Estimating vaccine coverage in conflict settings using geospatial methods: a case study in Borno state, Nigeria

Area of Science:

• Epidemiology
• Geospatial analysis
• Public Health

Background:

• Subnational vaccination coverage data are crucial for global immunization targets and health equity.
• Conflict zones present challenges for traditional household surveys, impacting data reliability and population estimates.
• Model-based geostatistical (MBG) approaches offer an alternative for estimating vaccine coverage in insecure areas.

Purpose of the Study:

• To estimate diphtheria-tetanus-pertussis (DTP) vaccine coverage in conflict-affected Borno state, Nigeria, using a spatiotemporal MBG approach.
• To compare MBG-derived coverage estimates with those from household-based surveys in the same region.
• To investigate the impact of conflict on survey sampling and the importance of accurate population data.

Main Methods:

• Employed a spatiotemporal model-based geostatistical (MBG) approach to estimate DTP vaccine coverage.
• Utilized geolocated conflict data and sampling cluster locations from household surveys for comparison.
• Assessed the influence of reliable population estimates on coverage assessments in conflict settings.

Main Results:

• Spatiotemporal MBG modeling provided valuable vaccination coverage estimates in Borno state, Nigeria.
• Comparison highlighted limitations of household surveys in conflict-affected areas due to sampling constraints.
• Geospatially modelled estimates proved useful for understanding coverage where conflict impedes representative sampling.

Conclusions:

• Model-based geostatistical methods are a valuable supplement to traditional surveys for estimating vaccination coverage in conflict settings.
• These approaches enhance the ability to track immunization progress and ensure equitable health outcomes despite security challenges.
• Accurate population data are essential for robust coverage estimations, particularly in complex humanitarian emergencies.