When the Europeans arrived Cameroon in the 17th Century, they were surprised to meet a king in the Bamoum Kingdom North of the Rio dos Cameroes with a map, statistics, and writings of his kingdom. The king knew the importance of maps and data for decision making. In the 21st century, as the world grapples with ending poverty, increasing access to basic services, promoting the agenda of the sustainable development goals, and leaving no one behind, it is even data and maps will play an even more critical role.
The availability of high-quality research evidence gives decision-makers additional tools needed for decision making, including evidence relevant to poorer communities and disadvantaged populations. eBASE Africa, a member of the Africa evidence Network, has been active in improving livelihoods through the use of innovation and best practices in basic services for underserved populations in Africa, including women, children, people living with a disability, and indigenous populations. One of the approaches currently being used is that of DataMaps.
What are DataMaps?
A systematic approach of using existing data, evidence of ‘what works’, and qualitative data from household visits by community workers in basic services to make decisions using maps. It consists of nine steps which begins from data mining of existing data. The approach can be used for all basic services including health (HealthMaps), education (AttainMaps), disability (RedMaps) or environment (WasteMaps).
The first step in DataMaps is mining existing data, for example in health we have used data from DHIS2 databases. In disability we have used databases from the ministry of social affairs in Cameroon and primary data collected by eBASE Africa using the Washington Group tools.
The second step is ensuring data is trustworthy. One of the biggest challenges in Africa is having trustworthy data. Trustworthy data requires skilled data collectors and enough financial resources to travel to collect data both of which may be absent on most instances in Africa. It is therefore important to conduct rigorous data validation process. In our experiences we have used an approach developed at eBASE Africa with an integration of the WHO Data Quality Tool to ensure DHIS2 data validity before using these for decision making.
The third step is looking for research evidence on ‘what works’ for the data indicator we like to improve. A PICO question must be developed and the search must be rigorous and systematic. Searches usually look for research evidence recommendations with possibility for GRADEing the evidence. Over the past 2 years, we have worked with practitioners to develop clinical, pedagogy, climate, and financial PICO questions. Our searches focus on relevant evidence, therefore, national databases are our first stop over. The next stopover is usually WHO or UN databases. After exhausting these options and GRADEing the evidence therein, we then search other evidence hubs databases like Africa Evidence Network, Cochrane, Campbell, Education Endowment Foundation, and Joanna Briggs Databases.
The fourth step consists of providing feedback to all stakeholders on data and options for improving data outcomes based on ‘what works’. We have used district coordination meetings in health or council meetings in environment to provide feedback in our past projects. The feedback session is usually an opportunity to engage stakeholders, and get their opinions and contributions on developing a roadmap for change. Stakeholder meetings usually engages an African ‘indaba’ approach where a consensus team is nominated to moderate and validate discussions.
The fifth step consists of community triaging. Here communities with the worst indicators and populations at most risks are identified and community workers are tasked to do household visits for homes in the affected areas.
The sixth step consist of community workers collect qualitative data through open ended discussion topics, record the discussions and send these to a central server through WhatsApp or MaxAPP. They also geotag household responses using WhatsApp or Magpi. Transcripts are later transcribed using MaxQDA.
The seventh step consist of combining household data in a 3-dimensional manner where we report voice, time, and space. This is the 3-Dimensional Qualitative Data (3D Q-Data). The voice is the speech from the community member (service user) which could be available as text (transcript) or as audio recording. It could also be represented by pictures or images. The time is the date when interview was made. The space is the GPS location where the community members house is located. This can be monitored over time for change. The decision-maker the livelihood outcome that is affecting data outcome in the users own voice and located in space and time.
The eighth step consist of decision making, where the decision-maker takes into consideration the 3D Q-Data, and the evidence to decide on interventions to improve an indicator. In one example in the Bali District Hospital, the decision-maker used clinical audits and feedback to improve communication between midwives and women in the delivery room.
The ninth step is an evaluation and feedback on the process of change.
The process is IT cumbersome, using six softwares – WhatsApp, MaxApp, Magpi, MaxQDA, GoogleMaps, and Microsoft PowerPoint. We are currently discussing with experts including IT experts and academicians on how to develop a single platform to manage all tasks. Because data uses geotagging, it becomes extremely difficult to protect participants identity. We are exploring approaches to ethically manage this data, coining the informed consent message, and protecting participants identity.
The concept of DataMaps for decision-making is still being developed with help from global partners, development agencies, and the Ministry of Public Health in Cameroon. This approach uses existing platforms, for example, existing DHIS2 data, existing community workers, and existing evidence databases to facilitate decision making. This significantly reduces use of resources and builds on existing expertise, thus reducing the need for external support. DataMaps help with prioritizing issues and targeting populations hence is critical if we must leave no one behind. The inclusion of best practices ensures that the most effective interventions are used to solve the most pressing issues for populations at the greatest risk.
Related upcoming events: eBASE Africa will be presenting a workshop on this approach at the upcoming Qualitative Evidence Symposium in Brasilia, Brazil from 9th to 11th October 2019. www.qesymposium.org