Child Nutrition: Food Security and Safety

Citation:
 
Study Design:
Class:
- Click here for explanation of classification scheme.
Quality Rating:
Research Purpose:

To determine the prevalence of unsafe food practices of individuals in a Canadian-based population, specifically, high-risk food consumption and home food safety.

Inclusion Criteria:
  • English-speaking
  • Older than 18 months.
Exclusion Criteria:

Traveling outside of Canada in the seven days before the interview.

Description of Study Protocol:

Recruitment

Residents of the Waterloo Region, Ontario, Canada who were randomly selected based on residential telephone numbers.

Design

Cross-sectional study.

Statistical Analysis
  • Individuals who refused to answer a question were excluded from the analysis of that question
  • Prevalence estimates were calculated for the consumption of high-risk food items and for food handling and preparation habits
  • Exact confidence intervals (CIs) were computed for all proportions at the 95% level
  • Differences in proportions were tested with Pearson's chi square (x2) test and Fischer's exact test when the expected frequencies for a contingency table were less than five
  • To assess the relationship between high-risk practices and demographic variables, odds ratios (ORs) were used to estimate the strength of association for statistically significant differences between binary variables such as gender and the Cochran-Armitage test for trend was used to test for linear trends among ordinal variables such as age, income and education
  • Multiple x2 tests were then used for any non-linear trends identified among age, income and education groups as well as for non-ordered variables such as residence
  • Data on high-risk practices were sub-divided into single demographic variables
  • Cross-tabulations and x2 were used to determine differences between high-risk food consumption practices and food-handling behaviors
  • ORs were used to estimate the strength of association for statistically significant differences between high-risk practices
  • In all analyses, statistically significant differences were determined by a two-tailed probability of less than 0.05.
Data Collection Summary:

Timing of Measurements

Telephone survey occurred from November 2005 to March 2006.

Dependent Variables

  • Consumption of high-risk foods
  • Prevalence of unsafe food practices:
    • Hand-washing practices
    • Safe food-handling knowledge
    • Source of food safety education
    • Meat thawing and cooking practices
    • Cross-contamination after raw food preparation
    • Refrigeration temperatures.

Independent Variables

Demographics of the Canadian community:

  • Age
  • Income
  • Education
  • Type of living area.
Description of Actual Data Sample:
  • Initial N: 7,142 contacted and eligible respondents
  • Attrition (final N): 2,332 completed interviews
  • Age: Described elsewhere
  • Ethnicity: Described elsewhere
  • Other relevant demographics: Described elsewhere
  • Location: Waterloo Region, Ontario, Canada.
Summary of Results:

Key Findings

High-Risk Food Items 

  • The prevalence of drinking unpasteurized milk decreased with increasing education level (P=0.009). Drinking unpasteurized milk was significantly more prevalent among rural residents (9%) than among urban residents (0.4%, P<0.001). However, the prevalence of consumption of cheese made from unpasteurized milk was not significantly different between rural residents (3%) and urban residents (0.9%, P=0.075).
  • The prevalence of undercooked egg consumption increased with increasing age (P<0.001), education (P<0.001) and income (P=0.0004). Eating undercooked eggs was significantly more common among suburban residents (49.6%) than among urban residents (40.5%, P=0.006). Overall, fewer (5.9%) respondents reported eating foods containing raw eggs, such as cookie dough, cake batter or steak tartar; the prevalence of this behavior increased with increasing income level (P<0.001).
  • Males were significantly more likely than females to consume chicken nuggets (OR=1.37; 95% CI=1.09, 1.73; P=0.005). Chicken nugget consumption increased with increasing income level (P=0.006) and decreased with increasing education (P<0.001). Consumption was highest (59.9%) in children younger than 12 years and decreased with increasing age (P<0.001).
  • Individuals aged 13 to 64 years were significantly more likely to consume spouts than were individuals older than 64 years of age (OR=1.6; 95% CI=1.05, 2.51; P=0.025). Males were significantly more likely to drink unpasteurized juice than were females (OR=1.48; 95% CI=1.03, 2.1; P=0.025). Raw shellfish consumption increased as education level increased (P=0.011).
  • Males were significantly less likely to eat lettuce (OR=.65; 95% CI=0.51, 0.82; P<0.001) and pre-bagged mixed salad greens (OR=0.77; 95% CI=0.65, 0.93; P=0.004) than were females. Children younger than 12 years were significantly less likely to eat lettuce (OR=0.19; 95% CI=0.12, 0.29; P<0.001 and pre-bagged mixed salad greens (OR=0.52; 95% CI=0.34, 0.79; P=0.001) than were elderly individuals. Adults aged 25 to 54 years were significantly more likely to eat lettuce than were the elderly (OR=1.45; 95% CI=1.01, 2.07; P=0.033).
  • Suburban residents (79.4%) were significantly less likely to eat lettuce than urban residents were (85.5%, P=0.006), whereas suburban residents (49.8%) were significantly more likely than were urban residents (37.6%, P<0.001) to eat pre-bagged mixed salad greens. Eating lettuce increased as education (P<0.001) and total annual household income level (P<0.001) increased.
  • Males were significantly more likely to consume fresh basil than were females (OR=1.33; 95% CI=1.01, 1.73; P=0.032.) and adults aged 18 to 64 years were significantly more likely to eat fresh basil than were elderly respondents (OR=1.78; 95% CI=1.17, 2.8; P=0.005)
  • Children younger than 12 years of age were significantly more likely to consume melon than elderly respondents were (OR=1.51; 95% CI=1, 2.28; P=0.04). As total annual household income level increased, the prevalence of eating melon increased (P=0.015). Residents in suburban areas (33.6%) were significantly more likely to consume melon than were residents of urban areas (22.1%, P<0.001).

Hand-washing Practices

  • Males were significantly more likely to not always wash their hands before eating or handling food (OR=1.61; 95% CI=1.34, 1.94; P<0.001) and to not wash their hands with soap and water after handling raw meal (OR=2.55; 95% CI=1.78, 3.66; P<0.001) than were females. Also, males were significantly more likely to think that hand washing is not important in the prevention of disease (OR=2.11; 95% CI=1.31, 3.41; P=0.001) compared with females. As age increased, both the prevalence of people who always washed their hands before eating or handling food (P<0.001) and those who thought hand washing was important in the prevention of disease (P<0.001) increased.
  • As education level increased, the prevalence of people who thought hand washing was important in the prevention of disease (P<0.001) increased. Respondents without a high school diploma were significantly less likely to always wash their hands before eating or handling food compared with respondents with an education beyond high school (P<0.001) .  Respondents with a high school or college diploma were significantly more likely to always wash their hands with soap and water after handling food compared with respondents without a high school diploma (P=0.045 and P=0.001, respectively).  
  • As total household income level increased, always washing hands before eating or handling food (P=0.002) and reporting that hand washing was important in the prevention of disease (P<0.001) decreased
  • Suburban residents were significantly more likely to always wash their hands before eating or handling food than were urban residents (P<0.001). Rural residents were significantly more likely than were urban residents to always wash hands with soap and water after handling raw meat. (P=0.042).

Individual Food Safety Knowledge

  • Respondents who had been given information about foods to avoid eating were significantly less likely  to eat undercooked eggs (OR=0.78; 95% CI=0.65, 0.95; P=0.009) and chicken nuggets (OR=0.52; 95% CI=0.41, 0.66; P<0.001) than were those who had not been given information. However, respondents who had been given information about foods to avoid eating were significantly more likely to eat raw nuts (OR=1.32; 95% CI=1.03, 1.67; P=0.022), lettuce (OR=1.37; 95% CI=1.08, 1.73; P=0.007) and fresh basil (OR=1.56; 95% CI=1.19, 2.04; P=0.001).
  • Respondents who reported electronic media as a source of information were significantly less likely to consume:
Less Likely to Consume OR 95% CI P
Sprouts  0.68 0.49, 0.93  0.012
Chicken Nuggets  0.35 0.26, 0.47   <0.001
Unpasteurized Juice  0.45 0.27, 0.071  <0.001 
More Likely to Consume  
Lettuce 1.53 1.17, 2.02 0.001
  • Raw oyster was the only high-risk food item whose consumption was significantly associated with knowledge of associated disease risks. Respondents who had heard of the risks associated with raw oysters were significantly more likely (OR=1.96; 95% CI=1.36, 2.84; P<0.001) to eat raw shellfish than were those that had not heard of risks.
  •  Males were significantly less likely than females were to have heard of food-borne disease risks associated with:
Food OR 95% CI P
Hamburger 0.47 0.38, 0.59 <0.001
Chicken 0.61 0.48, 0.76 <0.001
Unpasteurized juice 0.72 0.59, 0.88 <0.001
Sprouts 0.57 0.48, 0.69 <0.001
  • As age increased, knowledge of disease risks associated with consumption of each food item increased, then decreased among individuals over the age of 64 years. Knowledge of disease risks associated with consumption of each food item increased with increasing education level (P<0.001). Individuals who reported a total annual household income between Can$40,000 and Can$80,000 were significantly more likely to have heard of risks associated with raw oysters (47.6%) than were individuals who reported a total annual household income of less than Can$20,000 (30.2%, P<0.001).
  • Knowledge of risks associated with drinking unpasteurized juice was significantly higher among individuals with a total annual household income between Can$60,000 and Can$80,000 (40.7%) than among individuals with a total annual household income of less than Can$20,000 (28.6%, P=0.026). Rural residents (68%) were significantly more likely to have heard of disease risks associated with drinking unpasteurized milk than were urban residents (53.4%, P=0.004), while suburban residents were significantly less likely to have heard of risks associated with drinking unpasteurized milk (38.3%) and unpasteurized juice (21.8%) then were urban residents (53.4%, P<0.001 and 30.4%, P=0.002, respectively).

Household Food-Handling and Cooking Practices

  • As age increased, the prevalence of noticing a safe handling label on meat packages increased (P=0.011). Urban residents (53.6%) were significantly more likely to notice safe handling labels on meat packages than were all other residents (41.1%, P<0.001). Males were significantly more likely to thaw frozen meat at room temperature than were females (OR=1.22; 95% CI=1, 1.48; P=0.046).
  • Rural residents (40%) were significantly more likely to thaw frozen meat at room temperature than urban residents were (26.5%, P=0.003), while suburban residents (12.8%, P<0.001) were significantly less likely to thaw frozen meat at room temperature. As total annual household income level increased, the use of thermometers to determine the doneness of cooked meat increased (P<0.001). Suburban residents (21.8%) were significantly more likely to use a thermometer to determine when meat was cooked well enough to eat compared with urban residents (12.2%, P<0.001).

Cross-contamination

  • As age increased, there was a decrease in the prevalence of using soap and water to clean the kitchen sink or cutting board after preparing raw meat (P<0.001). Urban residents (62.3%) were significantly less likely to use soap and water to clean the kitchen sink and cutting board after preparing raw meat than were other residents (71.2%, P<0.001). 
  • Males were significantly more likely to not always wash raw fruits before eating them than were females (OR=1.34; 95% CI=1.1, 1.64; P=0.003). Children younger than 12 years (OR=1.74; 95% CI=1.12, 2.71; P=0.009) and respondents between 18 and 54 years of age (OR=1.57; 95% CI=1.16, 2.13; P=0.002) were significantly more likely to not always wash fruits before eating them than were respondents older than 64 years.
  • As age increased, the prevalence of washing vegetables before consumption increased (P<0.001). As total annual household income level increased, the prevalence of washing fruits and vegetables before consumption decreased (P=0.001 and P=0.03, respectively). Suburban residents were significantly more likely to always wash fruits and vegetables before consumption then were urban residents (P<0.001 and P<0.001, respectively).

Refrigerator Temperatures

  • Males were significantly more likely to know the correct refrigerator temperature (OR=1.85; 95% CI=1.53, 2.23; P<0.001) as well as personal refrigerator temperature (OR=1.6; 95% CI=1.21, 2.1; P<0.001 than were females. Adults aged 25 to 64 years were significantly more knowledgeable of the correct recommended refrigerator temperature than elderly individuals were (OR=1.43; 95% CI=1.08, 1.89; P=0.009). As age increased, knowledge of personal refrigerator temperature also increased (P=0.012).
  • Respondents with a college diploma (25.5%) were significantly less likely to know the correct recommended refrigerator temperature than were respondents without a high school diploma (37.3%, P=0.001). However, both respondents with a high school diploma (22%) and a university degree (21.1%) were significantly more likely to know the temperature of their own refrigerator than were respondents without a high school diploma (14.1%; P=0.011 and P=0.008, respectively).
  • Individuals who reported a total annual household income between Can$40,000 and Can$60,000 were significantly more likely to know the correct recommended refrigerator (51.5%) and personal refrigerator temperature (25.1%) than were individuals who reported a total annual household income of less than Can$20,000 (35.7%; P=0.003 and 12.1%; P=.012, respectively)
  • Individuals who reported a total annual household income between Can$20,000 and Can$40,000 (22.7%) were also significantly more likely to know their own refrigerator temperatures than were individuals who reported a total annual household income of less then Can$20,000 (12.1%, P=0.041)
  • Suburban resident were significantly more likely to know the correct recommended refrigerator temperature (54.9%) and personal refrigerator temperature (29.9%) than were urban residents (31.9%; P<0.001 and 15.9%; P<0.001, respectively)
  • Respondents who knew the temperature inside their refrigerators were significantly less likely to eat chicken nuggets (OR=0.49; 95% CI=0.31, 0.75; P=0.001) than were those that did not know the temperature inside their refrigerators, but were significantly more likely to eat fresh basil (OR=1.55; 95% CI=1.03, 2.3; P=0.023).
Author Conclusion:
  • The survey assessed consumption of high-risk foods and evaluated consumer food safety knowledge and practice in the home, and the results suggest that certain high-risk food consumption patterns and unsafe food-handling and hygiene practices are fairly common among respondents and are often associated with demographic characteristics. Despite awareness of concepts such as hand-washing and high-risk food consumption, respondents still engaged in high-risk behaviors that varied according to demographic characteristics. These findings indicate that education messages should stress that consumer behavior is an essential component of the safety of foods prepared at home and additional research is needed regarding consumer perceptions of the nature and origin of food-borne illness in order to design educational programs that will motivate behavioral change.
  • This study had also outlined the difference in views and knowledge between public health scientists and the public with respect to high-risk foods. Although high-risk foods highlighted in this study pose different risks, more work is needed through food attribution studies to better understand the role of particular food items and infectious disease. Once this information is available, educational campaigns could be better directed to make sure that the public is aware as much as possible of all high-risk foods that public health officials are concerned about and combine those messages with strategies to promote healthy diets with appropriate nutritional guidance.
Funding Source:
Government: Public Health Agency of Canada
Reviewer Comments:

Respondents were not well described, although many details were published elsewhere. The authors note the following limitations:

  • Food safety questions on the preparation and handling of food were asked of the person most familiar with these practices in the household and are thus not representative of individuals in the general population
  • Since consumer awareness of food safety issues reported by household food preparers is higher than that of the general population, it is possible the prevalence of positive food safety practices here were overestimated
  • These results relied on self-reported practices and knowledge, which can lead to biases toward reporting socially desirable responses
  • If respondents reported what they believed to be correct rather than what they practiced, the prevalence of unsafe practices among the general population may be even higher than reported here
  • The results presented are not validated by observation. Since studies show discrepancies between self-reported practices and actual food-handling behaviors, it is possible that actual food safety practices are poorer than are the self-reported practices here.
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) Yes
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) Yes
 
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? No
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? N/A
  2.3. Were health, demographics, and other characteristics of subjects described? No
  2.4. Were the subjects/patients a representative sample of the relevant population? No
3. Were study groups comparable? N/A
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? N/A
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? No
  4.1. Were follow-up methods described and the same for all groups? N/A
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) No
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? N/A
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) N/A
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? N/A
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? Yes
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? N/A
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? N/A
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? Yes
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? N/A
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes