INMATE FACTORS ASSOCIATED WITH HIV TRANSMISSION IN PRISON

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INMATE FACTORS ASSOCIATED WITH HIVTRANSMISSION IN PRISON*

CHRISTOPHER P. KREBSRTI International—Center for Crime, Violence, and JusticeResearch

Research Summary:The prevalence of AIDS infection is approximately four times higher instate and Federal prisons than among the general U.S. population. It isalso apparent that high-risk HIV transmission behaviors occur insideprison; however, data that validly document cases of HIV transmissionin prison are rare. This study uses data from a large sample of stateprison inmates and logistic regression to determine what inmate charac-teristics are associated with contracting HIV inside prison. Findingsindicate that inmates who are nonwhite and younger and who havebeen convicted of sexual crimes and have served longer sentences aremore likely to contract HIV inside prison.

Policy Implications:Documenting that HIV is transmitted inside prisons justifies the needfor additional research and effective prevention strategies. Modelingwhat types of inmates might be at risk for contracting HIV insideprison can help public and correctional health researchers and officialsimprove their current prevention practices and ultimately reduce or pre-vent HIV transmission both inside and outside prison.

KEYWORDS: HIV, AIDS, Prison, Jail, Correctional Health

Although anyone can contract HIV and develop AIDS, some groupsare at greater risk than others. Research indicates that the prevalence ofAIDS infection is approximately 3.5 times higher in state and Federal pris-ons than among the general U.S. population (Maruschak, 2004), and thisfigure has been considerably higher in previous years. Numerous studieshave determined that many inmates engage in high-risk HIV transmissionactivities, such as consensual and nonconsensual sex, injection drug use,and tattooing while incarcerated (Correctional Service Canada, 1996;Decker et al., 1984; Gaes and Goldberg, 2004; Hensley, 2000; Hensley etal., 2003; Mahon, 1996; Monroe et al., 1988; Nacci and Kane, 1982; Pag-liaro and Pagliaro, 1992; Saum et al., 1995; Struckman-Johnson and

* I would like to thank the anonymous reviewers whose comments strengthenedthis manuscript. I would also like to express my appreciation for the many stateemployees and professors who supported this research.

VOLUME 5 NUMBER 1 2006 PP 113–136 R


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Struckman-Johnson, 2000, 2002; Tewksbury, 1989). Although the preva-lence of HIV/AIDS in prison and the incidence of high-risk behaviorwithin prison have been well documented, there is not an abundance ofevidence documenting that HIV is indeed transmitted inside prison, andthere are no published studies that use multivariate methods to modelHIV transmission in prison.

It may be that conclusive evidence of HIV transmission among inmatesis needed before adequate prevention measures will be implemented. Thisstudy fills several gaps in the research literature by (1) documenting multi-ple cases of HIV transmission in prison, (2) using descriptive statistics tocompare inmates who did and did not contract HIV inside prison, and (3)using logistic regression to model HIV transmission and thus identify char-acteristics of inmates who contract HIV inside prison in a multivariateframework. At a minimum, these findings may encourage additionalresearch on the transmission of infectious diseases in correctional facilities.Researching HIV transmission inside correctional facilities is controver-sial, complicated, and challenging, but this study employed innovativemethods to overcome many impediments. Data and research that informpolicy and efforts aimed at preventing HIV transmission both within andoutside correctional facilities have the potential to improve correctionaland public health.

PREVIOUS RESEARCH ON HIGH-RISK HIVTRANSMISSION BEHAVIOR AND HIV

TRANSMISSION IN PRISON

Numerous researchers have documented that high-risk HIV transmis-sion behaviors occur inside prison. Estimates of the proportion of inmateswho indulge in homosexual sex while in prison range from 2% to 65%(Nacci and Kane, 1982; Saum et al., 1995; Tewksbury, 1989; Wooden andParker, 1982). Most of this sexual contact is likely “unsafe” because fewcorrectional facilities address the issue of sex in prison or distribute con-doms, the most effective mechanism (short of abstinence) for preventingthe sexual transmission of HIV. Most state prison systems (48 out of 50)and all but five jails consider condoms to be contraband and do not allowthem inside facilities (Braithwaite and Arriola, 2003). The fact that muchof the sexual contact occurring in male prisons may be of the anal varietyfurther increases the risk of HIV transmission behind bars. Anal inter-course, especially receptive anal intercourse, is considered to be signifi-cantly more risky than either vaginal intercourse or oral sex because themucosal lining of the rectum is significantly more delicate than both thevaginal and the oral linings and, therefore, more susceptible to rupture,thus making HIV transmission more likely (Schoub, 1995).


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Whereas the research discussed above covers voluntary participation,rape, another fact of prison life, occurs involuntarily. The controversialnature of rape in prison makes estimating its prevalence problematic; how-ever, it does occur and is germane to any discussion regarding HIV trans-mission inside prison. Estimates of the proportion of inmates who aresexually assaulted inside prison range from 0% to 40% (Davis, 1982; Gaesand Goldberg, 2004; Hensley, 2000; Hensley et al., 2003; Lockwood, 1994;Struckman-Johnson and Struckman-Johnson, 2000, 2002; Wooden andParker, 1982).

Several researchers have documented that injection drug use (IDU)occurs inside prison (Carvell and Hart, 1990; Turnbull et al., 1992).Although the proportion of offenders who continue to inject drugs oncethey are incarcerated seems to decrease, those who do are more likely toshare equipment and less likely to clean the shared equipment betweenuses (Correctional Service Canada, 1996; Mahon, 1996). Clean injectionequipment and bleach, which can be used to clean the equipment, are dif-ficult to acquire in the correctional setting because they are often consid-ered contraband and illegal for inmates to posses (Mahon, 1996).Furthermore, inmates who are sharing injection equipment are doing sowith a population (i.e., fellow inmates) with a high rate of HIV/AIDSinfection (Hammett et al., 1999).

An additional high-risk HIV transmission activity that occurs insideprison is tattooing. In Canada, it was estimated that 45% of inmatesacquired a tattoo while in prison (Correctional Service Canada, 1996).Like injection equipment, needles, which are used to make tattoos, aredifficult to obtain in the correctional setting. Many inmates are thereforeforced to share tattooing equipment, thereby increasing their risk of con-tracting HIV (Doll, 1988; Mahon, 1996).

Few studies have tackled the issue of HIV transmission in prison. Thosethat have employed varying methods to test inmates entering and exitingprison. Unfortunately, many of these studies have had to compromise reli-able research techniques for convenient alternatives. This compromiselargely involves not controlling for a sufficient “window period” of sixmonths, which is essential to determining when subjects contract the virus(Centers for Disease Control and Prevention, 2001). This period is thetime between when an individual contracts HIV and is certain to test posi-tive on an HIV screening.

Horsburgh et al. (1990) identified 1,069 inmates who tested HIV nega-tive during mandatory screening procedures upon entry into the NevadaState prison system. Two of the 1,069 later tested positive after a cumula-tive 1,207 years of potential exposure to HIV in prison. The authors con-cluded that the seroconversion rate was 1 per 604 inmate years or 0.17%annually. Brewer et al. (1988) identified a convenience sample of 393


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inmates who tested HIV negative upon entry into the Maryland Stateprison system. This sample provided a total of 482 years of potential HIVexposure in prison. Two inmates in the sample later tested positive, so theannual rate of transmission was estimated to be 0.41%. In a study by Cas-tro et al. (1994), almost 2,400 inmates, who tested HIV negative uponentry into the Illinois State prison system and had spent at least threemonths in jail before entry, were tested for HIV after one year of incarcer-ation. Requiring the three-month stint in jail represents an effort to con-trol for a potential window period. Seven inmates from the original samplesubsequently tested positive for HIV, which translates into an annualintraprison transmission rate of 0.3%.

Since the above researchers failed to control for a window period of atleast six months, the findings lack utility and validity because it is not pos-sible to be certain any of the inmates actually contracted HIV insideprison. The inmates who seemingly contracted HIV in prison could verywell have contracted the virus before entering prison. Similarly, severalinmates in the sample might have contracted HIV in prison and it simplydid not show up on the screening upon exit, because of the potential six-month window period.

Dolan et al. (1994) identified one case in Australia of a man who hadcontracted HIV while in prison. The man was continuously incarceratedsince before 1980 until after 1990. He tested negative for HIV in 1987, butthen tested positive several years later. Mutter et al. (1994) identified Stateprison inmates who had been continuously incarcerated since before HIVexisted (1978) and identified which inmates had been tested for HIV.These researchers used a similar dataset and approach to that employed inthe current study. Eighty-seven of the original sample of 556 had beentested for HIV, and 18 (21%) tested positive. The authors concluded thatthe HIV transmission rate in prison for this group was 21%. This appearsto be an extremely high rate of transmission for any population and ismuch higher than any other estimate for a correctional sample. However,Mutter et al. (1994) fail to emphasize the level of selection bias in thesample. Many of those tested may have volunteered for testing becausethey knew they engaged in high-risk HIV transmission activities, such assharing needles with fellow drug users, having unprotected sex, or beingtattooed with unclean equipment. Similarly, inmates might have beentested by the prison administration because they showed signs of sicknessor were known to engage in high-risk HIV transmission activities. Regard-less of the reasons for testing, the group of inmates represents a non-ran-dom sample, and selection bias likely inflates the 21% HIV transmission inprison figure dramatically. Taylor et al. (1995) identified eight inmates in aScottish prison who contracted HIV, seemingly via injection drug use in



prison, although the methods employed are complicated and it is not clearthe authors controlled for a sufficient window period.

Researchers in Thailand have used samples of inmates and have identi-fied factors associated with HIV infection, such as history of IDU, positivedrug tests for opiates, experience with methadone maintenance treatment,having tattoos, and IDU and sharing of injection equipment while inprison (Choopanya et al., 2002; Raktham et al., 2000; Thaisri et al., 2003;Wright et al., 1994). However, these studies model HIV status generallyand therefore do not actually determine whether HIV was acquired duringincarceration, so the factors they identify could be associated with HIVtransmission inside prison or simply HIV transmission more generallyamong subjects who have spent some time in prison.

The previous research is somewhat limited. Several studies do not defin-itively identify cases of HIV transmission in prison. Of the studies thatdefinitively document cases of HIV transmission in prison, few actualcases are identified and little is known about the characteristics of thoseinmates. A study by Krebs and Simmons (2002) identified 33 cases of HIVtransmission in prison and presented some demographic differencesbetween inmates who contracted HIV inside prison and those who did not;however, no study has analyzed characteristics of inmates who contractedHIV inside prison within a multivariate framework. This article uses logis-tic regression to model the transmission of HIV, thus determining whatinmate characteristics might be associated with contracting HIV in the cor-rectional setting.

METHODOLOGY

This study employs a design engineered to overcome some methodolog-ical impediments encountered by previous researchers. Rather than test-ing inmates and coping with the inevitable difficulties of conducting acontrolled longitudinal analysis within a correctional facility or system, thisstudy relies on existing data from an unnamed southern state and a back-ward-looking longitudinal research design, which naturally accounts for asix-month window period and tracks a sample of inmates for a 22-yearperiod beginning January 1, 1978 and ending January 1, 2000.

THE INMATE SAMPLE

The first step in this study involved obtaining a dataset containing infor-mation on all male inmates who were in the state prison system on January1, 1978. This date is significant because HIV and AIDS were not identifiedin the U.S. population before 1978 (Food and Drug Administration, 1992).Researchers have tested blood samples of high-risk groups taken beforeJanuary 1, 1978, but they have been unable to confirm its existence in the


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United States before 1978 (Rushing, 1995). It is therefore assumed that ifan inmate was in prison on January 1, 1978, there is virtually no chancethey would be infected with HIV/AIDS as of that date.

The state Department of Corrections (DOC) has limited electronicrecords for inmates before June 30, 1980. Therefore, this study uses adataset of male inmates who were in prison on June 30, 1980 but excludesinmates who entered prison after January 1, 1978. Inmates who were inprison on January 1, 1978 but were released from secure custody beforeJune 30, 1980 are similarly excluded. Women were excluded from thedataset because the transmission dynamics of HIV/AIDS are substantiallydifferent for women and men in the correctional setting and because therewere too few women.

When the state DOC furnished this dataset, it contained 5,265 cases andhad the following information on each inmate: Social Security number,date of birth, last name, first name, gender, race, highest level of educationclaimed, Test of Adult Basic Education (TABE) score, date the inmateentered prison for the qualifying sentence (i.e., the sentence that led tothem being in prison on January 1, 1978), whether the inmate was dead oralive as of January 1, 2000, and date of first leave from prison on the quali-fying sentence. Date of first leave indicates the date the inmate first leftsecure custody after January 1, 1978. As described later, it is the first leavedate that will be analyzed in relation to the date of HIV/AIDS diagnosis, ifthere is one, to identify inmates who were found to have HIV/AIDSbefore leaving prison and therefore contracted HIV inside prison.

THE HIV/AIDS REPORTING SYSTEM (HARS)

The second step involved accessing the state HIV/AIDS Reporting Sys-tem (HARS) dataset. This dataset contains information on all persons inthe state who have HIV or AIDS. Gaining access to this dataset was verydifficult, as the information is highly confidential. Special arrangementswere made with the state Department of Health (DOH) to match theHARS and inmate datasets. Subsequent analyses were designed so theycould be run without compromising the security of the information or theidentities of the HIV positive cases. When data are being released to anexternal researcher, the state DOH has stringent standards about thenature of the data being released. In accordance with DOH Policy: Secur-ity 17, dated September 29, 1997:

DOH staff who release aggregate HIV/AIDS data outside theDepartment will comply with the convention of suppressing all non-zero cross tabulated cells with <3 cases (i.e., all cells containing only 1or 2 cases). All marginal totals shown in table form will be inspectedto ensure that values of internal cells expressed as ‘<3’ cannot be



exactly determined. Consolidation with other data subgroups may benecessary to avoid such disclosure.

Due to this policy, some descriptive data presented herein lack specificityand sometimes certain comparisons across groups are not possible. Forexample, the ages of inmates in the sample had to be collapsed andpresented in broad categories (i.e., <20, 20–39, 40+), and the primaryoffense for which the inmate was imprisoned is crude (i.e., murder, sexualcrime, other). This lack of specificity, however, does not affect our abilityto identify and model cases of HIV transmission in prison because themodels were run by DOH staff using individual-level data. This study, thelinking of the DOC and HARS data, and subsequent analyses werereviewed and approved by the DOH Institutional Review Board (IRB).

It is important to mention that in the state prisons, HIV testing is con-ducted on a voluntary basis. There is, therefore, some self-selection bias interms of who seeks testing, but this is also true of community-based stud-ies that rely on voluntary testing data.

RESEARCH STRATEGY

Once the inmate dataset was prepared and access to the HARS datasethad been granted, several procedures generated the necessary findings.First, the two datasets were cross-matched by DOH staff using the name,race, date of birth, and social security number variables, which identifiedthose persons in the inmate dataset who are known to have HIV/AIDS. Asoftware program called Automatch (Jaro, 1997; Matchware Technologies,1997) was used for this procedure, which is a probability matching pro-gram well suited for this operation.

Second, the date of HIV/AIDS diagnosis for each matched case wasimported from the HARS dataset into the inmate dataset. At this pointthe HARS dataset was no longer needed and the remainder of the datapreparation and the subsequent analyses were conducted by DOH staffusing the inmate dataset and the imported date of diagnosis variable.

Third, inmates who contracted HIV inside prison were identified byDOH staff. If an inmate was known to have HIV/AIDS before their firstleave date, they were deemed to have contracted HIV inside prison. Onthe other hand, if HIV/AIDS was diagnosed after an inmate had leftprison, then they were deemed to have contracted HIV after leavingprison. Although they could have contracted HIV inside prison, it isimpossible to definitively draw this conclusion because their conditionswere diagnosed only after spending some time in the community.

Fourth, the inmates in the sample were grouped so logistic regressionmodels could be run to determine what inmate characteristics are associ-ated with contracting HIV inside prison or after leaving prison, factors


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that might help us understand what types of inmates might be at acceler-ated risk of contracting HIV. The groups are compared using bivariateanalyses in a previously published manuscript (Krebs and Simmons, 2002).The first group, IN PRISON HIV, consists of inmates who contracted HIVinside prison. The second group, HIV OUT, consists of inmates who con-tracted HIV, but not necessarily inside prison (i.e., their conditions werediagnosed after they left prison for the first time). The third group, NOHIV, contains inmates who are not known to have HIV/AIDS and areassumed to be uninfected.

Comparisons are made among the three groups, thus indicating whetherinmates in the various groups are different in terms of age (as of January1,1978), race, level of education, and criminal offense. Unfortunately, due tostringent legal confidentiality requirements, the state DOH could notrelease individual-level data to outside researchers. Some of the descrip-tive data therefore lack specificity.

Beyond providing descriptive data, DOH staff ran several logisticregression models with various specifications. Although individual-leveldata could not be released to researchers outside of DOH, staff runningthe regression models could use all data when running the models andrelease the model results for publication. For the logistic regression mod-els, dichotomous dependent variables were constructed based on whetheran inmate was in one of the three mutually exclusive groups describedabove (i.e., IN PRISON HIV, HIV OUT, and NO HIV). Although multi-ple models were run and a variety of stepwise procedures were used toenter independent variables in the models, the results did not differ sub-stantially. Therefore, two of the best-fitting and more interesting modelsare presented herein, both of which include all available independent vari-ables. Specifically, in the first model inmates in all three groups (INPRISON HIV, HIV OUT, and NO HIV) are included and IN PRISONHIV is regressed on the independent variables. This model addresseswhether inmates who contract HIV inside prison are different from thoseinmates who do not. In the second model, inmates in the HIV OUT andNO HIV groups are included and HIV OUT is regressed on the indepen-dent variables (inmates in the IN PRISON HIV group are excluded). Thismodel addresses whether inmates who contract HIV after leaving prisonare different from those inmates who do not. The purpose of these twomodels is to use multivariate methods to determine whether inmates whocontract HIV, inside prison (Model 1) or outside prison (Model 2), aredifferent from inmates who do not, thus indicating what inmate character-istics might be associated with contracting HIV, inside and outside ofprison.

Several independent variables were included in the models. Specifically:



1. Two continuous age measures were included in separate specifica-tions: age at admission to prison and age as of January 1, 1978 (thedate the risk of contracting HIV began). Both age measures per-formed similarly in models, so age as of January 1, 1978 is used inthe models presented herein.

2. Race was dummy coded into white and nonwhite (which in mostcases means black). A nonwhite indicator was included with whiteas the reference category.

3. Two continuous measures of education were modeled simultane-ously because these measures were not highly correlated: highestlevel of education claimed and the Test of Adult Basic Educationscore (TABE score). The first measure might be a better indicationof years of formal education, whereas the second measure is likelya better indication of an inmate’s actual abilities.

4. The criminal offense that led to the qualifying sentence wasdummy coded into murder, sexual crime, and other crime, withother crime as the reference category.

5. Total number of days spent in prison before January 1, 2000 orbeing released from prison for the first time (total prison in days).

All of these variables were provided by the DOC. The total prison in dayswas included in the models because it could theoretically be associatedwith HIV transmission risk. As it has been argued, the total amount oftime an inmate spends in the carceral society might correlate with theirwillingness to become involved in high-risk behaviors, some of whichincrease risk of HIV transmission. Gresham Sykes (1958), in his classicstudy The Society of Captives, referred to the “pains of imprisonment,”which are brought about by the loss of liberty, goods and services, hetero-sexual relationships, autonomy, and security. The pains, which escalateover time, result in an array of behavioral responses, many of whichinvolve opposing the institutional authority and entail the internalizationof deviant normative prescriptions. In other words, inmates becomeprisonized over time and adopt and adhere to an inmate code, whichsometimes embodies behaviors that put one at risk of contracting HIV,such as sex, drug use, and violence. Other variables have been includedbecause they afford some statistical control for variables of interest (e.g.,age, race, criminal offense) and allow us to determine whether theseinmate characteristics are associated with HIV transmission.

FINDINGS

The matching protocol identified 271 inmates (5.2%) in the originalsample of 5,265 who tested positive for HIV/AIDS. Of the 271 inmateswho tested positive for HIV/AIDS, 33 (0.63% of sample) were determined


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to have contracted HIV inside prison, and 238 (4.6% of sample) werefound to have HIV/AIDS after leaving prison.

Data indicating whether inmates in a particular group are different interms of age (as of January 1, 1978), race, level of education claimed, andcriminal offense are presented in Table 1. Chi-squares were used to iden-tify differences that are statistically significant at the 0.05 level. Inmates inthe NO HIV group seem to have a younger age distribution, followed bythe inmates in the IN PRISON HIV and HIV OUT groups, suggestingthat older inmates were more likely to contract HIV. The NO HIV groupseems to have the largest proportion of white inmates (45%) comparedwith the IN PRISON HIV (21%) and HIV OUT (12%) groups. Inmatesin the IN PRISON HIV group seem to have the lowest level of education,with 36% of its members not getting past eighth grade. Inmates in the INPRISON HIV groups are much more likely to have been convicted ofmurder or a sexual offense than inmates in the HIV OUT or NO HIVgroups.

TABLE 1. AGE, RACE, EDUCATION, AND OFFENSECHARACTERISTICS OF THREE GROUPS

OF INMATES

IN PRISON HIV HIV OUT NO HIV(n = 33) (n = 238) (n = 4,994) Total

AGE AS OF 1/1/1978

<20 0 (0%) 0 (0%) 300 (6%) 314 (6%)20–39 15 (45%)* 86 (36%)** 3845 (77%)*,** 3976 (76%)Over 40 18 (55%)* 152 (64%)** 849 (17%)*,** 975 (18%)

RACE

White 7 (21%)* 29 (12%) 2247 (45%)* 2292 (44%)Nonwhite 26 (79%)* 209 (88%)** 2707 (55%)*,** 2963 (56%)

LEVEL OF EDUCATION

Grades 1–8 12 (36%) 50 (21%) 1348 (27%) 1385 (26%)Grades 9–12 19 (58%) 150 (63%) 2897 (58%) 3091 (59%)Other 2 (6%) 38 (16%) 749 (15%) 789 (15%)

OFFENSE

Murder 17 (52%)* 23 (10%)** 1342 (27%)*,** 1382 (26%)Sexual crime 10 (30%)* 26 (11%) 625 (13%)* 661 (13%)Other crime 6 (18%)* 189 (79%)** 3027 (60%)*,** 3222 (61%)

*Statistically significant age, race, and offense differences at the 0.05 level between inmateswho contracted HIV inside prison and inmates who have not contracted HIV**Statistically significant age, race, and offense differences at the 0.05 level between inmateswho contracted HIV outside of prison and inmates who have not contracted HIV

For the logistic regression models, variables were entered using several



stepwise procedures, but the resulting models did not fit any better thanthe full models. Models using entry, rather than stepwise, methods aretherefore presented. In the first model presented, the IN PRISON HIVmeasure is regressed on the set of covariates and inmates in the INPRISON HIV, HIV OUT, and NO HIV groups are included. The resultsof this first model are presented in Table 2.

TABLE 2. LOGISTIC REGRESSION MODEL OFCONTRACTING HIV INSIDE PRISON

95%Standard Confidence

Variable B Error Exp(B) IntervalLower Upper

Age as of 1/1/1978 −0.086 0.260 0.918*** 0.871 0.966Nonwhite 1.042 0.460 2.835* 1.150 6.990Education claimed 0.029 0.054 1.030 0.926 1.145TABE Score −0.107 0.058 0.899 0.802 1.008Murder 1.044 0.543 2.839 0.979 8.235Sexual crime 1.667 0.546 5.299** 1.818 15.444Total prison in days 0.133 0.029 1.142*** 1.078 1.209NOTE: Nagelkerke R Square: 0.229.*Statistically significant at the 0.05 level.**Statistically significant at the 0.01 level.***Statistically significant at the 0.001 level.

Four variables have a statistically significant association with havingcontracted HIV inside prison: age as of January 1, 1978, being nonwhite,having been convicted of a sexual crime, and the total number of daysspent in prison before being released for the first time or January 1, 2000(whichever came first). Each year an inmate is older deceases the oddsthat he will contract HIV while in prison by 1.09 times (1/0.918 = 1.09).The odds a nonwhite inmate will contract HIV inside prison are 2.84 timeshigher than for a white inmate. Having been convicted of a sexual crimeincreases the odds that an inmate contracts HIV inside prison by 5.3 timesrelative to inmates who are convicted of crimes other than a sexual crimeor murder. The total amount of time spent in prison increases the oddsthat an inmate will contract HIV inside prison. In fact, each additional dayof incarceration increases the odds that an inmate will contract HIV by1.14 times. Two additional variables, TABE score and having been con-victed of murder, approached statistical significance (0.07 and 0.06, respec-tively), suggesting that inmates with higher TABE scores might be lesslikely to contract HIV inside prison and inmates who were convicted of


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murder were more likely to contract HIV inside prison. The Nagelkerke Rsquare for this model is 0.229, so approximately 23% of the variation inthe dependent variable is explained by the independent variables includedin the model. Although the use of pseudo R square measures like theNagelkerke R square is somewhat controversial, it is offered in this articlefor those who believe it has some utility.

In the second model presented, the HIV OUT measure is regressed onthe set of covariates and inmates in the HIV OUT and NO HIV groupsare included (inmates in the IN PRISON HIV group are excluded). Someof the 238 inmates in the HIV OUT group may have contracted HIVinside prison, but HIV or AIDS was not diagnosed until after they hadbeen released from prison for the first time.

The results of the first and second models have some similarities at leastwith regard to the age and race measures. Again, the older an inmate is asof January 1, 1978 is associated with a decrease in the odds that they con-tract HIV. Each year an inmate is older deceases the odds that he willcontract HIV while in prison by 1.03 times. The odds a nonwhite inmatewill contract HIV are 6.25 times higher than for a white inmate. Havingbeen convicted of murder decreases the odds that an inmate contractsHIV by 2.13 times relative to inmates who are convicted of crimes otherthan murder or a sexual crime. The total amount of time spent in prisondecreases the odds that an inmate will contract HIV post-release (Table3). In fact, each additional day of incarceration decreases the odds that aninmate will contract HIV by 1.1 times. No additional variables evenapproached statistical significance in the second model. The Nagelkerke Rsquare for this model is 0.128, so approximately 13% of the variation inthe dependent variable is explained by the independent variables includedin the model.

An additional variable that was not included in the models presentedabove is an indicator of whether the inmate was known to be deceased asof January 1, 2000. The deceased measure was positively associated withboth dependent variables and statistically significant at the 0.001 level inboth models. Inclusion of the deceased measure did not substantiallyaffect the other variables in the models, but it did increase the pseudo Rsquare measures from 0.227 to 0.4463 and from 0.128 to 0.196 in the twomodels, respectively. The deceased measure was not included in the finalmodels, however, because of missing data. Including the deceased variablewould have decreased the number of HIV positive inmates from 33 to 25in the first model and from 238 to 223 in the second model. Regardless, thepositive association between being deceased and HIV/AIDS status is notsurprising; inmates with HIV/AIDS are more likely to die than uninfectedinmates. In fact, HIV/AIDS was likely diagnosed for some of theseinmates during a post-mortem examination.



TABLE 3. LOGISTIC REGRESSION MODEL OFCONTRACTING HIV AFTER LEAVING PRISON

95%Standard Confidence

Variable B Error Exp(B) IntervalLower Upper

Age as of 1/1/1978 –0.031 0.009 0.970*** 0.953 0.986Nonwhite 1.833 0.204 6.251*** 4.189 9.330Education claimed 0.017 0.018 1.017 0.981 1.055TABE Score 0.011 0.019 1.011 0.974 1.050Murder –0.755 0.240 0.470** 0.294 0.752Sexual crime –0.158 0.220 0.853 0.554 1.314Total prison in days –0.091 0.024 0.913*** 0.871 0.956NOTE: Nagelkerke R Square: 0.128.*Statistically significant at the 0.05 level.**Statistically significant at the 0.01 level.***Statistically significant at the 0.001 level.

DISCUSSION

The number of inmates in the sample who contracted HIV inside prison(33) is the absolute minimum number of inmates who contracted HIVinside prison and therefore likely a highly conservative figure. The figureis conservative for several reasons. First, there are likely numerousinmates (up to 238, 271 – 33 = 238) who contracted HIV inside prison, buttested positive for HIV/AIDS only after leaving prison. Second, severalinmates in the sample likely have HIV/AIDS but have never been tested.As their infection status is unknown, it is impossible to determine if, when,or where they contracted HIV. Additionally, this figure is conservativebecause much of the time the sample spent in prison was during the earlyand mid-1980s when rates of HIV/AIDS infection were relatively low.Today, the risk of HIV transmission in prison may be higher than it was inthe early 1980s simply because HIV/AIDS infection rates are significantlyhigher, although this likelihood may be counterbalanced by increases inawareness and prevention activity.

The limitations of this study include the somewhat convenient nature ofthe sample (i.e., the sample included only inmates who were in prison in1978, which may not be representative of today’s prison population), thelimited robustness of the data, and the parsimonious number of variables.Certainly, the methodological strength and policy implications of thisresearch design would be enhanced if random sampling and routine HIVtesting procedures were possible, if the data DOH would release were


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coded more robustly or at the individual level, and if more variables wereavailable for analysis. For example, in this study, the concept of prison isused to reference the environment in which HIV transmission is said tohave occurred, at least for 33 inmates in the sample. Certainly, environ-mental or population conditions or factors (e.g., the HIV prevalence rate)that likely affect the risk of HIV transmission in prison vary, perhaps dra-matically, from facility to facility and within different parts or subpopula-tions of a prison. The inmates in this sample were exposed to different andpossibly multiple facilities and populations and for varying periods of time.Ideally, data on facility and population characteristics that could relate tothe risk of HIV transmission would be included in such an analysis. Unfor-tunately, due to the difficult nature of researching HIV transmission inprison, compromises had to be made and it was not possible to avoid someof the inherent limitations of these data and this methodology. As a result,some differences across racial/ethnic and/or offense categories may havebeen obscured. Another limitation relates to the types of analyses thatcould be run with the data. Under an agreement with DOH, all analyseshad to be run by a DOH statistician and logistic regression was chosen asthe most viable approach. Certainly, other analytic techniques (e.g., sur-vival analysis or those with nonlinear specifications) may have been pref-erable, but logistic regression is a methodologically appropriate techniquefor analyzing these data. Despite its limitations, this study breaks newground and generates data and findings not previously published in theresearch literature. The simple fact that this article helps document that infact HIV is transmitted inside prison is important. Neither this finding northe regression findings are necessarily surprising, and one could have rea-sonably assumed what the results would be, but without data that validlyconfirm such assumptions, they would be only assumptions.

The relationship in the first model between age and contracting HIVinside prison suggests that younger inmates who were incarcerated as of1978 were perhaps more likely to engage in or become involved in activi-ties that increase their risk of contracting HIV, such as drug use, consen-sual and nonconsensual sex, and tattooing. This finding might not besurprising but does seem at odds with the data presented in Table 1, whichsuggested that inmates who contract HIV inside prison are older thaninmates who do not. This apparent contradiction, however, is similar to aproblem known as Simpson’s Paradox, in which two variables may seem tobe related in a certain way (e.g., contracting HIV in prison and age), butthe apparent association disappears, or even reverses direction, whenadditional data or more complete data are introduced (Agresti, 1996). Inthis case, the age data are presented in a categorical format in Table 1, andcrudely so (i.e., in broad categories), because that is how DOH agreed to



release the data to outside researchers. Although in Table 1 the age distri-bution for inmates who contracted HIV inside prison seems to be consid-erably younger than for inmates who did not contract HIV, it may be thatthe mean age for inmates who contracted HIV inside prison is in fact sig-nificantly lower. In the logistic regression model, in which a continuousage measure is used and additional covariates (e.g., length of incarcera-tion) are included, the actual relationship between contracting HIV inprison and age emerges: The younger the inmate, the more likely they areto contract HIV inside prison. Although this finding seems to contradictTable 1, the reality is that the data presented in Table 1 do not accuratelyreflect the actual age distribution of the inmates in the two groups.

The finding that nonwhite inmates are more likely to contract HIVinside prison suggests nonwhite inmates may also be more likely tobecome involved in activities that increase HIV transmission risk. Thisfinding is not terribly surprising given that nonwhites have higher rates ofHIV/AIDS in the general population and are believed to engage in high-risk HIV transmission behavior at higher rates (Centers for Disease Con-trol and Prevention, 2003; Wohl et al., 2000). The impact HIV and AIDSare having on the African-American community is especially profound.AIDS is the leading cause of death for African-American men aged 25through 44 years, a population for whom experience in the correctionalsystem is relatively common (Braithwaite and Arriola, 2003; Hoyert et al.,1999). The reality that African-American women are infected with HIV at25 times the rate of white women (Centers for Disease Control and Pre-vention, 2004) has been attributed, in part, to the possibility that African-American men are contracting HIV inside prison and transmitting it, per-haps heterosexually, to their partners upon release (Johnson and Raphael,2005).

It is unclear what the sexual crime finding suggests other than thatinmates who were convicted of sexual crimes are also more likely toengage or become involved in activities that increase their level of HIVtransmission risk. The amount of time an inmate spends in prison beingassociated with contracting HIV inside prison is an expected result, largelybecause the nature of the research design essentially requires inmates whocontract HIV inside prison to have been in prison for an extended periodof time, especially given the sample and the years these data represent.Due to the low HIV prevalence and rarity of HIV testing in the early1980s, an inmate in this sample likely would have had to remain incarcer-ated throughout most of the 1980s, if not longer, to have been determinedto have contracted HIV inside prison.

In the second model, it again seems that younger and nonwhite inmatesare at higher risk of contracting HIV; however, inmates convicted of mur-der are at a lower risk of contracting HIV than inmates convicted of other


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crimes. The relationship between age and contracting HIV might not besurprising, but it is at odds with the bivariate results presented in Table 1for reasons already explained. The longer an inmate spends in prison, theless likely it is that HIV will be diagnosed after leaving prison. This lastfinding is somewhat surprising, but perhaps less so when one considersthat the inmates who are found to have HIV after leaving prison had to bereleased (i.e., have their sentence end) at some point before January 1,2000 (i.e., they were perhaps not the inmates likely to contract HIV andserve very long sentences, many of whom were likely in the IN PRISONHIV group).

The paucity of empirical evidence related to HIV transmission in cor-rections reflects the difficult nature of conducting research on this contro-versial and complex topic. The various difficulties involved in researchingHIV transmission in corrections include the complexity of documentingthe transmission of the virus, the controversial nature of studying HIV/AIDS in the corrections setting, the confidential status of HIV/AIDS-related data, and a related host of legal, ethical, and philosophical issues.Nonetheless, and not withstanding these difficulties, useful research onthis topic is important if the burden posed by HIV transmission in thecorrectional setting is going to be fully understood and dealt withappropriately.

This study validly documents that 33 out of the original 5,265 inmates inthe sample (0.63%) contracted HIV inside prison. These data challenge aproposition that is common in the field of corrections that HIV and otherinfectious diseases are not in fact transmitted inside correctional facilities.Given the documented prevalence of high-risk HIV transmission activitiesinside prisons, these data confirm that risk of transmission exists and sug-gest that, at least in this sample, the risk of HIV transmission was signifi-cantly higher for inmates who were young, nonwhite, convicted of a sexualcrime, and serving long sentences. A valid assessment of HIV transmissionin prison needs to be conducted with a more current prison population, sothe current burden posed by HIV transmission in prison can be furtherestablished and appropriate prevention and response approaches can bedeveloped and implemented. This study helps establish the need for suchan inquiry and provides guidance on what might constitute an appropriateand affordable methodology for generating valid and more representativedata on HIV transmission inside correctional facilities.

Until a valid study of current inmates is conducted, this study serves asan indicator that perhaps more can be done to prevent the transmission ofinfectious diseases in the correctional setting. Knowing whether a risk ofHIV transmission in prison exists and who is at risk are essential pieces of



information when designing any infectious disease education and preven-tion strategy. An article published jointly by The National Institute of Jus-tice and the Centers for Disease Control and Prevention, entitled “PublicHealth/Corrections Collaborations: Prevention and Treatment of HIV/AIDS, STDs, and TB,” assesses the level of collaboration between depart-ments of health and departments of corrections in the fight against HIV,AIDS, and other communicable diseases (Hammett, 1998). Several collab-orative programs exist, but additional efforts are needed. The number onefactor that is “common to successful collaborations between public healthand correctional agencies” is “data and events demonstrating a need forcollaboration” (Hammett, 1998, p. 6). The article goes on to explain (p. 6):

Successful public health/corrections collaborations are much easier todevelop and sustain when data documenting the burden of disease inthe inmate population are readily available. . .Better surveillancemight produce a whole series of positive effects: more resources forcorrectional health programs, better services, a safer and healthierenvironment for inmates and staff, less disease returned to the com-munity, and ultimately improved public health.

We hope that this study adds to the literature that documents the burdenposed by HIV transmission in prison (e.g., see Hammett et al., 2002) andwill help facilitate more of the necessary collaborative relationshipsbetween public health agencies and correctional health departments.

Although many prisons offer some form of HIV/AIDS education, it isnot clear whether these programs are effective at actually changing inmatebehavior either during or after incarceration (Braithwaite et al., 1996).According to Hammett et al. (1999), only 10% of state and Federal prisonsprovide comprehensive HIV/AIDS education and prevention program-ming, which consists of instructor-led education, peer-led programs, pre-and post-test counseling, and multi-session prevention counseling. As therisk posed by HIV transmission in prison is largely undocumented, correc-tional health officials have seen little reason to intensify their preventionand education efforts. This study, and others like it, might facilitate neces-sary policy reform and encourage correctional health officials to capitalizeon this valuable opportunity to prevent the transmission of a fatal diseasein a high-risk population, most of whom will be released into the generalcommunity.

A relatively affordable prevention strategy involves distributing con-doms to inmates. Consensual and nonconsensual sex occurs inside correc-tional facilities, and if inmates used condoms when indulging in riskysexual activities, the risk of contracting HIV inside prison would almostcertainly diminish. “Significantly, no system that has adopted the policy of


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making condoms available in prisons has reversed the policy, and the num-ber of systems that make condoms available [internationally] continues togrow every year” (Jurgens, 2000, p. 7). Despite this fact, only two stateprison systems and five jails in the United States provide condoms toinmates (Braithewaite and Arriola, 2003). Even if inmates are effectivelyeducated about how to eliminate or reduce their risk of contracting HIV,they are denied the very mechanisms to do so while in prison. Addition-ally, methadone maintenance, a program that results in less drug injectionbehavior and therefore diminishes HIV transmission risk, is typically notoffered inside American prisons (Advisory Committee on the Misuse ofDrugs, 1993; Dolan et al., 1994; Hammett et al., 1999; McLeod, 1991;World Health Organization, 1993). Prison officials may be unwilling to domore to prevent transmission until there is valid empirical evidence that,in fact, HIV is transmitted inside prison. This is not to say that inmates arenot at all responsible for their behavior. In fact, many inmates mightknowingly engage in high-risk HIV transmission behaviors despite beingeducated about risk reduction or being provided with the tools that enablerisk reduction. Intensified education initiatives could, however, makeinmates more aware of the risks, which could potentially change theirbehavioral patterns.

The contributions of this study are of value because previous researchhas had difficulty finding valid methods to document that risk of HIVtransmission in prison exists and has not used multivariate approaches toassess how the level of risk might vary across inmates. Most prison inmatesserve sentences of a few years (e.g., state and Federal prison inmatesreleased in 2002 had served an average of 30 months; Harrison and Beck,2004), and if they are leaving prison equipped to infect others, all of soci-ety should be concerned. Current correctional policies seem to be basedon the assumption that HIV is not transmitted inside prison or that pre-vention is not possible or cost-effective. Data proving otherwise mightalert the public to this potentially serious social problem and ultimatelyencourage policy reform, which may result in fewer HIV transmissionsboth inside and outside prison. Preventing HIV transmission inside prisonis a correctional health issue and a public health concern. Inmates andpeople in the community, especially the African-American community,are being impacted. Additional research is needed to better document andunderstand this phenomenon, so appropriate policies can be drafted andimprovements can be made to a system that has largely ignored the call forbetter HIV prevention services (Braithwaite and Arriola, 2003).

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Dr. Christopher P. Krebs is a Senior Research Social Scientist at RTI Internationalwhere he conducts studies on juvenile justice and delinquency; adult offender andinmate behavior; substance abuse policy, epidemiology, and treatment; and corrections.Dr. Krebs has led and worked on several projects for the National Institute of Justice(NIJ), Bureau of Justice Statistics (BJS), National Institute on Drug Abuse (NIDA),Centers for Disease Control and Prevention (CDC), and Substance Abuse and MentalHealth Services Administration (SAMHSA). He has employed both quantitative andqualitative methods in his research and has extensive experience designing studies,developing survey instruments, analyzing data, and reporting findings.

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