Obesity and Overweight Surveillance in England: what is measured and where are the gaps?

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1 Obesity and Overweight Surveillance in England: what is measured and where are the gaps? November 2009

2 Executive Summary This paper aims to identify and describe the main sources of national level surveillance data on obesity in England. It also highlights gaps in these data sources and makes recommendations for the national surveillance of obesity in the future. The paper focuses on data from surveys and other data collections which record the population prevalence of obesity among adults and children. Some of these data sources also include information on the determinants of obesity and on relevant anthropometric measures. The sources discussed in this report are all publicly available: commercial data sources have not been examined. Information about obesity among adults is available from: Health Survey for England (HSE) measures Body Mass Index (BMI), waist and hip circumference as well as socio-demographic and lifestyle information that may help improve our understanding of the determinants of obesity; Quality and Outcomes Framework (QOF) clinical register of obese patients from general practice; Neighbourhood Statistics: Model-based Estimates estimates of obesity prevalence using HSE and Census data. Information about obesity among children is available from: HSE measures BMI, waist and hip circumference and includes information on the determinants of obesity; National Child Measurement Programme (NCMP) measures height and weight of children aged 4 to 5 and 10 to 11 years; Health Behaviour in School-aged Children Study (HBSC) records selfreported BMI for secondary school pupils. The data collection systems for obesity in England are reasonably strong. BMI data collected over many years, principally via the HSE, enable population prevalence to be monitored, and the NCMP s large scale data collection provides data for more indepth investigation among children in specific age groups. Various enhancements to data collection systems are suggested (for example, larger sample sizes for certain subgroups in particular surveys, and the inclusion of additional measures in some cases). These would provide a greater understanding of obesity at population level and allow in-depth analyses across specific sub-groups of the population. The difficulties are not easy to tackle, however, and reflect the challenges of population level data collection and monitoring. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 2

3 Introduction Assessment of the health risk associated with excess adiposity requires the measurement of individuals. However, obesity is not only an individual issue. It has a major impact on society and is recognised as one of the greatest public health challenges in England today. Data on the prevalence of overweight and obesity help in the targeting of interventions, and trend data can help our understanding of how the situation changes over time. Obesity is a complex condition influenced by many factors. Data collection on a large scale allows the monitoring of obesity at population level and may provide data for in-depth analysis of different causal and contributory factors, supporting the development of effective interventions and public health approaches to tackle obesity. This paper aims to describe the main sources of national level surveillance data on obesity currently available in England. It discusses approaches to the measurement of obesity, including their value in predicting future ill health and their suitability for large scale data collection. The main sources of data on obesity in both adults and children are identified and gaps are highlighted. The paper also identifies limitations of current surveillance systems and suggests improvements that will allow a greater understanding of population trends in obesity and its determinants across all sub groups of the population in the future. Measuring obesity There are a number of different approaches to the measurement of obesity. They range from simple proxies that can be used at population level to technically sophisticated methods that provide more accurate measurements but are too complex to be used for large scale data collection. The principal methods of measuring obesity are discussed below. Further details of the advantages and limitations of each method are provided in Appendix A. Body Mass Index (BMI) The most commonly used measure of obesity is BMI, which provides a proxy measure of total adiposity (the amount of fat around the body). There is a substantial body of literature demonstrating links between an elevated BMI and an increased risk of current and future ill health. 1 BMI is calculated by dividing body weight in kilograms by height in metres squared. A BMI between 25.0 and 29.9 kg/m 2 is frequently used to classify adults as overweight, whereas a BMI of 30 kg/m 2 or over is classified as obese (Table 1). NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 3

4 Table 1: World Health Organisation BMI classification system for adults BMI range (kg/m2) Classification < 18.5 Underweight Normal weight Overweight Obese > 40 Morbidly obese BMI is a very useful measure for studying trends or investigating differences between populations. Height and weight measures can be taken with a good degree of accuracy across large number of individuals in a short space of time. The equipment required is not expensive and little is required in the way of operator training. Height and weight can also be measured with minimal body contact between subject and operator, and the subject is only required to remove outer clothing. Because of its simplicity and the proven links between BMI and future ill-health, it is the most frequently used measure of obesity for population surveillance. In children and adolescents BMI varies with age and sex, which makes it more difficult to classify children as obese, overweight or underweight. The BMI thresholds given in Table 1 are only suitable for adults. For children, a set of thresholds which vary by age and sex must be used. Thresholds are obtained by choosing a specific BMI centile on a child growth reference curve. In England, the 85 th and 95 th centiles of the British 1990 Growth Reference (UK90) are most frequently used to classify children as overweight or obese for the purposes of population monitoring. These thresholds are used in the published obesity and overweight prevalence figures from the HSE and NCMP. Other thresholds do exist for classifying children s BMI, including those used in a clinical context in the UK, or those recommended by international bodies such as the International Obesity Taskforce (IOTF) and the World Health Organisation (WHO). Although it is the measure of obesity most commonly used for population surveillance, BMI does have some disadvantages. It may be less useful on an individual basis since it is not a direct measure of body fat mass or distribution and does not account for differences in body composition. For example, BMI does not accurately control for large differences in height between individuals, and it may be skewed by high muscle mass. The relationship between BMI and health also appears to vary with ethnicity. 2 BMI has been found to overestimate overweight and obesity in black ethnic groups for all age groups in the United States, 3 and findings from the NCMP suggest that height differences between ethnic groups, rather than body fat, may account for the higher prevalence of obesity amongst children from black ethnic groups in England. 4 Alternative BMI thresholds have been proposed for some ethnic groups. Such thresholds NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 4

5 require further validation, however, and a greater understanding is needed of the way BMI varies with age and sex in different ethnic groups. All the data sources included in this report collect the height and weight measurements required to calculate BMI, or provide prevalence figures based on BMI. As BMI has been collected for many years, it is the most useful measure when studying obesity trends in England. However, there may be differences in body composition between population cohorts as well as between different ethnic groups and sexes. It is thought that current generations may have a higher proportion of fat mass (as opposed to lean or non-fat mass) for a given BMI compared to past generations. 5 This poses challenges for the interpretation of BMI measurements for specific age, sex and ethnic groups over time. Increases in BMI in boys during puberty, for example, may be due mainly to increases in fat-free mass, whilst the tendency for BMI to decrease in the elderly may correspond to reduction in fat-free rather than fat mass. It is useful therefore, to collect other anthropometric measurements of body fat. These measures are described below, and issues such as measurement technique, accuracy and appropriateness for large scale data collection are considered for each. Waist circumference and waist-to-hip and waist-to-height ratios Waist circumference is the most frequently used measure of central obesity, particularly for population surveillance. Only one measurement is required per individual, and the cost of the equipment (a tape measure) is negligible. A number of studies have suggested that the accumulation of body fat around the waist (central or abdominal adiposity) may present a higher risk to health than fat deposited in other parts of the body. 1 In adults, central adiposity is known to be associated with increased risk of a number of obesity-related conditions, including type 2 diabetes, 5 hypertension 6,7 and heart disease. 6 Although measures of central adiposity are closely correlated with BMI, they have been shown to predict future ill health independently of BMI. 8 However, the accurate measurement of waist circumference does require training, and measurements are likely to show greater intraobserver and interobserver a error than height or weight measurements. 9,10 In addition, taking waist measurements requires body contact between the operator and subject, which can lead to ethical issues in the collection of child data. According to National Institute for Health and Clinical Excellence (NICE) guidelines, waist circumference thresholds for adults are as follows: a Intraobserver error refers to the differences in interpretation by an individual making observations of the same phenomenon at different times. Interobserver error refers to differences in interpretation by two or more individuals making observations of the same phenomenon. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 5

6 Increased risk of health problems: Men 94cm Women 80cm Greatly increased risk of health problems: Men 102cm Women 88cm Although most of the evidence for waist circumference as a predictor of future morbidity has been derived from adult studies, similar correlations have been found for children. 11 The UK90 reference contains a waist circumference distribution for British children, but to date there are no agreed thresholds that can be used to classify children s waist circumference. Further work is needed on the use of waist measurements in children. As with BMI, waist circumference has a number of disadvantages as a proxy measure of obesity. Waist circumference does not adjust for body stature in any way, and some research suggests different waist circumference thresholds may be needed for different ethnic groups. 1 More complex measures of central adiposity exist, such as waist-to-hip and waist-to-height ratios, and these provide a measure of abdominal obesity that adjusts for an individual s body shape. There is, however, less agreement over which thresholds should be used with these measures, and as they require more than one measurement to be taken per individual, they tend to be used less frequently for population surveillance. As waist circumference is highly correlated to BMI, some may question its relevance as an additional measure. However, variation in waist circumference for a given BMI has been found to be a strong predictor of allcause mortality. A combination of both measurements may thus be the most useful in both population monitoring and clinical practice. 12 Of the different data sources included in this report, only the HSE includes waist measurements. Height and hip measurements are also collected, so waist-to-height and waist-to-hip ratios can be calculated from HSE data. Bioelectrical impedance analysis (BIA) BIA measures impedance, or the resistance to flow of an electric current through the body. Lean tissue, which is made up of over 70% water, acts as an electrical conductor, whereas fat mass, which has no water content, acts as an insulator. By measuring impedance across the body an estimate of the percentage of body fat can be obtained. Small and inexpensive BIA devices are available which can be used by individuals in their own homes. However, such machines may be inaccurate as they often estimate from the limbs only, e.g. legs in stand-on scales and arms in hand-held devices. Portable instruments suitable for use in large scale data collection are available, but the large number of different devices in use vary in quality and accuracy. Impedance measurements can also vary with skin conditions such as dryness, the presence of sweat, and temperature. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 6

7 BIA relies on prediction models for the estimation of fat-free mass (FFM). 13 Very few of these models have been developed in non-white populations. 14 Their validity is therefore questionable when applied to populations other than those in which they were generated because of ethnic differences in body composition. For example, prediction models developed in predominantly white populations have been found to be less valid for black populations. 15 BIA is a useful research tool where consistency of measurement and validity of prediction models can be carefully controlled, but its limitations mean that it is rarely used for population-level surveillance. Skinfold thickness Skinfold thickness refers to the measurement of subcutaneous fat located directly beneath the skin. This is done by grasping a fold of skin and subcutaneous fat and measuring its thickness using callipers. These skinfold measurements are then converted into an estimate of an individual s percentage body fat, typically using a formula that takes account of age and sex. Although the equipment needed to measure skinfold thickness is inexpensive, considerable training is required to take these measures accurately. Intraobserver and interobserver reliability has been found to be an issue for skin fold measurement 15, although reliability can be improved by training and standardisation of technique. Skinfold measurement involves body contact between operator and subject, and it is usually necessary to remove clothing from the upper part of the body. As a result, measurement may be considered intrusive or embarrassing by some individuals, limiting the use of this measure in large scale population surveillance. In addition, there are a number of different approaches both to taking skinfold measurements and to converting these into estimates of an individual s percentage body fat. Different results are possible with different methods, and as a result skinfold thickness is most appropriate for monitoring change in body composition in individuals over time, where measurements can be directly compared rather than converted to percentage body fat. If it is used for population surveillance, it is important to ensure that consistent methodology is used for the estimation of percentage body fat. Dunk tank - hydrodensitometry The dunk tank compares an individual s weight in water to their weight in air, whilst controlling for lung volume. Different body tissues have different densities: individuals with proportionally higher body fat will have less overall body density and will be more buoyant. This technique requires expensive specialist equipment, requires the individual to get wet, and may take a long time. Conditions such as temperature and humidity can also affect the results. Consequently this method is unsuitable for large scale surveillance. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 7

8 Body pod - air-displacement plethysmography Measuring the amount of air displaced by an individual is an alternative to using water to calculate body density. A Body Pod consists of two chambers, separated by a sensitive membrane that measures pressure. The change in air pressure between when the test chamber contains a person and when it does not are compared, and body density is calculated, adjusting for the height and weight of the individual. Body Pods are expensive and the measurement requires individuals to wear a tight fitting bathing suit and a swim cap. As a result they are again unsuitable for population surveillance of obesity. Dual-energy X-ray absorptiometry (DXA) As bone, muscle and fat all absorb and transmit X-rays differently, patterns of X-ray absorption can be used to estimate the amounts and densities of various tissues within the body. However, as it measures absorption only in two-dimensions, DXA cannot differentiate directly between visceral and subcutaneous fat. 16 DXA is unsuitable for large scale population surveillance as it requires very expensive machinery as well as highly trained operators and interpreters. However, DXA has gained wide acceptance as the reference standard for comparing the accuracy of other methods. 17 A study by Sardinha et al. (1998) 17 used DXA as a reference for comparing four other methods of estimating body fat: a body pod, skinfold thickness, BMI and BIA. The body pod was found to be the most accurate of the four methods, whilst BIA was found to be the least accurate (Table 2). Table 2: Correlation between DXA and other methods of measuring body fat (A correlation coefficient of 1 describes a perfect agreement) Method of measuring body fat Correlation coefficient with DXA Body pod 0.93 Skinfold thickness 0.87 BMI 0.81 BIA 0.68 Computerized tomography (CT) Computerized tomography (CT) has been shown to be an accurate and precise technique for measuring soft tissue. 18 It permits differentiation between visceral and subcutaneous fat in a cross-section of the body in a way that other methods, including DXA, do not. 19 Effective methods for assessing visceral fat are important to investigate the role of visceral fat in the increased health risks associated with obesity. However, CT scans are expensive, time-consuming and require a relatively high radiation dose, 20 making them unsuitable for large scale data collection. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 8

9 There is still a need, therefore, to develop and test alternative ways of estimating visceral fat. 16 Magnetic resonance imaging (MRI) Magnetic resonance imaging (MRI) compares well with CT for the measurement of body fat and both techniques have a similar accuracy in comparison with chemical analysis. 18 MRI scans use magnetic and radio waves, meaning that there is no exposure to X-rays or any other damaging forms of radiation. However, MRI is also expensive and time-consuming and similarly unsuitable for population monitoring. Measures of obesity for population surveillance CT and MRI scans are too slow and expensive to be used for collecting large scale data on obesity prevalence. Despite showing a closer correlation with gold standard DXA readings than BMI, skinfold thickness and body pods cannot be easily used for population surveillance because of the practical difficulties outlined earlier. BMI shows an acceptable correlation with DXA which, combined with its simplicity and other advantages, supports its position as the most commonly used measure of obesity for population surveillance. Although there is substantial evidence that the distribution of fatty tissue around the body has an important effect on health, it is worth noting that many of the biggest studies linking obesity and ill health use proxy measures such as BMI, waist circumference and waist-to-hip ratio, rather than methods that measure the proportion of body fat more accurately, such as CT, MRI, DXA, the body pod or skinfold thickness. Including waist measurements in population surveillance datasets should enable the proportion of the population at risk of obesity-related ill health to be more accurately described, however, and would add minimal expense or inconvenience to the data collection process. None of the other proxy measures currently available could be easily adopted for population surveillance programmes, and it is unclear whether including other measures would add significantly to our understanding of the epidemiology of obesity. In conclusion, BMI is currently the best measure of obesity for population surveillance. The ease with which it can be measured makes it a convenient method for surveys involving large numbers of individuals, and proven links between BMI and future obesity-related ill health, as well as the history of published statistics using this measure, makes BMI highly suitable for the analysis of trends over time. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 9

10 Surveillance data for obesity in adults This section discusses the main sources of surveillance data describing the prevalence of overweight and obesity among adults, and highlights any concerns and weaknesses for each. A summary of key details for each data source is given in Appendix B. Information on obesity prevalence in adults in England comes principally from the following sources: Health Survey for England (HSE) This is an annual survey designed to measure health and health related behaviour in adults and children living in private households in England; Quality and Outcome Framework (QOF) The QOF clinical register of obesity in general practice is based on patients aged 16 and over with a BMI greater than or equal to 30 recorded in the previous 15 months; Neighbourhood Statistics: Model-based Estimates These estimates of adult obesity (and other lifestyle indicators) are modelled using HSE, Census and other data. Health Survey for England (HSE) - adults The HSE is currently seen as the most robust source of information on obesity prevalence in England and is the only survey discussed within this report that collects data from a nationally representative population sample. The HSE is an annual survey, designed to measure the health and health related behaviour of adults and children living in private households in England. It has been undertaken since 1991 and provides many years of data for trend analysis at national level. HSE data can provide estimates of obesity prevalence at national and regional level, but not at lower geographical levels. Height and weight measurements are taken by a trained interviewer, whilst waist and hip circumference measures are taken by a nurse, allowing both BMI and measures of central adiposity to be calculated. The HSE also collects information on many of the recognised determinants of obesity and a range of socio-demographic factors. These include dietary habits, physical activity, attitudes to physical activity and healthy eating, education, income, urban/rural classification of dwelling, as well as other measures including mental and physical health. HSE data thus enables investigation into both population surveillance and the determinants of obesity. The HSE collects data from a large number of individuals, and attempts to sample from a cross-section of the national population. As the epidemiology of obesity is a complex process influenced by many individual factors, including sex, age, socioeconomic status and ethnicity, sample sizes need to be large enough to consider all these variables in trend analyses. However, the sample size is not large enough for some factors, such as ethnicity, to be accurately controlled for. Segmentation by age and socio-economic status also reduces sub-group sample size, but both these variables, unlike ethnicity, can be manipulated so that sub-group numbers are increased to a level suitable for analysis. For example, values for age and socio economic status can be combined into wider categories, whereas values for ethnicity NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 10

11 cannot. If sampling occurs in proportions that reflect the proportion of different ethnic groups in the national population, 20 this is unlikely to provide large enough numbers of individuals of all minority ethnic groups for important issues such as age, sex and socio-economic status to be considered as well. In the 2007 HSE, a total of 14,337 individuals were measured. Of these 12,412 (86.3%) were White, 378 (2.6%) were mixed race, 925 (6.4%) Asian or Asian British, 508 (3.5%) Black or Black British, and 114 (0.8%) Chinese or other ethnic groups (Figure 1). Figure 1: Percentage of individuals from different ethnic groups measured in the 2007 HSE White Mixed Asian or Asian British Black or Black British Chinese and other ethnic group In 2004, the HSE sample included a boost to increase the proportion of people from minority ethnic groups. This boost provided useful data to help investigate the influence of ethnicity on obesity, and future boosted samples will make it possible to monitor trends over time in these populations. Similarly, boosting of specific age groups in the HSE sample would allow a focus on narrower age bands and more precise age specific analyses. However, boosting the HSE sample in this manner would either require extra resources or would have to be undertaken at the cost of sampling from other population sub-groups. Quality and Outcome Framework (QOF) QOF is a voluntary reward and incentive programme for GP practices, which rewards practices for the quality of care they provide to their patients. Although voluntary, participation in QOF is very high, with nearly all GP practices taking part in the scheme. In 2007/08 the sum of the practice list sizes for all practices participating was over 54 million, which represents 99.8% of registered patients in England. To support the QOF programme, the Quality Management and Analysis System (QMAS) is used to collect information from GP practice IT systems. QMAS can determine the number of patients coded as having particular clinical conditions. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 11

12 The QOF clinical register on obesity was started in 2006/07 and records the number of patients aged 16 and over with a BMI of greater than or equal to 30 kg/m 2, recorded by the GP practice in the previous 15 months. These data can be used to provide prevalence figures at national, SHA, PCT and GP practice level. These figures must be interpreted with some care, however, as QOF registers do not equate to prevalence as defined by epidemiologists. 21 As well as being influenced by the underlying prevalence of obesity, QOF-based prevalence figures are highly influenced by other factors, such as the proportion of registered patients who visit the practice over a 15 month period, the proportion of these patients that have their BMI measured by the GP, and how many of those identified as obese are correctly coded as such on the practice IT system. Also, QOF data does not provide information on factors such as age and sex, which are important for standardisation in comparative studies. At national level the reported prevalence of obesity through QOF data is substantially below estimates from other sources. The HSE 2007 suggested that 24% of the English population aged 16 years and over had a BMI greater than 30 kg/m 2, compared to 9.4% from 2007/08 QOF data. This suggests that QOF can currently provide little useful information about the absolute prevalence of obesity in England. QOF data is often used for comparisons between GP practices and between different years, where relative differences, rather than absolute prevalence, is what matters. Unfortunately, at this level, use of QOF data is still problematic. There is likely to be substantial variation in the factors outlined above between GP practices, or even between PCTs or SHAs, which in itself could result in large differences in reported prevalence figures. Neighbourhood Statistics: Model-based Estimates The NHS Information Centre for Health and Social Care commissioned the National Centre for Social Research (NatCen) to provide model-based estimates of adult obesity, as well as other lifestyle behaviours, using HSE, Census and other data. The model-based estimate for a particular local authority (LA), or primary care organisation (PCO) is the expected prevalence for that area based on its population characteristics, and as such does not represent an estimate of the actual prevalence. Modelled estimates are specific to the area for which they are calculated and cannot be translated onto any other geographical boundary system. The estimates allow for some boosting of under-sampled groups such as minority ethnic populations, by using Census data and merging three years of HSE data (2003, 2004 and 2005) in order to maximise sample size. 22 Model-based PCO estimates can be used in a number of ways, most obviously to identify PCOs that have an expected prevalence of healthy lifestyle indicators that is significantly higher or lower than England as a whole. These model-based estimates have been used to estimate obesity prevalence in adult ethnic groups at a sub-national level (London, Midlands and Eastern, North and South) and included the following ethnic groups: Black Caribbean, Black African, Indian, Pakistani, Bangladeshi, Chinese and Irish. 20 The methodology used, however, does not enable NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 12

13 separate estimates for specific population sub-groups (eg defined by age, sex, social class or ethnicity) to be produced within each PCO. Model-based estimates are essentially local and do not provide national level data. As they are estimates they do not provide a true reflection of prevalence rates for a particular LA or PCO, nor do they take account of any additional local factors that may impact on the true prevalence rate. They cannot, therefore, be used to monitor change over time an important limitation. Also, as they are experimental statistics they may be subject to consultation, modification and further development. 22 NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 13

14 Surveillance data for obesity in children Obesity prevalence data for children are mainly collected from the following sources: Health Survey for England (HSE) children The HSE has collected annual data on obesity in children since 1995 and is currently the primary source of childhood obesity trend data; National Childhood Measurement Programme (NCMP) The NCMP has weighed and measured children in Reception (4 to 5 years old) and Year 6 (10 to 11 years old) in maintained schools in England every year since 2005/06; Health Behaviour in School-aged Children study (HBSC) The HBSC is a cross-national research study conducted in collaboration with the WHO Regional Office for Europe. It samples children aged 11 to 16. Health Survey for England (HSE) - children The HSE has included children aged 2-15 years living in private households since 1995, and infants under two years old since Interviewers take height and weight measurements for all children, except those under two years of age, for whom nurses measure length instead of height. Nurses also measure waist and hip circumference of all children aged 15 and under. As well as these measures, interviewers collect data on the child s perception of their own weight (for children aged 8 to 15 years), and reported birth weight for all children. This provides interesting information that can be used to investigate weight determination in more depth. As with adults, the HSE collects information for children on many of the recognised determinants of overweight and obesity. These include dietary habits, physical activity and attitudes to physical activity and healthy eating. Data are collected by interviewing either the child or the parent, depending on the age of the child. This information can be used in conjunction with other factors measured in the HSE such as socio-demographic and household measures to further explore influences on obesity. As for adults, HSE data for children are not sufficiently robust to describe prevalence or trends for areas smaller than SHAs. Furthermore, the collection of data relating to children from minority ethnic groups or specific age groups in childhood and adolescence is limited by small sub-population sample sizes. For example, the 2006 HSE collected data from over 7000 children under 15. Segmenting this data by sex and age results in a measured population of around 500 children for each two year age group between 2 and 15, providing an average of 250 individuals per year of age, per sex (Table 3). These low numbers do not allow trends over small age ranges to be monitored with confidence. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 14

15 Table 3: Number of children measured by age and sex by the 2006 HSE Age in years Boys Girls Total Total During the childhood years growth rates change dramatically between specific age ranges, and by sex. Larger sample sizes would be very useful in monitoring trends in obesity among children and adolescents, yielding more robust age-specific estimates of overweight and obesity prevalence. However, resource constraints are a limiting factor for all surveys. As the HSE includes children of all ages up to 15 in its data collection, its resources are not targeted at specific age ranges. In contrast, surveys or programmes like the NCMP and the HBSC, in which large numbers of children are measured, focus on narrower age ranges in order to monitor trends among children of these ages. They complement the HSE by providing richer data for these age groups. National Child Measurement Programme (NCMP) The NCMP measures the height and weight of all children in Reception (4 to 5 years of age) and Year 6 (10 to 11 years) in mainstream maintained primary and middle schools in England. Measurements are taken by PCT staff within schools on an annual basis, with the first programme conducted in the 2005/06 academic year. 23 This provides measured data for over 400,000 children in each year group. With such a large sample size a robust analysis of issues such as ethnicity can be conducted for the specific age groups included in the NCMP. However, the NCMP does not measure as large a selection of individual characteristics as some other data sources, such as the HSE or the HBSC, and although sex and ethnic group are recorded, no data on health-related behaviour are collected. The presence of the lower super output area of residence (LSOA), derived from home postcode of the child, allows for some consideration of the effect of geographical determinants. Data quality issues which affect NCMP data include variation between PCTs in terms of participation and opt out rates, accuracy of measurements and the use of different measurement tools, as well as when in the school year the measurements are taken. 23 The fact that NCMP data are collected from a very high proportion of the target population is a major strength. They can be used for investigation of patterns and NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 15

16 trends at local level, but only for the specific age groups they cover, which limits the use of the data for population monitoring to some extent. Health Behaviour in School-aged Children study (HBSC) The HBSC collects data every four years and takes place in around 55 schools in England, sampling more than 6000 pupils. 24 The study aims to gain insight into, and increase our understanding of, young people's health and well-being, including health behaviour and its social context. The survey is co-ordinated by the World Health Organisation (WHO) and is conducted in 41 countries, enabling cross-national comparisons to be made. 24 In England the survey spans the compulsory secondary school years, during which many physiological and psycho-social changes take place. It samples around 2000 young people in each of three year groups: Year 7 (11 to 12 years), Year 9 (13 to 14 years) and Year 11 (15 to 16 years). 25 Unlike the HSE, the HBSC focuses on specific age groups; but like the HSE its samples include relatively small numbers of children and young people from minority ethnic groups, making analysis of differences between ethnic groups difficult. The major caveat with this study, however, is that all the information is collected through self-completed questionnaires delivered in school, including self-reported height and weight. The resulting BMI data may therefore be inaccurate, since selfreported height and weight is often unreliable. 26,27 Height tends to be overestimated by individuals, whilst weight tends to be underestimated, leading to an overall underestimation of BMI in studies where these measures are selfreported. The overestimation of height has been found to be more prominent in older individuals, shorter men and heavier women, 28 whilst weight underestimation is greater in heavier men and women. This leads to bias in prevalence estimates for obesity and overweight obtained in this way. Differences in self-reported and measured BMI are found to be greatest amongst those over 60 years of age, 29 and in teenagers, who tend to under-report weight and over-report height. 26 NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 16

17 Data collection issues for surveillance data When examining obesity surveillance data sources it is important to consider the data collection issues that may influence findings. In particular, results may be biased by differences in participation rates and by inconsistency in measurement techniques. In the 2007 HSE, 66% of eligible households (4,200) in the general population sample took part in the survey. The remaining 34% of households did not agree to take part and provided no measurements. To calculate the participation rate at individual level, the number of people in non-participating households had to be estimated, since the size of these households is not known. Using these estimates, together with data from participating households, it was estimated that 53% of adults sampled by the HSE had their height measured and 51% had had their weight measured. 30 The figures for children were similar: 54% of children aged 0 16 had their weight measured, and 53% of children had their height (or length, for those under 2 years) measured. 30 Even within households that agreed to take part in the survey, individuals could opt out from height and weight measurements, and a proportion did so. The numbers of participating children was increased through a boost sample, but more than 10% of both boys and girls aged 2 and over did not participate in height and weight measurements (Table 4). 30 Table 4: Proportion of total children in co-operating households participating in height and weight measurements Boys % Girls % All children Height measured Weight measured This equates to over 700 non-participating children There is little information about these individuals to inform our understanding of non-participation, apart from some description of response rate by region, type of dwelling, age and sex. Nonparticipation can occur for many reasons and different motivations, but no analysis has yet been carried out to shed light on these or to explore the influence of nonparticipation and opt-out on HSE findings concerning obesity prevalence data. NCMP analyses have emphasised the importance of considering opt-out b and nonparticipation c rates in relation to obesity prevalence data. Opt-out rates have been found to be significantly associated with prevalence of obesity at PCT level, suggesting that individuals opting out were more likely to be classified as obese. 23 Although the findings from the NCMP cannot be generalised to all large surveys, they do highlight the concern that non-participation and opt-out may influence results. This is especially so if opt-out is more common among certain sub-groups, such as those defined by age, sex, ethnicity and socio-economic status, or indeed weight status. When making comparisons within and between data sets (for example, comparing different years of the NCMP or comparing the HSE to the b Opt-out from the NCMP is where the child or parent actively chooses to opt-out of measurement. c Non-participation in the NCMP includes active opt-out by the child or parent, as well as other factors such as where the whole school does not take part or the child is away or sick on the day of measurement. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 17

18 Health Surveys for Wales, Scotland or Northern Ireland), assumptions have to be made that the non-participating individuals or households are similar in each case. This caveat must be borne in mind when using such data. Analysis of NCMP data also found that there were differences between PCTs in the rounding up or rounding down of height and weight 4 measurements, revealing another data quality issue. In any large study where data collection is carried out locally, discrepancies between local approaches can make a difference to the results. For example, the tools used to measure individuals (e.g. weighing scales) may vary in accuracy, and within the NCMP the time in the school year when the pupils are measured may also make a difference to obesity prevalence rates 4. Any information on data collection procedures would therefore be useful when interpreting data from surveys such as these. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 18

19 Discussion The data collection systems for obesity measurement in England are reasonably strong. There are many years of data available on BMI, allowing population monitoring and national trends to be monitored. Data sources such as QOF provide information on the treatment of obesity within the health care system. Large scale collection of obesity data from specific childhood age groups allows more detailed investigation of factors affecting weight status in these age groups. There are various ways in which data collection might be enhanced to improve the quality and range of information available from these sources and provide a greater understanding of the obesity problem at the population level, especially across different population sub-groups. The points below highlight some of the issues in collecting data at population level and reflect the challenges of population monitoring: Issues and problems: The most accurate methods of measuring body fat are too complex and expensive to be used within a large scale data collection programme; Collection of large scale data segmented by ethnic group is difficult. Although the NCMP does collect from a large enough sample to compare between ethnic groups, it does so only for specific age ranges. The HSE last boosted its sample to enhance data collection from minority ethnic groups in 2004; Data collection within surveys is often organised according to administrative boundaries, and as sampling takes place within these boundaries there can be restrictions on the analysis of data at a more local level; It is very difficult to obtain any data on non-participants or individuals who opt out. Suggested areas for improvement: Although BMI is a reasonably accurate and well-established measure that can be used for trend monitoring across many years, wherever possible it is useful to include other measures of body fat alongside BMI; Boosted HSE samples would be helpful in that they provide large enough sample sizes for sub-group analysis. This is particularly the case for issues such as ethnicity, specific adult and child age groups and socio-economic status; Particularly regarding ethnicity, boosted samples along the lines of the HSE minority ethnic boost in 2004 would be valuable, enabling useful trend monitoring to take place; It would be particularly useful to collect large scale measured data across specific childhood and adolescent age groups. Many of the behaviours that influence overweight and obesity are thought to become established in childhood and adolescence. Collecting such data would allow close NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 19

20 comparison across the short age ranges that produce great variations in psycho-social and physiological characteristics and would offer greater insight into these factors and their effect on weight status. For example, the NCMP currently collects height and weight measurements for a large number of children spanning the primary school years. Similar measurements collected within the secondary school age groups, over which major psychosocial and physiological changes occur, would be very valuable, albeit difficult to implement; Collecting information about individuals who opt out of measurement would allow some investigation of the reasons for and trends in non-participation, shedding light on the way such factors influence results. However, collecting data on non-participants is very challenging; The main gaps identified in this report as priorities which should be addressed are the collection of measured, rather than self-reported, anthropometric measures for adolescents and in-depth, large scale data collection among minority ethnic groups. The continued collection of large scale trend data for children through the NCMP is also important. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 20

21 Conclusion All population surveys are limited by factors such as access to and availability of individuals and the types of measurements collected. The complexity of the nature of weight determination means that a vast amount of information could potentially be collected. Resources are limited, however, and a pragmatic approach is needed. But there are some areas that deserve more detailed investigation and/or larger scale data collection. For example, comparisons based on ethnicity require larger sample sizes than are currently collected. After controlling for important factors such as age and sex, current routine surveys do not include a large enough sample of individuals from minority ethnic groups to allow a rigorous investigation, or are restricted to collecting from individuals of specific ages. Boosted samples such as that conducted in the past for the HSE should be repeated to enable the monitoring of trends by ethnicity. It would also be useful to record more information on the data collection process itself, particularly on issues such as non-participation. Without an accurate indication of the factors affecting participation by particular individuals or households, comparisons between years of data collection and between data sources rely on assumptions about the nature (and even the number) of nonparticipants. Clearly, collecting data from non-participating individuals is fraught with difficulties and a separate study on non-participation would provide valuable insights. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 21

22 References 1. World Health Organization. Obesity: Preventing and managing the global epidemic, WHO Technical Report Series 894. World Health Organisation: Geneva; National Institute for Health and Clinical Excellence. Obesity guidance on prevention, identification, assessment and management of overweight and obesity in adults and children, NICE clinical guideline 43. National Institute for Health and Clinical Excellence: London; Dagogo-Jack, S (2009) Fatness in Blacks Overestimated by Widely Used Body Fat Measurements, The Endocrine Society's 91st Annual Meeting, Washington, D.C as reported in Medical News Today 13 th June Available from: [Accessed June 25th 2009]. 4. Ridler, C et al. (2009) National Child Measurement Programme: Detailed Analysis of the 2007/08 National Dataset. NOO: Oxford. 5. Wells J, Coward W, Cole T and Davies P. The contribution of fat and fat-free tissue to body mass index in contemporary children and the reference child. International Journal of Obesity 2002; 26: Wei M, Gaskill S, Haffner S, Stern M. Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans a 7 year prospective study. Obesity Research 1997; 5(1): Dalton M, Cameron A, Zimmet P, Shaw J. Waist circumference, waist-hip and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. Journal of Internal Medicine 2003; 254(6): Savva S, Tornaritis M, Savva M, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. International Journal of Obesity Related Metabolic Disorders 2000; 24(11): Janssen I, Katzmarsyk P and Ross R. Waist circumference and not body mass index explains obesity-related health risk. American Journal of Clinical Nutrition 2004; 79: Klipstein-Grobusch K, Georg T and Boeing H. Interviewer variability in anthropometric measurements and estimates of body composition. International Journal of Epidemiology 1997; 26 (Suppl. 1): S174-S Bassali R, Waller J L, Gower B, et al. Utility of waist circumference percentile for risk evaluation in obese children International. Journal of Pediatric Obesity 2009; 1_5, ifirst article. NOO OBESITY AND OVERWEIGHT SURVEILLANCE IN ENGLAND 22