Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia; School of Behavioural and Health Sciences, Australian Catholic University, Australia; Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Australia.
Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
Prev Med. 2020 Dec;141:106260. doi: 10.1016/j.ypmed.2020.106260. Epub 2020 Oct 2.
Although socioeconomic, behavioural, psychological, and biological factors have been individually linked to multimorbidity, data on the importance of these factors are limited. Our study aimed to determine the leading predictors for multimorbidity of chronic conditions in middle-aged Australian adults using machine learning methods. We included 53,867 participants aged 45-64 years from the 45 and Up Study who were free of eleven predefined chronic conditions at baseline (2006-2009) in the analysis. Incident multimorbidity was defined by the co-existence of ≥2, ≥3, or ≥ 4 conditions during follow-up until December 31, 2016. The five leading predictors for multimorbidity in men were age (7.2-20.5% of total variance), body mass index (6.5-15.4%), smoking (4.0-8.3%), chicken intake (3.6-7.5%), and red meat intake (4.6-6.3%) across the three definitions. Leading predictors varied across the three definitions in women, but the four common ones were body mass index (6.3-20.1%), age (6.2-16.4%), chicken intake (4.1-8.3%), and red meat intake (4.2-4.7%). The ten leading modifiable health factors accounted for 39.4-46.1% of total variance across the three definitions. Men with 6-10 health factors had 46-54% lower risks for multimorbidity compared with those reporting ≤2. The corresponding percentage for women was 45-52%. Non-behavioural factors including psychological distress, low education and income and high relative economic disadvantage were among the leading risk factors for multimorbidity. In conclusion, modifications on behavioural factors including diets, physical activity, smoking, alcohol consumption may reduce the risk of multimorbidity in middle-aged adults, whereas individuals with low socioeconomic status or psychological distress are at the highest priority for intervention.
尽管社会经济、行为、心理和生物学因素已分别与多种疾病相关,但这些因素的重要性数据有限。我们的研究旨在使用机器学习方法确定中年澳大利亚成年人慢性疾病多种疾病的主要预测因素。我们在分析中纳入了来自 45 岁及以上研究的 53867 名年龄在 45-64 岁之间的参与者,这些参与者在基线(2006-2009 年)时没有 11 种预先定义的慢性疾病。在随访期间(截至 2016 年 12 月 31 日)共存≥2、≥3 或≥4 种疾病定义为多种疾病。男性多种疾病的五个主要预测因素是年龄(7.2-20.5%的总方差)、体重指数(6.5-15.4%)、吸烟(4.0-8.3%)、鸡肉摄入(3.6-7.5%)和红肉摄入(4.6-6.3%),这三个定义都是如此。在女性中,这三个定义的主要预测因素各不相同,但有四个共同的因素是体重指数(6.3-20.1%)、年龄(6.2-16.4%)、鸡肉摄入(4.1-8.3%)和红肉摄入(4.2-4.7%)。这十个主要的可改变健康因素占三个定义总方差的 39.4-46.1%。与报告≤2 种健康因素的男性相比,有 6-10 种健康因素的男性多种疾病的风险降低了 46-54%。女性的相应百分比为 45-52%。非行为因素,包括心理困扰、低教育和收入以及相对较高的经济劣势,是多种疾病的主要危险因素之一。总之,包括饮食、身体活动、吸烟、饮酒在内的行为因素的改变可能会降低中年成年人多种疾病的风险,而社会经济地位低或心理困扰的个体则是干预的重中之重。