Evidence map›Paper›PMID 33737137›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2021

Serial population-based serosurveys for COVID-19 in two neighbourhoods of Karachi, Pakistan.

Muhammad Imran Nisar, Nadia Ansari, Farah Khalid, Mashal Amin, Hamna Shahbaz, Aneeta Hotwani, Najeeb Rehman, Sierra Pugh, Usma Mehmood, Arjumand Rizvi and 9 more

Abstract read
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Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
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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

19 citing papers in PubMed.

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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

19 authors.

Muhammad Imran NisarDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan. Electronic address: Imran.nisar@aku.edu.
Nadia AnsariDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Farah KhalidDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Mashal AminDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Hamna ShahbazDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Aneeta HotwaniDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Najeeb RehmanDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Sierra PughColorado State University, Fort Collins, CO, USA.
Usma MehmoodDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Arjumand RizviDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Arslan MemonHealth Department, Government of Sindh, Karachi, Pakistan.
Zahoor AhmedHealth Department, Government of Sindh, Karachi, Pakistan.
Ashfaque AhmedHealth Department, Government of Sindh, Karachi, Pakistan.
Junaid IqbalDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Ali Faisal SaleemDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Uzma Bashir AamirWorld Health Organization Country Office, Karachi, Sind, Pakistan.
Daniel B LarremoreUniversity of Colorado, Boulder, CO, USA.
Bailey FosdickColorado State University, Fort Collins, CO, USA.
Fyezah JehanDepartment of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan. Electronic address: fyezah.jehan@aku.edu.

Funding

Medical Research Council MR/L004283/1World Health Organization 001
6 · The paper itself

Abstract

objectiveTo determine population-based estimates of coronavirus disease 2019 (COVID-19) in a densely populated urban community of Karachi, Pakistan.

methodsThree cross-sectional surveys were conducted in April, June and August 2020 in low- and high-transmission neighbourhoods. Participants were selected at random to provide blood for Elecsys immunoassay for detection of anti-severe acute respiratory syndrome coronavirus-2 antibodies. A Bayesian regression model was used to estimate seroprevalence after adjusting for the demographic characteristics of each district.

resultsIn total, 3005 participants from 623 households were enrolled in this study. In Phase 2, adjusted seroprevalence was estimated as 8.7% [95% confidence interval (CI) 5.1-13.1] and 15.1% (95% CI 9.4-21.7) in low- and high-transmission areas, respectively, compared with 0.2% (95% CI 0-0.7) and 0.4% (95% CI 0-1.3) in Phase 1. In Phase 3, it was 12.8% (95% CI 8.3-17.7) and 21.5% (95% CI 15.6-28) in low- and high-transmission areas, respectively. The conditional risk of infection was 0.31 (95% CI 0.16-0.47) and 0.41 (95% CI 0.28-0.52) in low- and high-transmission neighbourhoods, respectively, in Phase 2. Similar trends were observed in Phase 3. Only 5.4% of participants who tested positive for COVID-19 were symptomatic. The infection fatality rate was 1.66%, 0.37% and 0.26% in Phases 1, 2 and 3, respectively.

conclusionContinuing rounds of seroprevalence studies will help to improve understanding of secular trends and the extent of infection during the course of the pandemic.

Indexed as

COVID-19 Serological TestingAdolescentAdultAntibodies, ViralBayes TheoremChildChild, PreschoolCOVID-19Cross-Sectional StudiesFemaleHumansImmunoassayInfantMaleMiddle AgedPakistanAntibodies, ViralAntibodyCOVID-19PakistanSeroepidemiologySeroprevalenceSurveillance

Identifiers

PMID33737137
PMCPMC8752032

What OpenQuestion holds

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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.