A systematic review of the impact of the Great Recession on food intake
Bibliographic record
Abstract
Abstract Background The 2007-2009 Great Recession significantly impacted global economies, industries, and individuals around the world, with a potential impact on health behaviours including dietary intake. Given the global reach and severity of the Great Recession, changes in food intakes may impacted health and health inequalities internationally. We conducted a systematic review examining whether the Great Recession had an impact on food intakes. Methods We searched MEDLINE, Embase, PsycINFO, Health Management Information Consortium (HMIC), CINAHL and Web of Science databases, along with relevant grey literature, in June 2020. Primary quantitative studies with the Great Recession as the exposure and food intake as the outcome and with two time points were eligible for inclusion in this review, assessed independently by two reviewers. The Newcastle Ottawa scale was used for quality assessment. The study was registered with PROSPERO (CRD42019135864). Results Forty-one studies from twenty-five countries were included in this review. Studies were heterogeneous in methods and results. Nine of ten studies on energy intake found that total energy intake decreased over the recession. Eight of eleven studies on dietary quality found that the recession was associated with poorer diet. Thirty-four studies assessed impact on individual food groups. They found that consumption of fruits and vegetables, meat and fish, and fast food, confectionery and soft drinks generally decreased while egg and legume consumption increased and carbohydrate consumption exhibited little change. The impact was generally greater on those of lower socio-economic position. Conclusions While our review presents mixed findings about the impact of the Great Recession on food intake, there is consistent evidence of a negative impact on diets. Identifying effective policies which mitigate adverse changes in nutrition during economic downturns should be prioritised at the national and international level. Key messages Our systematic review of the Great Recession and food intakes demonstrates potential relationships between economic downturns and nutrition – these relationships may be important for health globally. Our systematic review demonstrates that it is important for policy-makers and public health professionals to consider mitigating potential nutritional impacts during economic recessions.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".