Impact of Psychiatric Diagnosis and Treatment on Medication Adherence in Youth With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Youth with systemic lupus erythematosus (SLE) experience high rates of psychiatric comorbidities, which may affect medication adherence. We undertook this study to examine the association between psychiatric disorders and hydroxychloroquine adherence and to determine whether psychiatric treatment modifies this association. METHODS: We identified incident hydroxychloroquine users among youth with SLE (ages 10-24 years) using de-identified US commercial insurance claims in Optum Clinformatics Data Mart (2000-2016). Adherence was estimated using medication possession ratios (MPRs) over a 365-day time period. Multivariable linear regression models were used to estimate the effect of having any psychiatric disorder on MPRs, as well as the independent effects of depression, anxiety, adjustment, and other psychiatric disorders. We tested for interactions between psychiatric diagnoses and treatment with psychotropic medications or psychotherapy. RESULTS: Among 873 subjects, 20% had a psychiatric diagnosis, most commonly depression. Only adjustment disorders were independently associated with decreased MPRs (β -0.12, P = 0.05). We observed significant crossover interactions, in which psychiatric disorders had opposite effects on adherence depending on the receipt of psychiatric treatment. Among youth with any psychiatric diagnosis, psychotropic medication use was associated with a 0.15 increase in the MPR compared with no psychotropic medication use (P = 0.02 for interaction). Among youth with depression or anxiety, psychotherapy was also associated with a higher MPR compared with no psychotherapy (P = 0.05 and P < 0.01 for interaction, respectively). CONCLUSION: The impact of psychiatric disorders on medication adherence differed by whether youth had received psychiatric treatment. Improving recognition and treatment of psychiatric conditions may increase medication adherence in youth with SLE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".