Risk, individual perception of risk and population health
Notice bibliographique
Résumé
We thank Annie Britton for her thoughtful comments 1 on our study investigating the life-time risk of mortality at differing levels of alcohol consumption in seven European countries 2. These comments consider key concepts of epidemiology, social psychology and causality. One comment concerns differences in the risk of an alcohol-attributable death across European Union countries, despite cultural and socio-economic similarities. Alcohol use and other factors affect the risk of death via complex interacting pathways 3, 4; for a death to occur, a combination of biopsychosocial factors is needed. Indeed, differences in alcohol-attributable death risk across countries, despite similar levels of consumption, are due in part to these factors differing across countries. Furthermore, different mortality risks also exist across socio-economic strata for similar levels of alcohol consumption 5, 6. Thus, differences in risk across countries and socio-economic strata result from differing health risk behaviours and environmental factors which form part of the interacting pathways affecting risk 7, 8. While epidemiological concepts of causality are based on these complex pathways, empirical studies, including the underlying studies used in our paper, simplify the relationships between the biopsychosocial factors by isolating the impact of a single factor on mortality through regressions while accounting for relatively few confounding and interacting factors. The results from such studies are often interpreted as an absolute ‘biological impact’ of this single risk factor (‘one drink leads to x fewer minutes of life’), irrespective of other factors. However, this approach is limited, as exemplified by the marked differences in alcohol-attributable mortality across countries with similar drinking levels. The implications for advice based on the mortality risk are not straightforward. Our analyses found that national risk curves vary widely at higher levels of drinking, while at lower levels of drinking, which confer a life-time mortality risk below one in 1000, the variations in risk across European countries are not large 2. Another consideration is whether guidelines should extend beyond the basic advice of low-risk average consumption. It is now common to also specify a risk threshold for a single drinking occasion—found, for example, in the current Australian and UK guidelines 9, 10. Thus, a consideration of drinking patterns addresses a dimension of risk obscured by cumulative guidelines; drinking patterns affect the risk of injuries, and also some chronic, mental and infectious diseases 11, 12. Additionally, separate guidelines are often formulated for drinking during pregnancy and teenage drinking, although these implicit risk thresholds are set based on social norms, more or less at ‘any risk’. Another comment concerns the potential of the presented risk information to change behaviour. Cognitive psychology has shown that humans are far from the rational homo economicus 11, 12, as despite being informed about the effects of alcohol, humans underestimate the overall risks of drinking 13. Therefore, comprehensive alcohol policies which include low-risk drinking guidelines also require pricing, marketing and availability policies (the ‘best buys’: 14, 15), and other promising interventions such as minimum pricing 16, 17 or lowering of the ethanol concentration in alcoholic beverages 18. None.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».