使用预测风险模型来优先考虑家庭的预防服务:宾夕法尼亚州阿勒盖尼县的Hello Baby计划
Rhema Vaithianathan1, Diana Benavides-Prado2, Rebecca Rebbe3
1Centre for Social Data Analytics, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand. rhema.vaithianathan@aut.ac.nz.
概括
预测风险模型 (PRM) 可以识别高危儿童虐待的婴儿,改善有针对性的预防服务. 这种方法比传统的分层家庭的方法更有效,并将资源分配给最需要的人.
科学领域:
- 儿童福利 儿童福利
- 公共卫生 公共卫生
- 数据科学是数据科学.
背景情况:
- 基于人口的儿童虐待预防是具有挑战性的,因为发病率低.
- 通过传统的标准,很难确定干预的高风险家庭.
- 预测风险模型 (PRM) 提供了一种数据驱动的风险分层方法.
研究的目的:
- 开发和验证一个PRM,用于识别高风险的婴儿虐待相关的移除年龄为3岁.
- 根据预测的风险,将家庭分为优先级的服务级别.
- 将PRM的有效性与传统的资格标准进行比较.
主要方法:
- 利用了阿勒盖尼县2012-2015年出生儿童的行政数据.
- 使用最少绝对收缩和选择运算符 (LASSO) 开发了一个PRM.
- 使用儿童移除数据和其他不良结果 (例如死亡率) 验证了模型.
主要成果:
- 该PRM实现了曲线下的面积 (AUC) 为0.93.
- 在前5%的风险层中,虐待相关的死亡/接近死亡的相对风险比率为5.54.
- 在风险向方面,PRM显著优于传统方法,如贫困或青少年孕产妇年龄.
结论:
- PRM有效地识别高风险婴儿,用于针对性儿童福利干预.
- 这种数据驱动的方法提高了预防服务的效率和影响.
- 在儿童保护服务中,PRM提供了一种优越的资源分配方法.
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