Abstract
Background
Although mental disorders are significant predictors of educational attainment throughout the entire educational career, most research on mental disorders among students has focused on the primary and secondary school years.
Methods
The World Health Organization World Mental Health Surveys were used to examine the associations of mental disorders with college entry and attrition by comparing college students (n = 1,572) and nonstudents in the same age range (18–22; n = 4,178), including nonstudents who recently left college without graduating (n = 702) based on surveys in 21 countries (4 low/lower-middle income, 5 upper middle-income, 1 lower-middle or upper-middle at the times of two different surveys, and 11 high income). Lifetime and 12-month prevalence and age-of-onset of DSM-IV anxiety, mood, behavioural and substance disorders were assessed with the Composite International Diagnostic Interview.
Results
One-fifth (20.3%) of college students had 12-month DSM-IV/CIDI disorders. 83.1% of these cases had pre-matriculation onsets. Disorders with pre-matriculation onsets were more important than those with post-matriculation onsets in predicting subsequent college attrition, with substance disorders and, among women, major depression the most important such disorders. Only 16.4% of students with 12-month disorders received any 12-month healthcare treatment for their mental disorders.
Conclusions
Mental disorders are common among college students, have onsets that mostly occur prior to college entry, in the case of pre-matriculation disorders are associated with college attrition, and are typically untreated. Detection and effective treatment of these disorders early in the college career might reduce attrition and improve educational and psychosocial functioning.
Keywords: Mental Illness, College, Education, College Dropout, College attrition, Epidemiology
INTRODUCTION
Although prevalence (Costello et al., 2005; Merikangas et al., 2009) and treatment (Fazel et al., 2014a; Fazel et al., 2014b) of mental disorders among elementary and secondary school students has been the subject of considerable attention, less is known about mental disorder prevalence or treatment among college students other than in the United States (Eisenberg et al., 2007; Blanco et al., 2008; Cho et al., 2015; Kendler et al., 2015; Mojtabai et al., 2015). We know somewhat more about the associations of early-onset mental disorders with significant reductions in subsequent educational attainment (Kessler et al., 1995; Fergusson & Horwood, 1998; Johnson et al., 1999; Miech et al., 1999; Woodward & Fergusson, 2001; Fergusson & Woodward, 2002; Fletcher, 2008; Lee et al., 2009; Mojtabai et al., 2015), but this work is limited by either being based on small restricted samples or by being subject to long-term recall bias. Given the importance of an educated workforce for the human capital potential of a country, it would be valuable to know more about five questions: First, what is the prevalence of mental disorders among college students? Second, what proportion of those disorders had onsets prior to college entry? Third, to what extent are disorders with pre-matriculation onsets associated with college entry? Fourth, what is the relative importance of disorders with pre-matriculation and post-matriculation onsets in predicting college attrition? Fifth, what proportion of college students with mental disorders receives treatment? We address these five questions using data from community epidemiological surveys carried out in 21 different countries in the WHO World Mental Health (WMH) Survey Initiative.
METHOD
Samples
The WMH surveys are a cross-national series of community epidemiological surveys using consistent sampling designs, field procedures, and instruments to facilitate pooled cross-national comparative analyses (Kessler & Üstun, 2011). The focus is on prevalence and correlates of common mental disorders. The data reported here come from the 23 WMH surveys carried out in 21 countries that assessed college student status and had a sufficiently large sample to: (i) estimate prevalence among college students ages 18–22; and (ii) estimate and compare prevalence in disaggregated subsamples of nonstudents in the same age range as students, distinguishing college attriters from respondents who never entered college. The 18–22 age range was chosen because the vast majority of college students were in that age range across countries. We excluded college graduates because it was rare to find them in the 18–22 age range.
The surveys were carried out in 21 countries: 5 classified by the World Bank (2012) as low or lower-middle income countries (national surveys in Colombia, Iraq, and Peru, a regional survey in Nigeria, and regional surveys in Beijing-Shanghai and Shenzhen in the People’s Republic of China [PRC]), 6 classified as upper-middle-income countries (national surveys in Bulgaria, Lebanon, Mexico, and Romania and regional surveys in São Paulo Brazil and Medellin Colombia), and 11 classified as high income countries (national surveys in Australia, Belgium, France, Italy, Netherlands, New Zealand, Northern Ireland, Poland, Portugal, Spain, and the United States). The national survey in Colombia was classified as lower-middle income but the regional survey in Medellin Colombia upper-middle income because the World Bank classification of Colombia’s income level changed between the times of the two surveys.
Each WMH survey was based on a probability sample of household residents using a multi-stage clustered area probability sample design. Response rates ranged between 50.4% (Poland) and 97.2% (Colombia) with a weighted mean of 71.4% across surveys. A detailed description of sampling procedures is presented elsewhere (Heeringa et al., 2008). We focus here on all respondents in the age range 18–22 who were either college students (n=1,572), college attriters (n=702), secondary school graduates who never went to college (n=1,571), or people who never completed secondary school (n=1,905). We exclude the small number of 18–22 year olds in the surveys who were college graduates (n=183) because this was an uncommon outcome. In order to increase precision of prevalence comparisons, the joint age-sex distributions of students and nonstudents within each country were standardized to equal the pooled student distribution across all countries combined. All results reported here combine men and women. However, all analyses were also carried out in separate samples of men and women (detailed results are available on request) and important differences in results are noted throughout the text.
Field procedures
Interviews were administered face-to-face in respondent homes (or, in the case of students living in campus group housing, in their student residences) after obtaining informed consent using procedures approved by local Institutional Review Boards. All procedures used complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975 as revised in 2008. The interview schedule was developed in English and translated into other languages using a standardized WHO protocol (Harkness et al., 2008). Interviews were administered in two parts. Part I, administered to all respondents, assessed core DSM-IV mental disorders. Part II assessed additional disorders and correlates. Part II was administered to 100% of Part I respondents who met lifetime criteria for any Part I disorder and a probability subsample of other Part I respondents. Part II respondents were weighted to adjust for differential probabilities of selection into Part II, making estimates of prevalence in the weighted Part II sample identical to those in the Part I sample. The current analysis is based on the Part II sample. More details about WMH sample design and weighting are presented elsewhere (Heeringa et al., 2008).
Measures
Student status
All respondents were asked if they were currently students or had finished their education and how many years of education they had completed. College students were defined as those in the age range 18–22 who had completed secondary school and were currently students (either part-time or full-time). College students were compared to college attriters (i.e., respondents in the same age range who completed some college but did not graduate and were no longer students) and other nonstudents in the same age range with education less than or equal to a secondary school education.
Mental disorders
Mental disorders were assessed with Version 3.0 of the Composite International Diagnostic Interview (CIDI, Kessler & Ustun, 2004), a fully-structured diagnostic interview administered by trained lay interviewers. DSM-IV criteria are used here. The CIDI assessed two lifetime mood disorders (major depressive disorder and broadly-defined bipolar disorder [BPD; including BPD-I, BPD-II, and subthreshold BPD, which was defined using criteria described elsewhere (Kessler et al., 2006), five lifetime anxiety disorders (separation anxiety disorder, panic disorder with or without agoraphobia, generalized anxiety disorder [GAD], phobia [either agoraphobia without a history of panic disorder, social phobia, or specific phobia], posttraumatic stress disorder [PTSD]), four disruptive behaviour disorders (attention-deficit/hyperactivity disorder [ADHD], oppositional-defiant disorder, conduct disorder, intermittent explosive disorder), and four substance disorders (alcohol abuse with or without dependence; alcohol dependence with abuse, drug abuse with or without dependence, drug dependence with abuse). Age-of-onset of each disorder was assessed using special probing techniques shown experimentally to improve recall accuracy (Knäuper et al., 1999). Age-of-onset reports were used to determine whether each respondent had a history of each disorder prior to the typical age of beginning college (i.e., ages 0–17). DSM-IV organic exclusion rules and diagnostic hierarchy rules were used other than for substance abuse, which was defined with or without dependence. As detailed elsewhere (Haro et al., 2006), generally good concordance was found between these CIDI diagnoses and blinded clinical diagnoses based on clinical reappraisal interviews with the SCID (First et al., 1994).
Treatment of 12-month disorders
Respondents who met criteria for any 12-month DSM-IV/CIDI disorder were presented with a list of mental health care providers (e.g., psychiatrist, psychologist, psychotherapist), general medical providers (e.g., general practitioner, cardiologist or other medical specialist, nurse), human services professionals (e.g., social workers, spiritual advisor), alternative therapists-(e.g., herbalist, spiritualist), and self-help groups and asked if they ever sought help from each of these types of individuals or settings “for problems with your emotions, nerves, or your use of alcohol or drugs,” and, if so, how recently they did so. Respondents who reported seeking treatment at any time in the past 12 month were then asked how many visits they made in the past over that time period to each type of individual or setting. Consistent with earlier WMH analyses (Wang et al., 2007), we defined minimally adequate treatment as either (i) making at least 4 visits in the past 12 months to any type of treatment provider, (ii) making at least 2 visits and using any type of medication for their emotional problems, or (iii) still being in treatment at the time of interview.
Analysis methods
All analyses used weighted data. Cross-tabulations were used to estimate 12-month prevalence of each DSM-IV/CIDI disorder as of the time of interview and to decompose prevalence estimates into those with pre-matriculation onsets (i.e., ages 0–17) and post-matriculation onsets (ages 18+), noting that the definition of pre-matriculation disorders was conservative in that virtually of those classified pre-matriculation did, in fact, start before college entry while some unknown number of disorders with onsets after age 17 started before the respondent’s college entry. These estimates were made separately for each disorder pooled across countries among college students, college attriters, and respondents who had never entered college. A second set of cross-tabulations was then estimated for lifetime prevalence of each disorder in the total sample separately among respondents in each of the same subsamples. Logistic regression analysis was used to estimate the odds-ratio of 12-month and lifetime prevalence of each disorder within each of three pairs of subgroups: (i) students vs. respondents who never entered college, (ii) attriters vs. respondents who never entered college, and (iii) students vs. attriters. Logistic regression coefficients and standard errors were exponentiated to produce odds-ratios (ORs) with 95% confidence intervals (95% CIs). Logistic models included dummy control variables for surveys. We also evaluated significance of differences in ORs across countries at different income levels. Statistical significance was consistently evaluated using .05-level two-tailed tests. The design-based Taylor series method (Wolter, 1985) implemented in the SAS software system (SAS Institute Inc., 2010) was used to adjust significance tests for the weighting and clustering of observations. Overall fit of the logistic models was assessed by calculating area under the receiver operating characteristic curve (AUC).
RESULTS
Distribution of students
An average (mean [standard error]) of 30.9% (0.9) of respondents ages 18–22 across surveys were students, with a range between 4.1% (Shenzhen PRC) and 59.0% (Beijing-Shanghai PRC and Medellin Colombia) Aggregate rates were similar for males (32.0%) and females (29.7%). (Detailed results are available on request.)
What is the 12-month prevalence of DSM-IV/CIDI disorders among college students?
Twelve-month prevalence of any DSM-IV/CIDI disorder was 20.3% among college students compared to 25.0% among attriters and 21.4% among other nonstudents. (Table 1) Students also had a lower mean number of overall disorders (23.9/100 persons) than either attriters (39.7/100) or other nonstudents (32.8/100). Anxiety disorders were the most prevalent class of disorders across all groups in the full sample (11.7–14.7%) followed by mood disorders (6.0–9.9%), substance disorders (4.5–6.7%), and behavioral disorders (2.8–5.3%). The most prevalent individual disorders were phobias (9.0–11.1%) and MDD (4.5–7.7%). Logistic models controlling age-sex and survey found that students had significantly higher prevalence than attriters of panic disorder and lower prevalence of behavioral disorders, while students had significantly lower prevalence than other nonstudents of quite a few disorders (GAD, PTSD, any mood disorder, ADHD, oppositional-defiant disorder, drug dependence) as well as of number of disorders.
Table 1.
Pooled 12-month prevalence of DSM-IV/CIDI mental disorders separately among respondents ages 18–22 who were current students, college attriters, and nonstudents in the same age range1
| Students (ST) | Attriters (AT) | Other (OT) | ST:AT2 | ST:OT2 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | OR | (95 % CI) | OR | (95 % CI) | AUC3 | |
|
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| I. Anxiety disorders | |||||||||||
| Separation Anxiety Disorder | 1.3 | (0.7) | 1.5 | (0.5) | 1.2 | (0.2) | 1.2 | (0.3–3.8) | 1.1 | (0.3–3.6) | 0.84 |
| Panic Disorder | 1.2 | (0.3) | 1.1 | (0.3) | 1.4 | (0.2) | 2.6* | (1.1–5.9) | 0.9 | (0.5–1.5) | 0.72 |
| GAD | 0.4 | (0.1) | 0.8 | (0.3) | 0.8 | (0.1) | 0.9 | (0.3–2.5) | 0.5* | (0.3–1.0) | 0.70 |
| Any Phobia | 9.0 | (1.1) | 11.1 | (1.5) | 9.9 | (0.6) | 0.9 | (0.6–1.3) | 0.9 | (0.7–1.2) | 0.68 |
| PTSD | 1.3 | (0.3) | 2.7 | (0.6) | 2.0 | (0.3) | 0.9 | (0.5–1.7) | 0.6* | (0.3–1.0) | 0.83 |
| Any | 11.7 | (1.3) | 14.7 | (1.6) | 12.9 | (0.6) | 1.0 | (0.7–1.4) | 0.9 | (0.7–1.2) | 0.69 |
| II. Mood disorders | |||||||||||
| MDD | 4.5 | (0.5) | 7.7 | (0.9) | 5.1 | (0.4) | 0.7 | (0.5–1.1) | 0.9 | (0.6–1.2) | 0.67 |
| Bipolar | 1.8 | (0.4) | 2.3 | (0.5) | 2.6 | (0.3) | 1.1 | (0.6–2.2) | 0.6 | (0.4–1.0) | 0.75 |
| Any | 6.0 | (0.7) | 9.9 | (1.0) | 7.6 | (0.5) | 0.8 | (0.6–1.1) | 0.7* | (0.6–1.0) | 0.68 |
| III. Behavioral disorders | |||||||||||
| ADHD | 0.4 | (0.2) | 1.5 | (0.7) | 1.0 | (0.1) | 0.4 | (0.1–1.4) | 0.3* | (0.1–0.9) | 0.83 |
| Conduct Disorder | 0.2 | (0.1) | 0.4 | (0.3) | 0.5 | (0.1) | 0.4 | (0.0–4.3) | 0.3 | (0.1–1.4) | 0.86 |
| ODD | 0.2 | (0.1) | 1.0 | (0.5) | 0.6 | (0.1) | 0.3 | (0.1–1.6) | 0.3* | (0.1–0.9) | 0.82 |
| IED | 2.4 | (0.4) | 3.0 | (0.7) | 2.4 | (0.3) | 0.8 | (0.5–1.3) | 0.9 | (0.6–1.3) | 0.81 |
| Any | 2.8 | (0.4) | 5.3 | (1.1) | 3.8 | (0.3) | 0.6* | (0.4–1.0) | 0.7* | (0.5–0.9) | 0.79 |
| IV. Substance disorders | |||||||||||
| Alcohol Abuse | 2.5 | (0.4) | 4.2 | (0.9) | 3.1 | (0.3) | 0.7 | (0.4–1.3) | 0.7 | (0.5–1.0) | 0.77 |
| Alcohol Dependence | 1.4 | (0.4) | 1.6 | (0.5) | 1.5 | (0.2) | 1.1 | (0.5–2.7) | 0.8 | (0.4–1.5) | 0.77 |
| Drug Abuse | 0.7 | (0.2) | 1.3 | (0.5) | 1.0 | (0.2) | 0.7 | (0.2–1.9) | 0.6 | (0.3–1.4) | 0.82 |
| Drug Dependence | 0.2 | (0.1) | 1.1 | (0.4) | 0.9 | (0.2) | 0.3 | (0.1–1.2) | 0.2* | (0.1–0.8) | 0.83 |
| Any | 4.5 | (0.6) | 6.7 | (1.1) | 5.8 | (0.4) | 0.9 | (0.5–1.4) | 0.7* | (0.5–0.9) | 0.78 |
| V. Total disorders | |||||||||||
| Low/lower middle countries | 12.8 | (1.9) | 13.4 | (2.5) | 14.7 | (1.1) | 1.1 | (0.6–1.9) | 0.8 | (0.6–1.2) | 0.64 |
| Upper-middle countries | 21.8 | (4.9) | 31.8 | (6.5) | 21.9 | (1.9) | 0.8 | (0.3–2.0) | 1.0 | (0.5–1.8) | 0.63 |
| High countries | 25.2 | (1.7) | 27.5 | (2.6) | 27.3 | (1.4) | 1.1 | (0.8–1.5) | 0.9 | (0.7–1.1) | 0.66 |
| Total | 20.3 | (1.4) | 25.0 | (2.0) | 21.4 | (0.8) | 1.0 | (0.8–1.3) | 0.9 | (0.7–1.1) | 0.67 |
| (n) | (1,572) | (702) | (3,476) | – | – | – | |||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Based on a pooled within-survey logistic regression model adjusting for between-survey variation in the association of age-sex with student status.
The AUCs were generated from logistic models in which dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an order logistic framework, controlling age-sex and survey.
What proportion of 12-month DSM-IV/CIDI disorders had onsets prior to college entry?
The vast majority (83.1%) of students with 12-month disorders reported pre-matriculation onsets. (Table 2) The only disorder-specific exceptions were that post-matriculation onsets were more common than pre-matriculation onsets among students for 12-month panic disorder (51.8%) and alcohol abuse-dependence (70.0−67.6%) and that post-matriculation onsets characterized sizable minorities of students with 12-month GAD (38.4%), PTSD (41.3%), mood disorders (41.6%), and drug abuse (46.2%). Relative proportions of pre-versus post-matriculation onsets were generally comparable among attriters and other nonstudents as among students (χ21 = 0.0–3.4, p = .92−.06). The two exceptions to this general pattern were that a significantly higher proportion of students (70.0%) than attriters (42.5%; χ21 = 8.6, p = .003) with 12-month alcohol abuse had post-matriculation onsets and a significantly higher proportion of students (16.9%) than nonstudents (12.3%; χ21 = 5.4, p = .020) with any 12-month disorders had at least one such disorder with a post-matriculation onset.
Table 2.
Pooled percentages of 12-month DSM-IV/CIDI disorders with pre-matriculation onsets1 separately among respondents ages 18–22 who were current students, college attriters, and nonstudents2 in the same age range3
| Students (ST) | Attriters (AT) | Other4 (OT) | ST vs. AT | ST vs. OT | ||||
|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | χ2 | χ2 | |
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| I. Anxiety disorders | ||||||||
| Separation Anxiety Disorder | 70.4 | (18.4) | 44.9 | (17.5) | 62.6 | (7.0) | 0.3 | 2.2 |
| Panic Disorder | 48.2 | (12.1) | 75.6 | (13.4) | 84.6 | (4.3) | 0.1 | 0.7 |
| GAD | 61.6 | (12.6) | 48.5 | (17.4) | 76.9 | (6.5) | 0.4 | 2.3 |
| Any Phobia | 95.6 | (1.5) | 89.8 | (3.6) | 97.6 | (0.9) | 0.2 | 1.2 |
| PTSD | 58.7 | (11.2) | 73.8 | (9.3) | 77.4 | (5.2) | 0.0 | 1.5 |
| Any | 91.6 | (1.8) | 86.9 | (3.1) | 93.8 | (1.1) | 0.0 | 1.5 |
| II. Mood disorders | ||||||||
| MDD | 53.7 | (6.2) | 63.5 | (6.3) | 64.9 | (3.4) | 0.5 | 2.9 |
| Bipolar | 61.4 | (10.8) | 58.6 | (11.1) | 61.8 | (4.9) | 0.5 | 0.1 |
| Any | 58.4 | (5.3) | 64.4 | (5.5) | 63.9 | (2.8) | 0.6 | 0.4 |
| III. Behavior disorders | ||||||||
| ADHD | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| Conduct Disorder | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| ODD | 100.0 | (0.0) | 100.0 | (0.0) | 99.0 | (1.0) | – | – |
| IED | 90.5 | (5.7) | 84.3 | (7.5) | 88.2 | (2.8) | 0.1 | 0.0 |
| Any | 92.1 | (4.8) | 92.0 | (4.3) | 94.2 | (1.6) | 0.0 | 0.1 |
| IV. Substance disorders | ||||||||
| Alcohol Abuse | 30.0 | (6.0) | 57.5 | (11.9) | 43.6 | (4.9) | 8.6* | 3.4 |
| Alcohol Dependence | 32.4 | (13.7) | 32.3 | (12.9) | 45.5 | (6.3) | 0.0 | 0.1 |
| Drug Abuse | 53.8 | (15.7) | 52.4 | (19.8) | 61.7 | (7.9) | 1.5 | 1.3 |
| Drug Dependence | 100.0 | (0.0) | 66.4 | (17.3) | 60.8 | (8.5) | – | – |
| Any | 46.6 | (6.7) | 61.2 | (8.5) | 55.8 | (3.6) | 3.3 | 0.9 |
| V. Total disorders | ||||||||
| Any | 83.1 | (2.3) | 83.6 | (3.0) | 87.7 | (1.2) | 0.9 | 5.4* |
Significant at the .05 level.
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
“All other” respondents were defined as those who never entered college whether or not they graduated from secondary school.
Do pre-matriculation onset DSM-IV/CIDI disorders predict college entry
Somewhat smaller proportions of respondents who entered college (25.3%) than those who did not (27.2%; 28.2% of those who graduated from secondary school) met criteria for at least one lifetime pre-matriculation DSM-IV/CIDI disorder. (Table 3) Mean number of disorders was also lower for students (36.2/100) than nonstudents (48.9−46.4/100). Anxiety disorders were consistently much more common across these samples (15.7–19.0%) than either mood (7.9–8.3%), behavioral (6.8–7.8%), or substance (5.0–6.9%) disorders. Phobia was by far the most prevalent individual disorder (11.6–13.9%) followed by major depressive disorder (5.8–6.2%). Logistic regression analysis controlling age-sex and survey found that history of one or more pre-matriculation disorders was associated with significantly reduced odds of subsequent matriculation among respondents who graduated from secondary school (OR 0.8; 95% CI 0.7–1.0), although further analysis (detailed results available on request) showed that this was true only in high income countries (OR 0.7; 95% CI 0.6–0.9). A similar pattern was found for number of disorders. Disaggregated analysis showed that a number of individual anxiety (separation anxiety disorder, panic disorder, PTSD), behavioral (conduct disorder, oppositional-defiant disorder), and substance (alcohol and drug abuse) disorders were implicated in these aggregate associations, with mood disorders the only class of disorders not significant in any of the comparisons. Replication of these analyses separately among males and females (detailed results available on request) revealed that the significant inverse associations between pre-matriculation disorders and subsequent matriculation were for the most part stronger among females than males.
Table 3.
Pooled lifetime prevalence of pre-matriculation onset DSM-IV/CIDI mental disorders1 separately among respondents ages 18–22 who entered college (both current students and attriters), secondary school graduates in the same age range who never entered college, and all other respondents2 in the same age range3
| College entrants (CE)
|
Other respondents who were secondary school…
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Graduates (GR) | Total4 | CE: GR5 | CE: Total5 | |||||||||
| % | (se) | % | (se) | % | (se) | OR | (95% CI) | AUC | OR | (95% CI) | AUC | |
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| I. Anxiety disorders | ||||||||||||
| Separation Anxiety Disorder | 2.4 | 0.3 | 4.2 | 0.5 | 4.0 | 0.4 | 0.6 | (0.3–1.1) | 0.85 | 0.5* | (0.3–0.9) | 0.85 |
| Panic Disorder | 1.1 | 0.2 | 1.4 | 0.2 | 1.4 | 0.2 | 0.6* | (0.3–1.0) | 0.70 | 0.6 | (0.3–1.1) | 0.69 |
| GAD | 0.9 | 0.2 | 1.2 | 0.2 | 1.2 | 0.2 | 0.6 | (0.3–1.2) | 0.75 | 0.6 | (0.3–1.1) | 0.75 |
| Any Phobia | 11.6 | 0.7 | 13.9 | 0.8 | 12.5 | 0.6 | 0.8 | (0.6–1.0) | 0.66 | 0.9 | (0.7–1.1) | 0.67 |
| PTSD | 1.9 | 0.3 | 2.7 | 0.4 | 2.4 | 0.3 | 0.6* | (0.3–1.0) | 0.83 | 0.6* | (0.4–0.9) | 0.83 |
| Any | 15.7 | 0.8 | 19.0 | 1.0 | 17.3 | 0.7 | 0.8* | (0.6–1.0) | 0.69 | 0.8 | (0.7–1.0) | 0.69 |
| II. Mood disorders | ||||||||||||
| MDD | 5.9 | 0.5 | 6.2 | 0.4 | 5.8 | 0.4 | 0.9 | (0.6–1.1) | 0.69 | 0.8 | (0.6–1.1) | 0.69 |
| Bipolar | 2.1 | 0.3 | 2.3 | 0.4 | 2.4 | 0.3 | 0.8 | (0.5–1.4) | 0.74 | 0.8 | (0.5–1.2) | 0.74 |
| Any | 7.9 | 0.5 | 8.3 | 0.6 | 8.1 | 0.5 | 0.8 | (0.6–1.1) | 0.70 | 0.8 | (0.6–1.0) | 0.70 |
| III. Behavioral disorders | ||||||||||||
| ADHD | 2.1 | 0.3 | 2.0 | 0.4 | 2.0 | 0.3 | 1.1 | (0.6–1.8) | 0.85 | 0.8 | (0.5–1.2) | 0.84 |
| Conduct Disorder | 1.6 | 0.3 | 2.9 | 0.4 | 2.8 | 0.3 | 0.5* | (0.3–0.8) | 0.88 | 0.4* | (0.2–0.6) | 0.88 |
| ODD | 2.3 | 0.3 | 3.1 | 0.4 | 2.9 | 0.3 | 0.7 | (0.4–1.2) | 0.86 | 0.6* | (0.4–0.9) | 0.85 |
| IED | 3.1 | 0.5 | 2.6 | 0.3 | 2.7 | 0.3 | 1.3 | (0.9–1.9) | 0.83 | 1.0 | (0.8–1.4) | 0.83 |
| Any | 6.8 | 0.6 | 7.8 | 0.7 | 7.5 | 0.5 | 0.9 | (0.6–1.2) | 0.83 | 0.7* | (0.6–0.9) | 0.82 |
| IV. Substance disorders | ||||||||||||
| Alcohol Abuse | 2.4 | 0.3 | 3.5 | 0.4 | 4.0 | 0.4 | 0.6* | (0.4–0.9) | 0.78 | 0.4* | (0.3–0.6) | 0.78 |
| Alcohol Dependence | 0.8 | 0.2 | 0.8 | 0.2 | 1.3 | 0.2 | 1.0 | (0.5–2.0) | 0.82 | 0.6 | (0.3–1.1) | 0.82 |
| Drug Abuse | 1.7 | 0.3 | 1.9 | 0.3 | 2.3 | 0.3 | 0.7 | (0.4–1.2) | 0.83 | 0.6* | (0.4–0.9) | 0.82 |
| Drug Dependence | 1.0 | 0.2 | 1.0 | 0.2 | 1.2 | 0.2 | 0.9 | (0.4–1.8) | 0.81 | 0.7 | (0.4–1.2) | 0.80 |
| Any | 5.0 | 0.5 | 5.7 | 0.5 | 6.9 | 0.4 | 0.7* | (0.5–1.0) | 0.80 | 0.5* | (0.4–0.7) | 0.80 |
| V. Total disorders | ||||||||||||
| Low/lower middle countries | 15.8 | (1.8) | 19.9 | (2.0) | 16.5 | (1.2) | 0.8 | (0.5–1.1) | 0.67 | 0.9 | (0.6–1.3) | 0.67 |
| Upper-middle countries | 29.5 | (4.5) | 28.9 | (3.5) | 29.1 | (2.1) | 1.0 | (0.6–1.9) | 0.63 | 0.9 | (0.5–1.4) | 0.63 |
| High countries | 29.3 | (1.6) | 33.2 | (1.8) | 35.7 | (1.4) | 0.7* | (0.7–1.0) | 0.70 | 0.7* | (0.5–0.8) | 0.70 |
| Total | 25.3 | 1.0 | 28.2 | 1.1 | 27.2 | 0.9 | 0.8* | (0.7–1.0) | 0.70 | 0.8* | (0.6–0.9) | 0.70 |
| (n) | (2,274) | (1,571) | (3,476) | – | – | |||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
“All other” respondents were defined as those who never entered college whether or not graduated from secondary school.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Secondary school graduates or non-graduates.
Based on a single pooled logistic model for each row in which separate dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an ordered logistic framework, controlling age-sex and survey.
Do pre- and post-matriculation onset DSM-IV/CIDI disorders predict college attrition?
A significantly lower proportion of students than attriters had one or more pre-matriculation onset mental disorders (22.9% versus 30.3%; t=2.7, p=.007). This pattern is consistent with pre-matriculation disorders predicting subsequent attrition among college students, although this difference became nonsignificant in the logistic regression analysis controlling age-sex and survey (OR 0.9; 95% CI 0.6–1.1). (Table 4) However, a comparable model that looked at number of pre-matriculation disorders (0.1,2.3,4+) found a significantly lower odds of being a student than attriter (OR 0.8; 95% CI 0–6–1.0). The individual disorders significantly implicated in this association were all substance disorders. The association was significant only in high income countries. Replication of these analyses separately among males and females (detailed results available on request) showed that pre-matriculation alcohol and drug abuse predicted attrition among males (with ORs of 0.2–0.4), while MDD, alcohol abuse, and drug dependence were significant among females (with ORs of 0.2–0.6).
Table 4.
Pooled lifetime prevalence of pre-matriculation1 and post-matriculation2 onset DSM-IV/CIDI mental disorders separately among respondents ages 18–22 who were current students or college attriters3
| Students (ST)
|
Attriters (AT)
|
Students vs. Attriters
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pre- | Post-4 | Pre- | Post-4 | Pre-5 | Post-6 | |||||||||
| % | (se) | % | (se) | % | (se) | % | (se) | OR | (95 % CI) | AUC | OR | (95 % CI) | AUC | |
|
|
|
|
|
|
|
|||||||||
| I. Anxiety disorders | ||||||||||||||
| Separation Anxiety Disorder | 2.7 | (0.8) | 0.7 | (0.2) | 1.8 | (0.5) | 1.1 | (0.3) | 1.5 | (0.6–3.5) | 0.85 | 1.2 | (0.6–2.5) | 0.89 |
| Panic Disorder | 1.1 | (0.3) | 0.6 | (0.3) | 1.1 | (0.3) | 0.3 | (0.2) | 1.8 | (0.8–3.9) | 0.69 | 5.0* | (1.1–22.1) | 0.82 |
| GAD | 0.7 | (0.2) | 0.2 | (0.1) | 1.3 | (0.5) | 0.9 | (0.3) | 0.7 | (0.3–1.9) | 0.75 | 0.6 | (0.2–1.4) | 0.79 |
| Any Phobia | 11.3 | (1.1) | 0.5 | (0.1) | 12.3 | (1.5) | 1.3 | (0.5) | 1.0 | (0.7–1.4) | 0.67 | 0.7 | (0.2–2.6) | 0.74 |
| PTSD | 1.1 | (0.3) | 0.6 | (0.1) | 3.6 | (0.7) | 0.7 | (0.3) | 0.5 | (0.3–1.1) | 0.83 | 2.0 | (0.8–4.8) | 0.83 |
| Any | 15.1 | (1.4) | 1.5 | (0.2) | 16.9 | (1.7) | 2.8 | (0.6) | 1.0 | (0.8–1.4) | 0.69 | 1.1 | (0.6–2.0) | 0.77 |
| II. Mood disorders | ||||||||||||||
| MDD | 4.8 | (0.5) | 3.4 | (0.5) | 8.3 | (1.1) | 4.0 | (0.8) | 0.7 | (0.5–1.1) | 0.69 | 1.4 | (0.8–2.3) | 0.72 |
| Bipolar | 1.9 | (0.4) | 0.8 | (0.3) | 2.6 | (0.6) | 1.2 | (0.4) | 0.8 | (0.4–1.5) | 0.74 | 1.2 | (0.4–3.2) | 0.79 |
| Any | 6.6 | (0.6) | 3.9 | (0.6) | 10.8 | (1.3) | 5.2 | (0.9) | 0.7 | (0.5–1.1) | 0.70 | 1.3 | (0.8–2.0) | 0.72 |
| III. Behavioral disorders | ||||||||||||||
| ADHD | 1.9 | (0.4) | 0.0 | (0.0) | 2.6 | (0.7) | 0.0 | (0.0) | 1.2 | (0.5–2.6) | 0.84 | –7 | – | – |
| Conduct Disorder | 1.2 | (0.3) | 0.0 | (0.0) | 2.4 | (0.7) | 0.0 | (0.0) | 0.8 | (0.3–1.7) | 0.88 | –7 | – | – |
| ODD | 1.9 | (0.4) | 0.0 | (0.0) | 3.3 | (0.7) | 0.0 | (0.0) | 0.8 | (0.4–1.5) | 0.85 | –7 | – | – |
| IED | 2.9 | (0.5) | 0.2 | (0.1) | 3.7 | (0.8) | 0.5 | (0.2) | 0.8 | (0.5–1.4) | 0.83 | 0.8 | (0.2–3.7) | 0.88 |
| Any | 5.9 | (0.7) | 0.2 | (0.2) | 8.7 | (1.2) | 0.5 | (0.3) | 0.9 | (0.6–1.3) | 0.82 | 0.8 | (0.2–4.6) | 0.88 |
| IV. Substance disorders | ||||||||||||||
| Alcohol Abuse | 1.5 | (0.2) | 2.6 | (0.4) | 4.5 | (0.9) | 5.6 | (1.1) | 0.4* | (0.2–0.7) | 0.78 | 0.9 | (0.5–1.5) | 0.79 |
| Alcohol Dependence | 0.6 | (0.2) | 1.1 | (0.3) | 1.4 | (0.4) | 1.7 | (0.6) | 0.4 | (0.1–1.0) | 0.83 | 1.3 | (0.5–3.2) | 0.79 |
| Drug Abuse | 0.9 | (0.3) | 0.4 | (0.1) | 3.4 | (0.7) | 1.2 | (0.5) | 0.3* | (0.1–0.6) | 0.84 | 0.8 | (0.3–2.1) | 0.77 |
| Drug Dependence | 0.4 | (0.2) | 0.1 | (0.1) | 2.2 | (0.6) | 0.6 | (0.3) | 0.2* | (0.1–0.6) | 0.83 | 0.5 | (0.1–2.3) | 0.81 |
| Any | 3.2 | (0.5) | 3.5 | (0.5) | 8.8 | (1.1) | 7.7 | (1.2) | 0.4* | (0.3–0.6) | 0.80 | 0.9 | (0.5–1.5) | 0.79 |
| V. Total disorders | ||||||||||||||
| Low/lower middle countries | 15.4 | (2.2) | 4.8 | (1.3) | 17.3 | (2.9) | 6.9 | (2.6) | 0.9 | (0.5–1.6) | 0.67 | 1.2 | (0.4–3.7) | 0.74 |
| Upper-middle countries | 26.8 | (5.7) | 2.3 | (0.9) | 38.8 | (6.5) | 9.4 | (4.1) | 0.8 | (0.3–1.9) | 0.63 | 0.6 | (0.1–2.4) | 0.72 |
| High countries | 26.9 | (1.8) | 10.3 | (1.3) | 32.9 | (2.8) | 12.8 | (2.1) | 0.8 | (0.61.1) | 0.70 | 1.3 | (0.8–2.1) | 0.70 |
| Total | 22.9 | (1.6) | 6.7 | (0.7) | 30.3 | (2.1) | 10.9 | (1.5) | 0.9 | (0.6–1.1) | 0.70 | 1.2 | (0.8–1.8) | 0.73 |
| (n) | (1,572) | –8 | (702) | –9 | ||||||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation among students varies across disorders from a low of 1,336 who had no pre-matriculation history of specific phobias to a high of 1,563 who had no pre-matriculation history of drug dependence, with a median sample size of 1,548. The sample size for having no pre-matriculation history of any disorder is 1,102 among students. Among attriters, the sample size for estimating prevalence of post-matriculation onsets for respondents who did not have a pre-matriculation history of the disorder varies from a low of 574 among attriters who had no pre-matriculation history of specific phobias to a high of 690 among attriters who had no pre-matriculation history of generalized anxiety disorder, with a median sample size of 676. The sample size for having no pre-matriculation history of any disorder is 429 among attriters.
Based on a pooled logistic model in which a dummy variable for the pre-matriculation disorder in the row heading or, in the case of Part VI, a count of number of pre-matriculation disorders, was used to predict whether the respondent was a student (coded 1) or an attriter (coded 0) within the sample of respondents who entered college, controlling age-sex and survey.
Based on a pooled logistic model in which a dummy variable for the post-matriculation disorder in the row heading among respondents who did not have that disorder prior to matriculation or, in the case of Part VI, a count of number of pre-matriculation disorders in the total sample, was used to predict whether the respondent was a student (coded 1) or an attriter (coded 0) within the sample of respondents who entered college, controlling age-sex and survey.
There were no post-matriculation onsets of these disorders by definition, as the CIDI required onsets before age 8 (ADHD) or 18 (Conduct Disorder, Oppositional Defiant Disorder).
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 653 among male students and 683 among female students who had no pre-matriculation history of specific phobias to a high of 742 among male students and 821 among female students who had no pre-matriculation history of drug dependence. The sample size for having no pre-matriculation history of any disorder is 544 among male students and 558 among female students.
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 239 among male attriters and 335 among female attriters who had no pre-matriculation history of specific phobias to a high of 267 among male attriters and 425 among female attriters who had no pre-matriculation history of panic disorder. The sample size for having no pre-matriculation history of any disorder is 170 among male attriters and 259 among female attriters.
A significantly lower proportion of students than attriters also had one or more post-matriculation onset disorders (6.7% versus 10.9%; t=2.5, p=.006), but again this difference became nonsignificant in the logistic regression analysis (OR 1.2; 95% CI 0.8–1.8). A parallel model for number of disorders was also insignificant. Logistic regression found only one individual disorder, panic disorder, with significantly different odds of post-matriculation onset among attriters compared to students, but in this case the odds were significantly higher, not lower, among students than attriters (OR 5.0; 95% CI 1.1–22.1). Replication separately among males and females (detailed results available on request) showed that the pattern differed by sex, with the significantly elevated odds of post-matriculation panic disorder among students compared to attriters confined to males and the only significant OR among females involving post-matriculation onset GAD being lower among students than attriters (OR 0.2; 95% CI 0.1–0.5).
What proportion of students with 12-month DSM-IV/CIDI disorders receive treatment?
Roughly one-sixth (16.4%) of students with 12-month DSM-IV/CIDI disorders received minimally adequate treatment for these disorders in the 12 months before interview. (Table 5) This treatment rate was not significantly different from that of attriters (16.6%; χ21 = 0.8, p = .37) or other nonstudents (10.6%; χ21 = 2.8, p = .09). However, the treatment rate of students decreased monotonically with country income level, from 23.1% in high income countries, to 11.4% in upper-middle and 6.7% in lower-middle/low income countries (χ22 = 7.4, p = .025).
Table 5.
Pooled proportions of respondents with 12-month DSM-IV/CIDI mental disorders who received minimally adequate treatment for these disorders in the 12 months before interview by country income level separately among respondents who were current students, college attriters, and nonstudents in the same age range1
| Students | Attriters | Others | |||||||
|---|---|---|---|---|---|---|---|---|---|
| % | (se) | (n) | % | (se) | (n) | % | (se) | (n) | |
|
|
|
|
|||||||
| Low/lower-middle | 6.7 | (3.9) | (79) | 2.2 | (2.2) | (28) | 6.1 | (2.8) | (206) |
| Upper-middle | 11.4 | (5.1) | (60) | 9.4 | (4.9) | (30) | 8.5 | (2.2) | (237) |
| High | 23.1 | (3.4) | (128) | 21.5 | (4.7) | (123) | 15.8 | (2.2) | (260) |
| Total | 16.4 | (2.4) | (267) | 16.6 | (3.3) | (181) | 10.6 | (1.4) | (703) |
| χ22ε | 7.4* | 7.9* | 6.7* | ||||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
DISCUSSION
We presented here the first large-scale cross-national investigation of 12-month and lifetime prevalence of mental disorders among college students, the associations of pre- and post-matriculation mental disorders with college matriculation and attrition, and 12-month treatment of mental disorders among college students. The results showed that a substantial proportion of college students have 12-month DSM-IV/CIDI disorders, that the vast majority of cases had pre-matriculation onsets, that pre-matriculation disorders are associated with both reduced odds of college matriculation and elevated odds of attrition, and that only a small minority of college students receive even minimally adequate treatment for their mental disorders.
In considering intervention possibilities, an important question unanswered by our results is whether the pre-matriculation disorders we found to be associated with attrition are stable. This question arises because we made a great many comparisons in the analyses, leading to the possibility that some of the associations judged to be significant were false positives. Replication is needed to evaluate this issue. This replication would ideally be carried out in a prospective sample that assessed student at the time of college entry and follows them through their college careers to evaluate the importance of -matriculation predictors of college outcomes.
Assuming that this replication supports our finding that pre-matriculation mental disorders are associated with attrition, a second important unanswered question is whether these disorders are causal risk factors or only risk markers of attrition (Kraemer et al., 1997). Our findings that pre-matriculation substance disorders (males and females) and MDD (females) predicted subsequent attrition were also found in previous epidemiological studies (Kessler et al., 1995; Eisenberg et al., 2009; Hunt et al., 2010; Mojtabai et al., 2015), but none of these studies provided any evidence that these disorders are causes rather than non-causal correlates. It is plausible to think that the associations are non-causal because risk factors for adolescent substance (Kilpatrick et al., 2000) and mood (Lewinsohn et al., 1998; Costello et al., 2002) disorders have many similarities with risk factors for college attrition (Ishitani & DesJardins, 2002; Stratton et al., 2006; Hartley, 2010), such as high stress, interpersonal discord, and diminished social support, none of which we controlled in our analyses. To the extent that these (or other) joint predictors account for the associations of pre-matriculation mental disorders with college attrition, interventions to detect and treat these pre-existing disorders among entering freshmen would not reduce subsequent attrition. On the other hand, to the extent that the pre-matriculation disorders associated with college attrition represent common causal pathways for many of the more traditional predictors of attrition documented by educational researchers, interventions focused on college entrants with these pre-existing disorders might be of great value in reducing attrition.
There is no definitive way to adjudicate between these competing possibilities with the data presented here. A definitive evaluation would require experimental intervention where students with pre-matriculation disorders were detected and randomized either into best-practices treatments or usual care. Such an intervention would be most feasible in high income countries, where access to outpatient mental health treatment in high, even though there is wide variation in the number of outpatient mental health workers per 100,000 people in the populations of these countries (e.g., 23 in Portugal; 40–46 in the Netherlands, Poland and Spain; 125 in the U.S.; 158 in France) (World Health Organization, 2015). Given the much lower availability of outpatient mental health treatment providers in upper-middle and lower-middle/low income (e.g., 9.5/100,000 in Mexico and 0.9/100,000 in Nigeria), it seems unrealistic to think that resources would be available to provide outpatient treatment for college students in such countries.
In carrying out such an intervention, a question would arise whether the intervention should be limited to active disorders or also include relapse prevention for students with lifetime disorders that were not active at the time of matriculation. The results presented here are moot on this point because the WMH surveys did not collect data on whether disorders with pre-matriculation onsets were active or remitted at the time of college entry. However, it is noteworthy that 12-month prevalence of the pre-matriculation lifetime disorders associated with subsequent attrition was generally not more common among attriters than students. If pre-matriculation lifetime disorders were causal risk factors rather than risk markers for subsequent attrition, we would expect that 12-month presence of these same disorders would predict subsequent attrition, in which case our failure to find higher 12-month prevalence among attriters than students might be taken as evidence that these pre-matriculation disorders were risk markers rather than causal risk factors. It is important to recognize, though, that questions about disorder recency were not sufficiently specific in the WMH surveys to allow us to determine prevalence in the 12-months prior to attrition. As a result, our failure to find higher 12-month prevalence among attriters than students could be due to the consequences of attrition (e.g., being dismissed from college leading to a reduction in the heavy substance use that led to the dismissal). A similar kind of reciprocal influence might account for the fact that disorders with post-matriculation onsets were not more common among attriters than students.
Several limitations of our study are noteworthy. Some of these involve sampling. Perhaps the most obvious one is that the number of college students in the WMH surveys was too small to support separate analyses in each country. We carried out some cross-national analyses comparing results in high, upper-middle, and lower-middle/low income countries, but this level of disaggregation was less complete than we would have been preferred. The small sample sizes also resulted in wide confidence intervals of estimates, with many of the odds-ratios considered significant having confidence intervals where one bound was very close to 1.0 and in a number of cases rounded to 1.0 with a single decimal of precision. Another sampling-related limitation is that not all WMH surveys included group housing in their sample frames. This means that these surveys under-represented students living in campus dormitories or fraternity-sorority houses. Most WMH surveys also failed to distinguish between students living in off-campus housing with their families versus with roommates. These sample limitation have the potential to be important in that some mental disorders (e.g., separation anxiety disorder, substance disorders) are likely to be higher among students living away from home and possibly more so in group housing than elsewhere.
Another set of limitation involves the assessment of mental disorders. Lifetime mental disorders and ages-of-onset were assessed retrospectively. Although the WMH assessment used procedures shown experimentally to improve accuracy of recall (Scott et al., 2014), there are likely to be downward biases in reports of lifetime prevalence and possibly also in dating age-of-onset of disorders due to recall errors. It is unclear whether these biases would be different for students, attriters, and respondents who never entered college. In addition, diagnoses were based on fully-structured lay-administered interviews rather than semi-structured clinical interviews, although the WMH clinical appraisal data are reassuring (Haro et al., 2006). In addition, we assessed only prevalence, not severity, of disorders. This omission could be important given that other research has shown that educational attainment is associated not only with prevalence but also severity of mental disorders (Ten Have et al., 2013).
A third set of limitations involves the imprecision in our information on timing of disorder onset, matriculation, and attrition. This imprecision made it impossible for us to draw firm distinctions between mental disorders that occurred before versus after matriculation or, in the case of disorders with clear post-matriculation onsets, those that occurred before versus subsequent to attrition. Importantly, we dealt with this limitation by erring on the side of being conservative by considering only disorders with onsets prior to age 18 as being pre-matriculation disorders. As a result, correction of this problem would only lead to an increase in the strength of our finding that the vast majority of the disorders of college students are pre-matriculation disorders.
Despite these limitations, our results are clear in showing that mental disorders are common among college students around the world, that the vast majority of these disorders have pre-matriculation onsets (which, if anything, were under-estimated by our lack of precision in the dating of matriculation), and that pre-matriculation mental disorders are associated both with failure to enter college and with attrition. We also found that only a small minority of college students with mental disorders receive even minimally adequate treatment and that the treatment rate is even lower in low-income countries. These results could have important human capital implications that would make it cost-effective from a societal perspective to invest in screening and increased treatment of college student mental disorders.
Acknowledgments
The WHO World Mental Health Survey Collaborators are Tomasz Adamowski, PhD, MD, Sergio Aguilar-Gaxiola, MD, PhD, Ali Al-Hamzawi, MD, Mohammad Al-Kaisy, MD, Abdullah Al Subaie, FRCP, Jordi Alonso, MD, PhD, Yasmin Altwaijri, MS, PhD, Laura Helena Andrade, MD, PhD, Lukoye Atwoli, MD, Randy Auerbach, PhD, William Axinn, PhD, Corina Benjet, PhD, Guilherme Borges, ScD, Robert Bossarte, PhD, Evelyn Bromet, PhD, Ronny Bruffaerts, PhD, Brendan Bunting, PhD, Ernesto Caffo, MD, Jose Miguel Caladas de Almeida, MD, PhD, Graca Cardoso, MD, PhD, Stephanie Chardoul, Somnath Chatterji, MD, Alexandre Chiavegatto Filho, PhD, Pim Cuijpers, PhD, Louisa Degenhardt, PhD, Giovanni de Girolamo, MD, Ron deGraaf, MS, PhD, Peter deJonge, PhD, Koen Demyttenaere, MD, PhD, David Ebert, PhD, Sara Evans-Lacko, PhD, John Fayyad, MD, Marina Piazza Ferrand, DSc, MPH, Fabian Fiestas, MD, PhD, Silvia Florescu, MD, PhD, Barbara Forresi, PhD, Sandro Galea, DrPH,MD,MPH, Laura Germine, PhD, Stephen Gilman, ScD, Dirgha Ghimire, PhD, Oye Gureje, MD, PhD, Josep Maria Haro, MD, MPH, PhD, Yanling He, MD, Hristo Hinkov, MD, Chi-yi Hu, PhD, MD, Yueqin Huang, MD, MPH, PhD, Aimee Nasser Karam, PhD, Elie G. Karam, MD, Norito Kawakami, MD, PhD, Andrzej Kiejna, PhD,MD, Karestan Koenen, PhD, Viviane Kovess-Masfety, MS, MD, PhD, Luise Lago, PhD, Carmen Lara, MD, PhD, Sing Lee, PhD, Jean-Pierre Lepine, MD, Itzhak Levav, MD, Daphna Levinson, PhD, Zhaorui Liu, MD, MPH, Silvia Martins, MD, PhD, Herbert Matschinger, PhD, John McGrath, PhD, Katie McLaughlin, PhD, Maria Elena Medina-Mora, PhD, Zeina Mneimneh, PhD, MPH, Jacek Moskalewicz, DrPH, Samuel Murphy, DrPH, Fernando Navarro-Mateu, MD, PhD, Matt Nock, PhD, Siobhan O’Neill, PhD, Mark Oakley-Browne, MB, ChB, PhD, FRANZCP, J. Hans Ormel, PhD, Beth-Ellen Pennell, MA, Stephanie Pinder-Amaker, PhD, Patryk Piotrowski, MD, PhD, Jose Posada-Villa, MD, Ayelet Ruscio, PhD, Kate Scott, PhD, Vicki Shahly, PhD, Derrick Silove, PhD, Tim Slade, PhD, Jordan Smoller, ScD,MD, Dan J Stein, MBA,MSc,PhD, Amy Street, PhD, Hisateru Tachimori, PhD, Nezar Taib, MS, Margreet ten Have, PhD, Graham Thornicroft, MD, Yolanda Torres de Galvis, MPH, Maria Carmen Viana, MD, PhD, Gemma Vilagut, MS, Elisabeth Wells, PhD, David R. Williams, MPH, PhD, Michelle Williams, ScD, Bogdan Wojtyniak, ScD, Alan Zaslavsky, PhD.
The World Health Organization World Mental Health Survey collaborators are Tomasz Adamowski, PhD, MD, Sergio Aguilar-Gaxiola, MD, PhD, Ali Al-Hamzawi, MD, Mohammad Al-Kaisy, MD, Abdullah Al Subaie, MBBS, FRCP, Jordi Alonso, MD, PhD, Yasmin Altwaijri, MS, PhD, Laura Helena Andrade, MD, PhD, Lukoye Atwoli, MD, Randy P. Auerbach, PhD, William G. Axinn, PhD, Corina Benjet, PhD, Guilherme Borges, ScD, Robert M. Bossarte, PhD, Evelyn J. Bromet, PhD, Ronny Bruffaerts, PhD, Brendan Bunting, PhD, Ernesto Caffo, MD, Jose Miguel Caldas de Almeida, MD, PhD, Graca Cardoso, MD, PhD, Alfredo H. Cia, MD, Stephanie Chardoul, Somnath Chatterji, MD, Alexandre Chiavegatto Filho, PhD, Pim Cuijpers, PhD, Louisa Degenhardt, PhD, Giovanni de Girolamo, MD, Ron de Graaf, MS, PhD, Peter de Jonge, PhD, Koen Demyttenaere, MD, PhD, David D. Ebert, PhD, Sara Evans-Lacko, PhD, John Fayyad, MD, Fabian Fiestas, MD, PhD, Silvia Florescu, MD, PhD, Barbara Forresi, PhD, Sandro Galea, DrPH, MD, MPH, Laura Germine, PhD, Stephen E. Gilman, ScD, Dirgha J. Ghimire, PhD, Meyer D. Glantz, PhD, Oye Gureje, PhD, DSc, FRCPsych, Josep Maria Haro, MD, MPH, PhD, Yanling He, MD, Hristo Hinkov, MD, Chi-yi Hu, PhD, MD, Yueqin Huang, MD, MPH, PhD, Aimee Nasser Karam, PhD, Elie G. Karam, MD, Norito Kawakami, MD, DMSc, Ronald C. Kessler, PhD, Andrzej Kiejna, MD, PhD, Karestan C. Koenen, PhD, Viviane Kovess-Masfety, MSc, MD, PhD, Luise Lago, PhD, Carmen Lara, MD, PhD, Sing Lee, PhD, Jean-Pierre Lepine, MD, Itzhak Levav, MD, Daphna Levinson, PhD, Zhaorui Liu, MD, MPH, Silvia S. Martins, MD, PhD, Herbert Matschinger, PhD, John J. McGrath, PhD, Katie A. McLaughlin, PhD, Maria Elena Medina-Mora, PhD, Zeina Mneimneh, PhD, MPH, Jacek Moskalewicz, DrPH, Samuel D. Murphy, DrPH, Fernando Navarro-Mateu, MD, PhD, Matthew K. Nock, PhD, Siobhan O’Neill, PhD, Mark Oakley-Browne, MB, ChB, PhD, J. Hans Ormel, PhD, Beth-Ellen Pennell, MA, Marina Piazza, MPH, ScD, Stephanie Pinder-Amaker, PhD, Patryk Piotrowski, MD, PhD, Jose Posada-Villa, MD, Ayelet M. Ruscio, PhD, Kate M. Scott, PhD, Vicki Shahly, PhD, Derrick Silove, PhD, Tim Slade, PhD, Jordan W. Smoller, ScD, MD, Juan Carlos Stagnaro, MD, PhD, Dan J. Stein, MBA, MSc, PhD, Amy E. Street, PhD, Hisateru Tachimori, PhD, Nezar Taib, MS, Margreet ten Have, PhD, Graham Thornicroft, PhD, Yolanda Torres, MPH, Maria Carmen Viana, MD, PhD, Gemma Vilagut, MS, Elisabeth Wells, PhD, David R. Williams, MPH, PhD, Michelle A. Williams, ScD, Bogdan Wojtyniak, ScD, Alan M. Zaslavsky, PhD.
Financial Support
The World Health Organization World Mental Health (WMH) Survey Initiative is supported by the National Institute of Mental Health (NIMH; R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, GlaxoSmithKline, and Bristol-Myers Squibb. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. None of the funders had any role in the design, analysis, interpretation of results, or preparation of this paper. This report was prepared under the auspices of the World Health Organization ICD-11 Chapter 5 (Mental and Behavioural Disorders) epidemiology working group, which is co-chaired by Chatterji and Kessler. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of the sponsoring organizations, agencies, or governments.
The 2007 Australian National Survey of Mental Health and Wellbeing is funded by the Australian Government Department of Health and Ageing. The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The Bulgarian Epidemiological Study of common mental disorders EPIBUL is supported by the Ministry of Health and the National Center for Public Health Protection. The Chinese World Mental Health Survey Initiative is supported by the Pfizer Foundation. The Shenzhen Mental Health Survey is supported by the Shenzhen Bureau of Health and the Shenzhen Bureau of Science, Technology, and Information. The Colombian National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection. The Mental Health Study Medellín – Colombia was carried out and supported jointly by the Center for Excellence on Research in Mental Health (CES University) and the Secretary of Health of Medellín. The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123, and EAHC 20081308), (the Piedmont Region (Italy), Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnología, Spain (SAF 2000-158-CE), Departament de Salut, Generalitat de Catalunya, Spain, Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP), and other local agencies and by an unrestricted educational grant from GlaxoSmithKline. Implementation of the Iraq Mental Health Survey (IMHS) and data entry were carried out by the staff of the Iraqi MOH and MOP with direct support from the Iraqi IMHS team with funding from both the Japanese and European Funds through United Nations Development Group Iraq Trust Fund (UNDG ITF). The Lebanese National Mental Health Survey (L.E.B.A.N.O.N.) is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health/Fogarty International Center (R03 TW006481-01), Sheikh Hamdan Bin Rashid Al Maktoum Award for Medical Sciences, anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from AstraZeneca, Eli Lilly, GlaxoSmithKline, Hikma Pharmaceuticals, Janssen Cilag, Lundbeck, Novartis, and Servier. The Mexican National Comorbidity Survey (MNCS) is supported by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (CONACyT-G30544- H), with supplemental support from the PanAmerican Health Organization (PAHO). Corina Benjet has received funding from the (Mexican) National Council of Science and Technology (grant CB-2010-01-155221) Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS) is supported by the New Zealand Ministry of Health, Alcohol Advisory Council, and the Health Research Council. The Nigerian Survey of Mental Health and Wellbeing (NSMHW) is supported by the WHO (Geneva), the WHO (Nigeria), and the Federal Ministry of Health, Abuja, Nigeria. The Northern Ireland Study of Mental Health was funded by the Health & Social Care Research & Development Division of the Public Health Agency. The Peruvian World Mental Health Study was funded by the National Institute of Health of the Ministry of Health of Peru. The Polish project Epidemiology of Mental Health and Access to Care –EZOP Poland was carried out by the Institute of Psychiatry and Neurology in Warsaw in consortium with Department of Psychiatry - Medical University in Wroclaw and National Institute of Public Health-National Institute of Hygiene in Warsaw and in partnership with Psykiatrist Institut Vinderen – Universitet, Oslo. The project was funded by the Norwegian Financial Mechanism and the European Economic Area Mechanism as well as Polish Ministry of Health. No support from pharmaceutical industry neither other commercial sources was received. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. The Romania WMH study projects “Policies in Mental Health Area” and “National Study regarding Mental Health and Services Use” were carried out by National School of Public Health & Health Services Management (former National Institute for Research & Development in Health), with technical support of Metro Media Transilvania, the National Institute of Statistics-National Centre for Training in Statistics, SC. Cheyenne Services SRL, Statistics Netherlands and were funded by Ministry of Public Health (former Ministry of Health) with supplemental support of Eli Lilly Romania SRL. The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), and the John W. Alden Trust. A complete list of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/. The views and opinions expressed in this article are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or governments.
Appendix Table 1.
WMH sample characteristics by World Bank income categories1
| Sample sizes
|
||||||||
|---|---|---|---|---|---|---|---|---|
| Country by income category | Survey2 | Sample characteristics3 | Field dates | Age range | Response rate4 | Part 1 | Part 2 | Part 2 age 18–22 and not graduated from college |
| I. Low and lower middle income countries | ||||||||
| China - Beijing/Shanghai | B-WMH/S-WMH | Beijing and Shanghai metropolitan areas. | 2002–3 | 18–70 | 74.7 | 5,201 | 1,628 | 133 |
| China - Shenzhen5 | Shenzhen | Shenzhen metropolitan area. Included temporary residents as well as household residents. | 2006–7 | 18–88 | 80.0 | 7,132 | 2,475 | 426 |
| Colombia | NSMH | All urban areas of the country (approximately 73% of the total national population) | 2003 | 18–65 | 87.7 | 4,426 | 2,381 | 337 |
| Iraq | IMHS | Nationally representative. | 2006–7 | 18–96 | 95.2 | 4,332 | 4,332 | 561 |
| Nigeria | NSMHW | 21 of the 36 states in the country, representing 57% of the national population. The surveys were conducted in Yoruba, Igbo, Hausa and Efik languages. | 2002–3 | 18–100 | 79.3 | 6,752 | 2,143 | 217 |
| Peru | EMSMP | Nationally representative. | 2004–5 | 18–65 | 90.2 | 3,930 | 1,801 | 251 |
| Total | 82.9 | (31,773) | (14,760) | (1,925) | ||||
| II. Upper-middle income countries | ||||||||
| Brazil - São Paulo | São Paulo Megacity | São Paulo metropolitan area. | 2005–7 | 18–93 | 81.3 | 5,037 | 2,942 | 262 |
| Bulgaria | NSHS | Nationally representative. | 2003–7 | 18–98 | 72.0 | 5,318 | 2,233 | 74 |
| Colombia - Medellin6 | MMHHS | Medellin metropolitan area | 2011–12 | 19–65 | 97.2 | 3,261 | 1,673 | 147 |
| Lebanon | LEBANON | Nationally representative. | 2002–3 | 18–94 | 70.0 | 2,857 | 1,031 | 79 |
| Mexico | M-NCS | All urban areas of the country (approximately 75% of the total national population). | 2001–2 | 18–65 | 76.6 | 5,782 | 2,362 | 372 |
| Romania | RMHS | Nationally representative. | 2005–6 | 18–96 | 70.9 | 2,357 | 2,357 | 102 |
| Total | 77.2 | (24,612) | (12,598) | (1,036) | ||||
| III. High income countries | ||||||||
| Australia5 | NSMHWB | Nationally representative. | 2007 | 18–85 | 60.0 | 8,463 | 8,463 | 649 |
| Belgium | ESEMeD | Nationally representative. The sample was selected from a national register of Belgium residents | 2001–2 | 18–95 | 50.6 | 2,419 | 1,043 | 41 |
| France | ESEMeD | Nationally representative. The sample was selected from a national list of households with listed telephone numbers. | 2001–2 | 18–97 | 45.9 | 2,894 | 1,436 | 76 |
| Italy | ESEMeD | Nationally representative. The sample was selected from municipality resident registries. | 2001–2 | 18–100 | 71.3 | 4,712 | 1,779 | 108 |
| Netherlands | ESEMeD | Nationally representative. The sample was selected from municipal postal registries. | 2002–3 | 18–95 | 56.4 | 2,372 | 1,094 | 31 |
| New Zealand5 | NZMHS | Nationally representative. | 2003–4 | 18–98 | 73.3 | 12,790 | 7,312 | 545 |
| Northern Ireland | NISHS | Nationally representative. | 2004–7 | 18–97 | 68.4 | 4,340 | 1,986 | 144 |
| Poland | EZOP | Nationally representative. | 2010–11 | 18–65 | 50.4 | 10,081 | 4,000 | 432 |
| Portugal | NMHS | Nationally representative. | 2008–9 | 18–81 | 57.3 | 3,849 | 2,060 | 113 |
| Spain | ESEMeD | Nationally representative. | 2001–2 | 18–98 | 78.6 | 5,473 | 2,121 | 113 |
| United States | NCS-R | Nationally representative. | 2002–3 | 18–99 | 70.9 | 9,282 | 5,692 | 537 |
| Total | 80.6 | (20,940) | (11,101) | (2,789) | ||||
| IV. Total | 71.4 | (51,002) | (25,819) | (5,750) | ||||
The World Bank (2012) Data. Accessed May 12, 2012 at: http://data.worldbank.org/country. Some of the WMH countries have moved into new income categories since the surveys were conducted. The income groupings above reflect the status of each country at the time of data collection. The current income category of each country is available at the preceding URL.
NSMH (The Colombian National Study of Mental Health); IMHS (Iraq Mental Health Survey); NSMHW (The Nigerian Survey of Mental Health and Wellbeing); B-WMH (The Beijing World Mental Health Survey); S-WMH (The Shanghai World Mental Health Survey); EMSMP (La Encuesta Mundial de Salud Mental en el Peru); (Bulgaria National Survey of Health and Stress); MMHHS (Medellín Mental Health Household Study); LEBANON (Lebanese Evaluation of the Burden of Ailments and Needs of the Nation); M-NCS (The Mexico National Comorbidity Survey); RMHS (Romania Mental Health Survey); NSMHWB (National Survey of Mental Health and Wellbeing); ESEMeD (The European Study Of The Epidemiology Of Mental Disorders); NZMHS (New Zealand Mental Health Survey); NISHS (Northern Ireland Study of Health and Stress); EZOP (Epidemiology of Mental Disorders and Access to Care Survey); NMHS (Portugal National Mental Health Survey); PEGASUS-Murcia (Psychiatric Enquiry to General Population in Southeast Spain-Murcia); NCS-R (The US National Comorbidity Survey Replication).
Most WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the US were selected in the first stage followed by one or more subsequent stages of geographic sampling (e.g., towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed. These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and the Netherlands (where postal registries were used to select households). Several WMH surveys (Belgium, Italy) used municipal resident registries to select respondents without listing households. 16 of the 23 surveys are based on nationally representative household samples.
The response rate is calculated as the ratio of the number of households in which an interview was completed to the number of households originally sampled, excluding from the denominator households known not to be eligible either because of being vacant at the time of initial contact or because the residents were unable to speak the designated languages of the survey. The weighted average response rate is 71.4%.
For the purposes of cross-national comparisons we limit the sample to those 18+.
Colombia moved from the “lower and lower-middle income” to the “upper-middle income” category between 2003 (when the Colombian National Study of Mental Health was conducted) and 2010 (when the Medellin Mental Health Household Study was conducted), hence Colombia’s appearance in both income categories. For more information, please see footnote 1.
Appendix Table 2.
Distribution of Part II WMH survey respondents ages 18–22 in participating surveys by country income level and respondent education and student status1
| College entrants
|
Other respondents who were secondary school…
|
Total | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Students | Attriters | Graduates | Non-graduates | ||||||
| % | (se) | % | (se) | % | (se) | % | (se) | (n) | |
|
|
|
|
|
|
|||||
| I. Low and lower-middle | |||||||||
| China - Beijing/Shanghai | 59.0 | (5.3) | 13.9 | (2.7) | 20.9 | (4.1) | 6.2 | (1.7) | (133) |
| China - Shenzhen | 4.1 | (0.4) | 10.6 | (1.4) | 30.2 | (2.5) | 55.1 | (2.6) | (426) |
| Colombia | 32.6 | (5.3) | 13.3 | (2.5) | 30.3 | (3.5) | 23.9 | (2.9) | (337) |
| Iraq | 18.8 | (2.6) | 3.0 | (0.8) | 8.7 | (2.1) | 69.4 | (2.4) | (561) |
| Nigeria | 44.0 | (4.3) | 2.3 | (0.6) | 23.1 | (3.2) | 30.6 | (3.7) | (217) |
| Peru | 40.7 | (3.7) | 8.2 | (1.7) | 39.1 | (3.0) | 12.0 | (2.3) | (251) |
| II. Upper-middle | |||||||||
| Brazil - São Paulo | 7.1 | (2.4) | 13.5 | (2.6) | 55.9 | (3.8) | 23.5 | (3.1) | (262) |
| Bulgaria | 34.9 | (4.9) | 0.4 | (0.4) | 41.4 | (4.2) | 23.4 | (4.1) | (74) |
| Colombia - Medellin | 59.0 | (7.1) | 13.6 | (3.3) | 13.0 | (3.7) | 14.4 | (4.8) | (147) |
| Lebanon | 42.6 | (5.0) | 6.7 | (2.6) | 15.0 | (4.7) | 35.7 | (4.5) | (79) |
| Mexico | 23.1 | (2.6) | 4.7 | (1.2) | 12.0 | (1.9) | 60.2 | (3.3) | (372) |
| Romania | 27.3 | (7.2) | 0.5 | (0.0) | 26.9 | (5.5) | 45.3 | (7.4) | (102) |
| III. High | |||||||||
| Australia | 44.0 | (2.3) | 16.5 | (1.7) | 17.1 | (1.8) | 22.4 | (1.9) | (649) |
| Belgium | 39.9 | (8.9) | 29.7 | (7.2) | 26.6 | (6.3) | 3.7 | (2.1) | (41) |
| France | 15.3 | (4.5) | 26.7 | (6.4) | 58.1 | (6.4) | 0.0 | (0.0) | (76) |
| Italy | 19.3 | (3.2) | 14.8 | (3.9) | 40.3 | (3.7) | 25.6 | (4.2) | (108) |
| Netherlands | 56.5 | (7.7) | 34.7 | (7.2) | 4.6 | (3.2) | 4.2 | (1.3) | (31) |
| New Zealand | 30.0 | (3.8) | 5.3 | (1.1) | 25.0 | (2.4) | 39.7 | (3.1) | (545) |
| Northern Ireland | 35.1 | (3.8) | 37.0 | (5.1) | 27.9 | (4.6) | 0.0 | (0.0) | (144) |
| Poland | 42.5 | (3.4) | 0.2 | (0.2) | 41.6 | (2.7) | 15.6 | (2.1) | (432) |
| Portugal | 46.8 | (3.6) | 1.9 | (0.4) | 19.0 | (2.8) | 32.4 | (4.4) | (113) |
| Spain | 49.8 | (4.7) | 23.3 | (4.7) | 6.9 | (1.8) | 20.0 | (3.0) | (113) |
| United States | 23.7 | (3.5) | 21.5 | (2.1) | 34.2 | (3.6) | 20.5 | (2.4) | (537) |
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Appendix Table 3.
Distribution of Part II WMH male survey respondents ages 18–22 in participating surveys by education and student status1
| College entrants
|
Other respondents who were secondary school…
|
Total | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Students | Attriters | Graduates | Non-graduates | ||||||
| % | (se) | % | (se) | % | (se) | % | (se) | (n) | |
|
|
|
|
|
|
|||||
| I. Low and lower-middle | |||||||||
| China - Beijing/Shanghai | 57.4 | (7.8) | 8.1 | (2.4) | 22.8 | (5.8) | 11.8 | (2.8) | (74) |
| China - Shenzhen | 6.1 | (1.0) | 11.2 | (3.3) | 30.3 | (4.6) | 52.5 | (5.7) | (163) |
| Colombia | 27.6 | (6.1) | 13.6 | (3.6) | 32.7 | (5.4) | 26.1 | (3.8) | (138) |
| Iraq | 21.7 | (3.9) | 3.9 | (1.4) | 9.0 | (2.6) | 65.3 | (4.3) | (277) |
| Nigeria | 55.1 | (5.8) | 2.9 | (0.6) | 14.4 | (3.6) | 27.5 | (4.8) | (107) |
| Peru | 39.7 | (4.0) | 7.2 | (2.0) | 41.5 | (3.5) | 11.5 | (4.3) | (125) |
| II. Upper-middle | |||||||||
| Brazil - São Paulo | 4.9 | (2.2) | 13.3 | (4.6) | 60.3 | (5.1) | 21.5 | (5.1) | (110) |
| Bulgaria | 37.6 | (5.9) | 0.0 | (0.0) | 40.3 | (5.9) | 22.1 | (6.3) | (32) |
| Colombia - Medellin | 65.4 | (9.8) | 9.1 | (3.2) | 9.2 | (3.6) | 16.2 | (8.3) | (65) |
| Lebanon | 53.0 | (6.4) | 1.0 | (1.0) | 13.3 | (3.8) | 32.8 | (6.3) | (33) |
| Mexico | 24.1 | (4.0) | 4.8 | (1.8) | 11.8 | (2.3) | 59.4 | (4.4) | (155) |
| Romania | 22.8 | (7.7) | 1.0 | (0.2) | 33.9 | (9.0) | 42.3 | (7.8) | (44) |
| III. High | |||||||||
| Australia | 47.8 | (3.2) | 14.2 | (2.1) | 17.3 | (2.4) | 20.8 | (2.4) | (302) |
| Belgium | 40.9 | (14.1) | 18.3 | (9.0) | 36.6 | (12.6) | 4.2 | (3.5) | (16) |
| France | 12.5 | (6.7) | 32.9 | (9.5) | 54.6 | (9.6) | 0.0 | (0.0) | (34) |
| Italy | 24.8 | (5.0) | 20.4 | (6.9) | 29.1 | (3.6) | 25.7 | (7.2) | (44) |
| Netherlands | 38.6 | (12.8) | 54.4 | (12.6) | 7.1 | (5.7) | 0.0 | (0.0) | (13) |
| New Zealand | 25.4 | (4.1) | 4.0 | (1.4) | 26.8 | (3.2) | 43.9 | (4.0) | (236) |
| Northern Ireland | 37.1 | (7.1) | 36.5 | (8.7) | 26.4 | (6.6) | 0.0 | (0.0) | (62) |
| Poland | 38.1 | (4.6) | 0.5 | (0.5) | 42.3 | (4.0) | 19.0 | (3.1) | (201) |
| Portugal | 43.3 | (6.5) | 3.1 | (0.4) | 14.4 | (4.9) | 39.2 | (7.0) | (48) |
| Spain | 57.1 | (6.4) | 17.2 | (4.5) | 10.3 | (2.8) | 15.3 | (5.8) | (51) |
| United States | 23.5 | (4.8) | 20.5 | (2.3) | 30.5 | (3.8) | 25.6 | (3.3) | (237) |
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Appendix Table 4.
Distribution of Part II WMH female survey respondents ages 18–22 in participating surveys by education and student status1
| College entrants
|
Other respondents who were secondary school…
|
Total | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Students | Attriters | graduates | non-graduates | ||||||
| % | (se) | % | (se) | % | (se) | % | (se) | (n) | |
|
|
|
|
|
|
|||||
| I. Low and lower-middle | |||||||||
| China - Beijing/Shanghai | 60.8 | (6.1) | 20.3 | (4.9) | 18.8 | (5.8) | 0.0 | (0.0) | (59) |
| China - Shenzhen | 3.0 | (0.9) | 10.3 | (1.7) | 30.2 | (3.5) | 56.6 | (4.1) | (263) |
| Colombia | 36.8 | (7.8) | 13.0 | (3.7) | 28.3 | (4.9) | 22.0 | (4.2) | (199) |
| Iraq | 15.6 | (3.1) | 2.0 | (0.6) | 8.4 | (2.6) | 74.0 | (3.4) | (284) |
| Nigeria | 33.0 | (4.6) | 1.7 | (0.8) | 31.6 | (5.2) | 33.7 | (4.5) | (110) |
| Peru | 41.6 | (5.2) | 9.1 | (2.9) | 36.9 | (4.8) | 12.5 | (2.8) | (126) |
| II. Upper-middle | |||||||||
| Brazil - São Paulo | 9.5 | (4.2) | 13.6 | (3.3) | 51.3 | (5.1) | 25.6 | (3.9) | (152) |
| Bulgaria | 31.7 | (8.8) | 0.8 | (0.9) | 42.6 | (7.4) | 24.9 | (6.3) | (42) |
| Colombia - Medellin | 51.7 | (8.6) | 18.6 | (5.8) | 17.3 | (6.6) | 12.3 | (4.3) | (82) |
| Lebanon | 28.3 | (6.4) | 14.6 | (5.0) | 17.4 | (8.6) | 39.8 | (7.5) | (46) |
| Mexico | 22.0 | (4.0) | 4.6 | (1.7) | 12.2 | (3.2) | 61.2 | (5.5) | (217) |
| Romania | 31.3 | (10.2) | 0.0 | (0.0) | 20.8 | (5.0) | 47.9 | (10.9) | (58) |
| III. High | |||||||||
| Australia | 40.0 | (3.0) | 18.9 | (2.4) | 16.9 | (2.6) | 24.1 | (2.7) | (347) |
| Belgium | 38.5 | (8.5) | 45.2 | (7.0) | 13.2 | (6.7) | 3.1 | (0.5) | (25) |
| France | 18.5 | (4.1) | 19.3 | (7.1) | 62.2 | (8.3) | 0.0 | (0.0) | (42) |
| Italy | 13.8 | (4.2) | 9.0 | (1.6) | 51.8 | (6.3) | 25.4 | (4.9) | (64) |
| Netherlands | 80.8 | (5.3) | 8.1 | (2.3) | 1.3 | (0.7) | 9.9 | (3.7) | (18) |
| New Zealand | 34.4 | (4.9) | 6.6 | (1.7) | 23.3 | (3.0) | 35.7 | (3.8) | (309) |
| Northern Ireland | 33.1 | (4.0) | 37.5 | (5.5) | 29.4 | (4.3) | 0.0 | (0.0) | (82) |
| Poland | 46.7 | (3.7) | 0.0 | (0.0) | 40.9 | (3.4) | 12.3 | (2.6) | (231) |
| Portugal | 50.1 | (3.6) | 0.8 | (0.7) | 23.3 | (4.5) | 25.8 | (5.7) | (65) |
| Spain | 43.3 | (5.0) | 28.7 | (7.6) | 3.9 | (2.2) | 24.1 | (4.1) | (62) |
| United States | 24.0 | (3.8) | 22.5 | (4.0) | 37.9 | (4.9) | 15.6 | (3.4) | (300) |
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Appendix Table 5.
Pooled 12-month prevalence of DSM-IV/CIDI mental disorders separately among male respondents ages 18–22 who were current students, college attriters, and nonstudents in the same age range1
| Students (ST) | Attriters (AT) | Other (OT) | ST:AT2 | ST:OT2 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | OR | (95 % CI) | OR | (95 % CI) | AUC3 | |
|
|
|
|
|
|
|
||||||
| I. Anxiety disorders | |||||||||||
| Separation Anxiety Disorder | 1.9 | (1.4) | 0.7 | (0.3) | 1.0 | (0.3) | 3.4 | (0.6–19.3) | 2.0 | (0.4–11.0) | 0.81 |
| Panic Disorder | 1.5 | (0.6) | 0.7 | (0.5) | 1.0 | (0.2) | 5.2 | (0.8–34.1) | 1.4 | (0.6–3.3) | 0.75 |
| GAD | 0.2 | (0.1) | 0.8 | (0.5) | 0.8 | (0.3) | 0.4 | (0.1–3.2) | 0.3 | (0.1–1.1) | 0.74 |
| Any Phobia | 5.3 | (1.0) | 8.1 | (2.7) | 6.8 | (0.7) | 0.7 | (0.3–1.4) | 0.7 | (0.5–1.1) | 0.69 |
| PTSD | 0.7 | (0.4) | 0.8 | (0.5) | 0.6 | (0.3) | 1.7 | (0.3–8.8) | 1.1 | (0.3–4.8) | 0.75 |
| Any | 8.6 | (1.7) | 10.0 | (2.7) | 8.6 | (0.8) | 1.0 | (0.5–2.0) | 1.0 | (0.6–1.6) | 0.66 |
| II. Mood disorders | |||||||||||
| MDD | 3.2 | (0.7) | 6.0 | (1.2) | 3.7 | (0.5) | 0.8 | (0.4–1.7) | 0.8 | (0.5–1.3) | 0.62 |
| Bipolar | 1.1 | (0.3) | 2.2 | (0.6) | 2.9 | (0.5) | 0.7 | (0.3–1.9) | 0.3* | (0.2–0.7) | 0.79 |
| Any | 4.3 | (0.8) | 8.3 | (1.3) | 6.6 | (0.7) | 0.8 | (0.4–1.5) | 0.6* | (0.4–0.9) | 0.68 |
| III. Behavior disorders | |||||||||||
| ADHD | 0.5 | (0.2) | 2.0 | (1.3) | 0.7 | (0.2) | 0.3 | (0.1–1.4) | 0.6 | (0.2–1.8) | 0.83 |
| Conduct Disorder | 0.3 | (0.2) | 0.8 | (0.7) | 0.7 | (0.2) | 0.3 | (0.0–3.5) | 0.4 | (0.1–1.8) | 0.84 |
| ODD | 0.4 | (0.2) | 1.0 | (0.7) | 0.5 | (0.2) | 0.3 | (0.1–1.7) | 0.7 | (0.2–2.8) | 0.86 |
| IED | 3.1 | (0.7) | 3.0 | (1.0) | 2.8 | (0.4) | 1.0 | (0.5–2.0) | 1.0 | (0.6–1.7) | 0.80 |
| Any | 4.1 | (0.8) | 5.9 | (1.7) | 4.3 | (0.5) | 0.7 | (0.3–1.3) | 0.8 | (0.5–1.3) | 0.77 |
| IV. Substance disorders | |||||||||||
| Alcohol Abuse | 3.3 | (0.7) | 4.7 | (1.4) | 4.4 | (0.5) | 0.9 | (0.4–2.0) | 0.7 | (0.4–1.1) | 0.71 |
| Alcohol Dependence | 1.4 | (0.5) | 1.3 | (0.5) | 2.5 | (0.4) | 1.2 | (0.4–3.5) | 0.5 | (0.2–1.2) | 0.76 |
| Drug Abuse | 0.9 | (0.5) | 1.2 | (0.6) | 1.6 | (0.3) | 0.8 | (0.2–3.2) | 0.5 | (0.2–1.6) | 0.81 |
| Drug Dependence | 0.4 | (0.3) | 1.2 | (0.7) | 1.4 | (0.3) | 0.5 | (0.1–3.1) | 0.3* | (0.1–1.0) | 0.84 |
| Any | 5.7 | (1.0) | 7.3 | (1.6) | 8.6 | (0.8) | 1.0 | (0.5–1.9) | 0.6 | (0.4–0.9) | 0.74 |
| V. Total disorders | |||||||||||
| Any | 17.9 | (2.0) | 22.3 | (3.2) | 19.7 | (1.2) | 0.9 | (0.6–1.5) | 0.8 | (0.6–1.1) | 0.68 |
| (n) | (747) | (270) | (1,550) | – | – | – | |||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Based on a pooled within-survey logistic regression model adjusting for between-survey variation in the association of age-sex with student status.
The AUCs were generated from logistic models in which dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an order logistic framework, controlling age-sex and survey.
Appendix Table 6.
Pooled 12-month prevalence of DSM-IV/CIDI mental disorders separately among female respondents ages 18–22 who were current students, college attriters, and nonstudents in the same age range1
| Students (ST) | Attriters (AT) | Other (OT) | ST:AT2 | ST:OT2 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | OR | (95 % CI) | OR | (95 % CI) | AUC3 | |
|
|
|
|
|
|
|
||||||
| I. Anxiety disorders | |||||||||||
| Separation Anxiety Disorder | 0.7 | (0.2) | 2.2 | (0.9) | 1.4 | (0.2) | 0.4 | (0.2–1.1) | 0.4* | (0.2–0.9) | 0.86 |
| Panic Disorder | 1.0 | (0.2) | 1.5 | (0.4) | 1.7 | (0.3) | 1.4 | (0.7–2.9) | 0.5* | (0.3–0.9) | 0.75 |
| GAD | 0.7 | (0.2) | 0.8 | (0.3) | 0.8 | (0.2) | 1.2 | (0.4–3.4) | 0.7 | (0.4–1.5) | 0.75 |
| Any Phobia | 12.8 | (2.0) | 13.8 | (1.4) | 12.8 | (0.9) | 1.0 | (0.6–1.5) | 1.0 | (0.7–1.5) | 0.66 |
| PTSD | 2.0 | (0.5) | 4.4 | (1.0) | 3.3 | (0.5) | 0.8 | (0.4–1.6) | 0.5* | (0.3–0.9) | 0.79 |
| Any | 15.1 | (2.0) | 19.0 | (1.8) | 17.1 | (1.0) | 1.0 | (0.7–1.4) | 0.8 | (0.6–1.2) | 0.67 |
| II. Mood disorders | |||||||||||
| MDD | 5.8 | (0.8) | 9.2 | (1.4) | 6.3 | (0.6) | 0.7 | (0.4–1.1) | 0.9 | (0.6–1.3) | 0.67 |
| Bipolar | 2.5 | (0.7) | 2.4 | (0.7) | 2.4 | (0.3) | 1.5 | (0.7–3.5) | 1.0 | (0.5–1.9) | 0.72 |
| Any | 7.8 | (1.0) | 11.5 | (1.5) | 8.7 | (0.7) | 0.8 | (0.5–1.2) | 0.9 | (0.6–1.2) | 0.68 |
| III. Behavior disorders | |||||||||||
| ADHD | 0.2 | (0.2) | 1.1 | (0.3) | 1.1 | (0.2) | 0.4 | (0.1–2.4) | 0.2 | (0.0–1.3) | 0.83 |
| Conduct Disorder | 0.0 | (0.0) | 0.0 | (0.0) | 0.2 | (0.1) | – | – | – | – | – |
| ODD | 0.0 | (0.0) | 1.0 | (0.7) | 0.8 | (0.1) | 0.0* | (0.0–0.6) | 0.0* | (0.0–0.2) | 0.85 |
| IED | 1.5 | (0.4) | 3.1 | (0.9) | 2.1 | (0.3) | 0.5 | (0.3–1.1) | 0.7 | (0.4–1.4) | 0.83 |
| Any | 1.6 | (0.4) | 4.8 | (1.1) | 3.3 | (0.4) | 0.4 | (0.2–0.8) | 0.4* | (0.3–0.8) | 0.82 |
| IV. Substance disorders | |||||||||||
| Alcohol Abuse | 1.7 | (0.3) | 3.7 | (1.2) | 1.9 | (0.4) | 0.6 | (0.3–1.3) | 0.8 | (0.4–1.4) | 0.80 |
| Alcohol Dependence | 1.3 | (0.5) | 2.0 | (0.9) | 0.6 | (0.2) | 1.3 | (0.4–4.7) | 1.8 | (0.7–4.8) | 0.77 |
| Drug Abuse | 0.5 | (0.2) | 1.4 | (0.8) | 0.4 | (0.1) | 0.6 | (0.2–2.1) | 1.1 | (0.5–2.4) | 0.83 |
| Drug Dependence | 0.1 | (0.1) | 1.1 | (0.5) | 0.4 | (0.1) | 0.1* | (0.0–0.7) | 0.2 | (0.0–1.2) | 0.81 |
| Any | 3.3 | (0.6) | 6.2 | (1.6) | 3.0 | (0.4) | 0.8 | (0.4–1.6) | 0.9 | (0.6–1.5) | 0.78 |
| V. Total disorders | |||||||||||
| Any | 22.8 | (2.1) | 27.5 | (2.4) | 23.1 | (1.1) | 1.0 | (0.7–1.4) | 0.9 | (0.7–1.2) | 0.68 |
| (n) | (825) | (432) | (1,926) | – | – | – | |||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Based on a pooled within-survey logistic regression model adjusting for between-survey variation in the association of age-sex with student status.
The AUCs were generated from logistic models in which dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an order logistic framework, controlling age-sex and survey.
Appendix Table 7.
Pooled percentages of 12-month DSM-IV/CIDI disorders with pre-matriculation onsets1 separately among male respondents ages 18–22 who were current students, college attriters, and nonstudents2 in the same age range3
| Students (ST) | Attriters (AT) | Other4 (OT) | ST vs. AT | ST vs. OT | ||||
|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | χ2 | χ2 | |
|
|
|
|
|
|
||||
| I. Anxiety disorders | ||||||||
| Separation Anxiety Disorder | 79.6 | (18.1) | 51.9 | (23.5) | 75.0 | (9.7) | 0.0 | 0.2 |
| Panic Disorder | 28.1 | (15.5) | 100.0 | (0.0) | 67.9 | (8.5) | – | 0.4 |
| GAD | 24.5 | (19.6) | 77.0 | (21.7) | 78.3 | (11.5) | 53.2* | 742.2* |
| Any Phobia | 93.4 | (4.5) | 77.5 | (10.9) | 98.8 | (0.7) | 0.3 | 7.9* |
| PTSD | 65.2 | (23.2) | 100.0 | (0.0) | 75.9 | (14.4) | – | 0.0 |
| Any | 88.0 | (3.9) | 78.3 | (9.0) | 93.6 | (1.6) | 0.0 | 3.1 |
| II. Mood disorders | ||||||||
| MDD | 31.4 | (9.9) | 51.9 | (10.0) | 58.3 | (6.5) | 0.1 | 3.6 |
| Bipolar | 68.8 | (14.9) | 72.1 | (10.9) | 66.9 | (7.0) | 0.7 | 0.0 |
| Any | 40.7 | (8.6) | 57.4 | (8.5) | 61.6 | (5.2) | 0.4 | 2.2 |
| III. Behavior disorders | ||||||||
| ADHD | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| Conduct Disorder | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| ODD | 100.0 | (0.0) | 100.0 | (0.0) | 97.4 | (2.7) | – | – |
| IED | 94.8 | (3.1) | 94.3 | (3.4) | 88.1 | (3.5) | 0.0 | 0.6 |
| Any | 96.0 | (2.4) | 97.0 | (1.7) | 94.2 | (1.9) | 0.0 | 0.0 |
| IV. Substance disorders | ||||||||
| Alcohol Abuse | 32.8 | (7.8) | 66.1 | (13.8) | 44.1 | (6.0) | 10.9* | 3.3 |
| Alcohol Dependence | 44.1 | (18.9) | 33.7 | (17.5) | 51.2 | (7.2) | 0.2 | 0.8 |
| Drug Abuse | 72.0 | (18.3) | 60.2 | (21.4) | 58.7 | (9.4) | 0.5 | 0.1 |
| Drug Dependence | 100.0 | (0.0) | 76.9 | (17.6) | 60.7 | (9.7) | – | – |
| Any | 58.4 | (8.1) | 65.2 | (10.4) | 58.2 | (4.2) | 0.3 | 0.2 |
| V. Total disorders | ||||||||
| Any | 78.3 | (3.8) | 81.4 | (4.9) | 84.9 | (2.1) | 0.9 | 4.5* |
Significant at the .05 level.
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
“All other” respondents were defined as those who never entered college whether or not they graduated from secondary school.
Appendix Table 8.
Pooled percentages of 12-month DSM-IV/CIDI disorders with pre-matriculation onsets1 separately among female respondents ages 18–22 who were current students, college attriters, and nonstudents2 in the same age range3
| Students (ST) | Attriters (AT) | Other4 (OT) | ST vs. AT | ST vs. OT | ||||
|---|---|---|---|---|---|---|---|---|
| % | (se) | % | (se) | % | (se) | χ2 | χ2 | |
|
|
|
|
|
|
||||
| I. Anxiety disorders | ||||||||
| Separation Anxiety Disorder | 42.0 | (13.1) | 42.9 | (21.7) | 54.0 | (8.5) | 0.3 | 2.3 |
| Panic Disorder | 80.0 | (5.4) | 64.6 | (16.5) | 93.7 | (3.7) | 0.0 | 0.5 |
| GAD | 75.4 | (8.4) | 24.0 | (15.4) | 75.5 | (6.5) | 272.1* | 1.6 |
| Any Phobia | 96.6 | (0.7) | 96.4 | (1.6) | 97.0 | (1.3) | 0.2 | 0.4 |
| PTSD | 56.2 | (11.6) | 69.6 | (10.2) | 77.6 | (5.6) | 0.0 | 1.3 |
| Any | 93.9 | (1.5) | 91.1 | (2.3) | 93.9 | (1.4) | 0.1 | 0.0 |
| II. Mood disorders | ||||||||
| MDD | 66.7 | (6.0) | 70.4 | (7.3) | 68.6 | (3.9) | 1.6 | 1.2 |
| Bipolar | 57.9 | (14.3) | 47.1 | (15.6) | 55.9 | (6.8) | 0.3 | 0.1 |
| Any | 68.7 | (5.2) | 69.1 | (6.9) | 65.6 | (3.4) | 1.3 | 0.0 |
| III. Behavior disorders | ||||||||
| ADHD | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| Conduct Disorder | – | – | – | – | 100.0 | (0.0) | – | – |
| ODD | 100.0 | (0.0) | 100.0 | (0.0) | 100.0 | (0.0) | – | – |
| IED | 81.2 | (14.9) | 75.2 | (12.6) | 88.4 | (4.9) | 0.9 | 0.9 |
| Any | 81.4 | (14.8) | 86.3 | (8.3) | 94.3 | (3.0) | 1.4 | 1.6 |
| IV. Substance disorders | ||||||||
| Alcohol Abuse | 24.0 | (10.2) | 47.5 | (17.3) | 42.4 | (8.6) | 4.2* | 1.1 |
| Alcohol Dependence | 19.3 | (14.1) | 31.5 | (17.6) | 23.0 | (9.2) | 320.2* | 2.2 |
| Drug Abuse | 20.3 | (13.3) | 46.0 | (30.3) | 73.1 | (9.4) | 2.5 | 2.6 |
| Drug Dependence | 100.0 | (0.0) | 55.9 | (24.1) | 61.1 | (18.2) | – | – |
| Any | 24.9 | (8.5) | 56.8 | (13.0) | 48.9 | (6.6) | 6.3* | 3.0 |
| V. Total disorders | ||||||||
| Any | 87.1 | (2.2) | 85.2 | (3.9) | 89.9 | (1.5) | 0.2 | 1.3 |
Significant at the .05 level.
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
“All other” respondents were defined as those who never entered college whether or not they graduated from secondary school
Appendix Table 9.
Pooled lifetime prevalence of pre-matriculation onset DSM-IV/CIDI mental disorders1 separately among male respondents ages 18–22 who entered college (both current students and attriters), secondary school graduates in the same age range who never entered college, and all other respondents2 in the same age range3
| College entrants (CE)
|
Other respondents who were secondary school…
|
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Graduates (GR) | Total4 | CE: GR5 | CE: Total5 | |||||||||
| % | (se) | % | (se) | % | (se) | OR | (95 % CI) | AUC | OR | (95 % CI) | AUC | |
|
|
|
|
|
|
||||||||
| I. Anxiety disorders | ||||||||||||
| Separation Anxiety Disorder | 2.8 | (0.4) | 3.9 | (0.7) | 3.4 | (0.5) | 0.8 | (0.3–2.2) | 0.85 | 0.8 | (0.3–1.8) | 0.85 |
| Panic Disorder | 1.0 | (0.2) | 1.2 | (0.2) | 0.9 | (0.2) | 0.7 | (0.3–1.7) | 0.69 | 1.1 | (0.4–2.8) | 0.66 |
| GAD | 0.7 | (0.2) | 1.1 | (0.2) | 0.9 | (0.3) | 0.5 | (0.2–1.7) | 0.74 | 0.7 | (0.2–1.9) | 0.74 |
| Any Phobia | 8.9 | (0.5) | 10.1 | (0.9) | 9.3 | (0.8) | 0.8 | (0.5–1.3) | 0.65 | 0.9 | (0.6–1.3) | 0.65 |
| PTSD | 0.9 | (0.3) | 1.0 | (0.6) | 0.8 | (0.3) | 0.8 | (0.2–4.3) | 0.75 | 0.9 | (0.2–3.2) | 0.75 |
| Any | 12.7 | (0.8) | 13.9 | (1.0) | 12.3 | (0.9) | 0.9 | (0.6–1.3) | 0.66 | 1.0 | (0.7–1.4) | 0.66 |
| II. Mood disorders | ||||||||||||
| MDD | 3.9 | (0.5) | 4.7 | (0.5) | 4.5 | (0.5) | 0.6 | (0.4–1.0) | 0.66 | 0.6* | (0.4–0.9) | 0.66 |
| Bipolar | 2.4 | (0.5) | 3.2 | (0.6) | 2.8 | (0.5) | 0.7 | (0.3–1.4) | 0.75 | 0.7 | (0.4–1.3) | 0.75 |
| Any | 6.2 | (0.7) | 7.7 | (0.8) | 7.2 | (0.7) | 0.7 | (0.4–1.0) | 0.68 | 0.7* | (0.5–1.0) | 0.68 |
| III. Behavior disorders | ||||||||||||
| ADHD | 2.7 | (0.4) | 2.6 | (0.2) | 2.2 | (0.4) | 1.0 | (0.5–1.9) | 0.85 | 1.0 | (0.6–1.7) | 0.85 |
| Conduct Disorder | 2.1 | (0.5) | 3.2 | (0.4) | 3.7 | (0.5) | 0.6* | (0.3–1.0) | 0.88 | 0.4* | (0.2–0.8) | 0.87 |
| ODD | 2.7 | (0.4) | 3.0 | (0.5) | 3.0 | (0.5) | 0.9 | (0.4–1.7) | 0.87 | 0.8 | (0.4–1.3) | 0.87 |
| IED | 4.2 | (0.7) | 2.6 | (0.3) | 3.1 | (0.4) | 1.7 | (1.0–2.8) | 0.83 | 1.2 | (0.8–1.9) | 0.82 |
| Any | 8.2 | (0.8) | 8.8 | (0.7) | 8.5 | (0.8) | 0.9 | (0.6–1.3) | 0.82 | 0.8 | (0.6–1.1) | 0.82 |
| IV. Substance disorders | ||||||||||||
| Alcohol Abuse | 3.0 | (0.4) | 3.7 | (0.5) | 4.8 | (0.6) | 0.7 | (0.4–1.2) | 0.75 | 0.5* | (0.3–0.8) | 0.75 |
| Alcohol Dependence | 1.2 | (0.4) | 1.1 | (0.4) | 2.0 | (0.3) | 1.1 | (0.4–2.7) | 0.81 | 0.5 | (0.3–1.1) | 0.80 |
| Drug Abuse | 2.5 | (0.4) | 2.5 | (0.5) | 2.8 | (0.5) | 0.9 | (0.5–1.7) | 0.79 | 0.7 | (0.4–1.2) | 0.78 |
| Drug Dependence | 1.1 | (0.3) | 0.9 | (0.2) | 1.5 | (0.3) | 1.2 | (0.4–3.7) | 0.83 | 0.7 | (0.3–1.5) | 0.81 |
| Any | 6.6 | (0.7) | 6.1 | (0.7) | 8.8 | (0.7) | 1.0 | (0.7–1.5) | 0.77 | 0.6* | (0.4–0.8) | 0.76 |
| V. Total disorders | ||||||||||||
| Any | 23.5 | (1.1) | 24.3 | (1.2) | 24.8 | (1.2) | 0.9 | (0.7–1.2) | 0.70 | 0.8 | (0.6–1.0) | 0.70 |
| (n) | (1,017) | (695) | (1,550) | |||||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
“All other” respondents were defined as those who never entered college whether or not graduated from secondary school.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Secondary school graduates or non-graduates.
Based on a single pooled logistic model for each row in which separate dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an ordered logistic framework, controlling age-sex and survey
Appendix Table 10.
Pooled lifetime prevalence of pre-matriculation onset DSM-IV/CIDI mental disorders1 separately among female respondents ages 18–22 who entered college (both current students and attriters), secondary school graduates in the same age range who never entered college, and all other respondents2 in the same age range3
| College entrants (CE)
|
Other respondents who were secondary school…
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Graduates (GR) | Total4 | CE: GR5 | CE: Total5 | ||||||||||
| % | (se) | % | (se) | % | (se) | OR | (95 % CI) | AUC | OR | (95 % CI) | AUC | ||
|
|
|
|
|
|
|||||||||
| I. Anxiety disorders | |||||||||||||
| Separation Anxiety Disorder | 2.0 | (0.2) | 4.5 | (0.6) | 4.5 | (0.6) | 0.4* | (0.3–0.8) | 0.85 | 0.4* | (0.2–0.6) | 0.85 | |
| Panic Disorder | 1.2 | (0.2) | 1.7 | (0.3) | 1.9 | (0.3) | 0.5* | (0.3–0.9) | 0.71 | 0.5* | (0.3–0.8) | 0.72 | |
| GAD | 1.1 | (0.3) | 1.3 | (0.2) | 1.4 | (0.3) | 0.7 | (0.3–1.4) | 0.78 | 0.6 | (0.3–1.2) | 0.77 | |
| Any Phobia | 14.4 | (0.9) | 17.4 | (1.0) | 15.7 | (1.0) | 0.8 | (0.6–1.1) | 0.66 | 0.9 | (0.7–1.2) | 0.66 | |
| PTSD | 2.9 | (0.4) | 4.2 | (0.6) | 3.9 | (0.5) | 0.5* | (0.3–0.9) | 0.80 | 0.5* | (0.3–0.8) | 0.80 | |
| Any | 18.7 | (1.0) | 23.7 | (1.2) | 22.2 | (1.2) | 0.7* | (0.5–0.9) | 0.69 | 0.7* | (0.6–0.9) | 0.69 | |
| II. Mood disorders | |||||||||||||
| MDD | 7.9 | (0.6) | 7.5 | (0.5) | 7.1 | (0.6) | 1.0 | (0.7–1.4) | 0.69 | 1.0 | (0.7–1.4) | 0.69 | |
| Bipolar | 1.8 | (0.4) | 1.5 | (0.3) | 2.0 | (0.3) | 1.2 | (0.6–2.1) | 0.76 | 0.9 | (0.5–1.5) | 0.75 | |
| Any | 9.7 | (0.7) | 9.0 | (0.6) | 9.0 | (0.7) | 1.0 | (0.8–1.4) | 0.71 | 1.0 | (0.7–1.2) | 0.70 | |
| III. Behavior disorders | |||||||||||||
| ADHD | 1.5 | (0.2) | 1.5 | (0.4) | 1.9 | (0.4) | 1.3 | (0.5–3.4) | 0.85 | 0.7 | (0.4–1.3) | 0.83 | |
| Conduct Disorder | 1.1 | (0.1) | 2.6 | (0.3) | 2.0 | (0.4) | 0.4* | (0.2–0.9) | 0.90 | 0.4* | (0.2–0.7) | 0.90 | |
| ODD | 2.0 | (0.3) | 3.1 | (0.5) | 2.8 | (0.5) | 0.7 | (0.3–1.5) | 0.86 | 0.5 | (0.3–1.0) | 0.85 | |
| IED | 2.0 | (0.3) | 2.6 | (0.4) | 2.3 | (0.3) | 0.9 | (0.6–1.5) | 0.84 | 0.8 | (0.5–1.3) | 0.84 | |
| Any | 5.3 | (0.4) | 6.9 | (0.6) | 6.6 | (0.7) | 0.8 | (0.5–1.3) | 0.83 | 0.6* | (0.5–0.9) | 0.82 | |
| IV. Substance disorders | |||||||||||||
| Alcohol Abuse | 1.9 | (0.4) | 3.4 | (0.4) | 3.1 | (0.4) | 0.4* | (0.2–0.8) | 0.82 | 0.4* | (0.2–0.7) | 0.81 | |
| Alcohol Dependence | 0.5 | (0.1) | 0.6 | (0.1) | 0.6 | (0.1) | 0.9 | (0.3–2.3) | 0.89 | 0.8 | (0.4–1.7) | 0.89 | |
| Drug Abuse | 0.9 | (0.3) | 1.5 | (0.2) | 1.8 | (0.3) | 0.5 | (0.2–1.1) | 0.87 | 0.4* | (0.2–0.8) | 0.87 | |
| Drug Dependence | 0.8 | (0.2) | 1.1 | (0.3) | 0.9 | (0.2) | 0.6 | (0.3–1.6) | 0.81 | 0.7 | (0.3–1.4) | 0.81 | |
| Any | 3.3 | (0.5) | 5.3 | (0.5) | 5.2 | (0.5) | 0.5* | (0.3–0.8) | 0.83 | 0.4* | (0.3–0.7) | 0.83 | |
| V. Total disorders | |||||||||||||
| Any | 27.1 | (1.2) | 31.8 | (1.3) | 29.4 | (1.3) | 0.7* | (0.6–0.9) | 0.71 | 0.8* | (0.6–1.0) | 0.71 | |
| (n) | (1,257) | (876) | (1,926) | ||||||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
“All other” respondents were defined as those who never entered college whether or not graduated from secondary school.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Secondary school graduates or non-graduates.
Based on a single pooled logistic model for each row in which separate dummy variables for being an attriter or other nonstudent were included as predictors of the mental disorder in the row heading or, in the case of Part VI, number of disorders estimated in an ordered logistic framework, controlling age-sex and survey.
Appendix Table 11.
Pooled lifetime prevalence of pre-matriculation1 and post-matriculation2 onset DSM-IV/CIDI mental disorders separately among male respondents ages 18–22 who were current students or college attriters3
| Students (ST) | Attriters (AT) | Students vs. Attriters | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pre- | Post- | Pre- | Post- | Pre-4 | Post-4 | |||||||||
| % | (se) | % | (se) | % | (se) | % | (se) | OR | (95 % CI) | AUC | OR | (95 % CI) | AUC | |
|
|
|
|
|
|
||||||||||
| I. Anxiety disorders | ||||||||||||||
| Separation Anxiety Disorder | 3.7 | (1.5) | 1.0 | (0.3) | 0.8 | (0.5) | 0.6 | (0.3) | 3.7 | (0.9–15.9) | 0.91 | 2.7 | (0.7–10.2) | 0.88 |
| Panic Disorder | 1.0 | (0.5) | 1.1 | (0.5) | 1.0 | (0.6) | 0.0 | (0.0) | 1.1 | (0.3–4.7) | 0.67 | 115.4* | (9.3–999.9+) | 0.87 |
| GAD | 0.5 | (0.3) | 0.3 | (0.1) | 1.2 | (0.7) | 0.3 | (0.2) | 0.5 | (0.1–2.4) | 0.74 | 1.9 | (0.3–11.7) | 0.85 |
| Any Phobia | 8.9 | (1.2) | 0.4 | (0.3) | 8.8 | (2.7) | 2.0 | (0.9) | 1.2 | (0.6–2.3) | 0.66 | 0.2 | (0.0–1.6) | 0.80 |
| PTSD | 0.6 | (0.3) | 0.3 | (0.2) | 1.5 | (0.9) | 0.0 | (0.0) | 0.7 | (0.2–3.5) | 0.76 | – | – | – |
| Any | 13.2 | (1.9) | 1.7 | (0.4) | 11.4 | (2.8) | 3.0 | (1.0) | 1.3 | (0.7–2.5) | 0.66 | 1.0 | (0.4–2.6) | 0.80 |
| II. Mood disorders | ||||||||||||||
| MDD | 3.2 | (0.6) | 3.0 | (0.7) | 5.6 | (1.3) | 4.4 | (1.2) | 0.9 | (0.4–1.8) | 0.66 | 1.1 | (0.4–2.9) | 0.70 |
| Bipolar | 1.8 | (0.5) | 0.5 | (0.2) | 3.6 | (1.1) | 1.1 | (0.4) | 0.5 | (0.2–1.3) | 0.75 | 0.7 | (0.2–3.3) | 0.80 |
| Any | 5.0 | (0.8) | 3.5 | (0.7) | 9.2 | (1.8) | 5.7 | (1.4) | 0.7 | (0.4–1.3) | 0.68 | 1.0 | (0.4–2.4) | 0.72 |
| III. Behavior disorders | ||||||||||||||
| ADHD | 2.2 | (0.7) | 0.0 | (0.0) | 3.7 | (1.4) | 0.0 | (0.0) | 0.9 | (0.3–2.6) | 0.85 | –5 | – | – |
| Conduct Disorder | 1.7 | (0.6) | 0.0 | (0.0) | 3.0 | (1.2) | 0.0 | (0.0) | 0.7 | (0.2–2.1) | 0.87 | –5 | – | – |
| ODD | 2.2 | (0.6) | 0.0 | (0.0) | 3.7 | (1.1) | 0.0 | (0.0) | 0.7 | (0.3–1.6) | 0.87 | –5 | – | – |
| IED | 4.1 | (0.9) | 0.2 | (0.1) | 4.6 | (1.1) | 0.2 | (0.1) | 1.0 | (0.5–1.9) | 0.82 | 1.2 | (0.2–6.9) | 0.87 |
| Any | 7.6 | (1.2) | 0.2 | (0.1) | 9.7 | (1.9) | 0.2 | (0.1) | 1.0 | (0.5–1.8) | 0.82 | 1.1 | (0.2–7.0) | 0.88 |
| IV. Substance disorders | ||||||||||||||
| Alcohol Abuse | 2.0 | (0.4) | 3.5 | (0.7) | 5.2 | (1.3) | 8.4 | (1.9) | 0.4* | (0.2–0.8) | 0.75 | 0.7 | (0.4–1.5) | 0.76 |
| Alcohol Dependence | 0.8 | (0.4) | 1.1 | (0.4) | 2.0 | (0.7) | 1.3 | (0.5) | 0.4 | (0.1–1.5) | 0.81 | 1.4 | (0.5–4.0) | 0.78 |
| Drug Abuse | 1.3 | (0.5) | 0.4 | (0.2) | 5.3 | (1.2) | 0.7 | (0.4) | 0.2* | (0.1–0.7) | 0.81 | 0.9 | (0.2–4.0) | 0.76 |
| Drug Dependence | 0.6 | (0.3) | 0.0 | (0.0) | 2.4 | (1.0) | 0.5 | (0.3) | 0.2 | (0.1–1.0) | 0.82 | 0.2 | (0.0–2.1) | 0.84 |
| Any | 4.5 | (0.8) | 4.0 | (0.8) | 11.3 | (1.8) | 10.3 | (2.1) | 0.4* | (0.2–0.7) | 0.77 | 0.6 | (0.3–1.3) | 0.77 |
| V. Total disorders | ||||||||||||||
| Any | 20.9 | (2.2) | 7.0 | (0.9) | 29.5 | (3.3) | 13.2 | (2.3) | 0.8 | (0.5–1.2) | 0.70 | 0.9 | (0.5–1.6) | 0.70 |
| (n) | (747) | –6 | (270) | –7 | ||||||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Based on a pooled within-survey logistic regression equation adjusting for between-survey variation in the association of age-sex with student status
There were no post-matriculation onsets of these disorders by definition, as the CIDI required onsets before age 8 (ADHD) or 18 (Conduct Disorder, Oppositional Defiant Disorder).
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 653 among male students who had no pre-matriculation history of specific phobias to a high of 742 among male students who had no pre-matriculation history of drug dependence, with a median sample size of 731. The sample size for having no pre-matriculation history of any disorder is 544 among male students.
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 239 among male attriters who had no pre-matriculation history of specific phobias to a high of 267 among male attriters who had no pre-matriculation history of panic disorder, with a median sample size of 256. The sample size for having no pre-matriculation history of any disorder is 170 among male attriters.
Appendix Table 12.
Pooled lifetime prevalence of pre-matriculation1 and post-matriculation2 onset DSM-IV/CIDI mental disorders separately among female respondents ages 18–22 who were current students or college attriters3
| Students (ST) | Attriters (AT) | Students vs. Attriters | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pre- | Post- | Pre- | Post- | Pre-4 | Post-4 | |||||||||
| % | (se) | % | (se) | % | (se) | % | (se) | OR | (95 % CI) | AUC | OR | (95 % CI) | AUC | |
|
|
|
|
|
|
||||||||||
| I. Anxiety disorders | ||||||||||||||
| Separation Anxiety Disorder | 1.7 | (0.5) | 0.5 | (0.2) | 2.8 | (0.9) | 1.6 | (0.5) | 0.7 | (0.3–1.7) | 0.85 | 0.5 | (0.2–1.3) | 0.89 |
| Panic Disorder | 1.2 | (0.2) | 0.2 | (0.1) | 1.3 | (0.4) | 0.6 | (0.3) | 1.8 | (0.9–3.8) | 0.72 | 0.9 | (0.2–3.5) | 0.93 |
| GAD | 1.0 | (0.3) | 0.2 | (0.0) | 1.4 | (0.7) | 1.4 | (0.5) | 1.0 | (0.3–3.0) | 0.77 | 0.2* | (0.1–0.5) | 0.80 |
| Any Phobia | 13.8 | (2.0) | 0.6 | (0.1) | 15.5 | (1.6) | 0.6 | (0.2) | 0.9 | (0.6–1.3) | 0.66 | 3.0 | (0.9–10.5) | 0.71 |
| PTSD | 1.6 | (0.5) | 1.0 | (0.2) | 5.5 | (1.1) | 1.4 | (0.5) | 0.5 | (0.2–1.0) | 0.82 | 1.9 | (0.8–4.7) | 0.79 |
| Any | 17.1 | (2.0) | 1.3 | (0.3) | 22.0 | (2.0) | 2.7 | (0.7) | 0.9 | (0.6–1.2) | 0.69 | 1.1 | (0.5–2.4) | 0.76 |
| II. Mood disorders | ||||||||||||||
| MDD | 6.5 | (0.9) | 3.8 | (0.8) | 10.8 | (1.5) | 3.6 | (0.9) | 0.6* | (0.4–1.0) | 0.69 | 1.7 | (0.9–3.3) | 0.73 |
| Bipolar | 1.9 | (0.6) | 1.1 | (0.5) | 1.6 | (0.4) | 1.4 | (0.7) | 1.3 | (0.6–2.8) | 0.75 | 1.6 | (0.4–6.0) | 0.78 |
| Any | 8.3 | (0.9) | 4.4 | (0.9) | 12.3 | (1.6) | 4.7 | (1.1) | 0.7 | (0.4–1.1) | 0.71 | 1.6 | (0.9–2.9) | 0.73 |
| III. Behavior disorders | ||||||||||||||
| ADHD | 1.5 | (0.6) | 0.0 | (0.0) | 1.6 | (0.4) | 0.0 | (0.0) | 1.4 | (0.6–3.3) | 0.83 | –5 | – | – |
| Conduct Disorder | 0.7 | (0.3) | 0.0 | (0.0) | 1.9 | (0.6) | 0.0 | (0.0) | 0.6 | (0.2–1.6) | 0.89 | –5 | – | – |
| ODD | 1.5 | (0.6) | 0.0 | (0.0) | 2.8 | (0.8) | 0.0 | (0.0) | 0.6 | (0.2–1.9) | 0.85 | –5 | – | – |
| IED | 1.6 | (0.3) | 0.3 | (0.3) | 2.9 | (0.9) | 0.8 | (0.5) | 0.6 | (0.3–1.1) | 0.84 | 0.8 | (0.1–5.8) | 0.90 |
| Any | 4.0 | (0.7) | 0.3 | (0.3) | 7.8 | (1.3) | 0.7 | (0.5) | 0.6 | (0.3–1.0) | 0.83 | 0.9 | (0.1–7.7) | 0.92 |
| IV. Substance disorders | ||||||||||||||
| Alcohol Abuse | 1.0 | (0.3) | 1.7 | (0.3) | 3.8 | (1.2) | 3.1 | (1.2) | 0.4* | (0.1–0.9) | 0.81 | 1.1 | (0.4–2.9) | 0.80 |
| Alcohol Dependence | 0.4 | (0.2) | 1.2 | (0.5) | 0.8 | (0.3) | 2.1 | (1.0) | 0.3 | (0.1–1.3) | 0.89 | 1.4 | (0.4–4.9) | 0.81 |
| Drug Abuse | 0.5 | (0.2) | 0.5 | (0.1) | 1.6 | (0.8) | 1.7 | (0.9) | 0.4 | (0.1–1.7) | 0.87 | 0.8 | (0.3–2.4) | 0.86 |
| Drug Dependence | 0.2 | (0.2) | 0.2 | (0.1) | 1.9 | (0.6) | 0.7 | (0.4) | 0.2* | (0.0–0.8) | 0.84 | 0.7 | (0.1–5.2) | 0.89 |
| Any | 1.7 | (0.4) | 3.1 | (0.6) | 6.4 | (1.5) | 5.5 | (1.5) | 0.4* | (0.2–0.7) | 0.84 | 1.3 | (0.7–2.6) | 0.82 |
| V. Total disorders | ||||||||||||||
| Any | 25.1 | (2.4) | 6.4 | (1.1) | 31.0 | (2.5) | 8.9 | (1.8) | 0.9 | (0.6–1.2) | 0.71 | 1.8 | (1.0–3.3) | 0.75 |
| (n) | (825) | –6 | (432) | –7 | ||||||||||
Significant at the .05 level, two-sided test
Pre-matriculation onset disorders were defined as those with onsets at ages 0–17.
Post-matriculation onset disorders were defined as those with onsets at ages 18+.
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Based on a pooled within-survey logistic regression equation adjusting for between-survey variation in the association of age-sex with student status
There were no post-matriculation onsets of these disorders by definition, as the CIDI required onsets before age 8 (ADHD) or 18 (Conduct Disorder, Oppositional Defiant Disorder).
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 683 among female students who had no pre-matriculation history of specific phobias to a high of 821 among female students who had no pre-matriculation history of drug dependence, with a median sample size of 810. The sample size for having no pre-matriculation history of any disorder is 558 among female students.
Prevalence of post-matriculation onsets was estimated in the subsample of respondents who did not have a pre-matriculation history of the disorder. As a result, the sample size for each calculation varies across disorders from a low of 335 among female attriters who had no pre-matriculation history of specific phobias to a high of 425 among female attriters who had no pre-matriculation history of drug abuse, with a median sample size of 418. The sample size for having no pre-matriculation history of any disorder is 259 among female attriters.
Appendix Table 13.
Pooled proportions of male respondents with 12-month DSM-IV/CIDI mental disorders who received minimally adequate treatment for these disorders in the 12 months before interview by country income level separately among respondents who were current students, college attriters, and nonstudents in the same age range1
| Students
|
Attriters
|
Others
|
|||||||
|---|---|---|---|---|---|---|---|---|---|
| % | (se) | (n) | % | (se) | (n) | % | (se) | (n) | |
| Low/lower-middle | 11.1 | (6.5) | (4) | 0.0 | – | (0) | 5.7 | (3.2) | (4) |
| Upper-middle | 9.1 | (8.6) | (1) | 14.5 | (10.6) | (3) | 7.5 | (3.5) | (5) |
| High | 26.7 | (7.6) | (15) | 13.5 | (5.8) | (8) | 14.9 | (3.6) | (17) |
| Total | 18.3 | (4.5) | (20) | 13.0 | (4.8) | (11) | 9.9 | (2.0) | (26) |
| χ22 | 6.836* | 0.0 | 2.0 | ||||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Appendix Table 14.
Pooled proportions of female respondents with 12-month DSM-IV/CIDI mental disorders who received minimally adequate treatment for these disorders in the 12 months before interview by country income level separately among respondents who were current students, college attriters, and nonstudents in the same age range1
| Students
|
Attriters
|
Others
|
|||||||
|---|---|---|---|---|---|---|---|---|---|
| % | (se) | (n) | % | (se) | (n) | % | (se) | (n) | |
| Low/lower-middle | 0.7 | (0.7) | (1) | 2.7 | (2.7) | (1) | 6.5 | (4.3) | (4) |
| Upper-middle | 13.2 | (6.3) | (5) | 4.7 | (3.5) | (2) | 9.1 | (2.8) | (13) |
| High | 20.3 | (3.4) | (18) | 31.6 | (5.4) | (26) | 16.7 | (3.3) | (30) |
| Total | 14.7 | (2.5) | (24) | 20.1 | (4.1) | (29) | 11.0 | (2.0) | (47) |
| χ22 | 13.741* | 11.683* | 5.8 | ||||||
Significant at the .05 level, two-sided test
Respondents were limited to those in the Part II sample who were 18–22 years old at the time of interview and had not graduated from college.
Footnotes
Author contributions
Auerbach, Bruffaerts, Cuijpers, Ebert, and Kessler conceived of the paper and designed the analysis plan. Kessler and Sampson supervised the statistical analysis, which was carried out by Hwang and Liu. All coauthors participated in discussions to interpret the results. Auerbach and Kessler wrote the first draft, while all other coauthors participated in revisions and approved the final version of the manuscript for submission.
Declaration of Interest
In the past three years, Dr. Kessler has served as a consultant for or received research support from Johnson & Johnson Wellness and Prevention, the Lake Nona Life Project, and Shire Pharmaceuticals. Dr. Kessler is a co-owner of DataStat, Inc., a market research company that carries out healthcare research. The other authors report no biomedical financial interests or potential conflicts of interest.
Dr. Demyttenaere has served on advisory boards and speaker bureaus with Astra Zeneca, Eli Lilly, Lundbeck, Johnson & Johnson, Naurex and Servier. Dr. Demyttenaere has received grants from Eli Lilly, Fonds Ga Voor Geluk and Fonds voor Wetenschappelijk Onderzoek Vlaandereden.
References
- Blanco C, Okuda M, Wright C, Hasin DS, Grant BF, Liu SM, Olfson M. Mental health of college students and their non-college-attending peers: Results from the national epidemiologic study on alcohol and related conditions. Archives of General Psychiatry. 2008;65:1429–1437. doi: 10.1001/archpsyc.65.12.1429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cho SB, Llaneza DC, Adkins AE, Cooke M, Kendler KS, Clark SL, Dick DM. Patterns of substance use across the first year of college and associated risk factors. Frontiers in Psychiatry. 2015;6:152. doi: 10.3389/fpsyt.2015.00152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Costello EJ, Egger H, Angold A. 10-year research update review: The epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. Journal of the American Academy of Child and Adolescent Psychiatry. 2005;44:972–986. doi: 10.1097/01.chi.0000172552.41596.6f. [DOI] [PubMed] [Google Scholar]
- Costello EJ, Pine DS, Hammen C, March JS, Plotsky PM, Weissman MM, Biederman J, Goldsmith HH, Kaufman J, Lewinsohn PM, Hellander M, Hoagwood K, Koretz DS, Nelson CA, Leckman JF. Development and natural history of mood disorders. Biological Psychiatry. 2002;52:529–542. doi: 10.1016/s0006-3223(02)01372-0. [DOI] [PubMed] [Google Scholar]
- Eisenberg D, Golberstein E, Hunt J. Mental health and academic success in college. The BE Journal of Economic Analysis & Policy. 2009;9 [Google Scholar]
- Eisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. The American Journal of Orthopsychiatry. 2007;77:534–542. doi: 10.1037/0002-9432.77.4.534. [DOI] [PubMed] [Google Scholar]
- Fazel M, Hoagwood K, Stephan S, Ford T. Mental health interventions in schools 1: Mental health interventions in schools in high-income countries. The Lancet Psychiatry. 2014a;1:377–387. doi: 10.1016/S2215-0366(14)70312-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fazel M, Patel V, Thomas S, Tol W. Mental health interventions in schools in low-income and middle-income countries. The Lancet Psychiatry. 2014b;1:388–398. doi: 10.1016/S2215-0366(14)70357-8. [DOI] [PubMed] [Google Scholar]
- Fergusson DM, Horwood LJ. Early conduct problems and later life opportunities. Journal of Child Psychology and Psychiatry, and Allied Disciplines. 1998;39:1097–1108. [PubMed] [Google Scholar]
- Fergusson DM, Woodward LJ. Mental health, educational, and social role outcomes of adolescents with depression. Archives of General Psychiatry. 2002;59:225–231. doi: 10.1001/archpsyc.59.3.225. [DOI] [PubMed] [Google Scholar]
- First M, Spitzer R, Gibbon M, Williams B. Structured clinical interview for axis I DSM-IV disorders. New York State Psychiatric Institute: Biometrics Research Department; 1994. [Google Scholar]
- Fletcher JM. Adolescent depression: Diagnosis, treatment, and educational attainment. Health Economics. 2008;17:1215–1235. doi: 10.1002/hec.1319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Harkness J, Pennell B, Villar A, Gebler N, Aguilar-Gaxiola S, Bilgen I. Translation procedures and translation assessment in the World Mental Health survey initiative. In: Kessler RC, editor. The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press; New York: 2008. pp. 91–113. [Google Scholar]
- Haro JM, Arbabzadeh-Bouchez S, Brugha TS, de Girolamo G, Guyer ME, Jin R, Lepine JP, Mazzi F, Reneses B, Vilagut G, Sampson NA, Kessler RC. Concordance of the Composite International Diagnostic Interview version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research. 2006;15:167–180. doi: 10.1002/mpr.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hartley MT. Increasing resilience: Strategies for reducing dropout rates for college students with psychiatric disabilities. American Journal of Psychiatric Rehabilitation. 2010;13:295–315. [Google Scholar]
- Heeringa S, Wells E, Hubbard F, Mneimneh Z, Chiu W, Sampson N, Berglund P. Sample designs and sampling procedures. In: Kessler RC, editor. The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press; New York: 2008. pp. 14–32. [Google Scholar]
- Hunt J, Eisenberg D, Kilbourne AM. Consequences of receipt of a psychiatric diagnosis for completion of college. Psychiatric Services. 2010;61:399–404. doi: 10.1176/ps.2010.61.4.399. [DOI] [PubMed] [Google Scholar]
- Ishitani T, DesJardins S. A longitudinal investigation of dropout from college in the united states. Journal of College Student Retention: Research, Theory & Practice. 2002;4:173–201. [Google Scholar]
- Johnson JG, Cohen P, Dohrenwend BP, Link BG, Brook JS. A longitudinal investigation of social causation and social selection processes involved in the association between socioeconomic status and psychiatric disorders. Journal of Abnormal Psychology. 1999;108:490–499. doi: 10.1037//0021-843x.108.3.490. [DOI] [PubMed] [Google Scholar]
- Kendler KS, Myers J, Dick D. The stability and predictors of peer group deviance in university students. Social Psychiatry and Psychiatric Epidemiology. 2015;50:1463–1470. doi: 10.1007/s00127-015-1031-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kessler R, Üstun T. The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press; Cambridge: 2011. [Google Scholar]
- Kessler RC, Akiskal HS, Angst J, Guyer M, Hirschfeld RM, Merikangas KR, Stang PE. Validity of the assessment of bipolar spectrum disorders in the WHO CIDI 3.0. Journal of Affective Disorders. 2006;96:259–269. doi: 10.1016/j.jad.2006.08.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kessler RC, Foster CL, Saunders WB, Stang PE. Social consequences of psychiatric disorders, I: Educational attainment. The American Journal of Psychiatry. 1995;152:1026–1032. doi: 10.1176/ajp.152.7.1026. [DOI] [PubMed] [Google Scholar]
- Kessler RC, Ustun TB. The World Mental Health (WMH) survey initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) International Journal of Methods in Psychiatric Research. 2004;13:93–121. doi: 10.1002/mpr.168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kilpatrick DG, Acierno R, Saunders B, Resnick HS, Best CL, Schnurr PP. Risk factors for adolescent substance abuse and dependence: Data from a national sample. Journal of Consulting and Clinical Psychology. 2000;68:19–30. doi: 10.1037//0022-006x.68.1.19. [DOI] [PubMed] [Google Scholar]
- Knäuper B, Cannell CF, Schwarz N, Bruce ML, Kessler RC. Improving accuracy of major depression age-of-onset reports in the US National Comorbidity Survey. International Journal of Methods in Psychiatric Research. 1999;8:39–48. [Google Scholar]
- Kraemer HC, Kazdin AE, Offord DR, Kessler RC, Jensen PS, Kupfer DJ. Coming to terms with the terms of risk. Archives of General Psychiatry. 1997;54:337–343. doi: 10.1001/archpsyc.1997.01830160065009. [DOI] [PubMed] [Google Scholar]
- Lee S, Tsang A, Breslau J, Aguilar-Gaxiola S, Angermeyer M, Borges G, Bromet E, Bruffaerts R, de Girolamo G, Fayyad J, Gureje O, Haro JM, Kawakami N, Levinson D, Oakley Browne MA, Ormel J, Posada-Villa J, Williams DR, Kessler RC. Mental disorders and termination of education in high-income and low- and middle-income countries: Epidemiological study. The British Journal of Psychiatry: the Journal of Mental Science. 2009;194:411–417. doi: 10.1192/bjp.bp.108.054841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lewinsohn PM, Rohde P, Seeley JR. Major depressive disorder in older adolescents: Prevalence, risk factors, and clinical implications. Clinical Psychology Review. 1998;18:765–794. doi: 10.1016/s0272-7358(98)00010-5. [DOI] [PubMed] [Google Scholar]
- Merikangas KR, Nakamura EF, Kessler RC. Epidemiology of mental disorders in children and adolescents. Dialogues in Clinical Neuroscience. 2009;11:7–20. doi: 10.31887/DCNS.2009.11.1/krmerikangas. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miech RA, Caspi A, Moffitt TE, Wright BRE, Silva PA. Low socioeconomic status and mental disorders: A longitudinal study of selection and causation during young adulthood. American Journal of Sociology. 1999;104:1096–1131. [Google Scholar]
- Mojtabai R, Stuart EA, Hwang I, Susukida R, Eaton WW, Sampson N, Kessler RC. Long-term effects of mental disorders on employment in the national comorbidity survey ten-year follow-up. Social Psychiatry and Psychiatric Epidemiology. 2015;50:1657–1668. doi: 10.1007/s00127-015-1097-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- SAS Institute Inc. SAS/STATR software. SAS Institute Inc; Cary, NC: 2010. [Google Scholar]
- Scott KM, Al-Hamzawi AO, Andrade LH, Borges G, Caldas-de-Almeida JM, Fiestas F, Gureje O, Hu C, Karam EG, Kawakami N, Lee S, Levinson D, Lim CC, Navarro-Mateu F, Okoliyski M, Posada-Villa J, Torres Y, Williams DR, Zakhozha V, Kessler RC. Associations between subjective social status and DSM-IV mental disorders: Results from the World Mental Health Surveys. JAMA Psychiatry. 2014;71:1400–1408. doi: 10.1001/jamapsychiatry.2014.1337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stratton SL, O’Toole MD, Wetzel NJ. Are the factors affecting dropout behavior related to initial enrollment intensity for college undergraduates? Research in Higher Education. 2006;48:453–485. [Google Scholar]
- Ten Have M, Nuyen J, Beekman A, de Graaf R. Common mental disorder severity and its association with treatment contact and treatment intensity for mental health problems. Psychological Medicine. 2013;43:2203–2213. doi: 10.1017/S0033291713000135. [DOI] [PubMed] [Google Scholar]
- The World Bank. Data: Countries and economies. 2012 May 12; http://data.worldbank.org/country.
- Wang PS, Aguilar-Gaxiola S, Alonso J, Angermeyer MC, Borges G, Bromet EJ, Bruffaerts R, de Girolamo G, de Graaf R, Gureje O, Haro JM, Karam EG, Kessler RC, Kovess V, Lane MC, Lee S, Levinson D, Ono Y, Petukhova M, Posada-Villa J, Seedat S, Wells JE. Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO World Mental Health surveys. Lancet. 2007;370:841–850. doi: 10.1016/S0140-6736(07)61414-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wolter K. Introduction to variance estimation. Springer-Verlag; New York: 1985. [Google Scholar]
- Woodward LJ, Fergusson DM. Life course outcomes of young people with anxiety disorders in adolescence. Journal of the American Academy of Child and Adolescent Psychiatry. 2001;40:1086–1093. doi: 10.1097/00004583-200109000-00018. [DOI] [PubMed] [Google Scholar]
- World Health Organization. Mental health atlas 2014. Geneva: World Health Organization; 2015. [Google Scholar]