From Paper Maps to Digital Maps: Enhancing Routine Immunisation Microplanning in Northern Nigeria

"Engaging communities and healthcare providers is important to validate and resolve context-specific challenges and improve understanding of geospatial data for decision-making."

There is growing international interest in applying geospatial data and technology for immunisation programme strengthening in low-resource settings, such as Nigeria. Since 2012, the Global Polio Eradication Initiative (GPEI) has applied geographical information systems (GIS) in Nigeria to track polio vaccination, provide near real-time monitoring of team performance, and identify service gaps. In 2017, the Bauchi and Sokoto (Nigeria) State Primary Health Care Development Agencies (SPHCDAs), with support from the Maternal and Child Survival Program (MCSP) of the United States Agency for International Development (USAID), developed an approach to improve routine immunisation (RI) microplanning using GIS technology. This paper details the process of developing digital maps for primary health centre (PHC) catchment areas in 6 local government authorities (LGAs) and documents the lessons learned.

The project was motivated by the fact that microplans that are based on outdated population estimates and inaccurate estimates of the distances between communities and service delivery points can result in poor resource allocation and the inability to reach infants in need of immunisation services. Thus, the approach aimed to apply updated target population estimates generated through GPEI activities using GIS technology and then to develop digital PHC maps addressing location of services, reach, and service gaps based on geocoordinates that would provide more accurate vaccination strategy determination.

The process of generating PHC catchment area digital maps involved 3 stages:

Information gathering and reconciliation - For this stage, the study teams identified the data inputs needed for microplanning. These included PHC locations; settlement names and locations; points of interest such as churches, schools, mosques, markets, and village head houses; roads, rails, and waterways; target population data; and state, LGA, and ward boundaries. Data sources included the geodatabase of the GPEI and the Bauchi and Sokoto SPHCDAs' lists of health facilities and corresponding settlements. Discrepancies between these sources were reconciled into a harmonised dataset to the extent possible, and then field-level data collectors, after a two-day training, worked with LGA leaders and government-assigned ward officers to determine the exact locations of PHCs, other health facilities, and settlements where information was missing or inconsistent.

Geospatial data processing and analysis - This stage involved delineating PHC catchment areas, digitising road networks, and developing population estimates. The teams used the open-source GIS application QGIS v2.8.9 to estimate the boundary of each PHC catchment area and create a polygon connecting the furthest settlement points. The digitised network of roads and road types used an online reference map to develop a road network layer with estimated travel distances. The study teams linked the road network layer to the PHC and settlement maps to calculate travel times, taking into consideration distances, topography, and barriers such as mountains and bodies of water. Revised population estimates were extracted from the vaccination tracking system (VTS) when possible or by linking residential types with population density observations.

Production and validation of maps - The study teams used QGIS v2.8.9 to generate a set of maps for each PHC catchment area showing the PHC, other health facilities, settlements, administrative and catchment area boundaries, points of interest, bodies of water, and rail and road networks. The teams then met with key informants drawn from state partners, LGA and ward RI supervisors, PHC managers, RI service providers, and community stakeholders to validate the maps and build confidence in using them. The informants made suggestions that improved the maps' readability and clarity for use in RI microplanning. RI providers and community stakeholders also identified data quality issues and explained why some settlements might prefer health facilities that did not align with their geography due to local considerations such as market days or ethnic tensions.

Challenges the study team encountered in moving from RI paper maps to digital maps in a low-income country included:

• Lack of comprehensive SPHCDA lists and inconsistency in naming facilities and settlements;
• Mismatch between settlements in the SPHCDA list and GPEI operational boundary names, meaning that VTS population estimates could not be used at the settlement level for this activity;
• OSM's dependency on volunteers to generate spatial data, calling into question estimations of travel distances for vaccination strategy sessions;
• Challenges inherent in reconciling data (e.g., settlements preferring to access a facility that did not align with geographic guidance due to issues such as ethnic tensions) - challenges that could only be addressed through early involvement of local stakeholders; and
• Lack of capacity among SPHCDA staff on how to use GIS data, indicating the need for training.

The approach used in this study offers a methodology for generating digital maps. Despite challenges identified, it provides an opportunity to provide more accurate population estimates, and it provides service providers and communities with information that introduces transparency and efficiency into the microplanning process. It also demonstrates reasons to consider use of this approach not only in RI but also in broader primary healthcare planning.