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.

As of January 12, 2023, COVID-19 has been responsible for 202,157 deaths in the UK overall. The North West of England has been the most affected area in terms of deaths at 28,116, followed by the South East of England with 26,221 coronavirus deaths. Furthermore, there have been 22,264 mortalities in London as a result of COVID-19.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

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.

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.

This statistic illustrates the number of aircraft landings and take-offs at East Midlands International Airport in the UK from 2005 to 2020. In 2020, due to the coronavirus pandemic, there were only ****** aircraft movements at East Midlands International Airport.

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.

Background: The Coronavirus disease (COVID-19) has emphasised the critical need to investigate the mental well-being of healthcare professionals working during the pandemic. It has been highlighted that healthcare professionals display a higher prevalence of mental distress and research has largely focused on frontline professions. Social restrictions were enforced during the pandemic that caused rapid changes to the working environment (both clinically and remotely). The present study aims to examine the mental health of a variety of healthcare professionals, comparing overall mental wellbeing in both frontline and non-frontline professionals and the effect of the working environment on mental health outcomes.

Method: A cross-sectional mixed methods design, conducted through an online questionnaire. Demographic information was optional but participants were required to complete: (a) Patient Health Questionnaire, (b) Generalised Anxiety Disorder, (c) Perceived Stress Scale, and (d) Copenhagen Burnout Inventory. The questionnaire included one open-ended question regarding challenges experienced working during the pandemic.

Procedure:
Upon ethical approval the online questionnaire was advertised for six weeks from 1st May 2021 to 12th June 2021 to maximise the total number of respondents able to partake. The survey was hosted on the survey platform “Online Surveys”. It was not possible to determine a response rate because identifying how many people had received the link was unattainable information. The advert for the study was placed on social media platforms (WhatsApp, Instagram, Facebook and Twitter) and shared through emails.

Participants were recruited through the researchers’ existing professional networks and they shared the advertisement and link to questionnaire with colleagues. The information page explained the purpose of the study, eligibility criteria, procedure, costs and benefits of partaking and data storage. Participants were made aware on the information page that completing and submitting the questionnaire indicated their informed consent. It was not possible to submit complete questionnaires unless blank responses were optional demographic data. Participants were informed that completed questionnaires could not be withdrawn due to anonymity.

The questionnaire consisted of four sections: demographic data, mental health information and the four psychometric tools, PHQ-9, GAD-7, PSS-10 and CBI. Due to the sensitive nature of this research, only the psychometric measures required an answer for each question, thus all demographic information was optional to encourage participant contentment. Once participants had completed the questionnaire and submitted, they were automatically taken to a debrief page. This revealed the hypothesis of the questionnaire and rationalised why it was necessary to conceal this prior to completion. Participants were signposted to mental health charities and a self-referral form for psychological support. Participants could contact the researcher via email to express an interest in the results. It was explained that findings would be analysed using descriptive statistics to investigate any correlations or patterns in the responses. Data collected was stored electronically, on a password protected laptop. It will be kept for three years and then destroyed.

Instruments: PHQ-9, GAD-7, PSS-10 and CBI.

Other questions included:

Thank you for considering taking part in the questionnaire! Please remember by completing and submitting the questionnaire you are giving your informed consent to participate in this study.

Demographic:

Gender: please select one of the following:

Male Female Non-binary Prefer not to answer

Age: what is your age?

Open question: Prefer not to answer

What is your current region in the UK?

South West, East of England, South East, East Midlands, Yorkshire and the Humber, North West, West Midlands, North East, London, Scotland, Wales, Northern Ireland Prefer not to answer

Ethnicity: please select one of the following:

White English, Welsh, Scottish, Northern Irish or British Irish Gypsy or Irish Traveller Any other White background Mixed or Multiple ethnic groups White and Black Caribbean White and Black African White and Asian Any other Mixed or Multiple ethnic background Asian or Asian British Indian Pakistani Bangladeshi Chinese Any other Asian background Black, African, Caribbean or Black British African Caribbean Any other Black, African or Caribbean background Other ethnic group Arab Option for other please specify Prefer not to answer

Employment/environment:

What was your employment status in 2020 prior to COVID-19 pandemic?

Please select the option that best applies. Employed Self-employed Unpaid work (homemaker/carer) Out of work and looking for work Out of work but not currently looking for work Student Volunteer Retired Unable to work Prefer not to answer Option for other please specify

What is your current employment status?

Please tick the option that best applies. Employed Self-employed Unpaid work (homemaker/carer) Out of work and looking for work Out of work but not currently looking for work Student Volunteer Retired Unable to work Prefer not to answer Option for other please specify

What is your healthcare profession/helping profession?

Please state your job title. Open question

How often did you work from home before the COVID-19 pandemic?

Not at all, rarely, some, most, everyday Option for N/A

How often did you work from home during the first UK national lockdown for COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often did you work from home during the second UK national lockdown during COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often have you worked from home during the third UK national lockdown during COVID-19?

Not at all, rarely, some, most, everyday Option for N/A

How often are you currently working from home during the COVID-19 pandemic?

Not at all, rarely, some, most, everyday Option for N/A

Mental health:

How would you describe your mental health leading up to the COVID-19 pandemic?

Excellent, Very good, Good, Fair, Poor

How would you describe your mental health during the COVID-19 pandemic?

Excellent, Very good, Good, Fair, Poor

What have been the main challenges working as a healthcare professional/helping profession during COVID-19 pandemic? Open question

Data analysis: Firstly, any missing data was checked by the researcher and noted in the results section. The data was then analysed using a statistical software package called Statistical Package for the Social Sciences version 28 (SPSS-28). Descriptive statistics were collected to organise and summarise the data, and a correlation coefficient describes the strength and direction of the relationship between two variables. Inferential statistics were used to determine whether the effects were statistically significant. Responses to the open-ended question were coded and examined for key themes and patterns utilising the Braun and Clarke (2006) thematic analysis approach.

Ethical considerations: The study was approved by the Health Science, Engineering and Technology Ethical Committee with Delegated Authority at the University of Hertfordshire.

The potential benefits and risks of partaking in the research were contemplated and presented on the information page to promote informed consent. Precautions to prevent harm to participants included eligibility criteria, excluding those under eighteen years older or experiencing mental health distress. As the questionnaire was based around employment and the working environment, another exclusion involved experiencing a recent job change which caused upset.

An anonymous questionnaire and optional input of demographic data fostered the participants’ right to autonomy, privacy and respect. Specific employment and organisation or company information were not collected to protect confidentiality. Although participants were initially deceived regarding the hypotheses, they were provided with accurate information about the purpose of the study. Deceit was appropriate to collect unbiased information and participants were subsequently informed of the hypotheses on the debrief page.

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.