D568 Task 2 Population Determinants Analysis Example

A Red Line Drawn in 1938: How Social Determinants Left by Redlining Shape Asthma and Heat Illness for Residents of a Composite Neighborhood Today

Student Name

Leavitt School of Health, Western Governors University

D568: Health Equity and Social Determinants of Health, Task 2

Course Instructor

Month Day, Year

A Red Line Drawn in 1938: How Social Determinants Left by Redlining Shape Asthma and Heat Illness for Residents of a Composite Neighborhood Today

The Population

Eastgate is a composite neighborhood of about 18,000 residents east of downtown in a mid-sized American city. About 70% of residents are Black and 15% Latino, and 34% live below the federal poverty level, compared with 13% citywide. Two health problems stand out in local data. Children in Eastgate visit the emergency department for asthma at more than twice the city's rate, and during the heat wave two summers ago, Eastgate accounted for a quarter of the city's heat-related emergency visits while holding a twentieth of its population. This paper analyzes how social determinants explain those outcomes, beginning with a decision made almost 90 years ago.

How the Past Created the Present

In the 1930s, the federal Home Owners' Loan Corporation graded urban neighborhoods by perceived mortgage risk, and its assessments took residents' race into account; neighborhoods with Black residents were typically given the lowest grade and outlined in red on the maps (Swope et al., 2022). The Federal Housing Administration used similar appraisals. The practice, known as redlining, steered lending away from these neighborhoods for decades. Residents could not easily buy or improve homes, property values stayed low, and businesses and public investment went elsewhere. Eastgate was graded D, the lowest level, in 1938.

Redlining ended legally with the Fair Housing Act of 1968, but its effects remain in the structures it shaped. A scoping review of studies on redlining and present-day neighborhoods found that nearly all reported associations between historical redlining and health-relevant outcomes, although differences in study design prevent direct comparison and causality remains debated (Swope et al., 2022). The review proposes that redlining works through several pathways: segregation, disinvestment, lost wealth and the placement of environmental hazards in neighborhoods with little political power.

Neighborhood and Built Environment

Eastgate's built environment shows those pathways. In the 1960s, planners routed an interstate highway through the neighborhood, where land was cheap and residents had little influence, and a bus depot and two warehouses followed. Diesel exhaust from the highway and trucks raises levels of fine particles and nitrogen dioxide, both asthma triggers. Much of the housing is older rental stock with leaks, mold and pests, which also trigger asthma, because owners who could not get loans in the past, and landlords who bought later, invested little. Tree canopy covers about 9% of Eastgate, compared with 31% in the city's historically top-graded neighborhoods, and much of the land is paved.

These conditions have measurable effects. In an ecological study of 1,431 census tracts in eight California cities, the median age-adjusted rate of emergency visits for asthma was 2.4 times higher in formerly redlined tracts than in tracts given the best grade, 63.5 versus 26.5 visits per 10,000 residents per year, and redlining remained associated with a 39% higher rate after adjusting for poverty, diesel emissions and fine particles (Nardone et al., 2020). For heat, an analysis of 108 U.S. urban areas found that in 94% of them, formerly redlined neighborhoods had higher summer land surface temperatures than their neighbors, by about 2.6 degrees Celsius on average nationally and by as much as 7 degrees in some cities, partly because of more pavement and fewer trees (Hoffman et al., 2020).

Economic Stability

Lost opportunities for homeownership translated into lost wealth, which passes across generations. Eastgate families are less likely to own homes and more likely to rent from landlords who do not fix leaks or install air conditioning. When the heat wave came, many households had no air conditioning or could not afford to run it. Lower incomes also mean less ability to buy air purifiers, replace carpets that hold allergens or move. Jobs available to residents are more often outdoors or in warehouses without cooling, raising heat exposure at work as well as at home.

Health Care Access and Quality

Eastgate has one federally qualified health center and no pediatric practice. Children with asthma are more likely to be seen for flare-ups in the emergency department than for regular controller medication visits, and asthma action plans are less common. Pharmacies are farther away, which makes refilling inhalers harder. Heat illness also depends on care: older adults with heart disease or diabetes, and people taking medications that impair heat regulation, are at greater risk, and those with less regular care are less likely to have been warned.

Social and Community Context

Decades of disinvestment and highway construction broke up social networks and displaced long-standing businesses and churches. During heat waves, isolation is dangerous; people living alone, especially older adults, are less likely to be checked on. Residents' distrust of city institutions, rooted in the neighborhood's history, can make them less likely to use cooling centers run by the city. At the same time, Eastgate has strong churches and a tenants' association, which are assets any response should build on.

What the Analysis Shows

The determinants interact. Diesel exhaust, poor housing and limited primary care each raise asthma risk, and together they explain why a child in Eastgate is more likely to end up in the emergency department than a child with the same diagnosis across town. For heat, pavement, few trees and no air conditioning raise exposure, while isolation and limited care raise vulnerability. The evidence has limits. The studies cited are ecological, so they show associations at the neighborhood level and cannot show that any individual's asthma was caused by redlining; neighborhoods graded D in 1938 may have differed in other ways. Even so, the consistency of findings across cities and outcomes, and the clear pathways from policy to present conditions, make the pattern hard to dismiss.

The analysis also points to action. Because the causes are structural, the responses should be too: enforcing housing codes on mold and pests, planting trees and reducing paved surface, routing truck traffic away from homes, requiring cooling in rental housing, placing pediatric asthma care in the neighborhood and working through churches and the tenants' association to reach isolated residents during heat waves.

Conclusion

Eastgate's higher rates of asthma and heat illness are the present-day outcomes of a policy drawn on a map in 1938. Redlining shaped the neighborhood's built environment, economic stability, access to care and social context, and each of those determinants now shapes the health of its residents. Understanding that history turns what looks like individual illness into a population problem with structural causes and structural solutions.

References

Hoffman, J. S., Shandas, V., & Pendleton, N. (2020). The effects of historical housing policies on resident exposure to intra-urban heat: A study of 108 US urban areas. Climate, 8(1), 12. https://doi.org/10.3390/cli8010012

Nardone, A., Casey, J. A., Morello-Frosch, R., Mujahid, M., Balmes, J. R., & Thakur, N. (2020). Associations between historical residential redlining and current age-adjusted rates of emergency department visits due to asthma across eight cities in California: An ecological study. The Lancet Planetary Health, 4(1), e24-e31. https://doi.org/10.1016/S2542-5196(19)30241-4

Swope, C. B., Hernández, D., & Cushing, L. J. (2022). The relationship of historical redlining with present-day neighborhood environmental and health outcomes: A scoping review and conceptual model. Journal of Urban Health, 99(6), 959-983. https://doi.org/10.1007/s11524-022-00665-z