AIDS and Behavior

, Volume 21, Issue 10, pp 3047–3056 | Cite as

Identifying Patterns of Social and Economic Hardship Among Structurally Vulnerable Women: A Latent Class Analysis of HIV/STI Risk

  • Meredith L. Brantley
  • Deanna Kerrigan
  • Danielle German
  • Sahnah Lim
  • Susan G. Sherman
Original Article


Women who are structurally vulnerable are at heightened risk for HIV/STIs. Identifying typologies of structural vulnerability that drive HIV/STI risk behavior is critical to understanding the nature of women’s risk. Latent class analysis (LCA) was used to classify exotic dancers (n = 117) into subgroups based on response patterns of four vulnerability indicators. Latent class regression models tested whether sex- and drug-related risk behavior differed by vulnerability subgroup. Prevalence of vulnerability indicators varied across housing instability (39%), financial insecurity (39%), limited education (67%), and arrest history (36%). LCA yielded a two-class model solution, with 32% of participants expected to belong to a “high vulnerability” subgroup. Dancers in the high vulnerability subgroup were more likely to report sex exchange (OR = 8.1, 95% CI, 1.9–34.4), multiple sex partnerships (OR = 6.4, 95% CI, 1.9–21.5), and illicit drug use (OR = 17.4, 95% CI, 2.5–123.1). Findings underscore the importance of addressing inter-related structural factors contributing to HIV/STI risk.


HIV Sexually transmitted infections Social determinants Exotic dance club 



We are grateful for the support of the Baltimore City Health Department, recruitment and data collection by the STILETTO study team, and for the women who participated.


The STILETTO Study was supported by the National Institute of Drug Abuse (NIDA R21 DA033855) and the Johns Hopkins Center for AIDS Research (JHU CFAR; NIAID 1P30AI094189). M.L. Brantley was supported by the National Institute of Allergy and Infectious Disease (T32 AI050056-12) and the National Institute on Drug Abuse (F31 DA038540). S. Lim was supported by the National Institute of Allergy and Infectious Disease (T32 AI050056-12).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

The study was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. Study procedures were in accordance with the ethical standards of the Johns Hopkins Bloomberg School of Public Health and with the 1964 Helsinki declaration and its later amendments.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Centers for disease control and prevention. 2014 Diagnoses of HIV Infection in the United States and dependent areas. HIV Surveillance Report 2015.Google Scholar
  2. 2.
    Centers for Disease Control and Prevention. Sexually transmitted disease surveillance 2012. Atlanta: U.S. Department of Health and Human Services; 2013.Google Scholar
  3. 3.
    Rhodes T, Wagner K, Strathdee SA, Shannon K, Davidson P, Bourgois P. Structural violence and structural vulnerability within the risk environment: theoretical and methodological perspectives for a social epidemiology of HIV risk among injection drug users and sex workers. In: O’Campo P, Dunn JR, editors. Rethinking social epidemiology: towards a science of change. Dordrecht: Springer; 2012. p. 205–30.CrossRefGoogle Scholar
  4. 4.
    Gupta GR, Ogden J, Warner A. Moving forward on women’s gender-related HIV vulnerability: the good news, the bad news and what to do about it. Glob Public Health. 2011;6(Suppl 3):S370–82.CrossRefPubMedGoogle Scholar
  5. 5.
    Gupta GR, Parkhurst JO, Ogden JA, Aggleton P, Mahal A. Structural approaches to HIV prevention. Lancet. 2008;372(9640):764–75.CrossRefPubMedGoogle Scholar
  6. 6.
    Blum RW, McNeely C, Nonnemaker J. Vulnerabilty, risk, and protection. J Adolesc Health. 2002;31(1 Suppl):28–39.CrossRefPubMedGoogle Scholar
  7. 7.
    Quesada J, Hart LK, Bourgois P. Structural vulnerability and health: Latino migrant laborers in the United States. Med Anthropol. 2011;30(4):339–62.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Holmes SM. Structural vulnerability and hierarchies of ethnicity and citizenship on the farm. Med Anthropol. 2011;30(4):425–49.CrossRefPubMedGoogle Scholar
  9. 9.
    Baral S, Beyrer C, Muessig K, Poteat T, Wirtz AL, Decker MR, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis. 2012;12(7):538–49.CrossRefPubMedGoogle Scholar
  10. 10.
    German D, Latkin CA. Social stability and health: exploring multidimensional social disadvantage. J Urban Health. 2012;89(1):19–35.CrossRefPubMedGoogle Scholar
  11. 11.
    German D, Latkin CA. Social stability and HIV risk behavior: evaluating the role of accumulated vulnerability. AIDS Behav. 2012;16(1):168–78.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Aidala A, Cross JE, Stall R, Harre D, Sumartojo E. Housing status and HIV risk behaviors: implications for prevention and policy. AIDS Behav. 2005;9(3):251–65.CrossRefPubMedGoogle Scholar
  13. 13.
    Rhodes SD, Tanner A, Duck S, et al. Female sex work within the rural immigrant Latino community in the southeast United States: an exploratory qualitative community-based participatory research study. Prog Community Health Partnersh. 2012;6(4):417–27.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Dank M, Khan B, Downey PM, et al. Estimating the size and structure of the underground commercial sex economy in eight major US cities. Washington, DC: The Urban Institute; 2014.CrossRefGoogle Scholar
  15. 15.
    Beyrer C, Crago AL, Bekker LG, et al. An action agenda for HIV and sex workers. Lancet. 2015;385(9964):287–301.CrossRefPubMedGoogle Scholar
  16. 16.
    Jewkes R, Dunkle K, Nduna M, et al. Factors associated with HIV sero-status in young rural South African women: connections between intimate partner violence and HIV. Int J Epidemiol. 2006;35(6):1461–8.CrossRefPubMedGoogle Scholar
  17. 17.
    Pettifor AE, Measham DM, Rees HV, Padian NS. Sexual power and HIV risk South Africa. Emerg Infect Dis. 2004;10(11):1996–2004.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Dunkle KL, Jewkes RK, Brown HC, Gray GE, McIntryre JA, Harlow SD. Transactional sex among women in Soweto, South Africa: prevalence, risk factors and association with HIV infection. Soc Sci Med. 2004;59(8):1581–92.CrossRefPubMedGoogle Scholar
  19. 19.
    Hallfors DD, Iritani BJ, Miller WC, Bauer DJ. Sexual and drug behavior patterns and HIV and STD racial disparities: the need for new directions. Am J Public Health. 2007;97(1):125–32.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Maticka-Tyndale E, Lewis J, Clark JP, Zubick J, Young S. Social and cultural vulnerability to sexually transmitted infection: the work of exotic dancers. Can J Public Health. 1999;90(1):19–22.PubMedGoogle Scholar
  21. 21.
    Sherman SG, Lilleston P, Reuben J. More than a dance: the production of sexual health risk in the exotic dance clubs in Baltimore, USA. Soc Sci Med. 2011;73(3):475–81.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Reuben J, Serio-Chapman C, Welsh C, Matens R, Sherman SG. Correlates of current transactional sex among a sample of female exotic dancers in Baltimore, MD. J Urban Health. 2011;88(2):342–51.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Stall R, Mills TC, Williamson J, et al. Association of co-occurring psychosocial health problems and increased vulnerability to HIV/AIDS among urban men who have sex with men. AJPH. 2003;93(6):939–42.CrossRefGoogle Scholar
  24. 24.
    Reilly ML, German D, Serio-Chapman C, Sherman SG. Structural vulnerabilities to HIV/STI risk among female exotic dancers in Baltimore, Maryland. AIDS Care. 2015;27(6):777–82.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Wilson PA, Nanin J, Amesty S, Wallace S, Cherenack EM, Fullilove R. Using syndemic theory to understand vulnerability to HIV infection among Black and Latino men in New York City. J Urban Health. 2014;91(5):983–98.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Mizuno Y, Purcell DW, Knowlton AR, Wilkinson JD, Gourevitch MN, Knight KR. Syndemic vulnerability, sexual and injection risk behaviors, and HIV continuum of care outcomes in HIV-positive injection drug users. AIDS Behav. 2015;19(4):684–93.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Sherman SG, Duong Q, Taylor R, Reilly ML, Zelaya CE, Huettner S, Ellen J. Measuring a novel STI risk environment: the exotic dance club. STD Prevention Conference, Atlanta, GA, Accessed 10 June 2014.Google Scholar
  28. 28.
    German D, Davey M, Latkin C. Residential transience and HIV risk behaviors among injection drug users. AIDS Behav. 2007;11(6 Suppl):21–30.CrossRefPubMedGoogle Scholar
  29. 29.
    Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The adverse childhood experiences (ACE) study. Am J Prev Med. 1998;14(4):245–58.CrossRefPubMedGoogle Scholar
  30. 30.
    Straus MA, Hamby SL, Boney-McCoy S, Sugerman DB. The revised conflict tactics scale (CTS2): development and preliminary psychometric data. J Fam Issues. 1996;17(3):283–316.CrossRefGoogle Scholar
  31. 31.
    Radloff LS. The CES-D scale: a self report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.CrossRefGoogle Scholar
  32. 32.
    Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for Epidemiological Studies-Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging. 1997;12:277–87.CrossRefPubMedGoogle Scholar
  33. 33.
    Dziak JJ, Lanza ST, Tan X. Effect size, statistical power and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. Struct Equ Model. 2014;21(4):534–52.CrossRefGoogle Scholar
  34. 34.
    Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model. 2007;14(4):535–69.CrossRefGoogle Scholar
  35. 35.
    Akaike H. Factor analysis and AIC. Psychometrika. 1987;52(3):317–32.CrossRefGoogle Scholar
  36. 36.
    Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–4.CrossRefGoogle Scholar
  37. 37.
    Sclove L. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika. 1987;52(3):333–43.CrossRefGoogle Scholar
  38. 38.
    Celeux G, Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. J Classif. 1996;13:195–212.CrossRefGoogle Scholar
  39. 39.
    LCA outcome probability calculator (Version 1.0). 2011. The Methodology Center, Penn State: University Park.Google Scholar
  40. 40.
    Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157–68.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Development Core Team R. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015.Google Scholar
  42. 42.
    Linzer DA, Lewis JB. poLCA: an R package for polytomous variable latent class analysis. J Stat Softw. 2011;42(10):1–29.CrossRefGoogle Scholar
  43. 43.
    Muthén LK, Muthén BO. Mplus User’s Guide. Seventh Edition. 1998–2015, Muthén & Muthén: Los Angeles, CA.Google Scholar
  44. 44.
    Schwartz H, Ecola L, Leuschner KJ, Kofner A. Inclusionary zoning can bring poor families closer to good schools, in how housing matters. Chicago: MacArthur Foundation; 2014.Google Scholar
  45. 45.
    Cunningham M, MacDonald G. Housing as a platform for improving education outcomes among low-income children. Washington, DC: Urban Institute; 2012.Google Scholar
  46. 46.
    Pellowski JA, Kalichman SC, Matthews KA, Adler N. A pandemic of the poor: social disadvantage and the U.S. HIV epidemic. Am Psychol. 2013;68(4):197–209.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    El-Bassel N, Gilbert L, Wu E, Hill J. Relationship between drug abuse and intimate partner violence: a longitudinal study among women receiving methadone. Am J Public Health. 2005;95(3):465–70.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Ulibarri MD, Roesch S, Rangel MG, Staines H, Amaro H, Strathdee SA. “Amar te Duele” (“love hurts”): sexual relationship power, intimate partner violence, depression symptoms and HIV risk among female sex workers who use drugs and their non-commercial, steady partners in Mexico. AIDS Behav. 2015;19(1):9–18.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Decker MR, Miller E, McCauley HL, Tancredi DJ, Anderson H, Levenson RR, Silverman JG. Recent partner violence and sexual and drug-related STI/HIV risk among adolescent and young adult women attending family planning clinics. Sex Transm Dis. 2014;90:145–9.CrossRefGoogle Scholar
  50. 50.
    Illangasekare SL, Burke JG, Chander G, Gielen AC. Depression and social support among women living with the substance abuse, violence, and HIV/AIDS syndemic: a qualitative exploration. Womens Health Issues. 2014;24(5):551–7.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Rudolph L, Caplan J, Ben-Moshe K, Dillon L. Health in all policies: a guide for state and local governments. Washington, DC and Oakland, CA: American Public Health Association and Public Health Institute; 2013.Google Scholar
  52. 52.
    Wernham A, Teurtsch SM. Health in all policies for big cities. J Public Health Manage Pract. 2015;21(Suppl 1):S56–65.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Health, Behavior and SocietyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Department of Population, Family and Reproductive HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  3. 3.Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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