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Background <p>This study examined risk factors for post-COVID respiratory infection using data from the <i>Tracking the Burden</i>,<i> Distribution</i>,<i> Impact of Tracking Post-COVID-19 Conditions in Diverse Populations for Children</i>,<i> Adolescents</i>,<i> Adults</i> (Track PCC) passive surveillance cohort.</p> Methods <p>This retrospective study included adult Temple Health patients in Philadelphia, Pennsylvania with SARS-CoV-2 (COVID-19) infections from March 2020 to December 2022 and ≥ 90 days follow-up. COVID-19 infection was identified via laboratory testing, billing codes, or clinical documentation. The primary outcome was post-COVID respiratory infection identified by billing codes. Predictors included social, clinical, and COVID-related correlates. Adjusted logistic regression models were used on 17,539 complete cases and multiple imputed datasets (<i>n</i> = 45,513) with SuperMICE.</p> Results <p>In complete-case analysis, uninsured coverage, Alpha variant, total comorbidities, and hospitalization were associated with lower odds of respiratory post-COVID conditions (ORs: 0.02–0.99). Dual Medicare/Medicaid and current smokers, increased odds (ORs: 1.33–1.58). Imputed analyses showed consistent results with and additionally observed higher odds of respiratory infection among those with Medicaid and higher number of vaccinations, and lower odds among those of any non-white race, Hispanic ethnicity.</p> Conclusions <p>Temple Track PCC findings identify high-risk populations and underscore the utility of advanced imputation in surveillance-based research.</p>

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Investigating respiratory infection as a post-COVID-19 condition using a large passive surveillance cohort from Track PCC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Marina Oktapodas Feiler,
  • Recai M. Yucel,
  • Bari J. Dzomba,
  • Resa M. Jones

摘要

Background

This study examined risk factors for post-COVID respiratory infection using data from the Tracking the Burden, Distribution, Impact of Tracking Post-COVID-19 Conditions in Diverse Populations for Children, Adolescents, Adults (Track PCC) passive surveillance cohort.

Methods

This retrospective study included adult Temple Health patients in Philadelphia, Pennsylvania with SARS-CoV-2 (COVID-19) infections from March 2020 to December 2022 and ≥ 90 days follow-up. COVID-19 infection was identified via laboratory testing, billing codes, or clinical documentation. The primary outcome was post-COVID respiratory infection identified by billing codes. Predictors included social, clinical, and COVID-related correlates. Adjusted logistic regression models were used on 17,539 complete cases and multiple imputed datasets (n = 45,513) with SuperMICE.

Results

In complete-case analysis, uninsured coverage, Alpha variant, total comorbidities, and hospitalization were associated with lower odds of respiratory post-COVID conditions (ORs: 0.02–0.99). Dual Medicare/Medicaid and current smokers, increased odds (ORs: 1.33–1.58). Imputed analyses showed consistent results with and additionally observed higher odds of respiratory infection among those with Medicaid and higher number of vaccinations, and lower odds among those of any non-white race, Hispanic ethnicity.

Conclusions

Temple Track PCC findings identify high-risk populations and underscore the utility of advanced imputation in surveillance-based research.