Hulland Erin N, Blanton Curtis J, Leidman Eva Z, Bilukha Oleg O
Emergency Response and Recovery Branch, Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, GA 30333 USA.
Emerg Themes Epidemiol. 2016 Dec 7;13:13. doi: 10.1186/s12982-016-0054-y. eCollection 2016.
Cluster surveys provide rapid but representative estimates of key nutrition indicators in humanitarian crises. For these surveys, an accurate estimate of the design effect is critical to calculate a sample size that achieves adequate precision with the minimum number of sampling units. This paper describes the variability in design effect for three key nutrition indicators measured in small-scale surveys and models the association of design effect with parameters hypothesized to explain this variability.
380 small-scale surveys from 28 countries conducted between 2006 and 2013 were analyzed. We calculated prevalence and design effect of wasting, underweight, and stunting for each survey as well as standard deviations of the underlying continuous Z-score distribution. Mean cluster size, survey location and year were recorded. To describe design effects, median and interquartile ranges were examined. Generalized linear regression models were run to identify potential predictors of design effect.
Median design effect was under 2.00 for all three indicators; for wasting, the median was 1.35, the lowest among the indicators. Multivariable linear regression models suggest significant, positive associations of design effect and mean cluster size for all three indicators, and with prevalence of wasting and underweight, but not stunting. Standard deviation was positively associated with design effect for wasting but negatively associated for stunting. Survey region was significant in all three models.
This study supports the current field survey guidance recommending the use of 1.5 as a benchmark for design effect of wasting, but suggests this value may not be large enough for surveys with a primary objective of measuring stunting or underweight. The strong relationship between design effect and region in the models underscores the continued need to consider country- and locality-specific estimates when designing surveys. These models also provide empirical evidence of a positive relationship between design effect and both mean cluster size and prevalence, and introduces standard deviation of the underlying continuous variable (Z-scores) as a previously unexplored factor significantly associated with design effect. The magnitude and directionality of this association differed by indicator, underscoring the need for further investigation into the relationship between standard deviation and design effect.
整群抽样调查能快速提供人道主义危机中关键营养指标的代表性估计值。对于这些调查，准确估计设计效应对于计算能以最少抽样单位实现足够精度的样本量至关重要。本文描述了小规模调查中测量的三个关键营养指标的设计效应变异性，并对设计效应与假设用于解释这种变异性的参数之间的关联进行建模。
分析了2006年至2013年期间在28个国家进行的380项小规模调查。我们计算了每次调查中消瘦、体重不足和发育迟缓的患病率及设计效应，以及潜在连续Z评分分布的标准差。记录了平均群大小、调查地点和年份。为描述设计效应，检查了中位数和四分位间距。运行广义线性回归模型以确定设计效应的潜在预测因素。
所有三个指标的设计效应中位数均低于2.00；消瘦指标的中位数为1.35，是所有指标中最低的。多变量线性回归模型表明，所有三个指标的设计效应与平均群大小均呈显著正相关，与消瘦和体重不足的患病率呈正相关，但与发育迟缓无关。标准差与消瘦的设计效应呈正相关，但与发育迟缓呈负相关。调查区域在所有三个模型中均具有显著性。
本研究支持当前现场调查指南建议将1.5作为消瘦设计效应的基准值，但表明对于主要目标是测量发育迟缓或体重不足的调查，该值可能不够大。模型中设计效应与区域之间的强关系强调了在设计调查时持续需要考虑国家和地区特定估计值。这些模型还提供了设计效应与平均群大小和患病率之间正相关关系的实证证据，并引入潜在连续变量（Z评分）的标准差作为与设计效应显著相关的先前未探索因素。这种关联的大小和方向性因指标而异，强调了进一步研究标准差与设计效应之间关系的必要性。
