Chronic disease burden associated with overweight and obesity in Ireland: the effects of a small BMI reduction at population level

BMC Public Health

Ivan J Perry²  Edel Doherty⁵  Anthony P Fitzgerald⁴  Anne Dee³  Karen Kearns¹ 

[1] Healthcare Pricing Office, ESRI Building, Whitaker Square, Sir John Rogerson’s Quay, Dublin 2, Ireland;Department of Epidemiology and Public Health, University College Cork, Cork, Ireland;HSE Department of Public Health, Mount Kennett House, Henry Street, Limerick, Ireland;Department of Statistics, University College Cork, Cork, Ireland;J.E. Cairnes School of Business and Economics, National University of Ireland Galway, Galway, Ireland

关键词: Population attributable fraction;    Prevalence;    Chronic disease;    Burden;    BMI;    Obesity;    Overweight;   

Others  :  1145374
DOI  :  10.1186/1471-2458-14-143

 received in 2013-08-08, accepted in 2014-02-06,  发布年份 2014

【 摘 要 】

Background

Overweight and obesity prevalence has risen dramatically in recent decades. While it is known that overweight and obesity is associated with a wide range of chronic diseases, the cumulative burden of chronic disease in the population associated with overweight and obesity is not well quantified. The aims of this paper were to examine the associations between BMI and chronic disease prevalence; to calculate Population Attributable Fractions (PAFs) associated with overweight and obesity; and to estimate the impact of a one unit reduction in BMI on the population prevalence of chronic disease.

Methods

A cross-sectional analysis of 10,364 adults aged ≥18 years from the Republic of Ireland National Survey of Lifestyle, Attitudes and Nutrition (SLÁN 2007) was performed. Using binary regression, we examined the relationship between BMI and the selected chronic diseases. In further analyses, we calculated PAFs of selected chronic diseases attributable to overweight and obesity and we assessed the impact of a one unit reduction in BMI on the overall burden of chronic disease.

Results

Overweight and obesity prevalence was higher in men (43.0% and 16.1%) compared to women (29.2% and 13.4%), respectively. The most prevalent chronic conditions were lower back pain, hypertension, and raised cholesterol. Prevalence of chronic disease generally increased with increasing BMI. Compared to normal weight persons, the strongest associations were found in obese women for diabetes (RR 3.9, 95% CI 2.5-6.3), followed by hypertension (RR 2.9, 95% CI 2.3-3.6); and in obese men for hypertension (RR 2.1, 95% CI 1.6-2.7), followed by osteoarthritis (RR 2.0, 95% CI 1.2-3.2). Calculated PAFs indicated that a large proportion of chronic disease is attributable to increased BMI, most noticeably for diabetes in women (42%) and for hypertension in men (30%). Overall, a one unit decrease in BMI results in 26 and 28 fewer cases of chronic disease per 1,000 men and women, respectively.

Conclusions

Overweight and obesity are major contributors to the burden of chronic disease in the population. The achievement of a relatively modest reduction in average BMI in the population has the potential to make a significant impact on the burden of chronic disease.

【 授权许可】

   
2014 Kearns et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]World Health Organisation: WHO Factsheet No. 311, March 2011. [http://www.who.int/mediacentre/factsheets/fs311/en/ webcite]
• [2]Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M: Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011, 378:815-825.
• [3]Jones A, Bentham G, Foster C, Hillsdon M, Panter J: Tackling Obesities: Future Choices–Obesogenic Environments–Evidence Review. London: Government Office for Science; 2007.
• [4]Rodgers A, Vaughan P, Prentice T, Edejer T, Evans D, Lowe J: World health report 2002: reducing risks, promoting healthy life. Geneva, Switzerland: World Health Organization; 2002.
• [5]World Health Organization: Global Status Report on Noncommunicable Diseases 2010. Geneva, Switzerland: World Health Organization; 2011.
• [6]Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, Dietz W: Obesity and severe obesity forecasts through 2030. Am J Prevent Med 2012, 42:563-570.
• [7]Ezzati M, Lopez AD, Rodgers A, Murray CJ: Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva, Switzerland: World Health Organization; 2004.
• [8]Anis AH, Zhang W, Bansback N, Guh D, Amarsi Z, Birmingham C: Obesity and overweight in Canada: an updated cost-of-illness study. Obes Rev 2010, 11:31-40.
• [9]Costa-Font J, Gil J: Obesity and the incidence of chronic diseases in Spain: a seemingly unrelated probit approach. Econ Hum Biol 2005, 3:188-214.
• [10]Visscher TL, Seidell JC: The public health impact of obesity. Ann Rev Publ Health 2001, 22:355-375.
• [11]World Health Organization: The world health report 2002: reducing risks, promoting healthy life. In World Health Organization. Geneva: WHO; 2002.
• [12]Zhao G, Ford E, Dhingra S, Li C, Strine T, Mokdad A: Depression and anxiety among US adults: associations with body mass index. Int J Obes 2009, 33:257-266.
• [13]Morgan K, McGee H, Watson D, Perry I, Barry M, Shelley E, Harrington J, Molcho M, Layte R, Tully N, et al.: SLAN 2007: Survey of Lifestyle, Attitudes & Nutrition in Ireland: main report. In Department of Health and Children. Dublin: The Stationery Office; 2008. [http://www.dohc.ie/publications/slan07_report.html webcite]
• [14]Harrison E, Rose D: The European Socio-economic Classification (ESeC) User Guide. Colchester, UK: Institute for Social and Economic Research, University of Essex; 2006. [http://www.iser.essex.ac.uk/files/esec/guide/docs/UserGuide.pdf webcite]
• [15]StataCorp: Stata statistical software: release 12. College Station, TX: StataCorp LP; 2011.
• [16]Robbins AS, Chao SY, Fonseca VP: What’s the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes. Ann Epidemiol 2002, 12:452-454.
• [17]Hanley J: A heuristic approach to the formulas for population attributable fraction. J Epidemiol Commun Health 2001, 55:508-514.
• [18]Miettinen OS: Proportion of disease caused or prevented by a given exposure, trait or intervention. Am J Epidemiol 1974, 99:325-332.
• [19]Greenland S, Drescher K: Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993, 49:865-872.
• [20]Zou G: A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004, 159:702-706.
• [21]CSO: Annual Population Estimates, Central Statistics Office, Ireland. [http://www.cso.ie/px/pxeirestat/Database/eirestat/Annual%20Population%20Estimates/Annual%20Population%20Estimates_statbank.asp?SP=Annual webcite]
• [22]Turley M, Tobias M, Paul S: Non‒fatal disease burden associated with excess body mass index and waist circumference in New Zealand adults. Aust New Zealand J Publ health 2006, 20:231-237.
• [23]Kress AM, Hartzel MC, Peterson MR: Burden of disease associated with overweight and obesity among US military retirees and their dependents, aged 38–64, 2003. Prevent Med 2005, 41:63-69.
• [24]Jiang Y, Chen Y, Manuel D, Morrison H, Mao Y: Quantifying the impact of obesity category on major chronic diseases in Canada. Sci World J 2007, 7:1211-1221.
• [25]Dal Grande E, Gill T, Wyatt L, Chittleborough CR, Phillips PJ, Taylor AW: Population attributable risk (PAR) of overweight and obesity on chronic diseases: South Australian representative, cross-sectional data, 2004–2006. Obes Res Clin Pract 2009, 3:159-168.
• [26]The Cost of Overweight and Obesity on the Island of Ireland, Safefood, Dublin, 2012. [http://www.safefood.eu/Publications/Research-reports/The-cost-of-overweight-and-obesity-on-the-island-o.aspx webcite]
• [27]Rose G, Khaw K-T, Marmot M: Rose’s Strategy of Preventive Medicine: The Complete Original Text. USA: Oxford University Press; 2008.
• [28]Rupp I, Triemstra M, Boshuizen HC, Jacobi CE, Dinant HJ, van den Bos GA: Selection bias due to non-response in a health survey among patients with rheumatoid arthritis. Eur J Publ Health 2002, 12:131-135.
• [29]Gorber SC, Tremblay M, Moher D, Gorber B: A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev 2007, 8:307-326.
• [30]Villanueva EV: The validity of self-reported weight in US adults: a population based cross-sectional study. BMC Public Health 2001, 1:11.
• [31]Shiely F, Perry IJ, Lutomski J, Harrington J, Kelleher CC, McGee H, Hayes K: Temporal trends in misclassification patterns of measured and self-report based body mass index categories-findings from three population surveys in Ireland. BMC Public Health 2010, 10:560.
• [32]Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ: Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 2004, 57:1096-1103.
• [33]Kriegsman DM, Penninx BW, Van Eijk JTM, Boeke AJP, Deeg DJ: Self-reports and general practitioner information on the presence of chronic diseases in community dwelling elderly: a study on the accuracy of patients’ self-reports and on determinants of inaccuracy. J Clin Epidemiol 1996, 49:1407-1417.
• [34]Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH: The disease burden associated with overweight and obesity. JAMA 1999, 282:1523-1529.
• [35]Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS: Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003, 289:76-79.
• [36]Paeratakul S, Lovejoy J, Ryan D, Bray G: The relation of gender, race and socioeconomic status to obesity and obesity comorbidities in a sample of US adults. Int J Obes Relat Metabol Disord 2002, 26:1205.
• [37]Hernán M, Taubman S: Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes 2008, 32:S8-S14.