Childhood vaccines in Uganda and Zambia: Determinants and barriers to vaccine coverage
Childhood vaccines in Uganda and Zambia: Determinants and barriers to vaccine coverage
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DOI：
10.1016/j.vaccine.2018.05.116
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发表时间：
2018-07-05
期刊：
影响因子：
5.5
通讯作者：
Lim, Stephen S.
中科院分区：
文献类型：
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作者：
Phillips, David E.;Dieleman, Joseph L.;Lim, Stephen S.
提高儿童疫苗覆盖率是全球卫生的优先事项，但在低收入和中等收入国家具有挑战性。尽管之前的研究试图衡量疫苗接种的决定因素，但大多数都有局限性。我们使用明确定义的假设模型、多方面的数据以及充分利用假设和数据的建模策略来衡量决定因素。方法：我们使用来自乌干达和赞比亚的家庭、卫生设施、患者和卫生办公室的相关横断面调查数据，以及贝叶斯结构方程模型来量化儿童疫苗接种的方差比例，这些方差由关键决定因素解释，控制已知的混杂因素。结果：我们发现证据表明，疫苗接种的主要决定因素对于不同的结果是不同的。对于三剂五价疫苗，接种意图（母亲）是主要驱动因素，但对于一剂疫苗，社区获得是一个更大的因素。对于肺炎球菌结合疫苗，卫生设施的准备情况是主要驱动力。考虑到具体的可修改的决定因素，成本，设施集水区人口和人员配备的改善预计将导致最大的增长覆盖率根据model.Conclusions：这项分析措施疫苗接种的决定因素，使用改进的方法在大多数现有的研究。它提供的证据表明，应在相关成果的背景下处理决定因素，并提供证据表明，如果有针对性，可能对这两个国家产生最大影响的具体决定因素。未来的研究应该寻求改进我们的分析框架，将其应用于不同的环境，并利用更强大的研究设计。专注于特定决定因素的程序应该使用这些结果来选择适合于衡量其有效性的结果。这些国家的疫苗接种计划应该利用我们的发现来更好地针对干预措施，并继续在疫苗可预防的疾病方面取得进展。(C)2018作者爱思唯尔有限公司出版
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. (C) 2018 The Authors. Published by Elsevier Ltd.