  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.
