Abstract
This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25–40 years and gave births for the first time after a singleton pregnancy during 2015–2017. The dependent variable was preterm birth during 2015–2017 and 90 independent variables were included (demographic/socioeconomic information, particulate matter, disease information, medication history, obstetric information). Random forest variable importance was used to identify major determinants of preterm birth including depression and particulate matter. Based on random forest variable importance, the top 40 determinants of preterm birth during 2015–2017 included socioeconomic status, age, proton pump inhibitor, benzodiazepine, tricyclic antidepressant, sleeping pills, progesterone, gastroesophageal reflux disease (GERD) for the years 2002–2014, particulate matter for the months January–December 2014, region, myoma uteri, diabetes for the years 2013–2014 and depression for the years 2011–2014. In conclusion, preterm birth has strong associations with depression and particulate matter. What is really needed for effective prenatal care is strong intervention for particulate matters together with active counseling and medication for common depressive symptoms (neglected by pregnant women).
Highlights
Preterm birth is a major part of disease burden for newborns and children on the globe [1,2,3,4]
A recent review reports that the following maternal variables are important predictors of preterm birth: demographic/socioeconomic determinants, disease information, medication history and obstetric information [5]
The following 90 independent variables were included: (1) demographic/socioeconomic determinants in 2014 such as age, socioeconomic status measured by an insurance fee with the range of 1 to 20, and region; (2) particulate matter (PM10) for each of the months January–December 2014; (3) disease information for each of the years 2002–2014, i.e., depression, diabetes, gastroesophageal reflux disease (GERD), hypertension and periodontitis; (4) medication history in 2014, i.e., benzodiazepine, calcium channel blocker, nitrate, progesterone, proton pump inhibitor, sleeping pills and tricyclic antidepressant; (5) obstetric information in 2014 such as in vitro fertilization, myoma uteri and prior cone
Summary
Preterm birth is a major part of disease burden for newborns and children on the globe [1,2,3,4]. Two systematic reviews [8,9] and several population-based cohort studies [10,11,12,13,14,15] confirmed a positive association between air pollution and preterm birth. The number of predictors in the existing literature has been limited to 14 and no effort has been made based on machine learning in this direction In this context, this study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. This study includes a population-based cohort of 405,586 participants and the most comprehensive set of 90 predictors such as demographic/socioeconomic determinants, particulate matter, disease information, medication history and obstetric information
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