The Association Between Chronic Pain Conditions and Subclinical and Clinical Anxiety Among Community-Dwelling Older Adults Consulting in Primary Care
Bibliographic record
Abstract
OBJECTIVE: To examine associations between chronic pain conditions, pain level, and subclinical/clinical anxiety in community-dwelling older adults. DESIGN: Cross-sectional associations were analyzed using multinomial logistic regression to compare the odds of having subclinical/clinical anxiety by painful condition and pain level, controlling for confounders. SETTING: Participants were recruited in primary care waiting rooms to take part in the first wave of the Étude sur la Santé des Aînés (ESA)-Services study. SUBJECTS: In total, 1,608 older adults aged 65+. METHODS: Clinical anxiety was assessed using DSM-IV criteria. Subclinical anxiety was considered present when participants endorsed symptoms of anxiety but did not fulfill clinical diagnostic criteria for an anxiety disorder. Painful chronic conditions included arthritis, musculoskeletal conditions, gastrointestinal problems, and headaches/migraines. Presence of painful conditions was assessed using combined self-report and health administrative data sources. Pain level was self-reported on an ordinal scale. Physical comorbidities were identified from ICD-9/10 diagnostic codes and depression was evaluated based on the DSM-IV. RESULTS: Sixty-six percent of home-living older adults suffer from a chronic pain condition. Older adults with clinical anxiety are more likely to experience musculoskeletal pain, gastrointestinal problems, headaches/migraines, and higher pain levels compared to those with no anxiety. Also, those with ≥3 painful conditions are at greater risk for subclinical and clinical anxiety compared to those with no painful condition. CONCLUSIONS: These results emphasize the need for assessing anxiety symptoms in older adults with chronic pain conditions. Comprehensive management of comorbid chronic pain and psychopathology might help reduce the burden for patients and the healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".