Associations between 1930s HOLC grades and estimated population burden of cardiovascular disease risk factors in 2020.
Hanxue WeiBenjamin R SpoerAndrea R TitusTaylor M LampeMarc N GourevitchJacob W FaberSteven J KorzeniewskiSamantha J BauerLorna E ThorpePublished in: PNAS nexus (2024)
Studies have recently begun to explore the potential long-term health impacts of homeownership policies implemented in the New Deal era. We investigated the association between assigned grades of lending risk by the Home Owners' Load Corporation (HOLC) maps from the 1930s and present-day prevalence of three cardiovascular risk factors (diabetes and obesity in 2020, and hypertension in 2019), estimated at the census tract level in the United States. To minimize potential confounding, we adjusted for sociodemographic data from the time period when HOLC maps were made. We calculated propensity scores (predicted probability of receiving a HOLC grade) and created a pseudo-population using inverse probability weighting. We then employed marginal structural models to estimate prevalence differences comparing A vs. B, B vs. C, and C vs. D HOLC grades. Adjusting only for regions, a less desirable HOLC grade was associated with higher estimated prevalence rates of present-day cardiovascular risk factors; however, most differences were no longer significant after applying propensity score methods. The one exception was that the prevalence of diabetes, hypertension, and obesity were all higher in C vs. B graded census tracts, while no differences were observed for C and D and A and B comparisons. These results contribute to a small body of evidence that suggests historical "yellowlining" (as C grade was in color yellow) may have had persistent impacts on neighborhood-level cardiovascular risk factors 80 years later.
Keyphrases
- cardiovascular risk factors
- cardiovascular disease
- risk factors
- metabolic syndrome
- type diabetes
- blood pressure
- public health
- insulin resistance
- healthcare
- weight loss
- coronary artery disease
- cardiovascular events
- glycemic control
- high fat diet induced
- big data
- deep learning
- mental health
- high speed
- machine learning
- single molecule
- breast cancer risk
- atomic force microscopy