Evidence map›Paper›PMID 38499832›Full record

ArticleInfectious diseases and therapy2024

Estimation of Symptomatic Respiratory Syncytial Virus Infection Incidence in Adults in Multiple Countries: A Time-Series Model-Based Analysis Protocol.

Robin Bruyndonckx, Aleksandra Polkowska-Kramek, Caihua Liang, Charles Nuttens, Thao Mai Phuong Tran, Bradford D Gessner, Elizabeth Begier

Abstract read
In one paragraph

Article in Infectious diseases and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Article
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  6. Observational
  7. Article
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  9. Journal of preventive medicine and hygiene · 2024
    Article
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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

7 authors.

Robin BruyndonckxEpidemiology and Pharmacovigilance, P95, Leuven, Belgium.ORCID http://orcid.org/0000-0002-4217-2869
Aleksandra Polkowska-KramekEpidemiology and Pharmacovigilance, P95, Leuven, Belgium.
Caihua LiangPfizer Inc, New York, USA.
Charles NuttensPfizer Inc, Paris, France.
Thao Mai Phuong TranEpidemiology and Pharmacovigilance, P95, Leuven, Belgium.
Bradford D GessnerPfizer Inc, New York, USA.
Elizabeth BegierPfizer Inc, Dublin, Republic of Ireland. Elizabeth.begier@pfizer.com.ORCID http://orcid.org/0000-0002-1287-5416

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEstimating respiratory syncytial virus (RSV) burden in adults is challenging because of non-specific symptoms, infrequent standard-of-care testing, resolution of viral shedding before seeking medical care, test positivity that varies by specimen site in the upper airway and lower diagnostic test sensitivity compared to children. Conducting prospective observational studies to assess RSV burden in adults is time- and resource-intensive. Thus, model-based approaches can be applied using existing data to obtain more accurate estimates of RSV burden. This protocol establishes essential elements for estimating RSV incidence rate in adults using a time series model-based approach. It can be tailored to specific databases and applied globally across countries, enabling estimation of local RSV disease burden to inform public health decision-making, including immunization policy.

methodsData are analysed using a quasi-Poisson regression model, considering the effect of baseline trends and pathogen co-circulation, stratified by age and risk status. Pathogen co-circulation is represented by viral proxies defined based on ICD code groupings indicating RSV and influenza-specific hospitalizations, lagged 0 up to 4 weeks based on the model selection. A final model is constructed in two steps: optimization of the time trend (using p-values) and selection of the viral proxy lag time (using test statistics, to prioritize the most biologically plausible option). The yearly incidence rate and percentage of events attributable to RSV are estimated from the final model. Confidence intervals are calculated using residual bootstrapping. PLANNED OUTCOMES: Outcomes to be modelled are based on administrative ICD code groupings and include the number of cardiorespiratory, respiratory and cardiovascular events in a specific care setting (e.g., general practitioner visit, emergency department visit, hospitalization and death). Cardiovascular events are limited to those for which existing evidence suggests an association with RSV infection. Additional secondary outcomes are constructed as a subset of the primary outcomes based on specific ICD code groups.

Indexed as

Disease burdenEmergency department visitsGeneral practitioner visitsHospitalizationMortalityRespiratory syncytial virusTime series analysis

Identifiers

PMID38499832
PMCPMC11058168

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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.