A Joint Model for Disease Mapping with Spatially Correlated Count Data and Covariate Measurement Error
Bibliographic record
Abstract
Abstract Cross-sectional and case-control studies are popular individual- level designs. Cross-sectional studies measure/determine prevalence of an adverse health condition, while case-control studies compare two groups based on the exposure measured retrospectively. Individuals within a population can be divided into groups based on observed characteristics, and a particular group may be more or less susceptible to an adverse health condition. In contrast to individual-level study designs, disease mapping and ecological regression models are well-known methods for modeling population-level characteristics using aggregated data. Such designs emphasize on group-level characteristics and ignore variability within groups. However, optimal prediction of an adverse health condition requires us to integrate these techniques into a general frame-work for modeling individual- and group-level factors simultaneously. To overcome this methodological gap, we formulate the joint Besag-York-Mollie (BYM2) model for modeling adverse health condition, integrating individual- and group-level factors into a single framework. The individual- and group-level factors are modelled using submodels linked through association parameters in the joint BYM2 model. The group-level submodel can incorporate spatial auto-correlation in the outcome and covariate measurement error. We propose a Bayesian approach for inference and present comparative studies, via both real and simulated data. The simulation results demonstrate better performance of the joint BYM2 model for capturing parameter values with reasonable uncertainty. We demonstrate an application for modeling the risk of developing adverse health condition among Canadian secondary school students using individual-, school-, and neighbourhood-level risk factors.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".