Prospective evaluation of a model of risk for HIV infection among injecting drug users

DEPENDENCE Drug and Alcohol Dependence 40 (1995)63-71

ELSEVIER

Prospective evaluation of a model of risk for HIV infection among injecting drug users Martin Y. Iguchi*“, Donald A. Bux Jr.“, Harvey Kushnera, Victor Lidz”, John F. Frenchb, Jerome J. Platt” ‘Division of Addiction Research and Treatment (DART), Department of Psychiatry, Medical Coiiege of Pennsylvania and Hahnemann University, Mail Stop 984, Broad and Vine, Philadelphia, PA 19102-1192, USA bDepartment of Psychology, Rutgers - the State University of New Jersey, Piscataway, NJ, USA cDivision of Alcoholism. Drug Abase, and Addiction Services, New Jersey State Department of Health, Trenton. NJ, USA

Received18 May 1995;accepted7 September1995

Abstract

Data on 3016 out-of-treatment injecting drug users (IDUs) were analyzed in order to replicate findings from an earlier study on risk factors for HIV infection (Iguchi et al., 1992)and evaluate a model for estimating probability of infection. Logistic regression analysesyielded a set of risk factors highly consistent with previous findings. A logistic function was used to estimate subjects’ probabilities of infection, and theseestimateswere strongly correlated with actual HIV prevalence in both the original and current samples.The current study represents a successful replication of earlier Iindings.and supports the validity of the risk model. Keywork

Substanceabuse; Intravenous; HIV seropositivity; Risk factors; Risk models; Replication study

1. Introductioo Nationwide, 25% of AIDS cases reported to date have been attributed to infection through the use of injected drugs, with an additional 7% of cases associated with both injected drug use and homosexual activity as risk factors (Centers for Disease Control and Prevention,

1995). In contrast, New Jersey reports a majority of AIDS casesassociated with injected drug use (52% of reported cases),with an additional 4% of casesattributed to both drug injection and male homosexual activity (New JerseyState Department of Health, 1995).The problem of HIV and AIDS is especially acute in the large cities in northern New Jersey that are adjacent to New York City. The number of injecting drug users (IDUs) in this area has been estimated at a minimum of 20 000 individuals (Pate1et al., 1991). HIV seroprevalence in this region is reported to be as high as 56% among IDUs not enrolled in treatment (Iguchi et al.,

l

Correspondingauthor. Tel.: (215)7628387.

037~8716/95/$09.50 0 1995ElsevierScienceIreland Ltd. All SSDI 0376-8716(95)01190-A

1992), and 43% among IDUs entering drug treatment (Allen et al., 1992). Previously reported research has identified specific behavioral and demographic risk factors that independently contribute to risk for HIV infection among IDUs in Newark and Jersey City, NJ (Iguchi et al., 1992).Among these are duration of drug use, injection of certain drugs, frequency of injection, use of certain non-injected drugs, time spent in jail, sexual activity, self-rating of AIDS risk, health history, race, gender, and educational level. The current report has three purposes. First, we describe the replication of our earlier findings on risk factors for HIV with a second sample of IDUs recruited from the same cities, among whom both the seroprevalenceand levels of risk behavior were significantly lower than in the initial study sample. Becausethe original study was exploratory in nature, a large number of variables were analyzed, and some data analytic procedures had been data-driven rather than based on a priori hypotheses. Thus, we believed that a replication of the findings was necessary to rule out the possibility

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that some of the original findings were spurious. Second, we develop a multivariate model for HIV risk based on the simultaneous consideration of the previously identified risk variables that conceptualizes risk in terms of the accumulation of multiple factors. Third, we discuss the validity of the multiple-factor statistical model in estimating HIV prevalence in each sample, and its potential utility in guiding public health policy and risk reduction interventions. 2. Method 2.1. General procedures 2.1.1. Recruitment and sample. All procedures were

approved by the Institutional Review Boards of the University of Medicine and Dentistry of New Jersey(under whose auspices the project was initiated), Hahnemann University, and the New Jersey State Department of Health. Subjectswere active IDUs in Newark and Jersey City, NJ who had not been in addiction treatment for at least six months prior to recruitment. Subjects were

recruited by community-based outreach workers and word of mouth to centrally located field offices in each city. Recruitment efforts were city-wide at both locations, initially focusing on neighborhoods identified by the staff ethnographer as having a high concentration of IDUs, and then expanding to include additional areasas they becameknown to the project staff. The sampleconsisted of two setsof research subjects. The first, or generative sample (GS), consisted of 1271 IDUs interviewed between May and December 1989, that was used to identify the variables associated with risk of HIV infection as described above (Iguchi et al., 1992).One hundred sixty-nine (11.7%) subjectshad been dropped from the original analyses due to incomplete data. Another 7 subjectswere dropped from the current analyseswhen they were identified as individuals who had previously been interviewed. The characteristics of the GS are provided in the first column of Table 1. The variables identified in the earlier report make up the risk model described in the current study. The second, or prospective sample (PS), consisted of

Table 1 Subject characteristics, generative and prospective samples Characteristic

Age <25 25-29 30-34 35-39 40-49 250 Mean age (SD.) Race Non-Hispanic Black Hispanic Non-Hispanic White

Generative sample (n : 1271) n (%) 75 (5.9) 183 (14.4) 349 (27.5) 381 (30.0) 247 (19.4) 36 (2.8) 35.40 (6.70)

prospective sample (n = 3016) n (%) 354 (11.7) 620 (20.6) 806 (26.7) 696 (23.1) 471 (15.6) 69 (2.3) 33.65 (7.22)

Comments

x* = 74.5; P < 0.0001 F= 53.94; P < 0.0001

985 (77.5) 192 (15.1) 94 (7.4)

2064 (68.4) 678 (22.5) 274 (9.1)

x2 = 39.2; P < 0.0001

Female Male Education Grade l-8 Grade 9-l 1 High school diploma or equivalent Some college College graduate HIV serostatus HIV+ HlV-

302 (23.8) 969 (76.2)

714 (23.7) 2302 (76.3)

x*=0004 . 1 P=O.95

115 (9.0) 616 (48.5) 296 (23.3) 224 (17.6) 20 (1.6)

260 (8.6) 1471(48.8) 839 (27.8) 390 (12.9) 56 (1.9)

x2 = 23.1; P = 0.0003

656 (51.6) 615 (48.4)

1060(35.1) 1958(64.9)

x*= 101.0;P < 0.0001

Risk behavior history

Mean (SD.)

Mean (S.D.)

17.68(6.80) 14.94(8.10) 2.51 (6.51) 15.24(4.12) 26.32 (50.54) 15.44(37.71)

16.38(7.55) 12.60(8.65) 2.20 (7.17) 14.45(3.75) 27.23 (53.56) 8.02 (25.61)

SCX

Years of drug use Years of injection drug use Sex partners, past year Age first intoxicated Weeks in jail, past 5 years Weeks in drug treatment, past 5 years

F=28.14; I’ < 0.0001 F = 67.82; P < 0.0001 F= 1.76; P=O.1852 F= 35.75; P < 0.0001 F = 0.27; P = 0.6036 F= 55.60; P < 0.0001

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3328 IDUs interviewed between January 1990 and December 1991,of whom 312 (9.4%) were dropped due to incomplete data, leaving 3016 casesto be included in the analyses.This sample was used to validate the risk variables. 2.1.2. Data collection. At the research sites, subjects provided informed consent and then completed a detailed, structured interview (the AIDS Initial Assessment, or AIA, v. 8.0) with trained staff. The interview included questions regarding demographics, current and past drug use patterns (types of drugs used, routes of administration, and frequency of use), needle sharing and cleaning, sexual practices, AIDS knowledge, and health history. Subjectsthen received pre-test HIV counseling, and provided a blood sample for testing for HIV antibodies. Subjectswere offered the results of the test and appropriate post-test counseling if they desired them. Subjects were paid US%10for the interview and US$5 for the blood sample. 2.1.3. HIV testing. Blood samples were drawn by either venipuncture or finger stick and sent to the NJ State Department of Health Immunology Laboratory Services in Trenton, NJ. Samples were classified as seronegative if they were non-reactive on the ELISA (Genetic Systems,LEV 960). Sampleswere classified as seropositive if they were reactive on repeat administrations of the ELISA, and in at least two out of three of the major HIV protein bands P24, GP41, and GP1201160on the Western (immune) blot method. A classification of indeterminate was given to samplestesting positive on repeat administrations of the ELISA, but reactive on less than two of the major protein bands on the Western blot. 2.2. Development of risk model

In order to develop the initial set of risk variables, a seriesof univariate and multivariate analyseswere conducted on the GS. Over 500 behavioral and demographic variables from the AIA were examined in univariate analyses to develop a set of variables that correlated with HIV infection in the GS. Continuous variables (e.g., age, years of injection drug use, number of incarcerations in the past live years) were examined using one-way analysis of variance with and without covariates. Categorical variables (e.g., race, gender, type of drugs used) were analyzed using simple chi-square analyses.Some categorical variables, such as frequency of injection of certain drugs, were transformed into dichotomous variables by dividing them at the point in the series of categories where the greatest increase in HIV infection rates was observed. Decisions regarding the point at which these variables would be dichotomized were made post-hoc after examination of the actual data obtained rather than on the basis of a priori hypotheses. The validation of these decisions was

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therefore a major focus of the replication study undertaken here. Over 200 variables were correlated with HIV infection in the GS. Among those, 24 were selectedthat had the strongestrelationship with HIV and that were consistent with a priori hypothesesor with previous studies; these were entered into a stepwise logistic regression. This analysis yielded a set of thirteen variables that contributed independent variance to the HIV infection status of the subjects(Iguchi et al., 1992).The results of the original logistic analysis are provided in Table 2. 2.3. Prospective evaluation

Replication of these findings involved two sets of analyses.First, the previous stepwiselogistic regression analyseswere repeated on data from the PS using the sameset of 24 variables, in order to evaluate the degree to which the equation resulting from the PS data was consistent with that obtained from the GS data. Second, we used the regressionequation derived from the GS to calculate risk scores for all subjects, and grouped subjects according to their scores. Finally, the regression model was used to calculate a predicted rate of HIV infection for eachgroup basedon its mean risk score. The accuracy of the model in estimating infection rates was then evaluated by means of a linear regression between predicted and actual rates of infection for each group. 3. Results 3.1. Sample

Demographic and seroprevalencedata of the PS are presentedin Table 1, along with comparable data from the GS. A number of significant differences were noted between the two samples. Subjects in the PS were significantly younger, less likely to be Black, and had shorter injection drug use careers than subjects in the GS. Moreover, seroprevalencein the PS was significantly lower than in the GS. In general, successivecohorts of subjects exhibited progressively lower rates of HIV seropositivity as well as lower levels of risk variables; the rates of HIV infection ranged from a high of 63% and 56%for the first 100subjectsin Newark and JerseyCity, respectively, to lows of 20% and 18%, respectively, for the last cohorts in the two cities. Thesetrends have been reported previously (Iguchi et al., 1994). 3.2. Replication of the logistic regression

The results of the logistic regression analysis with the GS and PS are shown in Table 2, with the 13 variables that contributed independent variance to HIV serostatus in the GS (listed in the order in which they entered into the original GS model) and the regression constant. The repetition of the logistic regression analysesusing the PSdata yielded very similar results to those

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Table 2 Results of logistic regression on the GS and PS Variable

Years of injection drug use No. sex partners - past 6 months (none=l, zzl partner=-I) Times in jail - past 5 years (>2 times= I, 52 times=-I) Education (I illness=l, sl illness=-I) Sex (female = I, male = -I) Constant

Generative sample (n = 1271)

Prospective sample (n = 3016)

Regression coefficient

Standard error

P

Regression coefficient

Standard error

P

0.0340 0.2301

0.0091 0.0918


0.0495 0.2051

0.0056 0.055I

<0.0001 0.0002

0.2602

0.0655

<0.0001

0.1269

0.0449

0.0847

0.1775

0.0653

0.0066

0.1685

0.0448

0.0002

0.1758

0.0809

0.0298

0.2166

0.0495

<0.0001

0.3258

0.0731

<0.0001

0.2425

0.0486

<0.0001

0.2195

0.0675

0.001I

0.2262

0.0450

<0.0001

0.197

0.0668

0.0032

0.2676

0.0459


0.4337

0.1177

0.0002

0.4038

0.0601


0.2985

0.0792

0.0002

0.3933

0.0720

<0.0001

(Did not load on the second regression function) 0.3982 0.0508 <0.0001

0.1648

0.0798

0.0388

0.1982

0.0559

0.0004

0.1676

0.0791

0.0341

0.1440

0.0540

0.0076

-0.4535

0.1997

0.023I

-1.1348

0.1154


%cludes chlamydia, endocarditis, genital herpes, gonorrhea, hepatitis, pneumonia, syphilis and tuberculosis.

produced for the GS data, and remarkably similar regression coefficients. Specifically, the PS analyses resulted in the sameset of variables included in the final regression,with the single exception that solvent abuse was dropped from the PS model. No variables were included in the PS model that had not been included in the GS model. The order in which the variables were entered into the regressionwas also altered somewhat in the PS model; although years of injection was still entered first, it was more strongly weighted as a contributor of risk, while the use/non-use of crack cocaine diminished somewhat in importance. The Spearman rank-order correlation between the order of entry of the twelve variables entered in both regressionswas 0.8. Because of the strong similarity between the two models, further analyseswere limited to evaluating the validity of the G&generated regression model in both samples.

rounded to the nearest integer in order to obtain a coefficient (weight) for each variable in the index. The one exception was years of drug use, for which the calculation yielded a value of 0.2493. This coefficient was rounded to 0.25, and the weight for this variable was defined as (number of years of use)M. The selection of the (0.15)-l multiplier was arbitrary, but yielded the best results in terms of simplifying the calculation of a risk score. Thus, we calculated risk scoreswith a possible range of -23 (subject negative on all risk variables with an injection drug use career of less than one year) to +27 (subject positive on all risk variables with a career of 220 years injecting drugs). These risk scores can then be used to estimatethe probability that an IDU with a particular risk score is infected with HIV. This is the predicted probability of HIV infection, or P(HIV+), and is based on the logistic function (1):

3.3. Calculating risk for HIV infection

(1) P(HIV+) = (1 + exp(-Z/6))-’

Using the coefficients from the logistic regression equation derived from the GS, a risk score was calculated for each subject. In order to simplify the model for use in the intervention servicesof the project, each regression coefficient was divided by 0.15 and

where Z equals the risk scorecalculated from the regression equation as described above. The range of probabilities is from P(-23) = 0.0212 to P(+27) = 0.9890. Table 3 presentsthe predicted and actual rates of HIV

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Table 3 Risk score distribution and flHIV+), GS and PS Risk score -wv

Generative sample (n = 1271) n

HIV+ (%)

Average P(HIV+)

”

HIV+ (%)

Average P(HIV+)

-23 -20 -15 -10 -5 +o +5 +10 +15 +20 +25

3 44 86 159 232 294 280 134 35 4 ida

1 (33.33) 7 (15.91) 9 (10.47) 39 (24.53) 95 (40.95) 164 (55.78) 203 (72.50) 105 (78.36) 29 (82.86) 4 (100.00) +27

0.03 f 0.00 0.06 f 0.01 0.12 l 0.02 0.23 rt 0.04 0.40 ZIZ0.06 0.60 f 0.06 0.77 f 0.04 0.88 f 0.02 0.94 f 0.01 0.98 f 0.01 0

20 202 310 513 562 475 393 173 51 4 n/a

I (5.00) 18 (8.91) 41 (13.23) 103 (20.08) 190 (33.81) 238 (50.1I) 254 (64.63) 122 (70.52) 41 (80.39) 3 (75.00) n/a

0.03 f 0.00 0.06 f 0.01 0.12 t 0.02 0.23 f 0.04 0.40 f 0.06 0.60 f 0.06 0.77 f 0.04 0.88 zt 0.02 0.94 f 0.01 0.98 zt 0.00 0

-20 -15 -10 -5 -0 +5 +10 +15 +20 +25

Prospective sample (n = 2703)

infection for both the GS and PS, grouped according to risk scores.The first column of the table consists of risk scorecategoriesdefining eachgroup of subjects;the next three columns indicate the number of subjects in each

category, the actual (observed) rate of HIV infection for the category, and the predicted rate of infection based on that group’s average risk score, respectively, for the GS. The final three columns provide analogous data for

q

1.0

0 0.8

>r .Z .P 2

Generative Sample y = 0.12218t 0.78447x; P2 = 0.921

0.6

E P

q

0.0

0.2

0.4

Predicted

0.6

0.8

probability

Fig. 1. Predicted versus observed probability of infection based on logistics model.

1.0

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the PS. As these data suggest,the predicted probability of HIV infection is a very accurate estimate of actual levels of HIV seroprevalence associated with various categories of risk score both in the GS and in the PS.

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discussed the combined impact of multiple injecting drug users risk factors in particular populations of individuals. 4.1. Applications

3.4. Predicted versus observed probabilities infection

of HIV

In order to examine the validity of the model in estimating HIV infection, we compared the predicted and observedfrequenciesof HIV infection in Table 3 for both samples,using the chi-squared goodness-of-fit test. There was a statistically significant departure from the expected (x2 = 38.05, df = 9, P c 0.01) for the PS (reflecting the significantly lower seroprevalence in the PS) and, as expected, no statistically significant differencefor the GS; the tendency of the model was to overestimate slightly the rate of infection in the PS. We also computed a linear regression between the observed and predicted probabilities of HIV infection for both samples (Fig. 1). In support of the validity of the model, the coefficient of proportionality was very high in the GS and even higher in the PS. In addition, the slopes of the two regressionswere nearly identical. The validity of the model in the PS as well as the GS suggeststhat the risk factors remained highly stable even though the rate of infection declined from the GS to the PS. The difference in the intercept for the two regressionsis the result of the lower HIV seroprevalencein the PS. 4. Discussion Becauseof the large number of variables (over 500) originally analyzed in developing the model for HIV risk described here and the data-driven selection of cut-off points for creating dichotomous variables, it was necessaryto consider the possibility that one or more of the 13 variables identified in the first linear regression analysis might have a spurious relationship to HIV risk. The linear regression calculated on the PS yielded very similar results, however, suggesting that the original model was highly reliable. Moreover, there was a strong positive correlation between predicted HIV infection rates using the model generated from the GS, and the actual infection rates observed in both the GS and the PS (Fig. l), suggestingthat the model has considerable validity as an index of HIV risk among IDUs in Newark and Jersey City. Practically speaking, risk for HIV infection is better conceptualized in terms of an accumulation of risk from multiple factors. An important and unique characteristic of our model is that it portrays cumulative risk basedon the simultaneous consideration of thirteen variables that cover a range of behavioral, demographic, and historical factors. Whereas previous studies have identified individual variables that contribute independently to HIV risk, none to date has directly

The model developed here can be adapted for use in service delivery, particularly in the context of an outreach-basedprogram targeting IDUs. In developing an intervention for the delivery of casemanagementservices to this population, we (Lidz et al., 1992)developed a brief screening instrument, the HIV Risk Index (see Fig. 2), for assessingan individual’s likelihood of infection with HIV. Staff used the Index to guide a systemof prioritizing and tailoring social service delivery to our clients. All individuals in this population are considered at substantial risk for infection with HIV. However, it was an unfortunate reality in this setting that the demand for servicesgreatly exceededtheir availability. In addition, given the transient nature of this population it had been our experience that many clients would not return for follow-up visits if they were not immediately engagedin services,and that many clients did not follow through on referrals when offered without substantial support from and direct involvement by our staff. For these reasons,the initial research interview provided the best opportunity for arranging service placements for our clients, even though their HIV test results typically were not available until weeks later. Given this context, the use of the Risk Index as a basis for triage of clients provided the highest-risk clients with immediate contact with a casemanager and in-depth counseling on the importance of receiving HIV test results (Lidz et al., 1992). This expedited intake processincreasedthe chancesthat the highest risk clients would return for subsequentcase managementsessions,receive HIV antibody test results when they becameavailable, and successfullybe referred for appropriate services. Models that convey the importance of cumulative risk for infection, such as that described here, may also have great utility in guiding public health interventions targeting groups of individuals or neighborhoods where particular sets of risk factors tend to cluster. Knowing the cumulative risk associated with the three or four most important risk factors, for example, public health officials could target social networks of IDUs with particular behavioral profiles, such as old-timers who inject speedball, Black IDUs who do not use crack, or Hispanics IDUs who frequent shooting galleries, and thereby design interventions that more precisely reach those at highest risk for infection. The notion of cumulative risk can also be useful in a harm-reduction approach to risk reduction counseling with individuals presenting for HIV testing; an individual whose risk factors indicate a particular likelihood of infection, but who neverthelesstests negative, can be counseled on the

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HIV RISK INDEX SCORE

QUESTION

female Gender of respondent: -male Iffemale, enter +I; if male, enter -I How old are you? (enter response): How old were you when you first shot drugs? (enter response):

Calculateyears sincefirst injection, divide byfour, round to nearestwhole number,and enter result What race or ethnic group do you consider yourself (enter response):

to belong to?

If Black, enter +I; if non-Black, enter -1 What is the highest level of education you have reached?(enter

response):

If-&S diploma or GED, enter +I; if2HS diploma or GED, enter -I How many times have you been in jail or prison in the past 5 years? (enter response):

If more than twice, enter +2; if twice or less,enter -2 Have you had any of the following

in the past 6 months (read list; check all that apply):

-endocarditis -syphilis gonorrhea --genital herpes -tuberculosis hepatitis -chlarnydia If two or more of the above,enter +I; if less than two, enter -1 Have you used crack cocaine In the past 6 months?

--pneumonia

-no

-yes

If no, enter +I; ifyes, enter-Z Have you used heroin

without

injecting

(e.g., by snorting

or swallowing)

in the past 6

months? -yes -no IJno, enter +2; if yes, enter -2 On average, how frequently one):

did you inject cocaine by itself in the past 6 months? (check

never -<4x/month -once/week -2-6xlweek If > once/week,enter +I; if< once/week,enter -I

-once/day

-2-3xlday

>4xlday

Have you ever injected cocaine & heroin mixed together in the past 6 months? -yes

-no

chance -some chance high

chance -sure

chance

Fig. 2. HIV Risk Index.

-no

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risk of continuing to engagein specific types of behavior on the basis of his or her current risk profile and on specific behavioral changesthat may reduce risk. For IDUs unwilling to make the global change of stopping drug use, such an approach could also have considerable utility under a harm-reduction model in counseling IDUs on which behaviors pose the greatest risk for acquiring HIV infection. We believe that the current findings also have implications for future research, in that they underscore the importance of social networks of drug users as a factor in determining HIV risk (Needle et al., 1995).Our original report (Iguchi et al., 1992) and the current replication describe primarily behavioral factors contributing to risk; we believe that future research will require an examination of immediate social and community factors that also influence risk. For example, Blacks were found to be at higher risk relative to non-Blacks, while crack users appear to be at lower risk for infection. Since it is unlikely that either of theseeffectsis the result of behavioral differences associated with these variables, we believe they are best explained by a cohort effect that mediatesboth relationships. In other words, Blacks may be at higher risk than non-Blacks not becauseof behavioral differences between these two groups, but because Black IDUs tend to associateand use drugs with other IDUs who are, as a group, more often infected with the virus. Studies that examine the homogeneity of serostatus within drug-using networks, and that trace mixing patterns and vectors of HIV transmission within and between networks (Morris, 1994; Service and Blower, 1995),will be an important next step in understanding the epidemiology of HIV.

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local public health policy and interventions. Moreover, methods similar to those described here could be used elsewhereto develop locally valid models for identifying and targeting especially high-risk clients. A related issue is the extent to which the present results can be generalized to future cohorts of IDUs within the region studied here. Either large-scalebehavior changes among IDUs in response to the threat of AIDS, or changing levels of HIV seroprevalenceamong IDUs, could affect the long-term validity of the model in thesetwo cities. That is, our model may be temporally as well as geographically bound. Finally, since several items in the model relate to current injection and other drug use practices, the current findings may be less applicable to individuals at risk for HIV infection through past injection drug use, but who have not injected recently (for example, currently abstinent, in-treatment subjects). 4.3. Summary The primary purposes of this report were (a) to replicate earlier findings on correlates of HIV infection among IDUs in a region with a high prevalence of HIV, and (b) to develop and validate a model for estimating likelihood of infection that accounts for the cumulative effectsof multiple risk factors. The variables included in the model proved to be highly stable correlates of HIV infection even though the two cohorts under study were characterized by markedly different rates of HIV infection and of HIV-risk behavior. The model as a whole is highly valid as a measureof likelihood of HIV infection among IDUs in Newark and Jersey City at this time. Acknowledgments

4.2. Limitations

Several limitations are evident with regard to the applicability of these findings to populations other than the one studied here. First, the model is region-specific. It was developed in a limited geographic area (Newark and JerseyCity, NJ) with high prevalence of HIV infection among IDUs. To the extent that any region is unique in terms of the extent and pattern of spread of the virus among IDUs, variables included in our model will not necessarily be reliable indicators of risk in other geographic regions, for example, where the HIV epidemic has spread less extensively or along different social lines. This issue, however, does not necessarily representa shortcoming of the current study, and in our opinion points to the larger problem of examining risk variables in data collected across different regions. While studies designed to assesscommon risk factors among IDUs in different geographic areas are valuable in identifying common factors, they run the risk of obscuring important regional differences in patterns of transmission of HIV. We feel it is equally important to identify region-specific risk factors in order to guide

This research was supported by two grants from the National Institutes on Drug Abuse (R18-DA05286 and R18-02589). The authors also gratefully acknowledge Robert Baxter and Lenore Robison for their extensive contributions to the outreach and researchoperations in Newark and JerseyCity. We also wish to thank the staff of the Health Behavior Projects for their invaluable hard work and dedication. References Allen, D.M., Onorato, I.M., Green, T.A. and the Field Services Branch of the Centers for DiseaseControl (1992) HIV infection in intravenous drug users entering drug treatment, United States, 1988-1989. Am. I. Public Health 82, 541-546. Centersfor DiseaseControl and Prevention (1995)HIV/AIDS Surveillance: US AIDS Casesreported through December, 1994,Centers for Disease Control and Prevention, Atlanta, GA. Iguchi, MY., Bux, D.A., Lidz, V., Kushner, H., French, J.F. and Platt, J.J. (1994) Interpreting HIV seroprevalence data from a street-basedoutreach program. J. Acquir. Immune Detic. Syndr. 7, 491-499. Iguchi, M.Y., Platt, J.J., French, J.F., Baxter, R.C., Kushner, H.,

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