Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

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 \u2009=\u2009111, 75.0%). Cluster based analyses ( n \u2009=\u200937, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic ( n \u2009=\u200966, 44.6%), diabetes related ( n \u2009=\u200954, 36.5%) and non-diabetes medical related ( n \u2009=\u200918, 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 \u2009=\u200971, 48%), assessment of diabetes related complications ( n \u2009=\u200957, 38.5%) and non-diabetes metabolic derangements ( n \u2009=\u200942, 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.