The interactive Cancer Services profile tool has been updated to include the latest financial year of data that are available, collated by the National Disease Registration Service (NDRS). For most indicators, for example on screening, diagnostics and urgent suspected cancer referrals, the latest data is now available for the financial year 2023 to 2024. For the cancer incidence indicator, the tool has been updated to include the 2022 to 2023 data.

Cancer Prevalence Statistics from the National Disease Registration Service publishes data on the number of people living with and beyond a cancer diagnosis on a given date (index date). The index date for the present publication is 31 December 2022. Understanding the size of the population living with and beyond a cancer diagnosis at a point in time can support planning for the delivery of local health and social care services, quantify the ‘burden’ of disease in an area or population and could determine the number of people who may have unmet health needs that could potentially benefit from new treatment interventions. The publication includes an interactive dashboard and data workbook for download. Detailed methodology notes are included at the Cancer Prevalence Statistics home page. The data are anonymous and available in an open format for anyone to access and use. Cancer prevalence Statistics are provided for all cancers combined as well as by cancer sites for England and sub-national geographies as follows: Cancer Alliance; Integrated Care Board; Local Authority. If you have feedback or any other queries about Cancer Prevalence, please email us at NDRSenquires@nhs.net and mention 'Cancer Prevalence Statistics' in your email.

This statistic shows the registrations of newly diagnosed cases of cancer in England from 1995 to 2021, by gender. Cancer is an aggregation disease in which cells within a part of the body begin to grow abnormally, often spreading to other parts of the body. In 2021, approximately 168 thousand men and 162 thousand women were newly diagnosed with cancer in England.

United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 12.900 NA in 2016. This records a decrease from the previous number of 13.300 NA for 2015. United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 14.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 20.000 NA in 2000 and a record low of 12.900 NA in 2016. United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 10.900 % in 2016. This records a decrease from the previous number of 11.200 % for 2015. United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 12.200 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 16.400 % in 2000 and a record low of 10.900 % in 2016. United Kingdom UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

The Office for Health Improvement and Disparities (OHID) has updated the mortality profile.

The profile brings together a selection of mortality indicators, including from other OHID data tools such as the https://fingertips.phe.org.uk/profile/public-health-outcomes-framework/data">Public Health Outcomes Framework, making it easier to assess outcomes across a range of causes of death.

For the March 2023 update, 12 new indicators have been added to the profile:

• mortality rate for deaths due to COVID-19, all ages
• under 75 mortality rate for deaths due to COVID-19
• mortality rate from all cardiovascular diseases, all ages
• mortality rate from heart disease, all ages
• mortality rate from stroke, all ages
• mortality rate from cancer, all ages
• mortality rate from breast cancer, all ages
• mortality rate from colorectal cancer, all ages
• mortality rate from liver disease, all ages
• mortality rate from respiratory disease, all ages
• under 75 mortality rate from lung cancer
• mortality rate from dementia and Alzheimer’s disease, all ages

If you would like to send us feedback on the tool please contact pha-ohid@dhsc.gov.uk.

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.

In 2022, 26.3 males and 12.9 females per 100,000 population in England were registered as newly diagnosed with kidney cancer. This represents an increase in the diagnosis rate compared to the previous year. This statistic shows the rate of newly diagnosed cases of kidney cancer per 100,000 population in England from 1995 to 2022, by gender.

This is the economic model developed as part of the technology assessment of low-dose CT for lung cancer screening conducted by the Peninsula Technology Assessment Group (PenTAG). Full details are provided in the Snowsill T, Yang H, Griffin E, Long L, Varley-Campbell J, Coelho H, Robinson S, Hyde C publication "Low-dose computed tomograph for lung cancer screening in high-risk populations: a systematic review and economic evaluation" published in Health Technol Assess.

The dataset contains the microsimulation used in the report base case, and is capable of performing the majority of sensitivity and scenario analyses through the use of included macros, although it should be noted that due to Monte Carlo variation there will be small differences in results from new runs from what is reported.

It is released under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) for the purposes of transparency, reproducibility and education. Requests for more permissive licenses should be addressed to the corresponding author.

This statistic shows the rate of mortality to cancer incidence excluding non-melanoma skin cancer (NMSC) in England in 2016, by region and gender. Cancer is an aggregation of diseases in which cells within the body grow abnormally, often spreading to other parts of the body. In this year, in the north east of England 51 percent of males and 46 percent females who were diagnosed died as a result of cancer, excluding NMSC, this is the highest recorded rate among males and females.

UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 9.000 NA in 2016. This records a decrease from the previous number of 9.200 NA for 2015. UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 9.800 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 12.900 NA in 2000 and a record low of 9.000 NA in 2016. UK: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Disability in activities of daily living (ADL) is a common unmet need among people with advanced respiratory disease. Rehabilitation could help prolong independence, but indicators for timely intervention in this population are lacking. This study aimed to identify trajectories of disability in ADLs over time, and predicting factors, in advanced respiratory disease. Multi-site prospective cohort study in people with advanced non-small cell lung cancer (NSCLC), chronic obstructive pulmonary disease (COPD) or interstitial lung disease (ILD), recruited from hospital or community services, throughout England. Disability in basic (Barthel Index) and instrumental (Lawton–Brody IADL Scale) ADLs were assessed monthly over six months. Visual graphical analysis determined individual trajectories. Multivariate logistic regression examined predictors of increasing disability in basic and instrumental ADLs. Between March 2020 and January 2021, we recruited participants with a diagnosis of NSCLC (n = 110), COPD (n = 72), and ILD (n = 19). 151 participants completed ≥3 timepoints and were included in the longitudinal analysis. Mobility limitation was an independent predictor of increasing disability in instrumental ADLs (odds ratio, 1⋅41 [CI: 1⋅14–1⋅74], p = 0⋅002). Mobility limitation could be used as a simple referral criterion across people with advanced respiratory disease to ensure timely rehabilitation that targets independence in ADLs. To our knowledge this is the first prospective cohort study of trajectories of disability in activities of daily living (ADL) in advanced respiratory disease, including recruitment during the Covid-19 pandemic.It adds to existing evidence by identifying individual variability in trajectories of ADL disability which are undetected at group level.The identification of mobility limitation as a predictor of increasing ADL disability, while controlling for malignant or non-malignant respiratory disease, is novel and has practical utility.Our findings have implications for clinical care, as early identification of functional decline through use of mobility limitation tools could flag early referral to rehabilitation services, potentially preventing or delaying forthcoming functional decline and avoiding reactive crisis management.Mobility limitation is a predictor of increasing disability in activities of daily living in advanced disease, which could be used to flag early referral to rehabilitation services, to help prevent or delay forthcoming functional decline and avoid reactive crisis management To our knowledge this is the first prospective cohort study of trajectories of disability in activities of daily living (ADL) in advanced respiratory disease, including recruitment during the Covid-19 pandemic. It adds to existing evidence by identifying individual variability in trajectories of ADL disability which are undetected at group level. The identification of mobility limitation as a predictor of increasing ADL disability, while controlling for malignant or non-malignant respiratory disease, is novel and has practical utility. Our findings have implications for clinical care, as early identification of functional decline through use of mobility limitation tools could flag early referral to rehabilitation services, potentially preventing or delaying forthcoming functional decline and avoiding reactive crisis management. Mobility limitation is a predictor of increasing disability in activities of daily living in advanced disease, which could be used to flag early referral to rehabilitation services, to help prevent or delay forthcoming functional decline and avoid reactive crisis management

Data License

The data is signed off as non-disclosive and is released under an Open Government Licence. link

This work uses data that has been provided by patients and collected by the NHS as part of their care and support. The data are collated, maintained and quality assured by the National Disease Registration Service, which is part of NHS England.

Recommended Reading

A previous project of mine on Kaggle; The benefits of early diagnosis are manifold. As documented in the project, diagnosing cancer in its nascent stages significantly bolsters survival rates, elevates the experience and quality of care received by patients, enhances the overall quality of life, and importantly, drives down both the costs and intricacies associated with cancer treatments. Such benefits underscore the profound importance of prompt diagnosis and also cast a light on the tangible repercussions of delays in such processes.

Cancer is a serious business ! Can technology be leveraged to help an early diagnosis ? link

Introduction

Cancer remains one of the most critical health challenges worldwide, impacting millions and posing substantial burdens on healthcare systems. Early diagnosis is a key factor in improving cancer outcomes, as it allows for timely intervention, often leading to better survival rates and improved quality of life for patients. This project uses data collected by the NHS, managed by the National Disease Registration Service under the Open Government License, to explore patterns in cancer incidence, diagnostic pathways, and survival rates across various cancer types. Through data visualisation and statistical analysis, this work seeks to deepen our understanding of the factors influencing early diagnosis, the effectiveness of different diagnostic routes, and the progression of survival rates over time

Data Visualisation: GDO_data_wide.csv

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The above chart displays the average percentages of different cancer presentation methods across various cancer sites.

Key observations about the chart: - Emergency Presentation (Red): This is a common presentation method for many cancers, especially pancreatic and brain cancers. This likely reflects the difficulty in detecting these cancers early. - GP Referral (Orange): A significant proportion of cancers are diagnosed via GP referral, highlighting the importance of primary care in cancer detection. This is particularly noticeable for skin cancer. - Two-Week Wait (Green): This is most prominent for suspected testicular, prostate, head and neck cancers. - Screening (Blue): Plays a crucial role in detecting specific cancers, notably breast and cervical cancers, where established screening programs exist. However, the impact is small. - Other Outpatient (Purple): This is prominent for eye cancer and varies across other cancer types, likely encompassing a range of planned diagnostic procedures and follow-up appointments.

By combining the information from the chart, we can gain a clearer understanding of how different cancers are typically diagnosed. This information can be valuable for raising awareness, promoting early detection, and improving cancer care.

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The cleaned summary tabl...

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.

Presents information on the number of newly diagnosed cases of cancer (incidence) and the age-standardised incidence rates for England.

This publication has been discontinued and the data are now included within Cancer Statistics Registrations, England (Series MB1) so that all cancer registration data can be found in one place. The last volume to be published was Cancer Registrations in England - 2010

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Cancer Registrations in England

The National Cancer Patient Experience Surveys (NCPES) began in 2010, after the 2007 'Cancer Reform Strategy' set out a commitment to establish a new survey programme. The NCPES is intended to be a vehicle enabling and supporting quality improvement in the NHS and has been used by national bodies, NHS Hospitals, specialist cancer teams, and national and condition specific charities to improve services for patients. It is designed to monitor national progress on cancer care and to help gather vital information on the Transforming Inpatient Care Programme, the National Cancer Survivorship Initiative and the National Cancer Equality Initiative. An Advisory Group was set up for the NCPES with the National Cancer Director, professionals, voluntary sector representatives, academics and patient survey experts. The Group agreed on the following guiding principles and objectives:

• a standard national survey tool was to be used
• surveys would be conducted at Trust level and identify cancer groups
• the survey would cover all cancers and include the whole care pathway
• the survey should use the word 'cancer' unlike the 2000 and 2004 surveys
• the survey focus would be on patients (rather than carers)
• the data would be used for benchmarking performance across Trusts and by cancer groups where numbers allow
• the data would be used to inform national and local policy
• the data would be made publicly available whilst observing patient data protection requirements and maintaining confidentiality.

The survey is intended to be a vehicle enabling and supporting quality improvement in the NHS and has been used by national bodies, NHS Hospitals, specialist cancer teams, and national and condition specific charities to improve services for patients.

The NCPES has been replicated in Wales (see SN 7510), Northern Ireland, the Isle of Man, parts of Australia, and the Middle East. Further information can be found on the Quality Health Limited National Cancer Patient Experience Survey webpage and the NHS England Cancer Patient Experience Survey webpage.

2010-2015 surveys temporarily withdrawn
The data for the 2010-2014 surveys were temporarily withdrawn at the request of the depositor in October 2015. The 2015 data (SN 8163 and the Special Licence version, SN 8164) were temporarily withdrawn at the request of the depositor in February 2020.

2015 survey:
The purposes of the Cancer Patient Experience Survey, 2015 were:

• to secure continuous improvement by building on the results of the 2014 survey and to enable local providers to assess their performance improvement with other providers;
• to enable commissioners to assess local improvements in cancer patient experience via locally agreed Commissioning for Quality and Innovation (CQUIN) schemes;
• to provide NHS England with an up to date overview of cancer patient experience across England;
• to provide NHS Improving Quality with data on each participating trust and the areas on which quality improvement needs to be focussed; and
• to enable patients to make informed choices about where to go for cancer treatment via publishing the provider level analysis on publicly available websites.

The 2015 data are based on the experience of an initial sample of 116,991 cancer patients, reduced to 108,272 through checking and deceased status measures, and in total 71,186 responded, either by post or online, phone or through the Quality Health translation service. The response rate was 65.7% overall, in line with the levels attained in previous years.

This study, SN 8163, is an edited version of the data collection, subject to standard End User Licence access conditions. NHS England has devised a set of rules to be applied to the data and edited the dataset in order to suppress any personal identifiable information in accordance with the ICO's guidelines on anonymisation and the National Statistician's guidance on suppressing disclosive data. These rules are designed such that there will be no less than three respondents shown when any given cross-tabulation is applied to the data. The rules by which the data are suppressed are as follows. Age suppressed into 4 categories: <55, 55-64, 65-74, 75+. Ethnicity suppressed into 3 categories: White British, non-white British, not specified. Response mode suppressed into three categories: online/postal/other. Cancer type suppressed into the following: Anal; Bladder; Breast; Cervical; Colon; DCIS; Endometrial; Leukaemia; Lung; Melanoma; Multiple myeloma; Non Hodgkins lymphoma; Oesophageal; Ovarian; Pancreatic; Prostate; Rectal; Renal; Secondary; Soft Tissue; Stomach; Testicular; Thyroid; Lip oral cavity and pharynx*; Other** - *this group is defined as C00-C14; ** this group contains all ICD10 codes not represented in other groups. Geographical information has been removed.

A more detailed version of the 2015 data, held under SN 8164, is available under restrictive Special Licence Access conditions. This contains hospital of treatment, geographic information and more detailed demographic information.

The end of life care profiles data update for July 2019 has been published by Public Health England (PHE).

This version includes 8 updated indicators with 2017 data for clinical commissioning groups (CCGs) and higher geographies:

• percentage of deaths in usual place of residence (all ages, 0 to 64 years, 65 to 74 years, 75 to 84 years, 85 years and over)
• percentage of deaths in usual place of residence by underlying cause of death (cancer, circulatory disease, respiratory disease)

The end of life care profiles are designed to improve the availability and accessibility of information around end of life care. The data is presented in an interactive tool that allows users to view and analyse it in a user-friendly format.

The profiles provide a snapshot overview of end of life care across England. They are intended to help local government and health services to improve care at the end of life.

This statistic shows the registrations of newly diagnosed cases of Hodgkin's disease in England from 1995 to 2022, by gender. Hodgkin's disease, also known as Hodgkin's lymphoma, is cancer of the blood, particularly the white blood cells or the lymphatic system. In 2022, 1,001 men and 830 women were registered as newly diagnosed with Hodgkin's disease.

The aim of the National Prostate Cancer Audit (NPCA) is to evaluate the patterns of care and outcomes for patients with prostate cancer in England and Wales, and to support services to improve the quality of care. National guidelines underpin the management of patients with prostate cancer and the NPCA evaluates current patterns of care against these standards including guidance and quality standards from the National Institute for Health and Care Excellence (NICE). The information presented here reports on prostate cancer services in England and Wales, showing variation across providers. For the first time since the NPCA Annual Report 2020, we report results from all six of our performance indicators for both England and Wales, using the most recently available data to the audit. Four performance indicators: • proportion of men with low-risk localised cancer undergoing radical prostate cancer treatment • proportion of men with high-risk/locally advanced disease undergoing radical prostate cancer treatment • proportion of patients experiencing at least one genitourinary (GU) complication requiring a procedural/surgical intervention within 2 years of radical prostatectomy • proportion of patients receiving a procedure of the large bowel and a diagnosis indicating radiation toxicity up to 2 years following radical prostate radiotherapy (RT) require risk stratification using the Gleason score, which is not currently available in the Rapid Cancer Registration Dataset (RCRD) used by the NPCA for recent Annual Reports (2021 and 2022). Therefore, to include these, we have used the National Cancer Registration Dataset (NCRD) in England. The most recently available data to the audit from the NCRD in England is between 1st April 2020 and 31st March 2021. In Wales, the data we receive includes the Gleason score, and the most recently available data to the audit covers patients newly diagnosed with prostate cancer between 1st April 2021 and 31st March 2022. Previous analysis has shown that RCRD underestimated the proportion of men diagnosed with metastatic disease when compared to the NCRD, therefore we have used the NCRD in England to report this indicator. This means we report on different time frames for England and Wales. The proportion of patients who had an emergency readmission within 90 days of radical prostate cancer surgery however can be accurately calculated using the RCRD. Therefore, to compare rates between England and Wales, we selected the same timeframe for this indicator. To report on the impact of and recovery from Covid-19 for prostate cancer services, we use the most recently available data in England from the RCRD between 1st January 2022 and 31st January 2023, and in Wales between 1st April 2021 and 31st March 2022. Individual provider results and reports are available enabling regional and national comparisons to support local QI.

Summary-level GWAS data for 53 traits generated by Genomics plc as presented in:

Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (https://doi.org/10.1101/2022.06.16.22276246)

If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com

NOTES

These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.

Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses.

All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.

GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.

The results are not adjusted for genomic control.

DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)

    cpra
    Variant ID in ‘CPRA’ format. Position reflects position in b37


    chrom
    Chromosome


    pos
    Position in base pairs (b37, 1-based)


    alt
    Alternative allele (effect allele)


    beta
    Effect size (log odds ratio)


    standard_error
    Standard error of beta


    minus_log10_p
    Minus log(base 10) of P-value


    ref
    Reference allele (non-effect allele)


    ncase
    Number of cases


    ncontrol
    Number of controls

DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)

    cpra
    Variant ID in ‘CPRA’ format. Position reflects position in b37


    chrom
    Chromosome


    pos
    Position in base pairs (b37, 1-based)


    alt
    Alternative allele (effect allele)


    beta
    Effect size (log odds ratio


    standard_error
    Standard error of beta


    minus_log10_p
    Minus log(base 10) of P-value


    ref
    Reference allele (non-effect allele)


    ntotal
    Total sample size

PHENOTYPE CODES

The following is a list of traits and their phenotype codes (as used in file naming).

DISEASE TRAITS

    Age-related macular degeneration
    AMD


    Alzheimer's disease
    AD


    Asthma
    AST


    Atrial fibrillation
    AF


    Bipolar disorder
    BD


    Bowel cancer
    CRC


    Breast cancer
    BC


    Cardiovascular disease
    CVD


    Coeliac disease
    CED


    Coronary artery disease
    CAD


    Crohn's disease
    CD


    Epithelial ovarian cancer
    EOC


    Hypertension
    HT


    Ischaemic stroke
    ISS


    Melanoma
    MEL


    Multiple sclerosis
    MS


    Osteoporosis
    OP


    Prostate cancer
    PC


    Parkinson's disease
    PD


    Primary open angle glaucoma
    POAG


    Psoriasis
    PSO


    Rheumatoid arthritis
    RA


    Schizophrenia
    SCZ


    Systemic lupus erythematosus
    SLE


    Type 1 diabetes
    T1D


    Type 2 diabetes
    T2D


    Ulcerative colitis
    UC


    Venous thromboembolic disease
    VTE

QUANTITATIVE TRAITS

    Age at menopause
    AAM


    Apolipoprotein A1
    APOEA


    Apolipoprotein B
    APOEB


    Body mass index
    BMI


    Calcium
    ACALMD


    Docosahexaenoic acid
    DOA


    Estimated bone mineral density T-score
    EBMDT


    Estimated glomerular filtration rate (creatinine based)
    EGCR


    Estimated glomerular filtration rate (cystatin based)
    EGCY


    Glycated haemoglobin
    HBA1C_DF


    High density lipoprotein cholesterol
    HDL


    Height
    HEIGHT


    Intraocular pressure
    IOP


    Low density lipoprotein cholesterol
    LDL_SF


    Omega-6 fatty acids
    OSFA


    Omega-3 fatty acids
    OTFA


    Phosphatidylcholines
    PDCL


    Phosphoglycerides
    PHG


    Polyunsaturated fatty acids
    PFA


    Resting heart rate
    RHR


    Remnant cholesterol (Non-HDL, Non-LDL cholesterol)
    RMNC


    Sphingomyelins
    SGM


    Total cholesterol
    TCH


    Total fatty acids
    TFA


    Total triglycerides
    TTG