Saving lives and spurring growth: A hopeful path in the pandemic

This summary relates to research published by the Minneapolis Fed in early November. The arrival of effective vaccines alters quantitative estimates, but the authors note elsewhere that even assuming an optimistic vaccine rollout scenario, a well-designed screening testing program would pay for itself and save lives.

Despite the arrival of effective coronavirus vaccines, months, and lives, will pass before we can return to what we once knew as normal life. During that indefinite time span, the economy will continue to underperform, as businesses remain shuttered because workers and potential customers fear infection, and working parents care for school-age children at home.

Hope for a quicker recovery may lie with fast, inexpensive screening tests, argues a staff report from the Minneapolis Fed, “Economic Benefits of COVID-19 Screening Tests,” published in early November 2020. Widespread deployment of these tests, says the report, would identify infected cases quickly and instruct people to isolate, thereby limiting contagion.

According to Minneapolis Fed consultant Andrew Atkeson of UCLA and his co-authors, the fiscal, macroeconomic, and health benefits of the screening testing program “far exceed their costs.” Even assuming that screening tests are imperfect and that a high fraction of those testing positive will therefore not comply with isolation instructions, the economists calculate that economic benefits would exceed costs by a factor of five to 10 for weekly testing.

Confirmatory tests, using diagnostic tests with higher accuracy—though slower and more expensive—would increase net economic benefits, they say, by increasing adherence to quarantine. They would also reduce false positives, allowing more healthy people to remain economically active. Targeting tests to specific age groups, and adding the economic value of lives saved, would raise the benefit-cost ratio still higher.

Failed efforts and false solutions?

Screening tests to detect and isolate individuals who are likely to spread the virus would help, but the current gold standard for testing, the laboratory-based polymerase chain reaction (PCR) test, is slow and relatively expensive.

Much less-costly screening tests that provide results quickly are widely available, but some health professionals have raised reasonable concerns about their quality and effectiveness. These techniques exhibit low “specificity” (meaning more false positives), potentially undermining confidence in results and adherence to recommendations to isolate if found positive. If individuals falsely identified as infected did isolate, they’d leave the work force needlessly, if temporarily.

Such tests are also less sensitive than PCR tests; infected individuals would exhibit false negatives at higher rates than with PCRs, leading them to infect others and undercutting policy effectiveness. Moreover, widespread testing of this sort could be prohibitively expensive and programmatically infeasible if administered daily, as some propose.

The economists address these concerns with a cost-benefit analysis that compares economic and mortality outcomes of a range of potential large-scale testing programs against a baseline that assumes continued use of current testing practices. To do so, they develop a model that blends dynamics of disease transmission and macroeconomic activity. It’s “behavioral” in the sense that people respond to their perceptions of disease vectors—becoming less economically active when current death rates and trends in death rates increase. People also exhibit “lockdown fatigue”; their desire to resume activity rises when unemployment has been high and rising.

Adherence—or lack thereof—is a key element in the analysis. A testing program will work only if those who test positive comply with instructions to isolate until no longer infectious. Because current evidence shows low compliance levels, the economists’ model varies adherence rates by the specificity level of the given screening test. Adding a more-definitive confirmatory test to the program to verify or negate initial results increases adherence by convincing individuals of their actual status.

Promising results

The economists present results from three possible testing programs. The first uses an inexpensive screening test with low specificity relative to a PCR’s. Half of those testing positive on this initial test get a PCR test to validate. Just 25 percent of those who test positive with the initial test isolate per instruction, but 75 percent comply after the confirmatory test.

Assuming weekly tests, this testing regime would cost an estimated $50 billion over the seven-month simulation period. (See table). Death rates would decline relative to a no-screening-test scenario, leading consumers and workers to return to economic activity. GDP would be $244 billion higher (relative to a no-screening test scenario), resulting in an additional $67 billion in federal tax receipts, more than enough to finance the testing. An estimated 65,000 deaths would be averted.

A second hypothetical program gives a confirmatory PCR test to every individual who tests positive with the screening test, increasing costs somewhat but leading to a much greater economic boost of $541 billion, higher tax revenue of $149 billion, and more than doubling the lives saved to 152,000.

The third scenario eliminates confirmatory PCRs but uses a two-stage screening test procedure that raises overall specificity (that is, lowers the false positive rate) and evokes a higher adherence of 50 percent. Under this program, incremental testing costs are less than under the other two programs, just $28 billion, because the initial test it uses is cheaper. It averts 117,000 deaths and boosts GDP by $395 billion and tax receipts by $108 billion.

Extensions

The researchers dig deeper, tweaking program features such as testing frequency, test specificity, adherence rates, and test costs. Results vary, but no one program provides markedly greater benefits than the three primary scenarios. They also investigate outcomes under an alternative economic feedback rule, where people respond to the number of current COVID-19 cases, rather than the weekly death rate and its trend. Net benefits are lower under this assumption, but even so, all programs “continue to yield large net economic and net total benefits.”

A more significant result comes from their exploration of age-targeted screening testing regimens. Because age groups vary in contact patterns, work activity, and health, focusing testing toward some cases more than others could change mortality and economic dynamics. The economists find that net benefits are highest from targeting young adults (ages 20-44), followed by targeting those ages 45-64, and then ages 65-74, suggesting that testing and isolation curbs transmission up the age ladder. Under any testing frequency—daily, weekly, or monthly—net benefits are always highest with an age-targeted strategy, even though it would withdraw more economically active adults from the workforce.

A brighter path

Although they report estimated outcomes under many potential scenarios, the economists emphasize that specific numbers are less important than the overall direction of their results: “A screening testing program … designed to evoke high adherence has the potential to avert deaths and to provide large economic gains.”

Their analysis rebuts many of the major arguments against screening testing, showing that affordable, practical programs using less-than-perfect test methods can save lives and revive the economy. It’s a promising path out of a bleak pandemic.