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. 2022 Jan 5;10(1):e4005.
doi: 10.1097/GOX.0000000000004005. eCollection 2022 Jan.

Contextualizing Breast Implant Removal Patterns with Google Trends: Big Data Applications in Surgical Demand

William M Tian  1 , Jess D Rames  1 , Jared A Blau  2 , Mahsa Taskindoust  1 , Scott T Hollenbeck  2
Affiliations

Contextualizing Breast Implant Removal Patterns with Google Trends: Big Data Applications in Surgical Demand

William M Tian et al. Plast Reconstr Surg Glob Open. .

Abstract

The demand for breast implant removal (BIR) has increased substantially in recent years. This study leveraged large datasets available through Google Trends to understand how changes in public perception could be influencing surgical demand, both geographically and temporally.

Methods: Using Google Trends, we extracted relative search volume for BIR-related search terms in the United States from 2006 to 2019. A network of related search terms was established using pairwise correlative analysis. Terms were assessed for correlation with national BIR case volume based on annual reports provided by the American Society of Plastic Surgeons. A surgical demand index for BIR was created on a state-by-state basis.

Results: A network of internally correlated BIR search terms was found. Search volumes for such terms, including "explant" [ρ = 0.912], "breast implant removal" [ρ = 0.596], "breast implant illness" [ρ = 0.820], "BII" [ρ = 0.600], and "ALCL" [ρ = 0.895] (P < 0.05), were found to be positively correlated with national BIR case volume, whereas "breast augmentation" [ρ = -0.596] (P < 0.05) was negatively correlated. Our 2019 BIR surgical demand index revealed that Nevada, Arizona, and Louisiana were the states with the highest BIR demand per capita.

Conclusions: Google Trends is a powerful tool for tracking public interest and subsequently, online health information seeking behavior. There are clear networks of related Google search terms that are correlated with actual BIR surgical volume. Understanding the online health queries patients have can help physicians better understand the factors driving patient decision-making.

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Figures

Fig. 1.
Fig. 1.
Case volume trends. A, 2006-2019 Case volume for breast implant removal (augmentation, reconstruction, and total) as reported by ASPS. B, 2006-2019 Google Trends relative search volume for related terms.
Fig. 2.
Fig. 2.
Correlogram for BIR-related terms and three unrelated plastic surgery terms (“liposuction,” “facelift,” and “cheek implant”). “X” marks nonsignificant pairwise comparisons (P > 0.05). The red to blue gradient display represents correlation outputs ranging from −1 to 1, respectively.
Fig. 3.
Fig. 3.
Distribution of BIR surgical demand index in 2019, for the 22 hotspot states identified by GT. BIR surgical demand index was defined as (BIR RSV)/(# of active plastic surgeons per 10,000 individuals).
See this image and copyright information in PMC

References

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