Addressing bias in artificial intelligence for public health surveillance.

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

Potential Effects of Digital Inequality on Treatment Seeking for Opioid Use Disorder.

Despite the availability of effective treatment, medications for opioid use disorder are underutilized due to a variety of practical, political, and psychological reasons. Digital inequalities, such as limited access to technology, skills to leverage the technology for desirable outcomes, and social resources, may be contributing to negative health outcomes. In addition, broader health literacy plays an integral part in the capacity of individuals to appraise opioid medication-related online information. This paper explores the role of digital inequalities in the uptake of treatment for opioid use disorder. Given the shift toward telemedicine and online counseling for substance use treatment as a consequence of the COVID-19 pandemic, more research into the digital inequalities faced by those who misuse opioids may provide insight into ways of engaging and encouraging this population to utilize treatment.

Potential application of conversational agents in HIV testing uptake among high-risk populations.

Human Immunodeficiency Virus (HIV) continues to be a significant public health problem, with ~1.2 million Americans living with HIV and ~14% unaware of their infection. The Centers for Disease Control and Prevention recommends that patients 13 to 64 years of age get screened for HIV at least once, and those with higher risk profiles screen at least annually. Unfortunately, screening rates are below recommendations for high-risk populations, leading to problems of delayed diagnosis. Novel technologies have been applied in HIV research to increase prevention, testing and treatment. Conversational agents, with potential for integrating artificial intelligence and natural language processing, may offer an opportunity to improve outreach to these high-risk populations. The feasibility, accessibility and acceptance of using conversational agents for HIV testing outreach is important to evaluate, especially amidst a global coronavirus disease 2019 pandemic when clinical services have been drastically affected. This viewpoint explores the application of a conversational agent in increasing HIV testing among high-risk populations.

An online advertising intervention to increase adherence to stay-at-home-orders during the COVID-19 pandemic: An efficacy trial monitoring individual-level mobility data.

The COVID-19 pandemic has led public health departments to issue several orders and recommendations to reduce COVID-19-related morbidity and mortality. However, for various reasons, including lack of ability to sufficiently monitor and influence behavior change, adherence to these health orders and recommendations has been suboptimal. Starting April 29, 2020, during the initial stay-at-home orders issued by various state governors, we conducted an intervention that sent online website and mobile application advertisements to people's mobile phones to encourage them to adhere to stay-at-home orders. Adherence to stay-at-home orders was monitored using individual-level cell phone mobility data, from April 29, 2020 through May 10, 2020. Mobile devices across 5 regions in the United States were randomly-assigned to either receive advertisements from our research team advising them to stay at home to stay safe (intervention group) or standard advertisements from other advertisers (control group). Compared to control group devices that received only standard corporate advertisements (i.e., did not receive public health advertisements to stay at home), the (intervention group) devices that received public health advertisements to stay at home demonstrated objectively-measured increased adherence to stay at home (i.e., smaller radius of gyration, average travel distance, and larger stay-at-home ratios). Results suggest that 1) it is feasible to use mobility data to assess efficacy of an online advertising intervention, and 2) online advertisements are a potentially effective method for increasing adherence to government/public health stay-at-home orders.

An online community peer support intervention to promote COVID-19 vaccine information among essential workers: a randomized trial.

INTRODUCTION: Vaccine hesitancy is still rampant in the United States, including health care personnel. Vaccination of frontline essential workers (e.g. health care workers) is very important, especially during a pandemic. We tested the efficacy of a 4-week online, peer-led intervention (Harnessing Online Peer Education) to promote requests for COVID-19 vaccine information among essential workers. METHODS: Participants (N = 120) and peer leaders (N = 12) were recruited through online advertisements from July 23 to August 20, 2021. Eligibility criteria included: 18 years or older, U.S. resident, English speaker, part of phase 1a or 1 b of COVID-19 vaccine rollout (e.g. frontline essential workers), hadn't received a COVID-19 vaccine but able to receive one. This was a parallel assignment randomised trial. STATA was used to create a randomisation using a random number generator so that all possible assignments of participants and peer leaders to groups were equally likely. Participants were randomly assigned to intervention or control arms that consisted of two private, hidden Facebook groups, each with 30 participants. Peer leaders were randomly assigned to an intervention group, each with six peer leaders. Participants in the intervention arm were randomly assigned to three peer leaders. Participants were blinded after assignment. Peer leaders were tasked with reaching out to their assigned participants at least three times each week. Participants completed a baseline and a post intervention survey. The study is registered on ClinicalTrials.org under identifier NCT04376515 and is no longer recruiting. This work was supported by the NIAID under grant 5R01AI132030-05. RESULTS: A total of 101 participants analysed (50 intervention and 51 control). Six people in the intervention group and 0 people in the control group requested vaccine information. Ten people in the intervention group and six people in the control group provided proof of vaccination. The odds of requesting vaccine information in the intervention group was 13 times that in the control group (95% confidence interval: (1.5, 1772), p-value = 0.015). Thirty-seven participants in the intervention group and 31 in the control group were engaged at some point during the study. CONCLUSIONS: Results suggest peer-led online community groups may help to disseminate health information, aid public health efforts, and combat vaccine hesitancy. Key MessagesThe odds of requesting vaccine information was 13 times in the intervention group.Peer-led online communities may help to disseminate information and aid public health efforts to combat vaccine hesitancy.

Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data.

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people's use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor social media to understand the public's perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process.

Mental Health among African American and Latinx Men who have sex with men after the COVID-19 Lockdown in Los Angeles - Findings from the HOPE cohort.

This study aimed to examine the depression and anxiety among men of color (primarily African American and Latinx) who have sex with men after the lockdown due to the COVID-19 pandemic. Outcomes included 21-item Beck Depression Inventory (BDI), 7-item Generalized Anxiety Disorder (GAD), and a 10-item COVID-related anxiety measure using a modified H1N1-related anxiety question. Independent variables were food insecurity and belief in government efficiency. Data were analyzed by Regression models with random cluster effects. Food insecurity experiences were significantly associated with higher depression (p < 0.001), higher anxiety (p < 0.001), and higher pandemic-related anxiety (p < 0.001). Higher levels of belief in government efficiency were significantly associated with lower depression (p < 0.05), less anxiety (p < 0.05), and less pandemic-related anxiety (p-value < 0.001). These findings emphasize the importance of establishing trust between government and at-risk communities when issuing public health policies, especially during unforeseen circumstances, as well as to ensure basic human rights, such as food security.

Ethical Views on Sharing Digital Data for Public Health Surveillance: Analysis of Survey Data Among Patients.

Digital data, including social media, wearable device data, electronic health records, and internet search data, are increasingly being integrated into public health research and policy. Because of the current issues around public distrust of science and other ethical issues in public health research, it is essential that researchers conduct ongoing studies assessing people's perceptions around and willingness to share digital data. This study aims to examine participants' social media use and comfort sharing their data with health researchers. One hundred and sixty-one participants with medical conditions were recruited through social media paid advertisements and referral from a website, and invited to complete surveys on social media use and ethical perspectives on data sharing. Eligibility criteria were adults 18 years old or older, living in the US, self-reported having been diagnosed by a physician with a medical condition, belonging to at least one social media platform, using social media at least twice a week, and owning a smartphone. Study participants were mostly female, White, and with a mean age of 36.31 years. More than one third of participants reported being very comfortable sharing electronic health data and social media data for personalized healthcare and to help others. Findings suggest that participants are very uncomfortable sharing their location and text message data with researchers, with primary concerns centered around loss of privacy, disclosing private information, and that friends, family, and others may find out that they shared text messages with researchers. We discuss the implications of this research before and after the COVID-19 pandemic, along with its potential implications for future collection of digital data for public health.

Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: Content Analysis.

BACKGROUND: Innovative surveillance methods are needed to assess adherence to COVID-19 recommendations, especially methods that can provide near real-time or highly geographically targeted data. Use of location-based social media image data (eg, Instagram images) is one possible approach that could be explored to address this problem. OBJECTIVE: We seek to evaluate whether publicly available near real-time social media images might be used to monitor COVID-19 health policy adherence. METHODS: We collected a sample of 43,487 Instagram images in New York from February 7 to April 11, 2020, from the following location hashtags: #Centralpark (n=20,937), #Brooklyn Bridge (n=14,875), and #Timesquare (n=7675). After manually reviewing images for accuracy, we counted and recorded the frequency of valid daily posts at each of these hashtag locations over time, as well as rated and counted whether the individuals in the pictures at these location hashtags were social distancing (ie, whether the individuals in the images appeared to be distanced from others vs next to or touching each other). We analyzed the number of images posted over time and the correlation between trends among hashtag locations. RESULTS: We found a statistically significant decline in the number of posts over time across all regions, with an approximate decline of 17% across each site (P<.001). We found a positive correlation between hashtags (#Centralpark and #Brooklynbridge: r=0.40; #BrooklynBridge and #Timesquare: r=0.41; and #Timesquare and #Centralpark: r=0.33; P<.001 for all correlations). The logistic regression analysis showed a mild statistically significant increase in the proportion of posts over time with people appearing to be social distancing at Central Park (P=.004) and Brooklyn Bridge (P=.02) but not for Times Square (P=.16). CONCLUSIONS: Results suggest the potential of using location-based social media image data as a method for surveillance of COVID-19 health policy adherence. Future studies should further explore the implementation and ethical issues associated with this approach.

Online misinformation and vaccine hesitancy.

Although rates of vaccination have increased worldwide, the rise in nonmedical exemptions for vaccination may have caused a resurgence of childhood vaccine-preventable diseases. Vaccine hesitancy plays an important role in the decreasing rates of vaccination and is considered by the World Health Organization as a top ten global threat to public health. Online vaccine misinformation is present in news outlets, websites, and social media, and its rapid and extensive dissemination is aided by artificial intelligence (AI). In combating online misinformation, public health experts, the medical community, and lay vaccination advocates can correct false statements using language that appeal to those who are undecided about vaccination. As the gatekeepers to online information, they can implement and enforce policy that limits or bans vaccine misinformation on their platforms. AI tools might also be used to address misinformation, but more research is needed before implementing this approach more broadly in health policy. This commentary examines the role that different online platforms appear to be playing in the spread of misinformation about vaccines. We also discuss the implications of online misinformation on attitudes about COVID-19 vaccine uptake and provide suggestions for ways to combat online misinformation.

Vaccine hesitancy, the rejection or delay to get vaccinated even if there is an effective vaccine available, may be instrumental in the resurgence of vaccine-preventable disease. Studies have shown that the rise in nonmedical exemptions for vaccination increases rates of childhood vaccine-preventable disease. One factor that influences vaccine hesitancy is online misinformation. False or misleading information online regarding vaccines can be found in independent news outlets, websites, and social media. The spread of vaccine misinformation is especially important during the COVID-19 pandemic as false information can decrease pro-vaccine opinions. The recent announcement of an effective COVID-19 vaccine became a hot topic online, with many adults hesitant to take the vaccine. Public health experts, medical professionals, and pro-vaccine individuals can help curb the spread of misinformation by correcting false statements online. Social media companies can also aid in stopping misinformation by implementing and enforcing policy that limits misinformation on their platforms.

The Role of Conversational Agents for Substance Use Disorder in Social Distancing Contexts.

The COVID-19 pandemic and its related policies, such as social distancing orders, are affecting the ability for people with substance use disorders (SUD) to seek prevention and treatment. In this commentary, we introduce conversational agents, a type of social technology. We discuss the role of conversational agents in the prevention and treatment of SUD in social distancing contexts and the potential benefits and limitations of designing and implementing such technology in the prevention and care for patients with SUD.

Adolescents and opioid-related outcomes amidst the COVID-19 pandemic.

Morbidity and mortality attributable to opioid use and misuse among adolescents and young adults are evident. Although recent trend data suggest a decrease in both opioid misuse and opioid use disorder among adolescents and young adults in the last few years, overdose cases continue to rise. The opioid epidemic among this population is complex and has a different profile compared to adults, with family facilitating exposure to opioids more often than other sources. Additionally, despite recommendations by experts to initiate medications for opioid use disorder, few initiate treatment. The recent COVID-19 pandemic has impacted many facets of daily life and its effects on the opioid crisis are largely unknown. Stay-at-home mandates resulting in online schooling and limited social interaction has had deleterious consequences for adolescents, especially their mental health. This viewpoint attempts to explore the effects of the pandemic on the opioid crisis in this vulnerable population.

Perceived impacts of the COVID-19 pandemic on cannabis-using emerging adults.

Cannabis-using youth are a large epidemiologic subgroup whose age and smoking-related risks underscore the importance of examining the impact of the COVID-19 pandemic in this population. Within a clinical trial (n = 36 received an intervention prior to data collection reported herein), we surveyed cannabis-using emerging adults (ages 18-25) about perceived COVID-19 impacts. Participants (n = 141) reporting weekly cannabis use (M = 18.6 use days in the past 30) were enrolled and completed online surveys as part of either their baseline or 3 month assessment. COVID-19-related measures included symptoms, substance use, mood, etc. Participants were 57% female (mean age = 21, standard deviation = 2.2), with 21% Hispanic/Latinx, 70% White, 20% Black/African American, and 10% of other races. Most participants (86%) reported quarantine/self-isolation (M = 59 days). Several had COVID-19 symptoms (16%), but none reported testing COVID-19 positive. Many respondents felt their cannabis use (35%-50%, across consumption methods) and negative emotions (e.g., loneliness, stress, and depression; 69.5%, 69.5%, and 61.8%, respectively) increased. They reported decreased in-person socialization (90.8%) and job losses (23.4%). Reports of increased cannabis smoking were associated with increased negative emotions. On an open-response item, employment/finances and social isolation were frequently named negative impacts (33.3% and 29.4%, respectively). Although cannabis-using emerging adults' reports of increases in cannabis use, coupled with mental health symptoms and social isolation, are concerning, the full impact of the pandemic on their health and well-being remains unknown. Future studies examining the relationship between social isolation, mental health, and cannabis use among young people are needed.

Ethical Perspectives in Sharing Digital Data for Public Health Surveillance Before and Shortly After the Onset of the COVID-19 Pandemic.

Data from digital technologies are increasingly integrated in public health research. In April of 2020, we interviewed a subset of participants (N=25) who completed a survey approximately one month earlier (just prior to the declaration of the COVID-19 pandemic in the United States). Using the survey, we contacted and interviewed participants who had expressed their willingness or unwillingness to share digital data (e.g., from contact tracing apps) for use in public health. We followed a directed content analysis approach for the analysis of the interview data. Among participants who had reported being unwilling to share data, concerns about privacy, confidentiality, and the purpose of the research were cited. During the interviews, 76.9% of the participants who had previously indicated that they were unwilling to share their data, expressed willingness to share data in order to assist with COVID-19 prevention. Our results contribute to our understanding of people's perspectives on sharing personal data and of the way their perspectives can vary as a function of potential uses of their personal information (e.g., prevention of COVID-19).

Applying social norms interventions to increase adherence to COVID-19 prevention and control guidelines.

Despite widespread national, state, and local guidelines for COVID-19 prevention, including social distancing and mask orders, many people continue to not adhere to recommendations, including congregating in groups for non-essential activities, putting themselves and others at risk. A social psychological perspective can be used to understand reasons for lack of adherence to policies and methods for increasing adherence based on successes from other behavior change campaigns. This manuscript seeks to describe some of the social psychological research that may be relevant to COVID-19 prevention and behavior change, describe how these theories have been previously applied in various domains to change behavior, and provide examples of how these approaches might be similarly applied to control the pandemic. We provide concrete examples of actions that can be taken based on social psychological research that might help to increase adherence to COVID-19 recommendations and improve prevention and control of the virus.

Network interventions for managing the COVID-19 pandemic and sustaining economy.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).