Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
-
SciScore for 10.1101/2021.09.30.21264337: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Evidence was integrated using facilitated expert elicitation, with subject matter experts (JL, AB, KRS, AKE, CLL, Thrombosis & Haemostasis Society of Australia and New Zealand [THANZ]) working closely with experienced BN modellers (HJM, KM, CLL). ABsuggested: RRID:BDSC_203)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our results should be interpreted …
SciScore for 10.1101/2021.09.30.21264337: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources Evidence was integrated using facilitated expert elicitation, with subject matter experts (JL, AB, KRS, AKE, CLL, Thrombosis & Haemostasis Society of Australia and New Zealand [THANZ]) working closely with experienced BN modellers (HJM, KM, CLL). ABsuggested: RRID:BDSC_203)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our results should be interpreted considering the model’s limitations. There are uncertainties associated with some of our model inputs, either because of limited data, or the use of data from other countries. As illustrated with the anomaly regarding the risk of dying in younger persons, model outputs should be considered as broad estimates rather than exact risks, but estimates can be improved over time as more data become available. Our model provides population level estimates and does not consider individual risks such as behaviour and comorbidities. We plan to develop future models that include the individual’s comorbidities, similar to the QCOVID tool [44] but specific for the Australian context. In the results provided, we have assumed that 100% of infections were from the delta variant. Assumptions of age distribution of delta cases (if unvaccinated) were obtained from data during the early stages of the delta outbreak in NSW from June 2021. While vaccination rates were relatively low then, older ages had higher vaccine coverage so infection rates for delta may have been underestimated in these groups. Data on CVST and PVT were obtained from studies outside Australia and may not reflect the local experience. The current model focuses on fatalities from COVID-19, TTS, and atypical blood clots, but does not consider other risks (e.g., adverse events) or other benefits (e.g., cases of severe COVID prevented, or broader societal benefits). Our model was not parameterised...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
