The data availability landscape in seven sub-Saharan African countries and its role in strengthening sugar-sweetened beverage taxation
Bibliographic record
Abstract
Background: Credible data and indicators are necessary for country-specific evidence to support the design, implementation, monitoring and evaluation of sugar-sweetened beverage (SSB) taxation.Objective: A cross-country analysis was undertaken in seven Sub-Saharan African countries to describe the potential role of available data in strengthening SSB taxation. The objectives were to: document currently available data sources; report on public access; discuss strengths and limitations for use in monitoring SSB taxation; describe policy maker's data needs, and propose improvements in data collection.Methods: The study used a mixed-methods approach involving a secondary data analysis of publicly available documents, and a qualitative exploration of the data needs of policy makers’ using primary data. Findings were synthesised and assessed for data strengths and weaknesses, including usability and availability. SSB taxation-related data availability was critically assessed with respect to adequacy in strengthening taxation policy on SSBs.Results: Findings showed a paucity of SSB taxation-related data in all seven countries. National survey data are inadequate regarding the intake of SSBs and household expenditure on SSBs. Fiscal data from SSB tax revenue, value added tax from SSB sales, corporate income tax from SSB companies and SSB custom duty revenues, are lacking. Accurate information on the soft drink industry is not easily accessed.Conclusion: Timely, easily understood, concise, and locally relevant evidence is needed in order to inform policy development on SSBs. The relevant data are drawn from multiple sectors. Cross- sector collaboration is therefore needed. Indicators for SSBs should be developed and included in current data collection tools to ensure monitoring and evaluation for SSB taxation.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".