Evidence map›Paper›PMID 40082976›Full record

ArticleBMC health services research2025

Drivers of primary care appointment volumes before and after the COVID-19 pandemic: a longitudinal study.

Tianchang Zhao, Rachel Meacock, Matt Sutton

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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

3 authors.

Tianchang ZhaoHealth Organisation, Policy & Economics (HOPE), School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK. tianchang.zhao@manchester.ac.uk.ORCID http://orcid.org/0000-0001-6063-3860
Rachel MeacockHealth Organisation, Policy & Economics (HOPE), School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK.ORCID http://orcid.org/0000-0001-8933-5058
Matt SuttonHealth Organisation, Policy & Economics (HOPE), School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK.ORCID http://orcid.org/0000-0002-6635-2127

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic led to significant reconfiguration of primary care systems internationally. For example, a large proportion of face-to-face appointments have been replaced by telephone or online consultations. As a result, the relationship between primary care appointment provision and some of its key determinants, such as workforce and demographic characteristics, are likely to have changed. Conclusions from previous studies which used only pre-pandemic data may no longer be applicable under these new configurations.

methodsThis study aims to investigate whether the relationships between primary care appointment rates and their determinants, including workforce composition and the age structure of registered patients, have changed after the COVID-19 pandemic. We conducted longitudinal analysis on the 106 Clinical Commissioning Groups (CCGs) in England over two periods of 24 months (March 2018 to February 2020 and March 2021 to February 2023). We used fixed-effects regression models to relate monthly general practice appointment rates per registered patient to workforce size and composition and population age structure, and compared the results between the two periods.

resultsIn the pre-pandemic period, changes in full time equivalent (FTE) numbers of GP trainees and nurses were the only time-varying variables associated with appointment rates. In the post-lockdown period, the age profile of registered patients became a key determinant of appointment rates. A 1% increase in the proportion of registered patients over 80 years was associated with a 0.165 (38.7%) increase in appointments per patient per month. Changes in FTE numbers of qualified GPs and direct patient care staff were not found to influence the appointment rate in either period.

conclusionThe relationships between primary care appointment rates and the workforce composition and population age structure have changed following the COVID-19 pandemic. The proportion of registered patients over the age of 80 years is now the most significant time-varying driver of appointment rates. General practices serving older patients may face much higher demand and have a bigger challenge providing sufficient appointments in the future compared to before the pandemic.

Indexed as

Appointments and SchedulesCOVID-19Primary Health CareAdolescentAdultAgedEnglandFemaleHumansLongitudinal StudiesMaleMiddle AgedPandemicsSARS-CoV-2Young AdultAppointment volumeCOVID-19General practicePrimary care

Identifiers

PMID40082976
PMCPMC11905703

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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.