The Black Zones: Harnessing Tech Enabled Community Driven Data, Big Population Health Data, & Agent-Based Models to Transform the Measures & Metrics around Longevity

Abstract Shorter Black life expectancy of African Americans (AAs) and Black Immigrants (BI) in the U.S. is well documented and has been linked to a variety of factors such as food deserts, pollution, low socioeconomic position, and resource scarcity. The Black Progress Index (BPI) launched by the Brookings Institution in 2022, comprises 13 social factors that predict longer life expectancy among Black populations at the county level, such as home and business ownership, fathers in the households, and social relationships. The BPI utilizes secondary data to reveal the factors and locales where Black people are living the longest and begins to (1) identify the environmental, social, cultural, and behavioral factors that impact life expectancy for AAs and BIs and; (2) develop strategies to improve the health status and promote longer life expectancy for AAs and BIs. However, while secondary data and social environment factors are useful to describe some factors that impact Black longevity, these data do not effectively capture the lived experience of residents at the neighborhood level. Thus, new community-led measures that capture lived experience that function as risk and resiliency factors that are associated with Black life expectancy are needed. Mobile data collection platforms that collect real-time, location-based data on users’ lived experiences and perceptions of their neighborhoods can gather much-needed data to disentangle negative and positive characteristics Black neighborhoods with relatively low versus high Black progress (e.g., Black longevity) as measured by the Black Progress Index and the impact those characteristics have on AA and BI residents’ life expectancy. We propose a sequential mixed-methods approach that uses mobile technology to enhance community based participatory research in order to develop new measures of risk and resiliency factors that capture AA and BIs lived experience of their neighborhoods and Black longevity. Additionally, we will develop dynamic agent-based models that integrate multiple types of data to capture previously documented risks to life expectancy gaps such as geography, social context, and resources available in Black communities. These models will provide insights on how decision making of community groups and others can influence Black longevity. For example, our agent-based models will be able to provide insights on how high-level financial decision impacts patterns in education funding, health funding, parks and public space, police expenditures, cash assistance, and other pecuniary choices related to downstream forces that influence Black longevity. Our research contributes to filling the gap left by previous studies that have been slow to quantify and measure both risks and protective factors that impact Black longevity and sheds new light on the extent to which compositional and contextual effects interact and impact the variation of Black life expectancy in different regions across the US.