Evidence map›Paper›PMID 34331165›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2022

Use of past care markers in risk-adjustment: accounting for systematic differences across providers.

Laura Anselmi, Yiu-Shing Lau, Matt Sutton, Anna Everton, Rob Shaw, Stephen Lorrimer

Open access · hybridAbstract read
In one paragraph

Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 33% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 4 institutions in 2 countries.

Laura AnselmiHealth Organisation, Policy and Economics, University of Manchester, Manchester, UK. laura.anselmi@manchester.ac.uk.ORCID http://orcid.org/0000-0002-2499-7656
Yiu-Shing LauHealth Organisation, Policy and Economics, University of Manchester, Manchester, UK.
Matt SuttonHealth Organisation, Policy and Economics, University of Manchester, Manchester, UK.
Anna EvertonAnalysis and Insight for Finance, NHS England and NHS Improvement, London, UK.
Rob ShawAnalytical Services, NHS England and NHS Improvement, London, UK.
Stephen LorrimerAnalysis and Insight for Finance, NHS England and NHS Improvement, London, UK.
NHS EnglandManchester Academic Health Science Centre · GBThe University of Melbourne · AUUniversity of Manchester · GB

Funding

Medical Research Council MR/S022554/1
6 · The paper itself

Abstract

Risk-adjustment models are used to predict the cost of care for patients based on their observable characteristics, and to derive efficient and equitable budgets based on weighted capitation. Markers based on past care contacts can improve model fit, but their coefficients may be affected by provider variations in diagnostic, treatment and reporting quality. This is problematic when distinguishing need and supply influences on costs is required.We examine the extent of this bias in the national formula for mental health care using administrative records for 43.7 million adults registered with 7746 GP practices in England in 2015. We also illustrate a method to control for provider effects.A linear regression containing a rich set of individual, GP practice and area characteristics, and fixed effects for local health organisations, had goodness-of-fit equal to R

Indexed as

BudgetsRisk AdjustmentAdultEnglandHealth ServicesHumansCostMental healthNeedResource allocationRisk-adjustmentWeighted capitation

Identifiers

PMID34331165
PMCPMC8882116
OpenAlexW3185774308

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.