The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.

The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.

Background

Rwandan adolescents have limited access to high-quality family planning and reproductive health (FP/RH) information and care to prevent unplanned pregnancy and HIV/STIs. In addition to the immediate implications for health and well-being, teenage pregnancy is a significant cause of school drop-out, limiting girls' future potential and employment opportunities. This study introduces a direct-to-consumer digital education program that uses storytelling to deliver age-appropriate FP/RH information and economic empowerment training to adolescents. It also facilitates access to high-quality, youth-friendly FP/RH care and products. We evaluate two different school-based models of its implementation to understand how to optimize the uptake of contraception and HIV testing among adolescents.

Methods

The study consists of two distinct phases. The first formative intervention design phase, conducted from 2016 to 2019, used a human-centered design methodology to develop the intervention alongside over 600 Rwandan adolescents, their parents, teachers, and healthcare providers. Through this methodology, we sought to maximize the fit between evidence-based practices (uptake of modern contraception and HIV testing) and the implementation context of adolescents in Rwanda. The second phase is an impact evaluation, in which we will use a Hybrid Trial Type 2 Effectiveness-Implementation study design to determine the overall effectiveness of this digital intervention as well as the relative effectiveness of the two different school-based implementation models. This takes the form of a 3-arm cluster-randomized non-inferiority trial, with a sample of 6000 youth aged 12-19 in 60 schools across 8 districts in Rwanda. Primary outcome measures include use of modern contraception, delayed initiation of childbearing, and uptake of HIV testing.

Discussion

This study will yield insights into not only whether this digital intervention is successful in achieving the intended sexual and reproductive health outcomes, but also which mechanisms are likely to drive this effectiveness. The methodologies used are broadly applicable to the design, implementation, and evaluation of other behavior-based health programs in low and middle-income countries.

Trial registration

ClinicalTrials.gov Identifier: NCT04198272 . Prospectively registered 13 December 2019.