Association of neighborhood-level disadvantage beyond individual sociodemographic factors in patients with or at risk of knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Lower socioeconomic status (SES) is a risk factor for poorer pain-related outcomes. Further, the neighborhood environments of disadvantaged communities can create a milieu of increased stress and deprivation that adversely affects pain-related and other health outcomes. Socioenvironmental variables such as the Area Deprivation Index, which ranks neighborhoods based on socioeconomic factors could be used to capture environmental aspects associated with poor pain outcomes. However, it is unclear whether the ADI could be used as a risk assessment tool in addition to individual-level SES. METHODS: The current study investigated whether neighborhood-level disadvantage impacts knee pain-related outcomes above sociodemographic measures. Participants were 188 community-dwelling adults who self-identified as non-Hispanic Black or non-Hispanic White and reported knee pain. Area Deprivation Index (ADI; measure of neighborhood-level disadvantage) state deciles were derived for each participant. Participants reported educational attainment and annual household income as measures of SES, and completed several measures of pain and function: Short-form McGill Pain Questionnaire, Western Ontario and McMaster Universities Osteoarthritis Index, and Graded Chronic Pain Scale were completed, and movement-evoked pain was assessed following the Short Physical Performance Battery. Hierarchical linear regression analyses were used to assess whether environmental and sociodemographic measures (i.e., ADI 80/20 [80% least disadvantaged and 20% most disadvantaged]; education/income, race) were associated with pain-related clinical outcomes. RESULTS: Living in the most deprived neighborhood was associated with poorer clinical knee pain-related outcomes compared to living in less deprived neighborhoods (ps < 0.05). Study site, age, BMI, education, and income explained 11.3-28.5% of the variance across all of the individual pain-related outcomes. However, the ADI accounted for 2.5-4.2% additional variance across multiple pain-related outcomes. CONCLUSION: The ADI accounted for a significant amount of variance in pain-related outcomes beyond the control variables including education and income. Further, the effect of ADI was similar to or higher than the effect of age and BMI. While the effect of neighborhood environment was modest, a neighborhood-level socioenvironmental variable like ADI might be used by clinicians and researchers to improve the characterization of patients' risk profile for chronic pain outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".