The effect of education and income on need and willingness to undergo total joint arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Individuals with lower socioeconomic status (SES) receive less arthritis care, including joint arthroplasty. However, no studies have considered the expectations or needs of the patients. Our objective was to assess the effect of education and income on the potential need for, and the willingness to consider hip and knee arthroplasty. METHODS: Through a mail/telephone survey of 48,218 persons ages 55 years or older residing in 2 areas of Ontario, Canada, 3,307 individuals with moderate-to-severe hip/knee problems were identified. These individuals received a questionnaire to assess education, income, arthritis severity, and comorbidity. In a subset of these subjects, we conducted interviews to evaluate the willingness to consider arthroplasty, and we also performed clinical and radiographic examinations of the joints to validate self-reports of arthritis. The potential need for arthroplasty was defined as the presence of severe arthritis (as scored by the Western Ontario and McMaster Universities Osteoarthritis Index), with no absolute contraindications to surgery. Separate logistic regression models examined the independent effects of education and income on the potential need for, and definite willingness to consider arthroplasty, after controlling for age, sex, and region of residence. Potential unmet need was estimated as the proportion of subjects with the need for arthroplasty who were not already on a surgery waiting list, who were definitely willing to consider arthroplasty, and who had evidence of arthritis by examination and radiography. RESULTS: Response rates were at least 72% for all questionnaires and interviews. Less education (adjusted odds ratio [OR] 1.57 for less than high school versus postsecondary education, 95% confidence interval [95% CI] 1.17-2.11) and lower income (adjusted OR 1.83 for </=$20,000 versus >$40,000, 95% CI 1.24-2.70) were independently associated with a greater likelihood of having the potential need for arthroplasty. Among the subjects with potential need, neither education nor income was independently associated with a definite willingness to consider arthroplasty. Thus, taking willingness into consideration, individuals with less education and/or lower income were more likely to have potential unmet need for arthroplasty. CONCLUSION: Persons with lower SES had a greater need for, and were equally willing to consider arthroplasty, compared with those with higher SES. Thus, observed SES disparities in the rates of performed arthroplasties cannot be explained by a lower need or less willingness to undergo arthroplasty in those with lower SES.
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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) | 0.000 | 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".