VE&VOC Long Covid Infographic

Long COVID Infographic

VE&VOC Infographic - Long COVID results

We have summarized our long outcome results from our evidence synthesis on vaccine effectiveness against of concern in an infographic that provides a plain language summary. This infographic was developed in collaboration with a citizen partner team and aims to make our findings more accessible.

You can access the PDF version here:

VE&VOC Infographic (English PDF version)
VE&VOC Infographic (English PDF, Large text version)

Glossary of Terms

Long Covid: New or persistent symptoms more than 12 weeks after COVID-19 infection. Long Covid is also referred to as Post-COVID Condition (PCC) or Post-Acute Sequelae of SARS-CoV-2 Infection (PASC).
Variant of Concern (VOC): A virus variant that increases disease spread or severity, or decreases the effectiveness of diagnostic tests, vaccines, and therapeutics, compared to previous variants.
Primary series: Refers to the initial sequence of vaccine doses that are required to provide full protection. Depending on the vaccine, the primary series may consist of one or two doses.
Booster doses: Additional doses of a vaccine that are given after the initial primary series to help maintain and extend the protection provided by the vaccine.
Vaccine Effectiveness: In this report, vaccine effectiveness is a measure of how well vaccines protect against long COVID outcomes between fully vaccinated vs unvaccinated and booster vs primary series.
Comparators: The different groups or options being compared in a study to see how they affect the outcomes. In this report, we’re comparing different numbers of vaccine doses.
Analysis: In research, an analysis refers to the process of examining data to draw meaningful conclusions. Some studies conduct multiple analyses, whereby researchers use different methods or approaches to evaluate multiple relationships in the same set of data.

Visual Summary of Results

Figure 1. Forest plot of effect estimates with 95% confidence intervals for Post-Acute Sequelae of SARS-CoV-2 infection (PASC) by number of doses.

Tips for reading a forest plot (adapted from source)

What is a forest plot?

The forest plot is a key way researchers can summarise data from multiple research findings in a single image.

Tips on how to interpret a forest plot

Look for the diamond shape at the bottom of the figure, which shows the overall analysis's effect size. The width of the diamond represents the amount of uncertainty surrounding the estimated effect size. The diamonds at the bottom of the subgroups represent the overall effect size for those subgroups (in this case, the effect of two doses compared to no vaccination or three doses compared to no vaccination).
The horizontal lines stretching from each study show the confidence ranges for each research's effect sizes. If the line includes one, it signifies that there is insufficient data to demonstrate a meaningful effect from the intervention or treatment under consideration (vaccination).
Examine the findings for outliers or discrepancies, which may be indicated by horizontal lines that are substantially longer or shorter than the rest. This might imply that the findings of one study are inconsistent with those of others, which could have an impact on the overall conclusion.
Take into account the general direction of the effect sizes. If most of the lines are to the left of one, it indicates that the intervention or treatment had a favorable effect. In this case, it means that vaccination is favourable for preventing overall PASC. A negative influence is shown if most of the lines are to the right of one (those who receive vaccination are more likely to develop PASC).
Consider the magnitude of each study, as indicated by the weight allocated to each study in the analysis. Larger, better-designed research may have a higher influence on the final finding than smaller ones.

Citation

Dahroug, L., Shaver, N., Katz, M., Asamoah, G. D., McGuire, M., Linkins, L. A., Abdelkader, W., Bennett, A., Hughes, S. E., Smith, M., Begin, M., Coyle, D., Piggott, T., Kagina, B., Welch, V., Colijn, C., Earn, D., El Emam, K., Heffernan, J., O’Brien, S., Wilson, K., Collins, E., Navarro, T., Beyene, J., Boutron, I., Bowdish, D., Cooper, C., Costa, A., Curran, J., Griffith, L., Hsu, A., Grimshaw, J., Langlois, M. A., Li, X., Pham-Huy, A., Raina, P., Rubini, M., Thabane, L., Wang, H., Xu, L., Brouwers, M., Horsley, T., Lavis, J., Iorio, A., Little, J. Living evidence synthesis on variants of concern and COVID-19 vaccine effectiveness.

How can I find more information about this project?

View protocol on the Open Science Framework: https://osf.io/zn2ky

PROSPERO registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=359790

Open Science Framework registration: https://osf.io/qacw4/

Report #1

Report #2