May 15, 2020
DOI: 10.1056/NEJMp2014836
DOI: 10.1056/NEJMp2014836
Quote:
Tragically, the United States, unable to match other countries' response, has tallied the most cases and deaths in the world and recent data suggest that those tallies are underestimates. Why has the U.S. response been so ineffectual? One key answer is testing, which has been a cornerstone of Covid-19 control elsewhere. U.S. testing to identify people infected with SARS-CoV-2 has been slow to start and to this day has not sufficiently ramped up. Testing was delayed in January and February as the Centers for Disease Control and Prevention (CDC) distributed faulty test kits, then failed to approve a working test developed by the World Health Organization or those developed by local public health laboratories. Since March, the number of tests per day has never reached the number needed because of shortages of reagents, materials, and personal protective equipment (see graph).
Quote:
To date, efforts to bolster testing have focused on operational issues: whether testing capacity is adequate, why shortages and supply chain failures are so pervasive, and how to scale up testing to the massive numbers needed to mitigate the U.S. epidemic. Yet offering more tests is not a strategy in and of itself. If enough tests were available, we would still need to answer a fundamental question: What decisions are the results meant to inform? Testing has many purposes beyond diagnosis and protection of health care workers. Testing data are needed to manage all aspects of a pandemic. For instance, they are a cornerstone of epidemic forecasting models, which are sorely needed to reveal the future demand for care, including the timing of case surges and the magnitude of required emergency medical services, hospital staff, hospital beds, ventilator equipment, and mortuary services. Without good testing data, forecasters have to rely on guesswork and assumptions.
Quote:
That the United States is failing such a simple test of its capacity to protect public health is shocking. Collecting and reporting public health data are not rocket science. Other countries, notably Canada and Belgium, are already reporting nationwide data on testing at the individual level, including individual demographic data (using ranges for each person to protect privacy) and other key attributes for each test.4 The United States was once a leader in collecting systematic federal data on population health. Now our national disease-tracking effort seems stuck with well-meaning but scattershot efforts by tech companies using cellular phone signals, social media surveys, online searches, and smart thermometers as we try to guess where Covid-19 outbreaks may be lurking. Small one-off studies using convenience samples have popped up to try to fill the vacuum with basics such as percentages of cases that are asymptomatic and of symptomatic people who seek care. Because of sampling bias, these studies are producing wildly different and nearly uninterpretable results. Estimates are so wide ranging that modelers have little choice but to default back to imprecise assumptions.
Quote:
In the information age, the United States seems to be swimming in big data. This country has generated many of the world's largest, most innovative, most profitable data companies. Yet when it comes to forecasting the spread of a major pandemic that is killing Americans and wreaking havoc on our economy, we seem oddly lost. With more than 80,000 dead and no end in sight, our national efforts seem feebler and more halting than the 19th-century work of Florence Nightingale in the Crimean War and William Farr in England, where they used systematically collected epidemiologic data and rigorous analysis to save countless lives. Would that our statistical models had such standardized, systematically collected, and readily reported data to inform them. Reopening state economies without the precision provided by analysis of rigorously reported testing data seems a peculiarly American form of madness.
