Cargando…

Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis

BACKGROUND: Racial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of soc...

Descripción completa

Detalles Bibliográficos
Autores principales: Lama, Yuki, Chen, Tao, Dredze, Mark, Jamison, Amelia, Quinn, Sandra Crouse, Broniatowski, David A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231890/
https://www.ncbi.nlm.nih.gov/pubmed/30217792
http://dx.doi.org/10.2196/10244
_version_ 1783370322240602112
author Lama, Yuki
Chen, Tao
Dredze, Mark
Jamison, Amelia
Quinn, Sandra Crouse
Broniatowski, David A
author_facet Lama, Yuki
Chen, Tao
Dredze, Mark
Jamison, Amelia
Quinn, Sandra Crouse
Broniatowski, David A
author_sort Lama, Yuki
collection PubMed
description BACKGROUND: Racial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease. OBJECTIVE: The objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages. METHODS: We identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source. RESULTS: Findings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation. CONCLUSIONS: This study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection.
format Online
Article
Text
id pubmed-6231890
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-62318902018-12-10 Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis Lama, Yuki Chen, Tao Dredze, Mark Jamison, Amelia Quinn, Sandra Crouse Broniatowski, David A J Med Internet Res Original Paper BACKGROUND: Racial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease. OBJECTIVE: The objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages. METHODS: We identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source. RESULTS: Findings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation. CONCLUSIONS: This study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection. JMIR Publications 2018-09-14 /pmc/articles/PMC6231890/ /pubmed/30217792 http://dx.doi.org/10.2196/10244 Text en ©Yuki Lama, Tao Chen, Mark Dredze, Amelia Jamison, Sandra Crouse Quinn, David A Broniatowski. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.09.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Lama, Yuki
Chen, Tao
Dredze, Mark
Jamison, Amelia
Quinn, Sandra Crouse
Broniatowski, David A
Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title_full Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title_fullStr Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title_full_unstemmed Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title_short Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
title_sort discordance between human papillomavirus twitter images and disparities in human papillomavirus risk and disease in the united states: mixed-methods analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231890/
https://www.ncbi.nlm.nih.gov/pubmed/30217792
http://dx.doi.org/10.2196/10244
work_keys_str_mv AT lamayuki discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis
AT chentao discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis
AT dredzemark discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis
AT jamisonamelia discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis
AT quinnsandracrouse discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis
AT broniatowskidavida discordancebetweenhumanpapillomavirustwitterimagesanddisparitiesinhumanpapillomavirusriskanddiseaseintheunitedstatesmixedmethodsanalysis