A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries

• Published in: Spatial statistics 2023-10, Vol.57, p.100772, Article 100772
• Main Authors: Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D., Tatem, A.J.
• Format: Article
• Language: English
• Subjects: Bayesian inference; Demographic and Health Surveys; INLA-SPDE approach; Multiple Indicator Cluster Surveys; Vaccination coverage
• ISSN: 2211-6753; 2211-6753
• DOI: 10.1016/j.spasta.2023.100772

Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs. •Different approaches for creating the index yielded very similar results.•The index had strong spatial patterns within countries.•The vulnerability classes had fairly common major defining risk factors across countries.•Vaccination coverage declined rapidly or gradually with increasing level of vulnerability.•Children aged under 1 were (un)even