Introducing risk inequality metrics in tuberculosis policy development
Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions1,2. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to cohort selection imposed by dynamic forces of infection. Individuals with higher risk of acquiring infection are predominantly affected by the pathogen, leaving the unaffected pool with those whose intrinsic risk is lower. This causes susceptibility pools to attain average risks which are lower under higher forces of infection. Interventions that modify the force of infection change the strength of selection, and therefore alter average risks in the pools which feed further incidence. Inability to account for these dynamics is what makes homogenous models unsuitable. We introduce concrete metrics to approximate risk inequality in tuberculosis, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.