Search

Background: As evidence related to the COVID-19 pandemic surged, databases, platforms, and repositories evolved with features and functions to assist users in promptly finding the most relevant evidence. In response, research synthesis teams adopted novel searching strategies to sift through the vast amount of evidence to synthesize and disseminate the most up-to-date evidence. This paper explores the key database features that facilitated systematic searching for rapid evidence synthesis during the COVID-19 pandemic to inform knowledge management infrastructure during future global health emergencies. Methods: This paper outlines the features and functions of previously existing and newly created evidence sources routinely searched as part of the NCCMT's Rapid Evidence Service methods, including databases, platforms, and repositories. Specific functions of each evidence source were assessed as they pertain to searching in the context of a public health emergency, including the topics of indexed citations, the level of evidence of indexed citations, and specific usability features of each evidence source. Results: Thirteen evidence sources were assessed, of which four were newly created and nine were either pre-existing or adapted from previously existing resources. Evidence sources varied in topics indexed, level of evidence indexed, and specific searching functions. Conclusion: This paper offers insights into which features enabled systematic searching for the completion of rapid reviews to inform decision makers within 5–10 days. These findings provide guidance for knowledge management strategies and evidence infrastructures during future public health emergencies. [ABSTRACT FROM AUTHOR]

Background: Rapid timescales for the design and delivery of research were common during the COVID-19 pandemic. The recruitment and retention of healthcare workers (HCWs) as participants in research studies are notoriously challenging, but this was exacerbated during the pandemic by the unprecedented demand placed on the workforce. The SARS-CoV-2 Immunity and Reinfection Evaluation (SIREN study) is a prospective multicentre cohort study following HCWs in the UK. This paper discusses the strategies and challenges associated with recruitment and retention of HCW participants in Scotland., Methods: There were 44,546 HCWs recruited to the SIREN study, of whom 6,285 were recruited by research teams at ten different research sites in Scotland between October 2020 and March 2021. Information on target and actual sample size, availability of resource, recruitment rate, and recruitment and engagement strategies by site was collated from SIREN study documentation and discussions with local key SIREN site staff. Individual-level data from 6,153 HCW participants with ongoing consent for all data usage were also collated, including socio-demographic data and information on withdrawal (in first year) and opt-in to a study extension after one year. Factors associated with these outcomes were explored in logistic regression analyses., Results: Different recruitment strategies were used in each site according to local agreements, protocol and staff capacity, with the recruitment period ranging from 13 to 160 days. The locally-agreed recruitment target was met in four sites. The proportion of participants who withdrew in the first year ranged from 3.1 to 24.8% by site, while subsequent opt-in to a 12-month study extension ranged from 28.6 to 74.8%. The sites with the highest proportions of withdrawals were the same four sites with lowest proportions of opt-in. On an individual level, there was a lower level of retention among younger participants, and those from lower socio-economic backgrounds and minority ethnic groups., Conclusions: Site-specific factors including research-readiness likely had a significant influence on recruitment and retention, more so than the specific recruitment or retention strategies employed. Independent of site factors, individual-level variables influenced recruitment and retention, suggesting targeted strategies may be needed to promote research engagement among particular socio-demographic groups., (© 2024. The Author(s).)

Background: The failure of randomised controlled trials to adequately reflect areas of highest health need have been repeatedly highlighted. This has implications for the validity and generalisability of findings, for equity and efficiency, but also for research capacity-building. Rai et al. (BMC Med Res Methodol 21:80, 2021) recently argued that the poor alignment between UK clinical research activity (specifically multi-centre RCTs) and local prevalence of disease was, in part, the outcome of behaviour and decision-making by Chief Investigators involved in trial research. They argued that a shift in research culture was needed. Following our recent multi-site mixed methods evaluative study about NHS 111 online we identify some of the additional structural barriers to delivering health research "where populations with the most disease live", accounting for the Covid-19 disruption to processes and delivery.Methods: The NHS 111 study used a mixed-method research design, including interviews with healthcare staff and stakeholders within the primary, urgent and emergency health care system, and a survey of users and potential users of the NHS 111 online service. This paper draws on data collated by the research team during site identification and selection, as we followed an action research cycle of planning, action, observation and reflection. The process results were discussed among the authors, and grouped into the two themes presented.Results: We approached 22 primary and secondary care sites across England, successfully recruiting half of these. Time from initial approach to first participant recruitment in successful sites ranged from one to ten months. This paper describes frontline bureaucratic barriers to research delivery and recruitment in the local Clinical Research Network system and secondary care sites carrying large research portfolios, alongside the adaptive practices of research practitioners that mitigate these.Conclusions: This paper augments the recommendations of Rai et al., describing delays encountered during the COVID-19 pandemic, and suggesting in addition to cultural change, it may be additionally important to dismantle infrastructural barriers and improve support to research teams so they can conduct health research "where populations with the most disease live". [ABSTRACT FROM AUTHOR]

Background: COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance., Methods: We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic., Results: The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods., Conclusion: We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results., (© 2023. The Author(s).)

Background: Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques., Methods: We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power., Results: The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions., Conclusion: This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses., (© 2023. The Author(s).)

Background: Identifying mechanisms to maintain CBPR studies during an infectious disease pandemic is vital. The current paper describes the changes in methods and processes conducted within a CBPR mixed-methods study to a virtual setting during the novel coronavirus (COVID-19) pandemic., Method: The DC Community Organizing for Optimal Culinary Knowledge study with Heart (DC COOKS with Heart) was designed to assess the feasibility of a dietary behavior intervention among African-American adults that are at risk for cardiovascular disease (CVD). The study is under the umbrella of an ongoing CBPR study and community advisory board that facilitates community involvement in study design and promotes ongoing engagement with community members and leaders. The study population for D.C. COOKS with Heart consists of adult African-American individuals who live in two low-resource neighborhoods in Washington, D.C., which were impacted disproportionately by COVID. Eligible study participants who previously participated in the DC CHOC community-based studies were contacted to participate in Phase 1. The quantitative part of the mixed-methods included survey data collection., Results: Due to the pandemic, the mode of data collection for surveys changed from self-administered face-to-face to internet-based. All virtual study procedures were conducted between March and April, 2021. Anticipated benefits of the virtual setting included participant safety during the pandemic, ease of logistics for participants. Anticipated challenges included administration of electronic devices to participants, research team training, and potential threats to established trust related to the privacy and confidentiality of participants., Conclusion: The transition to a virtual setting for study procedures in a mixed-methods study was conducted successfully in terms of recruitment, retention of participants, and training of research team members. The virtual transition required established and ongoing engagement through the community advisory board and CBPR practices, institutional support through virtual research policies, collaborations with information technology-based teams, and equipment administration for the study., Trials Registration: NCT04305431 . Registered on March 12, 2020., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: The existing Z-test for uncertainty events does not give information about the measure of indeterminacy/uncertainty associated with the test., Methods: This paper introduces the Z-test for uncertainty events under neutrosophic statistics. The test statistic of the existing test is modified under the philosophy of the Neutrosophy. The testing process is introduced and applied to the Covid-19 data., Results: Based on the information, the proposed test is interpreted as the probability that there is no reduction in uncertainty of Covid-19 is accepted with a probability of 0.95, committing a type-I error is 0.05 with the measure of an indeterminacy 0.10. Based on the analysis, it is concluded that the proposed test is informative than the existing test. The proposed test is also better than the Z-test for uncertainty under fuzzy-logic as the test using fuzz-logic gives the value of the statistic from 2.20 to 2.42 without any information about the measure of indeterminacy. The test under interval statistic only considers the values within the interval rather than the crisp value., Conclusions: From the Covid-19 data analysis, it is found that the proposed Z-test for uncertainty events under the neutrosophic statistics is efficient than the existing tests under classical statistics, fuzzy approach, and interval statistics in terms of information, flexibility, power of the test, and adequacy., (© 2022. The Author(s).)

Background: Ring-based studies are a novel research design commonly used for research involving infectious diseases: contacts of newly infected individuals form a ring that is targeted for interventions (e.g., vaccine, post-exposure prophylaxis). Given the novelty of the research design, it is critical to obtain feedback from participants on their experiences with ring-based studies to help with the development of future trials. Methods: In 2021, we conducted 26 semi-structured interviews with adult participants of a COVID-19 ring-based post-exposure prophylaxis trial based in Canada. We applied a purposive sampling approach and electronically recruited participants who tested positive for COVID-19 (Index Cases) and either agreed or declined for the study team to contact their potentially exposed contacts. We also included individuals who participated in the trial after being potentially exposed to an Index Case (known as Ring Members), and those who declined to participate after potential exposure. The methodological design of semi-structured interviews allowed participants to share their opinions and experiences in the trial (e.g., elements they enjoyed and disliked regarding their participation in the study). Results: The majority of participants in our study were women (62%) and the average age was 37.3 years (SD = 13.2). Overall, participants reported being highly satisfied with partaking in the ring-based trial. Notably, no substantial complaints were voiced about the trial's design involving contact after exposure. The most common reason of satisfaction was the knowledge of potentially helping others by advancing knowledge for a greater cause (e.g., development of potential treatment to prevent SARS-CoV-2 infection). Other reasons were curiosity about participating in a trial, and an activity to fill free time during the pandemic. A central element of dislike was confusion about instructions with the trial (e.g., independent at home SARS-CoV-2 testing). Additionally, maintaining confidentiality was a crucial concern for participants, who sought assurance that their data would not be shared beyond the scope of the study. Conclusions: Our results have the potential to inform future research, including clinical trials such as ring-based studies, by incorporating insights from participants' experiences into the development of study protocols. Despite some protocol-related challenges, participants expressed high satisfaction, driven by the desire to advance science and potentially aid others. [ABSTRACT FROM AUTHOR]

Background: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment. [ABSTRACT FROM AUTHOR]

Abstract Background Rapid timescales for the design and delivery of research were common during the COVID-19 pandemic. The recruitment and retention of healthcare workers (HCWs) as participants in research studies are notoriously challenging, but this was exacerbated during the pandemic by the unprecedented demand placed on the workforce. The SARS-CoV-2 Immunity and Reinfection Evaluation (SIREN study) is a prospective multicentre cohort study following HCWs in the UK. This paper discusses the strategies and challenges associated with recruitment and retention of HCW participants in Scotland. Methods There were 44,546 HCWs recruited to the SIREN study, of whom 6,285 were recruited by research teams at ten different research sites in Scotland between October 2020 and March 2021. Information on target and actual sample size, availability of resource, recruitment rate, and recruitment and engagement strategies by site was collated from SIREN study documentation and discussions with local key SIREN site staff. Individual-level data from 6,153 HCW participants with ongoing consent for all data usage were also collated, including socio-demographic data and information on withdrawal (in first year) and opt-in to a study extension after one year. Factors associated with these outcomes were explored in logistic regression analyses. Results Different recruitment strategies were used in each site according to local agreements, protocol and staff capacity, with the recruitment period ranging from 13 to 160 days. The locally-agreed recruitment target was met in four sites. The proportion of participants who withdrew in the first year ranged from 3.1 to 24.8% by site, while subsequent opt-in to a 12-month study extension ranged from 28.6 to 74.8%. The sites with the highest proportions of withdrawals were the same four sites with lowest proportions of opt-in. On an individual level, there was a lower level of retention among younger participants, and those from lower socio-economic backgrounds and minority ethnic groups. Conclusions Site-specific factors including research-readiness likely had a significant influence on recruitment and retention, more so than the specific recruitment or retention strategies employed. Independent of site factors, individual-level variables influenced recruitment and retention, suggesting targeted strategies may be needed to promote research engagement among particular socio-demographic groups.

Background: To describe the methodology for conducting the CalScope study, a remote, population-based survey launched by the California Department of Public Health (CDPH) to estimate SARS-CoV-2 seroprevalence and understand COVID-19 disease burden in California. Methods: Between April 2021 and August 2022, 666,857 randomly selected households were invited by mail to complete an online survey and at-home test kit for up to one adult and one child. A gift card was given for each completed survey and test kit. Multiple customized REDCap databases were used to create a data system which provided task automation and scalable data management through API integrations. Support infrastructure was developed to manage follow-up for participant questions and a communications plan was used for outreach through local partners. Results: Across 3 waves, 32,671 out of 666,857 (4.9%) households registered, 6.3% by phone using an interactive voice response (IVR) system and 95.7% in English. Overall, 25,488 (78.0%) households completed surveys, while 23,396 (71.6%) households returned blood samples for testing. Support requests (n = 5,807) received through the web-based form (36.3%), by email (34.1%), and voicemail (29.7%) were mostly concerned with the test kit (31.6%), test result (26.8%), and gift card (21.3%). Conclusions: Ensuring a well-integrated and scalable data system, responsive support infrastructure for participant follow-up, and appropriate academic and local health department partnerships for study management and communication allowed for successful rollout of a large population-based survey. Remote data collection utilizing online surveys and at-home test kits can complement routine surveillance data for a state health department. [ABSTRACT FROM AUTHOR]

Background: Surveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys. Objective: To develop a new reweighting method for overlapping panel surveys affected by non-response. Methods: We chose the Healthcare and Social Survey which has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement. Results: Our reweighting methodological approach is the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. It is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health. Conclusion: The proposed method produces suitable estimators for both cross-sectional and longitudinal samples. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study. [ABSTRACT FROM AUTHOR]

Background: Preprints are increasingly used to disseminate research results, providing multiple sources of information for the same study. We assessed the consistency in effect estimates between preprint and subsequent journal article of COVID-19 randomized controlled trials. Methods: The study utilized data from the COVID-NMA living systematic review of pharmacological treatments for COVID-19 (covid-nma.com) up to July 20, 2022. We identified randomized controlled trials (RCTs) evaluating pharmacological treatments vs. standard of care/placebo for patients with COVID-19 that were originally posted as preprints and subsequently published as journal articles. Trials that did not report the same analysis in both documents were excluded. Data were extracted independently by pairs of researchers with consensus to resolve disagreements. Effect estimates extracted from the first preprint were compared to effect estimates from the journal article. Results: The search identified 135 RCTs originally posted as a preprint and subsequently published as a journal article. We excluded 26 RCTs that did not meet the eligibility criteria, of which 13 RCTs reported an interim analysis in the preprint and a final analysis in the journal article. Overall, 109 preprint–article RCTs were included in the analysis. The median (interquartile range) delay between preprint and journal article was 121 (73–187) days, the median sample size was 150 (71–464) participants, 76% of RCTs had been prospectively registered, 60% received industry or mixed funding, 72% were multicentric trials. The overall risk of bias was rated as 'some concern' for 80% of RCTs. We found that 81 preprint–article pairs of RCTs were consistent for all outcomes reported. There were nine RCTs with at least one outcome with a discrepancy in the number of participants with outcome events or the number of participants analyzed, which yielded a minor change in the estimate of the effect. Furthermore, six RCTs had at least one outcome missing in the journal article and 14 RCTs had at least one outcome added in the journal article compared to the preprint. There was a change in the direction of effect in one RCT. No changes in statistical significance or conclusions were found. Conclusions: Effect estimates were generally consistent between COVID-19 preprints and subsequent journal articles. The main results and interpretation did not change in any trial. Nevertheless, some outcomes were added and deleted in some journal articles. [ABSTRACT FROM AUTHOR]

Background: Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. Methods: We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank's High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. Results: In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). Conclusion: With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Understanding how SARS-CoV-2 infection impacts long-term patient outcomes requires identification of comparable persons with and without infection. We report the design and implementation of a matching strategy employed by the Department of Veterans Affairs' (VA) COVID-19 Observational Research Collaboratory (CORC) to develop comparable cohorts of SARS-CoV-2 infected and uninfected persons for the purpose of inferring potential causative long-term adverse effects of SARS-CoV-2 infection in the Veteran population. Methods: In a retrospective cohort study, we identified VA health care system patients who were and were not infected with SARS-CoV-2 on a rolling monthly basis. We generated matched cohorts within each month utilizing a combination of exact and time-varying propensity score matching based on electronic health record (EHR)-derived covariates that can be confounders or risk factors across a range of outcomes. Results: From an initial pool of 126,689,864 person-months of observation, we generated final matched cohorts of 208,536 Veterans infected between March 2020-April 2021 and 3,014,091 uninfected Veterans. Matched cohorts were well-balanced on all 37 covariates used in matching after excluding patients for: no VA health care utilization; implausible age, weight, or height; living outside of the 50 states or Washington, D.C.; prior SARS-CoV-2 diagnosis per Medicare claims; or lack of a suitable match. Most Veterans in the matched cohort were male (88.3%), non-Hispanic (87.1%), white (67.2%), and living in urban areas (71.5%), with a mean age of 60.6, BMI of 31.3, Gagne comorbidity score of 1.4 and a mean of 2.3 CDC high-risk conditions. The most common diagnoses were hypertension (61.4%), diabetes (34.3%), major depression (32.2%), coronary heart disease (28.5%), PTSD (25.5%), anxiety (22.5%), and chronic kidney disease (22.5%). Conclusion: This successful creation of matched SARS-CoV-2 infected and uninfected patient cohorts from the largest integrated health system in the United States will support cohort studies of outcomes derived from EHRs and sample selection for qualitative interviews and patient surveys. These studies will increase our understanding of the long-term outcomes of Veterans who were infected with SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Objectives: The main objective of this study is to evaluate the methodological quality and reporting quality of living systematic reviews (LSRs) on Coronavirus disease 2019 (COVID-19), while the secondary objective is to investigate potential factors that may influence the overall quality of COVID-19 LSRs. Methods: Six representative databases, including Medline, Excerpta Medica Database (Embase), Cochrane Library, China national knowledge infrastructure (CNKI), Wanfang Database, and China Science, Technology Journal Database (VIP) were systematically searched for COVID-19 LSRs. Two authors independently screened articles, extracted data, and then assessed the methodological and reporting quality of COVID-19 LSRs using the "A Measurement Tool to Assess systematic Reviews-2" (AMSTAR-2) tool and "Preferred Reporting Items for Systematic reviews and Meta-Analyses" (PRISMA) 2020 statement, respectively. Univariate linear regression and multivariate linear regression were used to explore eight potential factors that might affect the methodological quality and reporting quality of COVID-19 LSRs. Results: A total of 64 COVID-19 LSRs were included. The AMSTAR-2 evaluation results revealed that the number of "yes" responses for each COVID-19 LSR was 13 ± 2.68 (mean ± standard deviation). Among them, 21.9% COVID-19 LSRs were rated as "high", 4.7% as "moderate", 23.4% as "low", and 50% as "critically low". The evaluation results of the PRISMA 2020 statement showed that the sections with poor adherence were methods, results and other information. The number of "yes" responses for each COVID-19 LSR was 21 ± 4.18 (mean ± standard deviation). The number of included studies and registration are associated with better methodological quality; the number of included studies and funding are associated with better reporting quality. Conclusions: Improvement is needed in the methodological and reporting quality of COVID-19 LSRs. Researchers conducting COVID-19 LSRs should take note of the quality-related factors identified in this study to generate evidence-based evidence of higher quality. [ABSTRACT FROM AUTHOR]

Abstract Background Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. Methods We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. Results The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment’s effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. Conclusion This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

Abstract Background Identifying mechanisms to maintain CBPR studies during an infectious disease pandemic is vital. The current paper describes the changes in methods and processes conducted within a CBPR mixed-methods study to a virtual setting during the novel coronavirus (COVID-19) pandemic. Method The DC Community Organizing for Optimal Culinary Knowledge study with Heart (DC COOKS with Heart) was designed to assess the feasibility of a dietary behavior intervention among African-American adults that are at risk for cardiovascular disease (CVD). The study is under the umbrella of an ongoing CBPR study and community advisory board that facilitates community involvement in study design and promotes ongoing engagement with community members and leaders. The study population for D.C. COOKS with Heart consists of adult African-American individuals who live in two low-resource neighborhoods in Washington, D.C., which were impacted disproportionately by COVID. Eligible study participants who previously participated in the DC CHOC community-based studies were contacted to participate in Phase 1. The quantitative part of the mixed-methods included survey data collection. Results Due to the pandemic, the mode of data collection for surveys changed from self-administered face-to-face to internet-based. All virtual study procedures were conducted between March and April, 2021. Anticipated benefits of the virtual setting included participant safety during the pandemic, ease of logistics for participants. Anticipated challenges included administration of electronic devices to participants, research team training, and potential threats to established trust related to the privacy and confidentiality of participants. Conclusion The transition to a virtual setting for study procedures in a mixed-methods study was conducted successfully in terms of recruitment, retention of participants, and training of research team members. The virtual transition required established and ongoing engagement through the community advisory board and CBPR practices, institutional support through virtual research policies, collaborations with information technology-based teams, and equipment administration for the study. Trials registration NCT04305431 . Registered on March 12, 2020.

Background: In the context of the COVID-19 pandemic, social science research has required recruiting many prospective participants. Many researchers have explicitly taken advantage of widespread public interest in COVID-19 to advertise their studies. Leveraging this interest, however, risks creating unrepresentative samples due to differential interest in the topic. In this study, we investigate the design of survey recruitment materials with respect to the views of resultant participants.Methods: Within a pan-Canadian survey (stratified random mail sampling, n = 1969), the design of recruitment invitations to prospective respondents was experimentally varied, with some prospective respondents receiving COVID-specific recruitment messages and others receiving more general recruitment messages (described as research about health and health policy). All respondents participated, however, in the same survey, allowing comparison of both demographic and attitudinal features between these groups.Results: Respondents recruited via COVID-19 specific postcards were more likely to agree that COVID-19 is serious and believe that they were likely to contract COVID-19 compared to non-COVID respondents (odds = 0.71, p = 0.04; odds = 0.74, p = 0.03 respectively; comparing health to COVID-19 framed respondents). COVID-19 specific respondents were more likely to disagree that the COVID-19 threat was exaggerated compared to the non-COVID survey respondents (odds = 1.44, p = 0.02).Conclusions: COVID-19 recruitment framing garnered a higher response rate, as well as a sample with greater concern about coronavirus risks and impacts than respondents who received more neutrally framed recruitment materials. [ABSTRACT FROM AUTHOR]

Background: One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic.Methods: The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation.Results: By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021).Conclusions: This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities. [ABSTRACT FROM AUTHOR]

Background: The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or 'fixed' randomisation to allocate patients to treatments. We assessed the impact of implementing response adaptive randomisation within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP platform trial.Methods: Trial data was simulated to closely match the data for patients allocated to standard care, dexamethasone, hydroxychloroquine, or lopinavir-ritonavir in the RECOVERY trial from March-June 2020, representing four out of five arms tested throughout this period. Trials were simulated in both a two-arm trial setting using standard care and dexamethasone, and a four-arm trial setting utilising all above treatments. Two forms of fixed randomisation and two forms of response-adaptive randomisation were tested. In the two-arm setting, response-adaptive randomisation was implemented across both trial arms, whereas in the four-arm setting it was implemented in the three non-standard care arms only. In the two-arm trial, randomisation strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients' respiratory support level.Results: All response-adaptive randomisation strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific response-adaptive randomisation reduced mortality rates even further. In the two-arm trial, response-adaptive randomisation reduced statistical power compared to FR, with subgroup level adaptive randomisation exhibiting the largest power reduction. In the four-arm trial, response-adaptive randomisation increased statistical power in the dexamethasone arm but reduced statistical power in the lopinavir arm. Response-adaptive randomisation did not induce any meaningful bias in treatment effect estimates nor did it cause any inflation in the type 1 error rate.Conclusions: Using response-adaptive randomisation within RECOVERY could have increased the number of patients receiving the optimal COVID-19 treatment during the trial, while reducing the number of patients needed to attain the same study power as the original study. This would likely have reduced patient deaths during the trial and lead to dexamethasone being declared effective sooner. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question for the trials community to address. Response-adaptive randomisation deserves to be considered as a design feature in future trials of COVID-19 and other diseases. [ABSTRACT FROM AUTHOR]

Background: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation.Methods: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan.Results: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method.Conclusions: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time. [ABSTRACT FROM AUTHOR]

Background: Missing data prove troublesome in data analysis; at best they reduce a study's statistical power and at worst they induce bias in parameter estimates. Multiple imputation via chained equations is a popular technique for dealing with missing data. However, techniques for combining and pooling results from fitted generalized additive models (GAMs) after multiple imputation have not been well explored.Methods: We simulated missing data under MCAR, MAR, and MNAR frameworks and utilized random forest and predictive mean matching imputation to investigate a variety of rules for combining GAMs after multiple imputation with binary and normally distributed outcomes. We compared multiple pooling procedures including the "D2" method, the Cauchy combination test, and the median p-value (MPV) rule. The MPV rule involves simply computing and reporting the median p-value across all imputations. Other ad hoc methods such as a mean p-value rule and a single imputation method are investigated. The viability of these methods in pooling results from B-splines is also examined for normal outcomes. An application of these various pooling techniques is then performed on two case studies, one which examines the effect of elevation on a six-minute walk distance (a normal outcome) for patients with pulmonary arterial hypertension, and the other which examines risk factors for intubation in hospitalized COVID-19 patients (a dichotomous outcome).Results: In comparison to the results from generalized additive models fit on full datasets, the median p-value rule performs as well as if not better than the other methods examined. In situations where the alternative hypothesis is true, the Cauchy combination test appears overpowered and alternative methods appear underpowered, while the median p-value rule yields results similar to those from analyses of complete data.Conclusions: For pooling results after fitting GAMs to multiply imputed datasets, the median p-value is a simple yet useful approach which balances both power to detect important associations and control of Type I errors. [ABSTRACT FROM AUTHOR]

Background: Many clinical trial procedures were often undertaken in-person prior to the COVID-19 pandemic, which has resulted in adaptations to these procedures to enable trials to continue. The aim of this study was to understand whether the adaptations made to clinical trials by UK Clinical Trials Units (CTUs) during the pandemic have the potential to improve the efficiency of trials post-pandemic.Methods: This was a mixed methods study, initially involving an online survey administered to all registered UK CTUs to identify studies that had made adaptations due to the pandemic. Representatives from selected studies were qualitatively interviewed to explore the adaptations made and their potential to improve the efficiency of future trials. A literature review was undertaken to locate published evidence concerning the investigated adaptations. The findings from the interviews were reviewed by a group of CTU and patient representatives within a workshop, where discussions focused on the potential of the adaptations to improve the efficiency of future trials.Results: Forty studies were identified by the survey. Fourteen studies were selected and fifteen CTU staff were interviewed about the adaptations. The workshop included 15 CTU and 3 patient representatives. Adaptations were not seen as leading to direct efficiency savings for CTUs. However, three adaptations may have the potential to directly improve efficiencies for trial sites and participants beyond the pandemic: a split remote-first eligibility assessment, recruitment outside the NHS via a charity, and remote consent. There was a lack of published evidence to support the former two adaptations, however, remote consent is widely supported in the literature. Other identified adaptations may benefit by improving flexibility for the participant. Barriers to using these adaptations include the impact on scientific validity, limitations in the role of the CTU, and participant's access to technology.Conclusions: Three adaptations (a split remote-first eligibility assessment, recruitment outside the NHS via a charity, and remote consent) have the potential to improve clinical trials but only one (remote consent) is supported by evidence. These adaptations could be tested in future co-ordinated 'studies within a trial' (SWAT). [ABSTRACT FROM AUTHOR]

Background: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas.Methods: The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process.Results: The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude.Conclusions: Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. Methods: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. Results: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. Conclusions: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units. [ABSTRACT FROM AUTHOR]

Background: Rapid Advice Guidelines (RAG) provide decision makers with guidance to respond to public health emergencies by developing evidence-based recommendations in a short period of time with a scientific and standardized approach. However, the experience from the development process of a RAG has so far not been systematically summarized. Therefore, our working group will take the experience of the development of the RAG for children with COVID-19 as an example to systematically explore the methodology, advantages, and challenges in the development of the RAG. We shall propose suggestions and reflections for future research, in order to provide a more detailed reference for future development of RAGs.Result: The development of the RAG by a group of 67 researchers from 11 countries took 50 days from the official commencement of the work (January 28, 2020) to submission (March 17, 2020). A total of 21 meetings were held with a total duration of 48 h (average 2.3 h per meeting) and an average of 16.5 participants attending. Only two of the ten recommendations were fully supported by direct evidence for COVID-19, three recommendations were supported by indirect evidence only, and the proportion of COVID-19 studies among the body of evidence in the remaining five recommendations ranged between 10 and 83%. Six of the ten recommendations used COVID-19 preprints as evidence support, and up to 50% of the studies with direct evidence on COVID-19 were preprints.Conclusions: In order to respond to public health emergencies, the development of RAG also requires a clear and transparent formulation process, usually using a large amount of indirect and non-peer-reviewed evidence to support the formation of recommendations. Strict following of the WHO RAG handbook does not only enhance the transparency and clarity of the guideline, but also can speed up the guideline development process, thereby saving time and labor costs. [ABSTRACT FROM AUTHOR]

The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants. [ABSTRACT FROM AUTHOR]

Background: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients.Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date.Results: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations.Conclusions: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use. [ABSTRACT FROM AUTHOR]

Background: We consider cluster size data of SARS-CoV-2 transmissions for a number of different settings from recently published data. The statistical characteristics of superspreading events are commonly described by fitting a negative binomial distribution to secondary infection and cluster size data as an alternative to the Poisson distribution as it is a longer tailed distribution, with emphasis given to the value of the extra parameter which allows the variance to be greater than the mean. Here we investigate whether other long tailed distributions from more general extended Poisson process modelling can better describe the distribution of cluster sizes for SARS-CoV-2 transmissions.Methods: We use the extended Poisson process modelling (EPPM) approach with nested sets of models that include the Poisson and negative binomial distributions to assess the adequacy of models based on these standard distributions for the data considered.Results: We confirm the inadequacy of the Poisson distribution in most cases, and demonstrate the inadequacy of the negative binomial distribution in some cases.Conclusions: The probability of a superspreading event may be underestimated by use of the negative binomial distribution as much larger tail probabilities are indicated by EPPM distributions than negative binomial alternatives. We show that the large shared accommodation, meal and work settings, of the settings considered, have the potential for more severe superspreading events than would be predicted by a negative binomial distribution. Therefore public health efforts to prevent transmission in such settings should be prioritised. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, the scientific world is in urgent need for new evidence on the treatment of COVID patients. The reporting quality is crucial for transparent scientific publication. Concerns of data integrity, methodology and transparency were raised. Here, we assessed the adherence of observational studies comparing treatments of COVID 19 to the STROBE checklist in 2020.Methods: Design: We performed a retrospective, cross-sectional study.Setting: We conducted a systematic literature search in the Medline database. This study was performed at the RWTH Aachen University Hospital, Department of Anaesthesiology Participants: We extracted all observational studies on the treatment of COVID-19 patients from the year 2020.Main Outcome Measures: The adherence of each publication to the STROBE checklist items was analysed. The journals' impact factor (IF), the country of origin, the kind of investigated treatment and the month of publication were assessed.Results: We analysed 147 observational studies and found a mean adherence of 45.6% to the STROBE checklist items. The percentage adherence per publication correlated significantly with the journals' IF (point estimate for the difference between 1st and 4th quartile 11.07%, 95% CI 5.12 to 17.02, p < 0.001). U.S. American authors gained significantly higher adherence to the checklist than Chinese authors, mean difference 9.10% (SD 2.85%, p = 0.023).Conclusions: We conclude a poor reporting quality of observational studies on the treatment of COVID-19 throughout the year 2020. A considerable improvement is mandatory. [ABSTRACT FROM AUTHOR]