Abstract
BackgroundThe 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. MethodsUsing 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. ResultsMeasles 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. ConclusionThe 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.
Highlights
Health policy decision-making based on spatially heterogeneous vaccination has resulted in a shift from pursuing coverage targets at the national-level to ensuring that high coverage levels⇑ Corresponding author at: WorldPop, Department of Geography and are evenly distributed across provinces or districts [1]
Cross-sectional data on the spatial distribution of measles vaccination coverage in children under 5 years of age for Cambodia, Nigeria and Mozambique were obtained from the Demographic and Health Surveys (DHS) database [12]
The measles vaccination status, i.e. whether they had ever received a measles vaccine or not, as determined from the vaccination card or as reported by the mother, was extracted. In this definition of measles vaccination coverage, used by the Demographic and Health Surveys program [12,13,14], there is an implicit assumption that the child has at least received the first dose of measles containing vaccine (MCV-1), but could have had the second dose (MCV-2)
Summary
⇑ Corresponding author at: WorldPop, Department of Geography and are evenly distributed across provinces or districts [1] While this likely represents a more effective strategy over targeting country-level goals, administrative area summaries may still mask important geographical inequities in coverage [2]. 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. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationallyrelevant administrative unit levels
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