Improved estimates of child malnutrition trends in Bangladesh using remote-sensed data
研究利用夜间灯光、降水等遥感数据，结合贝叶斯模型估计孟加拉国2000-2018年各区县儿童慢性营养不良（发育迟缓）趋势，发现全国患病率从约50%降至约30%，但部分区域仍居高不下。
Abstract This study investigates the trends in chronic malnutrition (stunting) among young children across Bangladesh’s 64 districts and 544 sub-districts from 2000 to 2018. We utilized remote-sensed data–nighttime light intensity to indicate urbanization, and environmental factors like precipitation and vegetation levels–to examine patterns of stunting. Our primary data source was the Bangladesh Demographic and Health Survey, conducted six times within the study period. Using Bayesian multilevel time-series models, we integrated cross-sectional, temporal, and spatial data to estimate stunting rates for years not covered by the direct survey information. This approach, enhanced by remote-sensed data, allowed for greater prediction accuracy by incorporating information from neighboring areas. Our findings show a significant reduction in national stunting rates, from nearly 50% in 2000 to about 30% in 2018. Despite this overall progress, some districts have consistently high levels of stunting, while others show fluctuating levels. Our model gives more precise sub-district estimates than previous methods, which were limited by data gaps. The study highlights Bangladesh’s advancements in reducing child stunting, highlighting the value of integrating remote-sensed data for more precise and credible analysis.