Evaluating spillover of HIV knowledge from study participants to their network members in a stepped-wedge behavioural intervention in Tanzania

Jeffrey Rewley, Mary C Smith Fawzi, Keith McAdam, Sylvia Kaaya, Yuanyuan Liu, Jim Todd, Irene Andrew, Jukka Pekka Onnela, Jeffrey Rewley, Mary C Smith Fawzi, Keith McAdam, Sylvia Kaaya, Yuanyuan Liu, Jim Todd, Irene Andrew, Jukka Pekka Onnela

Abstract

Objectives: We aim to describe the social network members of participants of a behavioural intervention, and examine how the effects of the intervention may spillover among network members.

Design: Secondary analysis of a step-wedge randomised controlled trial.

Setting: Change agents (CAs) were recruited from waiting rooms of HIV treatment facilities in Dar es Salaam, Tanzania, and their network members (NMs) were recruited directly by CAs.

Participants: We enrolled 662 CAs in an HIV behavioural intervention. They, along with 710 of their NMs, completed baseline and follow-up interviews from 2011 to 2013.

Primary and secondary outcomes: The primary outcome of this study was change in NMs' HIV knowledge, and the secondary outcome was whether the NM was lost to follow-up.

Results: At baseline, many characteristics were different between NMs and CAs. We found a number of NM characteristics significantly associated with follow-up of NMs, particularly female gender (OR=1.64, 95% CI: 1.02 to 2.63) and HIV knowledge (OR=20.0, 95% CI: 3.70 to 125); only one CA variable was significantly associated with NM follow-up: having a private source of water (OR=2.17, 95% CI: 1.33 to 3.57). The 14.2% increase in NMs' HIV knowledge was largely due to CAs feeling empowered to pass on prior knowledge, rather than transmitting new knowledge to their NMs.

Conclusions: Characteristics of social network members of persons living with HIV persons living with HIV may play a role in study retention. Additionally, the HIV knowledge of these NMs increased largely as a function of CA participation in the intervention, suggesting that intervening among highly-connected individuals may maximise benefits to the potential population for whom spillover can occur.

Trial registration number: Clinical Trial: NCT01693458; Post-results.

Keywords: epidemiology; public health; statistics & research methods.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Schematic of natural direct effect (NDE) and natural indirect effect (NIE). The NDE indicates the increase in NMs’ HIV knowledge happens without a concomitant increase in their CA’s HIV knowledge. The NIE, on the other hand, indicates that the increase in NMs’ HIV knowledge is mediated by their CA’s HIV knowledge increasing. Solid lines indicate paths of causality between variables. Dashed lines represent the line or lines composing the effect of interest. CA, change agent; NM, network member.

References

    1. Naghavi M, Abajobir AA, Abbafati C, et al. . Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016. Lancet 2017;390:1151–210. 10.1016/S0140-6736(17)32152-9
    1. Crepaz N, Tungol-Ashmon MV, Higa DH, et al. . A systematic review of interventions for reducing HIV risk behaviors among people living with HIV in the United States, 1988-2012. AIDS 2014;28:633–56. 10.1097/QAD.0000000000000108
    1. Arnold EA, Kegeles SM, Pollack LM, et al. . A randomized controlled trial to reduce HIV-related risk in African American men who have sex with men and women: the Bruthas project. Prev Sci 2019;20:115–25. 10.1007/s11121-018-0965-7
    1. García de Olalla P, Knobel H, Carmona A, et al. . Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients. J Acquir Immune Defic Syndr 2002;30:105–10. 10.1097/00042560-200205010-00014
    1. Joint United Nations Programme on HIV/AIDS and Joint United Nations Programme on HIV/Aids and others 90-90-90: an ambitious treatment target to help end the AIDS epidemic. Geneva: UNAIDS, 2014.
    1. Fisher JD, Fisher WA. Changing AIDS-risk behavior. Psychol Bull 1992;111:455–74. 10.1037/0033-2909.111.3.455
    1. McNeill C, George N, Glover R. An evaluation of sisters informing healing living empowering: increasing HIV knowledge among African American adolescent females using an evidence-based HIV prevention intervention. J Dr Nurs Pract 2017;10:4–10. 10.1891/2380-9418.10.1.4
    1. Carey MP, Braaten LS, Maisto SA, et al. . Using information, motivational enhancement, and skills training to reduce the risk of HIV infection for low-income urban women: a second randomized clinical trial. Health Psychol 2000;19:3–11. 10.1037/0278-6133.19.1.3
    1. Sinclair B, McConnell M, Green DP. Detecting spillover effects: design and analysis of multilevel experiments. Am J Pol Sci 2012;56:1055–69. 10.1111/j.1540-5907.2012.00592.x
    1. Benjamin-Chung J, Arnold BF, Berger D, et al. . Spillover effects in epidemiology: parameters, study designs and methodological considerations. Int J Epidemiol 2018;47:332–47. 10.1093/ije/dyx201
    1. Truelove HB, Carrico AR, Weber EU, et al. . Positive and negative spillover of pro-environmental behavior: an integrative review and theoretical framework. Global Environmental Change 2014;29:127–38. 10.1016/j.gloenvcha.2014.09.004
    1. Ghosh D, Krishnan A, Gibson B, et al. . Social network strategies to address HIV prevention and treatment continuum of care among at-risk and HIV-infected substance users: a systematic scoping review. AIDS Behav 2017;21:1183–207. 10.1007/s10461-016-1413-y
    1. Benjamin-Chung J, Abedin J, Berger D, et al. . Spillover effects on health outcomes in low- and middle-income countries: a systematic review. Int J Epidemiol 2017;46:1251–76. 10.1093/ije/dyx039
    1. Self S, Grabowski R. Factors influencing knowledge of HIV/AIDS in Nepal: role of socioeconomic interactions. Journal of Social and Economic Development 2018;20:174–91. 10.1007/s40847-018-0065-3
    1. Banerjee A, La Ferrara E, Orozco-Olvera VH. The entertaining way to behavioral change: fighting HIV with MTV. The World Bank, 2019.
    1. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med 2007;357:370–9. 10.1056/NEJMsa066082
    1. Perkins JM, Subramanian SV, Christakis NA. Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries. Soc Sci Med 2015;125:60–78. 10.1016/j.socscimed.2014.08.019
    1. Coleman J, Katz E, Menzel H. The diffusion of an innovation among physicians. Sociometry 1957;20:253–70. 10.2307/2785979
    1. VanderWeele TJ. Inference for influence over multiple degrees of separation on a social network. Stat Med 2013;32:591–6. discussion 597–9. 10.1002/sim.5653
    1. Lienert J, Marcum CS, Finney J, et al. . Social influence on 5-year survival in a longitudinal chemotherapy ward co-presence network. Netw Sci 2017;5:308–27. 10.1017/nws.2017.16
    1. Hughes JJ. Paying injection drug users to educate and recruit their Peers: why participant-driven interventions are an ethical public health model. Qual Manag Health Care 1999;7:4–12. 10.1097/00019514-199907040-00004
    1. Ssali S, Wagner G, Tumwine C, et al. . Hiv clients as agents for prevention: a social network solution. AIDS Res Treat 2012;2012:1–7. 10.1155/2012/815823
    1. Cislaghi B, Denny EK, Cissé M, et al. . Changing Social Norms: the Importance of "Organized Diffusion" for Scaling Up Community Health Promotion and Women Empowerment Interventions. Prev Sci 2019;20:936–46. 10.1007/s11121-019-00998-3
    1. Magnani R, Sabin K, Saidel T, et al. . Review of sampling hard-to-reach and hidden populations for HIV surveillance. AIDS 2005;19 Suppl 2:S67–72. 10.1097/01.aids.0000172879.20628.e1
    1. Smith R, Rossetto K, Peterson BL. A meta-analysis of disclosure of one's HIV-positive status, stigma and social support. AIDS Care 2008;20:1266–75. 10.1080/09540120801926977
    1. Watters JK, Biernacki P. Targeted sampling: options for the study of hidden populations. Soc Probl 1989;36:416–30. 10.2307/800824
    1. Neema S, Atuyambe LM, Otolok-Tanga E, et al. . Using a clinic based creativity initiative to reduce HIV related stigma at the infectious diseases Institute, Mulago national referral Hospital, Uganda. Afr Health Sci 2012;12:231–9. 10.4314/ahs.v12i2.24
    1. Smith Fawzi MC, Siril H, Liu Y, et al. . Agents of change among people living with HIV and their social networks: stepped-wedge randomised controlled trial of the NAMWEZA intervention in Dar es Salaam, Tanzania. BMJ Glob Health 2019;4:e000946. 10.1136/bmjgh-2018-000946
    1. Kaaya S, Arthur G, Siril H, et al. . Leadership of PLWH as agents of change: comparing HIV-related risk behavior of people attending art clinics in Dar ES Salaam with members of their social networks. 20th International AIDS Conference, 2014.
    1. Brown CA, Lilford RJ. The stepped wedge trial design: a systematic review. BMC Med Res Methodol 2006;6:54. 10.1186/1471-2288-6-54
    1. Zou J, Yamanaka Y, John M, et al. . Religion and HIV in Tanzania: influence of religious beliefs on HIV stigma, disclosure, and treatment attitudes. BMC Public Health 2009;9:75. 10.1186/1471-2458-9-75
    1. McCarthy B. Appreciative inquiry: an alternative to behaviour management. Dementia 2017;16:249–53. 10.1177/1471301216634921
    1. Siril HN, Kaaya SF, Smith Fawzi MK, et al. . Clinical outcomes and loss to follow-up among people living with HIV participating in the NAMWEZA intervention in Dar ES Salaam, Tanzania: a prospective cohort study. AIDS Res Ther 2017;14:18. 10.1186/s12981-017-0145-z
    1. Carey MP, Schroder KEE. Development and psychometric evaluation of the brief HIV knowledge questionnaire. AIDS Educ Prev 2002;14:172–82. 10.1521/aeap.14.2.172.23902
    1. Haile BJ, Chambers JW, Garrison JL. Correlates of HIV knowledge and testing: results of a 2003 South African survey. Journal of Black Studies 2007;38:194–208.
    1. Aarø LE, Breivik K, Klepp K-I, et al. . An HIV/AIDS knowledge scale for adolescents: item response theory analyses based on data from a study in South Africa and Tanzania. Health Educ Res 2011;26:212–24. 10.1093/her/cyq086
    1. Peltzer K, Matseke G, Mzolo T, et al. . Determinants of knowledge of HIV status in South Africa: results from a population-based HIV survey. BMC Public Health 2009;9:174. 10.1186/1471-2458-9-174
    1. Fedorov V, Mannino F, Zhang R. Consequences of dichotomization. Pharm Stat 2009;8:50–61. 10.1002/pst.331
    1. Easterly W. How the millennium development goals are unfair to Africa. World Dev 2009;37:26–35. 10.1016/j.worlddev.2008.02.009
    1. Barbarin OA, Khomo N. Indicators of economic status and social capital in South African townships: what do they reveal about the material and social conditions in families of poor children? Childhood 1997;4:193–222.
    1. Smith JA, McPherson M, Smith-Lovin L. Social distance in the United States: sex, race, religion, age, and education homophily among confidants, 1985 to 2004. American Sociological Review 2014;79:432–56.
    1. Lesko CR, Edwards JK, Cole SR, et al. . When to Censor? Am J Epidemiol 2018;187:623–32. 10.1093/aje/kwx281
    1. Hernán MA. The hazards of hazard ratios. Epidemiology 2010;21:13–15. 10.1097/EDE.0b013e3181c1ea43
    1. VanderWeele T. Explanation in causal inference: methods for mediation and interaction. Oxford University Press, 2015.
    1. Hong G. Causality in a social world: moderation, mediation and spill-over. John Wiley & Sons, 2015.
    1. Forastiere L, Mealli F, VanderWeele TJ. Identification and estimation of causal mechanisms in clustered Encouragement designs: disentangling bed nets using Bayesian principal stratification. J Am Stat Assoc 2016;111:510–25. 10.1080/01621459.2015.1125788
    1. Imai K, Jiang Z. Identification and sensitivity analysis of contagion effects in randomized placebo‐controlled trials. J R Stat Soc Ser A Stat Soc 2019;11 10.1111/rssa.12528
    1. Shalizi CR, Thomas AC. Homophily and contagion are generically confounded in observational social network studies. SMR 2012;40:211–39.
    1. de la Haye K, Robins G, Mohr P, et al. . Homophily and contagion as explanations for weight similarities among adolescent friends. J Adolesc Health 2011;49:421–7. 10.1016/j.jadohealth.2011.02.008
    1. Vanderweele TJ, Hong G, Jones SM, et al. . Mediation and spillover effects in group-randomized trials: a case study of the 4Rs educational intervention. J Am Stat Assoc 2013;108:469–82. 10.1080/01621459.2013.779832
    1. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods 2013;18:137–50. 10.1037/a0031034
    1. Leung MP. Treatment and spillover effects under network interference. Review of Economics and Statistics 2016:1–42.
    1. Latkin CA, Davey-Rothwell MA, Knowlton AR, et al. . Social network approaches to recruitment, HIV prevention, medical care, and medication adherence. J Acquir Immune Defic Syndr 2013;63 Suppl 1:S54–8. 10.1097/QAI.0b013e3182928e2a
    1. Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol 2007;17:643–53. 10.1016/j.annepidem.2007.03.013
    1. Berkman LF, support S, networks social. Social support, social networks, social cohesion and health. Soc Work Health Care 2000;31:3–14. 10.1300/J010v31n02_02
    1. Langford CP, Bowsher J, Maloney JP, et al. . Social support: a conceptual analysis. J Adv Nurs 1997;25:95–100. 10.1046/j.1365-2648.1997.1997025095.x
    1. Schneider JA, Zhou AN, Laumann EO. A new HIV prevention network approach: sociometric peer change agent selection. Soc Sci Med 2015;125:192–202. 10.1016/j.socscimed.2013.12.034
    1. He P, Eriksson F, Scheike TH, et al. . A proportional hazards regression model for the Sub-distribution with covariates adjusted Censoring weight for competing risks data. Scand Stat Theory Appl 2016;43:103–22. 10.1111/sjos.12167

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