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159 Investigating violence disparities through an intersectional lens: using additive interaction approaches to explore the relationship of redlining and racialized economic segregation on non-fatal shootings in Baltimore city, Maryland
  1. Mudia Uzzi,
  2. Kyle Aune,
  3. Forrest Jones,
  4. Lorraine Dean,
  5. Carl Latkin
  1. Johns Hopkins Bloomberg School of Public Health, Baltimore, USA


Statement of Purpose A multitude of social and structural factors give rise to disparities in neighborhood violent crime rates. Research examining these factors from an intersectional perspective is limited. We investigated how two factors related to structural racism and economic isolation (redlining and racialized economic segregation) interact to produce spatial variation and disparities of neighborhood-level Non-Fatal Shooting (NFS) rates in Baltimore City, Maryland.

Methods/Approach We performed an ecological cross-sectional study of 169 Census Tracts (CT) in Baltimore. For each CT, we calculated a weighted ‘redlining-score’ from historical redlining maps. We also generated an Index of Concentration at the Extremes Score (ICE), using variables in the US Census Bureau’s American Community Survey to operationalize racialized economic segregation. The redlining and ICE scores were both dichotomized into binary variables with two categories signifying a CT’s level of disadvantage or advantage. To determine NFS rates for each CT, we geocoded and aggregated by CT, 3,435 NFSs occurring between 2015–2019. We used various measures of additive interaction (e.g. joint disparity, excess intersectional disparity) to investigate the relationship of redlining and racialized economic segregation on neighborhood-level NFS rates.

Results Of the study’s 169 CTs, 24% were dually advantaged, 37% were dually disadvantaged, 39% were advantaged in one factor and disadvantaged in the other factor (e.g. advantaged in redlining and disadvantaged in racialized economic segregation and vice versa). The median yearly average NFS rate was 9 per 10,000 residents and the range for yearly average NFS rates was between 0–57 per 10,000 residents. The joint disparity of NFS was 24 per 10,000 residents and the excess intersectional disparity for NFS was 11 per 10,000 residents. Moreover, we found the attributable proportion of excess intersectional disparity was 42%. This suggests that close to half of the NFS rate in dually disadvantaged CTs can be explained by the intersection of redlining and racialized economic segregation.

Conclusions Dually disadvantaged CTs (Historically redlined Black working class neighborhoods) had higher yearly rates of NFS compared to a similar dually advantaged CTs. The intersection of redlining and racialized economic segregation contributes to the disparities of NFS rates in Baltimore City.

Significance Researchers can use additive interaction measures to improve the understanding of how social and structural factors act both individually and simultaneously to contribute to violence disparities. Moreover, an intersectional perspective to violence inequities could provide policymakers with valuable insights on where to target resources for violence reduction strategies.

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