socioeconomic disadvantage
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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.


2022 ◽  
Vol 97 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Alanna M. Chamberlain ◽  
Jennifer L. St. Sauver ◽  
Lila J. Finney Rutten ◽  
Chun Fan ◽  
Debra J. Jacobson ◽  
...  

2022 ◽  
Vol 226 (1) ◽  
pp. S628
Author(s):  
Virginia Y. Watkins ◽  
peinan Zhao ◽  
Antonina I. Frolova ◽  
Ebony B. Carter ◽  
Jeannie C. Kelly ◽  
...  

2021 ◽  
Vol 21 (8) ◽  
pp. 475-476
Author(s):  
Colin Tukuitonga ◽  
Alec Ekeroma

The Covid-19 outbreak in Aotearoa/New Zealand is a timely reminder of the chronic inequities in health and the importance of socioeconomic factors in the origins of the disease. The pandemic has affected mainly indigenous Maori and Pacific people.  There were 5,371 confirmed and probable cases of Covid-19 as at 13 November 2021, of which 2,104 (39%) were in Maori and 1,646 (31%) were in Pacific people.  Furthermore, 228 (70%) of all hospital admissions were Maori and Pacific people


Author(s):  
Stephanie Schrempft ◽  
Daniel W Belsky ◽  
Bogdan Draganski ◽  
Matthias Kliegel ◽  
Peter Vollenweider ◽  
...  

Abstract Background Socioeconomic disadvantage is a well-established predictor of morbidity and mortality, and is thought to accelerate the aging process. This study examined associations between life course socioeconomic conditions and the Pace of Aging, a longitudinal measure of age-related physiological decline. Methods Data were drawn from a Swiss population-based cohort of individuals originally recruited between 2003 and 2006, and followed up for 11 years (2834 women, 2475 men aged 35 – 75 years (mean 52)). Pace of Aging was measured using three repeated assessments of 12 biomarkers reflecting multiple body systems. Analysis tested associations of socioeconomic conditions with physiological status at baseline and with the Pace of Aging. Results Participants with more life course socioeconomic disadvantage were physiologically older at baseline and experienced faster Pace of Aging. Effect-sizes (β) for associations of childhood socioeconomic disadvantage with baseline physiological status ranged from 0.1-0.2; for adulthood socioeconomic disadvantage, effect-sizes ranged from 0.2-0.3. Effect-sizes were smaller for associations with the Pace of Aging (< 0.05 for childhood disadvantage, 0.05-0.1 for adulthood disadvantage). Those who experienced disadvantaged socioeconomic conditions from childhood to adulthood aged 10% faster over the 11 years of follow-up as compared with those who experienced consistently advantaged socioeconomic conditions. Covariate adjustment for health behaviors attenuated associations, but most remained statistically significant. Conclusions Socioeconomic inequalities contribute to a faster Pace of Aging, partly through differences in health behaviors. Intervention to slow aging in at risk individuals is needed by midlife, before aetiology of aging-related diseases become established.


2021 ◽  
Vol 4 (12) ◽  
pp. e2139593
Author(s):  
En Cheng ◽  
Pamela R. Soulos ◽  
Melinda L. Irwin ◽  
Elizabeth M. Cespedes Feliciano ◽  
Carolyn J. Presley ◽  
...  

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260788
Author(s):  
Kate E. Mooney ◽  
Stephanie L. Prady ◽  
Mary M. Barker ◽  
Kate E. Pickett ◽  
Amanda H. Waterman

Background and objective Working memory is an essential cognitive skill for storing and processing limited amounts of information over short time periods. Researchers disagree about the extent to which socioeconomic position affects children’s working memory, yet no study has systematically synthesised the literature regarding this topic. The current review therefore aimed to investigate the relationship between socioeconomic position and working memory in children, regarding both the magnitude and the variability of the association. Methods The review protocol was registered on PROSPERO and the PRISMA checklist was followed. Embase, Psycinfo and MEDLINE were comprehensively searched via Ovid from database inception until 3rd June 2021. Studies were screened by two reviewers at all stages. Studies were eligible if they included typically developing children aged 0–18 years old, with a quantitative association reported between any indicator of socioeconomic position and children’s working memory task performance. Studies were synthesised using two data-synthesis methods: random effects meta-analyses and a Harvest plot. Key findings The systematic review included 64 eligible studies with 37,737 individual children (aged 2 months to 18 years). Meta-analyses of 36 of these studies indicated that socioeconomic disadvantage was associated with significantly lower scores working memory measures; a finding that held across different working memory tasks, including those that predominantly tap into storage (d = 0.45; 95% CI 0.27 to 0.62) as well as those that require processing of information (d = 0.52; 0.31 to 0.72). A Harvest plot of 28 studies ineligible for meta-analyses further confirmed these findings. Finally, meta-regression analyses revealed that the association between socioeconomic position and working memory was not moderated by task modality, risk of bias, socioeconomic indicator, mean age in years, or the type of effect size. Conclusion This is the first systematic review to investigate the association between socioeconomic position and working memory in children. Socioeconomic disadvantage was associated with lower working memory ability in children, and that this association was similar across different working memory tasks. Given the strong association between working memory, learning, and academic attainment, there is a clear need to share these findings with practitioners working with children, and investigate ways to support children with difficulties in working memory.


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