E164: Understanding Racial Segregation and Cancer Care Analysis: A Scoping Review

Racial segregation and its role in cancer care inequity is a rapidly growing field utilizing a wide array of statistical and programming methodologies. This in part makes understanding racial segregation research hard to interpret as often one measure cannot capture the true landscape of inequity in our communities. The present review seeks to synthesize the recent work being done around racial segregation in cancer care, focusing particularly on the statistical measures being employed to understand segregation.


Methods: A PubMed search was performed using the search terms “segregation cancer race.” Results were limited to the last 5 years (2018-2023) and full text primary research only. This yielded 46 studies. All 46 abstracts were reviewed and 13 studies were excluded for not utilizing any segregation measure. The remaining 33 studies were evaluated for their chosen outcome measure, segregation measure, study area, and small level of analysis.

Results:

33 studies were included in our review. The index of dissimilarity (IoD) (16 studies) and index of concentration at extremes (ICE) (11 studies) were overwhelmingly selected as the segregation measure of choice. Interestingly, there was little consensus on whether to measure these indices as ordinal or continuous variables with 22 studies analyzing their segregation measure as ordinal and 12 treating them as continuous. 23 studies focused on some combination of cancer stage at diagnosis (13), receipt of surgery/treatment (11), and/or mortality (19) as their primary outcome measures. Study areas were largely nationwide (16) or single state (11) and then drilled down to the county (15) or census tract (15) level.


Conclusions: The vast majority of studies seem to rely on either the IoD or ICE to measure racial segregation. However, both these measures are solely measures of racial concentration and evenness, ignoring the other dimensions that define segregation, namely isolation, exposure, and clustering. Only two studies utilized more than one index of segregation in order to understand segregation comprehensively. There is also considerable variation in how variables are analyzed. Further work is needed to both define best practices for how to treat these indices of segregation and also which combinations of measures provide the greatest estimation of racial segregation.