Disease identification is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical to ensure that selected cohorts are representative of all patients, independently of their demographics or social determinants of health. While there are multiple potential sources of bias when constructing phenotype definitions which may affect their fairness, it is not standard in the field of phenotyping to consider the impact of different definitions across subgroups of patients. In this paper, we propose a set of best practices to assess the fairness of phenotype definitions. We leverage established fairness metrics commonly used in predictive models and relate them to commonly used epidemiological cohort description metrics. We describe an empirical study for Crohn's disease and diabetes type 2, each with multiple phenotype definitions taken from the literature across two sets of patient subgroups (gender and race). We show that the different phenotype definitions exhibit widely varying and disparate performance according to the different fairness metrics and subgroups. We hope that the proposed best practices can help in constructing fair and inclusive phenotype definitions.
翻译:在观察性健康研究中,疾病识别是一项核心的日常活动。科霍特人对下游分析产生了影响,例如对病情的定性、对病人风险的界定和对治疗方法的研究。因此,关键是确保选定的组群代表所有病人,而不论其人口统计或健康的社会决定因素。虽然在构建可能影响其公平性的苯型定义时,有多种潜在的偏向来源,但在构筑可能影响其公平性的苯型定义时,考虑不同病人分组不同定义的影响并不是典型的。在本文中,我们提出一套最佳做法,以评估苯型定义的公平性。我们利用一套在预测性模型中常用的公平性指标,并将它们与常用的流行病学组群描述指标联系起来。我们描述关于克罗恩人疾病和糖尿病类型2的经验性研究,每个类型都有从两组病人分组(性别和种族)的文献中得出的多重型的苯型定义。我们表明,不同的苯型定义根据不同的公平性指标和分组,表现差异很大。我们希望拟议的最佳做法有助于构建公平和包容性的苯型定义。