Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
Population Health Metrics
Published in: Population Health Metrics 1/2011

Open Access 01-12-2011 | Research

Burden of type 2 diabetes attributed to lower educational levels in Sweden

Authors: Emilie E Agardh, Anna Sidorchuk, Johan Hallqvist, Rickard Ljung, Stefan Peterson, Tahereh Moradi, Peter Allebeck

Published in: Population Health Metrics | Issue 1/2011

Login to get access

Abstract

Background

Type 2 diabetes is associated with low socioeconomic position (SEP) in high-income countries. Despite the important role of SEP in the development of many diseases, no socioeconomic indicator was included in the Comparative Risk Assessment (CRA) module of the Global Burden of Disease study. We therefore aimed to illustrate an example by estimating the burden of type 2 diabetes in Sweden attributed to lower educational levels as a measure of SEP using the methods applied in the CRA.

Methods

To include lower educational levels as a risk factor for type 2 diabetes, we pooled relevant international data from a recent systematic review to measure the association between type 2 diabetes incidence and lower educational levels. We also collected data on the distribution of educational levels in the Swedish population using comparable criteria for educational levels as identified in the international literature. Population attributable fractions (PAF) were estimated and applied to the burden of diabetes estimates from the Swedish burden of disease database for men and women in the separate age groups (30-44, 45-59, 60-69, 70-79, and 80+ years).

Results

The PAF estimates showed that 17.2% of the diabetes burden in men and 20.1% of the burden in women were attributed to lower educational levels in Sweden when combining all age groups. The burden was, however, most pronounced in the older age groups (70-79 and 80+), where lower educational levels contributed to 22.5% to 24.5% of the diabetes burden in men and 27.8% to 32.6% in women.

Conclusions

There is a considerable burden of type 2 diabetes attributed to lower educational levels in Sweden, and socioeconomic indicators should be considered to be incorporated in the CRA.
Literature
1.
Mackenbach JP, Bakker MJ: Tackling socioeconomic inequalities in health: analysis of European experiences. Lancet 2003,362(9393):1409-14. 10.1016/S0140-6736(03)14639-9CrossRefPubMed
2.
Health SNCFP: Health on equal terms--national goals for public health. Scand J Public Health Suppl 2001, 57: 1-68.
3.
Mackenbach JP: Socioeconomic inequalities in health in The Netherlands: impact of a five year research programme. BMJ 1994,309(6967):1487-91. 10.1136/bmj.309.6967.1487CrossRefPubMedPubMedCentral
4.
Mackenbach JP: Can we reduce health inequalities? An analysis of the English strategy (1997-2010). J Epidemiol Community Health 2011.
5.
Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ: Selected major risk factors and global and regional burden of disease. Lancet 2002,360(9343):1347-60. 10.1016/S0140-6736(02)11403-6CrossRefPubMed
6.
Editorial: Stemming the global tsunami of cardiovascular disease. Thelancet 2011,377(February 12):529-532.
7.
8.
Murray CJ, Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S: Comparative quantification of health risks conceptual framework and methodological issues. Popul Health Metr 2003,1(1):1. 10.1186/1478-7954-1-1CrossRefPubMedPubMedCentral
9.
De Vogli R, M M: Should socioeconomic factors be considered as traditional risk factors for cardiovascular disease, as confounders, or as risk modifiers? Dialogues in Cardiovascular Medicine 2008,13(2):103-110.
10.
Townsend P, D N, Whitehead M, eds: Inequalities in health (The Black Report and the health divide). London: Pengiun Books; 1988.
11.
Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A: Type 2 diabetes incidence and socioeconomic position: a systematic review and meta-analysis. International Journal of Epidemiology, (Advanced Access, published February 19, 2001) 2011, 1-15.
12.
Hackman DA, Farah MJ, Meaney MJ: Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat Rev Neurosci 2010,11(9):651-9. 10.1038/nrn2897CrossRefPubMedPubMedCentral
13.
Kaye SA, Folsom AR, Sprafka JM, Prineas RJ, Wallace RB: Increased incidence of diabetes mellitus in relation to abdominal adiposity in older women. J Clin Epidemiol 1991,44(3):329-34. 10.1016/0895-4356(91)90044-ACrossRefPubMed
14.
Kouvonen AM, Vaananen A, Woods SA, Heponiemi T, Koskinen A, Toppinen-Tanner S: Sense of coherence and diabetes: a prospective occupational cohort study. BMC Public Health 2008, 8: 46. 10.1186/1471-2458-8-46CrossRefPubMedPubMedCentral
15.
Lidfeldt J, Li TY, Hu FB, Manson JE, Kawachi I: A prospective study of childhood and adult socioeconomic status and incidence of type 2 diabetes in women. Am J Epidemiol 2007,165(8):882-9. 10.1093/aje/kwk078CrossRefPubMed
16.
Medalie JH, Papier C, Herman JB, Goldbourt U, Tamir S, Neufeld HN, Riss E: Diabetes mellitus among 10,000 adult men. I Five-year incidence and associated variables. Isr J Med Sci 1974,10(7):681-97.PubMed
17.
Robbins JM, Vaccarino V, Zhang H, Kasl SV: Socioeconomic status and diagnosed diabetes incidence. Diabetes Res Clin Pract 2005,68(3):230-6. 10.1016/j.diabres.2004.09.007CrossRefPubMed
18.
Cochran W: The combination of estimates from different experiments. Biometrics 1954, 9: 101-29.CrossRef
19.
Statistics Sweden: SCB-DATA för Forskning 2009, Innehållsbeskrivning av olika register. 2009.
20.
Report WH: World Health Report 2004: Changing History. Geneva: World Health Organization; 2004.
21.
Mathers CD, B C, Moesgaard Iburgh K, Inoue M, Ma Fat D, et al.: Global Burden of Disease in 2002: data sources, methods and results. In Global programme on evidence for health policy paper No. 54. Geneva: World Health Organization; 2003.
22.
Efron B, Tibshirani RJ, ed: An introduction to the Bootstrap. Chapman & Hall: London; 1993.
23.
Mackenbach JP, Kunst AE: Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med 1997,44(6):757-71. 10.1016/S0277-9536(96)00073-1CrossRefPubMed
24.
Moussa K, Ostergren PO, Grahn M, Kunst AE, Eek F, Essen B: Socioeconomic differences in smoking trends among pregnant women at first antenatal visit in Sweden 1982-2001: increasing importance of educational level for the total burden of smoking. Tob Control 2009,18(2):92-7. 10.1136/tc.2008.026336CrossRefPubMed
25.
Global Health Risks. Mortality and burden of disease attributable to selected major risk factors World Health Organisation; 2009.
26.
Espelt A, Arriola L, Borrell C, Larranaga I, Sandin M, Escolar-Pujolar A: Socioeconomic position and type 2 diabetes mellitus in europe 1999-2009: a panorama of inequalities. Curr Diabetes Rev 2011,7(3):148-58. 10.2174/157339911795843131CrossRefPubMed
27.
28.
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G: Indicators of socioeconomic position (part 1). J Epidemiol Community Health 2006,60(1):7-12. 10.1136/jech.2004.023531CrossRefPubMedPubMedCentral
29.
Espelt A, Borrell C, Roskam AJ, Rodriguez-Sanz M, Stirbu I, Dalmau-Bueno A, Regidor E, Bopp M, Martikainen P, Leinsalu M, Artnik B, Rychtarikova J, Kalediene R, Dzurova D, Mackenbach J, Kunst AE: Socioeconomic inequalities in diabetes mellitus across Europe at the beginning of the 21st century. Diabetologia 2008,51(11):1971-9. 10.1007/s00125-008-1146-1CrossRefPubMed
30.
Ljung R, Peterson S, Hallqvist J, Heimerson I, Diderichsen F: Socioeconomic differences in the burden of disease in Sweden. Bull World Health Organ 2005,83(2):92-9.PubMedPubMedCentral
31.
Norberg M, Stenlund H, Lindahl B, Andersson C, Eriksson JW, Weinehall L: Work stress and low emotional support is associated with increased risk of future type 2 diabetes in women. Diabetes Res Clin Pract 2007,76(3):368-77. 10.1016/j.diabres.2006.09.002CrossRefPubMed
32.
Begg CB, Mazumdar M: Operating characteristics of a rank correlation test for publication bias. Biometrics 1994,50(4):1088-101. 10.2307/2533446CrossRefPubMed
33.
Huisman M, Kunst AE, Andersen O, Bopp M, Borgan JK, Borrell C, Costa G, Deboosere P, Desplanques G, Donkin A, Gadeyne S, Minder C, Regidor E, Spadea T, Valkonen T, Mackenbach JP: Socioeconomic inequalities in mortality among elderly people in 11 European populations. J Epidemiol Community Health 2004,58(6):468-75. 10.1136/jech.2003.010496CrossRefPubMedPubMedCentral
34.
Diagnosis and classification of diabetes mellitus Diabetes Care 2004,27(Suppl 1):S5-S10.
Metadata
Title
Burden of type 2 diabetes attributed to lower educational levels in Sweden
Authors
Emilie E Agardh
Anna Sidorchuk
Johan Hallqvist
Rickard Ljung
Stefan Peterson
Tahereh Moradi
Peter Allebeck
Publication date
01-12-2011
Publisher
BioMed Central
Published in
Population Health Metrics / Issue 1/2011
Electronic ISSN: 1478-7954
DOI
https://doi.org/10.1186/1478-7954-9-60

Other articles of this Issue 1/2011

Population Health Metrics 1/2011 Go to the issue

Commentary

Verbal autopsy and global mortality statistics: if not now, then when?

Review

Social autopsy for maternal and child deaths: a comprehensive literature review to examine the concept and the development of the method

Research

Patterns of multimorbidity in working Australians

Research

Testing for fertility stalls in demographic and health surveys

Research

Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations

Research

Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths