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.

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

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

In December 2019, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) was described and by March 2020, the World Health Organization had declared the disease (Coronavirus disease 2019, COVID-19) a pandemic. Whilst respiratory symptoms are the fundamental feature of the disease, evidence indicates that the disease is associated with coagulation dysfunction which predisposes patients to an increased risk of both venous and arterial thromboembolism (TE) and potentially increased mortality risk as a consequence. Biomarkers associated with TE (D-dimers) are often raised in people with COVID but without clear evidence of TE. It is important to understand who is at most risk of TE, to manage disease effectively. This dataset (in OMOP) describes patients with and without COVID who were admitted to UHB including all those with and without TE.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. Birmingham was hard hit by all COVID waves and University Hospitals Birmingham NHS Foundation Trust (UHB) had >8000 COVID admissions by the end of December 2020.

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 patients admitted during the first wave of the COVID-19, both with and without COVID. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, SARS-CoV-2 swab result, diagnosis of TE, clotting parameters, D-Dimers, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Matched controls; ambulance, 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.

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.

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.

The CONTRAST study explored how the Covid-19 (lockdown) restrictions affected lives of older children in the UK, particularly how they have influenced learning, eating, physical and other activities and wellbeing.

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.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

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.

A deeply phenotyped dataset of hospitalised COVID-19 patients in Birmingham; including granular ethnicity and multi-morbidity data confirmed in primary care; physiology, blood biomarkers, treatments, interventions, ITU admissions and outcomes.

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.

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.

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.

Background and Aims: Angiotensin-converting enzyme II (ACE2) is the key molecule for understanding the pathophysiology of COVID-19. The risk of COVID-19 and impact of immunosuppressive treatment on disease course in patients with inflammatory bowel disease (IBD) remain controversial. We aimed to determine the change of intestinal ACE2 expression before and after biologics treatment including anti-tumor necrosis factor α (anti-TNFα), anti-integrin, and anti-interleukin (IL)12/23 in IBD patients.Methods: We analyzed the ACE2 expression through the public database of paired intestinal biopsies from IBD patients before and after biologic therapy. Change of ACE2 RNA and protein expression were validated in two independent cohorts (Birmingham cohort and Guangzhou cohort). The correlation between ACE2 expression and disease activity was also analyzed.Results: Mining information from the GEO database showed that compared with healthy control, intestinal ACE2 expression was downregulated in ileum of CD patients, while upregulated in colon of both CD and UC patients. Colonic ACE2 RNA expression was decreased significantly in patients responding to anti-TNFα but not anti-integrin and anti-IL12/23, which was validated in the Birmingham cohort. Using the Guangzhou cohort including 53 patients matched by pre- and post-anti-TNFα therapy, colonic ACE2 protein expression was significantly downregulated after anti-TNFα treatment in responders (P < 0.001) rather than non-responders. Colonic ACE2 expression was significantly higher in patients with severe histologically active disease compared with those with moderate (P < 0.0001) and mild (P = 0.0002) histologically active disease.Conclusion: Intestinal inflammation influences the expression of intestinal ACE2 in IBD patients, with different alterations in the ileum and colon. Colonic ACE2 expression was downregulated after anti-TNFα therapy in IBD patients responding to treatment. This might provide new clues regarding the risk of SARS-CoV-2 infection and the potential benefit of sustaining anti-TNFα treatment in patients with IBD.

These indicators are designed to accompany the SHMI publication. COVID-19 activity is included in the SHMI if the discharge date is on or after 1 September 2021. Contextual indicators on the number of provider spells which are related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. The number of spells as a percentage of pre-pandemic activity indicator is being published as an official statistic in development. Official statistics in development are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). This new organisation structure is reflected from this publication onwards. 2. There is a shortfall in the number of records for Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), The Shrewsbury and Telford Hospital NHS Trust (trust code RXW), and Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), East Lancashire Hospitals NHS Trust (trust code RXR), Great Western Hospitals NHS Foundation Trust (trust code RN3), Harrogate and District NHS Foundation Trust (trust code RCD), Milton Keynes University Hospital NHS Foundation Trust (trust code RD8), Portsmouth Hospitals University NHS Trust (trust code RHU), Royal United Hospitals Bath NHS Foundation Trust (trust code RD1), University Hospitals Birmingham NHS Foundation Trust (trust code RRK), University Hospitals of North Midlands NHS Trust (trust code RJE), and University Hospitals Plymouth NHS Trust (trust code RK9). Values for these trusts should therefore be interpreted with caution. 4. A number of trusts 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 about this is available in the Background Quality Report. 5. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

All infectious syphilis (primary, secondary and early latent) diagnoses among people accessing sexual health services* in England who are also residents in England, expressed as a rate per 100,000 population. Data is presented by area of patient residence and includes those residents in England and those with an unknown residence (data for those residents outside of England is not included).*Sexual health services providing STI related care (Levels 2 and 3). Further details on the levels of sexual healthcare provision are provided in the https://www.bashh.org/about-bashh/publications/standards-for-the-management-of-stis/ .RationaleSyphilis is an important public health issue in men who have sex with men (MSM) among whom incidence has increased over the past decade.Definition of numeratorThe number of infectious syphilis (primary, secondary and early latent) diagnoses among people accessing sexual health services in England who are also residents in England.Episode Activity codes (SNOMED or Sexual Health and HIV Activity Property Types (SHHAPT)) relating to diagnosis of infectious syphilis (primary, secondary and early latent) were used. The clinical criteria used to diagnose the conditions are given at https://www.bashh.org/guidelines .Data was de-duplicated to ensure that a patient received a diagnostic code only once for each episode. Patients cannot be tracked between clinics and therefore de-duplication relies on patient consultations at a single service.Definition of denominatorThe denominators for 2012 to 2022 are sourced from Office for National Statistics (ONS) population estimates based on the 2021 Census.Population estimates for 2023 were not available at the time of publication – therefore rates for 2023 are calculated using estimates from 2022 as a proxy.Further details on the ONS census are available from the https://www.ons.gov.uk/census .CaveatsEvery effort is made to ensure accuracy and completeness of GUMCAD data, including web-based reporting with integrated checks on data quality. However, responsibility for the accuracy and completeness of data lies with the reporting service.Data is updated on an annual basis due to clinic or laboratory resubmissions and improvements to data cleaning. Data may differ from previous publications.Figures reported in 2020 and 2021 are notably lower than previous years due to the disruption to SHSs during the national response to the COVID-19 pandemic.

Objective To determine how the first wave of the COVID-19 pandemic affected outcomes for all operatively managed neurosurgical patients, not only those positive for SARS-CoV-2.

Design Matched cohort (pairwise method).

Setting A single tertiary neurosurgical referral centre at a large UK Major Trauma Centre.

Participants During the first COVID-19 wave, 231 neurosurgical cases were performed. These cases were matched to cases from 2019. Cases were matched for age (±10 years), primary pathology and surgical procedure. Cases were excluded from analysis if either the age could not be matched to within 10 years, or the primary pathology or procedure was too unique. After exclusions, 191 cases were included in final analysis

Outcome measures Primary outcomes were 30-day mortality and postoperative pulmonary complications. Secondary outcomes included Glasgow Outcome Score (GOS) on discharge, length of stay (LoS), operative and anaesthetic times and grade of primary surgeon. An exploratory o...

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

All diagnoses of first episode genital herpes among people accessing sexual health services* in England who are also residents in England, expressed as a rate per 100,000 population. Data is presented by area of patient residence and include those residents in England and those with an unknown residence (data for those residents outside of England is not included).*Sexual health services providing STI related care (Levels 2 and 3). Further details on the levels of sexual healthcare provision are provided in the https://www.bashh.org/about-bashh/publications/standards-for-the-management-of-stis/ .RationaleGenital herpes is the most common ulcerative sexually transmitted infection seen in England. Infections are frequently due to herpes simplex virus (HSV) type 2, although HSV-1 infection is also seen. Recurrent infections are common with patients returning for treatment.Definition of numeratorThe number of diagnoses of genital herpes (first episode) among people accessing sexual health services in England who are also residents in England.Episode Activity codes (SNOMED or Sexual Health and HIV Activity Property Types (SHHAPT)) relating to diagnosis of genital herpes (first episode) were used. The clinical criteria used to diagnose the conditions are given at https://www.bashh.org/guidelines .Data was de-duplicated to ensure that a patient received a diagnostic code only once for each episode. Patients cannot be tracked between services and therefore de-duplication relies on patient consultations at a single service.Definition of denominatorThe denominators for 2012 to 2022 are sourced from Office for National Statistics (ONS) population estimates based on the 2021 Census.Population estimates for 2023 were not available at the time of publication – therefore rates for 2023 are calculated using estimates from 2022 as a proxy.Further details on the ONS census are available from the https://www.ons.gov.uk/census .CaveatsEvery effort is made to ensure accuracy and completeness of GUMCAD data, including web-based reporting with integrated checks on data quality. However, responsibility for the accuracy and completeness of data lies with the reporting service.Data is updated on an annual basis due to clinic or laboratory resubmissions and improvements to data cleaning. Data may differ from previous publications.Figures reported in 2020 and 2021 are notably lower than previous years due to the disruption to SHSs during the national response to the COVID-19 pandemic.

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.