지리정보시스템을 이용한 대전 광역시 응급의료 취약지 분석
The Analysis of Underserved Emergency Medical Services Areas in Daejeon Metropolitan City Using a Geographic Information System
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초록
이 연구는 대전광역시의 응급의료 취약지를 도출하고 그 지역의 보건학적 특성을 규명하기 위해시행하였다. 응급의료 취약지는 GIS의 비용가중분석 기법을 이용하여 분석하였으며, 응급의료센터로부터 30분 이내에 접근이 어려운 지역으로 정의하였다. 응급의료 취약 여부에 따른 보건학적 특성은 SPSS 17.0을 이용하여 교차분석을 시행하였다. 연구 결과, 대전광역시의 응급의료 취약지는12개의 행정동을 포함하고 있으며 이 지역에 약8,100명이 거주하고 있었다. 응급의료 취약지여부에따른 보건학적 특성의 차이를 교차 분석한 결과, 농어임업 인구, 국민기초생활수급권자 수, 장애인수, 노인인구의 수가 카이제곱검정에서 유의하였다(p<0.05). 이번 연구를 통해 대전광역시의 응급의료 취약지를 선정할 수 있었으며 GIS 분석 시도로데이터를 적용하여 기존의 Buffer 방식(단순반경거리)보다 더 정확하고 현실적으로 응급의료취약지를 선정할 수 있었다. 하지만 이 연구는대전광역시에 국한되어 시행했기 때문에 향후에는이 연구에 적용한 GIS 분석 기법과 응급의료 취약지 선정기준 등을 활용하여 전국을 대상으로 한연구가 필요하다.
Abstract
Objectives: The purpose of this study was to define the underserved emergency medical services (EMS)areas in Daejeon metropolitan city, as well as to identify their distinctive characteristics in public health perspectives. Methods: An underserved EMS area was operationally defined as an area in which it is difficult to arrive at an emergency medical center within 30 minutes. Using a cost-weighted distance algorithm with a geographic information system (GIS), the underserved EMS area was calculated. The characteristics of the underserved areas were analyzed by the Chi-square test. The SPSS statistical software package was used to perform the statistical analysis. All statistical tests were two-sided, and a p-value<0.05 was considered statistically significant. Results: Twelve administrative sectors (‘Dong’ in Korean) were included in the underserved areas, accounting for a population of approximately 8,100 citizens. The relationships between underserved EMS area and populations of agriculture, fishery, and forestry; citizens who are recipients of national basic livelihood security program; disabled; or aged 65 or older were statistically significant. Conclusion: It was found that 12 administrative sectors were included in the underserved EMS areas. Revealing underserved EMS areas using GIS analysis based on a cost-weighted distance algorithm of road data was an effective analytic method. However, as this study was confined to Daejeon City, South Korea, a nation-wide study should be performed to provide a more accurate conclusion.
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