Kesse-Guyot Emmanuelle, Assmann Karen, Andreeva Valentina, Castetbon Katia, Méjean Caroline, Touvier Mathilde, Salanave Benoît, Deschamps Valérie, Péneau Sandrine, Fezeu Léopold, Julia Chantal, Allès Benjamin, Galan Pilar, Hercberg Serge
Équipe de Recherche en Epidémiologie Nutritionnelle (EREN), Centre de Recherche en Epidémiologie et Statistiques, COMUE Sorbonne Paris Cité, Inserm (U1153), Inra (U1125), Cnam, Université Paris 13, Bobigny, France.
JMIR Public Health Surveill. 2016 Oct 18;2(2):e160. doi: 10.2196/publichealth.5880.
Traditional epidemiological research methods exhibit limitations leading to high logistics, human, and financial burden. The continued development of innovative digital tools has the potential to overcome many of the existing methodological issues. Nonetheless, Web-based studies remain relatively uncommon, partly due to persistent concerns about validity and generalizability.
The objective of this viewpoint is to summarize findings from methodological studies carried out in the NutriNet-Santé study, a French Web-based cohort study.
On the basis of the previous findings from the NutriNet-Santé e-cohort (>150,000 participants are currently included), we synthesized e-epidemiological knowledge on sample representativeness, advantageous recruitment strategies, and data quality.
Overall, the reported findings support the usefulness of Web-based studies in overcoming common methodological deficiencies in epidemiological research, in particular with regard to data quality (eg, the concordance for body mass index [BMI] classification was 93%), reduced social desirability bias, and access to a wide range of participant profiles, including the hard-to-reach subgroups such as young (12.30% [15,118/122,912], <25 years) and old people (6.60% [8112/122,912], ≥65 years), unemployed or homemaker (12.60% [15,487/122,912]), and low educated (38.50% [47,312/122,912]) people. However, some selection bias remained (78.00% (95,871/122,912) of the participants were women, and 61.50% (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred.
Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Santé cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies.
传统流行病学研究方法存在局限性,导致后勤、人力和财务负担高昂。创新数字工具的不断发展有可能克服许多现有的方法学问题。尽管如此,基于网络的研究仍然相对少见,部分原因是人们一直担心其有效性和可推广性。
本观点的目的是总结在法国基于网络的队列研究NutriNet-Santé中开展的方法学研究结果。
基于NutriNet-Santé电子队列先前的研究结果(目前纳入了超过150,000名参与者),我们综合了关于样本代表性、有利的招募策略和数据质量的电子流行病学知识。
总体而言,报告的结果支持基于网络的研究在克服流行病学研究中常见方法学缺陷方面的有用性,特别是在数据质量方面(例如,体重指数[BMI]分类的一致性为93%),减少社会期望偏差,以及能够接触到广泛的参与者特征,包括难以触及的亚组,如年轻人(12.30%[15,118/122,912],<25岁)和老年人(6.60%[8112/122,912],≥65岁)、失业者或家庭主妇(12.60%[15,487/122,912])以及低学历者(38.50%[47,312/122,912])。然而,仍存在一些选择偏差(78.00%(95,871/122,912)的参与者为女性,61.50%(75,590/122,912)拥有高等教育学历),这是队列研究纳入的一个固有方面;其他特定类型的偏差也可能已经出现。
鉴于社会各阶层对互联网的接入迅速增加,招募具有不同社会经济特征和健康风险暴露的参与者是非常可行的。仍需要继续努力识别电子队列中的特定偏差并收集全面有效的数据。NutriNet-Santé队列方法学研究结果的这一总结可能有助于研究人员开展下一代高质量的基于网络的流行病学研究。