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Abstract Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

BackgroundThe COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. ObjectiveThis paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in scientific communication. MethodsAccesoCovid.com was developed through a partnership between the University of California, San Francisco (UCSF) and Universidad Nacional Autónoma de México (UNAM). The website’s performance and user engagement were evaluated using Google Analytics over a span of 2 years. Key metrics included user language preference, geographical distribution, and site traffic. The website summarized and translated 1032 articles on various COVID-19 topics, such as “Pharmaceutical Interventions and Vaccines.” ResultsFrom February 2021 to February 2023, the platform attracted 57,000 users. Of the 43,000 unique new visitors, 84.2% (n=36,219) hailed from Spanish-speaking regions. The majority accessed the site organically through search engines, with 88.4% (n=38,000) of users arriving this way, while 5000 (11.6%) users accessed the site directly. Most users (n=30,894, 72.1%) preferred the Spanish version of the site. The website’s most accessed category was “Pharmaceutical Interventions and Vaccines,” followed by “Clinical Presentation and Management” and “Mental Health.” Regarding language distribution, 72.1% (n=30,894) of users primarily used Spanish; 21.4% (n=9215) used English; and 6.7% (n=2891) spoke other languages, including Portuguese, Chinese, and German. Geographically, the website attracted visitors from 179 countries, with the highest visitor counts from Mexico (n=12,342, 28.7%), Spain (n=6405, 14.9%), the United States (n=4416, 10.3%), and Peru (n=3821, 8.9%). ConclusionsAccesoCovid.com successfully bridged a critical language gap in the dissemination of COVID-19 research. Its success underscores the pressing need for multilingual scientific resources. The platform demonstrated significant user engagement and reach, particularly in Spanish-speaking countries. This highlights the potential for similar platforms to ensure equitable access to scientific knowledge across diverse linguistic communities. Future efforts should focus on expanding to other languages and conducting formal evaluations to enhance user satisfaction and impact.

Introduction Causal methods have been adopted and adapted across health disciplines, particularly for the analysis of single studies. However, the sample sizes necessary to best inform decision-making are often not attainable with single studies, making pooled individual-level data analysis invaluable for public health efforts. Researchers commonly implement causal methods prevailing in their home disciplines, and how these are selected, evaluated, implemented and reported may vary widely. To our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies leveraging individual-level data pooled from several studies. We undertake this review to uncover patterns in the implementation and reporting of causal methods used across disciplines in research focused on health outcomes. We will investigate variations in methods to infer causality used across disciplines, time and geography and identify gaps in reporting of methods to inform the development of reporting standards and the conversation required to effect change.Methods and analysis We will search four databases (EBSCO, Embase, PubMed, Web of Science) using a search strategy developed with librarians from three universities (Heidelberg University, Harvard University, and University of California, San Francisco). The search strategy includes terms such as ‘pool*’, ‘harmoniz*’, ‘cohort*’, ‘observational’, variations on ‘individual-level data’. Four reviewers will independently screen articles using Covidence and extract data from included articles. The extracted data will be analysed descriptively in tables and graphically to reveal the pattern in methods implementation and reporting. This protocol has been registered with PROSPERO (CRD42020143148).Ethics and dissemination No ethical approval was required as only publicly available data were used. The results will be submitted as a manuscript to a peer-reviewed journal, disseminated in conferences if relevant, and published as part of doctoral dissertations in Global Health at the Heidelberg University Hospital.

IntroductionPooling (or combining) and analysing observational, longitudinal data at the individual level facilitates inference through increased sample sizes, allowing for joint estimation of study- and individual-level exposure variables, and better enabling the assessment of rare exposures and diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in the health sciences in recent years. The adoption of so-called "causal" methods to account for both/either measured and/or unmeasured confounders is an important addition to the methodological toolkit for understanding the distribution, progression, and consequences of infectious diseases (IDs) and interventions on IDs. In the face of the Covid-19 pandemic and in the absence of systematic randomization of exposures or interventions, the value of these methods is even more apparent. Yet to our knowledge, no studies have assessed how causal methods involving pooling individual-level, observational, longitudinal data are being applied in ID-related research. In this systematic review, we assess how these methods are used and reported in ID-related research over the last 10 years. Findings will facilitate evaluation of trends of causal methods for ID research and lead to concrete recommendations for how to apply these methods where gaps in methodological rigor are identified.Methods and analysisWe will apply MeSH and text terms to identify relevant studies from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, and Web of Science. Eligible studies are those that apply causal methods to account for confounding when assessing the effects of an intervention or exposure on an ID-related outcome using pooled, individual-level data from 2 or more longitudinal, observational studies. Titles, abstracts, and full-text articles, will be independently screened by two reviewers using Covidence software. Discrepancies will be resolved by a third reviewer. This systematic review protocol has been registered with PROSPERO (CRD42020204104).