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Original Article
- Analysis of the relationship between community characteristics and depression using geographically weighted regression
- Hyungyun Choi, Ho Kim
- Epidemiol Health. 2017;39:e2017025. Published online June 21, 2017
- DOI: https://doi.org/10.4178/epih.e2017025
- 14,442 View
- 237 Download
- 1 Web of Science
- 4 Crossref
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Abstract
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AbstractOBJECTIVES
Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.METHODSUsing health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran’s I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.RESULTSIn the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.CONCLUSIONSThe indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions. -
SummaryKorean summary
정신질환 중 가장 흔한 우울증의 경우 집단의 특성 간 발생 현황에 차이를 보이고 있어 지역별 접근을 통한 연구가 요구됨에 따라 본 연구에서는 우울증의 지역적 변이요인을 분석하여 지역별 중재가 필요한 건강결정요인을 파악하고자 지역사회건강조사 자료를 이용하여 공간적 지리가중회귀분석을 시행하였다. 본 연구를 통해 지역단위보건관련지표는 지역의 우울증 발생과 유의미한 연관성이 있으며 연관성 우선순위는 지역별 차이가 있음이 밝혀졌다. 지역적 특성에 따른 우선순위를 제시하였음에 본 연구의 의의가 있으며 공중 보건 영역의 다른 사례에 본 연구방법론 및 연구결과 제시 방안을 적용함에 따라 지역의 건강수준향상 프로그램 개발에 유용한 기초자료의 제공을 기대할 수 있다. -
Citations
Citations to this article as recorded by
- A geographically weighted artificial neural network
Julian Hagenauer, Marco Helbich
International Journal of Geographical Information Science.2022; 36(2): 215. CrossRef - Spatial Dependence in Local Suicide Ideation and Actual Suicide among the Elderly: A Comparative Study between Men and Women
Taewan Kim, Hee-Jung Jun
Journal of Korea Planning Association.2021; 56(4): 49. CrossRef - Geographic Disparities in Stress Levels during the COVID-19 Pandemic in Kuwait
Mohammad Alnasrallah, Ibrahim Alshehab
Papers in Applied Geography.2020; 6(4): 449. CrossRef - Visual Analysis of Regional Variations in Antidepressant Use and Suicide Rate
Ho-Jung Kim, Iyn-Hyang Lee
Korean Journal of Clinical Pharmacy.2018; 28(4): 308. CrossRef
- A geographically weighted artificial neural network
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