The Sensitivity and Costs of Testing for SARS-CoV-2 Infection With Saliva Versus Nasopharyngeal Swabs
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal swabs are the primary sampling method used for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but they require a trained health care professional and extensive personal protective equipment. PURPOSE: To determine the difference in sensitivity for SARS-CoV-2 detection between nasopharyngeal swabs and saliva and estimate the incremental cost per additional SARS-CoV-2 infection detected with nasopharyngeal swabs. DATA SOURCES: Embase, Medline, medRxiv, and bioRxiv were searched from 1 January to 1 November 2020. Cost inputs were from nationally representative sources in Canada and were converted to 2020 U.S. dollars. STUDY SELECTION: Studies including at least 5 paired nasopharyngeal swab and saliva samples and reporting diagnostic accuracy for SARS-CoV-2 detection. DATA EXTRACTION: Data were independently extracted using standardized forms, and study quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). DATA SYNTHESIS: Thirty-seven studies with 7332 paired samples were included. Against a reference standard of a positive result on either sample, the sensitivity of saliva was 3.4 percentage points lower (95% CI, 9.9 percentage points lower to 3.1 percentage points higher) than that of nasopharyngeal swabs. Among persons with previously confirmed SARS-CoV-2 infection, saliva's sensitivity was 1.5 percentage points higher (CI, 7.3 percentage points lower to 10.3 percentage points higher) than that of nasopharyngeal swabs. Among persons without a previous SARS-CoV-2 diagnosis, saliva was 7.9 percentage points less (CI, 14.7 percentage points less to 0.8 percentage point more) sensitive. In this subgroup, if testing 100 000 persons with a SARS-CoV-2 prevalence of 1%, nasopharyngeal swabs would detect 79 more (95% uncertainty interval, 5 fewer to 166 more) persons with SARS-CoV-2 than saliva, but with an incremental cost per additional infection detected of $8093. LIMITATION: The reference standard was imperfect, and saliva collection procedures varied. CONCLUSION: Saliva sampling seems to be a similarly sensitive and less costly alternative that could replace nasopharyngeal swabs for collection of clinical samples for SARS-CoV-2 testing. PRIMARY FUNDING SOURCE: McGill Interdisciplinary Initiative in Infection and Immunity. (PROSPERO: CRD42020203415).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".