In England, there has been fairly significant increase in the mean weight of the population over the last quarter-century. In 1998, the mean weight was under 81 kilograms for men and around 68 kilograms for women. By 2022, the mean weight of men was 85.8 kilograms and the mean weight of women was 72.8 kilograms.

In 2022, men aged 55 to 64 years had an average body mass index (BMI) of 29 kg/m2 and women in the same age group had a BMI of 28.8 kg/m2, the highest mean BMI across all the age groups. Apart from individuals aged 16 to 24 years, every demographic in England had an average BMI which is classified as overweight.An increasing problem It is shown that the mean BMI of individuals for both men and women has been generally increasing year-on-year in England. The numbers show in England, as in the rest of the United Kingdom (UK), that the prevalence of obesity is an increasing health problem. The prevalence of obesity in women in England has increased by around nine percent since 2000, while for men the share of obesity has increased by six percent. Strain on the health service Being overweight increases the chances of developing serious health problems such as diabetes, heart disease and certain types of cancers. In the period 2019/20, England experienced over 10.7 thousand hospital admissions with a primary diagnosis of obesity, whereas in 2002/03 this figure was only 1,275 admissions. Furthermore, the number of bariatric surgeries taking place in England, particularly among women, has significantly increased over the last fifteen years. In 2019/20, over 5.4 thousand bariatric surgery procedures were performed on women and approximately 1.3 thousand were carried out on men.

This statistic displays the breakdown of body weight class among men and women in the United Kingdom (UK) in 2015. Of respondents, 39 percent of men and 46 percent of women had a healthy body weight.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Supplementary files for article "Are associations of adulthood overweight and obesity with all-cause mortality, cardiovascular disease, and obesity-related cancer modified by comparative body weight at age 10 years in the UK Biobank study?"Article abstractObjectiveAdults living with overweight or obesity do not represent a single homogenous group in terms of mortality and disease risks. The aim of our study was to evaluate how the associations of adulthood overweight and obesity with mortality and incident disease are modified by (i.e., differ according to) self-reported childhood body weight categories.MethodsThe sample comprised 191,181 men and 242,806 women aged 40-69 years (in 2006-2010) in the UK Biobank. The outcomes were all-cause mortality, incident cardiovascular disease (CVD), and incident obesity-related cancer. Cox proportional hazards regression models were used to estimate how the associations with the outcomes of adulthood weight status (normal weight, overweight, obesity) differed according to perceived body weight at age 10 years (about average, thinner, plumper). To triangulate results using an approach that better accounts for confounding, analyses were repeated using previously developed and validated polygenic risk scores (PRSs) for childhood body weight and adulthood BMI, categorised into three-tier variables using the same proportions as in the observational variables.ResultsIn both sexes, adulthood obesity was associated with higher hazards of all outcomes. However, the associations of obesity with all-cause mortality and incident CVD were stronger in adults who reported being thinner at 10 years. For example, obesity was associated with a 1.28 (1.21, 1.35) times higher hazard of all-cause mortality in men who reported being an average weight child, but among men who reported being a thinner child this estimate was 1.63 (1.53, 1.75). The ratio between these two estimates was 1.28 (1.17, 1.40). There was also some evidence that the associations of obesity with all-cause mortality and incident CVD were stronger in adults who reported being plumper at 10 years. In genetic analyses, however, there was no evidence that the association of obesity (according to the adult PRS) with mortality or incident CVD differed according to childhood body size (according to the child PRS). For incident obesity-related cancer, the evidence for effect modification was limited and inconsistent between the observational and genetic analyses.ConclusionsGreater risks for all-cause mortality and incident CVD in adults with obesity who perceive themselves to have been a thinner or plumper than average child may be due to confounding and/or recall bias.

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Reception age children (three year average between academic years 2016/17, 2017/18, 2018/19).Although this layer is symbolised based on an overall score for excess weight, the underlying data, including the raw data for Reception children, is included in the dataset.ANALYSIS METHODOLOGYEach MSOA was given a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the NUMBER of Reception children with excess weight and;B) the PERCENTAGE of Reception children with excess weight.An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of Reception children with excess weight, compared to other MSOAs. In other words, those are areas where a large number of children have excess weight, and where those children make up a large percentage of the population of that age group, suggesting there is a real issue with childhood obesity in that area that needs addressing.DATA SOURCESNational Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEBased on data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. Data analysed and published by Ribble Rivers Trust © 2021.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The spreadsheet contains regional level obesity trend data from the the HSE, BMI data from Understanding Society, and adjusted prevalence of underweight, healthy weight, overweight, and obesity by local authority from the Active People Survey.

Understanding Society data shows the percentage of the population aged 10 and over by their Body Mass Index Classification, covering underweight, normal weight, overweight, and three classes of obesity.

Questions on self-reported height and weight were added to the Sport England Active People Survey (APS) in January 2012 to provide data for monitoring excess weight (overweight including obesity, BMI ≥25kg/m2) in adults (age 16 and over) at local authority level for the Public Health Outcomes Framework (PHOF).

Health Survey for England (HSE) results at a national level are available on the NHS Information Centre website.

Other NHS indicators on obesity are available for Strategic Health Authorities (SHA).

Relevant links: http://discover.ukdataservice.ac.uk/series/?sn=2000053

http://www.noo.org.uk/visualisation/adult_obesity

This statistics display the distribution of weight classification in England in 2022, based on self--reported body mass index (BMI). In this year, 39 percent of men and 31 percent of women were classed as being overweight. Those with a BMI of between 25 and 29.9 are classed as overweight.

Observed and age-standardised proportion of adults with a Body Mass Index (BMI) greater than 30 kg/m2. To help reduce the prevalence of obesity. Legacy unique identifier: P00848

The Health Survey for England is a series of annual surveys designed to measure health and health-related behaviours in adults and children living in private households in England. The survey was commissioned originally by the Department of Health and, from April 2005 by The NHS Information Centre for health and social care. Since 1994, the survey has been carried out by the National Centre for Social Research and the Department of Epidemiology at University College Medical School. The trend tables focus upon key changes in core topics and measurements. The number estimates presented here for the first time convert the prevalences in the key trend tables into estimates of the numbers of people in the population in England that they represent. Detailed findings for 2007 are reported in the main report: Health Survey for England 2007: Healthy lifestyles: knowledge, attitudes and behaviour.

Progress made by NHS Boards in the first year against implementing the HEAT H3 target on child healthy weight interventions

Source agency: Scottish Government

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Child Healthy Weight Interventions

The Health Survey for England (HSE) is part of a programme of surveys commissioned by the Health and Social Care Information Centre. It has been carried out since 1994 by the Joint Health Surveys Unit of NatCen Social Research and the Research Department of Epidemiology and Public Health at UCL (University College London). The study provides regular information that cannot be obtained from other sources on a range of aspects concerning the public's health and many of the factors that affect health. The series of Health Surveys for England was designed to monitor trends in the nation's health, to estimate the proportion of people in England who have specified health conditions, and to estimate the prevalence of certain risk factors and combinations of risk factors associated with these conditions. The survey is also used to monitor progress towards selected health targets. Each survey in the series includes core questions and measurements (such as blood pressure, anthropometric measurements and analysis of blood and saliva samples), as well as modules of questions on specific issues that vary from year to year. In some years, the core sample has also been augmented by an additional boosted sample from a specific population subgroup, such as minority ethnic groups, older people or children; there was no boost in 2012. This is the 22nd annual Health Survey for England. All surveys have covered the adult population aged 16 and over living in private households in England. Since 1995, the surveys have included children who live in households selected for the survey; children aged 2-15 were included from 1995, and infants under two years old were added in 2001. Those living in institutions were outside the scope of the survey. This should be borne in mind when considering survey findings, since the institutional population is likely to be older and less healthy than those living in private households. The HSE in 2012 provided a representative sample of the population at both national and regional level. 9,024 addresses were randomly selected in 564 postcode sectors, issued over twelve months from January to December 2012. Where an address was found to have multiple dwelling units, a random selection was made and a single dwelling unit was included. Where there were multiple households at a dwelling unit, again one was selected at random. All adults and children in selected households were eligible for inclusion in the survey. Where there were three or more children aged 0-15 in a household, two of the children were selected at random to limit the respondent burden for parents. A nurse visit was arranged for all participants who consented. A total of 8,291 adults and 2,043 children were interviewed. A household response rate of 64 per cent was achieved. 5,470 adults and 1,203 children had a nurse visit. It should be noted that, as in 2011, there was no child boost sample in 2012. Thus the scope for analyses of some data for children may be limited by relatively small sample sizes.

Average birth weight and height for sample population percentiles for males and females.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

This statistic displays the body mass index (BMI) distribution of pregnant women in England in 2018/19, by age group. In this period, **** percent of pregnant women aged 30 to 39 years were classed as normal weight.

Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 5.400 % in 2024. This records an increase from the previous number of 5.300 % for 2023. Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 4.000 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 5.400 % in 2024 and a record low of 3.000 % in 2001. Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Contains tabulated outputs on each topic from the Health Survey for England 2018

Comparisons of the average weight (AW), average BMI (ABMI), and prevalence of obesity (POB) changes resulting from the optimization experiment.