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Network dynamics of HIV risk and prevention in a population-based cohort of young Black men who have sex with men

Published online by Cambridge University Press:  01 February 2017

J. SCHNEIDER ,
B. CORNWELL ,
A. JONAS ,
N. LANCKI ,
R. BEHLER ,
B. SKAATHUN ,
L. E. YOUNG ,
E. MORGAN ,
S. MICHAELS  and
R. DUVOISIN
...Show all authors
J. SCHNEIDER
Affiliation:
Department of Medicine, University of Chicago, Chicago, IL, USA Department of Public Health Sciences, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA NORC, Chicago, IL, USA (e-mail: jschnei1@medicine.bsd.uchicago.edu)
B. CORNWELL
Affiliation:
Department of Sociology, Cornell University, Ithaca, NY, USA (e-mail: btc49@cornell.edu)
A. JONAS
Affiliation:
Department of Medicine, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: ajonas1@medicine.bsd.uchicago.edu, nlancki1@medicine.bsd.uchicago.edu)
N. LANCKI
Affiliation:
Department of Medicine, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: ajonas1@medicine.bsd.uchicago.edu, nlancki1@medicine.bsd.uchicago.edu)
R. BEHLER
Affiliation:
Department of Sociology, Cornell University, Ithaca, NY, USA (e-mail: rlb296@cornell.edu)
B. SKAATHUN
Affiliation:
Department of Public Health Sciences, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: brittsl@uchicago.edu)
L. E. YOUNG
Affiliation:
Department of Medicine, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: lyoung1@medicine.bsd.uchicago.edu)
E. MORGAN
Affiliation:
Department of Public Health Sciences, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: morgane@uchicago.edu)
S. MICHAELS
Affiliation:
NORC, Chicago, IL, USA (e-mail: Stuart@norc.org, Rebeccah@norc.org)
R. DUVOISIN
Affiliation:
NORC, Chicago, IL, USA (e-mail: Stuart@norc.org, Rebeccah@norc.org)
A. S. KHANNA
Affiliation:
Department of Medicine, University of Chicago, Chicago, IL, USA Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, USA (e-mail: akhanna@medicine.bsd.uchicago.edu)
S. FRIEDMAN
Affiliation:
National Development Research Institute, New York, NY, USA (e-mail: Friedman@ndri.org)
P. SCHUMM
Affiliation:
Department of Public Health Sciences, University of Chicago, Chicago, IL, USA (e-mail: pschumm@uchicago.edu)
E. LAUMANN
Affiliation:
NORC, Chicago, IL, USA Department of Sociology, University of Chicago, Chicago, IL, USA
FOR THE uCONNECT STUDY TEAM
Affiliation:
(e-mail: ob01@uchicago.edu)
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Abstract

Critical to the development of improved HIV elimination efforts is a greater understanding of how social networks and their dynamics are related to HIV risk and prevention. In this paper, we examine network stability of confidant and sexual networks among young black men who have sex with men (YBMSM). We use data from uConnect (2013–2016), a population-based, longitudinal cohort study. We use an innovative approach to measure both sexual and confidant network stability at three time points, and examine the relationship between each type of stability and HIV risk and prevention behaviors. This approach is consistent with a co-evolutionary perspective in which behavior is not only affected by static properties of an individual's network, but may also be associated with changes in the topology of his or her egocentric network. Our results indicate that although confidant and sexual network stability are moderately correlated, their dynamics are distinct with different predictors and differing associations with behavior. Both types of stability are associated with lower rates of risk behaviors, and both are reduced among those who have spent time in jail. Public health awareness and engagement with both types of networks may provide new opportunities for HIV prevention interventions.

Keywords

networkblack MSMHIVprevention
Type
Research Article
Information
Network Science , Volume 5 , Special Issue 3: Networks and Health , September 2017 , pp. 381 - 409
Copyright
Copyright © Cambridge University Press 2017 

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References

Anderson, B. J., & Stein, M. D. (2011). A behavioral decision model testing the association of marijuana use and sexual risk in young adult women. AIDS and Behavior, 15 (4), 875–884.CrossRefGoogle ScholarPubMed
Andrade, L. F., Carroll, K. M., & Petry, N. M. (2013). Marijuana use is associated with risky sexual behaviors in treatment-seeking polysubstance abusers. The American Journal of Drug and Alcohol Abuse, 39 (4), 266–271.CrossRefGoogle ScholarPubMed
Armbruster, B., Roy, S., Kapur, A., & Schneider, J. A. (2013). Sex role segregation and mixing among men who have sex with men: Implications for biomedical HIV prevention interventions. PLoS One, 8 (8), e70043.CrossRefGoogle ScholarPubMed
Arnold, E. A., & Bailey, M. M. (2009). Constructing home and family: How the ballroom community supports African American GLBTQ youth in the face of HIV/AIDS. Journal of Gay & Lesbian Social Services, 21 (2–3), 171–188.CrossRefGoogle ScholarPubMed
Arnold, E. A., Sterrett-Hong, E., Jonas, A., & Pollack, L. M. (2016). Social networks and social support among ball-attending African American men who have sex with men and transgender women are associated with HIV-related outcomes. Global Public Health. 1–15. [Epub ahead of print]Google Scholar
Benotsch, E. G., Kalichman, S., & Cage, M. (2002). Men who have met sex partners via the Internet: Prevalence, predictors, and implications for HIV prevention. Archives of Sexual Behavior, 31 (2), 177–183.CrossRefGoogle ScholarPubMed
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.Google Scholar
Bouris, A., Voisin, D., Pilloton, M., Flatt, N., Eavou, R., Hampton, K., . . . Schneider, J. A. (2013). Project nGage: Network supported HIV care engagement for younger black men who have sex with men and transgender persons. Journal of AIDS & Clinical Research, 4 (9), 1–7.Google ScholarPubMed
Braine, N., van Sluytman, L., Acker, C., Friedman, S., & Des Jarlais, D. C. (2011). Sexual contexts and the process of risk reduction. Culture Health & Sexuality, 13 (7), 797–814.CrossRefGoogle ScholarPubMed
Bryan, A. D., Schmiege, S. J., & Magnan, R. E. (2012). Marijuana use and risky sexual behavior among high-risk adolescents: Trajectories, risk factors, and event-level relationships. Developmental Psychology, 48 (5), 1429–1442.CrossRefGoogle ScholarPubMed
Burt, R. S. (2000). The network of social capital. Research in Organizational Behavior, 22, 345–423.CrossRefGoogle Scholar
CDC (2016). HIV among gay and bisexual men. Retrieved July 7, 2016, from http://www.cdc.gov/nchhstp/newsroom/docs/factsheets/cdc-msm-508.pdf.Google Scholar
Cerhan, J. R., & Wallace, R. B. (1997). Change in social ties and subsequent mortality in rural elders. Epidemiology, 8 (5), 475–481.CrossRefGoogle ScholarPubMed
Chicago Police Department (2013). CLEARMAP crime summary. Retrieved January 5, 2013, from http://gis.chicagopolice.org/CLEARMap_crime_sums/startPage.htm#.Google Scholar
Chicago Policy Research Team (2010). Deserted?: A policy report on food access in four south side Chicago neighborhoods. The Chicago Policy Research Team.Google Scholar
Cohen, M. S., Chen, Y. Q., McCauley, M., Gamble, T., Hosseinipour, M. C., Kumarasamy, N., . . . H.S. Team, H. S. (2011). Prevention of HIV-1 infection with early antiretroviral therapy. The New England Journal of Medicine, 365 (6), 493–505.CrossRefGoogle ScholarPubMed
Cornwell, B. (2003). The dynamic properties of social support: Decay, growth, and staticity and their effects on adolescent depression. Social Forces, 81 (3), 953–978.CrossRefGoogle Scholar
Cornwell, B., & Laumann, E. O. (2015). The health benefits of network growth: New evidence from a national survey of older adults. Social Science & Medicine, 125, 94–106.CrossRefGoogle ScholarPubMed
Cornwell, B., Schumm, L. P., Laumann, E. O., Kim, J., & Kim, Y. J. (2014). Assessment of social network change in a National Longitudinal Survey. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69 (Suppl. 2), S75–S82.CrossRefGoogle Scholar
d'Epinay, C. J., Cavalli, S., & Guillet, L. A. (2010). Bereavement in very old age: Impact on health and relationships of the loss of a spouse, a child, a sibling, or a close friend. OMEGA – Journal of Death and Dying, 60 (4), 301–325.CrossRefGoogle Scholar
DeBlaere, C., Brewster, M. E., Sarkees, A., & Moradi, B. (2010). Conducting research with LGB people of color: Methodological challenges and strategies. Counselling Psychologist, 38, 331–362.CrossRefGoogle Scholar
Desmond, M. (2012). Disposable ties and the Urban poor. American Journal of Sociology, 117 (5), 1295–1335.CrossRefGoogle Scholar
Dombrowski, K., Curtis, R., Friedman, S., & Khan, B. (2013a). Topological and historical considerations for infectious disease transmission among injecting drug users in Bushwick, Brooklyn (USA). World Journal of AIDS, 3 (1), 1–9.CrossRefGoogle ScholarPubMed
Dombrowski, K., Khan, B., McLean, K., Curtis, R., Wendel, T., Misshula, E., & Friedman, S. (2013b). A reexamination of connectivity trends via exponential random graph modeling in two IDU risk networks. Substance Use & Misuse, 48 (14), 1485–1497.CrossRefGoogle ScholarPubMed
Dyer, T. P., Shoptaw, S., Guadamuz, T. E., Plankey, M., Kao, U., Ostrow, D., . . . Stall, R. (2012). Application of syndemic theory to black men who have sex with men in the Multicenter AIDS Cohort Study. Journal of Urban Health, 89 (4), 697–708.CrossRefGoogle ScholarPubMed
Eng, P. M., Rimm, E. B., Fitzmaurice, G., & Kawachi, I. (2002). Social ties and change in social ties in relation to subsequent total and cause-specific mortality and coronary heart disease incidence in men. American Journal of Epidemiology, 155 (8), 700–709.CrossRefGoogle ScholarPubMed
Fischer, C. S., & Beresford, L. (2015). Changes in support networks in late middle age: The extension of gender and educational differences. Journal of Gerontology, Series B: Psychological Sciences and Social Sciences, 70 (1), 123–131.CrossRefGoogle ScholarPubMed
Friedman, S. R., & Aral, S. (2001). Social networks, risk-potential networks, health, and disease. Journal of Urban Health, 78 (3), 411–418.CrossRefGoogle ScholarPubMed
Friedman, S. R., Bolyard, M., Maslow, C., Mateu-Gelabert, P., & Sandoval, M. (2005). Harnessing the power of social networks to reduce HIV risk. Focus, 20 (1), 5–6.Google ScholarPubMed
Friedman, S. R., Bolyard, M., Mateu-Gelabert, P., Goltzman, P., Pawlowicz, M. P., Singh, D. Z., . . . Flom, P. L. (2007a). Some data-driven reflections on priorities in AIDS network research. AIDS Behavior, 11 (5), 641–651.CrossRefGoogle ScholarPubMed
Friedman, S. R., Downing, M. J. Jr., Smyrnov, P., Nikolopoulos, G., Schneider, J. A., Livak, B., . . . Hatzakis, A. (2014). Socially-integrated transdisciplinary HIV prevention. AIDS Behavior, 18 (10), 1821–1834.CrossRefGoogle ScholarPubMed
Friedman, S. R., Maslow, C., Bolyard, M., Sandoval, M., Mateu-Gelabert, P., & Neaigus, A. (2004). Urging others to be healthy: “Intravention” by injection drug users as a community prevention goal. AIDS Education and Prevention, 16 (3), 250–263.CrossRefGoogle ScholarPubMed
Friedman, S. R., Mateu-Gelabert, P., Curtis, R., Maslow, C., Bolyard, M., Sandoval, M., & Flom, P. L. (2007b). Social capital or networks, negotiations, and norms? A neighborhood case study. American Journal of Preventive Medicine, 32 (Suppl. 6), S160–S170.CrossRefGoogle ScholarPubMed
Friedman, S. R., Sandoval, M., Mateu-Gelabert, P., Rossi, D., Gwadz, M., Dombrowski, K., . . . Perlman, D. (2013). Theory, measurement and hard times: Some issues for HIV/AIDS research. AIDS Behavior, 17 (6), 1915–1925.CrossRefGoogle ScholarPubMed
Fujimoto, K., Wang, P., Ross, M. W., & Williams, M. L. (2015). Venue-mediated weak ties in multiplex HIV transmission risk networks among drug-using male sex workers and associates. American Journal of Public Health, 105 (6), 1128–1135.CrossRefGoogle ScholarPubMed
Gayman, M., & Barragan, J. (2013). Multiple perceived reasons for major discrimination and depression. Society and Mental Health, 3, 203–220.CrossRefGoogle Scholar
Gerstorf, D., Rocke, C., & Lachman, M. E. (2011). Antecedent-consequent relations of perceived control to health and social support: Longitudinal evidence for between-domain associations across adulthood. The Journal of Gerontology, Series B: Psychological Sciences and Social Sciences, 66B (1), 61–71.CrossRefGoogle Scholar
Gile, K. J., & Handcock, M. S. (2010). Respondent-driven sampling: An assessment of current methodology. Sociological Methodology, 40 (1), 285–327.CrossRefGoogle ScholarPubMed
Giordano, G. N., & Lindstrom, M. (2010). The impact of changes in different aspects of social capital and material conditions on self-rated health over time: A longitudinal cohort study. Social Sciences & Medicine, 70 (5), 700–710.CrossRefGoogle ScholarPubMed
Goodreau, S. M., Carnegie, N. B., Vittinghoff, E., Lama, J. R., Sanchez, J., Grinsztejn, B., . . . Buchbinder, S. P. (2012). What drives the US and Peruvian HIV epidemics in men who have sex with men (MSM)? PLoS One, 7 (11), e50522.CrossRefGoogle ScholarPubMed
Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44 (2), 174–199.CrossRefGoogle Scholar
Hernandez-Romieu, A. C., Sullivan, P. S., Rothenberg, R., Grey, J., Luisi, N., Kelley, C. F., & Rosenberg, E. S. (2015). Heterogeneity of HIV prevalence among the sexual networks of black and white men who have sex with men in Atlanta: Illuminating a mechanism for Increased HIV risk for young black men who have sex with men. Sexually Transmitted Diseases, 42 (9), 505–512.CrossRefGoogle ScholarPubMed
Holloway, I. W., Pulsipher, C. A., Gibbs, J., Barman-Adhikari, A., & Rice, E. (2015). Network influences on the sexual risk behaviors of gay, bisexual and other men who have sex with men using geosocial networking applications. AIDS Behavior, 19 (Suppl. 2), 112–122.CrossRefGoogle ScholarPubMed
Hoyert, D. L. (2012). 75 years of mortality in the United States, 1935–2010, NCHS Data Brief, No. 88.Google Scholar
Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. Berkeley: University of California Berkeley Press.Google Scholar
Kelly, J. A., Lawrence, J. S. St, Diaz, Y. E., Stevenson, L. Y., Hauth, A. C., Brasfield, T. L., . . . Andrew, M. E. (1991). HIV risk behavior reduction following intervention with key opinion leaders of population: An experimental analysis. American Journal of Public Health, 81 (2), 168–171.CrossRefGoogle ScholarPubMed
Khan, B., Dombrowski, K., Saad, M., Mclean, K., & Friedman, S. R. (2013). Network firewall dynamics and the sub-saturation stabilization of HIV. Discrete Dynamics in Nature and Society, 2013. doi: 10.1155/2013/720818.CrossRefGoogle Scholar
Khanna, A. S., Goodreau, S. M., Gorbach, P. M., Daar, E., & Little, S. J. (2014). Modeling the impact of post-diagnosis behavior change on HIV prevalence in Southern California men who have sex with men (MSM). AIDS Behavior, 18 (8), 1523–1531.CrossRefGoogle ScholarPubMed
Khanna, A. S., Michaels, S., Skaathun, B., Morgan, E., Green, K., Young, L., . . . uConnect Study team (2016). Preexposure prophylaxis awareness and use in a population-based sample of young black men who have sex with men. JAMA Internal Medicine, 176 (1), 136–138.CrossRefGoogle Scholar
Kohler, H. P., Behrman, J. R., & Watkins, S. C. (2007). Social networks and HIV/AIDs risk perceptions. Demography, 44 (1), 1–33.CrossRefGoogle ScholarPubMed
Kubicek, K., McNeeley, M., Holloway, I. W., Weiss, G., & Kipke, M. D. (2013). “It's like our own little world'': Resilience as a factor in participating in the Ballroom community subculture. AIDS Behavior, 17 (4), 1524–1539.CrossRefGoogle ScholarPubMed
Laumann, E. O., Ellingson, S., Mahay, J., Paik, A., & Youm, Y. (2004). The sexual organization of the city. Chicago: University of Chicago.CrossRefGoogle Scholar
Laumann, E. O., Gagnon, J. H., Michael, R., & Michaels, S. (1994). The social organization of sexuality: Sexual Practices in the United States. Chicago: University of Chicago Press.Google Scholar
Laumann, E. O. & Youm, Y. (1999). Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: A network explanation. Sexually Transmitted Disease, 26 (5), 250–261.CrossRefGoogle ScholarPubMed
Livak, B., Michaels, S., Green, K., Nelson, C., Westbrook, M., Simpson, Y., . . . Schneider, J. A. (2013). Estimating the number of young Black men who have sex with men (YBMSM) on the south side of Chicago: Towards HIV elimination within US urban communities. Journal of Urban Health, 90 (6), 1205–1213.CrossRefGoogle ScholarPubMed
Mari Gallagher Research & Consulting Group (2007). Examining the impact of food deserts on public health in Chi cago.Google Scholar
Mateu-Gelabert, P., Bolyard, M., Maslow, C., Sandoval, M., Flom, P. L., & Friedman, S. R. (2008). For the common good: Measuring residents' efforts to protect their community from drug- and sex-related harm. SAHARA Journal, 5 (3), 144–157.CrossRefGoogle ScholarPubMed
Mayer, K. H., Wang, L., Koblin, B., Mannheimer, S., Magnus, M., del Rio, C., . . . H.P. Team (2014). 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, 9 (1), e87298.CrossRefGoogle ScholarPubMed
McCreesh, N., Frost, S. D. W., Seeley, J., Katongole, J., Tarsh, M. N., Ndunguse, R., . . . White, R. G. (2012). Evaluation of respondent-driven sampling. Epidemiology, 23 (1), 138–147.CrossRefGoogle ScholarPubMed
McFadden, R. B., Bouris, A. M., Voisin, D. R., Glick, N. R., & Schneider, J. A. (2014). Dynamic social support networks of younger black men who have sex with men with new HIV infection. AIDS Care, 26 (10), 1275–1282.CrossRefGoogle ScholarPubMed
Millett, G. A., Flores, S. A., Peterson, J. L., & Bakeman, R. (2007). Explaining disparities in HIV infection among black and white men who have sex with men: A meta-analysis of HIV risk behaviors. AIDS, 21 (15), 2083–2091.CrossRefGoogle ScholarPubMed
Millett, G. A., Peterson, J. L., Flores, S. A., Hart, T. A., Jeffries, W. L. t., Wilson, P. A., . . . Remis, R. S. (2012). 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, 380 (9839), 341–348.CrossRefGoogle ScholarPubMed
Millett, G. A., Peterson, J. L., Wolitski, R. J., & Stall, R. (2006). Greater risk for HIV infection of black men who have sex with men: A critical literature review. American Journal of Public Health, 96 (6), 1007–1019.CrossRefGoogle ScholarPubMed
Morris, M. e. (2004). Network epidemiology. Oxford: Oxford University Press.CrossRefGoogle Scholar
Murrill, C. S., Liu, K. L., Guilin, V., Colon, E. R., Dean, L., Buckley, L. A., . . . Torian, L. V. (2008). HIV prevalence and associated risk behaviors in New York City's house ball community. American Journal of Public Health, 98 (6), 1074–1080.CrossRefGoogle ScholarPubMed
Mustanski, B., Garofalo, R., Herrick, A., & Donenberg, G. (2007). Psychosocial health problems increase risk for HIV among urban young men who have sex with men: Preliminary evidence of a syndemic in need of attention. Annals of Behavioral Medicine, 34 (1), 37–45.CrossRefGoogle ScholarPubMed
Mustanski, B. S. (2007). Are sexual partners met online associated with HIV/STI risk behaviours? Retrospective and daily diary data in conflict. AIDS Care, 19 (6), 822–827.CrossRefGoogle ScholarPubMed
Phillips, G. 2nd, Peterson, J., Binson, D., Hidalgo, J., Magnus, M. & YMSM of color SPNS Imitative Study Group (2011). House/ball culture and adolescent African-American transgender persons and men who have sex with men: A synthesis of the literature. AIDS Care, 23 (4), 515–520.CrossRefGoogle ScholarPubMed
Reisner, S. L., Mimiaga, M. J., Skeer, M., Bright, D., Cranston, K., Isenberg, D., . . . Mayer, K. H. (2009). Clinically significant depressive symptoms as a risk factor for HIV infection among black MSM in Massachusetts. AIDS Behavior, 13 (4), 798–810.CrossRefGoogle ScholarPubMed
Rothenberg, R. B., Potterat, J. J., Woodhouse, D. E., Muth, S. Q., Darrow, W. W., & Klovdahl, A. S. (1998). Social network dynamics and HIV transmission. AIDS, 12 (12), 1529–1536.CrossRefGoogle ScholarPubMed
Sasovova, Z., Mehra, A., Borgatti, S. P., & Schippers, M. (2010). Network churn: The effects of self-monitoring personality on brokerage dynamics. Administrative Science Quarterly, 55, 639–670.CrossRefGoogle Scholar
Schneider, J. A., Cornwell, B., Ostrow, D., Michaels, S., Schumm, P., Laumann, E. O., & Friedman, S. (2013). Network mixing and network influences most linked to HIV infection and risk behavior in the HIV epidemic among black men who have sex with men. American Journal of Public Health, 103 (1), e28–e36.CrossRefGoogle Scholar
Schneider, J. A., Kozloski, M., Michaels, S., Skaathun, B., Voisin, D., Lancki, N., . . . Schumm, P. (2017). Criminal justice involvement history is associated with better HIV care continuum metrics among a population-based sample of young Black men who have sex with men. AIDS, 31 (1), 159–165.CrossRefGoogle Scholar
Schneider, J. A., Walsh, T., Cornwell, B., Ostrow, D., Michaels, S., & Laumann, E. O. (2012). HIV health center affiliation networks of black men who have sex with men: Disentangling fragmented patterns of HIV prevention service utilization. Sexually Transmitted Disease, 39 (8), 598–604.CrossRefGoogle Scholar
Schneider, J. A., Zhou, A. N., & Laumann, E. O. (2015). A new HIV prevention network approach: Sociometric peer change agent selection. Social Sciences & Medicine, 125, 192–202.CrossRefGoogle ScholarPubMed
Seeman, T. E., Miller-Martinez, D. M., Stein Merkin, S., Lachman, M. E., Tun, P. A., & Karlamangla, A. S. (2011). Histories of social engagement and adult cognition: Midlife in the U.S. study. Journal of Gerontology, Series B: Psychological Sciences and Social Sciences, 66 (Suppl. 1), i141–i152.CrossRefGoogle ScholarPubMed
Seng, J. S., Lopez, W. D., Sperlich, M., Hamama, L. & Reed Meldrum, C. D. (2012). Marginalized identities, discrimination burden, and mental health: Empirical exploration of an interpersonal-level approach to modeling intersectionality. Social Science & Medicine, 75 (12), 2437–2445.CrossRefGoogle ScholarPubMed
Shah, N. S., Iveniuk, J., Muth, S. Q., Michaels, S., Jose, J. A., Laumann, E. O., & Schneider, J. A. (2014). Structural bridging network position is associated with HIV status in a younger Black men who have sex with men epidemic. AIDS Behavior, 18 (2), 335–345.CrossRefGoogle Scholar
Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociol Methods & Research Journal, 40 (2), 211–239.CrossRefGoogle ScholarPubMed
Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. New York: Chapman & Hall/CRC.CrossRefGoogle Scholar
Stack, C. (1974). All our kin: Strategies for survival in a black community. New York: Harper and Row.Google Scholar
Stall, R., Mills, T. C., Williamson, J., Hart, T., Greenwood, G., Paul, J., . . . Catania, J. A. (2003). Association of co-occurring psychosocial health problems and increased vulnerability to HIV/AIDS among urban men who have sex with men. American Journal of Public Health, 93 (6), 939–942.CrossRefGoogle ScholarPubMed
StataCorp (2015). Stata: Release 14. Statistical software. College Station, TX: StataCorpLP.Google Scholar
Stroebe, M., Schut, H., & Stroebe, W. (2007). Health outcomes of bereavement. Lancet, 370, 1960–1973.CrossRefGoogle ScholarPubMed
Census Bureau, U.S. (2013). 2005–2009 American community survey 5-year estimates. Retrieved January 3, 2013 from http://www.census.gov/library/publications/time-series/statistical_abstracts.html.Google Scholar
University of Illinois at Chicago (2005). The case for transit oriented development in the Greater Roseland Area.Google Scholar
University of Illinois at Chicago (2009). Transit equity matters: An equity index and regional analysis of the Red line and two other proposed CTA transit extensions.Google Scholar
Valente, T. W. (1995). Network models of the diffusion of innovations. Creskill, NJ: Hampton Press.Google Scholar
Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York: Oxford University Press.CrossRefGoogle Scholar
Valente, T. W. (2012). Network interventions. Science, 337 (6090), 49–53.CrossRefGoogle ScholarPubMed
Van Sluytman, L., Braine, N., Acker, C., Friedman, S., & Desjarlais, D. C. (2013). Migration narratives: expanding methods to examine the interaction of person and environment among aging gay men. Journal of Gerontological Social Work, 56 (3), 219–236.CrossRefGoogle ScholarPubMed
Vaughan, D. (1986). Uncoupling: Turning points in intimate relationships. New York: Oxford University Press.Google Scholar
Wasserheit, J. N., & Aral, S. O. (1996). The dynamic topology of sexually transmitted disease epidemics: Implications for prevention strategies. Journal of Infectious Diseases, 174 (Suppl. 2), S201–S213.CrossRefGoogle ScholarPubMed
Williams, D. R., & Sternthal, M. (2010). Understanding racial-ethnic disparities in Health: Sociological contributions. Journal of Health and Social Behavior, 51, S15–S27.CrossRefGoogle ScholarPubMed
Youm, Y., & Laumann, E. O. (2002). Social network effects on the transmission of sexually transmitted diseases. Sexually Transmitted Disease, 29 (11), 689–697.CrossRefGoogle ScholarPubMed
Zellner, A. (1970). Estimation of regression relationships containing unobservable variables. International Economic Review, 11 (441–454).CrossRefGoogle Scholar
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