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Open Access Highly Accessed Commentary

The impact of excess body weight at the hospital frontline

Andrew G Renehan123* and Iain E Buchan1

Author Affiliations

1 MRC Health eResearch Centre, Farr Institute for Health Informatics Research, University of Manchester, Manchester, UK

2 Institute of Cancer Sciences, University of Manchester, Manchester, UK

3 Department of Surgery, University of Manchester, Manchester Academic Health Science Centrem The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK

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BMC Medicine 2014, 12:64  doi:10.1186/1741-7015-12-64


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/12/64


Received:24 March 2014
Accepted:24 March 2014
Published:17 April 2014

© 2014 Renehan and Buchan; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Quantification of disease burden by deaths or years lived with disability is a useful indicator as it informs prevention by accounting for health loss but it does not reflect the needs for health services. An alternative indicator is to quantify the impact of a risk factor on health care utilization. In an article published in BMC Medicine, Reeves and colleagues describe the relationship between body mass index in 1.2 million women (England) and hospital admission rates. The main finding was that around one in eight hospital admissions was attributable to overweight or obesity, translating to around 420,000 extra hospital admissions, and two million extra days spent in hospital, annually. These findings reinforce the evidence that excess body weight is associated with extensive healthcare utilization and emphasize the need to scale-up and speed-up research if global problems, such as obesity, are to be tackled with due alacrity.

Please see related research: http://www.biomedcentral.com/1741-7015/12/45 webcite.

Keywords:
Body mass index; Hospital admission rates; Data linkage

Background

Excess body weight, commonly measured as body mass index (BMI) ≥25 kg/m2 (overweight) and BMI ≥30 (obese), is an established risk factor for mortality and morbidity from cardiovascular disease [1], type 2 diabetes [2], cancer [3] and osteoarthritis [4]. An estimated three million deaths per year worldwide are attributed to excess body weight [5]. The Global Burden of Disease project [6] ranked excess body weight as the sixth largest cause of death and disability-adjusted life years (DALYs; sum of years lived with disability and years of life lost) after considering the independent effects of 67 different risk factors, clustered for 21 regions in the year 2010. This ranking increased from seventh in 1990 and was mainly accounted for by obesity/overweight-related cardiovascular disease, diabetes, cancer and musculoskeletal disorders [6]. Quantification of the disease burden by deaths or DALYs is useful as it informs prevention by accounting for health loss but it does not reflect the needs for health services. An alternative indicator is to quantify the impact of a risk factor, in this example, excess body weight, on the utilization of health care services.

BMI and hospital utilization (England)

In an article published in BMC Medicine, Reeves and colleagues describe the relationship between BMI, determined at baseline (1996 to 2001) in the Million Women Study (age 50 to 64 years) and hospital admission rates over a 9.2 year follow-up period [7]. The main finding was, that among these women in England, around one in eight hospital admissions was attributable to overweight or obesity, translating to around 420,000 extra hospital admissions, and two million extra days spent in hospital, annually. The authors examined 25 types of indications for admission – of these, significant increases in the risk of admission with increasing BMI were observed for 19. Almost two-thirds (62% of first time admission) were for diabetes, ischemic heart disease, stroke, joint replacements, gallbladder disease or cancer.

To clinicians at the hospital frontline, these figures come as no surprise and are readily believed. By the nature of the study, the analysis is limited to women; therefore, the numbers of hospital admissions attributed to excess body weight for the general population may be twice the reported rates. The hospital admission data covered a period up to the end of 2008 – so, as trends of median BMI values in England have continued to increase since then (albeit with some slowing down) [8], current admission rates attributed to excess body weight may be higher.

The impressive numbers (over 1.2 million women with BMI measurements) included in this study allowed for powerful analyses, not only in the full group of women but also in subgroups defined by ten age bands after age 50 years. The near parallel curves for the relationships of BMI with rates of hospital admission suggest no interaction between age and BMI; in other words, the excess numbers of hospitalizations attributable to overweight and obesity impact similarly in middle and older ages. Given that much of the obesity epidemic in England is driven by weight gain in early adulthood (‘the fat getting fatter’) [8], these relationships make grim reading for those managing the already overstretched health care systems [9].

A particularly useful insight from this study is that a third of first admissions in the overweight and obese women were due mainly to venous thromboembolism, diverticular disease, diaphragmatic hernia, cataracts or carpal tunnel syndrome. These observations stretch the usual radar beyond common conditions causally associated with BMI, such as diabetes, thereby giving a fuller reflection of the impacts of excess body weight. Consider Saint’s triad of diverticular disease, hiatus hernia and gallbladder disease, traditionally thought to have no common pathophysiology and used as a counterexample to the commonly used ‘single cause of disease’ principle in diagnostic medicine of Occam's Razor [10]. Here, obesity may be the common pathophysiological mechanism and large datasets will allow one to test the hypothesis that these overlapping conditions are explained by one pathway.

Several avenues for more investigation

The study by Reeves and colleagues confirms and extends the evidence that a high burden of hospital admissions in the United Kingdom is attributable to excess bodyweight [11,12]. This adds to the need for policy-makers to prioritize anti-obesity strategies, but also raises questions about entry points for intervention. The words of British epidemiologist, William Farr (1807 to 1883) ‘Diseases are more easily prevented than cured and the first step to their prevention is the discovery of their exciting causes’ are germane to this situation. The current study illuminates several avenues for more investigation:

First, there are opportunities to use hospital admissions as an outcome measure to assess population-level interventions. For example, hospital admissions rates are likely to reflect the effects of public health interventions sooner than mortality and with greater relevance than the usual process measures. This was clearly the case in tobacco control when demonstrating significant reductions in acute admissions for myocardial infarction following the introduction of the smoking bans in Scotland [13] and Liverpool [14].

Second, there is a need to distinguish unplanned from planned admissions, on which anti-obesity programs might have differential impacts. Third, it is unlikely that excess body weight, as a risk factor, and hospital admission rates, are causal in isolation. For example, data from two Scottish populations show that excess BMI is associated with a 30% increase in liver disease mortality, which is dwarfed by the observation of a 300% increase associated with excess alcohol consumption [15]. However, when these two risk factors occur together, there is a supra-additive interaction, and a 900% increase in liver disease mortality.

Fourth, against these complexities, there is a need to evaluate the cost of excess hospital admission attributable to excess weight. Withrow and Alter [16] estimated that obesity accounts for between 0.7% and 2.8% of world country's total healthcare expenditures, while the FORESIGHT project forecasted that billions of pounds will be consumed in 2020 due to obesity [17]. However, these models do not directly include hospital admission costs, and for example, they do not rank these costs against those attributable to smoking, alcohol or trauma. Such data are required for health resource allocation decisions at a governmental level.

Patients and the public are best served if such research is done in a timely manner using all relevant data. In the UK, there are particularly strong primary care data which may be linked with hospital admissions and used to adjust for many potential confounding factors. This work can be challenging in terms of data acquisition, information extraction, cleaning and analysis, requiring a concert of informatics, statistics and epidemiology. To harness such data for research, the UK recently established the Farr Institute for Health Informatics Research [18] creating a network of expertise alongside the physical and electronic infrastructure required to pursue research questions with linked health datasets at the national scale, quickly. Initiatives like the Farr Institute will need to be developed and linked internationally if policies about global public health problems, such as obesity, are to be informed properly.

Conclusions

The findings of Reeves and colleagues reinforce the evidence that excess body weight is associated with extensive healthcare utilization. Similar signals may have languished for many years across other data sources. It is time to consider scaling-up and speeding-up research with linked data if global problems, such as obesity, are to be tackled with due alacrity.

Competing interests

The authors declare they have no competing interests.

Authors’ contributions

Both authors contributed to conception of the article. AGR drafted the article. Both authors were involved in editing and revision of the manuscript and both agreed to its publication.

Acknowledgements

Both authors are employed by the University of Manchester. There was no funding to directly support the writing of this manuscript.

References

  1. Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR, Lawlor DA, Nordestgaard BG, Ridker P, Salomaa V, Stevens J, Woodward M, Sattar N, Collins R, Thompson SG, Whitlock G, Danesh J: Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.

    Lancet 2011, 377:1085-1095. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Vazquez G, Duval S, Jacobs DR Jr, Silventoinen K: Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis.

    Epidemiol Rev 2007, 29:115-128. PubMed Abstract | Publisher Full Text OpenURL

  3. Renehan A, Tyson M, Egger M, Heller RF, Zwahlen M: Body mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies.

    Lancet 2008, 371:569-578. PubMed Abstract | Publisher Full Text OpenURL

  4. Bijlsma JW, Berenbaum F, Lafeber FP: Osteoarthritis: an update with relevance for clinical practice.

    Lancet 2011, 377:2115-2126. PubMed Abstract | Publisher Full Text OpenURL

  5. World Health Organization: Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva: World Health Organization; 2009. OpenURL

  6. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, Atkinson C, Bacchus LJ, Bahalim AN, Balakrishnan K, Balmes J, Barker-Collo S, Baxter A, Bell ML, Blore JD, Blyth F, Bonner C, Borges G, Bourne R, Boussinesq M, Brauer M, Brooks P, Bruce NG, Brunekreef B, Bryan-Hancock C, Bucello C, Buchbinder R, et al.: A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010.

    Lancet 2012, 380:2224-2260. PubMed Abstract | Publisher Full Text OpenURL

  7. Reeves GK, Balkwill A, Cairns BJ, Green J, Beral V: Hospital admissions in relation to body mass index in UK women: a prospective cohort study.

    BMC Med 2014, 12:45. PubMed Abstract | BioMed Central Full Text OpenURL

  8. Sperrin M, Marshall AD, Higgins V, Buchan IE, Renehan AG: Slowing down of adult body mass index trend increases in England: a latent class analysis of cross-sectional surveys (1992–2010).

    Int J Obes (Lond) 2013.

    doi:10.1038/ijo.2013.161

    OpenURL

  9. NHS winter crisis as 40 per cent rise in cancelled operations Telegraph http://www.telegraph.co.uk/news/uknews/10453428/NHS-winter-crisis-as-40-per-cent-rise-in-cancelled-operations.html webcite (accessed 20 March 2014)

  10. Hilliard AA, Weinberger SE, Tierney LM Jr, Midthun DE, Saint S: Clinical problem-solving, Occam's razor versus Saint's Triad.

    N Engl J Med 2004, 350:599-603. PubMed Abstract | Publisher Full Text OpenURL

  11. Hart CL, Hole DJ, Lawlor DA, Smith GD: Obesity and use of acute hospital services in participants of the Renfrew/Paisley study.

    J Public Health (Oxf) 2007, 29:53-56. Publisher Full Text OpenURL

  12. Wulff J, Wild SH: The relationship between body mass index and number of days spent in hospital in Scotland.

    Scott Med J 2011, 56:135-140. PubMed Abstract | Publisher Full Text OpenURL

  13. Pell JP, Haw S, Cobbe S, Newby DE, Pell AC, Fischbacher C, McConnachie A, Pringle S, Murdoch D, Dunn F, Oldroyd K, Macintyre P, O'Rourke B, Borland W: Smoke-free legislation and hospitalizations for acute coronary syndrome.

    N Engl J Med 2008, 359:482-491. PubMed Abstract | Publisher Full Text OpenURL

  14. Liu A, Guzman Castillo M, Capewell S, Lucy J, O'Flaherty M: Reduction in myocardial infarction admissions in Liverpool after the smoking ban: potential socioeconomic implications for policymaking.

    BMJ Open 2013, 3:e003307. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  15. Hart CL, Morrison DS, Batty GD, Mitchell RJ, Davey Smith G: Effect of body mass index and alcohol consumption on liver disease: analysis of data from two prospective cohort studies.

    BMJ 2010, 340:c1240. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Withrow D, Alter DA: The economic burden of obesity worldwide: a systematic review of the direct costs of obesity.

    Obes Rev 2011, 12:131-141. PubMed Abstract | Publisher Full Text OpenURL

  17. 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. PubMed Abstract | Publisher Full Text OpenURL

  18. The Farr Institute of Health Informatics Research [http://www.farrinstitute.org/ webcite]