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. 2023 Jul 8;13(1):11085.
doi: 10.1038/s41598-023-37947-8.

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

Alyssa N Sbarra  1 , Sam Rolfe  2 , Emily Haeuser  2 , Jason Q Nguyen  2 , Aishatu Adamu  3   4 , Daniel Adeyinka  5   6 , Olufemi Ajumobi  7   8 , Chisom Akunna  6   9 , Ganiyu Amusa  10   11 , Tukur Dahiru  12 , Michael Ekholuenetale  13   14 , Christopher Esezobor  15   16 , Kayode Fowobaje  13   17 , Simon I Hay  2   18 , Charles Ibeneme  19   20 , Segun Emmanuel Ibitoye  21 , Olayinka Ilesanmi  22   23 , Gbenga Kayode  24   25 , Kris Krohn  2 , Stephen S Lim  2   18 , Lyla E Medeiros  2 , Shafiu Mohammed  26   27 , Vincent Nwatah  28   29 , Anselm Okoro  30 , Andrew T Olagunju  31   32 , Bolajoko O Olusanya  33 , Osayomwanbo Osarenotor  34 , Mayowa Owolabi  35   36 , Brandon Pickering  2 , Mu'awiyyah Babale Sufiyan  12 , Benjamin Uzochukwu  37 , Ally Walker  2 , Jonathan F Mosser  38   39
Affiliations

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

Alyssa N Sbarra et al. Sci Rep. .

Abstract

Reliable estimates of subnational vaccination coverage are critical to track progress towards global immunisation targets and ensure equitable health outcomes for all children. However, conflict can limit the reliability of coverage estimates from traditional household-based surveys due to an inability to sample in unsafe and insecure areas and increased uncertainty in underlying population estimates. In these situations, model-based geostatistical (MBG) approaches offer alternative coverage estimates for administrative units affected by conflict. We estimated first- and third-dose diphtheria-tetanus-pertussis vaccine coverage in Borno state, Nigeria, using a spatiotemporal MBG modelling approach, then compared these to estimates from recent conflict-affected, household-based surveys. We compared sampling cluster locations from recent household-based surveys to geolocated data on conflict locations and modelled spatial coverage estimates, while also investigating the importance of reliable population estimates when assessing coverage in conflict settings. These results demonstrate that geospatially-modelled coverage estimates can be a valuable additional tool to understand coverage in locations where conflict prevents representative sampling.

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Conflict of interest statement

This study was funded by the Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance. The funders had no contribution to study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had full access to all data and results, as well as final responsibility for the decision to submit for publication. A.N.S., E.H., K.K., S.M., B.P., and J.F.M. report support from the Bill & Melinda Gates Foundation during the conduct of the study. Additionally, J.F.M. also reports support from Gavi, the Vaccine Alliance.

Figures

Figure 1
Figure 1
Locations of conflict and survey data collection within Borno state. For both the 2016–17 Multiple Indicator Cluster Survey (MICS/NICS) and 2018 Demographic and Health Survey (DHS), the location of each cluster sample during data collection is shown in green triangles, for the DHS sized by number of children sampled. Purple circles show the location of all types of conflict data points as part of the Armed Conflict Location & Event Data Project (ACLED) database in each survey year.
Figure 2
Figure 2
DTP1 and DTP3 coverage in Nigeria in 2018 with survey cluster locations in Borno state. Local governing authority (LGA) level coverage of DTP1 (a) and DTP3 (b) in Nigeria in 2018, aggregated from 5 × 5-km2 geospatial modelled estimates using second administrative units from the Database of Global Administrative Areas. In Borno state inset, locations of MICS/NICS sampling clusters (open circles) and DHS clusters ( +) are shown.
Figure 3
Figure 3
Borno state LGA-level time series of DTP3 coverage. Local governing authority (LGA) level estimates in Borno state for DTP3 coverage in 2018. A time series for select LGAs is shown with data sources included in the model aggregated to the LGA unit for years 2000 to 2018.
See this image and copyright information in PMC

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