Mosites Emily, Dawson-Hahn Elizabeth, Walson Judd, Rowhani-Rahbar Ali, Neuhouser Marian L
a Department of Epidemiology , University of Washington , Seattle , USA.
b Paul G. Allen School for Global Animal Health , Washington State University , Pullman , USA.
Paediatr Int Child Health. 2017 Aug;37(3):158-165. doi: 10.1080/20469047.2016.1230952. Epub 2016 Sep 29.
Reducing the burden of stunting in childhood is critical to improving health in low- and middle-income settings. However, because many aetiologies underlie linear growth failure, stunting has proved difficult to prevent and reverse. Understanding the contributions these aetiologies make to the burden of stunting can help the development of targeted, effective interventions. To begin to frame these causes, a qualitative and a quantitative framework of the primary drivers of stunting in low-resource settings were developed. Population attributable fractions (PAF) were estimated to inform the quantitative framework. According to these estimates, infectious diseases were responsible for large attributable fractions in all settings, and a combination of dietary indicators also comprised a large fraction in Africa. However, the PAF calculation was found to have several limitations, including a requirement for a binary outcome and sensitivity to confounding, which necessitate broad interpretation of the results. More robust tools to model complex causality are needed in order to understand the causal aetiology of stunting.
减轻儿童期发育迟缓负担对于改善低收入和中等收入地区的健康状况至关重要。然而,由于线性生长发育不良存在多种病因,发育迟缓已被证明难以预防和扭转。了解这些病因对发育迟缓负担的影响有助于制定有针对性的有效干预措施。为了梳理这些原因,研究人员构建了一个定性和定量框架,用于分析资源匮乏地区发育迟缓的主要驱动因素。通过估算人群归因分数(PAF)来完善定量框架。根据这些估算结果,传染病在所有地区都占据了较大的归因比例,而在非洲,饮食指标的综合影响也占了很大一部分。然而,研究发现PAF计算存在若干局限性,包括需要二元结局以及对混杂因素敏感,这就需要对结果进行宽泛解读。为了理解发育迟缓的因果病因,需要更强大的工具来模拟复杂的因果关系。