Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions
IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development...
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| Published in | BMJ open Vol. 10; no. 10; p. e043010 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
British Medical Journal Publishing Group
21.10.2020
BMJ Publishing Group LTD BMJ Publishing Group |
| Series | Protocol |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2044-6055 2044-6055 |
| DOI | 10.1136/bmjopen-2020-043010 |
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| Abstract | IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. |
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| AbstractList | The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.
Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.
The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.INTRODUCTIONThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.METHODS AND ANALYSISTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.ETHICS AND DISSEMINATIONThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. Introduction The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysis Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and dissemination The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. |
| Author | Emmerson, Chris Taylor, Chris Lyons, Ronan Brophy, Sinead Williams, Christopher Hollinghurst, Joseph Au-Yeung, Lee Davies, Jan Connor, Thomas R Diggle, Peter Mizen, Amy Akbari, Ashley Szakmany, Tamas Rafferty, James Cottrell, Simon Lyons, Jane Davies, Chris Lucini, Biagio Hunt, Joe John, Ann Cunningham, Anne M Davies, Gareth I Thomas, Daniel Rh Pugh, Richard J Gravenor, Mike B Helliwell, Kathryn Cross, Lynsey Scourfield, Simon Torabi, Fatemeh Fry, Richard Thompson, Daniel John, Gareth North, Laura Bailey, Rowena Turner, Samantha L Griffiths, Rowena Thompson, Simon Orton, Chris |
| AuthorAffiliation | 9 Glan Clwyd Hospital , Betsi Cadwaladr University Health Board , Rhyl , UK 13 Welsh Government , Cardiff , Cardiff , UK 7 School of Biosciences , Cardiff University , Cardiff , South Glamorgan , UK 3 Department of Anaesthesia, Intensive Care and Pain Medicine, Division of Population Medicine , Cardiff University , Cardiff , UK 11 Epidemiology and Population Health , University of Liverpool , Liverpool , Merseyside , UK 5 Members of the public , Swansea , UK 1 Population Data Science , Swansea University Medical School , Swansea , UK 8 School of Social Sciences , Cardiff University , Cardiff , South Glamorgan , UK 10 Faculty of Health and Medicine , Lancaster University , Lancaster , Lancashire , UK 6 Public Health Wales NHS Trust , Cardiff , Cardiff , UK 2 Institute of Life Sciences , Swansea University Medical School , Swansea , UK 4 Aneurin Bevan University Health Board , Newport , UK 12 NHS Wales Informatics Service , Cardiff , Wales , UK |
| AuthorAffiliation_xml | – name: 8 School of Social Sciences , Cardiff University , Cardiff , South Glamorgan , UK – name: 9 Glan Clwyd Hospital , Betsi Cadwaladr University Health Board , Rhyl , UK – name: 2 Institute of Life Sciences , Swansea University Medical School , Swansea , UK – name: 11 Epidemiology and Population Health , University of Liverpool , Liverpool , Merseyside , UK – name: 3 Department of Anaesthesia, Intensive Care and Pain Medicine, Division of Population Medicine , Cardiff University , Cardiff , UK – name: 12 NHS Wales Informatics Service , Cardiff , Wales , UK – name: 7 School of Biosciences , Cardiff University , Cardiff , South Glamorgan , UK – name: 10 Faculty of Health and Medicine , Lancaster University , Lancaster , Lancashire , UK – name: 13 Welsh Government , Cardiff , Cardiff , UK – name: 4 Aneurin Bevan University Health Board , Newport , UK – name: 1 Population Data Science , Swansea University Medical School , Swansea , UK – name: 6 Public Health Wales NHS Trust , Cardiff , Cardiff , UK – name: 5 Members of the public , Swansea , UK |
| Author_xml | – sequence: 1 givenname: Jane orcidid: 0000-0002-4407-770X surname: Lyons fullname: Lyons, Jane email: j.lyons@swansea.ac.uk organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 2 givenname: Ashley orcidid: 0000-0003-0814-0801 surname: Akbari fullname: Akbari, Ashley organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 3 givenname: Fatemeh surname: Torabi fullname: Torabi, Fatemeh organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 4 givenname: Gareth I surname: Davies fullname: Davies, Gareth I organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 5 givenname: Laura surname: North fullname: North, Laura organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 6 givenname: Rowena surname: Griffiths fullname: Griffiths, Rowena organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 7 givenname: Rowena surname: Bailey fullname: Bailey, Rowena organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 8 givenname: Joseph orcidid: 0000-0002-3556-2017 surname: Hollinghurst fullname: Hollinghurst, Joseph organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 9 givenname: Richard orcidid: 0000-0002-7968-6679 surname: Fry fullname: Fry, Richard organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 10 givenname: Samantha L surname: Turner fullname: Turner, Samantha L organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 11 givenname: Daniel surname: Thompson fullname: Thompson, Daniel organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 12 givenname: James surname: Rafferty fullname: Rafferty, James organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 13 givenname: Amy surname: Mizen fullname: Mizen, Amy organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 14 givenname: Chris orcidid: 0000-0002-9561-2493 surname: Orton fullname: Orton, Chris organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 15 givenname: Simon surname: Thompson fullname: Thompson, Simon organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 16 givenname: Lee surname: Au-Yeung fullname: Au-Yeung, Lee organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 17 givenname: Lynsey surname: Cross fullname: Cross, Lynsey organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 18 givenname: Mike B surname: Gravenor fullname: Gravenor, Mike B organization: Institute of Life Sciences, Swansea University Medical School, Swansea, UK – sequence: 19 givenname: Sinead surname: Brophy fullname: Brophy, Sinead organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 20 givenname: Biagio surname: Lucini fullname: Lucini, Biagio organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 21 givenname: Ann orcidid: 0000-0002-5657-6995 surname: John fullname: John, Ann organization: Population Data Science, Swansea University Medical School, Swansea, UK – sequence: 22 givenname: Tamas surname: Szakmany fullname: Szakmany, Tamas organization: Aneurin Bevan University Health Board, Newport, UK – sequence: 23 givenname: Jan surname: Davies fullname: Davies, Jan organization: Members of the public, Swansea, UK – sequence: 24 givenname: Chris surname: Davies fullname: Davies, Chris organization: Members of the public, Swansea, UK – sequence: 25 givenname: Daniel Rh surname: Thomas fullname: Thomas, Daniel Rh organization: Public Health Wales NHS Trust, Cardiff, Cardiff, UK – sequence: 26 givenname: Christopher surname: Williams fullname: Williams, Christopher organization: Public Health Wales NHS Trust, Cardiff, Cardiff, UK – sequence: 27 givenname: Chris surname: Emmerson fullname: Emmerson, Chris organization: Public Health Wales NHS Trust, Cardiff, Cardiff, UK – sequence: 28 givenname: Simon surname: Cottrell fullname: Cottrell, Simon organization: Public Health Wales NHS Trust, Cardiff, Cardiff, UK – sequence: 29 givenname: Thomas R surname: Connor fullname: Connor, Thomas R organization: School of Biosciences, Cardiff University, Cardiff, South Glamorgan, UK – sequence: 30 givenname: Chris surname: Taylor fullname: Taylor, Chris organization: School of Social Sciences, Cardiff University, Cardiff, South Glamorgan, UK – sequence: 31 givenname: Richard J surname: Pugh fullname: Pugh, Richard J organization: Glan Clwyd Hospital, Betsi Cadwaladr University Health Board, Rhyl, UK – sequence: 32 givenname: Peter surname: Diggle fullname: Diggle, Peter organization: Epidemiology and Population Health, University of Liverpool, Liverpool, Merseyside, UK – sequence: 33 givenname: Gareth surname: John fullname: John, Gareth organization: NHS Wales Informatics Service, Cardiff, Wales, UK – sequence: 34 givenname: Simon surname: Scourfield fullname: Scourfield, Simon organization: NHS Wales Informatics Service, Cardiff, Wales, UK – sequence: 35 givenname: Joe surname: Hunt fullname: Hunt, Joe organization: NHS Wales Informatics Service, Cardiff, Wales, UK – sequence: 36 givenname: Anne M surname: Cunningham fullname: Cunningham, Anne M organization: NHS Wales Informatics Service, Cardiff, Wales, UK – sequence: 37 givenname: Kathryn surname: Helliwell fullname: Helliwell, Kathryn organization: Welsh Government, Cardiff, Cardiff, UK – sequence: 38 givenname: Ronan orcidid: 0000-0001-5225-000X surname: Lyons fullname: Lyons, Ronan organization: Population Data Science, Swansea University Medical School, Swansea, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33087383$$D View this record in MEDLINE/PubMed |
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volume: 9 year: 2009 article-title: The Sail databank: linking multiple health and social care datasets publication-title: BMC Med Inform Decis Mak doi: 10.1186/1472-6947-9-3 – volume: 383 start-page: 1517 year: 2014 article-title: Use of data linkage to measure the population health effect of non-health-care interventions publication-title: Lancet doi: 10.1016/S0140-6736(13)61750-X – volume: 10 year: 2020 article-title: Early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II): protocol for an observational study using linked Scottish national data publication-title: BMJ Open doi: 10.1136/bmjopen-2020-039097 – volume: 3 start-page: e279 year: 2018 article-title: Risk of emergency hospital admission in children associated with mental disorders and alcohol misuse in the household: an electronic birth cohort study publication-title: Lancet Public Health doi: 10.1016/S2468-2667(18)30069-0 – volume: 5 start-page: 1121 year: 2020 article-title: The secure Anonymised information linkage databank dementia e-cohort (SAIL-DeC) publication-title: Int J Popul Data Sci doi: 10.23889/ijpds.v5i1.1121 – volume: 1 start-page: e99-e100 year: 2020 article-title: An integrated national scale SARS-CoV-2 genomic surveillance network publication-title: Lancet Microbe doi: 10.1016/S2666-5247(20)30054-9 – volume: 354 start-page: i4515 year: 2016 article-title: Self controlled case series methods: an alternative to standard epidemiological study designs publication-title: BMJ doi: 10.1136/bmj.i4515 – volume: 369 year: 2020 article-title: Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study publication-title: BMJ doi: 10.1136/bmj.m1985 – volume: 9 year: 2019 article-title: Longitudinal access and exposure to green-blue spaces and individual-level mental health and well-being: protocol for a longitudinal, population-wide record-linked natural experiment publication-title: BMJ Open doi: 10.1136/bmjopen-2018-027289 – volume: 8 year: 2018 article-title: Study protocol for investigating the impact of community home modification services on hospital utilisation for fall injuries: a controlled longitudinal study using data linkage publication-title: BMJ Open doi: 10.1136/bmjopen-2018-026290 – volume: 391 start-page: 1775 year: 2018 article-title: Development and validation of a hospital frailty risk score focusing on older people in acute care settings using electronic hospital records: an observational study publication-title: Lancet doi: 10.1016/S0140-6736(18)30668-8 – volume: 48 start-page: 922 year: 2019 article-title: External validation of the electronic frailty index using the population of Wales within the secure anonymised information linkage databank publication-title: Age Ageing doi: 10.1093/ageing/afz110 – volume: 40 start-page: 373 year: 1987 article-title: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation publication-title: J Chronic Dis doi: 10.1016/0021-9681(87)90171-8 – volume: 47 start-page: 15 year: 2019 article-title: Risk factors for 1-year mortality and hospital utilization patterns in critical care survivors publication-title: Crit Care Med doi: 10.1097/CCM.0000000000003424 – year: 2017 article-title: Development and validation of an electronic frailty index using routine primary care electronic health record data publication-title: Age Ageing doi: 10.1093/ageing/afx001 – volume: 72 start-page: 896 year: 2018 article-title: Emergency hospital admissions associated with a non-randomised housing intervention meeting national housing quality Standards: a longitudinal data linkage study publication-title: J Epidemiol Community Health doi: 10.1136/jech-2017-210370 – volume: 26 start-page: 1037 year: 2020 article-title: Real-time tracking of self-reported symptoms to predict potential COVID-19 publication-title: Nat Med doi: 10.1038/s41591-020-0916-2 – volume: 31 start-page: 582 year: 2009 article-title: Residential anonymous linking fields (RALFs): a novel information infrastructure to study the interaction between the environment and individuals' health publication-title: J Public Health doi: 10.1093/pubmed/fdp041 – volume: 9 year: 2009 article-title: The Sail Databank: building a national architecture for e-health research and evaluation publication-title: BMC Health Serv Res doi: 10.1186/1472-6963-9-157 – year: 2020 article-title: Ethnicity and outcomes from COVID-19: the ISARIC CCP-UK prospective observational cohort study of hospitalised patients publication-title: SSRN J doi: 10.2139/ssrn.3618215 – volume: 15 year: 2020 article-title: Measuring follow-up time in routinely-collected health datasets: challenges and solutions publication-title: PLoS One doi: 10.1371/journal.pone.0228545 – volume: 1 start-page: e63 year: 2019 article-title: A chronological map of 308 physical and mental health 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2020102114205122000_10.10.e043010.28 doi: 10.1136/bmj.m1985 – volume: 1 start-page: e99-e100 year: 2020 ident: 2020102114205122000_10.10.e043010.24 article-title: An integrated national scale SARS-CoV-2 genomic surveillance network publication-title: Lancet Microbe – ident: 2020102114205122000_10.10.e043010.39 doi: 10.1136/bmj.i4515 – ident: 2020102114205122000_10.10.e043010.34 doi: 10.1093/ageing/afx001 – ident: 2020102114205122000_10.10.e043010.38 – volume: 26 start-page: 1037 year: 2020 ident: 2020102114205122000_10.10.e043010.21 article-title: Real-time tracking of self-reported symptoms to predict potential COVID-19 publication-title: Nat Med doi: 10.1038/s41591-020-0916-2 – ident: 2020102114205122000_10.10.e043010.11 doi: 10.1136/jech-2017-210370 – volume: 47 start-page: 15 year: 2019 ident: 2020102114205122000_10.10.e043010.10 article-title: Risk factors for 1-year mortality and hospital utilization patterns in critical care survivors publication-title: Crit Care Med doi: 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| Snippet | IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to... The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and... Introduction The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to... |
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| SubjectTerms | At risk populations Betacoronavirus Censuses Coronavirus Infections - epidemiology Coronavirus Infections - therapy Coronaviruses COVID-19 Critical care Datasets Delivery of Health Care - standards Demography Disease transmission Epidemiology Ethnicity Governance health informatics Health services Humans Infections Information dissemination Information management Intensive care International conferences Intervention Laboratories Linked Data Morbidity Mortality Pandemics Pandemics - prevention & control Pneumonia, Viral - epidemiology Pneumonia, Viral - therapy Policy making Population Privacy Public Health Risk Factors SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Social workers Socioeconomic factors Structured Query Language-SQL Wales - epidemiology |
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| Title | Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions |
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