WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK.
WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK.
Vaccine. 2018 Mar 14;36(12):1583-1591. doi: 10.1016/j.vaccine.2018.02.020. Epub 2018 Feb 14.
The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.
Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.
Measles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.
The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.
儿童疫苗接种计划在中低收入国家的扩展是公共卫生领域的一项重大成功。干预计划的执行情况指标（如覆盖率和覆盖人数）通常是通过国家统计数据或由于调查设计、行政便利或操作限制而在大区域范围内进行测量。这些数据掩盖了覆盖率的异质性和“冷点”，这些“冷点”可能使疾病持续存在，即使总体覆盖率较高。因此，为了减少不平等，加速实现消除疾病目标的进展，应更好地描述覆盖率的细微变化。
以麻疹为例，使用贝叶斯地质统计学技术，该技术建立在一系列公开可用的地理空间协变量基础上，并通过马尔可夫链蒙特卡罗（MCMC）方法实现，利用五岁以下儿童不同年龄组的聚类人口与健康调查（DHS）数据，以 1km 空间分辨率绘制柬埔寨、莫桑比克和尼日利亚的疫苗接种覆盖率图。
在地质统计学模型中，仅 4-5 个协变量就可以很好地预测麻疹疫苗接种覆盖率，其中偏远程度一直被选为关键变量。输出的 1km×1km 地图显示，在这三个国家中存在显著的异质性，而这些异质性无法通过省级汇总来捕捉。与人口数据的整合表明，在调查时，很少有地区达到 80%的覆盖率，这是世界卫生组织 2020 年全球疫苗行动计划的一个目标。
消除疫苗可预防疾病需要有坚实的证据基础来指导策略，并为有效利用有限资源提供信息。这里概述的方法提供了一种超越疫苗接种覆盖率的大区域汇总的方法，这些汇总掩盖了具有重要流行病学意义的异质性，而提供了详细的地图，这些地图可以捕捉到国家以下的脆弱性。输出数据集是基于开放数据和方法构建的，格式灵活，可以聚合到更具操作性的行政单位级别。
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