Validating a brief screening measure for early-onset substance use during adolescence in a diverse, nationwide birth cohort

被引:0
作者
Pelham, William E., III [1 ]
Corbin, William R. [2 ]
Meier, Madeline H. [2 ]
机构
[1] Univ Calif San Diego, Ctr Human Dev, 9500 Gilman Dr, La Jolla, CA 92093 USA
[2] Arizona State Univ, Dept Psychol, Tempe, AZ 85287 USA
关键词
Screening; Adolescence; Cannabis; Alcohol; Nicotine; PROTECTIVE FACTORS; RISK; PREVENTION; ALCOHOL; PREDICTORS; DEPENDENCE; ADULTHOOD; SMOKING; FAMILY; ABUSE;
D O I
10.1016/j.addbeh.2022.107277
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
The Loeber Risk Score (LRS) was developed to predict early-onset cannabis use in adolescence from late childhood, facilitating early identification. However, the LRS was developed in non-representative historical samples, leaving uncertain its generalizability to children/adolescents across the U.S. today. We externally validated the LRS in a diverse, nationwide cohort (N = 4,898) weighted to the composition of the U.S. Census. Participants in 20 cities completed assessments when youth were approximately 5, 9, and 15 years old. Parents completed the LRS at the age similar to 5 and similar to 9 interviews. At the age similar to 15 interview, youth reported on the onset of alcohol/drug use before age 15, monthly drinking/binge drinking at ages 14-16, and use of cannabis multiple times per month at ages 14-16. First, we validated the LRS measured at age similar to 9. Area under the receiver operating curve was 0.62 for onset of cannabis use before age 15, 0.68 for onset of cigarette use before age 15, and 0.62 for use of cannabis multiple times per month at ages 14-16. For drinking outcomes, LRS performance could not be distinguished from chance prediction. The recommended screening cutoff of LRS >= 2 identified 24% of children, among whom early-onset cannabis/cigarette use outcomes occurred 1.4-2.2 times more frequently than the general population. The LRS' performance did not vary significantly by sex, race, or ethnicity. When the LRS was measured at age similar to 5, AUROC was significantly lower for some outcomes. Together, findings support the LRS measure as a potential tool for identifying children in early or late childhood at risk of early-onset drug use in adolescence.
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页数:8
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共 55 条
  • [41] Fragile families: Sample and design
    Reichman, NE
    Teitler, JO
    Garfinkel, I
    McLanahan, SS
    [J]. CHILDREN AND YOUTH SERVICES REVIEW, 2001, 23 (4-5) : 303 - 326
  • [42] Ruggles S., 2021, **DATA OBJECT**, DOI 10.18128/D010.V11.0
  • [43] Correction for range restriction: An expanded typology
    Sackett, PR
    Yang, H
    [J]. JOURNAL OF APPLIED PSYCHOLOGY, 2000, 85 (01) : 112 - 118
  • [44] Review of inverse probability weighting for dealing with missing data
    Seaman, Shaun R.
    White, Ian R.
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2013, 22 (03) : 278 - 295
  • [45] Racial/Ethnic Differences in Adolescent Substance Use: Mediation by Individual, Family, and School Factors
    Shih, Regina A.
    Miles, Jeremy N. V.
    Tucker, Joan S.
    Zhou, Annie J.
    D'Amico, Elizabeth J.
    [J]. JOURNAL OF STUDIES ON ALCOHOL AND DRUGS, 2010, 71 (05) : 640 - 651
  • [46] Prediction models need appropriate internal, internal-external, and external validation
    Steyerberg, Ewout W.
    Harrell, Frank E., Jr.
    [J]. JOURNAL OF CLINICAL EPIDEMIOLOGY, 2016, 69 : 245 - 247
  • [47] Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
    Steyerberg, Ewout W.
    Moons, Karel G. M.
    van der Windt, Danielle A.
    Hayden, Jill A.
    Perel, Pablo
    Schroter, Sara
    Riley, Richard D.
    Hemingway, Harry
    Altman, Douglas G.
    [J]. PLOS MEDICINE, 2013, 10 (02)
  • [48] Introduction: Theoretical and operational framework for research into the etiology of substance use disorders
    Tarter, RE
    Vanyukov, MM
    [J]. JOURNAL OF CHILD & ADOLESCENT SUBSTANCE ABUSE, 2001, 10 (04) : 1 - 12
  • [49] Teresi JA, 2006, MED CARE, V44, pS3, DOI 10.1097/01.mlr.0000245437.46695.4a
  • [50] Calibration: the Achilles heel of predictive analytics
    van Calster, Ben
    McLernon, David J.
    van Smeden, Maarten
    Wynants, Laure
    Steyerberg, Ewout W.
    [J]. BMC MEDICINE, 2019, 17 (01)