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Background <p>The COVID-19 pandemic placed immense strain on Canada’s healthcare system and nosocomial infections increased risk for both healthcare workers and high-risk patients. Nosocomial COVID-19 surveillance typically relies on time-based case definitions that aim to balance sensitivity against specificity, yielding inconsistent estimates of relative mortality risk. We developed a latent class analysis approach to probabilistically classify healthcare-associated infections using multiple indicator variables and evaluated mortality risk in hospitalized COVID-19 patients.</p> Methods <p>We analyzed COVID-19 surveillance data from Ontario’s Case Contact and Management System and COVaxON vaccine registry (March 17, 2020 to September 4, 2022). Latent class analysis (LCA) integrated five binary indicators (nosocomial flag, outbreak linkage, and three time intervals from admission to positive test) to classify 53,191 hospitalized cases by likelihood of healthcare-acquired infection. We estimated mortality odds and survival by classification using multivariable logistic regression and time-varying Cox models, adjusting for age, comorbidities, immunocompromised status, long-term care residence, vaccination, and pandemic wave.</p> Results <p>LCA identified three classes: unlikely (<i>n</i> = 46,819), moderately likely (<i>n</i> = 2,687), and likely (<i>n</i> = 3,685) healthcare-acquired infection. Compared to unlikely, moderately likely cases had elevated mortality (OR: 1.26, 95% CI: 1.14–1.40); likely cases showed no elevation in cumulative mortality (OR: 1.05, 95% CI: 0.96–1.15). Time-varying survival models revealed substantially higher mortality risk early in the period following infection: hazard ratios were 3.25 (95% CI: 2.99–3.52) for moderately likely and 4.88 (95% CI: 4.53–5.25) for likely healthcare-acquired infection. Single-indicator variable sensitivities ranged from 34% to 70%.</p> Conclusion <p>Defining nosocomial infections early in the COVID-19 pandemic was challenging due to the volume of cases and number of patients hospitalized. Several different characteristics can indicate a case’s likelihood of being healthcare-acquired, and using a single variable to define a nosocomial case often presents trade-offs in terms of sensitivity or specificity. These findings underscore the limitations of single-indicator definitions and the risk of misclassifying cases in a way that would underestimate the relative severity of hospital-acquired infection by biasing effects toward the null. Latent class modeling addresses sensitivity-specificity trade-offs inherent in time-based nosocomial definitions and reveals elevated mortality risk that strict definitions miss.</p>

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Healthcare-associated COVID-19 in Ontario, Canada: case classification and relative mortality

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Natalie J. Wilson,
  • Alicia A. Grima,
  • Clara Eunyoung Lee,
  • David N. Fisman

摘要

Background

The COVID-19 pandemic placed immense strain on Canada’s healthcare system and nosocomial infections increased risk for both healthcare workers and high-risk patients. Nosocomial COVID-19 surveillance typically relies on time-based case definitions that aim to balance sensitivity against specificity, yielding inconsistent estimates of relative mortality risk. We developed a latent class analysis approach to probabilistically classify healthcare-associated infections using multiple indicator variables and evaluated mortality risk in hospitalized COVID-19 patients.

Methods

We analyzed COVID-19 surveillance data from Ontario’s Case Contact and Management System and COVaxON vaccine registry (March 17, 2020 to September 4, 2022). Latent class analysis (LCA) integrated five binary indicators (nosocomial flag, outbreak linkage, and three time intervals from admission to positive test) to classify 53,191 hospitalized cases by likelihood of healthcare-acquired infection. We estimated mortality odds and survival by classification using multivariable logistic regression and time-varying Cox models, adjusting for age, comorbidities, immunocompromised status, long-term care residence, vaccination, and pandemic wave.

Results

LCA identified three classes: unlikely (n = 46,819), moderately likely (n = 2,687), and likely (n = 3,685) healthcare-acquired infection. Compared to unlikely, moderately likely cases had elevated mortality (OR: 1.26, 95% CI: 1.14–1.40); likely cases showed no elevation in cumulative mortality (OR: 1.05, 95% CI: 0.96–1.15). Time-varying survival models revealed substantially higher mortality risk early in the period following infection: hazard ratios were 3.25 (95% CI: 2.99–3.52) for moderately likely and 4.88 (95% CI: 4.53–5.25) for likely healthcare-acquired infection. Single-indicator variable sensitivities ranged from 34% to 70%.

Conclusion

Defining nosocomial infections early in the COVID-19 pandemic was challenging due to the volume of cases and number of patients hospitalized. Several different characteristics can indicate a case’s likelihood of being healthcare-acquired, and using a single variable to define a nosocomial case often presents trade-offs in terms of sensitivity or specificity. These findings underscore the limitations of single-indicator definitions and the risk of misclassifying cases in a way that would underestimate the relative severity of hospital-acquired infection by biasing effects toward the null. Latent class modeling addresses sensitivity-specificity trade-offs inherent in time-based nosocomial definitions and reveals elevated mortality risk that strict definitions miss.