Examine the results of the food frequency analysis using binomial probabilities below [original table available in the third tab of the Excel spreadsheet for Exercise 2 (Module 2 – Exercise 2)].
Note: In practice, binomial probabilities will be calculated for all food items, and then factors outlined in Parts 1 and 2 of this exercise will be taken into consideration.
The table shows that multiple food items are reported more commonly than would be expected: spinach, blueberries, almonds, walnuts, and sesame seeds.
|
Food Item |
Confirmed Cases |
Reference |
Binomial Probability | ||||
|
Yes |
Prob |
No |
DK |
%Y+P |
Foodbook Canada* |
p-value | |
|
MEATS | |||||||
|
Any chicken (not including deli meat) |
3 |
0 |
3 |
1 |
50.0 |
85.6 |
0.0375 |
|
Any pork (not including deli meat) |
1 |
2 |
3 |
1 |
50.0 |
55.1 |
0.3028 |
|
Any beef (not including deli meat) |
1 |
1 |
4 |
1 |
33.3 |
78.4 |
0.0201 |
|
EGGS | |||||||
|
Any eggs |
2 |
3 |
2 |
0 |
71.4 |
80.7 |
0.2677 |
|
DAIRY PRODUCTS | |||||||
|
Any dairy (excluding cheese) |
3 |
1 |
3 |
0 |
57.1 |
84.6 |
0.0655 |
|
Non-dairy milk |
3 |
0 |
3 |
1 |
50.0 |
No data |
No data |
|
Any cheese |
4 |
0 |
3 |
0 |
57.1 |
88.8 |
0.00306 |
|
VEGETABLES | |||||||
|
Tomatoes |
3 |
1 |
3 |
0 |
57.1 |
72.9 |
0.1967 |
|
Any lettuce or leafy greens |
4 |
1 |
1 |
1 |
83.3 |
82.4 |
0.4011 |
|
Iceberg |
0 |
2 |
3 |
2 |
40.0 |
41.1 |
0.3452 |
|
Romaine |
2 |
1 |
3 |
1 |
50.0 |
48.8 |
0.312 |
|
Spinach |
4 |
0 |
1 |
2 |
80.0 |
28.4 |
0.0233 |
|
Sprouts |
2 |
1 |
4 |
0 |
42.9 |
12.9 |
0.0432 |
|
Cucumbers |
3 |
2 |
2 |
0 |
71.4 |
62.9 |
0.2846 |
|
Bell peppers |
4 |
0 |
2 |
1 |
66.7 |
63.6 |
0.3252 |
|
Broccoli |
3 |
0 |
3 |
1 |
50.0 |
55.5 |
0.3013 |
|
Cauliflower |
4 |
0 |
3 |
0 |
57.1 |
33.0 |
0.1248 |
|
Mushrooms |
4 |
0 |
3 |
0 |
57.1 |
50.0 |
0.2734 |
|
Zucchini |
3 |
1 |
3 |
0 |
57.1 |
21.1 |
0.0341 |
|
FRUITS | |||||||
|
Melons |
3 |
0 |
3 |
1 |
50.0 |
39.7 |
0.2744 |
|
Apples |
4 |
1 |
2 |
0 |
71.4 |
72.3 |
0.3183 |
|
Bananas |
4 |
2 |
1 |
0 |
85.7 |
76.7 |
0.3321 |
|
Citrus fruits |
4 |
0 |
3 |
0 |
57.1 |
65.0 |
0.2679 |
|
Any berries |
5 |
0 |
2 |
0 |
71.4 |
65.2 |
0.2997 |
|
Strawberries |
2 |
2 |
2 |
1 |
66.7 |
49.6 |
0.2306 |
|
Raspberries |
2 |
0 |
3 |
2 |
40.0 |
27.5 |
0.2882 |
|
Blueberries |
3 |
2 |
2 |
0 |
71.4 |
31.3 |
0.0298 |
|
Blackberries |
3 |
1 |
3 |
0 |
57.1 |
10.5 |
0.003 |
|
Mangoes |
4 |
0 |
3 |
0 |
57.1 |
15.7 |
0.0127 |
|
Pineapple |
1 |
1 |
5 |
0 |
28.6 |
30.0 |
0.3177 |
|
NUTS & SEEDS | |||||||
|
Peanuts |
4 |
0 |
3 |
0 |
57.1 |
33.6 |
0.1306 |
|
Almonds |
2 |
3 |
1 |
1 |
83.3 |
41.0 |
0.041 |
|
Walnuts |
3 |
1 |
2 |
1 |
66.7 |
18.5 |
0.0117 |
|
Hazelnuts (filberts) |
0 |
0 |
6 |
1 |
0.0 |
10.1 |
0.5279 |
|
Cashews |
2 |
0 |
1 |
4 |
66.7 |
26.8 |
0.1577 |
|
Pecans |
2 |
1 |
3 |
1 |
50.0 |
12.9 |
0.0284 |
|
Pistachios |
0 |
0 |
4 |
3 |
0.0 |
No data |
No data |
|
Other nuts |
1 |
0 |
3 |
3 |
25.0 |
No data |
No data |
|
Peanut butter |
4 |
0 |
3 |
0 |
57.1 |
55.0 |
0.2918 |
|
Other nut butters/pastes/spreads |
2 |
1 |
3 |
1 |
50.0 |
18.3 |
0.0668 |
|
Sunflower seeds |
2 |
1 |
3 |
1 |
50.0 |
18.3 |
0.0668 |
|
Sesame seeds |
2 |
2 |
2 |
1 |
66.7 |
17.1 |
0.0088 |
|
Chia seeds |
3 |
2 |
2 |
0 |
71.4 |
No data |
No data |
|
Flax seeds |
2 |
2 |
2 |
1 |
66.7 |
No data |
No data |
|
Other seeds |
1 |
0 |
3 |
3 |
25.0 |
No data |
No data |
|
OTHER | |||||||
|
Cold cereals |
2 |
0 |
4 |
1 |
33.3 |
54.3 |
0.1929 |
|
Hot cereals |
2 |
0 |
2 |
3 |
50.0 |
28.5 |
0.2491 |
|
Vegetarian/Vegan |
2 |
0 |
3 |
2 |
40.0 |
No data |
No data |
|
Supplements |
3 |
0 |
4 |
0 |
42.9 |
28.2 |
0.2086 |
*Based on the Foodbook Survey, 2015, Public Health Agency of Canada
Question 2-9: What do these results mean? Why are multiple items identified?
Items that are reported more commonly than expected should be examined in further detail and assessed as a possible source of the outbreak (e.g., for packaged goods such as frozen berries, did cases report the same brand? For general produce items such as tomatoes, did cases report a particular type, e.g., cherry?). Statistically, with so many questions on a hypothesis-generating questionnaire, there will be some items that come up by chance alone (especially with a small sample size like the one in this case study).
Additionally, the outbreak under investigation represents a unique case demographic – there is one vegetarian and one vegan, as well as cases reporting diets rich in fresh produce. The produce items and nuts and seeds may be flagging because these are items typically consumed by this case demographic. Exposures may also flag if they are reported very infrequently compared to expected – it is important to look at the total number of cases reporting an exposure, while always keeping in mind what we discussed earlier around foods that might be more difficult to recall.
On the other hand, it is important to keep in mind that some foods with high expected consumption levels (e.g., any eggs) may not flag statistically, but could still be potential sources. It is important to look for commonalities among commonly reported exposures.
Although expected consumption data were not available for chia and flax seeds, a high proportion of cases reported consuming these food items, suggesting these food items may be of interest as potential sources of the outbreak.
Question 2-10: If data from the Foodbook Study, or a similar study, were not available, what other studies could be conducted to help identify foods of interest? Why is the Foodbook study preferable in this situation?
An analytic study, such as a case-control study, could be conducted in place of the Foodbook study in order to compare the food consumption of cases to the food consumption of the general population (controls).
Use of an analytic study such as a case control study is not commonly used in a national outbreak investigation. These studies are costly to run, and take time. While analytic studies are very valuable in other situations, in this case the Foodbook data is readily available and representative of the Canadian population.
Further reading on analytic studies is available here