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Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis

Romeiser, Jamie L, Jusko, Nicole, Williams, Augusta A·2024·Journal of Medical Internet Research
DOI 10.2196/49928PMC PMC11112475

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Abstract

Background: Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. Objective: The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Methods: Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Results: Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. Conclusions: The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

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Figure 1 Absolute search volume trends for alpha gal (red), food allergy (green), lone star tick (blue), and meat allergy (purple), 2004-2023.
Figure 1 Absolute search volume trends for alpha gal (red), food allergy (green), lone star tick (blue), and meat allergy (purple), 2004-2023.CC BY · PubMed Central
Figure 2 Estimated number of monthly searches for alpha gal (red), food allergy (green), lone star tick (blue), and meat allergy (purple) after adjustment for long-term trends, season, and media reports. The mean number of searches for each term is shown by a solid line, and 95% CIs are shaded around each respective line.
Figure 2 Estimated number of monthly searches for alpha gal (red), food allergy (green), lone star tick (blue), and meat allergy (purple) after adjustment for long-term trends, season, and media reports. The mean number of searches for each term is shown by a solid line, and 95% CIs are shaded around each respective line.CC BY · PubMed Central
Figure 3 Spatial and temporal correlations between alpha gal and lone star tick information seeking over time. A. All Spearman ρ correlations for state-based RSV values were significant at the P<.001 level. Orange values indicate the 3 primary correlations of interest. B. Correlations can be further visualized in the choropleth maps. Dark blue states on the map indicate high RSV values. RSV: relative search volume.
Figure 3 Spatial and temporal correlations between alpha gal and lone star tick information seeking over time. A. All Spearman ρ correlations for state-based RSV values were significant at the P<.001 level. Orange values indicate the 3 primary correlations of interest. B. Correlations can be further visualized in the choropleth maps. Dark blue states on the map indicate high RSV values. RSV: relative search volume.CC BY · PubMed Central
Figure 4 Content analysis of related search queries. Ten primary themes were identified, and multiple subcategories within the themes revealed diverse information-seeking behavior. ICD-10: International Classification of Diseases 10th Revision.
Figure 4 Content analysis of related search queries. Ten primary themes were identified, and multiple subcategories within the themes revealed diverse information-seeking behavior. ICD-10: International Classification of Diseases 10th Revision.CC BY · PubMed Central

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Research works printed but not linked: 31

  1. 10.1016/j.iac.2017.09.002 · DOI
  2. 10.3390/ijms21041474 · DOI
  3. 10.1016/j.waojou.2021.100598 · DOI
  4. Ng, AE, 2023, NCHS Data Brief, 1
  5. 10.1001/jamapediatrics.2013.2376 · DOI
  6. 10.1111/j.1398-9995.2009.02121.x · DOI
  7. 10.1159/000375106 · DOI
  8. 10.1080/1744666X.2020.1782745 · DOI
  9. 10.4049/jimmunol.2100712 · DOI
  10. 10.1016/j.jaip.2019.03.031 · DOI
  11. 10.3389/fimmu.2019.01056 · DOI
  12. 10.1186/s13071-020-04343-4 · DOI
  13. 10.1016/j.jaci.2011.02.019 · DOI · Paper page
  14. 10.1016/j.anai.2022.11.021 · DOI
  15. 10.3389/fcimb.2021.680264 · DOI
  16. 10.1016/j.ttbdis.2021.101674 · DOI
  17. 10.1056/NEJMp1911661 · DOI
  18. 10.1016/j.anai.2020.12.019 · DOI
  19. 10.1016/j.jaip.2019.12.003 · DOI
  20. 10.1016/j.onehlt.2022.100424 · DOI
  21. 10.1177/2150131917705714 · DOI
  22. 10.1177/2374373518808310 · DOI
  23. 10.2196/13680 · DOI
  24. 10.1016/j.jegh.2017.06.001 · DOI
  25. 10.3389/fdata.2023.1132764 · DOI
  26. 10.1093/aje/kwac171 · DOI
  27. 10.1371/journal.pone.0211778 · DOI
  28. 10.1016/j.jaip.2020.07.032 · DOI
  29. 10.1002/sim.3733 · DOI
  30. 10.3390/insects12030225 · DOI
  31. 10.14423/SMJ.0000000000001223 · DOI

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Metadata

Authors
Romeiser, J. L., Jusko, N., & Williams, A. A.
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2024-05-08
Volume
26
Pages
e49928
ISSN
1438-8871
DOI
10.2196/49928
PMC
PMC11112475
Cited by
6
References
4
Language
en

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