Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12875-019-0939-2
Title: Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study
Authors: Yan, Shi
Seng, Benjamin Jun Jie
Kwan, Yu Heng
Tan, Chuen Seng 
Quah, Joanne Hui Min 
Thumboo, Julian 
Low, Lian Leng 
Keywords: Science & Technology
Life Sciences & Biomedicine
Primary Health Care
Medicine, General & Internal
General & Internal Medicine
Primary care
Latent class analysis
Population segmentation
LATENT CLASS ANALYSIS
CLUSTER-ANALYSIS
SEGMENTATION
RISK
PATTERNS
SYSTEMS
MODEL
Issue Date: 23-Apr-2019
Publisher: BMC
Citation: Yan, Shi, Seng, Benjamin Jun Jie, Kwan, Yu Heng, Tan, Chuen Seng, Quah, Joanne Hui Min, Thumboo, Julian, Low, Lian Leng (2019-04-23). Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study. BMC FAMILY PRACTICE 20 (1). ScholarBank@NUS Repository. https://doi.org/10.1186/s12875-019-0939-2
Abstract: © 2019 The Author(s). Background: Heterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1 year follow up healthcare utilizations and all-cause mortality across the classes. Methods: Using de-identified administrative data, we included all adult Singapore citizens or permanent residents who utilized Singapore Health Services (SingHealth) primary care services in 2012. Latent class analysis was used to identify patient subgroups having unique disease patterns in the population. The models were assessed by Bayesian Information Criterion and clinical interpretability. We compared healthcare utilizations in 2013 and one-year all-cause mortality across classes and performed regression analysis to assess predictive ability of class membership on healthcare utilizations and mortality. Results: We included 100,747 patients in total. The best model (k = 6) revealed the following classes of patients: Class 1 "Relatively healthy" (n = 58,213), Class 2 "Stable metabolic disease" (n = 26,309), Class 3 "Metabolic disease with vascular complications" (n = 2964), Class 4 "High respiratory disease burden" (n = 1104), Class 5 "High metabolic disease without complication" (n = 11,122), and Class 6 "Metabolic disease with multi-organ complication" (n = 1035). The six derived classes had different disease patterns in 2012 and 1 year follow up healthcare utilizations and mortality in 2013. "Metabolic disease with multiple organ complications" class had the highest healthcare utilization (e.g. incidence rate ratio = 19.68 for hospital admissions) and highest one-year all-cause mortality (hazard ratio = 27.97). Conclusions: Primary care utilizers are heterogeneous and can be segmented by latent class analysis into classes with unique disease patterns, healthcare utilizations and all-cause mortality. This information is critical to population level health resource planning and population health policy formulation.
Source Title: BMC FAMILY PRACTICE
URI: https://scholarbank.nus.edu.sg/handle/10635/154883
ISSN: 14712296
DOI: 10.1186/s12875-019-0939-2
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