Relationships of Antidepressant Medication With Its Various Factors Including Nitrogen Dioxides Seasonality: Machine Learning Analysis Using National Health Insurance Data

ABSTRACT

Objective@#This study employs machine learning and population-based data to examine major factors of antidepressant medication including nitrogen dioxides (NO2) seasonality. @*Methods@#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. @*Results@#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). @*Conclusion@#Antidepressant medication has strong associations with neighborhood conditions including NO2 seasonality and welfare support.