Definitions and Methods for Analysis of Multiple Cause of Death: A Scoping Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».