HIV Preexposure Prophylaxis, HIV Prevention
Conditions
Keywords
HIV prevention, HIV Pre-exposure prophylaxis, Sentiment analysis
Brief summary
The impact of effective HIV prevention tools is limited because many people do not know that they are at risk for HIV acquisition, despite the availability of various risk assessment scores and criteria. This proposal aims to use a novel data science approach to assessing HIV prevention needs among 400 young women in Kisumu, Kenya- namely, topic modeling and network analysis of text and/or social media messages (e.g., WhatsApp, Instagram, Twitter). The study will involve in-depth assessment of relevant ethical and logistical factors to ensure appropriate and optimized use of a sentiment analysis tool for implementation in routine clinical care.
Detailed description
In the Social Media as a Risk Tool (SMaaRT) Study, the investigators hypothesize that topic modeling of SMS/social media data combined with network analysis among young women in Kenya will correlate well with existing HIV risk scales and ultimately yield a better understanding of HIV prevention needs. The investigators propose the following aims: 1. Explore ethical factors that may influence analysis of SMS and social media messages. Research assistants will conduct individual qualitative interviews with up to 32 young women (16 who would and 16 who would not provide SMS/social media data, stratified among four clinic sites) and one focus group of five Kenyan bioethicists. Questions will explore ethical concerns from individual and bystander (e.g., contacts involved in SMS/social media) perspectives and differences in ethical issues by type of social media (e.g., conversations vs posts). Follow-up interviews will be conducted with the women who provide SMS and/or social media data (in Aim 2). 2. Conduct topic modeling and network analysis of SMS and social media messages to predict HIV prevention needs among young women in Kenya. Working with four clinical sites in Kisumu, study staff will ask approximately 400 women (ages 18-24) seeking HIV testing, PrEP, and other health services to download six months of SMS/social media messages (e.g., WhatsApp, Instagram, Twitter) as a one-time procedure. For those providing data, research assistants will assess social networks engaged via SMS/social media (e.g., anonymously labeled as peers, sexual partners), administer multiple HIV risk assessments (e.g., VOICE, Wand risk scores), and obtain HIV test results. Data analysts will use automated structural topic modelling to determine "topics" (word clusters) and assess for association with other risk assessments (primary outcome) and HIV test results (exploratory outcome), and will also evaluate the impact of social networks, SMS/social media type, data volume, and language type on outcomes. Data collection and analysis will conform to Aim 1 findings. 3. Assess practical factors that may influence use of a sentiment analysis tool in routine care. In a needs assessment based on Implementation Mapping, research assistants will conduct four focus groups with five staff per clinic and two focus groups with five young women each to explore staffing best suited to implement a sentiment analysis tool and how it could be best integrated into routine care. The investigators will also assess available resources to determine optimal efficiency in developing a preliminary implementation strategy.
Interventions
None listed
Sponsors
Study design
Eligibility
Inclusion criteria
* Identifying as a young woman (age 18-24 years) * Attending clinic for any health services, including PrEP and HIV testing * Smart phone ownership * Ability to understand Kiswahili, DhoLuo, and/or English * Use of SMS, WhatsApp, and/or other types of social media
Exclusion criteria
• Inability to provide informed consent (e.g., intoxication, developmental delay)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Association of Artificial Intelligence Measure Datasets With the Wand Risk Score | 6 months of data given at one time | The investigators will compare the above-noted measure datasets with the Wand risk score as assessed in the study participants at the time of SMS/social media data collection. The Wand risk score is an empirically validated HIV prediction tool designed to estimate the probability of HIV acquisition in women. It calculates a total score (max 50 pts): age \<20 years (11 pts), 20-24 years (10 pts), 25-29 years (6 pts), 30+ years (0 pts); marital status of single/not cohabitating (13 pts), married/cohabitating (0 pts); # sexual partners in last 3 months of 3 or more (5 pts), \<3 (0 pts); age at sexual debut of \<16 years (3 pts), 16+ years (0 pts); parity (# of births) of 0-1 births (10 pts), 2 births (8 pts), 3+ births (0 pts); ever diagnosed with an STI as yes (4 pts), no (0 pts); and use of injectable contraception as yes (4 pts), no (0 pts). A higher score denotes a higher HIV risk. The total possible range is 0-50. |
| Association of Artificial Intelligence Measure Datasets With the VOICE Risk Score | 6 months | Analysts will examine 6 months of SMS/social media message content from each of the 400 study participants using three computational linguistic methods: 1) sentiment, valence, and arousal analysis; 2) topic modeling; 3) simple textual counts. Analysts will also perform network analysis with up to 20 contacts from each participant to understand how often and with which parties the participant communicates most frequently. These networks will be examined temporally to see if any of the connections have grown or weakened over time. The VOICE risk score (modified to not use biological markers) is a measure of behavioral and demographic factors to determine HIV risk: \<25 y/o (2 pts), unmarried or not living with a partner (2 pts), primary partner has other partners (2 pts), partner does not provide financial support (1 pt), alcohol use in the last 3 months (1 pt). A higher score denotes a greater HIV risk. The total possible range is 0-8. |
Countries
Kenya
Contacts
Massachusetts General Hospital
Participant flow
Recruitment details
Note: Ineligible participants provided baseline demographic/behavioral information only under verbal consent (according to IRB approval).
Baseline characteristics
| Characteristic | — |
|---|---|
| Age, Categorical <=18 years | 0 Participants |
| Age, Categorical >=65 years | 0 Participants |
| Age, Categorical Between 18 and 65 years | 400 Participants |
| Age, Continuous | 22.0 years STANDARD_DEVIATION 1.6 |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants |
| Race (NIH/OMB) Asian | 0 Participants |
| Race (NIH/OMB) Black or African American | 400 Participants |
| Race (NIH/OMB) More than one race | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Race (NIH/OMB) White | 0 Participants |
| Region of Enrollment Kenya | 400 Participants |
| Sex: Female, Male Female | 400 Participants |
| Sex: Female, Male Male | 0 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 400 |
| other Total, other adverse events | 0 / 400 |
| serious Total, serious adverse events | 0 / 400 |