Twenty years of recrudescent influenza and pneumonia mortality after the 1918 influenza pandemic in Newfoundland
Bibliographic record
Abstract
There is substantial and growing knowledge of the severe and unequal impacts of the 1918 flu worldwide, but the long-term impacts of the pandemic have not been studied widely despite its unique epidemiological signatures. The goal of this paper is to identify significant recrudescent influenza and pneumonia (P&I) mortality events after the 1918 flu through 1939 in Newfoundland, with specific attention to the changes in age-based mortality. I use a Serfling regression model using monthly P&I mortality rates from 1910-17 to identify baseline patterns preceding the 1918 flu. I assess monthly P&I mortality rates from 1918-39 against the baseline Serfling regression model to identify months of significant excess P&I mortality rates for the ~20 post-pandemic years. I calculate a ratio of excess mortality in <65-year-olds and excess mortality in 65+-year-olds (RR <>65 ) to identify major shifts in age-based P&I mortality during and after the flu pandemic. Results show that there were six significant recrudescent P&I mortality events after the 1918-20 flu in 1923, 1926, 1928, 1929, 1931, and 1935 (18 months of excess P&I mortality and ~1548 excess deaths). Age-based analyses (RR <>65 ) show that individuals <65 had highest excess mortality during the flu pandemic RR <>65 reverted to <1.0 in the following years, with some important exceptions. These results suggest that the novelty of the H1N1 influenza A virus in 1918 contributed to significant long-term P&I activity and recrudescent mortality in Newfoundland for at least two decades after the end of the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".