TY - JOUR
T1 - ORIGINAL ARTICLE
T2 - Associations of antidepressant medication with its various predictors including particulate matter: Machine learning analysis using national health insurance data
AU - Lee, Kwang Sig
AU - Kim, Geunyeong
AU - Ham, Byung Joo
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( NRF-2020M3E5D9080792 ). The funder had no role in the design of the study, the collection, analysis and interpretation of the data and the writing of the manuscript.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/3
Y1 - 2022/3
N2 - This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15–79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002–2012. The dependent variable was antidepressant-free months during 2013–2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013–2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM.2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM.2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.
AB - This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15–79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002–2012. The dependent variable was antidepressant-free months during 2013–2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013–2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM.2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM.2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.
KW - Antidepressant
KW - Neighborhood condition
KW - Particulate matter
KW - Socioeconomic satisfaction
UR - http://www.scopus.com/inward/record.url?scp=85122528107&partnerID=8YFLogxK
U2 - 10.1016/j.jpsychires.2022.01.011
DO - 10.1016/j.jpsychires.2022.01.011
M3 - Article
C2 - 35026595
AN - SCOPUS:85122528107
SN - 0022-3956
VL - 147
SP - 67
EP - 78
JO - Journal of Psychiatric Research
JF - Journal of Psychiatric Research
ER -
