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 Jingjing Guan
Additional contact information
Eman Leung: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Albert Lee: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Yilin Liu: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Chi-Tim Hung: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Ning Fan: Health in Action Limited, Hong Kong SAR, China
Sam C. C. Ching: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Hilary Yee: Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
Yinan He: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
Richard Xu: Department of Rehabilitation Science, Hong Kong Polytechnic University, Hong Kong SAR, China
Hector Wing Hong Tsang: Department of Rehabilitation Science, Hong Kong Polytechnic University, Hong Kong SAR, China
Jingjing Guan: JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China

IJERPH, 2024, vol. 21, issue 2, 1-21

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.

Keywords: pain interference; working poor; built environment; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/21/2/179/pdf (application/pdf)
https://www.mdpi.com/1660-4601/21/2/179/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:21:y:2024:i:2:p:179-:d:1333403

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().