Case Studies

In this section, existing methodologies for the creation of health catchment area databases have been summarized. Organizations/programs profiled in these case studies include MSF; GRID3; the Global Polio Eradication Initiative (Global Polio administrative boundaries) and the Digital Microplanning for COVAX (UNICEF/WHO GIS Working Group).

In the first case study, Médecins Sans Frontières (MSF) in 2016 created a dashboard to support integrated disease surveillance and response in Sierra Leone by creating polygons of geo-spatial data into Tonkolili district (See Appendix 2). These maps were created by field workers in collaboration with community health workers and included lists of health facilities and villages. This dashboard is currently being used to monitor health surveillance in Tonkolili. This method for creating a health catchment area database took a grounded approach, which was facilitated by strong partnerships with community health workers and the Ministry of Health. This method is best suited if data is needed to measure access to target groups for programme interventions. However, this approach is very resource intensive and applicable mostly in areas devoid of conflict, thus allowing secure access to communities.

In the second case study, the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) initiative sought to produce referenced geospatial data and model population density to improve health catchment area layers and micro-planning tools. Data sources included DHIS2, health zones created by other organizations and field workers (See Appendix 3). This methodology, which is similar to the one employed by MSF in Sierra Leone utilized a bottom-up approach, grounded in the knowledge and experiences of community health workers. The partnerships formed at community level allow for accurate data collection, data validation and created a sense of ownership for the data. This method however demands huge human resources and logistics, and is best suited in non-conflict settings, where enough security, resources and time are available to conduct field mapping and validation.

The third case study describes how the Global Polio Eradication Initiative amassed available administrative units that best connects with data from immunization campaigns to create geospatial hierarchies that meet the mapping needs of the initiative (See Appendix 4). This method adopted a top-to-bottom approach, allowing for remote mapping of data covering very large geographical areas. This method is best suited for scoping, monitoring, or evaluation purposes, as it may lack the fine details to inform community outreach and impact assessment on the ground. These databases are relatively easy to construct, less expensive and are readily integrated into national databases since existing administrative areas are utilized in mapping. This method is also best suited in conflict or other humanitarian regions where secure access to communities cannot be guaranteed. It is also best suited for surveillance purposes during pandemics, to track the speed and distribution of disease outbreaks.

The fourth case study explores how the UNICEF/WHO GIS working group has created a digital micro planning initiative to promote an equitable access to COVID-19 vaccination centres. Data components include spatial population, modes of transport and healthcare facility capability. This initiative enables countries to map refusal populations (See Appendix 5). Digital microplanning has shown demonstrable impact in increasing immunization coverage in several countries in recent years.8 TheThis application of this tool to augment COVID-19 vaccination strategies counts as an appropriate use of relevant technology to meet health needs. This method employs a more coordinated and collaborative approach, where a market is created for data producers (GIS experts, NGOs, Humanitarian organizations, academic institutions) and data consumers (countries, MoH, communities). This high-level coordination ensures uniform support is provided to countries and streamlines data collection and metrics. This is best suited when data is open, a high-level of coordination is applicable to enhance cooperation between international organizations and country governments, and enough financial resources are available to ensure equity in service provision and support. A key challenge in using this method is navigating the complex web of data sharing policies that limit the extent to which organizations and countries are mandated to share their data onto a single collaborative space.