36 A YEAR WITHOUT THE FLU: MODELLING THE EFFECTS ON CARDIOVASCULAR MORTALITY FROM INFLUENZA IN IRELAND
Bibliographic record
Abstract
Abstract Background Cardiovascular diseases (CVDs) are consistently ranked among the leading causes of death among older adults in Ireland. COVID-19 and influenza infection are associated with cardiovascular complications. However, percentage of deaths caused by CVD among adults aged 75 and over in Ireland decreased from 32.9% to 31.0% from 2019 to 2020. Government-imposed social distancing measures resulted in abolition of influenza activity (IA). We analysed population data from the 2010/11–2019/20 influenza seasons to estimate the impact of reduced IA on CVD mortality rates during the COVID-19 pandemic season. Methods Quarterly mortality data for acute myocardial infarction (AMI) and cerebrovascular disease from first quarter (Q1) 2010 to fourth quarter (Q4) 2020 was obtained from the Central Statistics Office. Weekly data on influenza-like illness (ILI) rates and positive percentages (PP) (i.e. proportion of influenza-positive sentinel respiratory specimens) from week 40 2010 to week 20 2020 was obtained from the Health Protection Surveillance Centre. Excess mortality rate during influenza season was calculated as the percentage difference between Q4/Q1 and preceding third quarter (Q3) mortality rates. We adopted the Goldstein index (ILI rate × PP) as an indicator of IA. Time series analyses, Pearson correlation coefficients (r) and linear regression models were used to evaluate the relationships between IA and excess AMI and cerebrovascular disease mortality rates. Results Statistically significant positive associations were observed between IA and excess AMI (r = 0.557, p = 0.011) and cerebrovascular disease (r = 0.858, p < 0.001) mortality rates. Linear regression models predicted 0.072% (95% confidence interval 0.019%, 0.125%) and 0.095% (0.067%, 0.123%) increases in excess AMI and cerebrovascular disease mortality rates respectively per unit increase in IA levels. Conclusion Elimination of IA may have contributed towards limiting the effects of COVID-19 on CVD mortality rates, and consequently total excess mortality, among older adults in Ireland.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".