Mobility Data Tends to Leave Out Older and People of Color

Study finds that mobility data used for COVID-19 research may leave out older and non-white people.

Research on reliability and bias mobility data finds that older and non-White voters are less likely to be captured by these data.

Mobility data of individuals has been used widely in developing strategies against COVID-19 - to study the effectiveness of social distancing policies, how people's travel affects transmission of the virus, and how social distancing has affected different sectors of the economy.

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‘Public health resources based on such information could cause disproportionate harms to high-risk elderly and minority groups’
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However, little data is available on its reliability and demographic bias.

Amanda Coston, who led the study, says, "Older age is a major risk factor for COVID-19-related mortality, and African-American, Native-American, and Latinx communities bear a disproportionately high burden of COVID-19 cases and deaths.”

She adds that allocating public health resources based on such information could cause disproportionate harms to high-risk elderly and minority groups.

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For the study, researchers assessed the validity of SafeGraph data which contains information from nearly 47 million mobile devices in the US. They aimed to determine whether SafeGraph data accurately represent the broader population.

Since SafeGraph data do not contain demographics such as age and race, researchers faced a major challenge. They tackled this by using administrative data (voter registration and turnout records with information on age, gender, and race, along with voters' travel to a polling location) to supplement the demographic information necessary.

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The study included 539,000 voters from North Carolina who voted at 558 locations during the 2018 general election.

Findings revealed that the SafeGraph data under-represents two high-risk groups - older and minority voters. This could result in jurisdictions under-allocating essential health resources like pop-up testing sites and masks, to these vulnerable populations.

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Alexandra Chouldechova, co-author, suggests, "While SafeGraph information may help people make policy decisions, auxiliary information, including prior knowledge about local populations, should also be used to make policy decisions about allocating resources."

The authors also recommend devising ways to make mobility data more representative.

The authors point out that in the US, voters tend to be older and include more White people, so the sampling bias may have been underestimated. Additionally, since SafeGraph provides researchers with an aggregated version of the data for privacy reasons, researchers could not test for bias at the individual voter level.

Source-Medindia