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Record W3174003681 · doi:10.1016/s2214-109x(21)00218-7

Global burden of acute lower respiratory infection associated with human parainfluenza virus in children younger than 5 years for 2018: a systematic review and meta-analysis

2021· review· en· W3174003681 on OpenAlexaboutno aff
Xin Wang, You Li, Maria Deloria Knoll, Shabir A. Madhi, Cheryl Cohen, Vina Lea Arguelles, Sudha Basnet, Quique Bassat, W. Abdullah Brooks, Marcela Echavarría, Rodrigo Fasce, Ángela Gentile, Doli Goswami, Nusrat Homaira, Stephen R. C. Howie, Karen L. Kotloff, Najwa Khuri‐Bulos, Anand Krishnan, Marilla Lucero, Socorro Lupisan, Maria Mathisen, Kenneth A McLean, Ainara Mira‐Iglesias, Cinta Moraleda, Michiko Okamoto, Histoshi Oshitani, Katherine L. O’Brien, Betty E. Owor, Zeba Rasmussen, Barbara Rath, Vahid Salimi, Pongpun Sawatwong, J. Anthony G. Scott, Eric A. F. Simões, Viviana Sotomayor, Donald M. Thea, Florette K. Treurnicht, Lay‐Myint Yoshida, Heather J. Zar, Harry Campbell, Harish Nair

Bibliographic record

VenueThe Lancet Global Health · 2021
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
FundersFogarty International CenterMedical Research CouncilJohns Hopkins Bloomberg School of Public HealthInnovative Medicines InitiativeChina Scholarship CouncilGovernment of the United KingdomUniversity of New South WalesNational Institute for Health and Care ResearchUniversitetet i BergenTribhuvan UniversityUniversitat de BarcelonaNational Research FoundationHorizon 2020International Centre for Diarrhoeal Disease Research, BangladeshEuropean Federation of Pharmaceutical Industries and AssociationsUniversity of JordanJapan Agency for Medical Research and DevelopmentUniversité Mohammed V de RabatAstraZenecaCidara TherapeuticsWellcome TrustNational Institutes of HealthRegeneron PharmaceuticalsCenters for Disease Control and PreventionInstitució Catalana de Recerca i Estudis AvançatsAlereSanofiGlaxoSmithKlineTehran University of Medical Sciences and Health ServicesGAVI AllianceBill and Melinda Gates FoundationJohns Hopkins UniversityPfizerNovavaxU.S. Department of Health and Human Services
KeywordsMeta-analysisMedicineCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MEDLINE2019-20 coronavirus outbreakHuman Parainfluenza VirusVirologyRespiratory systemVirusIntensive care medicinePediatricsInternal medicineBiologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Human parainfluenza virus (hPIV) is a common virus in childhood acute lower respiratory infections (ALRI). However, no estimates have been made to quantify the global burden of hPIV in childhood ALRI. We aimed to estimate the global and regional hPIV-associated and hPIV-attributable ALRI incidence, hospital admissions, and mortality for children younger than 5 years and stratified by 0-5 months, 6-11 months, and 12-59 months of age. METHODS: We did a systematic review of hPIV-associated ALRI burden studies published between Jan 1, 1995, and Dec 31, 2020, found in MEDLINE, Embase, Global Health, Cumulative Index to Nursing and Allied Health Literature, Web of Science, Global Health Library, three Chinese databases, and Google search, and also identified a further 41 high-quality unpublished studies through an international research network. We included studies reporting community incidence of ALRI with laboratory-confirmed hPIV; hospital admission rates of ALRI or ALRI with hypoxaemia in children with laboratory-confirmed hPIV; proportions of patients with ALRI admitted to hospital with laboratory-confirmed hPIV; or in-hospital case-fatality ratios (hCFRs) of ALRI with laboratory-confirmed hPIV. We used a modified Newcastle-Ottawa Scale to assess risk of bias. We analysed incidence, hospital admission rates, and hCFRs of hPIV-associated ALRI using a generalised linear mixed model. Adjustment was made to account for the non-detection of hPIV-4. We estimated hPIV-associated ALRI cases, hospital admissions, and in-hospital deaths using adjusted incidence, hospital admission rates, and hCFRs. We estimated the overall hPIV-associated ALRI mortality (both in-hospital and out-hospital mortality) on the basis of the number of in-hospital deaths and care-seeking for child pneumonia. We estimated hPIV-attributable ALRI burden by accounting for attributable fractions for hPIV in laboratory-confirmed hPIV cases and deaths. Sensitivity analyses were done to validate the estimates of overall hPIV-associated ALRI mortality and hPIV-attributable ALRI mortality. The systematic review protocol was registered on PROSPERO (CRD42019148570). FINDINGS: 203 studies were identified, including 162 hPIV-associated ALRI burden studies and a further 41 high-quality unpublished studies. Globally in 2018, an estimated 18·8 million (uncertainty range 12·8-28·9) ALRI cases, 725 000 (433 000-1 260 000) ALRI hospital admissions, and 34 400 (16 400-73 800) ALRI deaths were attributable to hPIVs among children younger than 5 years. The age-stratified and region-stratified analyses suggested that about 61% (35% for infants aged 0-5 months and 26% for 6-11 months) of the hospital admissions and 66% (42% for infants aged 0-5 months and 24% for 6-11 months) of the in-hospital deaths were in infants, and 70% of the in-hospital deaths were in low-income and lower-middle-income countries. Between 73% and 100% (varying by outcome) of the data had a low risk in study design; the proportion was 46-65% for the adjustment for health-care use, 59-77% for patient groups excluded, 54-93% for case definition, 42-93% for sampling strategy, and 67-77% for test methods. Heterogeneity in estimates was found between studies for each outcome. INTERPRETATION: We report the first global burden estimates of hPIV-associated and hPIV-attributable ALRI in young children. Globally, approximately 13% of ALRI cases, 4-14% of ALRI hospital admissions, and 4% of childhood ALRI mortality were attributable to hPIV. These numbers indicate a potentially notable burden of hPIV in ALRI morbidity and mortality in young children. These estimates should encourage and inform investment to accelerate the development of targeted interventions. FUNDING: Bill & Melinda Gates Foundation.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.542
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.174
GPT teacher head0.492
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations100
Published2021
Admission routes1
Has abstractyes

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