Multilevel analysis of individual, household, and community factors influencing child growth in Nepal
Multilevel analysis of individual, household, and community factors influencing child growth in Nepal
复制标题
影响尼泊尔儿童成长的个人、家庭和社区因素的多层次分析
DOI：
10.1186/s12887-019-1469-8
复制
发表时间：
2019
期刊：
影响因子：
2.4
通讯作者：
G. Shively
中科院分区：
文献类型：
--
作者：
Timothy M. Smith;G. Shively
BackgroundChildhood malnutrition and growth faltering is a serious concern in Nepal. Studies of child growth typically focus on child and mother characteristics as key factors, largely because Demographic and Health Surveys (DHS) collect data at these levels. To control for and measure the importance of higher-level factors this study supplements 2006 and 2011 DHS data for Nepal with data from coincident rounds of the Nepal Living Standards Surveys (NLSS). NLSS information is summarized at the district level and matched to children using district identifiers available in the DHS.MethodsThe sample consists of 7533 children aged 0 to 59 months with complete anthropometric measurements from the 2006 and 2011 NDHS. These growth metrics, specifically height-for-age and weight-for-height, are used in multilevel regression models, with different group designations as upper-level denominations and different observed characteristics as upper-level predictors.ResultsCharacteristics of children and households explain most of the variance in height-for-age and weight-for-height, with statistically significant but relatively smaller overall contributions from community-level factors. Approximately 6% of total variance and 22% of explained variance in height-for-age z-scores occurs between districts. For weight-for-height, approximately 5% of total variance, and 35% of explained variance occurs between districts.ConclusionsThe most important district-level factors for explaining variance in linear growth and weight gain are the percentage of the population belonging to marginalized groups and the distance to the nearest hospital. Traditional determinants of child growth maintain their statistical power in the hierarchical models, underscoring their overall importance for policy attention.
