Why are Non-Linear Relationships Important in Epidemiology?
Non-linear relationships are crucial in understanding the dynamics of disease spread, risk factors, and health outcomes. For example, the relationship between alcohol consumption and health outcomes like heart disease or certain cancers can be non-linear. Recognizing and accurately modeling these relationships can lead to more effective interventions and policy decisions.
