Dynamics of combatting market-driven epidemics: Insights from U.S. reduction of cigarette, sugar, and prescription opioid consumption
Bibliographic record
Abstract
Misuse and overconsumption of certain consumer products have become major global risk factors for premature deaths, with their total costs in trillions of dollars. Progress in reducing such deaths has been slow and difficult. To address this challenge, this review introduces the definition of market-driven epidemics (MDEs), which arise when companies aggressively market products with proven harms, deny these harms, and resist mitigation efforts. MDEs are a specific within the broader landscape of commercial determinants of health. We selected three illustrative MDE products reflecting different consumer experiences: cigarettes (nicotine delivery product), sugar (food product), and prescription opioids (medical product). Each met the MDE case definition with proven adverse health impacts, well-documented histories, longitudinal product consumption and health impact data, and sustained reduction in product consumption. Based on these epidemics, we describe five MDE phases: market expansion, evidence of harm, corporate resistance, mitigation, and market adaptation. From the peak of consumption to the most recent data, U.S. cigarette sales fell by 82%, sugar consumption by 15%, and prescription opioid prescriptions by 62%. For each, the consumption tipping point occurred when compelling evidence of harm, professional alarm, and an authoritative public health voice and/or public mobilization overcame corporate marketing and resistance efforts. The gap between suspicion of harm and the consumption tipping point ranged from one to five decades-much of which was attributable to the time required to generate sufficient evidence of harm. Market adaptation to the reduced consumption of target products had both negative and positive impacts. To our knowledge, this is the first comparative analysis of three successful efforts to change the product consumption patterns and the associated adverse health impacts of these products. The MDE epidemiological approach of shortening the latent time to effective mitigation provides a new method to reduce the impacts of harmful products.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".