如何将社会脆弱性纳入流行病数学建模:来自国际Delphi会议的建议
Megan Naidoo1, Whitney Shephard2, Nokuthula Mtshali3
1Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), Barcelona, Spain; Barcelona Institute for Global Health Campus Clínic, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
Social science & medicine (1982)
|July 25, 2025
概括
流行病的数学模型往往忽视了社会脆弱性 (SV). 这项研究提供了研究建议,将SV纳入流行病建模,提高风险社区的准确性和干预措施.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 公共卫生 公共卫生
背景情况:
- 流行病的数学建模对于疾病应对至关重要,但往往忽略了社会脆弱性 (SV).
- 综合性疾病,包括社会,经济和健康不平等,显著影响疾病动态和健康结果.
- 目前的流行病模型缺乏常规包括SV,尽管其至关重要.
研究的目的:
- 制定研究建议,将社会脆弱性纳入流行病数学建模中.
- 通过为 SV 集成提供框架来弥补当前建模实践中的差距.
- 通过考虑社会因素,提高流行病模型的准确性和相关性.
主要方法:
- 采用了德尔菲技术,调查了来自12个国家的22位跨学科专家.
- 使用了三轮在线调查,包括自由文本和利克尔特尺度问题.
- 通过描述性统计和诱导性定性分析,就研究建议达成共识.
主要成果:
- 在七个主题的27个建议上达成共识:协作,设计,数据选择,数据源,关系动态,报告和校准/敏感性.
- 专家们确定了92个社会脆弱性指标,其中获得卫生设施,医疗保健和家庭组成是最常被引用的.
- 该研究为SV集成提供了流程和技术建议.
结论:
- 将社会脆弱性纳入流行病模型提供了对疾病传播的更全面的理解.
- 这种方法突出了面临风险的社区,提高了预测准确性和干预效率.
- 这些建议支持制定更有弹性和公平的公共卫生战略.
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