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.

An Evaluation of the User Experience and Privacy Concerns of Individuals Misusing Opioids Using a Location Tracking Mobile Application.

Opioid use disorder is a growing public health concern in the United States, causing economic burden and hindered by stigma. New forms of data, including location data, may improve the effectiveness of interventions for preventing and treating opioid use disorder and/or misuse, increase access to treatment and address racial and ethnic disparities. This qualitative study aimed to identify factors that contribute to users' experience with a publicly available location-tracking mobile app - and investigate their privacy and ethical concerns. The study was conducted through two 15-minute interviews within a 48-h time frame. Participants were recruited from a pool of past research participants, Facebook ads, and referrals, and had to meet certain inclusion criteria related to opioid use disorder and/or misuse. The study had a final sample of 30 participants, 15 male and 15 female. The study suggests that a simple onboarding process and convenient experience can enhance participant adherence to the study app and other similar location-based research apps. However, the study also found that participants had concerns about privacy and transparency about locational privacy when sharing their location data. To improve the app, researchers suggest incorporating user behavior earlier in the app development stage. The study also highlights the importance of addressing ethical and privacy concerns such as limiting the types of collected data, incorporating data encryption and retention strategies, giving access to research staff only, and not sharing the data with third-party companies or law enforcement agencies to increase user satisfaction.

Direct and Indirect Effects of Cannabis Risk Perceptions on Cannabis Use Frequency.

Background: Perceived risk of harm associated with cannabis use has decreased in recent decades, particularly among emerging adults who show the highest prevalence of use. Cannabis-related protective behavioral strategies (PBS) are associated with lower cannabis use and fewer consequences; however, individuals who perceive using cannabis as low risk may use cannabis PBS less often. Therefore, using cross-sectional data, we examined the associations between perceived risk of harm associated with cannabis use, cannabis PBS, and cannabis use frequency. Method: Participants were 146 emerging adults between the ages of 18-25 (56.2% female) who reported consuming cannabis at least 3 times/week and completed measures of past-month cannabis use, past three-month use of cannabis PBS, and perceived risk of harm associated with cannabis use. Path analyses examined direct and indirect effects of perceived risk of cannabis-related harm on cannabis frequency through cannabis PBS. Results: Most (66.4%) participants reported no perceived risk of harm associated with occasional cannabis use, whereas 30.1% reported no perceived risk of harm associated with regular cannabis use. Findings indicated a significant indirect effect between perceived risk of harm and cannabis use frequency through cannabis PBS, b = -10.23, SE = 3.80, 95% CI [-17.67, -2.80], p = .007. Conclusions: Among emerging adults who consume cannabis regularly, findings suggest that a greater perceived risk of cannabis-related harm is associated with decreased cannabis use frequency via increased use of cannabis PBS. Although future analyses evaluating causal mechanisms are needed, these findings have clinical implications for harm reduction interventions focused on cannabis use.

Ethical Perspectives in Using Technology-Enabled Research for Key HIV Populations in Rights-Constrained Settings.

PURPOSE OF REVIEW: People who use illicit drug substances (e.g., heroin) and sex workers are vulnerable to acquiring HIV. Due to the criminalization of illicit drug substances and sex work in many countries, these populations often reside in rights-constrained settings where their well-being, freedom, and access to HIV prevention and care services may be compromised due to legal prosecutions and societal stigma. RECENT FINDINGS: This study conducted a literature review on papers that evaluated a combination of the following three components: ethics, technology-based research, and populations who use drug substances and/or sex workers. We explored research on these ethical perspectives from both key populations and researchers. Findings revealed potential risks in data security and possible harmful implications of compromised data within these rights-constrained settings. Best practices were explored within the literature to identify potential methods for addressing these ethical concerns and improving HIV prevention and care.

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.

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).

Risky Drinking in Adolescents and Emerging Adults: Differences between Individuals Using Alcohol Only versus Polysubstances.

BACKGROUND: Factors related to risky drinking (e.g., motives, protective behavioral strategies [PBS]) may vary between youth who engage in polysubstance use compared to those who consume alcohol only. We examined differences in factors among youth who consume alcohol only compared to alcohol with other substances (i.e., polysubstance use), and correlates associated with risky drinking between the groups. METHODS: Participants (N = 955; ages 16-24; 54.5% female) who reported recent risky drinking completed measures of alcohol/substance use, alcohol-related consequences, drinking motives, alcohol PBS, mental health symptoms, and emotion dysregulation. Participants were in the polysubstance group if they reported using at least one other substance (e.g., cannabis, stimulants) in addition to alcohol in the past three months. Chi-square and t-tests examined differences between the two groups and multiple regression analyses examined correlates of risky drinking. RESULTS: Most participants (70.4%, n = 672) reported polysubstance use; these individuals engaged in riskier patterns of drinking, experienced more alcohol-related consequences, used fewer PBS, had stronger drinking motives (enhancement, social, coping), endorsed more mental health symptoms, and reported more emotion dysregulation. Regression models showed that emotion dysregulation significantly associated with risky drinking in the alcohol-only group; conformity and coping motives, alcohol PBS, and anxiety symptoms significantly associated with risky drinking in the polysubstance group. CONCLUSIONS: Among risky drinking youth, results indicated youth engaging in polysubstance use have greater comorbidities and individual-level factors associated with risky drinking than youth who consume alcohol only. These findings may inform the tailoring of interventions for individuals who engage in risky drinking and polysubstance use.

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual's physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google's Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.

Role of Peer Coaches in Digital Interventions for MOUD Initiation and Maintenance.

Millions of Americans suffer from opioid use disorder (OUD) in the United States, resulting in thousands of deaths. The ongoing opioid crisis necessitates novel approaches to reduce the morbidity and mortality associated with misusing opioids. Studies using peer support models show success in engaging persons living with OUD in initiating treatment and decreasing relapse. Although most studies have focused on patients in clinical settings, community studies integrating peer community leaders also show promise. This viewpoint paper explores the use of peer coaches in online interventions in the community setting.

A Qualitative Exploration of the Potential Role of Using Online Social Media Support Communities to Increase Initiation of Medications for Opioid Use Disorder (MOUD).

Objectives: This study sought to explore the potential role of peer-led online communities to increase use of medications for opioid use disorder. Methods: From January through March 2020, participants with opioid use disorder and their family members/friends were recruited from paid Facebook ads; public health key stakeholders were recruited from referrals from the study team and opioid experts. Thirty participants from California were interviewed; 23 persons reporting opioid misuse, 3 family members/friends of persons misusing opioids, and 4 public health key stakeholders. We conducted semi-structured interviews asking about preferences, barriers and facilitators of treatment options for opioid use disorder, and perspectives around the use of digital/online communities. The categories of participants interviewed were each asked slightly different questions depending upon their role. Results: Results suggest that participants who misuse opioids (1) may prefer to engage in online communities rather than in-person meetings to discuss their opioid use, (2) generally prefer to receive opioid-related information from other patients with opioid use disorder and/or those in recovery rather than from health providers or other individuals, and (3) thought that an online community could be beneficial for helping address their opioid use. Conclusion: Results suggest an openness and interest in a peer-led online community to discuss opioid use and treatment among people who misuse opioids.