Evidence map›Paper›PMID 35564537›Full record

ArticleInternational journal of environmental research and public health2022

The Impacts of Housing Characteristics and Built-Environment Features on Mental Health.

Zihan Kan, Mei-Po Kwan, Mee Kam Ng, Hendrik Tieben

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

4 authors.

Zihan KanInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Mei-Po KwanInstitute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.ORCID 0000-0001-8602-9258
Mee Kam NgDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China.ORCID 0000-0003-2599-098X
Hendrik TiebenSchool of Architecture, The Chinese University of Hong Kong, Shatin, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we examined the relationships between housing characteristics, neighborhood built-environment features, and people's mental health in Hong Kong, an Asian city well known for its high-density and high-rise housing. The potential mediating effects of people's perceived living environment were also considered in the analysis. We collected data from 221 participants from two communities in Hong Kong, i.e., Sham Shui Po (SSP) and Tin Shui Wai (TSW), using a stratified random sampling approach. Big datasets were also used to derive relevant built-environment features at the street block level. We used structural equation modeling to explore the complex relationships among housing characteristics, built-environment features, and mental health. The results indicate that the associations between built-environment quality and people's mental health are weak. For communities with relatively poor housing conditions (i.e., SSP in this study), the impact of housing characteristics on mental health may be more direct; for communities with relatively good housing conditions (i.e., TSW in this study), the effect of housing characteristics on mental health may be indirect. Our findings shed light on the importance of considering different contexts in developing policies related to housing and built environment and mental health.

Indexed as

HousingMental HealthBuilt EnvironmentCitiesHumansResidence Characteristicsbuilt environmenthousing characteristicsmental healthstructural equation modeling

Identifiers

PMID35564537
PMCPMC9100191

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Registered trials

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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.