The WHO World Mental Health Surveys International College Student Project: Prevalence and Distribution of Mental Disorders

Abstract

Increasingly, colleges across the world are contending with rising rates of mental disorders, and in many cases, the demand for services on campus far exceeds the available resources. The present study reports initial results from the first stage of the WHO World Mental Health International College Student project, in which a series of surveys in 19 colleges across eight countries (Australia, Belgium, Germany, Mexico, Northern-Ireland, South-Africa, Spain, United States) were carried out with the aim of estimating prevalence and basic socio-demographic correlates of common mental disorders among first-year college students. Web-based self-report questionnaires administered to incoming first-year students (45.5% pooled response rate) screened for six common lifetime and 12-month DSM-IV mental disorders: major depression, mania/hypomania, generalized anxiety disorder, panic disorder, alcohol use disorder, and substance use disorder. We focus on the 13,984 respondents who were full-time students: 35% of whom screened positive for at least one of the common lifetime disorders assessed and 31% screened positive for at least one 12-month disorder. Syndromes typically had onsets in early-middle adolescence and persisted into the year of the survey. Although relatively modest, the strongest correlates of screening positive were older age, female sex, unmarried-deceased parents, no religious affiliation, non-heterosexual identification and behavior, low secondary school ranking, and extrinsic motivation for college enrollment. The weakness of these associations means that the syndromes considered are widely distributed with respect to these variables in the student population. Although the extent to which cost-effective treatment would reduce these risks is unclear, the high level of need for mental health services implied by these results represents a major challenge to institutions of higher education and governments.

Full text

Journal of Abnormal Psychology
WHO World Mental Health Surveys International College
Student Project: Prevalence and Distribution of Mental
Disorders
Randy P. Auerbach, Philippe Mortier, Ronny Bruffaerts, Jordi Alonso, Corina Benjet, Pim Cuijpers,
Koen Demyttenaere, David D. Ebert, Jennifer Greif Green, Penelope Hasking, Elaine Murray,
Matthew K. Nock, Stephanie Pinder-Amaker, Nancy A. Sampson, Dan J. Stein, Gemma Vilagut, Alan
M. Zaslavsky, Ronald C. Kessler, and WHO WMH-ICS Collaborators
Online First Publication, September 13, 2018. http://dx.doi.org/10.1037/abn0000362
CITATION
Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., Demyttenaere, K.,
Ebert, D. D., Green, J. G., Hasking, P., Murray, E., Nock, M. K., Pinder-Amaker, S., Sampson, N. A.,
Stein, D. J., Vilagut, G., Zaslavsky, A. M., Kessler, R. C., & WHO WMH-ICS Collaborators (2018,
September 13). WHO World Mental Health Surveys International College Student Project:
Prevalence and Distribution of Mental Disorders. Journal of Abnormal Psychology. Advance online
publication. http://dx.doi.org/10.1037/abn0000362

WHO World Mental Health Surveys International College Student Project:
Prevalence and Distribution of Mental Disorders
Randy P. Auerbach
Columbia University Philippe Mortier and Ronny Bruffaerts
Universitair Psychiatrisch Centrum - Katholieke Universiteit
Leuven (UPC-KUL), Campus Gasthuisberg, Leuven
Jordi Alonso
Health Services Research Unit, IMIM (Hospital del Mar
Medical Research Institute), Barcelona, Spain; Pompeu Fabra
University (UPF); and CIBER en Epidemiología y Salud Pública
(CIBERESP), Madrid, Spain
Corina Benjet
National Institute of Psychiatry Ramón de la Fuente Muñiz
Pim Cuijpers
Amsterdam Public Health Research Institute, Vrije Universiteit
Amsterdam
Koen Demyttenaere
Universitair Psychiatrisch Centrum - Katholieke Universiteit
Leuven (UPC-KUL), Campus Gasthuisberg, Leuven
David D. Ebert
Friedrich-Alexander University Erlangen Nuremberg Jennifer Greif Green
Boston University
Penelope Hasking
Curtin University Elaine Murray
Ulster University
Matthew K. Nock
Harvard University Stephanie Pinder-Amaker and Nancy A. Sampson
Harvard Medical School
Dan J. Stein
University of Cape Town Gemma Vilagut
Health Services Research Unit, IMIM (Hospital del Mar
Medical Research Institute), Barcelona, Spain; Pompeu Fabra
University (UPF); and CIBER en Epidemiología y Salud Pública
(CIBERESP), Madrid, Spain
Alan M. Zaslavsky and Ronald C. Kessler
Harvard Medical School WHO WMH-ICS Collaborators
Randy P. Auerbach, Department of Psychiatry, Columbia University.
Philippe Mortier and Ronny Bruffaerts, Universitair Psychiatrisch Centrum
- Katholieke Universiteit Leuven (UPC-KUL), Campus Gasthuisberg, Leu-
ven. Jordi Alonso, Health Services Research Unit, IMIM (Hospital del Mar
Medical Research Institute), Barcelona, Spain; Pompeu Fabra University
(UPF); and CIBER en Epidemiología y Salud Pública (CIBERESP), Ma-
drid, Spain. Corina Benjet, Department of Epidemiologic and Psychosocial
Research, National Institute of Psychiatry Ramón de la Fuente Muñiz. Pim
Cuijpers, Department of Clinical, Neuro and Developmental Psychology,
Amsterdam Public Health Research Institute, Vrije Universiteit Amster-
dam. Koen Demyttenaere, Universitair Psychiatrisch Centrum - Katholieke
Universiteit Leuven (UPC-KUL), Campus Gasthuisberg, Leuven. David D.
Ebert, Department for Psychology, Clinical Psychology and Psychother-
apy, Friedrich-Alexander University Erlangen Nuremberg. Jennifer Greif
Green, School of Education, Boston University. Penelope Hasking, School
of Psychology and Speech Pathology, Curtin University. Elaine Murray,
School of Biomedical Sciences, Ulster University. Matthew K. Nock,
Department of Psychology, Harvard University. Stephanie Pinder-Amaker,
Department of Psychiatry, Harvard Medical School. Nancy A. Sampson,
Department of Health Care Policy, Harvard Medical School; Dan J. Stein,
Department of Psychiatry and MRC Unit on Risk and Resilience in Mental
Disorders, University of Cape Town. Gemma Vilagut, Health Services
Research Unit, IMIM (Hospital del Mar Medical Research Institute),
Barcelona, Spain; Pompeu Fabra University (UPF); and CIBER en Epide-
miología y Salud Pública (CIBERESP), Madrid, Spain. Alan M. Zaslavsky,
Ronald C. Kessler, and on behalf of the WHO WMH-ICS Collabora-
tors, Department of Health Care Policy, Harvard Medical School.
continued
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Journal of Abnormal Psychology
© 2018 American Psychological Association 2018, Vol. 1, No. 999, 000
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1

Increasingly, colleges across the world are contending with rising rates of mental disorders, and in many
cases, the demand for services on campus far exceeds the available resources. The present study reports
initial results from the first stage of the WHO World Mental Health International College Student project,
in which a series of surveys in 19 colleges across 8 countries (Australia, Belgium, Germany, Mexico,
Northern Ireland, South Africa, Spain, United States) were carried out with the aim of estimating
prevalence and basic sociodemographic correlates of common mental disorders among first-year college
students. Web-based self-report questionnaires administered to incoming first-year students (45.5%
pooled response rate) screened for six common lifetime and 12-month DSM–IV mental disorders: major
depression, mania/hypomania, generalized anxiety disorder, panic disorder, alcohol use disorder, and
substance use disorder. We focus on the 13,984 respondents who were full-time students: 35% of whom
screened positive for at least one of the common lifetime disorders assessed and 31% screened positive
for at least one 12-month disorder. Syndromes typically had onsets in early to middle adolescence and
persisted into the year of the survey. Although relatively modest, the strongest correlates of screening
positive were older age, female sex, unmarried-deceased parents, no religious affiliation, nonheterosexual
identification and behavior, low secondary school ranking, and extrinsic motivation for college enroll-
ment. The weakness of these associations means that the syndromes considered are widely distributed
with respect to these variables in the student population. Although the extent to which cost-effective
treatment would reduce these risks is unclear, the high level of need for mental health services implied
by these results represents a major challenge to institutions of higher education and governments.
WHO WMH-ICS Collaborators: Australia: Mark Boyes, School of
Psychology & Speech Pathology, Curtin University; Glenn Kiekens,
School of Psychology & Speech Pathology, Curtin University and RG
Adult Psychiatry KU Leuven, Belgium; Germany: Harald Baumeister,
University of Ulm; Fanny Kaehlke, Matthias Berking, Friedrich-Alexander
University Erlangen Nuremberg; Mexico: Adrián Abrego Ramírez, Uni-
versidad Politécnica de Aguascalientes; Guilherme Borges, Instituto Na-
cional de Psiquiatría Ramón de la Fuente; Anabell Covarrubias Díaz,
Universidad La Salle Noroeste; Ma. Socorro Durán, Universidad De La
Salle Bajío; Rogaciano González, Universidad De La Salle Bajío, campus
Salamanca; Raúl A. Gutiérrez-García, Universidad De La Salle Bajío,
campus Salamanca & Universidad Politécnica de Aguascalientes; Alicia
Edith Hermosillo de la Torre, Universidad Autónoma de Aguascalientes;
Kalina Isela Martinez Martínez, Universidad Autónoma de Aguascalientes,
Departamento de Psicología, Centro Ciencias Sociales y Humanidades;
María Elena Medina-Mora, Instituto Nacional de Psiquiatría Ramón de la
Fuente; Humberto Mejía Zarazúa, Universidad La Salle Pachuca; Gustavo
Pérez Tarango, Universidad De La Salle Bajío; María Alicia Zavala Ber-
bena, Universidad De La Salle Bajío; Northern Ireland: Siobhan O’Neill,
Psychology Research Institute, Ulster University; Tony Bjourson, School
of Biomedial Sciences, Ulster University; South Africa: Christine Lochner,
Janine Roos and Lian Taljaard, MRC Unit on Risk & Resilience in Mental
Disorders, Department of Psychiatry, Stellenbosch University; Jason
Bantjes and Wylene Saal, Department of Psychology, Stellenbosch Uni-
versity; Spain: The UNIVERSAL study group also includes Itxaso Alayo,
Pompeu Fabra University; José Almenara, Cadiz University; Laura Ball-
ester, IMIM (Hospital del Mar Medical Research Institute); Gabriela Bar-
baglia, Pompeu Fabra University; Maria Jesús Blasco, Pompeu Fabra
University; Pere Castellví, IMIM (Hospital del Mar Medical Research
Institute); Ana Isabel Cebria
`, Parc Taulí Hospital Universitari; Enrique
Echeburúa, Basque Country University; Andrea Gabilondo, Osakidetza-
Basque Health Service; Carlos García-Forero, Pompeu Fabra University;
Álvaro Iruin, Hospital Universitario Donostia-Osakidetza; Carolina
Lagares, Cadiz University; Andrea Miranda-Mendizábal, Pompeu Fabra
University; Oleguer Parès-Badell, Pompeu Fabra University; María Teresa
Pérez-Vázquez, Miguel Hernández University; José Antonio Piqueras,
Miguel Hernández University; Miquel Roca, Illes Balears University; Jesús
Rodríguez-Marín, Miguel Hernández University; Margalida Gili, Illes
Balears University; Victoria Soto-Sanz, Miguel Hernández University and
Margarida Vives, Illes Balears University.
Funding to support this project was received from the National Institute
of Mental Health (NIMH) R56MH109566 (Randy P. Auerbach), and the
content is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institutes of Health or NIMH;
the Belgian Fund for Scientific Research (11N0514N/11N0516N/
1114717N; Philippe Mortier), the King Baudouin Foundation (2014-
J2140150-102905) (RB), and Eli Lilly (IIT-H6U-BX-I002) (RB, PM);
BARMER, a health care insurance company, for project StudiCare (DDE);
ZonMw (Netherlands Organisation for Health Research and Development;
grant number 636110005) and the PFGV (PFGV; Protestants Fonds voor
de Geestelijke Volksgezondheid) in support of the student survey project
(PC); South African Medical Research Council (DJS); Fondo de Investi-
gación Sanitaria, Instituto de Salud Carlos III - FEDER (PI13/00343),
ISCIII (Río Hortega, CM14/00125), ISCIII (Sara Borrell, CD12/00440);
European Union Regional Development Fund (ERDF) EU Sustainable
Competitiveness Programme for Northern Ireland, Northern Ireland Public
Health Agency (HSC R&D), and Ulster University (TB); Ministerio de
Sanidad, Servicios Sociales e Igualdad, PNSD (Exp. 2015I015); DIUE
Generalitat de Catalunya (2017 SGR 452; 2014 SGR 748), FPU (FPU15/
05728) (JA); The World Mental Health International College Student
project is carried out as part of the WHO World Mental Health (WMH)
Survey Initiative. The WMH survey is supported by the National Institute
of Mental Health NIMH R01MH070884, the John D. and Catherine T.
MacArthur Foundation, the Pfizer Foundation, the U.S. Public Health
Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fog-
arty International Center (FIRCA R03-TW006481), the Pan American
Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceuti-
cal, GlaxoSmithKline, and Bristol-Myers Squibb (RK). None of the
funders had any role in the design, analysis, interpretation of results, or
preparation of this article. We thank the staff of the WMH Data Collection
and Data Analysis Coordination Centres for assistance with instrumenta-
tion, fieldwork, and consultation on data analysis. A complete list of all
within-country and cross-national WMH publications can be found at: http://
www.hcp.med.harvard.edu/wmh/. Declarations of Interest: the past 3 years,
Ronald C. Kessler received support for his epidemiological studies from
Sanofi Aventis; was a consultant for Johnson & Johnson Wellness and
Prevention, Shire, Takeda; and served on an advisory board for the Johnson
& Johnson Services Inc. Lake Nona Life Project. Ronald C. Kessler is a
co-owner of DataStat, Inc., a market research firm that carries out health-
care research. David D. Ebert has received consultant fees and served on
the scientific advisory board for several companies, including MindDis-
trict, Lantern, Schoen Kliniken, and German health insurance companies
(BARMER, Techniker Krankenkasse). He also is a stakeholder in the
institute for health training online (GET.ON), which aims to implement
scientific findings related to digital health interventions into routine care.
Dan J. Stein has received research grants and/or consultancy honoraria
from Biocodex, Lundbeck, Servier, and Sun.
Correspondence concerning this article should be addressed to Randy P.
Auerbach, Department of Psychiatry, Columbia University, 1051 Riverside
Drive, New York, NY 10032. E-mail: rpa2009@columbia.edu
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2AUERBACH ET AL.

General Scientific Summary
Roughly 1/3 of first-year students in 19 colleges across 8 countries who participated in a self-report
survey screened positive for at least 1 common DSM–IV anxiety, mood, or substance disorder (35.3%
lifetime, 31.4% 12 months). Basic sociodemographic correlates were modest, showing that the
syndromes were widely distributed rather than concentrated in 1 small segment of the student
population.
Keywords: college, mental disorders, lifetime prevalence, 12-month prevalence
Supplemental materials: http://dx.doi.org/10.1037/abn0000362.supp
College students are a key population segment for determining
the economic growth and success of a country. Until recently, little
attention was paid to identifying mental disorders among college
students other than in the United States (Blanco et al., 2008; Cho
et al., 2015; Eisenberg, Golberstein, & Gollust, 2007; Kendler,
Myers, & Dick, 2015; Mojtabai et al., 2015). However, given that
the college years are a peak period for onset of many common
mental disorders, particularly mood, anxiety, and substance use
disorders (de Girolamo, Dagani, Purcell, Cocchi, & McGorry,
2012; Kessler et al., 2007), it is not surprising that epidemiological
studies consistently find high prevalence of these disorders among
college students (Hunt & Eisenberg, 2010; Ibrahim, Kelly, Adams,
& Glazebrook, 2013; Pedrelli, Nyer, Yeung, Zulauf, & Wilens,
2015). This high prevalence is significant not only for the distress
it causes at a time of major life transition, but also because it is
associated with substantial impairment in academic performance
(Auerbach et al., 2016; Bruffaerts et al., 2018) as well as suicidal
thoughts and behaviors (Mortier, Auerbach, et al., 2018). While
timely and effective treatment is important, the number of students
in need of treatment for these disorders far exceeds the resources
of most counseling centers, resulting in substantial unmet need for
treatment of mental disorders among college students (Auerbach et
al., 2016; Beiter et al., 2015; Xiao et al., 2017).
Emerging adulthood—which includes the college years—repre-
sents a distinct period of development straddling the adolescent
and young adulthood life stages. While emerging adulthood (ages
18–29 years) shares many features with these earlier and later
periods, it is defined by increased autonomy from parents (e.g.,
leaving the home), marked shifts in social roles, and relational
instability (Arnett, 2000; Sussman & Arnett, 2014). In contrast to
adolescents, emerging adults have reached sexual maturity and
often pursue a range of educational and occupational opportunities
(e.g., tertiary education, full-time work, combination of education
and work). However, in comparison with adults, emerging adults
have not yet established a stable life structure (e.g., long-term
romantic relationship, stable job). More broadly, Sussman and
Arnett (2014) differentiate emerging adulthood from other life
stages across five dimensions: (a) identity exploration, (b) feeling
in-between, (c) entertaining possibilities, (d) self-focus, and (e)
instability. While these dimensions are developmentally normative
among college students, each has potential mental health implica-
tions, especially during a period when there is a high likelihood of
disengaging from treatment (see Auerbach et al., 2016; Stroud,
Mainero, & Olson, 2013). For example, although identity explo-
ration is developmentally appropriate, within collegiate environ-
ments in which students can reinvent themselves, it is not without
its challenges, particularly if students feel they have made the
wrong choices. Similarly, college is characterized by substantial
instability—changes in romantic status (including sexual orienta-
tion), peer groups, course selection (i.e., major, concentration), and
career choices. This instability may contribute to reduced social
support and increased stress, which are known contributors to
mental disorders (Slavich & Auerbach, 2018). Thus, while there is
doubtlessly overlap with other life stages, the college years repre-
sent a distinct period in which there is a critical need to improve
early identification and treatment for debilitating mental disorders.
It is a challenge for universities to determine whether and, if so,
how to identify college students for outreach and treatment of
existing mental disorders or for preventive interventions when at
high risk of mental disorders and, once identified, how to offer
services to the very large proportion of students likely to profit
from either treatment or preventive interventions. Internet-based
cognitive behavior therapy (CBT), which has been shown to have
effects equivalent to those of face-to-face CBT (Andersson, Cui-
jpers, Carlbring, Riper, & Hedman, 2014), is an attractive option
for addressing the latter challenges based on its low cost and ease
of implementation. However, little is known about the disorders
for which such interventions are most needed or the effectiveness
of internet-based CBT among college students. The WHO World
Mental Health (WMH) International College Student (WMH-ICS)
project was launched in an effort to address this critical knowledge
gap. The first stage of the WMH-ICS is administering web-based
mental health needs assessment surveys to convenience samples of
entering first-year students in colleges and universities throughout
the world and then following these students over their college
careers to examine patterns and baseline predictors of onset and
persistence of common mental disorders and impairments in aca-
demic performance associated with those disorders. As part of this
initiative, a number of surveys also embed pragmatic clinical trials
that screen for mood and anxiety disorders and then randomize
screened positives either to Internet-based CBT or usual care.
Baseline survey data are then being used in the latter samples to
develop precision medicine treatment models aimed at guiding the
subsequent targeting of Internet-based interventions to the students
most likely to be helped by them.
The current report presents data from the first year of baseline
WMH-ICS surveys among first-year college students from eight
countries. In carrying out these surveys, we aimed to determine the
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3
COLLEGE MENTAL HEALTH

feasibility of successfully implementing large-scale cross-national
surveys of first-year college students across a number of institu-
tions using a web-based screening assessment of common mental
disorders. We also aimed to determine whether such surveys
would yield similarly high prevalence estimates of common
DMS-IV disorders and low estimates of treatment as in previous
college surveys and in the representative sample of 1,572 college
students across 21 countries surveyed in 2-hr face-to-face inter-
views as part of the larger WMH surveys (Auerbach et al., 2016).
The WHO Composite International Diagnostic Interview (CIDI;
Kessler & Ustün, 2004), a validated fully structured diagnostic
interview that generates diagnoses according to the definitions and
criteria of the Diagnostic and Statistical Manual of Mental Disor-
ders, Fourth Edition (American Psychiatric Association, 1994),
was used in the WMH surveys. One fifth of college students in
those surveys had 12-month DSM–IV/CIDI disorders, with anxiety
and mood disorders the most common class of disorders. Only
16.4% of all 12-month cases received any treatment for these
disorders. One of our aims in the current report is to determine
whether comparable estimates of prevalence and treatment are
found in the web-based WMH-ICS surveys. We also aimed in
the WMH-ICS surveys to determine if the sociodemographic cor-
relates of 12-month mental disorders in the WMH-ICS surveys
would be the same as in previous surveys of college student mental
health. These associations have typically been found to be small,
but with women having higher rates of anxiety and mood disorders
than men, men having higher rates of substance use disorders than
women, and socioeconomic background being inversely related to
prevalence of all disorders (Chen & Jacobson, 2012; Eisenberg,
Hunt, & Speer, 2013).
Method
Samples
The initial round of WMH-ICS surveys was administered in a
convenience sample of 19 colleges and universities (henceforth
referred to as “colleges”) in eight mostly high-income countries
(Australia, Belgium, Germany, Mexico, Northern Ireland, South
Africa, Spain, and United States). Each institution received ethics
approval to participate in the project and all participants provided
consent. web-based self-report questionnaires were administered
to all incoming first-year students in each participating school
(seven private, 12 public) between October 2014 and February
2017. A total of 14,371 questionnaires were completed, with
sample sizes ranging from a low of 633 in Australia to a high of
4,580 in Belgium. The response rates were quite variable across
countries, from a low of 7.0% in Australia to a high of 79.3% in
Mexico. The weighted (by achieved sample size) mean response
rate across all surveys was 45.5%. Table 1 summarizes the sample
design in each participating country.
Procedures
Before initiating data collection, the country-specific Institu-
tional Review Boards provided approval for a project entitled,
Survey on College Adjustment (Australia: HR65/2016; Belgium:
S54803(ML8724); Germany: 193_16 B; Mexico: CEI/C/032/
2016; Northern Ireland: REC/15/0004; South Africa: N13/10/149;
Spain: 2013/5252/I; United States: 2015P002664). All incoming
first-year students in the participating schools were invited to
participate in a web-based self-report health survey. Mode of
contact varied widely across schools but in all cases other than in
Mexico consisted of an approach that attempted to recruit 100% of
incoming first-year students either as part of a health evaluation, as
part of the registration process, or in a stand-alone survey admin-
istered to students via their student e-mail addresses. Attempts
were then made to convert initial nonrespondents through a series
of personalized reminder e-mails. Incentives were used in the final
stages of recruitment (e.g., a raffle for store credit coupons, movie
passes) in 10 schools. In addition, one country (Spain) used an
“end-game” strategy consisting of a random sample of nonrespon-
dents at the end of the normal recruitment period that was offered
incentives for participation. The sampling scheme was quite dif-
ferent in Mexico, where 100% of entering first-year students were
invited to participate in conjunction with mandatory activities that
varied from school to school (e.g., student health evaluations;
tutoring sessions) and time was set aside for completing the survey
during those activities. No follow-up of nonrespondents was car-
ried out in Mexico because it was assumed that students who failed
to complete the survey even though time was set aside for it during
mandatory activities were firm nonrespondents. Informed consent
was obtained before administering the survey in all countries. The
text statement used to obtain informed consent varied across
schools and was approved by the institutional review boards of the
organizations coordinating the surveys in each country.
Measures
The self-report questionnaire was developed in English and
translated into local languages using a translation, back-
translation, and harmonization protocol that expanded on the
standard WHO protocol in ways developed by survey method-
ologists to maximize cross-national equivalence of meaning and
consistency of measurement (Harkness, Pennell, Villar, Gebler,
& Aguilar-Gaxiola, 2008).
Mental disorders. The questionnaire included short validated
self-report screening scales for lifetime and 12-month prevalence
of six common DSM–IV mood (major depressive disorder, mania/
hypomania), anxiety (generalized anxiety disorder, panic disor-
der), and substance (alcohol abuse or dependence [AUD], drug
abuse or dependence, involving either cannabis, cocaine, any other
street drug, or a prescription drug either used without a prescrip-
tion or used more than prescribed to get high, buzzed, or numbed
out). This is a larger set of disorders than used in most previous
college mental health surveys, some of which focused only on
depression (for review see Ibrahim et al., 2013) or screening scales
of current anxious and depressive symptoms (Mahmoud, Staten,
Hall, & Lennie, 2012). Although a larger set of disorders is used
in the face-to-face WMH surveys (Scott, de Jonge, Stein, &
Kessler, in press), participating colleges were unwilling to admin-
ister student surveys that would be long enough to include all those
disorders. The six disorders in the core WMH-ICS surveys were a
compromise that included the disorders associated with the highest
levels of role impairment among college students in the WMH
surveys. As an indication that these disorders capture the vast
majority of students with seriously impairment psychopathology,
83% of the college students in the WMH surveys who reported
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4AUERBACH ET AL.

Table 1
WMH-ICS Sample Characteristics
Country Number of participating
universities Total size of
the universities Number of incoming
freshmen eligible
Number of incoming
freshmen
participated Response
rate Survey field
dates Sampling and procedures
Australia 1 public ⬃45,000 9,042 633 7.0% 2016 All incoming freshmen were invited to
participate through e-mail. Five
reminder e-mails were sent with
personalized links to the survey.
Conditional incentives were applied
(movie passes).
Belgium 1 public ⬃40,000 8,530 4,580 53.7% 2014–16 All incoming freshmen were invited
for a psychomedical check-up in the
student mental health center.
Surveys were completed in the
waiting room. Students who did not
show up for the psychomedical
check-up received up to eight
reminder emails. Conditional
incentives were applied (store credit
coupons).
Germany 1 public ⬃40,000 5,064 677 13.4% 2016–17 All incoming freshmen were invited to
participate through e-mail. Six
reminder e-mails were sent with
personalized links to the survey.
Conditional incentives were applied
(store credit coupons).
Mexico 4 private/2 public ⬃28,000 5,293 4,199 79.3% 2016 All incoming freshmen were eligible
for the survey. Initial contact
differed by university: survey
included in an obligatory health
evaluation (one university), as part
of obligatory group tutoring sessions
(one university), or as part of
required classes (two universities) or
teacher evaluations (two
universities). Two universities sent
reminder e-mails (tutors sent out
e-mails to their tutees; in a required
class of personal development,
reminders were sent out by faculty).
No incentives were applied.
(table continues)
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5
COLLEGE MENTAL HEALTH

Table 1 (continued)
Country Number of participating
universities Total size of
the universities Number of incoming
freshmen eligible
Number of incoming
freshmen
participated Response
rate Survey field
dates Sampling and procedures
Northern Ireland 1 public ⬃25,000 4,359 739 17.0% 2015 All incoming freshmen due to register
were invited to participate.
Following registration, ID numbers
and links to the survey were
provided. Five reminder e-mails/text
messages were sent with
personalized links to the survey. A
sixth reminder involved a researcher
telephoning nonresponders. All
responders were entered into a
number of draws to win an iPad.
South Africa 1 public ⬃30,000 5,338 686 12.9% 2015 All incoming freshmen were invited to
participate through e-mail. Eight
reminder e-mails and one text
message were sent with
personalized links to the survey.
Conditional incentives were applied
(5x R1000 draw).
Spain 5 public ⬃96,000 16,332 2,118 13.0% 2014–15 All incoming freshmen were eligible
for the survey. Initial contact
differed by university [information
stands, information sessions in
classrooms, through the university’s
website]. Four reminder emails were
sent with personalized links to the
survey. Conditional monetary
incentives were applied.
Additionally, an end-game strategy
was implemented by selecting a
random proportion of
nonrespondents and offering all of
them a monetary incentive.
United States 3 private ⬃21,800 4,382 739 16.9% 2015–16 All incoming freshmen were invited to
participate through e-mail. Three
reminder e-mails were sent with
personalized links to the survey.
Conditional incentives were applied
(gift cards).
Total 12 public/7 private ⬃326,000 58,340 14,371 45.5
ⴱ
2014–17
ⴱ
Weighted by achieved sample size.
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6AUERBACH ET AL.

suicidal ideation in the 12 months before interview met criteria for
one or more of these six disorders during that same 12-month time
period (Mortier, Cuijpers, et al., 2018).
The assessments of five of the six disorders were based on the
Composite International Diagnostic Interview Screening Scales
(CIDI-SC; Kessler, Calabrese et al., 2013; Kessler & Ustün, 2004).
The exception was the screen for AUD, which was based on the
Alcohol Use Disorders Identification Test (AUDIT; Saunders,
Aasland, Babor, de la Fuente, & Grant, 1993). The CIDI-SC scales
have been shown to have good concordance with blinded clinical
diagnoses based on the Structured Clinical interview for DSM–IV
(SCID; First, Spitzer, Gibbon, & Williams, 1994), with AUC in the
range 0.70–0.78 (Kessler, Calabrese et al., 2013; Kessler, Santi-
ago et al., 2013). However, these validation studies have not yet
been carried out in samples of college students. The version of the
AUDIT we used, which defined alcohol use disorder as either a
total score of 8⫹or a score of 4⫹on the AUDIT dependence
questions (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001),
has been shown to have concordance with clinical diagnoses in the
range AUC ⫽0.78–0.91 (Reinert & Allen, 2002). Additional
items taken from the CIDI (Kessler & Ustün, 2004) were used to
assess age-of-onset of each disorder and number of lifetime years
with symptoms.
Sociodemographic correlates. Only a handful of basic so-
ciodemographic variables were included in the survey. Gender was
assessed by asking respondents whether they identified themselves
as male, female, transgender (male-to-female, female-to-male), or
“other.” Respondent age was divided into three categories (18
years, 19 year, 20 or more years old). Parental educational level
was assessed for father and mother separately (none, elementary,
secondary, some postsecondary, college graduate, doctoral de-
gree), and was categorized into high (college graduate or more),
medium (some postsecondary education), and low (secondary
school or less) based on the highest-of-both parents’ educational
level. Parental marital status was dichotomized into “parents not
married or parent(s) deceased” versus “parents married and both
alive.” Respondents were asked about the urbanicity of the place
they were raised (small city, large city, town or village, suburbs,
rural area), and their religious background (categorized into Chris-
tian, other religion, no religion). Sexual orientation was classified
into heterosexual, gay or lesbian, bisexual, asexual, not sure, and
other. Additional questions were asked about the extent to which
respondents were attracted to men and women and the gender(s) of
people they had sex with (if any) in the past 5 years. Respondents
were categorized into the following categories: heterosexual with
no same-sex attraction, heterosexual with same-sex attraction,
nonheterosexual without same-sex sexual intercourse, and nonhet-
erosexual with same-sex sexual intercourse.
College-related correlates. Respondents were asked where
they ranked academically compared with other students at the time
of their high school graduation (from top 5% to bottom 10%;
categorized into quartiles) and what their most important reason
was to go to university. Based on the results of a tetrachoric factor
analysis (see online supplemental Table 1) the most important
reason to go to university was categorized into extrinsic reasons
(i.e., family wanted me to, my friends were going, teachers advised
me to, did not want to get a job right away) versus intrinsic reasons
(to achieve a degree, I enjoy learning and studying, to study a
subject that really interests me, to improve job prospects generally,
to train for specific type of job). Respondents were also asked
where they were living during the first semester of the academic
year (parents’, other relative’s, or own home, college hall of
residence, shared house, apartment, or flat/private hall of resi-
dence, other), and if they expect to work during the school year.
Analysis Methods
Weighting. We noted above that one Spanish survey used an
“end-game” strategy in which a random sample of nonrespondents
at the end of the normal recruitment period was offered incentives
for participation. Respondents in this end-phase were given a
weight equal to 1/p, where p represented the proportion of nonre-
spondents at the end of the normal recruitment period that was
included in the end-game, to adjust for the undersampling of these
hard-to-recruit respondents. In addition, in an effort to make in-
crease the representativeness of the WMH-ICS sample in each
college with respect to known population characteristics, a post-
stratification weight was applied to the survey data to adjust for
differences between survey respondents and nonrespondents on
sociodemographic information made available about the student
body by college officials. Standard methods for poststratification
weighting were used for this purpose (Groves & Couper, 1998). In
the case of the Spanish survey, this meant that the data were
doubly weighted: once to include the end-game weight and then
with the poststratification weight applied to those weighted data.
Item-level missing data in the completed surveys were imputed
using the method of multiple imputation (MI) by chained equa-
tions (van Buuren, 2012). Four kinds of item-missing data were
imputed simultaneously in this way. The first was a 50% random
subsampling of the drug use section in Belgium, which was done
to reduce interview length. The second was the complete absence
of the panic disorder section in Mexico, Northern Ireland, and
South Africa due to a skip logic error. The third was the complete
absence of some sociodemographic variables in Australia, Bel-
gium, and Spain because of a decision by school administrators not
to assess those variables (sexual orientation, current living situa-
tion, expected student job, and most important reason for going to
college in all these countries; parent education and marital status in
Australia and Belgium; religion in Australia; self-reported high
school ranking in Belgium). The fourth were invalid responses
to individual questions made by some respondents in each
country, although this fourth category was uncommon: less than
0.1% for lifetime disorders, 0.0%–2.3% for 12-month disorders
other than AUD, and in the range 3.0%–9.3% (3.8%–7.0%
interquartile range) for AUD, 0.0%–12.0% (interquartile range
1.9%–2.7%) for disorder age-of-onset, 0.0%–24.6% (interquar-
tile range 2.4%–8.8%) for disorder persistence, 1.8%–25.4%
(interquartile range 8.8%–24.1%) for most important reasons for
attending college, 1.0%–10.8% (interquartile range 3.0%–3.4%)
for high school ranking, and 0.0%–7.0% for the other sociodemo-
graphic and college-related variables.
Prevalence estimates are reported as weighted within-country
proportions, with associated MI-adjusted standard errors obtained
through the Taylor series linearization method. Estimates of age of
onset and proportional persistence (i.e., the percentage of lifetime
years with symptoms of each disorder from the age-of-onset to the
age when survey was completed) are reported as median values
with associated interquartile ranges. To obtain pooled estimates of
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7
COLLEGE MENTAL HEALTH

prevalence, age of onset, and proportional persistence across coun-
tries, each country was given an equal sum of weights.
Substantive analyses. All substantive analyses were con-
ducted with SAS Version 9.4 (SAS Institute Inc, 2010), and
weighted data were used in all data analytic procedures. Logistic
regression analyses were used to identify correlates of lifetime and
12-month mental disorders in the total sample and 12-month
disorders among lifetime cases. Logistic regression coefficients
and their 95% confidence intervals (CIs; ⫹/⫺1.96 times their
MI-based standard errors) were exponentiated to create odds ratios
(OR) and associated 95% CIs. Negative binomial regression was
used to identify correlates of number of years with symptoms
among lifetime cases. These regression coefficients and their 95%
CIs were exponentiated to create persistence rate ratios (RR) and
their associated 95% CIs. Estimates were pooled across countries
to examine both main effects and all possible two-way interactions
among correlates, with risk for Type I error adjusted for using the
false discovery rate method (Q ⫽0.05; Benjamini & Hochberg,
1995). We then examined between-country variation in associa-
tions by including correlate-by-country interactions and an ad-
justed interaction dummy coding scheme that kept the product of
all country-specific ORs and RRs equal to one. The latter method
allowed us to detect significant between-country variation by
evaluating the statistical significance of deviation of within-
country coefficients from the median 1.0 value. Statistical signif-
icance in all analyses was evaluated using two-sided MI-based
tests with significance level ␣set at 0.05.
Results
Preliminary Analyses
Although there were 14,371 respondents in the total sample, 35
respondents were excluded because of missing information on
gender or full-time status, which we required as anchor variables
for purposes of imputing other missing values. An additional 302
respondents were excluded because they were part-time students.
Most of these students came from the Australian sample and were
older, full-time employed people who would normally be expected
to access mental health services, if they were needed, through their
employer or employer-sponsored health insurance rather than
through their college. In addition, preliminary analyses reported
below showed that the majority of the 50 remaining students who
identified either as transgender or “other” rather than as male or
female endorsed a number of mental disorders and experienced
considerable impairment, leading us to focus on them in a separate
report. The analyses reported here are based on the remaining
13,984 respondents.
Prevalence of Common Mental Disorders
Thirty-five percent of the 13,984 respondents in the main sam-
ple reported at least one of the lifetime mental disorders assessed
in the survey (see Table 2). Prevalence was similar for the addi-
tional respondents excluded because of missing information on
gender or full-time student status (35.9%) and because of being
part-time (41.2%), whereas the students who self-identified as
either transgender or “other” had much higher lifetime prevalence
of any disorder (76.5%). Twelve-month prevalence of any of the
Table 2
Prevalence, Age of Onset, and Proportional Persistence of Any Mental Disorder in the WMH-ICS by Country
Country Sample
Size Lifetime % [95% CI] 12-month % [95% CI] 12-month/lifetime
% [95% CI] Age of onset median
[95% CI] [IQR]
Proportional persistence
a
median
[95% CI] [IQR]
All countries
b
13,984 35.3 [34.1, 36.6] 31.4 [30.2, 32.6] 89.0 [87.6, 90.4] 14.2 [14.1, 14.4] [12.0–15.9] 65.0 [62.5, 67.5] [41.2–80.3]
Australia 529 48.3 [43.7, 52.9] 43.3 [38.7, 47.9] 89.7 [85.7, 93.7] 14.5 [13.8, 15.1] [12.2–16.5] 69.4 [62.9, 75.9] [45.3–83.9]
Belgium 4,490 22.4 [21.2, 23.7] 19.1 [17.9, 20.2] 85.0 [82.5, 87.4] 14.2 [14.0, 14.5] [11.7–15.8] 60.9 [56.6, 65.2] [34.5–78.5]
Germany 652 41.1 [37.1, 45.1] 36.2 [32.3, 40.0] 88.0 [83.9, 92.1] 13.9 [13.3, 14.4] [11.4–15.9] 60.8 [55.0, 66.6] [40.2–78.3]
Mexico 4,190 27.0 [25.6, 28.5] 23.7 [22.3, 25.2] 87.8 [85.8, 89.9] 14.3 [14.0, 14.6] [11.5–15.7] 50.3 [46.6, 54.1] [28.7–75.5]
Northern Ireland 711 39.1 [35.5, 42.8] 36.9 [33.2, 40.5] 94.2 [91.4, 97.0] 14.4 [13.9, 14.9] [12.1–16.0] 67.6 [60.9, 74.3] [44.0–80.4]
South Africa 666 36.1 [32.2, 39.9] 32.2 [28.5, 36.0] 89.3 [84.8, 93.9] 14.3 [13.6, 14.9] [11.6–15.8] 70.3 [63.9, 76.6] [42.8–83.2]
Spain 2,046 39.8 [36.2, 43.5] 33.2 [29.7, 36.6] 83.3 [78.7, 87.9] 14.6 [14.3, 14.9] [13.0–16.1] 58.9 [50.9, 66.9] [31.7–77.0]
United States 700 28.7 [25.3, 32.2] 27.0 [23.6, 30.3] 93.9 [90.2, 97.5] 13.6 [13.1, 14.0] [11.7–15.4] 72.2 [68.1, 76.3] [48.9–84.9]
F(ndf,ddf)[p-value]
c
42.93 [7,201814] [⬍.01]
ⴱ
38.49 [7,144393] [⬍.01]
ⴱ
5.90 [7,6978] [⬍.01]
ⴱ
11.26 [7,692] [⬍.01]
ⴱ
Note. Age of onset of any mental disorder was defined as the minimum age of onset across disorders; for proportional persistence, this was the maximum proportional persistence across disorders.
95% CI ⫽95% confidence interval; IQR ⫽interquartile range. Significant findings are marked with an asterisk; ndf ⫽numerator degrees of freedom; ddf ⫽denominator degrees of freedom; ␣⫽
.05.
a
Proportional persistence of mental disorder is defined as the percentage of lifetime years with mental disorder symptoms from age-of-onset to age at the completion of the survey.
b
To obtain pooled
estimates of prevalence, age of onset, and proportional persistence across countries, each country was given an equal sum of weights.
c
F-test to evaluate significant between-country difference in
estimates.
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8AUERBACH ET AL.

disorders considered in the main sample was 31%. Lifetime and
12-month prevalence estimates ranged from a high of 48.3%–
43.3% in Australia to a low of 22.4%–19.1% in Belgium. Median
age-of-onset was 14.2 years of age, from a high of 14.6 in Spain
to a low of 13.6 in the United States. Median proportional annual
persistence (i.e., the proportion of years in episode between age-
of-onset and age at interview) was 65.0%, from a high of 72.2% in
the U.S. to a low of 50.3% in Mexico. The vast majority (89.0%)
of respondents with a lifetime disorder had 12-month prevalence,
from a high of 94.2% in Northern Ireland to a low of 83.3% in
Spain.
Major depressive disorder (MDD) was the most common of the
disorders examined across all countries combined (21.2% lifetime
prevalence; 18.5% 12-month prevalence) followed by generalized
anxiety disorder (18.6–16.7%; see Table 3). The other disorders
had comparatively much lower prevalence, from a high of
6.8%–6.3% for AUD to a low of 3.5%–3.1% for broadly defined
bipolar disorder. Median ages-of-onset of individual disorders
were in the range 14.3 (major depressive disorder) to 16.2 (drug
use disorder). Proportional annual persistence was considerably
lower for drug use disorder (45.9%) than other disorders
(62.4%–73.3%). Twelve-month prevalence among lifetime cases
also was considerably lower for drug use disorder (59.8%) than the
other disorders (87.1%–92.8%).
Sociodemographic and College-Related Correlates of
Mental Disorders
Female gender and older age (i.e., aged 19 and 20⫹years)
emerged as significant positive correlates of both lifetime and
12-month prevalence (see Table 4). Parental education was unre-
lated to the disorders assessed, but students with unmarried parents
or a parent who was deceased had significantly elevated odds of
both lifetime and 12-month disorders. Respondents who endorsed
no religious affiliation had a greater likelihood of reporting the
presence of lifetime and 12-month mental disorders than those
identifying as Christian. Relative to students reporting heterosex-
ual identification with no same-sex attraction (72.6%), students
identifying as heterosexual with some same-sex attraction
(14.1%), nonheterosexual without same-sex intercourse (8.0%), or
nonheterosexual with same-sex intercourse (5.4%) had two- to
threefold elevated odds of lifetime and 12-month disorders. Fi-
nally, extrinsically motivated (as compared with intrinsically mo-
tivated) students and students with lower high school rankings
(relative to students with higher high school rankings) had elevated
odds of mental disorders. Importantly, these associations were
quite stable across countries, with only 6.3% of country-specific
odds-ratios differing significantly from the cross-national average
(see Table 5).
Discussion
The present study reports initial results from the WHO WMH-
ICS project administered to first-year college students—a series of
surveys in 19 colleges across eight countries (Australia, Belgium,
Germany, Mexico, Northern Ireland, South Africa, Spain, United
States). At least one third of the college students that participated
in the surveys reported a history of one or more of the mental
disorders examined in the survey. This finding is broadly consis-
Table 3
Prevalence, Age of Onset, and Proportional Persistence of Mental Disorders in the WMH-ICS Surveys (n ⫽13,984)
Type of disorder
Lifetime
prevalence %
[95% CI] 12-month prevalence
% [95% CI] 12-month prevalence among
lifetime cases % [95% CI] Age of onset median
[95% CI] [IQR]
Proportional persistence
a
median
[95% CI] [IQR]
Major depressive episode 21.2 [20.2, 22.3] 18.5 [17.5, 19.5] 87.1 [85.2, 89.0] 14.3 [14.1, 14.5] [12.4–15.9] 62.4 [59.1, 65.7] [37.7–79.0]
Generalized anxiety disorder 18.6 [17.6, 19.6] 16.7 [15.7, 17.7] 90.0 [88.2, 91.8] 14.6 [14.3, 14.9] [12.2–16.3] 65.0 [61.4, 68.6] [41.5–80.9]
Panic disorder 5.0 [4.4, 5.6] 4.5 [3.9, 5.1] 90.1 [85.5, 94.6] 14.6 [14.0, 15.2] [12.1–16.5] 68.0 [61.4, 74.7] [45.3–83.6]
Broad mania 3.5 [3.0, 3.9] 3.1 [2.6, 3.5] 88.6 [84.9, 92.2] 15.0 [14.6, 15.4] [13.6–16.6] 72.8 [69.2, 76.5] [55.5–88.1]
Alcohol use disorder 6.8 [6.1, 7.5] 6.3 [5.7, 7.0] 92.8 [90.2, 95.3] 15.6 [15.4, 15.9] [14.3–16.9] 73.3 [70.1, 76.6] [49.4–91.4]
Substance use disorder 5.1 [4.5, 5.7] 3.0 [2.6, 3.5] 59.8 [53.4, 66.1] 16.2 [15.8, 16.5] [14.9–17.7] 45.9 [39.2, 52.7] [26.3–73.5]
Any mental disorder 35.3 [34.1, 36.6] 31.4 [30.2, 32.6] 89.0 [87.6, 90.4] 14.2 [14.1, 14.4] [12.0–15.9] 65.0 [62.5, 67.5] [41.2–80.3]
Note. To obtain pooled estimates of prevalence, age of onset, and proportional persistence across countries, each country was given an equal sum of weights. For any mental disorder, age of onset
was defined as the minimum age of onset across disorders; for proportional persistence, this was the maximum proportional persistence across disorders. 95% CI ⫽95% confidence interval; IQR ⫽
interquartile range.
a
Proportional persistence of mental disorder is defined as the percentage of lifetime years with mental disorder symptoms from age-of-onset to age at the completion of the survey.
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9
COLLEGE MENTAL HEALTH

Table 4
Sociodemographic and College-Related Correlates for Any Mental Disorder in the WMH-ICS Surveys
Correlates Predictor
distribution
a
% (SE) Lifetime aOR [95% CI] 12-month aOR
[95% CI] 12-month/lifetime
aOR [95% CI] Proportional persistence
b
aPRR [95% CI]
Being female 54.4 (.7) 1.4 [1.2, 1.5]
ⴱ
1.4 [1.3, 1.6]
ⴱ
1.5 [1.2, 1.9]
ⴱ
1.0 [1.0, 1.0]
Age
18
c
51.7 (.6) (ref) (ref) (ref) (ref)
19 26.2 (.6) 1.3 [1.1, 1.4]
ⴱ
1.2 [1.1, 1.4]
ⴱ
.9 [.6, 1.2] 1.0 [.9, 1.0]
20⫹22.1 (.6) 1.5 [1.3, 1.7]
ⴱ
1.3 [1.2, 1.5]
ⴱ
.6 [.4, .8]
ⴱ
.9 [.8, .9]
ⴱ
F(ndf,ddf)[p-value]
d
20.89 [2,25240] [⬍.01]
ⴱ
10.19 [2,16785] [⬍.01]
ⴱ
6.42 [2,3714] [⬍.01]
ⴱ
21.62 [2,993] [⬍.01]
ⴱ
Parental education
High 57.3 (.7) (ref) (ref) (ref) (ref)
Medium 24.3 (.6) 1.0 [.9, 1.1] 1.0 [.9, 1.2] 1.3 [.9, 1.8] 1.0 [.9, 1.0]
Low 18.4 (.5) .9 [.8, 1.1] .9 [.8, 1.1] 1.1 [.8, 1.5] 1.0 [.9, 1.0]
F(ndf,ddf)[p-value]
d
.61 [2,294] [.54] .98 [2,556] [.37] 1.18 [2,256] [.31] .37 [2,98] [.69]
Parents not married or parent[s] deceased 25.8 (.6) 1.3 [1.2, 1.5]
ⴱ
1.3 [1.2, 1.5]
ⴱ
1.2 [.9, 1.6] 1.0 [1.0, 1.1]
Place raised
e
Small city 28.0 (.6) (ref) (ref) (ref) (ref)
Large city 26.8 (.6) 1.0 [.9, 1.1] 1.0 [.9, 1.2] 1.3 [.9, 1.8] 1.0 [1.0, 1.1]
Town/village 20.5 (.6) 1.0 [.9, 1.2] 1.0 [.8, 1.1] .8 [.6, 1.2] 1.0 [1.0, 1.1]
Suburbs 17.1 (.6) 1.0 [.8, 1.2] 1.0 [.8, 1.2] 1.2 [.7, 2.1] 1.0 [1.0, 1.1]
Rural area 7.6 (.4) 1.1 [.9, 1.3] 1.1 [.9, 1.4] 1.4 [.8, 2.6] 1.0 [1.0, 1.1]
F(ndf,ddf)[p-value]
d
.34 [4,686] [.85] .41 [4,379] [.80] 1.62 [4,384] [.17] .56 [4,390] [.69]
Religion
Christian 61.9 (.7) (ref) (ref) (ref) (ref)
No religion 30.8 (.7) 1.4 [1.2, 1.6]
ⴱ
1.3 [1.1, 1.4]
ⴱ
.7 [.5, .9]
ⴱ
1.0 [1.0, 1.0]
Another religion 7.3 (.4) 1.2 [.9, 1.5] 1.1 [.9, 1.4] .7 [.4, 1.1] 1.0 [.9, 1.1]
F(ndf,ddf)[p-value]
d
12.85 [2,316] [⬍.01]
ⴱ
5.83 [2,333] [⬍.01]
ⴱ
4.16 [2,823] [.02]
ⴱ
.19 [2,544] [.82]
Sexual orientation
Heterosexual: no same-sex attraction 72.6 (.6) (ref) (ref) (ref) (ref)
Heterosexual: some same-sex attraction 14.1 (.5) 1.8 [1.5, 2.1]
ⴱ
1.7 [1.5, 2.0]
ⴱ
1.1 [.8, 1.6] 1.0 [1.0, 1.1]
Nonheterosexual without same-sex sexual intercourse 8.0 (.4) 2.6 [2.1, 3.3]
ⴱ
2.6 [2.1, 3.4]
ⴱ
1.6 [1.0, 2.5] 1.1 [1.0, 1.1]
ⴱ
Nonheterosexual with same-sex sexual intercourse
f
5.4 (.3) 2.8 [2.3, 3.6]
ⴱ
2.9 [2.3, 3.6]
ⴱ
1.7 [1.1, 2.8]
ⴱ
1.1 [1.0, 1.1]
F(ndf,ddf)[p-value]
d
43.82 [3,61] [⬍.01]
ⴱ
42.98 [3,60] [⬍.01]
ⴱ
2.29 [3,198] [.08] 2.13 [3,118] [.10]
Current living situation
Parents or other relative or own home 56.3 (.7) (ref) (ref) (ref) (ref)
University or college hall of residence 27.8 (.7) 1.1 [.9, 1.3] 1.2 [1.0, 1.4] 1.6 [1.1, 2.5]
ⴱ
1.0 [1.0, 1.1]
Shared house or apartment/flat 11.1 (.4) 1.0 [.9, 1.2] 1.1 [.9, 1.3] 1.4 [.9, 2.0] 1.0 [1.0, 1.1]
Private hall of residence 3.2 (.3) 1.0 [.8, 1.3] 1.1 [.8, 1.4] 1.4 [.7, 2.9] 1.0 [.9, 1.1]
Other 1.6 (.2) .9 [.6, 1.3] .8 [.5, 1.2] .6 [.3, 1.4] .9 [.8, 1.1]
F(ndf,ddf)[p-value]
d
.44 [4,174] [.78] 1.37 [4,131] [.25] 2.44 [4,433] [.05]
ⴱ
.96 [4,306] [.43]
Expected to work a student job 72.4 (.6) 1.0 [.9, 1.1] 1.0 [.9, 1.1] .9 [.7, 1.2] 1.0 [.9, 1.0]
Self-reported ranking in high school
Top 5% 24.8 (.6) (ref) (ref) (ref) (ref)
Top 10% to 5% 22.3 (.6) 1.1 [1.0, 1.3] 1.2 [1.0, 1.4] 1.3 [.9, 1.9] 1.0 [1.0, 1.1]
(table continues)
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10 AUERBACH ET AL.

tent with earlier college student surveys in documenting high
recent prevalence of common mental disorders (Blanco et al.,
2008; Cho et al., 2015; Eisenberg et al., 2007; Kendler et al.,
2015); although most earlier surveys were carried out in the U.S.
and assessed only current disorders (Merikangas et al., 2010).
Direct comparisons of prevalence estimates are not possible, as the
measures, time frames (12-month and lifetime in the current sur-
veys vs. current prevalence in most other surveys) and populations
represented differed across surveys. It is noteworthy in the latter
regard that the colleges in the WMH-ICS project were not selected
to be representative of all colleges in their countries but were
instead a convenience sample of the colleges in which WMH
collaborators worked or had close contacts. It is also noteworthy
that the response rates in the college surveys were quite variable
and were lower overall than in the nationally representative face-
to-face community household surveys in the WMH initiative. An
earlier WMH report based on face-to-face interviews with the
subset of WMH household survey respondents in 21 countries who
were college students found somewhat lower lifetime (29.3%) and
12-month (25.2%) prevalence estimates of any disorder in mostly
high-income countries, but this result was based on a wider range
of DSM–IV disorders and on most in-depth assessments of these
disorders than in the WMH-ICS surveys (Auerbach et al., 2016).
It is impossible to tell the extent to which these differences
reflect the fact that the colleges included in the WMH-ICS surveys
were atypical of all colleges in their countries, that the eight
countries considered in the WMH-ICS surveys were different from
the 21 included in the WMH surveys, that the mode of data
collection was different in the two sets of surveys (self-
administration in the WMH-ICS surveys vs. face-to-face in the
WMH surveys, with self-administration known to be associated
with increased rates of reporting embarrassing behaviors; Gnambs
& Kaspar, 2015), that the diagnostic measures were different, or
some combination of these factors. It is noteworthy, though, that
both sets of surveys documented that most lifetime mental disor-
ders among college students started prior to college entrance and
that persistence of these disorders was very high, suggesting that
clinical interventions early in the college career might be war-
ranted. Given the limited mental health resources that exist on
most college campuses relative to the scope of the problem, there
is also a need to consider cost-effective approaches to reduce the
treatment gap for this important segment of the population (e.g.,
group psychotherapy, internet-based psychotherapy).
We found a number of sociodemographic and college-related
variables that had statistically significant but substantively modest
associations (OR ⫽1.4–1.5) with overall disorder prevalence:
being female, having parents who were not married or deceased,
having no religious affiliation, graduating in the bottom 70% of
their high school class, and having primarily extrinsic reasons for
going to college. Odds-ratios of this size are equivalent to values
of Cohen’s dindicative of small effect sizes, whereas the 27% of
students who reported either a nonheterosexual orientation or some
same-sex attraction had relative-odds of disorder (OR ⫽2.0–3.4)
equivalent to values of Cohen’s din the small to medium range,
and the roughly 0.4% of respondents who reported themselves to
be either transsexual or “other” had a relative-odds of disorder
(OR ⫽5.6) equivalent to a Cohen’s din the large range (Hassel-
blad & Hedges, 1995). The small effects for basic sociodemo-
graphic and college-related factors are in line with prior research
Table 4 (continued)
Correlates Predictor
distribution
a
% (SE) Lifetime aOR [95% CI] 12-month aOR [95%
CI] 12-month/lifetime
aOR [95% CI] Proportional persistence
b
aPRR [95% CI]
Top 30% to 10% 30.2 (.6) 1.3 [1.1, 1.4]
ⴱ
1.3 [1.1, 1.5]
ⴱ
1.2 [.9, 1.7] 1.0 [1.0, 1.1]
Bottom 70% 22.7 (.6) 1.5 [1.3, 1.7]
ⴱ
1.5 [1.3, 1.8]
ⴱ
1.3 [.9, 1.8] 1.0 [1.0, 1.1]
F(ndf,ddf)[p-value]
d
10.53 [3,958] [⬍.01]
ⴱ
10.16 [3,605] [⬍.01]
ⴱ
.88 [3,706] [.45] .34 [3,438] [.80]
Most important reason to go to college extrinsic 10.6 (.5) 1.2 [1.0, 1.4]
ⴱ
1.2 [1.0, 1.4] .9 [.6, 1.4] 1.0 [1.0, 1.1]
Note. All models adjusted for the predictors shown in the rows, and for country membership. Models for 12-month prevalence among lifetime cases, and models for proportional persistence
additionally adjusted for age of onset of disorder. We additionally tested all possible two-way interactions between predictors shown in the rows; none were significant after adjusting for false discovery
rate (Q ⫽.05). Significant findings are indicated in bold and marked with an asterisk; ndf ⫽numerator degrees of freedom; ddf ⫽denominator degrees of freedom; ␣⫽.05. aOR ⫽adjusted odds
ratio; aPRR ⫽adjusted persistence rate ratio; CI ⫽confidence interval; SE ⫽standard error.
a
To obtain pooled estimates of predictor distributions across countries, each country was given an equal sum of weights.
b
Proportional persistence of mental disorder is defined as the percentage
of lifetime years with mental disorder symptoms from age-of-onset to age at the completion of the survey.
c
16- and 17-year-old respondents (n⫽2[⬍.01%], and n⫽307[.8%], respectively) were
classified in the 18-year-old respondent group for all analyses.
d
F-test to evaluate joint significance of categorical predictor levels.
e
For places raised, small city was selected as a reference category
because it represented the largest group.
f
Nonheterosexual orientation and/or same-sex sexual intercourse.
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11
COLLEGE MENTAL HEALTH

Table 5
Sociodemographic and College-Specific Predictors for Any Lifetime Mental Disorder in the WMH-ICS Surveys: Country Effect Versus Overall Effect
Overall effect Australia Belgium Germany Mexico Northern
Ireland South Africa Spain U.S.
Correlates aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI] aOR [95% CI]
Being female 1.4 [1.3, 1.6]
ⴱ
.9 [.6, 1.2] .9 [.7, 1.0] 1.1 [.8, 1.4] 1.0 [.9, 1.2] .9 [.7, 1.3] 1.1 [.8, 1.5] 1.0 [.8, 1.2] 1.1 [.8, 1.6]
Age
18
a
(ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
19 1.1 [1.0, 1.3]
ⴱ
.9 [.6, 1.4] 1.4 [1.1, 1.7]
ⴱ
1.1 [.8, 1.7] 1.1 [.9, 1.4] .9 [.6, 1.3] .9 [.6, 1.2] 1.1 [.8, 1.3] .7 [.5, 1.1]
20⫹1.4 [1.2, 1.8]
ⴱ
1.0 [.6, 1.5] 1.8 [1.3, 2.6]
ⴱ
1.1 [.7, 1.7] 1.1 [.8, 1.4] 1.2 [.8, 1.7] 1.0 [.6, 1.6] .6 [.4, .8]
ⴱ
.7 [.2, 2.1]
Parental education
High (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
Medium 1.0 [.9, 1.2] 1.1 [.6, 1.9] 1.1 [.9, 1.5] 1.0 [.7, 1.4] .9 [.7, 1.1] .8 [.6, 1.2] 1.0 [.6, 1.5] 1.0 [.8, 1.3] 1.1 [.7, 1.9]
Low 1.0 [.8, 1.2] .9 [.5, 1.6] 1.1 [.8, 1.5] 1.0 [.7, 1.4] .7 [.5, .9]
ⴱ
1.0 [.7, 1.4] 1.5 [.9, 2.5] 1.1 [.8, 1.4] 1.0 [.5, 2.2]
Parents not married or parent(s) deceased 1.4 [1.2, 1.6]
ⴱ
1.0 [.6, 1.8] 1.0 [.8, 1.3] 1.2 [.8, 1.6] .8 [.7, 1.0] 1.1 [.8, 1.5] 1.0 [.7, 1.4] .9 [.7, 1.2] 1.0 [.7, 1.4]
Place raised
b
Small city (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
Large city 1.1 [.9, 1.3] 1.0 [.4, 2.1] .9 [.7, 1.2] .8 [.5, 1.3] .8 [.6, 1.0] 1.3 [.7, 2.6] 1.5 [.8, 2.8] 1.1 [.8, 1.5] .8 [.5, 1.4]
Town/village 1.0 [.8, 1.3] 1.2 [.6, 2.6] 1.1 [.8, 1.6] 1.1 [.7, 1.7] .8 [.6, 1.1] .8 [.4, 1.3] .8 [.2, 2.7] .9 [.7, 1.3] 1.6 [.8, 3.0]
Suburbs 1.0 [.8, 1.2] 1.2 [.7, 2.3] 1.1 [.7, 1.6] .7 [.4, 1.3] .7 [.4, 1.2] .8 [.4, 1.4] 1.5 [.8, 2.9] .8 [.5, 1.3] 1.6 [1.0, 2.6]
Rural area 1.2 [.9, 1.6] 1.1 [.4, 2.9] 1.0 [.6, 1.6] .6 [.3, 1.1] .8 [.5, 1.2] .8 [.4, 1.5] 1.7 [.7, 3.8] .8 [.4, 1.5] 1.9 [.7, 4.7]
Religion
Christian (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
No religion 1.4 [1.2, 1.6]
ⴱ
1.3 [.8, 2.1] 1.1 [.9, 1.3] 1.1 [.7, 1.5] 1.2 [.9, 1.4] .9 [.6, 1.3] .9 [.6, 1.4] .8 [.6, 1.0]
ⴱ
.9 [.6, 1.3]
Another religion 1.2 [.9, 1.5] 1.3 [.7, 2.6] 1.2 [.8, 1.9] .8 [.4, 1.4] 1.4 [.9, 2.1] 2.3 [.7, 7.6] 1.1 [.6, 2.0] .3 [.2, .7]
ⴱ
.7 [.4, 1.2]
Sexual orientation
Heterosexual: no same-sex attraction (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
Heterosexual: some same-sex attraction 2.0 [1.7, 2.4]
ⴱ
1.1 [.6, 2.2] .9 [.7, 1.3] 1.1 [.7, 1.6] .9 [.7, 1.2] 2.3 [1.3, 4.2]
ⴱ
.9 [.5, 1.7] .6 [.5, .8]
ⴱ
.8 [.5, 1.1]
Nonheterosexual without same-sex sexual intercourse 2.8 [2.2, 3.7]
ⴱ
1.8 [.7, 4.3] 1.2 [.8, 1.7] 1.1 [.6, 2.0] .7 [.5, .9]
ⴱ
.9 [.4, 1.7] 1.2 [.6, 2.5] .7 [.5, 1.2] .9 [.5, 1.4]
Nonheterosexual with same-sex sexual intercourse 3.4 [2.6, 4.5]
ⴱ
1.7 [.7, 3.7] 1.1 [.6, 1.7] 1.3 [.6, 2.8] .6 [.4, .9]
ⴱ
1.2 [.6, 2.6] 1.2 [.4, 3.3] .5 [.3, .7]
ⴱ
1.1 [.6, 2.1]
Current living situation
Parents or other relative or own home (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
University or college hall of residence 1.4 [.9, 2.2] .8 [.4, 1.7] .8 [.5, 1.2] .8 [.4, 1.4] .5 [.2, 1.2] .9 [.5, 1.5] .8 [.4, 1.4] .7 [.4, 1.3] 7.6 [.5, 107.8]
Shared house or apartment/flat 1.1 [.6, 2.0] 1.0 [.4, 2.6] 1.0 [.5, 1.9] .9 [.4, 1.8] 1.0 [.5, 1.8] 1.0 [.5, 2.0] .8 [.3, 2.3] .9 [.5, 1.8] 1.7 [.0, 85.4]
Private hall of residence 1.5 [.9, 2.6] .5 [.2, 1.6] .7 [.3, 1.5] .9 [.4, 1.9] .6 [.3, 1.1] 2.4 [.6, 9.7] .8 [.2, 3.5] .5 [.2, 1.4] 5.1 [.3, 97.9]
Other 1.1 [.5, 2.3] 1.0 [.2, 4.5] 1.0 [.3, 2.8] 1.3 [.4, 4.2] .5 [.2, 1.6] .8 [.2, 3.5] .7 [.1, 8.5] .8 [.3, 2.1] 3.4 [.0, 237.7]
Expected to work a student job 1.0 [.9, 1.1] 1.0 [.5, 1.8] 1.0 [.8, 1.3] 1.0 [.7, 1.4] 1.1 [.9, 1.4] .9 [.6, 1.3] 1.1 [.7, 1.8] 1.0 [.8, 1.2] 1.0 [.7, 1.4]
Self-reported high school ranking
Top 5% (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref) (ref)
Top 10% to 5% 1.1 [1.0, 1.3] 1.5 [.8, 2.7] 1.0 [.7, 1.4] 1.1 [.6, 1.8] .9 [.7, 1.1] .8 [.5, 1.5] 1.0 [.6, 1.5] 1.2 [.9, 1.6] .7 [.5, 1.1]
Top 30% to 10% 1.2 [1.1, 1.4]
ⴱ
1.1 [.7, 1.9] 1.1 [.8, 1.5] 1.0 [.7, 1.7] 1.1 [.8, 1.3] .9 [.5, 1.5] 1.2 [.8, 1.7] .9 [.7, 1.2] .8 [.5, 1.2]
Bottom 70% 1.5 [1.2, 1.8]
ⴱ
1.3 [.7, 2.3] 1.1 [.9, 1.5] 1.2 [.8, 1.9] 1.0 [.8, 1.3] .8 [.4, 1.3] 1.2 [.7, 2.1] .8 [.6, 1.1] .7 [.4, 1.2]
Most important reason to go to college extrinsic 1.4 [1.1, 1.7]
ⴱ
.8 [.4, 1.6] .8 [.6, 1.2] 1.0 [.6, 1.7] .8 [.6, 1.0] 1.5 [.8, 2.8] .9 [.5, 1.8] 1.8 [1.0, 3.1]
ⴱ
.8 [.4, 1.5]
Note. Each row shows a separate logistic regression model with any lifetime mental disorder as the outcome variable, adjusting for all other predictor variables (rows), country membership, and
predictor-by-country interaction dummies. The second column shows the overall adjusted predictor variable effect; the country columns show to what extent the country-specific adjusted predictor
variable effect deviates from the overall adjusted predictor variable effect. Significant findings are indicated in bold and marked with an asterisk; ␣⫽.05. aOR ⫽adjusted odds ratio; CI ⫽confidence
interval; SE ⫽standard error.
a
16- and 17-year-old respondents (n⫽2[⬍.01%], and n⫽307 [.8%], respectively) were classified in the 18-year-old respondent group for all analyses.
b
For places raised, small city was selected
as a reference category because it represented the largest group.
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12 AUERBACH ET AL.

(mostly conducted in the United States; e.g., Eisenberg et al., 2013;
Pedrelli, Borsari, Lipson, Heinze, & Eisenberg, 2016), and simi-
larly, the elevated odds of disorder among students with nonhet-
erosexual orientations are consistent with previous studies of the
association between sexual orientation and mental health among
college students (Kerr, Santurri, & Peters, 2013; Kisch, Leino, &
Silverman, 2005; Oswalt & Wyatt, 2011; Przedworski et al.,
2015).
While our results show a median age-of-onset in early to middle
adolescence, these findings are not easy to reconcile with prior
epidemiological research that has assessed individuals across a
much broader age range (⬃18–65 years; e.g., National Comor-
bidity Replication [NCS-R], National Epidemiologic Survey on
Alcoholism and Related Conditions [NESARC]). Moreover, even
among studies that stratify the prevalence of disorders across age
groups, there is no delineation among students and nonstudents,
which has important implications (Auerbach et al., 2016). Of note,
the majority of WMH-ICS respondents were aged 18–19 years,
and this necessarily impacts the interpretation of age-of-onset. For
example, in both NCS-R and NESEARC, median age-of-onset for
major depression (Hasin, Goodwin, Stinson, & Grant, 2005) and
mood disorders (Kessler et al., 2005) was ⬃30 years compared
with ⬃14 years within the WMH-ICS sample. Similarly, age-of-
onset for substance use disorders also is older (⬃20 years) in the
NCS-R sample relative to the WMH-ICS (⬃14–16 years). These
differences most likely reflect the age ranges of the samples as
opposed to methodological differences (e.g., survey vs. face-to-
face interviews). That said, relative to the NCS-R, the WMH-ICS
shows an older age-of-onset for anxiety disorders (⬃11 years vs.
⬃14 years); potentially indicating subtle differences in reporting
accuracy (and potential recall biases) across instruments or across
retrospective recall periods in samples where respondents are
either mostly young (WMH-ICS) or have an unrestricted age range
(NCS-R).
Trajectory of Mental Disorders and
Associated Outcomes
The WMH-ICS was designed to follow first-year students
though their college years to address key questions about illness
onset, course, and consequences. Of particular importance, we
want to determine if the syndromes detected in this baseline survey
predict a range of key outcomes that are the focus of considerable
concern on college campuses, including academic functioning
(e.g., grades, attrition), sexual assault, and suicidal thoughts and
behaviors. There is some precedent for expecting associations with
these outcomes to be found. For example, in a prospective study of
college students implemented as a forerunner to the WMH-ICS
surveys, reports obtained during students’ first year identified
students with persistent suicidal thoughts and behaviors during
subsequent college years (Mortier et al., 2017). If similar prospec-
tive associations are obtained between the richer set of baseline
symptoms probed in the current survey and a wider range of
outcomes, such results could be important in targeting cost-
effective interventions.
There also is strong reason to believe that rates of disorders,
particularly externalizing disorders (e.g., substance use disorder)
and serious mental illness (e.g., bipolar disorder, thought disor-
ders), will show higher prevalence during later college years.
Indeed, substance use disorders, bipolar disorder, and thought
disorders typically emerge in the early to-mid 20s, and the typical
college lifestyle—irregular sleep, increased interpersonal stress,
experimental substance use—may confer increased risk of disorder
onset (Arnett, 2005; Sussman & Arnett, 2014). Additionally, al-
though our results show that female gender is a meaningful cor-
relate of increased lifetime and 12-month disorder prevalence of
the disorders considered, it also may be that (a) our assessment
reflects an imbalanced assessment of internalizing versus external-
izing disorders but (b) perhaps more critically, the assessment of
these disorders is conducted before their peak period of onset. As
first-year students are being followed throughout their collegiate
career, the WMH-ICS project has a unique opportunity to identify
factors that may be present before the unfolding of symptoms,
which again, will ultimately afford institutions an opportunity to
identify high-risk students who might benefit from preventative-
intervention efforts.
Improving Access to Care
The finding that one third of students from a range of countries
in the WMH-ICS screened positive for at least one of the six
12-month mental disorders assessed represents a key global mental
health issue and raises questions about appropriate screening and
intervention. As noted earlier, precise population prevalence esti-
mates cannot be obtained because our surveys are not nationally
representative and survey response rates are generally low, but it is
nonetheless clear from our results, in conjunction with the larger
literature, that a substantial proportion of college students meet
criteria for common mental disorders. Furthermore, as symptoms
of mental disorders range from subclinical through to severe, it is
likely that more than one third of our respondents suffered from
significant distress and that fewer than the one third suffered from
a serious mental disorder. Fortunately, colleges often have a range
of resources, and in recent years have developed programs to
reduce stigma and increase mental health literacy, to screen and
link students to mental health services, and to train key gatekeepers
about mental disorders and treatment (Eisenberg, Hunt, & Speer,
2012).
As screening mental disorders on college campuses becomes
more commonplace, early identification will increase. However,
one third of students have one or more of the 12-month disorders
considered here and other disorders that we did not consider are
likely to be present among a substantial number of other students.
It is unlikely in light of this that college campuses will have
sufficient resources to support student needs for mental health
services, exacerbating the problems that already exist in the mental
health treatment system of escalating financial expenses and long
waitlists (Andersson & Titov, 2014; Webb, Rosso, & Rauch,
2017). As noted earlier, one practical response would be to offer
internet-based interventions in addition to the services already
offered by student mental health and counseling centers. A number
of internet-based interventions exist for a broad range of psychi-
atric disorders (e.g., depression, anxiety, eating disorders) and
associated problems (e.g., sleep, stress) and have been shown to be
effective for both prevention and treatment of these conditions
(e.g., Ebert et al., 2015; Josephine, Josefine, Philipp, David, &
Harald, 2017; Olthuis, Watt, Bailey, Hayden, & Stewart, 2015;
Riper et al., 2014; Rosso et al., 2017; van Straten, Cuijpers, &
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13
COLLEGE MENTAL HEALTH

Smits, 2008); particularly guided Internet-based CBT interven-
tions (e.g., Baumeister, Reichler, Munzinger, & Lin, 2014;
Palmqvist, Carlbring, & Andersson, 2007; V. Spek et al., 2007). In
addition to their low cost, these interventions address a number of
other important barriers to treatment, most notably stigma and
inconvenience. Internet-based interventions could be especially
useful if they are used in campus mental health counseling centers
to triage care, with students experiencing less severe symptoms
receiving these interventions. Importantly, subthreshold cases are
known to have substantial impairment (Cuijpers, de Graaf, & van
Dorsselaer, 2004; Fergusson, Horwood, Ridder, & Beautrais,
2005) and to benefit from Internet-based interventions (Andersson
& Cuijpers, 2009; Spek et al., 2008); potentially reducing the
incidence of threshold cases (Buntrock et al., 2016).
Limitations and Summary
Our findings should be considered in light of several limitations.
First, the cross-national prevalence estimates are based on a con-
venience sample of colleges with relatively low and quite variable
response rates, limiting generalizability of results. Second, only six
common mental disorders were assessed in the surveys. The
omission of attention-deficit/hyperactivity disorder, eating dis-
orders, phobias, posttraumatic stress disorder, conduct disorder,
oppositional-defiant disorder, and intermittent explosive disorder
are especially noteworthy because of their comparatively high
prevalence in the WMH surveys (Auerbach et al., 2016), and
therefore, the true prevalence of mental disorders among college
students is likely to be a good deal higher than reported in the
current study; particularly as we are only including first-years
students who are not yet through the high-risk periods for many
common disorders. Although it would have been desirable to
include a more comprehensive assessment, this was rejected by the
administrations of participating schools. However, as an alterna-
tive we developed screening scales for omitted disorders, and we
are experimenting with a design in which subsets of these screen-
ing scales are rotated in future iterations of the surveys at random
to provide partial information about prevalence and correlates of a
wider range of disorders. This approach, which is referred to in the
survey methodology literature as matrix sampling (Merkouris,
2015), is becoming an increasingly popular approach to reduce
respondent burden when the number of questions of interest in a
survey exceeds the number that causes respondent burden
(Hughes, Beaghen, & Asiala, 2015; Thomas, Raghunathan, Schen-
ker, Katzoff, & Johnson, 2006). Third, our results indicated that
female gender emerged as a positive correlate of both lifetime and
12-month mental disorder prevalence. While this is not unex-
pected, it also important to note that this difference may be driven
by an imbalance in our assessment of number of internalizing
(four) disorders, which are known to be more common among
women, and externalizing (two) disorders, which are known to be
more common among men. Last, although the surveys used well-
validated screening scales calibrated to yield unbiased prevalence
estimates in general population samples, calibration studies have
not yet been carried out in samples of college students. Nor do we
know if calibration studies in separate countries would show that
concordance of the structured questions in our diagnostic screens
are equally valid in all countries. Fourth, lifetime prevalence and
age-of-onset were assessed retrospectively, which may contribute
to downward biases given recall errors.
Despite these limitations, our study clearly underscores the fact
that mental disorders are common among college students. In line
with the precision medicine initiative approach (Insel, 2014), the
next step in this work will be to begin constructing personalized
approaches that both identify each student’s risk profile and then,
provide access to intervention resources designed to ameliorate the
negative effects of mental disorders on this important segment of
the population.
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Received August 10, 2017
Revision received April 7, 2018
Accepted April 13, 2018 䡲
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