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Accepted manuscripts

Contents

Systematic Review

  • Child Mental Health Services Access and Efficacy Post-Disaster: A Scoping Review

  • Gavin Rienne, John Lyons, Erin Abner, Jennifer Horney
  • Published online by Cambridge University Press:
    16 October 2023, pp. 1-21
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  • Introduction:

    The increased threat of natural disasters makes understanding the relationship between community resources and children’s mental health critical. Mental health care efficacy and access are crucial to assessing the quality of community mental health care availability.

    Objectives:

    The primary objective of this scoping review is to investigate the relationship between children’s mental health and community mental health resource efficacy and accessibility after a major disaster

    Methods:

    Conducted a systematic search to identify epidemiologic and health service utilization studies assessing the relationship between disasters and subsequent health service utilization amongst children and adolescents.

    Results:

    The research returned 1682 potentially relevant studies and 31 articles were selected based on identified criteria from pre-selected databases.

    Conclusion:

    The studies conclude a gender and age-based disparity in access and efficiency of children’s mental health services. The studies also identify the need for greater resource distribution and organizational structure.

Brief Report

  • Analyzing Social Media Messaging on Masks and Vaccines: A Case Study on Misinformation During the COVID-19 Pandemic

  • Marc Trotochaud, Elizabeth Smith, Divya Hosangadi, Tara Kirk Sell
  • Published online by Cambridge University Press:
    10 January 2023, pp. 1-9
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  • Misinformation and disinformation during infectious disease outbreaks can hinder public health responses. This analysis examines comments about masks and COVID-19 vaccines on Twitter during the first six months of the COVID-19 pandemic. We conducted a content analysis of 6,600 randomly selected English-language tweets, examining tweets for health, political, of societal frames; inclusion of true information, false information, partially true/misleading information, and/or opinion; political components; risk frames; and use of specific types of rumor. We found false and partially false information in 22% of tweets in which we were able to assess veracity. Tweets with misinformation were more likely to mention vaccines, be political in nature, and promote risk elevating messages (p<0.5). We also found false information about vaccines as early as January 2020, nearly a year before COVID-19 vaccines became widely available. These findings highlight a need for new policies and strategies aimed to counter harmful and misleading messaging.

Original Research

  • Enhancing Artificial Intelligence for Twitter-based Public Discourse on Food Security During the COVID-19 Pandemic

  • Nina M. Martin, Lisa Poirier, Andrew J. Rosenblum, Melissa M. Reznar, Joel Gittelsohn, Daniel J. Barnett
  • Published online by Cambridge University Press:
    04 August 2022, pp. 1-25
  • Objective:

    Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.

    Methods:

    Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.

    Results:

    237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.

    Conclusions:

    AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Letter to the Editor