Browse > Article
http://dx.doi.org/10.4332/KJHPA.2015.25.1.3

Difference in Healthcare Utilization for Percutaneous Transluminal Coronary Angioplasty Inpatients by Insurance Types: Propensity Score Matching Analysis  

Seo, Eun-Won (Department of Health Administration, Yonsei University Graduate School)
Lee, Kwang-Soo (Department of Health Administration, Yonsei University College of Health Sciences)
Publication Information
Health Policy and Management / v.25, no.1, 2015 , pp. 3-10 More about this Journal
Abstract
Background: Previous studies showed differences in healthcare utilization among insurance types. This study aimed to analyze the difference in healthcare utilization for percutaneous transluminal coronary angioplasty inpatients by insurance types after controlling factors affecting healthcare utilization using propensity score matching (PSM). Methods: The 2011 national inpatient sample based on health insurance claims data was used for analysis. PSM was used to control factors influencing healthcare utilization except insurance types. Length of stay and total charges were used as healthcare utilization variables. Patients were divided into National Health Insurance (NHI) and Medical Aid (MA) patients. Factors representing inpatients (gender, age, admission sources, and Elixhauser comorbidity index) and hospitals (number of doctors, number of beds, and location of hospitals) were used as covariates in PSM. Results: Tertiary hospitals didn't show significant difference in length of stay and total charges after PSM between two insurance types. However, MA patients showed significantly longer length of stay than that of NHI patients after PSM in general hospitals. Multivariate regression analysis provided that admission sources, Elixhauser comorbidity index, insurance types, number of doctors, and location of hospitals (province) had significant influences on the length of stay in general hospitals. Conclusion: Study results provided evidences that healthcare utilization was differed by insurance types in general hospitals. Health policy makers will need to prepare interventions to influence the healthcare utilization differences between insurance types.
Keywords
Angioplasty, balloon, coronary; Healthcare utilization; Insurance type; Propensity score matching;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Song YJ. The South Korean health care system. Jpn Med Assoc J 2009; 52 (3):206-209.
2 Korea Institute for Health and Social Affairs. Improvement and development strategies of medical aid program in Korea. Seoul: Korea Institute for Health and Social Affairs; 2012.
3 Lee YJ. Differences of cancer patient's health care utilizations between medical aid program and national health insurance in the elderly. J Korea Contents Assoc 2013;11(5):270-279. DOI: http://dx.doi.org/10.5392/jkca.2011.11.5.270   DOI   ScienceOn
4 Lee DH, Park EC, Nam CM, Lee SG, Lee DH, Yu SH. Comparing difference of volume of psychiatric treatments between the patient with health insurance and those with medical assistance: for inpatients of Korean psychiatric hospitals. Korean J Prev Med 2003;36(1):33-38.
5 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. DOI: http://dx.doi.org/10.1186/s12913-014-0570-9   DOI   ScienceOn
6 Shin HY. Actual expenditure and efficiency method on medical expenses of medical aid. Health Welf Issue Focus 2012;134:1-8.
7 Lee SG, Jeon SY. The relations of socioeconomic status to health status, health behaviors in the elderly. J Prev Med Public Health 2005;38(2):154-162.
8 Kim SR. A study on the comparison of inpatients healthcare utilization between the Medicaid recipients and the insured [dissertation]. Seoul: Yonsei University; 2000.
9 Kim YR. A study on the association between types of public health insurance scheme and length of stay: based on data of medical records a public hospital [dissertation]. Seoul: Yonsei University; 2013.
10 Kinsella K. Urban and rural dimensions of global population aging: an overview. J Rural Health 2001l;17(4):314-322. DOI: http://dx.doi.org/ 10.1111/j.1748-0361.2001.tb00280.x   DOI   ScienceOn
11 Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70(1):41-55. DOI: http://dx.doi.org/10.2307/2335942   DOI   ScienceOn
12 Wilson LD. Rapid progression of coronary artery disease in the setting of chronic cocaine abuse. J Emerg Med 1998;16(4):631-634. DOI: http://dx.doi.org/10.1016/s0736-4679(98)00058-4   DOI   ScienceOn
13 Eleftherios T. Latest-generation drug eluting and bioabsorbable stents. Hosp Chron 2014;9(1):168-169.
14 Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1983; 79(387): 516-524. DOI: http://dx.doi.org/c   DOI   ScienceOn
15 Kim L, Kim JA, Kim S. A guide for the utilization of Health Insurance Review and Assessment Service national patient samples. Epidemiol Health 2014;36:e2014008. DOI: http://dx.doi.org/10.4178/epih/e2014008   DOI
16 Kim SJ, Park EC, Jang SI, Lee MF, Kim TH. An analysis of the impatient charge and length of stay for patients with joint diseases in Korea: specialty versus small general hospitals. Health Policy 2013;113:93-99. DOI: http://dx.doi.org/10.1016/j.healthpol.2013.09.013   DOI   ScienceOn
17 Kim WJ, Kim SS, Kim EJ, Kang SH. Severity-adjusted LOS model of AMI patients based on the Korean national hospital discharge in-depth injury survey data. J Korea Acad-Ind Coop Soc 2013;14(10);4910-4918. DOI: http://dx.doi.org/10.5762/kais.2013.14.10.4910   DOI   ScienceOn
18 Chang H, Kwon YD, Yoon SS. Impact of health insurance type on health care utilization in patients with acute cerebral infarction. J Korean Neurol Assoc 2011;29(1):9-15.
19 Korean Hospital Association. Medical expenses of health insurance. Korean Hospital Association; 2013.
20 Choi HS, Lim JH, Kim WJ, Kang SH. The effective management of length of stay for patients with acute myocardial infarction in the era of digital hospital. Soc Digit Policy Manag 2012;10(1):413-422.
21 Zhan C, Miller MR. Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization. JAMA 2003;290(14): 1868-1874. DOI: http://dx.doi.org/10.1001/jama.290.14.1868   DOI   ScienceOn
22 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   DOI   ScienceOn
23 Li B, Evans D, Faris P, Dean S, Quan H. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res 2008;8:12. DOI: http://dx.doi.org/10.1186/1472-6963-8-12   DOI   ScienceOn
24 Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997;127(8 Pt 2):757-763. DOI: http://dx.doi.org/10.7326/0003-4819-127-8_part_2-199710151-00064   DOI
25 Jang EJ, Ann JH, Jung SY, Hwang JS, Lee JY, Shim JI. Methods for the control of measured confounders in outcomes research. Seoul: National Evidence-based Healthcare Collaboration Agency; 2013.
26 Lee KO. A Study on nonresponse adjustment by using propensity scores. Surv Res 2009;10(1);169-186.
27 Park YH. The characteristics and utilization factors of tertiary hospital inpatients: evidence from Korea Health Panel (2008-2011). Korean J Health Serv Manag 2012;8(3):13-25. DOI: http://dx.doi.org/10.12811/kshsm.2014.8.3.013   DOI   ScienceOn
28 Hong JS. Distribution and operative status of hospital bed in Korea. Korean J Health Policy Admin 2012;6:181-186.
29 Park YH. Utilization patterns of national health insurance and medical aid inpatients in tertiary hospitals. Korean J Health Serv Manag 2012; 6(4):83-98. DOI: http://dx.doi.org/10.12811/kshsm.2012.6.4.083   DOI   ScienceOn