The explanation of a complex problem: A content analysis of causality in cancer news.

Understanding causality is a critical part of developing preventive and treatment actions against cancer. Three main causality models-necessary, sufficient-component, and probabilistic causality have been commonly used to explain the causation between causal factors and risks in health science. However, news media do not usually follow a strict protocol to report the causality of health risks. The purpose of this study was to describe and understand how the causation of cancer was articulated on news media. A content analysis of 471 newspaper articles published in the United States during two time-frames (2007-2008 and 2017-2018) was conducted. The analysis showed that probabilistic causality was most frequently used to explain the causal relationship between risk factors and cancer. The findings also uncovered other important details of news framing, including types and characteristics of risk factors, intervention measures, and sources of evidence. The results provided theoretical and practical implications for public understanding and assessment of cancer risks.