使用梯度提升建模构建的共变量依赖马尔科夫链可以有效地产生对肥胖趋势的长期预测
Alexander A Huang1,2, Samuel Y Huang3
1Cornell University, Ithaca, NY, USA. alexander.huang@northwestern.edu.
BMC research notes
|November 24, 2023
概括
美国的成年人面临着显著的体重增加风险,随着时间的推移,许多人逐渐超重或肥胖. 先进的建模准确地预测了长期的体重变化,有助于公共卫生战略.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 在美国成年人中,肥胖患病率从30.5% (1999) 升至41.9% (2020).
- 长期体重增加是一个关键的公共卫生问题.
- 模拟长期体重增加预测的研究是有限的.
研究的目的:
- 分析美国成年人的长期体重增加趋势.
- 为长期体重变化开发预测模型.
主要方法:
- 使用NHANES (2017-2020) 数据进行的回顾性,横截面的队列研究.
- 包括调查和10年前体重数据的参与者.
- 采用多态梯度提升建模和马尔科夫链进行预测.
主要成果:
- 分析了6146名参与者,61%的参与者在10年内增加了体重.
- 最初的重量状态对10年过渡有重大影响.
- 正常体重的个体显示有17%的过渡到超重和2%的肥胖;超重的个体有15%的过渡到肥胖;肥胖的个体在很大程度上保持肥胖 (84%).
结论:
- 美国成年人有很高的风险从正常体重过渡到超重或肥胖.
- 与共变量依赖的马尔科夫链的梯度增强建模有效预测长期重量轨迹.
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