Evidence map›Paper›PMID 34545792›Full record

ArticleEmerging infectious diseases2021

Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA.

Daniel M Parker, Tim Bruckner, Verónica M Vieira, Catalina Medina, Vladimir N Minin, Philip L Felgner, Alissa Dratch, Matthew Zahn, Scott M Bartell, Bernadette Boden-Albala

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Adolescent COVID-19 Vaccine Decision-Making among Parents in Southern California.International journal of environmental research and public health · 2022
    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

10 authors.

Daniel M Parker
Tim Bruckner
Verónica M Vieira
Catalina Medina
Vladimir N Minin
Philip L Felgner
Alissa Dratch
Matthew Zahn
Scott M Bartell
Bernadette Boden-Albala

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We conducted a detailed analysis of coronavirus disease in a large population center in southern California, USA (Orange County, population 3.2 million), to determine heterogeneity in risks for infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the actual burden of disease and testing intensity, test positivity, and mortality. In the first month of the local epidemic (March 2020), case incidence clustered in high-income areas. This pattern quickly shifted, and cases next clustered in much higher rates in the north-central area of the county, which has a lower socioeconomic status. Beginning in April 2020, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race or ethnicity, and ZIP codes with low educational attainment) strongly affected risk for seropositivity and death.

Indexed as

COVID-19EpidemicsCaliforniaHumansSARS-CoV-2Seroepidemiologic StudiesCaliforniacoronavirus diseaseCOVID-19health equitymortalityOrange Countyrespiratory infectionsSARS-CoV-2seropositivitysevere acute respiratory syndrome coronavirus 2test positivityUnited Statesviruseszoonoses

Identifiers

PMID34545792
PMCPMC8462316

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.