PMID- 33706717
OWN - NLM
STAT- MEDLINE
DCOM- 20210624
LR  - 20210624
IS  - 1471-2288 (Electronic)
IS  - 1471-2288 (Linking)
VI  - 21
IP  - 1
DP  - 2021 Mar 11
TI  - Population segmentation of type 2 diabetes mellitus patients and its clinical 
      applications - a scoping review.
PG  - 49
LID - 10.1186/s12874-021-01209-w [doi]
LID - 49
AB  - 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(R), Embase(R), Scopus(R) and PsycInfo(R). 
      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.
FAU - Seng, Jun Jie Benjamin
AU  - Seng JJB
AUID- ORCID: 0000-0002-3039-3816
AD  - Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
AD  - SingHealth Regional Health System PULSES Centre, Singapore Health Services, 
      Outram Rd, Singapore, 169608, Singapore.
FAU - Monteiro, Amelia Yuting
AU  - Monteiro AY
AUID- ORCID: 0000-0001-8764-9627
AD  - Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
FAU - Kwan, Yu Heng
AU  - Kwan YH
AUID- ORCID: 0000-0001-7802-9696
AD  - SingHealth Regional Health System PULSES Centre, Singapore Health Services, 
      Outram Rd, Singapore, 169608, Singapore.
AD  - Program in Health Services and Systems Research, Duke-NUS Medical School, 8 
      College Road, Singapore, 169857, Singapore.
AD  - Department of Pharmacy, Faculty of Science, National University of Singapore, 
      Singapore, Singapore.
FAU - Zainudin, Sueziani Binte
AU  - Zainudin SB
AUID- ORCID: 0000-0003-1686-0358
AD  - Department of General Medicine (Endocrinology), Sengkang General Hospital, 
      Singapore, Singapore.
FAU - Tan, Chuen Seng
AU  - Tan CS
AUID- ORCID: 0000-0002-6513-2309
AD  - Saw Swee Hock School of Public Health, National University of Singapore and 
      National University Health System, Singapore, Republic of Singapore.
FAU - Thumboo, Julian
AU  - Thumboo J
AUID- ORCID: 0000-0001-6712-5535
AD  - SingHealth Regional Health System PULSES Centre, Singapore Health Services, 
      Outram Rd, Singapore, 169608, Singapore.
AD  - Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, 
      Singapore.
AD  - SingHealth Regional Health System, Singapore Health Services, Singapore, 
      Singapore.
FAU - Low, Lian Leng
AU  - Low LL
AUID- ORCID: 0000-0003-4228-2862
AD  - SingHealth Regional Health System PULSES Centre, Singapore Health Services, 
      Outram Rd, Singapore, 169608, Singapore. low.lian.leng@singhealth.com.sg.
AD  - SingHealth Regional Health System, Singapore Health Services, Singapore, 
      Singapore. low.lian.leng@singhealth.com.sg.
AD  - Department of Family Medicine and Continuing Care, Singapore General Hospital, 
      Outram Road, Singapore, 169608, Singapore. low.lian.leng@singhealth.com.sg.
AD  - SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, 
      Singapore. low.lian.leng@singhealth.com.sg.
AD  - Outram Community Hospital, SingHealth Community Hospitals, 10 Hospital Boulevard, 
      Singapore, 168582, Singapore. low.lian.leng@singhealth.com.sg.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20210311
PL  - England
TA  - BMC Med Res Methodol
JT  - BMC medical research methodology
JID - 100968545
SB  - IM
MH  - *Diabetes Mellitus, Type 2/diagnosis/epidemiology
MH  - Humans
PMC - PMC7953703
OTO - NOTNLM
OT  - Cluster analysis
OT  - Data analysis
OT  - Diabetes mellitus, type 2
OT  - Latent class analysis
OT  - Outcome assessment, health care
OT  - Patient outcome assessment
OT  - Population segmentation
OT  - Scoping review
COIS- The authors declare that they have no competing interests.
EDAT- 2021/03/13 06:00
MHDA- 2021/06/25 06:00
PMCR- 2021/03/11
CRDT- 2021/03/12 05:46
PHST- 2020/07/08 00:00 [received]
PHST- 2021/01/13 00:00 [accepted]
PHST- 2021/03/12 05:46 [entrez]
PHST- 2021/03/13 06:00 [pubmed]
PHST- 2021/06/25 06:00 [medline]
PHST- 2021/03/11 00:00 [pmc-release]
AID - 10.1186/s12874-021-01209-w [pii]
AID - 1209 [pii]
AID - 10.1186/s12874-021-01209-w [doi]
PST - epublish
SO  - BMC Med Res Methodol. 2021 Mar 11;21(1):49. doi: 10.1186/s12874-021-01209-w.