Literature DB >> 15209936

Dead reckoning: demographic determinants of the accuracy of mortality risk perceptions.

Jahn Karl Hakes1, W Kip Viscusi.   

Abstract

General patterns of bias in risk beliefs are well established in the literature, but much less is known about how these biases vary across the population. Using a sample of almost 500 people, the regression analysis in this article yields results consistent with the well-established pattern that small risks are overassessed and large risks are underassessed. The accuracy of these risk beliefs varies across demographic factors, as does the switch point at which people go from underassessment to overassessment, which we found to be 1500 deaths annually for the full sample. Better educated people have more accurate risk beliefs, and there are important differences in the risk perception by race and gender that also may be of policy interest.

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Year:  2004        PMID: 15209936     DOI: 10.1111/j.0272-4332.2004.00465.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  7 in total

Review 1.  Incidence of fatal food anaphylaxis in people with food allergy: a systematic review and meta-analysis.

Authors:  T Umasunthar; J Leonardi-Bee; M Hodes; P J Turner; C Gore; P Habibi; J O Warner; R J Boyle
Journal:  Clin Exp Allergy       Date:  2013-12       Impact factor: 5.018

2.  Public Perceptions on Cancer Incidence and Survival: A Nation-wide Survey in Korea.

Authors:  Soyeun Kim; Dong Wook Shin; Hyung Kook Yang; So Young Kim; Young-Jin Ko; BeLong Cho; Young Sung Lee; Dukhyoung Lee; Keeho Park; Jong Hyock Park
Journal:  Cancer Res Treat       Date:  2015-05-26       Impact factor: 4.679

3.  The Human Cost of Anthropogenic Global Warming: Semi-Quantitative Prediction and the 1,000-Tonne Rule.

Authors:  Richard Parncutt
Journal:  Front Psychol       Date:  2019-10-16

4.  Risk-Perception Change Associated with COVID-19 Vaccine's Side Effects: The Role of Individual Differences.

Authors:  Laura Colautti; Alice Cancer; Sara Magenes; Alessandro Antonietti; Paola Iannello
Journal:  Int J Environ Res Public Health       Date:  2022-01-21       Impact factor: 3.390

Review 5.  The Prediction of Public Risk Perception by Internal Characteristics and External Environment: Machine Learning on Big Data.

Authors:  Qihui Xie; Yanan Xue
Journal:  Int J Environ Res Public Health       Date:  2022-08-03       Impact factor: 4.614

6.  Measuring, and identifying predictors of women's perceptions of three types of breast cancer risk: population risk, absolute risk and comparative risk.

Authors:  C Apicella; S J Peacock; L Andrews; K Tucker; M B Daly; J L Hopper
Journal:  Br J Cancer       Date:  2009-02-10       Impact factor: 7.640

Review 7.  What do we know about communicating risk? A brief review and suggestion for contextualising serious, but rare, risk, and the example of cox-2 selective and non-selective NSAIDs.

Authors:  R Andrew Moore; Sheena Derry; Henry J McQuay; John Paling
Journal:  Arthritis Res Ther       Date:  2008-02-07       Impact factor: 5.156

  7 in total

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