Background <p>Influenza-like illness (ILI) and severe acute respiratory infection (SARI) remain significant public health burdens, particularly among older adults and in resource-limited settings. However, long-term epidemiologic trends across different pandemic phases remain poorly understood, especially for vulnerable populations.</p> Methods <p>Surveillance and hospital records collected between 2017 and 2024 from two sentinel hospitals in Shanghai, China, were analyzed. Interrupted time-series (ITS) models were used to evaluate temporal changes in ILI and SARI incidence. Large-scale data processing techniques were applied to extract structured information from unstructured medical records, enabling comprehensive analysis of clinical characteristics and outcomes.</p> Results <p>Both ILI and SARI cases declined sharply during the COVID-19 pandemic but rebounded thereafter. Older adults (≥60 years) experienced disproportionately greater increases in these cases, along with higher risks of ILI-to-SARI progression and ICU admission. Although clinical recovery rates improved in the post-pandemic period, the demand for oxygen therapy and hospital-based care remained elevated.</p> Conclusion <p>These findings highlight the long-term impact of the COVID-19 pandemic on respiratory infection dynamics and underscore the need for targeted interventions for older adults. Despite highest susceptibility and baseline risk for severe illness, older adults demonstrated improved post-pandemic clinical outcomes due to enhanced clinical prioritization. Strengthening vaccination programs, improving health education, and enhancing resource preparedness are crucial to reduce the post-pandemic disease burden, particularly in settings with limited healthcare capacity. These findings provide important evidence to strengthen post-pandemic respiratory surveillance and improve protection strategies for older adults in China.</p>

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Long-term trends of influenza-like illness and severe acute respiratory infection across pandemic phases: an interrupted time-series study

  • Ying Guo,
  • Ning Ma,
  • Huimin Xu,
  • Fangyi Zhu,
  • Wanyu Qiao,
  • Ye Yao,
  • Weibing Wang

摘要

Background

Influenza-like illness (ILI) and severe acute respiratory infection (SARI) remain significant public health burdens, particularly among older adults and in resource-limited settings. However, long-term epidemiologic trends across different pandemic phases remain poorly understood, especially for vulnerable populations.

Methods

Surveillance and hospital records collected between 2017 and 2024 from two sentinel hospitals in Shanghai, China, were analyzed. Interrupted time-series (ITS) models were used to evaluate temporal changes in ILI and SARI incidence. Large-scale data processing techniques were applied to extract structured information from unstructured medical records, enabling comprehensive analysis of clinical characteristics and outcomes.

Results

Both ILI and SARI cases declined sharply during the COVID-19 pandemic but rebounded thereafter. Older adults (≥60 years) experienced disproportionately greater increases in these cases, along with higher risks of ILI-to-SARI progression and ICU admission. Although clinical recovery rates improved in the post-pandemic period, the demand for oxygen therapy and hospital-based care remained elevated.

Conclusion

These findings highlight the long-term impact of the COVID-19 pandemic on respiratory infection dynamics and underscore the need for targeted interventions for older adults. Despite highest susceptibility and baseline risk for severe illness, older adults demonstrated improved post-pandemic clinical outcomes due to enhanced clinical prioritization. Strengthening vaccination programs, improving health education, and enhancing resource preparedness are crucial to reduce the post-pandemic disease burden, particularly in settings with limited healthcare capacity. These findings provide important evidence to strengthen post-pandemic respiratory surveillance and improve protection strategies for older adults in China.