Estimating national and state-level suicide deaths using a novel online symptom search data source
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Estimating national and state-level suicide deaths using a novel online symptom search data source



Public Access Version Available on: December 01, 2024, 12:00 AM
Please check back on the date listed above.
  • English

  • Details:

    • Alternative Title:
      J Affect Disord
    • Description:
      Background:

      Suicide mortality data are a critical source of information for understanding suicide-related trends in the United States. However, official suicide mortality data experience significant delays. The Google Symptom Search Dataset (SSD), a novel population-level data source derived from online search behavior, has not been evaluated for its utility in predicting suicide mortality trends.

      Methods:

      We identified five mental health related variables (suicidal ideation, self-harm, depression, major depressive disorder, and pain) from the SSD. Daily search trends for these symptoms were utilized to estimate national and state suicide counts in 2020, the most recent year for which data was available, via a linear regression model. We compared the performance of this model to a baseline autoregressive integrated moving average (ARIMA) model and a model including all 422 symptoms (All Symptoms) in the SSD.

      Results:

      Our Mental Health Model estimated the national number of suicide deaths with an error of −3.86 %, compared to an error of 7.17 % and 28.49 % for the ARIMA baseline and All Symptoms models. At the state level, 70 % (N = 35) of states had a prediction error of <10 % with the Mental Health Model, with accuracy generally favoring larger population states with higher number of suicide deaths.

      Conclusion:

      The Google SSD is a new real-time data source that can be used to make accurate predictions of suicide mortality monthly trends at the national level. Additional research is needed to optimize state level predictions for states with low suicide counts.

    • Pubmed ID:
      37704053
    • Pubmed Central ID:
      PMC10958391
    • Document Type:
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