A Screening Tool for Assessing Alcohol Use Risk among Medically Vulnerable Youth

Background: In an effort to reduce barriers to screening for alcohol use in pediatric primary care, the National Institute on Alcoholism and Alcohol Abuse (NIAAA) developed a two-question Youth Alcohol Screening Tool derived from population-based survey data. It is unknown whether this screening tool, designed for use with general populations, accurately identifies risk among youth with chronic medical conditions (YCMC). This growing population, which comprises nearly one in four youth in the US, faces a unique constellation of drinking-related risks. Method To validate the NIAAA Youth Alcohol Screening Tool in a population of YCMC, we performed a cross-sectional validation study with a sample of 388 youth ages 9–18 years presenting for routine subspecialty care at a large children’s hospital for type 1 diabetes, persistent asthma, cystic fibrosis, inflammatory bowel disease, or juvenile idiopathic arthritis. Participants self-administered the NIAAA Youth Alcohol Screening Tool and the Diagnostic Interview Schedule for Children as a criterion standard measure of alcohol use disorders (AUD). Receiver operating curve analysis was used to determine cut points for identifying youth at moderate and highest risk for an AUD. Results: Nearly one third of participants (n = 118; 30.4%) reported alcohol use in the past year; 86.4% (106) of past year drinkers did not endorse any AUD criteria, 6.8% (n = 8) of drinkers endorsed a single criterion, and 6.8% of drinkers met criteria for an AUD. Using the NIAAA tool, optimal cut points found to identify youth at moderate and highest risk for an AUD were ≥ 6 and ≥12 drinking days in the past year, respectively. Conclusions: The NIAAA Youth Alcohol Screening Tool is highly efficient for detecting alcohol use and discriminating disordered use among YCMC. This brief screen appears feasible for use in specialty care to ascertain alcohol-related risk that may impact adversely on health status and disease management

Master of Science

thesisExecutive functioning (EF) is the neurocognitive domain most strongly associated with performance of instrumental activities of daily living (IADLs). However, performance-based tests of EF have long been criticized for having poor ecological validity, largely because of their imperfect association with functional outcomes and their low face validity. However, past research has not examined whether high face validity improves a test's ability to predict IADLs. The present study examined this question. We pitted the Pillbox Test (i.e., a measure with high verisimilitude) against the Delis-Kaplan Executive Function System (D-KEFS; a measure of EF that has low verisimilitude) with respect to their ability to (a) predict actual at-home management of medications among community-dwelling older adults and (b) classify participants based on the degree to which they mismanaged their medications at home across the 8 weeks. As a secondary aim, we sought to establish the degree to which a commonly used laboratory-based behavioral measure of IADLs, specifically Timed Instrumental Activities of Daily Living (TIADL), reflects actual daily functioning. Fifty community-dwelling, older adults completed the Pillbox Test, D-KEFS, and TIADL. Weekly pill counts were conducted as an index of real-world daily functioning. Analyses using medication management as the dependent variable revealed that (a)the D-KEFS, the completion time (but not errors) on the Pillbox Test, and thecompletion time (but not errors) on TIADL were significantly related to medication management after controlling for age, (b) although the time to complete the Pillbox Test accounted for additional variance in medication management beyond age and the D-KEFS, the D-KEFS proved to be superior in terms of classifying participants based on their difficulties managing their medications, and (c) time to complete TIADL did not account for additional variance in medication management beyond age and the D-KEFS, or beyond age and time to complete the Pillbox Test. The present study does not support the notion that face validity, in and of itself, improves the ability of performance-based EF tests to predict functional outcomes. The results also suggest that time to complete TIADL may be considered an equal predictor of at-home functioning as tests of EF. i

Naturally occurring expressive suppression is associated with lapses in instrumental activities of daily living among community-dwelling older adults

OBJECTIVES: Expressive suppression (i.e., effortful regulation of overt affect) has a deleterious impact on executive functioning (EF). This relationship has potential ramifications for daily functioning, especially among older adults, because a close relationship exists between EF and functional independence. However, past research has not directly examined whether expressive suppression impacts instrumental activities of daily living (IADL). The present study examined this association among older adults. METHODS: One hundred ten community-dwelling older adults completed a self-report measure of acute (past 24 hr) and chronic (past 2 weeks) expressive suppression, a timed test of IADL, and the Behavioral Dyscontrol Scale as a measure of EF. RESULTS: High chronic expressive suppression was related to slow IADL performance beyond covariates (age, IQ, depression), but only for individuals with low EF. High acute expressive suppression was associated with lower accuracy on IADL tasks beyond covariates (IQ, depression), but this association was fully explained by EF. CONCLUSIONS: The current results suggest that expressive suppression is associated with less efficient and more error-prone IADL performance. EF fully accounted for the relationship between acute expressive suppression and IADL performance, showing that suppression is a risk factor for both poorer EF performance and functional lapses in daily life. Furthermore, individuals with weaker EF may be particularly vulnerable to the effect of chronic expressive suppression. (JINS, 2019, 25, 718-728)

Substance Use Among Adolescents with Attention-Deficit/Hyperactivity Disorder: Reasons for Use, Knowledge of Risks, and Provider Messaging/Education.

ObjectiveAdolescents with attention-deficit/hyperactivity disorder (ADHD) are at increased risk for alcohol and marijuana use. This study's objective is to describe adolescents' ADHD-specific reasons for marijuana use, knowledge of ADHD-specific alcohol risks, and reported subspecialty provider messaging/education regarding alcohol use among adolescents with ADHD.MethodsYouths with ADHD aged 12 to 18 years completed a survey about alcohol and marijuana use, ADHD-specific reasons for marijuana use, knowledge of ADHD-specific alcohol risks, and reported provider messaging/education regarding alcohol use. We assessed knowledge toward substance use using descriptive statistics. We used χ and t tests to determine whether knowledge or provider messaging/education differed by sociodemographic characteristics.ResultsOf the 96 participants, 61.5% were male, average age was 15.7 years; 31.3% reported past-year alcohol use and 20.8% reported past-year marijuana use. The majority (65.2%) said "no/don't know" to both "Can alcohol make ADHD symptoms worse?" and "Can alcohol interfere or get in the way of the medications you take?" Older participants were more likely to correctly answer the medication question "yes." Despite most (74%) participants reporting that their provider asked about alcohol use, few youth reported that their providers gave specific messages/education that alcohol could make ADHD symptoms worse (9.4%) or interfere with ADHD medications (14.6%); older participants and past-year alcohol users were more likely to have received these alcohol-specific messages.ConclusionMany youth with ADHD are unaware of the risks of alcohol use in relation to ADHD and providers are not consistently discussing these risks in the context of clinical ADHD care

Comparison of estimated risk levels by age and response using NIAAA Youth Alcohol Screening Tool for all youth and YCMC.

<p>Screening question assessed the number of drinking days in the past year [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156240#pone.0156240.ref019" target="_blank">19</a>].</p

Cut-points in drinking days used to identify AUD risk: Prevalence, Sensitivity, Specificity, and Area Under the ROC Curve.

<p>Cut-points in drinking days used to identify AUD risk: Prevalence, Sensitivity, Specificity, and Area Under the ROC Curve.</p

Sample Sociodemographic Characteristics by Past Year Alcohol Use Disorder (AUD) Risk^a.

<p>Sample Sociodemographic Characteristics by Past Year Alcohol Use Disorder (AUD) Risk<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156240#t002fn001" target="_blank">^a</a>.</p

The NIAAA Youth Alcohol Screening Tool.

<p>[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156240#pone.0156240.ref019" target="_blank">19</a>]</p