COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Analysis of people who are potentially suitable for antibody and antiviral out-of-hospital treatments for coronavirus (COVID-19). Includes analysis of their behaviours, opinions and well-being in relation to the COVID-19 pandemic. Data covering attitudes towards treatments and experiences of those who have been offered treatments are also presented.

The UK Government has made available a range of COVID-19 treatment options for non-hospitalised individuals at highest risk of severe complications from COVID-19. This is a release of management information (MI) on digitally identified individuals at highest risk of severe complications from COVID-19. Where an individual in this cohort tests positive for COVID-19, they may be eligible for COVID-19 treatment in a non-hospitalised setting. The purpose of this MI is to make anonymous and summarised data available for use in public health analysis.

According to a survey carried out in the UK as the end of April 2020, there were shortages of several items of personal protective equipment (PPE) for doctors working in high-risk areas during the coronavirus (COVID-19) pandemic. Over 32 percent of doctors reported shortages of scrubs, while approximately 30 percent were experiencing shortages of long-sleeved disposable gowns. For 11 percent of doctors in the UK, there was no supply at all of disposable goggles. There are also reported shortages of PPE for doctors in other areas of the health system in contact with coronavirus patients, and also for general practitioners.

The latest number of cases in the UK can be found here. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

COVID-19 Cases in England

Tracking Infection Risk at a Local Level

By Dan Winchester [source]

About this dataset

This dataset contains the total number of confirmed COVID-19 cases in each English Upper Tier Local Authority over the past eight days. Aggregated from Public Health England data, this dataset provides unprecedented insight into how quickly the virus has been able to spread in local communities throughout England. Despite testing limitations, understanding these localized patterns of infection can help inform important public health decisions by local authorities and healthcare workers alike.

It is essential to bear in mind that this data is likely an underestimation of true infection rates due to limited testing -- it is critical not to underestimate the risk the virus poses on a local scale! Use this dataset at your own discretion with caution and care; consider supplementing it with other health and socio-economic metrics for a holistic picture of regional trends over time.

This dataset features information surrounding GSS codes and names as well as total numbers of recorded COVID-19 cases per English Upper Tier Local Authority on January 5th 2023 (TotalCases_2023-01-05)

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Research Ideas

• Comparing the total cases in each local authority to population density of the region, to identify areas with higher incidence of virus
• Tracking changes in total cases over a period of time to monitor trend shifts and detect possible outbreak hotspots
• Establishing correlations between the spread of COVID-19 and other non-coronavirus related health issues, such as mental health or cardiovascular risk factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: utla_by_day.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------------------| | GSS_CD | Government Statistical Service code for the local authority. (String) | | GSS_NM | Name of the local authority. (String) | | TotalCases_2023-01-05 | Total number of confirmed COVID-19 cases in the local authority on the 5th of January 2023. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Dan Winchester.

Coronavirus affects some members of the population more than others. Emerging evidence suggests that older people, men, people with health conditions such as respiratory and pulmonary conditions, and people of a Black, Asian Minority Ethnic (BAME) background are at particular risk. There are also a number of other wider public health risk factors that have been found to increase the likelihood of an individual contracting coronavirus. This briefing presents descriptive evidence on a range of these factors, seeking to understand at a London-wide level the proportion of the population affected by each.

The Chief Medical Officer (CMO) for England, working with the CMOs of the devolved nations and other senior clinicians, commissioned NHS Digital to produce a list of people at “high risk” of complications from COVID-19, who should be shielded for at least 12 weeks. The CMO for Wales commissioned a collaboration of national bodies in Wales (NWIS, DU, NWSSP, PHW) to identify “high risk” people for the Welsh population, based largely on the NHS Digital methodology. This list is referred to as the Shielded Patient List (SPL).

The “high risk” list was defined as a subset of a wider group of people who may be “at risk”. Specific advice applies to these groups, currently this advice is: • “At Risk” – large group normally at risk from the flu - should practice strict social distancing • “At high risk” – a smaller sub-group (circa 70k), defined by CMO – should practice complete social “shielding” NHS Digital have described the methodology that has been used to identify patients who meet the high risk criteria due to their inclusion in one or more of the disease groups.

As there are differences in some of the systems used across the devolved nations, nuanced differences in application and interpretation of CMO guidance, this document describes the Welsh methodology.

https://nwis.nhs.wales/coronavirus/coronavirus-content/coronavirus-documents/covid-19-high-risk-shielded-patient-list-identification-methodology/

Dataset received it's final update in March 2022

Estimates of the risk of hospital admission for coronavirus (COVID-19) and death involving COVID-19 by vaccination status, overall and by age group, using anonymised linked data from Census 2021. Experimental Statistics.

Outcome definitions

For this analysis, we define a death as involving COVID-19 if either of the ICD-10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified) is mentioned on the death certificate. Information on cause of death coding is available in the User Guide to Mortality Statistics. We use date of occurrance rather than date of registration to give the date of the death.

We define COVID-109 hospitalisation as an inpatient episode in Hospital Episode Statistics where the primary diagnosis was COVID-19, identified by the ICD-19 codes (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified). Where an individual had experienced more than one COVID-19 hospitalisation, the earliest that occurred within the study period was used. We define the date of COVID-19 hospitalisation as the start of the hospital episode.

ICD-10 code

U07.1 :

COVID-19, virus identified

U07.2:

COVID-19, virus not identified

Vaccination status is defined by the dose and the time since the last dose received

Unvaccinated:

no vaccination to less than 21 days post first dose

First dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post first dose or less than 21 days post second dose

First dose 3+ months:

more than or equal to 91 days post first dose to less than 21 days post second dose

Second dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post second dose or less than 21 days post third dose

Second dose 3-6 months:

more than or equal to 91 days post second dose to earliest of less than 182 days post second dose or less than 21 days post third dose

Second dose 6+ months:

more than or equal to 182 days post second dose to less than 21 days post third dose

Third dose 21 days to 3 months:

more than or equal to 21 days post third dose to less than 91 days post third dose

Third dose 3+ months:

more than or equal to 91 days post third dose

Model adjustments

Three sets of model adjustments were used

Age adjusted:

age (as a natural spline)

Age, socio-demographics adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status)

Fully adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status), plus health-related characteristics (disability, self-reported health, care home residency, number of QCovid comorbidities (grouped), BMI category, frailty flag and hospitalisation within the last 21 days.

Age

Age in years is defined on the Census day 2021 (21 March 2021). Age is included in the model as a natural spline with boundary knots at the 10th and 90th centiles and internal knots at the 25th, 50th and 75th centiles. The positions of the knots are calculated separately for the overall model and for each age group for the stratified model.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

Black men and women in the United Kingdom were four times more likely to die from Coronavirus than white people of the same gender as of April 2020. Several other ethnic groups were also at an increased risk from Coronavirus than the white population, with men of Bangladeshi or Pakistani origin 3.6 times more likely, and women 3.4 more likely to die from Coronavirus.

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

Analysis of people previously considered to be clinically extremely vulnerable (CEV) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

This is a release of management information for anonymous summary data for those patients that have been identified on the Shielded Patient List (SPL). Its purpose is to make the summary data available to a wider audience, as open data, to enable a broad base of users to perform analysis from it. The purpose behind releasing this data is to present regional and local data to allow for its use in public health. It will also allow for greater analysis, modelling and planning to be performed using the latest data, to aid in the response to the pandemic. We will update this weekly and we would welcome your feedback to help us develop our open data sets. The data that is published is based on version 51 of the SPL clinical methodology, with the data extracted as at 28 March 2021.

COVID-19 case-control definitions based on conventions from the Covid19 Host Genetics Initiative working group (https://www.covid19hg.org).

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents. Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London: ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis. Number of people ages 18 and over on the National Immunisation Management Service (NIMS) ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population. Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues: Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates. For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available, The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011. Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

The role COVID-19 vaccination on healthcare resource utilization (HCRU) and cost remains unclear, especially during Omicron predominance and among high risk UK populations. A retrospective cohort study using UK The Health Improvement Network (THIN) primary care data included adults (≥18 years) with confirmed or suspected COVID-19 between September 2022- May 2023. Three cohorts were defined: Highest risk (eligible for two seasonal doses), High Risk (eligible for one dose), and All COVID-19 patients. Long COVID was identified as ≥ 1 symptom or diagnostic/referral code, ≥4 weeks post COVID-19 diagnosis. Inverse probability of treatment weighting assessed associations between vaccination status (yes/no and time since vaccination) and long COVID, HCRU, and costs. In both Risk Cohorts, COVID-19 vaccination was not associated with long COVID incidence. However, in the High Risk (n = 1,889) and All Patients cohorts (n = 8,507) outpatient specialist referrals were significantly lower in the 3–6-month post-vaccination group versus > 6 months (rate ratio: 0.28; 95% CI: 0.10-0.79, p 

Men working in working-class jobs were at a higher risk of dying from Coronavirus in England and Wales, when compared their counterparts working in white-collar professions, as of April 2020. The death rate was highest for men working occupations classified as elementary trades at 27.8 per 100,000 population, compared with just 3.9 for those working in scientific research.

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

Introduction

This page comprises the additional datasets used for the COVID-19 Global Forecasting Challenge (currently in week 3). Only datasets that have not been hosted on Kaggle will be uploaded here: * Oxford COVID-19 Government Response Tracker * Assessment Capacities Project COVID-19 Government Measures

UPDATE: Please see my notebook on the COVID-19 Global Forecasting Challenge (Week 3) competition here for merging the data.

Oxford COVID-19 Government Response Tracker

The Oxford COVID-19 Government Response Tracker (OxCGRT) provides a systematic cross-national, cross-temporal measure to understand how government responses have evolved over the full period of the disease’s spread. The project tracks governments’ policies and interventions across a standardized series of indicators and creates a composite index to measure the stringency of these responses. Data is collected and updated in real time by a team of dozens of students and staff at Oxford University. Read the white paper here. Access the OxCGRT website here.

Indicators

The OxCGRT tracks 11 indicators of government response:

1. School closing (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
2. Workplace closing (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
3. Cancel public events (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend cancelling
     • 2 = Require cancelling
4. Close public transport (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
5. Public info campaigns(with geographic scope)
   • Ordinal scale:
     • 0 = No COVID-10 public information campaign
     • 1 = COVID-10 public information campaign
6. Restrictions on internal movement (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend movement restriction
     • 2 = Restricted movement
7. International travel controls
   • Ordinal scale:
     • 0 = No measures
     • 1 = Screening
     • 2 = Quarantine on high-risk regions
     • 3 = Ban on high-risk regions
8. Fiscal measures
   • Value of fiscal stimuli in USD
9. Monetary measures
   • Value of interest rate in %
10. Emergency investment in health care
    • Value of new short-term spending on health in USD
11. Investment in vaccines
    • Value of investment in USD

Indicators with geographic scope are coded in the following way: - 0 = Targeted - 1 = General

The Assessment Capacities Project (ACAPS) COVID-19 Government Measures Dataset

This dataset comprises government measures and descriptions of these measures by country and date. The measures include:

1. Social distancing
2. Movement restrictions
3. Public health measures
4. Social and economic measures
5. Lockdowns

Descriptors of these measures include: - Date of implementation - Specific measure - Penalties for non-compliance - Source (e.g. government, media)

Objectives: To evaluate the associations of status, amount, and frequency of alcohol consumption across different alcoholic beverages with coronavirus disease 2019 (COVID-19) risk and associated mortality.Methods: This study included 473,957 subjects, 16,559 of whom tested positive for COVID-19. Multivariate logistic regression analyses were used to evaluate the associations of alcohol consumption with COVID-19 risk and associated mortality. The non-linearity association between the amount of alcohol consumption and COVID-19 risk was evaluated by a generalized additive model.Results: Subjects who consumed alcohol double above the guidelines had a higher risk of COVID-19 (1.12 [1.00, 1.25]). Consumption of red wine above or double above the guidelines played protective effects against the COVID-19. Consumption of beer and cider increased the COVID-19 risk, regardless of the frequency and amount of alcohol intake. Low-frequency of consumption of fortified wine (1–2 glasses/week) within guidelines had a protective effect against the COVID-19. High frequency of consumption of spirits (≥5 glasses/week) within guidelines increased the COVID-19 risk, whereas the high frequency of consumption of white wine and champagne above the guidelines decreased the COVID-19 risk. The generalized additive model showed an increased risk of COVID-19 with a greater number of alcohol consumption. Alcohol drinker status, frequency, amount, and subtypes of alcoholic beverages were not associated with COVID-19 associated mortality.Conclusions: The COVID-19 risk appears to vary across different alcoholic beverage subtypes, frequency, and amount. Red wine, white wine, and champagne have chances to reduce the risk of COVID-19. Consumption of beer and cider and spirits and heavy drinking are not recommended during the epidemics. Public health guidance should focus on reducing the risk of COVID-19 by advocating healthy lifestyle habits and preferential policies among consumers of beer and cider and spirits.

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.