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KMID : 0387320170270030241
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Korean Journal of Health Policy and Administration
2017 Volume.27 No. 3 p.241 ~ p.246
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Induced Abortion Trends and Prevention Strategy Using Social Big-Data
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Park Myung-Bae
Chae Seong-Hyun
Lim Jin-Seop
Kim Chun-Bae
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Abstract
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Background: The purpose of this study is to investigate the trends on the induced abortion in Korea using social big-data and confirm whether there was time series trends and seasonal characteristics in induced abortion.
Methods: From October 1, 2007 to October 24, 2016, we used Naver`s data lab query, and the search word was `induced abortion` in Korean. The average trend of each year was analyzed and the seasonality was analyzed using the cosinor model.
Results: There was no significant changes in search volume of abortion during that period. Monthly search volume was the highest in May followed by the order of June and April. On the other hand, the lowest month was December followed by the order of January, and September. The cosinor analysis showed statistically significant seasonal variations (amplitude, 4.46; confidence interval, 1.46-7.47; p<0.0036). The search volume for induced abortion gradually increased to the lowest point at the end of November and was the highest at the end of May and declined again from June.
Conclusion: There has been no significant changes in induced abortion for the past nine years, and seasonal changes in induced abortion have been identified. Therefore, considering the seasonality of the intervention program for the prevention of induced abortion, it will be effective to concentrate on the induced abortion from March to May.
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KEYWORD
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Big-data, Induced abortion, Contraception, Seasonality, Naver
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