- Title
- Influencing indicators and spatial variation of diabetes mellitus prevalence in Shandong, China: a framework for using data-driven and spatial methods
- Creator
- Li, Yizhuo; Fei, Teng; Wang, Jian; Nicholas, Stephen; Li, Jun; Xu, Lizheng; Huang, Yanran; Li, Hanqi
- Relation
- GeoHealth Vol. 5, Issue March 2021, no. e2020GH000320
- Publisher Link
- http://dx.doi.org/10.1029/2020GH000320
- Publisher
- John Wiley & Sons
- Resource Type
- journal article
- Date
- 2021
- Description
- 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 < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.
- Subject
- diabetes mellitus; indicators; lasso regression; spatial regression
- Identifier
- http://hdl.handle.net/1959.13/1424655
- Identifier
- uon:38124
- Identifier
- ISSN:2471-1403
- Rights
- © 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
- Language
- eng
- Full Text
- Reviewed
- Hits: 4841
- Visitors: 5138
- Downloads: 299
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT02 | Publisher version (open access) | 1 MB | Adobe Acrobat PDF | View Details Download |