David E Phillips1, Joseph L Dieleman2, Jessica C Shearer3, Stephen S Lim2. 1. Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA. Electronic address: davidp6@uw.edu. 2. Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA. 3. PATH, Seattle, USA.
Abstract
BACKGROUND: Improving childhood vaccine coverage is a priority for global health, but challenging in low and middle-income countries. Although previous research has sought to measure determinants of vaccination, most has limitations. We measure determinants using a clearly-defined hypothetical model, multi-faceted data, and modeling strategy that makes full use of the hypothesis and data. METHODS: We use linked, cross-sectional survey data from households, health facilities, patients and health offices in Uganda and Zambia, and Bayesian Structural Equation Modeling to quantify the proportion of variance in childhood vaccination that is explained by key determinants, controlling for known confounding. RESULTS: We find evidence that the leading determinant of vaccination is different for different outcomes. For three doses of pentavalent vaccine, intent to vaccinate (on the part of the mother) is the leading driver, but for one dose of the vaccine, community access is a larger factor. For pneumococcal conjugate vaccine, health facility readiness is the leading driver. Considering specifically-modifiable determinants, improvements in cost, facility catchment populations and staffing would be expected to lead to the largest increase in coverage according to the model. CONCLUSIONS: This analysis measures vaccination determinants using improved methods over most existing research. It provides evidence that determinants should be approached in the context of relevant outcomes, and evidence of specific determinants that could have the greatest impact in these two countries, if targeted. Future studies should seek to improve our analytic framework, apply it in different settings, and utilize stronger study designs. Programs that focus on a particular determinant should use these results to select an outcome that is appropriate to measure their effectiveness. Vaccination programs in these countries should use our findings to better target interventions and continue progress against vaccine preventable diseases.
BACKGROUND: Improving childhood vaccine coverage is a priority for global health, but challenging in low and middle-income countries. Although previous research has sought to measure determinants of vaccination, most has limitations. We measure determinants using a clearly-defined hypothetical model, multi-faceted data, and modeling strategy that makes full use of the hypothesis and data. METHODS: We use linked, cross-sectional survey data from households, health facilities, patients and health offices in Uganda and Zambia, and Bayesian Structural Equation Modeling to quantify the proportion of variance in childhood vaccination that is explained by key determinants, controlling for known confounding. RESULTS: We find evidence that the leading determinant of vaccination is different for different outcomes. For three doses of pentavalent vaccine, intent to vaccinate (on the part of the mother) is the leading driver, but for one dose of the vaccine, community access is a larger factor. For pneumococcal conjugate vaccine, health facility readiness is the leading driver. Considering specifically-modifiable determinants, improvements in cost, facility catchment populations and staffing would be expected to lead to the largest increase in coverage according to the model. CONCLUSIONS: This analysis measures vaccination determinants using improved methods over most existing research. It provides evidence that determinants should be approached in the context of relevant outcomes, and evidence of specific determinants that could have the greatest impact in these two countries, if targeted. Future studies should seek to improve our analytic framework, apply it in different settings, and utilize stronger study designs. Programs that focus on a particular determinant should use these results to select an outcome that is appropriate to measure their effectiveness. Vaccination programs in these countries should use our findings to better target interventions and continue progress against vaccine preventable diseases.
Authors: Sarah N Cox; Patrick T Wedlock; Sarah W Pallas; Elizabeth A Mitgang; Tatenda T Yemeke; Sarah M Bartsch; Taiwo Abimbola; Sheryl S Sigemund; Aaron Wallace; Sachiko Ozawa; Bruce Y Lee Journal: Vaccine Date: 2021-05-25 Impact factor: 3.641
Authors: Morgan Brown; Paul Bouanchaud; Kemi Tesfazghi; Saysana Phanalasy; May Me Thet; Hoa Nguyen; Jennifer Wheeler Journal: Malar J Date: 2022-03-09 Impact factor: 2.979
Authors: Caroline Soi; Jessica C Shearer; Ashwin Budden; Emily Carnahan; Nicole Salisbury; Gilbert Asiimwe; Baltazar Chilundo; Haribondhu Sarma; Sarah Gimbel; Moses Simuyemba; Jasim Uddin; Felix Masiye; Moses Kamya; Dai Hozumi; Julie K Rajaratnam; Stephen S Lim Journal: Health Policy Plan Date: 2020-11-01 Impact factor: 3.344