- Click here for explanation of classification scheme.

To use meta-analysis to identify data gaps where additional research is needed to:

• Fully evaluate consumers' risky food handling and consumption practices
• Evaluate differences in knowledge vs. reported practices for various consumer behaviors and demographic sub-population categories
• Evaluate differences in reported risky behaviors among demographic sub-populations.

• Studies that evaluated US consumers knowledge and reported behaviors in units of mean percentages or proportions; these are referred to as effect sizes
• Studies published since 1992 that provided sample size information and reported demographic characteristics of the respondents
• Behavioral measures included in the meta-analysis:
  • Consumption of raw or undercooked ground beef, eggs, shellfish, and milk
  • Knowledge of good hygiene practices
  • Practices to prevent cross-contamination
  • Proper defrosting methods
  • Apparently safe food sources
  • Proper cooking and heating practices and handling practices for hygiene
  • Prevention of cross-contamination
  • Food holding
  • Cold storage
  • Avoidance of unsafe foods
  • Cooking and heating.
• Demographics included in the analysis:
  • Gender
  • Ethnicity (Caucasian, Hispanic, African American, Asian, others)
  • Age (young, 18 to 29 years, mid-age, 30 to 54, seniors older than 55 years)
  • Education (less than high school, high school, more than high school)
  • Geographic region (Pacific, Mountain, West North Central, West South Central, East North Central, East South Central, Middle Atlantic, South Atlantic)
  • Metropolitan (1 million or more population) vs. non-metropolitan (less than 1 million population).

Not described.

Recruitment

Studies were retrieved from various journals and surveys from government and private organizations, but search terms were not described.

Design

Meta-analysis.

Statistical Analysis

• After collecting, indexing and arranging the data in proper format, the ES, standard error (SE) and the inverse variance weight (w) for each study were calculated for each risky behavior and demographic category combination
• The mean ES then was calculated by averaging individual ESs weighted by inverse variance weights
• Similarly, the SE of the mean ES was calculated by averaging SEs of individual effects weighted by inverse variance weights
• A Z-test was used to evaluate whether the difference between knowledge and the reported use of a safe handling practice for a demographic category was significant
• To evaluate whether differences in behaviors among demographic groups such as gender and ethnicity (Caucasian, Hispanic, African American, Asian) were significant, an analysis of variance (ANOVA, PROC ANOVA) to perform multiple comparisons was used
• When the P value was less than 0.05, the difference in the behavioral measure for the demographic category analyzed was significant at α=0.05.

 

Dependent Variables

Risky food handling and consumer practices:

• Consumption of raw food
• Poor hygiene
• Cross-contamination.

Independent Variables

• Gender
• Ethnicity (Caucasian, Hispanic, African American, Asian, others)
• Age (young, 18 to 29 years, mid-age, 30 to 54, senior older than 55)
• Education (less than high school, high school, more than high school)
• Geographic region (Pacific, Mountain, West North Central, West South Central, East North Central, East South Central, Middle Atlantic, South Atlantic)
• Metropolitan (1 million or more population) vs. non-metropolitan (less than 1 million population).

• Initial N: Combined findings from 20 studies (262 male respondents, 738 female respondents)
• Attrition (final N): Combined findings from 20 studies (262 male respondents, 738 female respondents)
• Age: 336 young (18 to 29 years), 669 mid-age (30 to 54 years), 410 senior (55 or more years)
• Ethnicity: Caucasian, Hispanic, African American, Asian, Other (reported separately per study)
• Other relevant demographics: Reported separately per study
• Location: United States.

Key Findings

• There were considerable differences in behaviors across demographic categories, possibly because of socioeconomic and cultural differences
• Compared with women, men reported:
  • Greater consumption of raw or undercooked foods
  • Poorer hygiene
  • Poorer practices to prevent cross-contamination
  • 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
  • Poorer cross-contamination practices
• The highest raw ground beef and egg consumption and the poorest hygiene and cross-contamination practices were found in the US Mountain region
• For all behaviors evaluated in the meta-analysis, consumer knowledge of safe handling practices does not correspond with reported use of the practices, suggesting that knowledge is a poor indicator of actual behavior
• The largest difference between knowledge and reported use was observed for individuals with more than a high school education (25.6%), men (24.9%), and mid-age adults (21.8%)
• The largest difference between knowledge of practices to prevent cross-contamination and reported use was observed for men (13.1%) and young adults (10.4%)
• More people consumed raw or undercooked eggs (47%) than consumed raw or undercooked ground beef (21%), shellfish (12%) and raw milk (2.1%). Consumption of raw or undercooked food varied by gender, ethnicity, age, income, education level and region.
• Consumers were more knowledgeable about good hygiene practices (88%), practices to prevent cross-contamination (86%) and safe food holding practices (84%) than they were about the other practices included in the analysis
• Middle-aged adults consumed more raw food and younger adults had the poorest safe handling practices and were the least knowledgeable about safe handling practices compared to senior adults.  

The meta-analysis findings can be used to:

• Identify important data gaps
• Prioritize further research to conduct statistical analysis
• Identify demographic categories with risky consumption and handling practices
• Help target risk communication and risk management efforts.

Government:

Cooperative State Research, Education and Extension Service and the US Department of Agriculture

• Studies were retrieved from various journals and surveys from government and private organizations, but search terms were not described. 
• Quality of studies was not addressed.