Regional variations in the economic burden attributable to excess weight, physical inactivity and tobacco smoking across British Columbia
Bibliographic record
Abstract
INTRODUCTION: Prevalence rates of excess weight, tobacco smoking and physical inactivity vary substantially by geographical region within British Columbia (B.C.). The purpose of this study is to determine the potential reduction in economic burden in B.C. if all regions in the province achieved prevalence rates of these three risk factors equivalent to those of the region with the lowest rates. METHODS: We used a previously developed approach based on population-attributable fractions to estimate the economic burden associated with the various risk factors. Sex-specific relative risk and age/sex-specific prevalence data was used in the modelling. RESULTS: The annual economic burden attributable to the three risk factors in B.C. was about $5.6 billion in 2013, with a higher proportion of this total attributable to excess weight ($2.6 billion) than to tobacco smoking ($2.0 billion). While B.C. has lower prevalence rates of the risk factors than any other Canadian province, there is significant variation within the province. If each region in the province were to achieve the best prevalence rates for the three risk factors, then $1.4 billion (24% of the $5.6 billion) in economic burden could be avoided annually. CONCLUSION: There are notable disparities in the prevalence of each risk factor across health regions within B.C., which were mirrored in each region's attributable economic burden. A variety of social, environmental and economic factors likely drive some of this geographical variation and these underlying factors should be considered when developing prevention programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".