High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries

Abstract

Background: The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.

Methods: Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.

Results: Measles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.

Conclusion: The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.

Keywords: Bayesian geostatistics; Coverage heterogeneities; Demographic and Health Surveys; Measles vaccine.

Fig. 1

A schematic diagram of the modelling approach used to produce high resolution age-structured estimates of vaccination coverage.

Fig. 2

Predicted measles vaccination coverage in children under 5 years old at 1 × 1 km for (A) Nigeria 2013, (B) Cambodia 2014 and (C) Mozambique 2011, with associated uncertainty maps, measured as standard deviations, in (D), (E) and (F).

Fig. 3

Predicted measles vaccination coverage at 1 × 1 km for Nigeria children (left) 9–11 months old, (middle) 12–23 months old, and (right) 24–59 months old.

Fig. 4

Maps showing those areas estimated to be the 20% lowest measles vaccination coverage areas in each country through using DHS region estimates (left column) and 1 × 1 km map estimates (right column).

Fig. 5

Differences in proportions and numbers of under 5 year old children vaccinated against measles through estimates constructed at varying levels of spatial aggregation. The variability in percentage covered estimates through national, sub-national administrative units and 1 × 1 km grid squares are shown in (a). The change in numbers vaccinated through moving from national to DHS region level and to 1 × 1 km grid squares is shown in (b). Further details are provided in supplementary materials (Tables S6–7).

Fig. 6

Maps of proportions of under 5 children estimated to be vaccinated against measles, with districts highlighted in green if they were above the WHO Global Vaccination Action Plan (GVAP) threshold of 80%, for (A) Nigeria in 2013, (B) Cambodia in 2014 and (C) Mozambique in 2011. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)