Estimating prevalence using indirect information and Bayesian evidence synthesis
Bibliographic record
Abstract
Abstract We focus on the analysis of health count data, aggregated over disjoint geographical locations, by combining information from data sources in a coherent fashion using a Bayesian hierarchical model. The overall objective is to estimate prevalence of a medical condition in the population given that the sampled counts arise from a subset of all cases, and when there is no additional information available from the data. We develop a hierarchical model to predict the overall prevalence using an external data set for calibration. We demonstrate that the Bayesian methodology can account fully for the uncertainty, variability and spatial dependence for the estimate. We apply our model to dispensing data obtained by the Public Health Agency of Canada for 2014, and assess the prevalence of treated Attention Deficit Hyperactivity Disorder (ADHD) from records of drugs dispensed to treat the condition. We demonstrate that our final model fits the data well in an out‐of‐sample assessment. We estimate the prevalence of treated ADHD in Canada to be 1.14% with 95% credible interval (0.86%, 1.27%), with prevalence noted to be higher in the eastern part of Canada, most notably in Nova Scotia. The Canadian Journal of Statistics 46: 673–689; 2018 © 2018 Société statistique du Canada
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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 | 0.096 | 0.323 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".