Evidence map›Paper›PMID 34714344›Full record

ArticleJAMA network open2021

Characteristics of the Residential Environment and Their Association With Depression in Hong Kong.

Chinmoy Sarkar, Ka Yan Lai, Sarika Kumari, Gabriel M Leung, Chris Webster, Michael Y Ni

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

  1. Pooled it
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  8. Factors affecting major depression in Iran: a mixed-method study.Journal of health, population, and nutrition · 2024
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Chinmoy SarkarHealthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Ka Yan LaiHealthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Sarika KumariHealthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Gabriel M LeungSchool of Public Health, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Chris WebsterHealthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
Michael Y NiHealthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.
University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: With rapid urbanization, understanding the role of residential environments in the development of depression has gained importance. However, the potential associations of residential space and density with depression have been understudied. Objective: To investigate longitudinal associations of residential livable space and density with depression. Design, Setting, and Participants: This longitudinal cohort study used data from 2 waves of the FAMILY Cohort, a population-based cohort in the Hong Kong Special Administrative Region, China, recruited between February 28, 2009, and March 28, 2011, at baseline and followed up between August 3, 2011, and June 19, 2013, at wave 2. Data were analyzed from September 1, 2020, through August 10, 2021. A total of 16 968 participants aged 16 years or older underwent assessment using the Patient Health Questionnaire-9 (PHQ-9) and had complete data across all variables. Exposures: Residential density was objectively assessed at 3 scales (within apartment, building block, and at neighborhood level) at baseline and follow-up. Main Outcomes and Measures: Depressive symptoms were defined in terms of a PHQ-9 threshold greater than or equal to 5 and probable major depression with a cutoff value greater than or equal to 10. A continuous PHQ-9 score was used as a secondary outcome. Multilevel logistic regression models were used to examine associations of the residential density with probable major depression and depressive symptoms adjusted for time-varying sociodemographic and lifestyle factors, comorbidities, and other environment variables. Results: Of the 16 968 participants with complete data, the mean (SD) age was 45.5 (16.7) years, and 9328 participants (55.0%) were women at baseline. The mean follow-up was 2.2 years (range, 1.3-3.6 years). At the household level, after full adjustments, each IQR increment in residential livable area was associated with lower odds of probable major depression (adjusted odds ratio [aOR], 0.84; 95% CI, 0.71-0.98; P = .03) and depressive symptoms (aOR, 0.93; 95% CI, 0.86-1.00; P = .04). At the building-block level, each IQR increment in block density was associated with higher odds of depressive symptoms (aOR, 1.11; 95% CI, 1.01-1.22; P = .03), but only in single-housing environment models. The results remained consistent in models with continuous PHQ-9 evaluation. Conclusions and Relevance: This study's findings suggest that residential livable space and block density may play a role in the development of depression. Integrated mental health and housing policies promoting mental capital in cities, such as health-optimized density at the household and building-block levels, might serve as a population approach to reduce the burden of depression.

Indexed as

Residence CharacteristicsSocial EnvironmentDepressionFemaleHong KongHumansInterviews as TopicMaleStress, PsychologicalSurveys and Questionnaires

Identifiers

PMID34714344
PMCPMC8556618
OpenAlexW3208226777

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.