Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Junming Li; Sixian Wang; Xiulan Han; Gehong Zhang; Min Zhao; Ling Ma

doi:10.1016/j.socscimed.2020.113062

Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Soc Sci Med. 2020 Jul:256:113062. doi: 10.1016/j.socscimed.2020.113062. Epub 2020 May 19.

Authors

Junming Li¹, Sixian Wang², Xiulan Han³, Gehong Zhang⁴, Min Zhao⁵, Ling Ma⁶

Affiliations

¹ School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan, 030006, China. Electronic address: Lijm@sxufe.edu.cn.
² School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan, 030006, China. Electronic address: Wangsixian_a@163.com.
³ School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan, 030006, China. Electronic address: Hanxl@sxufe.edu.cn.
⁴ First Hospital of Shanxi Medical University, 85 Jiefang Road, Taiyuan, 030001, China. Electronic address: Zhanggh88666@sina.com.
⁵ School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan, 030006, China. Electronic address: Zhaomin_er@163.com.
⁶ School of Statistics, Shanxi University of Finance and Economics, 696 Wucheng Road, Taiyuan, 030006, China. Electronic address: Maling_starry@sina.com.

PMID: 32464417
DOI: 10.1016/j.socscimed.2020.113062

Abstract

Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM_2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM_2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM_2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).

Keywords: Bayesian LASSO regression; Bayesian space-time model; Global diabetes prevalence; Influence factors; Spatiotemporal trends.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
China / epidemiology
Diabetes Mellitus* / epidemiology
Global Health*
Humans
Mexico / epidemiology
Prevalence
Spatial Analysis
Time Factors