Cancer Information Scanning and Seeking in the General Population

Abstract

The amount of cancer-related information available in the media and other sources continues to increase each year. We wondered how people make use of such content in making specific health decisions. We studied both the information they actively seek (“seeking”) and that which they encounter in a less purposive way (“scanning”) through a nationally representative survey of adults aged 40–70 years (n = 2,489) focused on information use around three prevention behaviors (dieting, fruit and vegetable consumption, and exercising) and three screening test behaviors (prostate-specific antigen, colonoscopy, mammogram). Overall, respondents reported a great deal of scanning and somewhat less seeking (on average 62% versus 28% for each behavior), and they used a range of sources including mass media, interpersonal conversations, and the Internet, alongside physicians. Seeking was predicted by female gender, age of 55–64 vs. 40–44, higher education, Black race and Hispanic ethnicity, and being married. Scanning was predicted by older age, female gender, and education. Respondents were fairly consistent in their place on a typology of scanning and seeking across behaviors. Seeking was associated with all six behaviors, and scanning was associated with three of six behaviors.

The authors acknowledge the funding support of the National Cancer Institute's Center of Excellence in Cancer Communication (CECCR) located at the Annenberg School for Communication, University of Pennsylvania (P50-CA095856-05).

Notes

*Coefficients significantly different from zero (p < .05); **p < .01; ***p < .001.

Note: The range for individual sources is 0–5. The doctor is included here for the sake of comparison with the other sources. Since we were more interested in the influence of other sources on prevention and lifestyle behavior, however, it has been excluded from indices computed by adding the other sources together in some subsequent analyses.

*Coefficients significantly different from zero (p < .05); **p < .01; ***p < .001. These regression models use the weighted data.

Nonmissing = 2,341.

Nonmissing = 2,363.

Nonmissing = 2,326.

Nonmissing = 2,347.

Nonmissing = 1,176.

Nonmissing = 1,158.

*Kappas significantly different from zero (p < .05); **p < .01; ***p < .001. Standard errors are likely underestimated as Stata does not allow for kappas to be run with weights, so we could not adjust appropriately.

Results are for six independent logistic regression models (one for each behavior). Analyses for colonoscopy are restricted to those over age 50. Those for diet are restricted to individuals with a BMI of 25 or greater. All analyses control for age, gender, education, race, ethnicity, and marital status.