Examining the impact of the number of regions used in cluster detection methods: An application to childhood asthma visits to a hospital in Manitoba, Canada
Why this work is in the frame
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Bibliographic record
Abstract
The level of spatial aggregation is a major concern in cluster investigations. Combining regions to protect privacy may result in a loss of power and thus, can limit the information researchers can obtain. The impact of spatial aggregation on the ability to detect clusters is examined in this study, which shows the importance of choosing the correct level of spatial aggregation in cluster investigations. We applied the circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS) and Bayesian disease mapping (BYM) approaches to a dataset containing childhood asthma visits to a hospital in Manitoba, Canada, using three different levels of spatial aggregation. Specifically, we used 56, 67 and 220 regions in the analysis, respectively. It is expected that the three scenarios will yield different results and will highlight the importance of using the right level of spatial aggregation. The three methods (CSS, FSS, BYM) examined in this study performed similarly when detecting potential clusters. However, for different levels of spatial aggregation, the potential clusters identified were different. As the number of regions used in the analysis increased, the total area identified in the cluster decreased. In general, potential clusters were identified in the central and northern parts of Manitoba. Overall, it is crucial to identify the appropriate number of regions to study spatial patterns of disease as it directly affects the results and consequently the conclusions. Additional investigation through future work is needed to determine which scenario of spatial aggregation is best.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it