Adhikari Tulsi, Yadav Jeetendra, Tolani Himanshu, Tripathi Niharika, Kaur Harpreet, Rao M Vishnu Vardhana
ICMR-National Institute of Medical Statistics, New Delhi, India.
ICMR-National Institute of Medical Statistics, New Delhi, India.
Spat Spatiotemporal Epidemiol. 2022 Feb;40:100459. doi: 10.1016/j.sste.2021.100459. Epub 2021 Oct 29.
Exploring Bayesian spatio-temporal methods to analyze spatial dependence in malnutrition at the state level for tribal children (less than 3 years) population of India and change over time (three rounds of NFHS-2(1998-99),3(2005-06) and 4(2015-16)). The Bayesian model, fitted by Markov chain Monte Carlo simulation using OpenBUGS, for spatial autocorrelation (through spatial random effects modeling). The model estimated (1) mean time trend and (2) spatial random effects. Results of spatio-temporal modeling for stunting, wasting and underweight exhibited a declining mean trend across the study region from NFHS-2 to NFHS-4. Spatial random effects exhibited spatial dependence for various states in stunting, wasting and underweight tribal children. Future research should analyze spatio-temporal distribution for malnutrition at district level which will require NFHS-5 data. Also, analysis can be done capturing spatio-temporal interaction and identifying hot spots and cold spots at district level.
探索贝叶斯时空方法，以分析印度部落儿童（3岁以下）人口在邦一级的营养不良空间依赖性以及随时间的变化（三轮全国家庭健康调查 - 2（1998 - 1999年）、3（2005 - 2006年）和4（2015 - 2016年））。通过使用OpenBUGS的马尔可夫链蒙特卡罗模拟拟合的贝叶斯模型，用于空间自相关（通过空间随机效应建模）。该模型估计了（1）平均时间趋势和（2）空间随机效应。发育迟缓、消瘦和体重不足的时空建模结果显示，从全国家庭健康调查 - 2到全国家庭健康调查 - 4，整个研究区域的平均趋势呈下降趋势。空间随机效应在发育迟缓、消瘦和体重不足的部落儿童的各个邦表现出空间依赖性。未来的研究应分析地区一级营养不良的时空分布，这将需要全国家庭健康调查 - 5的数据。此外，可以进行分析以捕捉时空相互作用并识别地区一级的热点和冷点。
