Spatio-temporal analysis of pneumonia and influenza hospitalizations in Ontario, Canada
Bibliographic record
Abstract
Pneumonia and influenza represent a significant public health and health care system burden that is expected to increase with the aging of developed nations' populations. The burden of these illnesses is far from uniform however, with recent studies showing that they are both highly spatially and temporally variable. We have combined spatial and time-series analysis techniques to examine pneumonia and influenza hospitalizations in the province of Ontario, Canada, to determine how temporal patterns vary over space, and how spatial patterns of hospitalizations vary over time. Knowledge of these patterns can provide clues to disease aetiology and inform the effective management of health care system resources. Spatial analysis revealed significant clusters of high hospitalization rates in northern and rural counties (Moran's I = 0.186; P <0.05), while county level time series analysis demonstrated significant upward trends in rates in almost a quarter of the counties (P <0.05), and significant seasonality in all but one county (Fisher-Kappa and Barlett Kolmogorov Smirnov tests significant at the level P <0.01). Areas of weak seasonality were typically seen in rural areas with high rates of hospitalizations. The highest levels of spatial clustering of pneumonia and influenza hospitalizations were found to occur in months when rates were lowest. The findings provide evidence of spatio-temporal interaction over the study period, with marked spatial variability in temporal patterns, and temporal variability in spatial patterns. Results point to the need for the effective allocation of services and resources based on regional and seasonal demands, and more regionally focused prevention strategies. This research represents an important step towards understanding the dynamic nature of these illnesses, and sets the stage for the application of spatio-temporal modelling techniques to explain them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".