Evidence map›Paper›PMID 34295126›Full record

ArticleEthnicity & disease2021

Use of Zip Code Based Aggregate Indicators to Assess Race Disparities in COVID-19.

Kevin D Long, Steven M Albert

Abstract read
In one paragraph

Article in Ethnicity & disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Kevin D LongUniversity of Pittsburgh, Graduate School of Public Health, Pittsburgh, PA.
Steven M AlbertUniversity of Pittsburgh, Behavioral and Community Health Sciences, Pittsburgh, PA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In the first six months of the pandemic, information on race and ethnicity was missing for half of the US COVID-19 cases. Combining case ascertainment with census-based zip code indicators may identify COVID-19 race-ethnicity disparities in the absence of individual-level data. Design: Ecological retrospective study for the period March-July 2020. Setting: Population-based investigation, Allegheny County, Pennsylvania. Participants: All COVID-19 cases, adjusted for zip code area population, in the early period of the pandemic. Main Outcome Measures: Monthly COVID-19 incidence and requests for human services by zip code level indicators of race-ethnicity and poverty. Results: In the early period of the pandemic, COVID-19 incidence was higher in zip codes with a greater proportion of racial and ethnic minorities. Zip codes with the highest quartile of minority residents (>25.1% of population) had a COVID-19 incidence of 60.1 (95% CI: 51.7-68.5) per 10,000 in this period; zip codes with the lowest quartile of minority residents (<6.3%) had an incidence of 31.3 (95% CI: 14.4-48.2). Requests for human services during this period (volume of 211 calls and county services) confirm these disparities. Conclusion: Use of census-defined race-ethnicity proportions by zip code offers a way to identify disparities when individual race-ethnicity data are unavailable.

Indexed as

COVID-19Cross-Sectional StudiesHumansMinority GroupsRetrospective StudiesSARS-CoV-2COVID-19Ethnic GroupsMinority GroupsPovertyRacial DisparitiesZip Codes

Identifiers

PMID34295126
PMCPMC8288471

What OpenQuestion holds

Textmetadata
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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.