Why some women have an optimistic or a pessimistic bias about their breast cancer risk: experiences, heuristics, and knowledge of risk factors

Cancer Nurs. 2010 Jan-Feb;33(1):64-73. doi: 10.1097/NCC.0b013e3181b430f9.

Abstract

Perceived risk to a health problem is formed by inferential rules called heuristics and by comparative judgments that assess how one's risk compares to the risk of others. The purpose of this cross-sectional, community-based survey was to examine how experiences with breast cancer, knowledge of risk factors, and specific heuristics inform risk judgments for oneself, for friends/peers, and comparative judgments for breast cancer (risk friends/peers - risk self). We recruited an English-speaking, multicultural (57% nonwhite) sample of 184 middle-aged (47 + or - 12 years old), well-educated women. Fifty percent of participants perceived that their breast cancer risk was the same as the risk of their friends/peers; 10% were pessimistic (risk friends/peers - risk self < 0), whereas 40% were optimistic (risk friends/peers - risk self > 0). Family history of breast cancer and worry informed risk judgments for oneself. The availability and cultural heuristics specific for black women informed risk judgments for friends/peers. Knowledge of risk factors and interactions of knowledge with the availability, representativeness, and simulation heuristics informed comparative judgments (risk friends/peers - risk self). We discuss cognitive mechanisms with which experiences, knowledge, and heuristics influence comparative breast cancer risk judgments. Risk communication interventions should assess knowledge deficits, contextual variables, and specific heuristics that activate differential information processing mechanisms.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Breast Neoplasms*
  • Cross-Sectional Studies
  • Data Collection
  • Female
  • Health Education
  • Health Knowledge, Attitudes, Practice*
  • Humans
  • Middle Aged
  • Perception*
  • Psychometrics
  • Regression Analysis
  • Risk Assessment
  • Risk Factors
  • Statistics as Topic