The Role of the Economic Factors with Pain Medication among Individuals Over 50
Notice bibliographique
Résumé
Background: The experience of pain is a widespread phenomenon among adults, and entails high costs to both individuals and society. The objective of the current research is to determine if the ability to pay and supplementary insurance are factors associated with pain medication among individuals over 50. Methods: Data came from Survey of Health, Aging and Retirement in Europe. The sample included 64,281 individuals 50+ from nineteen European countries and Israel. Results: Joint pain was common with one out of three reporting joint pain. Prevalence of pain was similar among different age groups, and more women reported joint pain. Among those in pain, about 21.5% of the individuals reported mild pain, 52.9% moderate and 26% severe pain. In the multivariate logistic regression, we found that men and those older than 60 suffered more from joint pain, while controlling for education and subjective assessment of the ability to cope economically. A large percentage of those with pain were not taking medication to manage their pain, and there were significant demographic differences between those that did and did not take medication. Those that took medication were younger, male, had more education, were able to cope economically and had supplementary insurance. In a multivariable logistic regression analysis, we found that among those with joint pain, significant predictors of taking pain medication were those who were male, younger than 59, had more education, able to cope economically and had supplementary insurance. When controlling for economic factors the likelihood of taking pain medication decreased with increasing age. Conclusions: Joint pain is an important public health problem. The study showed that about half of the individuals with pain were not taking medication to manage their pain. Our results demonstrate that among individuals over 50 in Europe income is strongly associated with taking pain medication and that there is economic inequity in medication access. Key messages: Access to medication deserves attention from healthcare workers and policy makers. Although pain management is multifaceted, prescription medication is essential in pain management.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».