Evidence map›Paper›PMID 37821860›Full record

ArticleBMC health services research2023

Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals - a longitudinal national study based on official hospital statistics.

Olaf Schoffer, Dirk Schriefer, Andreas Werblow, Andrea Gottschalk, Peter Peschel, Linda A Liang, Alexander Karmann, Stefanie J Klug

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Article in BMC health services research, 2023. 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
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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

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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.

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4 · The record

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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

8 authors.

Olaf SchofferCenter for Evidence-based Healthcare, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, Dresden, 01307, Germany. olaf.schoffer@ukdd.de.ORCID http://orcid.org/0000-0001-6922-7148
Dirk SchrieferChair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
Andreas WerblowHealth Economics Centre and Faculty of Business and Economics, TU Dresden, Dresden, Germany.
Andrea GottschalkInstitute For Medical informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany.
Peter PeschelHealth Sciences and Public Health, TU Dresden, Dresden, Germany.
Linda A LiangChair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
Alexander KarmannHealth Economics Centre and Faculty of Business and Economics, TU Dresden, Dresden, Germany.
Stefanie J KlugChair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-3523-1362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffects of demographic change, such as declining birth rates and increasing individual life expectancy, require health system adjustments offering age- and needs-based care. In addition, healthcare factors can also influence health services demand.

methodsThe official German hospital statistics database with odd-numbered years between 1995 and 2011 was analysed. This is a national comprehensive database of all general hospital inpatient services delivered. Official data from hospital statistics were linked at the district level with demographic and socio-economic data as well as population figures from the official regional statistics. Panel data regression, modelling case numbers per hospital, was performed for 13 diagnosis groups that characterised the patient structure. Socio-demographic variables included age, sex, household income, and healthcare factors included bed capacity, personnel and hospital characteristics.

resultsThe median number of annual treatments per hospital increased from 6 015 (5th and 95th percentile [670; 24 812]) in 1995 to 7 817 in 2011 (5th and 95th percentile [301; 33 651]). We developed models characterising the patient structure of health care in Germany, considering both socio-demographic and hospital factors. Demographic factors influenced case numbers across all major diagnosis groups. For example, the age groups 65-74 and 75 + influenced cerebrovascular disease case numbers (p < 0.001). Other important factors included human and material resources of hospitals or the household income of patients. Distinct differences between the models for the individual diagnosis groups were observed.

conclusionsHospital planning should not only consider demographic change but also hospital infrastructure and socio-economic factors.

Indexed as

Delivery of Health CareHospitalsBirth RateHealth ServicesHumansLife ExpectancyDemographic changeHospital infrastructureHospital planningInpatient carePatient structure

Identifiers

PMID37821860
PMCPMC10566170

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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.