Medical and Psychiatric Comorbidity and Health Care Use Among Children 6 to 17 Years Old
Bibliographic record
Abstract
BACKGROUND: The association of psychiatric disorders (PDs) with other PDs and medical disorders (MDs) has been insufficiently explored in children and adolescents. OBJECTIVES: To estimate medical and psychiatric comorbidity present in children with PDs and to determine the medical service usage of children with PDs. DESIGN: We use administrative health care data to describe the health care provided for study children. Psychiatric disorders were classified into the following 3 categories: psychosis, emotion, and behavior. We used logistic regression to assess medical comorbidity for each category. Psychiatric comorbidity was determined using chi(2) test analysis. Health care use was determined by comparing the frequency of visits for MDs and PDs between children with PDs and children without PDs. SETTING: We studied 406,640 children (50.6% male) between 6 and 17 years old, living in Alberta, Canada, during the fiscal year April 1, 1995, through March 31, 1996. RESULTS: A PD was diagnosed in 32,214 (60.3% male) children. Psychiatric comorbidity was present in 13.6% of the children; comorbidity existed in all 3 psychiatric groups and peaked in postpubertal children. More girls than boys had significant medical comorbidity. Significant odds ratios (ORs) for girls varied from 1.2 (behavior and sinusitis, bronchitis, and chronic disorders; psychosis, and menstrual problems) to 15.3 (behavior and developmental delay). Among boys, the highest OR was seen with the combination of behavior and developmental delay (OR, 8.3) and psychosis and poisoning (OR, 8.2). With ORs ranging from 4.6 to 15.3, developmental delay consistently had high ORs for both sexes and all 3 types of PDs. Poisoning also had high ORs (3.3-14.1) with all 3 PDs and both sexes. Among girls, disorders associated with pregnancy and the genitourinary system had modest associations (OR, 1.9-2.2, for behavior) to moderate (OR, 2.5-4.0, for emotion). Children with PDs had significantly greater medical service usage than did children without PDs. Girls had greater medical health care usage than boys. Psychiatric service usage was similar for both sexes. CONCLUSIONS: Medical and psychiatric comorbidity exist in children with PDs. Girls are more commonly affected. Health care usage is higher in children with PDs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".