在特罗姆索研究中探索社会经济指标与非传染性疾病之间的关联的变化:一种算法方法
Sigbjørn Svalestuen1,2, Emre Sari2, Petja Lyn Langholz3
1Department of Social Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
Scandinavian journal of public health
|June 11, 2024
概括
这项研究使用了算法方法,根据挪威的社会经济因素来预测非传染性疾病. 教育,收入和职业在很大程度上预测了疾病风险,突出了健康不平等.
科学领域:
- 健康研究方法的方法论.
- 流行病学 流行病学
- 计算式健康科学 计算式健康科学
背景情况:
- 健康不平等仍然存在,社会经济地位 (SES) 是非传染性疾病 (NCD) 患病率的关键决定因素.
- 评估健康不平等的传统方法可能无法完全捕捉到SES和疾病风险之间的复杂相互作用.
研究的目的:
- 应用算法方法来评估社会经济变量的NCD预测能力.
- 通过机器学习为评估健康不平等提供方法论见解.
主要方法:
- 利用了来自特罗姆索研究 (2015-2016) 的数据,其中有21,083名参与者 (年龄40岁以上).
- 使用随机森林算法预测四种NCD结果 (心脏病发作,癌症,糖尿病,中风),使用SES指标和健康行为.
- 使用分类错误,平均精度下降和部分依赖统计数据来评估模型性能.
主要成果:
- 包括教育,家庭收入和职业在内的社会经济因素表现出不同程度的NCD预测能力.
- 预测错误分类率在25.1%至35.4%之间,这表明社会经济群体内的差异很大.
- 部分依赖性分析揭示了预期的健康梯度,以及SES和NCD之间的复杂功能关系,并成功地进行了样本外验证.
结论:
- 算法建模为评估病率中的异质健康不平等提供了有价值的指标.
- 教育,收入和职业的预测性贡献因特定的NCD和SES指标而异.
- 错误分类率的观察到的变化表明,通过在未来的研究中进一步探索子群异质性,可以提高预测准确性的潜力.
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