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Record W4307054504 · doi:10.1093/pch/pxac100.090

91 The impact of non-pharmaceutical public health interventions on hospital admissions and mortality from common causes of pediatric respiratory distress: a single center perspective

2022· article· en· W4307054504 on OpenAlexaffabout
Madeline Parker, Olivia Griffin, Félix Lévesque, Ayisha Kurji, Erin Woods

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsRoyal University HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineBronchiolitisAsthmaPneumoniaPsychological interventionRespiratory distressPublic healthPediatricsExacerbationEmergency medicineIntensive care medicineInternal medicineRespiratory systemSurgery

Abstract

fetched live from OpenAlex

Abstract Background In response to the COVID-19 pandemic, jurisdictions around the world implemented policies to reduce COVID-19 transmission through public masking, travel restrictions, and closure of non-essential businesses. Collectively known as non-pharmaceutical interventions (NPI), these strategies reliably reduce the spread of COVID-19. International data suggests NPI also reduce hospitalizations for pediatric respiratory infections and their consequences, particularly asthma exacerbation. However, few Canadian studies have examined the impact of NPI on hospitalizations for common causes of pediatric respiratory distress. Objectives This study describes the impact of NPI on admissions for bronchiolitis, pneumonia, and asthma at a Canadian pediatric tertiary care centre. Design/Methods A retrospective chart review was conducted including all pediatric patients <18 years admitted to the general pediatric and pediatric intensive care units with bronchiolitis, pneumonia, or asthma. Data regarding diagnosis, length of hospitalization, and mortality were collected before (September 2016-March 2020) and in the 6 months after provincial NPI implementation (March 2020-September 2020). NPI were present throughout this period, however, specific measures varied due to evolving public health orders. Chi-squared testing was conducted to describe the impact of NPI on number of admissions, length of hospitalization, and mortality. Results Participants (n=1631) included 111 (6.8%) patients <1 month, 878 (53.8%) patients 1-23 months, 331 (20.3%) patients 24 months-4 years, and 311 (19.1%) patients ≥5 years. A mean of 205 patients were admitted every 6 months with respiratory distress (bronchiolitis, pneumonia, and/or asthma) prior to NPI implementation. During this timeframe, the 6-month mean admissions due to asthma, pneumonia, and bronchiolitis were 48, 56, and 101, respectively. In the 6 months following NPI implementation, there were 56 admissions for respiratory distress, including 15 for asthma, 19 for pneumonia, and 22 for bronchiolitis. Mean length of stay increased following the implementation of NPI from 8.49 to 11.68 days, whereas 6-month mean mortality decreased from two to zero deaths. Results did not attain statistical significance (p>0.05). Conclusion Results suggest NPI reduce hospitalizations and mortality from bronchiolitis, pneumonia, and asthma. Given the similar seasonality of these conditions, periodic use of NPI beyond the COVID-19 pandemic may reduce pediatric morbidity and mortality from common causes of respiratory distress. However, additional research is needed to describe the relationship between NPI and length of hospitalization. Future studies should also examine the impact of NPI on other pediatric infectious diseases to better characterize their utility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.728

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.113
GPT teacher head0.462
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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