Obesity Predicts Primary Health Care Visits: A Cohort Study
Bibliographic record
Abstract
The objective of this study was to explore the relationship between body mass index (BMI), its association with chronic disease, and its impact on health services utilization in the province of Newfoundland and Labrador, Canada, from 1998 to 2002. A data linkage study was conducted involving a provincial health survey linked to 2 health care use administrative databases. The study population comprised 2345 adults between the ages of 20 and 64 years. Self-reported height and weight measures and other covariates, including chronic diseases, were obtained from a provincial survey. BMI categories include: normal weight (BMI 18.5-24.9), overweight (BMI 25-29.9), obese class I (BMI 30-34.9), obese class II (BMI ≥ 35), and obese class III (BMI ≥ 40). Survey responses were linked with objective physician and hospital health services utilization over a 5-year period. Weight classifications in the study sample were as follows: 37% normal, 39% overweight, 17% obese, and 6% morbidly obese. The obese and morbidly obese were more likely to report having serious chronic conditions after adjusting for age and sex. Only the morbidly obese group (BMI ≥ 35 kg/m(2)) had a significantly higher number of visits to a general practitioner (GP) over a 5-year period compared to the normal weight group (median 22.0 vs. 17.0, P<0.05). Using multivariate models and controlling for the number of chronic conditions and other relevant covariates, being morbidly obese remained a significant predictor of GP visits (P<0.001), but was not a predictor for visits to a specialist or any type of hospital use. The increase in the prevalence of obesity is placing a burden at the primary health care level. More resources are needed in order to support GPs in their efforts to manage and treat obese adults who have associated comorbidities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".