This dataset reports the uptake of at least one dose of the COVID-19 vaccine among patients registered with GP practices in England. It provides a measure of immunisation coverage and supports monitoring of public health efforts to reduce the spread and severity of COVID-19. The data is sourced from Immform, EMIS Health, and TPP systems.

Rationale

Vaccination is a critical tool in controlling the COVID-19 pandemic. Monitoring vaccine uptake helps identify gaps in coverage, inform targeted outreach, and evaluate the effectiveness of immunisation campaigns. This indicator supports efforts to increase vaccine uptake and protect vulnerable populations.

Numerator

The numerator is the number of patients who have received at least one dose of a COVID-19 vaccine.

Denominator

The denominator is the total number of patients registered with GP practices, as recorded in the Immform-EMIS Health and TPP systems.

Caveats

Automated data collection is only possible from GP practices whose IT suppliers support automatic extraction. Some organisations may not have responded or submitted data, which could affect completeness and accuracy.

External References

More information is available from the following source:

Immform COVID-19 Collections Portal

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis.  TB remains a significant global health problem. The UK has one of the highest rates of TB in Europe, with almost 5000 new cases notified in 2019. Within the UK, Birmingham and the West Midlands are particular hotspots for TB, with over 300 cases of active disease and approximately 10 times that of new latent infections diagnosed each year.

Birmingham and the West Midlands have experienced particularly high rates of COVID-19 during the pandemic and there is increasing evidence that individuals of Black, Asian and minority ethnicities (BAME) experience the most significant morbidity and highest mortality rates due to COVID-19. These groups also experience the highest burdens of TB, both in the UK and overseas.

Epidemiological data suggests that current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. There is also evidence of immunopathogenic overlap between the two infections with in vitro studies finding that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of TB-infected macrophages.

This dataset would enable a deeper analysis of demography and clinical outcomes associated with COVID-19 in patients with concurrent TB.

PIONEER geography: the West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

EHR. 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 & an expanded 250 ITU bed capacity during COVID. 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”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic, curated to focus on Mycobacterium tuberculosis and SARS-CoV-2. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (A&E, triage, IP, ITU admissions), presenting complaint, DNAR teal, all physiology readings (AVPU scale, Covid CFS, blood pressure, respiratory rate, oxygen saturations and others), all blood results, imaging reports, all prescribed & administered treatments, all outcomes.

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Background: Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 125 million cases, and more than 2.7 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonitis, adult respiratory distress syndrome (ARDS) and death. Many patients required ventilatory support including high flow oxygen, continuous positive airway pressure and intubated with or without tracheotomy. There was considerable learning on how to manage COVID-19 during the pandemic and new drugs became available during the different waves. This secondary care COVID dataset contains granular ventilatory, demographic, morbidity, serial acuity, medications and outcome data in COVID-19 across all waves and will be continuously refreshed.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day, more than 100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions across all waves.

Electronic Health Records (EHR): University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. ITU capacity increased to 250 beds during the COVID pandemic. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. UHB has cared for more than 10,000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January 2020 to the current date. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), severity, ventilatory requirements, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed and administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support, dexamethasone, remdesivir, tocilizumab), all outcomes.

Available supplementary data: Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

This data was generated as part of an 18 month ESRC funded project,as part of UKRI’s rapid response to COVID-19. The project examines how UK period poverty initiatives mitigated Covid-19 challenges in light of lockdown measures and closure of services, and how they continued to meet the needs of those experiencing period poverty across the UK. Applied social science research methodologies were utilised to collect and analyse data as this project, about the Covid-19 pandemic, was undertaken during an ongoing ‘real world’ pandemic. Data collection was divided into two phases. Phase 1 (October 2020 – February 2021) collected data from period poverty organisations in the UK using semi-structured interviews and an online survey to develop an in-depth understanding of how period poverty organisations were responding to and navigating the Covid-19 Pandemic. Having collected and analysed this data, phase 2 (June – September 2021) used an online survey to collect data from people experiencing period poverty in order to better understand their lived experiences during the pandemic. Our dataset comprises of phase 1 interview transcripts and online survey responses, and phase 2 online survey responses.

Period poverty refers not only to economic hardship with accessing period products, but also to a poverty of education, resources, rights and freedom from stigma for girls and menstruators (1). Since March 2020, and the introduction of lockdown/social distancing measures as a result of the Covid-19 pandemic, more than 1 of every 10 girls (aged 14-21) cannot afford period products and instead must use makeshift products (toilet roll, socks/other fabric, newspaper/paper). Nearly a quarter (22%) of those who can afford products struggle to access them, mostly because they cannot find them in the shops, or because their usual source/s is low on products/closed (2).

Community /non-profit initiatives face new challenges related to Covid-19 lockdown measures as they strive to continue to support those experiencing period poverty. Challenges include accessing stocks of period products, distribution of products given lockdown restrictions, availability of staff/volunteer assistance and the emergence of 'new' vulnerable groups. There is an urgent need to capture how initiatives are adapting to challenges, to continue to support the needs of those experiencing period poverty during the pandemic. This data is crucial to informing current practice, shaping policy, developing strategies within the ongoing crisis and any future crises, and ensuring women and girls' voices are centralised.

The project builds upon existing limited knowledge by providing insight into how UK based initiatives and projects are mitigating challenges linked to Covid-19, by examining how they are continuing to meet the needs of those experiencing period poverty and identifying any gaps in provision.

1. Montgomery P., et al., 2016. Menstruation and the Cycle of Poverty. PLoS ONE 11(12): e0166122.
2. Plan International UK, 2020. The State of Girls' Rights in the UK: Early insights into the impact of the coronavirus pandemic on girls. London: Plan International UK

PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases& more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS)& death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The percentage of extra deaths that occurred due to winter, including those that had COVID-19 mentioned on the death certificate. The Excess Winter Mortality (EWM) index is calculated as the number of excess winter deaths divided by the average non-winter deaths, expressed as a percentage. Calculated so that comparisons can be made between sexes, age groups, and regions.

An EWM index of 20 shows that there were 20 percent more deaths in winter compared with the non-winter period. Provisional figures at country and region level are produced for the most recent winter using estimation methods, and so are rounded to the nearest 100 deaths. Data post 2019/20 should be treated with caution due to high numbers of deaths from COVID-19 in the summer period.

For data years 2020/21 onwards, instances where the number of winter deaths compared to non-winter deaths were equal to zero or a negative value, an EWM index is presented. (For earlier years, the EWM index was removed). A zero value for winter deaths compared to non-winter deaths is often affected by rounding, so in these instances, the winter mortality index can either be a positive or negative value. A negative winter mortality index means there were a higher number of deaths in the non-winter periods than the winter period.

Alternatively, figures are available for deaths excluding COVID-19, calculated using all-cause deaths that did not have COVID-19 mentioned on the death certificate.

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

This indicator is designed to accompany the SHMI publication. The SHMI includes all deaths reported of patients who were admitted to non-specialist acute trusts in England and either died while in hospital or within 30 days of discharge. Deaths related to COVID-19 are excluded from the SHMI. A contextual indicator on the percentage of deaths reported in the SHMI which occurred in hospital and the percentage which occurred outside of hospital is produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells for England from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for Chelsea and Westminster Hospital NHS Foundation Trust (trust code RQM). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), East and North Hertfordshire NHS Trust (trust code RWH), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), Sandwell and West Birmingham Hospitals NHS Trust (trust code RXK), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG) are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information is available in the Background Quality Report. 6. On 1 July 2023 Southport and Ormskirk Hospital NHS Trust (trust code RVY) was acquired by St Helens and Knowsley Teaching Hospitals NHS Trust (trust code RBN). The new organisation is known as Mersey and West Lancashire Teaching Hospitals NHS Trust (trust code RBN). This new organisation structure is reflected from this publication onwards. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   ​​   ​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.  

This highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications, procedures, drugs, mortality and readmission.

Geography: 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. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year. Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. 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 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.

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (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 & 100 ITU beds. 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”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The winter mortality index (WMI) is a measure expressed as a ratio of the difference in all cause mortality during winter months (December to March) compared to the average in the non winter months (the preceding August to November and following April to July).The terminology used to describe this indicator has changed to provide clearer explanation of what the analysis represents. The measures have been renamed to winter deaths compared to non winter deaths (previously excess winter deaths) and winter mortality index (WMI) (previously excess winter mortality index). There have been no methodology changes.

RationaleThe purpose of the winter mortality measure is to compare the number of deaths that occurred in the winter period (December to March) with the average of the non winter periods (August to November and April to July). Winter mortality is not solely a reflection of temperature, but of other factors as well. These include respiratory diseases and pressure on services, which have been more intense than usual during and following the height of the pandemic (1).It is an important measure as it allows users to assess whether policies are having an impact on mortality risks during the winter period (2). (1) Office for National Statistics (ONS), released 19 January 2023, ONS website, statistical bulletin, Winter mortality in England and Wales: 2021 to 2022 (provisional) and 2020 to 2021 (final). (2) Office for National Statistics (ONS), released 19 January 2023, ONS website, QMI, Winter mortality in England and Wales QMI: 19 January 2023Definition of numeratorTotal number of winter deaths for all ages in defined year 20xx/20xx+1 (number of deaths occurring in December in year 20xx and January to March in 20xx plus 1) minus half the number of deaths in the non winter months (preceding August to November in year 20xx and following April to July in year 20xx plus 1) and registered by 31 December 20xx plus 1.Definition of denominatorThe average number of deaths for all ages ( in defined year 20xx/20xx plus 1) occurring in the non winter months, i.e. the total number of deaths occurring in the preceding August to November in year 20xx and the following April to July in year 20xx plus 1 divided by two and registered by 31 December 20xx plus 1.CaveatsIn 2020, the coronavirus (COVID 19) pandemic led to a large increase of deaths mostly in the non-winter months of April to July 2020. This has impacted the WMI for 2019 to 2020. Because we rely on using the difference between deaths occurring in the winter and the average of non winter months; specifically, the scale of COVID 19 deaths during non winter months has fundamentally disturbed the data time series and so data for 2019 to 2020 should be interpreted with caution.The Office for National Statistics (ONS) Annual Births and Mortality Extract is based on registered deaths (Date of registration) and the Winter deaths compared to non winter deaths and WMI calculations are based on the date of death occurrences (Date of death). It is possible that a number of deaths might not have been registered when the data were released and this could vary between areas. This indicator only includes deaths which are registered by the end of the calendar year 20xx plus 1.Data published in the PHOF will differ from published ONS results which uses an extract of mortality data taken approximately five months after the annual ONS mortality extract is taken, in order to give more time for late registrations (for example, deaths that were referred to a coroner) to appear in the data.The WMI will be partly dependent on the proportion of older people in the population as most winter deaths effect older people (there is no standardisation in this calculation by age or any other factor).This winter period was selected as they are the months which over the last 50 years have displayed above average monthly mortality. However, if mortality starts to increase prior to this, for example in November, the number of deaths in the non winter period will increase, which in turn will decrease the estimate of winter deaths compared to non winter deaths.The counts are presented rounded to the nearest 10, in line with how data is presented by the ONS.

Background. Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonitis, adult respiratory distress syndrome (ARDS) & death. Many patients required ventilatory support including high flow oxygen, continuous positive airway pressure and intubated with or without tracheotomy. Different centres took different approaches to care delivery depending on ITU bed availability. This secondary care COVID OMOP dataset contains granular ventilatory, demographic, morbidity, serial acuity and outcome data in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (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 & 100 ITU beds. ITU capacity increased to 250 beds during the COVID pandemic. 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”. UHB has cared for >5000 COVID admissions to date. This data is in the OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – September 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), severity, ventilatory requirements, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Local authority level vaccine coverage estimates for the school-based meningococcal ACWY adolescent vaccination programme for 14 to 15 year olds.

Rationale The MenACWY vaccination was introduced into the national immunisation programme in autumn 2015 to respond to a rapid and accelerating increase in cases of invasive meningococcal group W (MenW) disease, which was declared a national incident. The MenACWY conjugate vaccine provides direct protection to the vaccinated cohort and, by reducing MenW carriage, will also provide indirect protection to unvaccinated children and adults. This follows advice from the Joint Committee on Vaccination and Immunisation (JCVI). It is routinely offered through schools in academic school Years 9 and 10 (rising 14 and rising 15 year olds). The indicator measures local authority level MenACWY vaccine coverage for students at the end of school Yr 10. Vaccination coverage is the best indicator of the level of protection a population will have against vaccine preventable communicable diseases. Coverage is closely correlated with levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise. Previous evidence shows that highlighting vaccination programmes encourages improvements in uptake levels. May also have relevance for NICE guidance PH21: Reducing differences in the uptake of immunisations (The guidance aims to increase immunisation uptake among those aged under 19 years from groups where uptake is low).

Definition of numerator Total number of adolescents in LA responsible population whose 15th birthday falls within the time period who have ever received MenACWY vaccine.

Definition of denominator Total number of adolescents attending school in LA plus adolescents resident in the LA not linked to any school whose 15th birthday falls within the time period.

Caveats On 23 March 2020, all educational settings in England were advised to close by the UK Government as part of COVID-19 pandemic measures. Although the importance of maintaining good vaccine uptake was impressed, operational delivery of all school-aged immunisation programmes was paused for a short period of time as a consequence of school closures limiting access to venues for providers and children who were eligible for vaccination and to ensure that lockdown regulations were not breached.

The NHSEI central public health commissioning and operations team rapidly established an Immunisation Task and Finish Group, with regional NHSEI and UKHSA representation. The group was established to:

assess the impact of COVID-19 on all immunisation programmes, including school-aged programmes develop technical guidance and a plan for restoration and recovery of school-aged programmes, once education settings were reopened

From 1 June 2020, some schools partially reopened for some year groups for a mini summer term. NHSEI published clinical guidance for healthcare professionals on maintaining immunisation programmes during COVID-19, and the Department of Education published further guidance which led to schools allowing vaccination sessions to resume on site.

NHSEI commissioned, school-aged immunisation providers were able to implement their restoration and recovery plans to commence catch-up during the summer of 2020. This included delivery of programmes in school and community settings following a robust risk assessment and in line with UK Government Public Health COVID-19 guidance.

In September 2020, schools across the UK reopened for general in-person attendance. During the 2020 to 2021 academic year, students were required to stay at home and learn remotely if they tested positive for COVID-19 or if they were a contact of a confirmed COVID-19 case, and so school attendance rates in England were lower than normal, especially in areas with very high COVID-19 incidence rates. In England, as part of a wider national lockdown in January 2021, schools were closed to all except children of keyworkers and vulnerable children. From early March 2021, primary schools reopened, with a phased reopening of secondary schools.

Although this led to some disruption of school-based elements of programme delivery in the 2020 to 2021 academic year, NHSEI Regional Public Health Commissioning teams worked with NHSEI commissioned school-aged immunisation providers to maintain the delivery of the routine programme and catch-up. As the routine programme is commissioned for a school-aged cohort rather than a school-based cohort, providers were able to build on existing arrangements such as community-based clinics in place for children not in mainstream education. A wide variety of local arrangements were established to ensure programme delivery continued effectively and safely in the school and community premises, during the term time and school breaks.

Overall Absence Rate in State-Funded Schools: The overall absence rate is calculated as the total number of absence sessions (authorised + unauthorised) divided by the total number of possible sessions for each enrolment. One session equals half a day.

Legal Requirement: Parents of children aged 5 to 15 (compulsory school age) must ensure their child receives a suitable education through regular school attendance or otherwise.

Scope of Data: Data includes state-funded primary and secondary schools (maintained schools, city technology colleges, academies) and special schools. It is based on the geographical location of the school and counts enrolments, not individual pupils.

Types of Absence:

Authorised Absence: Absence with permission and a satisfactory explanation (e.g., illness). Unauthorised Absence: Absence without permission or explanation. Includes late arrivals after the register closes.

COVID-19 Impact: No data was published for the 2019/20 academic year due to the pandemic. From 2020/21, total possible sessions include those recorded on the school census and sessions missed due to coronavirus-related circumstances.

Source: Data uploaded from DfE annual release. Other term-time releases are available.

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