Browsing by Author "Bourdon, Jessica L."

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    Characterization of Service Use for Alcohol Problems Across Generations and Sex in Adults With Alcohol Use Disorder
    (Wiley, 2020-03) Bourdon, Jessica L.; Tillman, Rebecca; Francis, Meredith W.; Dick, Danielle M.; Stephenson, Mallory; Kamarajan, Chella; Edenberg, Howard J.; Kramer, John; Kuperman, Samuel; Bucholz, Kathleen K.; McCutcheon, Vivia V.; Biochemistry and Molecular Biology, School of Medicine
    Background: There are gaps in the literature on service use (help-seeking and treatment utilization) for alcohol problems among those with alcohol use disorder (AUD). First, policy changes and cultural shifts (e.g., insurance) related to AUD have occurred over the last few decades, making it important to study generational differences. Second, multiple studies have found that females receive fewer services than males, and exploring whether these sex differences persist across generations can inform public health and research endeavors. The current study examined service use for alcohol problems among individuals with AUD. The aims were as follows: (i) to describe service use for alcohol problems; (ii) to assess generational differences (silent [b. 1928 to 1945], boomer [b. 1946 to 1964], generation X [b. 1965 to 1980], millennial [b. 1981 to 1996]) in help-seeking and treatment utilization; and (iii) to examine sex differences across generations. Methods: Data were from affected family members of probands who participated in the Collaborative Study on the Genetics of Alcoholism (N = 4,405). First, frequencies for service use variables were calculated across generations. Pearson chi-square and ANOVA were used to test for differences in rates and types of service use across generations, taking familial clustering into account. Next, Cox survival modeling was used to assess associations of generation and sex with time to first help-seeking and first treatment for AUD, and time from first onset of AUD to first help-seeking and first treatment. Interactions between generation and sex were tested within each Cox regression. Results: Significant hazards were found in all 4 transitions. Overall, younger generations used services earlier than older generations, which translated into higher likelihoods of these behaviors. Regardless of generation, younger females were less likely to use services than males. Conclusions: There are generational and sex differences in service use for alcohol problems among individuals with AUD. Policy and clinical implications are discussed.
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    Deriving a Measure of Social Recovery Capital From the Important People and Activities Instrument: Construction and Psychometric Properties
    (Oxford University Press, 2022) Francis, Meredith W.; Bourdon, Jessica L.; Chan, Grace; Dick, Danielle M.; Edenberg, Howard J.; Kamarajan, Chella; Kinreich, Sivan; Kramer, John; Kuo, Sally I-Chun; Pandey, Ashwini K.; Pandey, Gayathri; Smith, Rebecca L.; Bucholz, Kathleen K.; McCutcheon, Vivia V.; Psychiatry, School of Medicine
    Aim: This study presents a measure of Social Recovery Capital (SRC) derived from the Important People and Activities instrument (IPA). Methods: The sample comprised young adults who participated in the Collaborative Study on the Genetics of Alcoholism, a high-risk family study of alcohol use disorder (N = 2472). Exploratory and confirmatory factor analysis identified influential items and factor structure, adjusting for family relatedness. The final scale was tested for reliability and validity. Results: Factor analysis retained 10 items loading on three factors (Network Abstinence Behaviors, Basic Network Structure and Network Importance) that together explained 42% of the variance in SRC. The total model showed adequate fit (Comparative Fit Index = 0.95; Tucker Lewis Index = 0.93; Root Mean Square Error of Approximation = 0.06; Standardized Root Mean Squared Residual = 0.05) and acceptable reliability (α = 0.60; McDonald's ω = 0.73) and correlated with validation measures mostly in the weak to moderate range. Due to variable factor scores for reliability and validity, we only recommend using the total score. Conclusion: The SRC-IPA is a novel measure of SRC derived from the IPA that captures social network data and has applications in research and clinical work. Secondary data analyses using the SRC-IPA in studies that collected the IPA can further demonstrate the interaction of SRC with a wide variety of clinical indicators and demographic characteristics, making it a valuable addition to other measures of SRC.
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    Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
    (Springer Nature, 2021-03-15) Kinreich, Sivan; McCutcheon, Vivia V.; Aliev, Fazil; Meyers, Jacquelyn L.; Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Zhang, Jian; Kuang, Weipeng; Pandey, Gayathri; Subbie-Saenz de. Viteri, Stacey; Francis, Meredith W.; Chan, Grace; Bourdon, Jessica L.; Dick, Danielle M.; Anokhin, Andrey P.; Bauer, Lance; Hesselbrock, Victor; Schuckit, Marc A.; Nurnberger, John I., Jr.; Foroud, Tatiana M.; Salvatore, Jessica E.; Bucholz, Kathleen K.; Porjesz, Bernice; Psychiatry, School of Medicine
    Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.