Socioeconomic status and the utilization of diagnostic imaging in an urban setting
Bibliographic record
Abstract
BACKGROUND: In publicly funded health care systems, the utilization of health care services should be equitable, irrespective of socioeconomic status (SES). Although the association between SES and health care utilization has been examined in Canada relative to surgical, cardiac and preventive health care services, no published studies have specifically explored the association between SES and diagnostic imaging. METHODS: We examined over 300,000 diagnostic imaging claims made in the Winnipeg Regional Health Authority between Apr. 1, 2001, and Mar. 31, 2002. Using patient postal codes, we assigned SES on the basis of average household incomes in Canada's 1996 census. Using multiple regression, we examined the association between income quintile, patient age group (< or =16, 17-64, > or = 65 years), patient morbidity level according to the Johns Hopkins University Adjusted Clinical Group method (high, moderate, low), and imaging modality (general radiology, vascular, computed tomography, magnetic resonance, and general and obstetric ultrasound). RESULTS: Relative rates (RR) of diagnostic imaging utilization (highest v. lowest income quintile) were significantly increased in pediatric and adult patient groups at all morbidity levels receiving general radiology (highest RR 2.47, 95% confidence interval [CI] 2.07-2.93); pediatric and adult patient groups at high and low morbidity levels and elderly patient groups at low morbidity levels receiving general ultrasound (highest RR 2.26, 95% CI 1.20-4.26); pediatric and adult patient groups at all morbidity levels and elderly patients at high and moderate morbidity levels receiving magnetic resonance imaging (highest RR 2.51, 95% CI 1.78- 3.52); and adult patient groups at all morbidity levels receiving computed tomography (highest RR 1.46, 95% CI 1.35- 1.59). A lower RR of diagnostic imaging utilization in the highest income quintile was found only among patients receiving obstetric ultrasound (RR 0.80, 95% CI 0.73-0.87). No significant associations were found among elderly patients receiving general radiology or computed tomography or adult patients receiving vascular imaging. INTERPRETATION: We found a pattern of increased diagnostic imaging utilization in patient groups with a higher SES. Further research is needed to better understand the nature of this finding and how it contributes to health outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".