Fink Günther, Victora Cesar G, Harttgen Kenneth, Vollmer Sebastian, Vidaletti Luís Paulo, Barros Aluisio J D
Günther Fink is with the Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA. Cesar G. Victora, Luís Paulo Vidaletti, and Aluisio J. D. Barros are with the International Center for Equity in Health, Federal University of Pelotas, Pelotas, Brazil. Kenneth Harttgen is with ETH Zürich, Zürich, Switzerland. Sebastian Vollmer is with the University of Göttingen, Göttingen, Germany.
Am J Public Health. 2017 Apr;107(4):550-555. doi: 10.2105/AJPH.2017.303657. Epub 2017 Feb 16.
To compare the predictive power of synthetic absolute income measures with that of asset-based wealth quintiles in low- and middle-income countries (LMICs) using child stunting as an outcome.
We pooled data from 239 nationally representative household surveys from LMICs and computed absolute incomes in US dollars based on households' asset rank as well as data on national consumption and inequality levels. We used multivariable regression models to compare the predictive power of the created income measure with the predictive power of existing asset indicator measures.
In cross-country analysis, log absolute income predicted 54.5% of stunting variation observed, compared with 20% of variation explained by wealth quintiles. For within-survey analysis, we also found absolute income gaps to be predictive of the gaps between stunting in the wealthiest and poorest households (P < .001).
Our results suggest that absolute income levels can greatly improve the prediction of stunting levels across and within countries over time, compared with models that rely solely on relative wealth quintiles.
以儿童发育迟缓为结果，比较合成绝对收入指标与中低收入国家基于资产的财富五分位数的预测能力。
我们汇总了来自中低收入国家的239项具有全国代表性的家庭调查数据，并根据家庭资产排名以及国家消费和不平等水平数据计算了以美元为单位的绝对收入。我们使用多变量回归模型来比较所创建的收入指标的预测能力与现有资产指标的预测能力。
在跨国分析中，对数绝对收入预测了观察到的发育迟缓变异的54.5%，相比之下，财富五分位数解释的变异为20%。对于调查内分析，我们还发现绝对收入差距可预测最富有和最贫穷家庭之间发育迟缓的差距（P < 0.001）。
我们的结果表明，与仅依赖相对财富五分位数的模型相比，随着时间推移，绝对收入水平能够极大地改善对各国及国家内部发育迟缓水平的预测。
