Cost-effectiveness of a whole-area testing pilot of asymptomatic SARS-CoV-2 infections with lateral flow devices: a modelling and economic analysis study
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Abstract
Background
Mass community testing for SARS-CoV-2 by lateral flow devices (LFDs) aims to reduce prevalence in the community. However its effectiveness as a public heath intervention is disputed.
Method
Data from a mass testing pilot in the Borough of Merthyr Tydfil in late 2020 was used to model cases, hospitalisations, ICU admissions and deaths prevented. Further economic analysis with a healthcare perspective assessed cost-effectiveness in terms of healthcare costs avoided and QALYs gained.
Results
An initial conservative estimate of 360 (95% CI: 311–418) cases were prevented by the mass testing, representing a would-be reduction of 11% of all cases diagnosed in Merthyr Tydfil residents during the same period. Modelling healthcare burden estimates that 24 (16—36) hospitalizations, 5 (3–6) ICU admissions and 15 (11–20) deaths were prevented, representing 6.37%, 11.1% and 8.2%, respectively of the actual counts during the same period. A less conservative, best-case scenario predicts 2333 (1764–3115) cases prevented, representing 80% reduction in would-be cases. Cost -effectiveness analysis indicates 108 (80–143) QALYs gained, an incremental cost-effectiveness ratio of £2,143 (£860-£4,175) per QALY gained and net monetary benefit of £6.2 m (£4.5 m-£8.4 m). In the best-case scenario, this increases to £15.9 m (£12.3 m-£20.5 m).
Conclusions
A non-negligible number of cases, hospitalisations and deaths were prevented by the mass testing pilot. Considering QALYs gained and healthcare costs avoided, the pilot was cost-effective. These findings suggest mass testing with LFDs in areas of high prevalence (> 2%) is likely to provide significant public health benefit. It is not yet clear whether similar benefits will be obtained in low prevalence settings or with vaccination rollout.
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SciScore for 10.1101/2021.05.10.21256816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
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:Strengths and weaknesses of the study: The model underlying these estimates makes a number of assumptions about the transmission dynamics of SARS-CoV-2. For example, the transmissibility of SARS_CoV-2 and the level of adherence to testing and self-isolation …
SciScore for 10.1101/2021.05.10.21256816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
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:Strengths and weaknesses of the study: The model underlying these estimates makes a number of assumptions about the transmission dynamics of SARS-CoV-2. For example, the transmissibility of SARS_CoV-2 and the level of adherence to testing and self-isolation in symptomatic cases. Where possible, we used existing regional data to back up these assumptions. However, there is considerable uncertainty surrounding the definition of some of the parameters used. Our sensitivity analysis (See Appendix B) shows that the model is very sensitive to the relative infectability of asymptomatic cases, which is one parameter for which there is high uncertainty [14] and is inconstantly defined [26]. This makes the output of the model particular susceptible to errors in this parameter and should be considered when evaluating these estimates. Another factor that was not considered in the model is differences in transmissibility between age and demographic groups. These groups will likely differ in their likelihood of transmitting and acquiring an infection. Such considerations would require generating Rt estimates for sub-populations for which sample sizes are likely to be too small. Also, the impact of new variants of concern and vaccine roll-out were not considered. These were not factors at the time of the pilot, but have subsequently become significant factors in the transmission dynamics of COVID-19. We have been quite conservative and not included costs of non-hospital cases or long COVID ...
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.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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