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Criminal justice involvement history is associated with better HIV care continuum metrics among a population-based sample of young black MSM

Schneider, John A.; Kozloski, Michael; Michaels, Stuart; Skaathun, Britt; Voisin, Dexter; Lancki, Nicola; Morgan, Ethan; Khanna, Aditya; Green, Keith; Coombs, Robert W.; Friedman, Samuel R.; Laumann, Edward; Schumm, Phil for the uConnectand BARS study teams

Author Information
doi: 10.1097/QAD.0000000000001269



HIV care continuum research that includes black MSM (BMSM) has primarily utilized a disparities framework which compares BMSM with a white MSM reference group [1,2]. Although this approach may suggest broad differences between these two populations, such analyses are unable to account for the marked heterogeneity within BMSM communities [3–5]. In addition, existing studies have not adequately explored the role of criminal justice involvement (CJI), which is higher among black versus white MSM. Such CJI might be related to the HIV care continuum [6]. Therefore, next-generation analyses that move beyond a disparities framework can begin to illuminate factors and processes that are most amenable to intervention among BMSM. Such approach recognizes BMSM as a diverse group, and in particular younger BMSM where prevention interventions are largely absent [7]. More specifically, in the context of CJI, studies [8,9] have demonstrated high rates of HIV transmission among criminal justice involved BMSM. However, there is little understanding of how CJI might be related to the HIV care continuum among young BMSM (YBMSM). Whether CJI might benefit or detract from HIV care engagement is an important question that can have significant primary and secondary HIV intervention implications within this population, who are disproportionately burdened by staggering rates of CJI and HIV incidence [8,10].

To examine the relationship between CJI and the HIV care continuum, we use recent data from the uConnect cohort, a population-based sample of younger BMSM 16–29 years of age. Because drivers of the HIV care continuum are poorly characterized among YBMSM [11,12], a population-based sample helps avoid some of the biases associated with samples of YBMSM based in clinics or jails, or other convenience samples, that would impact our understanding of factors most associated with the entire breadth of the HIV care continuum.


Sample generation

Respondent-driven sampling (RDS) was used to generate the uConnect cohort and estimation of population parameters from the data. Additional information regarding justification for RDS, population parameter estimates, weights computation of finite population correction, sensitivity analyses, implementation of RDS sampling, and sourcing of seeds has been previously described [13,14] and is available in the Supplementary Materials 1.0–3.0 (

Data collection: eligibility criteria

Using an RDS approach, a baseline sample of eligible YBMSM was recruited in South Chicago and adjacent South suburbs between June 2013 and July 2014 [13]. This geographic area was sampled because it represents the largest region of highest HIV incidence in Chicago [15] and, as the most populous contiguous majority black community area in the United States [16], provides the opportunity to generate a large sample with limited variation in environmental exposures [17]. Study respondents were eligible to be interviewed if they were self-identified as African-American or black, were born male, were between 16 and 29 years of age (inclusive), reported oral or anal sex with a man within the past 24 months, resided in South Chicago or the adjacent South suburbs, and were willing and able to provide informed consent at the time of the study visit.

HIV care continuum measures

HIV care continuum measures included HIV-infected unaware, linkage to care, retention in care, adherence to antiretrovirals, and viral suppression. HIV infection was determined by three assays applied to samples eluted from dry blood spot samples: ARCHITECT HIV Ag/Ab Combo, Multispot HIV-1/HIV-2 Bio-Rad, and RealTime HIV-1 RNA (Abbot, Lake Bluff, Illinois, USA). HIV-infected but unaware individuals were identified as clients who reported HIV-negative status (or never tested), and who were found to be HIV seropositive. All clients found to be HIV-infected-but-unaware were reviewed and confirmed by a trained social worker following the study visit and corroboration with Department of Public Health surveillance records. Linkage to care, retention in care, and adherence to antiretrovirals were based upon self-report. Multiple definitions of linkage to care exist [18–26]. In the uConnect cohort, linkage was defined as reporting at least one HIV medical care visit within 6 months after diagnosis. Retention in care was defined as reporting two or more appointments 90 days apart in the previous 12 months [27]. Viral load frequency measures of retention collected from Chicago Department of Public Health laboratory surveillance data were used to verify self-report, and was correlated (data not shown). Adherence to antiretrovirals was defined as self-report of missing HIV medications on fewer than 4 days in the previous month to be consistent with more than 85% adherence. Viral suppression was defined as having an HIV RNA less than 2000 nucleic acid copies/ml in whole blood (RealTime, Abbott).

Independent variable measures

The variables selected for this study were informed by prior empirical considerations suggesting that racial disparities associated with HIV care continuum metrics were associated with sociodemographic, risk behavior, health, and social factors [28–32]. Additional information on these variables and CJI definitions can be found in Supplementary Materials 1.0–4.0 (

Statistical analysis

First, we assessed the bivariate relationships between each of the independent variables with our primary HIV care continuum outcomes using simple logistic regressions, giving us unadjusted bivariate odds ratios for each of the independent/outcome variable pairings. Next, we accounted for sociodemographic characteristics and preexisting situations that may affect one's place on the continuum in each of the logistic regressions to produce adjusted bivariate odds ratios. Our final comprehensive models included each HIV care continuum variable against the entire set of independent variables and controls. Significant associations between independent variables and HIV care continuum variables were assessed via the odds ratios in those final six multivariate logistic regression models (α=0.05). Joint P values for the effect of each independent variable across the span of the HIV care continuum were calculated using techniques to adjust for overall effects in seemingly unrelated regressions [33].

Finally, to more clearly understand the association between the HIV care continuum and CJI, we substituted in total time served, average time served per detainment (days), and total number of separate detainments for the original dichotomous CJI variable in the final multivariate logistic regression models. All regression analyses were conducted using Stata 14 software (StataCorp, College Station, Texas, USA) [34].


A final analytic sample of 622 eligible participants was generated through RDS chains of up to 13 waves in length and with a mean of 2.1 recruits per participant. At enrollment, 71.1% of the sample was 24 years of age and under, 40.8% had prior history of CJI, 54.4% had healthcare coverage, and 34.6% were HIV seropositive. Of those who were HIV seropositive, 58.4% had been linked to care within 6 months of diagnosis, 40.2% were retained in care, 32.2% were adherent to antiretrovirals, and 24.3% were virally suppressed. uConnect cohort descriptives can be found in the Supplementary Materials (

In adjusted analyses, CJI was associated with the overall HIV care continuum (adjusted odds ratio = 2.35; 95% confidence interval 1.13–4.88) including viral suppression (adjusted odds ratio = 3.00; 95% confidence interval 1.15–7.79). In addition, several covariates were associated with each step in the HIV care continuum as demonstrated in Table 1. Length and frequency of CJI was also associated with care continuum metrics albeit in a more nuanced way (Fig. 1).

Table 1:
Multivariable logistic regression models of potential HIV care continuum drivers among a population-based sample of younger black MSM, uConnect 2013–2014.
Fig. 1:
HIV treatment continuum success.Adjusted estimates of HIV treatment continuum success by length and frequency of criminal justice detention, uConnect 2013–2014 (n = 214). * P < 0.05; ** P < 0.01; *** P < 0.001.


There are several new and important findings from these analyses. First, we documented that CJI YBMSM versus those with no such histories are more likely to be successfully engaged in the HIV care continuum. These findings were robust across a variety of HIV care continuum stag. A critical caveat to these findings is that there are competing forces between total days spent per detention and frequency of detention episodes. Repeated episodes of CJI resulted in worse HIV care engagement across a number of measures, yet longer stays were associated with better HIV care engagement across all HIV care continuum measures. Frequent and recurrent cycling within the criminal justice system has also been reported by others as a risk factor for ongoing HIV risk and transmission [35,36]. It is unclear from our analysis whether frequent cycling of YBMSM in and out of CJI is a symptom of other social factors that limit HIV care engagement, or whether repeated CJI is disruptive to maintaining engagement with HIV care.

Our finding of the association between longer CJI stays and improved HIV care engagement substantiates those of prior studies which have documented that CJI is associated with improved HIV care continuum metrics. A recent systematic review suggests that HIV care continuum measures such as achievement of viral suppression were highest during the incarcerated period [37]. The review also highlights that following incarceration, populations involved in criminal justice are less likely to engage in the HIV care continuum and at slightly lower rates to those engaged prior to incarceration. Our findings extend those of earlier studies by characterizing the nuance of HIV care engagement based upon the frequency of exposure and duration of CJI. Some of the differences in our study may be because of several factors including that the majority of studies in the systematic review were focused on prison populations that included both men and women sexual identity was rarely reported, and race/ethnicity was not assessed. Clearly, the association between duration of CJI and HIV care engagement is of concern and may reflect the poor HIV care engagement infrastructure outside of jails in communities of color.

The current study also finds that having a ‘mother figure’ is an important factor related to improved care continuum metrics. It may be that having important kin relationships is associated with better care outcomes, although having a mother figure did not modify the findings when added to the model. Previous work has shown that family in the lives of YBMSM matters with respect to HIV testing and other HIV-related risk behaviors [38,39]. Relatedly, network interventions have been found to be effective including group-level interventions [40], which may hold promise for improving the HIV care continuum [41].

In summary, this analysis of the YBMSM care continuum highlights the nuanced nature of the possible impacts of CJI on the HIV care continuum. Understanding these nuances was possible because we moved away from an examination of disparities that compares white to BMSM, and instead focused on the heterogeneity within YBMSM communities. Although CJI systems may be good for HIV care retention, its overall social role is likely very negative, especially considering outcomes that go beyond public health (police brutality, disruption of community lives, lack of opportunity upon release, etc.). In addition, although limiting criminalization of YBMSM may reduce the risk of HIV, the criminal justice system is structured to implement many steps of the HIV continuum, including testing and treatment which may engage some in care even postrelease. However, frequent and cycling CJI was associated with less care engagement and may reflect disruptions in HIV care engagement or represents other structural factors that would limit care engagement. Consequently, forming more coordinated joint partnerships with criminal justice systems, departments of public health and other community-based initiatives are needed to improve the HIV continuum for YBMSM.


We would like to thank the uConnect study participants, as well as Ishida Robinson, Eve Zurawski, Billy Davis, Rebecca Duvoisin, and the NORC field team for generating the data for analysis. We would like to thank Michelle Taylor and Iman Little for providing linkage services for all participants. We would also like to thank the National Institutes of Health (R01 DA039934, R01DA033875) for funding the study and analysis.

Presented in part: Conference on Retroviruses and Opportunistic Infections (CROI) February 2015.

Conflicts of interest

There are no conflicts of interest.


1. Rosenberg ES, Millett GA, Sullivan PS, Del Rio C, Curran JW. Understanding the HIV disparities between black and white men who have sex with men in the USA using the HIV care continuum: a modeling study. Lancet HIV 2014; 1:e112–e118.
2. Millett GA, Peterson JL, Flores SA, Hart TA, Jeffries WL 4th, Wilson PA, et al. Comparisons of disparities and risks of HIV infection in black and other men who have sex with men in Canada, UK, and USA: a meta-analysis. Lancet 2012; 380:341–348.
3. Mays VM, Cochran SD, Zamudio A. HIV prevention research: are we meeting the needs of African American men who have sex with men?. J Black Psychol 2004; 30:78–105.
4. Peterson JL, Jones KT. HIV prevention for black men who have sex with men in the United States. Am J Public Health 2009; 99:976–980.
5. Malebranche DJ, Arriola KJ, Jenkins TR, Dauria E, Patel SN. Exploring the ‘bisexual bridge’: a qualitative study of risk behavior and disclosure of same-sex behavior among black bisexual men. Am J Public Health 2010; 100:159–164.
6. Iroh PA, Mayo H, Nijhawan AE. The HIV care cascade before, during, and after incarceration: a systematic review and data synthesis. Am J Public Health 2015; 105:e5–16.
7. Hemmige V, McFadden R, Cook S, Tang H, Schneider JA. HIV prevention interventions to reduce racial disparities in the United States: a systematic review. J Gen Intern Med 2012; 27:1047–1067.
8. Brewer RA, Magnus M, Kuo I, Wang L, Liu TY, Mayer KH. The high prevalence of incarceration history among black men who have sex with men in the United States: associations and implications. Am J Public Health 2014; 104:448–454.
9. Brewer RA, Magnus M, Kuo I, Wang L, Liu TY, Mayer KH. Exploring the relationship between incarceration and HIV among black men who have sex with men in the United States. J Acquir Immune Defic Syndr 2014; 65:218–225.
10. Brewer RA, Magnus M, Kuo I, Wang L, Liu TY, Mayer KH. Exploring the relationship between incarceration and HIV among black men who have sex with men in the United States. J Acquir Immune Defic Syndr 2014; 65:218–225.
11. Singh S, Bradley H, Hu X, Skarbinski J, Hall HI, Lansky A, et al. Men living with diagnosed HIV who have sex with men: progress along the continuum of HIV care: United States, 2010. MMWR Morb Mortal Wkly Rep 2014; 63:829–833.
12. Whiteside YO, Cohen SM, Bradley H, Skarbinski J, Hall HI, Lansky A, et al. Progress along the continuum of HIV care among blacks with diagnosed HIV: United States, 2010. MMWR Morb Mortal Wkly Rep 2014; 63:85–89.
13. Khanna AS, Michaels S, Skaathun B, Morgan E, Green K, Young L, et al. Preexposure prophylaxis awareness and use in a population-based sample of young black men who have sex with men. JAMA Intern Med 2016; 176:136–138.
14. Livak B, Schneider JA. Using sociometric measures to assess non-response bias. Ann Epidemiol 2014; 24:554–557.
15. Chicago Department of Public HealthHIV/STI surveillance report, 2015. Chicago, IL: City of Chicago; 2015.
16. U.S. Census Bureau. State and County Quick Facts. Data derived from population estimates, American community survey, census of population and housing, county business patterns, economic census, survey of business owners, building permits, consolidated federal funds report, census of governments. In.
17. Heckathorn DD. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl 1997; 44:174–199.
18. Hightow-Weidman LB, Jones K, Wohl AR, Futterman D, Outlaw A, Phillips G 2nd, et al. Early linkage and retention in care: findings from the outreach, linkage, and retention in care initiative among young men of color who have sex with men. AIDS Patient Care STDS 2011; 25 (suppl 1):S31–38.
19. Reed JB, Hanson D, McNaghten AD, Bertolli J, Teshale E, Gardner L, et al. HIV testing factors associated with delayed entry into HIV medical care among HIV-infected persons from eighteen states, United States, 2000–2004. AIDS Patient Care STDS 2009; 23:765–773.
20. Craw J, Gardner L, Rossman A, Gruber D, Noreen O, Jordan D, et al. Structural factors and best practices in implementing a linkage to HIV care program using the ARTAS model. BMC Health Serv Res 2010; 10:246.
21. Ikard K, Janney J, Hsu LC, Isenberg DJ, Scalco MB, Schwarcz S, et al. Estimation of unmet need for HIV primary medical care: a framework and three case studies. AIDS Educ Prev 2005; 17:26–38.
22. Bamford LP, Ehrenkranz PD, Eberhart MG, Shpaner M, Brady KA. Factors associated with delayed entry into primary HIV medical care after HIV diagnosis. AIDS 2010; 24:928–930.
23. Keller S, Jones J, Erbelding E. Choice of rapid HIV testing and entrance into care in Baltimore city sexually transmitted infections clinics. AIDS Patient Care STDS 2011; 25:237–243.
24. Holtgrave DR. On the epidemiologic and economic importance of the National AIDS Strategy for the United States. J Acquir Immune Defic Syndr 2010; 55:139–142.
25. Rothman RE, Kelen GD, Harvey L, Shahan JB, Hairston H, Burah A, et al. Factors associated with no or delayed linkage to care in newly diagnosed human immunodeficiency virus (HIV)-1-infected patients identified by emergency department-based rapid HIV screening programs in two urban EDs. Acad Emerg Med 2012; 19:497–503.
26. Bertolli J, Shouse RL, Beer L, Valverde E, Fagan J, Jenness SM, et al. Using HIV surveillance data to monitor missed opportunities for linkage and engagement in HIV medical care. Open AIDS J 2012; 6:131–141.
27. Mugavero MJ, Davila JA, Nevin CR, Giordano TP. From access to engagement: measuring retention in outpatient HIV clinical care. AIDS Patient Care STDS 2010; 24:607–613.
28. Sullivan PS, Rosenberg ES, Sanchez TH, Kelley CF, Luisi N, Cooper HL, et al. Explaining racial disparities in HIV incidence in black and white men who have sex with men in Atlanta, GA: a prospective observational cohort study. Ann Epidemiol 2015; 25:445–454.
29. Maulsby C, Millett G, Lindsey K, Kelley R, Johnson K, Montoya D, et al. HIV among Black men who have sex with men (MSM) in the United States: a review of the literature. AIDS Behav 2014; 18:10–25.
30. Mayer KH, Wang L, Koblin B, Mannheimer S, Magnus M, del Rio C, et al. Concomitant socioeconomic, behavioral, and biological factors associated with the disproportionate HIV infection burden among Black men who have sex with men in 6 U.S. cities. PLoS One 2014; 9:e87298.
31. Christopoulos KA, Das M, Colfax GN. Linkage and retention in HIV care among men who have sex with men in the United States. Clin Infect Dis 2011; 52 (suppl 2):S214–222.
32. Giordano TP, Gifford AL, White AC Jr, Suarez-Almazor ME, Rabeneck L, Hartman C, et al. Retention in care: a challenge to survival with HIV infection. Clin Infect Dis 2007; 44:1493–1499.
33. Zellner A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 1962; 57:348–368.
34. Stata CorpStata Statistical Software: Release 14. College Station, TX: StataCorp LP; 2015.
35. Khan MR, Miller WC, Schoenbach VJ, Weir SS, Kaufman JS, Wohl DA, et al. Timing and duration of incarceration and high-risk sexual partnerships among African Americans in North Carolina. Ann Epidemiol 2008; 18:403–410.
36. Khan MR, Rosen DL, Epperson MW, Goldweber A, Hemberg JL, Richardson J, et al. Adolescent criminal justice involvement and adulthood sexually transmitted infection in a nationally representative US sample. J Urban Health 2013; 90:717–728.
37. Iroh PA, Mayo H, Nijhawan AE. The HIV care cascade before, during, and after incarceration: a systematic review and data synthesis. Am J Public Health 2015; 105:e5–e16.
38. Bouris A, Hill BJ, Fisher K, Erickson G, Schneider JA. Mother-son communication about sex and routine human immunodeficiency virus testing among younger men of color who have sex with men. J Adolesc Health 2015; 57:515–522.
39. Schneider J, Michaels S, Bouris A. Family network proportion and HIV risk among black men who have sex with men. J Acquir Immune Defic Syndr 2012; 61:627–635.
40. Valente TW. Network interventions. Science 2012; 337:49–53.
41. Bouris A, Voisin D, Pilloton M, Flatt N, Eavou R, Hampton K, et al. Project nGage: network supported HIV care engagement for younger black men who have sex with men and transgender persons. J AIDS Clin Res 2013; 4:

criminal justice involvement; HIV; HIV care; HIV care continuum; MSM; respondent-driven sampling

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