Evidence map›Paper›PMID 22110252›Full record

ArticleBMJ (Clinical research ed.)2011

A person based formula for allocating commissioning funds to general practices in England: development of a statistical model.

Jennifer Dixon, Peter Smith, Hugh Gravelle, Steve Martin, Martin Bardsley, Nigel Rice, Theo Georghiou, Mark Dusheiko, John Billings, Michael De Lorenzo and 1 more

Open access · hybridAbstract read
In one paragraph

Article in BMJ (Clinical research ed.), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
36.4field-weighted citation impact, top 1% of its field
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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.

  1. Pooled it
  2. Workload and funding in general practice: a nationwide cross-sectional study in England.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
  3. Article
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  5. Use of past care markers in risk-adjustment: accounting for systematic differences across providers.The European journal of health economics : HEPAC : health economics in prevention and care · 2022
    Article
  6. Article
  7. Generalized Zero-Adjusted Models to Predict Medical Expenditures.Computational intelligence and neuroscience · 2021
    Article
  8. Article
  9. Demand-side determinants of rising hospital admissions in Germany: the role of ageing.The European journal of health economics : HEPAC : health economics in prevention and care · 2019
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 6 institutions in 3 countries.

Jennifer DixonThe Nuffield Trust, London W1G 7LP, UK. jennifer.dixon@nuffieldtrust.org.uk
Peter Smith
Hugh Gravelle
Steve Martin
Martin Bardsley
Nigel Rice
Theo Georghiou
Mark Dusheiko
John Billings
Michael De Lorenzo
Colin Sanderson
University of York · GBNuffield Trust · GBDepartment of Public Service · USDialog Semiconductor (Germany) · DEImperial College London · GBUniversity of London · GB

Funding

Department of Health
6 · The paper itself

Abstract

objectivesTo develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice

designMultivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices.

settingAll general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date. SUBJECTS: All individuals registered with a general practice in England at 1 April 2007.

main outcome measuresPower of the statistical models to predict the costs of the individual patient or each practice's registered population for 2007-8 tested with a range of metrics (R(2) reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice.

resultsModels including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups.

conclusionsA model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.

Indexed as

Financial ManagementModels, EconomicAdultAgedBudgetsCosts and Cost AnalysisEnglandFemaleGeneral PracticeHumansMaleMiddle AgedMultivariate AnalysisProspective StudiesResource AllocationState Medicine

Identifiers

PMID22110252
PMCPMC3222692
OpenAlexW1991611483

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.