Public understanding of risks from gene-environment interaction in common diseases

Implications for public communications

Celeste M. Condit, Lijiang Shen

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Background/Aims: Public understanding of the relationship between health behaviors and genes is likely to affect the motivational impact of learning information about one's own genes. Extant research has featured difficulty measuring public understandings of this relationship. This essay explores public understanding of the relationship between genes and behavior, especially with regard to the mathematical relationships to risk concept. It contributes a psychometrically valid scale for measuring beliefs about gene- behavior relationships. Methods: Three population representative surveys (n = 633, 658, 1,218) were conducted using the Knowledge Networks panel platform. Results: Interpretations of risk vary depending on whether genes and behavior are conceived of as health-damaging (loss frame) or health-protecting (gain frame). In the loss frame, the majority of the population adopts an additive model of the relationship with approximately one-third adopting an amplificative model. In the gain frame, beliefs are divided roughly equally among additive, amplificative and sub-additive models. Scores on the nonmathematically based scale indicate higher belief in the existence of interaction than scores on the more concrete question format. Conclusions: The existence of different interpretations of gene-behavior relationships based on gain/loss frame and abstract/concrete modes indicates the need to select frame and mode carefully in both teaching and research. Research is needed to identify optimal configurations for teaching and presenting this relatively complex material.

Original languageEnglish (US)
Pages (from-to)115-124
Number of pages10
JournalPublic Health Genomics
Volume14
Issue number2
DOIs
StatePublished - Mar 1 2011

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Gene-Environment Interaction
Communication
Genes
Teaching
Research
Health Behavior
Health
Population
Learning

All Science Journal Classification (ASJC) codes

  • Genetics(clinical)
  • Public Health, Environmental and Occupational Health

Cite this

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abstract = "Background/Aims: Public understanding of the relationship between health behaviors and genes is likely to affect the motivational impact of learning information about one's own genes. Extant research has featured difficulty measuring public understandings of this relationship. This essay explores public understanding of the relationship between genes and behavior, especially with regard to the mathematical relationships to risk concept. It contributes a psychometrically valid scale for measuring beliefs about gene- behavior relationships. Methods: Three population representative surveys (n = 633, 658, 1,218) were conducted using the Knowledge Networks panel platform. Results: Interpretations of risk vary depending on whether genes and behavior are conceived of as health-damaging (loss frame) or health-protecting (gain frame). In the loss frame, the majority of the population adopts an additive model of the relationship with approximately one-third adopting an amplificative model. In the gain frame, beliefs are divided roughly equally among additive, amplificative and sub-additive models. Scores on the nonmathematically based scale indicate higher belief in the existence of interaction than scores on the more concrete question format. Conclusions: The existence of different interpretations of gene-behavior relationships based on gain/loss frame and abstract/concrete modes indicates the need to select frame and mode carefully in both teaching and research. Research is needed to identify optimal configurations for teaching and presenting this relatively complex material.",
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Public understanding of risks from gene-environment interaction in common diseases : Implications for public communications. / Condit, Celeste M.; Shen, Lijiang.

In: Public Health Genomics, Vol. 14, No. 2, 01.03.2011, p. 115-124.

Research output: Contribution to journalArticle

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