Evidence map›Paper›PMID 40302730›Full record

ArticleBMJ public health2025

Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan.

Daniel S Farrar, Lisa G Pell, Yasin Muhammad, Sher Hafiz Khan, Lauren Erdman, Diego G Bassani, Zachary Tanner, Imran Ahmed Chauhadry, Muhammad Karim, Falak Madhani and 8 more

Abstract read
In one paragraph

Article in BMJ public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

18 authors.

Daniel S FarrarCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.ORCID 0000-0002-7823-1912
Lisa G PellCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.
Yasin MuhammadGilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan.
Sher Hafiz KhanGilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan.
Lauren ErdmanVector Institute, The Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada.
Diego G BassaniCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.
Zachary TannerCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.
Imran Ahmed ChauhadryCentre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan.ORCID 0000-0001-7425-3113
Muhammad KarimCentre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan.
Falak MadhaniAga Khan Health Service Pakistan, Karachi, Sindh, Pakistan.
Shariq ParachaAga Khan Health Service Pakistan, Karachi, Sindh, Pakistan.
Masood Ali KhanGilgit Regional Office, Aga Khan Health Service Pakistan, Gilgit, Gilgit-Baltistan, Pakistan.
Sajid SoofiCentre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan.ORCID 0000-0003-4192-8406
Monica TaljaardClinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, Ontario, Canada.
Rachel F SpitzerDepartment of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario, Canada.
Sarah M Abu FadalehCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.
Zulfiqar A BhuttaCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.
Shaun K MorrisCentre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Robust estimates of COVID-19 prevalence in settings with limited capacity for SARS-CoV-2 molecular and serologic testing are scarce. We aimed to describe the epidemiology of confirmed and probable COVID-19 in Gilgit-Baltistan, and to develop a symptom-based predictive model to identify infected but undiagnosed individuals with COVID-19. Methods: We conducted a cross-sectional survey in 10 257 randomly selected households in Gilgit-Baltistan from June to August 2021. Data regarding SARS-CoV-2 testing, healthcare worker (HCW) diagnoses, symptoms and outcomes since March 2020 were self-reported by households. 'Confirmed/probable' infection was defined as a positive test, HCW COVID-19 diagnosis or HCW pneumonia diagnosis with COVID-19-positive contact. Robust Poisson regression was conducted to assess differences in symptoms, outcomes and SARS-CoV-2 testing rates. We developed a symptom-based machine learning model to differentiate confirmed/probable infections from those with negative tests. We applied this model to untested respondents to estimate the total prevalence of SARS-CoV-2 infection. Results: Data were collected for 77 924 people. Overall, 314 (0.5%) had confirmed/probable infections, 3263 (4.4%) had negative tests and 74 347 (95.1%) were untested. Children were tested less often than adults (adjusted prevalence ratio (aPR) 0.08, 95% CI 0.06 to 0.12 for ages 1-4 years vs 30-39 years), while males were tested more often than females (aPR 1.51, 95% CI 1.40 to 1.63). In the predictive model, area under the receiver operating characteristic curve was 0.92 (95% CI 0.90 to 0.93). We estimate there were 8-17 total SARS-CoV-2 infections for each positive test (8-17:1). The ratio of estimated to confirmed cases was higher for ages 1-4 years (211-480:1), 5-9 years (80-185:1) and for females (13-25:1). Conclusions: From March 2020 to August 2021, the majority of SARS-CoV-2 infections in Gilgit-Baltistan went unconfirmed, particularly among women and children. Predictive models which incorporate self-reported symptoms may improve understanding of the burden of disease in settings lacking diagnostic capacity.

Indexed as

COVID-19Cross-Sectional StudiesEpidemiologyPopulation SurveillanceStatistics as Topic

Identifiers

PMID40302730
PMCPMC12039044

What OpenQuestion holds

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LicenceCC BY
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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.