27
0.26, 6.10
71.4
28.6
92.1 7.9
1.00 4.68
1.32, 16.48
1.00 3.29
0.84,12.92
38.1 61.9
65.8 34.2
1.00 3.13
1.15, 8.50
2.34
0.81, 6.79
80.0 20.0
96.3 3.7
1.00 6.50
0.98, 43.29
1.90
0.18, 20.69
Adjusted OR a
95% Cl
...
...
...
...
...
...
...
...
...
...
...
...
...
...
aAdjusted for age, gender, and ethnicity.
bArnong those who had sex.
1540 American Journal of Public Health
-i November 1995, Vol. 85, No. 11
Drug Users' Risk Factors
TABLE 2-Final Logistic Regression Model Exact Method
Asymptotic Method
OR
95% Cl
OR
TABLE 3-Cross Validation for the Logistic Regression Models
95% Cl
Covariates
CVL
Change in CVLa
None Heroin use, cocaine use, share, STD Heroin use, cocaine use, share Heroin use, cocaine use, STD
102.37 99.56
0.00 -2.81
98.67
-3.70
98.65
-3.72
98.51 98.46
-3.91
97.97 97.96
97.95
-4.40 -3.41 -3.42
97.87
-4.50
97.73 97.52 97.50
-4.64 -4.85 -4.86
97.28
-5.09
96.55
-5.82 -6.13
Age,y
