Adolescent school absenteeism and service use in a population-based study

Study design

This population-based study employs previously collected data from the youth@hordaland-survey
of adolescents in the county of Hordaland in Western Norway, conducted during spring
in 2012. The youth@hordaland-survey is a cross sectional study with a main aim to
assess mental health problems and service use in adolescents.

Sample

All adolescents in the three age cohorts in Hordaland were invited to participate
in the study (n?=?19 430). The adolescents received information about the study and login details
via their official school e-mail, followed by an SMS reminder for the majority of
the students. One school class (about 45 min) during regular school hours was allocated
for the completion of the Internet based questionnaire. A teacher was present to organize
the data collection and to ensure confidentiality. For those not at school during
the allocated school completion, the questionnaire could be completed at other times
at their convenience during the study period. Some schools arranged catch up days,
and we also arranged for participation for adolescents in hospitals or institutions
during the study period. Those not enrolled in school at the time of the study received
log on information through postal mail. However, adolescents who had dropped out of
school were not included in the current study sample, as one of the main variables
was school absenteeism.

Data from the youth@hordaland-survey include information on sociodemographic variables,
familial socioeconomic status, use of health care and social services, daily life
functioning, as well as extensive information on mental health. Of the 19 430 adolescents
who were invited to participate, 10 220 (53 %) agreed to participate and 8988 (87.9 %
of the original sample) approved the linkage to administrative data on school absence.

The study and the link between youth@hordaland and data on school absence were approved
by the Regional Committee for Medical and Health Research Ethics in Western Norway.

Instruments

Demographic information

Gender and year of birth are based on the personal identity number in the Norwegian
national population registry. All participants were asked about their mother’s education,
with the response options: ‘primary school’, ‘secondary school’, college or university:
less than 4 years’ and ‘college or university: 4 years or more’.

Living situation

The participants’ living situation was based on self-report of a range of situations
that were recoded as ‘living with family’, ‘living alone/with friends’, and ‘other’
for the present study. The variable ‘living with family’ includes living with biological
parents, foster parents, adoptive parents, grandparents or another family. ‘Living
alone/with friends’ includes living alone, living with friends or with a boyfriend/girlfriend.

School program

The educational programs reported by the participants were categorized into ‘general
studies’, ‘vocational subjects in school’ (this categorization is based on the Norwegian
high school system; including a program for general studies preparing for higher academic
education, and a vocational education program), and a third option of ‘vocational
training (work placement)’.

School absence

Administrative data on non-attendance were provided by Hordaland County Council. It
included the number of days and school-hours each participant had been absent during
the last semester (6 months), converted into percentage of absence relative to the
total number of school days.

For the purpose of the present study high absence was defined as 15 % absence or more,
based on Kearney’s criteria for problematic absence and the cut-off used in previous
research on absenteeism 4], 6]. The participants were divided into three groups: Adolescents with low absence (less
than 3 %), adolescents with moderate absence (between 3 and 15 %) and adolescents
with high absence (15 % or more).

Self-reported absence

The participants were asked to report the number of days and school hours they had
been absent during the past month. In addition, they reported location and behavior
while absent, with the response alternatives: ‘I’m home’, ‘I’m out with friends’,
‘I’m at work’ or ‘I’m sick’. Other responses could be specified in an open field and
multiple responses were also an option. The open responses were categorized into:
‘organizational work/politics/sport’, ‘unexcused absence’ and ‘other’.

Use of services

Service use was measured by the following question: “Have you had contact with the
following services within the last school year? If yes, check how often”. The response
categories used in the present study were; ‘school health services’, ‘special needs
education’, ‘educational psychological service’, ‘mental health services for adolescents’,
‘mental health services for adults’, ‘general practitioner’, and ‘adolescent health
clinic’. Additional services could be specified in an open field. In the present study
the category ‘mental health services’ is a combination of mental health services for
adolescents and adults. The participants who had been in contact with one or more
services were asked to indicate the frequency of the contacts, measured by a Likert
scale with the alternatives: ‘weekly’, ‘monthly’, ‘every three months’, ‘every six
months’, and ‘less than every six months’, with the exception of ‘special needs education’.
For the purpose of the present study, the latter two categories were combined in ‘every
six months or less’ and ‘weekly’ and ‘monthly’ were combined in ‘monthly or more’.

Statistics

In this study, we investigated service use in adolescents with low absence compared
to adolescents with moderate and high absence. Service use was measured by numbers
and category of services visited and frequency of contact. Chi-square tests were used
to examine differences between adolescents with low, moderate and high absence with
regards to demographic variables, rate of contact with specific services and self-reported
absence. Differences in contact with each of the services studied were examined by
logistic regression, using the absence variable as the exposure variable. Age, gender,
maternal education and school program were included as control variables in the regression
analyses. Multinomial logistic regression was used to calculate odds ratios for the
number of services visited, ranging from ‘1’ to ‘4 and more’, and the frequency of
contact for the participants according to absence. Results were considered significant
at the p??.05 level. IBM SPSS version 21 for Windows was used for all analyses.