Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave

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Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave

Fri, 2020-12-04 19:22 — mike kraft

No medication has proven effective in slowing transmission of the novel coronaviru

s (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). Lacking a v

Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave | Health Affairs Journal Experts agree that reverse transcription–polymerase chain reaction (PCR) testing is critical in controlling coronavirus disease 2019 (COVID-19), but decision-makers disagree on how much testing is optimal. Controlling for interventions and ecological factors, we used linear regression to quantify testing’s impact on COVID-19’s average reproduction number, representing transmissibility, in 173 countries and territories, accounting for 99% of the world’s COVID-19 cases, during March to June 2020. Amongst interventions, PCR testing had the greatest influence—a ten-fold increase in the ratio of tests to new cases reported reduced the average reproduction number by 9% across a range of testing levels. Our results imply that mobility reductions (e.g., shelter-in-place orders) were less effective in developing countries than in developed countries. Our results help explain how some nations achieved near-elimination of COVID-19 and the failure of lockdowns to slow COVID-19 in others. Our findings suggest that World Health Organization and other testing benchmarks are insufficient for COVID-19 control. Increased testing and isolation may represent the most effective, least costly alternative in terms of money, economic growth and human life for controlling COVID-19. [Editor’s Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.]

No medication has proven effective in slowing transmission of the novel coronavirus (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). Lacking a vaccine, governments have relied on non-pharmaceutical interventions (NPIs), including physical distancing, travel restrictions, and hygiene measures. After China sequenced and shared the viral genome, detection of active infection using reverse transcription–polymerase chain reaction (PCR) testing has been part of the response, extending beyond clinical care and epidemiological tracking.

Country experience and simulations show that testing, tracing and isolation can reduce transmission,¹^,² since substantial asymptomatic transmission occurs in COVID-19. The World Health Organization (WHO) has urged countries to “test, test and test,”³ and has suggested a rate of 10 negative to 1 positive test result as an indicator of adequacy.⁴

Despite this, decision-makers disagree on what constitutes adequate testing.⁵^,⁶ The legacy of pandemic influenza planning, which focused on reducing morbidity and mortality and never envisaged testing for controlling spread, may contribute to this disagreement, and most research focuses on other NPIs.⁷ ^–¹⁰ In a PubMed search, we found only 30 quantitative analyses of the impact of testing: almost all involved modeling and simulation, and none quantified real-world impacts.¹¹ Research problems include: difficulties of isolating impacts when multiple NPIs are simultaneously implemented; increases in per capita testing rates with cases which makes them a poor indicator of testing strategy; lack of a global testing database; failure to control for confounding factors; and unrepresentative geographical samples.¹²

Our study addressed these research problems by compiling data on numerous factors to quantify the association of PCR testing with COVID-19 spread during the initial pandemic wave—when some countries, such as China and New Zealand, achieved near-elimination—using a study design that robustly managed data gaps to maximize sample size and covariates. ...

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