Jia Peng, Anderson John D, Leitner Michael, Rheingans Richard
Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, 7500, The Netherlands.
Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States of America.
PLoS One. 2016 Jul 12;11(7):e0158490. doi: 10.1371/journal.pone.0158490. eCollection 2016.
Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals.
The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation.
The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing.
There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.
获得卫生设施对于降低多种不良健康结果的风险至关重要。在许多低收入国家，不同财富水平之间在卫生设施方面存在明显差距，这可能会阻碍千年发展目标各项工作的进展。
根据2008 - 2009年肯尼亚人口与健康调查中397个群组的被调查家庭的国家资产得分，将其分为五个财富五分位数组。应用了一系列空间分析方法，包括超额风险、局部空间自相关和空间插值，以观察不同财富类别在改善卫生设施覆盖率方面的差异。通过对调查覆盖率进行插值、时间调整并乘以高分辨率人口网格，估算了拥有改善卫生设施的人口总数。然后将其与联合国人口司以及世界卫生组织/联合国儿童基金会供水与卫生联合监测计划的年度估计数进行比较。
经验贝叶斯克里金插值法在预测改善卫生设施的原始覆盖率和空间覆盖率时，对所有群组和五个五分位数组产生的均方根误差最小。南部地区的覆盖率普遍高于北部和东部，南部的覆盖率从内罗毕向各个方向递减，而尼扬扎省和东北省的覆盖率相对较低。空间平滑后，证实了被调查群组中卫生设施改善程度高和低的总体聚类趋势。
肯尼亚不同财富类别之间在卫生设施方面存在明显差距，与原始覆盖率相比，空间平滑后的覆盖率能更接近现有统计数据的估计值。未来的干预活动需要针对不同财富类别以及全国范围内资源有限但需求较大的地区进行调整。
