Publications by authors named "W Scott Comulada"

88 Publications

Psychiatric hospitalization among youth at high risk for HIV.

AIDS Care 2021 Jun 24:1-10. Epub 2021 Jun 24.

College of Osteopathic Medicine, Nova Southeastern University, Miami, FL, USA.

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 ( = 839) and New Orleans ( = 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.
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http://dx.doi.org/10.1080/09540121.2021.1944599DOI Listing
June 2021

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

Prev Sci 2021 May 11. Epub 2021 May 11.

University of California, UCLA Center for Community Health, 10920 Wilshire Blvd Suite 350, Los AngelesLos Angeles, CA, 90024, USA.

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.
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http://dx.doi.org/10.1007/s11121-021-01255-2DOI Listing
May 2021

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

Authors:
W Scott Comulada

Stata J 2021 Mar 30;21(1):195-205. Epub 2021 Mar 30.

Department of Psychiatry and Biobehavioral Sciences, Department of Health Policy and Management, University of California, Los Angeles, Los Angeles, CA.

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.
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http://dx.doi.org/10.1177/1536867x211000022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087284PMC
March 2021

Drawing open the curtain on home-based interventions.

Mhealth 2021 20;7:18. Epub 2021 Apr 20.

Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.

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http://dx.doi.org/10.21037/mhealth.2020.01.02DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063024PMC
April 2021

Failure to recognize Low non-treponemal titer syphilis infections in pregnancy May lead to widespread under-treatment.

Int J Infect Dis 2021 Mar 2;104:27-33. Epub 2021 Jan 2.

David Geffen School of Medicine, University of California Los Angeles, 10833 Le Conte Ave., 90095‑1688, CHS 37‑121, Los Angeles, CA, USA.

Objectives: Rates of maternal syphilis have increased five-fold in Brazil in the past decade. While penicillin remains the only appropriate treatment for maternal syphilis, we hypothesized that low non-treponemal titers (<1:16) may lead to reduced penicillin treatment in Brazil.

Methods: Using Brazilian Ministry of Health data on women diagnosed with maternal syphilis between January 1, 2010, and December 31, 2018, we conducted a random-effects logistic regression model with a cluster correction at the state level to evaluate predictive factors of penicillin treatment.

Results: We observed yearly increases in cases of pregnant women with syphilis from 2010 to 2018. There was significant variation by state: 52,451 cases were reported in São Paulo, followed by 26,838 in Rio de Janeiro. Among 215,937 cases of maternal syphilis, 91·3% received penicillin. In the random-effects model, a non-treponemal titer ≥1:16 was associated with 1·44 higher odds of receiving penicillin (95% confidence interval [CI]: 1·391·48), and prenatal care was associated with a 2·12 increased odds of receiving penicillin (95% CI: 2·022·21). Although there is an association between the absence of prenatal care and inadequate treatment for syphilis, 83·2% of women in this cohort who did not receive penicillin were engaged in prenatal care.

Conclusions: Providers may inappropriately exclude low non-treponemal titers and thereby fail to use penicillin treatment in maternal syphilis. While the cause of the maternal syphilis epidemic in Brazil is multifactorial, we believe our findings can be used to develop targeted interventions throughout Brazil as well as shape public health initiatives globally.
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http://dx.doi.org/10.1016/j.ijid.2020.12.076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012229PMC
March 2021
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