Evidence map›Paper›PMID 39619267›Full record

ArticleGeoHealth2024

Effect of Regional Housing Hardship on Spatial Variation in Cancer Incidence: Does Housing Stress Increase Cancer Incidence?

Haishi Yu, Jinyu Huang, Yang Wang, Xiaoli Yue, Yingmei Wu, Hong'ou Zhang

Abstract read
In one paragraph

Article in GeoHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Haishi YuYunnan Normal University Hospital Yunnan Normal University Kunming China.
Jinyu HuangFaculty of Geography Yunnan Normal University Kunming China.ORCID https://orcid.org/0009-0007-0621-4789
Yang WangFaculty of Geography Yunnan Normal University Kunming China.ORCID https://orcid.org/0000-0002-3651-7517
Xiaoli YueFaculty of Geography Yunnan Normal University Kunming China.
Yingmei WuFaculty of Geography Yunnan Normal University Kunming China.
Hong'ou ZhangGuangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Housing hardship can create a range of health issues. However, little attention has been paid to the relationship between housing hardship and cancer incidence. This study examines the Yangtze River Economic Belt (YREB) in China as a case study to develop a model of factors related to housing hardship that can affect cancer incidence. This study employs spatial regression models to investigate the correlation between housing hardship and cancer incidence and further explores the variation in the correlation between urban areas (UAs) and non-urban areas (NUAs). The research conclusions are as follows: (a) a palpable correlation exists between housing hardship and cancer incidence. The housing price-to-income ratio (HPIR) and the rental household proportion (RHP) are positively correlated to cancer incidence, whereas the per capita living area (PCLA) has a negative correlation with cancer incidence. (b) The differences in the impact of housing hardship on cancer incidence between the UAs and the NUAs are reflected mainly in the differences in the PCLA and the RHP. The PCLA has a strong association with cancer incidence in the UAs, whereas the RHP demonstrates a strong correlation with cancer incidence in the NUAs. (c) Significant spatial heterogeneity is observed in housing hardship in the YREB.

Indexed as

cancer incidencehousing hardshiphousing overcrowdinghousing pricehousing stressYangtze River Economic Belt

Identifiers

PMID39619267
PMCPMC11607661

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.