86 Impact of Community-Level Socioeconomic Disparities on Quality of Life After Burn Injury: A Burn Model Systems Database Study
Bibliographic record
Abstract
Abstract Introduction Individual- and community-level socioeconomic disparities impact overall health and injury incidence, severity, and outcomes. However, the impact of community-level socioeconomic disparities on recovery after burn injury is unknown. We aimed to characterize the association between community-level socioeconomic disparities and health-related quality of life (HRQL) after burn injury. These findings might inform rehabilitation service delivery and policy making at administrative levels. Methods Participants with the NIDILRR Burn Model System who were ≥14 years with a zip code were included. Sociodemographic and injury characteristics and 12-item Short Form Health Survey (SF-12) and Veterans RAND (VR-12) physical (PCS) and mental (MCS) component summary scores 6 months after injury were extracted. Data were deterministically linked by zip code to the Distressed Communities Index (DCI), which combines seven census-derived metrics into a single indicator of economic well-being that ranges from 0 (lowest distress) to 100 (highest distress). Multilevel linear regression models estimated the association between DCI and HRQL. Results The 342 participants were mostly male (239, 69%) had a median age of 48 years (IQR 33–57) and sustained a median burn size of 10% TBSA (IQR 3–28%). More than one-third of participants (117, 34%) lived in a neighborhood within the two most distressed quintiles. After adjusting for age, race/ethnicity, and pre-injury HRQL, increasing neighborhood distress was negatively associated with PCS (ß-0.05, SE 0.02, p=0.01). Age and pre-injury PCS were also significantly associated with 6-month PCS. There was no association between neighborhood distress and 6-month MCS. However, pre-injury MCS was significantly associated with 6-month MCS (0.56, SE 0.07, p< 0.001). Conclusions Neighborhood distress is associated with lower PCS after burn injury but is not associated with MCS. Regardless of neighborhood distress, pre-injury HRQL is significantly associated with both PCS and MCS during recovery.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".