Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines
Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines
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.
Bayesian inference, Demographic and Health Surveys, binomial geostatistical model, diphtheria-tetanus-pertussis vaccine, vaccination coverage
5662-5678
Utazi, Chigozie
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Aheto, Justice
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Chan, Ho Man Theophilus
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Tatem, Andrew
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Sahu, Sujit
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20 December 2022
Utazi, Chigozie
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Aheto, Justice
dfdbcbd6-229b-4af8-86b5-e698e62fe29f
Chan, Ho Man Theophilus
5bf76c72-ef36-45cb-990e-d6a00d8781f0
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf
Utazi, Chigozie, Aheto, Justice, Chan, Ho Man Theophilus, Tatem, Andrew and Sahu, Sujit
(2022)
Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines.
Statistics in Medicine, 41 (29), .
(doi:10.1002/sim.9586).
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.
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Statistics in Medicine - 2022 - Utazi - Conditional probability and ratio‐based approaches for mapping the coverage of (2)
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More information
Accepted/In Press date: 9 September 2022
e-pub ahead of print date: 21 September 2022
Published date: 20 December 2022
Additional Information:
Funding Information:
We are grateful to the DHS program for providing the data for this study. We also thank the Bill and Melinda Gates Foundation for funding this study.
Funding Information:
This work was supported by funding from the Bill and Melinda Gates Foundation (Investment ID INV‐003287). Chigozie Edson Utazi and Andrew J. Tatem received the Grant. The funder did not play any role in the study design, data collection, analysis and interpretation of data, the report writing, and the decision to submit the manuscript for publication.
Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Keywords:
Bayesian inference, Demographic and Health Surveys, binomial geostatistical model, diphtheria-tetanus-pertussis vaccine, vaccination coverage
Identifiers
Local EPrints ID: 470622
URI: http://eprints.soton.ac.uk/id/eprint/470622
ISSN: 0277-6715
PURE UUID: da4fe9ab-7a18-4ea4-a166-1465079ce0ee
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Date deposited: 14 Oct 2022 16:48
Last modified: 17 Mar 2024 04:04
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Author:
Justice Aheto
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