Article

Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination

Department of Health Behavior and Health Education, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Health Psychology (Impact Factor: 3.95). 04/2007; 26(2):136-45. DOI: 10.1037/0278-6133.26.2.136
Source: PubMed

ABSTRACT Risk perceptions are central to many health behavior theories. However, the relationship between risk perceptions and behavior, muddied by instances of inappropriate assessment and analysis, often looks weak.
A meta-analysis of eligible studies assessing the bivariate association between adult vaccination and perceived likelihood, susceptibility, or severity was conducted.
Thirty-four studies met inclusion criteria (N = 15,988). Risk likelihood (pooled r = .26), susceptibility (pooled r = .24), and severity (pooled r = .16) significantly predicted vaccination behavior. The risk perception-behavior relationship was larger for studies that were prospective, had higher quality risk measures, or had unskewed risk or behavior measures.
The consistent relationships between risk perceptions and behavior, larger than suggested by prior meta-analyses, suggest that risk perceptions are rightly placed as core concepts in theories of health behavior.

0 Followers
 · 
134 Views
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Awareness represents a major modulator for the uptake of preventive measures and healthy life-style choices. Women underestimate the role of cardiovascular diseases as causes of mortality, yet little information is available about their subjective risk awareness. Methods The Berlin Female Risk Evaluation (BEFRI) study included a randomized urban female sample aged 25–74 years, in which 1,066 women completed standardized questionnaires and attended an extensive clinical examination. Subjective estimation was measured by a 3-point Likert scale question asking about subjective perception of absolute cardiovascular risk with a 10 year outlook to be matched to the cardiovascular risk estimate according to the Framingham score for women. Results An expected linear increase with age was observed for hypertension, hyperlipidemia, obesity, and vascular compliance measured by pulse pressure. Knowledge about optimal values of selected cardiovascular risk factor indicators increased with age, but not the perception of the importance of age itself. Only 41.35% of all the participants correctly classified their own cardiovascular risk, while 48.65% underestimated it, and age resulted as the most significant predictor for this subjective underestimation (OR = 3.5 for age >50 years compared to <50, 95% CI = 2.6–4.8, P <0.0001). Therefore, although socioeconomic factors such as joblessness (OR = 1.9, 95% CI = 1.4–2.6, P <0.0001) and combinations of other social risk factors (low income, limited education, simple job, living alone, having children, statutory health coverage only; OR = 1.5, 95% CI = 1.1–2.1, P = 0.009) also significantly influenced self-awareness, age appeared as the strongest predictor of risk underestimation and at the same time the least perceived cardiovascular risk factor. Conclusions Less than half of the women in our study population correctly estimated their cardiovascular risk. The study identifies age as the strongest predictor of risk underestimation in urban women and at the same time as the least subjectively perceived cardiovascular risk factor. Although age itself cannot be modified, our data highlights the need for more explicit risk counseling and information campaigns about the cardiovascular relevance of aging while focusing on measures to control coexisting modifiable risk factors.
    BMC Medicine 01/2015; 13:52. DOI:10.1186/s12916-015-0304-9 · 7.28 Impact Factor
  • Source

Full-text (2 Sources)

Download
80 Downloads
Available from
Jun 4, 2014