Effect of the Presence of Emergency Departments With 300 or More Hospital Beds in Health Service Areas on 30-Day Mortality in Korea: A Nationwide Retrospective Cross-sectional Study

Document Type : Original Article

Authors

1 Department of Emergency Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea

2 Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

3 Center for Public Healthcare, National Medical Center, Seoul, South Korea

4 Department of Public Health and Community Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea

5 Department of Health Policy and Management, Seoul National University College of Medicine, Seoul, South Korea

Abstract

Background 
Disparities in emergency care accessibility exist between health service areas (HSAs). There is limited evidence on whether the presence of an emergency department (ED) that exceeds a certain hospital bed capacity is associated with emergency patient outcomes at the regional level. The objective of this study was to evaluate the effect of HSAs with or without of regional or local emergency centers with 300 or more hospital beds (EC300 or nEC300, respectively) by comparing the 30-day mortality of patients with severe emergency diseases (SEDs) admitted to the hospital through the ED.
 
Methods 
The study retrospectively evaluated data from the National Health Information Database (NHID) of the National Health Insurance Service (NHIS) Claims database and enrolled patients who were admitted from the ED for SEDs. SEDs were defined using ICD-10 (International Classification of Diseases 10th Revision) codes for 28 disease categories with high severity, and 56 HSAs were designated as published by the NHIS. We performed hierarchical logistic regression analysis using multilevel models with the generalized linear mixed model (GLIMMIX) procedure to evaluate whether EC300 was associated with the 30-day mortality of SED patients, adjusting for patient-level, prehospital-level, hospital-level, and HSA-level variables.
 
Results 
In total, 662 478 patients were analyzed, of whom 54 839 (8.3%) died within 30 days after hospital discharge. Of the 56 HSAs, 46 (82.1%) were included in the EC300 group. After adjustment for patient-level, prehospital-level, hospital-level, and HSA-level variables, nEC300 was significantly associated with increased 30-day mortality in SED patients (adjusted odds ratio [AOR]: 1.33, 95% CI: 1.137-1.153). In addition, patients who visited EDs with fewer annual SED admissions were associated with higher 30-day mortality.
 
Conclusion 
nEC300 had a greater risk of 30-day mortality in patients treated with SEDs than EC300. The results indicate that not only the number of EDs in each HSA is important for ensuring adequate patient outcomes but also the presence of EDs with adequate receiving capacity.

Keywords

References

  1. Carr BG, Branas CC. Time, distance, and access to emergency care in the United States. LDI Issue Brief. 2009;14(4):1-4.
  2. Salhi RA, Edwards JM, Gaieski DF, Band RA, Abella BS, Carr BG. Access to care for patients with time-sensitive conditions in Pennsylvania. Ann Emerg Med. 2014;63(5):572-579. doi:1016/j.annemergmed.2013.11.018
  3. Pham H, Puckett Y, Dissanaike S. Faster on-scene times associated with decreased mortality in Helicopter Emergency Medical Services (HEMS) transported trauma patients. Trauma Surg Acute Care Open. 2017;2(1):e000122. doi:1136/tsaco-2017-000122
  4. Kang HJ, Kwon S. Regional disparity of cardiovascular mortality and its determinants. Health Policy and Management. 2016;26(1):12-23. doi:4332/kjhpa.2016.26.1.12
  5. Chang I, Kim BHS. Regional disparity of medical resources and its effect on age-standardized mortality rates in Korea. Ann Reg Sci. 2019;62(2):305-325. doi:1007/s00168-019-00897-z
  6. Carr BG, Branas CC, Metlay JP, Sullivan AF, Camargo CA Jr. Access to emergency care in the United States. Ann Emerg Med. 2009;54(2):261-269. doi:1016/j.annemergmed.2008.11.016
  7. Carr BG, Addyson DK. Geographic information systems and emergency care planning. Acad Emerg Med. 2010;17(12):1274-1278. doi:1111/j.1553-2712.2010.00947.x
  8. Gerard WA, Camargo CA Jr, Cullen J. The role of family physicians in an evolving emergency medicine workforce. Ann Emerg Med. 2022;80(1):91-92. doi:1016/j.annemergmed.2022.03.016
  9. Casey MM, Moscovice I, Holmes GM, Pink GH, Hung P. Minimum-distance requirements could harm high-performing critical-access hospitals and rural communities. Health Aff (Millwood). 2015;34(4):627-635. doi:1377/hlthaff.2014.0788
  10. Dong E, Liu S, Chen M, et al. Differences in regional distribution and inequality in health-resource allocation at hospital and primary health centre levels: a longitudinal study in Shanghai, China. BMJ Open. 2020;10(7):e035635. doi:1136/bmjopen-2019-035635
  11. Korean Ministry of Health and Welfare. Policy to Strengthen Local Medical Care. Korea: Korean Ministry of Health and Welfare; 2019.
  12. Nimptsch U, Mansky T. Hospital volume and mortality for 25 types of inpatient treatment in German hospitals: observational study using complete national data from 2009 to 2014. BMJ Open. 2017;7(9):e016184. doi:1136/bmjopen-2017-016184
  13. Nally DM, Sørensen J, Valentelyte G, et al. Volume and in-hospital mortality after emergency abdominal surgery: a national population-based study. BMJ Open. 2019;9(11):e032183. doi:1136/bmjopen-2019-032183
  14. Han KT, Kim SJ, Kim W, et al. Associations of volume and other hospital characteristics on mortality within 30 days of acute myocardial infarction in South Korea. BMJ Open. 2015;5(11):e009186. doi:1136/bmjopen-2015-009186
  15. Ramkumar PN, Navarro SM, Frankel WC, Haeberle HS, Delanois RE, Mont MA. Evidence-based thresholds for the volume and length of stay relationship in total hip arthroplasty: outcomes and economies of scale. J Arthroplasty. 2018;33(7):2031-2037. doi:1016/j.arth.2018.01.059
  16. Ravaghi H, Alidoost S, Mannion R, Bélorgeot VD. Models and methods for determining the optimal number of beds in hospitals and regions: a systematic scoping review. BMC Health Serv Res. 2020;20(1):186. doi:1186/s12913-020-5023-z
  17. Emergency Medical Services Act. Reliable Ministry of Government Legislation. Emergency Medical Services Act; 2019.
  18. Fareed N. Size matters: a meta-analysis on the impact of hospital size on patient mortality. Int J Evid Based Healthc. 2012;10(2):103-111. doi:1111/j.1744-1609.2012.00264.x
  19. Mukamel DB, Zwanziger J, Tomaszewski KJ. HMO penetration, competition, and risk-adjusted hospital mortality. Health Serv Res. 2001;36(6 Pt 1):1019-1035.
  20. Hartz AJ, Krakauer H, Kuhn EM, et al. Hospital characteristics and mortality rates. N Engl J Med. 1989;321(25):1720-1725. doi:1056/nejm198912213212506
  21. Carretta HJ, Chukmaitov A, Tang A, Shin J. Examination of hospital characteristics and patient quality outcomes using four inpatient quality indicators and 30-day all-cause mortality. Am J Med Qual. 2013;28(1):46-55. doi:1177/1062860612444459
  22. Cheol Seong S, Kim YY, Khang YH, et al. Data resource profile: the national health information database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46(3):799-800. doi:1093/ije/dyw253
  23. Han KS, Jeong J, Kang H, Kim WY, Kim SJ, Lee SW. Association between the emergency department length of stay time and in-hospital mortality according to 28 diagnosis groups in patients with severe illness diagnosis codes. J Korean Soc Emerg Med. 2021;32(1):77-88. doi:5694/j.1326-5377.2006.tb00203.x
  24. National Emergency Medical Center (NEMC). 2022 Emergency Medical Center Evaluation Guidelines. NEMC; 2021:1.

 

  1. Kim Y, Lee TS, Park SY, et al. Study on the establishment of a health insurance medical use map. Kangwondo: National Health Insurance Service; 2016.
  2. Bay KS, Nestman LJ. A hospital service population model and its application. Int J Health Serv. 1980;10(4):677-695. doi:2190/pw30-8jy9-jymx-37mt
  3. Ross JS, Normand SL, Wang Y, et al. Hospital volume and 30-day mortality for three common medical conditions. N Engl J Med. 2010;362(12):1110-1118. doi:1056/NEJMsa0907130
  4. Roessler M, Walther F, Eberlein-Gonska M, et al. Exploring relationships between in-hospital mortality and hospital case volume using random forest: results of a cohort study based on a nationwide sample of German hospitals, 2016-2018. BMC Health Serv Res. 2022;22(1):1. doi:1186/s12913-021-07414-z
  5. Luft HS, Hunt SS, Maerki SC. The volume-outcome relationship: practice-makes-perfect or selective-referral patterns? Health Serv Res. 1987;22(2):157-182.
  6. Kocher KE, Haggins AN, Sabbatini AK, Sauser K, Sharp AL. Emergency department hospitalization volume and mortality in the United States. Ann Emerg Med. 2014;64(5):446-457.e6. doi:1016/j.annemergmed.2014.06.008
  7. Georgakakos PK, Swanson MB, Ahmed A, Mohr NM. Rural stroke patients have higher mortality: an improvement opportunity for rural emergency medical services systems. J Rural Health. 2022;38(1):217-227. doi:1111/jrh.12502
  8. Jarman MP, Castillo RC, Carlini AR, Kodadek LM, Haider AH. Rural risk: geographic disparities in trauma mortality. Surgery. 2016;160(6):1551-1559. doi:1016/j.surg.2016.06.020
  9. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery. A study of adverse occurrence and failure to rescue. Med Care. 1992;30(7):615-629. doi:1097/00005650-199207000-00004
  10. Kim AM, Park JH, Kang S, Yoon TH, Kim Y. An ecological study of geographic variation and factors associated with cesarean section rates in South Korea. BMC Pregnancy Childbirth. 2019;19(1):162. doi:1186/s12884-019-2300-0
  11. Kim AM, Park JH, Cho S, Kang S, Yoon TH, Kim Y. Factors associated with the rates of coronary artery bypass graft and percutaneous coronary intervention. BMC Cardiovasc Disord. 2019;19(1):275. doi:1186/s12872-019-1264-3
  12. Kim JY, Shin SD, Ro YS, et al. Post-resuscitation care and outcomes of out-of-hospital cardiac arrest: a nationwide propensity score-matching analysis. Resuscitation. 2013;84(8):1068-1077. doi:1016/j.resuscitation.2013.02.010
  13. National Health Service. Summary Hospital-Level Mortality Indicator, Indicator Specification. 2018 Health and Social Care Information Centre. 2018.
  14. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi:1016/0021-9681(87)90171-8
  15. Gaughan J, Siciliani L, Gravelle H, Moscelli G. Do small hospitals have lower quality? Evidence from the English NHS. Soc Sci Med. 2020;265:113500. doi:1016/j.socscimed.2020.113500

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History

  • Receive Date: 06 March 2023
  • Revise Date: 25 January 2024
  • Accept Date: 16 March 2024
  • First Publish Date: 17 March 2024

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