Pilot trial results of D-HOMES: a behavioral-activation based intervention for diabetes medication adherence and psychological wellness among people who have been homeless.

Introduction: People living with type 2 diabetes who experience homelessness face a myriad of barriers to engaging in diabetes self-care behaviors that lead to premature complications and death. This is exacerbated by high rates of comorbid mental illness, substance use disorder, and other physical health problems. Despite strong evidence to support lay health coach and behavioral activation, little research has effectively engaged people living with type 2 diabetes who had experienced homelessness (DH). Methods: We used community engaged research and incremental behavioral treatment development to design the Diabetes HOmeless MEdication Support (D-HOMES) program, a one-on-one, 3 month, coaching intervention to improve medication adherence and psychological wellness for DH. We present results of our pilot randomized trial (with baseline, 3 mo., 6 mo. assessments) comparing D-HOMES to enhanced usual care (EUC; brief diabetes education session and routine care; NCT05258630). Participants were English-speaking adults with type 2 diabetes, current/recent (<24 mo.) homelessness, and an HbA1c_7.5%. We focused on feasibility (recruitment, retention, engagement) and acceptability (Client Satisfaction Questionnaire, CSQ-8). Our primary clinical outcome was glycemic control (HbA1c) and primary behavioral outcome was medication adherence. Secondary outcomes included psychological wellness and diabetes self-care. Results: Thirty-six eligible participants enrolled, 18 in each arm. Most participants identified as Black males, had high rates of co-morbidities, and lived in subsidized housing. We retained 100% of participants at 3-months, and 94% at 6-months. Participants reported high satisfaction (mean CSQ-8 scores=28.64 [SD 3.94] of 32). HbA1c reduced to clinically significant levels in both groups, but we found no between group differences. Mean blood pressure improved more in D-HOMES than EUC between baseline and 6 mo. with between group mean differences of systolic -19.5 mmHg (p=0.030) and diastolic blood pressure -11.1 mmHg (p=0.049). We found no significant between group differences in other secondary outcomes. Conclusion: We effectively recruited and retained DH over 6 months. Data support that the D-HOMES intervention was acceptable and feasible. We observe preliminary blood pressure improvement favoring D-HOMES that were statistically and clinically significant. D-HOMES warrants testing in a fully powered trial which could inform future high quality behavioral trials to promote health equity. Clinical trial registration: https://clinicaltrials.gov/study/NCT05258630?term=D-HOMES&rank=1, identifier NCT05258630.

A necessary conversation to develop chatbots for HIV studies: qualitative findings from research staff, community advisory board members, and study participants.

Chatbots increase business productivity by handling customer conversations instead of human agents. Similar rationale applies to use chatbots in the healthcare sector, especially for health coaches who converse with clients. Chatbots are nascent in healthcare. Study findings have been mixed in terms of engagement and their impact on outcomes. Questions remain as to chatbot acceptability with coaches and other providers; studies have focused on clients.To clarify perceived benefits of chatbots in HIV interventions we conducted virtual focus groups with 13 research staff, eight community advisory board members, and seven young adults who were HIV intervention trial participants (clients). Our HIV healthcare context is important. Clients represent a promising age demographic for chatbot uptake. They are a marginalized population warranting consideration to avoid technology that limits healthcare access.Focus group participants expressed the value of chatbots for HIV research staff and clients. Staff discussed how chatbot functions, such as automated appointment scheduling and service referrals, could reduce workloads while clients discussed the after-hours convenience of these functions. Participants also emphasized that chatbots should provide relatable conversation, reliable functionality, and would not be appropriate for all clients. Our findings underscore the need to further examine appropriate chatbot functionality in HIV interventions.

Using Machine Learning to Identify Predictors of Sexually Transmitted Infections Over Time Among Young People Living With or at Risk for HIV Who Participated in ATN Protocols 147, 148, and 149.

BACKGROUND: Sexually transmitted infections (STIs) among youth aged 12 to 24 years have doubled in the last 13 years, accounting for 50% of STIs nationally. We need to identify predictors of STI among youth in urban HIV epicenters. METHODS: Sexual and gender minority (gay, bisexual, transgender, gender-diverse) and other youth with multiple life stressors (homelessness, incarceration, substance use, mental health disorders) were recruited from 13 sites in Los Angeles and New Orleans (N = 1482). Self-reports and rapid diagnostic tests for STI, HIV, and drug use were conducted at 4-month intervals for up to 24 months. Machine learning was used to identify predictors of time until new STI (including a new HIV diagnosis). RESULTS: At recruitment, 23.9% of youth had a current or past STI. Over 24 months, 19.3% tested positive for a new STI. Heterosexual males had the lowest STI rate (12%); African American youth were 23% more likely to acquire an STI compared with peers of other ethnicities. Time to STI was best predicted by attending group sex venues or parties, moderate but not high dating app use, and past STI and HIV seropositive status. CONCLUSIONS: Sexually transmitted infections are concentrated among a subset of young people at highest risk. The best predictors of youth's risk are their sexual environments and networks. Machine learning will allow the next generation of research on predictive patterns of risk to be more robust.

Comparing Factors Associated with Increased Stimulant Use in Relation to HIV Status Using a Machine Learning and Prediction Modeling Approach.

Stimulant use is an important driver of HIV/STI transmission among men who have sex with men (MSM). Evaluating factors associated with increased stimulant use is critical to inform HIV prevention programming efforts. This study seeks to use machine learning variable selection techniques to determine characteristics associated with increased stimulant use and whether these factors differ by HIV status. Data from a longitudinal cohort of predominantly Black/Latinx MSM in Los Angeles, CA was used. Every 6 months from 8/2014-12/2020, participants underwent STI testing and completed surveys evaluating the following: demographics, substance use, sexual risk behaviors, and last partnership characteristics. Least absolute shrinkage and selection operator (lasso) was used to select variables and create predictive models for an interval increase in self-reported stimulant use across study visits. Mixed-effects logistic regression was then used to describe associations between selected variables and the same outcome. Models were also stratified based on HIV status to evaluate differences in predictors associated with increased stimulant use. Among 2095 study visits from 467 MSM, increased stimulant use was reported at 20.9% (n = 438) visits. Increased stimulant use was positively associated with unstable housing (adjusted [a]OR 1.81; 95% CI 1.27-2.57), STI diagnosis (1.59; 1.14-2.21), transactional sex (2.30; 1.60-3.30), and last partner stimulant use (2.21; 1.62-3.00). Among MSM living with HIV, increased stimulant use was associated with binge drinking, vaping/cigarette use (aOR 1.99; 95% CI 1.36-2.92), and regular use of poppers (2.28; 1.38-3.76). Among HIV-negative MSM, increased stimulant use was associated with participating in group sex while intoxicated (aOR 1.81; 95% CI 1.04-3.18), transactional sex (2.53; 1.40-2.55), and last partner injection drug use (1.96; 1.02-3.74). Our findings demonstrate that lasso can be a useful tool for variable selection and creation of predictive models. These results indicate that risk behaviors associated with increased stimulant use may differ based on HIV status and suggest that co-substance use and partnership contexts should be considered in the development of HIV prevention/treatment interventions.

Increasing Participation in a TelePrEP Program for Sexual and Gender Minority Adolescents and Young Adults in Louisiana: Protocol for an SMS Text Messaging-Based Chatbot.

BACKGROUND: Sexual and gender minority (SGM) adolescents and young adults (AYAs) are at increased risk of HIV infection, particularly in the Southern United States. Despite the availability of effective biomedical prevention strategies, such as pre-exposure prophylaxis (PrEP), access and uptake remain low among SGM AYAs. In response, the Louisiana Department of Health initiated the LA TelePrEP Program, which leverages the power of telemedicine to connect Louisiana residents to PrEP. A virtual TelePrEP Navigator guides users through the enrollment process, answers questions, schedules appointments, and facilitates lab testing and medication delivery. To increase the participation of SGM AYAs in the program, the TelePrEP program partnered with researchers to develop a chatbot that would facilitate access to the program and support navigator functions. Chatbots are capable of carrying out many functions that reduce employee workload, and despite their successful use in health care and public health, they are relatively new to HIV prevention. OBJECTIVE: In this paper, we describe the iterative and community-engaged process that we used to develop an SMS text messaging-based chatbot tailored to SGM AYAs that would support navigator functions and disseminate PrEP-related information. METHODS: Our process was comprised of 2 phases: conceptualization and development. In the conceptualization phase, aspects of navigator responsibilities, program logistics, and user interactions to prioritize in chatbot programming (eg, scheduling appointments and answering questions) were identified. We also selected a commercially available chatbot platform that could execute these functions and could be programmed with minimal coding experience. In the development phase, we engaged Department of Health staff and SGM AYAs within our professional and personal networks. Five different rounds of testing were conducted with various groups to evaluate each iteration of the chatbot. After each iteration of the testing process, the research team met to discuss feedback, guide the programmer on incorporating modifications, and re-evaluate the chatbot's functionality. RESULTS: Through our highly collaborative and community-engaged process, a rule-based chatbot with artificial intelligence components was successfully created. We gained important knowledge that could advance future chatbot development efforts for HIV prevention. Key to the PrEPBot's success was resolving issues that hampered the user experience, like asking unnecessary questions, responding too quickly, and misunderstanding user input. CONCLUSIONS: HIV prevention researchers can feasibly and efficiently program a rule-based chatbot with the assistance of commercially available tools. Our iterative process of engaging researchers, program personnel, and different subgroups of SGM AYAs to obtain input was key to successful chatbot development. If the results of this pilot trial show that the chatbot is feasible and acceptable to SGM AYAs, future HIV researchers and practitioners could consider incorporating chatbots as part of their programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42983.

Factors associated with lifetime HIV testing among women in four Southeast Asian countries: Evidence from the demographic and health surveys.

BACKGROUND: Southeast Asian countries have been trying to increase HIV testing coverage of women since awareness of HIV status is essential to eliminate mother-to-child transmission of HIV. This study determined factors related to lifetime HIV testing uptake among women aged 15-49 years in four Southeast Asian countries: Myanmar, Cambodia, the Philippines and Timor-Leste. METHODS: This study used cross-sectional data from the 2015-16 Myanmar Demographic and Health Survey (DHS), the 2014 Cambodia DHS, the 2017 Philippines National DHS and the 2016 Timor-Leste DHS. We conducted multivariable logistic regression analyses to identify factors associated with lifetime HIV testing among women aged 15-49 years who completed the surveys in each country and ran a fixed effects logistic regression model using pooled data. RESULTS: The proportions of lifetime HIV testing uptake among women aged 15-49 years were 42.1% in Cambodia, 19.5% in Myanmar, 4.6% in the Philippines, and 3.7% in Timor-Leste. Marital status, age, education, and wealth were significantly associated with lifetime HIV testing uptake among women in all four countries. Other factors (e.g., comprehensive knowledge of HIV, rural/urban residence, positive attitudes towards negotiation for safer sex) were also significant determinants of HIV testing uptake among women in some of these countries. CONCLUSIONS: A multi-sectoral collaboration of related sectors and organizations is necessary to increase access to HIV testing and HIV knowledge of women to overcome the barriers to HIV testing. It is critical to make HIV testing services available and accessible to women, especially in rural areas.

Adolescents Living With or at Risk for HIV: A Pooled Descriptive Analysis of Studies From the Adolescent Medicine Trials Network for HIV/AIDS Interventions.

PURPOSE: This study aims to describe the cohort of Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program participants and evaluate whether the ATN's recently completed 5-year cycle recruited study participants who parallel the populations most impacted by HIV in the United States. METHODS: Harmonized measures across ATN studies collected at baseline were aggregated for participants aged 13-24 years. Pooled means and proportions stratified by HIV status (at risk for or living with HIV) were calculated using unweighted averages of study-specific aggregate data. Medians were estimated using a weighted median of medians method. Public use 2019 Centers for Disease Control and Prevention surveillance data for state-level new HIV diagnoses and HIV prevalence among US youth aged 13-24 years were obtained for use as reference populations for ATN at-risk youth and youth living with HIV (YLWH), respectively. RESULTS: Data from 3,185 youth at-risk for HIV and 542 YLWH were pooled from 21 ATN study phases conducted across the United States. Among ATN studies tailored to at-risk youth, a higher proportion of participants were White and a lower proportion were Black/African American and Hispanic/Latinx compared to youth newly diagnosed with HIV in the United States in 2019. Participants in ATN studies tailored to YLWH were demographically similar to YLWH in the United States. DISCUSSION: The development of data harmonization guidelines for ATN research activities facilitated this cross-network pooled analysis. These findings suggest the ATN's YLWH are representative, but that future studies of at-risk youth should prioritize recruitment strategies to enroll more participants from African American and Hispanic/Latinx populations.

How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales.

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest.

Substance Use, Violence, and Sexual Risk Among Young Cis-Gender Women Placed at High-Risk for Human Immunodeficiency Virus Infection.

The substance use, violence, and AIDS (SAVA) syndemic framework is used to study risk for HIV/AIDS. As a secondary analysis from a large HIV/AIDS prevention study, we categorized participants into having from zero to three SAVA conditions based on the presence or absence of self-reported substance use in the past 4 months, history of lifetime sexual abuse, and intimate partner violence. We used Poisson regression models to examine the association between the number of SAVA conditions and sexual risk behavior. Among all participants (n = 195, median age, 20), 37.9%, 19.5%, and 6.7% reported occurrence of one, two, and all three SAVA conditions, respectively. We found that more than one SAVA condition experienced by women was significantly associated with having more than one sex partner (adjusted prevalence ratio [aPR] = 1.88; 95% confidence interval [CI] = 1.28, 2.76) and with substance use before sex (aPR = 1.61 95% CI = 1.06, 2.45).

Where you live matters: Township neighborhood factors important to resilience among south African children from birth to 5 years of age.

OBJECTIVE: This is a secondary analysis examining neighborhood factors predicting high rates of child resilience in South African Township neighborhoods. METHODS: A population cohort of South African pregnant women (98%; n = 1238), were recruited and assessed across five years with high follow-up rates (83-96%). Resilient children were identified based on consistently meeting global standards for growth, cognitive functioning, and behavior. Community infrastructure, maternal risks, and caretaking behaviors were examined based on neighborhood as predictors of childhood resilience. RESULTS: The rate of resilient children varied significantly by neighborhood (9.5% to 27%). Mothers living in high prevalence neighborhoods (HPN) compared to low prevalence neighborhoods (LPN) were older and more likely be living with three or more people in formal housing with access to water and electricity. In the HPN, resilient children had more food security and were less likely to have mothers with depressed mood. Migration to rural areas occurred more frequently among resilient compared to non-resilient children in the HPN. CONCLUSION: This study applies a novel measure of resilience that is multidimensional and longitudinally defined. Living in formal housing with consistent access to food was associated with resilience. Migration to rural areas among families living in HPN suggests that rural areas could be protective. TRIAL REGISTRATION: ClinicalTrials.gov registration #NCT00996528.

Response Patterns to Weekly Short Message Service Health Surveys Among Diverse Youth at High Risk for Acquiring HIV.

HIV researchers use short messaging service (SMS)-based surveys to monitor health behaviors more closely than what would be possible with in-person assessment. Benefits are tempered by nonresponse to completing surveys. Understanding response patterns and their associated study participant characteristics would guide more tailored use of SMS-based surveys for HIV studies. We examined response to weekly 7-item SMS surveys administered as part of an HIV prevention trial. Using Mixture hidden Markov models (MHMM), we identified the underlying response patterns shared by subgroups of participants over time and quantified the association between these response patterns and participant characteristics. Three underlying response patterns were identified; responders, responders with phone-related errors, and non-responders. Non-responders versus responders were more likely to be younger, male, cis-gender, Black and Latinx participants with histories of homelessness, incarceration, and social support service utilization. Responders with phone-related errors compared to non-responders were more likely to be Black, Latinx, female, students, and have a history of incarceration and social support service utilization. More nuanced results from MHMM analyses better inform what strategies to use for increasing SMS response rates, including assisting in securing phone ownership/service for responders with phone-related errors and identifying alternative strategies for non-responders. Actively collecting and monitoring non-delivery notification data available from SMS gateway service companies offers another opportunity to identify and connect with participants when they are willing but unable to respond during follow-up.

Risk Behaviors Associated with Patterns of Sexualized Stimulant and Alcohol Use among Men Who Have Sex with Men: a Latent Class Analysis.

Substance use during sexual encounters (sexualized substance use) is an important driver of HIV and sexually transmitted infection (STI) disparities that are experienced by men who have sex with men (MSM). This analysis aimed to identify patterns of sexualized substance use and their associations with HIV risk behaviors. We utilized visit-level data from a longitudinal cohort of predominantly Black/Latinx MSM, half with HIV and half with substance use in Los Angeles, California. Every 6 months from 8/2014 to 3/2020, participants underwent STI testing and completed surveys on demographics, sexualized substance use (stimulant and/or alcohol intoxication during oral sex, receptive anal intercourse [RAI] and/or insertive anal intercourse [IAI]), transactional sex, biomedical HIV prevention (pre-/post-exposure prophylaxis use or undetectable viral load), and depressive symptoms. Latent class analysis was used to identify patterns of sexualized substance use. Multinomial logit models evaluated risk behaviors associated with latent classes. Among 2386 study visits from 540 participants, 5 classes were identified: no substance use, sexualized stimulant use, sexualized alcohol use, sexualized stimulant and alcohol use, and stimulant/alcohol use during oral sex and RAI. Compared to the no sexualized substance use class, sexualized stimulant use was associated with transactional sex, current diagnosis of STIs, not using HIV biomedical prevention, and depressive symptoms. Sexualized alcohol use had fewer associations with HIV risk behaviors. Patterns of sexual activities, and the substances that are used during those activities, confer different risk behavior profiles for HIV/STI transmission and demonstrate the potential utility of interventions that combine substance use treatment with HIV prevention.

Examining the Relative Contributions of Methamphetamine Use, Depression, and Sexual Risk Behavior on Rectal Gonorrhea/Chlamydia Among a Cohort of Men Who Have Sex With Men in Los Angeles, California.

BACKGROUND: Methamphetamine use, sexual risk behaviors, and depression contribute to ongoing human immunodeficiency virus (HIV) and sexually transmitted infection (STI) disparities among men who have sex with men (MSM). The relative contributions of these effects longitudinally are not well understood. METHODS: This analysis used visit-level data from a longitudinal cohort of MSM, half with HIV, in Los Angeles, CA. From August 2014 to March 2020, participants completed follow-up visits every 6 months and underwent testing for rectal gonorrhea/chlamydia (GC/CT) and completed questionnaires including depressive symptoms, number of receptive anal intercourse (RAI) partners, and methamphetamine use. Path analysis with structural equation modeling using concurrent and lagged covariates was used to identify relative contributions of methamphetamine use and depression on number of RAI partners and rectal GC/CT across time. RESULTS: Five hundred fifty-seven MSM with up to 6 visits (3 years) were included for a total of 2437 observations. Methamphetamine use and depressive symptoms were positively associated with number of RAI partners (ß = 0.28, P < 0.001; ß = 0.33, P = 0.018, respectively), which was positively associated with rectal GC/CT (ß = 0.02, P < 0.001). When stratified by HIV status, depressive symptoms were positively associated with RAI partners for HIV-negative MSM (ß = 0.50, P = 0.007) but were not associated for MSM living with HIV (ß = 0.12, P = 0.57). Methamphetamine use was positively associated with RAI partners in both strata. CONCLUSIONS: Factors and patterns, which contribute to risk behaviors associated with rectal GC/CT, may differ by HIV status. Our findings demonstrate the importance of combined treatment and prevention efforts that link screening and treatment of stimulant use and depression with STI prevention and treatment.

Preliminary Efficacy and Acceptability of an Online Exercise and Nutrition Workplace Wellness Program: A Brief Report.

BACKGROUND: Workplace wellness programs (WWP) offer physiological and psychological benefits to employees and financial and productivity benefits to employers. However, the COVID-19 pandemic has prevented in-person sessions and has required WWP's to transition to online platforms. The purpose of this brief report was to assess the preliminary feasibility, acceptability, and efficacy of a mobile version of the Bruin Health Improvement Program (BHIP mobile) WWP. METHODS: Participants virtually attended (i.e., via Zoom) twice weekly physical activity sessions and a once weekly nutrition seminar for 10-weeks with the option of a 30-minute one-on-one consultation with a registered dietician. Demographics, anthropometric indices, stress, muscular endurance, and aerobic fitness were assessed at baseline and follow-up. All analyses were conducted in SPSS v. 27. RESULTS: Twenty-seven participants (96% female) enrolled and 13 (100% female) completed the 10-week program. There were significant reductions in bodyweight (p < .01) and body mass index (p < .02) but not stress (p > .05), and significant increases in muscular endurance (p < .01) but not aerobic fitness (p > .05). Overall, BHIP mobile appears to be acceptable to participants but logistical concerns such as inconsistent internet connection was noted as potential downfalls. CONCLUSIONS/APPLICATIONS TO PRACTICE: Improvements in health outcome among completers of a mobile WWP were promising. Completer feedback highlighted program strengths as the flexibility and comfort of attending exercise sessions and nutrition classes from home. Future versions of the program will allocate resources to improve completion and expand appeal for men and women.

Psychiatric hospitalization among youth at high risk for HIV.

Youth at-risk for HIV are also at-risk for mental health disorders and psychiatric hospitalization. Understanding the association between engagement in HIV prevention, concurrent risk behaviors, and psychiatric hospitalization may lead to improvements in integrated prevention and mental health treatment efforts. Youth at-risk for HIV, aged 14-24 years old, predominantly Black/African American and Latinx (75%) were recruited through youth-serving clinics and community sites in Los Angeles (n = 839) and New Orleans (n = 647). We compared youth with and without histories of psychiatric hospitalization on engagement in HIV prevention, concurrent risk behaviors, and demographic characteristics. We examined predictors of hospitalization using multiple imputations for missing data. Hospitalized youth (30%) were more involved in HIV programs, but were less likely to use PrEP/PEP or condoms than non-hospitalized youth. The odds of hospitalization were higher for transgender/gender nonconforming youth relative to cisgender youth; the OR was increased after adjustment for concurrent risk behaviors. Hospitalization was associated with homelessness, trauma, incarceration, substance use, and involvement in substance abuse treatment programs. There is a continuing need to integrate the diagnosis and treatment of mental health disorders into HIV prevention programs to better address multiple challenges faced by vulnerable youth.

Adolescents may accurately self-collect pharyngeal and rectal clinical specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae infection.

BACKGROUND: The COVID-19 pandemic illuminated the benefits of telemedicine. Self-collected specimens are a promising alternative to clinician-collected specimens when in-person testing is not feasible. In this study, we assessed the adequacy of self-collected pharyngeal and rectal specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae among individuals undergoing chlamydia and gonorrhea screening. METHODS: We used data from a large cohort study that included male and female adolescents between the ages of 12-24 years. We considered self-collected specimens adequate for clinical use if the human synthase gene (a control target of the assay) was detected in the specimen. RESULTS: In total, 2,458 specimens were included in the analysis. The human synthase gene was detected in 99.2% (2,439/2,458) of all self-collected specimens, 99.5% (1,108/1,114) of the pharyngeal specimens, and 99.0% (1,331/1,344) of the rectal specimens. CONCLUSION: Self-collected pharyngeal and rectal specimens demonstrated a very high proportion of human gene presence, suggesting that self-collection was accurate. A limitation of this study is that the sample adequacy control detects the presence or absence of the human hydroxymethylbilane synthase gene, but it does not indicate the specific anatomic origin of the human hydroxymethylbilane synthase gene. Self-collected specimens may be an appropriate alternative to clinician-collected specimens.

Calculating level-specific SEM fit indices for multilevel mediation analyses.

Stata's gsem command provides the ability to fit multilevel structural equation models (sem) and related multilevel models. A motivating example is provided by multilevel mediation analyses (ma) conducted on patient data from Methadone Maintenance Treatment clinics in China. Multilevel ma conducted through the gsem command examined the mediating effects of patients' treatment progression and rapport with counselors on their treatment satisfaction. Multilevel models accounted for the clustering of patient observations within clinics. sem fit indices, such as the comparative fit index and the root mean squared error of approximation, are commonly used in the sem model selection process. Multilevel models present challenges in constructing fit indices because there are multiple levels of hierarchy to account for in establishing goodness of fit. Level-specific fit indices have been proposed in the literature but have not been incorporated into the gsem command. I created the gsemgof command to fill this role. Model results from the gsem command are used to calculate the level-specific comparative fit index and root mean squared error of approximation fit indices. I illustrate the gsemgof command through multilevel ma applied to two-level Methadone Maintenance Treatment data.

Using Machine Learning to Predict Young People's Internet Health and Social Service Information Seeking.

Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH's health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH's self-reports of internet use. The YARH were aged 14-24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH's lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥ .75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms.