Prevalence and Treatment of Depression in Children and Adolescents With Sickle Cell Disease
Bibliographic record
Abstract
Article Abstract Objective: To describe the prevalence and treatment of comorbid depressive disorders in children and adolescents diagnosed with sickle cell disease. Method: A retrospective cohort design evaluating South Carolina Medicaid medical and pharmacy claims between January 1, 1996, and December 31, 2006, was employed to identify 2,194 children and adolescents aged 17 years and younger diagnosed with sickle cell disease. Cohorts diagnosed with and without comorbid unipolar depressive disorders (using DSM-IV-TR criteria) were then compared. Results: Forty-six percent of the sickle cell disease cohort was diagnosed with a depressive disorder (n = 1,017), either dysthymia (90%) or major depressive disorder (10%). Dysthymia was diagnosed at approximately 9 years of age, whereas major depressive disorder was diagnosed at approximately 14 years of age. Compared with the controls, the sickle cell disease cohort with depression had more acute vaso-occlusive pain and acute chest syndrome visits per year, developed more complications with related organ damage, and incurred significantly higher outpatient, acute (emergency + inpatient), and total sickle cell disease care costs. The depression cohort was primarily treated with selective serotonin reuptake inhibitors (SSRIs; 12%) or serotonin-norepinephrine reuptake inhibitors (SNRIs; 10%) for approximately 9 months. Although alleviating the comorbid depression might positively affect their sickle cell disease pain, over 80% of the patients received no antidepressant medications, and many of the prescribed SSRIs and SNRIs have previously shown no impact on relieving chronic pain. Conclusions: Comorbid depression in sickle cell disease is associated with adverse course and outcomes. These findings underscore the need for earlier and more aggressive treatment of comorbid depression by primary care or psychiatric providers in order to reduce the chronic, severe pain-depression burden on these patients. Prim Care Companion CNS Disord 2011;13(2):e1-e7 Submitted: August 2, 2010; accepted September 22, 2010. Published online: March 10, 2011 (doi:10.4088/PCC.10m01063). Corresponding author: Jeanette M. Jerrell, PhD, Department of Neuropsychiatry and Behavioral Science, University of South Carolina School of Medicine, 3555 Harden St Ext, CEB 301, Columbia, SC 29203 (Jeanette.Jerrell@uscmed.sc.edu).
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".