Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality – a retrospective cohort study

Abstract

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. Electronic supplementary material The online version of this article (10.1186/s12875-019-0939-2) contains supplementary material, which is available to authorized users.

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

“…LCA is a data-driven method utilizing individual level observable data (indicator variables) to identify underlying latent groups of individuals (classes) (33). Examples of successful LCA utilization in population segmentation has been demonstrated by Low et al in the Singapore regional health system (10) and Yan et al in a primary care population respectively (34). In this study, 9 identified medical and socioeconomic variables were selected and described above.…”

The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

“…LCA is a data-driven method utilizing individual level observable data (indicator variables) to identify underlying latent groups of individuals (classes) (32). Examples of successful LCA utilization in population segmentation has been demonstrated by Low et al in the Singapore regional health system (10) and Yan et al in a primary care population respectively (33). In this study, 9 identified medical and socioeconomic variables were selected and described above.…”

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study

“…As part of a range of descriptive analyses associated with PHM, population segmentation supports this by cutting through the complexity of large and multivariate linked datasets in determining a manageable number of population groups separable by differences in health determinants or outcomes. [17][18][19] This may be an important tool in understanding the composition of the high-risk group considered here.…”