Skip to main content
SpringerLink
Log in

Global Research on Natural Disasters and Human Health: a Mapping Study Using Natural Language Processing Techniques

  • Published:
Current Environmental Health Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

This review aimed to systematically synthesize the global evidence base for natural disasters and human health using natural language processing (NLP) techniques.

Recent Findings

We searched Embase, PubMed, Scopus, PsycInfo, and Web of Science Core Collection, using titles, abstracts, and keywords, and included only literature indexed in English. NLP techniques, including text classification, topic modeling, and geoparsing methods, were used to systematically identify and map scientific literature on natural disasters and human health published between January 1, 2012, and April 3, 2022. We predicted 6105 studies in the area of natural disasters and human health. Earthquakes, hurricanes, and tsunamis were the most frequent nature disasters; posttraumatic stress disorder (PTSD) and depression were the most frequently studied health outcomes; mental health services were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries. Co-occurrence of natural disasters and psychological distress was common. Psychological distress was one of the top three most frequent topics in all continents except Africa, where infectious diseases was the most prevalent topic.

Summary

Our findings demonstrated the importance and feasibility of using NLP to comprehensively map natural disasters and human health in the growing literature. The review identifies clear topics for future clinical and public health research and can provide an empirical basis for reducing the negative health effects of natural disasters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The database used during the current study is available from the corresponding author upon reasonable requests.

Abbreviations

NLP :

Natural language processing

PTSD :

Posttraumatic stress disorder

NMF :

Non-negative matrix factorization

LDA :

Latent Dirichlet allocation

References 

  1. Kouadio IK, et al. Infectious diseases following natural disasters: prevention and control measures. Expert Rev Anti Infect Ther. 2012;10(1):95–104.

    Article  PubMed  Google Scholar 

  2. Leaning J, Guha-Sapir D. Natural disasters, armed conflict, and public health. N Engl J Med. 2013;369(19):1836–42.

    Article  CAS  PubMed  Google Scholar 

  3. Hallegatte S and Przyluski V. The economics of natural disasters: concepts and methods. World Bank Policy Research Working Paper, 2010(5507).

  4. Cherry KE, et al. Longitudinal assessment of cognitive and psychosocial functioning after Hurricanes Katrina and Rita: exploring disaster impact on middle-aged, older, and oldest-old adults. J Appl Biobehav Res. 2011;16(3–4):187–211.

    Article  PubMed  Google Scholar 

  5. LaJoie AS, Sprang G, McKinney WP. Long-term effects of Hurricane Katrina on the psychological well-being of evacuees. Disasters. 2010;34(4):1031–44.

    Article  PubMed  Google Scholar 

  6. Morganstein JC and Ursano RJ. Ecological disasters and mental health: causes, consequences, and interventions. Frontiers in psychiatry, 2020;11:1.

  7. Gautier D, Denis D, Locatelli B. Impacts of drought and responses of rural populations in West Africa: a systematic review. Wiley Interdisciplinary Reviews: Climate Change. 2016;7(5):666–81.

    Google Scholar 

  8. Xu J, et al. Natural disasters and social conflict: a systematic literature review. Int J Disaster Risk Reduction. 2016;17:38–48.

    Article  Google Scholar 

  9. Rodriguez-Llanes JM, et al. Child malnutrition and recurrent flooding in rural eastern India: a community-based survey. BMJ Open. 2011;1(2):e000109.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Berrang-Ford L, et al. Systematic mapping of global research on climate and health: a machine learning review. Lancet Planet Health. 2021;5(8):e514–25.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Haddaway NR, et al. On the use of computer-assistance to facilitate systematic mapping. Campbell Syst Rev. 2020;16(4):e1129.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nunez-Mir GC, et al. Automated content analysis: addressing the big literature challenge in ecology and evolution. Methods Ecol Evol. 2016;7(11):1262–72.

    Article  Google Scholar 

  13. Nakagawa S, et al. Research weaving: visualizing the future of research synthesis. Trends Ecol Evol. 2019;34(3):224–38.

    Article  PubMed  Google Scholar 

  14. Garousi V, Bauer S, Felderer M. NLP-assisted software testing: a systematic mapping of the literature. Inf Softw Technol. 2020;126:106321.

    Article  Google Scholar 

  15. The World Bank. World Bank country and lending groups. 2021 [cited 2022 April 14 th]; Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

  16. O’Mara-Eves A, et al. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4(1):1–22.

    Google Scholar 

  17. Endert A et al. The state of the art in integrating machine learning into visual analytics. in Computer Graphics Forum. 2017;36(8):458–86. Wiley Online Library.

  18. Lee J-S, Hsiang J. Patent classification by fine-tuning BERT language model. World Patent Inf. 2020;61: 101965.

    Article  Google Scholar 

  19. Halterman A. Mordecai: Full text geoparsing and event geocoding. J Open Source Softw. 2017;2(9):91.

    Article  ADS  Google Scholar 

  20. Lesnikowski A, et al. Frontiers in data analytics for adaptation research: topic modeling. Wiley Interdiscip Rev: Clim Change. 2019;10(3):e576.

    Google Scholar 

  21. Cichocki A, Phan A-H. Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans Fundam Electron Commun Comput Sci. 2009;92(3):708–21.

    Article  ADS  Google Scholar 

  22. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. Journal of machine Learning research. 2003;3(Jan):993–1022.

    Google Scholar 

  23. Pedregosa F, et al. Scikit-learn: machine learning in Python. J Machine Learn Res. 2011;12:2825–30.

    MathSciNet  Google Scholar 

  24. Jogia J, et al. Culture and the psychological impacts of natural disasters: implications for disaster management and disaster mental health. Built and human environment review. 2014;7(1):1.

    Google Scholar 

  25. Datar A, et al. The impact of natural disasters on child health and investments in rural India. Soc Sci Med. 2013;76:83–91.

    Article  PubMed  Google Scholar 

  26. Keim ME. Building human resilience: the role of public health preparedness and response as an adaptation to climate change. Am J Prev Med. 2008;35(5):508–16.

    Article  PubMed  Google Scholar 

  27. Chan E, Man A, Lam H. Scientific evidence on natural disasters and health emergency and disaster risk management in Asian rural-based area. Br Med Bull. 2019;129(1):91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ivers LC, Ryan ET. Infectious diseases of severe weather-related and flood-related natural disasters. Curr Opin Infect Dis. 2006;19(5):408–14.

    Article  PubMed  Google Scholar 

  29. ESCAP U. ESCAP Asia-Pacific disaster report 2017 disaster resilience for sustainable development. 2017. [cited 2022 April 20th]; Available from: https://www.unescap.org/sites/default/files/1_Disaster%20Report%202017%20Low%20res.pdf.

  30. Owoaje ET, et al. A review of the health problems of the internally displaced persons in Africa. Nigerian postgraduate medical journal. 2016;23(4):161.

    Article  PubMed  Google Scholar 

  31. Waring SC, Brown BJ. The threat of communicable diseases following natural disasters: a public health response. Disaster Manag Response. 2005;3(2):41–7.

    Article  PubMed  Google Scholar 

Download references

Funding

The authors declare that they received no specific funding for this work.

Author information

Authors and Affiliations

  1. Institute for Global Public Policy; LSE-Fudan Research Centre for Global Public Policy, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433, China

    Xin Ye

  2. CentraleSupélec, Paris-Saclay University, 91192, Paris, France

    Hugo Lin

Authors
  1. Xin Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hugo Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XY and HL designed the study. HL and XY analyzed the data, performed the statistical analyses, and designed figures and tables. XY drafted the initial manuscript. XY and HL revised and edited manuscript. Both authors reviewed the drafted manuscript for critical content. Both authors approved the final version of the manuscript.

Corresponding author

Correspondence to Xin Ye.

Ethics declarations

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Human and Animal Rights and Informed Consent

Not applicable, because this article does not contain any studies with human or animal subjects.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 78 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, X., Lin, H. Global Research on Natural Disasters and Human Health: a Mapping Study Using Natural Language Processing Techniques. Curr Envir Health Rpt 11, 61–70 (2024). https://doi.org/10.1007/s40572-023-00418-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40572-023-00418-3

Keywords

Search

Navigation