TY - JOUR
T1 - Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines
AU - Utazi, Chigozie Edson
AU - Aheto, Justice Moses K.
AU - Chan, Ho Man Theophilus
AU - Tatem, Andrew J.
AU - Sahu, Sujit K.
N1 - Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PY - 2022/12/20
Y1 - 2022/12/20
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - Demographic and Health Surveys
KW - binomial geostatistical model
KW - diphtheria-tetanus-pertussis vaccine
KW - vaccination coverage
UR - http://www.scopus.com/inward/record.url?scp=85138334475&partnerID=8YFLogxK
U2 - 10.1002/sim.9586
DO - 10.1002/sim.9586
M3 - Article
C2 - 36129171
AN - SCOPUS:85138334475
SN - 0277-6715
VL - 41
SP - 5662
EP - 5678
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 29
ER -