To describe the methodological characteristics used in large health surveys conducted in Spain early on in the COVID-19 pandemic.
Scoping review. Inclusion criteria: observational studies published between January 2020 and December 2021, with sample sizes of over 2,000 persons resident in Spain. Databases consulted: PubMed, CINAHL, Literatura Latinoamericana y del Caribe en CC de la Salud, Scopus, PsycINFO, Embase, Sociological Abstracts, Dialnet and Web of Science Core Collection. We analyzed the characteristics of the literature references, methodologies and information gathered in the surveys selected. Fifty five studies were included.
Sixty percentage of the studies included had mental health as their main topic and 75% were conducted on the general adult population. Thirteen percentage had a longitudinal design, 93% used the internet to gather information and the same percentage used non-probability sampling. Thirty percentage made some type of sampling correction to reduce coverage or non-response biases, but not selection biases. Sixty seven percentage did not state the availability of their data.
Consistent with the extensive use of non-probability sampling without any bias correction in the extraordinary setting created by COVID-19, quality population frameworks are required so that probability and representative samples can be extracted quickly to promptly address other health crises, as well as to reduce potential coverage, non-response and particularly selection biases by utilizing reweighting techniques. The low data accessibility despite the huge opportunity that COVID-19 provided for Open Science-based research is striking.
描述在COVID-19大流行早期在西班牙进行的大型健康调查中使用的方法学特征。
■范围审查。纳入标准:2020年1月至2021年12月发表的观察性研究,样本量超过2,000人居住在西班牙。咨询的数据库:PubMed,CINAHL,LiteraturaLatinoamericanaydelCaribeenCCdelaSalud,Scopus,PsycINFO,Embase,社会学文摘,Dialnet和WebofScience核心合集。我们分析了文献参考文献的特点,所选调查中收集的方法和信息。纳入了55项研究。
■60%的研究以心理健康为主要主题,75%的研究对象是普通成年人。百分之十三的人有纵向设计,93%的人使用互联网收集信息,相同比例的人使用非概率抽样。30%的人进行了某种类型的抽样校正,以减少覆盖率或无反应偏差,但不是选择偏见。67%的人没有说明他们数据的可用性。
与在COVID-19创建的特殊环境中广泛使用非概率抽样而没有任何偏差校正一致,需要高质量的人群框架,以便可以快速提取概率和代表性样本,以迅速解决其他健康危机,以及减少潜在的覆盖范围,无反应,特别是通过利用重新加权技术的选择偏差。尽管COVID-19为基于开放科学的研究提供了巨大的机会,但数据可访问性低是惊人的。