Which government-led social distancing measures are effective at stopping the spread of the novel Coronovirus? One company has contributed data to answering this question. A private health technology company, Kinsa sells a "smart thermometer" which links to an app that guides people through what to do if they have a fever. When people use the app, they agree to have their temperature information (including fever and other symptoms they report) shared with Kinsa.
Kinsa has used body temperature data points from all over the U.S. to create maps showing areas of "atypical illness." They define atypical illness on their website as "an unusual incidence of elevated flu-like illness levels."
It's really interesting to explore Kinsa's atypical illness maps of the U.S. here.
More relevant for a research-methods course are the figures that Kinsa presents that map "flu-like illness" rates over time in certain high-population counties (Boston, New York, Los Angeles). I've pasted two of them here. Take a look at these figures, and then answer the questions below.
a) These figures should remind you of one of the quasi-experimental designs covered in Chapter 13. Which design is it?
b) The Kinsa company has used these charts to argue that social distancing measures work to reduce cases of COVID-19. Journalists (for example) have used the charts to specifically argue that limiting large gatherings and closing schools alone does not do much to stop the spread; it was only when bars and restaurants were shuttered that these counties saw reduced rates of atypical illness. Look carefully at the three counties above. What parts of the data are relevant to this argument? Do you think the data support the claim that closing bars and restaurants was effective? Can you think of alternative explanations for the pattern, other than "closing bars and restaurants"?
c) Finally, consider the ethics and scientific openness principles. Do you think it's ethical for Kinsa to use and publicize the private health information of people who buy its products? Consider arguments both for and against the scientific ethics of this practice. Frame your arguments around the ethical principles of respect for persons, beneficence, and justice.
In your reflections, you might consider that users of Kinsa's products agree to data sharing via its terms and conditions. You might consider that the data from users is kept anonymous and is aggregated over more than a million users. You might consider the public health benefits of sharing the data. And you might also consider the extent to which Kinsa is following open science practices--that is, sharing all the data the way most university researchers now do (relevant Kinsa research information here).
d) If you're construct-validity-curious, you might be wondering if Kinsa's definition of flu-like illness actually tracks COVID-19 cases. Kinsa do not claim to be diagnosing actual COVID (you need a nasal swab for that). However, they present some maps showing how their temperature data has tracked with confirmed COVID cases in several counties. The data are in the middle of this webpage.
By the way, here's another example of a company that collects our health data, using it to share national patterns. This one is Fitbit, sharing how sleep time has increased (in most age groups) since the COVID quarantine started.
