Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48548, first published .
HIV Prevention and Care Among Black Cisgender Sexual Minority Men and Transgender Women: Protocol for an HIV Status–Neutral Cohort Study Using an Observational-Implementation Hybrid Approach

HIV Prevention and Care Among Black Cisgender Sexual Minority Men and Transgender Women: Protocol for an HIV Status–Neutral Cohort Study Using an Observational-Implementation Hybrid Approach

HIV Prevention and Care Among Black Cisgender Sexual Minority Men and Transgender Women: Protocol for an HIV Status–Neutral Cohort Study Using an Observational-Implementation Hybrid Approach

Protocol

1Mailman School of Public Health, Columbia University, New York, NY, United States

2Chicago Center for HIV Elimination, University of Chicago, Chicago, IL, United States

3Department of Psychiatry, Rutgers University, New Brunswick, NJ, United States

4Division of Immunology, School of Medicine, Tulane University, New Orleans, LA, United States

5Center for Human Toxicology, University of Utah, Salt Lake City, UT, United States

6Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Coral Gables, FL, United States

7Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC, United States

8Einstein-CUNY-Rockefeller Center for AIDS Research, School of Public Health and Health Policy, City University of New York, New York, NY, United States

9Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Corresponding Author:

Dustin T Duncan, ScD

Mailman School of Public Health

Columbia University

722 168th St

New York, NY, 10032

United States

Phone: 1 212 247 8023

Email: dd3018@cumc.columbia.edu


Background: Black cisgender gay, bisexual, and other sexual minority men (SMM) and transgender women (TW) continue to be heavily affected by HIV. Further research is needed to better understand HIV prevention and care outcomes in this population. In particular, there is a need for research examining the impact of substance use and sleep health on HIV prevention and treatment outcomes among Black SMM and TW.

Objective: This paper outlines the study methods being used in the recently launched follow-up study to the Neighborhoods and Networks (N2) study, which we refer to as N2 Part 2 (N2P2). N2P2 aims to address this gap in the literature, build off the findings of the original N2 study, and identify socioenvironmental determinants of health, including whether neighborhood and network factors mediate and moderate these relationships.

Methods: Building on the N2 cohort study in Chicago from 2018 to 2022, N2P2 used a prospective longitudinal cohort design and an observational-implementation hybrid approach. With sustained high levels of community engagement, we aim to recruit a new sample of 600 Black SMM and TW participants residing in the Chicago metropolitan statistical area. Participants are asked to participate in 3 study visits across an 18-month study period (1 visit every 9 months). Four different forms of data are collected per wave: (1) an in-person survey, (2) biological specimen collection, (3) a daily remote ecological momentary assessment for 14 days after each study visit, and (4) data from electronic health records. These forms of data collection continue to assess neighborhood and network factors and specifically explore substance use, sleep, immune function, obesity, and the implementation of potential interventions that address relevant constructs (eg, alcohol use and pre-exposure prophylaxis adherence).

Results: The N2P2 study was funded in August 2021 by the National Institute of Drug Abuse (R01DA054553 and R21DA053156) and National Heart, Lung, and Blood Institute (R01HL160325). This study was launched in November 2022. Recruitment and enrollment for the first wave of data collection are currently ongoing.

Conclusions: The N2P2 study is applying innovative methods to comprehensively explore the impacts of substance use and sleep health on HIV-related outcomes among an HIV status–neutral cohort of Black SMM and TW in Chicago. This study is applying an observational-implementation hybrid design to help us achieve findings that support rapid translation, a critical priority among populations such as Black SMM and TW that experience long-standing inequities with regard to HIV and other health-related outcomes. N2P2 will directly build off the findings that have resulted from the original N2 study among Black SMM and TW in Chicago. These findings provide a better understanding of multilevel (eg, individual, network, and neighborhood) factors that contribute to HIV-related outcomes and viral suppression among Black SMM and TW.

International Registered Report Identifier (IRRID): DERR1-10.2196/48548

JMIR Res Protoc 2023;12:e48548

doi:10.2196/48548

Keywords



Background

Black cisgender gay, bisexual, and other sexual minority men (SMM) and transgender women (TW) continue to be heavily affected by HIV, with both groups at increased vulnerability for HIV acquisition [1-8]. Despite accounting for <1% of the US population, Black SMM constituted 28% of newly diagnosed HIV cases in 2020 [1]. Black TW also experience disparately high rates of HIV prevalence, with a recent systematic review estimating HIV prevalence to be 14.1% among TW, overall, and 44.2% among Black TW, in particular [9]. Despite these long-standing inequities, much of the research on HIV among SMM and TW has been conducted among racially and ethnically diverse samples, and, in some instances, focused on comparing Black and White populations. In contrast, this study focuses exclusively on Black SMM and TW as a more intentional, effective, and efficient research approach that recognizes the nuanced challenges faced by these communities.

Within this context, intersectionality theory provides a framework for understanding and examining the ways in which intersecting societal oppressions have multiplicative effects on health outcomes among multiply marginalized people. Indeed, in a society structured by White supremacy, cisnormativity, and heteronormativity, Black SMM and TW experience multilevel, interrelated oppressions that directly impact their life experiences, including factors related to HIV prevention and care [10-13]. It is important to recognize that Black SMM and TW experience these oppressions in distinct ways [14]. However, in certain local contexts (eg, Chicago), there is also a significant overlap of social and sexual networks as well as neighborhood-related factors between Black SMM and TW [15-17]. Given the long-standing health inequities experienced by Black SMM and TW [4,9,18,19], such as those related to HIV prevention, care, and its determinants, research to further understand salient issues in the lives of these multiply marginalized populations is urgently needed.

Current research on HIV prevention and care should be responsive to a dynamic biomedical landscape. Specifically, HIV pre-exposure prophylaxis (PrEP) represents an unrealized yet potentially critical tool for ending the HIV epidemic (EHE) [20] and reducing inequities [21-28]. Although PrEP awareness and use have been increasing [29-32], there has been limited uptake of PrEP among marginalized populations, especially Black SMM and TW [33-38]. For example, in 2020, among those for whom PrEP is recommended, only 9% of Black individuals were on PrEP (as opposed to 66% of White individuals) [39]. Similar levels of suboptimal PrEP uptake have been noted in TW populations as well [26]. Antiretroviral therapy (ART) is another critical tool for EHE as it functions to maintain undetectable levels of HIV viral load among people living with HIV [40]. Achieving sustainable levels of effective ART adherence is critical for HIV prevention and treatment, as research demonstrates that viral load is associated with onward HIV transmission [41], such that no HIV transmission events occur when viral loads are below a certain threshold (eg, undetectable=untransmissible) [42]. However, evidence indicates that many Black SMM and TW face challenges in achieving sustained viral suppression, including factors such as stigmatizing experiences, depression, anxiety, intimate partner violence, and financial stressors [43-48].

Further research is needed to better understand HIV prevention and care outcomes among Black SMM and TW, including the impact of prevalent behaviors such as substance use. Recent findings indicate that cannabis use, including heavy cannabis use, is highly prevalent among Black SMM and TW. For example, in the Neighborhoods and Networks (N2) study among Black SMM and TW in Chicago (mainly the south side of Chicago) [49], two-thirds of participants currently use cannabis (past month), half of whom use cannabis daily [50], similar to other studies among Black SMM and TW [48,51,52]. Cannabis use also likely increased during the COVID-19 pandemic [53,54], including for Black SMM and TW [50]. Thus far, however, there have been mixed findings as to whether cannabis use impacts HIV-related outcomes [55,56]; although among Black SMM and TW, studies have shown that cannabis use is associated with HIV acquisition [51], being connected to an HIV transmission cluster [57], and decreased HIV testing [58]. The impact of cannabis use on the PrEP care continuum has not been studied as much, although initial results suggest that there may be an association [55,59-61], including a few studies on Black SMM [59,60]. Additional substance use behaviors among Black SMM and TW (beyond cannabis use) are also potentially relevant to understanding HIV-related outcomes. Alcohol misuse, including heavy drinking and binge drinking, is prevalent and has been shown to negatively impact HIV-related outcomes, including in Black SMM and TW [62-64]. Increasing methamphetamine use among Black SMM and TW is also a growing concern [65,66], especially given its negative impact on HIV prevention [67,68], the acquisition of other sexually transmitted infections (STIs) [69-71], and HIV treatment outcomes [72,73].

There are critical gaps in our understanding of substance use among Black SMM and TW, including identifying its socioenvironmental determinants as well as its impacts on HIV-related outcomes. Further research into whether substance use impacts HIV-related outcomes should also focus on potential mechanisms (eg, biological vulnerability [74-79] and neurocognitive impacts [80-82]), which will help identify potential intervention points along causal pathways and enhance external validity, which can also help inform future intervention and implementation strategies [83]. When studying HIV prevention and care among Black SMM and TW, it is also critical to take contextual factors into account [84,85], particularly those operating at multiple socioecological levels [86]. Neighborhood and social factors have been shown to be associated with HIV-related outcomes in general populations as well as among intersectional minoritized populations, such as Black SMM and TW [87]. One especially important issue is policing and police brutality, as research has demonstrated that Black SMM and TW frequently encounter law enforcement [88,89], and the presence of police has been linked to be various HIV-related outcomes, including among Black SMM [90].

Sleep health is another salient factor in the lives of Black SMM and TW, as studies have found a high prevalence of sleep problems (eg, excessive or short sleep) [91,92], and this has worsened during the COVID-19 pandemic [93]. Sleep health has been found to be associated with HIV-related outcomes in the general population [94] as well as in samples of sexual and gender minority groups [95,96]. Research regarding the relationship between sleep and HIV outcomes among Black SMM and TW is scant, although 1 study from our team has demonstrated that poor sleep health is associated with lower PrEP adherence among Black SMM [93].

Generally, there have been limitations to the collective research that has been conducted, thus far, on substance use, sleep health, and their impacts on HIV-related outcomes among Black SMM and TW. First, most studies used cross-sectional data and relied on self-reporting (which are subject to social desirability bias). Studies that use objective measures, such as biological specimens and data from electronic health records (EHRs), allow researchers to assess the accuracy of self-report data and minimize self-report bias. Furthermore, most studies have used retrospective measures, which summarize across periods and are subject to recall bias [97]. Prospective data, especially granular data, can be collected using innovative methods such as ecological momentary assessment (EMA) [97,98], which captures day-to-day and event-level variation of experiential, spatial, and behavioral data in real time. Longitudinal studies that include objective, prospective, and granular measurement of relevant constructs and that evaluate mechanisms and context will allow us to better study these complex constructs and how they interrelate among Black SMM and TW.

Objectives

To overcome these limitations, we are conducting a prospective study to explore the impact of substance use and sleep health on HIV-related outcomes among a well-characterized HIV status–neutral cohort of Black SMM and TW in Chicago, Illinois. The study objectives are conceptualized in Figure 1, and the primary objectives are summarized as follows:

  1. Determine the causal effects of substance use (eg, cannabis use and stimulant use) and sleep health on HIV outcomes, including acquisition, prevention (eg, HIV testing, PrEP uptake, and PrEP adherence), and care outcomes (eg, retention in care and viral suppression).
  2. Assess whether associations between substance use, sleep health, and HIV prevention and care outcomes vary by contextual characteristics (eg, individual-, network-, and neighborhood-level constructs) and are mediated by relevant constructs (neurocognitive impacts and biological vulnerability).

To prioritize the potential expedited translation of study findings into public health impact, which is of critical importance among populations such as Black SMM and TW that experience long-standing health inequities, we will use an innovative observational-implementation hybrid approach [99]. The observational-implementation hybrid approach prioritizes the collection of implementation data while conducting observation research. This paper outlines the study methods being used in the recently launched follow-up study to the N2 study [49], which we refer to as N2 Part 2 (N2P2).

Figure 1. Conceptual model for the Neighborhoods and Networks Part 2 (N2P2) study. Originally looking at the neighborhood and network factors of HIV-related outcomes in the Neighborhoods and Networks Part 1 (N2P1) study, we have now launched the N2P2 study to expand our understanding of substance use, sleep, and HIV-related outcomes.

Study Design

Building on the N2 cohort study in Chicago, which was completed between January 2018 and June 2022 (N=412), the N2P2 study uses a prospective longitudinal cohort design and aims to recruit a sample of 600 Black SMM and TW participants residing in the Chicago metropolitan statistical area (MSA). Following updated recommendations for labeling hybrid studies [100], we will apply an observational-implementation hybrid approach in the context of the cohort study design [99]. Following an initial baseline assessment (conducted in person), participants are asked to participate in 2 subsequent in-person visits every 9 months, totaling 3 visits across an 18-month study period. The participants are also asked to participate in a daily remote EMA survey occurring for 14 days after the in-person study assessment. As has been our long-standing practice for work with this heterogeneous community in Chicago, we are using an HIV status–neutral approach with Black SMM and TW to increase inclusivity and minimize the risk that participation in the study could disclose HIV status [101]. The HIV status–neutral approach (also known as an integrated continuum) is a person-centered approach that uses the same treatment approach for all individuals, regardless of HIV serostatus. This approach has been noted as a strategy that works toward achieving health equity among marginalized populations and is in line with ongoing calls for prioritizing the social determinants of health in health equity research [101,102]. Relatedly, all study procedures are identical for participants regardless of their HIV serostatus.

Hybrid Observational-Implementation Approach

As part of the observational-implementation hybrid approach, we are preparing to conduct multiple activities as part of N2P2 that will provide data on implementation constructs related to evidence-based interventions that address salient health issues impacting Black SMM and TW. For example, we are currently collecting data that will inform how to improve the delivery of evidence-based interventions to reduce alcohol use among Black SMM and TW who could benefit from reducing their drinking. Specifically, we are developing a Discrete Choice Experiment (DCE) [103,104] to determine preferences for alcohol interventions among N2P2 participants who have an Alcohol Use Disorders Identification Test-Consumption score ≥4 [105,106]. DCE’s are a preference elicitation method that asks participants to complete multiple-choice tasks (eg, paired comparisons of profiles A vs B) with different levels and combinations of questions to compare various attributes (ie, key intervention design features). The DCE that we are currently developing will ask N2P2 participants for their preferences regarding the delivery of several evidence-based alcohol interventions, including pharmacotherapy [107,108], behavioral therapies [109,110], and mobile health interventions [111,112]. Attributes that will be included in the DCE tasks include type of intervention, person delivering the service, delivery settings, and communication platforms. We will consult with the N2P2 community advisory board as part of the formative work for refining the DCE, and it will be incorporated into the second wave of N2P2 data collection using a logic step in the survey.

In addition to the DCE on preferences for alcohol intervention delivery, we will also apply a human-centered design approach [113] to a subsample of N2P2 participants to create journey maps of Black SMM and TW experiences as they access HIV prevention and care settings. We are also using the same approach to understand the experiences of health care providers as they deliver HIV prevention and care services to Black SMM and TW. These data will then be used to inform how to tailor alcohol interventions to be more contextually informed to better suit the needs of Black SMM and TW and how to integrate them into community-based and clinical HIV prevention and care service settings. In subsequent waves of data collection, we plan to explore the collection of data on implementation constructs related to evidence-based interventions that address cannabis use, such as cognitive behavioral therapy. In addition, as recruitment settings for N2P2 seeds include HIV testing and substance use treatment facilities, and referral participants are likely to have accessed these services as well, we will also collect data from N2P2 participants about their experiences with these interventions. Collectively, these data can be used to better understand the implementation of evidence-based interventions and related determinants.

Recruitment

Soft-launch recruitment for N2P2 commenced in September 2022. Respondent-driven sampling (RDS), a systematic form of snowball sampling that leverages existing social networks, is being used for recruitment [114]. RDS has previously been used to sample marginalized populations [115], including Black SMM and TW [49,116]. A total of 60 nonrandomly selected “seed” participants will be recruited from a variety of community sources with attention paid to diversity in geography and prior involvement in research and substance use (eg, recruiting seeds who use methamphetamine) [117,118]. In the cohort before N2, uConnect (n=612), we achieved a representative sample using approximately 60 seeds, which generated a large sample (n=620) [119]. Although a diverse sample was generated, homophily was achieved across several demographics in fewer waves (including some nonproductive seeds) given the inclusion criteria based on race, geography, and age. We hope to replicate a successful RDS in N2P2. Study facilitators will monitor the recruitment process and track referrals along recruitment threads. During recruitment, participants will receive a flyer with a unique identifier code, allowing them to be added to the study, which will also be used to indicate who referred them. For each additional individual that a participant successfully recruits to the study, the participant will receive US $30 compensation (up to a maximum of 6 additional participants) via cash or an electronic money transfer service of their choice (eg, PayPal, Cash App, and Venmo). Recruitment materials were developed to increase awareness of the study among the community, including the development of an N2P2 study logo (Figure 2).

Eligibility criteria for inclusion in the N2P2 study include (1) assigned male sex at birth, (2) identify as being Black or African American, (3) aged 18 to 34 years, (4) report of at least 1 sexual encounter with another man or transgender woman within the past year, and (5) currently residing in the Chicago MSA with no plans to move or relocate during the proposed study period. Individuals who are unable to provide informed consent are excluded from participating in the study. Individuals who screen eligible will then be scheduled for an appointment at the study site, the Village, the Chicago Center for HIV Elimination’s, off-campus community service and research space that has served Chicago’s South Side community for over a decade and that specializes in HIV prevention and care services [120].

Regarding the eligibility criteria, we made a conscious decision to be inclusive in the N2P2 study and engage Black TW as well as Black SMM. There are multiple reasons for this decision. First, the environments in Chicago in which both Black SMM and TW predominantly live include hyper-segregated neighborhoods that create similar exposures that are major drivers of HIV [121-123]. In addition, networks are often similar and overlapping with social networks including members of the house or ball community and sexual networks including many SMM. Indeed, research has shown that Black SMM and TW are members of social and sexual networks that are associated with HIV-related outcomes in nuanced ways [15-17,124-127]. In addition, many Black TW transition later than their White counterparts, and they identify at younger ages as SMM [128-130]. The reasons for this are complex, but the situation is likely not helped by most adolescent gender programs across the United States (including in Chicago), which underuse gender-affirming hormone therapy [131,132]. Finally, the decision to intentionally include both Black SMM and TW in the N2P2 study was made with community input, including from participants in the original N2 study, and with the understanding that social and sexual networks among Black SMM and TW in Chicago significantly overlap [126,133]. Importantly, community input highlighted increasing inclusivity, with particular attention needed to ensure that survey materials, procedures, and protocols are culturally tailored and gender affirming, with appropriate skip patterns and language to ensure that community members feel included and respected.

We recognize that transgender individuals describe themselves in a variety of different ways; for the purposes of our study and in congruence with established research on TW with HIV [134,135], we define TW as individuals who were assigned male sex at birth and have a feminine gender identity. We acknowledge that it is important to note that Black SMM and TW are distinct populations that experience unique social contexts and challenges related to substance use, sleep health, and HIV-related outcomes [14,136]. Indeed, studies have demonstrated that compared with Black SMM, Black TW experience substantially higher rates of financial instability, incarceration, and HIV and STIs [137,138].

Figure 2. Neighborhoods and Networks Part 2 (N2P2) study logo.

Procedures and Measures

Assessments are conducted every 9 months for 3 waves. Assessments are conducted in person in private interview rooms at an off-campus research and service space, The Village, which is located in the same building that has been engaging Black sexual and gender minority community members for >20 years. Four different forms of data are collected at each assessment: (1) a self-report survey, (2) biological specimen collection, (3) the administration of a daily remote EMA survey, and (4) extraction of EHR data. The prospective design of N2P2 enables the study team to alter and expand future surveys based on preliminary findings, including measuring relevant implementation issues, as per the hybrid observational-implementation approach [99]. After completing the survey, blood samples are drawn by trained study staff and screened for HIV antibodies to determine participant HIV serostatus. To encourage engagement with community members from all backgrounds, reduce barriers to entry, and increase accessibility, participants have the option to complete assessments remotely on Zoom (Zoom Video Communications), thus reducing the in-person requirement of participation to only collection of the biospecimens, which are scheduled at a separate date and time following survey completion. At the end of the assessment, survey technicians confirm contact information, schedule follow-up visits, provide instructions on completing the daily EMA survey, and disburse compensation for completion of the survey (US $100) and provision of biological samples (US $50) in cash or other preferred mechanism for compensation. Finally, as we did for the original N2 study [139], we continue to seek permission to access EHR data and health department data of each participant via release of information forms and subsequently link these data with the participant’s study identification number. Details on each type of data collection are described in the following sections.

Self-Report Survey

Overview

The self-report survey was developed by the study investigators in tandem with the University of Chicago Survey Laboratory. Of note, certain constructs from N2 Part 1 continue to be used in this N2P2 study, allowing for robust longitudinal analyses. Trained survey technicians obtain written informed consent before participants complete a survey using computer-assisted participant interview (CAPI) technology. Self-report survey measures assessed using CAPI technology are described by construct in the paragraphs below and summarized in Table 1.

Table 1. Neighborhoods and Networks Part 2 measures assessed via computer-assisted participant interview.
ConstructMeasurementReliability (Cronbach α)
Demographics (age, sex assigned at birth, height, weight, gender identity, sexual orientation, sexual attraction, history of incarceration, housing status, education, employment, income, and health insurance status)Sociodemographic questionnaireN/Aa
Potential moderating and mediating variables

Neighborhood factorsNeighborhood preferences: questions adapted from the Belgian Environmental Physical Activity Study [140]N/A

Neighborhood factorsNeighborhood disorder and violence scales [141,142].70 [141] and .913-.921 [142]

Network factorsNetwork composition: questions adapted from the Chicago Community Adult Health Study [143].85

Network factors Network size and PrEPb use: questions adapted from the National HIV Behavioral Surveillance System [144]N/A

ObesitySociodemographic questionnaireN/A

Anxiety and depressionPatient Health Questionnaire-4 [145].85

Self-efficacySherer Self-Efficacy Scale [146].71-.86

Race-based harassmentBlack Men’s Experiences Scale [147].86

Intersectional discriminationIntersectional Discrimination Index [148].70-.72

Interpersonal violenceQuestions adapted from the original Neighborhoods and Networks cohort studyN/A

Social supportMedical Outcomes Study Social Support Scale [149].91-.96

LonelinessLoneliness Scale for Emotional and Social Loneliness [150].70-.76
Implementation data

Preferences for alcohol interventionsDiscrete choice experiment [103,104]N/A
Exposures

Cannabis useDaily Sessions, Frequency, Age of Onset, and Quantity of cannabis use inventory [151].69-.95

Cannabis useCannabis Use Disorders Identification Test-Revised [152].73

Cannabis useMarijuana Motives Measure: Cannabis Coping Motives Subscale [153].80-.89

Cannabis useQuestions adapted from The International Cannabis Policy Study [154]N/A

Alcohol useAlcohol Use Disorders Identification Test [106].88

Other substancesQuestions adapted from the 2020 National Survey on Drug Use and Health [155]N/A

Other substancesDrug Abuse Screening Test [156].71-.94

SleepSleep duration and quality: Pittsburgh Sleep Quality Index [157].83

SleepSleep-related problems: Functional Outcomes of Sleep Questionnaire [158].87
Primary outcomes

Antiretroviral therapy adherenceWilson self-report scale [159].83

Retention in HIV careMissed visit proportionN/A

PrEP uptakePrEP uptake: questions adapted from the National HIV Behavioral Surveillance System [144]N/A

PrEP AdherencePrEP adherence: Wilson self-report scale [159].87
Secondary outcomes

Sexual behaviorsQuestions adapted from the National HIV Behavioral Surveillance System [144]N/A

aN/A: not applicable.

bPrEP: pre-exposure prophylaxis.

HIV Care

Self-reported HIV care outcomes include ART adherence and linkage to HIV care, which are assessed using validated measures [144,159].

PrEP Cascade of Care

To assess self-reported PrEP outcomes, we are providing participants with an adapted description of PrEP used successfully with samples of SMM and TW vulnerable to HIV [160]. The PrEP cascade of care measures include current PrEP use [144], PrEP modality (pill or injection), PrEP initiation and discontinuation, and PrEP adherence [159].

Cannabis Use and Other Substance Use

Cannabis use is being assessed via multiple validated and reliable measures that describe the frequency of use [151], modes of use [151], timing of use [151], risk of disordered use [152], motivations for cannabis use [153], and reasons for seeking cannabis treatment (if sought out by participants) [154]. The use of other substances such as alcohol, stimulants, heroin or opiates, and nicotine is also assessed and described in terms of frequency, method of consumption, and risk of disordered use [105,155,156].

Sleep

Sleep health outcomes include the self-reporting of sleep duration [157], sleep quality [157], and sleep-related problems (eg, apnea and trouble falling asleep or insomnia) [158].

Sexual Behavior

We are measuring partnered sexual behavior in the past 3 months [161], including characteristics of sexual partners (eg, gender, relationship type, HIV status, PrEP use, and undetectable viral load), sexual acts, and condom use.

Moderating Variables

Additional relevant individual-, network-, and neighborhood-level characteristics will be assessed as moderators of the impact of cannabis use on PrEP outcomes. Individual-level characteristics will include sociodemographics, reasons for using cannabis, cannabis use expectations, intersectional discrimination [148], and race-based harassment [147]. Obesity will also be assessed as a potential moderator for analyses involving sleep health as an exposure variable. Social and sexual network characteristics will include the racial and ethnic composition of network members, network size, network substance use, and network PrEP use. Neighborhood-level characteristics will include socioeconomic disadvantage, racial and ethnic composition, and locations of dispensaries. Many of these measures were captured in the original N2 study. Whenever feasible, preexisting N2 data will be used.

Covariates

Data are being collected on the following relevant sociodemographic variables: age, ethnicity, sexual orientation, sexual attraction, socioeconomic status (eg, education, income, and employment), current living situation (eg, housing instability), nativity, relationship status, mental health, history of incarceration, health insurance status, and existing or recent STIs (eg, chlamydia, gonorrhea, and syphilis).

Biological Specimen Collection

Collected blood samples are screened on site for HIV and syphilis, after which the plasma from each sample is objectively measured for quantitative levels of cannabis metabolites via liquid chromatography–tandem mass spectrometry [74]. These metabolites will also be measured using solid-phase extraction and gas chromatography–negative ion chemical ionization mass spectrometry [162]. The detection of cannabis metabolites in plasma will be used to confirm self-reported cannabis use by the study participants. In addition, we will explore the utility of using plasma levels of tetrahydrocannabinol to stratify participants into cannabis use groups (eg, heavy user or moderate user) [163]. Cytokine and chemokine levels are also assessed in the systemic circulation (plasma samples) using the Human Inflammation Panel via the LEGENDplex assay (BioLegend, Inc), which measures 13 different cytokines or chemokines, including those relevant to HIV infection [164].

After instruction by study staff, the participants also self-administer 3 rectal swabs and 1 oral swab. The 1 oral swab and 1 of the 3 rectal swabs are tested on site for chlamydia and gonorrhea nucleic acid amplification tests [165]. The remaining 2 rectal swabs are evaluated for the levels of proinflammatory cytokine and chemokine in the rectal mucosal secretions. Finally, participants are asked to provide a urine sample, which is used to conduct on-site toxicological screening of marijuana, methamphetamine, opioids, and cocaine as well as chlamydia and gonorrhea nucleic acid amplification tests. Participants who test positive for HIV or STIs or who are interested in PrEP, primary care, or substance use treatment are linked to care with services available in the same building as the data collection. A summary of the biological measures is presented in Textbox 1.

Textbox 1. Biological data in Neighborhoods and Networks Part 2 data collection.

Constructs and measurements

  • Cannabis use
    • Biological presence in blood or urine samples
  • Other substances
    • Biological presence in blood or urine samples
  • Sexually transmitted infections
    • Biological data for chlamydia, gonorrhea, or syphilis in urine, blood, saliva, or rectal mucosa samples
  • Inflammation
    • Biological data on cytokine and chemokine levels in systemic circulation and rectal mucosa

EMA Survey

The EMA survey is administered over a 2-week period following the in-person assessment. If participants are unable to complete the EMA survey using their personal phone, the study team sends the EMA survey via email. Participants receive a daily notification through their cell phones at the same time each day asking to complete the brief survey. The participants have 2 hours to complete the survey. Participants receive an additional reminder notification 1 hour before the survey expires as well as personalized reminder text messages from the study staff. Daily assessments (rather than more frequent assessments) were chosen to minimize participant burden, based on feedback from community members who participated in the original N2 study. Participants receive US $2 for each daily survey completed within the first week and US $3 for each daily survey completed during the second week. The participants also receive a US $5 bonus if all 14 days are completed. The EMA survey includes multiple measures regarding policing, substance use, HIV outcomes, and mental health. Example questions and response options from the EMA survey are listed in Table 2.

Table 2. Ecological momentary assessment survey measures in Neighborhoods and Networks Part 2 data collection.
MeasureExample questionExample response options
Policing“In the past 24 hours, did you encounter someone in law enforcement that:”“(Select all that apply) Pushed or grabbed you?; Followed you?; Made you feel unsafe?; etc.”
HIV outcomes“In the past 24 hours, did you take a daily PrEPa pill”“Yes; No”
Cannabis use“In the past 24 hours, how many times did you use cannabis?”“(Numerical drop-down selection) 1-7+”
Other substance use“In the past 24 hours, did you use any of the drugs listed below?”“Heroin (Smack, Junk, Dope); Fentanyl (China White, China Girl, Tango); Benzos; Methamphetamine (Speed, Meth, Crystal, Crystal Meth, Ice, Crank); Cocaine (Coke, Snow, Crack, Rock); Prescription opioids; Unknown combination of opioids; Other; None of the above”
Alcohol use“In the past 24 hours, how many drinks of alcohol did you have?”“(Numerical drop-down selection) 1-7+”
Anxiety and depression [145]“In the past 24 hours how often were you bothered by the following problems (if at all)?”“Not at all; Several times throughout the day; More than half the day; Nearly all day”

aPrEP: pre-exposure prophylaxis.

EHR Data

Overview

As we did for the original N2 study [139], we continue to seek permission to access the EHR data of each participant via release of information forms from a local partnering health clinic and surveillance data from the health department and subsequently link these data with the participant’s study identification number. The outcomes described in the following subsections will be extracted from participants’ EHRs (Textbox 2).

Textbox 2. Electronic health record data in Neighborhoods and Networks Part 2 data collection.

Constructs and measurements

  • Viral suppression
    • Biological viral load
  • Retention to HIV care
    • Missed visit proportion
  • Pre-exposure prophylaxis (PrEP) outcomes
    • PrEP persistence and type of PrEP product
Viral Suppression

Viral suppression data will be obtained from the participants’ EHRs. Viral suppression is defined as having a viral load of ≤200 HIV RNA copies/mL.

Retention to HIV Care

Retention to HIV care is measured by calculating a missed visit proportion, or the proportion of total scheduled visits (eg, nonacute appointments with an ART-prescribing provider) that are missed across the 27-month follow-up period [166,167]. Appointment and visit history to calculate the missed visit proportion will be collected from each participant’s EHR. To permit longitudinal analyses, we will divide the 27-month follow-up period into three 9-month sections and record, for each participant, whether they completed or missed their scheduled appointment during that section.

PrEP Outcomes

Several PrEP-related outcomes will be obtained from the participants’ EHR data, including PrEP persistence, which will be measured in several ways (eg, number of dedicated PrEP visits and number of prescriptions for PrEP) [168]. The type of PrEP product will also be collected, although we expect limited uptake of long-acting injectable PrEP in this community owing to a number of social and structural barriers; notably, these barriers are repeatedly observed with each new biomedical advance purportedly developed for community members classified as most vulnerable [169,170].

Data Management

Each N2P2 participant receives a unique study identification number, which is used to link all 3 forms of the primary data into a centralized database in the same manner as our original N2 study [139]. Data will be analyzed with the aim of maintaining participant anonymity. Access to the participant identification number, which links data to unique identifiers, will be restricted to designated members of the research team. All survey, biological, EMA, and EHR data will be uploaded to the encrypted University of Chicago servers and will be backed up on a weekly basis.

Data Triangulation

For data triangulation, objective measures will be prioritized, using previously validated decision rules for combining objective biomarker data with self-reported measures, an approach that has been used in other studies with regard to substance use [171,172] and that we will adapt as needed. This approach will allow us to account for the limitations of respective measures (eg, inaccuracy of self-report [173] and varying rates of metabolizing substance use [174]) and also allow us to identify relevant categories, as has been done in other studies [74,175]. Using multiple measures to assess constructs, particularly those regarding sensitive topics, is superior to relying on a single method [176].

Statistical Analyses

The longitudinal design of N2P2 will allow researchers to evaluate changes in constructs over time as well as cross-sectional, series cross-sectional, and longitudinal associations between exposures and outcomes [177]. We will use generalized linear models to evaluate the associations between relevant exposures and outcomes. These models will include the appropriate time-invariant and time-varying covariates to adjust for confounding of each exposure-outcome relationship. In addition, we will apply generalized estimating equations to account for repeated measures within individuals across study visits. For analyses that assess mediation, we will use statistical tests that leverage the latest statistical methodological advances in the causal inference field and that test for and estimate the effect of mediation (ie, the natural indirect effect). Our inferences will support a causal mediation interpretation if the covariates effectively control for confounding in 4 key relationships: the impact of the candidate exposure on the outcome, the effect of the candidate exposure on the candidate mediator, the influence of the candidate mediator on the outcome, and if the candidate exposure has no impact on any confounding variables related to the effect of the candidate mediator on the outcome. We recognize that this approach is among various mediation analysis methods, and we will also consider other commonly applied approaches [178]. To assess effect modification, we will conduct regression analyses by adding 2-way interaction terms (eg, cannabis use × moderator) to the previously described generalized linear models. We also plan to evaluate cross-level interactions to determine whether our 2-way interaction analyses vary by other variables in the associations between our candidate exposures and outcomes. We further plan to use additional modeling approaches, including egocentric analysis, mesolevel sociometric analysis, multilevel (affiliation network) modeling, 2-mode person or place network modeling, synergistic place or network models, and spatial autocorrelation regression modeling. In some analyses, moderating variables that are considered confounders will be adjusted for as covariates.

Power Calculations

We have adequate power to address our primary study aims. With a planned baseline sample size of 600 Black SMM and TW with 80% attrition over the study period and assuming α=.05 and power=0.80, we estimate that we will have 80% power to detect Cohen d effect sizes of 0.40 in differences of continuous HIV-related outcomes (based on distributions of those outcomes in N2 baseline data) in the highest versus lowest quintiles of exposure in the cohort. This is a conservative calculation, as it does not account for the adjustment of covariates, which can serve to increase power. To maximize the sample size and minimize potential selection bias as a result of loss of participants who do not attend all follow-up waves, we will use a pooled analysis approach and include participants who attend at least 1 of the 3 waves over the study period. A total of 600 participants will contribute 1800 potential observations across waves. This approach has been used in our prior work and in other cohort studies to efficiently maximize analytic capabilities [179]. Moderation analyses may not be optimally powered, especially to examine cross-level interactions. Mediation analyses may also not be optimally powered. However, to mitigate power issues, we plan to dichotomize sociodemographic characteristics when possible and appropriate [180]. Moreover, the potential mechanisms linking study exposures to HIV-related outcomes will be the focus of future research proposals (ie, adequate power will be prioritized).

Governance and Organizational Study

Research strategies and protocols for the N2P2 study were developed using a community-based participatory approach [181] where study staff members regularly meet with the community advisory board to guide the study. These meetings involve receiving community feedback on survey assessments and protocols, presenting progress reports on the study, discussing preliminary data, and determining the best practices for dissemination to community members and policy makers. In addition, 1 staff member who is from the Chicago Black SMM and TW community further guides community engagement through periodic social events and social media development. Moreover, we have also created a scientific advisory board to provide expert feedback on the overall study design and methodology of N2P2, thereby supporting the collection and analyses of high-quality data.

Ethical Considerations

This study was conducted in accordance with the guidelines of the Declaration of Helsinki. The N2P2 study protocol has been approved by the institutional review board (16-1419) of the University of Chicago (institutional review board of record), and a reliance agreement has been established with Columbia University. Written informed consent is required from all participants before participation in the study. Benefits to participation in the study include sharing the results of HIV serostatus and STI testing at multiple time points as well as referral to relevant health services when necessary (substance use treatment and mental health).


The N2P2 study was funded in August 2021 by the National Institute of Drug Abuse (R01DA054553 and R21DA053156) and National Heart, Lung, and Blood Institute (R01HL160325), and approved by the institutional review board in September 2022. Data collection was launched in November 2022. As of manuscript submission, 431 participants have been enrolled. Recruitment and enrollment for the first wave of data collection are currently ongoing.


Expected Findings

In summary, the N2P2 study applies innovative methods to comprehensively explore the impact of substance use and sleep health on HIV-related outcomes among an HIV status–neutral cohort of Black SMM and TW in Chicago. This study applies an observational-implementation hybrid design to prioritize the expedited translation of findings into public health impact, a critical priority among populations such as Black SMM and TW that experience long-standing inequities with regard to HIV and other health-related outcomes owing to systemic racism, homophobia, and transphobia. N2P2 directly builds on the findings of the original N2 study among Black SMM and TW in Chicago. These findings contribute to a better understanding of how multilevel (eg, individual, network, and neighborhood) factors are associated with HIV prevention and care outcomes among Black SMM and TW [139,182,183]. Relatedly, the N2 COVID-19 study contributed additional findings related to factors associated with PrEP and ART use, among other health outcomes and health behaviors, at the initial peak of the COVID-19 pandemic [50,184-186].

A major goal of the N2P2 study is to inform the implementation of evidence-based practices that already exist to address many of the issues that we are studying. For example, we observed a high prevalence of heavy drinking in the original N2 study, which aligns with other studies among Black SMM and TW [187]. We also observed from the N2 COVID-19 study that alcohol use increased among Black SMM and TW during the pandemic [50], as it did among the general population [188,189]. However, very few individuals who could benefit from alcohol treatment receive it [190,191], despite the availability of multiple evidence-based interventions [107], indicating a clear implementation gap. Therefore, in this observational cohort study, we are working to incorporate elements of implementation science related to evidence-based interventions that address alcohol use. We have named this the hybrid observational-implementation approach, and we have previously described how this practical approach can be applied to observational epidemiologic research to make it more consequential by contributing more directly to the growing implementation science movement [99]. This may also help to make the research pipeline more efficient and faster [192] and help observational research achieve its intended public health impacts, an innovation that is especially important for health equity research. In future waves of the N2P2 study, we intend to expand this work to address other salient health issues, particularly those that we learn about through findings from the existing N2 study data, or in the upcoming waves of N2P2 data collection.

In addition, given the longitudinal nature of our data collection, we are able to continue examining COVID-19 as well as recently emerged STI threats (eg, mpox [193]), evolving STI epidemics such as syphilis, and other infections that disproportionately impact these communities (ie, meningococcus). This can be an important extension for future N2P2 waves, as it is known that there are clear racial and ethnic differences in new and emerging infectious disease epidemics, including among Black SMM and TW [194,195].

Maintaining meaningful community engagement at each step of the research process and enhancing those efforts are top priorities in the N2P2 study, an approach used in the original N2 study. For example, community members provided iterative feedback that has informed this study’s design. In addition, the study team organized several community dinners and social events (eg, study relaunch event at the Village) to share the findings of the original N2 study and further strengthen community trust and inclusion. We aim to continue prioritizing shared decision-making and using innovative community-based research approaches (eg, crowdsourcing and human-centered design) as the N2P2 study progresses forward [196].

Relatedly, many papers from the original N2 study were led by trainees and early-stage investigators (eg, postbaccalaureate research coordinators, master’s-level students, PhD students, and postdoctoral fellows), including those who hold minoritized identities, particularly trainees who hold the same intersectional identities as those in the N2 study. This has enhanced our ability to situate interpretations of data in the community context and obtain high-quality data. For example, because many research coordinators hold similar intersectional identities as N2 study participants, we were able to build trust with the community. This hopefully will contribute to less misclassification in self-reported measures and a greater willingness to adhere to study protocols. In N2P2, we plan to continue this work, especially because we recognize the harms that can arise from power differentials between researchers and study participants [197]. We view this component of our work as part of our intersectional and health equity–focused research program, which aims to ultimately achieve better health outcomes among Black SMM and TW [198,199].

Finally, we note that the N2P2 study currently does not have funding for recruitment in the Southeastern United States, which was a component of the original N2 study. We are actively working to acquire funding to study Black SMM and Black TW in the Southeastern sites included in N2, including New Orleans and Baton Rouge, given that the burden of the domestic HIV and AIDS epidemic is heaviest among Black SMM and TW living in that part of the country [200].

Strengths and Limitations

The N2P2 study has some limitations. First, our sample of Black SMM and TW is sampled exclusively from the Chicago MSA and may have limited generalizability to other geographic contexts, such as smaller cities or rural areas. We decided to continue our focus on the Chicago MSA as >80% of the US population lives in urban settings [201]. Chicago shares similar contextual and environmental characteristics with many other hyper-segregated EHE jurisdictions in the United States, such as Houston and Detroit. In addition, owing to the duration of the cohort, our study’s ability to assess causality may be limited by participant loss to follow-up. This limitation is particularly significant for minoritized populations such as Black SMM and TW because of the structural barriers that these groups experience. For example, in the original N2 study cohort, 70% of the participants had an annual income of <US $25,000 and 35% reported unstable housing. These structural barriers have also been shown to be related to HIV-related outcomes [48], and they also serve as barriers to participation in research. To mitigate this challenge, participant retention is prioritized within the study’s community integration efforts as well as through protocol-based approaches such as recording participant contact information and consistent efforts to connect with participants and remind them of their engagement with the study. Moreover, owing to social desirability bias, it is possible that substance use, sexual behaviors, and PrEP and ART nonadherence are underreported. We aim to reduce the effects of this bias by obtaining objective biomarker data and using CAPI to collect potentially sensitive information. Finally, we recognize heterogeneity in Black SMM and TW and the importance of meaningful intersectional analyses; however, because we are not intentionally focusing on this heterogeneity, we may have limited power for subgroup analyses such as by ethnicity and socioeconomic status. For example, in the original N2 study, although we have variation in socioeconomic status, only 6.6% of the sample reported being Latinx or Hispanic ethnicity. We are also making efforts to ensure variability in other factors that are related to health, such as our intentional geographical sampling approach, and collect data on experiences that are relevant to Black SMM and TW, such as membership in the house or ball community and other queer family structures.

Conclusions

These limitations are offset by many notable strengths. In N2P2, we are using a unique and innovative approach to simultaneously explore multiple salient questions about what potentially drives the long-standing HIV-related inequities experienced by Black SMM and TW [1,9]. Combining resources to conduct a comprehensive study, such as N2P2, marks a potentially more efficient use of resources, one that may reduce the logistical challenges associated with establishing independent cohorts to address each of these topics independently. The inclusive HIV status–neutral approach minimizes potential stigmatization and enhances the potential to connect participants with relevant HIV prevention and treatment services. Furthermore, the multiple innovative methods that are being used to collect primary data in N2P2, including EMA, biobehavioral overlap, preference elicitation, and human-centered design, will allow a more holistic understanding of how substance use, sleep, and HIV-related outcomes interrelate. Similarly, because of the pressing need for additional research on the HIV epidemic, specifically within Black SMM and TW communities, the study findings from N2P2 may have a substantial potential public health impact. As such, the N2P2 study provides a pivotal opportunity to better understand the impact of substance use and sleep on HIV among Black SMM and TW, which may inform the development of future evidence-based culturally relevant interventions and policy approaches.

Acknowledgments

The authors thank the participants for engaging in this research. The Neighborhoods and Networks Part 2 (N2P2) study is funded by grants from the National Institute on Drug Abuse (R01DA054553, multiple principal investigator [MPI]: DTD and JRK; U2CDA050098, principal investigator [PI]: JAS and the National Heart, Lung, and Blood Institute (R01HL160325, MPI: DTD or JAS). The N2P2 study is further supported by several pilot awards from the HIV Center for Clinical and Behavioral Studies (P30MH043520, PI: Remien), the National Center for Advancing Translational Sciences (UL1TR001873, PI: Reilly), and the New York University Center for Drug Use and HIV Research (P30DA011041, PI: Hagan) to JRK. JRK is also supported in part by grants from the National Institute on Alcohol Abuse and Alcoholism (K01AA028199), the National Institute on Drug Abuse (NIDA) (R01DA057351 and R21DA05315), and the National Institute on Mental Health (P30MH043520). YTC is supported in part by a grant from the National Institute on Drug Abuse (R03DA053161). Duncan was supported in part by grants from the National Institute on Minority Health and Health Disparities (R01MD013554 and R01MD013554-S1); the National Institute on Mental Health (R01MH129198); the National Institute on Drug Abuse (5P30DA011041-24); the National Heart, Lung, and Blood Institute (R01HL160325); and the National Institute on Allergy and Infectious Diseases (UG3AI169658). JR and the Center for Human Toxicology receives funding from NIDA (75N95019C00016) to support the development and validation of a bioanalytical assay for measuring cannabinoids. The work described herein is the sole responsibility of the authors and does not represent the official views of the National Institutes of Health or its institutes.

Data Availability

Once data are collected, they will be stored in a public repository hosted by Data Commons. In addition, investigators can obtain data via an email request to the Neighborhoods and Networks Part 2 staff.

Authors' Contributions

JRK, JAS, and DTD were responsible for conceptualization of the project. The methodology was developed by JRK, JAS, and DTD. Formal analysis was conducted by EA, AH, and CM. JRK, JAS, DTD, IM, MD, HH, and EA. Resources were provided by JAS. Data curation was conducted by HH, EA, and AH. JRK, BD, TM, and DTD prepared the original draft of this manuscript and IM, MD, HH, EA, AH, JP-B, Y-TC, DE, JM, JR, CM, BM, GJ-L, HJR, SSM, CG, DSH, AWC, SS, JAS, and DTD revised and edited the manuscript. JRK, JAS, and DTD supervised the project with project administration by IM, MD, HH, and JAS. Funding acquisition done by JRK, JAS, and DTD.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Peer-reviewed reports from NIH.

PDF File (Adobe PDF File), 352 KB

  1. Matthews DD, Herrick AL, Coulter RW, Friedman MR, Mills TC, Eaton LA, et al. Running backwards: consequences of current HIV incidence rates for the next generation of Black MSM in the United States. AIDS Behav. Jan 2016;20(1):7-16. [FREE Full text] [CrossRef] [Medline]
  2. HIV surveillance report, 2019; vol.32. Centers for Disease Control and Prevention. May 2021. URL: http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html [accessed 2023-11-01]
  3. Singh S, Song R, Johnson AS, McCray E, Hall HI. HIV incidence, prevalence and undiagnosed infections in men who have sex with men. In: Proceedings of the the 24th Conference on Retroviruses and Opportunistic Infections. Presented at: Summary reports of the 24th Conference on Retroviruses and Opportunistic Infections; February 13-15, 2017, 2017; Seattle, WA. URL: https://www.natap.org/2017/CROI/croi_116.htm [CrossRef]
  4. Prejean J, Song R, Hernandez A, Ziebell R, Green T, Walker F, et al. Estimated HIV incidence in the United States, 2006-2009. PLoS One. 2011;6(8):e17502. [FREE Full text] [CrossRef] [Medline]
  5. HIV surveillance report, 2016; vol. 28. Centers for Disease Control and Prevention. Nov 2017. URL: https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-report-2016-vol-28.pdf [accessed 2023-11-01]
  6. Mitsch A, Singh S, Li J, Balaji A, Linley L, Selik R. Age-associated trends in diagnosis and prevalence of infection with HIV among men who have sex with men - United States, 2008-2016. MMWR Morb Mortal Wkly Rep. Sep 21, 2018;67(37):1025-1031. [FREE Full text] [CrossRef] [Medline]
  7. HIV in NYC: statistics and reports. New York City Health. URL: https://www.nyc.gov/site/doh/data/data-sets/hiv-aids-surveillance-and-epidemiology-reports.page [accessed 2023-11-01]
  8. Hess KL, Hu X, Lansky A, Mermin J, Hall HI. Lifetime risk of a diagnosis of HIV infection in the United States. Ann Epidemiol. Apr 2017;27(4):238-243. [FREE Full text] [CrossRef] [Medline]
  9. Becasen JS, Denard CL, Mullins MM, Higa DH, Sipe TA. Estimating the prevalence of HIV and sexual behaviors among the US transgender population: a systematic review and meta-analysis, 2006-2017. Am J Public Health. Jan 2019;109(1):e1-e8. [FREE Full text] [CrossRef] [Medline]
  10. Watkins-Hayes C. Intersectionality and the sociology of HIV/AIDS: past, present, and future research directions. Annu Rev Sociol. Jul 2014;40:431-457. [CrossRef]
  11. Collins PH. Intersectionality as Critical Social Theory. Durham, NC. Duke University Press; Aug 23, 2019.
  12. Bowleg L. Evolving intersectionality within public health: from analysis to action. Am J Public Health. Jan 2021;111(1):88-90. [CrossRef] [Medline]
  13. Crenshaw K. Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Rev. Jul 1991;43(6):1241-1299. [CrossRef]
  14. Poteat T, German D, Flynn C. The conflation of gender and sex: gaps and opportunities in HIV data among transgender women and MSM. Glob Public Health. 2016;11(7-8):835-848. [FREE Full text] [CrossRef] [Medline]
  15. Brawner BM, Kerr J, Castle BF, Bannon JA, Bonett S, Stevens R, et al. A systematic review of neighborhood-level influences on HIV vulnerability. AIDS Behav. Mar 2022;26(3):874-934. [FREE Full text] [CrossRef] [Medline]
  16. Phillips G2, Neray B, Birkett M, Felt D, Janulis P, Mustanski B. Role of social and sexual network factors in PrEP utilization among YMSM and transgender women in Chicago. Prev Sci. Oct 2019;20(7):1089-1097. [FREE Full text] [CrossRef] [Medline]
  17. Arnold EA, Sterrett-Hong E, Jonas A, Pollack LM. Social networks and social support among ball-attending African American men who have sex with men and transgender women are associated with HIV-related outcomes. Glob Public Health. Feb 2018;13(2):144-158. [FREE Full text] [CrossRef] [Medline]
  18. Estimated HIV incidence in the United States, 2007-2010. Centers for Disease Control and Prevention. Dec 2012. URL: http://www.cdc.gov/hiv/topics/ surveillance/resources/reports/#supplemental [accessed 2023-11-01]
  19. HIV among African American gay and bisexual men. Centers for Disease Control and Prevention. URL: http://www.cdc.gov/hiv/risk/racialethnic/bmsm/facts/ [accessed 2023-10-26]
  20. Fauci AS, Redfield RR, Sigounas G, Weahkee MD, Giroir BP. Ending the HIV epidemic: a plan for the United States. JAMA. Mar 05, 2019;321(9):844-845. [FREE Full text] [CrossRef] [Medline]
  21. Cáceres CF, Borquez A, Klausner JD, Baggaley R, Beyrer C. Implementation of pre-exposure prophylaxis for human immunodeficiency virus infection: progress and emerging issues in research and policy. J Int AIDS Soc. Oct 18, 2016;19(7(Suppl 6)):21108. [FREE Full text] [CrossRef] [Medline]
  22. Cohen SE, Vittinghoff E, Bacon O, Doblecki-Lewis S, Postle BS, Feaster DJ, et al. High interest in preexposure prophylaxis among men who have sex with men at risk for HIV infection: baseline data from the US PrEP demonstration project. J Acquir Immune Defic Syndr. Apr 01, 2015;68(4):439-448. [FREE Full text] [CrossRef] [Medline]
  23. Haberer JE, Bangsberg DR, Baeten JM, Curran K, Koechlin F, Amico KR, et al. Defining success with HIV pre-exposure prophylaxis: a prevention-effective adherence paradigm. AIDS. Jul 17, 2015;29(11):1277-1285. [FREE Full text] [CrossRef] [Medline]
  24. Jenness SM, Maloney KM, Smith DK, Hoover KW, Goodreau SM, Rosenberg ES, et al. Addressing gaps in HIV preexposure prophylaxis care to reduce racial disparities in HIV incidence in the United States. Am J Epidemiol. Apr 01, 2019;188(4):743-752. [FREE Full text] [CrossRef] [Medline]
  25. Mimiaga MJ, Closson EF, Kothary V, Mitty JA. Sexual partnerships and considerations for HIV antiretroviral pre-exposure prophylaxis utilization among high-risk substance using men who have sex with men. Arch Sex Behav. Jan 2014;43(1):99-106. [FREE Full text] [CrossRef] [Medline]
  26. Jalil EM, Torres TS, Luz PM, Monteiro L, Moreira RI, de Castro CR, et al. Low PrEP adherence despite high retention among transgender women in Brazil: the PrEParadas study. J Int AIDS Soc. Mar 2022;25(3):e25896. [FREE Full text] [CrossRef] [Medline]
  27. Riddell J4, Cohn JA. Reaching high-risk patients for HIV preexposure prophylaxis. JAMA. Jul 12, 2016;316(2):211-212. [CrossRef] [Medline]
  28. Grulich AE, Guy R, Amin J, Jin F, Selvey C, Holden J, et al. Population-level effectiveness of rapid, targeted, high-coverage roll-out of HIV pre-exposure prophylaxis in men who have sex with men: the EPIC-NSW prospective cohort study. Lancet HIV. Nov 2018;5(11):e629-e637. [CrossRef] [Medline]
  29. Wu H, Mendoza MC, Huang YL, Hayes T, Smith DK, Hoover KW. Uptake of HIV preexposure prophylaxis among commercially insured persons-United States, 2010-2014. Clin Infect Dis. Jan 15, 2017;64(2):144-149. [CrossRef] [Medline]
  30. Sullivan PS, Giler RM, Mouhanna F, Pembleton ES, Guest JL, Jones J, et al. Trends in the use of oral emtricitabine/tenofovir disoproxil fumarate for pre-exposure prophylaxis against HIV infection, United States, 2012-2017. Ann Epidemiol. Dec 2018;28(12):833-840. [FREE Full text] [CrossRef] [Medline]
  31. Preexposure prophylaxis for the prevention of HIV infection in the United States - 2014: a clinical practice guideline. US Centers for Disease Control and Prevention, US Public Health Service. 2014. URL: https://www.cdc.gov/hiv/pdf/guidelines/PrEPguidelines2014.pdf [accessed 2023-11-01]
  32. Delaney KP, Sanchez T, Bowles K, Oraka E, DiNenno E, Sullivan P. Awareness and use of PrEP appear to be increasing among internet samples of US MSM. In: Proceedings of the Conference on Retroviruses and Opportunistic Infections 2016. Presented at: Conference on Retroviruses and Opportunistic Infections 2016; February 22-25, 2016, 2016; Boston, MA.
  33. Eaton LA, Driffin DD, Bauermeister J, Smith H, Conway-Washington C. Minimal awareness and stalled uptake of pre-exposure prophylaxis (PrEP) among at risk, HIV-negative, Black men who have sex with men. AIDS Patient Care STDS. Aug 2015;29(8):423-429. [FREE Full text] [CrossRef] [Medline]
  34. Smith DK, Toledo L, Smith DJ, Adams MA, Rothenberg R. Attitudes and program preferences of African-American urban young adults about pre-exposure prophylaxis (PrEP). AIDS Educ Prev. Oct 2012;24(5):408-421. [CrossRef] [Medline]
  35. Cohen SE, Vittinghoff E, Anderson PL, Doblecki-Lewis S, Bacon O, Chege W, et al. Implementation of PrEP in STD clinics and a community health center: high uptake and drug levels among MSM in the demo project. In: Proceedings of the 21st Conference on Retroviruses and Opportunistic Infections. Presented at: 21st Conference on Retroviruses and Opportunistic Infections; March 3-6, 2014, 2014; Boston, MA. URL: https://www.natap.org/2014/CROI/croi_157.htm
  36. Misra K, Udeagu CC. Disparities in awareness of HIV postexposure and preexposure prophylaxis among notified partners of HIV-positive individuals, New York City 2015-2017. J Acquir Immune Defic Syndr. Oct 01, 2017;76(2):132-140. [CrossRef] [Medline]
  37. Siegler AJ, Mouhanna F, Giler RM, Weiss K, Pembleton E, Guest J, et al. The prevalence of pre-exposure prophylaxis use and the pre-exposure prophylaxis-to-need ratio in the fourth quarter of 2017, United States. Ann Epidemiol. Dec 2018;28(12):841-849. [FREE Full text] [CrossRef] [Medline]
  38. Harris NS, Johnson AS, Huang YL, Kern D, Fulton P, Smith DK, et al. Vital signs: status of human immunodeficiency virus testing, viral suppression, and HIV preexposure prophylaxis - United States, 2013-2018. MMWR Morb Mortal Wkly Rep. Dec 06, 2019;68(48):1117-1123. [FREE Full text] [CrossRef] [Medline]
  39. PrEP for HIV prevention in the U.S. Centers for Disease Control and Prevention. URL: https:/​/www.​cdc.gov/​nchhstp/​newsroom/​fact-sheets/​hiv/​PrEP-for-hiv-prevention-in-the-US-factsheet.​html [accessed 2023-03-28]
  40. García F, Romeu J, Grau I, Sambeat MA, Dalmau D, Knobel H, et al. A randomized study comparing triple versus double antiretroviral therapy or no treatment in HIV-1-infected patients in very early stage disease: the Spanish Earth-1 study. AIDS. Dec 03, 1999;13(17):2377-2388. [CrossRef] [Medline]
  41. Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li C, Wabwire-Mangen F, et al. Viral load and heterosexual transmission of human immunodeficiency virus type 1. Rakai Project Study Group. N Engl J Med. Mar 30, 2000;342(13):921-929. [CrossRef] [Medline]
  42. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Antiretroviral therapy for the prevention of HIV-1 transmission. N Engl J Med. Sep 01, 2016;375(9):830-839. [FREE Full text] [CrossRef] [Medline]
  43. Rosenberg ES, Millett GA, Sullivan PS, Del Rio C, Curran JW. Understanding the HIV disparities between Black and White men who have sex with men in the USA using the HIV care continuum: a modeling study. Lancet HIV. Dec 2014;1(3):e112-e118. [FREE Full text] [CrossRef] [Medline]
  44. Buchacz K, Armon C, Tedaldi E, Palella FJ, Novak RM, Ward D, et al. Disparities in HIV viral load suppression by race/ethnicity among men who have sex with men in the HIV outpatient study. AIDS Res Hum Retroviruses. Apr 2018;34(4):357-364. [FREE Full text] [CrossRef] [Medline]
  45. Sevelius JM, Carrico A, Johnson MO. Antiretroviral therapy adherence among transgender women living with HIV. J Assoc Nurses AIDS Care. 2010;21(3):256-264. [FREE Full text] [CrossRef] [Medline]
  46. Kalichman SC, Hernandez D, Finneran S, Price D, Driver R. Transgender women and HIV-related health disparities: falling off the HIV treatment cascade. Sex Health. Oct 2017;14(5):469-476. [CrossRef] [Medline]
  47. Quinn KG, Voisin DR. ART adherence among men who have sex with men living with HIV: key challenges and opportunities. Curr HIV/AIDS Rep. Aug 2020;17(4):290-300. [FREE Full text] [CrossRef] [Medline]
  48. Sullivan PS, Knox J, Jones J, Taussig J, Valentine Graves M, Millett G, et al. Understanding disparities in viral suppression among Black MSM living with HIV in Atlanta Georgia. J Int AIDS Soc. Apr 2021;24(4):e25689. [FREE Full text] [CrossRef] [Medline]
  49. Duncan DT, Hickson DA, Goedel WC, Callander D, Brooks B, Chen YT, et al. The social context of HIV prevention and care among Black men who have sex with men in three U.S. cities: the neighborhoods and networks (N2) cohort study. Int J Environ Res Public Health. May 30, 2019;16(11):1922. [FREE Full text] [CrossRef] [Medline]
  50. Moody RL, Chen YT, Schneider JA, Knox J, Timmins L, Hanson H, et al. Polysubstance use in a community sample of Black cisgender sexual minority men and transgender women in Chicago during initial COVID-19 pandemic peak. Subst Abuse Treat Prev Policy. Jan 28, 2022;17(1):4. [FREE Full text] [CrossRef] [Medline]
  51. Knox J, Hwang G, Carrico AW, Duncan DT, Watson RJ, Eaton LA. Daily marijuana use predicts HIV seroconversion among Black men who have sex with men and transgender women in Atlanta, GA. AIDS Behav. Aug 2022;26(8):2503-2515. [FREE Full text] [CrossRef] [Medline]
  52. Mimiaga MJ, Reisner SL, Grasso C, Crane HM, Safren SA, Kitahata MM, et al. Substance use among HIV-infected patients engaged in primary care in the United States: findings from the Centers for AIDS Research Network of Integrated Clinical Systems cohort. Am J Public Health. Aug 2013;103(8):1457-1467. [FREE Full text] [CrossRef] [Medline]
  53. Kumar N, Janmohamed K, Nyhan K, Martins SS, Cerda M, Hasin D, et al. Substance, use in relation to COVID-19: a scoping review. Addict Behav. Apr 2022;127:107213. [FREE Full text] [CrossRef] [Medline]
  54. Schauer GL, Dilley JA, Roehler DR, Sheehy TJ, Filley JR, Broschart SC, et al. Cannabis sales increases during COVID-19: findings from Alaska, Colorado, Oregon, and Washington. Int J Drug Policy. Dec 2021;98:103384. [FREE Full text] [CrossRef] [Medline]
  55. Grov C, Rendina HJ, John SA, Parsons JT. Determining the roles that club drugs, marijuana, and heavy drinking play in PrEP medication adherence among gay and bisexual men: implications for treatment and research. AIDS Behav. May 2019;23(5):1277-1286. [FREE Full text] [CrossRef] [Medline]
  56. Viamonte M, Ghanooni D, Reynolds JM, Grov C, Carrico AW. Running with scissors: a systematic review of substance use and the pre-exposure prophylaxis care continuum among sexual minority men. Curr HIV/AIDS Rep. Aug 2022;19(4):235-250. [FREE Full text] [CrossRef] [Medline]
  57. Morgan E, Nyaku AN, DʼAquila RT, Schneider JA. Determinants of HIV phylogenetic clustering in Chicago among young Black men who have sex with men from the uConnect cohort. J Acquir Immune Defic Syndr. Jul 01, 2017;75(3):265-270. [FREE Full text] [CrossRef] [Medline]
  58. Morgan E, Khanna AS, Skaathun B, Michaels S, Young L, Duvoisin R, et al. Marijuana use among young Black men who have sex with men and the HIV care continuum: findings from the uConnect cohort. Subst Use Misuse. Nov 09, 2016;51(13):1751-1759. [FREE Full text] [CrossRef] [Medline]
  59. Okafor CN, Hucks-Ortiz C, Hightow-Weidman LB, Magnus M, Emel L, Beauchamp G, et al. Brief report: associations between self-reported substance use behaviors and PrEP acceptance and adherence among Black MSM in the HPTN 073 study. J Acquir Immune Defic Syndr. Sep 01, 2020;85(1):23-29. [FREE Full text] [CrossRef] [Medline]
  60. Serota DP, Rosenberg ES, Sullivan PS, Thorne AL, Rolle CP, Del Rio C, et al. Pre-exposure prophylaxis uptake and discontinuation among young Black men who have sex with men in Atlanta, Georgia: a prospective cohort study. Clin Infect Dis. Jul 27, 2020;71(3):574-582. [FREE Full text] [CrossRef] [Medline]
  61. Connolly MD, Dankerlui DN, Eljallad T, Dodard-Friedman I, Tang A, Joseph CL. Outcomes of a PrEP demonstration project with LGBTQ youth in a community-based clinic setting with integrated gender-affirming care. Transgend Health. Jun 08, 2020;5(2):75-79. [FREE Full text] [CrossRef] [Medline]
  62. Vagenas P, Azar MM, Copenhaver MM, Springer SA, Molina PE, Altice FL. The impact of alcohol use and related disorders on the HIV continuum of care: a systematic review : alcohol and the HIV continuum of care. Curr HIV/AIDS Rep. Dec 2015;12(4):421-436. [FREE Full text] [CrossRef] [Medline]
  63. Arrington-Sanders R, Alvarenga A, Galai N, Arscott J, Wirtz A, Carr R, et al. Social determinants of transactional sex in a sample of young Black and Latinx sexual minority cisgender men and transgender women. J Adolesc Health. Feb 2022;70(2):275-281. [FREE Full text] [CrossRef] [Medline]
  64. Williams EC, Hahn JA, Saitz R, Bryant K, Lira MC, Samet JH. Alcohol use and Human Immunodeficiency Virus (HIV) infection: current knowledge, implications, and future directions. Alcohol Clin Exp Res. Oct 2016;40(10):2056-2072. [FREE Full text] [CrossRef] [Medline]
  65. Hussen SA, Camp DM, Jones MD, Patel SA, Crawford ND, Holland DP, et al. Exploring influences on methamphetamine use among Black gay, bisexual and other men who have sex with men in Atlanta: a focus group study. Int J Drug Policy. Apr 2021;90:103094. [FREE Full text] [CrossRef] [Medline]
  66. Scheim AI, Bauer GR, Shokoohi M. Drug use among transgender people in Ontario, Canada: disparities and associations with social exclusion. Addict Behav. Sep 2017;72:151-158. [CrossRef] [Medline]
  67. Grov C, Westmoreland D, Morrison C, Carrico AW, Nash D. The crisis we are not talking about: one-in-three annual HIV seroconversions among sexual and gender minorities were persistent methamphetamine users. J Acquir Immune Defic Syndr. Nov 01, 2020;85(3):272-279. [FREE Full text] [CrossRef] [Medline]
  68. Vu NT, Maher L, Zablotska I. Amphetamine-type stimulants and HIV infection among men who have sex with men: implications on HIV research and prevention from a systematic review and meta-analysis. J Int AIDS Soc. Jan 16, 2015;18(1):19273. [FREE Full text] [CrossRef] [Medline]
  69. Wu E, El-Bassel N, McVinney LD, Hess L, Remien RH, Charania M, et al. Feasibility and promise of a couple-based HIV/STI preventive intervention for methamphetamine-using, Black men who have sex with men. AIDS Behav. Nov 2011;15(8):1745-1754. [FREE Full text] [CrossRef] [Medline]
  70. Reback CJ, Fletcher JB. Elevated HIV and STI prevalence and incidence among methamphetamine-using men who have sex with men in Los Angeles county. AIDS Educ Prev. Aug 2018;30(4):350-356. [FREE Full text] [CrossRef] [Medline]
  71. Sherman JP, Dyar C, Morgan E. Substance use treatment partially mitigates association between methamphetamine use and STI risk: findings from the NSDUH cohort. Sex Transm Infect. May 2022;98(3):210-214. [FREE Full text] [CrossRef] [Medline]
  72. Carrico AW, Shoptaw S, Cox C, Stall R, Li X, Ostrow D, et al. Stimulant use and progression to AIDS or mortality after the initiation of highly active antiretroviral therapy. J Acquir Immune Defic Syndr. Dec 15, 2014;67(5):508-513. [FREE Full text] [CrossRef] [Medline]
  73. Carrico AW, Johnson MO, Colfax GN, Moskowitz JT. Affective correlates of stimulant use and adherence to anti-retroviral therapy among HIV-positive methamphetamine users. AIDS Behav. Aug 2010;14(4):769-777. [FREE Full text] [CrossRef] [Medline]
  74. Manuzak JA, Gott T, Kirkwood J, Coronado E, Hensley-McBain T, Miller C, et al. Heavy cannabis use associated with reduction in activated and inflammatory immune cell frequencies in antiretroviral therapy-treated human immunodeficiency virus-infected individuals. Clin Infect Dis. Jun 01, 2018;66(12):1872-1882. [FREE Full text] [CrossRef] [Medline]
  75. McEwen BS. Stress, adaptation, and disease. Allostasis and allostatic load. Ann N Y Acad Sci. May 01, 1998;840:33-44. [CrossRef] [Medline]
  76. Dhabhar FS. Effects of stress on immune function: the good, the bad, and the beautiful. Immunol Res. May 2014;58(2-3):193-210. [FREE Full text] [CrossRef] [Medline]
  77. Chen W, Crawford RB, Kaplan BL, Kaminski NE. Modulation of HIVGP120 antigen-specific immune responses in Vivo by Δ9-Tetrahydrocannabinol. J Neuroimmune Pharmacol. Jun 2015;10(2):344-355. [FREE Full text] [CrossRef] [Medline]
  78. Milloy MJ, Marshall B, Kerr T, Richardson L, Hogg R, Guillemi S, et al. High-intensity cannabis use associated with lower plasma human immunodeficiency virus-1 RNA viral load among recently infected people who use injection drugs. Drug Alcohol Rev. Mar 2015;34(2):135-140. [FREE Full text] [CrossRef] [Medline]
  79. Rom S, Persidsky Y. Cannabinoid receptor 2: potential role in immunomodulation and neuroinflammation. J Neuroimmune Pharmacol. Jun 2013;8(3):608-620. [FREE Full text] [CrossRef] [Medline]
  80. Crane NA, Schuster RM, Fusar-Poli P, Gonzalez R. Effects of cannabis on neurocognitive functioning: recent advances, neurodevelopmental influences, and sex differences. Neuropsychol Rev. Jun 2013;23(2):117-137. [FREE Full text] [CrossRef] [Medline]
  81. Gonzalez R, Schuster RM, Vassileva J, Martin EM. Impact of HIV and a history of marijuana dependence on procedural learning among individuals with a history of substance dependence. J Clin Exp Neuropsychol. Aug 2011;33(7):735-752. [FREE Full text] [CrossRef] [Medline]
  82. Meier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RS, et al. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc Natl Acad Sci U S A. Oct 02, 2012;109(40):E2657-E2664. [FREE Full text] [CrossRef] [Medline]
  83. Geng EH, Baumann AA, Powell BJ. Mechanism mapping to advance research on implementation strategies. PLoS Med. Mar 8, 2022;19(2):e1003918. [FREE Full text] [CrossRef] [Medline]
  84. Knox J, Reddy V, Lane T, Lovasi GS, Hasin D, Sandfort T. Safer sex intentions modify the relationship between substance use and sexual risk behavior among Black South African men who have sex with men. Int J STD AIDS. Jul 2019;30(8):786-794. [FREE Full text] [CrossRef] [Medline]
  85. Knox J, Reddy V, Lane T, Hasin D, Sandfort T. Substance use and sexual risk behavior among Black South African men who have sex with men: the moderating effects of reasons for drinking and safer sex intentions. AIDS Behav. Jul 2017;21(7):2023-2032. [FREE Full text] [CrossRef] [Medline]
  86. Baral S, Logie CH, Grosso A, Wirtz AL, Beyrer C. Modified social ecological model: a tool to guide the assessment of the risks and risk contexts of HIV epidemics. BMC Public Health. May 17, 2013;13:482. [FREE Full text] [CrossRef] [Medline]
  87. Duncan DT, Ransome Y, Park SH, Jackson SD, Kawachi I, Branas CC, et al. Neighborhood social cohesion, religious participation and sexual risk behaviors among cisgender Black sexual minority men in the southern United States. Soc Sci Med. Jul 2021;279:113913. [FREE Full text] [CrossRef] [Medline]
  88. Khan MR, Kapadia F, Geller A, Mazumdar M, Scheidell JD, Krause KD, et al. Racial and ethnic disparities in "stop-and-frisk" experience among young sexual minority men in New York City. PLoS One. Aug 26, 2021;16(8):e0256201. [FREE Full text] [CrossRef] [Medline]
  89. Stenersen MR, Thomas K, McKee S. Police and transgender and gender diverse people in the United States: a brief note on interaction, harassment, and violence. J Interpers Violence. Dec 2022;37(23-24):NP23527-NP23540. [CrossRef] [Medline]
  90. Baker P, Beletsky L, Avalos L, Venegas C, Rivera C, Strathdee SA, et al. Policing practices and risk of HIV infection among people who inject drugs. Epidemiol Rev. Jan 31, 2020;42(1):27-40. [FREE Full text] [CrossRef] [Medline]
  91. Duncan DT, Schneider JA, Radix A, Harry-Hernandez S, Callander D. Sleep health among transgender women of color in New York City: preliminary analyses of interim baseline data from the TURNNT study cohort. Sleep Health. Apr 2021;7(2):153-154. [FREE Full text] [CrossRef] [Medline]
  92. Caceres BA, Hickey KT, Heitkemper EM, Hughes TL. An intersectional approach to examine sleep duration in sexual minority adults in the United States: findings from the Behavioral Risk Factor Surveillance System. Sleep Health. Dec 2019;5(6):621-629. [FREE Full text] [CrossRef] [Medline]
  93. Duncan DT, Park SH, Chen YT, Mountcastle H, Pagkas-Bather J, Timmins L, et al. Sleep characteristics among Black cisgender sexual minority men and Black transgender women during the COVID-19 pandemic: the role of multi-level COVID-19-related stressors. Sleep Health. Oct 2022;8(5):440-450. [FREE Full text] [CrossRef] [Medline]
  94. McKnight-Eily LR, Eaton DK, Lowry R, Croft JB, Presley-Cantrell L, Perry GS. Relationships between hours of sleep and health-risk behaviors in US adolescent students. Prev Med. Oct 2011;53(4-5):271-273. [CrossRef] [Medline]
  95. Millar BM, Goedel WC, Duncan DT. Sleep health among sexual and gender minorities. In: Duncan DT, Kawachi I, Redline S, editors. The Social Epidemiology of Sleep. New York, NY. Oxford Academic Press; 2019.
  96. Duncan DT, Goedel WC, Mayer KH, Safren SA, Palamar JJ, Hagen D, et al. Poor sleep health and its association with mental health, substance use, and condomless anal intercourse among gay, bisexual, and other men who have sex with men. Sleep Health. Dec 2016;2(4):316-321. [CrossRef] [Medline]
  97. Smiley SL, Milburn NG, Nyhan K, Taggart T. A systematic review of recent methodological approaches for using ecological momentary assessment to examine outcomes in U.S. Based HIV research. Curr HIV/AIDS Rep. Aug 2020;17(4):333-342. [CrossRef] [Medline]
  98. Paolillo EW, Obermeit LC, Tang B, Depp CA, Vaida F, Moore DJ, et al. Smartphone-based ecological momentary assessment (EMA) of alcohol and cannabis use in older adults with and without HIV infection. Addict Behav. Aug 2018;83:102-108. [FREE Full text] [CrossRef] [Medline]
  99. Knox J, Schwartz S, Duncan DT, Curran G, Schneider J, Stephenson R, et al. Proposing the observational-implementation hybrid approach: designing observational research for rapid translation. Ann Epidemiol. Sep 2023;85:45-50. [CrossRef] [Medline]
  100. Curran GM, Landes SJ, McBain SA, Pyne JM, Smith JD, Fernandez ME, et al. Reflections on 10 years of effectiveness-implementation hybrid studies. Front Health Serv. Dec 8, 2022;2:1053496. [FREE Full text] [CrossRef] [Medline]
  101. Pagkas-Bather J, Brewer R, Bouris A. Status-neutral interventions to support health equity for Black sexual minority men. Curr HIV/AIDS Rep. Aug 2022;19(4):265-280. [CrossRef] [Medline]
  102. McCree DH, Beer L, Prather C, Gant Z, Harris N, Sutton M, et al. An approach to achieving the health equity goals of the national HIV/AIDS strategy for the United States among racial/ethnic minority communities. Public Health Rep. 2016;131(4):526-530. [FREE Full text] [CrossRef] [Medline]
  103. Ryan M, Bate A, Eastmond CJ, Ludbrook A. Use of discrete choice experiments to elicit preferences. Qual Health Care. Oct 01, 2001;10 Suppl 1(Suppl 1):i55-i60. [FREE Full text] [CrossRef] [Medline]
  104. Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to Value Health and Health Care. Dordrecht, The Netherlands. Springer; Nov 5, 2007.
  105. Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption--II. Addiction. Jul 1993;88(6):791-804. [CrossRef] [Medline]
  106. Bohn MJ, Babor TF, Kranzler HR. The Alcohol Use Disorders Identification Test (AUDIT): validation of a screening instrument for use in medical settings. J Stud Alcohol. Jul 1995;56(4):423-432. [CrossRef] [Medline]
  107. Knox J, Hasin DS, Larson FR, Kranzler HR. Prevention, screening, and treatment for heavy drinking and alcohol use disorder. Lancet Psychiatry. Dec 2019;6(12):1054-1067. [FREE Full text] [CrossRef] [Medline]
  108. Kranzler HR, Soyka M. Diagnosis and pharmacotherapy of alcohol use disorder: a review. JAMA. Aug 28, 2018;320(8):815-824. [FREE Full text] [CrossRef] [Medline]
  109. Carroll KM, Kiluk BD. Cognitive behavioral interventions for alcohol and drug use disorders: through the stage model and back again. Psychol Addict Behav. Dec 2017;31(8):847-861. [FREE Full text] [CrossRef] [Medline]
  110. Magill M, Ray LA. Cognitive-behavioral treatment with adult alcohol and illicit drug users: a meta-analysis of randomized controlled trials. J Stud Alcohol Drugs. Jul 2009;70(4):516-527. [FREE Full text] [CrossRef] [Medline]
  111. Kaner EF, Beyer FR, Garnett C, Crane D, Brown J, Muirhead C, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev. Oct 25, 2017;9(9):CD011479. [FREE Full text] [CrossRef] [Medline]
  112. Hasin DS, Aharonovich E, Zingman BS, Stohl M, Walsh C, Elliott JC, et al. HealthCall: a randomized trial assessing a smartphone enhancement of brief interventions to reduce heavy drinking in HIV care. J Subst Abuse Treat. Jul 2022;138:108733. [FREE Full text] [CrossRef] [Medline]
  113. What is human-centered design? Design Kit. URL: https://www.designkit.org/human-centered-design.html [accessed 2023-11-01]
  114. Heckathorn DD. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl. May 1997;44(2):174-199. [CrossRef]
  115. Hathaway AD, Hyshka E, Erickson PG, Asbridge M, Brochu S, Cousineau MM, et al. Whither RDS? An investigation of respondent driven sampling as a method of recruiting mainstream marijuana users. Harm Reduct J. Jul 09, 2010;7:15. [FREE Full text] [CrossRef] [Medline]
  116. Khanna AS, Michaels S, Skaathun B, Morgan E, Green K, Young L, et al. Preexposure prophylaxis awareness and use in a population-based sample of young Black men who have sex with men. JAMA Intern Med. Jan 2016;176(1):136-138. [FREE Full text] [CrossRef] [Medline]
  117. Pierce SJ, Miller RL, Morales MM, Forney J. Identifying HIV prevention service needs of African American men who have sex with men: an application of spatial analysis techniques to service planning. J Public Health Manag Pract. Jan 2007;Suppl:S72-S79. [FREE Full text] [CrossRef] [Medline]
  118. Wang JF, Stein A, Gao BB, Ge Y. A review of spatial sampling. Spat Stat. Dec 2012;2:1-14. [CrossRef]
  119. Schneider JA, Kozloski M, Michaels S, Skaathun B, Voisin D, Lancki N, et al. Criminal justice involvement history is associated with better HIV care continuum metrics among a population-based sample of young black MSM. AIDS. Jan 02, 2017;31(1):159-165. [FREE Full text] [CrossRef] [Medline]
  120. Chicago Center for HIV Elimination - UChicago C4P. AmeriCorps. URL: https:/​/my.​americorps.gov/​mp/​listing/​viewListing.​do;jsessionid=7HW8j-SttyRUTCR4ABpgwxoDedlwXt1QQA3IofC9OJH4KVhD-kUg!-1162293846?fromSearch=​true&id=107618#:~:text=The%20University%20of%20Chicago%20Center,specific%20interventions%20that%20reduce%20the [accessed 2023-02-10]
  121. Massey DS, Tannen J. A research note on trends in black hypersegregation. Demography. Jul 2015;52(3):1025-1034. [FREE Full text] [CrossRef] [Medline]
  122. Millett GA, Honermann B, Jones A, Lankiewicz E, Sherwood J, Blumenthal S, et al. White counties stand apart: the primacy of residential segregation in COVID-19 and HIV diagnoses. AIDS Patient Care STDS. Oct 2020;34(10):417-424. [FREE Full text] [CrossRef] [Medline]
  123. Moore NY. The South Side: A Portrait of Chicago and American Segregation. New York City, NY. St. Martin's Publishing Group; Mar 22, 2016.
  124. Kuhns LM, Hotton AL, Schneider J, Garofalo R, Fujimoto K. Use of Pre-exposure Prophylaxis (PrEP) in young men who have sex with men is associated with race, sexual risk behavior and peer network size. AIDS Behav. May 2017;21(5):1376-1382. [FREE Full text] [CrossRef] [Medline]
  125. Arnold EA, Bailey MM. Constructing home and family: how the ballroom community supports African American GLBTQ youth in the face of HIV/AIDS. J Gay Lesbian Soc Serv. Jan 01, 2009;21(2-3):171-188. [FREE Full text] [CrossRef] [Medline]
  126. Ezell JM, Ferreira MJ, Duncan DT, Schneider JA. The social and sexual networks of Black transgender women and Black men who have sex with men: results from a representative sample. Transgend Health. Dec 18, 2018;3(1):201-209. [FREE Full text] [CrossRef] [Medline]
  127. Bukowski LA, Chandler CJ, Creasy SL, Matthews DD, Friedman MR, Stall RD. Characterizing the HIV care continuum and identifying barriers and facilitators to HIV diagnosis and viral suppression among Black transgender women in the United States. J Acquir Immune Defic Syndr. Dec 01, 2018;79(4):413-420. [FREE Full text] [CrossRef] [Medline]
  128. Graham LF, Crissman HP, Tocco J, Hughes LA, Snow RC, Padilla MB. Interpersonal relationships and social support in transitioning narratives of Black transgender women in Detroit. Int J Transgend. Aug 08, 2014;15(2):100-113. [CrossRef]
  129. Pinto RM, Melendez RM, Spector AY. Male-to-female transgender individuals building social support and capital from within a gender-focused network. J Gay Lesbian Soc Serv. Sep 01, 2008;20(3):203-220. [FREE Full text] [CrossRef] [Medline]
  130. Nemoto T, Bödeker B, Iwamoto M. Social support, exposure to violence and transphobia, and correlates of depression among male-to-female transgender women with a history of sex work. Am J Public Health. Oct 2011;101(10):1980-1988. [CrossRef] [Medline]
  131. Macapagal K, Bhatia R, Greene GJ. Differences in healthcare access, use, and experiences within a community sample of racially diverse lesbian, gay, bisexual, transgender, and questioning emerging adults. LGBT Health. Dec 2016;3(6):434-442. [FREE Full text] [CrossRef] [Medline]
  132. Call DC, Challa M, Telingator CJ. Providing affirmative care to transgender and gender diverse youth: disparities, interventions, and outcomes. Curr Psychiatry Rep. Apr 13, 2021;23(6):33. [CrossRef] [Medline]
  133. Phillips G2, Peterson J, Binson D, Hidalgo J, Magnus M, YMSM of color SPNS Initiative Study Group. House/ball culture and adolescent African-American transgender persons and men who have sex with men: a synthesis of the literature. AIDS Care. Apr 2011;23(4):515-520. [CrossRef] [Medline]
  134. Goldhammer H, Marc LG, Psihopaidas D, Chavis NS, Massaquoi M, Cahill S, et al. HIV care continuum interventions for transgender women: a topical review. Public Health Rep. 2023;138(1):19-30. [CrossRef] [Medline]
  135. Poteat T, Scheim A, Xavier J, Reisner S, Baral S. Global epidemiology of HIV infection and related syndemics affecting transgender people. J Acquir Immune Defic Syndr. Aug 15, 2016;72 Suppl 3(Suppl 3):S210-S219. [FREE Full text] [CrossRef] [Medline]
  136. Poteat TC, Keatley J, Wilcher R, Schwenke C. Evidence for action: a call for the global HIV response to address the needs of transgender populations. J Int AIDS Soc. Jul 17, 2016;19(3 Suppl 2):21193. [FREE Full text] [CrossRef] [Medline]
  137. Allan-Blitz LT, Menza TW, Cummings V, Gaydos CA, Wilton L, Mayer KH. Differing correlates of incident bacterial sexually transmitted infections among a cohort of Black cisgender men who have sex with men and transgender women recruited in 6 US cities (HIV prevention trials network 061). Sex Transm Dis. Jul 01, 2022;49(7):e79-e84. [FREE Full text] [CrossRef] [Medline]
  138. Russell JS, Hickson DA, Timmins L, Duncan DT. Higher rates of low socioeconomic status, marginalization, and stress in Black transgender women compared to Black cisgender MSM in the MARI study. Int J Environ Res Public Health. Feb 23, 2021;18(4):2183. [FREE Full text] [CrossRef] [Medline]
  139. Chen YT, Duncan DT, Issema R, Goedel WC, Callander D, Bernard-Herman B, et al. Social-environmental resilience, PrEP uptake, and viral suppression among young Black men who have sex with men and young Black transgender women: the neighborhoods and networks (N2) study in Chicago. J Urban Health. Oct 2020;97(5):728-738. [FREE Full text] [CrossRef] [Medline]
  140. Van Dyck D, Cardon G, Deforche B, Sallis JF, Owen N, De Bourdeaudhuij I. Neighborhood SES and walkability are related to physical activity behavior in Belgian adults. Prev Med. Jan 2010;50 Suppl 1:S74-S79. [CrossRef] [Medline]
  141. Sampson RJ, Raudenbush SW. Seeing disorder: neighborhood stigma and the social construction of “Broken Windows”. Soc Psychol Q. Dec 2004;67(4):319-342. [CrossRef]
  142. Ross CE, Mirowsky J. Disorder and decay: the concept and measurement of perceived neighborhood disorder. Urban Aff Rev. Jan 1999;34(3):412-432. [CrossRef]
  143. Mair C, Diez Roux AV, Morenoff JD. Neighborhood stressors and social support as predictors of depressive symptoms in the Chicago Community Adult Health Study. Health Place. Oct 2010;16(5):811-819. [FREE Full text] [CrossRef] [Medline]
  144. Wolitski RJ, Fenton KA. Sexual health, HIV, and sexually transmitted infections among gay, bisexual, and other men who have sex with men in the United States. AIDS Behav. Apr 2011;15 Suppl 1(S1):S9-17. [CrossRef] [Medline]
  145. Kroenke K, Spitzer RL, Williams JB, Löwe B. An ultra-brief screening scale for anxiety and depression: the PHQ–4. Psychosomatics. 2009;50(6):613-621. [CrossRef]
  146. Sherer M, Maddux JE, Mercandante B, Prentice-Dunn S, Jacobs B, Rogers RW. The self-efficacy scale: construction and validation. Psychol Rep. Aug 31, 2016;51(2):663-671. [CrossRef]
  147. Bowleg L, English D, Del Rio-Gonzalez AM, Burkholder GJ, Teti M, Tschann JM. Measuring the pros and cons of what it means to be a Black man: development and validation of the Black Men's Experiences Scale (BMES). Psychol Men Masc. Apr 2016;17(2):177-188. [FREE Full text] [CrossRef] [Medline]
  148. Scheim AI, Bauer GR. The Intersectional Discrimination Index: development and validation of measures of self-reported enacted and anticipated discrimination for intercategorical analysis. Soc Sci Med. Apr 2019;226:225-235. [FREE Full text] [CrossRef] [Medline]
  149. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. Jan 1991;32(6):705-714. [CrossRef] [Medline]
  150. Gierveld JD, Tilburg TV. A 6-item scale for overall, emotional, and social loneliness: confirmatory tests on survey data. Res Aging. Aug 18, 2016;28(5):582-598. [CrossRef]
  151. Cuttler C, Spradlin A. Measuring cannabis consumption: psychometric properties of the daily sessions, frequency, age of onset, and quantity of cannabis use inventory (DFAQ-CU). PLoS One. May 26, 2017;12(5):e0178194. [FREE Full text] [CrossRef] [Medline]
  152. Loflin M, Babson K, Browne K, Bonn-Miller M. Assessment of the validity of the CUDIT-R in a subpopulation of cannabis users. Am J Drug Alcohol Abuse. Oct 23, 2018;44(1):19-23. [CrossRef] [Medline]
  153. Simons J, Correia CJ, Carey KB, Borsari BE. Validating a five-factor marijuana motives measure: relations with use, problems, and alcohol motives. J Counsel Psychol. Jul 1998;45(3):265-273. [CrossRef]
  154. Hammond D, Goodman S, Wadsworth E, Rynard V, Boudreau C, Hall W. Evaluating the impacts of cannabis legalization: The International Cannabis Policy Study. Int J Drug Policy. Feb 26, 2020;77:102698. [CrossRef] [Medline]
  155. 2020 National Survey on Drug Use and Health (NSDUH): methodological summary and definitions. Substance Abuse and Mental Health Services Administration. Oct 2021. URL: https:/​/www.​samhsa.gov/​data/​sites/​default/​files/​reports/​rpt35330/​2020NSDUHMethodSummDefs092421/​2020NSDUHMethodsSummDefs092421.​htm [accessed 2023-11-01]
  156. Yudko E, Lozhkina O, Fouts A. A comprehensive review of the psychometric properties of the Drug Abuse Screening Test. J Subst Abuse Treat. Mar 2007;32(2):189-198. [CrossRef] [Medline]
  157. Buysse DJ, Reynolds CF3, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. May 1989;28(2):193-213. [CrossRef] [Medline]
  158. Chasens ER, Ratcliffe SJ, Weaver TE. Development of the FOSQ-10: a short version of the Functional Outcomes of Sleep Questionnaire. Sleep. Jul 2009;32(7):915-919. [FREE Full text] [CrossRef] [Medline]
  159. Wilson IB, Lee Y, Michaud J, Fowler FJJ, Rogers WH. Validation of a new three-item self-report measure for medication adherence. AIDS Behav. Nov 20, 2016;20(11):2700-2708. [FREE Full text] [CrossRef] [Medline]
  160. Goedel WC, Halkitis PN, Greene RE, Hickson DA, Duncan DT. HIV risk behaviors, perceptions, and testing and preexposure prophylaxis (PrEP) awareness/use in Grindr-using men who have sex with men in Atlanta, Georgia. J Assoc Nurses AIDS Care. 2016;27(2):133-142. [FREE Full text] [CrossRef] [Medline]
  161. Branson BM, Handsfield HH, Lampe MA, Janssen RS, Taylor AW, Lyss SB, et al. Revised recommendations for HIV testing of adults, adolescents, and pregnant women in health-care settings. MMWR Recomm Rep. Sep 22, 2006;55(RR-14):1-17; quiz CE1. [FREE Full text] [Medline]
  162. Huang W, Moody DE, Andrenyak DM, Smith EK, Foltz RL, Huestis MA, et al. Simultaneous determination of delta9-tetrahydrocannabinol and 11-nor-9-carboxy-delta9-tetrahydrocannabinol in human plasma by solid-phase extraction and gas chromatography-negative ion chemical ionization-mass spectrometry. J Anal Toxicol. Oct 2001;25(7):531-537. [FREE Full text] [CrossRef] [Medline]
  163. Huang W, Czuba LC, Manuzak JA, Martin JN, Hunt PW, Klatt NR, et al. Objective identification of cannabis use levels in clinical populations is critical for detecting pharmacological outcomes. Cannabis Cannabinoid Res. Dec 01, 2022;7(6):852-864. [CrossRef] [Medline]
  164. McGaugh A, Miller C, King J, McManus K, Alcaide ML, Bauermeister J, et al. 1289. Douching and rectal inflammation in sexual minority men: implications for HIV acquisition. Open Forum Infect Dis. Oct 2019;6(Supplement_2):S464-S465. [CrossRef]
  165. Gaydos CA, Cartwright CP, Colaninno P, Welsch J, Holden J, Ho SY, et al. Performance of the Abbott RealTime CT/NG for detection of chlamydia trachomatis and Neisseria gonorrhoeae. J Clin Microbiol. Sep 2010;48(9):3236-3243. [FREE Full text] [CrossRef]
  166. Mugavero MJ, Davila JA, Nevin CR, Giordano TP. From access to engagement: measuring retention in outpatient HIV clinical care. AIDS Patient Care STDS. Oct 2010;24(10):607-613. [FREE Full text] [CrossRef] [Medline]
  167. Mugavero MJ, Westfall AO, Zinski A, Davila J, Drainoni ML, Gardner LI, et al. Measuring retention in HIV care: the elusive gold standard. J Acquir Immune Defic Syndr. Dec 15, 2012;61(5):574-580. [FREE Full text] [CrossRef] [Medline]
  168. Pyra M, Rusie L, Castro M, Keglovitz Baker K, McNulty M, Bohm N, et al. A taxonomy of pragmatic measures of HIV preexposure prophylaxis use. AIDS. Nov 01, 2020;34(13):1951-1957. [FREE Full text] [CrossRef] [Medline]
  169. Rael CT, Lopez-Ríos J, McKenna SA, Das D, Dolezal C, Abascal E, et al. Transgender women's barriers, facilitators, and preferences on tailored injection delivery strategies to administer long-acting injectable cabotegravir (CAB-LA) for HIV pre-exposure prophylaxis (PrEP). AIDS Behav. Dec 03, 2021;25(12):4180-4192. [FREE Full text] [CrossRef] [Medline]
  170. Tran NK, Martinez O, Scheim AI, Goldstein ND, Welles SL. Perceived barriers to and facilitators of long-acting injectable HIV PrEP use among Black, Hispanic/Latino, and White gay, bisexual, and other men who have sex with men. AIDS Educ Prev. Oct 2022;34(5):365-378. [CrossRef] [Medline]
  171. Carrico AW, Hunt PW, Emenyonu NI, Muyindike W, Ngabirano C, Cheng DM, et al. Unhealthy alcohol use is associated with monocyte activation prior to starting antiretroviral therapy. Alcohol Clin Exp Res. Dec 2015;39(12):2422-2426. [FREE Full text] [CrossRef] [Medline]
  172. Winhusen T, Somoza E, Ciraulo DA, Harrer JM, Goldsmith RJ, Grabowski J, et al. A double-blind, placebo-controlled trial of tiagabine for the treatment of cocaine dependence. Drug Alcohol Depend. Dec 01, 2007;91(2-3):141-148. [FREE Full text] [CrossRef] [Medline]
  173. White D, Rosenberg ES, Cooper HL, del Rio C, Sanchez TH, Salazar LF, et al. Racial differences in the validity of self-reported drug use among men who have sex with men in Atlanta, GA. Drug Alcohol Depend. May 01, 2014;138:146-153. [FREE Full text] [CrossRef] [Medline]
  174. Mariani JJ, Brooks D, Haney M, Levin FR. Quantification and comparison of marijuana smoking practices: blunts, joints, and pipes. Drug Alcohol Depend. Jan 15, 2011;113(2-3):249-251. [FREE Full text] [CrossRef] [Medline]
  175. Fabritius M, Favrat B, Chtioui H, Battistella G, Annoni JM, Appenzeller M, et al. THCCOOH concentrations in whole blood: are they useful in discriminating occasional from heavy smokers? Drug Test Anal. 2014;6(1-2):155-163. [FREE Full text] [CrossRef] [Medline]
  176. Abaasa A, Hendrix C, Gandhi M, Anderson P, Kamali A, Kibengo F, et al. Utility of different adherence measures for PrEP: patterns and incremental value. AIDS Behav. Apr 2018;22(4):1165-1173. [FREE Full text] [CrossRef] [Medline]
  177. Halkitis P, Kapadia F, Ompad D. Incidence of HIV infection in young gay, bisexual, and other YMSM: the P18 cohort study. J Acquir Immune Defic Syndr. Aug 01, 2015;69(4):466-473. [FREE Full text] [CrossRef] [Medline]
  178. Baron RM, Kenny DA. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Personality Soc Psychol. 1986;51(6):1173-1182. [CrossRef]
  179. VanderWeele TJ. Sample size and power calculations for additive interactions. Epidemiol Methods. Aug 29, 2012;1(1):158. [CrossRef]
  180. Aguinis H, Gottfredson RK. Best-practice recommendations for estimating interaction effects using moderated multiple regression. J Organ Behav. Apr 30, 2010;31(6):776-786. [CrossRef]
  181. Duran B, Oetzel JG, Minkler M, Wallerstein N. Community-Based Participatory Research for Health Advancing Social and Health Equity. Hoboken, NJ. John Wiley & Sons; 2017.
  182. Timmins L, Schneider JA, Chen YT, Goedel WC, Brewer R, Callander D, et al. Sexual identity, sexual behavior and pre-exposure prophylaxis in Black cisgender sexual minority men: the N2 cohort study in Chicago. AIDS Behav. Oct 14, 2021;25(10):3327-3336. [FREE Full text] [CrossRef] [Medline]
  183. Pagkas-Bather J, Duncan DT, Chen YT, Cursio J, Del Vecchio N, Mayer KH, et al. Sleep disturbance is associated with missing PrEP doses among young Black sexual minority men in the N2 study. AIDS Behav. Dec 04, 2022;26(12):3827-3833. [FREE Full text] [CrossRef] [Medline]
  184. Chen YT, Duncan DT, Del Vecchio N, Timmins L, Pagkas-Bather J, Lacap S, et al. COVID-19-related stressors, sex behaviors, and HIV status neutral care among Black men who have sex with men and transgender women in Chicago, USA. J Acquir Immune Defic Syndr. Nov 01, 2021;88(3):261-271. [FREE Full text] [CrossRef] [Medline]
  185. Timmins L, Schneider JA, Chen YT, Pagkas-Bather J, Kim B, Moody RL, et al. COVID-19 stressors and symptoms of depression and anxiety among Black cisgender sexual minority men and Black transgender women during the initial peak of the COVID-19 pandemic. Soc Psychiatry Psychiatr Epidemiol. Oct 22, 2022;57(10):1999-2011. [FREE Full text] [CrossRef] [Medline]
  186. Chen YT, Duncan DT, Del Vecchio N, Timmins L, Pagkas-Bather J, Knox J, et al. COVID-19 conspiracy beliefs are not barriers to HIV status neutral care among Black cisgender sexual minority men and Black transgender women at the initial peak of the COVID-19 pandemic in Chicago, USA. AIDS Behav. Dec 22, 2022;26(12):3939-3949. [FREE Full text] [CrossRef] [Medline]
  187. Schuler MS, Prince DM, Breslau J, Collins RL. Substance use disparities at the intersection of sexual identity and race/ethnicity: results from the 2015-2018 national survey on drug use and health. LGBT Health. 2020;7(6):283-291. [FREE Full text] [CrossRef] [Medline]
  188. Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic - United States, June 24-30, 2020. MMWR Morb Mortal Wkly Rep. Aug 14, 2020;69(32):1049-1057. [FREE Full text] [CrossRef] [Medline]
  189. Ramalho R. Alcohol consumption and alcohol-related problems during the COVID-19 pandemic: a narrative review. Australas Psychiatry. Oct 2020;28(5):524-526. [FREE Full text] [CrossRef] [Medline]
  190. Grant BF, Chou SP, Saha TD, Pickering RP, Kerridge BT, Ruan WJ, et al. Prevalence of 12-month alcohol use, high-risk drinking, and DSM-IV alcohol use disorder in the United States, 2001-2002 to 2012-2013: results from the national epidemiologic survey on alcohol and related conditions. JAMA Psychiatry. Sep 01, 2017;74(9):911-923. [FREE Full text] [CrossRef] [Medline]
  191. Grant BF, Goldstein RB, Saha TD, Chou SP, Jung J, Zhang H, et al. Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions iii. JAMA Psychiatry. Aug 2015;72(8):757-766. [FREE Full text] [CrossRef] [Medline]
  192. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. Dec 2011;104(12):510-520. [FREE Full text] [CrossRef] [Medline]
  193. WHO recommends new name for monkeypox disease. World Health Organization. Nov 28, 2022. URL: https://www.who.int/news/item/28-11-2022-who-recommends-new-name-for-monkeypox-disease [accessed 2022-12-08]
  194. Hubach RD, Owens C. Findings on the monkeypox exposure mitigation strategies employed by men who have sex with men and transgender women in the United States. Arch Sex Behav. Nov 14, 2022;51(8):3653-3658. [FREE Full text] [CrossRef] [Medline]
  195. Kava CM, Rohraff DM, Wallace B, Mendoza-Alonzo JL, Currie DW, Munsey AE, et al. Epidemiologic features of the monkeypox outbreak and the public health response - United States, May 17-October 6, 2022. MMWR Morb Mortal Wkly Rep. Nov 11, 2022;71(45):1449-1456. [FREE Full text] [CrossRef] [Medline]
  196. Karris MY, Dubé K, Moore AA. What lessons it might teach us? Community engagement in HIV research. Curr Opin HIV AIDS. Mar 2020;15(2):142-149. [FREE Full text] [CrossRef] [Medline]
  197. Galupo MP. Researching while cisgender: identity considerations for transgender research. Int J Transgend. Jun 22, 2017;18(3):241-242. [CrossRef]
  198. Rosenberg S, Tilley PJ. ‘A point of reference’: the insider/outsider research staircase and transgender people’s experiences of participating in trans-led research. Qual Res. Oct 21, 2020;21(6):923-938. [CrossRef]
  199. Keene L, Guilamo-Ramos V. Racial and sexual minority scholar positionality: advancing health status and life opportunity among sexual minority men of color. Health Educ Behav. Jun 03, 2021;48(3):250-259. [CrossRef] [Medline]
  200. Reif S, Safley D, McAllaster C, Wilson E, Whetten K. State of HIV in the US deep south. J Community Health. Oct 28, 2017;42(5):844-853. [CrossRef] [Medline]
  201. Cross SH, Califf RM, Warraich HJ. Rural-urban disparity in mortality in the US from 1999 to 2019. JAMA. Jun 08, 2021;325(22):2312-2314. [FREE Full text] [CrossRef] [Medline]


ART: antiretroviral therapy
CAPI: computer-assisted participant interview
DCE: Discrete Choice Experiment
EHE: ending the HIV epidemic
EHR: electronic health record
EMA: ecological momentary assessment
MSA: metropolitan statistical area
N2: Neighborhoods and Networks
N2P2: Neighborhoods and Networks Part 2
PrEP: pre-exposure prophylaxis
RDS: respondent-driven sampling
SMM: sexual minority men
STI: sexually transmitted infection
TW: transgender women


Edited by A Mavragani; The proposal for this study was peer reviewed by the National Institute on Drug Abuse Special Emphasis Panel, PrEP for HIV Prevention among Substance Using Populations (National Institutes of Health, USA). See the Multimedia Appendix for the peer-review report; submitted 01.05.23; accepted 20.09.23; published 01.12.23.

Copyright

©Justin R Knox, Brett Dolotina, Tyrone Moline, Isabella Matthews, Mainza Durrell, Hillary Hanson, Ellen Almirol, Anna Hotton, Jade Pagkas-Bather, Yen-Tyng Chen, Devin English, Jennifer Manuzak, Joseph E Rower, Caleb Miles, Brett Millar, Girardin Jean-Louis, H Jonathon Rendina, Silvia S Martins, Christian Grov, Deborah S Hasin, Adam W Carrico, Steve Shoptaw, John A Schneider, Dustin T Duncan. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 01.12.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.