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

['Helen Strongman', 'London School Of Hygiene', 'Tropical Medicine', 'London', 'United Kingdom', 'Helena Carreira', 'Bianca L. De Stavola', 'University College London', 'Krishnan Bhaskaran', 'David A. Leon']

Date: 2022-01 

In our very large UK study population, the rate of death by any cause increased by 43% (95% CI 40% to 47%) during Wave 1 of the pandemic compared to that expected based on prepandemic levels and trends (2015 to 2019). There were small (+/− 10%) increases in the RR of death during Wave 1 associated with most of the factors we examined, compared to the prepandemic era. For example, chronic heart disease was associated with 2.03 (95% CI 2.01 to 2.05) times higher rate of death during Wave 1, while prepandemic, the RR was 2.05 (95% CI 1.98 to 2.13). However, bigger changes in the RR of death were seen for people with dementia or learning disabilities, with people in these groups having an approximate 5-fold increased rate of death during Wave 1, compared to people without these conditions, while prepandemic, this was 3.5 times higher. During Wave 1, we also observed an inversion of the mortality patterns by region and ethnicity: London registered the highest RR of death, while prepandemic had the lowest, and people of black or South Asian ethnicity had lower rates of death prepandemic, compared to the white population, but markedly increased rates during Wave 1.

We should note that the excess mortality in Wave 1 reported in our study cannot be dissociated from the widespread efforts to suppress the virus transmission that involved a national lockdown and imposed social distancing. We cannot assume that the risks observed during Wave 1 would apply to other waves, as population behaviour, risk perception, and transmissibility of the virus most likely have changed.

However, this study also has limitations. There may have been misclassification of the vital status of a small number of individuals in some weeks due to imprecise recording of the exact date of death in CPRD. We expect this to have little impact as 98% of the death dates in CPRD are within 30 days of the ONS date of death [ 26 ] and our sensitivity analysis using the ONS death date yielded similar results. There is also a potential for misclassification of the exposures, as information may be incomplete (e.g., diagnoses from secondary care not coded in the primary care record) or inaccurate (e.g., patients in correctly reporting their smoking behaviour) and vary between practices. However, the similar results of the sensitivity analyses including hospital diagnoses in addition to primary care ones suggest minor impact for most conditions. Mortality rate ratios for cancer recently diagnosed and multimorbidity were, however, higher in linked data compared to when primary care data only were used, reflecting known under ascertainment of cancer in primary care data [ 31 ]. This was probably exacerbated by the reduced diagnostic activity during Wave 1 reported previously [ 32 ] and depicted in our graphs of person weeks contributing over the study period. Thus, our main results may underestimate the rates of death in people with cancer because of misclassification, especially during the pandemic. Even though CPRD data are representative of the UK population in terms of age and sex, they are not regionally representative and include few practices from Eastern England [ 22 ]. This, together with likely clustering of practices within regions by Clinical Commissioning Group (CCG), may explain differences observed in RRs for ethnicity and region in our sensitivity analyses separating the CPRD GOLD and CPRD Aurum databases. For analyses of health factors, we see no reasons to doubt that the pattern observed elsewhere in the UK would not be applicable. Our models for ethnicity, BMI, and smoking included only patients with information on these variables. Our findings from these complete cases analyses are nevertheless valid, under the assumption that missingness for these variables is conditionally independent of the outcome [ 33 ].

A major strength of this study is the use of 2 very large and well-characterised datasets of primary care electronic health records. This allowed us to estimate effects for a large and diverse range of chronic conditions, including cardiovascular, respiratory, neurological, and renal diseases, and rarer conditions that would be difficult to study otherwise. CPRD data are of good quality, with high completeness and validity reported for both diagnoses and recorded deaths [ 26 , 30 ]. The availability of data from several years prior to the pandemic permitted us to account for secular and seasonal trends in mortality, and to time-update exposures such as smoking status, obesity, and asthma. Finally, we carried out multiple sensitivity analyses to assess the robustness of the results.

Strengths and weaknesses in relation to other studies

Our observation of an overall RR of death in Wave 1 compared to the prepandemic period of 1.43 (95% CI 1.40 to 1.47), following adjustment for seasonality, year, age, and sex, compares closely to a 47% increase in death observed in England and Wales during the same period in ONS mortality data compared to expected deaths based on an average from 2015 to 2019 (estimated from data provided by the ONS [34,35]).

There have been few studies of how excess mortality during the COVID-19 pandemic varied by health and wider demographic factors. de Lusignan and colleagues [17] reported excess mortality among patients from several demographic and clinical groups in England during Wave 1. Our results are broadly consistent with this previous study, but we are unable to directly compare the magnitude of the excess mortality due to methodological differences: In particular, the de Lusignan study used national life table data as an external basis for computing expected deaths in contrast to our use of prepandemic mortality within the same population. They were therefore unable to assess whether the relative risk of people with health and demographic factors differed in Wave 1 compared to previous years.

Our finding of an increased RR of death during Wave 1 of the pandemic in people with and without dementia and learning difficulties is consistent with cohort studies that have shown that excess mortality was higher in care home residents in England and Wales, especially those living in care homes catering for older people and those with dementia [12,36–38], and with ONS data that showed that 30% of all COVID-19–related deaths in England and Wales between March and June 2020 occurred in care homes, and 26% of all COVID-19–related deaths were in people with dementia [39]. Rates of SARS-CoV-2 infection were much higher among those living in care homes than among those living in private homes during Wave 1. This disproportionate exposure to SARS-CoV-2 may explain the increased mortality rate ratio in these 2 groups of the population and is consistent with studies conducted elsewhere that showed increased risk of infection among people with dementia [40]. However, we cannot rule out that factors other than infection may explain the increased risk of mortality. Joy and colleagues [41] quantified risk of mortality in a cohort of people with known SARS-CoV-2 infection status and found a similarly increased risk of mortality among people with learning disabilities and, compared to similar people without learning disabilities, suggesting that comorbidity and treatment may also explain part of the increased risk of mortality. We could not formally test these hypotheses, however, as we did not have data on the type of dwelling (collective versus private), and information on SARS-CoV-2 infection status in Wave 1 was limited by testing capacity.

Going in the other direction from dementia and learning disabilities, the RR of death among people with a recent diagnosis of cancer was lower during Wave 1 than it was prepandemic. This may be due to a lower risk of SARS-CoV-2 infection in cancer patients, compared to those without cancer consequent upon shielding. On 22 March 2020, the UK Government issued a list of preexisting clinical conditions that were considered at the time to put people at particularly high risk of serious disease or death if they contracted COVID-19, which included active cancer [42]. In July 2020, the ONS undertook a survey of clinically extremely vulnerable persons in England, concluding that 95% reported either completely or mostly following government shielding guidance [43].

Demographic factors associated with raised risk of exposure to SARS-CoV-2 may also explain the partially interrelated changes in the RR observed during Wave 1 for region and ethnicity. London is a global multicultural city with major international links and likely an important point of entry of imported SARS-CoV-2–infected cases. As the most densely populated region in the UK, with 5,701 people per squared kilometre [44], infection spread fast in London prior to the implementation of control measures [45]. The ethnic inequalities in COVID-19 mortality in Wave 1 in the UK have been reported consistently [7,8,17,41] and likely related to a complex interaction of factors including a predominance in some public facing occupations (e.g., food retail and healthcare), larger and often multigenerational households, and deprivation. Increases on excess mortality in ethnic minority groups have also been reported in the United States and Sweden [46–48]. Similar to other studies [16,41], our increase in the RR of mortality was disproportionately increased among those living in the most deprived areas, reflecting the perpetuation of the adverse effect of low socioeconomic status in health and mortality.

For many of the factors we examined, including most morbidities, there was little change in the RR of death in Wave 1 compared to prepandemic. This is in line with previous research suggesting similar strengths of associations with risk factors for mortality due to COVID-19 and other causes [49], but still striking. This firstly suggests that most of these characteristics are not particularly predictive of exposure to SARS-CoV-2. Beyond this, however, it can be interpreted as demonstrating that the net effect of COVID-19 in different subgroups of the population is to simply amplify baseline mortality risk by a constant amount. This is akin to David Spiegelhalter’s observation that the COVID-19 case-fatality age-curve in Wave 1 ran almost perfectly in parallel with the exponential increase with age in the risk of death prepandemic [50]. This has been interpreted as showing that COVID-19 has the effect of compressing one’s annual risk of death (whatever that may be) into fewer weeks. This insight, applied to our population study of excess deaths, suggests that the effect of the pandemic has been to accelerate the tempo of underlying mortality rate by a fixed proportional degree. However, the pathophysiological underpinning of this remains unclear and is beyond the scope of this study.

[END]

[1] Url: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003870

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