Open AccessPatterns of patients with multiple chronic conditions in primary care: A cross-sectional study
Author(s) -
Xiao Wei Tan,
Ying Xie,
Jeremy Kaiwei Lew,
Poay Sian Sabrina Lee,
Eng Sing Lee
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0238353
Subject(s) - medicine , latent class model , cross sectional study , kidney disease , comorbidity , population , diabetes mellitus , disease , medical record , physical therapy , environmental health , pathology , statistics , mathematics , endocrinology
Objective Our aim was to identify the patterns of multimorbidity among a group of patients who visited primary care in Singapore. Methods A cross-sectional study of electronic medical records was conducted on 437,849 individuals aged 0–99 years who visited National Healthcare Group Polyclinics from 1 Jul 2015 to 30 Jun 2016 for the management of chronic conditions. Patients’ health conditions were coded with the 10 th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), and patient records were extracted for analysis. Patients’ diagnosis codes were grouped by exploratory factor analysis (EFA), and patterns of multimorbidity were then identified by latent class analysis (LCA). Results EFA identified 19 groups of chronic conditions. Patients with at least three chronic conditions were further separated into eight classes based on demographics and probabilities of various diagnoses. We found that older patients had higher probabilities of comorbid hypertension, kidney disease and ischaemic heart disease (IHD), while younger patients had a higher probability of comorbid obesity. Female patients had higher probabilities of comorbid arthritis and anaemia, while male patients had higher probabilities of comorbid kidney diseases and IHD. Indian patients presented with a higher probability of comorbid diabetes than Chinese and Malay patients. Conclusions This study demonstrated that patients with multimorbidity in primary care could be classified into eight patterns. This knowledge could be useful for more precise management of these patients in the multiethnic Asian population of Singapore. Programmes for early intervention for at-risk groups can be developed based on the findings.