This study used population-based data for youth aged 12 to 18 to identify a developmentally appropriate, consumption-based single-item alcohol screen with high sensitivity and specificity in identifying alcohol-related problems. Results indicate that self-reported frequency of alcohol use in the past year provides an empirically based brief 1-item screen that efficiently identifies youth with alcohol-related problems (ie, any DSM-IV AUD symptom or alcohol dependence), which had better overall screening performance than quantity or HED frequency. A range of cut points for the frequency item that have high sensitivity and specificity for a moderate-risk outcome (ie, any past-year DSM-IV AUD symptom) could be used for early detection of alcohol-related problems in youth (relative to AUD), whereas a range of cut points with good overall screening performance in relation to a high-risk outcome (ie, DSM-IV alcohol dependence) could facilitate triaging of youth with high problem severity to further evaluation and possible treatment referral (bolded values in – indicate easy-to-remember cut scores). For both alcohol outcomes, age-specific cut points increase screening sensitivity and specificity, but within age, the same cut points can be used for males and females.
As predicted, frequency generally had better overall screening performance (ie, greater AUC) compared with quantity and HED frequency in relation to both outcomes, at ages 12 to 18, and by gender within age. Thus, frequency should be prioritized as a screen.13
The high sensitivity and specificity of frequency suggests that additional consumption items would not provide much improvement in overall screening performance, especially at younger ages. It is noteworthy that AUC for frequency and quantity decreased slightly with age (but remained high, >0.80). In contrast, AUC for HED frequency increased with age, suggesting that the frequency of consuming 5 or more drinks per occasion becomes more effective as a screener as a function of age. To improve the overall performance of the HED frequency measure at younger ages, lower HED quantity may be needed.19
A caveat that needs to be considered in interpreting the relative performance of the 3 consumption items is that the time frame for the quantity and HED frequency items was “past 30 days,” whereas the time frame for the frequency item and the 2 outcomes referred to “past year.” The shorter time frame, particularly for HED frequency, might have limited its screening performance here.
Although optimal drinking frequency cut points to identify youth with moderate- and high-risk outcomes differed by age, differences by gender within age were small, in general. As shown in –, sensitivity and specificity across gender within age were similar across a range of values, suggesting that more general, easy-to-remember guidelines for cut points across gender could be used. At ages 12 to 15, the use of drinking at least once in the past year to identify risk may seem overly conservative (ie, potentially producing “false positives”); however, given that early onset of alcohol use predicts later AUD,20
a conservative guideline at young ages may help to prevent future harm.
In applying developmentally appropriate screening cut points, use of a lower threshold incurs the “cost” of identifying possible false-positive cases, whereas the use of a higher threshold could increase the proportion of “false-negative” (“missed”) cases that might benefit from further evaluation. In the context of adolescent alcohol screening, depending on the screening context and available resources, it may be preferable to favor high sensitivity relative to specificity, given the importance of prevention and early intervention. However, when resources to manage positive screens are scarce, specificity may be emphasized. It should be noted that any screen with less than perfect specificity will produce a substantial proportion of false-positive cases when base rates are sufficiently low.21
Nevertheless, the current results show that both sensitivity and specificity are quite high across a range of cut points, especially in younger adolescents.
Advantages of a consumption-based, relative to a problem-based (eg, CRAFFT), screen are that even problem-based screens assume that the level of consumption has been assessed7
; problem-based screens may “miss” specific alcohol-related harm experienced by an adolescent; and assessing the pattern of alcohol use can identify potentially harmful use (eg, HED) that indicates the need for intervention. Because alcohol is the substance used most often by adolescents,2
and youth who do not use alcohol are less likely to use other substances (NIAAA, unpublished analysis of 2000–2009 data from NSDUH),22
a brief alcohol screen can help prioritize the need to screen for other substances and risk behaviors. Assessment of alcohol consumption can be complemented by problem-based screening (eg, CRAFFT7
). Obtaining an honest report of drinking behavior from an adolescent can be facilitated by establishing rapport, ensuring that the adolescent understands the clinic’s confidentiality policy, and discussing sensitive topics with the adolescent in private.23
DSM-based alcohol outcomes constitute an important standard used to evaluate screening performance. The higher prevalence of any AUD symptom in this study, relative to the alcohol abuse diagnosis, supports the use of this moderate-risk outcome for earlier detection of alcohol-related problems. The inclusion of a more severe outcome, DSM-IV alcohol dependence, provides an important advance in facilitating identification of youth who may benefit from more intensive evaluation and intervention. Despite the strengths of using DSM-based AUD outcomes, limitations of using DSM-based criteria with adolescents need to be considered.24
As in many large-scale fully structured surveys of diagnostic criteria, there may be overendorsement of certain symptoms.25
For example, hangover might be mistaken for withdrawal.24
In addition, endorsement of Tolerance at early ages may reflect a developmental process, or acute (within session) tolerance, rather than a high level of tolerance typically associated with dependence.26
Nevertheless, endorsement of any alcohol-related symptom by adolescents can signal a risky pattern of use that warrants further evaluation.
Other study limitations warrant comment. Youth self-reports of alcohol consumption were used without biochemical verification, and may have underestimated alcohol use, for example, because items did not specifically query use of alcohol energy drinks or sweetened alcoholic beverages. It is important to appreciate, however, that what predicted past-year AUD symptoms and alcohol dependence in this study was what the youth reported; the accuracy of those reports need not be presumed to use the item(s) as a screen. Although population-based data were analyzed, the context of responding to computerized survey items differs from that of clinic settings where consumption items might be asked in-person for the purpose of screening, and confidentiality concerns may compromise honest reporting. The performance of combinations of consumption items was not examined because of differences in item time frames. However, the high sensitivity and specificity of frequency in relation to the 2 outcomes, particularly at younger ages, suggests that a combination of consumption items would provide little incremental improvement. Ethnic differences in screening performance remain to be documented.