Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs.

尽管最近为覆盖错过的社区和减少零剂量流行率做出了努力，但许多低收入和中等收入国家（LMIC）在疫苗接种覆盖率方面仍然存在严重不平等。疫苗接种覆盖率的地理不平等往往表现为多种风险因素，应通过数据整合来实施这些风险因素，以便为更有效和公平的疫苗接种政策和计划提供信息。在这里，我们探索整合来自多个风险因素的信息以创建零剂量脆弱性指数的方法，以改进对脆弱社区的识别和优先排序以及对疫苗接种覆盖率不平等的理解。我们收集了六个中低收入国家的疫苗接种覆盖率和相关风险因素的地理定位数据，重点关注 DTP1 的覆盖率、DTP3 和 MCV1 疫苗作为零剂量和疫苗接种不足的指标。使用基于一套地理空间协变量信息的地理空间建​​模技术，我们制作了先前未绘制的风险因素和疫苗接种覆盖率的 1 × 1 公里和地区级地图。然后，我们使用不同的方法整合风险因素地图中的数据，构建零剂量脆弱性指数，将国家内的地区划分为不同的脆弱性组，范围从最不脆弱的地区 (1) 到最脆弱的地区 (5)。通过与人口数据整合，我们估计了生活在不同脆弱类别的 1 岁以下儿童的数量。我们的结果显示该指数的空间分布存在很大差异，尽管在某些情况下覆盖范围几乎没有变化，但仍揭示了最脆弱的地区。我们发现，最脆弱地区的最显着特征跨越了我们研究中包含的风险因素的不同子领域（健康、社会经济、人口和地理）。我们还证明，可以在风险因素较少的情况下稳健地估计该指数，并且无需与疫苗接种覆盖率信息联系起来。该指数构成了指导中低收入国家有针对性的疫苗接种策略的实用且有效的工具。我们还证明，可以在风险因素较少的情况下稳健地估计该指数，并且无需与疫苗接种覆盖率信息联系起来。该指数构成了指导中低收入国家有针对性的疫苗接种策略的实用且有效的工具。我们还证明，可以在风险因素较少的情况下稳健地估计该指数，并且无需与疫苗接种覆盖率信息联系起来。该指数构成了指导中低收入国家有针对性的疫苗接种策略的实用且有效的工具。