Evidence map›Paper›PMID 25746728›Full record

ArticlePloS one2015

Hospital expenditure at the end-of-life: what are the impacts of health status and health risks?

Claudia Geue, Paula Lorgelly, James Lewsey, Carole Hart, Andrew Briggs

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Age, morbidity, and time to death: End-of-life expenditures on health care for the young-old population.The European journal of health economics : HEPAC : health economics in prevention and care · 2025
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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

5 authors.

Claudia GeueHealth Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow, G12 8RZ, United Kingdom.
Paula LorgellyCentre for Health Economics, Building 75, Monash University, Clayton VIC 3800, Australia.
James LewseyHealth Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow, G12 8RZ, United Kingdom.
Carole HartPublic Health, Institute of Health & Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow, G12 8RZ, United Kingdom.
Andrew BriggsHealth Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow, G12 8RZ, United Kingdom.

Funding

Chief Scientist Office PDF/13/03
6 · The paper itself

Abstract

backgroundIt is important for health policy and expenditure projections to understand the relationship between age, death and expenditure on health care (HC). Research has shown that older age groups incur lower hospital costs than previously anticipated and that remaining time to death (TTD) was a much stronger indicator for expenditure than age. How health behaviour or risk factors impact on HC utilisation and costs at the end of life is relatively unknown. Smoking and Body Mass Index (BMI) have featured most prominently and mixed findings exist as to the exact nature of this association.

methodsThis paper considers the relationship between TTD, age and expenditure for inpatient care in the last 12 quarters of life; and introduces measures of health status and risks. A longitudinal dataset covering 35 years is utilised, including baseline survey data linked to hospital and death records. The effect of age, TTD and health indicators on expenditure for inpatient care is estimated using a two-part model.

resultsAs individuals approach death costs increase. This effect is highly significant (p<0.01) from the last until the 8th quarter before death and influenced by age. Statistically significant effects on costs were found for: smoking status, systolic blood pressure and lung function (FEV1). On average, smokers incurred lower quarterly costs in their last 12 quarters of life than non-smokers (~7%). Participants' BMI at baseline did show a negative association with probability of HC utilisation however this effect disappeared when costs were estimated.

conclusionsHealth risk measures obtained at baseline provide a good indication of individuals' probability of needing medical attention later in life and incurring costs, despite the small size of the effect. Utilising a linked dataset, where such measures are available can add substantially to our ability to explain the relationship between TTD and costs.

Indexed as

Health StatusHospital CostsAgedAged, 80 and overFemaleHumansMaleModels, EconometricRiskSurvival AnalysisTerminal Care

Identifiers

PMID25746728
PMCPMC4352086

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.