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Young LE, Nan Y, Jang E, Stevens R. Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review. Curr HIV/AIDS Rep 2023; 20:470-480. [PMID: 37917386 PMCID: PMC10719139 DOI: 10.1007/s11904-023-00673-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2023] [Indexed: 11/04/2023]
Abstract
PURPOSE OF REVIEW The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research. RECENT FINDINGS Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world.
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Affiliation(s)
- Lindsay E Young
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA.
| | - Yuanfeixue Nan
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
| | - Eugene Jang
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
| | - Robin Stevens
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
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Laureate CDP, Buntine W, Linger H. A systematic review of the use of topic models for short text social media analysis. Artif Intell Rev 2023:1-33. [PMID: 37362887 PMCID: PMC10150353 DOI: 10.1007/s10462-023-10471-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2023] [Indexed: 06/28/2023]
Abstract
Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-023-10471-x.
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Affiliation(s)
| | - Wray Buntine
- College of Engineering and Computer Science, VinUniversity, Vinhomes Ocean Park, Gia Lam District, Hanoi 10000 Vietnam
| | - Henry Linger
- Faculty of IT, Monash University, Wellington Rd, Clayton, VIC 3800 Australia
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Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. Int J Digit Earth 2023; 16:130-157. [PMID: 37997607 PMCID: PMC10664840 DOI: 10.1080/17538947.2022.2161652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/17/2022] [Indexed: 11/25/2023]
Abstract
Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.
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Affiliation(s)
- Fengrui Jing
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Banky Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Xu Q, Nali MC, McMann T, Godinez H, Li J, He Y, Cai M, Lee C, Merenda C, Araojo R, Mackey TK. Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study. JMIR Infodemiology 2022; 2:e35446. [PMID: 37113799 PMCID: PMC10014091 DOI: 10.2196/35446] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/18/2022] [Accepted: 03/07/2022] [Indexed: 04/29/2023]
Abstract
Background Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. Objective This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention-related themes discussed by users. Results We collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. Conclusions Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.
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Affiliation(s)
- Qing Xu
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
| | - Matthew C Nali
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
- Global Health Program, Department of Anthropology University of California La Jolla, CA United States
| | - Tiana McMann
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
- Global Health Program, Department of Anthropology University of California La Jolla, CA United States
| | | | - Jiawei Li
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
| | - Yifan He
- S-3 Research San Diego, CA United States
| | - Mingxiang Cai
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
| | - Christine Lee
- Office of Minority Health and Health Equity, U.S. Food and Drug Administration Silver Spring, MD United States
| | - Christine Merenda
- Office of Minority Health and Health Equity, U.S. Food and Drug Administration Silver Spring, MD United States
| | - Richardae Araojo
- Office of Minority Health and Health Equity, U.S. Food and Drug Administration Silver Spring, MD United States
| | - Tim Ken Mackey
- S-3 Research San Diego, CA United States
- Global Health Policy and Data Institute San Diego, CA United States
- Global Health Program, Department of Anthropology University of California La Jolla, CA United States
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Shah N, Nali M, Bardier C, Li J, Maroulis J, Cuomo R, Mackey TK. Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram. Tob Control 2021:tobaccocontrol-2021-056937. [PMID: 34857646 DOI: 10.1136/tobaccocontrol-2021-056937] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/16/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND Increased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms. OBJECTIVES We used unsupervised machine learning to identify and characterise sale offers of electronic nicotine delivery systems (ENDS) and associated products on Instagram. We examined types of sellers, geographic ENDS location and use of age verification. METHODS Our methodology was composed of three phases: data collection, topic modelling and content analysis. We used data mining approaches to query hashtags related to ENDS product use among young adults to collect Instagram posts. For topic modelling, we applied an unsupervised machine learning approach to thematically categorise and identify topic clusters associated with selling activity. Content analysis was then used to characterise offers for sale of ENDS products. RESULTS From 70 725 posts, we identified 3331 engaged in sale of ENDS products. Posts originated from 20 different countries and were roughly split between individual (46.3%) and retail sellers (43.4%), with linked online sellers (8.8%) representing a smaller volume. ENDS products most frequently offered for sale were flavoured e-liquids (53.0%) and vaping devices (20.5%). Online sellers offering flavoured e-liquids were less likely to use age verification at point of purchase (29% vs 64%) compared with other products. CONCLUSIONS Instagram is a global venue for unregulated ENDS sales, including flavoured products, and access to websites lacking age verification. Such posts may violate Instagram's policies and US federal and state law, necessitating more robust review and enforcement to prevent ENDS uptake and access.
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Affiliation(s)
- Neal Shah
- Department of Healthcare Research and Policy, University of California San Diego, La Jolla, California, USA.,Global Health Policy and Data Institute, San Diego, California, USA
| | - Matthew Nali
- Global Health Policy and Data Institute, San Diego, California, USA.,Department of Anesthesiology, University of California San Diego School of Medicine, La Jolla, California, USA
| | - Cortni Bardier
- Global Health Policy and Data Institute, San Diego, California, USA.,Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, California, USA
| | - Jiawei Li
- Global Health Policy and Data Institute, San Diego, California, USA
| | - James Maroulis
- Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, California, USA
| | - Raphael Cuomo
- Global Health Policy and Data Institute, San Diego, California, USA.,Department of Anesthesiology, University of California San Diego School of Medicine, La Jolla, California, USA
| | - Tim K Mackey
- Department of Healthcare Research and Policy, University of California San Diego, La Jolla, California, USA .,Global Health Policy and Data Institute, San Diego, California, USA.,Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, California, USA
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Marks C, Carrasco-Escobar G, Carrasco-Hernández R, Johnson D, Ciccarone D, Strathdee SA, Smith D, Bórquez A. Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action. Transl Res 2021; 234:88-113. [PMID: 33798764 PMCID: PMC8217194 DOI: 10.1016/j.trsl.2021.03.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/25/2021] [Accepted: 03/25/2021] [Indexed: 01/01/2023]
Abstract
The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.
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Affiliation(s)
- Charles Marks
- Interdisciplinary Research on Substance Use Joint Doctoral Program at San Diego State University and University of California, San Diego; Division of Infectious Diseases and Global Public Health, University of California, San Diego; School of Social Work, San Diego State University
| | - Gabriel Carrasco-Escobar
- Division of Infectious Diseases and Global Public Health, University of California, San Diego; Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Derek Johnson
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Dan Ciccarone
- Department of Family and Community Medicine, University of California San Francisco
| | - Steffanie A Strathdee
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Davey Smith
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Annick Bórquez
- Division of Infectious Diseases and Global Public Health, University of California, San Diego.
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Cuomo RE, Purushothaman VL, Li J, Bardier C, Nali M, Shah N, Obradovich N, Yang J, Mackey TK. Characterizing Self-Reported Tobacco, Vaping, and Marijuana-Related Tweets Geolocated for California College Campuses. Front Public Health 2021; 9:628812. [PMID: 33928062 PMCID: PMC8076505 DOI: 10.3389/fpubh.2021.628812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: College-aged youth are active on social media yet smoking-related social media engagement in these populations has not been thoroughly investigated. We sought to conduct an exploratory infoveillance study focused on geolocated data to characterize smoking-related tweets originating from California 4-year colleges on Twitter. Methods: Tweets from 2015 to 2019 with geospatial coordinates in CA college campuses containing smoking-related keywords were collected from the Twitter API stream and manually annotated for discussions about smoking product type, sentiment, and behavior. Results: Out of all tweets detected with smoking-related behavior, 46.7% related to tobacco use, 50.0% to marijuana, and 7.3% to vaping. Of these tweets, 46.1% reported first-person use or second-hand observation of smoking behavior. Out of 962 tweets with user sentiment, the majority (67.6%) were positive, ranging from 55.0% for California State University, Long Beach to 95.8% for California State University, Los Angeles. Discussion: We detected reporting of first- and second-hand smoking behavior on CA college campuses representing possible violation of campus smoking bans. The majority of tweets expressed positive sentiment about smoking behaviors, though there was appreciable variability between college campuses. This suggests that anti-smoking outreach should be tailored to the unique student populations of these college communities. Conclusion: Among tweets about smoking from California colleges, high levels of positive sentiment suggest that the campus climate may be less receptive to anti-smoking messages or adherence to campus smoking bans. Further research should investigate the degree to which this varies by campuses over time and following implementation of bans including validating using other sources of data.
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Affiliation(s)
- Raphael E. Cuomo
- Department of Anesthesiology, San Diego School of Medicine, University of California, San Diego, San Diego, CA, United States
- Global Health Policy and Data Institute, San Diego, CA, United States
| | - Vidya L. Purushothaman
- Department of Anesthesiology, San Diego School of Medicine, University of California, San Diego, San Diego, CA, United States
- Global Health Policy and Data Institute, San Diego, CA, United States
| | - Jiawei Li
- S-3 Research, San Diego, CA, United States
| | - Cortni Bardier
- Global Health Policy and Data Institute, San Diego, CA, United States
| | - Matthew Nali
- Global Health Policy and Data Institute, San Diego, CA, United States
| | - Neal Shah
- Global Health Policy and Data Institute, San Diego, CA, United States
| | - Nick Obradovich
- Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | - Joshua Yang
- Department of Public Health, California State University, Fullerton, Fullerton, CA, United States
| | - Tim K. Mackey
- Department of Anesthesiology, San Diego School of Medicine, University of California, San Diego, San Diego, CA, United States
- Global Health Policy and Data Institute, San Diego, CA, United States
- S-3 Research, San Diego, CA, United States
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Cuomo RE, Cai M, Shah N, Li J, Chen WH, Obradovich N, Mackey TK. Characterising communities impacted by the 2015 Indiana HIV outbreak: A big data analysis of social media messages associated with HIV and substance abuse. Drug Alcohol Rev 2020; 39:908-913. [PMID: 32406155 PMCID: PMC8533051 DOI: 10.1111/dar.13091] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 04/06/2020] [Accepted: 04/21/2020] [Indexed: 01/14/2023]
Abstract
INTRODUCTION AND AIMS Infoveillance approaches (i.e. surveillance methods using online content) that leverage big data can provide new insights about infectious disease outbreaks and substance use disorder topics. We assessed social media messages about HIV, opioid use and injection drug use in order to understand how unstructured data can prepare public health practitioners for response to future outbreaks. DESIGN AND METHODS We conducted an retrospective analysis of Twitter messages during the 2015 HIV Indiana outbreak using machine learning, statistical and geospatial analysis to examine the transition between opioid prescription drug abuse to heroin injection use and finally HIV transmission risk, and to test possible associations with disease burden and demographic variables in Indiana and Marion County. Tweets from October 2014 to June 2015 were compared to disease burden at the county level for Indiana, and classification of census blocks by presence of relevant messages was done at the census block level for Marion County. Marion County was used as it exhibited the highest total count of Tweets. RESULTS 257 messages about substance abuse and HIV were significantly related to HIV rates (P < 0.001) and opioid-related hospitalisations (P = 0.037). Using 157 characteristics from the American Community Survey, a linear classifier was computed with an appreciable correlation (r = 0.49) to risk-related social media messages from Marion County. DISCUSSION AND CONCLUSIONS Communities appear to communicate online in response to disease burden. Classification produced an accurate equation to model census block risk based on census data, allowing for high-dimensional estimation of risk for blocks with sparse populations.
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Affiliation(s)
- Raphael E Cuomo
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
- Department of Anesthesiology, University of California, San Diego, USA
| | - Mingxiang Cai
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
- Department of Computer Science and Engineering, University of California, San Diego, USA
| | - Neal Shah
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
| | - Jiawei Li
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
| | - Wen-Hao Chen
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
- Department of Computer Science and Engineering, University of California, San Diego, USA
| | - Nick Obradovich
- Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | - Tim K Mackey
- Global Health Policy Institute, San Diego, USA
- Department of Healthcare Research and Policy, University of California, San Diego, USA
- Department of Anesthesiology, University of California, San Diego, USA
- Division of Infectious Disease and Global Public Health, University of California, San Diego, USA
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