Shang Xianwen, Wang Wei, Keel Stuart, Wu Jinrong, He Mingguang, Zhang Lei
Department of Surgery, Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia.
School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia.
Front Public Health. 2019 Nov 8;7:320. doi: 10.3389/fpubh.2019.00320. eCollection 2019.
Identifying leading determinants for disease-free status may provide evidence for action priorities, which is imperative for public health with an expanding aged population worldwide. This study aimed to identify leading determinants, especially modifiable factors for disease-free status using machine learning methods. We included 52,036 participants aged 45-64 years from the 45 and Up Study who were free of 13 predefined chronic conditions at baseline (2006-2009). Disease-free status was defined as participants aging from 45-64 years at baseline to 55-75 years at the end of the follow-up (December 31, 2016) without developing any of the 13 chronic conditions. We used machine learning methods to evaluate the importance of 40 potential predictors and analyzed the association between the number of leading modifiable healthy factors and disease-free status. Disease-free status was found in about half of both men and women during a mean 9-year follow-up. The five most common leading predictors were body mass index (6.4-9.5% of total variance), self-rated health (5.2-8.2%), self-rated quality of life (4.1-6.8%), red meat intake (4.5-6.5%), and chicken intake (4.5-5.9%) in both genders. Modifiable behavioral factors including body mass index, diets, smoking, alcohol consumption, and physical activity, contributed to 37.2-40.3% of total variance. Participants having six or more modifiable health factors were 1.63-8.76 times more likely to remain disease-free status and had 0.60-2.49 more disease-free years (out of 9-year follow-up) than those having two or fewer. Non-behavioral factors including low levels of education and income and high relative socioeconomic disadvantage, were leading risk factors for disease-free status. Body mass index, diets, smoking, alcohol consumption, and physical activity are key factors for disease-free status promotion. Individuals with low socioeconomic status are more in need of care.
确定无病状态的主要决定因素可为行动重点提供依据，这对全球老年人口不断增加的公共卫生而言至关重要。本研究旨在使用机器学习方法确定主要决定因素，尤其是无病状态的可改变因素。我们纳入了来自“45岁及以上研究”的52036名年龄在45 - 64岁之间的参与者，他们在基线期（2006 - 2009年）没有13种预先定义的慢性病。无病状态定义为参与者从基线期的45 - 64岁到随访结束（2016年12月31日）的55 - 75岁期间未患上这13种慢性病中的任何一种。我们使用机器学习方法评估40个潜在预测因素的重要性，并分析主要可改变健康因素的数量与无病状态之间的关联。在平均9年的随访期间，约一半的男性和女性处于无病状态。两性中五个最常见的主要预测因素是体重指数（占总方差的6.4 - 9.5%）、自我评估健康状况（5.2 - 8.2%）、自我评估生活质量（4.1 - 6.8%）、红肉摄入量（4.5 - 6.5%）和鸡肉摄入量（4.5 - 5.9%）。包括体重指数、饮食、吸烟、饮酒和体育活动在内的可改变行为因素占总方差的37.2 - 40.3%。拥有六种或更多可改变健康因素的参与者保持无病状态的可能性是拥有两种或更少可改变健康因素参与者的1.63 - 8.76倍，并且在9年随访期间无病年限多0.60 -
2.49年。包括低教育水平、低收入和高相对社会经济劣势在内的非行为因素是无病状态的主要风险因素。体重指数、饮食、吸烟、饮酒和体育活动是促进无病状态的关键因素。社会经济地位低的个体更需要护理。
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