Challenges and Opportunities with Linking Malaria Data – Perspectives from Ghana and Malawi

On August 31, the USAID Country Health Information Systems and Data Use (CHISU) Project hosted their webinar titled, “Challenges and Opportunities with Linking Malaria Data – Perspectives from Ghana and Malawi.” Dr. Stephanie Watson-Grant, Deputy Director, CHISU Project, moderated the webinar with panelists: 

  • Austin Gumbo, National Malaria Control Programme (NMCP) in Malawi
  • Dr. Williams Ojo, President's Malaria Initiative (PMI)
  • Samuel Oppong, National Malaria Control Programme (NMCP) in Ghana 
  • Dr. Serge Somda, Health Information Expert, West African Health Organization (WAHO) 

Stephanie prefaced the discussion by noting that the data needed for Malaria response is complex: to fully respond to Malaria in a given country, experts have to pay attention to data related to surveillance, prevention, diagnostic, treatment, supply chain, and also climate, temperature, rainfall, humidity, and entomological data. Each of the data have a different structure and are typically managed in different systems—either on paper or electronic, or both. While there are substantial challenges to digitally linking these data, there are also opportunities as countries' health information systems experience digital transformation. 

In Malawi, Malaria data is manually collected at the facility and community level, with village clinics at the community level, and then entered into the aggregated DHIS2 platform at the district level (some data is now entered at the facility level). While some systems are interoperable with the DHIS2 platform, others such as the low and middle income countries are not. 

Austin noted, “The NMCP vision for better management and use is to have all the data integrated into DHIS2 [and] the system must be made interoperable to share the data.”

In Ghana, Malaria data needs have expanded since the early 2000s when data was donor/funder driven. At present, the National Malaria Control Programme (NMCP) draws upon an array of data sources to inform its decisions: health facility data from the routine systems, surveys, entomological data, etc. The NMCP however has not made use of climatic data. All of the aforementioned sources of data inform target interventions. 

Samuel noted, “This [data] has been used for vaccine rollout, where we are looking at areas of high burden and where there are other [supporting] interventions to help us radically reduce the burden in those [geographical] areas.”       

In the last few years, the President's Malaria Initiative (PMI) has moved one step closer to bringing together several data points in one place, and since 2018, has worked with all partner countries to bring together different types of data. Williams noted that it is important for countries to consider geographical administrative unit alignment when bringing data together for cooperative analysis and interpretation. He also noted that with respect to meta data and standards, different countries have their own standard of interpreting data/defining data sets. 

On the problem of different standards, Williams stated, “The use of a data dictionary is very vital. From one country [to another], the definition of Intermittent Preventive Treatment in Pregnancy (IPTp) or Malaria cases could be different. We need to have standards so that we can have a correct interpretation and cooperative analysis.”

For the West African Health Organization (WAHO), all of the West African countries’ national health management information system and sometimes the national malaria control program’s system of data collection and management use District Health Information Software version 2 (DHIS2). WAHO is working with all the countries to gather and put all the data together, and started a partnership with different national, regional, and international entities to support the implementation of an integrated system. Now, all 15 Economic Community of West African States (ECOWAS) countries have a national data warehouse built on DHIS2, where they manage the routine data and surveillance systems to manage epidemic data. 

Echoing Austin’s observation on NMCP’s challenges in Malawi, Serge noted for WAHO, “We have [a multitude] of information…take Malaria [data], which is multifactorial…but this information is stored in many places. So, you go in one place to get the laboratory information, another place to get the meteorological information, and another place to get information on care…interoperability is our principal challenge.”

In regard to some of the biggest challenges with linking Malaria data sources, for Ghana, Samuel observed that the private sector has different data collection tools. Also, when they are developing their data collection tools, there is little consideration of the national HMIS data needs. Consequently, when asked for a specific type of data, the private sector is unable to produce the data in conformance with national HMIS standards. 

Austin noted that while linking data sources, it is important to look at the data quality: in the DHIS2 system, there are inconsistencies, outliers, and missing data. To address this problem, the NMCP uses the WHO’s data quality tool to clean the data in the system. CHISU has also been supporting the NMCP with data cleaning efforts. 

In terms of promising developments in linking Malaria data, Serge noted that under their existing platform one country can see what is happening in another country at the district level. WAHO is working with different partners to have a Malaria forecast platform that will include 22 key indicators. This will allow monitoring of Malaria across the borders in the region.  

From PMI experience Williams noted that countries need to do an assessment to fine tune what type of data is needed and after that, while doing integration across platforms, it is important to leverage existing platforms. Other factors to consider are data security, governance, and quality. 

While the discussion highlighted the challenges associated with linking Malaria data at the country and regional level, including ensuring gender is considered in data linking efforts, there are many opportunities. The examples from WAHO and PMI showcase some of the promising cooperative activities and platforms that can address existing challenges at the country and regional level. 

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