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Development of the obesity epidemic in England David Boniface Health Behaviour Research Centre Epidemiology and Public Health University College London.

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Presentation on theme: "Development of the obesity epidemic in England David Boniface Health Behaviour Research Centre Epidemiology and Public Health University College London."— Presentation transcript:

1 Development of the obesity epidemic in England David Boniface Health Behaviour Research Centre Epidemiology and Public Health University College London

2 Talk Programme Description of the obesity epidemic. Effects of environment, lifestyle and ageing. Extrapolating current obesity trends. Issues and limitations.

3 Obesity Epidemic References Development of the epidemic: –Breslow L. Public health aspects of weight control. Am J Public Health 1952;42: –Rosenbaum S. 100 years of heights and weights. J R Statist Soc A 1988;151: –Harlan WR et al. Secular Trends in body mass in the United States, Am J Epidemiol 1988;128: –Flegal KM, et al. Prevalence and trends in obesity among US adults, JAMA. 2002; 288:

4 Data Sources (cross-sectional) National Heights and Weights Survey, 1980 Health and Lifestyle Survey, 1984/85 Diet and Nutritional Survey of British Adults, 1987 Health Survey for England (annual)

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10 Change of shape of the BMI distribution

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13 Changes in percentiles (Tukey mean-difference plot) Differences between the 1980 and 2006 values of each percentile are plotted against the mean of the 1980 and 2006 values Graph shows these percentiles: 2.5 5, 10, 20, 30, 40, 50, 60, 70, 80, 90,

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17 Interpretation of Tukey mean- difference plots Greater increases in BMI for higher vs lower percentiles Females: greater increases than males in the higher percentiles, especially in younger age groups Males: increases in lower percentiles for older men.

18 Obesity epidemic - contributions of environment, lifestyle and ageing

19 Pseudo-cohorts take a sequence of cross sectional surveys some years apart. in each survey identify participants whose ages match those of a hypothetical cohort repeatedly observed as it ages.

20 Compare real- with pseudo-cohorts A real longitudinal cohort study provides rich information about growth patterns A pseudo-longitudinal study can draw repeatedly on the same range of ages. A group of parallel pseudo-cohorts can help separate ageing and environmental effects.

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24 Linear annual increases in BMI MALESCOEFFICIENT95% C.I.Due to age (adj)0.079(0.076 to 0.081)ageing survey year(adj)0.108(0.103 to 0.113)environ. survey year (adj for cohort) (expct ) (0.170 to 0.184)cohort FEMALES age (adj)0.088(0.085 to 0.091)ageing survey year(adj)0.114(0.108 to 0.120)environ. survey year (adj for cohort) (expct ) (0.191 to 0.208)cohort

25 Obesity epidemic - contributions of environment, lifestyle and ageing Pseudo-cohorts can reproduce the development of adiposity throughout lifetimes Ageing and environmental effects are confounded within but not between cohorts. BMI increase can be decomposed into ageing and environmental contributions.

26 Concluding remarks on historical development of the BMI distribution 1.Significant environment based increases in BMI started after Male ageing effect mainly in 18-44y but females show increases 18-64y. 3.Environment effect > ageing effect. 4.Pseudo-cohorts BMI increase, total rate = environment + ageing.

27 Obesity rates and the future Attention is on future of obesity rates as BMI 30+ is the threshold for clinical consequences.

28 Overweight and Obesity Prevalence (ages 18-64) BMI 25 to 29 = overweight: BMI 30+ = obese: Base n% overwt% obese 1980 men women 3,683 4, men women 5,772 6, men women 4,151 4, [age standardised to 2001]

29 Alternative models for growth in obesity rates The governments Foresight project predicted very high obesity rates for England for 2050 using a tanh model and HSE data from How does this compare with using data and different models?

30 tanh - A simple and convenient set of slowly varying, monotonic functions that are asymptotic to 0 and 1 are provided by the set: Foresight (McPherson K. et al (2007))

31 Probability of males aged 21–60 belonging to a specific BMI group in a given year [95% C.I.]

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34 Probability of females aged 21–60 belonging to a specific BMI group in a given year [95% C.I.]

35 NatCen Prediction Zaninotto P, Wardle H, Stamatakis E. et al Forecasting Obesity to Report prepared for UK Department of Health. This report was by NatCen – who carry out the Health Survey for England. It used HSE Predictions for obesity rates were: Men (16-74) 21.3% 29.4% Women (16-74)21.7% 26.3%

36 Zaninotto et al, 2006 Two curves, …. power and exponential, were selected as being plausible models for the data that would allow for either acceleration or slowing down in changes in prevalence of obesity The choice between the two curves was made on the basis of the curve that best fits the data Power curve: Y = b 0 *(t**b 1 ) Logistic curve: Y = 1/{(1/u) + (b 0 *(b 1 **t))}

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39 Prediction using Gompertz curve and data

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42 Predictions to 2010 model Data used Male Obese % % C.I. tanh (Foresight) (2 parameters) (21-60y) 31.3(30.0 – 32.7) NATCEN (16-74y) 29.4 Gompertz (18-64y) 25.5(24.3 – 26.8)

43 Predictions to 2050 model Data used Male Obese % % C.I. tanh (Foresight) (2 parameters) (21-60y) 60.0(55.0 – 65.0) tanh (3 parameters) (21-60y) 31.5(19.7 – 43.3) Gompertz (18-64y) 33.0(23.1 – 43.3)

44 Modelling Issues Obtaining a predicted distribution of BMI rather than just a mean or % obese Taking account of predicted changes in population age distribution Choice of shape of fitted curve

45 Conclusions

46 Conclusions (pseudo-cohorts) A group of parallel pseudo-cohorts is advantageous in separating ageing and environmental effects.

47 Conclusions (BMI trends) Pseudo-cohorts showed BMI in men and women increasing as they aged by around 0.20 BMI points p.a. Environment-based change was the largest contributor at around 0.11 p.a. Ageing added around 0.08 p.a.

48 Conclusions (start of the epidemic) The main environment-based increases appear after 1987

49 Conclusions (BMI distribution shape change) There has been a marked change in the BMI distribution: –Higher percentiles have increased more than lower percentiles. –Especially true in women, where younger women have shown the largest increases. –Suggests faster than expected increase in prevalence of very obese (BMI 40+)

50 Conclusions (predicting the future) Predicting the future is important and there should be a debate about the methods.


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