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Abstract 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.

A team of researchers worked with public health officials and clinical decision makers to create real-time models to help predict COVID-19 cases and hospital bed needs in the East of England. These models used up-to-date data and allowed officials to explore different scenarios and make informed plans. The models have also been applied in other countries and can be a valuable tool in future epidemics. We describe the real-time participatory modeling work that our team of academics, public health officials, and clinical decision makers undertook to support the regional efforts to tackle COVID-19 in the East of England (EoE). Our team studied questions to address the pandemic's rapidly evolving current and near-future epidemiological state as well as short-term (a few weeks) and medium-term (several months) bed capacity demand. Frequent data input from and consultations with our public health and clinical partners allowed our academic team to apply dynamic data-driven approaches using time series and system dynamics modeling. Our portfolio of models provided decision makers with the ability to ask nuanced questions. It allowed them to explore and explain different aspects of the pandemic and make more informed capacity plans in the EoE and its subregions. Our novel time series models have already been applied to India in collaboration with Indian health authorities, and the system dynamics model has been used in the canton of Ticino in Switzerland. Therefore, our work may address future epidemiological crises beyond the EoE, especially when used in conjunction with other methods as an ensemble. Additionally, the knowledge gained through our experiences and documented in this paper may guide academic-practitioner collaborations in rapid response to future disasters. History: This paper was refereed. [ABSTRACT FROM AUTHOR]

Background: We aimed to estimate the relative risk of mortality following a first positive SARS-CoV-2 test during the first, second, and third waves of the COVID-19pandemic in England by age, sex, and vaccination status, taking into account pre-existing health conditions and lifestyle factors. Methods: We conducted a retrospective cohort study of all individuals registered with the National Health Service (NHS) in England from 1 March 2020 to September 2022. Data for all individuals were obtained and linked including primary care records, hospital admission episodes, SARS-CoV-2 test results, vaccinations, and death registrations. We fitted Cox Proportional Hazards models with time dependent covariates for confirmed SARS-CoV-2 infection to model the risk of subsequent mortality. Results: The hazard ratio for death after testing positive for subsequent, compared with those not testing positive, amongst unvaccinated individuals, ranged from 11 to 89 by age and sex, in the first four weeks following a positive test in wave one and reduced to 14 to 50 in wave three. This hazard was further reduced amongst those who had three vaccines to between 1.4 and 7 in wave three. Conclusions: This study provides robust estimates of increased mortality risk among those who tested positive over the first three waves of the COVID-19 pandemic in England. The estimates show the impact of various factors affecting the risk of mortality from COVID-19. The results provide the first step towards estimating the magnitude and pattern of mortality displacement due to COVID-19, which is essential to understanding subsequent mortality rates in England. [ABSTRACT FROM AUTHOR]

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.

Abstract Background Directly standardized rates (DSRs) adjust for different age distributions in different populations and enable, say, the rates of disease between the populations to be directly compared. They are routinely published but there is concern that a DSR is not valid when it is based on a “small” number of events. The aim of this study was to determine the value at which a DSR should not be published when analyzing real data in England. Methods Standard Monte Carlo simulation techniques were used assuming the number of events in 19 age groups (i.e., 0–4, 5–9, ... 90+ years) follow independent Poisson distributions. The total number of events, age specific risks, and the population sizes in each age group were varied. For each of 10,000 simulations the DSR (using the 2013 European Standard Population weights), together with the coverage of three different methods (normal approximation, Dobson, and Tiwari modified gamma) of estimating the 95% confidence intervals (CIs), were calculated. Results The normal approximation was, as expected, not suitable for use when fewer than 100 events occurred. The Tiwari method and the Dobson method of calculating confidence intervals produced similar estimates and either was suitable when the expected or observed numbers of events were 10 or greater. The accuracy of the CIs was not influenced by the distribution of the events across categories (i.e., the degree of clustering, the age distributions of the sampling populations, and the number of categories with no events occurring in them). Conclusions DSRs should not be given when the total observed number of events is less than 10. The Dobson method might be considered the preferred method due to the formulae being simpler than that of the Tiwari method and the coverage being slightly more accurate.

Many natural resources have degraded and collapsed despite being managed under rigorous institutional frameworks set up to ensure rational exploitation. Path dependency of dysfunction institutions has been suggested as an explanation for such undesired outcomes. We explore the role of path dependency in natural resource management by studying a 100-year evolution of Swedish fisheries. We rely on three main types of original longitudinal data collected for the period 1914-2016: (A) policy documents, (B) government spending on management and subsidies, and (C) catch and fleet data. Our analysis contrasts the periods before and after the Swedish entrance into the European Union (1995) because this marks the year when fisheries policy became beyond the direct influence of the Swedish government. We uncover four pieces of evidence suggesting the existence of a path dependent dynamic in the pre-EU period: (1) despite increasing insights on the vulnerability of fish stocks to overexploitation, national policy goals in relation to fisheries continuously promoted incompatible goals of social and economic growth but without any reference to the sustainability of the biological resources; (2) the same policy instruments were used over long periods; (3) actor constellations within the fisheries policy subsystem were stable over time; (4) neither political regime nor macroeconomic variables and fisheries performance (industry production, oil price, landing values) could explain observed temporal variation in subsidies. We conclude that key policy actors in the pre-EU period formed an "iron triangle" and thereby prevented necessary policy changes. These national reinforcing feedbacks have been weakened since EU entrance, and the indicators for path dependency show broader involvement of stakeholders, a shift in spending, and policy goals that now explicitly address ecological sustainability.