Prevalence thresholds for wasting, overweight and stunting in children under 5 years.

PUBLIC HEALTH NUTRITION(2019)

引用 164|浏览26
暂无评分
摘要
Objective: Prevalence ranges to classify levels of wasting and stunting have been used since the 1990s for global monitoring of malnutrition. Recent developments prompted a re-examination of existing ranges and development of new ones for childhood overweight. The present paper reports from the WHO-UNICEF Technical Expert Advisory Group on Nutrition Monitoring. Design: Thresholds were developed in relation to SD of the normative WHO Child Growth Standards. The international definition of 'normal' (2 SD below/above the WHO standards median) defines the first threshold, which includes 2.3% of the area under the normalized distribution. Multipliers of this 'very low level (rounded to 2.5%) set the basis to establish subsequent thresholds. Country groupings using the thresholds were produced using the most recent set of national surveys. Setting: One hundred and thirty-four countries. Subjects: Children under 5 years. Results: For wasting and overweight, thresholds are: 'very low (<2.5%), 'low' (approximate to 1-2 times 2.5%), 'medium' (approximate to 2-4 times 2.5%), 'high' (approximate to 4-6 times 2.5%) and 'very high' (> approximate to 6 times 2.5%). For stunting, thresholds are: 'very low' (<2.5%), 'low' (approximate to 1-4 times 2.5%), 'medium' (approximate to 4-8 times 2.5%), 'high' (approximate to 8-12 times 2.5%) and 'very high' (> approximate to 12 times 2.5 %). Conclusions: The proposed thresholds minimize changes and keep coherence across anthropometric indicators. They can be used for descriptive purposes to map countries according to severity levels; by donors and global actors to identify priority countries for action; and by governments to trigger action and target programmes aimed at achieving 'low' or 'very low' levels. Harmonized terminology will help avoid confusion and promote appropriate interventions.
更多
查看译文
关键词
Wasting,Overweight,Stunting,Malnutrition,Children
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要