Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BACKGROUND

Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.

METHODS

We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.

RESULTS

Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.

CONCLUSION

Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Leveraging the Million Veteran Program Infrastructure and Data for a Rapid Research Response to COVID-19

Background

The Veterans Health Administration Office of Research and Development (ORD) played a key role in the federal government's response to the COVID-19 pandemic. The ORD effectively leveraged existing resources to answer questions related to the SARS-CoV-2 virus and COVID-19.

Observations

When the COVID-19 pandemic hit in 2020, the Million Veteran Program (MVP), one of the largest genomic cohorts in the world, extended the centralized recruitment and enrollment infrastructure to develop a COVID-19 research volunteer registry to assist enrollment in the vaccine and treatment trials in which the US Department of Veterans Affairs (VA) participated. In addition, the MVP allowed for new data collection and a large genomic cohort to understand host contributions to COVID-19. This article describes ways the MVP contributed to the VA's rapid research response to COVID-19. Several host genetic factors believed to play a role in the development and severity of COVID-19 were identified. Furthermore, existing MVP partnerships with other federal agencies, particularly with the Department of Energy, were leveraged to improve understanding and management of COVID-19.

Conclusions

A previously established enterprise approach and research infrastructure were essential to the VA's successful and timely COVID-19 research response. This infrastructure not only supported rapid recruitment in vaccine and treatment trials, but also leveraged the unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations. Extending the models that VA research applied to the federal government at large and establishing centralized resources for shared or federated data analyses across federal agencies will better equip the nation to respond to future public health crises.

Unit information prior for incorporating real-world evidence into randomized controlled trials

Randomized controlled trials (RCTs) have been widely recognized as the gold standard to infer the treatment effect in clinical research. Recently, there has been growing interest in enhancing and complementing the result in an RCT by integrating real-world evidence from observational studies. The unit information prior (UIP) is a newly proposed technique that can effectively borrow information from multiple historical datasets. We extend this generic approach to synthesize the non-randomized evidence into a current RCT. Not only does the UIP only require summary statistics published from observational studies for ease of implementation, but it also has clear interpretations and can alleviate the potential bias in the real-world evidence via weighting schemes. Extensive numerical experiments show that the UIP can improve the statistical efficiency in estimating the treatment effect for various types of outcome variables. The practical potential of our UIP approach is further illustrated with a real trial of hydroxychloroquine for treating COVID-19 patients.

Novel antiviral effects of chloroquine, hydroxychloroquine, and green tea catechins against SARS CoV-2 main protease (Mpro) and 3C-like protease for COVID-19 treatment

OBJECTIVES

Coronaviruses are globally emerging viruses that threaten our health care systems and have become a popular pandemic around the world. This causes a sudden rise in positive coronavirus cases and related deaths to occur worldwide, representing a significant health hazard to humans and the economy.

METHODS

We examined predominantly catechins of green tea include epigallocatechin-3-gallate (EGCG), epicatechin-3-gallate (ECG), and drugs of chloroquine (CQ), and hydroxychloroquine (HCQ) appearing to reveal anti-viral activities. Data were collected from PubMed, Google Scholar, and Science Direct databases. To investigate the role of antiviral effects (CQ and HCQ), green tea catechins, beneficial use of convalescent plasma; covaxin in COVID-19 patients faced a dangerous healthiness issue. Computational docking analysis has been used for this purpose.

RESULTS

The lead compounds are EGCG and ECG act as potential inhibitors bind to the active site region of the HKU4-CoV 3CL protease and M-Pro protease enzymes of coronavirus. Conclusions: SARS-COV-2 is a pathogen of substantial vigour concern and the review unveils the role of catechins associated with many viral diseases. We suggested that the function of green tea catechins, novel drugs of CQ, and HCQ exhibit antiviral activities against positive-sense single-stranded RNA viruses (CoVs).

Antirheumatic Drug Intake Influence on Occurrence of COVID-19 Infection in Ambulatory Patients with Immune-Mediated Inflammatory Diseases: A Cohort Study

Introduction

We aimed to study the prevalence of a history of COVID-19 infection among patients suffering from systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren’s syndrome (SjS) or psoriatic arthritis (PsA), and the potential influence of long-term hydroxychloroquine (HCQ) intake.

Methods

We performed an observational monocentric cohort study at the Adolphe de Rothschild Foundation Hospital ophthalmology division (Paris, France). Electronic medical records (EMR) data were searched for keywords associated with SLE, RA, SjS, or PsA. Patients were contacted by phone and were interviewed using a standardized questionnaire. The primary outcome was the occurrence of a positive COVID-19 test result during the study period. We determined the adjusted association between various antirheumatic drugs intake, COVID-19 risk factors, and occurrence of COVID-19 using a logistic regression model. This study is registered on ClinicalTrials.gov (Identifier: NCT04345159).

Results

Patients were recruited between Apr 17, 2020, and Apr 30, 2020 and were recontacted between Oct 6, 2020, and Nov 2, 2020. A total of 569 patients were included, of whom 459 patients were eligible for data analysis. One hundred and eighty-one patients were treated with long-term HCQ and 18 patients had tested positive for COVID-19. No antirheumatic drug intake, including HCQ intake, was significantly associated with an increased or decreased risk of developing COVID-19 infection.

Conclusions

No antirheumatic drug intake was associated with an increased or decreased risk of developing COVID-19 infection in our cohort of patients suffering from immune-mediated inflammatory diseases.