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Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review
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. METHOD...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953703/ https://www.ncbi.nlm.nih.gov/pubmed/33706717 http://dx.doi.org/10.1186/s12874-021-01209-w |
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author | Seng, Jun Jie Benjamin Monteiro, Amelia Yuting Kwan, Yu Heng Zainudin, Sueziani Binte Tan, Chuen Seng Thumboo, Julian Low, Lian Leng |
author_facet | Seng, Jun Jie Benjamin Monteiro, Amelia Yuting Kwan, Yu Heng Zainudin, Sueziani Binte Tan, Chuen Seng Thumboo, Julian Low, Lian Leng |
author_sort | Seng, Jun Jie Benjamin |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01209-w. |
format | Online Article Text |
id | pubmed-7953703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79537032021-03-12 Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review Seng, Jun Jie Benjamin Monteiro, Amelia Yuting Kwan, Yu Heng Zainudin, Sueziani Binte Tan, Chuen Seng Thumboo, Julian Low, Lian Leng BMC Med Res Methodol Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01209-w. BioMed Central 2021-03-11 /pmc/articles/PMC7953703/ /pubmed/33706717 http://dx.doi.org/10.1186/s12874-021-01209-w Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Seng, Jun Jie Benjamin Monteiro, Amelia Yuting Kwan, Yu Heng Zainudin, Sueziani Binte Tan, Chuen Seng Thumboo, Julian Low, Lian Leng Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title | Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title_full | Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title_fullStr | Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title_full_unstemmed | Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title_short | Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
title_sort | population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953703/ https://www.ncbi.nlm.nih.gov/pubmed/33706717 http://dx.doi.org/10.1186/s12874-021-01209-w |
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