Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared.

During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.

COVID-19 severity scale for claims data research.

OBJECTIVE: To create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data. DATA SOURCE: Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020. STUDY DESIGN: The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality. DATA COLLECTION/EXTRACTION METHODS: The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC). PRINCIPAL FINDINGS: A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19. CONCLUSION: A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.

DIABETESvid: Web survey methods for diabetes self-care and resilience in the COVID-19 pandemic in Brazil

Aim: To describe Brazilian web survey methods termed DIABETESvid by assessing the diabetic individuals' self-care practices and their resilience in the COVID-19 pandemic. Method(s): This is a cross-sectional study on data collected from web surveys in the period between 1st September and 19th October 2020, in which socio-demographic, clinical, self-care and resilience variables were investigated. A questionnaire was elaborated and implemented by using the Research Electronic Data Capture platform. Result(s): A total of 1,633 participants were eligible for this study, with a higher frequency of females, 46.5% being aged between 18 and 39 years old, 40.9% being diagnosed with diabetes within 1 to 10 years and all having high level of education. Most of the participants was living in south-eastern Brazil, self-reported type 1 diabetes mellitus and had access to the survey link on WhatsApp. In this 7-week study, it was evidenced that the survey response rate was higher in the first week (38.5%) and number of accesses were increased on Thursdays (20.2%) and in the night (40.2%). Conclusion(s): The method used here can be useful as a baseline for future web surveys involving diabetic individuals so that several analyses can be conducted in the clinical care and academic contexts.Copyright © 2021 The Authors

Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak.

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 - 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 - 87.0% or 1.70 - 80.9%).

Using trajectory modeling of spatio-temporal trends to illustrate disparities in COVID-19 death in flint and Genesee County, Michigan.

COVID-19's rapid onset left many public health entities scrambling. But establishing community-academic partnerships to digest data and create advocacy steps offers an opportunity to link research to action. Here we document disparities in COVID-19 death uncovered during a collaboration between a health department and university research center. We geocoded COVID-19 deaths in Genesee County, Michigan, to model clusters during two waves in spring and fall 2020. We then aggregated these deaths to census block groups, where group-based trajectory modeling identified latent patterns of change and continuity. Linking with socioeconomic data, we identified the most affected communities. We discovered a geographic and racial gap in COVID-19 deaths during the first wave, largely eliminated during the second. Our partnership generated added and immediate value for community partners, including around prevention, testing, treatment, and vaccination. Our identification of the aforementioned racial disparity helped our community nearly eliminate disparities during the second wave.

Thromboprophylaxis with standard-dose vs. flexible-dose heparin for hospitalized COVID-19 patients: a target trial emulation.

OBJECTIVES: To compare mortality of hospitalized COVID-19 patients under two low-molecular weight heparin (LMWH) thromboprophylaxis strategies: standard dose and variable dose (standard dose increased to intermediate dose in the presence of laboratory abnormalities indicating an increased thrombosis risk). STUDY DESIGN AND SETTING: Target trial emulation using observational data from 2,613 adults admitted with a COVID-19 diagnosis in Madrid, Spain between March 16 and April 15, 2020. RESULTS: A total of 1,284 patients were eligible. Among 503 patients without increased baseline thrombotic risk, 28-day mortality risk (95% confidence interval [CI]) was 9.0% (6.6, 11.7) under the standard dose strategy and 5.6% (3.3, 8.3) under the variable dose strategy; risk difference 3.4% (95% CI: -0.24, 6.9); mortality hazard ratio 1.61 (95% CI: 0.97, 2.89). Among 781 patients with increased baseline thrombotic risk, the 28-day mortality risk was 25.8% (22.7, 29.0) under the standard dose strategy and 18.1% (9.3, 28.9) under the intermediate dose strategy; risk difference 7.7% (95% CI: -3.5, 17.2); mortality hazard ratio 1.45 (95% CI: 0.81, 3.17). Major bleeding and LMWH-induced coagulopathy were rare under all strategies. CONCLUSION: Escalating anticoagulation intensity after signs of thrombosis risk may increase the survival of hospitalized COVID-19 patients. However, effect estimates were imprecise and additional studies are warranted.

Investigating SARS-CoV-2 Susceptibility in Animal Species: A Scoping Review.

In the early stages of response to the SARS-CoV-2 pandemic, it was imperative for researchers to rapidly determine what animal species may be susceptible to the virus, under low knowledge and high uncertainty conditions. In this scoping review, the animal species being evaluated for SARS-CoV-2 susceptibility, the methods used to evaluate susceptibility, and comparing the evaluations between different studies were conducted. Using the PRISMA-ScR methodology, publications and reports from peer-reviewed and gray literature sources were collected from databases, Google Scholar, the World Organization for Animal Health (OIE), snowballing, and recommendations from experts. Inclusion and relevance criteria were applied, and information was subsequently extracted, categorized, summarized, and analyzed. Ninety seven sources (publications and reports) were identified which investigated 649 animal species from eight different classes: Mammalia, Aves, Actinopterygii, Reptilia, Amphibia, Insecta, Chondrichthyes, and Coelacanthimorpha. Sources used four different methods to evaluate susceptibility, in silico, in vitro, in vivo, and epidemiological analysis. Along with the different methods, how each source described "susceptibility" and evaluated the susceptibility of different animal species to SARS-CoV-2 varied, with conflicting susceptibility evaluations evident between different sources. Early in the pandemic, in silico methods were used the most to predict animal species susceptibility to SARS-CoV-2 and helped guide more costly and intensive studies using in vivo or epidemiological analyses. However, the limitations of all methods must be recognized, and evaluations made by in silico and in vitro should be re-evaluated when more information becomes available, such as demonstrated susceptibility through in vivo and epidemiological analysis.

Rooming-In Practice During the Pandemic: Results From a Retrospective Cohort Study.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic emerged in December 2019 and spread rapidly worldwide. So far, evidence regarding the breastfeeding and rooming-in management of mothers with COVID-19 and their newborn infants is scarce. RESEARCH AIMS: 1) To assess the rate of exclusive breastfeeding at discharge among mothers with COVID-19 and their newborn infants managed either using a rooming-in or a separation regimen; and 2) to evaluate different neonatal outcomes, including the need for re-hospitalization related to COVID-19 among newborn infants in the two groups. METHOD: We conducted a retrospective two-group comparative observational study. The sample was participants with COVID-19 and their newborn infants (N = 155 dyads) between March 1, 2020, and April 30, 2021. Two time periods were outlined resulting from the two different clinical practices of mother-infant separation and rooming-in. RESULTS: Within the sample, 145 (93.5%) were asymptomatic. All neonates had documented Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) test results, and six tested positive by reverse transcriptase polymerase chain reaction within 48 hr of life. The rate of exclusive breastfeeding was significantly higher (p < .0001) within the rooming-in group. Length of hospital stay was significantly lower (p = .001) within the rooming-in group. CONCLUSIONS: Protected rooming-in practice has proven to be safe and effective in supporting breastfeeding: None of the infants enrolled were hospitalized due to COVID-19 infection and the rate of exclusive breastfeeding at discharge was increased compared to those infants separated from their mothers.

A modified self-controlled case series method for event-dependent exposures and high event-related mortality, with application to COVID-19 vaccine safety.

We propose a modified self-controlled case series (SCCS) method to handle both event-dependent exposures and high event-related mortality. This development is motivated by an epidemiological study undertaken in France to quantify potential risks of cardiovascular events associated with COVID-19 vaccines. Event-dependence of vaccinations, and high event-related mortality, are likely to arise in other SCCS studies of COVID-19 vaccine safety. Using this case study and simulations to broaden its scope, we explore these features and the biases they may generate, implement the modified SCCS model, illustrate some of the properties of this model, and develop a new test for presence of a dose effect. The model we propose has wider application, notably when the event of interest is death.

Avoiding bias in self-controlled case series studies of coronavirus disease 2019.

Many studies, including self-controlled case series (SCCS) studies, are being undertaken to quantify the risks of complications following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). One such SCCS study, based on all COVID-19 cases arising in Sweden over an 8-month period, has shown that SARS-CoV-2 infection increases the risks of AMI and ischemic stroke. Some features of SARS-CoV-2 infection and COVID-19, present in this study and likely in others, complicate the analysis and may introduce bias. In the present paper we describe these features, and explore the biases they may generate. Motivated by data-based simulations, we propose methods to reduce or remove these biases.

Interrogating structural inequalities in COVID-19 mortality in England and Wales.

BACKGROUND: Numerous observational studies have highlighted structural inequalities in COVID-19 mortality in the UK. Such studies often fail to consider the hierarchical, spatial nature of such inequalities in their analysis, leading to the potential for bias and an inability to reach conclusions about the most appropriate structural levels for policy intervention. METHODS: We use publicly available population data on COVID-19-related mortality and all-cause mortality between March and July 2020 in England and Wales to investigate the spatial scale of such inequalities. We propose a multiscale approach to simultaneously consider three spatial scales at which processes driving inequality may act and apportion inequality between these. RESULTS: Adjusting for population age structure and number of local care homes we find highest regional inequality in March and June/July. We find finer grained within region inequality increased steadily from March until July. The importance of spatial context increases over the study period. No analogous pattern is visible for non-COVID-19 mortality. Higher relative deprivation is associated with increased COVID-19 mortality at all stages of the pandemic but does not explain structural inequalities. CONCLUSIONS: Results support initial stochastic viral introduction in the South, with initially high inequality decreasing before the establishment of regional trends by June and July, prior to reported regionality of the 'second-wave'. We outline how this framework can help identify structural factors driving such processes, and offer suggestions for a long-term, locally targeted model of pandemic relief in tandem with regional support to buffer the social context of the area.

Scaling COVID-19 against inequalities: should the policy response consistently match the mortality challenge?

BACKGROUND: The mortality impact of COVID-19 has thus far been described in terms of crude death counts. We aimed to calibrate the scale of the modelled mortality impact of COVID-19 using age-standardised mortality rates and life expectancy contribution against other, socially determined, causes of death in order to inform governments and the public. METHODS: We compared mortality attributable to suicide, drug poisoning and socioeconomic inequality with estimates of mortality from an infectious disease model of COVID-19. We calculated age-standardised mortality rates and life expectancy contributions for the UK and its constituent nations. RESULTS: Mortality from a fully unmitigated COVID-19 pandemic is estimated to be responsible for a negative life expectancy contribution of -5.96 years for the UK. This is reduced to -0.33 years in the fully mitigated scenario. The equivalent annual life expectancy contributions of suicide, drug poisoning and socioeconomic inequality-related deaths are -0.25, -0.20 and -3.51 years, respectively. The negative impact of fully unmitigated COVID-19 on life expectancy is therefore equivalent to 24 years of suicide deaths, 30 years of drug poisoning deaths and 1.7 years of inequality-related deaths for the UK. CONCLUSION: Fully mitigating COVID-19 is estimated to prevent a loss of 5.63 years of life expectancy for the UK. Over 10 years, there is a greater negative life expectancy contribution from inequality than around six unmitigated COVID-19 pandemics. To achieve long-term population health improvements it is therefore important to take this opportunity to introduce post-pandemic economic policies to 'build back better'.

Complex systems models for causal inference in social epidemiology.

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.