Integrating Approaches to Geographic Variation in Methodologies for Public Health Surveillance
Bibliographic record
Abstract
Epidemiology is increasingly recognizing the complexity of the underlying mechanisms determining health states. Public health surveillance needs to incorporate this knowledge into their regular reporting and analysis cycles. Aggregate data related to a multitude of health related states and risk factors is produced and publicly shared by public health surveillance. These large stores of aggregate data have the potential to be combined and analyzed to capture much of the underlying complexity. The aim of this thesis is to advance the methods used in public health surveillance for combining and analyzing these disparate sources of aggregate data. Three papers address this aim by focusing on (1) developing a sound methodology using funnel plots for the analysis of aggregate health data, especially addressing the issues of policy relevant analysis and overdispersion, (2) developing a spatial scan statistic capable of identify multiple irregularly shaped clusters in aggregate space-time data, and (3) applying the funnel plot and spatial scan techniques to childhood immunization surveillance in Alberta. These papers conclude that (1) the funnel plot methodology is a robust way of creating policy relevant analysis with understandable visualizations in the presence of overdispersion, (2) the novel MultScan spatial scan performs well at cluster detection, and (3) sophisticated surveillance of childhood immunization can be undertaken accounting for a wide variety of determinants using available aggregate data.
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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 | 0.105 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".