We’ve had some key (and difficult) pivots along the way. During our early years, one of our first program iterations utilized CCTs to prevent mother-to-child transmission of HIV. While effective, it wasn’t scalable because the number of HIV-positive women we were identifying on the ground didn’t match with publicly available data (learn more here). More recently, we pivoted from ORS and zinc co-pack distribution to ORS only. This decision was made after reviewing the available evidence and consulting with implementing partners and state health authorities (learn more here). Following the evidence sometimes means we fold an entire program; sometimes it means we make a program design pivot. But each time we make that hard decision, we become more committed to following the evidence even when it’s not the popular choice.
One thing that may not be obvious from the outside is how much of our work revolves around operational visibility, feedback loops, and continuous learning. Running a program across thousands of clinics means we are constantly trying to understand not just what should be happening, but what is actually happening on a given clinic day. One lesson we’ve learned while scaling in difficult environments is that policy, reported implementation, and operational reality can diverge meaningfully. Over time, we’ve built systems that help us identify discrepancies, surface patterns, investigate anomalies, and continuously refine the quality of implementation. That operational learning process has probably been one of the biggest drivers of our scaling journey. The key is not just collecting data, but building systems that surface discrepancies, create feedback loops, identify operational gaps, and continuously improve execution quality.
Our team noticed the same conversations happening at Skoll. Government engagement is extremely important and must be embedded throughout the program. However, we think about it not primarily as a funding pathway, but as a legitimacy and sustainability pathway. We operate under state health authority approval, and that integration is core to how the program functions. There’s been real discussion in global health over the past decade about the limits of donor-funded programs and the need for government ownership at scale. We take that seriously. But we’re also realistic that government fiscal capacity and priority-setting in northern Nigeria right now make direct government funding of the CCT program unlikely in the near term. One way we are considering further integration is to explore a lower-cost model in which incentives are provided by clinic staff or community mobilizers rather than by dedicated field staff. This could also unlock new geographies within Nigeria and beyond if we can develop a model that is suitable for different operational contexts.
Tony, thanks for your kind words!
We’ve had some key (and difficult) pivots along the way. During our early years, one of our first program iterations utilized CCTs to prevent mother-to-child transmission of HIV. While effective, it wasn’t scalable because the number of HIV-positive women we were identifying on the ground didn’t match with publicly available data (learn more here). More recently, we pivoted from ORS and zinc co-pack distribution to ORS only. This decision was made after reviewing the available evidence and consulting with implementing partners and state health authorities (learn more here). Following the evidence sometimes means we fold an entire program; sometimes it means we make a program design pivot. But each time we make that hard decision, we become more committed to following the evidence even when it’s not the popular choice.
One thing that may not be obvious from the outside is how much of our work revolves around operational visibility, feedback loops, and continuous learning. Running a program across thousands of clinics means we are constantly trying to understand not just what should be happening, but what is actually happening on a given clinic day. One lesson we’ve learned while scaling in difficult environments is that policy, reported implementation, and operational reality can diverge meaningfully. Over time, we’ve built systems that help us identify discrepancies, surface patterns, investigate anomalies, and continuously refine the quality of implementation. That operational learning process has probably been one of the biggest drivers of our scaling journey. The key is not just collecting data, but building systems that surface discrepancies, create feedback loops, identify operational gaps, and continuously improve execution quality.
Our team noticed the same conversations happening at Skoll. Government engagement is extremely important and must be embedded throughout the program. However, we think about it not primarily as a funding pathway, but as a legitimacy and sustainability pathway. We operate under state health authority approval, and that integration is core to how the program functions. There’s been real discussion in global health over the past decade about the limits of donor-funded programs and the need for government ownership at scale. We take that seriously. But we’re also realistic that government fiscal capacity and priority-setting in northern Nigeria right now make direct government funding of the CCT program unlikely in the near term. One way we are considering further integration is to explore a lower-cost model in which incentives are provided by clinic staff or community mobilizers rather than by dedicated field staff. This could also unlock new geographies within Nigeria and beyond if we can develop a model that is suitable for different operational contexts.