The National Diabetes Audit (NDA) provides a comprehensive view of diabetes care in England and Wales. It measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards. This is the Type 1 Diabetes report. It details the findings and recommendations relating to diabetes care process completion, treatment target achievement and structured education for people with type 1 diabetes. The 2019-20 audit covers the period 01 January 2019 to 31 March 2020. This is the first NDA report dedicated to people with type 1 diabetes. A new diagnosis validation process, which considers medication as well as recorded diagnosis, has been introduced to try to ensure that only people with true type 1 diabetes are included (see appendix). Results are to be taken in the context of low data submission from specialist services, possibly hampered due to COVID-19.

The Diabetes Foot Care Profiles present Clinical Commissioning Group (CCG) level information regarding people with diabetes who were admitted to hospital for foot disease.

The profiles include a range of analysis covering risk, treatment and outcomes, highlighting variation between areas and time-periods. They are intended to help guide equitable national, regional and local commissioning of diabetic foot care services and other preventative interventions.

The information in the profiles is compiled from Hospital Episode Statistics (HES). This latest update focuses on spells of inpatient care between 1 April 2015 and 31 March 2018. Where possible, earlier information has also been provided to allow trend analysis.

The https://fingertips.phe.org.uk/profile/diabetes-ft">Diabetes Foot Care Profiles are produced by the National Cardiovascular Intelligence Network (NCVIN).

Deaths registered in England and Wales in 2020 and how they compared with the five-year average (2015 to 2019), based on finalised 2020 mortality data. The figures are broken down by cause, place of death, age group, sex and deprivation.

The National Diabetes Audit (NDA) is part of the National Clinical Audit and Patient Outcomes Programme (NCAPOP) which is commissioned by the Healthcare Quality Improvement Partnership (HQIP) and funded by NHS England. The NDA is managed by NHS Digital in partnership with Diabetes UK. The NDA measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards, in England and Wales. The NDA collects and analyses data for use by a range of stakeholders to drive changes and improvements in the quality of services and health outcomes for people with diabetes. This report details the findings and recommendations relating to diabetes care process completion, treatment target achievement and structured education for the 2018-19 audit. The audit collected data during May and June 2019, for the period 01 January 2018 to 31 March 2019. This report follows the NDA short report publication in December 2019, which provided the top level findings for the 2018-19 audit, along with local level data made available to services in a timely manner that can help drive improvements in the quality of diabetes care locally. A new method of collecting structured education attendance data was trialled for 2018-19. In addition to extracting education data from GP practice systems, structured education providers could submit data directly via the Clinical Audit Platform (CAP). Around 20 providers submitted, however only a small proportion of records were submitted with the required data. This exercise has shown the potential value of this additional collection and improvements to the process are being developed to improve future collections. Included within this publication is the National Diabetes Insulin Pump Audit interactive reporting and supporting information. It provides information on people with Type 1 diabetes on an insulin pump at National, LHB, and Specialist Diabetes Service level for the 2018-19 audit period. Note: An error was identified in the original release of the National Diabetes Insulin Pump Audit 2018-19, Interactive Report for Specialist Services in England. The number of people with Type 1 diabetes in the 'HbA1c values' section was overstated and showed the total number of people with any type of diabetes seen at the service. This has been corrected and replaced with a new version of the report on 23 December 2020.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of diabetes mellitus in persons (aged 17+). 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 diabetes mellitus in persons (aged 17+).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 (aged 17+) with diabetes mellitus 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 diabetes mellitus 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 depression, 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 diabetes mellitusB) the NUMBER of people within that MSOA who are estimated to have diabetes mellitusAn 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 diabetes mellitus, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from diabetes mellitus, and where those people make up a large percentage of the population, indicating there is a real issue with diabetes mellitus 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 diabetes mellitus, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of diabetes mellitus.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.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.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This statistic show the number of children and young adults with cystic fibrosis related diabetes in England and Wales from 2019 to 2020, by age. During this time interval there were 100 number of children aged between 10 and 14 years with cystic fibrosis related diabetes.

Analysis of risk factors for pre-diabetes and undiagnosed type 2 diabetes among adults living in private households, using the Health Survey for England.

The National Diabetes Audit (NDA) is part of the National Clinical Audit and Patient Outcomes Programme (NCAPOP) which is commissioned by the Healthcare Quality Improvement Partnership (HQIP) and funded by NHS England. The NDA is managed by NHS Digital in partnership with Diabetes UK. The NDA measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards, in England and Wales. The NDA collects, analyses and reports data for use by primary care and specialist services, local and national commissioners to support change and improvement in the quality of services and health outcomes for people with diabetes. This data release includes the care process and treatment target measurements for 2019-20 (1st January 2019 – 31st March 2020). Data were collected during May and June 2020. The national report, scheduled for 2021, will contain commentary on the audit findings and recommendations. We will communicate to users when the publication date for this report has been finalised. GP practice participation in England and Wales has increased from 98.0 per cent in 2018-19 to 99.2 per cent in 2019-20. Diabetes specialist service participation stands at 98 services in 2019-20. For NDA 2019-20, Diabetes Eye Screening (DES) data has been collected directly from DES providers for the first time. All but one DES provider in England (Liverpool) successfully submitted data, although three providers made partial submissions. For Liverpool, eye examination information secondarily recorded in Primary Care systems has been used, which is likely to be incomplete. The new 'Retinal Screening' care process measure appears in the care process and treatment targets worksheets and also feeds into the new 'All Nine Care Processes' measure, which is reported in addition to the longstanding ‘All Eight Care Processes'. Please note that there is a potential issue with the SNOMED codes used to identify if a person has had their serum creatinine care process check. Two serum/plasma creatinine codes were removed from the NDA creatinine code set during the universal SNOMED code refresh. This has affected the measurement of creatinine care process completion in a small number of health economies, and thereby has the potential to influence the all eight/nine care process percentages for organisations/areas that still use these codes. To resolve the issue, the NDA business rules are currently being amended to add these codes back into future NDA data extractions.

Confirmed DM cases from 1,255,130 cats in VetCompass

This is the quarterly release of data from the National Diabetes Audit (NDA). It is the mid-year data release for the third extraction of NDA 2019-20 data. It shows progress to date covering the period 1 January to 31 December 2019. The first data extraction was withdrawn this year so that the delayed biannual SNOMED code release could be incorporated into the NDA 2019-20 extract. Quarterly data release summary: • First data extraction releases will cover the six month period 1 January to 30 June; • Second data extraction releases will cover the nine month period 1 January to 30 September; • Third data extraction releases will cover the twelve month period 1 January to 31 December; • The full audit year report will continue to cover the fifteen month period 1 January to 31 March the following year. This quarterly release of data should not be used to assess performance against the annual processes as it does not include the full fifteen month audit period. It shows partial year progress against care processes and treatment targets. It also provides the latest position on structured education. It therefore can be used as an operational planning tool to assess progress to date. Care process completion is expected to increase before the end of the year. Treatment target achievement is calculated from the latest reading for each target, so achievement may go down or up. The recording of structured education may also increase. The complete audit year performance, covering 1 January 2019 to 31 March 2020, will be made available later in the year. The data published here is for English GP practices only. The complete audit year publications will include Welsh GP practices, as well as participating specialist diabetes services in England. The data has not been through the full data assurance process that is carried out on the annual data, but is a provisional reflection of progress over this partial year period.

The Diabetes Foot Care Profiles presents Clinical Commissioning Group (CCG) and Sustainability and Transformation Partnership (STP) level information on diabetic lower-limb amputations and hospital admissions for diabetes-related foot disease. The data are intended to help guide equitable national, regional and local commissioning of diabetic foot care services and other preventative interventions.

The information is compiled from Hospital Episode Statistics (HES) and this latest update focuses on spells of inpatient care between 1 April 2016 and 31 March 2019.

The https://fingertips.phe.org.uk/profile/diabetes-ft">Diabetes Foot Care Profiles is produced by the National Cardiovascular Intelligence Network (NCVIN).

Note: due to reduced analytical capacity, influenced by the COVID-19 pandemic, the format of the 2020 publication is a simplified spreadsheet of data (rather than the previous PDF reports).

In addition, the April 2019 release of the major and minor amputations indicators (tax year 2015 to 2016 to tax year 2017 to 2018) has been replaced with the data in this workbook. A coding error was identified in the National Diabetes Audit data that supports the profiles and, therefore, they have been reissued.

The National Pregnancy in Diabetes Audit (NPID) is a workstream of the National Diabetes Audit (NDA) and is managed by NHS Digital under an agreement with the Healthcare Quality Improvement Partnership (HQIP) on behalf of NHS England and the Welsh Government. The NDA is delivered by NHS Digital, in partnership with Diabetes UK. The audit is a measurement system to support improvement in the quality of care for women with diabetes who are pregnant or planning pregnancy and seeks to address three key questions: Were women with diabetes adequately prepared for pregnancy? Were adverse maternal outcomes during pregnancy minimised? Were adverse fetal/infant outcomes minimised? NPID is the largest continuous audit of pregnancy in women with diabetes in the world (more than 4,500 pregnancies in 2020). We now have seven years of data which has allowed a depth of analysis not previously possible.

Chronic kidney disease (CKD) caused heavy burden globally. This study aimed to investigate the patterns and temporal variations in the burden of CKD in China, Japan, the United Kingdom (U.K.), and the United States (U.S.) from 1990 to 2019, and decompose the difference in CKD disease burden between 1990 and 2019 into demographic factors. From 1990 to 2019, although the age-standardized rate (ASR) of incidence remained stable in the four countries, and the ASR of mortality and disability-adjusted life years (DALY) have declined in four countries (except for the increase in U.S.), the number of CKD incidence, death, and DALY increased significantly. The average disease burden per case in U.S. has increased between 1990 and 2019, with an increasing proportion of death-related disease burden. For the CKD due to diabetes and hypertension, whose incidences accounted for < 25% of the total CKD, while it accounts for more than 70% of the deaths (except in U.K. with 54.14% in women and 51.75% in men). CKD due to diabetes and hypertension should be the focus of CKD prevention and control. Considering the high treatment costs of CKD and ESRD, it is urgent and necessary to transform CKD treatment into primary and secondary prevention.

The National Diabetes Audit (NDA) provides a comprehensive view of diabetes care in England and Wales. This report identifies current rates and trends of long term complications and outcomes of diabetes. The analysis helps to identify the scale of the additional health risks faced by people with diabetes. It covers the decade leading up to the start of the Covid-19 pandemic (April 2009-March 2020), but it reflects the cumulative effects of the previous 30 years of diabetes care. Routine diabetes care is separately reported by the NDA (1). Routine care aims to deliver NICE recommended standards, based on randomised controlled trials lasting up to 30 years, that specify interventions known to be effective in reducing long term complications. Routine care comprises blood sugar and blood pressure control, using statins when indicated by cardiovascular risk, identifying eye, foot and kidney disease at early, modifiable stages and encouraging beneficial lifestyle changes such as exercise, weight control and smoking cessation. This report on long term complications of diabetes includes information on 3.5 million people with diagnosed diabetes. The core information in the audit is recorded during routine care and submitted by GP practices and specialist diabetes services in England and Wales. To look at long term complications and outcomes, this core data was linked with other sources of data. These include:

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 is an overview of the treatment and demographics of 227,435 adults with type 1 diabetes. From 2019 to 2022 glucose control in people with type 1 diabetes in England and Wales improved while blood pressure control deteriorated. Use of diabetes technology (wearable glucose monitoring devices in England and insulin pumps in England and Wales) was associated with lower glucose levels. Diabetes technology was used less by those in the most deprived groups and in ethnic minorities. 30% of people with type 1 diabetes did not attend specialist care in 2021-22 and were less likely to receive annual checks or achieve treatment targets as recommended by the National Institute for Health and Care Excellence (NICE). There are 3 recommendations for commissioners of care.

BackgroundMore than 80% of individuals in low and middle-income countries (LMICs) are unvaccinated against coronavirus disease 2019 (COVID-19). In contrast, the greatest burden of cardiovascular disease is seen in LMIC populations. Hypertension (HTN), diabetes mellitus (DM), ischaemic heart disease (IHD) and myocardial injury have been variably associated with adverse COVID-19 outcomes. A systematic comparison of their impact on specific COVID-19 outcomes is lacking. We quantified the impact of DM, HTN, IHD and myocardial injury on six adverse COVID-19 outcomes: death, acute respiratory distress syndrome (ARDS), invasive mechanical ventilation (IMV), admission to intensive care (ITUadm), acute kidney injury (AKI) and severe COVID-19 disease (SCov), in an unvaccinated population.MethodologyWe included studies published between 1st December 2019 and 16th July 2020 with extractable data on patients ≥18 years of age with suspected or confirmed SARS-CoV-2 infection. Odds ratios (OR) for the association between DM, HTN, IHD and myocardial injury with each of six COVID-19 outcomes were measured.ResultsWe included 110 studies comprising 48,809 COVID-19 patients. Myocardial injury had the strongest association for all six adverse COVID-19 outcomes [death: OR 8.85 95% CI (8.08–9.68), ARDS: 5.70 (4.48–7.24), IMV: 3.42 (2.92–4.01), ITUadm: 4.85 (3.94–6.05), AKI: 10.49 (6.55–16.78), SCov: 5.10 (4.26–6.05)]. HTN and DM were also significantly associated with death, ARDS, ITUadm, AKI and SCov. There was substantial heterogeneity in the results, partly explained by differences in age, gender, geographical region and recruitment period.ConclusionCOVID-19 patients with myocardial injury are at substantially greater risk of death, severe disease and other adverse outcomes. Weaker, yet significant associations are present in patients with HTN, DM and IHD. Quantifying these associations is important for risk stratification, resource allocation and urgency in vaccinating these populations.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, registration no: CRD42020201435 and CRD42020201443.

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.