DOI QR코드

DOI QR Code

Factors Affecting the Outcome Indicators in Patients with Stroke

뇌졸중 환자의 결과지표에 영향을 주는 요인: 다변량 회귀분석과 다수준분석 비교

  • Kim, Sun Hee (Department of Medical Care and Hospital Administration, Hallym Polytechnic University) ;
  • Lee, Hae Jong (Department of Health Administration, Yonsei University College of Health Sciences)
  • 김선희 (한림성심대학교 의무행정과) ;
  • 이해종 (연세대학교 보건과학대학 보건행정학과)
  • Received : 2014.10.21
  • Accepted : 2015.04.02
  • Published : 2015.03.31

Abstract

Background: The purpose of this study is comparison of the results between regression and multi-level analysis to find out factors influencing outcome indicators (in-hospital death, length of stay, and medical charges) of stroke patients. Methods: By using patient sample data of Health Insurance Review & Assessment Service, patients admitted with stroke were selected as survey target and 15,864 patients and 762 hospitals were surveyed. Results: For the results of existing regression analysis and multi-level analysis, models were assessed through model suitability index value and as a result, the value of results of multi-level analysis decreased compared to the results of regression, showing it is a better model. Conclusion: Factors influencing in-hospital death of stroke patients were analyzed and as a result, intra-class correlation (ICC) was 13.6%. In factors influencing length of stay, ICC was 11.4%, and medical charges, ICC was 17.7%. It was found that factors influencing the outcome indicators of stroke patients may vary in every hospital. This study could carry out more accurate analysis than existing research findings through analysis of reflecting structure at patient level and hospital level factors and analysis on random effect.

Keywords

References

  1. Statistics Korea. 2011 Death cause statistics. Daejeon: Statistics Korea; 2012.
  2. Health Insurance Review & Assessment Service. Comprehensive quality report of national health insurance 2012. Seoul: Health Insurance Review & Assessment Service; 2013.
  3. Appelros P. Prediction of length of stay for stroke patients. Acta Neurol Scand 2007;116(1):15-19. DOI: http://dx.doi.org/10.1111/j.1600-0404. 2006.00756.x
  4. Kim KH, Choi BR, Park CS. A study on stroke assessment indicators expansion. Seoul: Health Insurance Review & Assessment Service; 2012.
  5. Birkmeyer JD, Siewers AE, Finlayson EV, Stukel TA, Lucas FL, Batista I, et al. Hospital volume and surgical mortality in the United States. N Engl J Med 2002;346(15):1128-1137. DOI: http://dx.doi.org/10.1056/nejmsa012337
  6. Saposnik G, Baibergenova A, O'Donnell M, Hill MD, Kapral MK, Hachinski V, et al. Hospital volume and stroke outcome: does it matter? Neurology 2007;69(11):1142-1151. DOI: http://dx.doi.org/10.1212/01.wnl.0000268485.93349.58
  7. Bardach NS, Zhao S, Gress DR, Lawton MT, Johnston SC. Association between subarachnoid hemorrhage outcomes and number of cases treated at California hospitals. Stroke 2002;33(7):1851-1856. DOI: http://dx.doi.org/10.1161/01.str.0000019126.43079.7b
  8. Svendsen ML, Ehlers LH, Ingeman A, Johnsen SP. Higher stroke unit volume associated with improved quality of early stroke care and reduced length of stay. Stroke 2012;43(11):3041-3045. DOI: http://dx.doi.org/10.1161/STROKEAHA.111.645184
  9. Lin HC, Xirasagar S, Chen CH, Lin CC, Lee HC. Association between physician volume and hospitalization costs for patients with stroke in Taiwan: a nationwide population-based study. Stroke 2007;38(5):1565-1569. DOI: http://dx.doi.org/10.1161/strokeaha.106.474841
  10. Ovbiagele B. Nationwide trends in in-hospital mortality among patients with stroke. Stroke 2010;41(8):1748-1754. DOI: http://dx.doi.org/10.1161/STROKEAHA.110.585455
  11. Reeves MJ, Gargano J, Maier KS, Broderick JP, Frankel M, LaBresh KA, et al. Patient-level and hospital-level determinants of the quality of acute stroke care: a multilevel modeling approach. Stroke 2010;41(12):2924-2931. DOI: http://dx.doi.org/10.1161/STROKEAHA.110.598664
  12. Reed SD, Blough DK, Meyer K, Jarvik JG. Inpatient costs, length of stay, and mortality for cerebrovascular events in community hospitals. Neurology 2001;57(2):305-314. DOI: http://dx.doi.org/10.1212/wnl.57.2.305
  13. Smith EE, Shobha N, Dai D, Olson DM, Reeves MJ, Saver JL, et al. Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program. Circulation 2010;122(15):1496-1504. DOI: http://dx.doi.org/10.1161/CIRCULATIONAHA.109.932822
  14. Lee KS, Lee SI. Does a higher coronary artery bypass graft surgery volume always have a low in-hospital mortality rate in Korea? J Prev Med Public Health 2006;39(1):13-20.
  15. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998;36(1):8-27. DOI: http://dx.doi.org/10.1097/00005650-199801000-00004
  16. Bottle A, Aylin P. Comorbidity scores for administrative data benefited from adaptation to local coding and diagnostic practices. J Clin Epidemiol 2011;64(12):1426-1433. DOI: http://dx.doi.org/10.1016/j.jclinepi.2011.04.004
  17. Lieffers JR, Baracos VE, Winget M, Fassbender K. A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer 2011;117(9):1957-1965. DOI: http://dx.doi.org/10.1002/cncr.25653
  18. Chu YT, Ng YY, Wu SC. Comparison of different comorbidity measures for use with administrative data in predicting short- and long-term mortality. BMC Health Serv Res 2010;10:140. DOI: http://dx.doi.org/10.1186/1472-6963-10-140
  19. Sharabiani MT, Aylin P, Bottle A. Systematic review of comorbidity indices for administrative data. Med Care 2012;50(12):1109-1118. DOI: http://dx.doi.org/10.1097/MLR.0b013e31825f64d0
  20. Son CK, Do SR, Jang YS, Choi JS, Chung YH, Kim NS, et al. In-depth analysis of patient survey in 2009. Seoul: Ministry of Health and Welfare; 2011.
  21. Min IS, Choi PS. Advanced panel data analysis. Seoul: Jiphil Media; 2012.
  22. Hox JJ. Multilevel analysis: techniques and applications. 2nd ed. New York: Routledge; 2010.
  23. Singer JD, Willett JB. Applied longitudinal data analysis. Oxford: Oxford University Press; 2003.
  24. Lee JY, Kang SJ, Bang HN, Lee MJ, Park KS, Eun KS, et al. Advanced quantitative methods in social science. Seoul: Seoul University Press; 2013.
  25. Kang SJ, Chun MJ, Chang JH. A comparative analysis of humanities and vocational high school students: 3 level multi-level analysis. Proceedings of the 1st KEEP Conference Dissertation; 2005.
  26. Heck RH, Thomas SL. An Introduction to multilevel modeling techniques. 2nd ed. New York: Routledge; 2009.