Evidence map›Paper›PMID 34140001›Full record

ArticleBMC health services research2021

Do remote dialysis services really cost more? An economic analysis of hospital and dialysis modality costs associated with dialysis services in urban, rural and remote settings.

Gillian Gorham, Kirsten Howard, Joan Cunningham, Federica Barzi, Paul Lawton, Alan Cass

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2021. 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
2.5field-weighted citation impact, top 10% 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

5 citing papers in PubMed, 17 citations in OpenAlex.

  1. Trial
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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

6 authors at 2 institutions in 1 country.

Gillian GorhamMenzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia. gillian.gorham@menzies.edu.au.ORCID http://orcid.org/0000-0002-9814-2204
Kirsten HowardSydney School of Public Health, The University of Sydney, Sydney, Australia.
Joan CunninghamMenzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia.
Federica BarziMenzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia.
Paul LawtonMenzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia.
Alan CassMenzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, 0810, Darwin, Australia.
Charles Darwin University · AUThe University of Sydney · AU

Funding

National Health and Medical Research Council 1058244National Health and Medical Research Council 1092092National Health and Medical Research Council 116826
6 · The paper itself

Abstract

backgroundRates of end-stage kidney disease in Australia are highest in the Northern Territory (NT), with the burden of disease heaviest in remote areas. However, the high cost of delivering dialysis services in remote areas has resulted in centralisation, requiring many people to relocate for treatment. Patients argue that treatment closer to home improves health outcomes and reduces downstream healthcare use. Existing dialysis cost studies have not compared total health care costs associated with treatment in different locations.

objectiveTo estimate and compare, from a payer perspective, the observed health service costs (all cause hospital admissions, emergency department presentations and maintenance dialysis) associated with different dialysis models in urban, rural and remote locations.

methodsUsing cost weights attributed to diagnostic codes in the NT Department of Health's hospital admission data set (2008-2014), we calculated the mean (SD) total annual health service costs by dialysis model for 995 dialysis patients. Generalized linear modeling with bootstrapping tested the marginal cost differences between different explanatory variables to estimate 'best casemix'/'worst casemix' cost scenarios.

resultsThe mean annual patient hospital expenditure was highest for urban models at $97 928 (SD $21 261) and $43 440 (SD $5 048) and lowest for remote at $19 584 (SD $4 394). When combined with the observed maintenance dialysis costs, expenditure was the highest for urban models at $148 510 (SD $19 774). The incremental cost increase of dialysing in an urban area, compared with a rural area, for a relocated person from a remote area, was $5 648 more and increased further for those from remote and very remote areas to $10 785 and $15 118 respectively.

conclusionsThis study demonstrates that dialysis treatment in urban areas for relocated people has health and cost implications that maybe greater than the cost of remote service delivery. The study emphasises the importance of considering all health service costs and cost consequences of service delivery models. KEY POINTS FOR DECISION MAKERS: Relocation for dialysis treatment has serious health and economic consequences. Relocated people have low dialysis attendance and high hospital costs in urban areas. While remote dialysis service models are more expensive than urban models, the comparative cost differences are significantly reduced when all health service costs are included. The delivery of equitable and accessible dialysis service models requires a holistic approach that incorporates the needs of the patient; hence dialysis cost studies must consider the full range of cost impacts beyond the dialysis treatments alone.

Indexed as

Renal DialysisRural Health ServicesHealth ServicesHospitalsHumansNorthern TerritoryRural Population

Identifiers

PMID34140001
PMCPMC8212525
OpenAlexW3168993408

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.