Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes. Issue 11 (10th November 2020)

• Record Type: Journal Article
• Authors: Chao, Yi-Sheng; Lin, Kuan-Fu; Wu, Chao-Jung; Wu, Hsing-Chien; Hsu, Hui-Ting; Tsao, Lien-Cheng; Cheng, Yen-Po; Lai, Yi-Chun; Chen, Wei-Chih
• Is Part Of: BMJ open. Volume 10:Issue 11(2020)
• Publication Date: 2020-11-10
• Subjects: frailty -- bias -- forward-stepwise regression -- the health and retirement study -- index mining; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-037022
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Composite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes). Settings: General psychiatric care. Participants: Without real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses. Primary and secondary outcomes: The relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured. Results: The prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses. Conclusions: There are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.