Evidence map›Paper›PMID 29550781›Full record

ArticleBMJ open2018

Development and validation of a casemix classification to predict costs of specialist palliative care provision across inpatient hospice, hospital and community settings in the UK: a study protocol.

Ping Guo, Mendwas Dzingina, Alice M Firth, Joanna M Davies, Abdel Douiri, Suzanne M O'Brien, Cathryn Pinto, Sophie Pask, Irene J Higginson, Kathy Eagar and 1 more

Abstract readValidation Study
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  7. Review
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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

11 authors.

Ping GuoDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Mendwas DzinginaDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Alice M FirthDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Joanna M DaviesDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Abdel DouiriDepartment of Primary Care and Public Health Sciences, King's College London, London, UK.
Suzanne M O'BrienDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Cathryn PintoDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Sophie PaskDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Irene J HigginsonDepartment of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, London, UK.
Kathy EagarUniversity of Wollongong, Australian Health Services Research Institute, Centre for Health Service Development, Wollongong, Australia.
Fliss E M MurtaghWolfson Palliative Care Research Centre, Hull York Medical School, University of Hull, Hull, UK.

Funding

Department of Health RP-PG-1210-12015
6 · The paper itself

Abstract

introductionProvision of palliative care is inequitable with wide variations across conditions and settings in the UK. Lack of a standard way to classify by case complexity is one of the principle obstacles to addressing this. We aim to develop and validate a casemix classification to support the prediction of costs of specialist palliative care provision. METHODS AND ANALYSIS: Phase I: A cohort study to determine the variables and potential classes to be included in a casemix classification. Data are collected from clinicians in palliative care services across inpatient hospice, hospital and community settings on: patient demographics, potential complexity/casemix criteria and patient-level resource use. Cost predictors are derived using multivariate regression and then incorporated into a classification using classification and regression trees. Internal validation will be conducted by bootstrapping to quantify any optimism in the predictive performance (calibration and discrimination) of the developed classification. Phase II: A mixed-methods cohort study across settings for external validation of the classification developed in phase I. Patient and family caregiver data will be collected longitudinally on demographics, potential complexity/casemix criteria and patient-level resource use. This will be triangulated with data collected from clinicians on potential complexity/casemix criteria and patient-level resource use, and with qualitative interviews with patients and caregivers about care provision across difference settings. The classification will be refined on the basis of its performance in the validation data set. ETHICS AND DISSEMINATION: The study has been approved by the National Health Service Health Research Authority Research Ethics Committee. The results are expected to be disseminated in 2018 through papers for publication in major palliative care journals; policy briefs for clinicians, commissioning leads and policy makers; and lay summaries for patients and public. TRIAL REGISTRATION NUMBER: ISRCTN90752212.

Indexed as

Cohort StudiesCommunity Health ServicesCosts and Cost AnalysisDelivery of Health CareDiagnosis-Related GroupsFemaleHospicesHospitals, PublicHumansMalePalliative CareSpecializationUnited Kingdomadult palliative carecasemix classificationcost predictorsvalidation

Identifiers

PMID29550781
PMCPMC5879599

What OpenQuestion holds

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LicenceCC BY
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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.