Natural Experiment on Trade and Investment Liberalization and Soft Drink Consumption
Bibliographic record
Abstract
Abstract Background Globalization is recognized to as a contributing factor to a health harming environment through a variety of mechanisms including through changes in food systems and food availability. Sugar-sweetened beverage (SSB) consumption is linked to obesity and diabetes and its regulation is a key priority for public health. The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is an international trade agreement between 11 countries. Methods This project uses of natural experiment methods to predict the impact of the entry into force of the CPTPP on SSB consumption. These methods allow quantitative inferences to be drawn in the situations where the exposure is not randomly assigned. Soft drink consumption data was collected from the Euromonitor database for 80 countries from all regions. This data was used to estimate the effect of agreements similar to the TPP. Results Eleven country trade agreement pairs were identified. In 5 cases out of the 11, the exposed country had a higher soft drink consumption at five years after the trade agreement. The effect of the trade agreement exposure for an average country in the sample in a trade agreement was found to be 1.10 (95% CI: 1.01-1.18; p-value: 0.03) after adjusting for GDP and the involvement of the US. In 7 of the 11 member-countries soft drink consumption is expected to increase yielding an average increase of 9.0% in those countries; the changes did not yield statistically significant differences in others. Conclusions This projected extended the use of synthetic methods to the projection of future effects of policy implementation. While it showed that there may be increasing trend of SSB consumption in certain scenarios, this could not be generalized to all cases. This illustrates the wide range of effects of international trade liberalization and highlights that national policy probably plays a strong modulating role on the impact that it has on local food environments. Key messages Globalization can lead to health harming environments and its impacts should further be studied by public health professionals and researchers. Many global policies have the potential to lead to significant health impacts but are negotiated without involving public health experts.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".