Effects of behavioural risk factors on high-cost users of healthcare: a population-based study
Bibliographic record
Abstract
OBJECTIVES: High-cost users (HCUs) are known to disproportionally incur the majority of healthcare utilization costs relative to their counterparts. A number of studies have highlighted the detrimental effects of risky health behaviours; however, only a few have demonstrated the link to HCUs, a meaningful endpoint for program and policy decision-makers. We investigated the association between health behaviour risks and downstream high-cost healthcare utilization. METHODS: A combined cohort of participants from the Canadian Community Health Survey (CCHS) cycles 2005-2009 was linked to future population-based health administrative data in Ontario. Using person-centered costing methodology, CCHS respondents were ranked according to healthcare utilization costs and categorized as ever having HCU status in the 4 years following interview. Logistic regression models were used to estimate the association between various health behaviours on future HCU status. RESULTS: Models estimated that smoking and physical inactivity were associated with a significant increase in the odds of becoming an HCU. Compared to individual behaviours, increasing the number of health behaviour risks significantly strengthened the odds of becoming an HCU in subsequent years. CONCLUSION: The analyses provide evidence that upstream health behaviours affect high-cost healthcare utilization. Health behaviours are a meaningful target for health promotion programs and policies. These findings can inform decision-makers on appropriate behavioural targets for those on an HCU trajectory and promote public health efforts to support healthcare system sustainability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".