Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19

Jinyan Lyu, Wanting Cui, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

Original languageEnglish
Title of host publicationChallenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
EditorsBrigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781643682846
StatePublished - 25 May 2022
Event32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France
Duration: 27 May 202230 May 2022

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference32nd Medical Informatics Europe Conference, MIE 2022


  • COVID-19
  • Conditional Logistic Regression
  • Mortality


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