Changes in Online Food Access During the COVID-19 Pandemic and Associations With Deprivation: Longitudinal Analysis
Bibliographic record
Abstract
BACKGROUND: Food prepared out of home is typically energy-dense and nutrient-poor. Online food delivery services have become a popular way to purchase such food. The number of accessible food outlets through these services can influence how frequently they are used. Anecdotally, food outlet access through online food delivery services increased in England between 2020 and 2022, in the context of the COVID-19 pandemic. However, the extent to which this access changed is poorly understood. OBJECTIVE: We aimed to investigate monthly changes in online access to food prepared out of home in England in the context of the first 2 years of the COVID-19 pandemic compared with November 2019 and the extent to which any changes were associated with deprivation. METHODS: In November 2019 and monthly between June 2020 and March 2022, we used automated data collection to construct a data set containing information about all food outlets in England registered to accept orders through the leading online food delivery service. Across postcode districts, we identified the number and percentage of food outlets registered to accept orders and the number that was accessible. We used generalized estimating equations (adjusted for population density, the number of food outlets in the physical food environment, and rural/urban classification) to investigate the change in outcomes compared with prepandemic levels (November 2019). We stratified analyses by deprivation quintile (Q). RESULTS: Across England, the summed number of food outlets registered to accept orders online increased from 29,232 in November 2019 to 49,752 in March 2022. Across postcode districts, the median percentage of food outlets registered to accept orders online increased from 14.3 (IQR 3.8-26.0) in November 2019 to 24.0 (IQR 6.2-43.5) in March 2022. The median number of food outlets accessible online decreased from 63.5 (IQR 16.0-156.0) in November 2019 to 57.0 (IQR 11.0-163.0) in March 2022. However, we observed variation by deprivation. In March 2022, the median number of outlets accessible online was 175.0 (IQR 104.0-292.0) in the most deprived areas (Q5) compared with 27.0 (IQR 8.5-60.5) in the least deprived (Q1). In adjusted analyses, we estimated that the number of outlets accessible online in the most deprived areas was 10% higher in March 2022 compared with November 2019 (incidence rate ratios: 1.10, 95% CI 1.07-1.13). In the least deprived areas, we estimated a 19% decrease (incidence rate ratios: 0.81, 95% CI 0.79-0.83). CONCLUSIONS: The number of food outlets accessible online increased only in the most deprived areas in England. Future research might attempt to understand the extent to which changes in online food access were associated with changes in online food delivery service use and the possible implications on diet quality and health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".