Health Services and Outcomes Research, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO 64108, USA; Department of Pediatrics, University of Missouri-Kansas City School of Medicine, 2411 Holmes Street, Kansas City, MO 64108, USA.
Research Informatics, Children's Mercy Kansas City, 2401 Gillham Road, Kansas City, MO 64108, USA.
J Biomed Inform. 2024 Sep;157:104687. doi: 10.1016/j.jbi.2024.104687. Epub 2024 Jul 8.
The ability to apply results from a study to a broader population remains a primary objective in translational science. Distinct from intrinsic elements of scientific rigor, the extrinsic concept of generalization requires there be alignment between a study cohort and population in which results are expected to be applied. Widespread efforts have been made to quantify representativeness of study cohorts. These techniques, however, often consider the study and target cohorts as monolithic collections that can be directly compared. Overlooking known impacts to health from socio-demographic and environmental factors tied to individual's geographical location, and potentially obfuscating misalignment in underrepresented population subgroups. This manuscript introduces several measures to account for geographic information in the assessment of cohort representation.
Metrics were defined across two themes: First, measures of recruitment, to assess if a study cohort is drawn at an expected rate and in an expected geographical pattern with respect to individuals in a reference cohort. Second, measures of individual characteristics, to assess if the individuals in the study cohort accurately reflect the sociodemographic, clinical, and geographic diversity observed across a reference cohort while accounting for the geospatial proximity of individuals.
As an empirical demonstration, methods are applied to an active clinical study examining asthma in Black/African American patients at a US Midwestern pediatric hospital. Results illustrate how areas of over- and under-recruitment can be identified and contextualized in light of study recruitment patterns at an individual-level, highlighting the ability to identify a subset of features for which the study cohort closely resembled the broader population. In addition they provide an opportunity to dive deeper into misalignments, to identify study cohort members that are in some way distinct from the communities for which they are expected to represent.
Together, these metrics provide a comprehensive spatial assessment of a study cohort with respect to a broader target population. Such an approach offers researchers a toolset by which to target expected generalization of results derived from a given study.
将研究结果应用于更广泛的人群的能力仍然是转化科学的主要目标。与科学严谨性的内在要素不同，推广的外在概念要求研究队列与预期应用结果的人群之间存在一致性。已经做出了广泛的努力来量化研究队列的代表性。然而，这些技术通常将研究和目标队列视为可以直接比较的整体集合。忽略了与个体地理位置相关的社会人口和环境因素对健康的已知影响，并可能掩盖代表性不足的人群亚组中的不一致性。本文介绍了几种在评估队列代表性时考虑地理信息的措施。
定义了两个主题的指标：首先，招募措施，以评估研究队列是否按照预期的比率在预期的地理模式中从参考队列中的个体中抽取。其次，个体特征措施，以评估研究队列中的个体是否在考虑个体地理空间接近度的情况下准确反映参考队列中观察到的社会人口统计学、临床和地理多样性。
作为实证示范，方法应用于一项在美国中西部儿科医院对黑人/非裔美国患者进行哮喘的主动临床研究。结果说明了如何根据个体层面的研究招募模式识别和理解过度和欠招募的区域，并强调了识别研究队列密切反映更广泛人群的特征子集的能力。此外，它们还提供了一个深入研究不一致性的机会，以确定研究队列成员在某种程度上与他们预期代表的社区不同。
这些指标共同为研究队列相对于更广泛的目标人群提供了全面的空间评估。这种方法为研究人员提供了一种工具，可用于针对从给定研究中得出的结果的预期推广。
