Inpatient care utilisation and expenditure associated with objective physical activity: econometric analysis of the UK Biobank
Bibliographic record
Abstract
BACKGROUND: Physical inactivity increases the risk of chronic disease and mortality. The high prevalence of physical inactivity in the UK is likely to increase financial pressure on the National Health Service. The UK Biobank Study offered an opportunity to assess the impact of physical inactivity on healthcare use and spending using individual-level data and objective measures of physical activity. The objective of this study was to assess the associations between objectively measured physical activity levels and future inpatient days and costs in adults in the UK Biobank study. METHODS: We conducted an econometric analysis of the UK Biobank study, a large prospective cohort study. The participants (n = 86,066) were UK adults aged 43-79 who had provided sufficient valid accelerometer data. Hospital inpatient days and costs were discounted and standardised to mean monthly values per person to adjust for the variation in follow-up times. Econometric models adjusted for BMI, long-standing illness, and other sociodemographic factors. RESULTS: Mean follow-up time for the sample was 28.11 (SD 7.65) months. Adults in the most active group experienced 0.037 fewer days per month (0.059-0.016) and 14.1% lower inpatient costs ( - £3.81 [ - £6.71 to - £0.91] monthly inpatient costs) compared to adults in the least active group. The relationship between physical activity and inpatient costs was stronger in women compared to men and amongst those in the lowest income group compared to others. The findings remained significant across various sensitivity analyses. CONCLUSIONS: Increasing physical activity levels in the UK may reduce inpatient hospitalisations and costs, especially in women and lower-income groups.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".