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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".