Notice bibliographique
Résumé
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».