relationship between epidemiology and public health

Introduction: Selection bias is a common concern in research that can significantly affect the validity and generalizability of study findings. It occurs when the selection of study participants is not representative of the target population, leading to skewed or inaccurate results. This bias can compromise the external validity of a study, making it challenging to […]

Introduction: Confounding bias is a critical concern in epidemiological research. It occurs when an extraneous variable (confounder) distorts the true association between an exposure and an outcome. Recognizing and addressing confounding is essential for ensuring the validity and accuracy of study findings. In this explanation, we will delve into the concept of confounding bias, provide

Introduction: Directed Acyclic Graphs (DAGs) are powerful graphical tools employed in epidemiology to elucidate and analyze the causal relationships between various variables in a study. In epidemiological research, the quest to establish causality is paramount. DAGs play a pivotal role in this process by providing a structured framework to visualize and understand the complex web

Directed Acyclic Graphs (DAGs) and Conceptual Framework: DAGs are graphical tools used in epidemiology to visualize and understand the causal relationships between variables in a study. Understanding the concept of causality and how to use DAGs to depict potential causal pathways is crucial. DAGs help in identifying and controlling for confounding variables in epidemiological research.