Evidence map›Paper›PMID 38373916›Full record

ArticleBMC health services research2024

The distribution structure of medical and care resources based on regional characteristics throughout Japan in 2020.

Takuya Kusunoki, Tohru Yoshikawa

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Takuya KusunokiDepartment of Architecture and Building Engineering, Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, 192-0397, Japan. takuya111510@gmail.com.
Tohru YoshikawaDepartment of Architecture and Building Engineering, Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.

Funding

JSPS KAKENHI JP21H03157JST SPRING JPMJSP2156
6 · The paper itself

Abstract

backgroundGiven Japan's rapidly aging population, the Ministry of Health, Labour and Welfare's policy of reducing hospital beds and replacing medical care with nursing care requires the establishment of a coordinated system of medical and care services tailored to regional characteristics. To gain useful knowledge for the development of such a system, this study aimed to identify differences in the structure of the relationship between medical and care resources due to differences in regional characteristics.

methodsInitially, regional characteristics were used to group all 334 secondary medical areas (SMA) in Japan by principal component analysis. Subsequently, the related structure of the distribution of medical and care resources for each group were compared. For these comparisons, first, the related structure of the distribution of medical and care resources nationwide was modeled using structural equation modeling. Secondly, multigroup analysis was conducted to investigate differences among the models across groups.

resultsThe nationwide SMAs were grouped largely based on urbanicity and middle-density regionality. The groups with high urbanicity and high middle-density regionality consisted of SMAs with a high and medium population density. By contrast, the low middle-density regionality group consisted of SMAs containing large cities with a high population density and depopulated areas with a low population density. The model of the related structure of the distribution of medical and care resources differed among these groups. In the non-urbanicity and middle-density regionality groups, nursing care abundance tended to increase acute care abundance. In addition, in all groups, nursing care abundance tended to increase long-term hospitalization care abundance and clinic care abundance (with beds).

conclusionsThe key finding of this study was that the government's objective of reducing hospital beds may not be achieved solely by expanding nursing homes. This is because many of the models did not show a tendency that higher nursing care abundance reduces the values of the factors which increase more hospital beds. This finding was particularly relevant in middle-density regionality groups. This finding suggests that the location of nursing homes should be monitored because of concerns about the oversupply of nursing homes and sprawl in those areas.

Indexed as

AgingNursing HomesAgedHospitalizationHumansJapanLong-Term CareAging populationHospital bedLabour and welfareMinistry of healthMultigroup analysisNursing homePrincipal component analysisRegional characteristicsSecondary medical areaStructural equation modeling

Identifiers

PMID38373916
PMCPMC10877860

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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.