Factors associated with perceived risk in automotive employees at increased risk of colorectal cancer.

Risk perception may be an important motivator of health-related behaviors. To develop effective risk communication messages, it is important to understand both the patterns of association between perceived risk and health-related behaviors as well as the correlates of risk perception. Very little is known about whether correlates of risk perception are similar in cross-sectional data compared with prospective data. Furthermore, there are scant data on consistency of correlates of risk perception across groups who vary in objective medical risk. If correlates differ, it would underscore the need to tailor intervention messages based on subgroup characteristics as well as increase awareness of the limitations of basing intervention messages only on cross-sectional data. We analyzed data on a subset of 5042 employees who participated in The Next Step Trial, a randomized health promotion trial to encourage colorectal cancer screening and dietary change. We restricted our analysis to only those automotive workers who were white, male, and did not have colorectal cancer (4477/5042) and who returned surveys both at baseline (2,684/4,477) and at year 2 of follow-up (1955/2684). Initial analyses detected interactions between a history of polyps and several of the other covariates. Therefore, univariate and multivariable analyses were conducted separately for men with and without a personal history of colorectal polyps. Within each of the four subgroups (those with or without polyps in the baseline or follow-up analyses), we examined associations between perceived risk measured at baseline (cross-sectional analyses) and at year 2 of follow-up (prospective analyses) in relation to intervention group status, demographic, medical history, psychosocial, and worksite characteristics measured at baseline. To assess the predictive ability of the models, we computed sensitivity and specificity as measures of each model's ability to correctly classify men into their respective subgroup. Although there was no association between perceived risk and intervention group status in the four subgroups analyzed, we included intervention group status as a covariate in all analyses. At baseline (cross-sectional analyses) among men with and without a history of polyps, perceived risk was positively associated with family history of colorectal polyps or cancer, family support for screening, and worry about being diagnosed with colorectal cancer. In addition, for men without polyps, perceived risk was positively associated with being a current smoker. At year 2 of follow-up (prospective analyses) for men with and without polyps, perceived risk at year 2 was positively associated with family history and baseline perceived risk and was negatively associated with having a normal screening examination or no examinations during the trial. In addition, for men with polyps, perceived risk was positively associated with belief in the salience and coherence of screening and with intention to be screened and was negatively associated with access to screening at the worksite. Specificity was higher than sensitivity in three of four subgroups and was >65% in all subgroups. Except for family history, messages to influence perceived risk would emphasize different factors, depending on whether associations were based on baseline or follow-up data and depending on whether men reported a personal history of polyps. For example, although intervention messages using baseline data would emphasize the same factors for men with or without polyps, messages based on follow-up data would emphasize psychosocial characteristics, such as salience and coherence of screening and intention for men with a history of polyps but not for men without. Our findings support the need to delineate subgroups in the study population to target and tailor health-related messages based on respondent characteristics. Our findings also underscore the need to base health-related messages on prospective data as well as cross-sectional data to better address health-related beliefs and behaviors.