Sexual Assault Among Young Adolescents in Informal Settlements in Nairobi, Kenya: Findings from the IMPower and SOS Cluster-Randomized Controlled Trial
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Published:2023-11-15
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ISSN:1389-4986
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Container-title:Prevention Science
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language:en
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Short-container-title:Prev Sci
Author:
Sarnquist CleaORCID, Friedberg RinaORCID, Rosenman Evan T. R.ORCID, Amuyunzu-Nyamongo MaryORCID, Nyairo Gavin, Baiocchi MichaelORCID
Abstract
AbstractSexual assault is a global threat to adolescent health, but empowerment self-defense (ESD) interventions have shown promise for prevention. This study evaluated the joint implementation of a girls’ ESD program and a concurrent boys’ program, implemented via a cluster-randomized controlled trial in informal settlements of Nairobi, Kenya, from January 2016 to October 2018. Schools were randomized to the 12-h intervention or 2-h standard of care. Students were randomly sampled to complete surveys at baseline and again at 24 months post-intervention. A total of 3263 girls, ages 10–14, who completed both baseline and follow-up surveys were analyzed; weights were adjusted for dropout. At follow-up, 5.9% (n = 194/3263) of girls reported having been raped in the prior 12 months. Odds of reporting rape were not significantly different in the intervention versus SOC group (OR: 1.21; 95% CI (0.40, 5.21), p = 0.63). Secondary outcomes, social self-efficacy (OR: 1.08; 95% CI (0.95, 1.22), p = 0.22), emotional self-efficacy (OR 1.07; 95% CI (0.89, 1.29), p = 0.49), and academic self-efficacy (OR: 0.90; 95% CI (0.82, 1.00), p = 0.06) were not significantly different. Exploratory analyses of boys’ victimization and perpetration are reported. This study improved on previous ESD studies in this setting with longitudinal follow-up of individuals and independent data collection. This study did not show an effect of the intervention on self-reported rape; findings should be interpreted cautiously due to limitations. Sexual assault rates are high in this young population, underscoring a dire need to implement and rigorously test sexual assault prevention interventions in this setting. The trial was registered with Clinical Trials.gov # NCT02771132. Version 3.1 registered on May 2017, first participant enrolled January 2017.
Funder
South African Medical Research Council Department for International Development, UK Government National Defense Science and Engineering Graduate Stanford University School of Medicine, Stanford University Claremont McKenna College
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
Reference50 articles.
1. Anwar, Y., Sall, M., Cislaghi, B., Miramonti, A., Clark, C., Bar Faye, M., & Canavera, M. (2020). Assessing gender differences in emotional, physical, and sexual violence against adolescents living in the districts of Pikine and Kolda. Senegal. Child Abuse & Neglect, 102, 104387. https://doi.org/10.1016/j.chiabu.2020.104387 2. Assink, M., van der Put, C. E., Meeuwsen, M. W. C. M., de Jong, N. M., Oort, F. J., Stams, G. J. J. M., & Hoeve, M. (2019). Risk factors for child sexual abuse victimization: A meta-analytic review. Psychological Bulletin, 145(5), 459. https://doi.org/10.1037/bul0000188 3. Baiocchi, M., Friedberg, R., Rosenman, E., Amuyunzu-Nyamongo, M., Oguda, G., Otieno, D., & Sarnquist, C. (2019). Prevalence and risk factors for sexual assault among class 6 female students in unplanned settlements of Nairobi, Kenya: Baseline analysis from the IMPower & Sources of Strength cluster randomized controlled trial. PLoS one, 14(6), e0213359. 4. Baiocchi, M., Omondi, B., Langat, N., Boothroyd, D. B., Sinclair, J., Pavia, L., Mulinge, M., Githua, O., Golden, N. H., & Sarnquist, C. (2017). A behavior-based intervention that prevents sexual assault: The results of a matched-pairs, cluster-randomized study in Nairobi, Kenya. Prevention Science: THe Official Journal of the Society for Prevention Research, 18(7), 818–827. https://doi.org/10.1007/s11121-016-0701-0 5. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), Article 1. https://doi.org/10.18637/jss.v067.i01
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