Lee Kwang-Sig, Kim Hae-In, Ham Byung-Joo
AI Center, Korea University College of Medicine, Seoul, Republic of Korea.
Industrial Management Engineering, Korea University School of Industrial Management Engineering, Seoul, Republic of Korea.
Psychiatry Investig. 2023 Jun;20(6):515-523. doi: 10.30773/pi.2022.0352. Epub 2023 May 30.
This study employs machine learning and population-based data to examine major factors of antidepressant medication including nitrogen dioxides (NO2) seasonality.
Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants with the age of 15-79 years, residence in the same districts of Seoul and no history of antidepressant medication during 2002-2012. The dependent variable was antidepressant-free months during 2013-2015 and the 103 independent variables for 2012 or 2015 were considered, e.g., particulate matter less than 2.5 micrometer in diameter (PM2.5), PM10, NO2, ozone (O3), sulphur dioxide (SO2) and carbon monoxide (CO) in each of 12 months in 2015.
It was found that the Cox hazard ratios of NO2 were statistically significant and registered values larger than 10 for every three months: March, June-July, October, and December. Based on random forest variable importance and Cox hazard ratios in brackets, indeed, the top 20 factors of antidepressant medication included age (0.0041 [1.69-2.25]), migraine and sleep disorder (0.0029 [1.82]), liver disease (0.0017 [1.33-1.34]), exercise (0.0014), thyroid disease (0.0013), cardiovascular disease (0.0013 [1.20]), asthma (0.0008 [1.19-1.20]), September NO2 (0.0008 [0.01]), alcohol consumption (0.0008 [1.31-1.32]), gender - woman (0.0007 [1.80-1.81]), July NO2 (0.0007 [14.93]), July PM10 (0.0007), the proportion of the married (0.0005), January PM2.5 (0.0004), September PM2.5 (0.0004), chronic obstructive pulmonary disease (0.0004), economic satisfaction (0.0004), January PM10 (0.0003), residents in welfare facilities per 1,000 (0.0003 [0.97]), and October NO2 (0.0003).
Antidepressant medication has strong associations with neighborhood conditions including NO2 seasonality and welfare support.
本研究采用机器学习和基于人群的数据来研究抗抑郁药物使用的主要因素，包括二氧化氮（NO₂）的季节性。
回顾性队列数据来自韩国国民健康保险服务的理赔数据，涉及43251名年龄在15 - 79岁之间、居住在首尔同一地区且在2002 - 2012年期间无抗抑郁药物使用史的参与者。因变量是2013 - 2015年期间未使用抗抑郁药物的月数，并考虑了2012年或2015年的103个自变量，例如2015年12个月中每个月直径小于2.5微米的颗粒物（PM2.5）、PM10、NO₂、臭氧（O₃）、二氧化硫（SO₂）和一氧化碳（CO）。
发现NO₂的Cox风险比具有统计学意义，且每三个月（3月、6 - 7月、10月和12月）的登记值大于10。基于随机森林变量重要性和括号内的Cox风险比，实际上，抗抑郁药物使用的前20个因素包括年龄（0.0041 [1.69 - 2.25]）、偏头痛和睡眠障碍（0.0029 [1.82]）、肝病（0.0017 [1.33 - 1.34]）、运动（0.0014）、甲状腺疾病（0.0013）、心血管疾病（0.0013 [1.20]）、哮喘（0.0008 [1.19 - 1.20]）、9月的NO₂（0.0008 [0.01]）、饮酒（0.0008 [1.31 - 1.32]）、性别 - 女性（0.0007 [1.80 - 1.81]）、7月的NO₂（0.0007 [14.93]）、7月的PM10（0.0007）、已婚比例（0.0005）、1月的PM2.5（0.0004）、9月的PM2.5（0.0004）、慢性阻塞性肺疾病（0.0004）、经济满意度（0.0004）、1月的PM10（0.0003）、每1000人中福利设施居民数（0.0003 [0.97]）和10月的NO₂（0.0003）。
抗抑郁药物的使用与包括NO₂季节性和福利支持在内的邻里环境密切相关。
