Evidence map›Paper›PMID 26398346›Full record

ArticlePloS one2015

Death by Segregation: Does the Dimension of Racial Segregation Matter?

Tse-Chuan Yang, Stephen A Matthews

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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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

2 authors.

Tse-Chuan YangDepartment of Sociology, Center for Social and Demographic Analysis, University at Albany, State University of New York, Albany, NY, United States of America.
Stephen A MatthewsDepartments of Sociology and Anthropology, Population Research Institute, Pennsylvania State University, University Park, PA, United States of America.

Funding

Population Research InstituteR24HD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI MATTHEWS, STEPHEN A · 2001 to 2016
$9.7M
Developmental Infrastructure CoreR24HD044943 · NICHD · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI DENTON, NANCY A · 2004 to 2016
$4.8M
NICHD NIH HHS R24 HD041025NICHD NIH HHS R24-HD041025NICHD NIH HHS R24 HD044943NICHD NIH HHS R24-HD04494309
6 · The paper itself

Abstract

The county-level geographic mortality differentials have persisted in the past four decades in the United States (US). Though several socioeconomic factors (e.g., inequality) partially explain this phenomenon, the role of race/ethnic segregation, in general, and the different dimensions of segregation, more specifically, has been underexplored. Focusing on all-cause age-sex standardized US county-level mortality (2004-2008), this study has two substantive goals: (1) to understand whether segregation is a determinant of mortality and if yes, how the relationship between segregation and mortality varies by racial/ethnic dyads (e.g., white/black), and (2) to explore whether different dimensions of segregation (i.e., evenness, exposure, concentration, centralization, and clustering) are associated with mortality. A third goal is methodological: to assess whether spatial autocorrelation influences our understanding of the associations between the dimensions of segregation and mortality. Race/ethnic segregation was found to contribute to the geographic mortality disparities. Moreover, the relationship with mortality differed by both race/ethnic group and the dimension of segregation. Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality. Among the five dimensions of segregation, evenness and exposure are more strongly related to mortality than other dimensions. Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality. These patterns offer possible insights that help identify omitted variables related to the persistent patterning of mortality in the US.

Indexed as

DeathRacismEthnicityHealthHumansLeast-Squares AnalysisMortality

Identifiers

PMID26398346
PMCPMC4580431

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.