Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines

Chigozie Edson Utazi, Justice Moses K. Aheto, Ho Man Theophilus Chan, Andrew J. Tatem, Sujit K. Sahu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Many vaccines are often administered in multiple doses to boost their effectiveness. In the case of childhood vaccines, the coverage maps of the doses and the differences between these often constitute an evidence base to guide investments in improving access to vaccination services and health system performance in low and middle-income countries. A major problem often encountered when mapping the coverage of multi-dose vaccines is the need to ensure that the coverage maps decrease monotonically with successive doses. That is, for doses (Formula presented.) and (Formula presented.), (Formula presented.), where (Formula presented.) is the coverage of dose (Formula presented.) at spatial location (Formula presented.). Here, we explore conditional probability (CP) and ratio-based (RB) approaches for mapping (Formula presented.), embedded within a binomial geostatistical modeling framework, to address this problem. The fully Bayesian model is implemented using the INLA and SPDE approaches. Using a simulation study, we find that both approaches perform comparably for out-of-sample estimation under varying point-level sample size distributions. We apply the methodology to map the coverage of the three doses of diphtheria-tetanus-pertussis vaccine using data from the 2018 Nigeria Demographic and Health Survey. The coverage maps produced using both approaches are almost indistinguishable, although the CP approach yielded more precise estimates on average in this application. We also provide estimates of zero-dose children and the dropout rates between the doses. The methodology is straightforward to implement and can be applied to other vaccines and geographical contexts.

Original languageEnglish
Pages (from-to)5662-5678
Number of pages17
JournalStatistics in Medicine
Volume41
Issue number29
DOIs
Publication statusPublished - 20 Dec 2022
Externally publishedYes

Keywords

  • Bayesian inference
  • Demographic and Health Surveys
  • binomial geostatistical model
  • diphtheria-tetanus-pertussis vaccine
  • vaccination coverage

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