The illness signals powering Kinsa Insights and published at healthweather.us are aggregated temperatures from a network of over 1 million Kinsa thermometers1 distributed to households across the U.S. The analysis and data behind these insights are based on what is known about how illnesses, like the flu and common cold, are spread. These types of upper respiratory and fever inducing illnesses are called influenza-like illness (ILI). There are many studies that show how these types of illnesses spread within communities, and how demographics like age, sex, and zip code, play a role in community spread.
Who Buys and Uses Kinsa Smart Thermometers?
Kinsa’s smart thermometers are sold at major retailers in the country and online, and are also given away for free through the Kinsa FLUency school health program (see the buy one give one offer to support this program). One in every 5 Kinsa thermometers in the US has been distributed to a family in a Title 1 school participating in FLUency.
Through retail sales and school donations, Kinsa has amassed a user base broadly consistent with the overall US population distribution by age. Mirroring US age distribution helps ensure that Kinsa’s illness signal is an accurate representation of illness in the country.
Children under age 18 have shown to play a major role in the transmission of influenza to the general public2. Understanding illness levels and how illness transmits from person to person in the under 18 population is crucial in detecting illness spread in the broader population. Thus, much of Kinsa’s work has focused on providing families with school age children with the tools and knowledge they need to triage symptoms and stop the spread to their broader community.
Knowing that families with school aged children are the largest part of Kinsa’s user base, it makes sense that the most engaged users are mothers with school-aged children. This is in part due to the fact that a large portion of Kinsa’s thermometers are given away for free to families that attend Title 1 elementary schools. This is shown in the chart below as a gender imbalance favoring women between the ages of 25 and 54.
Epidemiological evidence suggests that women of childbearing age are at higher risk for complications related to the flu, though there is mixed evidence that show that women suffer from higher infection rates than men3.
Where do Users of Kinsa Smart Thermometers live?
For any tool tracking the health of a population it’s important to have a strong representation of the community. The chart below shows the correlation between the US county population and Kinsa’s user base.
The chart shows that Kinsa’s thermometer distribution tends to mirror population density, with more active users in higher populated urban areas than in less populated rural areas. This is represented by the trend line that has an R2 value of .9. The closer this number is to 1, the higher the correlation. Due to the how contagious illness transmits from person to person, the more densely populated an area is, the more likely it is for illness to spread. This is, for example, a reason why COVID-19 has spread so easily in places like New York, San Francisco and Seattle.
The distribution of thermometers by state follows a similar pattern to the previous chart. There is a higher distribution of thermometers in more populous states like California, Texas and Florida compared to less populous states like Wyoming and South Dakota.
Kinsa continues to work towards ensuring our illness signal is a strong representation of the overall health of the U.S. population. We are collaborating with public health departments at the city and state level to distribute more thermometers to families and front line workers in areas hardest hit by COVID-19. By providing more thermometers to these communities, we strive to give the individuals in power the information they need to save lives.
Data Privacy Principles
2Viboud C, Boëlle PY, Cauchemez S, et al. Risk factors of influenza transmission in households, Br J Gen Pract, 2004, vol. 54 506(pg. 684-689)
3Klein, Sabra L et al. “Mechanisms of sex disparities in influenza pathogenesis.” Journal of leukocyte biology vol. 92,1 (2012): 67-73. doi:10.1189/jlb.0811427