Background
HIV infections
HIV INFECTIONS
Community health workers (CHWs) are a key solution to meeting the health needs of billions of persons lacking sufficient access to professional healthcare workers. Tasks are being shifted from professional health workers to paraprofessional CHWs globally [There are many reasons for these failures. Most randomized controlled trials (RCTs) devote considerable resources to staff selection/recruitment, training, and fidelity monitoring over time of manualized programs that target a single outcome. When scaled, most large public health systems fail to allocate sufficient resources to implement and manage these programs. Implementation often lacks fidelity [It is likely that the potential of CHWs to impact outcomes is contingent on the workforce strategies for recruiting, training, monitoring, and supportive supervision. Our underlying hypothesis is that if the potential impact of CHWs programs is to be realized, understanding the contribution of a well-trained and well-supervised workforce is essential.There have been few studies examining supervision, especially in low- and middle-income countries (LMICs). In 2017, Ballard and Montgomery [South Africa has 55,000 CHWs [CHWs’ job is complex. Pregnant South African mothers face multiple interrelated challenges. South Africa is the country with the most HIV infections [This study examines if supportive supervision of CHWs increases the impact of the existing government-employed CHWs workforce on the health outcomes of the mothers and children—examined in a cluster RCT. Given the structure of healthcare settings, we can only randomize CHWs by clinics, rather than individually assigning CHWs to receive supervision or not. In addition, because CHWs address multiple health challenges, our analytic strategy must concurrently examine a range of outcomes that shift over time. Improving one outcome (for example, increasing breastfeeding) is unlikely to positively impact the child’s completion of immunizations over time. We need an analytic strategy that allows us to analyze multiple outcomes that does not rely on a power-killing, Bonferroni-type correction. The generalist intervention requires that there be multiple significant outcomes to demonstrate an efficacious intervention and protect against Type 1 error. Our proposed strategy [