Kasomo Japheth Muema, Gayawan Ezra
African Institute for Mathematical Sciences (AIMS), Plots # 559-560-561, Kicukiro District, Niboye Sector, Nyakabanda Cell, PO BOX: 7150, KG3 ST, Rwanda.
Head, Biostatistics and Spatial Modelling Research Laboratory, Department of Statistics, Federal University of Technology, Akure, PMB 704, Akure, Ondo State, Nigeria.
SSM Popul Health. 2021 Oct 5;16:100939. doi: 10.1016/j.ssmph.2021.100939. eCollection 2021 Dec.
Studies have looked into how environmental and climate covariates affect under-and over-nutrition, but little is known about the spatial distribution of different forms of malnutrition in Kenya and whether there are locations that suffer from double-burden of malnutrition. This research quantifies spatial variations and estimates how climatic and environmental factors affect under-and over-nutrition among women in Kenya. This enables us to determine if the patterns in which these factors affect the malnutrition indicators are similar and whether there are overlaps in the spatial distributions. The study used data from the Demographic and Health Survey, which included cross-sectional data on malnutrition indicators as well as some climate and environmental variables. A multicategorical response variable that classified the women into one of four nutritional classes was generated from the body mass index (BMI) of the women, and a Bayesian geoadditive regression model with an estimate based on the Markov chain Monte Carlo simulation technique was adopted. Findings show that women in Turkana, Samburu, Isiolo, Baringo, Garissa, and West Pokot counties are more likely to be underweight than women in other counties while being overweight is prevalent in Kirinyag'a and Kitui counties. Obesity is prevalent in Kirinyag'a, Lamu, Kiambu, Murang'a, and Taita Taveta counties. The study further shows that as mean temperature and precipitation increase, the likelihood of being underweight reduces. The chances of being underweight are lower among literate women [OR: 0.614; 95% CrI: 0.513,0.739], married women [OR: 0.702; 95% CrI: 0.608,0.819] and those from rich households [OR: 0.617; 95% CrI: 0.489,0.772], which is not the case for overweight and obesity. The generated spatial maps identify hot spots of the double burden of malnutrition that can assist the government and donor agencies in channeling resources efficiently.
已有研究探讨了环境和气候协变量如何影响营养不足和营养过剩，但对于肯尼亚不同形式营养不良的空间分布，以及是否存在遭受双重营养不良负担的地区，我们却知之甚少。本研究对空间差异进行了量化，并估计了气候和环境因素如何影响肯尼亚女性的营养不足和营养过剩情况。这使我们能够确定这些因素影响营养不良指标的模式是否相似，以及空间分布上是否存在重叠。该研究使用了人口与健康调查的数据，其中包括营养不良指标的横断面数据以及一些气候和环境变量。根据女性的体重指数（BMI）生成了一个多分类响应变量，将女性分为四种营养类别之一，并采用了基于马尔可夫链蒙特卡罗模拟技术进行估计的贝叶斯地理加性回归模型。研究结果表明，图尔卡纳、桑布鲁、伊索洛、巴林戈、加里萨和西波科特等县的女性比其他县的女性更有可能体重过轻，而超重现象在基里尼亚加县和基图伊县较为普遍。肥胖现象在基里尼亚加、拉穆、基安布、穆朗加和泰塔塔韦塔等县较为普遍。研究还表明，随着平均温度和降水量的增加，体重过轻的可能性会降低。识字女性[比值比：0.614；95%可信区间：0.513,0.739]、已婚女性[比值比：0.702；95%可信区间：0.608,0.819]以及富裕家庭的女性[比值比：0.617；95%可信区间：0.489,0.772]体重过轻的几率较低，超重和肥胖的情况则并非如此。生成的空间地图确定了营养不良双重负担的热点地区，可协助政府和捐助机构有效地分配资源。
