about encouraging the rigorous collection of data from one group that may be of greater use to other groups .”
Data is used to formulate guidelines and recommendations , but as the pandemic has proven , the evidence is akin to shifting sands .
“ Even today , although we have a great deal more information about infections , we face the same ambiguities regarding wearing masks and vaccination ,” says Fischbeck . “ The experts initially said people shouldn ’ t wear masks ; then masks only prevented infected people from spreading the virus but did not protect the wearer from catching it ; then masks were effective in doing both ; and then two weeks ago wearing two or three masks was ‘ common sense ’ and today , there are , as of yet , no benefits seen in wearing two masks . This ambiguity endures because we don ’ t have the right data .”
“ Ambiguity exists because we don ’ t have enough information about the uncertainty involved in many of the decisions that healthcare professionals ( and others ) are called upon to make ,” Rode and Fischbeck say . “ It ’ s only natural , in the early stages of a new threat , to face uncertainty . Decisions must be made before all the facts are known . We accept that . But the decisions that are made should recognize that uncertainty , which implies exercising caution in drawing sweeping conclusions based on limited data . That ’ s what produces the ‘ shifting interpretations of the science .’ What is in actuality ambiguity — uncertainty about uncertainty — can often be interpreted as ‘ shifting interpretations ’ or , more pejoratively , arbitrariness . That can diminish trust in the recommendations offered .”
One of the key missing pieces of information has been the number of “ hidden ” cases , or undiagnosed individuals who have the virus , but do not display symptoms . These people are still capable of transmitting the virus , but because they are asymptomatic , they may take fewer precautions .
“ We had only very rough ideas of the size of this population , because there was virtually no testing of asymptomatic people early on ,” says Rode . “ This is the sort of information that largescale randomized testing would reveal .”
“ We think the evaluation of asymptomatic individuals deserves far more attention ,” Rode and Fischbeck say . “ A year into the pandemic , and there is still a lack of clear evidence as to the number of hidden cases of COVID-19 and the capacity to spread the virus . People who display symptoms are easy to quarantine and treat . But if we have no good estimates of the number of asymptomatic or hidden cases , it becomes impossible to design reasonable policies . Obviously , one strategy is ‘ lock everyone down .’ But if the risks from hidden cases are small ( relatively speaking ), especially for certain areas or populations , then the universal quarantine strategy may be overly costly ( both in economic terms and in health consequences such as mental health issues , increased suicides , and so forth ). It is potentially ‘ security theater ’; that is , the psychic benefit of such a policy outweighs its actual benefit .”
The paper by Rode and Fischbeck notes that , without this information , many mitigating strategies put in place are essentially “ security theater ” — actions that make people feel safer without having an appreciable impact on actual risk . Or actions that incur costs in return for unknown benefits .
Moving past this “ security theater ” requires creating strategies that impact risk management in a meaningful way .
“ To manage a risk , we must first be able to identify and quantify it ,” Rode and Fischbeck say . “ If we think of risk as the probability of a bad outcome occurring , it presumes first that we know , one , what the probability is , and two , what the consequences are . These are basic , fundamental parameters , and yet , there is considerable uncertainty as to what the correct values should be . We need to think backward from the decisions we want to be able to make to the information we would like to have to make those decisions , and ultimately back toward the sort of data we would need to collect to make good decisions .”
In addition to the challenges created by an absence of data , Rode and Fischbeck raise potential problems that could arise from the presence of data . In the article , they write “ disclosure informs , but it also divides .”
“ We mean that the availability of detailed demographic information on population infection rates must be treated with extreme caution ,” says Fischbeck .
“ If ‘ certificates of immunity ’ or ‘ vaccine passports ’ are put in place , are groups with historically limited access to healthcare effectively barred from large aspects of public life ? Should this form of COVID
Even today , although we have a great deal more information about infections , we face the same ambiguities regarding wearing masks and vaccination .”
— Paul Fischbeck
‘ redlining ’ or ‘ profiling ’ be illegal , even if it is useful in limiting the spread of the virus ?” said Rode . “ There are important policy and ethical questions that must be part of the discussion , even if having the information would make society as a whole better off .”
These questions , Rode and Fischbeck argue , create a clear need for the decision sciences to have a seat at the policy table .
The authors conclude that COVID-19 — and future viruses like it — are public health crises and solid , objective data are necessary to confront them . Reducing the ambiguity surrounding base rates and transmission rates is of considerable value to creating public health policy when testing is properly directed . But selective or misinterpreted data can also be a “ virus .” Released unintentionally or maliciously across social media , this “ information virus ” could cause societal harm to linger long after the physical harm is resolved . Careful application of the decision analyst ’ s tools is essential to traverse this uncharted territory .
Rode and Fischbeck say there are a number of ways to reduce the ambiguity surrounding base rates and transmission rates .
“ The simple answer is to collect the data ,” they say , “ But , of course , it ’ s not that simple . We need statistically representative samples of the population , but we generally can ’ t compel asymptomatic people to be tested . And if asymptomatic testing is to be voluntary , then extra effort must be devoted to ensuring that it is balanced across various demographics and locations . Messaging should be designed to highlight the benefits of testing for asymptomatic individuals to encourage their cooperation . The healthcare profession must also accept that there is value in devoting scarce resources to the asymptomatic population . Emphasizing the decision-making value in having information about the asymptomatic population , such as more narrowly-tailored restrictions and greater responsiveness to populations at greater risk , may help to justify using limited healthcare resources on seemingly ‘ healthy ’ people .”