DOI QR코드

DOI QR Code

Propensity score matching analysis on inpatient period differences of hemorrhagic stroke survivors depending on medical insurance coverage

  • Kim, Sang-Mi (Department of Business Administration, Graduate School of Business, Ewha Womans University) ;
  • Kim, Young (Wellness Coaching Service Research Center, Soonchunhyang University) ;
  • Lee, Seong-A (Department of Occupational Therapy, College of Medical Science, Soonchunhyang University)
  • 투고 : 2019.03.19
  • 심사 : 2019.05.27
  • 발행 : 2019.06.30

초록

Objective: The purpose of this study was to compare the differences in the length of hospital stay between hemorrhage stroke survivors with health insurance and those with medical care after controlling all factors except for the type of medical insurance by using the propensity score matching (PSM) method. Design: Retrospective cohort study. Methods: Data from the Korean National Centers for Disease Control and Prevention's In-Depth Discharge Injury Survey between the years 2006 and 2012 were used for analysis. A total of 4,538 cases were defined as persons with hemorrhagic stroke (I60-I62) based on the block of categories in the International Classification of Diseases (10th). In order to analyze the inpatient period differences depending on the type of health care, which reflects one's socio-economic level, the chi-square and t-test was conducted. Results: Frequency and percentage were presented, and regression analysis was used to determine the factors affecting the inpatient period. Age, severity of disease, treatment outcome, and post-discharge status were no longer statistically significant after matching. The inpatient period of the persons receiving medical aid benefits was found to be significantly longer than those with national health insurance (p<0.05). Conclusions: The factors influencing the inpatient period of hemorrhagic stroke survivors were treatment outcomes, severity of disease, hospital admission process, and the type of health care. It is necessary for systematic and comprehensive governmental management for persons with hemorrhagic stroke to be transferred to long-term care facilities.

키워드

참고문헌

  1. OECD. Health at a Glance 2015: OECD indicators. Paris: OECD Publishing; 2015.
  2. Ministry of Health and Welfare. Survey of the elderly [Internet]. Sejong: Ministry of Health and Welfare, 2018. [cited 2019 Mar 18] Available from: http://meta.narastat.kr/metasvc/index.do? confmNo=117071.
  3. Lee SG, Jeon SY. The relations of socioeconomic status to health status, health behaviors in the elderly. J Prev Med Public Health 2005;38:154-62.
  4. Statistics Korea. The prevalence rate (diagnosis criteria) and current treatment rate by sex of the elderly [Internet]. Daejeon: Statistics Korea, 2018. [cited 2019 Mar 18] Available from: http://kosis.kr/statHtml/statHtml.do?orgId=117&tblId=DT_117071_018&vw_cd=MT_ZTITLE&list_id=117_11771_003_117_11771_003_06.
  5. Russo CA, Andrews RM. Hospital stays for stroke and other cerebrovascular diseases, 2005: statistical brief #51. Healthcare Cost and Utilization Project (HCUP) statistical briefs. Rockville (MD): Agency for Healthcare Research and Quality (US); 2006.
  6. Kim SM, Hwang SW, Oh EH, Kang JK. Determinants of the length of stay in stroke patients. Osong Public Health Res Perspect 2013;4:329-41. https://doi.org/10.1016/j.phrp.2013.10.008
  7. Lim JH, Cheon SH. Analysis of variation in length of stay (LOS) after ischemic and hemorrhagic stroke using the Charlson Comorbidity Index (CCI). J Phys Ther Sci 2015;27:799-803. https://doi.org/10.1589/jpts.27.799
  8. Kang SH, Kim WJ, Seok HS. The variation of factors of severity-adjusted length of stay (LOS) in acute stroke patients. J Digit Policy Manag 2013;11:221-33.
  9. Lim SJ, Kim HJ, Nam CM, Chang HS, Jang YH, Kim S, et al. Socioeconomic costs of stroke in Korea: estimated from the Korea national health insurance claims database. J Prev Med Public Health 2009;42:251-60. https://doi.org/10.3961/jpmph.2009.42.4.251
  10. Kim YH, Moon JW, Kim KH. The determinant factors and medical charges pattern by length of stay in hospital. Korean J Hosp Manag 2010;15:15-26.
  11. Freitas A, Silva-Costa T, Lopes F, Garcia-Lema I, Teixeira-Pinto A, Brazdil P, et al. Factors influencing hospital high length of stay outliers. BMC Health Serv Res 2012;12:265. https://doi.org/10.1186/1472-6963-12-265
  12. Youn KI. Comparisons of health care utilization patterns and outcome for national health insurance and medical aid program cancer patients. J Korea Soc Health Inform Stat 2014;39:42-59.
  13. Joo JM, Kwon SM. Difference in outpatient medical expenditure and physician practice patterns between medicaid and health insurance patients. Health Policy Manag 2009;19:125-41. https://doi.org/10.4332/KJHPA.2009.19.3.125
  14. Kwon YD, Jang HJ, Choi YJ, Yoon SS. Nationwide trends in stroke hospitalization over the past decade. J Korean Med Assoc 2012;55:1014-25. https://doi.org/10.5124/jkma.2012.55.10.1014
  15. Evers S, Voss G, Nieman F, Ament A, Groot T, Lodder J, et al. Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups. Health Policy 2002;61:21-42. https://doi.org/10.1016/S0168-8510(01)00219-6
  16. Seo HJ, Yoon SJ, Lee SI, Lee KS, Yun YH, Kim EJ, et al. A comparison of the Charlson comorbidity index derived from medical records and claims data from patients undergoing lung cancer surgery in Korea: a population-based investigation. BMC Health Serv Res 2010;10:236. https://doi.org/10.1186/1472-6963-10-236
  17. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 2011;173:676-82. https://doi.org/10.1093/aje/kwq433
  18. Goldstein LB, Samsa GP, Matchar DB, Horner RD. Charlson Index comorbidity adjustment for ischemic stroke outcome studies. Stroke 2004;35:1941-5. https://doi.org/10.1161/01.STR.0000135225.80898.1c
  19. Suh HS, Kang HY, Kim J, Shin E. Effect of health insurance type on health care utilization in patients with hypertension: a national health insurance database study in Korea. BMC Health Serv Res 2014;14:570. https://doi.org/10.1186/s12913-014-0570-9
  20. Diringer MN, Edwards DF, Mattson DT, Akins PT, Sheedy CW, Hsu CY, et al. Predictors of acute hospital costs for treatment of ischemic stroke in an academic center. Stroke 1999;30:724-8. https://doi.org/10.1161/01.STR.30.4.724
  21. Heuschmann PU, Kolominsky-Rabas PL, Misselwitz B, Hermanek P, Leffmann C, Janzen RW, et al. Predictors of in-hospital mortality and attributable risks of death after ischemic stroke: the German Stroke Registers Study Group. Arch Intern Med 2004;164:1761-8. https://doi.org/10.1001/archinte.164.16.1761
  22. Reeves MJ, Bushnell CD, Howard G, Gargano JW, Duncan PW, Lynch G, et al. Sex differences in stroke: epidemiology, clinical presentation, medical care, and outcomes. Lancet Neurol 2008;7:915-26. https://doi.org/10.1016/S1474-4422(08)70193-5
  23. Bae HJ, Kim HS, Lee KS. Utilization of health care resources and costs of stroke patients: patients' perspective. J Korean Neurol Assoc 2004;22:583-9.