T217. MEDICATION ADHERENCE AND ITS CORRELATES AMONG PATIENTS WITH RECURRENT SCHIZOPHRENIA: A LARGE-SCALE STUDY IN CHINA
Bibliographic record
Abstract
Abstract Background The Medication Adherence Rating Scale (MARS) is a rapid, non-intrusive way of measuring adherence to medication in order to improve management of patients with schizophrenia. The current study evaluated the reliability of the Chinese (Mandarin) version of the MARS and explored clinical and demographic correlates to medication adherence in a large sample of patients with recurrent schizophrenia in China. Methods 1198 patients with recurrent schizophrenia were recruited from 37 different hospitals in 17 provinces/municipalities of China and evaluated with the Medication Adherence Rating Scale (MARS), Clinical Global Impression-Severity of illness (CGI-S) and Sheehan Disability Scale-Chinese version (SDS-C). Socio-demographic data included gender, age, marital status, education level, employment status and living with others or alone. Clinical data included duration of illness, number of relapses, and medication use, as well as current stage of disease evaluated by SCID. Pearson correlations were used to examine associations between MARS, socio-demographic, and clinical characteristics. Independent sample T-tests were used to compare MARS score between different socio-demographic and clinical characteristics. Finally, a cut-off score of 6 on the MARS (ranged from 1 to 10) was used to divide the sample into two groups (i.e. MARS score≥ 6 identified good adherence and MARS score< 6 indicated poor adherence). Bivariate logistic regression models with the two groups (MARS score<6 and MARS score≥6) as the dependent variable was used to identify influencing factors of medication adherence. Data processing and analyses were conducted on SPSS 22.0 and Mplus 7.4. Results The MARS showed good internal consistency and psychometric properties. MARS outcomes varied by demographic and clinical characteristics; only 28.5% recurrent schizophrenia patients met the criteria of good adherence to antipsychotic medication. Findings indicated older age (OR=1.04, 95%CI=1.02–1.06), unsteady income (OR=1.79, 95%CI=1.29–2.49), acute period (OR=4.23, 95%CI=3.21–5.59) and a higher CGI-S score (OR=1.44, 95%CI=1.03–2.01) had significantly predictive effects on poor medication adherence. MARS demonstrated good reliability in our sample (Cronbach’s α =0.83; Spearman-Brown = 0.72). Discussion This study of the MARS is unique for a few reasons. First, comparative reports on MARS use in mainland China have not been published internationally; similar tests on reliability and correlation have only been reported in Hong Kong and Taiwan (Hui et al., 2006; Kao and Liu, 2010). Second, in considering demographic and clinical correlates of medication adherence in patients with recurrent schizophrenia, our MARS study broadly represents China with 17 of 27 provinces/municipalities reporting data from multiple geographic regions, with the participation of hundreds of psychiatrists across China. Only 28.5% recurrent schizophrenia patients met the criteria of good adherence to antipsychotic medication in this study. Low levels of good medication adherence in schizophrenia patients are found across Asia, with 27% in Korea meeting the criteria of good adherence (Kim et al., 2006) and 26% in Hong Kong (Hui et al., 2006). Overall MARS total score in our study (3.68 ±2.90) is comparably lower to that of developed countries, as MARS total score had a mean of 6.0 to 7.7 in a UK sample (Fialko et al., 2008; Jaeger et al., 2012), and 5.5 for schizophrenia patients in France (Zemmour et al., 2016). Medication adherence of patients affected by recurrent schizophrenia in China was found to be relatively low. Risk factors for non-adherence to medication in recurrent schizophrenia patients include older age, unsteady income, acute period and severity of illness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".