Evidence map›Paper›PMID 29470673›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2018

How much does the treatment of each major disease cost? A decomposition of Swiss National Health Accounts.

Simon Wieser, Marco Riguzzi, Mark Pletscher, Carola A Huber, Harry Telser, Matthias Schwenkglenks

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In one paragraph

Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
10.2field-weighted citation impact, top 2% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

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  20. Factors related to the change in Swiss inpatient costs by disease: a 6-factor decomposition.The European journal of health economics : HEPAC : health economics in prevention and care · 2021
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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 5 institutions in 1 country.

Simon WieserWinterthur Institute of Health Economics, Zurich University of Applied Sciences, Gertrudstrasse 15, 8401, Winterthur, Switzerland. simon.wieser@zhaw.ch.ORCID http://orcid.org/0000-0003-1821-2944
Marco RiguzziWinterthur Institute of Health Economics, Zurich University of Applied Sciences, Gertrudstrasse 15, 8401, Winterthur, Switzerland.
Mark PletscherWinterthur Institute of Health Economics, Zurich University of Applied Sciences, Gertrudstrasse 15, 8401, Winterthur, Switzerland.
Carola A HuberDepartment of Health Sciences, Helsana Insurance Group, Zurich, Switzerland.
Harry TelserPolynomics, Olten, Switzerland.
Matthias SchwenkglenksEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
ZHAW Zurich University of Applied Sciences · CHHelsana Group (Switzerland) · CHPolynomics (Switzerland) · CHUniversity of Zurich · CHZHAW Zurich University of Applied Sciences · CH

Funding

Swiss Federal Office of Public Health 13.006625
6 · The paper itself

Abstract

In most countries, surprisingly little is known on how national healthcare spending is distributed across diseases. Single-disease cost-of-illness studies cover only a few of the diseases affecting a population and in some cases lead to untenably large estimates. The objective of this study was to decompose healthcare spending in 2011, according to Swiss National Health Accounts, into 21 collectively exhaustive and mutually exclusive major disease categories. Diseases were classified following the Global Burden of Disease Study. We first assigned the expenditures directly mapping from National Health Accounts to the 21 diseases. The remaining expenditures were assigned based on diagnostic codes and clues contained in a variety of microdata sources. Expenditures were dominated by non-communicable diseases with a share of 79.4%. Cardiovascular diseases stood out with 15.6% of total spending, followed by musculoskeletal disorders (13.4%), and mental and substance use disorders (10.6%). Neoplasms (6.0% of the total) ranked only sixth, although they are the leading cause of premature death in Switzerland. These results may be useful for the design of health policies, as they illustrate how healthcare spending is influenced by the epidemiological transition and increasing life expectancy. They also provide a plausibility check for single cost-of-illness studies. Our study may serve as a starting point for further research on the drivers of the constant growth of healthcare spending.

Indexed as

Communicable DiseasesCost of IllnessHealth ExpendituresHealth ResourcesHealth ServicesHumansLife ExpectancyMortality, PrematureNoncommunicable DiseasesState MedicineSwitzerlandCost-of-illnessDecomposition by diseasesHealthcare costsHealthcare expendituresNational Health AccountsSwitzerland

Identifiers

PMID29470673
OpenAlexW2791376161

What OpenQuestion holds

Textmetadata
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.