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


Journal

Plastic and reconstructive surgery. Global open
ISSN: 2169-7574
Titre abrégé: Plast Reconstr Surg Glob Open
Pays: United States
ID NLM: 101622231

Informations de publication

Date de publication:
Jan 2022
Historique:
received: 19 09 2021
accepted: 03 11 2021
entrez: 7 2 2022
pubmed: 8 2 2022
medline: 8 2 2022
Statut: epublish

Résumé

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. 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. 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] ( 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.

Identifiants

pubmed: 35127299
doi: 10.1097/GOX.0000000000004005
pmc: PMC8812673
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e4005

Informations de copyright

Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.

Références

J Health Commun. 2014;19(6):639-59
pubmed: 23557148
Annu Rev Public Health. 2010;31:349-69
pubmed: 20070196
Aesthet Surg J. 2018 Apr 6;38(5):575-577
pubmed: 29370334
Aesthet Surg J. 2022 Jan 12;42(2):171-180
pubmed: 33252630
Aesthet Surg J. 2021 Mar 12;41(4):448-459
pubmed: 32940709
Aesthetic Plast Surg. 2018 Feb;42(1):64-72
pubmed: 29270693
Aesthetic Plast Surg. 2020 Oct;44(5):1489-1497
pubmed: 32356152
Ann Plast Surg. 2019 Jun;82(6):593-594
pubmed: 31082845
Ann Plast Surg. 2013 Apr;70(4):427-31
pubmed: 23486144
Aesthet Surg J. 2021 May 18;41(6):661-668
pubmed: 32674141
JPRAS Open. 2020 Jul 24;25:88-92
pubmed: 32904136
Qual Health Res. 2001 Sep;11(5):706-14
pubmed: 11554197
Plast Reconstr Surg. 2020 Jan;145(1):225e-227e
pubmed: 31625990
Plast Reconstr Surg. 2017 Nov;140(5):765e-766e
pubmed: 28753149
Plast Reconstr Surg. 2018 May;141(5):1106-1113
pubmed: 29697604
Ann Plast Surg. 2020 Jul;85(S1 Suppl 1):S82-S86
pubmed: 32530850
Ann Surg. 2019 Jan;269(1):30-36
pubmed: 30222598

Auteurs

William M Tian (WM)

Duke University School of Medicine, Durham, N.C.

Jess D Rames (JD)

Duke University School of Medicine, Durham, N.C.

Jared A Blau (JA)

Department of Surgery, Division of Plastic and Maxillofacial Surgery, Duke University Medical Center, Durham, N.C.

Mahsa Taskindoust (M)

Duke University School of Medicine, Durham, N.C.

Scott T Hollenbeck (ST)

Department of Surgery, Division of Plastic and Maxillofacial Surgery, Duke University Medical Center, Durham, N.C.

Classifications MeSH