Evidence map›Paper›PMID 34800659›Full record

ArticleAnnals of epidemiology2022

Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area.

Christina P Lindan, Manisha Desai, Derek Boothroyd, Timothy Judson, Jenna Bollyky, Hannah Sample, Yingjie Weng, Yuteh Cheng, Alex Dahlen, Haley Hedlin and 7 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 31% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

17 authors at 6 institutions in 1 country.

Christina P LindanDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA. Electronic address: krysia.lindan@ucsf.edu.
Manisha DesaiQuantitative Sciences Unit, School of Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA, USA.
Derek BoothroydQuantitative Sciences Unit, School of Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA, USA.
Timothy JudsonDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA; Office of Population Health, University of California San Francisco, Brisbane, CA, USA.
Jenna BollykyDivision of Primary Care & Population Health, School of Medicine, Stanford University, Stanford, CA, USA.
Hannah SampleDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Yingjie WengQuantitative Sciences Unit, School of Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA, USA.
Yuteh ChengInstitute for Scientific Analysis, Alameda, CA, USA; The Henne Group, San Francisco, CA, USA.
Alex DahlenThe Chan-Zuckerberg Initiative, Redwood City, CA, USA.
Haley HedlinQuantitative Sciences Unit, School of Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA, USA.
Kevin GrumbachDepartment of Family and Community Medicine, University of California San Francisco, San Francisco General Hospital, San Francisco, CA, USA; Office of Population Health, University of California San Francisco, Brisbane, CA, USA.
Jeff HenneInstitute for Scientific Analysis, Alameda, CA, USA.
Sergio GarciaInstitute for Scientific Analysis, Alameda, CA, USA.
Ralph GonzalesDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA.
Charles S CraikDepartment of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA.
George RutherfordDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA.
Yvonne MaldonadoDepartment of Pediatrics, School of Medicine, Stanford University, CA, USA.
University of California, San Francisco · USArista (United States) · USInstitute for Scientific Analysis · USStanford University · USChan Zuckerberg Initiative (United States) · USSan Francisco General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties.

methodsUsing an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance.

resultsWe enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata.

conclusionsOur stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

Indexed as

COVID-19SARS-CoV-2AdultAntibodies, ViralCohort StudiesHumansIncidenceLongitudinal StudiesPrevalenceSan FranciscoAntibodies, ViralCOVID-19Population-based surveyProbability sampleSARS-CoV-2SARS-CoV-2 antibodySARS-CoV-2 viral detectionSurveillance

Identifiers

PMID34800659
PMCPMC8596645
OpenAlexW3213192168

What OpenQuestion holds

Textmetadata
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.