Lifestyle and socioeconomic determinants of diabetes: Evidence from country-level data

OBJECTIVE: The objectives of the study is to investigate the global socioeconomic risk factors associated with diabetes prevalence using evidence from available country-level data. DESIGN: A cross-sectional study based on (2010 and a 1% increase in alcohol consumption is associated with a -0.85% (95% CI -1.17% to -0.53%) decrease in diabetes prevalence. Statistically significant socioeconomic and lifestyle indices positively associated with diabetes prevalence included gross national income; overweight prevalence (BMI>25 kg/m2); and tobacco consumption. Statistically significant inverse associations with global diabetes prevalence included total population size; unemployment and alcohol consumption. The 2019 data was removed due to sparsity of data. CONCLUSION: Statistically significant global lifestyle and socioeconomic determinants of diabetes prevalence include alcohol consumption; tobacco consumption; overweight prevalence; per capita income; total population and unemployment rates. Determinants of diabetes include modifiable risk factors which are consistent at both the micro and macro level and include tobacco consumption and overweight prevalence. Factors which are non-modifiable and warrant further investigation include total population and unemployment rates, which were inversely associated with diabetes prevalence and are a product of other underlying factors. Other determinants such as alcohol consumption was also inversely associated with diabetes prevalence, but has been observed to have both negative and positive associations with diabetes at the micro-level. These associations were dependent upon the amount of alcohol consumed. Global cut-off point of alcohol consumption is critical to establish global policies to reduce diabetes prevalence. Overall, the use of cross-sectional based study for country level aggregate data is a critical tool that should be considered when making global joint strategies or policies against diabetes in both data analysis and decision making.