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1.
Orphanet J Rare Dis ; 17(1): 166, 2022 Apr 12.
Article in English | MEDLINE | ID: covidwho-1789126

ABSTRACT

BACKGROUND: Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. METHOD: To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher's exact test and logistic regression was used for univariable and multivariable analysis, respectively. RESULTS: People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21- 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65-5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61-10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60-11.08]). CONCLUSIONS: Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made.


Subject(s)
COVID-19 , COVID-19/genetics , Cohort Studies , England , Genomics , Humans , Pandemics , Rare Diseases/epidemiology , Rare Diseases/genetics , Retrospective Studies , SARS-CoV-2
2.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-330024

ABSTRACT

Background: Throughout the pandemic, research, public health, and policy emphasised prediction and surveillance of excess deaths, which have mostly occurred in older individuals with underlying conditions, highlighting importance of baseline mortality risk, infection rate (IR) and pandemic-related relative risk (RR). We now use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a model incorporating these factors for prediction of excess deaths. Methods: In development (Clinical Practice Research Datalink) and validation (NHS Digital Trusted Research Environment) cohorts in primary and secondary care EHR in England, we included 3·8 million and 35·1 million individuals aged ≥30 years, respectively. For model development, we predicted excess deaths using baseline one-year all-cause mortality risk and assumed RR=3 and IR=10%. For model validation, we observed number of excess deaths from March 2020 to March 2021. We used baseline mortality risk, IR and RR (assumed and observed) to predict excess deaths related to COVID-19. Findings: Among individuals with at least one high-risk condition, baseline (pre-pandemic) 1-year mortality risk at one year was 4·46% (95% CI 4·41–4·51) and 3.55% (3.54-3.57) in development and validation cohorts, respectively. In our original published model, we predicted 73,498 COVID-19 deaths over 1 year for the population of England. From 1st March 2020 to 1st March 2021, there were 127,020 observed excess deaths. Observed RR was 4·34 (4·31-4·38, 95% CI) and IR was 6·27% (6·26-6·28, 95%CI). In the validation cohort, predicted excess deaths over one year were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. We found that vaccination had a negligible effect on overall RR or IR between 1st December 2020 and 1st March 2021, compared to the likely effect of under-reported COVID-19 cases from the pre-vaccination period. Interpretation: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year infection rate and relative risk of the pandemic can be used to predict excess deaths. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to-date. Although infection dynamics are important in prediction of morbidity and mortality, future models should take greater account of underlying conditions and their associated risks. Funding Information: The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS Digital) of the trusted research environment, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK data and connectivity component of the UK Government Chief Scientific Adviser’s National Core Studies programme to coordinate national Covid-19 priority research. Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. AB, MAM, MHD and LP were supported by research funding from AstraZeneca. AB has received funding from the National Institute for Health Research (NIHR), British Medical Association, and UK Research and Innovation. AB, SD and HH are part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074. K.K. is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and NIHR Lifestyle BRC. Declaration of Interests: JBM and TM are employees of AstraZeneca. KK is chair of the ethnicity subgroup of the Independent Scientific Advisory Group for Emergencies (SAGE) and director of the University of Leicester Centre for Black Minority Ethnic Health. KK and AB are trustees of the South Asian Health Foundation (SAHF). CS is Director of the BHF Data Science Centre. All other authors report no competing interests. Ethics Approval Statement: Approval for the study in CPRD was granted by the Independent Scientific Advisory Committee (20_074R) of the Medicines and Healthcare products Regulatory Agency in the UK in accordance with the Declaration of Helsinki. The North East-Newcastle and North Tyneside 2 research ethics committee provided ethical approval for the CVD- COVID-UK research programme (REC No 20/NE/0161).

3.
BMC Med ; 20(1): 63, 2022 02 07.
Article in English | MEDLINE | ID: covidwho-1699213

ABSTRACT

BACKGROUND: Cardiovascular and renal diseases (CVRD) are major causes of mortality in individuals with type 2 diabetes (T2D). Studies of lifetime risk have neither considered all CVRD together nor the relative contribution of major risk factors to combined disease burden. METHODS: In a population-based cohort study using national electronic health records, we studied 473,399 individuals with T2D in England 2007-2018. Lifetime risk of individual and combined major adverse renal cardiovascular events, MARCE (including CV death and CVRD: heart failure; chronic kidney disease; myocardial infarction; stroke or peripheral artery disease), were estimated, accounting for baseline CVRD status and competing risk of death. We calculated population attributable risk for individual CVRD components. Ideal cardiovascular health was defined by blood pressure, cholesterol, glucose, smoking, physical activity, diet, and body mass index (i.e. modifiable risk factors). RESULTS: In individuals with T2D, lifetime risk of MARCE was 80% in those free from CVRD and was 97%, 93%, 98%, 89% and 91% in individuals with heart failure, chronic kidney disease, myocardial infarction, stroke and peripheral arterial disease, respectively at baseline. Among CVRD-free individuals, lifetime risk of chronic kidney disease was highest (54%), followed by CV death (41%), heart failure (29%), stroke (20%), myocardial infarction (19%) and peripheral arterial disease (9%). In those with HF only, 75% of MARCE after index T2D can be attributed to HF after adjusting for age, gender, and comorbidities. Compared with those with > 1, < 3 and ≥3 modifiable health risk behaviours, achieving ideal cardiovascular health could reduce MARCE by approximately 41.5%, 23.6% and 17.2%, respectively, in the T2D population. CONCLUSIONS: Four out of five individuals with T2D free from CVRD, and nearly all those with history of CVRD, will develop MARCE over their lifetime. Early preventive measures in T2D patients are clinical, public health and policy priorities.


Subject(s)
Cardiovascular Diseases , Diabetes Mellitus, Type 2 , Heart Failure , Renal Insufficiency, Chronic , Sodium-Glucose Transporter 2 Inhibitors , Cardiovascular Diseases/epidemiology , Cardiovascular Diseases/prevention & control , Cohort Studies , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/epidemiology , Heart Failure/epidemiology , Humans , Renal Insufficiency, Chronic/epidemiology , Risk Factors
4.
Clin Med (Lond) ; 21(6): e620-e628, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1551859

ABSTRACT

Patients and public have sought mortality risk information throughout the pandemic, but their needs may not be served by current risk prediction tools. Our mixed methods study involved: (1) systematic review of published risk tools for prognosis, (2) provision and patient testing of new mortality risk estimates for people with high-risk conditions and (3) iterative patient and public involvement and engagement with qualitative analysis. Only one of 53 (2%) previously published risk tools involved patients or the public, while 11/53 (21%) had publicly accessible portals, but all for use by clinicians and researchers.Among people with a wide range of underlying conditions, there has been sustained interest and engagement in accessible and tailored, pre- and postpandemic mortality information. Informed by patient feedback, we provide such information in 'five clicks' (https://covid19-phenomics.org/OurRiskCoV.html), as context for decision making and discussions with health professionals and family members. Further development requires curation and regular updating of NHS data and wider patient and public engagement.


Subject(s)
COVID-19 , Humans , Pandemics , Prognosis , SARS-CoV-2 , Surveys and Questionnaires
6.
Age Ageing ; 50(4): 1019-1028, 2021 06 28.
Article in English | MEDLINE | ID: covidwho-1132418

ABSTRACT

BACKGROUND: epidemiological data on COVID-19 infection in care homes are scarce. We analysed data from a large provider of long-term care for older people to investigate infection and mortality during the first wave of the pandemic. METHODS: cohort study of 179 UK care homes with 9,339 residents and 11,604 staff. We used manager-reported daily tallies to estimate the incidence of suspected and confirmed infection and mortality in staff and residents. Individual-level electronic health records from 8,713 residents were used to model risk factors for confirmed infection, mortality and estimate attributable mortality. RESULTS: 2,075/9,339 residents developed COVID-19 symptoms (22.2% [95% confidence interval: 21.4%; 23.1%]), while 951 residents (10.2% [9.6%; 10.8%]) and 585 staff (5.0% [4.7%; 5.5%]) had laboratory-confirmed infections. The incidence of confirmed infection was 152.6 [143.1; 162.6] and 62.3 [57.3; 67.5] per 100,000 person-days in residents and staff, respectively. Sixty-eight percent (121/179) of care homes had at least one COVID-19 infection or COVID-19-related death. Lower staffing ratios and higher occupancy rates were independent risk factors for infection.Out of 607 residents with confirmed infection, 217 died (case fatality rate: 35.7% [31.9%; 39.7%]). Mortality in residents with no direct evidence of infection was twofold higher in care homes with outbreaks versus those without (adjusted hazard ratio: 2.2 [1.8; 2.6]). CONCLUSIONS: findings suggest many deaths occurred in people who were infected with COVID-19, but not tested. Higher occupancy and lower staffing levels were independently associated with risks of infection. Protecting staff and residents from infection requires regular testing for COVID-19 and fundamental changes to staffing and care home occupancy.


Subject(s)
COVID-19 , Aged , COVID-19 Testing , Cohort Studies , Electronics , Humans , Nursing Homes , SARS-CoV-2 , United Kingdom/epidemiology , Watchful Waiting
7.
JAMIA Open ; 3(4): 545-556, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1096538

ABSTRACT

OBJECTIVES: The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers. MATERIALS AND METHODS: We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models. RESULTS: We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured. DISCUSSION AND CONCLUSION: Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.

8.
Eur J Prev Cardiol ; 28(14): 1599-1609, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1091243

ABSTRACT

AIMS: Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both 'direct', through infection, and 'indirect', through changes in healthcare. METHODS AND RESULTS: We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0).Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. CONCLUSION: Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic.


Subject(s)
COVID-19 , Cardiovascular Diseases , Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/epidemiology , Communicable Disease Control , Humans , Pandemics , SARS-CoV-2
10.
BMJ Open ; 10(11): e043828, 2020 11 17.
Article in English | MEDLINE | ID: covidwho-934100

ABSTRACT

OBJECTIVES: To estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer. METHODS: We employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England. RESULTS: Declines in urgent referrals (median=-70.4%) and chemotherapy attendances (median=-41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=-44.5%) and chemotherapy attendances (median=-31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity. CONCLUSIONS: Dramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.


Subject(s)
COVID-19/epidemiology , Models, Statistical , Neoplasms/epidemiology , Pandemics , Population Surveillance , SARS-CoV-2 , Adult , Cause of Death/trends , England/epidemiology , Female , Follow-Up Studies , Humans , Male , Middle Aged , Multimorbidity/trends , Survival Rate/trends , Time Factors
11.
BMJ ; 371: m3731, 2020 10 20.
Article in English | MEDLINE | ID: covidwho-883340

ABSTRACT

OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.


Subject(s)
Algorithms , Clinical Decision Rules , Coronavirus Infections , Hospitalization/statistics & numerical data , Mortality , Pandemics , Pneumonia, Viral , Risk Assessment , Adult , Aged, 80 and over , Betacoronavirus/isolation & purification , COVID-19 , Cohort Studies , Coronavirus Infections/mortality , Coronavirus Infections/therapy , Databases, Factual/statistics & numerical data , England/epidemiology , Female , Humans , Male , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Prognosis , Reproducibility of Results , Risk Assessment/methods , Risk Assessment/standards , SARS-CoV-2
12.
Heart ; 106(24): 1890-1897, 2020 12.
Article in English | MEDLINE | ID: covidwho-835511

ABSTRACT

OBJECTIVE: To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects. METHODS: Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. RESULTS: Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. CONCLUSIONS: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.


Subject(s)
COVID-19 , Cardiology Service, Hospital/trends , Cardiovascular Diseases/therapy , Delivery of Health Care, Integrated/trends , Health Services Needs and Demand/trends , Needs Assessment/trends , Cardiovascular Diseases/diagnosis , Cross-Sectional Studies , Emergency Service, Hospital/trends , Humans , Patient Admission/trends , Retrospective Studies , Time Factors , United Kingdom
14.
PLoS One ; 15(8): e0237298, 2020.
Article in English | MEDLINE | ID: covidwho-712951

ABSTRACT

OBJECTIVES: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research. DESIGN: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics). SETTING: Clinical academics in primary and secondary care in England. PARTICIPANTS: Equivalent of 3200 full-time clinical academics in England. INTERVENTIONS: Four policy approaches to COVID-19 with differing population infection rates: "Italy model" (6%), "mitigation" (10%), "relaxed mitigation" (40%) and "do-nothing" (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively. MAIN OUTCOME MEASURES: Number of full-time clinical academics available to conduct clinical research during the pandemic in England. RESULTS: In the "Italy model", "mitigation", "relaxed mitigation" and "do-nothing" scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively-with no clinical academics at all for 37 days in the "do-nothing" scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively. CONCLUSIONS: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.


Subject(s)
Betacoronavirus , Biomedical Research/methods , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , COVID-19 , Coronavirus Infections/virology , Delivery of Health Care/methods , England/epidemiology , Follow-Up Studies , Health Personnel/organization & administration , Health Workforce/organization & administration , Humans , Models, Statistical , Pandemics , Pneumonia, Viral/virology , Prospective Studies , Public Health/methods , SARS-CoV-2
16.
Lancet ; 395(10238): 1715-1725, 2020 05 30.
Article in English | MEDLINE | ID: covidwho-245277

ABSTRACT

BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.


Subject(s)
Coronavirus Infections/epidemiology , Mortality/trends , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , COVID-19 , Cohort Studies , Coronavirus Infections/complications , Female , Humans , Male , Middle Aged , Models, Statistical , Multimorbidity , Pandemics , Pneumonia, Viral/complications , Risk Factors , United Kingdom/epidemiology
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