Jun Jie Benjamin Seng1,2, Amelia Yuting Monteiro1, Yu Heng Kwan2,3,4, Sueziani Binte Zainudin5, Chuen Seng Tan6, Julian Thumboo2,7,8, Lian Leng Low9,10,11,12,13. 1. Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore. 2. SingHealth Regional Health System PULSES Centre, Singapore Health Services, Outram Rd, Singapore, 169608, Singapore. 3. Program in Health Services and Systems Research, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore. 4. Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore. 5. Department of General Medicine (Endocrinology), Sengkang General Hospital, Singapore, Singapore. 6. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore. 7. Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore. 8. SingHealth Regional Health System, Singapore Health Services, Singapore, Singapore. 9. SingHealth Regional Health System PULSES Centre, Singapore Health Services, Outram Rd, Singapore, 169608, Singapore. low.lian.leng@singhealth.com.sg. 10. SingHealth Regional Health System, Singapore Health Services, Singapore, Singapore. low.lian.leng@singhealth.com.sg. 11. Department of Family Medicine and Continuing Care, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore. low.lian.leng@singhealth.com.sg. 12. SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore. low.lian.leng@singhealth.com.sg. 13. Outram Community Hospital, SingHealth Community Hospitals, 10 Hospital Boulevard, Singapore, 168582, Singapore. low.lian.leng@singhealth.com.sg.
Abstract
BACKGROUND: Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients. METHODS: The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed. RESULTS: Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients' race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies. CONCLUSIONS: Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.
BACKGROUND: Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients. METHODS: The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed. RESULTS: Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients' race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies. CONCLUSIONS: Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.
Entities:
Keywords:
Cluster analysis; Data analysis; Diabetes mellitus, type 2; Latent class analysis; Outcome assessment, health care; Patient outcome assessment; Population segmentation; Scoping review
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