Many pediatricians use question-based substance use screening to detect substance use and prevent substance use disorder (SUD).1,2 Despite availability,3 implementing standardized, universal, question-based substance use screening with adolescents is low,4 hindering prevention in health care2,3 and increasing risk for inequity and bias because of gender, age, and race.5 To understand the impact of universal, standardized substance use screening, we compared substance use screening occurrences and results from clinical records before and after implementing a standardized tablet-based substance use screening to determine whether standardization was associated with equitable screening practices.

This 2-cohort comparison study used clinical data for adolescent patients before (cohort 1; n = 485) and after (cohort 2; n = 485) implementing standardized, tablet-based screening. Data was collected from a single freestanding pediatric health care system in a large metropolitan Midwest city, which includes 15 urban and suburban primary care clinics and 60 000 annual visits. Patients seen for primary care encounters between 2017 and 2021 were sampled to examine substance use documentation before standardized screening, and patients seen between 2022 and 2023 were sampled to examine substance use documentation after standardized screening. If patients experienced health care encounters before and after standardized screening, then only encounters after standardized screening occurred were included. Patients who received standardized screening were 1:1 matched to patients who did not receive standardized screening, such that each case was matched to a control of the same gender, race, ethnicity, and date of birth +/− 6 months (N = 970). When more than one individual demographically matched to a patient experiencing standardized screening, the comparison sample was selected at random. An a priori power analysis indicated that with this sample size, there was sufficient power to detect group differences in binomial outcomes ≥9.5% as statistically significant with a Bonferroni-corrected α = .008 and 80% power.

Prior to standardized screening (cohort 1), substance use screening occurrences and results could be captured in the patient record via (1) clinical staff–documented patient responses in a flowsheet, (2) lab results to substance use urine screens, (3) encounter diagnoses related to intoxication or SUD, (4) adding substance use to the problem list, or (5) in the clinic note. Any mention of substance use (or nonuse) in reference to the patient in any clinical note during an encounter was considered evidence of screening (0 = not screened, 1 = screened), and whether substance use was indicated (0 = no, 1 = yes) was captured using previously published methods.6,7 After implementing tablet-based screening (cohort 2), patients self-administered the CRAFFT 2.1+N substance use screening tool, which was integrated into Epic.4 CRAFFT administration (0 = not screened, 1 = screened) and past year substance use based on CRAFFT responses (0 = no, 1 = yes) were extracted from the medical record along with patient demographic characteristics for both cohorts. Differences in screening occurrences and results between cohorts were compared, stratified by sex assigned at birth (male, female), race (white, Black), and age in years. The local institutional review board approved the study; data are available upon request.

By design, the unstandardized and standardized screening cohorts were identical with respect to age, sex assigned at birth (55% female), race (54% Black), and ethnicity (1% Hispanic). With unstandardized screening, 48% of adolescents were screened, and 17% of screenings were positive. With standardized screening, more adolescents (99%) were screened, and more substance use was detected (29% positive screens).

With unstandardized screening, adolescents who were screened were more than 2 years older, on average, than adolescents who were not screened and more than 1 year older than those who received standardized screening (Table 1).

TABLE 1.

Sample Size, Distribution, Means, Standard Deviations, and Bivariate Comparisons for Substance Use Screening Occurrences and Results

Demographics by Screening GroupNot ScreenedScreenedComparisonaNegative Screening ResultPositive Screening ResultComparisona
Unstandardized n1 = 249 n2 = 227 F (3, 944) = 27.25 c n1 = 189 n2 = 38 F (3, 689) = 28.25c 
Age (years) M = 13.63 M = 15.71  M = 15.57 M = 16.39  
 SD = 2.75 SD = 2.24  SD = 2.30 SD = 1.70  
Standardized n3 = 6 n4 = 466 1vs2F = 80.92c n3 = 331 n4 = 135 1vs2F = 4.35b 
Age (years) M = 14.08 M = 14.67 3vs4F = 0.33 M = 14.17 M = 15.86 3vs4F = 49.26c 
 SD = 3.26 SD = 2.48 2vs4F = 28.56c SD = 2.55 SD = 1.80 2vs4F = 2.63 
Unstandardized   χ2 (3, N = 948) =   χ2 (3, N = 693) = 
Sex at birth n = 249 n = 227 315.79c n = 189 n = 38 14.54c 
% male1 n = 118 (47%) n = 96 (42%)  n = 75 (40%) n = 21 (55%)  
% female2 n = 131 (53%) n = 131 (58%)  n = 114 (60%) n = 17 (45%)  
Standardized   1vs2 χ2 = 1.25   1vs2 χ2 = 3.15 
Sex at birth n = 6 n = 466 3vs4 χ2 = 3.54 n = 331 n = 135 3vs4 χ2 = 0.01 
% male3 n = 5 (83%) n = 209 (45%) 1vs3 χ2 = 145.68c n = 149 (45%) n = 60 (44%) 1vs3 χ2 = 1.57 
% female4 n = 1 (17%) n = 257 (55%) 2vs4 χ2 = 168.93c n = 182 (55%) n = 75 (56%) 2vs4 χ2 = 12.60c 
Unstandardized   χ2 (3, N = 948) =   χ2 (3, N = 693) = 
Race n = 249 n = 227 335.66c n = 189 n = 38 12.87c 
% white1 n = 137 (55%) n = 82 (36%)  n = 70 (37%) n = 12 (32%)  
% Black2 n = 112 (45%) n = 145 (64%)  n = 119 (63%) n = 26 (68%)  
Standardized   1vs2 χ2 = 17.07c   1vs2 χ2 = 0.41 
Race n = 6 n = 466 3vs4 χ2 = 0.42 n = 331 n = 135 3vs4 χ2 = 0.34 
% white3 n = 2 (33%) n = 217 (47%) 1vs3 χ2 = 192.07c n = 157 (47%) n = 60 (44%) 1vs3 χ2 = 5.51b 
% Black4 n = 4 (67%) n = 249 (53%) 2vs4 χ2 = 127.98c n = 174 (53%) n = 75 (56%) 2vs4 χ2 = 7.14c 
Demographics by Screening GroupNot ScreenedScreenedComparisonaNegative Screening ResultPositive Screening ResultComparisona
Unstandardized n1 = 249 n2 = 227 F (3, 944) = 27.25 c n1 = 189 n2 = 38 F (3, 689) = 28.25c 
Age (years) M = 13.63 M = 15.71  M = 15.57 M = 16.39  
 SD = 2.75 SD = 2.24  SD = 2.30 SD = 1.70  
Standardized n3 = 6 n4 = 466 1vs2F = 80.92c n3 = 331 n4 = 135 1vs2F = 4.35b 
Age (years) M = 14.08 M = 14.67 3vs4F = 0.33 M = 14.17 M = 15.86 3vs4F = 49.26c 
 SD = 3.26 SD = 2.48 2vs4F = 28.56c SD = 2.55 SD = 1.80 2vs4F = 2.63 
Unstandardized   χ2 (3, N = 948) =   χ2 (3, N = 693) = 
Sex at birth n = 249 n = 227 315.79c n = 189 n = 38 14.54c 
% male1 n = 118 (47%) n = 96 (42%)  n = 75 (40%) n = 21 (55%)  
% female2 n = 131 (53%) n = 131 (58%)  n = 114 (60%) n = 17 (45%)  
Standardized   1vs2 χ2 = 1.25   1vs2 χ2 = 3.15 
Sex at birth n = 6 n = 466 3vs4 χ2 = 3.54 n = 331 n = 135 3vs4 χ2 = 0.01 
% male3 n = 5 (83%) n = 209 (45%) 1vs3 χ2 = 145.68c n = 149 (45%) n = 60 (44%) 1vs3 χ2 = 1.57 
% female4 n = 1 (17%) n = 257 (55%) 2vs4 χ2 = 168.93c n = 182 (55%) n = 75 (56%) 2vs4 χ2 = 12.60c 
Unstandardized   χ2 (3, N = 948) =   χ2 (3, N = 693) = 
Race n = 249 n = 227 335.66c n = 189 n = 38 12.87c 
% white1 n = 137 (55%) n = 82 (36%)  n = 70 (37%) n = 12 (32%)  
% Black2 n = 112 (45%) n = 145 (64%)  n = 119 (63%) n = 26 (68%)  
Standardized   1vs2 χ2 = 17.07c   1vs2 χ2 = 0.41 
Race n = 6 n = 466 3vs4 χ2 = 0.42 n = 331 n = 135 3vs4 χ2 = 0.34 
% white3 n = 2 (33%) n = 217 (47%) 1vs3 χ2 = 192.07c n = 157 (47%) n = 60 (44%) 1vs3 χ2 = 5.51b 
% Black4 n = 4 (67%) n = 249 (53%) 2vs4 χ2 = 127.98c n = 174 (53%) n = 75 (56%) 2vs4 χ2 = 7.14c 

Abbreviations: M, mean; SD, standard deviation

a

Age comparisons were conducted using a 2 x 2 factorial analysis of variance to account for differences by cohort (ie, unstandardized vs standardized) and result (ie, not screened vs screened, negative vs positive), with post hoc analyses to compare differences among groups (1vs2, 3vs2, 4vs4). Comparisons for sex assigned at birth and race were completed using a 4 x 2 Pearson’s χ2 test to account for differences by cohort and result, with post hoc analyses to compare differences among groups (1vs2, 3vs1, 4vs2, 3vs4).

b

P < .05,

c

P < .008, a Bonferroni-corrected P value adjustment for a priori analyses.

Screening occurrences did not significantly differ between males and females when screening was unstandardized or standardized (Table 1). With unstandardized screening, Black adolescents were more likely to be screened than white adolescents; no difference was detected with standardized screening.

Substance use was detected approximately 6 months earlier when screening was standardized (Table 1). Standardized screening resulted in a higher detection rate of substance use among females (29% positive with standardized; 13% positive with unstandardized screening).

Standardized substance use screening8 resulted in more completed screening, less bias, and better substance use detection. Adolescents may inaccurately report substance use, and screening occurrences may be undocumented, which are noted limitations. Investing in standardized universal screening may promote health equity in screening practices, contributing to reduced SUD-related morbidity and mortality.1–3 

Ms Ranginwala assisted with data collection and analysis, developed the first draft of the paper, and assisted with revisions. Dr Greiner assisted with study design and conceptualization and contributed to revised drafts of the paper. Ms Fox assisted with data collection and analysis and contributed to revised drafts of the paper. Ms Unkrich assisted with data collection and contributed to revised drafts of the paper. Dr Beal contributed to the study design, conceptualization, data analysis, and drafts of the paper.

CONFLICT OF INTEREST DISCLOSURES: The authors have no conflicts of interest to disclose.

FUNDING: This project was supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number 5UL1TR001425, the National Institute of Drug Abuse under Award Number R03DA054256, and the Substance Abuse and Mental Health Services Administration under Award Number 1H79TI084035. The content is solely the responsibility of the authors. The study sponsors had no role in the design and conduct of the study.

The following study team members contributed to coding the unstandardized clinical notes: Michelle Kalu, Myles Parks-Tiller, Elizabeth Hamik, Houley Sall, Meera Patel, SJ Jacobs, Luke Greiner, Maya Subedi, and Edward Owsley-Longino.

SUD

substance use disorder

1.
Clark
DB
,
Martin
CS
,
Cornelius
JR
.
Adolescent-onset substance use disorders predict young adult mortality
.
J Adolesc Health.
2008
;
42
(
6
):
637
639
. PubMed doi: 10.1016/j.jadohealth.2007.11.147
2.
Volkow
ND
,
Han
B
,
Einstein
EB
,
Compton
WM
.
Prevalence of substance use disorders by time since first substance use among young people in the US
.
JAMA Pediatr.
2021
;
175
(
6
):
640
643
. PubMed doi: 10.1001/jamapediatrics.2020.6981
3.
Levy
SJL
,
Williams
JF
,
Ryan
SA
, et al;
COMMITTEE ON SUBSTANCE USE AND PREVENTION
.
Substance use screening, brief intervention, and referral to treatment
.
Pediatrics.
2016
;
138
(
1
):
e20161211
. PubMed doi: 10.1542/peds.2016-1211
4.
Knight
JR
,
Sherritt
L
,
Harris
SK
,
Gates
EC
,
Chang
G
.
Validity of brief alcohol screening tests among adolescents: a comparison of the AUDIT, POSIT, CAGE, and CRAFFT
.
Alcohol Clin Exp Res.
2003
;
27
(
1
):
67
73
. PubMed doi: 10.1111/j.1530-0277.2003.tb02723.x
5.
Chen
P
,
Jacobson
KC
.
Developmental trajectories of substance use from early adolescence to young adulthood: gender and racial/ethnic differences
.
J Adolesc Health.
2012
;
50
(
2
):
154
163
. PubMed doi: 10.1016/j.jadohealth.2011.05.013
6.
Beal
SJ
,
Greiner
MV
,
Ammerman
RT
, et al
.
Patterns of substance use among adolescents in and out of foster care: an analysis of linked health and child welfare administrative data
.
Child Abuse Negl.
2023
;
146
:
106473
. PubMed doi: 10.1016/j.chiabu.2023.106473
7.
Ni
Y
,
Bachtel
A
,
Nause
K
,
Beal
S
.
Automated detection of substance use information from electronic health records for a pediatric population
.
J Am Med Inform Assoc.
2021
;
28
(
10
):
2116
2127
. PubMed doi: 10.1093/jamia/ocab116
8.
Harris
SK
,
Knight
JRJ
Jr
,
Van Hook
S
, et al
.
Adolescent substance use screening in primary care: validity of computer self-administered versus clinician-administered screening
.
Subst Abus.
2016
;
37
(
1
):
197
203
. PubMed doi: 10.1080/08897077.2015.1014615
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