Evidence map›Paper›PMID 42350003›Full record

Observational studyBMJ open2026

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.

Anthony C Dowell, Jayden MacRae, Rachel Blanch, Mika Hiroi, Lorraine Castelino, Nikki Turner

Abstract readObservational Study
In one paragraph

Observational study in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anthony C DowellUniversity of Otago, Wellington, New Zealand.ORCID http://orcid.org/0000-0003-0131-117X
Jayden MacRaeDatacraft Analytics, Wellington, New Zealand.
Rachel BlanchDatacraft Analytics, Wellington, New Zealand.
Mika HiroiImmunisation Advisory Centre, University of Auckland - Grafton Campus, Wellington, New Zealand.
Lorraine CastelinoUniversity of Auckland - Grafton Campus, Auckland, New Zealand lorraine.castelino@auckland.ac.nz.ORCID http://orcid.org/0009-0001-3081-6778
Nikki TurnerGeneral Practice and Primary Health Care, University of Auckland, Wellington, New Zealand.ORCID http://orcid.org/0000-0001-8760-5245

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo 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.

designA natural language processing (NLP) software interference algorithm was used to interrogate quantitative and qualitative cross-sectional and retrospective cohort data from clinical consultation notes.

settingPrimary care presentations across Aotearoa, NZ, encompassing both urban and rural areas.

participantsElectronic records were obtained from general practice interactions from 2018 to end of 2023. Participants consisted of 37 consenting practices from eight districts.

resultsA 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. Patterns vary markedly across different respiratory groups. While the degree of variance has reduced, there continues to be marked disruption of pre-COVID patterns.

conclusionsPrimary care respiratory presentations in NZ continue to experience persistent variation from the pre-COVID typical seasonal presentation patterns. There is significant change in presentations, likely due to changing disease prevalence, but also to changing patient and provider behaviour. These results have implications for primary care workload management and public health policy.

Indexed as

COVID-19Electronic Health RecordsNatural Language ProcessingPrimary Health CareCross-Sectional StudiesData MiningFemaleHumansMaleNew ZealandPandemicsRetrospective StudiesSARS-CoV-2SeasonsClinical auditCOVID-19Primary Health Care

Identifiers

PMID42350003
PMCPMC13311641

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.