What We Don’t Talk About When We Talk About Preventing Type 2 Diabetes—Addressing Socioeconomic Disadvantage

2016 ◽  
Vol 176 (8) ◽  
pp. 1053 ◽  
Author(s):  
Gabriela Spencer Bonilla ◽  
Rene Rodriguez-Gutierrez ◽  
Victor M. Montori
2022 ◽  
pp. 140349482110623
Author(s):  
Ina Tapager ◽  
Anne Mette Bender ◽  
Ingelise Andersen

Aims: It is well known that there is a socioeconomic gradient in the prevalence of many chronic diseases, including type 2 diabetes (T2DM). We present a simple assessment of the macro-level association between area socioeconomic disadvantage and the area-level prevalence of T2DM in Danish municipalities and the development in this relationship over the last decade. Methods: We used readily available public data on the socioeconomic composition of municipalities and T2DM prevalence to illustrate this association and report the absolute and relative summary measures of socioeconomic inequality over the time period 2008–2018. Results: The results show a persistent relationship between municipality socioeconomic disadvantage and T2DM prevalence across all analyses, with a modelled gap in T2DM prevalence between the most and least disadvantaged municipalities, the slope index of inequality, of 1.23 [0.97;1.49] in 2018. Conclusions: These results may be used to indicate areas with specific needs, to encourage systematic monitoring of socioeconomic gradients in health, and to provide a descriptive backdrop for a discussion of how to tackle these socioeconomic and geographic inequalities, which seem to persist even in the context of the comprehensive welfare systems in Scandinavia.


Author(s):  
Ramya Walsan ◽  
Darren J Mayne ◽  
Xiaoqi Feng ◽  
Nagesh Pai ◽  
Andrew Bonney

This study examined the association between neighbourhood socioeconomic disadvantage and serious mental illness (SMI)–type 2 diabetes (T2D) comorbidity in an Australian population using routinely collected clinical data. We hypothesised that neighbourhood socioeconomic disadvantage is positively associated with T2D comorbidity in SMI. The analysis considered 3816 individuals with an SMI living in the Illawarra and Shoalhaven regions of NSW, Australia, between 2010 and 2017. Multilevel logistic regression models accounting for suburb (neighbourhood) level clustering were used to assess the association between neighbourhood disadvantage and SMI -T2D comorbidity. Models were adjusted for age, sex, and country of birth. Compared with the most advantaged neighbourhoods, residents in the most disadvantaged neighbourhoods had 3.2 times greater odds of having SMI–T2D comorbidity even after controlling for confounding factors (OR 3.20, 95% CI 1.42–7.20). The analysis also revealed significant geographic variation in the distribution of SMI -T2D comorbidity in our sample (Median Odds Ratio = 1.35) Neighbourhood socioeconomic disadvantage accounted for approximately 17.3% of this geographic variation. These findings indicate a potentially important role for geographically targeted initiatives designed to enhance prevention and management of SMI–T2D comorbidity in disadvantaged communities.


2005 ◽  
Vol 173 (4S) ◽  
pp. 283-284
Author(s):  
Istvan Kovanecz ◽  
Monica G. Ferrini ◽  
Hugo H. Davila ◽  
Jacob Rajfer ◽  
Nestor F. Gonzalez-Cadavid
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