Evidence map›Paper›PMID 34414511›Full record

ArticleJournal of urban health : bulletin of the New York Academy of Medicine2021

Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission.

Olivia Biermann, Shaffi Fazaludeen Koya, Claire Corkish, Salma M Abdalla, Sandro Galea

Abstract readScoping Review
In one paragraph

Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Exploring Dietitians' Needs and Challenges in Food Data Literacy Training: A Mixed Methods Study.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2026
    Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Olivia BiermannDepartment of Global Public Health, Karolinska Institutet, Solna, Sweden.
Shaffi Fazaludeen KoyaRockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA.
Claire CorkishRockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA.
Salma M AbdallaRockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA. abdallas@bu.edu.
Sandro GaleaRockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.

Indexed as

Big DataSustainable DevelopmentData AnalysisHumansBig dataDataDecision-makingFood

Identifiers

PMID34414511
PMCPMC8440752

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.