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利用行政数据的机器学习方法研究医疗复杂性儿童的住院和急诊就诊的临床和社会经济学预测因素。

Clinical and socioeconomic predictors of hospital use and emergency department visits among children with medical complexity: A machine learning approach using administrative data.

机构信息

School of Public Health, University of Alberta, Edmonton, Canada.

Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.

出版信息

PLoS One. 2024 Oct 29;19(10):e0312195. doi: 10.1371/journal.pone.0312195. eCollection 2024.

Abstract

OBJECTIVES

The primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary objective was to estimate marginal associations between important predictors and resource use.

METHODS

This retrospective, population-cohort study of children with medical complexity in Alberta linked administrative health data with Canadian census data and used tree-based, gradient-boosted regression models to identify clinical and socioeconomic predictors of resource use. Separate analyses of cumulative numbers of hospital days and ED visits modeled the probability of any resource use and, when present, the amount of resource use. We used relative importance in each analysis to identify important predictors.

RESULTS

The analytic sample included 11 105 children with medical complexity. The best short- and long-term predictors of having a hospital stay and number of hospital days were initial length of stay and clinical classification. Initial length of stay, residence rurality, and other socioeconomic factors were top predictors of short-term ED use. The top predictors of ED use in the long term were almost exclusively socioeconomic, with rurality a top predictor of number of ED visits. Estimates of marginal associations between initial length of stay and resource use showed that average number of hospital days increases as initial length of stay increases up to approximately 90 days. Children with medical complexity living in rural areas had more ED visits on average than those living in urban or metropolitan areas.

CONCLUSIONS

Clinical factors are generally better predictors of hospital use whereas socioeconomic factors are more predictive of ED use among children with medical complexity in Alberta. The results confirm existing literature on the importance of socioeconomic factors with respect to health care use by children with medical complexity.

摘要

目的

本研究的主要目的是确定在 2010 年至 2013 年期间,初次出院后 1 年和 5 年内患有医疗复杂性的儿童在医院和急诊部使用的临床和社会经济预测因素。次要目的是估计重要预测因素与资源使用之间的边缘关联。

方法

这项针对艾伯塔省患有医疗复杂性的儿童的回顾性、人群队列研究,将行政健康数据与加拿大人口普查数据相链接,并使用基于树的梯度提升回归模型来确定资源使用的临床和社会经济预测因素。分别对住院天数和急诊就诊的累计次数进行分析,以建模任何资源使用的概率,以及在存在资源使用的情况下,资源使用的数量。我们在每个分析中使用相对重要性来识别重要的预测因素。

结果

分析样本包括 11105 名患有医疗复杂性的儿童。住院和住院天数的最佳短期和长期预测因素是初始住院时间和临床分类。初始住院时间、居住农村地区和其他社会经济因素是短期急诊就诊的主要预测因素。长期急诊就诊的主要预测因素几乎完全是社会经济因素,农村地区是急诊就诊次数的主要预测因素。初始住院时间与资源使用之间的边缘关联估计表明,随着初始住院时间的增加,平均住院天数会增加,直到大约 90 天。居住在农村地区的患有医疗复杂性的儿童比居住在城市或大都市地区的儿童平均有更多的急诊就诊。

结论

临床因素通常是预测医院使用的更好预测因素,而社会经济因素是预测艾伯塔省患有医疗复杂性的儿童急诊就诊的更重要因素。研究结果证实了现有文献中关于社会经济因素对患有医疗复杂性的儿童医疗保健使用的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e13/11521260/dd7248ba6976/pone.0312195.g001.jpg

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