Population health interventions to curb intake of sugars: Gaps in the evidence
Bibliographic record
Abstract
There is currently considerable attention focused on the role of sugars in health, including obesity and cardiovascular disease, and accordingly, the potential for population health interventions to curb sugars consumption. A rapid scoping review of systematic reviews related to interventions targeting sugar was conducted to identify gaps in the existing evidence on the effectiveness of such interventions. The databases Medline, EMBASE, CINAHL, and the Cochrane Database of Systematic Reviews were searched to identify systematic review articles related to interventions to reduce sugars intake, published in English over an eleven‐year period (January 2005 to December 2015). Sixteen systematic reviews meeting the inclusion criteria were identified. The interventions included price changes, initiatives to alter specific food environments, health promotion and education, and initiatives to limit exposure to advertising. A common thread among the reviews is the limited scope of available evidence combined with the heterogeneity of methods used in existing studies, including lack of consensus on definitions and measures used for sugars intake. There is a paucity of data on how interventions are implemented and the ways in which they interact with contextual factors, which hinders conclusions about how interventions introduced in one jurisdiction might function in another. Further, little is known about differential effects of interventions for population subgroups and potential compensatory behaviours on the part of both consumers and the food industry. Given current gaps in the evidence, implementation of interventions that show promise in terms of reducing intake of sugars should be accompanied by careful monitoring to assess intended and unintended consequences, including those related to equity. The application of a systems lens might be useful for considering the broad array of factors that impact the planning, implementation, and evaluation of interventions to alter sugars consumption and associated outcomes, including body weight. Support or Funding Information This research was completed under contract with the Canadian Institutes of Health Research Institute of Nutrition, Metabolism and Diabetes.
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 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 | 0.082 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".