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Analyzing the geographic distribution of major medical equipment with smart geographic system

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Abstract

This study examined geographic variations in major medical equipment used for patient diagnosis and treatment in Korea and analyzed their distributions in terms of sociodemographic variables. The study areas were all the city, gun (similar to a county), and gu (similar to a metropolitan ward office) administrative areas in Korea. The major medical equipment variable was measured by summing the numbers of major medical equipment in the study areas. The sociodemographic variables and the numbers of doctors and beds of each study area were collected from the Korean Statistical Information Services in 2010. Map data and study variables were imported into a geographic information system and used for the ordinary least squares regression and geographically weighted regression analysis. The results indicated that most of the sociodemographic variables were not statistically related to the distribution of medical equipment in Korea, but the numbers of doctors and beds were significant in explaining the variation in medical equipment.

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References

  1. Baicker K, Buckles KS, Chandra A (2006) Geographic variation in the appropriate use of cesarean delivery. Health Affair 25(5):355–367

    Article  Google Scholar 

  2. Bennema-Broos M, Groenewegen PP, Westert GP (2001) Social democratic government and spatial distribution of health care facilities. The case of hospital beds in Germany. Eur J Public Health 11(2):160–165

    Article  Google Scholar 

  3. Blumberg MS (1982) Regional differences in hospital use standardized by reported morbidity. Med Care 20(9):931–944

    Article  Google Scholar 

  4. Boros PW, Lubiáski W (2012) Health state and the quality of life in patients with chronic obstructive pulmonary disease in Poland: a study using the EuroQol-5D questionnaire. Polskie Archiwum Medycyny Wewnetrznej 122(3):73–81

    Google Scholar 

  5. Brunsdon C, Fotheringham A (2002) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298

    Article  Google Scholar 

  6. Burnham KP, Anderson D (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York

    Google Scholar 

  7. Busato A, Kunzi B (2008) Primary care physician supply and other key determinants of health care utilisation: the case of Switzerland. BMC Health Service Res. doi:10.1186/1472-6963-8-8

  8. Carlisle DM, Valdez RB, Shapiro MF, Brook RH (1995) Geographic variation in rates of selected surgical procedures within Los Angeles county. Health Service Res 30(1):27–42

    Google Scholar 

  9. Carlton M, Fotheringham A (2009) Geographically weighted regression: a tutorial on using GWR in ArcGIS 9.3. http://ncg.nuim.ie/ncg/gwr/gwr_tutorial.pdf. Accessed 16 Aug 2013

  10. Cashman S, Savageau J, McMullen M, Kinney R, Lemay C, Anthes F (2005) Health status of a low-income vulnerable population in a community health center. J Ambul Care Manag 28(1):60–72

    Article  Google Scholar 

  11. Comber AJ, Brunsdon C, Radburn R (2011) A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions. Int J Health Geogr. doi:10.1186/1476-072X-10-44

  12. Forder JE, Caiels J (2011) Measuring the outcomes of long-term care. Soc Sci Med 73(12):1766–1774

    Article  Google Scholar 

  13. Fotheringham AS, Brundson C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, West Sussex

    Google Scholar 

  14. Goodman DC, Fisher ES, Little GA, Stukel TA, Chang CH (2001) Are neonatal intensive care resources located according to need? Regional variation in neonatologists, beds, and low birth weight newborns. Pediatrics 108(2):426–431

    Article  Google Scholar 

  15. Goodman DC, Fisher ES, Little GA, Stukel TA, Chang CH, Schoendorf KS (2002) The relation between the availability of neonatal intensive care and neonatal mortality. N Engl J Med 346(20):1538–1544

    Article  Google Scholar 

  16. Graham RJ (1984) Population issues in economic planning: uses of demography in business. J Aust Popul Assoc 1:82–88

    Google Scholar 

  17. Hall O, Bloemer H, Cantrell B, Orebaugh M (1986) Using social indicators to locate mental health facilities. J Mental Health Admin 13(2):51–57

    Google Scholar 

  18. Holt JB, Zhang X, Presley-Cantrell L, Croft JB (2011) Geographic disparities in chronic obstructive pulmonary disease (COPD) hospitalization among medicare beneficiaries in the United States. Int J Chronic Obstruct Pulmon Dis 6:321–328

    Article  Google Scholar 

  19. Judge A, Welton NJ, Sandhu J, Ben-Shlomo Y (2009) Geographical variation in the provision of elective primary hip and knee replacement: the role of socio-demographic, hospital and distance variables. J Public Health 31(3):413–422

    Article  Google Scholar 

  20. Kanter RK (2002) Regional variation in child mortality at hospitals lacking a pediatric intensive care unit. Crit Care Med 30(1):94–99

    Article  Google Scholar 

  21. Kaufman JH, Edlund S, Ford DA, Powers C (2005) The social contract core. Electron Commer Res 5(1):141–165

    Article  Google Scholar 

  22. Kim HL, Yeo JY, Kang SO, Jung YH, Lee SH (2012) Review of the Korean health care system performance during 2000–2010 and policy implications based on OECD health data. Korean Institute for Health and Social Affairs, Seoul

    Google Scholar 

  23. Lee KS (2013) Disparity in the spatial distribution of clinics within a metropolitan city. Geospatial Health 7(2):199–207

    Article  Google Scholar 

  24. Lian M, Schootman M, Yun S (2008) Geographic variation and effect of area-level poverty rate on colorectal cancer screening. BMC Public Health 8:358

    Article  Google Scholar 

  25. Marmot MG, Kogevinas M, Elston MA (1987) Social/economic status and disease. Annu Rev Public Health 8:111–135

    Article  Google Scholar 

  26. Oh YH, Do SL, Son CK, Moon JW, Lee N, Park DS, Yu HS (2011) Development and management of monitoring system to improve the efficiency of health care resources allocation: health care resources, Korea. Korean Institute for Health and Social Affairs, Seoul

    Google Scholar 

  27. Oh YH, Kim JH (2007) The demand and supply of major medical equipment and policy recommendations. Health Soc Welfare Rev 27(2):96–121

    Article  Google Scholar 

  28. Petrova K, Wang B (2011) Location-based services deployment and demand: a roadmap model. Electron Commer Res 11(1):5–29

    Article  Google Scholar 

  29. Rabin R, Charro F (2001) EQ-SD: a measure of health status from the EuroQol group. Ann Med 33(5):337–343

    Article  Google Scholar 

  30. Rahim MM, Shanks G, Johnston RB (2011) A cross industry comparison of inter-organisational systems implementation activities. Electron Commer Res 11(2):215–243

    Article  Google Scholar 

  31. Roos NP (1984) Hysterectomy: variations in rates across small areas and across physicians’ practices. Am J Public Health 74(4):327–335

    Article  Google Scholar 

  32. Smith JP (1999) Health bodies and thick wallets: the dual relation between health and economic status. J Econ Perspect 13(2):145–165

    Article  Google Scholar 

  33. Wennberg J, Gittelsohn A (1982) Variations in medical care among small areas. Sci Am 246(4):120–134

    Article  Google Scholar 

  34. Wilkin CL, Riddett J (2009) IT governance challenges in a large not-for-profit healthcare organization: the role of intranets. Electron Commer Res 9(4):351–374

    Article  Google Scholar 

  35. Zhou T (2013) An empirical examination of user adoption of location-based services. Electron Commer Res 13(1):25–39

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Health Administration, Yonsei University, Wonju, South Korea

    Kwang-Soo Lee

  2. Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, South Korea

    Hyuk-Jun Kwon

Authors
  1. Kwang-Soo Lee
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  2. Hyuk-Jun Kwon
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Correspondence to Hyuk-Jun Kwon.

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Lee, KS., Kwon, HJ. Analyzing the geographic distribution of major medical equipment with smart geographic system. J Supercomput 71, 1996–2019 (2015). https://doi.org/10.1007/s11227-014-1218-6

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  • DOI: https://doi.org/10.1007/s11227-014-1218-6

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