ArticleNature communications2023
Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- Advanced machine learning model for Cobb angle progression in adolescent idiopathic scoliosis with surface topography: a multicenter, prospective, observational study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- What can shopping transactional data reveal about relative prevalence of menstrual pain and period poverty in England?PLOS digital health · 2026Article
- Using supermarket loyalty card data to investigate seasonal variation in laxative purchases in the UK.PLOS digital health · 2026Article
- Feasibility of using community pharmacy sales of over-the-counter respiratory products as a syndromic surveillance indicator: a pilot study from Sofia, Bulgaria.Frontiers in public health · 2026Article
- Shopping Data for Population Health Surveillance: Opportunities, Challenges, and Future Directions.Journal of medical Internet research · 2025Article
- Harnessing digital footprint data for population health: a discussion on collaboration, challenges and opportunities in the UK.BMJ health & care informatics · 2024Article
- A New Approach for Understanding International Hospital Bed Numbers and Application to Local Area Bed Demand and Capacity Planning.International journal of environmental research and public health · 2024Article
- Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved healthcare planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England, by using over 2 billion transactions logged by a UK high street retailer from March 2016 to March 2020. We report the results from the novel AI (Artificial Intelligence) explainability variable importance tool Model Class Reliance implemented on the PADRUS model (Prediction of Amount of Deaths by Respiratory disease Using Sales). PADRUS is a machine learning model optimised to predict registered deaths from respiratory disease in 314 local authority areas across England through the integration of shopping sales data and focused on purchases of non-prescription medications. We found strong evidence that models incorporating sales data significantly out-perform other models that solely use variables traditionally associated with respiratory disease (e.g. sociodemographics and weather data). Accuracy gains are highest (increases in R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.