Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

Link: https://jamanetwork.com/journals/jama/fullarticle/2818746?guestAccessKey=66ec96e3-d156-46cf-928b-ff8b2a8fc35e&utm_source=silverchair&utm_medium=email&utm_campaign=content_max-jamainternalmedicine&utm_content=olf&utm_term=051324&utm_adv=000004014036

Additional editors’ note: https://jamanetwork.com/journals/jama/fullarticle/2818747?guestAccessKey=8b28cc16-c1e5-4a09-bec6-1f77abfe98db&utm_source=silverchair&utm_medium=email&utm_campaign=content_max-jamainternalmedicine&utm_content=olf&utm_term=051324&utm_adv=000004014036

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Abstract:

Importance  Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.

Observations  A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?

Conclusions and Relevance  Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

Author(s): Issa J. Dahabreh, MD, ScD1,2,3,4,5Kirsten Bibbins-Domingo, PhD, MD, MAS6,7,8

Publication Date: 9 May 2024

Publication Site: JAMA

doi:10.1001/jama.2024.7741

Reports of COVID-19 Vaccine Adverse Events in Predominantly Republican vs Democratic States

Link: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2816958?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=032924

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Abstract:

Importance  Antivaccine sentiment is increasingly associated with conservative political positions. Republican-inclined states exhibit lower COVID-19 vaccination rates, but the association between political inclination and reported vaccine adverse events (AEs) is unexplored.

Objective  To assess whether there is an association between state political inclination and the reporting rates of COVID-19 vaccine AEs.

Design, Setting, and Participants  This cross-sectional study used the AE reports after COVID-19 vaccination from the Vaccine Adverse Event Reporting System (VAERS) database from 2020 to 2022, with reports after influenza vaccines from 2019 to 2022 used as a reference. These reports were examined against state-level percentage of Republican votes in the 2020 US presidential election.

Exposure  State-level percentage of Republican votes in the 2020 US presidential election.

Main Outcomes and Measures  Rates of any AE among COVID-19 vaccine recipients, rates of any severe AE among vaccine recipients, and the proportion of AEs reported as severe.

Results  A total of 620 456 AE reports (mean [SD] age of vaccine recipients, 51.8 [17.6] years; 435 797 reports from women [70.2%]; a vaccine recipient could potentially file more than 1 report, so reports are not necessarily from unique individuals) for COVID-19 vaccination were identified from the VAERS database. Significant associations between state political inclination and state AE reporting were observed for all 3 outcomes: a 10% increase in Republican voting was associated with increased odds of AE reports (odds ratio [OR], 1.05; 95% CI, 1.05-1.05; P < .001), severe AE reports (OR, 1.25; 95% CI, 1.24-1.26; P < .001), and the proportion of AEs reported as severe (OR, 1.21; 95% CI, 1.20-1.22; P < .001). These associations were seen across all age strata in stratified analyses and were more pronounced among older subpopulations.

Conclusions and Relevance  This cross-sectional study found that the more states were inclined to vote Republican, the more likely their vaccine recipients or their clinicians reported COVID-19 vaccine AEs. These results suggest that either the perception of vaccine AEs or the motivation to report them was associated with political inclination.

Author(s):David A. Asch, MD, MBA1,2; Chongliang Luo, PhD3; Yong Chen, PhD2,4,5Author(s):

Publication Date: 29 Mar 2024

Publication Site: JAMA Network Open