Evidence map›Paper›PMID 41619146›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2026

Healthcare cost for non-hospital services and pharmaceuticals among breast cancer survivors: an event study.

Aarushi Dhingra, Brenda Gannon, Terence C Cheng, Luke Connelly, Gita Mishra

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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2026. 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
–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

0 citing papers in PubMed.

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

5 authors.

Aarushi DhingraSchool of Economics, The University of Queensland, Faculty of Business, Economics and Law, St Lucia, Australia. aarushi.aarushi@unibo.it.ORCID http://orcid.org/0000-0002-3719-4414
Brenda GannonSchool of Economics, The University of Queensland, Faculty of Business, Economics and Law, St Lucia, Australia.
Terence C ChengCentre for Health Economics, Monash Business School, Monash University, Melbourne, Australia.
Luke ConnellyCentre for the Business and Economics of Health, The University of Queensland, Faculty of Business, Economics and Law, St Lucia, Australia.
Gita MishraSchool of Public Health, The University of Queensland, Faculty of Medicine, Herston, Australia.

Funding

National Health and Medical Research Council APP1153420
6 · The paper itself

Abstract

Breast cancer survivors require continuing long-term care, which leads to substantial resource use and subsequently increased healthcare costs (HC), having an impact on both the individual and society. This paper aims to investigate the impact of a breast cancer diagnosis on monthly HC and out-of-pocket (OOP) costs among survivors. We apply an event study methodology using a two-part fixed-effect model. Data from the Australian Longitudinal Study of Women's Health (2002-2017) is utilised, which is linked to extensive high-frequency administrative records from the Medicare Benefits Schedule, the Pharmaceutical Benefits Scheme, the Australian Cancer Database and the National Death Index. The findings reveal that a diagnosis leads to a persistent and substantial increase in total HC and OOP costs, with the largest increases occurring in the first six months. The effects' duration and magnitude vary by the type of healthcare service and pharmaceuticals.

Indexed as

Breast NeoplasmsCancer SurvivorsHealth Care CostsHealth ExpendituresAgedAustraliaFemaleHumansLongitudinal StudiesMiddle AgedBreast cancerEvent studyHealthcare costPanel data & administrative data

Identifiers

PMID41619146

What OpenQuestion holds

Textmetadata
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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.