Explaining Structural Change in Cardiovascular Mortality in Ireland 1995-2005: A Time Series Analysis
Bibliographic record
Abstract
Background: Deaths from circulatory respiratory causes among older age groups in Ireland fell sharply between 1995 and 2005 as did the seasonality of deaths from these causes.Objective:To examine whether a structural break has occurred in deaths from circulatory causes in Ireland between 1995 and 2005 and test whether this can be explained by changes in the prescribing of cardiovascular medications during the same period controlling for weather trends. Methods: Grouped logit Time series models were used to identify if and at which quarter a structural break occurred in Irish circulatory deaths between 1995 and 2005. Data on cardiovascular prescribing and temperature within the quarter were entered into the trend-break model to examine whether the structural break could be explained. Results: There was a reduction in circulatory deaths of 0.82%/quarter among men 1995-2005 which increased by 0.5%/quarter after the final quarter of 1999. The 25% excess winter deaths among men fell by 9% after Q4 1999. Among women the long term decline in deaths of 0.53%/quarter increased by 0.48% after Q1 2000 and seasonality was reduced by 6.8%. The structural break in trend and seasonality was higher among those aged 85+. Controlling for temperature, beta-blocker, ace-inhibitor and aspirin medications rendered the structural break indicator insignificant among all age groups for men. Diuretic, statin and calcium channel blocker medications could not explain the break point for men aged 75 to 84. Beta blocker, aspirin and calcium channel blocker medications explained mortality trends among all age groups among women. Ace inhibitor and statin could not explain trends amongst women aged 65-74 and nitrates and diuretics did not explain trends for any age group. Conclusions: Models suggest that cardiovascular prescribing significantly reduced circulatory mortality among men and women aged 65+ after 1999 in Ireland but the effect of prescribing was lower among women than men. Beta-blocker, ace inhibitor and aspirin medications were more successful than statin, diuretic and nitrates at explaining trends.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".