{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T19:39:14Z","timestamp":1789155554545,"version":"build-2803163510"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2013restricted 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u201385,982), while the COVID-19 pandemic disrupted typical seasonality, reducing admissions but increasing mortality among older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1863826","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:56:32Z","timestamp":1784699792000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Reconstructing severe acute respiratory infection dynamics from ICD-10 hospital discharge data: a 10-year analysis of 11.2 million discharges in Ecuador, 2014\u20132023"],"prefix":"10.3389","volume":"14","author":[{"given":"Jaime","family":"Angamarca-Iguago","sequence":"first","affiliation":[{"name":"Center for Evidence Ecosystems, Implementation Science and Decision-Making (CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad Tecnol\u00f3gica Indoam\u00e9rica","place":["Ambato, 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