Disrupted variance in respiratory illness presentation patterns in New Zealand primary care through and after the SARS-CoV-2 pandemic: an observational cohort study using automated mining of electronic health records and clinical text

25 Jun 20266 min read0 viewsJournal Feed

GIST (Key Takeaways)

Objectives To identify and compare respiratory illness presentation rates and service utilisation to primary care in Aotearoa, New Zealand (NZ) before, through and after the COVID-19 pandemic. Design A natural language processing (NLP) software interference algorithm was used to interrogate quantitative and qualitative cross-sectional and retrospective cohort data from clinical consultation notes. Setting Primary care presentations across Aotearoa, NZ, encompassing both urban and rural areas.
Participants Electronic records were obtained from general practice interactions from 2018 to end of 2023. Participants consisted of 37 consenting practices from eight districts. Results A total of 10 421 399 unique medical interactions were processed by the NLP software, of which 15.1% (1 573 631) were related to a respiratory issue.
This ranged from 14.2% and 13.7% in 2018 and 2019 to a peak of 25.2% in 2022. Expected patterns of seasonal variation were observed prepandemic with small variance in mean rates. Due to the COVID-19 pandemic, border closures and then SARS-CoV-2 arrival, there was significant disruption to previous seasonal patterns across all respiratory illness presentations in primary care.

Clinical Editorial

Summary

BMJ Open published a clinical update in Research Highlights on 25 Jun 2026.

The item focuses on Disrupted variance in respiratory illness presentation patterns in New Zealand primary care through and after the SARS-CoV-2 pandemic: an observational cohort study using automated mining of electronic health records and clinical text.

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