Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
National University of Singapore, 12 Science Drive 2, Singapore, 117549, Singapore.
BMC Fam Pract. 2019 Apr 23;20(1):54. doi: 10.1186/s12875-019-0939-2.
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.
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.
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).
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.
人群健康需求的异质性以及医疗资源规划的困难是全球初级保健系统面临的挑战。为了解决人口健康管理中的这一挑战，我们需要更好地了解初级保健利用者的不同健康状况。我们的目的是将初级保健利用者人群分为具有独特疾病模式的类别，并报告各分类人群在 1 年随访中的医疗保健利用情况和全因死亡率。
我们使用去标识化的行政数据，纳入了 2012 年在新加坡健康服务（SingHealth）初级保健服务中就诊的所有成年新加坡公民或永久居民。使用潜在类别分析来确定人群中具有独特疾病模式的患者亚组。通过贝叶斯信息准则和临床可解释性来评估模型。我们比较了 2013 年的医疗保健利用情况和各分类人群的 1 年全因死亡率，并进行回归分析，以评估类别成员对医疗保健利用情况和死亡率的预测能力。
我们共纳入了 100747 名患者。最佳模型（k=6）揭示了以下六类患者：第 1 类“相对健康”（n=58213）、第 2 类“稳定代谢性疾病”（n=26309）、第 3 类“代谢性疾病伴血管并发症”（n=2964）、第 4 类“高呼吸疾病负担”（n=1104）、第 5 类“高代谢性疾病无并发症”（n=11122）和第 6 类“代谢性疾病伴多器官并发症”（n=1035）。这六个衍生类别在 2012 年具有不同的疾病模式，2013 年的 1 年随访医疗保健利用情况和死亡率也不同。“多器官并发症的代谢性疾病”类别具有最高的医疗保健利用率（例如，住院率比=19.68）和最高的 1 年全因死亡率（风险比=27.97）。
初级保健利用者具有异质性，可以通过潜在类别分析分为具有独特疾病模式、医疗保健利用情况和全因死亡率的类别。这些信息对于人口层面的卫生资源规划和人口健康政策制定至关重要。
