Evidence map›Paper›PMID 42558866›Full record

ArticleFrontiers in public health2026

Reconstructing severe acute respiratory infection dynamics from ICD-10 hospital discharge data: a 10-year analysis of 11.2 million discharges in Ecuador, 2014-2023.

Jaime Angamarca-Iguago, Jaen Cagua-Ordónez, Juan Marcos Parise-Vasco, Natasha Bella Fuentes-Tumbaco, Mónica Escobar-Naranjo, Claudia Reytor-González, Daniel Simancas-Racines

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Jaime Angamarca-IguagoCenter for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnológica Indoamérica, Ambato, Ecuador.
Jaen Cagua-OrdónezCenter for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnológica Indoamérica, Ambato, Ecuador.
Juan Marcos Parise-VascoCenter for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnológica Indoamérica, Ambato, Ecuador.
Natasha Bella Fuentes-TumbacoLaboratorio Clínico, Unidad de Apoyo Diagnóstico, Hospital Pablo Arturo Suárez, Quito, Ecuador.
Mónica Escobar-NaranjoDirección Nacional de Vigilancia Epidemiológica, Ministerio de Salud Pública del Ecuador, Quito, Ecuador.
Claudia Reytor-GonzálezCenter for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnológica Indoamérica, Ambato, Ecuador.
Daniel Simancas-RacinesCenter for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnológica Indoamérica, Ambato, Ecuador.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In many low- and middle-income countries, surveillance of severe acute respiratory infections (SARI) relies on administrative hospital data without virological confirmation, and its ability to capture epidemic dynamics and age-specific burden remains uncertain. Methods: We analyzed 11,232,698 hospital discharges recorded by Ecuador's National Institute of Statistics and Censuses between 2014 and 2023, identified SARI episodes using International Classification of Diseases, 10th Revision (ICD-10) codes under broad and length-of-stay-restricted case definitions, and incorporated a neonatal component that included perinatal respiratory codes for infants younger than 1 year. Serfling harmonic regression was used to estimate seasonal baselines and excess SARI admissions at national, provincial, and age-stratified levels. Results: Administrative data identified 538,272 SARI discharges and revealed marked heterogeneity in incidence and case fatality across provinces and age groups. A previously unrecognized shift in ICD-10 coding from respiratory to perinatal chapters produced an apparent 99% decline in infant SARI after 2014; reclassifying perinatal respiratory codes restored stable high SARI rates in infants and increased their estimated burden by nearly ten-fold. Nationally, we identified 50 epidemic weeks with 77,352 excess SARI discharges (95% CI 70,030-85,982), while the COVID-19 pandemic disrupted typical seasonality, reducing admissions but increasing mortality among older adults. Conclusions: Routinely collected hospital discharge data can reconstruct SARI epidemic dynamics and geographic disparities in the absence of virological surveillance, but are highly sensitive to coding practices. Incorporating neonatal perinatal respiratory codes into SARI definitions is essential to avoid underestimating infant burden and misinforming public health priorities.

Indexed as

International Classification of DiseasesPatient DischargeRespiratory Tract InfectionsAcute DiseaseAdolescentAdultAgedChildChild, PreschoolEcuadorFemaleHumansIncidenceInfantInfant, NewbornMaleEcuadorepidemiologyhospital dischargesICD-10respiratory tract infectionsSARISerfling regressionsurveillance

Identifiers

PMID42558866
PMCPMC13438157

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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.