Understanding Nonadherence with Hydroxychloroquine Therapy in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Hydroxychloroquine (HCQ) is a cornerstone to managing systemic lupus erythematosus (SLE), yet adherence to medication is poor. We sought to measure the association of adherence with 5 "dimensions of adherence" as articulated by the World Health Organization for chronic conditions: the patient's socioeconomic status, and patient-, condition-, therapy-, and healthcare system-related factors. Our longterm goal is to generate evidence to design effective interventions to increase adherence. METHODS: The retrospective cohort study included Kaiser Permanente Northern California patients ≥ 18 years old during 2006-2014, with SLE and ≥ 2 consecutive prescriptions for HCQ. Adherence was calculated from the medication possession ratio and dichotomized as < 80% versus ≥ 80%. Predictor variables were obtained from the electronic medical record and census data. We used multivariable logistic regression to estimate adjusted OR and 95% CI. RESULTS: The study included 1956 patients. Only 58% of patients had adherence ≥ 80%. In adjusted analyses, socioeconomic variables did not predict adherence. Increasing age (65-89 yrs compared with ≤ 39 yrs: OR 1.44, 95% CI 1.07-1.93), white race (p < 0.05), and the number of rheumatology visits in the year before baseline (≥ 3 compared with 0 or 1: OR 1.47, 95% CI 1.18-1.83) were positively associated with adherence. The rheumatologist and medical center providing care were not associated with adherence. CONCLUSION: At our setting, as in other settings, about half of patients with SLE were not adherent to HCQ therapy. Differences in adherence by race/ethnicity suggest the possibility of using tailored interventions to increase adherence. Qualitative research is needed to elucidate patient preferences for adherence support.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".