Statistical modeling of pneumonia transmission rates in Manitoba
Bibliographic record
Abstract
Introduction: Pneumonia is a major respiratory infection that significantly strains Manitoba’s healthcare system, resulting a substantial number of hospitalizations and fatalities. Understanding the transmission dynamics and risk factors associated with pneumonia in this population is crucial for targeted interventions. Spatial variability in pneumonia hospitalizations has been observed in Manitoba, where various risk factors contribute to pneumonia infection. Therefore, it is essential to investigate the determinants of pneumonia, including spatial aspects and disease transmission rates. Methods: We applied a spatial Poisson regression model that incorporates the Intrinsic Conditional Autoregressive model to explore region level potential risk factors. To understand the influence of comorbidity, we utilized a mixed-effects Poisson regression model. Our analysis focused on hospital data spanning the years 2015 to 2019, and encompassing 96 Manitoba health regions. Moreover, to investigate disease transition rates, we employed both the Susceptible-Exposed-Infected-Recovery (SEIR) model (excluding reinfection) and the Susceptible-Exposed-Infected-Recovery-Susceptible (SEIRS) model (including reinfection). These compartmental models considered data from both hospital and physician-reported pneumonia cases during August 2017 to July 2018. Results: The raw incidence rate of pneumonia infection exhibited significant variation across different regions, ranging from 2 to 55 cases per 1000 population. After adjusting for potential risk factors, the incidence rate ratios ranged from 0.38 to 6.63, indicating substantial variations in incidence rates among regions. Factors such as age, immigration status, and comorbidities, notably Chronic Obstructive Pulmonary Disease (COPD) and Cardiovascular Disease (CVD), were identified as significant contributors to the risk of pneumonia infection. Conversely, vaccination was found to exert a protective effect, especially among individuals aged over 60 years. The domain-level analysis further demonstrated the significant impact of Inflammatory Bowel Diseases (IBD), COPD and CVD on pneumonia infection. The SEIR and SEIRS models were employed to analyze pneumonia transmission rates, indicated rates of 0.81 and 0.88, respectively. The SEIRS model suggested a reinfection rate of pneumonia was 0.003. The average reproduction number (R0) was calculated at 1.03 for both models, signifying the potential for disease spread in the population. Conclusion: The findings highlighted spatial variation in pneumonia incidence across the 96 health regions in Manitoba. Elder individuals, immigration status, and comorbidities, were identified as significant factors influencing pneumonia infections rates. Vaccination demonstrated a protective effect, particularly among the elderly population. The estimated reinfection interval for pneumonia was determined to be 333 days.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".