Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population
Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population
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DOI：
10.1186/s12875-020-01104-1
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发表时间：
2020-02-17
影响因子：
2.9
通讯作者：
Perez-Sust, P.
中科院分区：
文献类型：
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作者：
Monterde, D.;Vela, E.;Perez-Sust, P.
背景多病性与服务调试和临床决策高度相关。优化变量评估多病，以加强慢性护理管理是一个未满足的需求。为此，我们通过比较四种不同的多病指标的预测能力，探讨了多病对预测社区医疗资源使用的贡献。方法采用登记资料对2014年12月31日居住在加泰罗尼亚（ES）的所有居民（n = 6,102,595）进行人口健康研究。2015年的初级保健服务利用通过四个结果变量进行评估：A)频繁服务人员，B)家庭护理使用者，C)社会工作者使用者，D)综合药房。预测4个结果变量（A至D），并进行多病评估和不进行多病评估。我们比较了以下多发病指标对模型拟合的贡献：i) Charlson指数；ii)慢性病数量；iii)临床风险组（CRG）；iv)调整发病率组（GMA）。结果将多重发病作为协变量纳入模型后，模型的辨识度（AUC）增加，即：A)勤护（0.771 vs 0.853）， B)家庭护理使用者（0.862 vs 0.890）， C)社工使用者（0.809 vs 0.872）， D)多药（0.835 vs 0.912）。GMA对所有结果的预测能力最高，但对多药治疗的预测能力略低于CRG。结论：我们证实，多病评估增强了对社区医疗资源使用情况的预测。基于GMA的加泰罗尼亚人口风险评估工具表现出预测能力和适用性的最佳组合。
Background Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures. Methods A population health study including all citizens >= 18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA). Results The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG. Conclusions We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability.