Evidence map›Paper›PMID 42642417›Full record

ArticleNature communications2026

Disproportionate exposure to extreme floods in historically redlined U.S. communities.

Elizabeth Appel, S Amin Enderami, Elaina J Sutley, Admin Husic

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Elizabeth AppelDepartment of Civil, Environmental and Architectural Engineering, University of Kansas, Lawrence, KS, USA.
S Amin EnderamiDepartment of Civil, Environmental and Architectural Engineering, University of Kansas, Lawrence, KS, USA.ORCID http://orcid.org/0000-0002-2295-3262
Elaina J SutleyDepartment of Civil, Environmental and Architectural Engineering, University of Kansas, Lawrence, KS, USA.
Admin HusicDepartment of Civil, Environmental and Architectural Engineering, University of Kansas, Lawrence, KS, USA. husic@vt.edu.ORCID http://orcid.org/0000-0002-4225-2252

Funding

National Science Foundation (NSF) 2148878
6 · The paper itself

Abstract

Extreme floods and historical public policies both leave lasting impacts. We analyzed flood exposure across the neighborhoods of 202 US cities historically assessed for lending risk by the Home Owners' Loan Corporation on a scale from 'best (A) to 'hazardous' (D). Using high-resolution flood modeling and dasymetric population mapping, we found that D-graded neighborhoods bear a disproportionate flood burden in roughly three-quarters of cities. This inequality follows a systematic gradient: A- and B-graded neighborhoods are underrepresented in the flooded population, C-graded neighborhoods are proportionately represented, and D-graded neighborhoods are overrepresented. A greater fraction of D-graded residents are exposed to the 100-yr flood (6.4%) than A-graded residents to the 1000-yr flood (5.9%) with the gap widening at higher-magnitude floods. Flood disparities are most pronounced in the Midwest and Mid-Atlantic regions, aligning with the historical concentration of industrial labor and redlined neighborhoods along waterways. However, we find that proximity to waterways alone does not explain the observed inequality: flood disparities persist even among neighborhoods far from major rivers and coasts, implicating infrastructure deficits as a compounding factor. Neighborhoods marked as 'hazardous' nearly a century ago remain disproportionately vulnerable today, underscoring the need for equitable urban planning.

Identifiers

PMID42642417
PMCPMC13507297

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.