Health Care Utilization and Direct Costs Prior to Subspecialty Care in Children with Chronic Pain Compared with Other Chronic Childhood Diseases: A Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To understand the burden associated with pediatric chronic pain (CP) on the health care system compared with other costly chronic diseases prior to subspecialty care. STUDY DESIGN: In this retrospective cohort study, we assessed all-cause health care utilization and direct health care costs associated with pediatric CP (n = 91) compared with juvenile arthritis (n = 135), inflammatory bowel disease (n = 90), type 1 diabetes (n = 475) or type 2 diabetes (n = 289), anxiety (n = 7193), and controls (n = 273) 2 and 5 years prior to patients entering subspecialty care in Manitoba, Canada. Linked data from physician encounters, emergency department visits, hospitalizations, and prescriptions were extracted from administrative databases. Differences in health care utilization and direct health care costs associated with CP vs the other conditions were tested using negative binomial and zero-inflated negative binomial regression models, respectively. RESULTS: After adjustment for age at diagnosis, sex, location of residence, and socioeconomic status, CP continued to be associated with the highest number of consulted physicians and subspecialists and the highest number of physician billings compared with all other conditions (P < .01, respectively). CP was significantly associated with higher physician costs than juvenile arthritis, inflammatory bowel disease, type 1 diabetes, type 2 diabetes, or controls (P < .01, respectively); anxiety was associated with the highest physician and prescription costs among all cohorts (P < .01, respectively). CONCLUSION: Compared with chronic inflammatory and endocrinologic conditions, pediatric CP and anxiety were associated with substantial burden on the health care system prior to subspecialty care, suggesting a need to assess gaps and resources in the management of CP and mental health conditions in the primary care setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".