Evidence map›Paper›PMID 36151515›Full record

ArticleBMC public health2022

Regional variation in long-term care spending in Japan.

Xueying Jin, Masao Iwagami, Nobuo Sakata, Takahiro Mori, Kazuaki Uda, Nanako Tamiya

Abstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 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

6 authors.

Xueying JinDepartment of Social Science, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan. x.jin@ncgg.go.jp.
Masao IwagamiHealth Services Research and Development Center, University of Tsukuba, Tsukuba, Japan.
Nobuo SakataHealth Services Research and Development Center, University of Tsukuba, Tsukuba, Japan.
Takahiro MoriHealth Services Research and Development Center, University of Tsukuba, Tsukuba, Japan.
Kazuaki UdaHealth Services Research and Development Center, University of Tsukuba, Tsukuba, Japan.
Nanako TamiyaHealth Services Research and Development Center, University of Tsukuba, Tsukuba, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth inequalities are widening in Japan, and thus, it is important to understand whether (and to what extent) there is a regional variation in long-term care (LTC) spending across municipalities. This study assesses regional variation in LTC spending and identifies the drivers of such variation.

methodsWe conducted a cross-sectional study using publicly available municipality-level data across Japan in 2019, in which the unit of analysis was municipality. The outcome of interest was per-capita LTC spending, which was estimated by dividing total LTC spending in a municipality by the number of older adults (people aged ≥ 65). To further identify drivers of regional variation in LTC spending, we conducted linear regression of per-capita spending against a series of demand, supply, and structural factors. Shapley decomposition approach was used to highlight the contribution of each independent variable to the goodness of fit of the regression model.

resultsIn Fiscal 2019, per-capita LTC spending varied from 133.1 to 549.9 thousand yen (max/min ratio 4.1) across the 1460 municipalities analyzed, showing considerable regional variation. The included covariates explained 84.0% of the total variance in LTC spending, and demand-determined variance was remarkably high, which contributed more than 85.7% of the overall R

conclusionsOur results demonstrate that, even after adjusting for different municipalities' age and sex distribution, there is a large variation in LTC spending. Furthermore, our findings highlight that, to reduce the spending gap between municipalities, the issues underlying large variations in LTC spending across municipalities must be identified and addressed.

Indexed as

Long-Term CareAgedCross-Sectional StudiesHumansJapanLinear ModelsLong-term care claims dataLong-term care spendingRegional variation

Identifiers

PMID36151515
PMCPMC9508719

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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.