Girwar Shelley-Ann M, Jabroer Robert, Fiocco Marta, Sutch Stephen P, Numans Mattijs E, Bruijnzeels Marc A
Department of Public Health and Primary Care, LUMC Campus the Hague Leiden University Medical Centre The Hague The Netherlands.
Jan van Es Instituut Ede The Netherlands.
Health Sci Rep. 2021 Jul 23;4(3):e329. doi: 10.1002/hsr2.329. eCollection 2021 Sep.
In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.
We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and for continuous outcomes.
The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).
Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.
在当前的医疗保健形势下,医疗服务负担不断加重,成本更高且利用率增加。结构化人群健康管理已被开发出来,作为一种平衡质量与成本增加的方法。这种方法识别具有可比健康风险的亚人群,为那些将受益最大的人群量身定制干预措施。在全球范围内,从电子健康登记处提取的常规医疗数据用于风险分层方法的情况正在增加。不同的风险分层工具在医疗保健连续统一体的不同层面上使用。在这项系统的文献综述中,我们旨在探索在初级医疗保健环境中使用了哪些工具,并评估它们的性能。
我们对在初级保健人群中应用风险分层工具并得出健康结果的研究进行了系统的文献综述。纳入了在英语期刊上发表的经济合作与发展组织国家的研究。利用搜索引擎使用关键词,例如“初级保健”、“风险分层”和“模型”。基于不同的指标比较风险分层工具:二分结果的曲线下面积(AUC)和C统计量,以及连续结果的 。
搜索得到4718篇文章。特定的选择标准,如初级保健人群、一般健康利用结果和常规收集的数据源,确定了61篇文章,报告了31种不同的模型。应用最频繁的三种模型是调整后的临床分组(ACG,n = 23)、查尔森合并症指数(CCI,n = 19)和分层疾病分类(HCC,n = 7)。大多数AUC和C统计值高于0.7,与CCI和HCC相比,ACG的得分略有提高(通常在0.6至0.7之间)。
基于统计性能,ACG的有效性最高,其次是CCI和HCC。ACG似乎也是最灵活的,它使用不同的国际编码系统,并测量更广泛的健康结果。