Explore the vast range of titles available.

Background Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Each Indian state has its own mechanism for reporting COVID-19 data, and the quality of their reporting has not been systematically evaluated. We present a comprehensive assessment of the quality of COVID-19 data reporting done by the Indian state governments between 19 May and 1 June, 2020. Methods We designed a semi-quantitative framework with 45 indicators to assess the quality of COVID-19 data reporting. The framework captures four key aspects of public health data reporting – availability, accessibility, granularity, and privacy. We used this framework to calculate a COVID-19 Data Reporting Score (CDRS, ranging from 0–1) for each state. Results Our results indicate a large disparity in the quality of COVID-19 data reporting across India. CDRS varies from 0.61 (good) in Karnataka to 0.0 (poor) in Bihar and Uttar Pradesh, with a median value of 0.26. Ten states do not report data stratified by age, gender, comorbidities or districts. Only ten states provide trend graphics for COVID-19 data. In addition, we identify that Punjab and Chandigarh compromised the privacy of individuals under quarantine by publicly releasing their personally identifiable information. The CDRS is positively associated with the state’s sustainable development index for good health and well-being (Pearson correlation: r =0.630, p =0.0003). Conclusions Our assessment informs the public health efforts in India and serves as a guideline for pandemic data reporting. The disparity in CDRS highlights three important findings at the national, state, and individual level. At the national level, it shows the lack of a unified framework for reporting COVID-19 data in India, and highlights the need for a central agency to monitor or audit the quality of data reporting done by the states. Without a unified framework, it is difficult to aggregate the data from different states, gain insights, and coordinate an effective nationwide response to the pandemic. Moreover, it reflects the inadequacy in coordination or sharing of resources among the states. The disparate reporting score also reflects inequality in individual access to public health information and privacy protection based on the state of residence.

Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (\"why\"), content and data (\"what\"), and analyses and displays (\"how\" they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are \"close to home\"; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified.

The 2019 coronavirus disease (COVID-19) pandemic increased efforts for rapid data sharing and dissemination among researchers as well as to data repositories. Researchers and studies prioritized data sharing, which increased understanding of SARS-CoV-2's pathology. Eventually, this effort to maximize collaboration and data dissemination, led to the development of mRNA vaccines. This successful effort has highlighted the importance of data sharing and the implementation of data management policies, including the National Institutes of Health's (NIH) Data Sharing Policy of 2023. Moreover, programs such as the National Institute of Allergy and Infectious Diseases (NIAID) funded Collaborative Influenza Vaccine Innovation Centers (CIVICs), have beta-tested this policy, with the help of the Statistical, Data Management and Coordination Center (SDMCC) and its data standards, and deemed it useful. However, the process has also initiated pertinent discussion on potential improvements and optimizations for the future of data sharing. Here, I use the CIVICs data sharing reporting standards and process as a data sharing example, and suggest logistical improvements to propose a better-equipped model for the vaccinology community.

Background The COVID-19 pandemic has evolved into one of the most impactful health crises in modern history, compelling researchers to explore innovative ways to efficiently collect public health data in a timely manner. Social media platforms have been explored as a research recruitment tool in other settings; however, their feasibility for collecting representative survey data during infectious disease epidemics remain unexplored. Objectives This study has two aims 1) describe the methodology used to recruit a nationwide sample of adults residing in the United States (U.S.) to participate in a survey on COVID-19 knowledge, beliefs, and practices, and 2) outline the preliminary findings related to recruitment, challenges using social media as a recruitment platform, and strategies used to address these challenges. Methods An original web-based survey informed by evidence from past literature and validated scales was developed. A Facebook advertisement campaign was used to disseminate the link to an online Qualtrics survey between March 20–30, 2020. Two supplementary male-only and racial minority- targeted advertisements were created on the sixth and tenth day of recruitment, respectively, to address issues of disproportionate female- and White-oriented gender- and ethnic-skewing observed in the advertisement’s reach and response trends. Results In total, 6602 participant responses were recorded with representation from all U.S. 50 states, the District of Columbia, and Puerto Rico. The advertisements cumulatively reached 236,017 individuals and resulted in 9609 clicks (4.07% reach). Total cost of the advertisement was $906, resulting in costs of $0.09 per click and $0.18 per full response (completed surveys). Implementation of the male-only advertisement improved the cumulative percentage of male respondents from approximately 20 to 40%. Conclusions The social media advertisement campaign was an effective and efficient strategy to collect large scale, nationwide data on COVID-19 within a short time period. Although the proportion of men who completed the survey was lower than those who didn’t, interventions to increase male responses and enhance representativeness were successful. These findings can inform future research on the use of social media recruitment for the rapid collection of survey data related to rapidly evolving health crises, such as COVID-19.

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

The Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) conduct post-licensure vaccine safety monitoring using the Vaccine Adverse Event Reporting System (VAERS), a spontaneous (or passive) reporting system. This means that after a vaccine is approved, CDC and FDA continue to monitor safety while it is distributed in the marketplace for use by collecting and analyzing spontaneous reports of adverse events that occur in persons following vaccination. Various methods and statistical techniques are used to analyze VAERS data, which CDC and FDA use to guide further safety evaluations and inform decisions around vaccine recommendations and regulatory action. VAERS data must be interpreted with caution due to the inherent limitations of passive surveillance. VAERS is primarily a safety signal detection and hypothesis generating system. Generally, VAERS data cannot be used to determine if a vaccine caused an adverse event. VAERS data interpreted alone or out of context can lead to erroneous conclusions about cause and effect as well as the risk of adverse events occurring following vaccination. CDC makes VAERS data available to the public and readily accessible online. We describe fundamental vaccine safety concepts, provide an overview of VAERS for healthcare professionals who provide vaccinations and might want to report or better understand a vaccine adverse event, and explain how CDC and FDA analyze VAERS data. We also describe strengths and limitations, and address common misconceptions about VAERS. Information in this review will be helpful for healthcare professionals counseling patients, parents, and others on vaccine safety and benefit-risk balance of vaccination.

During the COVID-19 pandemic, vaccines were rapidly developed, but some potential adverse drug reactions (ADRs) were still undetected at the time of market authorization. The analysis of ADR reports received through the spontaneous reporting system (SRS) remains the cornerstone for safety signal detection. A limitation is that reporting ADRs is voluntary. In this study the added value of electronic health records (EHRs) was explored as an additional source to spontaneous reports, to strengthen safety signal detection concerning COVID-19 vaccines. The electronic health record adverse event (EHR-AE) search method was developed to enable targeted searches in EHRs using a natural language processing (NLP) tool with text-mining functionalities to identify additional potential cases. Searches were performed in EHRs of two Dutch hospitals concerning several established and non-established (potential) ADRs associated with COVID-19 vaccines. Identified cases were reported to Lareb and analyzed in addition to spontaneous reports for safety signal detection. Thirteen searches were conducted between January 1, 2023, and December 1, 2023, concerning different (potential) ADRs associated with COVID-19 vaccines. For 6 associations at least 1 case was identified, resulting in a total of 41 additional cases reported to Lareb. These cases contributed to the detection of two safety signals concerning COVID-19 vaccines. Two safety signals could have been detected approximately 18 and 2 months earlier, if the EHR-AE method had been implemented during the COVID-19 pandemic. The EHR-AE search method can strengthen and accelerate the signal detection process for potential ADRs associated with COVID-19 vaccination. Future studies should expand to more hospitals, aiming to further evaluate the added value of the method, including for which drug-ADR associations the EHR-AE search method is most beneficial. •The SRS is vital for pharmacovigilance, but its voluntary nature delays safety signal detection.•The EHR-AE search method was developed to find additional ADR cases in Dutch hospital EHRs using NLP.•The EHR-AE search method demonstrated that additional cases can be identified in EHRs.•Identified cases contributed to the detection of safety signals concerning COVID-19 vaccines.•The EHR-AE method can accelerate signal detection.

In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scripts at https://osf.io/renxy/ .

On March 8, 2020, there was a 650% increase in Twitter retweets using the term “Chinese virus” and related terms. On March 9, there was an 800% increase in the use of these terms in conservative news media articles. Using data from non-Asian respondents of the Project Implicit “Asian Implicit Association Test” from 2007–2020 (n = 339,063), we sought to ascertain if this change in media tone increased bias against Asian Americans. Local polynomial regression and interrupted time-series analyses revealed that Implicit Americanness Bias—or the subconscious belief that European American individuals are more “American” than Asian American individuals—declined steadily from 2007 through early 2020 but reversed trend and began to increase on March 8, following the increase in stigmatizing language in conservative media outlets. The trend reversal in bias was more pronounced among conservative individuals. This research provides evidence that the use of stigmatizing language increased subconscious beliefs that Asian Americans are “perpetual foreigners.” Given research that perpetual foreigner bias can beget discriminatory behavior and that experiencing discrimination is associated with adverse mental and physical health outcomes, this research sounds an alarm about the effects of stigmatizing media on the health and welfare of Asian Americans.

Background The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research. Methods We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease). For each publication, we recorded publication date, publication type, number of authors, whether the publication was related to COVID-19, whether the publication was based on a case series, and the number of patients included in the study if the publication was based on a case report or a case series. We estimated the publication dynamics with a locally estimated scatterplot smoothing method. A Natural Language Processing algorithm was designed to calculate the number of authors for each publication. We simulated the number of non-COVID-19 studies that could have been published during the pandemic by extrapolating the publication dynamics of 2019 to 2020, and comparing the expected number to the observed number of studies. Results Among the 22,525 studies assessed, 6319 met the inclusion criteria, of which 1022 (16.2%) were related to COVID-19 research. A dramatic increase in the number of publications in general journals was observed from February to April 2020 from a weekly median number of publications of 4.0 (IQR: 2.8–5.5) to 19.5 (IQR: 15.8–24.8) ( p  < 0.001), followed afterwards by a pattern of stability with a weekly median number of publications of 10.0 (IQR: 6.0–14.0) until December 2020 ( p  = 0.045 in comparison with April). Two prototypical editorial strategies were found: 1) journals that maintained the volume of non-COVID-19 publications while integrating COVID-19 research and thus increased their overall scientific production, and 2) journals that decreased the volume of non-COVID-19 publications while integrating COVID-19 publications. We estimated using simulation models that the COVID pandemic was associated with a 18% decrease in the production of non-COVID-19 research. We also found a significant change of the publication type in COVID-19 research as compared with non-COVID-19 research illustrated by a decrease in the number of original articles, (47.9% in COVID-19 publications vs 71.3% in non-COVID-19 publications, p  < 0.001). Last, COVID-19 publications showed a higher number of authors, especially for case reports with a median of 9.0 authors (IQR: 6.0–13.0) in COVID-19 publications, compared to a median of 4.0 authors (IQR: 3.0–6.0) in non-COVID-19 publications ( p  < 0.001). Conclusion In this meta-research gathering publications from high-impact medical journals, we have shown that the dramatic rise in COVID-19 publications was accompanied by a substantial decrease of non-COVID-19 research. Meta-research registration https://osf.io/9vtzp/ .