Healthcare expenditure statistics, produced to the international definitions of the System of Health Accounts 2011.

Subcategories may not sum to aggregates due to rounding.

UK healthcare expenditure data by financing scheme, function and provider, and additional analyses produced to internationally standardised definitions.

United Kingdom UK: Domestic Private Health Expenditure: % of Current Health Expenditure data was reported at 19.638 % in 2015. This records a decrease from the previous number of 19.675 % for 2014. United Kingdom UK: Domestic Private Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 16.913 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 19.813 % in 2013 and a record low of 14.803 % in 2009. United Kingdom UK: Domestic Private Health Expenditure: % of Current Health Expenditure 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. Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

United Kingdom UK: Current Health Expenditure Per Capita: Current Price data was reported at 0.004 USD mn in 2015. This records a decrease from the previous number of 0.005 USD mn for 2014. United Kingdom UK: Current Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.003 USD mn from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 0.005 USD mn in 2014 and a record low of 0.002 USD mn in 2000. United Kingdom UK: Current Health Expenditure Per Capita: Current Price 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. Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

A monthly-updated list of all financial transactions spending over £25,000 made by Ashton, Leigh and Wigan Community Healthcare NHS Trust, as part of the Government's commitment to transparency in expenditure. During the period from 1 April 2010 to 30 October 2010 we were operating as a provider arm of our local primary care trust - NHS Ashton, Leigh and Wigan.Our new metadata link is http://data.gov.uk/dataset/inancial-transaction-bridgewater-communityhealthcare

United Kingdom UK: Domestic General Government Health Expenditure: % of Current Health Expenditure data was reported at 80.352 % in 2015. This records an increase from the previous number of 80.316 % for 2014. United Kingdom UK: Domestic General Government Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 83.087 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 85.197 % in 2009 and a record low of 80.178 % in 2013. United Kingdom UK: Domestic General Government Health Expenditure: % of Current Health Expenditure 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. Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

Total healthcare expenditure in the UK as a percentage of GDP.

Introduction

Welcome to the UK English Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the UK English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of UK English call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

Please provide the following information under FOI law full schedule of uk databases used to check eligibility for Health Insurance Card eg NI, passport, register of births number of applications for HI Card received april 22-april 23 number of applications rejected due to lack of proof of eligibility april 22-april 23 number of people required to provide further proof following application NHS definition of legal criteria for eligibility for Health Insurance Card Your request was received on 16 August 2023 and I am dealing with it under the terms of the Freedom of Information Act 2000. On 3 December 2023 you clarified the following: 1) When assessing UK Global Health Insurance Card applications does the Authority have access to UK Government records? For example Registration of Births, National Insurance, EU Settlement Scheme records, UK Passport Office Records, DVA Records of Driving Licences? 2) Please give me the number of applications for UK Global Health Insurance Card applications in the last financial year. Please also indicate the number that were approved and the number rejected due to insufficient proof of residency. On 27th December 2023 you clarified the following: 5) I can confirm I want the information for EHIC, UK EHIC and UK GHIC. Response Question 1 When assessing UK Global Health Insurance Card applications, the NHSBSA has access to some UK Government records, such as EU settlement Scheme records. The NHSBSA does not have access to National Insurance records, Registration of Births, UK Passport Office Records or DVA Records. UK Global Health Insurance Card applications are based on a residency system and the NHSBSA will use third party data provider Equifax to establish UK residency. This is stated in our Privacy Notice. https://www.nhsbsa.nhs.uk/our-policies/privacy/overseas-healthcare-services-privacy-notice#:~:text=You%20have%20the%20right%20to,it%20for%20longer%20than%20necessary Question 2 There were 6,510,849 UK Global Health Insurance Card applications in the last financial year. Question 3 and 4 6,016,310 applications were approved and 145,876 were rejected because we were unable to establish proof of residency. The remaining applications were either rejected for other reasons, or we have not yet finished dealing with them. Question 5 The following links provide definitions of legal criteria for eligibility for UK GHIC and UK EHIC: • https://faq.nhsbsa.nhs.uk/knowledgebase/article/KA-26813 • https://www.nhs.uk/using-the-nhs/healthcare-abroad/apply-for-a-free-uk-global-health-insurance-card-ghic/ Please note that we do not issue EHIC anymore as that card has been replaced by the UK EHIC.

Our Europe B2B Data is a powerhouse of business intelligence, offering a comprehensive repository of over 52 million contacts, comprising decision-makers, owners, and founders. Delving into the intricacies of our dataset, here's what makes it a cut above the rest:

1. Unrivaled Accuracy: With verified email addresses, direct dials, and 16+ attributes, our data boasts an unparalleled accuracy rate of 100%. This ensures that your outreach efforts are targeted and effective, minimizing bounce rates and maximizing ROI.

2. Extensive Coverage: Spanning across various industries and countries, our dataset provides extensive coverage, enabling you to access key contacts from diverse sectors. From finance and healthcare to technology and manufacturing, we've got you covered.

3. Scale and Quality: Backed by high-scale and quality indicators, our data undergoes rigorous verification and validation processes to maintain its integrity and reliability. This ensures that you're working with the most up-to-date and actionable information available.

4. Sourcing Methodology: Our data is sourced from a multitude of reputable sources, including public records, industry-specific directories, and strategic partnerships with leading data providers. This multi-sourced approach ensures comprehensive coverage and accuracy.

5. Primary Use-Cases: Whether you're looking to expand your customer base, conduct market research, or enhance your B2B marketing campaigns, our dataset caters to a myriad of use cases. With detailed insights into key decision-makers, you can tailor your strategies for maximum impact.

6. Verticals and Industries: From startups to enterprise-level organizations, our data serves a wide array of verticals and industries. Some of the sectors covered include finance, healthcare, IT, manufacturing, retail, and more.

7. List of Countries in Europe: Our dataset covers the entire European continent, including but not limited to:

   • United Kingdom
   • Germany
   • France
   • Italy
   • Spain
   • Netherlands
   • Switzerland
   • Sweden
   • Belgium
   • Austria
   • Denmark
   • Finland
   • Norway
   • Ireland
   • Portugal
   • Greece
   • Poland
   • Czech Republic
   • Hungary
   • And many more.

In the broader context of our data offering, Europe B2B Data seamlessly integrates with our suite of global B2B data solutions. Whether you're targeting specific regions or expanding your reach globally, our datasets provide the foundation for success in today's competitive business landscape.

Industries We Cover: - Our dataset spans across a wide range of industries, including: - Technology - Finance - Healthcare - Manufacturing - Retail - Hospitality - Education - Real Estate - Transportation - Energy - Media & Entertainment - Agriculture - and many others.

Harness the power of our Europe B2B Data to unlock new opportunities, drive growth, and stay ahead of the curve in your industry. With its unmatched accuracy, extensive coverage, and versatile applications, our data is the key to unlocking your business's full potential.

Crude rate of cost of admissions for alcohol-related conditions (Broad definition) per head of population.

Rationale Alcohol misuse across the UK is a significant public health problem with major health, social, and economic consequences. This indicator aims to highlight the impact of alcohol-related conditions on inpatient hospital services in England. High costs of alcohol-related admissions are indicative of poor population health and high alcohol consumption. This indicator highlights the resource implications of alcohol-related conditions and supports the arguments for local health promotion initiatives. Publication of this indicator will allow national and local cost estimates to be updated and consistently monitored going forward. This measure accounts for just one aspect of the cost of alcohol to society, but there are others such as primary care, crime, ambulatory services, and specialist treatment services as well as broader costs such as unemployment and loss of productivity.

The Government has said that everyone has a role to play in reducing the harmful use of alcohol. This indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related admissions can be reduced through local interventions to reduce alcohol misuse and harm.

References: (1) PHE (2020) The Burden of Disease in England compared with 22 peer countries https://www.gov.uk/government/publications/global-burden-of-disease-for-england-international-comparisons/the-burden-of-disease-in-england-compared-with-22-peer-countries-executive-summary

Definition of numerator The total cost (£s) of alcohol-related admissions (Broad). Admissions to hospital where the primary diagnosis is an alcohol-related condition, or a secondary diagnosis is an alcohol-related external cause.

More specifically, hospital admissions records are identified where the admission is a finished episode [epistat = 3]; the admission is an ordinary admission, day case or maternity [classpat = 1, 2 or 5]; it is an admission episode [epiorder = 1]; the sex of the patient is valid [sex = 1 or 2]; there is a valid age at start of episode [startage between 0 and 150 or between 7001 and 7007]; the region of residence is one of the English regions, no fixed abode or unknown [resgor <= K or U or Y]; the episode end date [epiend] falls within the financial year, and an alcohol-attributable ICD10 code appears in the primary diagnosis field [diag_01] or an alcohol-related external cause code appears in any diagnosis field [diag_nn].

For each episode identified, an alcohol-attributable fraction is applied to the primary diagnosis field or an alcohol-attributable external cause code appears in one of the secondary codes based on the diagnostic codes, age group, and sex of the patient. Where there is more than one alcohol-related ICD10 code among the 20 possible diagnostic codes, the code with the largest alcohol-attributable fraction is selected; in the event of there being two or more codes with the same alcohol-attributable fraction within the same episode, the one from the lowest diagnostic position is selected. For a detailed list of all alcohol-attributable diseases, including ICD 10 codes and relative risks, see ‘Alcohol-attributable fractions for England: an update’ (2). Alcohol-related hospital admission episodes were extracted from HES according to the Broad definition and admissions flagged as either elective or non-elective based on the admission method field.

The cost of each admission episode was calculated using the National Cost Collection (published by NHS England) main schedule dataset for the corresponding financial year applied to elective and non-elective admission episodes. The healthcare resource group (HRG) was identified using the HES field SUSHRG [SUS Generated HRG], which is the SUS PbR derived HRG code at episode level. Healthcare Resource Groups (HRGs) are standard groupings of clinically similar treatments which use common levels of healthcare resource. The elective admissions were assigned an average of the elective and day-case costs. The non-electives were assigned an average of the non-elective long stay and non-elective short stay costs. Where the HRG was not available or did not match the National Reference Costs look-up table, an average elective or non-elective cost was imputed. This may result in the cost of these admissions being underestimated. For each record, the AAF was multiplied by the reference cost and the resulting values were aggregated by the required output geographies to provide numerators for the cost per capita indicator.

References: (2) PHE (2020) Alcohol-attributable fractions for England: an update https://www.gov.uk/government/publications/alcohol-attributable-fractions-for-england-an-update

Definition of denominator Mid-year population estimates.

Caveats Not all alcohol-related conditions require inpatient services, so this indicator is only one measure of the alcohol-related health problems in each local area. However, inpatient admissions are easily monitored, and this indicator provides local authorities with a routine method of monitoring the health impacts of alcohol in their local populations.

The Healthcare Resource Group cost assigned to each hospital admission is for the initial admission episode only and doesn’t include costs related to alcohol in any subsequent episodes in the hospital spell. Where the HRG was not available or did not match the National Reference Costs look-up table, an average elective or non-elective cost was imputed. This may result in the cost of these admissions being underestimated. It must be noted that the numerator is based on the financial year and the denominator on calendar mid-year population estimates, e.g., 2019/20 admission rates are constructed from admission counts for the 2019/20 financial year and mid-year population estimates for the 2020 calendar year. Data for England includes records with geography 'No fixed abode'. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator. This does not include attendance at Accident and Emergency departments. Hospital Episode Statistics overall is well completed. However, year-on-year variations exist due to poor completion from a proportion of trusts.

Analysis has revealed significant differences across the country in the coding of cancer patients in the Hospital Episode Statistics. In particular, in some areas, regular attenders at hospital for treatments like chemotherapy and radiotherapy are being incorrectly recorded as ordinary or day-case admissions. Since cancer admissions form part of the overarching alcohol-related admission national indicators, the inconsistent recording across the country for cancer patients has some implication for these headline measures.

Cancer admissions make up approximately a quarter of the total number of alcohol-related admissions. Analysis suggests that, although most Local Authorities would remain within the same RAG group compared with the England average if cancer admissions were removed, the ranking of Local Authorities within RAG groups would be altered. We are continuing to monitor the impact of this issue and to consider ways of improving the consistency between areas. The COVID-19 pandemic had a large impact on hospital activity with a reduction in admissions in 2020 to 2021. Because of this, NHS Digital has been unable to analyse coverage (measured as the difference between expected and actual records submitted by NHS Trusts) in the normal way. There may have been issues around coverage in some areas which were not identified as a result.

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for February 2020. The CSDS is a patient-level dataset and has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. These services can include NHS Trusts, health centres, schools, mental health trusts, and local authorities. The data collected in CSDS includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report.

Hearing Aid Usage and Satisfaction Among the Elderly - ELSA Dataset (Manipulated)

Dataset Overview

This dataset provides a modified version of variables related to hearing aid usage, hearing difficulties, and satisfaction levels among elderly individuals, based on the English Longitudinal Study of Ageing (ELSA), Wave 7. The dataset contains 1,164 samples and includes variables that examine hearing conditions, hearing aid adoption, interactions with healthcare professionals, and overall satisfaction with hearing aids.

Factors influencing the adaptation and continued use of hearing aids in the elderly population.

Dataset Structure

This dataset includes the following key variables:

Self_Reported_Hearing: Self-reported hearing quality (1 = Excellent, 5 = Poor).

Hearing_Difficulty: Whether the individual experiences hearing difficulties (1 = Yes, 2 = No).

Group_Conversation_Difficulty: Difficulty having a conversation in a group setting.

Background_Noise_Difficulty: Difficulty following conversations when there is background noise.

Doctor_Consultation: Whether the individual consulted a doctor or nurse about their hearing problems.

Hearing_Aid_Usage: Frequency of hearing aid usage (1 = Most of the time, 2 = Some of the time, 3 = No).

Satisfaction_Level: Satisfaction with hearing aids (1 = Very satisfied, 5 = Very dissatisfied).

Cochlear_Implant, Ear_Infection, Hearing_Test_Agreement, and more.

Additionally, the dataset includes variables related to hearing tests, such as the number of tones heard at different frequencies for both ears, as well as whether individuals were referred to a specialist or recommended a hearing aid by healthcare professionals.

Source

This dataset has been manipulated from the original English Longitudinal Study of Ageing (ELSA), Wave 7 dataset. The full original dataset, which includes a much larger set of variables and samples, is publicly available on the UK Data Service website for research and educational purposes: UK Data Service.

License

The modified data provided here is intended for educational and research purposes. Users are encouraged to refer to the original dataset and citation policies from the UK Data Service.

Hospital Associated Pneumonia (HAP) is a common healthcare associated infection, thought to affect 1-2% of all UK hospital admissions. Patients with HAP are more likely to need intensive care support and have increased length of stay and mortality rates. Unlike in community-acquired pneumonia, tools to stratify risk or severity are lacking. While there is some understanding of risk-factors that predispose people to HAP, prognostic factors are less well defined.  Treatment guidelines suggest broad spectrum antibiotics but there is little understanding of the causative organisms which cause HAP. 

​To explore HAP, PIONEER, with the NIHR Birmingham BRC Infection and Acute Care theme, have curated a highly granular dataset of 22,580 hospital acquired pneumonia spells. The data includes demography, co-morbidities including Charlson comorbidity index, symptoms, serial physiology and acuity, investigations including microbiology, imaging, medications (dose and route), ward locations including intensive care details and outcomes. The current dataset includes admissions from 01-01-2018 to 31-12-2022 but can be expanded to assess other timelines of interest.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

This dataset has been discontinued and replaced with the Mental Health Services Monthly Statistics dataset, available at https://data.gov.uk/dataset/mental-health-services-monthly-statistics The Mental Health Minimum Data Set (MHMDS) was renamed Mental Health and Learning Disabilities Data Set (MHLDDS) following an expansion in scope (from September 2014) to include people in contact with learning disability services for the first time. This monthly statistical release makes available the most recent Mental Health Minimum Dataset (MHMDS) data from April 2013 onwards. Further analysis to support currencies and payment in adult and older people's mental health services was added to the publication of April 2014 final data which can be found in the related links below. These changes are described in the Methodological Change paper referenced below. As well as providing timely data, it presents a wide range of information about care given to users of NHS-funded, secondary mental health services for adults and older people ('secondary mental health services') in England. This information will be of particular interest to organisations involved in giving secondary mental health care to adults and older people, as it presents timely information to support discussions between providers and commissioners of services. The MHMDS Monthly Report now includes the ten nationally recommended quality and outcome indicators to support the implementation of currencies and payment in mental health. For patients, researchers, agencies and the wider public it aims to provide up to date information about the numbers of people using services, spending time in psychiatric hospitals and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis.

• to provide annual data about the nation’s health;
• to estimate the proportion of people in England with specified health conditions;
• to estimate the prevalence of certain risk factors associated with these conditions;
• to examine differences between population subgroups in their likelihood of having specific conditions or risk factors;
• to assess the frequency with which particular combinations of risk factors are found, and which groups these combinations most commonly occur;
• to monitor progress towards selected health targets
• since 1995, to measure the height of children at different ages, replacing the National Study of Health and Growth;
• since 1995, monitor the prevalence of overweight and obesity in children.

Changes to the HSE from 2015:
Users should note that from 2015 survey onwards, only the individual data file is available. The household data file is no longer released for analysis. In addition, users may see other changes; for example only grouped age is now available instead of single year of age. NHS Digital have issued the following statement on changes to the HSE from 2015:

"NHS Digital has recently reviewed how we manage access to survey datasets. In doing this we have sought to strike a balance between protecting the privacy of individuals and enabling maximum use of these valuable, publicly funded data collections. We have thoroughly reviewed our disclosure control measures, including taking advice from experts at the Office of National Statistics. The result is that additional disclosure control measures have been applied to the 2015 survey [onwards] to enable a suitable dataset to be made available through the UK Data Service via end user licence. This involved providing less detail on some aspects, such as geographical classifications, ethnicity and household relationships. To provide greater protection of the answers of children and adults within households it is not possible to identify people within the same household on this dataset, however parent/guardian derived variables appended to their children (if they have any) have been added to enable some intra‐household analysis.”

It is hoped that a second dataset with more detail including family and household relationships will be made available via Special Licence. In the meantime, researchers who want to do analysis of health and behaviours within families or households, and the derived intra-household variables do not meet your needs, are advised to register their interest for a more detailed dataset with NatCen Social Research and provide information about their proposed research and which data they want.

Request I believe the above scheme needs to be put in place urgently. Can you please answer the following questions: 1. How many people have applied to you for Ill Health Retirement with Long Covid? 2. How many people have been rejected for Tier One and/or Tier Two levels of IHR when applying with Long Covid? 3. What evidence (listing guidance and research evidence) are being used to reject or confirm applications for IHR with Long Covid? Response Question 1 & 2 A copy of the information is attached. Question 3 Each Scheme Medical Adviser (SMA) is expected to adopt evidence-based practice in arriving at a decision. They do this by combining the following: Medical evidence provided in the Scheme member’s application, Further medical evidence that the SMA may have requested from the Scheme member’s treating healthcare professionals, Information that the employer may have provided in Part A of Form AW33E (e.g. demands of the work duties, any workplace adjustments tried, and the effectiveness of such adjustments), Information that the Scheme member may have provided in Part B of Form AW33E (for example, how long COVID affects them), Current medical literature on long COVID, And the SMA’s occupational health expertise. When assessing ill-health retirement applications from scheme members who have long COVID, the SMA might consult the following guidance and research evidence: • The Society of Occupational Medicine (SOM): ‘Long COVID and Return to Work – What Works?’ (https://www.som.org.uk/sites/som.org.uk/files/Long_COVID_and_Return_to_Work_What_Works_0.pdf) • The Faculty of Occupational Medicine (FOM): ‘Guidance for healthcare professionals on return to work for patients with post-COVID syndrome’ (https://www.fom.ac.uk/wp-content/uploads/FOM-Guidance-post-COVID_healthcare-professionals.pdf) • Occupational and Environmental Medicine (academic journal of the FOM: https://oem.bmj.com) • Occupational Medicine (academic journal of the SOM: https://academic.oup.com/occmed?login=false) • Industrial Injuries Advisory Council publication: ‘COVID-19 and Occupational Impacts’ (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1119955/covid-19-and-occupational-impacts.pdf) • NICE: https://cks.nice.org.uk/topics/long-term-effects-of-coronavirus-long-covid • Nature. An example of a recent publication in this journal is Davis, H., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Long covid: major findings, mechanisms and recommendations. Nature Reviews Microbiology, 21(3), 133-146. Full text available at https://www.nature.com/articles/s41579-022-00846-2 • British Medical Journal (BMJ) • Journal of the American Medical Association (JAMA) • The Lancet • New England Journal of Medicine In summary, the SMA is expected to adopt an individual approach to each case and use careful clinical judgement when applying the medical research literature and guidance to the specific medical circumstances of a Scheme member with long COVID.

What is the durability dataset?

This is the final, standardized dataset that was used for the integrative SIMON analysis in the publication: "Divergent trajectories of antiviral memory after SARS-Cov-2 infection" describing the durability of humoral and cellular immune responses (virus-specific B and T cell responses) longitudinally over 6 months after infection in the cohort of 78 UK Health workers who presented with symptomatic infection or who tested positive during an asymptomatic screening program. The integrated dataset contains combined data from 14 immunological assays, including antibody and T and B cell assays and clinical data which were merged using a donor-specific variable (Donor ID).

How to use the durability dataset?

Here, you can download the entire durability database as a CSV file. Briefly, in the CSV file, each row represents a sample with information about the time point when samples were acquired (timepoint). For each sample, there is information about the response status (column named, Responder). The responder status is the outcome of immune response durability and it was calculated based on the titer of the anti-nucleocapsid-specific antibodies measured 6 months post symptoms onset (pso). High responders were determined as individuals who are seropositive 6 months pso, i.e., have anti-N Ab titer ≥ 1.4, while low responders are individuals that are seronegative. The clinical symptoms are expressed as binary values: 0- no clinical symptoms, and 1 - clinical symptoms observed. Sex is indicated as male (m) and female (f). Age is determined in years. For each sample, there is information about which assays were performed and the value of the measured analytes.

How to cite FluPRINT?

If you use durability dataset in an academic publication, please use the following citation:

Tomic, A., et al. Divergent trajectories of antiviral memory after SARS-Cov-2 infection (2021).

Contact Information

If you are interested to find out more about the durability dataset, or if you experience any problems with downloading files, please contact us at info@adrianatomic.com.

UK: Proportion of Population Pushed Below the $3.10: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data was reported at 0.000 USD in 2013. This stayed constant from the previous number of 0.000 USD for 2010. UK: Proportion of Population Pushed Below the $3.10: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % data is updated yearly, averaging 0.000 USD from Dec 1995 (Median) to 2013, with 6 observations. UK: Proportion of Population Pushed Below the $3.10: Poverty Line by Out-of-Pocket Health Care Expenditure: 2011 PPP: % 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: Poverty. Proportion of population pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.; Weighted Average;

These contain my projections of Philippine health insurance expenditures from 2010 to 2014. The determinants of health expenditure growth are size and structure of the population, real growth rate, and GDP growth rate.