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The HIV epidemic has revealed considerable disparities in health among sexual and gender minorities of color within the Unites States, disproportionately affecting cisgender men who have sex with men (MSM) and trans women. Social inequities further disadvantage those with intersectional identities through homophobia, antitrans discrimination, and racism, shaping not only those at risk for HIV infection but also HIV prevention and care outcomes. Digital interventions have great potential to address barriers and improve HIV care among cisgender MSM and trans women; however, efficacy of digital HIV care interventions vary and need further examination.
This study assessed the 12-month efficacy of a 6-month digital HIV care navigation intervention among young people living with HIV in San Francisco, California. We examined dose-response relationships among intervention exposure (eg, text messaging), viral suppression, and mental health. Health electronic navigation (eNav) is a 6-month, text message–based, digital HIV care navigation intervention, in which young people living with HIV are connected to their own HIV care navigator through text messaging to improve engagement in HIV primary care.
This study had a single-arm, prospective, pre-post design. Eligibility criteria for the study included the following: identifying as cisgender MSM or trans women, being between the ages of 18 and 34 years, being newly diagnosed with HIV, or not being engaged or retained in HIV care or having a detectable viral load. We assessed and analyzed sociodemographics, intervention exposure, and HIV care and mental health outcome data for participants who completed the 6-month Health eNav intervention. We assessed all outcomes using generalized estimating equations to account for within-subjects correlation, and marginal effects of texting engagement on all outcomes were calculated over the entire 12-month study period. Finally, we specified an interaction between texting engagement and time to evaluate the effects of texting engagement on outcomes.
Over the entire 12-month period, this study shows that every one-text increase in engagement was associated with an increased odds of undetectable viral load (adjusted odds ratio 1.01, 95% CI 1.00-1.02;
Digital care navigation interventions including Health eNav may be a critical component in the health delivery service system as the digital safety net for those whose social vulnerability is exacerbated in times of crisis, disasters, or global pandemics owing to multiple social inequities. We found that increased engagement in a digital HIV care navigation intervention helped improve viral suppression and mental health—intersecting comorbid conditions—6 months after the intervention concluded. Digital care navigation may be a promising, effective, sustainable, and scalable intervention.
RR2-10.2196/16406
The HIV epidemic has revealed considerable disparities in health among sexual and gender minorities of color within the Unites States. This is most evident among cisgender men who have sex with men (MSM) and who accounted for 69% of new HIV diagnoses in in 2019 [
There are more factors at play that contextualize the drastic disparities among trans women and cisgender MSM. For example, in a meta-analysis of HIV prevalence in the US transgender population estimates that 37% of trans women reported having engaged in sex work, 36% reported the use of an illicit substance, only 39.2% reported being employed, and 30.3% of trans women and trans men reported homelessness or unstable housing [
Digital interventions have great potential to address barriers and improve HIV care among cisgender MSM and trans women. A systematic review of digital interventions found that overall, digital interventions had a positive impact on HIV care outcomes [
Health eNavigation (eNav) is a 6-month text message–based, digital HIV care navigation intervention where young people living with HIV were connected to their own digital HIV care navigator through bidirectional text messaging to improve engagement in HIV primary care. The intervention included delivery of personalized messages and content that addressed the following topics: (1) HIV care navigation, (2) health promotion and education, (3) motivational interviewing (MI), and (4) social support. HIV care navigation guides participants in knowing where, when, and how to access all health and related services, and increases access to appropriate resources (eg, primary medical care, mental health care, housing, insurance and benefits, etc) [
Informed by 2 health services frameworks, Health eNav transforms how HIV care navigation is delivered, seeking to improve health outcomes by amplifying the reach and value of the patient-centered medical home model and the chronic care model with the use of digital technology [
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study protocol was approved by the institutional review board at the University of California, San Francisco (IRB #16-19675).
Data for this analysis were obtained from the Health eNav study at San Francisco Department of Public Health (2017-2018). Health eNav was a digital care navigation intervention designed to improve HIV care linkage and retention and subsequent viral suppression among young cisgender MSM and trans women living with HIV. A digital care navigator delivered the intervention via bidirectional text messaging. This is a single-arm, prospective, pre-post design study. Study procedures are described in depth in a prior study [
Eligibility criteria for the study included the following: identifying as cisgender MSM or trans women, being between the ages of 18 and 34 years, and being newly diagnosed with HIV or not being engaged or retained in HIV care or having a detectable viral load. Participants were recruited via convenience sampling from 5 clinics and community-based organizations in San Francisco serving young people living with HIV. If eligible, participants met with research staff at study offices within the San Francisco Department of Public Health, where informed consent was obtained. Of 170 individuals screened, 140 were eligible. However, 20 were subsequently lost to follow-up and were not enrolled. This left a final sample of 120 young cisgender MSM or trans women living with HIV.
This analysis examines data collected from computer-assisted self-interview surveys of self-report data administered at baseline, 6 months, and 12 months and intervention exposure data that characterize the number of text messages sent during the 6-month intervention period. Intervention exposure data were collected and exported on the backend of our text messaging platform.
We analyzed the following sociodemographic information: age at interview (in years), gender identity (trans woman vs man), race or ethnicity (non-Hispanic, Latinx American Indian, or Alaska Native; Asian; Black or African American; Multiracial; White; or Hispanic or Latinx), education level (high school or General Educational Development or at least a college education), current living situation (stable vs unstable), income level in the last month (US $0-250, US $251-600, US $601-1300, or ≥US $1301), and incarceration status in the last 6 months.
We assessed 3 key HIV care continuum outcomes at baseline, 6 months, and 12 months of follow-up: whether participants received primary HIV care within the 6 months prior to their study visit, whether participants were currently taking HIV medications at each of these study visits, and whether participants had an undetectable viral load at each of these study visits.
We measured mental health using the mental health subscale of the 12-item Short-Form Health Survey [
To measure HIV stigma, we used the shortened revised HIV stigma scale tailored for young people living with HIV [
We measured the number of text messages sent between participants and the digital navigator during the 6-month intervention period, summing the number of text messages. From this, we created the intervention exposure variable, “texting engagement level,” defined as the total number of texts sent or received by each participant over the 6-month period. Text message conversations did not comprise preprogrammed, automated, repeated texts; instead, text messages were bidirectional and delivered by an interventionist in conversation with participants using motivational interviewing techniques to have conversations personalized to participants’ individual needs. For example, if a participant identified a need for social support to cope with a new HIV diagnosis, the conversation would center that topic. Alternatively, if a participant needed information about health insurance or a health care appointment or medication adherence reminder, the digital HIV care navigator would provide that information or provide follow-up tailored to participants’ individual needs. To ensure a baseline level of engagement in the case that participants were not initiating text messages, the digital navigator attempted to start a conversation with participants by sending one text message each week over the duration of the intervention period. The number of text messages ranged from 24 to 467 text messages.
Of the 120 participants in Health eNav, we restricted our analysis to the 60 participants who completed the 6-month digital care intervention. Participants who did not complete the intervention included people who moved out of our jurisdiction, were incarcerated, withdrew from the study, or lost to follow-up during the intervention period. We hypothesized that participants who completed the 6-month digital HIV care navigation intervention represent a different intervention and outcome experience from those who did not. As a result, analyses were restricted to intervention completers. Additionally, we excluded 4 participants who experienced interruptions in their phone service, lost their phone for a period of time, or deleted the text messaging app, and as a result, text messaging was not possible. The final analytic sample comprised 56 participants.
First, we characterized the entire sample with baseline sociodemographic data. We then described the mean texting engagement level by sociodemographics, HIV care continuum outcomes, mental health composite score (dichotomized into “low mental health issues” or a score of 0 to 10 vs “high mental health issues” or a score of 11 to 20), and HIV-related stigma composite score (dichotomized into “low HIV-related stigma experiences” or a score of 0 to 15 vs “high HIV-related stigma experiences” or a score of 16 to 30). Given the hypothesized difference in intervention effects from baseline to 6 months and then to 12 months, we assessed all outcomes (HIV care continuum, mental health, and HIV-related stigma outcomes) for a 6-month intervention period and 12-month intervention period using generalized estimating equations (GEE) to account for within-subjects correlation. Marginal effects of intervention exposure (or texting engagement) on all outcomes were calculated over the entire 12-month study period using GEE models. Finally, following the logic of differential intervention effects at 6 and 12 months, we specified an interaction between intervention exposure and time point to evaluate the possible effects of a dose response on all 5 outcomes by 6 months and 12 months. All statistical analyses were conducted in Stata 14 [
GEE models over the entire 12-month study period (
Sociodemographics, HIV care continuum outcomes, mental health, and HIV-related stigma among young cisgender men who have sex with men and trans women living with HIV who completed the intervention, overall and by texting engagement, Health eNavigation (N=56; 2017-2019).
Sociodemographics | Baseline, n (%)a | Texting engagement level, mean (SD) | ||||
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18-24 | 10 (17.86) | 141.60 (55.55) | |||
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25-36 | 46 (82.14) | 138.65 (66.26) | |||
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Trans woman | 8 (14.29) | 146.75 (48.21) | |||
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Cisgender Man | 48 (85.71) | 137.92 (66.62) | |||
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Black, non-Hispanic or Latinx | 11 (19.64) | 139.82 (61.57) | |||
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Hispanic or Latinx | 14 (25.00) | 156.57 (62.65) | |||
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Multiple races, non-Hispanic or Latinx | 14 (25.00) | 155.14 (73.83) | |||
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White, non-Hispanic or Latinx | 17 (30.36) | 111.29 (52.77) | |||
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High school or General Educational Development or less | 21 (37.50) | 149.05 (69.21) | |||
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Some college or more | 35 (62.50) | 133.26 (54.42) | |||
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Unstable | 35 (62.50) | 145.57 (61.31) | |||
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Stable | 21 (37.50) | 128.52 (68.47) | |||
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601-1300 | 13 (23.21) | 152.15 (66.45) | |||
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251-600 | 16 (28.57) | 125.56 (64.85) | |||
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0-250 | 14 (25.00) | 133.79 (53.17) | |||
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≥1301 | 13 (23.21) | 148.77 (74.04) | |||
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Yes | 7 (12.50) | 146.57 (55.81) | |||
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No | 49 (87.50) | 138.12 (65.55) | |||
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Yes | 50 (89.29) | 136.10 (64.01) | ||
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No | 6 (10.71) | 164.83 (59.88) | ||
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Yes | 47 (83.93) | 135.66 (63.75) | ||
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No | 8 (14.29) | 145.38 (58.73) | ||
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Yes | 36 (64.29) | 146.33 (63.12) | ||
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No | 16 (28.57) | 109.69 (53.36) | ||
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High mental health issues (11-20) | 23 (41.07) | 149.17 (72.72) | ||
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Low mental health issues (0-10) | 33 (58.93) | 132.21 (57.32) | ||
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High stigma experiences (16-30) | 17 (30.36) | 171.71 (67.07) | ||
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Low stigma experiences (0-15) | 39 (69.64) | 125.00 (57.94) |
aPercentages calculated out of total number of participants at baseline who completed the intervention and were included in the analysis (N=56), unless otherwise specified.
Differences in HIV care continuum, mental health, and HIV-related stigma outcomes at baseline and 6 months for cisgender men who have sex with men and trans women living with HIV who completed the intervention, Health eNavigation (2017-2019).
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Outcomes of generalized estimating equationsa over time: 6 months compared to baseline | Outcomes of generalized estimating equationsa over time: 12 months compared to baseline | |||||||||
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Effect estimateb (95% CI) | Effect estimateb (95% CI) | |||||||||
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No | Reference | Reference | |||||||
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Yes | 3.11 (0.56-17.18) | .19 | 0.67 (0.21-2.10) | .49 | |||||
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No | Reference | Reference | |||||||
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Yes | 0.75 (0.35-1.61) | .46 | 1.38 (0.42-4.55) | .59 | |||||
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No | Reference | Reference | |||||||
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Yes | 2.07 (1.04-4.11) | .04 | 2.98 (1.11-8.04) | .03 | |||||
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Mental health composite score | 0.18 (0.05-0.58) | <.01 | 0.41 (0.14-1.24) | .12 | ||||||
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HIV-related stigma composite score | 0.29 (0.05-1.75) | .18 | 0.21 (0.03-1.22) | .08 |
aFive models were created using generalized estimating equations to estimate the effects of each outcome over a 6- and 12-month intervention period. These models produced odds ratios for dichotomous outcomes and prevalence ratios for continuous outcomes.
bOdds ratios for dichotomous outcomes; mean change for continuous outcomes.
Differences in HIV care continuum, mental health, and HIV stigma outcomes over 12 months by mean texting engagement for cisgender men who have sex with men and trans women living with HIV who completed the intervention, Health eNavigation (2017-2019)a.
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GEE effects texting engagement over the 12-month study period | |||||
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Adjusted effect estimateb (95% CI) | |||||
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No | Reference | |||
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Yes | 1.00 (0.99-1.00) | .29 | ||
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No | Reference | |||
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Yes | 1.00 (0.99-1.01) | .75 | ||
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No | Reference | |||
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Yes | 1.01 (1.00-1.02) | .03 | ||
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Mental health composite score | 1.00 (0.99-1.02) | .61 | |||
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HIV-related stigma composite score | 1.03 (1.01-1.05) | .02 |
aFive models were created using generalized estimating equations to estimate the effects of each outcome over the entire 12-month period. These models produced odds ratios for dichotomous outcomes and prevalence ratios for continuous outcomes.
bOdds ratios for dichotomous outcomes; mean change for continuous outcomes.
Our study found evidence of dose-response effects associated with increases in intervention exposure or text message engagement that led to improved odds of undetectable viral load and decreases in negative mental health experiences. While there are studies supporting the application of mobile health (mHealth) approaches to improve HIV care continuum outcomes such as viral suppression, similar advances at the intersection of mental health and HIV care have lagged [
We also found that as text messaging increased, HIV stigma experiences also increased. The mean increase in HIV-related stigma experiences associated with increased engagement in text messaging was a surprising finding. Our post hoc analysis found that text messaging was associated with a mean increase in HIV-related stigma only among those who were recently diagnosed (estimate 1.04, 95% CI 1.01-1.07;
Our results should be interpreted with a number of limitations in mind. First, results from our sample of young cisgender MSM and trans women living with HIV in San Francisco may not generalize to other populations. Similarly, since we included only those who completed the 6-month digital care navigation intervention, the findings may only apply to young cisgender MSM and trans women living with HIV who adhere to interventions of this nature. This intervention was focused on changing how HIV care navigation was implemented to include digital methods for young people living with HIV, and owing to our local epidemic at the time of enrollment, this included both cisgender MSM and trans women. We hypothesized that both groups would benefit from participating because the digital navigation participants received was tailored to their individual needs. We did not sample participants to detect differences between these 2 groups. Measurement bias may be in issue as well. Texting engagement, defined as number of texts sent during the digital care navigation component of the intervention, precludes depictions of texting patterns on a day-by-day basis. Texting engagement could have been intermittent as well. However, restricting to those who completed this component of the intervention insured that texting patterns were likely consistent over the study period. While this intervention did not use standardized, preprogrammed text messages, our training and approach using MI as a client-centered communication framework was standardized and focused on supporting change talk. Selection bias may have played a role in our study as well. Participants who were actively engaged in substance use or encountering acute housing instability may not have had the time or capacity to participate in our intervention study. Finally, given the small sample size, it is possible that some of our analyses were underpowered to detect true effects.
The COVID-19 pandemic has disrupted the status quo systems of HIV care [
antiretroviral therapy
electronic navigation
generalized estimating equation
mobile health
motivational interviewing
men who have sex with men
odds ratio
The authors would like to thank all participants in the study. This work was funded by the Health Resources and Services Administration (award H97HA28895). This study’s funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
None declared.