Multiple causes of death data to track trends of mortality from chronic diseases: Insights from the COVID-19 pandemic in Switzerland
Bibliographic record
Abstract
PURPOSE: With the rising prevalence of multi-morbidity among aging populations and disruptive events as new infectious disease threats, the traditional focus on the underlying cause of death (UCOD) can obscure the contribution of chronic diseases to mortality trends. The multiple cause of death (MCOD) approach addresses this limitation by considering all causes recorded on a death certificate. We aimed to quantify the effect of the COVID-19 pandemic on trends of chronic disease mortality by comparing the UCOD and MCOD approaches. METHODS: We conducted a population-based time series analysis using all deaths occurred 2011-2022 in Switzerland. We modelled pre-pandemic (2011-2019) trends to predict the expected chronic disease deaths during 2020-2022 had the pandemic not occurred. We quantified the monthly effect of the pandemic as the observed minus expected chronic disease deaths and estimated these effects by sex and age using (1) the UCOD and (2) the MCOD. RESULTS: Both approaches revealed similar overall trends of effects. However, a marked discrepancy occurred at the end of 2020, when Switzerland experienced the highest COVID-19 mortality, with MCOD quantifying a substantially higher excess of chronic disease deaths compared to UCOD. CONCLUSION: MCOD complements UCOD in quantifying cause-specific mortality trends and may improve population health monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".