Combined analysis of 3 cross-sectional surveys of pain in 14 countries in Europe, the Americas, Australia, and Asia: impact on physical and emotional aspects and quality of life
Notice bibliographique
Résumé
Background and aims Recognition of the biopsychosocial aspects of pain is important for a true understanding of the burden of pain and the necessity of pain management. Biopsychosocial aspects of pain may differ between countries and cultures. Market research methods can be well suited and effective for assessing patient perspectives of pain and biopsychosocial differences. We conducted and combined 3 cross-sectional, international surveys to document the impact of pain on physical and emotional aspects of life, as well as quality of life (QOL). Methods Online panelists from 24 countries took part in our surveys in 2014, 2016, and 2017. Fourteen countries (Australia, Brazil, Canada, China, Germany, Italy, Japan, Poland, Russia, United Kingdom, United States, Mexico, Sweden, Saudi Arabia) contributed data in all 3 surveys and comprise the analysis population. A Global Pain Index (GPI) was constructed using 8 questions in 3 categories: Physical (frequency, duration, intensity of pain), Emotional (anxiety, impact on self-esteem, happiness), and Impact on QOL and ability to enjoy life. Each item was scored as the percentage of respondents meeting a prespecified threshold indicative of a substantial pain impact. Scores for the items within each category were averaged to obtain a category score, category scores were averaged to obtain a total score for each survey, and total scores from each survey were averaged to obtain a final combined score. Scores were assessed for the overall population, by individual countries, by age and gender, and by self-identified pain-treatment status (treat immediately, wait, never treat). Results Of the 50,952 adult respondents, 28,861 (56.6%) had ever experienced musculoskeletal pain; 50% of those with pain had pain with a multifaceted impact based on the GPI (Physical: 51%; Emotional: 40%; QOL Impact: 59%). Russia (57%) and Poland (56%) had the highest scores; Mexico (46%), Germany (47%), and Japan (47%) had the lowest. GPI score was higher in women (52%) than men (48%), and initially increased with age through age 54 (18‒24 years: 45%; 25‒34 years: 52%; 35‒44 years: 53%; 45‒54 years: 54%), after which it decreased again (55‒64 years: 51%; ≥65 years: 45%). A majority (65%) of respondents wait to treat their pain, whereas 21% treat their pain immediately and 14% never treat pain. The most common reason for waiting (asked in survey 3 only) was to avoid taking medication. Conclusions In this combined analysis of 3 international surveys using a novel biopsychosocial pain assessment tool, pain had a substantial impact on ~50% of respondents' lives, spanning physical (51%), emotional (40%), and QOL effects (59%). Despite the substantial impact, a majority of patients tried to avoid treating their pain. Implications Clinicians should take a biopsychosocial approach to pain by asking patients not only about the presence and severity of pain, but the extent to which it affects various aspects of their lives and daily functioning. Patients may also need education about the efficacy and safety of available treatments for self-management of pain. The GPI may be a useful new tool for future studies of the biopsychosocial effects of pain in large populations.
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 | 0,008 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 ».