Evidence map›Paper›PMID 38397670›Full record

ArticleInternational journal of environmental research and public health2024

Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference.

Eman Leung, Albert Lee, Yilin Liu, Chi-Tim Hung, Ning Fan, Sam C C Ching, Hilary Yee, Yinan He, Richard Xu, Hector Wing Hong Tsang and 1 more

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 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
1.3field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  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

11 authors at 3 institutions in 2 countries.

Eman LeungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Albert LeeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0002-5888-5660
Yilin LiuJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID 0009-0006-2706-4014
Chi-Tim HungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0003-2103-8377
Ning FanHealth in Action Limited, Hong Kong SAR, China.
Sam C C ChingJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Hilary YeeFaculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia.ORCID 0000-0001-5508-7797
Yinan HeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Richard XuDepartment of Rehabilitation Science, Hong Kong Polytechnic University, Hong Kong SAR, China.ORCID 0000-0002-4720-5172
Hector Wing Hong TsangDepartment of Rehabilitation Science, Hong Kong Polytechnic University, Hong Kong SAR, China.
Jingjing GuanJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Chinese University of Hong Kong · HKHong Kong Polytechnic University · HKThe University of Sydney · AU

Funding

Strategic Public Policy Research Fund S2019.A4.015.19S
6 · The paper itself

Abstract

Pain interferes with one's work and social life and, at a personal level, daily activities, mood, and sleep quality. However, little research has been conducted on pain interference and its socioecological determinants among the working poor. Noting the clinical/policy decision needs and the technical challenges of isolating the intricately interrelated socioecological factors' unique contributions to pain interference and quantifying the relative contributions of each factor in an interpretable manner to inform clinical and policy decision-making, we deployed a novel random forest algorithm to model and quantify the unique contribution of a diverse ensemble of environmental, sociodemographic, and clinical factors to pain interference. Our analyses revealed that features representing the internal built environment of the working poor, such as the size of the living space, air quality, access to light, architectural design conducive to social connection, and age of the building, were assigned greater statistical importance than other more commonly examined predisposing factors for pain interference, such as age, occupation, the severity and locations of pain, BMI, serum blood sugar, and blood pressure. The findings were discussed in the context of their benefit in informing community pain screening to target residential areas whose built environment contributed most to pain interference and informing the design of intervention programs to minimize pain interference among those who suffered from chronic pain and showed specific characteristics. The findings support the call for good architecture to provide the spirit and value of buildings in city development.

Indexed as

Air PollutionChronic PainWorking PoorHumansRandom Forestbuilt environmentmachine learningpain interferenceworking poor

Identifiers

PMID38397670
PMCPMC10888413
OpenAlexW4391535510

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.