邀请评论:激励更好的方法和更好的数据收集来衡量滥用药物的流行程度
Mathew V Kiang1, Monica J Alexander2,3
1Department of Epidemiology and Population Health, School of Medicine, Stanford University, Palo Alto, CA 94304, United States.
American journal of epidemiology
|June 27, 2024
概括
一项新的研究使用先进的建模估计了纽约各县的阿片类药物滥用率. 这种方法揭示了毒品使用的关键地理差异,有助于公共卫生资源分配和干预策略.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 美国面临着持续的药物过量危机,自2021年以来,每年有超过10万例死亡.
- 目前用于估计药物使用率的方法缺乏有效的资源分配和干预评估所需的时空分辨率.
- 现有的方法,如调查和捕获-重新捕获方法,具有显著的局限性.
研究的目的:
- 估计从2007年到2018年纽约州的阿片类药物滥用的县级流行率.
- 为了确定阿片类药物滥用患病率的显著局内变化.
- 为高分辨率的公共卫生洞察提供一种新的建模方法.
主要方法:
- 采用了一种新的联合贝叶斯空间时空建模方法.
- 整合了五个不同的数据来源,包括死亡,急诊室访问和治疗访问.
- 同时建模了多个与阿片类药物相关的结果,以捕捉全面的趋势.
主要成果:
- 成功估计了纽约州 (2007-2018) 的县级阿片类药物滥用患病率.
- 在该州内发现了阿片类药物滥用患病率的显著地理差异.
- 在高空间时空分辨率下对阿片类药物滥用和相关结果产生了与政策相关的见解.
结论:
- 这项研究证明了先进的关节贝叶斯空间时空建模对于理解药物使用的有用性.
- 这些发现强调了需要加强,标准化和可访问的数据,用于公共卫生监测.
- 这种复杂的建模方法可以通过揭示局部阿片类药物滥用模式来告知公共卫生政策和干预措施.
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