Information Management, GIS and Missing Maps for a Guinea Maternal Health Project
This is a look back at a project that the GIS team supported between 2016–2019, with information management and mapping services.
With thanks to British Red Cross Health Advisers, Viviana, Greg and Josie.
The Guinean Red Cross is a key player in the national system of emergency response in Guinea, evidenced in the Ebola response in West Africa. Capitalising on this, the Danish, Swiss and British Red Cross worked together to support the Guinea Red Cross develop integrated, high quality programmes.
One of these is the Guinea Red Cross’ Reproductive Health and Rights Project which aimed to improve access to reproductive health services, including emergency obstetric care, for women and girls in the Moyenne Guinee region; and shift practices and cultural viewpoints regarding reproductive health, including advocating for a reduction in harmful practices, such as Female Genital Mutilation (FGM). To achieve these objectives, the project increased knowledge, rights and access to reproductive health in the Moyenne Guinee region, with a focus on women and young people. It also provided training to community volunteers and midwives, to increase access to quality reproductive health and ante-natal care, which is much needed is the region
For a summary of reproductive health and rights in Guinea, an information section is at the bottom of this blog.
Can you help me with fieldwork planning?
It was a normal day in the office, when we received a query from our British Red Cross heath adviser for the programme. She was soon to prepare and run a baseline survey to gather data on knowledge of, and access to, reproductive health and rights in the programme location.
At the time there was a real lack of spatial data at village level needed for the baseline survey, such as locations of villages, place names, or building locations.
At the British Red Cross, we champion open geographic data, and OpenStreetMap, an open source wikipedia style map of the world, where anyone can edit, and use. We’re also founders of the Missing Maps Project, a collaborative project involving a number of organisations, aiming to map the worlds vulnerable communities.
Utilising the Missing Maps Project workflow and volunteer community we proceeded to map the programme areas to provide the detailed basemaps with which programme staff can effectively carry out a baseline survey to better understand the barriers that exist to accessing reproductive health services, and the prevalence of harmful practices.
A mammoth mapping task
We uploaded several projects to the HOT Tasking Manager, with instructions for volunteers to map roads, residential areas, buildings and other geographic features. Each project covered two sub-prefectures, and together covered a mammoth area of 777 square miles — almost 1.3 times the size of Greater London!
In less than two months, all six sub-prefectures, that the programme was focussing on, were mapped — an impressive feat, and a testament to the incredible impact that the Missing Maps community can have when it works together. In just the six study areas — remote volunteers mapped 40,734 buildings and 1283 residential areas.
But why would you need a map?
There are several reasons why a map was, or could be, required for fieldwork planning. Maps of the area allowed:
Planning movements by understanding the local geography:
- Identifying if areas are more remote and hard to access, or if there are rivers that will be needed to pass — the baseline study was conducted close to the beginning of the rainy season, and certain areas might have already been flooded.
- Identifying how large the total overall area is and how much time will be needed to travel between places.
Correcting your sample if required
- Maps help understand population spread and concentration, which is useful as it affects sampling methodologies.
- If the sample will need to be changed at the last minute due to missing information — as in fact became the case for the Guinea study — or some other issue, then a map can be used to reconfigure the methodology and allows for flexibility in the approach.
Supporting volunteers
- Mapping can illustrate to volunteers the areas that they are going to cover and how they are going to move between places.
- It also rewards volunteers for their efforts at the end by plotting where they surveyed providing a sense of achievement.
Rigorous in measuring change
- Knowing exactly where the baseline survey was collected allows us to make sure that we survey the same villages, and households, as part of the endline survey (taken at the end of the programme to identify change) and therefore remain rigorous in your approach. This was the case for the Guinea project, where the endline study was undertaken by a consultant. Because of the availability, and use, of maps, the consultant had no difficulty conducting interviews in the same baseline locations.
For this programme, the field teams used mobile data collection tools with GPS as part of the baseline survey. Post-fieldwork, we were then able to add village names to OpenStreetMap and produced maps of the results of the baseline survey to share back with the teams.
Building data literacy and information management capacity for decision making
These initial maps produced stemmed a hunger for more by the Guinea Red Cross, and assisted in providing the analytical capability to interpret and interrogate data and information at a local branch level.
But how did we go about this? In order to identify and prioritise what competencies to build, we assessed the programme aims, the location of the programme and any considerations, and who was involved. In all, we prioritised the building of mapping, information management and data literacy skills through their quarterly collection of community maternal health information.
The local branch was collecting a wealth of information including but not limited to: Childbirth and deliveries — if they were supported by a community healthcare worker, qualified me referred, had no assistance, or miscarried; The age of the woman — if they were over or under 18; Or if a woman was followed during and after their pregnancy and provided with health promotion, among others.
First, we enhanced the existing maps produced as part of the programme. During Ebola many health posts, centres and hospitals were mapped. We added these to the maps and with the heath adviser, and the Guinea Red Cross identified whether they still exist or if any are missing that need to be added. In addition, we began to map the maternal health activities and information collected by the programme.
However, all this information was collected in different Excel documents and within these, multiple sheets recording each month or quarter, and then dependent on the quarter or the data collected the table could be completely different.
So, we worked with the Guinea Red Cross and health adviser to order all the data into a single table with defined column headers and definitions. We focussed on the essential information that is collected as part of the work of the local branch and only the data that was going to be used for analysis thus enabling efficient data collection and entry, plus easier analysis for mapping and charting.
We took this one step further — how could we showcase and analyse the work and success of the volunteers and branch as part of the programme alongside maps of the datasets (which were already proving a hit)? The health adviser identified that the programme was having great success at meeting its aims, but this was not visible to the branch and volunteers, at the time only seen through mapping.
Working alongside our health adviser to understand the local context, we worked to develop an Excel Dashboard to highlight the programme information collected, activities and outcomes. Why Excel? The programme location could be regarded as a resource poor environment and lacking internet access. The teams were also already using Excel to record activities and so having an Excel Dashboard was not a massive leap into the unknown!
Using this as a tool, all the analyses and graphs were provided to the local teams through the health adviser, and they then had to decide what the graph showed, if they had any questions about what it showed and if there were steps they would now like to take having seen the graph. After, they then presented their chosen graph to the team.
This brought discussion about the programme, data collection and showcasing activities. It also helped to grow understanding about what graphs would be most useful and how they should be presented. We also shared skills on how to update and create similar dashboards and maps.
How was successful was this exercise in building data literacy capacity? After this, there were several enhancements suggested to be made to the Excel dashboard and maps, all including different ways to slice and dice the data collected by the field teams. In addition, this exercise assisted the Guinea Red Cross team in using these graphs and maps to present them in reports and meetings as well as to a wider audience thus building their own capacity.
Wrapping up
The lack of spatial data at the outset of this programme, focussed on reproductive health and rights in Guinea, and the resultant mapping derived from the global Missing Maps volunteers, stemmed an enthusiasm and drive to expand knowledge around geospatial data, mapping, information management and data literacy in the Guinea Red Cross branch.
Initially we were approached to support fieldwork planning, providing a base map which could be expanded on with local details. Yet this grew to provide health information management support — working with the Guinea Red Cross branch and health adviser to structure information collected and its visualisation whether this be a map, charts or in a dashboard. We assessed the requirements and capacity to work with and interpret the data and information. Producing maps allowed for the representation of activities in space, and if there were differences across locations. Creating a dashboard in Excel, allowed a stepping stone to understanding information management and data, whilst beginning to create conversations about the data collected and the successes of the programme. Both mapping and information management alongside data literacy worked cohesively as one to build the capacity of the Guinea Red Cross to be able to make decisions at a local level.
It was a real pleasure to work as part of this programme, building the capacity of a rural branch in Guinea to be able to interrogate, analyse and work with maps and charts to improve knowledge, rights and access to reproductive health.
Guinea — A summary of reproductive health and rights
In 2015, the West African country of Guinea had the 18th highest maternal mortality rate in the world — 679 deaths in pregnancy or childbirth per 100,000 live births, according to data from the World Health Organisation (WHO). For purposes of comparison, the number for the UK is 9.
One of the most significant contributing factors in this high number of deaths is the prevalence of female genital mutilation in the country. According to UNICEF, 96% of women in Guinea have undergone FGM — the second highest rate in the world, behind only Somalia.
In April 2016, the UN High Commissioner for Human Rights said that, “Although female genital mutilation appears to be decreasing worldwide, this is not the case in Guinea, where this practice is widespread in every region and among every ethnic, religious and social group.”
Because of this widespread practice, many Guinean women are at high risk of suffering complications during childbirth. According to the WHO, certain types of FGM result in an approximately 30% increase in the need for caesarean section, and a 70% increase in incidents of severe blood loss after giving birth. There is also a 15–55% increase in death rates for babies.
In its analysis of global maternal mortality rates meanwhile, the WHO reported that, “Most maternal deaths are preventable, as the health-care solutions to prevent or manage complications are well known.”
It also found that, “poor women in remote areas are the least likely to receive adequate health care,” with poverty, distance, lack of information, inadequate services and cultural practices all being key factors in the reasons for pregnant women not receiving the essential emergency care they need.
I’d like to thank the GIS team and heath advisers in the British, Danish and Swiss Red Cross as well as the Guinea Red Cross teams and Head of Health department.
