Modeling the need for hip and knee replacement surgery. Part 2. Incorporating census data to provide small‐area predictions for need with uncertainty bounds
Bibliographic record
Abstract
OBJECTIVE: To develop methods to produce small-area estimates of need for hip and knee replacement surgery to inform local health service planning. METHODS: Multilevel Poisson regression modeling was used to estimate rates of need for hip/knee replacement by age, sex, deprivation, rurality, and ethnic mix using a nationally representative population-based survey (the English Longitudinal Study of Ageing, n = 11,392 people age > or =50 years). Estimates of need from the regression model were then combined with stratified census population counts to produce small-area predictions of need. Uncertainty in the predictions was obtained by taking a Bayesian simulation-based approach using WinBUGS software. This allows correlations in parameter estimates to be appropriately incorporated in the credible intervals for the small-area predictions. RESULTS: Small-area estimates of need for hip/knee replacement have been produced for wards and districts in England. Rates of need are adjusted for the sociodemographic characteristics of an area and include 95% credible intervals. Need for hip/knee replacement varies geographically, dependant on the sociodemographic characteristics of an area. CONCLUSION: For the first time, small-area estimates of need for hip/knee replacement surgery have been produced together with estimates of uncertainty to inform local health planning. The methodologic approach described here could be reproduced in other countries and for other disease indicators. Further research is required to combine small-area estimates of need with provision to determine whether there is equitable access to care.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".