Are intentions to change, policy awareness, or health knowledge related to changes in dietary intake following a sugar-sweetened beverage tax in South Africa? A before-and-after study
Bibliographic record
Abstract
BACKGROUND: In April 2018, South Africa implemented the Health Promotion Levy (HPL), one of the first sugar-sweetened beverage (SSB) taxes to be based on each gram of sugar (beyond 4 g/100mL). The objectives of this study were to examine whether the psychological constructs tax awareness, SSB knowledge, SSB risk perception, and intentions to reduce SSB intake were associated with taxed beverage intake, whether they changed from pre- to post-tax, and whether they modified the effect of the HPL. METHODS: We collected single day 24-hour dietary recalls surveyed from repeat cross-sectional surveys of adults aged 18-39 years in Langa, South Africa. Participants were recruited in February-March 2018 (pre-tax, N = 2,481) and February-March 2019 (post-tax, N = 2,507) using door-to-door sampling. Surveys measured tax awareness, SSB knowledge, SSB risk perception, and intention to reduce SSB intake. SSB intake was estimated using a two-part model. To examine changes over time, logistic regression models were used for binary outcomes (tax awareness and intention to reduce SSB consumption) and linear regression models for continuous outcomes (SSB knowledge SSB risk perceptions). Effect modification was tested using interaction terms for each psychological construct with time. RESULTS: No constructs were associated with SSB intake at baseline. At post-tax, the predicted probability to consume taxed beverages was 33.5% (95% CI 28.5-38.5%) for those who expressed an intention to reduce SSB intake compared to 45.9% (95% CI 43.7-48.1%) for those who did not. Among consumers, intending to reduce SSB intake was associated with 55 (95% CI 28 to 82) kcal/capita/day less SSBs consumed. Tax awareness, SSB knowledge, and SSB risk perception increased by a small amount from pre- to post-tax. Intentions to reduce SSB intake was lower in the post-tax period. The tax effect on SSB intake was modified by SSB knowledge and intention to reduce SSB intake, with higher levels of each associated with lower SSB intake. CONCLUSION: After the South African SSB tax was implemented, SSB knowledge and risk perception increased slightly, tax awareness remained low, and only SSB knowledge and behavioral intention to change were significantly associated with taxed beverage intake among participants recruited from a low-income South African township.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".