scholarly journals Monitoring the opioid epidemic via social media discussions

2021 ◽  
Author(s):  
Adam Lavertu ◽  
Tymor Hamamsy ◽  
Russ B Altman
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Opioid Epidemic ◽  
Strongly Correlated ◽  
Drug Classes ◽  
Social Media Platforms

AbstractThe opioid epidemic persists in the United States; in 2019, annual drug overdose deaths increased by 4.6% to 70,980, including 50,042 opioid-related deaths. The widespread abuse of opioids across geographies and demographics and the rapidly changing dynamics of abuse require reliable and timely information to monitor and address the crisis. Social media platforms include petabytes of participant-generated data, some of which, offers a window into the relationship between individuals and their use of drugs. We assessed the utility of Reddit data for public health surveillance, with a focus on the opioid epidemic. We built a natural language processing pipeline to identify opioid-related comments and created a cohort of 1,689,039 geo-located Reddit users, each assigned to a city and state. We followed these users over a period of 10+ years and measured their opioid-related activity over time. We benchmarked the activity of this cohort against CDC overdose death rates for different drug classes and NFLIS drug report rates. Our Reddit-derived rates of opioid discussion strongly correlated with external benchmarks on the national, regional, and city level. During the period of our study, kratom emerged as an active discussion topic; we analyzed mentions of kratom to understand the dynamics of its use. We also examined changes in opioid discussions during the COVID-19 pandemic; in 2020, many opioid classes showed marked increases in discussion patterns. Our work suggests the complementary utility of social media as a part of public health surveillance activities.

scholarly journals Opioid Surveillance using Social Media: How URLs are shared among Reddit members

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Albert Park ◽  
Mike Conway
Keyword(s):  
Public Health ◽  
Social Media ◽  
Information Seeking ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Opioid Overdose ◽  
Unique Member ◽  
Surveillance Practice ◽  
Share Information

Objective: We aim to understand (1) the frequency of URL sharing and (2) types of shared URLs among opioid related discussions that take place in the social media platform called Reddit.Introduction: Nearly 100 people per day die from opioid overdose in the United States. Further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths1. However, with increasing use of social media comes increasing opportunity to seek and share information. For instance, 80% of Internet users obtain health information online2, including popular social interaction sites like Reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153. In Reddit, members often share information, and include URLs to supplement the information. Understanding the frequency of URL sharing and types of shared URLs can improve our knowledge of information seeking/sharing behaviors as well as domains of shared information on social media. Such knowledge has the potential to provide opportunities to improve public health surveillance practice. We use Reddit to track opioid related discussions and then investigate types of shared URLs among Reddit members in those discussions.Methods: First, we use a dataset4—made available on Reddit—that has been used in several informatics studies5,6. The dataset is comprised of 13,213,173 unique member IDs, 114,320,798 posts, and 1,659,361,605 associated comments that are made on 239,772 (including active and inactive) subreddits (i.e., sub-communities) from October 2007 to May 2015. Second, we identified 9 terms that are associated with opioids. The terms are 'opioid', 'opium', 'morphine', 'opiate', 'hydrocodone', 'oxycodone', 'fentanyl', 'heroin', and 'methadone'. Third, we preprocessed the entire dataset (i.e., converting text to lower cases and removing punctuation) and extracted discussions with opioid terms and their metadata (e.g., user ID, post ID) via a lexicon-based approach. Fourth, we extracted URLs using Python from these discussions, categorized the URLs by domain, and then visualized the results in a bubble chart7.Results: We extracted 1,121,187 posts/comments that were made by 328,179 unique member IDs from 8,892 subreddits. Of the 1,121,187 posts/comments, 82,639 posts/comments contained URLs (7.37%), and these posts consisted of 272,551 individual URLs and 138,206 unique URLs. The types of shared URLs in these opioid related discussions are summarized in Figure 1. The color and size represent the type and size respectively of shared URLs. The ‘.com’ is in blue; ‘.org’ is in orange; and ‘.gov’ is in green.Conclusions: We present preliminary findings concerning the types of shared URLs in opioid-related discussions among Reddit members. Our initial results suggest that Reddit members openly discuss opioid related issues and URL sharing is a part of information sharing. Although members share many URLs from reliable information sources (e.g., ‘ncbi.nlm.nih.gov’, ‘wikipedia.org, ‘nytimes.com’, ‘sciencedirect.com’), further investigation is needed concerning many of the ‘.com’ URLs, which have the potential to contain high and/or low quality information (e.g., ‘youtube.com’, ‘reddit.com’, ‘google.com’, ‘amazon.com’) to fully understand information seeking/sharing behaviors on social media and to identify opportunities, such as misinformation dissemination for improving public health surveillance practice.

scholarly journals Digital public health surveillance: a systematic scoping review

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zahra Shakeri Hossein Abad ◽  
Adrienne Kline ◽  
Madeena Sultana ◽  
Mohammad Noaeen ◽  
Elvira Nurmambetova ◽  
...  
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Scoping Review ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Successful Implementation ◽  
Public Health Action ◽  
Accessible Information ◽  
Scoping Reviews

AbstractThe ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health surveillance (DPHS) systems. We conducted a systematic scoping review in accordance with the PRISMA extension for scoping reviews to consolidate and characterize the existing research on DPHS and identify areas for further research. We used Natural Language Processing and content analysis to define the search strings and searched Global Health, Web of Science, PubMed, and Google Scholar from 2005 to January 2020 for peer-reviewed articles on DPHS, with extensive hand searching. Seven hundred fifty-five articles were included in this review. The studies were from 54 countries and utilized 26 digital platforms to study 208 sub-categories of 49 categories associated with 16 public health surveillance (PHS) themes. Most studies were conducted by researchers from the United States (56%, 426) and dominated by communicable diseases-related topics (25%, 187), followed by behavioural risk factors (17%, 131). While this review discusses the potentials of using Internet-based data as an affordable and instantaneous resource for DPHS, it highlights the paucity of longitudinal studies and the methodological and inherent practical limitations underpinning the successful implementation of a DPHS system. Little work studied Internet users’ demographics when developing DPHS systems, and 39% (291) of studies did not stratify their results by geographic region. A clear methodology by which the results of DPHS can be linked to public health action has yet to be established, as only six (0.8%) studies deployed their system into a PHS context.

Disaggregation of Latina/o Child and Adult Health Data: A Systematic Review of Public Health Surveillance Surveys in the United States

2021 ◽  
Vol 40 (1) ◽  
pp. 61-79
Author(s):  
Carmela Alcántara ◽  
Shakira F. Suglia ◽  
Irene Perez Ibarra ◽  
A. Louise Falzon ◽  
Elliot McCullough ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Systematic Review ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Data ◽  
Health Surveillance ◽  
Adult Health

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

2020 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

Structural Transphobia, Homophobia, and Biphobia in Public Health Practice: The Example of COVID-19 Surveillance

2021 ◽  
pp. e1-e7
Author(s):  
Randall L. Sell ◽  
Elise I. Krims
Keyword(s):  
Public Health ◽  
United States ◽  
Race And Ethnicity ◽  
Public Health Surveillance ◽  
Public Health Practice ◽  
The United States ◽  
Health Surveillance ◽  
Essential Information ◽  
And Gender ◽  
Public Health Surveillance System

Public health surveillance can have profound impacts on the health of populations, with COVID-19 surveillance offering an illuminating example. Surveillance surrounding COVID-19 testing, confirmed cases, and deaths has provided essential information to public health professionals about how to minimize morbidity and mortality. In the United States, surveillance has also pointed out how populations, on the basis of geography, age, and race and ethnicity, are being impacted disproportionately, allowing targeted intervention and evaluation. However, COVID-19 surveillance has also highlighted how the public health surveillance system fails some communities, including sexual and gender minorities. This failure has come about because of the haphazard and disorganized way disease reporting data are collected, analyzed, and reported in the United States, and the structural homophobia, transphobia, and biphobia acting within these systems. We provide recommendations for addressing these concerns after examining experiences collecting race data in COVID-19 surveillance and attempts in Pennsylvania and California to incorporate sexual orientation and gender identity variables into their pandemic surveillance efforts. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e7. https://doi.org/10.2105/AJPH.2021.3062727 )

scholarly journals Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities

2017 ◽  
Vol 133 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Alfonso Rodriguez-Lainz ◽  
Mariana McDonald ◽  
Maureen Fonseca-Ford ◽  
Ana Penman-Aguilar ◽  
Stephen H. Waterman ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Data Collection ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Monitoring Systems ◽  
Primary Language ◽  
Use Of Data ◽  
Race Ethnicity

Objective: Despite increasing diversity in the US population, substantial gaps in collecting data on race, ethnicity, primary language, and nativity indicators persist in public health surveillance and monitoring systems. In addition, few systems provide questionnaires in foreign languages for inclusion of non-English speakers. We assessed (1) the extent of data collected on race, ethnicity, primary language, and nativity indicators (ie, place of birth, immigration status, and years in the United States) and (2) the use of data-collection instruments in non-English languages among Centers for Disease Control and Prevention (CDC)–supported public health surveillance and monitoring systems in the United States. Methods: We identified CDC-supported surveillance and health monitoring systems in place from 2010 through 2013 by searching CDC websites and other federal websites. For each system, we assessed its website, documentation, and publications for evidence of the variables of interest and use of data-collection instruments in non-English languages. We requested missing information from CDC program officials, as needed. Results: Of 125 data systems, 100 (80%) collected data on race and ethnicity, 2 more collected data on ethnicity but not race, 26 (21%) collected data on racial/ethnic subcategories, 40 (32%) collected data on place of birth, 21 (17%) collected data on years in the United States, 14 (11%) collected data on immigration status, 13 (10%) collected data on primary language, and 29 (23%) used non-English data-collection instruments. Population-based surveys and disease registries more often collected data on detailed variables than did case-based, administrative, and multiple-source systems. Conclusions: More complete and accurate data on race, ethnicity, primary language, and nativity can improve the quality, representativeness, and usefulness of public health surveillance and monitoring systems to plan and evaluate targeted public health interventions to eliminate health disparities.

scholarly journals Social Media Behaviors and Experiences During the COVID-19 Pandemic: Associations With Anxiety, Depression, and Stress

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
J. Mitchell Vaterlaus ◽  
Lori A. Spruance ◽  
Emily V. Patten
Keyword(s):  
Public Health ◽  
Social Media ◽  
The United States ◽  
Media Content ◽  
Snowball Sampling ◽  
Emotional Experiences ◽  
Online Surveys ◽  
Social Media Platforms ◽  
Forward Stepwise ◽  
Anxiety Depression

The majority of research concerning public health crises and social media platforms has focused on analyzing the accuracy of information within social media posts. The current exploratory study explored social media users’ specific social media behaviors and experiences during the early weeks of the COVID-19 pandemic and whether these behaviors and experiences related to anxiety, depression, and stress. Data were collected March 21–31, 2020 from adults in the United States (<em>N</em> = 564) through snowball sampling on social media sites and Prime Panels. Online surveys included questions regarding social media use during the pandemic and the Depression Anxiety and Stress Scales (DASS). Forward stepwise modeling procedures were used to build three models for anxiety, stress, and depression. Participants who actively engaged with COVID-19 social media content were more likely to experience higher anxiety. Those who had emotional experiences via social media and used social media to connect during the pandemic were susceptible to higher levels of stress and depression. The current study suggests that during the pandemic specific behaviors and experiences via social media were related to anxiety, stress, and depression. Thus, limiting time spent on social media during public health crises may protect the mental health of individuals.

scholarly journals User Privacy Concerns and Expectations of Post-Pandemic Health Surveillance: A Scenario-Based Interview Study (Preprint)

2021 ◽  
Author(s):  
John S. Seberger ◽  
Sameer Patil
Keyword(s):  
Public Health ◽  
General Public ◽  
Public Health Surveillance ◽  
Effective Means ◽  
The United States ◽  
Health Surveillance ◽  
Personal Privacy ◽  
User Privacy ◽  
Privacy Concerns ◽  
Greater Good

BACKGROUND Smartphone-based apps designed and deployed to mitigate the ongoing COVID-19 pandemic are poised to become an infrastructure for post-pandemic public health surveillance. Yet people frequently identify deep-seated privacy concerns about such apps, invoking rationalizations such as contributing to ‘the greater good’ to justify their privacy-related discomfort. We adopt a future-oriented lens and consider participant perceptions of the potential routinization of such apps as a general public health surveillance infrastructure. This work focuses on the need to temper the surveillant achievement of public health with consideration for potential colonization of public health by the exploitative mechanisms of surveillance capitalism. OBJECTIVE This study develops an understanding of people’s perceptions of the potential routinization of apps as an infrastructure for public health surveillance after the COVID-19 pandemic has ended. METHODS We conducted scenario-based interviews (n = 19) with adults in the United States in order to understand how people perceive the short- and long-term privacy concerns associated with a fictional smart-thermometer app deployed to mitigate the ‘outbreak of a contagious disease.’ The scenario indicated that the app would continue functioning ‘after the disease outbreak as dissipated.’ We analyzed participant interviews using reflexive thematic analysis (TA). RESULTS Participants contextualized their perceptions of the app in a core trade-off between public health and personal privacy. They further evidenced the widespread expectation that data collected through health-surveillant apps would be shared with unknown third parties for financial gain. This expectation suggests a perceived alignment between health surveillant technologies and the broader economics of surveillance capitalism. Because of such expectations, participants routinely rationalized the use of the fictional app, which they viewed as always already privacy-invasive, by invoking ‘the greater good.’ We uncover that ‘the greater good’ is multi-faceted and self-contradictory, evidencing participants’ worry that health surveillance apps will contribute to an expansion of exploitative forms of surveillance. CONCLUSIONS While apps may be an effective means of pandemic-mitigation and preparedness, such apps are not exclusively beneficial in their outcomes. The potential routinization of apps as an infrastructure of general public health surveillance fosters end-user exploitation. Through its alignment with surveillance capitalism, such exploitation potentially erodes patient trust in the health care systems and providers that care for them. The inroads to such exploitation are present in participants’ manifestation of digital resignation, hyperbolic scaling, expectation of an infrastructure that works ‘too well,’ and generalized privacy fatalism.

Natural language processing and machine learning methods in public health surveillance: a narrative review (Preprint)

2020 ◽  
Author(s):  
Patrick James Ward ◽  
April M Young
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Surveillance Data ◽  
Online Media ◽  
Traditional Surveillance

BACKGROUND Public health surveillance is critical to detecting emerging population health threats and improvements. Surveillance data has increased in size and complexity, posing challenges to data management and analysis. Natural language processing (NLP) and machine learning (ML) are valuable tools for analysis of unstructured data involving free-text and have been used in innovative ways to examine a variety of health outcomes. OBJECTIVE Given the cross-disciplinary applications of NLP and ML, research on their applications in surveillance have been disseminated in a variety of outlets. As such, the aim of this narrative review was to describe the current state of NLP and ML use in surveillance science and to identify directions in future research. METHODS Information was abstracted from articles describing the use of natural language processing and machine learning in public health surveillance identified through a PubMed search. RESULTS Twenty-two articles met review criteria, 12 involving traditional surveillance data sources and 10 involving online media sources for surveillance. Traditional surveillance sources analyzed with NLP and ML consisted primarily of death certificates (n=6), hospital data (n=5), and online media sources (e.g., Twitter) (n=8). CONCLUSIONS The reviewed articles demonstrate the potential of NLP and ML to enhance surveillance data through improving timeliness of surveillance, identifying cases in the absence of standardized case definitions, and enabling mining of social media for public health surveillance.

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