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Differential patterns of association between PM1 and PM2.5 with symptoms of attention deficit hyperactivity disorder

Nature Mental Health volume 1, pages 402–409 (2023)Cite this article

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Abstract

Few studies have evaluated the association between ambient particulate matter (PM) exposure and childhood attention deficit hyperactivity disorder (ADHD). We explored associations between long-term exposure to ambient PM1 and PM2.5 (particles with airborne diameters of <1 µm and <2.5 µm, respectively) with positive screening for ADHD among 164,081 school-aged children in China. Parents or guardians completed a checklist using DSM-IV, and PM1 and PM2.5 exposures linked via residential addresses were estimated using a spatial statistical model. Long-term exposure to higher ambient PM1 (odds ratio = 1.74; 95% confidence interval = 1.47–2.06 per 10.0 μg m–3) and PM2.5 (odds ratio = 1.65; 95% confidence interval = 1.45–1.88 per 10.0 μg m–3) was associated with greater odds of screening positive for ADHD. The associations were heterogenous across regions, with stronger associations for PM1 exposure than for PM2.5. Our findings highlight the potential importance of ambient PM mass concentrations, sizes, components and sources for protecting children’s neurological health in China, and in the design of interventions to decrease the health burden of children’s ADHD.

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Data supporting the findings of this study are available in the Supplementary Information.

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The code for mixed-effects logistic regression models is provided in Supplementary Methods 3.

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Acknowledgements

The research was funded by the National Natural Science Foundation of China (grant no. 82073502 to R.-Q.L.); the Guangxi Key Research and Development Plan (grant no. GUIKEAB18050024 to G.-H.D.); the research was funded by the National Natural Science Foundation of China (grant nos. 81872582 to G.-H.D.; 81972992 to B.-Y.Y. and 81703179 to B.-Y.Y.); the National Key Research and Development Program of China (grant no. 2018YFE0106900 to X.-W.Z.) and the Fundamental Research Funds for the Central Universities (grant no. 19ykjc01 to G.-H.D.).

Author information

Author notes

  1. These authors contributed equally: Ru-Qing Liu, Yuming Guo, Michael S. Bloom.

  2. These authors jointly supervised this work: Yunjiang Yu, Xiao-Wen Zeng, Guang-Hui Dong.

Authors and Affiliations

  1. Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China

    Ru-Qing Liu, Michael S. Bloom, Bo-Yi Yang, Mo Yang, Li-Wen Hu, Hong-Yao Yu, Xiao-Wen Zeng & Guang-Hui Dong

  2. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

    Yuming Guo

  3. Department of Global and Community Health, George Mason University, Fairfax, VA, USA

    Michael S. Bloom

  4. Institute of Psychology, Jagiellonian University, Krakow, Poland

    Iana Markevych

  5. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

    Shyamali Dharmage

  6. Murdoch Children Research Institute, Melbourne, Victoria, Australia

    Shyamali Dharmage

  7. Department of Environmental and Biological Science, University of Eastern Finland, Kuopio, Finland

    Pasi Jalava & Mo Yang

  8. School of Public Health, The University of Queensland, Herston, Queensland, Australia

    Luke Knibbs

  9. PublicHealth Research Analtic and Methods for Evidence, Public Health Unit, Sydney local Health District, Camperdown, Sydney, Australia

    Luke Knibbs

  10. Departments of Environmental Health Sciences and Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY, USA

    Shao Lin

  11. International Laboratory for Air Quality and Health, Science and Engineering Faculty, Queensland University of Technology, Brisbane Queensland, Australia

    Lidia Morawska

  12. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, Munich, Germany

    Joachim Heinrich

  13. Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, China

    Xiao-Yun Zeng

  14. State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China

    Yang Zhou & Yunjiang Yu

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Contributions

R.-Q.L. designed the study, collected and analyzed the data, and wrote the manuscript. M.S.B. designed the study, analyzed the data and revised the study. Y.G. and B.-Y.Y. collected and analyzed the data, and revised the manuscript. I.M., S.D., P.J., L.K., S.L., L.M. and J.M. analyzed the data and revised the manuscript. X.-Y.Z., M.Y., Y.Z., L.-W.H. and H.-Y.Y. collected and analyzed the data. Y.Y., X.-W.Z. and G.-H.D. conceptualized the study, arranged the investigation, collected data and revised the manuscript.

Corresponding authors

Correspondence to Yunjiang Yu, Xiao-Wen Zeng or Guang-Hui Dong.

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Nature Mental Health thanks Aaron Reuben, Xueying Zhang and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Supplementary Fig. 1, Supplementary Tables 1–11 and Supplementary Methods 1–3.

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Supplementary Data 1

Dataset of the study presented as

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Liu, RQ., Guo, Y., Bloom, M.S. et al. Differential patterns of association between PM1 and PM2.5 with symptoms of attention deficit hyperactivity disorder. Nat. Mental Health 1, 402–409 (2023). https://doi.org/10.1038/s44220-023-00065-5

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