• Journal Article

Consumer Food Safety Knowledge, Practices, and Demographic Differences: Findings from a Meta-Analysis

Citation

Patil, S., Cates, S., & Morales, R. (2005). Consumer Food Safety Knowledge, Practices, and Demographic Differences: Findings from a Meta-Analysis. Journal of Food Protection, 68(9), 1884-1894.

Abstract

Risk communication and consumer education to promote safer handling of food can be the best way of managing the risk of foodborne illness at the consumer end of the food chain. Thus, an understanding of the overall status of food handling knowledge and practices is needed. Although traditional qualitative reviews can be used for combining information from several studies on specific food handling behaviors, a structured approach of meta-analysis can be more advantageous in a holistic assessment. We combined findings from 20 studies using meta-analysis methods to estimate percentages of consumers engaging in risky behaviors, such as consumption of raw food, poor hygiene, and cross-contamination, separated by various demographic categories. We estimated standard errors to reflect sampling error and between-study random variation. Then we evaluated the statistical significance of differences in behaviors across demographic categories and across behavioral measures. There were considerable differences in behaviors across demographic categories, possibly because of socioeconomic and cultural differences. For example, compared with women, men reported greater consumption of raw or undercooked foods, poorer hygiene, poorer practices to prevent cross-contamination, and less safe defrosting practices. Mid-age adults consumed more raw food (except milk) than did young adults and seniors. High-income individuals reported greater consumption of raw foods, less knowledge of hygiene, and poorer cross-contamination practices. The highest raw ground beef and egg consumption and the poorest hygiene and cross-contamination practices were found in the U.S. Mountain region. Meta-analysis was useful for identifying important data gaps and demographic groups with risky behaviors, and this information can be used to prioritize further research.