Evidence map›Paper›PMID 27894265›Full record

ArticleBMC family practice2016

Long term condition morbidity in English general practice: a cross-sectional study using three composite morbidity measures.

Charlotte Weston, Alexander Gilkes, Stevo Durbaba, Peter Schofield, Patrick White, Mark Ashworth

Abstract read
In one paragraph

Article in BMC family practice, 2016. 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. Impact of primary care funding on patient satisfaction: a retrospective longitudinal study of English general practice, 2013-2016.The British journal of general practice : the journal of the Royal College of General Practitioners · 2021
    Article
  4. Article
  5. 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.

Charlotte WestonDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK. c.f.weston@doctors.org.uk.ORCID 0000-0002-3074-8496
Alexander GilkesDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK.
Stevo DurbabaDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK.
Peter SchofieldDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK.
Patrick WhiteDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK.
Mark AshworthDivision of Health and Social Care Research, Department of Primary Care and Public Health Sciences, King's College London, Addison House, Guy's Campus, London, SE1 1UL, UK.

Funding

Department of Health NIHR-IPF-2014-08-06
6 · The paper itself

Abstract

backgroundThe burden of morbidity represented by patients with long term conditions (LTCs) varies substantially between general practices. This study aimed to determine the characteristics of general practices with high morbidity burden.

methodRetrospective cross-sectional study; general practices in England, 2014/15. Three composite morbidity measures (MMs) were constructed to quantify LTC morbidity at practice level: a count of LTCs derived from the 20 LTCs included in the UK Quality and Outcomes Framework (QOF) disease registers, expressed as 'number of QOF LTCs per 100 registered patients'; the % of patients with one or more QOF LTCs; the % of patients with one or more of 15 broadly defined LTCs included in the GP Patient Survey (GPPS). Determinants of MM scores were analysed using multi-level regression models. Analysis was based on a national dataset of English general practices (n = 7779 practices); GPPS responses (n = 903,357); general practice characteristics (e.g. list size, list size per full time GP); patient demographic characteristics (age, deprivation status); secondary care utilisation (out-patient, emergency department, emergency admission rates).

resultsMean MM scores (95% CIs) were: 57.7 (±22.3) QOF LTCs per 100 registered patients; 22.8% (±8.2) patients with a QOF LTC; 63.5% (±11.7) patients with a GPPS LTC. The proportion of elderly patients and social deprivation scores were the strongest predictors of each MM score; scores were largely independent of practice characteristics. MM scores were positive predictors of secondary care utilization and negative predictors' access, continuity of care and overall satisfaction.

conclusionsWide variation in LTC morbidity burden was observed across English general practice. Variation was determined by demographic factors rather than practice characteristics. Higher rates of secondary care utilisation in practices with higher morbidity burden have implications for resource allocation and commissioning budgets; lower reported satisfaction in these practices suggests that practices may struggle with increased workload. There is a need for a readily available metric to define the burden of morbidity and multimorbidity in general practice.

Indexed as

AgedAged, 80 and overAge FactorsChronic DiseaseContinuity of Patient CareCross-Sectional StudiesEnglandGeneral PracticeHealth Services AccessibilityHumansMiddle AgedPatient SatisfactionPrevalenceRetrospective StudiesSecondary Health CareTime FactorsLong term conditionsMultimorbidityPatient experiencePrimary careSecondary care utilisation

Identifiers

PMID27894265
PMCPMC5127084

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Registered trials

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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.