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Title: Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review
Authors: Seng, Jun Jie Benjamin
Monteiro, Amelia Yuting
Kwan, Yu Heng 
Zainudin, Sueziani Binte
Tan, Chuen Seng 
Thumboo, Julian 
Low, Lian Leng 
Keywords: Cluster analysis
Data analysis
Diabetes mellitus, type 2
Latent class analysis
Outcome assessment, health care
Patient outcome assessment
Population segmentation
Scoping review
Issue Date: 11-Mar-2021
Publisher: BioMed Central Ltd
Citation: Seng, Jun Jie Benjamin, Monteiro, Amelia Yuting, Kwan, Yu Heng, Zainudin, Sueziani Binte, Tan, Chuen Seng, Thumboo, Julian, Low, Lian Leng (2021-03-11). Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review. BMC Medical Research Methodology 21 (1) : 49. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2021, The Author(s).
Source Title: BMC Medical Research Methodology
ISSN: 1471-2288
DOI: 10.1186/s12874-021-01209-w
Rights: Attribution 4.0 International
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