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Enregistrement W2755946820 · doi:10.2298/stnv170610001m

Smoking as the main factor of preventable mortality in Serbia

2017· article· en· W2755946820 sur OpenAlex
Ivan Marinković

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Notice bibliographique

RevueStanovnistvo · 2017
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Public Health Policies and Epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
Mots-clésMedicineDemographyPopulationDiseaseQuarter (Canadian coin)Mortality rateEnvironmental healthCause of deathRisk factorSurgeryGeographyInternal medicine

Résumé

récupéré en direct d'OpenAlex

The use of tobacco in Serbia has for many years been one of the most frequent risk factors affecting disease development. Although its impact is often neglected and the effects on health minimised, reviewing the existing literature and calculating the tobacco consumption impact on the mortality of the population in Serbia (using the Peto-Lopez method) show a clear link between smoking and health of the population. Serbian population is heavily burdened with the negative effects of tobacco on health, especially men. At the beginning of the second decade of the 21st century, mortality from the illness or cause of death associated with smoking was at about 17% of the total mortality. In men, it is estimated that even a quarter of the total mortality is associated with smoking. In the female population, the share of smokers is considerably lower, and consequently the mortality from this factor is lower, about 9% of the total mortality. Of all major disease groups, tumours are most affected by smoking. The share of tobaccorelated mortality in neoplasms is high and accounts for 30% (43% in men and 14% in women). In cardiovascular diseases, the impact of smoking is much smaller and about 6,000 deaths per year are associated with the use of tobacco. Since the early 1990s, the number of smoking-attributable death has been growing. Relatively, the share of men has not changed, but for 20 years of analysis the share of women has significantly increased from 5% to 9%. In all age groups, the share of smoking-related mortality has increased in the female population, especially in the 45-69 age range where mortality has been doubled. Surveys on the health of the Serbian population also confirm the trend of increasing the share of women smokers in the population, especially in the categories of young people. Men in Serbia (35-69 years of age) have the highest smoking-attributable death rate in Europe. As much as 44% of total deaths in that age are directly related to smoking. Besides Hungary, where mortality in men is also relatively high (42%), other countries have significantly lower shares. Observed at the level of the entire continent, countries of the Balkan Peninsula (and their neighbours) have the highest shares of smoking-attributable death. Women in Serbia have a moderately high share of 9% and are among the ten most vulnerable countries in Europe. The biggest difference in smoking-related mortality by gender is observed in the Pyrenees Peninsula and in the eastern and south-eastern parts of Europe. These are also the countries with the largest absolute difference in the mortality rate of men and women, thus confirming the hypothesis that tobacco smoke, as a single mortality factor, plays the most important role in establishing a different gender mortality pattern. A high percentage of smokers in the total population limits the growth of life expectancy and affects the difference in gender mortality rate. If a certain mortality factor potentially affects the life expectancy of up to three years for men in Serbia, as shown in the paper, then it is especially important to pay attention to measures of prevention and awareness of the population regarding this issue. Moreover, it is particularly important to recognise the consequences of passive smoking the youth and children are exposed to, since in Serbia there is a great deal of tolerance for smoking indoors.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,351
Score d'incertitude au seuil0,920

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,068
Tête enseignante GPT0,361
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle