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Publications

Journal Article Research Support, Non-U.S. Gov't Epublish 1 citation

PubMed: 38245011 DOI: 10.1136/bmjopen-2022-071209

How does air pollution threaten mental health? Protocol for a machine-learning enhanced systematic map.

BMJ Open 14(1):e071209

Authors: Zhuanlan Sun, Zhe Han, Demi Zhu

Affiliations

  • High-Quality Development Evaluation Institute, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • School of Life Sciences and Technology, Tongji University, Shanghai, China.
  • School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China. China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China.

Abstract

INTRODUCTION

Air pollution exposure has influenced a broad range of mental health conditions. It has attracted research from multiple disciplines such as biomedical sciences, epidemiology, neurological science, and social science due to its importance for public health, with implications for environmental policies. Establishing and identifying the causal and moderator effects is challenging and is particularly concerning considering the different mental health measurements, study designs and data collection strategies (eg, surveys, interviews) in different disciplines. This has created a fragmented research landscape which hinders efforts to integrate key insights from different niches, and makes it difficult to identify current research trends and gaps.

METHOD AND ANALYSIS

This systematic map will follow the Collaboration for Environmental Evidence's guidelines and standards and Reporting Standards for Systematic Evidence Syntheses guidelines. Different databases and relevant web-based search engines will be used to collect the relevant literature. The time period of search strategies is conducted from the inception of the database until November 2022. Citation tracing and backward references snowballing will be used to identify additional studies. Data will be extracted by combining of literature mining and manual correction. Data coding for each article will be completed by two independent reviewers and conflicts will be reconciled between them. Machine learning technology will be applied throughout the systematic mapping process. Literature mining will rapidly screen and code the numerous available articles, enabling the breadth and diversity of the expanding literature base to be considered. The systematic map output will be provided as a publicly available database.

ETHICS AND DISSEMINATION

Primary data will not be collected and ethical approval is not required in this study. The findings of this study will be disseminated through a peer-reviewed scientific journal and academic conference presentations.

MeSH

Air Pollution, Machine Learning, Environmental Pollution, Research, Mental Health