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Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants s...

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Autores principales: Giorgi, Salvatore, Yaden, David B., Eichstaedt, Johannes C., Ungar, Lyle H., Schwartz, H. Andrew, Kwarteng, Amy, Curtis, Brenda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238775/
https://www.ncbi.nlm.nih.gov/pubmed/37270657
http://dx.doi.org/10.1038/s41598-023-34468-2
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author Giorgi, Salvatore
Yaden, David B.
Eichstaedt, Johannes C.
Ungar, Lyle H.
Schwartz, H. Andrew
Kwarteng, Amy
Curtis, Brenda
author_facet Giorgi, Salvatore
Yaden, David B.
Eichstaedt, Johannes C.
Ungar, Lyle H.
Schwartz, H. Andrew
Kwarteng, Amy
Curtis, Brenda
author_sort Giorgi, Salvatore
collection PubMed
description Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.
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spelling pubmed-102387752023-06-05 Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data Giorgi, Salvatore Yaden, David B. Eichstaedt, Johannes C. Ungar, Lyle H. Schwartz, H. Andrew Kwarteng, Amy Curtis, Brenda Sci Rep Article Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic. Nature Publishing Group UK 2023-06-03 /pmc/articles/PMC10238775/ /pubmed/37270657 http://dx.doi.org/10.1038/s41598-023-34468-2 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Giorgi, Salvatore
Yaden, David B.
Eichstaedt, Johannes C.
Ungar, Lyle H.
Schwartz, H. Andrew
Kwarteng, Amy
Curtis, Brenda
Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title_full Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title_fullStr Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title_full_unstemmed Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title_short Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data
title_sort predicting u.s. county opioid poisoning mortality from multi-modal social media and psychological self-report data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238775/
https://www.ncbi.nlm.nih.gov/pubmed/37270657
http://dx.doi.org/10.1038/s41598-023-34468-2
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