Definitions and Methods for Analysis of Multiple Cause of Death: A Scoping Review
Bibliographic record
Abstract
Objective: This review aims to identify and categorise demographic methods used in modelling multiple causes of death. The assumption that each death is caused by exactly one disease is debatable, as other possible diseases or causes may be associated with the main cause. Hence, the multiple causes of death approach is essential for understanding mortality. Therefore, through this study, we will carry out a Scoping Review of the existing literature on the topic of MCOD. Inclusion criteria: This review considers literature pertaining to methods for the analysis and utilization of multiple cause of death data. Papers that discuss the methods used as well as the strengths and limitations of multiple cause of death approach will be considered for this study. Methods: Preliminary searches were conducted in July 2022 and focussed on concepts of multiple cause of death mortality and multiple causes of death. Searches were conducted in PubMed, Web of Science, and Scopus and was conducted in English, French, Spanish and Portuguese. There were no time constraints on the studies to be included in this review. Articles were initially screened by title and abstract and then reviewed by full text by three independent reviewers. Two reviewers extracted the data from the eligible articles. Results: A total of 769 papers were reviewed at the abstract and title level. Of these, 124 were screened for full-text eligibility. A total of 53 articles were included in the final analysis. Among the articles included, 31 were articles from the United States, 14 were from Europe and 8 were from other countries. The papers were categorized as methodological (33) papers, data assessment papers (19), papers discussing socioeconomic differences in mortality (13) and mixed method papers (11). Conclusions: There are many different types of methodologies and procedures used to analyse multiple cause of death statistics. All papers included in this study used descriptive methods (mostly frequency tables and cross-tabulations) to analyze multiple cause of death data, and almost half of them use visualizations to model the results. One of the most common limitations cited among the articles is the comparability of the statistics. Accurate data and analysis of vital statistics require resources, and many countries do not have the to report high-quality statistics. This could explain why most of the papers selected for this study focused on data from developed countries.
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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 | 0.090 | 0.248 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.059 | 0.041 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".