Five steps to wrap technology and optimize impact: community malaria surveillance for elimination in Zambia

Image caption: Community health workers (CHWs) provide malaria testing and treatment; line-lists in paper registers and aggregated by “data” CHWs and submitted to the DHIS2. Malaria data are mapped to both CHW posts and health facilities.

Akros supports countries and their partners to design, test, and deploy interventions to scale for impact. Often this involves supporting governments to transition from paper-based to digital data collection and reporting. Below is a case study which outlines our approach of applying simple technology that is wrapped in the necessary processes, protocols, and trainings to deliver quality data and build intervention impact that scales.

The Government of Zambia (GRZ) has an ambitious goal of malaria elimination by 2021. One approach to achieve this goal is to improve malaria surveillance at the community level. Akros worked alongside the National Malaria Elimination Program and PATH-MACEPA to develop a community-based malaria surveillance system called “Component D.” The goal of Component D is to build community structures that will increase access to malaria testing and treating, identify and eliminate parasite reservoirs, and understand where (within health facility catchments) malaria transmission is happening. Effectively: to better understand and respond to the “needle in the haystack” which malaria can often be.

To assist the GRZ, Akros applied its five-part approach of “wrapping” technology with the processes, protocols, and training that ensure the technology contributes to impact and is able to scale:

1. Discover and assess—we explored the systems that were already in place in Zambia, evaluated the data needs, and identified the change agents that needed to understand and action information.
2. Build a “prototype design”—we leveraged simple technology (java feature phones and open data kit software) and documented and developed the processes, protocols, training materials, and supervision needed to support that technology and make it sustainable. We built feedback loops to assist programs to respond to those data.
3. Work with country governments and partners to test the design through small-scale implementation—we implemented Component D in Southern, Central, and Lusaka Province, Zambia.
4. Wrap that implementation with research to understand cost efficiency and disease impact—we learned that Component D was one of the primary drivers expanding access to care and treating reservoirs of parasite infection.
5. Work with our host governments and partners to iterate and smartly scale—from its initial small-scale implementation, Component D has become country policy. Since then, numerous partners and GRZ have now expanded to 36 districts in Zambia.

Did the country switch over to the new system en masse or was there a pilot area / time period? How was this decided? Discovery and assessment processes began in 2009, leading to implementation throughout several districts in Southern, Central, and Lusaka Province over a period of three years. These districts were determined based upon their malaria burden and their existing leadership being open to leveraging community health worker (CHW) activity to better understand the malaria burden at local levels. Component D has now become the GRZ policy for malaria surveillance in malaria elimination districts. Isdell Flowers, PATH-MACEPA, the Global Fund, USAID PMI, and others are leveraging their resources to assist GRZ to continue to expand and improve Component D.

Was there duplicative reporting on the legacy system during the transition period? Component D brought more rigorous “structure” to existing CHW registers, but still relied on the familiarity of these registers while also introducing simple aggregation processes for digital reporting of data.

What measures did you use (or are you using) to validate that data in the new system is adequately high quality in order to build confidence in the new system’s data and switch off the legacy system? Data validation includes periodic record reviews validating CHW registers with aggregated figures, locations and stock use, and in-built validations within the DHIS2.

What resources (e.g., licensing costs, technical support, new printed training materials) were required in order to make the transition? Resources required to transition toward digital data have been the greatest up front, including technical support to prototype and implement, cascade training costs, and initial “supervision visits” to support system continuity and health. As the system has become embedded in the GRZ, external supervision visits have reduced, while internal GRZ supervision has increased.

What have been some of the impacts of this system?

• The proportion of children with fever to malaria testing increased from only 9% prior to CHW scale-up to 81% by 2017.
• Parasite prevalence fell from the normal range of ~30% down to 4% by 2016 and remained stable.
• Access to care doubled.
• Community malaria data became available at central and district levels, which was not previously available. These data guide health facility stocking and intervention prioritization and planning.
• Sub-health facility malaria data now available and mapped within DHIS2.

Links to more information:

https://www.nmec.org.zm/enhanced-surveillance

https://www.ncbi.nlm.nih.gov/pubmed/26586264

https://akros.com/malaria-prevention/community-surveillance/