Skip to main content

Main menu

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • Other Publications
    • abfm

User menu

Search

  • Advanced search
American Board of Family Medicine
  • Other Publications
    • abfm
American Board of Family Medicine

American Board of Family Medicine

Advanced Search

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • JABFM on Bluesky
  • JABFM On Facebook
  • JABFM On Twitter
  • JABFM On YouTube
Research ArticleOriginal Research

Identifying Problematic Substance Use in a National Sample of Adolescents Using Frequency Questions

Laura J. Chavez, Katharine A. Bradley, Gwen T. Lapham, Thomas M. Wickizer and Deena J. Chisolm
The Journal of the American Board of Family Medicine July 2019, 32 (4) 550-558; DOI: https://doi.org/10.3122/jabfm.2019.04.180284
Laura J. Chavez
From the College of Public Health, Division of Health Services Management and Policy, Ohio State University, Columbus, OH (LJC, TMW, DJC); Nationwide Children's Hospital, Research Institute, Columbus (LJC, DJC); Kaiser Permanente Washington Health Research Institute, Seattle, WA (KAB, GTL); College of Medicine, Department of Pediatrics, Ohio State University, Columbus (DJC).
PhD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Katharine A. Bradley
From the College of Public Health, Division of Health Services Management and Policy, Ohio State University, Columbus, OH (LJC, TMW, DJC); Nationwide Children's Hospital, Research Institute, Columbus (LJC, DJC); Kaiser Permanente Washington Health Research Institute, Seattle, WA (KAB, GTL); College of Medicine, Department of Pediatrics, Ohio State University, Columbus (DJC).
MD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gwen T. Lapham
From the College of Public Health, Division of Health Services Management and Policy, Ohio State University, Columbus, OH (LJC, TMW, DJC); Nationwide Children's Hospital, Research Institute, Columbus (LJC, DJC); Kaiser Permanente Washington Health Research Institute, Seattle, WA (KAB, GTL); College of Medicine, Department of Pediatrics, Ohio State University, Columbus (DJC).
PhD, MPH, MSW
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas M. Wickizer
From the College of Public Health, Division of Health Services Management and Policy, Ohio State University, Columbus, OH (LJC, TMW, DJC); Nationwide Children's Hospital, Research Institute, Columbus (LJC, DJC); Kaiser Permanente Washington Health Research Institute, Seattle, WA (KAB, GTL); College of Medicine, Department of Pediatrics, Ohio State University, Columbus (DJC).
PhD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Deena J. Chisolm
From the College of Public Health, Division of Health Services Management and Policy, Ohio State University, Columbus, OH (LJC, TMW, DJC); Nationwide Children's Hospital, Research Institute, Columbus (LJC, DJC); Kaiser Permanente Washington Health Research Institute, Seattle, WA (KAB, GTL); College of Medicine, Department of Pediatrics, Ohio State University, Columbus (DJC).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Appendix 2.
    • Download figure
    • Open in new tab
    Appendix 2.

    Receiver Operating Characteristic (ROC) Curves for Tobacco Dependence, Alcohol Use Disorder, Cannabis Use Disorder, and Other Illicit Drug Use Disorder. AUD, alcohol use disorders; CUD, cannabis use disorders; DUD, drug use disorders.

Tables

  • Figures
    • View popup
    Table 1.

    Adolescent Sample Characteristics, by Age Group

    Age 12 to 15 Years N = 79,170Age 16 to 17 Years N = 42,356Age 18 to 20 Years N = 48,460Total N = 169,986
    N(%)N(%)N(%)N(%)
    Gender
        Males40,375(51.2)21,531(50.9)23,901(51.7)85,807(51.2)
        Females38,795(48.8)20,825(49.1)24,559(48.3)84,179(48.7)
    Race/ethnicity
        White Non-Hispanic45,509(56.1)24,450(57.1)27,670(56.7)97,629(56.5)
        Black Non-Hispanic10,543(14.2)5998(14.8)6863(14.5)23,404(14.5)
        Hispanic15,053(21.5)7762(20.2)9180(20.8)31,995(21.0)
        Asian3027(4.9)1644(5.0)2120(5.1)6791(5.0)
        Other5038(3.3)2502(2.9)2627(2.7)10,167(3.0)
    Criterion Standards
        Tobacco Dependence999(1.1)2588(5.2)6250(11.8)9837(5.8)
        AUD1,781(2.1)3314(7.3)6490(13.1)11,585(7.1)
        Any AUD Symptom3796(4.5)7264(16.5)14,094(28.8)25,154(15.7)
        CUD1509(1.7)2733(6.1)3324(6.9)7566(4.5)
        Any CUD Symptom2892(3.4)5727(12.9)8534(17.8)17,153(10.5)
        DUD976(1.1)1004(2.3)1483(3.0)3463(2.0)
        Any DUD Symptom2114(2.5)2043(4.7)2890(5.8)7047(4.2)
    • AUD, alcohol use disorder; CUD, cannabis use disorder; DUD, other illicit drug use disorder.

    • Ns are unweighted, proportions are weighted to account for complex survey design.

    • χ2 test comparing Criterion Standards across age groups all P < .001.

    • View popup
    Table 2.

    Performance of Substance Use Screens for Identifying Tobacco Dependence and Alcohol, Cannabis, and Other Illicit Drug Use Disorder

    Age 12 to 15 Years N = 79,170Age 16 to 17 Years N = 42,356Age 18 to 20 Years N = 48,460
    SeSpSeSpSeSp
    Smoking*
        ≥1 day1.000.971.000.901.000.79
        ≥2 days0.910.980.960.920.970.82
        ≥4 days0.810.990.890.940.940.86
        ≥25 days0.481.000.680.980.800.95
        AUC (SE)0.99 (0.0002)0.98 (0.0003)‡0.96 (0.0004)‡§
    Alcohol†
        ≥1 day1.000.851.000.571.000.38
        ≥2 days1.000.881.000.621.000.41
        ≥3 days1.000.901.000.661.000.44
        ≥12 days0.880.950.920.800.960.60
        ≥52 days0.490.980.580.920.730.79
        ≥270 days0.041.000.031.000.040.99
        AUC (SE)0.97 (0.0004)0.91 (0.0008)‡0.84 (0.0010)‡§
    Cannabis†
        ≥1 day1.000.941.000.791.000.71
        ≥2 days1.000.951.000.821.000.73
        ≥3 days1.000.961.000.831.000.75
        ≥12 days0.930.970.950.880.970.81
        ≥52 days0.660.990.740.930.830.87
        ≥270 days0.141.000.200.980.360.96
        AUC (SE)0.99 (0.0002)0.95 (0.0006)‡0.93 (0.0007)‡§
    Other Illicit Drugs†
        ≥1 day1.000.931.000.881.000.84
        ≥2 days0.950.940.970.900.980.87
        ≥3 days0.920.950.930.920.960.88
        ≥12 days0.750.970.800.950.850.93
        ≥52 days0.480.980.570.980.640.97
        ≥270 days0.141.000.191.000.261.00
        AUC (SE)0.98 (0.0004)0.97 (0.0005)‡0.97 (0.0005)‡§
    • AUC, Area under Receiver Operating Characteristic (ROC) Curve for Tobacco Dependence (Nicotine Dependence Syndrome Scale/Fagerström Test of Nicotine Dependence) or DSM-IV Alcohol, Cannabis, or Other Drug Use Disorders; Se, Sensitivity; Sp, Specificity.

    • Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • ↵* Frequency is coded as days of use in the past month.

    • ↵† Frequency is coded as days of use in the past year.

    • χ2 test for independence (relative to age 12 to 15 years),

    • ↵‡ P < .0001.

    • χ2 test for independence (comparing 16–17- and 18–20-year-olds),

    • ↵§ P < .0001.

    • View popup
    Table 3.

    Single-Item Screens for Identifying Substance Use Disorder (or Tobacco Dependence) in Adolescents (Age 12 to 20 years)

    Screening QuestionsScreening Cut-Points for Positive Screen
    1. During the past 30 days, on how many days did you smoke part or all of a cigarette?≥1 day all age groups
    2. On how many days in the past 12 months did you drink an alcoholic beverage?≥3 days for ages 12 to 15 years
    ≥12 days if older
    3. On how many days in the past 12 months did you use marijuana or hashish?≥3 days for ages 12 to 15 years
    ≥12 days if older
    4. On how many days in the past 12 months did you use cocaine, crack, heroin, hallucinogens, inhalants, or prescription medications including pain relievers, tranquilizers, stimulants, or sedatives that were not prescribed for you or that you took only for the feeling that it caused.≥1 day all age groups
    • Screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • View popup
    Appendix 1.

    Prevalence of Criterion Standards, by Age Group and Gender

    FemalesMales
    Age 12 to 15 YearsAge 16 to 17 YearsAge 18 to 20 YearsAge 12 to 15 YearsAge 16 to 17 YearsAge 18 to 20 Years
    N = 38,795N = 20,825N = 24,559N = 40,375N = 21,531N = 23,901
    N(%)N(%)N(%)N(%)N(%)N(%)
    Tobacco Dependence507(1.2)1200(4.9)*2891(10.5)†492(1.1)1388(5.6)3359(13.1)
    AUD1068(2.6)†1688(7.4)2822(11.2)†713(1.6)1626(7.1)3668(14.9)
    Any AUD Symptom2107(5.1)†3637(16.6)6383(25.7)†1,689(3.9)3627(16.4)7711(31.6)
    CUD773(1.8)1146(5.1)†1220(5.1)†736(1.7)1587(7.2)2104(8.6)
    Any CUD Symptom1349(3.1)*2358(10.6)†3260(13.5)†1,543(3.6)3369(15.2)5274(21.9)
    DUD624(1.4)†554(2.5)*680(2.7)†352(0.8)450(2.0)803(3.3)
    Any DUD Symptom1278(3.0)†1089(5.1)*1329(5.4)†836(2.1)954(4.3)1561(6.3)
    • AUD, alcohol use disorder; CUD, cannabis use disorder; DUD, other illicit drug use disorder.

    • Ns are unweighted, proportions are weighted to account for complex survey design.

    • χ2 test for independence (comparing females and males, within same age group),

    • ↵* P < .01,

    • ↵† P < .0001.

    • View popup
    Appendix 3.

    Performance of Substance Use Screens for Identifying Tobacco Dependence and Alcohol, Cannabis, and Other Illicit Drug Use Disorders, by Age and Gender

    MalesFemales
    12 to 15 Years16 to 17 Years18 to 20 Years12 to 15 Years16 to 17 Years18 to 20 Years
    SeSpSeSpSeSpSeSpSeSpSeSp
    Smoking*
        ≥1 day1.000.981.000.901.000.761.000.971.000.911.000.83
        ≥2 days0.890.980.950.920.970.800.930.980.960.930.980.85
        ≥4 days0.790.990.880.940.940.830.830.990.910.950.950.88
        ≥25 days0.451.000.650.980.770.940.511.000.720.990.840.96
        AUC (SE)0.99(0.0002)0.97(0.0005)‡0.95(0.0007)‡0.99(0.0002)0.98(0.0005)0.96(0.006)
    Alcohol†
        ≥1 day1.000.851.000.581.000.371.000.841.000.561.000.38
        ≥2 days1.000.881.000.621.000.411.000.871.000.611.000.42
        ≥3 days1.000.901.000.661.000.441.000.891.000.661.000.45
        ≥12 days0.890.950.940.800.970.580.880.950.910.810.950.62
        ≥52 days0.500.980.580.920.750.770.480.980.570.920.710.82
        ≥270 days0.031.000.031.000.050.990.041.000.031.000.030.99
        AUC (SE)0.97(0.0008)0.90(0.0012)0.82(0.0014)‡0.97(0.0006)0.90(0.0012)0.83(0.0015)
    Cannabis†
        ≥1 day1.000.941.000.781.000.681.000.941.000.811.000.74
        ≥2 days0.990.951.000.801.000.701.000.951.000.831.000.76
        ≥3 days0.990.951.000.821.000.721.000.961.000.850.990.78
        ≥12 days0.930.970.960.870.980.780.930.980.940.900.960.85
        ≥52 days0.660.980.780.920.850.840.660.990.670.940.790.90
        ≥270 days0.161.000.220.980.390.950.131.000.190.990.320.98
        AUC (SE)0.98(0.0004)‡0.94(0.0009)‡0.91(0.0010)‡0.99(0.0004)0.95(0.0008)0.94(0.0009)
    Other Illicit Drugs†
        ≥1 day1.000.941.000.881.000.821.000.921.000.881.000.86
        ≥2 days0.940.950.960.900.980.850.960.940.970.910.980.88
        ≥3 days0.920.950.920.920.960.870.920.940.940.920.960.90
        ≥12 days0.670.970.800.950.860.920.800.960.800.950.860.94
        ≥52 days0.450.980.560.980.650.960.500.980.580.980.650.97
        ≥270 days0.131.000.181.000.271.000.151.000.211.000.241.00
        AUC (SE)0.98(0.0005)0.97(0.0008)0.96(0.0008)‡0.98(0.0005)0.97(0.0007)0.97(0.0007)
    • AUC, area under receiver operating characteristic (ROC) curve for tobacco dependence (Nicotine Dependence Syndrome Scale/Fagerström Test of Nicotine Dependence) or DSM-IV alcohol, cannabis, or other drug use disorders; Se, sensitivity; Sp, specificity.

    • Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • ↵* Frequency is coded as days of use in the past month.

    • ↵† Frequency is coded as days of use in the past year.

    • χ2 test for independence (within age group, male relative to female adolescents),

    • ↵‡ P < .0001.

    • View popup
    Appendix 4.

    Performance of Substance Use Screens for Identifying Any Symptom of Alcohol, Cannabis, or Other Illicit Drug Use Disorder

    Age 12 to 15 YearsAge 16 to 17 YearsAge 18 to 20 Years
    N = 79,170N = 42,356N = 48,460
    SeSpSeSpSeSp
    Alcohol
        ≥1 day1.000.871.000.631.000.46
        ≥2 days1.000.901.000.691.000.50
        ≥3 days1.000.921.000.731.000.54
        ≥12 days0.840.960.890.880.940.72
        ≥52 days0.400.990.480.960.650.88
        ≥270 days0.021.000.021.000.031.00
        AUC (SE)0.98 (0.0003)0.93 (0.0006)*0.88 (0.0007)*†
    Cannabis
        ≥1 day1.000.961.000.861.000.80
        ≥2 days1.000.971.000.881.000.83
        ≥3 days0.990.971.000.901.000.85
        ≥12 days0.900.990.940.950.960.92
        ≥52 days0.610.990.710.980.800.96
        ≥270 days0.121.000.201.000.310.99
        AUC (SE)0.99 (0.0002)0.97 (0.0004)*0.96 (0.0004)*†
    Other Illicit Drugs
        ≥1 day1.000.941.000.901.000.87
        ≥2 days0.920.960.950.930.970.89
        ≥3 days0.880.960.900.940.940.91
        ≥12 days0.690.970.710.960.760.95
        ≥52 days0.400.990.440.980.520.98
        ≥270 days0.081.000.131.000.161.00
        AUC (SE)0.98 (0.0003)0.97 (0.0004)*0.97 (0.0004)*†
    • Notes: Se = Sensitivity; Sp = Specificity; AUC = Area under Receiver Operating Characteristic (ROC) Curve for any symptom of DSM-IV Alcohol, Cannabis, or Other Drug Use Disorders; Se, sensitivity; Sp, specificity. Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • Frequency is coded as days of use in the past year.

    • χ2 test for independence (relative to age 12 to 15 years),

    • ↵* P < .0001.

    • χ2 test for independence (comparing 16–17- and 18–20-year-olds),

    • ↵† P < .0001.

    • View popup
    Appendix 5.

    Performance of Substance Use Screens for Identifying Any Symptom of Alcohol, Cannabis, or Other Illicit Drug Use Disorder, by Age and Gender

    MalesFemales
    12 to 15 Years16 to 17 Years18 to 20 Years12 to 15 Years16 to 17 Years18 to 20 Years
    SeSpSeSpSeSpSeSpSeSpSeSp
    Alcohol
        ≥1 day1.000.871.000.641.000.471.000.861.000.621.000.45
        ≥2 days1.000.911.000.691.000.501.000.891.000.681.000.50
        ≥3 days1.000.931.000.741.000.541.000.921.000.731.000.54
        ≥12 days0.850.970.910.870.950.710.830.960.860.880.930.73
        ≥52 days0.400.990.490.950.680.870.390.990.470.960.620.89
        ≥270 days0.021.000.031.000.041.000.021.000.021.000.031.00
        AUC (SE)0.98(0.0004)†0.93(0.0009)0.86(0.0011)0.97(0.0004)0.93(0.0009)0.86(0.0011)
    Cannabis
        ≥1 day1.000.961.000.851.000.801.000.961.000.861.000.81
        ≥2 days1.000.971.000.881.000.820.990.971.000.891.000.83
        ≥3 days1.000.971.000.891.000.840.990.971.000.901.000.85
        ≥12 days0.910.990.950.940.970.910.890.990.920.950.940.92
        ≥52 days0.620.990.740.970.820.950.601.000.650.980.760.96
        ≥270 days0.131.000.221.000.340.990.111.000.161.000.261.00
        AUC (SE)0.99(0.0002)0.98(0.0004)0.97(0.0005)0.99(0.0002)0.98(0.0004)0.97(0.0005)
    Other Illicit Drugs
        ≥1 day1.000.951.000.901.000.851.000.941.000.911.000.88
        ≥2 days0.920.960.950.920.970.880.920.950.940.930.970.91
        ≥3 days0.880.960.900.930.940.900.870.960.900.940.940.92
        ≥12 days0.650.980.720.960.770.940.720.970.710.970.760.96
        ≥52 days0.370.990.430.980.520.980.420.990.450.990.520.98
        ≥270 days0.071.000.111.000.171.000.091.000.141.000.151.00
        AUC (SE)0.98(0.0004)*0.97(0.0006)0.96(0.0006)†0.98(0.0004)0.97(0.0005)0.97(0.0005)
    • AUC, area under receiver operating characteristic (ROC) curve for any symptom of DSM-IV alcohol, cannabis, or other drug use disorders; Se, sensitivity; Sp, specificity.

    • Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • Frequency is coded as days of use in the past year.

    • χ2 test for independence (within age group, male relative to female adolescents),

    • ↵* P < .01,

    • ↵† P < .0001.

    • View popup
    Appendix 6.

    Characteristics of Outpatient Subgroup (2013 to 2014)

    Outpatients
    N = 31,505
    N(%)
    Gender
        Males14,941(47.8)
        Females16,564(52.2)
    Age, years
        12 to 1514,699(41.9)
        16 to 178353(23.7)
        18 to 208453(34.4)
    Race/ethnicity
        White Non-Hispanic17,808(55.5)
        Black Non-Hispanic4254(13.8)
        Hispanic6079(21.7)
        Asian1402(5.6)
        Other1962(3.4)
    Criterion Standards
        Tobacco Dependence1313(4.3)
        AUD1683(5.8)
        Any AUD symptom3991(14.0)
        CUD1270(4.0)
        Any CUD Symptom3173(10.5)
        DUD505(1.6)
        Any DUD Symptom1106(3.5)
    • AUD, alcohol use disorder; CUD, cannabis use disorder; DUD, other illicit drug use disorder.

    • View popup
    Appendix 7.

    Performance of Substance Use Screens for Identifying Tobacco Dependence and Alcohol, Cannabis, and Other Illicit Drug Use Disorder among Outpatient Subgroup (2013 to 2014).

    Age 12 to 15 YearsAge 16 to 17 YearsAge 18 to 20 Years
    N = 14,699N = 8353N = 8453
    SeSpSeSpSeSp
    Smoking*
        ≥1 day1.000.981.000.931.000.83
        ≥2 days0.860.990.920.940.960.86
        ≥4 days0.760.990.820.960.940.89
        ≥25 days0.481.000.640.990.770.97
        AUC (SE)0.99 (0.0003)0.98 (0.0007)‡0.97 (0.0009)‡§
    Alcohol†
        ≥1 day1.000.861.000.571.000.38
        ≥2 days1.000.901.000.631.000.42
        ≥3 days1.000.911.000.671.000.46
        ≥12 days0.910.960.940.810.960.61
        ≥52 days0.500.990.590.930.720.81
        ≥270 days0.051.000.041.000.040.99
        AUC (SE)0.98 (0.0007)0.91 (0.0018)‡0.83 (0.0022)‡§
    Cannabis†
        ≥1 day1.000.941.000.791.000.69
        ≥2 days1.000.951.000.811.000.71
        ≥3 days1.000.961.000.821.000.73
        ≥12 days0.900.970.960.870.980.80
        ≥52 days0.590.990.720.920.800.86
        ≥270 days0.131.000.180.980.330.96
        AUC (SE)0.98 (0.0005)0.94 (0.0013)‡0.92 (0.0015)‡§
    Other Illicit Drugs†
        ≥1 day1.000.951.000.901.000.85
        ≥2 days0.980.950.970.920.970.88
        ≥3 days0.940.960.920.930.970.89
        ≥12 days0.790.970.800.960.840.94
        ≥52 days0.570.980.580.980.620.97
        ≥270 days0.141.000.181.000.261.00
        AUC (SE)0.99 (0.0004)0.97 (0.0008)‡0.97 (0.0009)‡§
    • AUC, area under receiver operating characteristic (ROC) curve for any symptom of DSM-IV alcohol, cannabis, or other drug use disorders; Se, sensitivity; Sp, specificity.

    • Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • ↵* Frequency is coded as days of use in the past month.

    • ↵† Frequency is coded as days of use in the past year.

    • χ2 test for independence (relative to age 12 to 15 years),

    • ↵‡ P < .0001.

    • χ2 test for independence (comparing 16 to 17 and 18 to 20 year olds),

    • ↵§ P < .0001.

    • View popup
    Appendix 8.

    Performance of Substance Use Screens for Identifying Any Symptom of Alcohol, Cannabis, or Other Illicit Drug Use Disorder among Outpatient Subgroup (2013 to 2014)

    Age 12 to 15Age 16 to 17Age 18 to 20
    N = 14,699N = 8353N = 8453
    SeSpSeSpSeSp
    Alcohol
        ≥1 day1.000.881.000.631.000.47
        ≥2 days1.000.921.000.691.000.51
        ≥3 days1.000.931.000.741.000.55
        ≥12 days0.840.970.870.880.950.72
        ≥52 days0.380.990.450.960.630.88
        ≥270 days0.021.000.031.000.031.00
        AUC (SE)0.98 (0.0006)0.92 (0.0013)*0.86 (0.0017)*†
    Cannabis
        ≥1 day1.000.961.000.851.000.79
        ≥2 days1.000.971.000.871.000.82
        ≥3 days1.000.971.000.891.000.84
        ≥12 days0.890.980.950.940.950.91
        ≥52 days0.610.990.700.970.790.96
        ≥270 days0.121.000.191.000.290.99
        AUC (SE)0.99 (0.0002)0.98 (0.0006)*0.97 (0.0007)*†
    Other Illicit Drugs
        ≥1 day1.000.961.000.921.000.87
        ≥2 days0.950.970.950.940.950.90
        ≥3 days0.910.970.890.940.920.92
        ≥12 days0.730.980.730.970.710.95
        ≥52 days0.460.990.440.990.480.98
        ≥270 days0.081.000.111.000.131.00
        AUC (SE)0.99(0.0004)0.97 (0.0008)*0.97 (0.0009)*†
    • AUC, area under receiver operating characteristic (ROC) curve for any symptom of DSM-IV alcohol, cannabis, or other drug use disorders; Se, sensitivity; Sp, specificity.

    • Bolded screening cut-points are shown that have high values of sensitivity and specificity and minimize the number of different age- and substance-specific cut-points.

    • Frequency is coded as days of use in the past year.

    • χ2 test for independence (relative to age 12 to 15 years),

    • ↵* P < .0001.

    • χ2 test for independence (comparing 16–17- and 18–20-year-olds),

    • ↵† P < .0001.

PreviousNext
Back to top

In this issue

The Journal of the American Board of Family     Medicine: 32 (4)
The Journal of the American Board of Family Medicine
Vol. 32, Issue 4
July-August 2019
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author
  • Back Matter (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on American Board of Family Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Identifying Problematic Substance Use in a National Sample of Adolescents Using Frequency Questions
(Your Name) has sent you a message from American Board of Family Medicine
(Your Name) thought you would like to see the American Board of Family Medicine web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
8 + 10 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Identifying Problematic Substance Use in a National Sample of Adolescents Using Frequency Questions
Laura J. Chavez, Katharine A. Bradley, Gwen T. Lapham, Thomas M. Wickizer, Deena J. Chisolm
The Journal of the American Board of Family Medicine Jul 2019, 32 (4) 550-558; DOI: 10.3122/jabfm.2019.04.180284

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Identifying Problematic Substance Use in a National Sample of Adolescents Using Frequency Questions
Laura J. Chavez, Katharine A. Bradley, Gwen T. Lapham, Thomas M. Wickizer, Deena J. Chisolm
The Journal of the American Board of Family Medicine Jul 2019, 32 (4) 550-558; DOI: 10.3122/jabfm.2019.04.180284
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Methods
    • Results
    • Discussion
    • Conclusions
    • Appendix
    • Appendix
    • Appendix
    • Appendix
    • Appendix
    • Appendix
    • Appendix
    • Appendix
    • Notes
    • References
  • Figures & Data
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Implementing Practice Changes in Family Medicine to Enhance Care and Prevent Disease Progression
  • Google Scholar

More in this TOC Section

  • Associations Between Modifiable Preconception Care Indicators and Pregnancy Outcomes
  • Perceptions and Preferences for Defining Biosimilar Products in Prescription Drug Promotion
  • Evaluating Pragmatism of Lung Cancer Screening Randomized Trials with the PRECIS-2 Tool
Show more Original Research

Similar Articles

Keywords

  • Adolescent
  • Cannabis
  • Substance-Related Disorders
  • Surveys and Questionnaires
  • Street Drugs
  • Tobacco

Navigate

  • Home
  • Current Issue
  • Past Issues

Authors & Reviewers

  • Info For Authors
  • Info For Reviewers
  • Submit A Manuscript/Review

Other Services

  • Get Email Alerts
  • Classifieds
  • Reprints and Permissions

Other Resources

  • Forms
  • Contact Us
  • ABFM News

© 2025 American Board of Family Medicine

Powered by HighWire