Changes in UK price disparities between healthy and less healthy foods over 10 years: An updated analysis with insights in the context of inflationary increases in the cost-of-living from 2021
Bibliographic record
Abstract
Food prices and affordability play an important role in influencing dietary choices, which in turn have implications for public health. With inflationary increases in the cost-of-living in the UK since 2021, understanding the dynamics of food prices becomes increasingly important. In this longitudinal study, we aimed to examine changes in food prices from 2013 to 2023 by food group and by food healthiness. We established a dataset spanning the years 2013-2023 by combining price data from the UK Consumer Price Index for food and beverage items with nutrient and food data from the UK nutrient databank and UK Department of Health & Social Care's National Diet and Nutrition Survey data. We calculated the price (£/100 kcal) for each food item by year as well as before and during the period of inflationary pressure, and classified items into food groups according to the UK Eatwell Guide and as either "more healthy" or "less healthy" using the UK nutrient profiling score model. In 2023, bread, rice, potatoes and pasta was cheapest (£0.12/100 kcal) and fruit and vegetables most expensive (£1.01/100 kcal). Less healthy food was cheaper than more healthy food (£0.33/100 kcal versus £0.81/100 kcal). Before the inflationary pressure period (from 2013 to late 2021), the price of foods decreased by 3%. After this period, the price of food increased by 22%: relative increases were highest in the food group milk and dairy food (31%) and less healthy category (26%). While healthier foods saw smaller relative price increases since 2021, they remain more expensive, potentially exacerbating dietary inequalities. Policy responses should ensure food affordability and mitigate price disparities via, for example, healthy food subsidies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".