Poverty is a Public Health Problem

Racial, economic and social inequality in the COVID-19 pandemic has been covered at length in the media. However, the impact of the COVID-19 pandemic on these disparities is not unique: health crises have long exacerbated inequities. But data show that it is really poverty which drives cycles of illness.

Data from Kinsa’s network of smart thermometers, which aggregates anonymous fever and symptom data from users, is particularly valuable for understanding where and when illness is spreading, but also for transmission dynamics within communities that typically lack adequate access to health care.

First, since Kinsa users take their temperature and enter symptoms when they first feel ill — long before they see a doctor or get a lab test — Kinsa’s data represents real time transmission. Second, Kinsa’s smart thermometers are affordable (and free for many families in Title I school communities), meaning the network gathers data from people who might avoid health care services due to cost. 

Analyzing income and illness levels in San Francisco, New York and Los Angeles shows a clear inverse relationship between income and illness. Even before the pandemic, wealthier individuals experienced less illness. 

The charts above examine illness levels compared to income, based on average income in a census tract. Data from the last two years consistently shows that lower incomes correlate to higher incidences of illness — a trend that was present even before the pandemic.

The same trend exists in New York City and San Francisco.

Poverty is clearly a public health problem that must be addressed, both acutely via targeted vaccine distribution, testing first responders and frontline workers, and reducing copays and other barriers to healthcare access; and systemically by addressing the social, racial and economic chasms this pandemic has brought to light. If we are to make real progress, these solutions need to be applied more broadly and continue long past the end of this particular pandemic. While COVID-19 may have brought these disparities to the national conversation, we need to understand they are not new and, unless we do something, they’re not going anywhere.

Methodology 

This analysis focuses on areas with enough thermometers to provide precise data to compare with individual census tracts. This requires greater resolution than county or zip code-level illness insights. This means restricting our analysis to population centers where we have high adoption rates such as New York City, Los Angeles and San Francisco Bay Area.

Even in these locations, thermometer distribution is more limited in lower income areas. We excluded any tracts that did not meet a minimum threshold for thermometers in use within a  12 month period. A reading was counted as a fever if they registered at least one temperature reading above 37.7° C (99.9° F) during the time period. To explore these trends over time, we worked using three-month periods.

In these cities, we looked at census tracts as they were defined in 2019, which typically contain around 4,000 people and only cover a few square blocks in dense areas. To understand income, we looked at data from the American Community Survey (ACS) which shows the average income per person in each tract.