Beyond weight: examining the association of obesity with cardiometabolic related inpatient costs among Canadian adults using linked population based survey and hospital administrative data
Bibliographic record
Abstract
BACKGROUND: The global population has transitioned to one where more adults are living with obesity than are underweight. Obesity is associated with the development of cardiometabolic diseases and widely attributed to increased hospital resource use; however, empirical evidence is limited regarding obesity prevention to support hospital cost containment. This study aims to test for obesity in predicting hospitalization costs for cardiometabolic conditions among the Canadian population aged 45 and over. METHODS: Data from the 2007-2011 Canadian Community Health Survey were linked to eight years of hospital discharge records. A cohort was identified of inpatients admitted for diabetes, hypertension, and other cardiometabolic diseases. Multiple linear regressions were used to investigate the association between obesity status and inpatient costs, controlling for sociodemographic and behavioural factors. RESULTS: The target cohort included 23,295 admissions for cardiometabolic diseases. Although inflation-adjusted inpatient costs generally increased over time, compared with the non-obese group, living with obesity was not a significant predictor of differences in cardiometabolic-related resource use (0.972 [95% CI: 0.926-1.021]). Being female and rural residence were found to be protective factors. CONCLUSIONS: Obesity was not found in this study to be independently linked to higher cardiometabolic hospitalization costs, suggesting that actions to mitigate disease progression in the population may be more beneficial than simply promoting weight loss. Results amplified the need to consider gender and urbanization when formulating which levers are most amenable to adoption of healthy lifestyles to reduce impacts of obesogenic environments to the healthcare system.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".