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.

In 2022, the mean body mass index (BMI) of both men and women in England stood at 27.6. Since 1998, mean BMI has creeped steadily upwards among both men and women. According to the NHS, a BMI of between 18.5 and 24.9 is classified as ideal for most adults, while a BMI of 25 and over is generally regarded as being overweight.

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

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 statistic displays the body mass index (BMI) distribution of pregnant women in England in 2018/19, by age group. In this period, 47.5 percent of pregnant women aged 30 to 39 years were classed as normal weight.

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

Note August 2011: A number of errors have been identified in Table 7.5 - GHQ 12 score by body mass index (BMI) and gender, 2006 on page 164 of the Statistics on Obesity, Physical Activity and Diet: England, 2009 report. The errors also affect the corresponding table in the accompanying Excel workbook. The commentary in the pdf report is unaffected. Please see the errata note for further information and corrected figures. The NHS IC apologises for any inconvenience this may have caused. Summary: This publication presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources. This publication also summarises government plans and targets in this area, as well as providing sources of further information and links to relevant documents and key sources. This, our third report on obesity, physical activity and diet includes: overweight and obesity prevalence among adults and children physical activity levels among adults and children trends in purchases and consumption of food and drink, and energy intake and health outcomes of being overweight or obese. For the first time, the report provides information on attitudes towards and knowledge about leading a healthy lifestyle for both adults and children. This includes information on how much physical activity should be done, barriers towards doing more physical activity, knowledge of the recommended daily number of portions of fruit and vegetables and attitudes towards a healthy diet.

This statistic displays the mean body mass index of adults in Scotland in 2023, by gender and age. In that year, men in the age group 45 to 54 years had a mean BMI of 28.8, while women in the same age group had a mean BMI of 28.6. A BMI between 25 and 30 is classified as overweight.

Observed body mass index (BMI) of adults. To help reduce the prevalence of obesity. Legacy unique identifier: P00844

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

The prevalence of obesity among adults in England has been generally been trending upwards since 2000. In that year, 21 percent of men and women in England were classified as obese. However, by 2022 this share was 30 percent among women and 28 percent among men. Obesity causing strain on health service As the prevalence of obesity is increasing in England, the number of hospital admissions as a result of obesity has also increased. In the period 2019/20, around eight thousand women and nearly 2.7 thousand men were admitted to hospital. A huge rise from the admission levels fifteen years previously. The highest number of admissions due to obesity were found in the age group 45 to 54 years, with over 3.1 thousand admissions in that age group. Situation in Scotland In Scotland in 2022, the mean Body Mass Index of women was 28.1 and for men it was 27.9. A BMI of over 25 is classed as overweight. While the prevalence of obesity or morbid obesity in Scotland in 2020 was 30 percent among women and 26 percent among men.

This is the percentage of year 6 children who are living with obesity and includes children who are living with severe obesity.

The BMI classification of each child is derived by calculating the child's BMI centile and assigning the BMI classification. Obese is defined as a BMI centile greater than or equal to the 95th centile. Severely obese is defined as a BMI centile greater than or equal to 99.6 (This BMI classification is a subset of the "Obese" classification).

The results are derived from the postcode of the school. Measurement of children's heights and weights, without shoes and coats and in normal, light, indoor clothing, was overseen by healthcare professionals and undertaken in school by trained staff. Measurements could be taken at any time during the academic year. Some children could be over one year older than others in the same school year at the point of measurement. This does not impact upon a child's BMI classification since BMI centile results are adjusted for age.

The National Child Measurement Programme (NCMP) collects height and weight measurements of children in reception (aged 4-5 years) and year 6 (aged 10-11 years) primarily in mainstream state-maintained schools in England. Local authorities are mandated to collect data from mainstream state-maintained schools but collection of data from special schools (schools for pupils with special educational needs and pupil referral units) and independent schools is encouraged.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Year 6 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 Year 6 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 Year 6 children with excess weight and;B) the PERCENTAGE of Year 6 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 Year 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.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (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.The percentage of each MSOA’s population (all ages) with cancer 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 registered patients that have that illness The estimated percentage of each MSOA’s population with cancer 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 cancer, within the relevant age range.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 cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn 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 that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP 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 ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ 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 (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), 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 populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework 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.GP Catchment Outlines. 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. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: 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. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

United States Prevalence of Overweight: % of Adults data was reported at 67.900 % in 2016. This records an increase from the previous number of 67.400 % for 2015. United States Prevalence of Overweight: % of Adults data is updated yearly, averaging 55.200 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 67.900 % in 2016 and a record low of 41.000 % in 1975. United States Prevalence of Overweight: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;;

This set of files contains public data used to validate the grocery data. All references to the original sources are provided below.CHILD OBESITYPeriodically, the English National Health Service (NHS) publishes statistics about various aspects of the health and habits of people living in England, including obesity. The NHS National Child Measurement (NCMP) measures the height and weight of children in Reception class (aged 4 to 5) and year 6 (aged 10 to 11), to assess overweight and obesity levels in children within primary schools. The program is carried out every year in England and statistics are produced at the level of Local Authority (that corresponds to Boroughs in London). We report the data for the school year 2015-2016 (file: child_obesity_london_borough_2015-2016.csv). For the school year 2013-2014, statistics in London are also available at ward-level (file: child_obesity_london_ward_2013-2014.csv)The files are comma-separated and contain the following fields: area_id: the id of the boroughnumber_reception_measured: number of children in reception year measurednumber_y6_measured: number of children in reception year measuredprevalence_overweight_reception: the prevalence (percentage) of overweight children in reception year prevalence_overweight_y6: the prevalence (percentage) of overweight children in year 6prevalence_obese_reception: the prevalence (percentage) of obese children in reception yearprevalence_obese_y6: the prevalence (percentage) of obese children in year 6ADULT OBESITYThe Active People Survey (APS) was a survey used to measure the number of adults taking part in sport across England and included two questions about the height and weight of participants. We report the results of the APS for the year 2012. Prevalence of underweight, healthy weight, overweight, and obese people at borough level are provided in the file london_obesity_borough_2012.csv.The file is comma-separated and contains the following fields: area_id: the id of the boroughnumber_measured: number of people who participated in the surveyprevalence_healthy_weight: the prevalence (percentage) of healthy-weight peopleprevalence_overweight: the prevalence (percentage) of overweight peopleprevalence_obese: the prevalence (percentage) of obese peopleBARIATRIC HOSPITALIZATIONThe NHS records and publishes an annual compendium report about the number of hospital admissions attributable to obesity or bariatric surgery (i.e., weight loss surgery used as a treatment for people who are very obese), and the number of prescription items provided in primary care for the treatment of obesity. The NHS provides both raw counts at the Local Authority level and numbers normalized by population living in those areas. In the file obesity_hospitalization_borough_2016.csv, we report the statistics for the year 2015 (measurements made between Jan 2015 and March 2016).The file is comma-separated and contains the following fields:area_id: the id of the boroughtotal_hospitalizations: total number of obesity-related hospitalizationstotal_bariatric: total number of hospitalizations for bariatric surgeryprevalence_hospitalizations: prevalence (percentage) of obesity-related hospitalizations prevalence_bariatric: prevalence (percentage) of bariatric surgery hospitalizations DIABETESThrough the Quality and Outcomes Framework, NHS Digital publishes annually the number of people aged 17+ on a register for diabetes at each GP practice in England. NHS also publishes the number of people living in a census area who are registered to any of the GP in England. Based on these two sources, an estimate is produced about the prevalence of diabetes in each area. The data (file diabetes_estimates_osward_2016.csv) was collected in 2016 at LSOA-level and published at ward-level.The file is comma-separated and contains the following fields:area_id: the id of the wardgp_patients: total number of GP patients gp_patients_diabetes: total number of GP patients with a diabetes diagnosisestimated_diabetes_prevalence: prevalence (percentage) of diabetesAREA MAPPINGMapping of Greater London postcodes into larger geographical aggregations. The file is comma-separated and contains the following fields:pcd: postcodelat: latitudelong: longitudeoa11: output arealsoa11: lower super output areamsoa11: medium super output areaosward: wardoslaua: borough

India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 2.800 % in 2022. This records an increase from the previous number of 2.700 % for 2021. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.300 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 2.800 % in 2022 and a record low of 2.200 % in 2015. India IN: 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 India – Table IN.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.

In 2023, the mean body mass index for men was 27.8 kilograms per meter squared and the mean body mass index for women was 28.3 kilograms per meter squared. The mean BMI in Scotland for both genders has been generally increasing since 1995.

Legacy unique identifier: P00846