149 research outputs found
Erratum to: Risky Sex and HIV Acquisition Among HIV Serodiscordant Couples in Zambia, 2002–2012: What Does Alcohol Have To Do With It?
No full textThe article Risky Sex and HIV Acquisition Among HIV Serodiscordant Couples in Zambia, 2002–2012: What Does Alcohol Have To Do With It?, written by Dvora Joseph Davey, William Kilembe, Kristin M. Wall, Naw Htee Khu, Ilene Brill, Bellington Vwalika, Elwyn Chomba, Joseph Mulenga, Amanda Tichacek, Marjan Javanbakht, W. Scott Comulada, Susan Allen, Pamina M. Gorbach, was originally published Online First without open access. After publication in volume 21, issue 7, pages 1892–1903, the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) [Year] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made
Mobile Phone Assessment in Egocentric Networks: A Pilot Study on Gay Men and Their Peers.
No full textMobile phone-based data collection encompasses the richness of social network research. Both individual-level and network-level measures can be recorded. For example, health-related behaviors can be reported via mobile assessment. Social interactions can be assessed by phone-log data. Yet the potential of mobile phone data collection has largely been untapped. This is especially true of egocentric studies in public health settings where mobile phones can enhance both data collection and intervention delivery, e.g. mobile users can video chat with counselors. This is due in part to privacy issues and other barriers that are more difficult to address outside of academic settings where most mobile research to date has taken place. In this article, we aim to inform a broader discussion on mobile research. In particular, benefits and challenges to mobile phone-based data collection are highlighted through our mobile phone-based pilot study that was conducted on egocentric networks of 12 gay men (n = 44 total participants). HIV-transmission and general health behaviors were reported through a mobile phone-based daily assessment that was administered through study participants' own mobile phones. Phone log information was collected from gay men with Android phones. Benefits and challenges to mobile implementation are discussed, along with the application of multi-level models to the type of longitudinal egocentric data that we collected
Model specification and bootstrapping for multiply imputed data: An application to count models for the frequency of alcohol use
No full textStata's mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention
Quantitative methods for HIV/AIDS researchCliburn Chan, Michael G. Hudgens, Shein‐Chung Chow, CRC Press
No full textCalculating level-specific SEM fit indices for multilevel mediation analyses
No full textStata'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 t 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 ll this role. Model results from the gsem command are used to calculate the level-specific comparative t index and root mean squared error of approximation t indices. I illustrate the gsemgof command through multilevel MA applied to two-level Methadone Maintenance Treatment data
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Model Specification and Bootstrapping for Multiply Imputed Data: An Application to Count Models for the Frequency of Alcohol Use
No full textStata's mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention
Model Specification and Bootstrapping for Multiply Imputed Data: An Application to Count Models for the Frequency of Alcohol Use
No full textCell phone-based ecological momentary assessment of substance use context for Latino youth in outpatient treatment: Who, what, when and where.
No full text
Cell phone-based ecological momentary assessment of substance use context for Latino youth in outpatient treatment: Who, what, when and where
No full textBACKGROUND: Relationships between alcohol, marijuana and other drug (AOD) use and contextual factors have mostly been established through retrospective self-report. Given the embeddedness of cell phones in adolescents' daily activities, cell phone-based ecological momentary assessment (CEMA) provides an opportunity to better understand AOD use in youth and how cell phones can be used to self-monitor and deliver interventions. We use CEMA to examine AOD use in Latino youth who have been especially understudied.
METHODS: Twenty-eight mostly Latino youth (ages 13-18) in outpatient substance abuse treatment recorded AOD use, contextual factors, cravings, and affect through once-daily CEMA over one month periods. Random-effects logistic regression was used to compare contextual factors between periods of AOD use and non-use.
RESULTS: The most frequent contextual factors reported during AOD use were being with close friends and "hanging out" as the primary activity. During AOD use compared to non-use, youth were more likely to be with close friends (OR=4.76; p<0.01), around users (OR=17.69; p<0.01), and at a friend's house (OR=5.97; p<0.01). Alcohol use was more frequently reported at night (63% vs 34%) and on weekends relative to other substances (64% vs 49%). Strong cravings were more frequently reported on AOD-use days (OR=7.34; p<0.01). Types of positive and negative affect were reported with similar frequencies, regardless of AOD use.
CONCLUSIONS: Reporting on social context, location, day and time of day, and cravings all show promise in developing cell phone-based interventions triggered by contextual data
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