Health care cost of severe obesity and obesity-related comorbidities: A retrospective cohort study from Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Estimates of health care costs associated with severe obesity, and those attributable to specific health conditions among adults living with severe obesity are needed. METHODS: . Two-part models were used to estimate the incremental health care cost of severe obesity and related health conditions during a 1-year observation period. RESULTS: Adjusting for potential confounders, the total health care cost ratio was higher in the investigational (n = 220,190) versus control (n = 1,955,548) cohort (1.32 [95 % CI: 1.32, 1.33]) with a predicted incremental cost of $2221 (95 % CI $2184, $22,265) per person-year; costs were less when obesity-related health conditions were controlled for (1.13 [95 % CI: 1.13, 1.14]; $1097 [95 % CI: $1084, $1110] per person-year). Among those living with severe obesity, incremental costs associated with specific health conditions ranged from $737 (95 % CI: $747, $728) lower (dyslipidemia) to $12,996 (95 % CI: $12,512, $13,634) higher (peripheral vascular disease) per person-year. CONCLUSIONS: Adults living with severe obesity had greater costs than those without, largely driven by obesity-related health conditions. For the Alberta adult population with a severe obesity prevalence of 11 %, severe obesity may account for an estimated additional $453-918 million in health care costs per year. Findings of this study provide rationale for resources and strategies to prevent and manage obesity and its complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".