Chronic disease clusters are associated with prolonged, bothersome, and multisite musculoskeletal pain: a population-based study on Northern Finns
Bibliographic record
Abstract
Background Chronic diseases often accumulate with musculoskeletal (MSK) pain. However, less evidence is available on idiosyncratic patterns of chronic diseases and their relationships with the severity of MSK pain in general MSK pain populations.Material and methods Questionnaire-based data on physician-diagnosed chronic diseases, MSK pain and its dimensions (frequency, intensity, bothersomeness, and the number of pain sites), and confounders were collected from the Northern Finland Birth Cohort 1966 at the age of 46. Latent Class Analysis (LCA) was used to identify chronic disease clusters among individuals who reported any MSK pain within the previous year (n = 6105). The associations between chronic disease clusters, pain dimensions, and severe MSK pain, which was defined as prolonged (over 30 d within the preceding year), bothersome (Numerical Rating Scale >5), and multisite (two or more pain sites) pain, were analyzed using logistic regression and general linear regression models, adjusted for sex and educational level (n for the full sample = 4768).Results LCA resulted in three clusters: Metabolic (10.8% of the full sample), Psychiatric (2.9%), and Relatively Healthy (86.3%). Compared to the Relatively Healthy cluster, the Metabolic and Psychiatric clusters had higher odds for daily pain and higher mean pain intensity, bothersomeness, and the number of pain sites. Similarly, the odds for severe MSK pain were up to 75% (95% confidence interval: 44%–113%) and 155% (81%–259%) higher in the Metabolic and Psychiatric clusters, respectively, after adjustments for sex and educational level.Conclusions Distinct patterns of chronic disease accumulation can be identified in the general MSK pain population. It seems that mental and metabolic health are at interplay with severe MSK pain. These findings suggest a potential need to screen for psychiatric and metabolic entities of health when treating working-aged people with MSK pain.Key messagesThis large study on middle-aged people with musculoskeletal pain aimed to examine the idiosyncratic patterns of chronic diseases and their relationships with the severity of musculoskeletal pain. Latent class cluster analysis identified three chronic disease clusters: Psychiatric, Metabolic, and Relatively Healthy. People with accumulated mental (Psychiatric cluster) or metabolic diseases (Metabolic cluster) experienced more severe pain than people who were relatively healthy (Relatively Healthy cluster). These findings suggest a potential need to screen for psychiatric and metabolic entities of health when treating working-aged people with MSK pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".