Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals.

BACKGROUND: Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed "harvesting" in the literature. METHODS: We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a "ball and urn" model. RESULTS: Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy. CONCLUSION: Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement - both in its length and prevalence - by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.

The impact of COVID-19 on mortality after hip fracture : a population cohort study from England.

AIMS: Hip fracture commonly affects the frailest patients, of whom many are care-dependent, with a disproportionate risk of contracting COVID-19. We examined the impact of COVID-19 infection on hip fracture mortality in England. METHODS: We conducted a cohort study of patients with hip fracture recorded in the National Hip Fracture Database between 1 February 2019 and 31 October 2020 in England. Data were linked to Hospital Episode Statistics to quantify patient characteristics and comorbidities, Office for National Statistics mortality data, and Public Health England's SARS-CoV-2 testing results. Multivariable Cox regression examined determinants of 90-day mortality. Excess mortality attributable to COVID-19 was quantified using Quasi-Poisson models. RESULTS: Analysis of 102,900 hip fractures (42,630 occurring during the pandemic) revealed that among those with COVID-19 infection at presentation (n = 1,120) there was a doubling of 90-day mortality; hazard ratio (HR) 2.09 (95% confidence interval (CI) 1.89 to 2.31), while the HR for infections arising between eight and 30 days after presentation (n = 1,644) the figure was greater at 2.51 (95% CI 2.31 to 2.73). Malnutrition (1.45 (95% CI 1.19 to 1.77)) and nonoperative treatment (2.94 (95% CI 2.18 to 3.95)) were the only modifiable risk factors for death in COVID-19-positive patients. Patients who had tested positive for COVID-19 more than two weeks prior to hip fracture initially had better survival compared to those who contracted COVID-19 around the time of their hip fracture; however, survival rapidly declined and by 365 days the combination of hip fracture and COVID-19 infection was associated with a 50% mortality rate. Between 1 January and 30 June 2020, 1,273 (99.7% CI 1,077 to 1,465) excess deaths occurred within 90 days of hip fracture, representing an excess mortality of 23% (99.7% CI 20% to 26%), with most deaths occurring within 30 days. CONCLUSION: COVID-19 infection more than doubles the rate of early hip fracture mortality. Those contracting infection between 8 and 30 days after initial presentation are at even higher mortality risk, signalling the potential for targeted interventions during this period to improve survival.Cite this article: Bone Joint J 2022;104-B(10):1156-1167.

Methods for modelling excess mortality across England during the COVID-19 pandemic.

Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates.The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period.Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend.

Inequalities in excess premature mortality in England during the COVID-19 pandemic: a cross-sectional analysis of cumulative excess mortality by area deprivation and ethnicity.

OBJECTIVES: To examine magnitude of the impact of the COVID-19 pandemic on inequalities in premature mortality in England by deprivation and ethnicity. DESIGN: A statistical model to estimate increased mortality in population subgroups during the COVID-19 pandemic by comparing observed with expected mortality in each group based on trends over the previous 5 years. SETTING: Information on deaths registered in England since 2015 was used, including age, sex, area of residence and cause of death. Ethnicity was obtained from Hospital Episode Statistics records linked to death data. PARTICIPANTS: Population study of England, including all 569 824 deaths from all causes registered between 21 March 2020 and 26 February 2021. MAIN OUTCOME MEASURES: Excess mortality in each subgroup over and above the number expected based on trends in mortality in that group over the previous 5 years. RESULTS: The gradient in excess mortality by area deprivation was greater in the under 75s (the most deprived areas had 1.25 times as many deaths as expected, least deprived 1.14) than in all ages (most deprived had 1.24 times as many deaths as expected, least deprived 1.20). Among the black and Asian groups, all area deprivation quintiles had significantly larger excesses than white groups in the most deprived quintiles and there were no clear gradients across quintiles. Among the white group, only those in the most deprived quintile had more excess deaths than deaths directly involving COVID-19. CONCLUSION: The COVID-19 pandemic has widened inequalities in premature mortality by area deprivation. Among those under 75, the direct and indirect effects of the pandemic on deaths have disproportionately impacted ethnic minority groups irrespective of area deprivation, and the white group the most deprived areas. Statistics limited to deaths directly involving COVID-19 understate the pandemic's impact on inequalities by area deprivation and ethnic group at younger ages.

Changes in health in the countries of the UK and 150 English Local Authority areas 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND: Previous studies have reported national and regional Global Burden of Disease (GBD) estimates for the UK. Because of substantial variation in health within the UK, action to improve it requires comparable estimates of disease burden and risks at country and local levels. The slowdown in the rate of improvement in life expectancy requires further investigation. We use GBD 2016 data on mortality, causes of death, and disability to analyse the burden of disease in the countries of the UK and within local authorities in England by deprivation quintile. METHODS: We extracted data from the GBD 2016 to estimate years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and attributable risks from 1990 to 2016 for England, Scotland, Wales, Northern Ireland, the UK, and 150 English Upper-Tier Local Authorities. We estimated the burden of disease by cause of death, condition, year, and sex. We analysed the association between burden of disease and socioeconomic deprivation using the Index of Multiple Deprivation. We present results for all 264 GBD causes of death combined and the leading 20 specific causes, and all 84 GBD risks or risk clusters combined and 17 specific risks or risk clusters. FINDINGS: The leading causes of age-adjusted YLLs in all UK countries in 2016 were ischaemic heart disease, lung cancers, cerebrovascular disease, and chronic obstructive pulmonary disease. Age-standardised rates of YLLs for all causes varied by two times between local areas in England according to levels of socioeconomic deprivation (from 14 274 per 100 000 population [95% uncertainty interval 12 791-15 875] in Blackpool to 6888 [6145-7739] in Wokingham). Some Upper-Tier Local Authorities, particularly those in London, did better than expected for their level of deprivation. Allowing for differences in age structure, more deprived Upper-Tier Local Authorities had higher attributable YLLs for most major risk factors in the GBD. The population attributable fractions for all-cause YLLs for individual major risk factors varied across Upper-Tier Local Authorities. Life expectancy and YLLs have improved more slowly since 2010 in all UK countries compared with 1990-2010. In nine of 150 Upper-Tier Local Authorities, YLLs increased after 2010. For attributable YLLs, the rate of improvement slowed most substantially for cardiovascular disease and breast, colorectal, and lung cancers, and showed little change for Alzheimer's disease and other dementias. Morbidity makes an increasing contribution to overall burden in the UK compared with mortality. The age-standardised UK DALY rate for low back and neck pain (1795 [1258-2356]) was higher than for ischaemic heart disease (1200 [1155-1246]) or lung cancer (660 [642-679]). The leading causes of ill health (measured through YLDs) in the UK in 2016 were low back and neck pain, skin and subcutaneous diseases, migraine, depressive disorders, and sense organ disease. Age-standardised YLD rates varied much less than equivalent YLL rates across the UK, which reflects the relative scarcity of local data on causes of ill health. INTERPRETATION: These estimates at local, regional, and national level will allow policy makers to match resources and priorities to levels of burden and risk factors. Improvement in YLLs and life expectancy slowed notably after 2010, particularly in cardiovascular disease and cancer, and targeted actions are needed if the rate of improvement is to recover. A targeted policy response is also required to address the increasing proportion of burden due to morbidity, such as musculoskeletal problems and depression. Improving the quality and completeness of available data on these causes is an essential component of this response. FUNDING: Bill & Melinda Gates Foundation and Public Health England.

PLCζ is the physiological trigger of the Ca2+ oscillations that induce embryogenesis in mammals but conception can occur in its absence.

Activation of the egg by the sperm is the first, vital stage of embryogenesis. The sperm protein PLCζ has been proposed as the physiological agent that triggers the Ca2+ oscillations that normally initiate embryogenesis. Consistent with this, recombinant PLCζ induces Ca2+ oscillations in eggs and debilitating mutations in the PLCZ1 gene are associated with infertility in men. However, there has been no evidence that knockout of the gene encoding PLCζ abolishes the ability of sperm to induce Ca2+ oscillations in eggs. Here, we show that sperm derived from Plcz1-/- male mice fail to trigger Ca2+ oscillations in eggs, cause polyspermy and thus demonstrate that PLCζ is the physiological trigger of these Ca2+ oscillations. Remarkably, some eggs fertilized by PLCζ-null sperm can develop, albeit at greatly reduced efficiency, and after a significant time-delay. In addition, Plcz1-/- males are subfertile but not sterile, suggesting that in the absence of PLCζ, spontaneous egg activation can eventually occur via an alternative route. This is the first demonstration that in vivo fertilization without the normal physiological trigger of egg activation can result in offspring. PLCζ-null sperm now make it possible to resolve long-standing questions in fertilization biology, and to test the efficacy and safety of procedures used to treat human infertility.