An analysis of complementary products associated with unhealthy food purchases using household grocery sales data in Montréal, Canada
Bibliographic record
Abstract
BackgroundConsumption of soft drinks and snack food contributes to the increasing global incidence of chronic illnesses such as cardiovascular diseases and type II diabetes. Previous studies addressing the purchasing patterns of such foods have emphasized the importance of complementary and co-occurring products, as both may undermine interventions seeking to limit the intake of unhealthy foods. However, research using household-level data to analyze such patterns in purchasing has traditionally been limited in terms of volume, objectivity, and representativeness. Moreover, few studies have searched explicitly for co-purchasing of soft drinks and snack foods in the same basket.Research objectiveThe objective of this study is to identify patterns in food categories purchased by households together with, or complementary to, soft drinks and snack foods as well as fresh fruits and vegetables.MethodsWe used longitudinal, household-level transaction data from 14,999 loyalty card members of a large grocery retailer in Montréal, Canada between February 2015 and September 2017 (1,522,501 transactions). Association rule mining was used to identify frequently co-purchased item categories for soft drinks, snack foods, juice, fruits, and vegetables.ResultsTransactions (baskets) containing snack foods and soft drinks were also likely to contain canned or highly-processed foods. For example, soft drinks were highly associated with salty snacks (confidence: 17%; odds ratio: 1.82 ± 0.02), bottled water (confidence: 16%, odds ratio: 1.77 ± 0.02), and frozen meals and sides (confidence: 16%; odds ratio: 1.78 ± 0.03). Conversely, purchases with qualitatively healthier foods were found to be associated with purchases of fruits and vegetables: purchases with vegetables were highly associated with fresh herbs (confidence: 84%; odds ratio: 1.90 ± 0.03) and packaged salads (confidence: 73%; odds ratio: 1.61 ± 0.01).ConclusionsThese empirical results quantify the extent to which healthy and unhealthy food-purchasing behaviours cluster within baskets. Public health practitioners seeking to design interventions that decrease the frequency of soft drink and snack food purchases in the grocery retail environment should consider the tendency for multiple unhealthy foods to be purchased concurrently. While loyalty card data do not capture the entirety of a household’s food purchasing behaviour, they represent objective and proximal outcomes to dietary patterns and should therefore be used alongside more traditional means of dietary assessment
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".