The cost-effectiveness of common strategies for the prevention of transmission of SARS-CoV-2 in universities
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Abstract
Most universities that re-open in the United States (US) for in-person instruction have implemented the Centers for Disease Prevention and Control (CDC) guidelines. The value of additional interventions to prevent the transmission of SARS-CoV-2 is unclear. We calculated the cost-effectiveness and cases averted of each intervention in combination with implementing the CDC guidelines.
Methods
We built a decision-analytic model to examine the cost-effectiveness of interventions to re-open universities. The interventions included implementing the CDC guidelines alone and in combination with 1) a symptom-checking mobile application, 2) university-provided standardized, high filtration masks, 3) thermal cameras for temperature screening, 4) one-time entry (‘gateway’) polymerase chain reaction (PCR) testing, and 5) weekly PCR testing. We also modeled a package of interventions (‘package intervention’) that combines the CDC guidelines with using the symptom-checking mobile application, standardized masks, gateway PCR testing, and weekly PCR testing. The direct and indirect costs were calculated in 2020 US dollars. We also provided an online interface that allows the user to change model parameters.
Results
All interventions averted cases of COVID-19. When the prevalence of actively infectious cases reached 0.1%, providing standardized, high filtration masks saved money and improved health relative to implementing the CDC guidelines alone and in combination with using the symptom-checking mobile application, thermal cameras, and gateway testing. Compared with standardized masks, weekly PCR testing cost $9.27 million (95% Credible Interval [CrI]: cost-saving-$77.36 million)/QALY gained. Compared with weekly PCR testing, the ‘package’ intervention cost $137,877 (95% CrI: $3,108-$19.11 million)/QALY gained. At both a prevalence of 1% and 2%, the ‘package’ intervention saved money and improved health compared to all the other interventions.
Conclusions
All interventions were effective at averting infection from COVID-19. However, when the prevalence of actively infectious cases in the community was low, only standardized, high filtration masks clearly provided value.
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SciScore for 10.1101/2020.08.13.20166975: (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
Software and Algorithms Sentences Resources We assessed risk using Mathematica’s 19andMe model. Mathematica’ssuggested: NoneResults 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:The major limitation of our analysis is the considerable uncertainty in parameter estimates. For example, estimates of infection fatality rates can quadruple when hospitals are overwhelmed with cases.14,17 However, the model is generally robust …
SciScore for 10.1101/2020.08.13.20166975: (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
Software and Algorithms Sentences Resources We assessed risk using Mathematica’s 19andMe model. Mathematica’ssuggested: NoneResults 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:The major limitation of our analysis is the considerable uncertainty in parameter estimates. For example, estimates of infection fatality rates can quadruple when hospitals are overwhelmed with cases.14,17 However, the model is generally robust to different parameter inputs and assumptions. Another limitation is that universities vary considerably with respect to sociodemographic composition and risk-taking among students. Additionally, the standard gamble exercises we used were administered to graduate students of public health, who may be more risk adverse than average students. We account for differences in student risk preferences by varying the number of assumed contacts between students, both on and off campus in sensitivity analyses. Our findings do not apply to universities in which a large number of students commute to and from multi-generational households.
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.
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