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Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods
- Source :
- GeoHealth, GeoHealth, Vol 5, Iss 3, Pp n/a-n/a (2021)
- Publication Year :
- 2020
-
Abstract
- To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data‐driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p<br />Key Points The significant spatial clusters of diabetes prevalence exist in the coastal northeast and northwest counties in Shandong Province, ChinaA methodological improvement combined data‐driven and spatial methods is proposed to identify automatically significant indicatorsSeveral economic, sociodemographic, education, and geographic environment indicators are found to be associated with diabetes prevalence
- Subjects :
- medicine.medical_specialty
Spatial methods
Epidemiology
spatial regression
lcsh:Environmental protection
Health, Toxicology and Mutagenesis
Management, Monitoring, Policy and Law
Diabetes mellitus
Environmental health
Linear regression
medicine
Per capita
lcsh:TD169-171.8
China
Waste Management and Disposal
Water Science and Technology
Global and Planetary Change
Public health
lasso regression
Public Health, Environmental and Occupational Health
Diabetes prevalence
Geohealth
medicine.disease
Pollution
indicators
Geography
diabetes mellitus
Spatial variability
Public Health
Research Article
Subjects
Details
- ISSN :
- 24711403
- Volume :
- 5
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- GeoHealth
- Accession number :
- edsair.doi.dedup.....047f5da105844a19ae707c99c08781a7