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Induced Abortion Trends and Prevention Strategy Using Social Big-Data

소셜 빅데이터를 이용한 낙태의 경향성과 정책적 예방전략

  • Park, Myung-Bae (Department of Gerontal Health and Welfare, Pai Chai University) ;
  • Chae, Seong Hyun (Department of Preventive Medicine, Yonsei University Wonju College of Medicine) ;
  • Lim, Jinseop (Department of Gerontal Health and Welfare, Pai Chai University) ;
  • Kim, Chun-Bae (Department of Preventive Medicine, Yonsei University Wonju College of Medicine)
  • 박명배 (배재대학교 실버보건학과) ;
  • 채성현 (연세대학교 원주의과대학 예방의학교실) ;
  • 임진섭 (배재대학교 실버보건학과) ;
  • 김춘배 (연세대학교 원주의과대학 예방의학교실)
  • Received : 2017.08.22
  • Accepted : 2017.09.11
  • Published : 2017.09.30

Abstract

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.

Keywords

References

  1. Wikidepia. Big data [Internet]. San Francisco (CA): Wikimedia Foundation [Cited 2017 Mar 1]. Available from: https://en.wikipedia.org/wiki/Big_data.
  2. McAfee A, Brynjolfsson E, Davenport TH. Big data: the management revolution. Harvard Bus Rev 2012;90(10):60-68.
  3. Gantz J, Reinsel D. The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. Framingham (MA): International Data Corporation; 2013.
  4. Hoover R, Sheth P, Burde A. Determining the accuracy of open-access databases for identifying commonly prescribed oral medications. J Am Pharm Assoc (2003) 2016;56(1):37-40. DOI: https://doi.org/10.1016/j.japh.2015.11.009.
  5. Laffer MS, Feldman SR. Improving medication adherence through technology: analyzing the managing meds video challenge. Skin Res Technol 2014;20(1):62-66. DOI: https://doi.org/10.1111/srt.12084.
  6. Yang HY. The methodology for using the bigdata. Seoul: Korea Institute of Science and Technology Evaluation and Planning; 2012.
  7. Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009;49(10):1557-1564. DOI: https://doi.org/10.1086/630200.
  8. Dugas AF, Hsieh YH, Levin SR, Pines JM, Mareiniss DP, Mohareb A, et al. Google flu trends: correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis 2012;54(4):463-469. DOI: https://doi.org/10.1093/cid/cir883.
  9. Kang M, Zhong H, He J, Rutherford S, Yang F. Using Google trends for influenza surveillance in South China. PLoS One 2013;8(1):e55205. DOI: https://doi.org/10.1371/journal.pone.0055205.
  10. Cho S, Sohn CH, Jo MW, Shin SY, Lee JH, Ryoo SM, et al. Correlation between national influenza surveillance data and google trends in South Korea. PLoS One 2013;8(12):e81422. DOI: https://doi.org/10.1371/journal.pone.0081422.
  11. Bhattacharya I, Ramachandran A, Bhattacharya J, Dogra NK. Google trends for formulating GIS mapping of disease outbreaks in India. Int J Geoinform 2013;9(3):9-19.
  12. Yang AC, Huang NE, Peng CK, Tsai SJ. Do seasons have an influence on the incidence of depression?: the use of an internet search engine query data as a proxy of human affect. PLoS One 2010;5(10):e13728. DOI: https://doi.org/10.1371/journal.pone.0013728.
  13. Ayers JW, Althouse BM, Allem JP, Rosenquist JN, Ford DE. Seasonality in seeking mental health information on Google. Am J Prev Med 2013; 44(5):520-525. DOI: https://doi.org/10.1016/j.amepre.2013.01.012.
  14. Lee BY, Lim JT, Yoo J. Utilization of social media analysis using big data. J Korea Contents Assoc 2013;13(2):211-219. DOI: https://doi.org/10.5392/JKCA.2013.13.02.211.
  15. Butler D. When Google got flu wrong. Nature 2013;494(7436):155-156. DOI: https://doi.org/10.1038/494155a.
  16. Lazer D, Kennedy R, King G, Vespignani A. Big data: the parable of Google flu: traps in big data analysis. Science 2014;343(6176):1203-1205. DOI: https://doi.org/10.1126/science.1248506.
  17. Song TM, Song J, An JY, Jin D. Multivariate analysis of factors for search on suicide using social big data. Korean J Health Educ Promot 2013; 30(3):59-73. DOI: https://doi.org/10.14367/kjhep.2013.30.3.059.
  18. Song TM, Song J, Cheon MK. Predicting tobacco risk factors by using social big data. J Korean Data Inf Sci Soc 2015;26(5):1047-1059. DOI: https://doi.org/10.7465/jkdi.2015.26.5.1047.
  19. Korea Institute for Health and Social Affairs. Prediction and implication development of at-risk youth based on bigdata. Sejong: Korea Institute for Health and Social Affairs; 2015.
  20. National Institute of Health. Abortion [Internet]. Bethesda (MD): National Institute of Health [Cited 2016 Nov 1]. Available from: https://medlineplus.gov/abortion.html.
  21. World Health Organization. Safe abortion: technical and policy guidance for health systems. Geneva: World Health Organization; 2012.
  22. Korea University, Ministry of Health and Welfare. The induced abortion survey and police development. Seoul: Korea University; 2005.
  23. Internet Trend. Search engine [Internet]. [place unknown]: Internet Trend [Cited 2016 Nov 1]. Available from: http://internettrend.co.kr/trendForward.tsp.
  24. Tanaka T, Natsume T, Shibata H, Nozawa K, Kojima S, Tsuchiya M, et al. Circadian rhythm of blood pressure in primary aldosteronism and renovascular hypertension: analysis by the cosinor method. Jpn Circ J 1983;47(7):788-794. DOI: https://doi.org/10.1253/jcj.47.788.
  25. Massin MM, Maeyns K, Withofs N, Ravet F, Gerard P. Circadian rhythm of heart rate and heart rate variability. Arch Dis Child 2000;83(2):179-182. DOI: https://doi.org/10.1136/adc.83.2.179.
  26. Portela A, Northrup G, Halberg F, Cornelissen G, Wendt H, Melby JC, et al. Changes in human blood pressure with season, age and solar cycles: a 26 year record. Int J Biometeorol 1996;39(4):176-181. DOI: https://doi.org/10.1007/bf01221388.
  27. Oberg AL, Ferguson JA, McIntyre LM, Horner RD. Incidence of stroke and season of the year: evidence of an association. Am J Epidemiol 2000;152(6):558-564. DOI: https://doi.org/10.1093/aje/152.6.558.
  28. Degerud E, Hoff R, Nygard O, Strand E, Nilsen DW, Nordrehaug JE, et al. Cosinor modelling of seasonal variation in 25-hydroxyvitamin D concentrations in cardiovascular patients in Norway. Eur J Clin Nutr 2016;70(4):517-522. DOI: https://doi.org/10.1038/ejcn.2015.200.
  29. Cornelissen G. Cosinor-based rhythmometry. Theor Biol Med Model 2014;11:16. DOI: https://doi.org/10.1186/1742-4682-11-16.
  30. Yonsei University, Ministry of Health and Welfare. The induced abortion survey and police development. Seoul: Yonsei University; 2011.
  31. Ministry of Health and Welfare. Falling induced abortion rate!: decreased 28% in the past three years. Sejong: Ministry of Health and Welfare; 2012.
  32. Park HM. A study on status and policy of induced abortion. Seoul: Korean Institute of Criminology; 2011.
  33. Lee BH. The issue of induced abortion. Suwon: Gyeonggi Research Institute; 2015.
  34. Jones RK, Kost K. Underreporting of induced and spontaneous abortion in the United States: an analysis of the 2002 National Survey of Family Growth. Stud Fam Plann 2007;38(3):187-197. DOI: https://doi.org/10.1111/j.1728-4465.2007.00130.x.
  35. Korean Statistical Information Service. Changes in the number and rate of population dynamics [Internet]. Daejeon: Statistics Korea; 2017 [cited 2017 Aug 7]. Available from: http://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1B8000F&vw_cd=MT_OTITLE&list_id=MT_CTITLE_C_CF&scrId=&seqNo=&lang_mode=ko&obj_var_id=&itm_id=&conn_path=E1#.
  36. Yadava KN, Dube D, Marwah SM. A study of seasonal trends in delivery and medical termination of pregnancy. J Obstet Gynaecol India 1979;29(2):256-257.
  37. Petersen DJ, Alexander GR. Seasonal variation in adolescent conceptions, induced abortions, and late initiation of prenatal care. Public Health Rep 1992;107(6):701-706.
  38. Wellings K, Macdowall W, Catchpole M, Goodrich J. Seasonal variations in sexual activity and their implications for sexual health promotion. J R Soc Med 1999;92(2):60-64. DOI: https://doi.org/10.1177/014107689909200204.
  39. Grimes DA, Benson J, Singh S, Romero M, Ganatra B, Okonofua FE, et al. Unsafe abortion: the preventable pandemic. Lancet 2006;368(9550):1908-1919. DOI: https://doi.org/10.1016/S0140-6736(06)69481-6.
  40. Korea Institute for Health and Social Affairs. Policy issues on abortion in Korea. Sejong: Korea Institute for Health and Social Affairs; 2010.
  41. Korea Institute for Health and Social Affairs. The 2015 national survey on fertility and family health and welfare. Sejong: Korea Institute for Health and Social Affairs; 2015.
  42. Lee MJ. Reviewing socioeconomic reasons for abortion in Korea. Seoul: Korean Women's Development Institute; 2010.
  43. Hwang SW, Chung CW. Contraception behaviors in unmarried men and women: a descriptive qualitative approach. Perspect Nurs Sci 2012;9(2):71-82.
  44. Calabretto H. Emergency contraception: knowledge and attitudes in a group of Australian university students. Aust N Z J Public Health 2009; 33(3):234-239. DOI: https://doi.org/10.1111/j.1753-6405.2009.00381.x.
  45. Choi SY, Kim YH, Oh HS. A study on sexual behavior, pregnancy and contraception knowledge in female adolescent. Korean J Women Health Nurs 2004;10(1):42-50. https://doi.org/10.4069/kjwhn.2004.10.1.42
  46. Choi JH, Kim KE, Shin MA. Contraceptive knowledge, contraceptive attitude, and contraceptive use among college students: function of gender, age, and residence. Korean J Hum Ecol 2010;19(3):511-522. DOI: https://doi.org/10.5934/KJHE.2010.19.3.511.
  47. Lee GR, Kim JA. Influencing factors on knowledge of contraception. J Converg Cult Technol 2015;1(4):19-26. DOI: https://doi.org/10.17703/JCCT.2015.1.4.19.
  48. Korea Centers for Disease Control and Prevention. The Korea youth risk behavior web-based survey report. Cheongju: Korea Centers for Disease Control and Prevention; 2015.
  49. Yoo SM. A study on unwed teenage mothers [master's thesis]. Seoul: Dongguk University; 2001.