Clusters of post-acute COVID-19 symptoms: a latent class analysis across 9 databases and 7 countries (preprint)

Prior evidence has suggested the multisystem symptomatic manifestations of post-acute COVID-19 condition (PCC). Here we conducted a network cluster analysis of 24 WHO proposed symptoms to identify potential latent subclasses of PCC. Individuals with a positive test of or diagnosed with SARS-CoV-2 after 09/2020 and with at least one symptom within ≥ 90 to 365 days following infection were included. Sub-analyses were conducted among people with ≥ 3 different symptoms. Summary characteristics were provided for each cluster. All analyses were conducted separately in 9 databases from 7 countries, including data from primary care, hospitals, national health claims and national health registries, allowing to validate clusters across the different healthcare settings. 787,078 persons with PCC were included. Single-symptom clusters were common across all databases, particularly for joint pain, anxiety, depression and allergy. Complex clusters included anxiety-depression and abdominal-gastrointestinal symptoms. Substantial heterogeneity within and between PCC clusters was seen across healthcare settings. Current definitions of PCC should be critically reviewed to reflect this variety in clinical presentation.

Collateral effects of the COVID-19 pandemic on endocrine treatments for breast and prostate cancer in the UK: implications for bone health (preprint)

BackgroundThe COVID-19 pandemic affected cancer screening, diagnosis and treatment pathways. This study examined the impact of the pandemic on incidence and trends of endocrine treatments in patients with breast or prostate cancer; and endocrine treatment-related side-effects. MethodsPopulation-based cohort study using UK primary care Clinical Practice Research Datalink (CPRD) GOLD database. There were 13,701 newly diagnosed breast cancer patients and 12,221 prostate cancer patients with [≥]1-year data availability since diagnosis between January 2017-June 2022. Incidence rates (IR) and incidence rate ratios (IRR) were calculated across multiple time periods before and after lockdown to examine the impact of changing social restrictions on endocrine treatments and treatment-related outcomes, including osteopenia, osteoporosis and bisphosphonate prescriptions. ResultsIn patients with breast cancer, aromatase inhibitor prescriptions increased during lockdown compared to pre-pandemic (IRR: 1.22 [95% Confidence Interval: 1.11-1.34]), followed by a decrease post-first lockdown (IRR: 0.79 [95%CI: 0.69-0.89]). In patients with prostate cancer, first-generation antiandrogen prescriptions increased compared to pre-pandemic (IRR: 1.23 [95% CI: 1.08-1.4]). For breast cancer patients on aromatase inhibitors, diagnoses of osteopenia, osteoporosis and bisphosphonate prescriptions were reduced across all lockdown periods compared to pre-pandemic (IRR range: 0.31-0.62). ConclusionDuring the first two years of the pandemic, newly diagnosed breast and prostate cancer patients were prescribed more endocrine treatments compared to pre-pandemic, due to restrictions on hospital procedures replacing surgeries with bridging therapies. But breast cancer patients had fewer diagnoses of osteopenia and osteoporosis, and bisphosphonate prescriptions. These patients should be followed up in the coming years for signs of bone thinning. Evidence of poorer management of treatment-related side-effects will allow us to determine whether there is a need to better allocate resources to patients at high risk for bone-related complications.

The Impact of the UK COVID-19 Lockdown on the Screening, Diagnostics and Incidence of Breast, Colorectal, Lung and Prostate Cancer in the UK: a Population-Based Cohort Study (preprint)

Objectives: This study aimed to assess the impact of the COVID-19 lockdown on the screening and diagnosis of breast, colorectal, lung, and prostate cancer. The study also investigated whether the rates returned to pre-pandemic levels by December 2021. Design: Cohort study. Setting: Electronic health records from UK primary care Clinical Practice Research Datalink (CPRD) GOLD database. Participants: The study included individuals registered with CPRD GOLD between January 2017 and December 2021, with at least 365 days of prior observation. Main outcome measures: The study focused on screening, diagnostic tests, referrals and diagnoses of first-ever breast, colorectal, lung, and prostate cancer. Incidence rates (IR) were stratified by age, sex and region, and incidence rate ratios (IRR) were calculated to compare rates during and after lockdown with the reference period before lockdown. Forecasted rates were estimated using negative binomial regression models. Results: Among 5,191,650 eligible participants, the initial lockdown resulted in reduced screening and diagnostic tests for all cancers, which remained dramatically reduced across the whole observation period for almost all tests investigated. For cancer incidence rates, there were significant IRR reductions in breast (0.69), colorectal (0.74), and prostate (0.71) cancers. However, the reduction in lung cancer incidence (0.92) was non-significant. Extrapolating to the entire UK population, an estimated 18,000 breast, 13,000 colorectal, 10,000 lung, and 21,000 prostate cancer diagnoses were missed from March 2020 to December 2021. Conclusion: The national COVID-19 lockdown in the UK had a substantial impact on cancer screening, diagnostic tests, referrals and diagnoses. Although incidence rates started to recover after the lockdown, they remained significantly lower than pre-pandemic levels for breast and prostate cancers and associated tests. Delays in diagnosis are likely to have adverse consequences on cancer stage, treatment initiation, mortality rates, and years of life lost. Urgent strategies are needed to identify undiagnosed cases and address the long-term implications of delayed diagnoses.

The role of COVID-19 vaccines in preventing post COVID-19 thromboembolic and cardiovascular complications: a multinational cohort study (preprint)

ImportanceThe overall effects of vaccination on the risk of cardiac, and venous and arterial thromboembolic complications following COVID-19 remain unclear. ObjectiveWe studied the association between COVID-19 vaccination and the risk of acute and subacute COVID-19 cardiac and thromboembolic complications. DesignMultinational staggered cohort study, based on national vaccination campaign rollouts. SettingNetwork study using electronic health records from primary care records from the UK, primary care data linked to hospital data from Spain, and national insurance claims from Estonia. ParticipantsAll adults with a prior medical history of [≥]180 days, with no history of COVID-19 or previous COVID-19 vaccination at the beginning of vaccine rollout were eligible. ExposureVaccination status was used as a time-varying exposure. Vaccinated individuals were classified by vaccine brand according to the first dose received. Main OutcomesPost COVID-19 complications including myocarditis, pericarditis, arrhythmia, heart failure (HF), venous (VTE) and arterial thromboembolism (ATE) up to 1 year after SARS-CoV-2 infection. MeasuresPropensity Score overlap weighting and empirical calibration based on negative control outcomes were used to minimise bias due to observed and unobserved confounding, respectively. Fine-Gray models were fitted to estimate sub-distribution Hazard Ratios (sHR) for each outcome according to vaccination status. Random effect meta-analyses were conducted across staggered cohorts and databases. ResultsOverall, 10.17 million vaccinated and 10.39 million unvaccinated people were included. Vaccination was consistently associated with reduced risks of acute (30-day) and subacute post COVID-19 VTE and HF: e.g., meta-analytic sHR 0.34 (95%CI, 0.27-0.44) and 0.59 (0.50-0.70) respectively for 0-30 days, sHR 0.58 (0.48 - 0.69) and 0.71 (0.59 - 0.85) respectively for 90-180 days post COVID-19. Additionally, reduced risks of ATE, myocarditis/pericarditis and arrhythmia were seen, but mostly in the acute phase (0-30 days post COVID-19). ConclusionsCOVID-19 vaccination reduced the risk of post COVID-19 complications, including cardiac and thromboembolic outcomes. These effects were more pronounced for acute (1-month) post COVID-19 outcomes, consistent with known reductions in disease severity following breakthrough vs unvaccinated SARS-CoV-2 infection. RelevanceThese findings highlight the importance of COVID-19 vaccination to prevent cardiovascular outcomes after COVID-19, beyond respiratory disease. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the impact of COVID-19 vaccination to prevent cardiac complications and thromboembolic events following a SARS-CoV-2 infection? FindingsResults from this multinational cohort study showed that COVID-19 vaccination reduced risk for acute and subacute COVID-19 heart failure, as well as venous and arterial thromboembolic events following SARS-CoV-2 infection. MeaningThese findings highlight yet another benefit of vaccination against COVID-19, and support the recommendations for COVID-19 vaccination even in people at high cardiovascular risk.

The burden of long COVID: a multinational cohort analysis of Spanish and UK data including SARS-CoV-2 infections, reinfections, and matched contemporaneous test negative controls (preprint)

As limited data was available on the effect of persisting COVID-19 symptoms, we characterised long COVID and identified key symptoms associated with persistent disease. Using primary care data from Spain and UK, we estimated incidence rates of long COVID in the population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential for long COVID by comparing their frequency in COVID-19 patients vs matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections. Fortunately, the proportion of COVID-19 cases resulting in long COVID declined over the study period.  Risk for altered smell/taste, dyspnoea, and fatigue were consistently higher in long COVID patient vs controls [RR between 5.97-1.09]. All persistent symptoms were less common after reinfection than first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.

Observational methods for COVID-19 vaccine effectiveness research: a trial emulation and empirical evaluation (preprint)

Despite much research on the topic, little work has been done comparing the use of methods to control for confounding in the estimation of COVID-19 vaccine effectiveness in routinely collected medical record data. We conducted a trial emulation study to replicate the ChAdOx1 (Oxford/AstraZeneca) and BNT162b2 (BioNTech/Pfizer) COVID-19 phase 3 efficacy studies. We conducted a cohort study including individuals aged 75+ from UK CPRD AURUM (N = 916,128) in early 2021. Three different methods were assessed: Overlap weighting, inverse probability treatment weighting, and propensity score matching. All three methods successfully replicated the findings from both phase 3 trials, and overlap weighting performed best in terms of confounding, systematic error, and precision. Despite lack of trial data beyond 3 weeks, we found that even 1 dose of BNT162b2 was effective against SARS-CoV-2 infection for up to 12 weeks before a second dose was administered. These results support the UK Joint Committee on Vaccination and Immunisation modelling and related UK vaccination strategies implemented in early 2021.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations (preprint)

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported from a standardised source over the next one to four weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models past predictive performance. Results: Over 52 weeks we collected and combined up to 28 forecast models for 32 countries. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 84% of participating models forecasts of incident cases (with a total N=862), and 92% of participating models forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than two weeks.

The impact of COVID passport mandates on the number of cases of and hospitalizations with COVID-19 in the UK: a difference-in-differences analysis (preprint)

BackgroundMandatory COVID-19 certification was introduced at different times in the four countries of the UK. We aimed to study the effect of this intervention on the incidence of cases and hospital admissions. MethodsThe main outcome was the weekly averaged incidence of COVID-19 confirmed cases and hospital admissions. We performed Negative Binomial Segmented Regression (NBSR) and Autoregressive Integrated Moving Average (ARIMA) analyses for the four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences (DiD) models to compare the latter three to England, where COVID-19 certification was imposed the latest. FindingsNBSR methods suggested COVID-19 certification led to a decrease in the incidence of cases in Northern Ireland, but not in hospitalizations. In Wales, they also caused a decrease in the incidence of cases but not in hospital admissions. In Scotland, we observed a decrease in both cases and admissions. ARIMA models confirmed these results. The DiD model showed that the intervention decreased the incidence of COVID compared to England in all countries except Wales, in October. Then, the incidence rate of cases already had a decreasing tendency, as well as in England, hence a particular impact of Covid Passport was less obvious. In Wales, the model coefficients were 2.2 (95% CI -6.24,10.70) for cases and -0.144 (95% CI -0.248, -0.039) for admissions in October and -7.75 (95% CI -13.1, -2.46) for cases and -0.169 (95% CI-0.308, -0.031) for admissions in November. In Northern Ireland, -10.1 (95% CI -18.4, -1.79) for cases and -0.269 (95% CI -0.385, -0.153) for admissions. In Scotland they were 7.91 (95% CI 4.46,11.4) for cases and -0.097 (95% CI - 0.219,0.024) for admissions. InterpretationThe introduction of mandatory certificates decreased cases in all countries except in England. Differences on concomitant measures, on vaccination uptake or Omicron variant prevalence could explain this discrepancy.

A Cost-Benefit Analysis of COVID-19 Vaccination in Catalonia (preprint)

(1) Background: in epidemiological terms, it has been possible to calculate the savings in health resources and the reduction in health effects of COVID vaccines. From the point of view of economic evaluation, some studies have estimated its cost-effectiveness with the vaccination showing highly favorable results, which in some cases is cost-saving; (2) Methods: a cost-benefit analysis of the vaccination campaign in the North Metropolitan Health Region (Catalonia). An epidemiological model based on observational data and before and after comparison is used. The information on the doses used and the resources assigned (conventional hospital beds, ICU, number of tests) has been extracted from administrative data from the largest Primary Care provider in the region (Catalan Institute of Health). A distinction is made between the social perspective and the health system; (3) Results: the costs of vaccination are estimated at 137 million euros (€48.05/dose administered). This figure is significantly lower than the positive impacts of the vaccination campaign, which are estimated at 470 million euros (€164/dose administered). Of these, 18% corresponds to the reduction of ICU discharges, 16% to the reduction in conventional hospital discharges, 5% to the reduction in PCR tests and 1% to the reduction of RAT tests. Monetization of deaths and cases with sequelae avoided account for 53% and 5% of total savings, respectively. The benefit/cost ratio is estimated at 3.4 from a social perspective and 1.2 from a health system perspective. The social benefits of vaccination are estimated at €116.67 per dose of vaccine given (€19.93 from the point of view of the health system); (4) Conclusions: the mass vaccination campaign against COVID is cost-saving. From a social perspective, most of these savings come from the monetization of the reduction in mortality and cases with sequelae, although the intervention is equally widely cost-effective from the point of view of the health system thanks to the reduction in the use of resources. It is concluded that, from an economic perspective, the vaccination campaign has high social returns.

A Cost-Benefit Analysis of the COVID-19 Asymptomatic Mass Testing Strategy in the North Metropolitan Area of Barcelona (preprint)

The epidemiological situation generated by COVID-19 has highlighted the importance of applying non-pharmacological measures. Among these, mass screening of the asymptomatic general population has been established as a priority strategy by carrying out diagnostic tests to limit the spread of the virus. In this article, we aim to evaluate the economic impact of mass COVID-19 screenings of an asymptomatic population through a Cost-Benefit Analysis based on the estimated total costs of mass screening versus health gains and associated health costs avoided. Excluding the value of monetized health, the Benefit-Cost ratio was estimated at approximately 0.45. However, if monetized health is included in the calculation, the ratio is close to 1.20. The monetization of health is the critical element that tips the scales in favour of the desirability of screening. Screenings with the highest return are those that maximize the percentage of positives detected.

The Impact of Prioritisation and Dosing Intervals on the Effects of COVID-19 Vaccination in Europe: An Agent-Based Cohort Model (preprint)

Background: Different strategies are being explored to maximise the effect of COVID-19 vaccination in Europe. We studied the impact of different population prioritisation and dose intervals on infections, hospitalisations, mortality, and public health restrictions.Method: An agent-based model was built to quantify the impact of different vaccination strategies over 6 months. Input parameters were derived from recent phase 3 trials and official European figures. We explored the prioritisation of vulnerable people, care-home staff and residents, and contagious groups and dose intervals from 3 to 12 weeks.Findings: Prioritising vulnerable people, rather than the most contagious groups, led to higher COVID-19 prevalence, but clear reductions in mortality, hospital admissions, and restrictions. At a realistic vaccination speed of ≤0·1% population/day, separating doses by 12 (instead of 3) weeks reduced hospitalisations, mortality, and restrictions for vaccines with similar first- and second-dose efficacy (e.g., the Oxford-AstraZeneca and Moderna vaccines), but not for those with lower first-dose efficacy (e.g., the Pfizer/BioNTech vaccine).Interpretation: Mass vaccination should dramatically reduce the effect of COVID-19 on Europe’s health and economy. Early vaccination of vulnerable populations will reduce mortality, hospitalisations, and public health restrictions compared to prioritisation of the most contagious groups. The choice of interval between doses should be based on expected vaccine availability and first-dose efficacy, with 12-week intervals preferred over shorter intervals in most realistic scenarios.Funding Statement: The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the department of Health. CP research is partially funded by Ministerio de Ciencia e Innovación, Gobierno de España through the grant PGC2018-095456-B-I00. MC is funded by La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003.Declaration of Interests: Dr. Prats reports grants from Ministerio de Ciencia e Innovación, Gobierno de España (PGC2018-095456-B-I00), during the conduct of the study; other from European Commission, DG-CONNECT (CNECT/LUX/2020/LVP/0085, LC-01591965), outside the submitted work. Dr. Prieto-Alhambra reports grants and other from AMGEN, grants, non-financial support and other from UCB Biopharma, grants from Les Laboratoires Servier, outside the submitted work; and HTA Funding Committee membership. Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DPA's department and open for external participants. Dr. CATALÀ reports grants from Ministerio de Ciencia e Innovación, Gobierno de España (PGC2018-095456-B- I00), grants from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003, during the conduct of the study; other from European Commission, DG-CONNECT (CNECT/LUX/2020/LVP/0085, LC-01591965), outside the submitted work. Ms. Li has nothing to disclose.Ethics Approval Statement: Only public aggregated data was used for this study. The use of such data does not require ethics approval.

Epidemiological dynamics of the incidence of COVID-19 in children and the relationship with the opening of schools in Catalonia (Spain) (preprint)

Here we analyse the epidemiological trend of the incidence of COVID-19 in children in Catalonia (Spain) during the first 20 weeks of the 2020-2021 school year. This study demonstrates that while schools were open the incidence rate among children remained significantly lower than in general population, despite a greater diagnostic effort in children. These results suggest that schools have not played a significant role in the SARS-CoV-2 dissemination in Catalonia.

Private Sources of Mobility Data Under COVID-19 (preprint)

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility is at the epicenter of that change, as the greatest facilitator for the spread of the virus. To study the change in mobility, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to possible future crisis, we need to properly understand all mobility data sources at our disposal. Our work is dedicated to the study of private mobility sources, gathered and released by large technological companies. This data is of special interest because, unlike most public sources, it is focused on people, not transportation means. i.e., its unit of measurement is the closest thing to a person in a western society: a phone. Furthermore, the sample of society they cover is large and representative. On the other hand, this sort of data is not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we set forth to explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting because of its large and fast pandemic peak, and for its implementation of a sustained, generalized lockdown. We find private mobility sources to be both correlated and complementary. Using them, we evaluate the efficiency of implemented policies, and provide a insights into what new normal means in Spain.

Empiric model for short-time prediction of COVID-19spreading (preprint)

Covid-19 appearance and fast spreading took by surprise the international community. Collaboration between researchers, public health workers and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both current state and short-term future trends can be carefully evaluated. Gompertz model has shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate that is able to show the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity. Moreover, it allows to carry out short-term predictions and long-term estimations that may facilitate policy decisions and the revision of in-place confinement measures and the development of new protocols. This model has been employed to fit the cumulative cases of Covid-19 from several Chinese provinces and from other countries with a successful containment of the disease. Results show that there are systematic differences in spreading velocity between countries. In countries that are in the initial stages of the Covid-19 outbreak, model predictions provide a reliable picture of its short-term evolution and may permit to unveil some characteristics of the long-term evolution. These predictions can also be generalized to short-term hospital and Intensive Care Units (ICU) requirements, which together with the equivalent predictions on mortality provide key information for health officials.

Robust estimation of diagnostic rate and real incidence of COVID-19 for European policymakers (preprint)

Policymakers need a clear and fast assessment of the real spread of the epidemic of COVID-19 in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths immediately indicate that countries like Italy and Spain have the worst situation as of mid April 2020, on its own, reported cases do not provide a correct picture of the situation. The reason is that different countries diagnose diversely and present very distinctive reported case fatality rate (CFR). The same levels of reported incidence and mortality might hide a very different underlying picture. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain an uniform unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empiric. The key assumption of the method is that the real CFR in Europe of COVID-19 is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor the way total deaths are reported. The estimation protocol has a dynamic nature, and it has been giving converging numbers for diagnostic rates in all European countries as of mid April 2020. From this diagnostic rate, policy makers can obtain an Effective Potential Growth (EPG) updated everyday providing an unbiased assessment of the countries with more potential to have an uncontrolled situation. The method developed will be used to track possible improvements on the diagnostic rate in European countries as the epidemic evolves.