보건행정학회지 (Health Policy and Management)

  • 제26권1호
  • /
  • Pages.30-38
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  • 2016
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  • 1225-4266(pISSN)
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  • 2289-0149(eISSN)
한국보건행정학회 (Korean Academy of Health Policy and Management)
권호 표지
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당뇨병 유병률의 지역 간 변이와 지역 특성과의 관계 분석

Spatial Distribution of Diabetes Prevalence Rates and Its Relationship with the Regional Characteristics

  • 조은경 (연세대학교 대학원 보건행정학과) ;
  • 서은원 (연세대학교 대학원 보건행정학과) ;
  • 이광수 (연세대학교 보건과학대학 보건행정학과)
  • Jo, Eun-Kyung (Department of Health Administration, Yonsei University Graduate School) ;
  • Seo, Eun-Won (Department of Health Administration, Yonsei University Graduate School) ;
  • Lee, Kwang-Soo (Department of Health Administration, Yonsei University College of Health Sciences)
  • 투고 : 2015.10.27
  • 심사 : 2016.03.18
  • 발행 : 2016.03.31
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초록

Background: This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. Methods: The unit of analysis was administrative districts of city gun gu. Dependent variable was the age- and sex- adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Results: Analysis results showed that age- and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Conclusion: Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.

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참고문헌

  1. Statistics Korea. Population census [Internet]. Daejeon: Statistics Korea; 2014 [cited 2015 Oct 20]. Available from: http://www.kostat.go.kr/.
  2. Amos AF, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabet Med 1997;14 Suppl 5:S1-S85. DOI: http://dx.doi.org/10.1002/(sici)1096-9136(199712)14:5+3.3.co;2-i.
  3. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract 2014;103(2):137-149. DOI: http://dx.doi.org/10.1016/j.diabres.2013.11.002.
  4. Park YL, Kim HS, Jeon MY, Jin CJ. A study on the prevalence rate of hypertension and diabetes among adults in the urban communities. J Korean Community Health Nurs Acad Soc 2003;17(1):144-152.
  5. Green C, Hoppa RD, Young TK, Blanchard JF. Geographic analysis of diabetes prevalence in an urban area. Soc Sci Med 2003;57(3):551-560. DOI: http://dx.doi.org/10.1016/s0277-9536(02)00380-5.
  6. Grubesic TH, Miller JA, Murray AT. Geospatial and geodemographic insights for diabetes in the United States. Appl Geogr 2014;55:117-126. DOI: http://dx.doi.org/10.1016/j.apgeog.2014.08.017.
  7. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 2002;55(1):125-139. DOI: http://dx.doi.org/10.1016/s0277-9536(01)00214-3.
  8. Schulz AJ, Zenk S, Odoms-Young A, Hollis-Neely T, Nwankwo R, Lockett M, et al. Healthy eating and exercising to reduce diabetes: exploring the potential of social determinants of health frameworks within the context of community-based participatory diabetes prevention. Am J Public Health 2005;95(4):645-651. DOI: http://dx.doi.org/10.2105/ajph.2004.048256.
  9. Kim SA, Song M. The relation between glucose control, self-care and depression in community dwelling older adults with diabetes. Perspect Nurs Sci 2012;9(2):94-101.
  10. Kim HS, Ro YJ, Kim NC, Yoo YS, Young JS, Oh JA. Prevalence and risk factors for diabetes mellitus and impaired fasting glucose of adults. J Korean Acad Nurs 2000;30(6):1479-1487. https://doi.org/10.4040/jkan.2000.30.6.1479
  11. Lee TY, Ko LH, Kim KH. Factors affecting on impaired blood glucose and diabetes in residents of a rural area. J Korea Acad-Ind Coop Soc 2008;9(5):1374-1385. DOI: http://dx.doi.org/10.5762/kais.2008.9.5.1374.
  12. Chun JD, Ryu SY, Han MA, Park J. Comparisons of health status and health behaviors among the elderly between urban and rural areas. J Agric Med Community Health 2013;38(3):182-194. DOI: http://dx.doi.org/10.5393/jamch.2013.38.3.182.
  13. Yang B, Hwang C. Spatial dependency and heterogeneity of adult diseases: in the cases of obesity, diabetes and high blood pressure in the U.S.A. J Korean Assoc Reg Geogr 2010;16(5):610-622.
  14. Jayasekara JM, Dissanayake DM, Adhikari SB, Bandara P. Geographical distribution of chronic kidney disease of unknown origin in North Central Region of Sri Lanka. Ceylon Med J 2013;58(1):6-10. DOI: http://dx.doi.org/10.4038/cmj.v58i1.5356.
  15. Dever GE. Community health analysis: global awareness at the local level. 2nd ed. Gaithersburg (MD): Aspen Publishers; 1991.
  16. Noh NY, Nam SY, Kang HS, Lee JE, Lee SK. Nutrition knowledge, dietary attitude, and dietary behavior among children and adolescents with type 1 diabetes. Korean J Community Nutr 2013;18(2):101-111. https://doi.org/10.5720/kjcn.2013.18.2.101
  17. Bocquier A, Cortaredona S, Nauleau S, Jardin M, Verger P. Prevalence of treated diabetes: geographical variations at the small-area level and their association with area-level characteristics: a multilevel analysis in Southeastern France. Diabetes Metab 2011;37(1):39-46. DOI: http://dx.doi.org/10.1016/j.diabet.2010.07.004.
  18. Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W. Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J Epidemiol Community Health 2000;54(3):173-177. DOI: http://dx.doi.org/10.1136/jech.54.3.173.
  19. Geraghty EM, Balsbaugh T, Nuovo J, Tandon S. Using geographic information systems (GIS) to assess outcome disparities in patients with type 2 diabetes and hyperlipidemia. J Am Board Fam Med 2010;23(1):88-96. DOI: http://dx.doi.org/10.3122/jabfm.2010.01.090149.
  20. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol 2011;40(3):804-818. DOI: http://dx.doi.org/10.1093/ije/dyr029.
  21. Cox M, Boyle PJ, Davey PG, Feng Z, Morris AD. Locality deprivation and type 2 diabetes incidence: a local test of relative inequalities. Soc Sci Med 2007;65(9):1953-1964. DOI: http://dx.doi.org/10.1016/j.socscimed.2007.05.043.
  22. Dijkstra A, Janssen F, De Bakker M, Bos J, Lub R, Van Wissen LJ, et al. Using spatial analysis to predict health care use at the local level: a case study of type 2 diabetes medication use and its association with demographic change and socioeconomic status. PLoS One 2013;8(8):e72730. DOI: http://dx.doi.org/10.1371/journal.pone.0072730.
  23. Brown AF, Ettner SL, Piette J, Weinberger M, Gregg E, Shapiro MF, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev 2004;26(1):63-77. DOI: http://dx.doi.org/10.1093/epirev/mxh002.
  24. Mennis J, Mason MJ. People, places, and adolescent substance use: integrating activity space and social network data for analyzing health behavior. Ann Assoc Am Geogr 2011;101(2), 272-291. DOI: http://dx.doi.org/10.1080/00045608.2010.534712.
  25. Kim EJ, Kang MG. Effects of built environment and individual characteristics on health condition. J KRSA 2011;27(3):27-42.
  26. Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med 2011;40(4):434-439. DOI: http://dx.doi.org/10.1016/j.amepre.2010.12.019.
  27. Frank LD, Sallis JF, Conway TL, Chapman JE, Saelens BE, Bachman W. Many pathways from land use to health: associations between neighborhood walkability and active transportation, body mass index, and air quality. J Am Plan Assoc 2006;72(1):75-87. https://doi.org/10.1080/01944360608976725
  28. Owen N, Cerin E, Leslie E, duToit L, Coffee N, Frank LD, et al. Neighborhood walkability and the walking behavior of Australian adults. Am J Prev Med 2007;33(5):387-395. DOI: http://dx.doi.org/10.1016/j.amepre.2007.07.025.
  29. Sallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain KL, et al. Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med 2009;68(7):1285-1293. DOI: http://dx.doi.org/10.1016/j.socscimed.2009.01.017.
  30. Sundquist K, Eriksson U, Kawakami N, Skog L, Ohlsson H, Arvidsson D. Neighborhood walkability, physical activity, and walking behavior: the Swedish Neighborhood and Physical Activity (SNAP) study. Soc Sci Med 2011;72(8):1266-1273. DOI: http://dx.doi.org/10.1016/j.socscimed.2011.03.004.
  31. Lee HY. GIS: geomatics. Paju: Bupmunsa; 2003.
  32. Moran PA. The interpretation of statistical maps. J R Stat Soc Series B Stat Methodol 1948;10(2):243-251.
  33. Lo CP, Yeung AK. Concepts and techniques of geographic information systems. 2nd ed. Upper Saddle River (NJ): Prentice Hall; 2007.
  34. Lee HY, Shim JH. GIS geomatics. 2nd ed. Paju: Bupmunsa; 2012.
  35. Choi HW, Park JM, Kim HW, Kim YO. Identifying spatial distribution pattern of water quality in Masan Bay using spatial autocorrelation index and Pearson's r. Ocean Polar Res 2007;29(4):391-400. DOI: http://dx.doi.org/10.4217/opr.2007.29.4.391.
  36. Seok HS, Kang SH. A study on the regional variation factor of hypertension prevalence. Health Soc Welf Rev 2013;33(3):210-236. DOI: http://dx.doi.org/10.15709/hswr.2013.33.3.210.
  37. Lee KS, Choi YJ. Analysis on the relationships between the spatial distribution of primary care organizations and socio-demographic characteristics in a metropolitan city using the geographic weighted regression method. Product Rev 2013;27(2):193-214. DOI: http://dx.doi.org/10.15843/kpapr.27.2.201306.193.
  38. Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis 1996;28(4):281-298. DOI: http://dx.doi.org/10.1111/j.1538-4632.1996.tb00936.x.
  39. Jo DG. GIS and geographically weighted regression in the survey research of small areas. Survey Res 2009;10(3):1-19.
  40. Sohn J. A quantitative analysis of the spatial agglomeration pattern among the Korean cities. J Korean Geogr Soc 2013;48(1):56-71.
  41. Griffith DA. Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environ Plann A 2008;40(11):2751-2769. DOI: http://dx.doi.org/10.1068/a38218.
  42. Anselin L, Getis A. Spatial statistical analysis and geographic information systems. Ann Reg Sci 1992;26(1):19-33. DOI: http://dx.doi.org/10.1007/bf01581478.