• February 21, 2024
• Public Health

Understanding the intricate dynamic between brain development in children, household poverty levels, and long-term economic outcomes has considerable implications for achieving societal objectives. Where we land on the economic distribution as adults is influenced by several factors affecting our development and output from an early age, including the household conditions and environment in which we are raised. Using data from digital or connected devices could provide health practitioners and policymakers with the insights needed at the most granular levels, to design microtargeted interventions to ameliorate the challenges impacting those outcomes.

The volumes of gray matter in the brain's temporal cortex and hippocampus regions in developing children have been shown to vary with household poverty levels. As these play an important role in memory-related processes including education performance and achievement, the implications are critical for long-term economic outcomes, including levels of household incomes. Most Americans – 72 percent – believe that it is possible for someone born into poverty to simply work hard enough and become wealthy. However, further studies show that this is not the case. A report from the Brookings Institute on the economic mobility of families across generations shows that 42 percent of children born into poverty end up in the bottom fifth of the economic distribution as adults and 23 percent rise to just the second fifth. Additionally, there is variation between subpopulation groups; 42 percent of Black Americans who lived more than half of their lives in poverty were poor by age 30, versus 25 percent of their white counterparts. In 2021, the U.S. Federal Interagency Forum on Child and Family Statistics reported that 15.3 percent of children under the age of 17 were in poverty, with higher rates for Black and Hispanic children – 27.3 percent and 22.4 percent, respectively.

Children living in poverty fare poorer on measures of academic ability, with as much as 20 percent of the gap in test scores explained by maturational lags in the frontal and temporal lobes, according to one study published in the Journal of the American Medical Association, JAMA Pediatrics. In the same study, the researchers found that gray matter volumes of children living below 1.5 times the federal poverty level were three to four percentage points below the developmental norm. If brain and cognitive development impact education achievement, then it should not be surprising that children from poor households are worse off in income levels in the long run. A paper published by the Alliance for Excellent Education argues that “higher education attainment improves a student’s future income, occupational status, and social prestige, all of which contribute to improved individual health.”  

These findings in no way validate any discourse on the aptitudes of minority populations and their IQ. Indeed, several studies refute any claim that, ceteris paribus, any single racial group is genetically more intelligent, but the findings do provide a window into how the conditions inflicted upon minority groups, through bad public policies, may have a significant impact from an early age, through adulthood, and spanning several generations.

But how much do these disparate gray matter volumes affect future income levels? A review of the annual earnings by educational attainment published by the National Center for Education Statistics outlines clearly that higher median earnings were associated with higher educational attainment. In 2021, individuals between ages 25 to 34 years old and who worked full-time had 21 percent higher earnings with a master’s degree or higher, than those with a bachelor’s degree. In the same year, those with a bachelor’s degree earned 55 percent higher than those who only completed high school.

The question, of course, becomes, how can cities and their leaders ensure that volumes of gray matter in the brains of developing children are not diminished due to external factors? To answer this question, one must understand that poverty-stricken households are typically confined to very particular neighborhoods in their communities. These areas are often referred to as slums, ghettos, or “the other side of the tracks,” among other euphemisms, and typically fall within proximity to factories, major roadways, and ports. Consequentially, these residents are exposed to toxic fumes and other harmful air pollutants. A study published in Science Advances showed that higher air pollution in low-income areas affects early childhood development. The research team found that exposure to toxic air reduces cognitive abilities by about one-tenth of a standard deviation, in children aged four.

Cities can utilize data from devices monitoring the factors known to affect brain and cognitive development, including gray matter volumes. A study published in Brain Science pointed to the association between widespread exposure to air pollution and gray matter and white matter (the large network of nerve fibers) volumes, indicating that monitoring of pollution levels and implementing mitigation interventions when harmful compounds are elevated, could have a possible impact on the community, beyond health outcomes. The Purple Air Sensors, for example, are small digital devices that enable indoor and outdoor air quality monitoring. Larger air sensors like those used by the Environmental Protection Agency (EPA) and used by cities’ environmental agencies are typically more robust in their monitoring, however, they are not able to provide readings at the levels of granularity that smaller devices can. A combination of the two approaches would be most effective.

Lead is another compound that affects gray matter levels and can be found in the water supplies of many cities. Several studies on water quality in the U.S. found associations between poverty-stricken neighborhoods and levels of contamination. One study conducted in Rhode Island found that blood lead levels increased across quintiles of poverty and old housing. A second study found that neighborhoods below the median income level in one Texas city were twice as likely to have detectable levels of lead in their water supply and 1.5 times the mean concentration. Devices capable of detecting lead in water supplies are readily available on the market. Local governments must make concerted efforts to monitor the concentration of lead in all the neighborhoods in their communities but pay particular attention to those neighborhoods bordering manufacturing facilities and other heavily trafficked areas.  

The long-term impact of neglecting the environmental factors affecting human health on economic output and subsequently on the ability of individuals from poor households to climb the economic ladder is significant. City governments must invest more resources in understanding their effects and designing solutions to mitigate them. American cities still have not configured appropriate data-sharing networks and protocols for combining both data sources readily available to government departments and agencies, and other non-traditional data sources like personal connected devices. For example, by allowing residents to opt into sharing anonymous health tracking data from their wearables such as Fitbit, WHOOP, and Apple Watch among others, and combining that data with pollution, traffic, and labor market participation data (including data on sick leave), we can begin to generate granular insights on how the brain health of residents in specific parts of the community are distributed and affected by environmental factors like PM2.5 and other noxious gases.

Navigating the complexities of poverty requires understanding the impacts of socioeconomic factors on brain development and this new era of digital connectivity provides access to vast pools of data which is invaluable to the policymaking process. Integrating research from diverse fields like neuroscience, economics, and data analytics can provide new pathways for designing strategies to uplift entire communities from poverty traps and propel individuals further along the economic distribution. This convergence of disciplines and data sources could usher in transformational change in communities.