Evidence map›Paper›PMID 39813208›Full record

ArticlePLOS global public health2025

Using internet-assisted geocoding of 1940 census addresses to reconstruct enumeration districts for use with redlining and longitudinal health datasets.

Shuo Jim Huang, Michel Boudreaux, Kellee White Whilby, Rozalina G McCoy, Neil Jay Sehgal

Abstract read
In one paragraph

Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Shuo Jim HuangUniversity of Maryland Institute for Health Computing, North Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0002-4474-463X
Michel BoudreauxUniversity of Maryland Institute for Health Computing, North Bethesda, Maryland, United States of America.
Kellee White WhilbyDepartment of Health Policy and Management, University of Maryland School of Public Health, College Park, Maryland, United States of America.
Rozalina G McCoyUniversity of Maryland Institute for Health Computing, North Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0002-2289-3183
Neil Jay SehgalDepartment of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0002-6326-1115

Funding

Sci-Tech Core-Maryland Population Research CenterP2CHD041041 · NICHD · UNIV OF MARYLAND, COLLEGE PARK · PI Susan W Parker · 2018 to 2026
$3.9M
Diabetes, Obesity, and Metabolic ComplicationsT32DK098107 · NIDDK · UNIVERSITY OF MARYLAND BALTIMORE · PI TAYLOR, SIMEON I. · 2015 to 2025
$2.4M
NICHD NIH HHS P2C HD041041NIDDK NIH HHS T32 DK098107
6 · The paper itself

Abstract

Many historical administrative documents, such as the 1940 census, have been digitized and thus could be merged with geographic data. Merged data could reveal social determinants of health, health and social policy milieu, life course events, and selection effects otherwise masked in longitudinal datasets. However, most exact boundaries of 1940 census enumeration districts have not yet been georeferenced. These exact boundaries could aid in analysis of redlining and other geographic and social contextual factors important for health outcomes today. Our objective is to locate and map a large set of 1940 enumeration districts. We use online resources and algorithmic solutions to locate and georeference unknown 1940 enumeration districts. We geocode addresses using the OpenCage API and construct "virtual" enumeration districts by using a convex hull algorithm on those geocoded addresses. We also merge in Home Owners' Loan Corporation (HOLC) redlining maps from the 1930s to demonstrate how 1940 enumeration districts could be used in future work to examine the association between historic redlining and current health. We geocode 7,228,656 1940 census addresses from the largest 191 US cities in 1940 that contained 84% of the 1940 US urban population from the Geographic Reference File and construct 34,472 virtual enumeration districts in areas that had HOLC redlining maps. 18,340 virtual enumeration districts were previously unmapped, covering cities containing an additional 40% of the 1940 US urban population. Where virtual enumeration districts match with previously mapped districts, 96.8% of paired districts share HOLC redlining categorization. Researchers can use algorithmic methods to quickly process, geocode, merge, and analyze large scale repositories of historical documents that provide important data on social determinants of health. These 1940 enumeration district maps could be used with studies such as the Health and Retirement Study, Panel Study for Income Dynamics, and Wisconsin Longitudinal Study.

Identifiers

PMID39813208
PMCPMC11734980

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.