埃塞俄比亚阿姆哈拉地区产前护理出席率预测模型的开发,埃塞俄比亚
Bryan Wilder1,2, Clara Pons-Duran1, Frederick G B Goddard1
1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
JAMA network open
|May 31, 2023
概括
开发了预测模型,以识别埃塞俄比亚农村地区高风险错过产前护理预约的孕妇. 这些模型表现适度,但可以帮助针对性干预改善母婴健康结果.
科学领域:
- 孕产妇健康 孕产妇健康
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 产前护理 (ANC) 对于预防孕产妇和新生儿死亡率以及改善分娩结果至关重要.
- 识别高风险ANC缺席的妇女是具有挑战性的,特别是在资源较低的环境中.
- 需要预测模型来主动识别有风险的怀孕,以便针对性干预.
研究的目的:
- 开发和评估用于识别可能错过产前护理 (ANC) 访问的妇女的预测模型.
- 在埃塞俄比亚农村环境中,评估怀孕不同阶段的模型性能.
主要方法:
- 一项预后研究利用了来自埃塞俄比亚Birhan健康和人口监测系统怀孕和儿童队列的数据.
- 包括各种预测因素:社会人口统计学,经济,医疗,环境和与怀孕有关的因素.
- 使用调整后勤回归和集合决策树进行预测模型的开发,以曲线下的面积 (AUC) 进行评估.
主要成果:
- 这项研究包括2195名妇女,其中26.5%的妇女未能参加至少一次ANC访问.
- 在受孕时,13周和24周开发的模型显示了适度的预测性能 (AUC从0.61到0.72不等).
- 与规范化后勤回归相比,整体决策树模型的性能略高.
结论:
- 开发的预测模型提供了一个适度但潜在的有价值的工具,用于识别面临不良产前护理出勤风险的妇女.
- 这些模型可以指导有针对性的干预措施,以改善资源有限的环境中ANC的利用.
- 这项研究强调了开发可访问的工具来预测服务不足人群的健康行为潜力.
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