scholarly journals Factors associated with excess all-cause mortality in the first wave of COVID-19 pandemic in the UK: a time-series analysis using the Clinical Practice Research Datalink

Author(s):  
Helen Strongman ◽  
Helena Carreira ◽  
Bianca L De Stavola ◽  
Krishnan Bhaskaran ◽  
David A Leon

Objectives: Excess mortality captures the total effect of the COVID-19 pandemic on mortality and is not affected by mis-specification of cause of death. We aimed to describe how health and demographic factors have been associated with excess mortality during the pandemic. Design: Time-series analysis. Setting: UK primary care data from practices contributing to the Clinical Practice Research Datalink on July 31st 2020. Participants: We constructed a time-series dataset including 9,635,613 adults (≥40 years old) who were actively registered at the general practice during the study period. Main outcome measures: We extracted weekly numbers of deaths between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during wave 1 of the UK pandemic (5th March to 27th May 2020) compared to pre-pandemic was estimated using seasonally adjusted negative binomial regression models. Relative rates of death for a range of factors were estimated before and during wave 1 by including interaction terms. Results: All-cause mortality increased by 43% (95% CI 40%-47%) during wave 1 compared with pre-pandemic. Changes to the relative rate of death associated with most socio-demographic and clinical characteristics were small during wave 1 compared with pre-pandemic. However, the mortality rate associated with dementia markedly increased (RR for dementia vs no dementia pre-pandemic: 3.5, 95% CI 3.4-3.5; RR during wave 1: 5.1, 4.87-5.28); a similar pattern was seen for learning disabilities (RR pre-pandemic: 3.6, 3.4-3.5; during wave 1: 4.8, 4.4-5.3), for Black or South Asian ethnicity compared to white, and for London compared to other regions. Conclusions: The first UK COVID-19 wave appeared to amplify baseline mortality risk by a relatively constant factor for most population subgroups. However disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.

PLoS Medicine ◽  
2022 ◽  
Vol 19 (1) ◽  
pp. e1003870
Author(s):  
Helen Strongman ◽  
Helena Carreira ◽  
Bianca L. De Stavola ◽  
Krishnan Bhaskaran ◽  
David A. Leon

Background Excess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic. Methods and findings We analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception. Conclusions The first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.


BMJ Open ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. e009147 ◽  
Author(s):  
Lamiae Grimaldi-Bensouda ◽  
Olaf Klungel ◽  
Xavier Kurz ◽  
Mark C H de Groot ◽  
Ana S Maciel Afonso ◽  
...  

2018 ◽  
Vol 78 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Dahai Yu ◽  
Kelvin P Jordan ◽  
Kym I E Snell ◽  
Richard D Riley ◽  
John Bedson ◽  
...  

ObjectivesThe ability to efficiently and accurately predict future risk of primary total hip and knee replacement (THR/TKR) in earlier stages of osteoarthritis (OA) has potentially important applications. We aimed to develop and validate two models to estimate an individual’s risk of primary THR and TKR in patients newly presenting to primary care.MethodsWe identified two cohorts of patients aged ≥40 years newly consulting hip pain/OA and knee pain/OA in the Clinical Practice Research Datalink. Candidate predictors were identified by systematic review, novel hypothesis-free ‘Record-Wide Association Study’ with replication, and panel consensus. Cox proportional hazards models accounting for competing risk of death were applied to derive risk algorithms for THR and TKR. Internal–external cross-validation (IECV) was then applied over geographical regions to validate two models.Results45 predictors for THR and 53 for TKR were identified, reviewed and selected by the panel. 301 052 and 416 030 patients newly consulting between 1992 and 2015 were identified in the hip and knee cohorts, respectively (median follow-up 6 years). The resultant model C-statistics is 0.73 (0.72, 0.73) and 0.79 (0.78, 0.79) for THR (with 20 predictors) and TKR model (with 24 predictors), respectively. The IECV C-statistics ranged between 0.70–0.74 (THR model) and 0.76–0.82 (TKR model); the IECV calibration slope ranged between 0.93–1.07 (THR model) and 0.92–1.12 (TKR model).ConclusionsTwo prediction models with good discrimination and calibration that estimate individuals’ risk of THR and TKR have been developed and validated in large-scale, nationally representative data, and are readily automated in electronic patient records.


Gut ◽  
2018 ◽  
Vol 68 (8) ◽  
pp. 1458-1464 ◽  
Author(s):  
Zhiwei Liu ◽  
Rotana Alsaggaf ◽  
Katherine A McGlynn ◽  
Lesley A Anderson ◽  
Huei-Ting Tsai ◽  
...  

ObjectiveTo evaluate the association between statin use and risk of biliary tract cancers (BTC).DesignThis is a nested case–control study conducted in the UK Clinical Practice Research Datalink. We included cases diagnosed with incident primary BTCs, including cancers of the gall bladder, bile duct (ie, both intrahepatic and extrahepatic cholangiocarcinoma), ampulla of Vater and mixed type, between 1990 and 2017. For each case, we selected five controls who did not develop BTCs at the time of case diagnosis, matched by sex, year of birth, calendar time and years of enrolment in the general practice using incidence density sampling. Exposures were defined as two or more prescription records of statins 1 year prior to BTC diagnosis or control selection. ORs and 95% CIs for associations between statins and BTC overall and by subtypes were estimated using conditional logistic regression, adjusted for relevant confounders.ResultsWe included 3118 BTC cases and 15 519 cancer-free controls. Current statin use versus non-use was associated with a reduced risk of all BTCs combined (adjusted OR=0.88, 95% CI 0.79 to 0.98). The reduced risks were most pronounced among long-term users, as indicated by increasing number of prescriptions (ptrend=0.016) and cumulative dose of statins (ptrend=0.008). The magnitude of association was similar for statin use and risk of individual types of BTCs. The reduced risk of BTCs associated with a record of current statin use versus non-use was more pronounced among persons with diabetes (adjusted OR=0.72, 95% CI 0.57 to 0.91). Among non-diabetics, the adjusted OR for current statin use versus non-use was 0.91 (95% CI 0.81 to 1.03, pheterogeneity=0.007).ConclusionCompared with non-use of statins, current statin use is associated with 12% lower risk of BTCs; no association found with former statin use. If replicated, particularly in countries with a high incidence of BTCs, our findings could pave the way for evaluating the value of statins for BTC chemoprevention.


2018 ◽  
Vol 2 (11) ◽  
pp. e478-e488 ◽  
Author(s):  
Carlos Santos-Burgoa ◽  
John Sandberg ◽  
Erick Suárez ◽  
Ann Goldman-Hawes ◽  
Scott Zeger ◽  
...  

2018 ◽  
Vol 28 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Rory J. Ferguson ◽  
Daniel Prieto‐Alhambra ◽  
Christine Walker ◽  
Dahai Yu ◽  
Jose M. Valderas ◽  
...  

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope


2018 ◽  
Vol 37 (8) ◽  
pp. 2103-2111 ◽  
Author(s):  
Jeremy G. Royle ◽  
Peter C. Lanyon ◽  
Matthew J. Grainge ◽  
Abhishek Abhishek ◽  
Fiona A. Pearce

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