Evidence map›Paper›PMID 33051221›Full record

ArticleBJGP open2020

Continuity of care as a predictor of ongoing frequent attendance in primary care: a retrospective cohort study.

Adam McDermott, Emily Sanderson, Christopher Metcalfe, Rebecca Barnes, Clare Thomas, Helen Cramer, David Kessler

Abstract read
In one paragraph

Article in BJGP open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study.International journal of environmental research and public health · 2022
    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

7 authors.

Adam McDermottCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK adam.mcdermott@bristol.ac.uk.
Emily SandersonBristol Randomised Trials Collaboration, Bristol Medical School, University of Bristol, Bristol, UK.
Christopher MetcalfeBristol Randomised Trials Collaboration, Bristol Medical School, University of Bristol, Bristol, UK.
Rebecca BarnesCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0001-8844-7496
Clare ThomasCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Helen CramerCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
David KesslerCentre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Funding

Department of Health PB-PG-0613-31099
6 · The paper itself

Abstract

backgroundFrequent attenders (FAs) in primary care receive considerable resources with uncertain benefit. Only some FAs attend persistently. Modestly successful models have been built to predict persistent attendance. Nevertheless, an association between relational continuity of care and persistent frequent attendance remains unclear, and could be important considering both the UK government and Royal College of General Practitioner's (RCGP) aim of improving continuity.

aimTo identify predictive measures (including continuity) for persistent frequent attendance that may be modified in future interventions. DESIGN &

settingThis is a retrospective cohort study sampling 35 926 adult patients registered in seven Bristol practices.

methodThe top 3% (1227) of patients by frequency of GP consultations over 6 months were classed as FAs. Individual relational continuity was measured over the same period using the Usual Provider Continuity (UPC) index. Attendance change was calculated for the following 6 months. Multivariable logistic regression analysis was used to determine variables that predicted attendance change.

resultsFAs were on average 8.41 years older (difference 95% confidence interval [CI] = 7.33 to 9.50,

conclusionContinuity does not predict change in frequent attendance. In addition to improving continuity, recent government policy is focused on increasing primary care access. If both aims are achieved it will be interesting to observe any effect on frequent attendance.

Indexed as

continuity of carefrequent attendersGeneral practiceprimary health careretrospective studies

Identifiers

PMID33051221
PMCPMC7880190

What OpenQuestion holds

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LicenceCC BY
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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.