Latent class analysis for identifying subclasses of depression using JMP Pro 16

• Main Authors: KARISHMA YADAV, SEET, Fei Fei Sue-ann, KAM, Tin Seong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Data Access and Manipulation; Latent Class Analysis; Data Visualization; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6871; https://ink.library.smu.edu.sg/context/sis_research/article/7874/viewcontent/Paper___Latent_Class_Analysis_for_Identifying_subclasses_of_Depression_using_JMP_Pro_16.pdf
• Institution: Singapore Management University

According to WHO, “Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease”. A major stumbling block in the care of depressed patients remains the accurate diagnosis of the severity of depression. Patient Health Questionnaire (PHQ-9), a 9-question instrument is widely used for diagnosing and determining the severity of depression. However, the popularly used 5-Category of depression severity based on the sum of responses to the 9 questions was overly subjective. In view of this limitation, our paper aims to demonstrate how Latent Class Analysis of JMP Pro can be used to provide a data-driven and objective approach to determine depression severity classes. The study was conducted using Mental Health-Depression Screener from National Health and Nutrition Examination Survey (NHANES) 2017-2018, conducted by the Centres for Disease Control and Prevention, USA. The analysis results reveal that Latent Class Analysis improves our understanding of the characteristics of depression classes better than the conventional 5-Category method.