Digital Public Health Surveillance Tools for Alcohol Use and HIV Risk Behaviors

Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review

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.

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.

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Potential Role of Conversational Agents in Encouraging PrEP Uptake

Approximately 1.2 million people are living with HIV, with many of them unaware of their infection. Pre-exposure prophylaxis (PrEP) is available to minimize transmission among those at high risk for infection, including men who have sex with men, people who inject drugs, and female sex workers. Despite its availability, there is low usage of PrEP. To address this problem, various digital tools have been examined in HIV research. Among those, conversational agents are still underused and their capacity warrants examination to reach at-risk populations. In this paper, we discuss the potential of conversational agents in increasing uptake of PrEP by addressing barriers experienced among those at high risk for infection.

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.