DOI QR코드

DOI QR Code

Assessing Seasonality of Acute Febrile Respiratory Tract Infections and Medication Use

인플루엔자 등 급성 호흡기계 질환과 의약품 사용의 계절적 상관성 분석

  • Park, Juhee (Pharmaceutical and Medical Device Research Team, Department of Research, Health Insurance Review and Assessment Service) ;
  • Choi, Won Suk (Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine) ;
  • Lee, Hye-Yeong (Pharmaceutical and Medical Device Research Team, Department of Research, Health Insurance Review and Assessment Service) ;
  • Kim, Kyoung-Hoon (Pharmaceutical and Medical Device Research Team, Department of Research, Health Insurance Review and Assessment Service) ;
  • Kim, Dong-Sook (Pharmaceutical and Medical Device Research Team, Department of Research, Health Insurance Review and Assessment Service)
  • 박주희 (건강보험심사평가원 연구조정실 의약기술연구팀) ;
  • 최원석 (고려대학교 의과대학 내과학교실 감염내과) ;
  • 이혜영 (건강보험심사평가원 연구조정실 의약기술연구팀) ;
  • 김경훈 (건강보험심사평가원 연구조정실 의약기술연구팀) ;
  • 김동숙 (건강보험심사평가원 연구조정실 의약기술연구팀)
  • Received : 2018.07.31
  • Accepted : 2018.08.21
  • Published : 2018.12.31

Abstract

Background: Monitoring appropriate medication categories can provide early warning of certain disease outbreaks. This study aimed to present a methodology for selecting and monitoring medications relevant to the surveillance of acute respiratory tract infections, such as influenza. Methods: To estimate correlations between acute febrile respiratory tract infection and some medication categories, the cross-correlation coefficient (CCC) was used and established. Two databases were used: real-time prescription trend of antivirals, anti-inflammatory drugs, antibiotics using Drug Utilization Review Program between 2012 and 2015 and physicians' number of encounters with acute febrile respiratory tract infections such as influenza outbreaks using the national level health insurance claims data. The seasonality was also evaluated using the CCC. Results: After selecting six candidate diseases that require extensive monitoring, influenza with highly specific medical treatment according to the health insurance claims data and its medications were chosen as final candidates based on a data-driven approach. Antiviral medications and influenza were significantly correlated. Conclusion: An annual correlation was observed between influenza and antiviral medications, anti-inflammatory drugs. Suitable models should be established for syndromic surveillance of influenza.

Keywords

BGHJBH_2018_v28n4_402_f0001.png 이미지

Figure 1. The process of research.

BGHJBH_2018_v28n4_402_f0002.png 이미지

Figure 2. Seasonality of ILI and medication use. (A) Anti-inflammatory drugs. (B) Cough remedy. (C) Antibiotics. (D) Anti-viral medications. ILI, influenza-like illness.

Table 1. The infectious disease and treatment

BGHJBH_2018_v28n4_402_t0001.png 이미지

Table 2. The specifcity of treatment2)

BGHJBH_2018_v28n4_402_t0002.png 이미지

References

  1. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013;309(13):1351-1352. DOI: https://doi.org/10.1001/jama.2013.393.
  2. Carneiro HA, Mylonakis E. Google trends: a web-based tool for realtime surveillance of disease outbreaks. Clin Infect Dis 2009;49(10):1557-1564. DOI: https://doi.org/10.1086/630200.
  3. Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron A. More diseases tracked by using Google Trends. Emerg Infect Dis 2009;15(8):1327-1328. DOI: https://doi.org/10.3201/eid1508.090299.
  4. Rossignol L, Pelat C, Lambert B, Flahault A, Chartier-Kastler E, Hanslik T. A method to assess seasonality of urinary tract infections based on medication sales and google trends. PLoS One 2013;8(10):e76020. DOI: https://doi.org/10.1371/journal.pone.0076020.
  5. Chun BC. Epidemiology of avian influenza and pandemic influenza. Korean J Public Health 2007;44(1):27-40.
  6. Chun BC. Modelling the impact of pandemic influenza. J Prev Med Public Health 2005;38(4):379-385.
  7. Korea Centers for Disease Control and Prevention. 2015 MERS white paper: lessons learned from MERS. Cheongju: Korea Centers for Disease Control and Prevention; 2016.
  8. Health Chosun. 21th Zika virus patient in South Korea...preventive measures to avoid the risks of infection? Health Chosun. 2017 Jun 17.
  9. Korea Centers for Disease Control and Prevention. 508 People infected with hepatitis C, concealed from public reports for 2 months. Cheongju: Korea Centers for Disease Control and Prevention; 2016.
  10. Infectious Disease Control and Prevention Act, Law No. 14316 (Dec 2 2016).
  11. Rogerson PA. Surveillance systems for monitoring the development of spatial patterns. Stat Med 1997;16(18):2081-2093. DOI: https://doi.org/10.1002/(sici)1097-0258(19970930)16:18<2081:aid-sim638>3.0.co;2-w.
  12. Lee GJ, Hong IY. A study on the methodology of spatial-temporal monitoring to establish a system of beforehand surveillance on influenza. J Korean Off Stat [Internet] 2011 [cited 2018 Mar 5];16(2):22-37. Available from: http://kostat.go.kr/file_total/16-2-02.pdf.
  13. Kim DS, Park JH, Kim SJ, Choi WS. Development of the surveillance system against the signs of infectious diseases. Wonju: Health Insurance Review and Assessment Service; 2016.
  14. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses 2014;8(3):309-316. DOI: https://doi.org/10.1111/irv.12226.
  15. Cowling BJ, Wong IO, Ho LM, Riley S, Leung GM. Methods for monitoring influenza surveillance data. Int J Epidemiol 2006;35(5):1314-1321. DOI: https://doi.org/10.1093/ije/dyl162.
  16. Pelat C, Boelle PY, Turbelin C, Lambert B, Valleron AJ. A method for selecting and monitoring medication sales for surveillance of gastroenteritis. Pharmacoepidemiol Drug Saf 2010;19(10):1009-1018. DOI: https://doi.org/10.1002/pds.1965.
  17. Korea Centers for Disease Control and Prevention. 2016 Standards for the diagnosis/reporting of communicable diseases. Cheongju: Korea Centers for Disease Control and Prevention; 2016.
  18. Suda KJ, Hunkler RJ, Matusiak LM, Schumock GT. Influenza antiviral expenditures and outpatient prescriptions in the United States, 2003-2012. Pharmacotherapy 2015;35(11):991-997. DOI: https://doi.org/10.1002/phar.1656.
  19. Suh M, Lee J, Chi HJ, Kim YK, Kang DY, Hur NW, et al. Mathematical modeling of the novel influenza A (H1N1) virus and evaluation of the epidemic response strategies in the Republic of Korea. J Prev Med Public Health 2010;43(2):109-116. DOI: https://doi.org/10.3961/jpmph.2010.43.2.109.
  20. Kim E, Lee S, Byun YT, Lee HJ, Lee T. Implementation of integrated monitoring system for trace and path prediction of infectious disease. J Internet Comput Serv 2013;14(5):69-76. DOI: https://doi.org/10.7472/jksii.2013.14.5.69.