Format

Send to

Choose Destination
Epidemiol Health. 2017 Jun 21;39:e2017025. doi: 10.4178/epih.e2017025. eCollection 2017.

Analysis of the relationship between community characteristics and depression using geographically weighted regression.

Author information

1
Korea Centers for Disease Control and Prevention, Cheongju, Korea.
2
Graduate School of Public Health, Seoul National University, Seoul, Korea.
3
Asian Institute for Energy, Environment and Sustainability, Seoul, Korea.

Abstract

OBJECTIVES:

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.

METHODS:

Using 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.

RESULTS:

In 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.

CONCLUSIONS:

The 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.

KEYWORDS:

Depression; Depressive disorder; Health status; Spatial analysis; Spatial regression

PMID:
28728351
PMCID:
PMC5668667
DOI:
10.4178/epih.e2017025
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Publishing M2Community Icon for PubMed Central
Loading ...
Support Center