Master's thesis - Hanna Määttänen

URBAN–RURAL DIFFERENCES
IN NIGHTMARE PREVALENCE IN FINLAND:
A POPULATION-BASED STUDY
Hanna Määttänen
Master’s thesis
Department of Psychology and Speech-Language Pathology
University of Turku
March 2016
The originality of this thesis has been checked in accordance with the University of Turku
quality assurance system using the Turnitin OriginalityCheck service.

UNIVERSITY OF TURKU
Department of Psychology and Speech-Language Pathology
MÄÄTTÄNEN, HANNA: Urban–rural differences in nightmare prevalence in Finland:
A population-based study
Master’s thesis, pp. 35.
Psychology
March 2016
Summary: The aims of the study were to examine urban–rural differences in nightmare
prevalence and to explore whether factors associated with nightmares confound the
possible urban–rural differences. In Germany and Austria, a higher prevalence of
nightmares had been found in the more populated areas. Findings from Finland had
indicated a higher prevalence of risk factors for frequent nightmares in the most urban
and in the most rural areas compared to semi-rural areas. Thus, it was hypothesized that
frequent nightmares would be more prevalent in the most urban and in the most rural
areas than in the semi-rural areas. Factors associated with nightmares were hypothesized
to partly confound the possible urban–rural differences in nightmares.
The data used in the study were from the cross-sectional population survey, the
National FINRISK Study 2012, with a total of 6,404 participants representing the
Finnish adult population aged 25–74 years. Nightmares were assessed as a self-reported
nightmare prevalence during the previous 30 days with answer options of “frequently,”
“occasionally,” and “not at all.” Urban–rural differences were analyzed according to a
modified version of the Finnish Environment Institute’s urban–rural classification based
on information on 250×250-meter grid squares, and according to population size of a
municipality as population size had been used in the previous studies. Factors related to
mental and physical health, and socio-demographic variables were controlled for.
Using the urban–rural classification as a variable, the highest prevalence rates of
both frequent and occasional nightmares were found in the sparsely populated rural
areas and the lowest in the semi-rural areas near cities. However, the size of the effect
was small. The differences in the nightmare prevalence were partly explained by poorer
mental and physical health in the sparsely populated rural areas. No higher nightmare
prevalence than average was detected in the inner urban areas, although such risk
factors as frequent symptoms of insomnia and alcohol intoxication were more common
in these areas than elsewhere. In the outer urban areas, however, there were slightly
higher odds of frequent nightmares compared to the semi-rural areas, which was not
explained by other factors in these data. Using the population size of a municipality as a
variable, the highest prevalence of frequent nightmares was observed in the most
populated city with over 500,000 inhabitants, and the lowest in municipalities of 20,000
to 49,999 inhabitants, yet the difference between these areas was very small.
The inconsistency of the results with the previous findings from Germany and
Austria was assumed to be caused by methodological differences and by geographical,
demographical, and cultural differences between the countries. As the inner urban areas
did not have a higher prevalence of nightmares in spite of various risk factors, they were
considered to possibly have some protective factors for nightmares. In light of these
results, more research attention to nightmares is needed in the most rural areas of
Finland. In the rural healthcare centers, nightmare problems should be carefully
assessed especially among people with other mental health problems.
Keywords: nightmares, urbanicity, rurality, epidemiology, environmental mental health,
environmental psychology

Table of contents
1. Introduction................................................................................................................... 1

1.1 Nightmares..............................................................................................................2

1.2 Urbanicity and rurality............................................................................................4

1.3 Urban–rural differences in nightmares ...................................................................5

1.4 Factors associated with nightmares and their urban–rural variation ......................5

1.4.1 Sleep disturbances..........................................................................................5

1.4.2 Psychiatric symptoms ....................................................................................6

1.4.3 Socio-demographic factors ............................................................................7

1.4.4 Other factors ..................................................................................................8

1.5 Aims of the study....................................................................................................9

2. Methods ...................................................................................................................... 10

2.1 Study population and design.................................................................................10

2.2 Questionnaire items ..............................................................................................12

2.3 Urban–rural variables............................................................................................13

2.4 Statistical methods ................................................................................................15

3. Results......................................................................................................................... 16

3.1 Socio-demographic characteristics .......................................................................17

3.2 Other factors associated with nightmares .............................................................18

3.3 Multinomial logistic regression analysis ..............................................................20

4. Discussion................................................................................................................... 22

4.1 Comparison with previous findings......................................................................24

4.2 The poor well-being in the sparsely populated rural areas ...................................25

4.3 The unexpected findings in the urban areas..........................................................26

4.4 Strengths and limitations.......................................................................................27

4.5 Future research......................................................................................................29

4.6 Conclusion ............................................................................................................30

REFERENCES ............................................................................................................... 31

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1. Introduction
About 2%–5% of the general adult population in developed countries have nightmares
at least once a week (e.g., Hublin, Kaprio, Partinen, & Koskenvuo, 1999; Li, Zhang, Li,
& Wing, 2010; Schredl, 2010). These vivid and emotionally negative dreams are
associated with other sleep disturbances, particularly with insomnia, and with such
daytime issues as fatigue and morning headache (e.g., Li et al., 2010). There is also a
robust association between nightmares and psychopathology, such as post-traumatic
stress disorder (American Psychiatric Association, 2013; Phelps, Forbes, & Creamer,
2008) and symptoms of depression (e.g., Li et al., 2012; Sandman et al., 2015;
Tanskanen et al., 2001). Having frequent nightmares may even increase the risk of
suicide (Li et al., 2012; Pigeon, Pinquart, & Conner, 2012).
In order to prevent the negative consequences of nightmares, it is essential to
gain information on their psychological and behavioral risk factors but also on the living
environment of people experiencing nightmares. Examining the living environment may
help to target resources to the most problematic areas and to develop these areas in such
a way as to improve the well-being of the inhabitants. Although there has been a great
interest in urban and rural mental health (Caracci, 2008; Philo, Parr, & Burns, 2003),
prior research on urban–rural differences in nightmares is scarce. These urban–rural
differences have been explored only in two studies (Schredl, 2013; Stepansky et al.,
1998) in which the findings have indicated that living in a city could increase the risk of
having nightmares compared to less populated areas. Schredl (2013) has suggested that
symptoms of mood disorders or some other psychiatric disorders might mediate the
relationship between urban living and nightmares, as these disorders have found to be
more prevalent in urban than in rural areas (for a review, see Peen, Schoevers, Beekman,
& Dekker, 2010).
However, psychiatric symptoms have not been controlled for in the studies of
Schredl (2013) or Stepansky et al. (1998). Furthermore, because the data of these
studies have been collected in Germany and Austria, it is unclear whether their findings
could be replicated elsewhere, such as in countries without large metropolitan areas.
Research from the sparsely populated country of Finland shows that there might be risk
factors for nightmares both in urban and in rural areas (e.g., Kaikkonen et al., 2014;
Saarsalmi et al., 2014). For example, symptoms of insomnia might be more prevalent in
the most urban areas (Kaikkonen et al., 2014), but the findings from the urban–rural

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differences in depressive symptoms have been mixed (Ayuso-Mateos et al., 2001;
Jokela, Lehtimäki, & Keltikangas-Järvinen, 2007; Kaikkonen et al., 2014). Hence, as
also the risk factors for nightmares can have regional variation, researchers examining
urban–rural differences in nightmares should pursue to control for the possible effect of
these risk factors.
In the present study, urban–rural differences in nightmare prevalence are
explored for the first time in a Finnish adult population. It is also examined whether
various factors previously associated with nightmares confound the possible urban–rural
differences in the prevalence of nightmares. Therefore, in this study the following
factors are controlled for: depressive symptoms, insomnia symptoms, diagnoses of other
psychiatric disorders than depression, feelings of exhaustion, working ability, headaches,
alcohol intoxication, and several socio-demographic factors.
1.1 Nightmares
Nightmares are vivid and emotionally negative dreams usually occurring during rapid
eye movement (REM) sleep and during the latter half of the sleep period (American
Academy of Sleep Medicine, 2014). Diagnostically, nightmares are defined as long and
disturbing dreams characterized by intensive dysphoric emotions, such as fear and
anxiety, and resulting in an awakening and a clear recall of the nightmare (American
Academy of Sleep Medicine, 2014; American Psychiatric Association, 2013; World
Health Organization, 1992). Among researchers, however, there has been no consensus
on the definition of a nightmare. The presence of unpleasant emotions, or specifically
fear, has often been the only criterion (e.g., Belicki, 1992a, Wood & Bootzin, 1990). In
addition, the criterion of an awakening from the dream has frequently been applied (e.g.,
Levin & Fireman, 2002; Schredl, 2010). An awakening has been considered as an
indicator of the emotional intensity of a dream (Zadra, Pilon, & Donderi, 2006), and
non-awakening disturbing dreams have been referred to as “bad dreams” (e.g., Levin &
Nielsen, 2007; Zadra & Donderi, 2000; Zadra et al., 2006). Some researchers have
relied on the judgment of participants and have not provided any definition for
nightmares (e.g., Hublin et al., 1999; Li et al., 2010).
Nightmares can be divided into two types depending on their origin: idiopathic
and post-traumatic nightmares (American Psychiatric Association, 2013; Levin &
Nielsen, 2007). Idiopathic nightmares have no known connection to the dreamer’s

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waking-life experiences, whereas post-traumatic nightmares appear to be strongly
related to a traumatic event in the dreamer’s waking life by either replicating the
traumatic event or including elements of it. Repeated post-traumatic nightmares are one
of the core symptoms of post-traumatic stress disorder, PTSD (American Psychiatric
Association, 2013). According to a few estimates, 52%–67% of people with a PTSD
diagnosis have post-traumatic nightmares (Neylan et al., 1998; Schreuder, Kleijn, &
Rooijmans, 2000). In nightmare research, the origin of nightmares is rarely addressed.
Among general adult population of developed countries, 2–5% report
nightmares at least once a week (Bjorvatn, Grønli, & Pallesen, 2010; Hublin et al.,
1999; Li et al., 2010; Schredl, 2010) and 10%–45% at least once a month (Hublin et al.,
1999; Li et al., 2010; Sandman et al., 2013; Schredl, 2010). In the study of Sandman et
al. (2013), the rate of “frequent nightmares” was similar to the rate of having nightmares
at least weekly: 3.5% of men and 4.8% of women reported frequent nightmares.
However, nightmare frequency may not be a direct correlate of the distress caused by
nightmares (Belicki, 1992a, 1992b; Miró & Martínez, 2005). Levin and Nielsen (2007)
have proposed that frequent nightmares do not necessarily produce distress for the
individual if one is not vulnerable to experience distress. Hence, one must be cautious in
interpreting results based on nightmare frequency alone because frequent nightmares
have not been a straightforward indicator of a clinical nightmare problem.
Most of the recent models of nightmare formation are based on the assumption
that one of the functions of dreaming is emotion or mood regulation (for a review, see
Nielsen & Levin, 2007). Nightmares are assumed to occur when this regulation system
either works more intensively than usual or fails to work as it should. For example,
stressful life events could cause a failure in the system that would lead to nightmares.
An evolutionary point of view has also been put forward: according to the threat
simulation theory by Revonsuo (2000), many nightmares, and dreams in general, are
rehearsals of events that have been potentially threatening in the human ancestral
environment. Rehearsing these events has enhanced survival skills of an individual and,
thus, reproductive success, but these rehearsals may not be beneficial for reproductive
success or psychological well-being anymore in the modern environment.

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1.2 Urbanicity and rurality
Before discussing the urban–rural differences in the prevalence of nightmares, the
definitions for urban, rural, urbanicity, and rurality are briefly presented. To begin with,
there are substantial international differences in the definitions for urban and rural
(Department of Economic and Social Affairs, Population Division, 2015). In Germany,
for example, an urban municipality has a population density of more than 500
inhabitants per square kilometer and a population of 50,000 inhabitants either in itself or
in a combined area of neighboring municipalities in the same density category (Federal
Statistical Office of Germany, 2013). According to Statistics Finland, on the other hand,
“at least 90 per cent of the population lives in urban settlements,” or “the population of
the largest urban settlement is at least 15,000” in urban municipalities (“Statistical
grouping of municipalities,” 2015). Rural municipalities in Germany have a population
density less than 100 inhabitants per square kilometer, whereas rural municipalities in
Finland have less than 60 per cent of the population living in urban settlements, or less
than 90 per cent if the population of the largest urban settlement is less than 4,000. The
diversity in the demographics of different countries and the variation in their definitions
of urban and rural areas present a challenge to the comparability of urban–rural studies.
The term urbanicity is widely used in the research literature (e.g., Jokela et al.,
2007; Penkalla & Kohler, 2014; Vlahov & Galea, 2002). It has been defined as “an
impact of living in urban areas at a given point in time” (Vlahov & Galea, 2002).
Rurality, a term used in some studies (e.g., Monnat & Pickett, 2011; Philo et al., 2003),
is viewed as a complementary term to urbanicity in the present study. The degree of
urbanicity or rurality of an area can be defined by such factors as population size,
population density, and traffic intensity. Some of the urban–rural studies have relied on
the participants’ own judgment on the degree of urbanicity of their place of residence
(e.g., Jokela et al., 2007; Paykel, Abbott, Jenkins, Brugha, & Meltzer, 2000), some have
defined urban and rural areas according to population size or density (e.g., Jokela et al.,
2007; Schredl, 2013; Stepansky et al., 1998), and some according to various
demographic characteristics (e.g., Saarsalmi et al., 2014; Weich, Twigg, & Lewis, 2006).
The borders of the areas have been based on administrative boundaries, such as
municipalities, or on boundaries created on geo- and demographic differences.

5
1.3 Urban–rural differences in nightmares
So far, the urban–rural differences in nightmare prevalence have been explored only in
one Austrian (Stepansky et al., 1998) and in one German study (Schredl, 2013). In these
studies, urban and rural areas were defined solely according to their population size. No
information on the study areas other than population size was provided in either of the
studies. Stepansky et al. observed that 7% of the participants suffered from nightmares
in areas with over 50,000 inhabitants compared to the average of 4%. They assessed the
self-reported suffering from nightmares with options of “yes,” “no,” or “no response,”
without addressing the frequency of nightmares. The finding of Schredl was in line with
the previous finding: in his logistic regression analysis, the odds of having nightmares
increased by the population size of an area of residence. Schredl used an eight-point
rating scale for addressing the nightmare frequency ranging from “never” to “several
times a week.” Altogether, the findings would indicate that people living in urban areas
could experience more nightmares than people living in rural areas.
1.4 Factors associated with nightmares and their urban–rural variation
If some factors associated with nightmares have urban–rural variation, they may
confound the possible urban–rural differences in nightmare prevalence. Schredl (2013)
and Stepansky et al. (1998) did not control for sleep disturbances, psychiatric symptoms,
or other health-related factors that have been associated with nightmares, which is why
their role in explaining the nightmare prevalence in the most populated areas remained
unknown in their studies. In the following, several important factors associated with
nightmares and their urban–rural variation are reviewed.
1.4.1 Sleep disturbances. Converging evidence indicates that nightmares are
strongly related to insomnia symptoms (Krakow, 2006; Li et al., 2010; Nakajima et al.,
2014; Ohayon, Morselli, & Guilleminault, 1997; Sandman et al., 2013; Sandman et al.,
2015; Schredl, 2009; Stepansky et al., 1998). According to the fifth edition of the
Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the symptoms of an
insomnia disorder are difficulties initiating sleep, difficulties maintaining sleep, and
early-morning awakenings with inability to return to sleep (American Psychiatric
Association, 2013). All of these symptoms in addition to restless sleep have correlated

6
strongly with nightmare frequency (Li et al., 2010). Sandman et al. (2015) reported that
symptoms of insomnia were the strongest independent risk factor for frequent
nightmares in the regression model of their study. It has been speculated that the
nocturnal awakenings of insomniacs could increase dream recall in general, and
therefore, insomniacs would report nightmares more frequently than non-insomniacs (Li
et al., 2010; Schredl, 2009). Another speculation is that nightmares could induce
insomnia by disrupting sleep cycle and causing fear of sleep.
As to the urban–rural differences in sleep disturbances, most of the findings
indicate that urban residents may have lower sleep quantity (Hale & Do, 2007; Ursin,
Bjorvatn, & Holsten, 2005) and quality (Kim et al., 2009) than their rural counterparts.
Also in Finland, the prevalence of insomnia symptoms was higher than average in inner
urban areas in the Regional Study of Health and Well-Being (“Alueellinen terveys- ja
hyvinvointitutkimus”), referred to henceforth as the “ATH study” (Kaikkonen et al.,
2014). By contrast, insomnia symptoms were less prevalent than average in peri-urban
areas, which surround the urban areas.
1.4.2 Psychiatric symptoms. Experiencing nightmares has been considerably
more common among people with post-traumatic stress disorder but also among people
with other psychiatric disorders. Those with weekly nightmares have had a five- to six-
fold risk of psychiatric disorders when compared to people with no nightmares at all
(Hublin et al., 1999) or people experiencing nightmares less than monthly (Li et al.,
2010). In the latter study, especially the risk of having mood disorders was high.
Symptoms of anxiety have also been associated with nightmares in different samples
(Levin & Fireman, 2002; Ohayon et al., 1997; Sandman et al., 2013).
Most importantly, symptoms of depression have correlated strongly with
nightmares in different populations (Levin & Fireman, 2002; Li et al., 2012; Sandman
et al., 2015; Zadra & Donderi, 2000). Depressive disorders in general are characterized
by “the presence of sad, empty, or irritable mood” (American Psychiatric Association,
2013). In addition to depressed mood, some of the symptoms in the diagnostic criteria
of a major depressive disorder (MDD) are loss of interest or pleasure, and fatigue or
loss of energy. A factor “negative attitude towards the self” of the 13-item Beck
Depression Inventory (BDI-13) assessing depressive symptoms was found to be an
independent risk factor for frequent nightmares in the study of Sandman et al. (2015).
Although insomnia is also one of the symptoms of MDD and insomniacs may have a

7
two-fold risk for developing depression (Baglioni et al., 2011), having nightmares is
probably associated with depressive symptoms regardless of insomnia (Nakajima et al.,
2014; Ohayon et al., 1997; Sandman et al., 2015).
In a meta-analysis including studies from 15 developed countries, living in an
urban area increased the risk of having a mood disorder (Peen et al., 2010). In Finland,
however, findings on affective symptoms vary slightly. Similarly to the insomnia
symptoms, the prevalence of depressive symptoms was the highest in inner urban areas
and the lowest in peri-urban areas in the ATH study (Kaikkonen et al., 2014), but in
earlier studies no significant urban–rural difference for depression has been detected
(Ayuso-Mateos et al., 2001; Jokela et al., 2007). Moreover, having low or anxious mood
has been significantly more common in sparsely populated rural municipalities than in
rural municipalities near cities (Heikkilä, Rintala, Airio, & Kainulainen, 2002; Rintala
& Karvonen, 2003). Hence, there could be more affective symptoms both in the most
urban and in the most rural areas in Finland compared to the semi-rural or semi-urban
areas surrounding the urban areas.
1.4.3 Socio-demographic factors. An extensive body of research shows that
adult women tend to report more nightmares than adult men (Bjorvatn et al., 2010; Li et
al., 2010; Sandman et al., 2013; Sandman et al., 2015; Schredl & Reinhard, 2011). No
gender difference has been found among the elderly (Mallon, Broman, & Hetta, 2000;
Sandman et al., 2013; Schredl & Reinhard, 2011). The findings on the effect of aging on
nightmare frequency are mixed, and no longitudinal studies have yet conducted. In
cross-sectional studies, Sandman et al. (2013) found that frequent nightmares increased
by advancing age in adulthood, but Schredl (2010) reported no significant difference in
nightmare frequency between adults and the elderly.
As to socioeconomic factors, Li et al. (2010) reported that low income level was
associated with nightmares in spite of controlling for other socio-demographic factors.
However, Sandman et al. (2015) found that although low household income level was
associated with nightmares it was not an independent risk factor for them. In both of
these studies, the statistically significant association between unemployment and
nightmares was explained by other factors. Controlling for other factors has not been
reported in other studies, in which nightmares have been associated with low income
level (Ohayon et al., 1997; Stepansky et al., 1998) and unemployment (Ohayon et al.,
1997; Tanskanen et al., 2001). Psychiatric symptoms could mediate the associations

8
between nightmares and socioeconomic factors, as low socioeconomic status has been
observed to be a risk factor for psychiatric disorders (Hudson, 2005).
Low income level and unemployment might be more prevalent in the most rural
areas than in urban areas (Heikkilä et al., 2002; Karvonen & Rintala, 2006), although in
recent studies the differences in subsistence have been minor between urban and rural
areas (Karvonen et al., 2010; Karvonen & Kauppinen, 2008; Kauppinen & Karvonen,
2014). The unemployment rate based on the statistics from the year 2010 was the
highest in the sparsely populated rural municipalities and the lowest in the rural
municipalities near cities (Ponnikas et al., 2014). However, this could be due to
differences in age structure and gender distribution (Kauppinen & Karvonen, 2014). In
addition to the low unemployment rate, the rural municipalities near cities have been
characterized by a high proportion of families with property ownership and high
household income level (Heikkilä et al., 2002).
1.4.4 Other factors. Current level of anxiety or stress has been associated with
nightmare frequency (Tanskanen et al., 2001; Zadra & Donderi, 2000) and nightmare
distress (Miró & Martínez, 2005). Additionally, Sandman et al. (2015) found that
experiencing feelings of exhaustion was a risk factor for having frequent nightmares
even when controlling for such factors as depressive symptoms. In a few recent studies,
having life stress has been reported the most in inner urban areas and the least in semi-
rural areas (Karvonen et al., 2010; Kauppinen & Karvonen, 2014).
Moreover, Sandman et al. (2015) discovered that having lowered ability to work
and having headaches were independent risk factors for frequent nightmares. In the
ATH study, there was a higher proportion of people with lowered ability to work in
sparsely populated rural areas than in urban or peri-urban areas despite controlling for
age (Saarsalmi et al., 2014). Although the association between nightmares and poor
self-rated health has been explained by other factors (Sandman et al., 2015), it should be
noted that self-rated health has been consistently worse in people living in rural than in
urban areas (Karvonen & Kauppinen, 2008; Karvonen, Kauppinen, & Ilmarinen, 2010;
Karvonen & Rintala, 2006; Kauppinen & Karvonen, 2014; Rintala & Karvonen, 2003).
Use of some psychopharmacological substances such as alcohol (Munezawa et
al., 2011; Sandman et al., 2015; Tanskanen et al., 2001) and serotonin reuptake
inhibitors, SSRIs (Pagel & Helfter, 2003), may produce nightmares, but the results are
still preliminary. Although the association was weak, frequent alcohol intoxication was

9
a risk factor for nightmares in the study of Sandman et al. (2015). Consuming moderate
or high doses of alcohol have been found to suppress REM sleep (Ebrahim, Shapiro,
Williams, & Fenwick, 2013), which is why withdrawal from alcohol has been proposed
to cause the REM sleep rebound effect and in this way to predispose to nightmares (e.g.,
Pagel & Helfter, 2003). Because drugs without known effect on REM sleep can cause
nightmares as well, Pagel (2010) has suggested that withdrawal from addictive agents
causes nightmares. In the ATH study, binge drinking was more common in the inner
urban than in other areas (Kaikkonen et al., 2014).
1.5 Aims of the study
In this study, urban–rural differences in nightmare prevalence are examined in a large
representative sample of the Finnish adult population from the year of 2012. The
primary aim of the study is to examine for the first time, which areas, ranging from the
most rural to the most urban, have the highest and the lowest prevalence of nightmares
in Finland. The study also aims to explore for the first time whether other factors
confound the possible urban–rural differences in nightmare prevalence. Factors
controlled for in this study are previously identified risk factors for nightmares in the
study of Sandman et al. (2015): gender, age, symptoms of insomnia, symptoms of
depression, feelings of exhaustion, working ability, headaches, and alcohol intoxication.
Also, diagnoses of other psychiatric disorders than depression and socioeconomic
factors are controlled for.
In two previous studies from Germany and Austria, nightmares have found to be
more prevalent in more populated than less populated areas. Based on findings from
Finland, there may be more risk factors for frequent nightmares in the most urban areas
than in the semi-rural areas located in-between the urban and the rural areas: symptoms
of insomnia, affective symptoms, life stress, and binge drinking. In the most rural areas,
affective symptoms and lowered ability to work may be higher than in semi-rural areas.
In light of these findings, the main hypothesis of the study is that frequent nightmares
are more prevalent in the most urban and in the most rural areas than in the semi-rural
areas. It is also hypothesized that the previously identified risk factors for frequent
nightmares partly confound the urban–rural differences in nightmare prevalence.

10
2. Methods
2.1 Study population and design
In the present study, the dataset from the Finnish National FINRISK Study collected in
2012 (referred to henceforth as FINRISK 2012) was used. FINRISK is a series of large
cross-sectional health examination surveys of the Finnish population carried out by
Finland’s National Institute for Health and Welfare every five years since 1972
(Borodulin et al., 2013). The primary aims of the surveys have been to assess risk
factors for chronic noncommunicable diseases and improve health of the Finnish
population. FINRISK 2012 was used in this study as it is the largest and the most recent
study in Finland in which the prevalence of nightmares, mental health, physical health,
and sleep disturbances have been assessed. The FINRISK data were also used in the
nightmare studies of Sandman et al. (2013; 2015) and Tanskanen et al. (2001). Sandman
et al. (2015) used FINRISK datasets from the surveys of 2007 and 2012, which is why
the risk factors for nightmares discovered in their study were expected to be associated
with nightmares also in this study.
The FINRISK 2012 study area is presented in Figure 1. The survey data were
collected from January to May in 2012 from five areas in Finland (Borodulin et al.,
2013). From each area, a sample of 2,000 citizens was randomly drawn from the
National Population Register representing population aged 25–74 years. The samples
were stratified according to gender, 10-year age groups, and geographical area.
Altogether 10,000 individuals were selected. The study targeted 9,905 individuals after
excluding people who had died or moved outside of the study area after the sampling.
Sixty-five per cent of the targeted individuals participated (n = 6,424) but since 20 of
them had moved outside the study area during the study, they were excluded from the
sample. Hence, the final sample size was 6,404. Participants were from 88
municipalities and 52.7% of them were female. Their age range was 25–74 years
(M = 51.1, Sd = 14.1, Md = 52.0). Exact coordinates of the participants’ residential
buildings provided by the National Population Register were available in the data.
The survey consisted of a self-report health questionnaire (referred to henceforth
as the basic questionnaire), a health examination, and an additional questionnaire for
those who participated in the health examination. The questionnaires included questions
on socio-demographic factors, the use of healthcare services, physical and mental health,

11
health behaviors, nutrition, and psychosocial factors. Both questionnaires included
items that were used in this study. The basic questionnaires were mailed to participants
who were invited to complete and return them to an assigned local primary healthcare
center where the health examination was carried out. In the healthcare center, it was
possible to ask further questions about the questionnaire and to complete it if necessary.
Specially trained nurses conducted the health examinations, gave participants the
additional questionnaires, and instructed to fill them in at home and return them by mail
to National Institute for Health and Welfare. The return rate of the additional
questionnaire was 84% (n = 4,905). The surveys received approval from the
Coordinating Ethics Committee of Helsinki and Uusimaa hospital district. Written
informed consent was obtained from the participants.
Figure 1. FINRISK 2012 study areas colored in black. © University of Turku, UTU-GIS 2015 &
National Land Survey of Finland 2012

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2.2 Questionnaire items
The questionnaire items used in this study assessed the prevalence of nightmares, socio-
demographic factors, and other factors previously associated with nightmares. The last
ones were symptoms of insomnia, feelings of exhaustion, headaches, diagnoses of other
psychiatric disorders than depression (referred to henceforth as “other psychiatric
disorders”), symptoms of depression, working ability, and frequency of intoxication.
The prevalence of nightmares, insomnia, feelings of exhaustion, and headaches were
assessed as follows: “During the previous 30 days, have you experienced nightmares /
insomnia / feelings of exhaustion / headaches?” For all these questions the answer
options were “frequently,” “occasionally,” and “not at all.” No definitions for
nightmares or other variables were provided. Other psychiatric disorders were assessed
as a self-reported diagnosis of “other psychiatric disorder than depression” received
during the previous 12 months.
Information on depressive symptoms were obtained by using a Finnish
translation of 13-item Beck Depression Inventory (BDI-SF-13) in which items 6, 8, 10,
11, 16, 19, 20, and 21 from the original 21-item BDI are excluded. Similarly to the
study of Sandman et al. (2015), 5 to 7 points were regarded as having mild symptoms of
depression, 8 to 15 points as moderate, and over 16 as severe (Spreen & Strauss, 1998).
In these data, BDI-SF-13 yielded a Cronbach Alpha of .865 indicating a high level of
internal consistency. Although a self-reported diagnosis of depression was also assessed
in FINRISK 2012, the BDI-SF-13 was presumed to estimate depressive symptoms
better than the diagnosis, as the diagnosis depends on the use of healthcare services.
Participants were asked to evaluate their working ability regardless of their
employment status by choosing one of the following options: full capacity, impaired
capacity, or incapacity. The frequency of intoxication was assessed as follows: “During
the last 12 months, how often have you considered yourself intoxicated after consuming
alcoholic beverages?” The categories were reduced from nine to four in this study: at
least once per week, one to three times per month, less than once per month, and not at
all during the last 12 months. BDI-SF-13, working ability, and frequency of intoxication
were assessed in the additional questionnaire, and thus, less participants replied to these
items compared to the ones in the basic questionnaire.
Socio-demographic variables used in the study were gender, age, marital status,
education level, employment status, and household income. Age was used both as a

13
continuous and as a categorical variable for which five 10-year age groups were formed:
25–34, 35–44, 45–54, 55–64, and 65–74 years. Four categories for marital status were
used: married or cohabiting (including “in a civil partnership”), unmarried, divorced
(shortened from “divorced or in judicial separation”), and widowed. Education level
was reduced to three categories: primary education, secondary education, and higher
education. The four categories of employment status were employed (including
“housewife”), student, retired, and unemployed. Household income was reduced from
nine to three categories: very low (15,000 or less), less than average (15,001–35,000),
and average or more (over 35,000). The division of the categories was based on the
average income of 38,060 euros and the low-income limit of 14,200 euros in 2012
according to Statistics Finland (“Income and consumption,” 2015).
2.3 Urban–rural variables
Two indicators of the degree of urbanicity and rurality were used in the analyses: a
modified version of the Finnish Environment Institute’s urban–rural classification of an
area of residence and the population size of a municipality. Population size was applied
in this study in order to compare the results with the previous nightmare studies in
which population size was used as an indicator of urbanicity and rurality (Schredl,
2013; Stepansky et al., 1998). However, the urban–rural classification based on the
Finnish Environment Institute’s urban–rural categories was considered as a more
sophisticated method in examining urban–rural differences in nightmares.
Helminen et al. (2014) created the urban–rural classification utilizing
information on population, workforce, commuting, and buildings from 250×250-meter
grid squares in Finland. The urban–rural categorization of the grid squares was based on
data that was calculated within a one-kilometer radius in urban areas and a five-
kilometer radius in rural areas. Thus, the aim was to form integrated units easily
distinguishable in the scale of the whole Finland by generalizing the information of the
grid squares. The first outline of the classification was published in the summer of 2012,
and the final version was accomplished in June 2013. Because the information on each
participant’s coordinates of their residential buildings was available in the FINRISK
data, the urban–rural classification based on the same coordinates was possible to match
with the FINRISK data.

14
In the classification, population centers with more than 15,000 inhabitants are
defined as urban areas. They are further divided into 1) inner urban areas, 2) outer urban
areas, and 3) peri-urban areas. The other areas are defined as rural and divided into 4)
rural areas close to urban areas, 5) local centers in rural areas, 6) rural heartland areas,
and 7) sparsely populated rural areas. The inner and the outer urban areas consist of
connected and densely built areas with city plans. The living space is denser in the inner
than in the outer urban areas; for example, there is more green space in the outer areas.
The peri-urban areas have no city plans but they are directly connected to the inner and
outer urban areas and defined according to specific distances from the city centers. In
the rural areas close to urban areas at least a third of the inhabitants go to work to the
inner or outer urban areas. They have a high “potential accessibility,” meaning
extensive road networks and short commuting distances. The local centers in rural areas
are regionally important population centers not large enough to be regarded as urban
areas. They have to fulfill specific criteria concerning population size average (average
>5,000 over three last years), population density of the center (>400 inhabitants/km2
),
employment (over 2,000 places of employment), and area density. Rural heartland areas
are densely populated rural areas typically with a strong primary production and
extensive land use. They are located relatively far away from urban areas. All the rural
areas that do not fit into the former categories are defined as sparsely populated rural
areas. As well as the rural heartland areas, they are located far away from urban areas
but they also have limited sources of livelihood and vast unpopulated areas.
In this study, the seven categories were reduced to five to have adequate cell
sizes in statistical analyses. Peri-urban areas and rural areas close to urban areas were
combined to semi-rural areas near cities, according to the endorsement of Helminen et
al. (2014). Since rural heartland areas generally surround the local centers in rural areas
and there were only 366 participants living in the local centers in rural areas, these two
areas were combined similarly to the study of Kauppinen and Karvonen (2014). This
category was named as rural centers. Sparsely populated rural areas, inner urban areas,
and outer urban areas remained unchanged.
Monthly statistics for population sizes of municipalities were obtained from the
Population Register Centre of Finland (e.g., “Municipalities sorted by population
31.01.2012,” 2012). Since data were collected during five months, the population
average of this period was calculated. Six population size categories were created
according to the categorization of Schredl’s (2013) study: 1) up to 4,999, 2) 5,000 to

15
19,999, 3) 20,000 to 49,999, 4) 50,000 to 99,999 (only two municipalities: 73,800;
97,600), 5) 100,000 to 499,999 (only three municipalities: 144,100; 178,800; 203,700),
and 6) 500,000 or more inhabitants (only the capital: 597,300).
2.4 Statistical methods
The associations between single categorical variables were analyzed using the Pearson
chi-square (χ²) test. Urban–rural differences in nightmare prevalence were first analyzed
both with the urban–rural classification and with population size of a municipality.
However, because the urban–rural classification and the categories of population size of
a municipality were strongly correlated (rs(6424) = .82, p < .001) and the former was
regarded as a more sophisticated urban–rural categorization, population size was
excluded from further analyses. Cramer’s V was used to calculate the effect sizes for the
associations.
Multinomial logistic regression analyses were performed to examine urban–rural
differences in nightmare prevalence when adjusted for confounding factors. In all the
models, nightmare prevalence was the dependent variable and the urban–rural
classification remained as the independent variable regardless of its statistical
significance. The first model was unadjusted. The second model was adjusted for
gender and age, and the third model for all the socio-demographic factors including
gender and age. In the fourth model, factors that had been associated most consistently
with nightmares in the previous studies were adjusted for: symptoms of insomnia,
symptoms of depression, and other psychiatric disorders. The fifth model was adjusted
for other factors associated with nightmares, that is, working ability, the frequency of
intoxication, feelings of exhaustion, and headaches. In the final and the sixth model, all
the previously adjusted variables were included. The odds ratios of frequent and
occasional nightmares and their level of significance were reported in all the urban–
rural categories. The level of significance was set at 5% (p < .05) in all the analyses.
The analyses were performed with IBM SPSS version 22.

16
3. Results
During the previous 30 days, 3.9% of the participants reported having frequent
nightmares and 44.7% occasional nightmares. The urban–rural differences in nightmare
prevalence according to the urban–rural classification and according to population size
of a municipality are presented in Table 1. There was a statistically significant but very
small association between the urban–rural classification and nightmare prevalence
(χ²(8, n = 6,239) = 17.13, p = .029, V = .037). Frequent and occasional nightmares were
the most prevalent in the sparsely populated rural areas, and the least prevalent in the
semi-rural areas near cities. There was also a very small but statistically significant
association between population size of a municipality and nightmare prevalence
(χ²(10, n = 6,239) = 21.60, p = .017, V = .042). Frequent nightmares were the most
prevalent in the capital of Helsinki with 597,300 inhabitants, and the least prevalent in
municipalities with 20,000 to 49,999 inhabitants. An additional analysis revealed that
the inner urban area of Helsinki (n = 838) did not differ from the inner urban areas of
other cities (n = 1,613) in experiencing frequent nightmares (χ²(2, n = 2,451) = 0.38,
p = .825, V = .010). The outer urban areas of Helsinki and of other cities were not
compared as only 45 participants lived in the outer urban area of Helsinki. Occasional
nightmares were the most prevalent in the least populated municipalities with up to
4,999 inhabitants, but otherwise there were only minor differences in occasional
nightmares.
Table 1.
The urban–rural differences in the nightmare prevalence.
Nightmares during the last 30 days
Urban–rural categories Frequently Occasionally Not at all N
The urban–rural classification
Inner urban areas 3.9% 43.9% 52.3% 2,440
Outer urban areas 4.2% 44.0% 51.8% 1,251
Semi-rural areas near cities 2.5% 42.0% 55.5% 764
Rural centers 3.9% 46.7% 49.4% 1,226
Sparsely populated rural areas 4.8% 48.9% 46.2% 558
Population size
500,000 or more
a
4.3% 43.1% 52.6% 890
100,000 to 499,999
b
3.7% 42.2% 54.1% 1,726
50,000 to 99,999 4.0% 45.1% 50.8% 1,090
20,000 to 49,999 3.3% 46.8% 49.9% 521
5,000 to 19,999 3.8% 44.7% 51.5% 1,535
Up to 4,999 4.0% 53.0% 43.0% 477
Total 3.9% 44.7% 51.5% 6,239
a
Only Helsinki with a population of 597,300.
b
The actual population size range of the cities was 144,200–203,700.

17
3.1 Socio-demographic characteristics
Women reported significantly more nightmares than men: 5.0% of women reported
frequent and 49.1% occasional nightmares, whereas the corresponding rates of men
were 2.6% and 39.8%, respectively (χ²(2, n = 6,258) = 96.26, p < .001, V = .124).
Nightmares were associated with lower household income (χ2
(4, n = 6,050) = 93.20,
p < .001, V = .088), being unemployed (χ2
(6, n = 6,229) = 82.93, p < .001, V = .082),
lower education level (χ2
(4, n = 6,241) = 47.83, p < .001, V = .062), advancing age
(χ2
(8, n = 6,258) = 43.96, p < .001, V = .059), and being divorced
(χ2
(6, n = 6,248) = 26.20, p < .001, V = .046).
Table 2.
The urban–rural differences in the socio-demographic factors.
Urban Semi-rural Rural
Factors Inner Outer Centers Sparse V p N
Gender .048 .006 6,404
Female 54.8% 52.0% 52.9% 51.8% 46.4% 3,373
Male 45.2% 48.0% 47.1% 48.2% 53.6% 3,031
Age .080 < .001 6,404
25–34 20.5% 17.2% 15.0% 11.2% 7.4% 1,035
35–44 16.9% 21.6% 23.9% 17.5% 14.6% 1,191
45–54 18.1% 22.7% 22.1% 20.1% 22.4% 1,299
55–64 20.9% 19.0% 20.7% 24.9% 26.2% 1,394
65–74 23.6% 19.4% 18.3% 26.3% 29.4% 1,485
Marital status .102 < .001 6,388
Married/cohabiting 63.2% 76.0% 84.0% 75.7% 75.5% 4,590
Unmarried 19.8% 11.9% 6.9% 10.5% 13.3% 909
Divorced 13.5% 9.2% 7.5% 9.6% 7.1% 675
Widowed 3.5% 2.9% 1.7% 4.2% 4.1% 214
Education level .181 < .001 6,383
Primary 14.8% 15.7% 18.1% 27.8% 33.6% 1,255
Secondary 47.3% 56.4% 55.6% 58.3% 56.2% 3,388
Higher 38.0% 27.9% 26.3% 13.9% 10.2% 1,740
Household income .089 < .001 6,170
Very low 12.8% 8.7% 5.4% 10.2% 12.7% 653
Less than average 29.8% 26.1% 27.3% 35.3% 38.2% 1,889
Average or more 57.3% 65.2% 67.3% 54.5% 49.2% 3,628
Employment status .089 < .001 6,377
Employed 61.5% 66.5% 70.7% 59.0% 56.7% 3,984
Student 6.3% 3.9% 1.6% 1.9% 0.7% 247
Retired 27.9% 23.2% 23.3% 34.0% 37.8% 1,811
Unemployed 4.3% 6.4% 4.4% 5.1% 4.9% 315
Note. Sparse = sparsely populated.

Socio-demographic characteristics of the areas of the urban–rural classification
are presented in Table 2. The inner urban areas were characterized by a high proportion
of people with low income, high divorce rate, and high education level. In the semi-
rural areas near cities, income level and the employment rate were the highest. The

18
outer urban areas fell mostly between the inner urban and the semi-rural areas except for
the slightly higher unemployment rate than in all the other areas. The rural areas
generally resembled each other, but in the rural centers there were slightly higher
divorce rate, higher education level, and higher income level than in the sparsely
populated rural areas. The latter areas were characterized by people with a very low
income level and people with only primary education. Participants living in the rural
areas were generally older than participants living in other areas.
3.2 Other factors associated with nightmares
Symptoms of insomnia had the strongest association with nightmare prevalence
(χ2
(4, n = 6,218) = 736.07, p < .001, V = .243). Of participants with frequent insomnia
symptoms, 16.3% had frequent nightmares. Nightmares were also moderately
associated with feelings of exhaustion (χ2
(4, n = 6,201) = 588.51, p < .001,
V = .218) and depressive symptoms (χ2
(6, n = 4,634) = 434.96, p < .001, V = .217). Of
participants with severe depressive symptoms, 29.5% reported frequent nightmares.
Having headaches (χ2
(4, n = 6,219) = 357.14, p < .001, V = .169), lowered ability to
work (χ2
(4, n = 4,401) = 157.95, p < .001, V = .134), and other psychiatric disorders
than depression (χ2
(2, n = 6,197) = 52.90, p < .001, V = .092) were associated with
nightmares as well, but the effect sizes were small. A very weak but statistically
significant association was also found between nightmares and being frequently
intoxicated (χ2
(6, n = 4,731) = 15.99, p = .014, V = .041).
The significant urban–rural differences in the nightmare-associated variables
according to the urban–rural classification are presented in Table 3. Frequent symptoms
of insomnia formed a U-shaped curve; the highest prevalence was in the inner urban and
in the sparsely populated rural areas and the lowest in the semi-rural areas near cities.
Likewise, symptoms of depression were the most prevalent in the inner urban and in the
sparsely populated rural areas and the least prevalent in the semi-rural areas.
The proportion of people with full capacity for work was the lowest in the
sparsely populated rural areas. Having been intoxicated at least once a month during the
previous 12 months was more common in the urban than in the rural areas. No
significant urban–rural difference was found in other psychiatric disorders than
depression (χ2
(4, n = 6,338) = 7.87, p = .097, V = .035), although 3.1% of people in the
inner urban and 1.4% in the semi-rural areas reported a diagnosis. There were also no

19
significant urban–rural differences in having headaches (χ2
(8, n = 6,273) = 12.60,
p = .126, V = .032) or experiencing feelings of exhaustion (χ2
(8, n = 6,251) = 7.59,
p = .475, V = .025).
Table 3.
The significant urban–rural differences in the factors associated with nightmares.
Urban Semi-rural Rural
Factors Inner Outer Centers Sparse V p N
Insomnia symptoms .043 .003 6,278
Frequently 11.3% 9.3% 6.4% 9.3% 10.9% 622
Occasionally 43.4% 42.2% 43.4% 43.1% 46.6% 2,723
Not at all 45.3% 48.5% 50.1% 47.6% 42.5% 2,933
Depressive symptoms .045 .005 4,729
Severe 2.2% 1.5% 1.2% 2.0% 2.5% 90
Moderate 13.3% 10.0% 8.5% 10.0% 15.1% 547
Mild 12.5% 11.6% 10.9% 11.8% 11.9% 564
None 72.0% 76.9% 79.3% 76.2% 70.4% 3,528
Working ability .086 < .001 4,490
Full capacity 79.5% 79.7% 78.7% 72.0% 64.0% 3,434
Impaired capacity 15.9% 16.2% 16.7% 23.4% 29.7% 847
Incapacity 4.6% 4.1% 4.6% 4.6% 6.2% 209
Intoxication .072 < .001 4,828
≥1 per week 9.9% 7.3% 6.5% 6.4% 6.6% 384
1–3 per month 18.7% 17.3% 14.5% 14.9% 13.7% 806
<1 per month 43.4% 48.6% 45.5% 41.0% 38.6% 2,112
Not at all 28.0% 26.8% 33.5% 37.6% 41.1% 1,526
Note. Sparse = sparsely populated.

20
3.3 Multinomial logistic regression analysis
In the multinomial logistic regression analyses, the class “semi-rural areas near cities”
was used as a reference category due to its lowest prevalence of frequent and occasional
nightmares. There was only little multicollinearity between the variables (all VIFs < 2).
The results of the multinomial logistic regression analyses are presented in Table 4.
Table 4.
Multinomial logistic regression models for nightmare prevalence and the urban–rural areas.
Frequent nightmares Occasional nightmares
Model 1 p OR 95% CI p OR 95% CI
Inner urban areas .050 1.66 1.00–2.74 .230 1.11 .94–1.31
Outer urban areas .035 1.79 1.04–3.07 .213 1.12 .94–1.35
Semi-rural areas near cities – 1.00 – 1.00
Rural centers .042 1.76 1.02–3.04 .023 1.24 1.03–1.49
Sparsely populated rural areas .006 2.33 1.27–4.27 .004 1.39 1.11–1.74
Model 2
Inner urban areas .062 1.62 .98–2.69 .278 1.10 .93–1.30
Rural centers .058 1.70 .98–2.94 .036 1.22 1.01–1.47
Model 3
Inner urban areas .075 1.64 .95–2.81 .374 1.08 .91–1.29
Rural centers .138 1.55 .87–2.77 .119 1.17 .96–1.41
Model 4
Inner urban areas .419 1.33 .67–2.64 .503 .93 .76–1.14
Rural centers .070 1.96 .95–4.05 .177 1.17 .93–1.46
Sparsely populated rural areas .109 1.94 .86–4.38 .107 1.25 .95–1.64
Model 5
Inner urban areas .031 2.18 1.07–4.44 .488 1.08 .87–1.33
Rural centers .085 1.95 .91–4.18 .110 1.21 .96–1.52
Model 6
Inner urban areas .122 1.90 .84–4.29 .465 .92 .73–1.15
Rural centers .161 1.86 .78–4.40 .451 1.10 .86–1.41
Note. OR = odds ratio, CI = confidence interval.
Model 1: Unadjusted.
Model 2: Adjusted for gender and age.
Model 3: Adjusted for gender, age, marital status, education level, employment status, and income.
Model 4: Adjusted for symptoms of insomnia, symptoms of depression, and other psychiatric disorders.
Model 5: Adjusted for working ability, frequency of intoxication, feelings of exhaustion, and headaches.
Model 6: Adjusted for gender, age, marital status, education level, employment status, income, symptoms
of insomnia, symptoms of depression, other psychiatric disorders, working ability, frequency of intoxication,
feelings of exhaustion, and headaches.

21
In the unadjusted regression model (model 1), the association between the
urban–rural classification and nightmare prevalence was statistically significant
(p = .031, Nagelkerke .003). The odds of having frequent nightmares were statistically
significant and the highest in the sparsely populated rural areas. The odds of having
frequent nightmares were also statistically significant in the rural centers and in the
outer urban areas but the effects were relatively weak. The odds of having occasional
nightmares were statistically significant but weak in the sparsely populated rural areas
and in the rural centers.
In the sparsely populated rural areas, adjusting for socio-demographic factors
(models 2 and 3) had only little effect on the odds of frequent and the odds of
occasional nightmares. When adjusted for symptoms of insomnia, symptoms of
depression, and other psychiatric disorders than depression (model 4), the odds changed
to statistically non-significant. In addition, when adjusted for working ability, frequency
of intoxication, feelings of exhaustion, and headaches (model 5), or all the variables
(model 6), both the odds of frequent and occasional nightmares were statistically non-
significant.
In the rural centers, the odds of frequent and the odds of occasional nightmares
were statistically non-significant in all of the models in which more factors than gender
and age were adjusted for. Socio-demographic factors (model 3) decreased the odds the
most. In the outer urban areas, the odds of frequent nightmares remained statistically
significant in all of the models.

22
4. Discussion
In this study, urban–rural differences in nightmare prevalence were examined for the
first time in a Finnish adult population. In the whole sample, 3.9% reported frequent and
44.7% occasional nightmares. A statistically significant difference in nightmare
prevalence was discovered within the categories of inner urban areas, outer urban areas,
semi-rural areas near cities, rural centers, and sparsely populated rural areas. Both
frequent and occasional nightmares were the most prevalent in the sparsely populated
rural areas (4.8% and 48.9%, respectively) and the least prevalent in the semi-rural areas
near cities (2.5% and 42.0%, respectively). In spite of the statistical significance, the
size of the effect was very small. When compared to the semi-rural areas in the
unadjusted regression model of the study, the odds of having frequent nightmares were
higher in the sparsely populated rural areas, in the outer urban areas, and in the rural
centers. The effects of the outer urban areas and the rural centers, however, were
relatively weak. The odds of having occasional nightmares were higher in the sparsely
populated rural areas and in the rural centers compared to the semi-rural areas, but these
effects were weak.
Nightmare prevalence was moderately associated with symptoms of insomnia,
feelings of exhaustion, and depressive symptoms in the present data. The effect size of
the association between nightmares and insomnia symptoms was over six times larger
than the effect size of the association between nightmares and the urban–rural
classification. Nightmares were weakly but statistically significantly associated with
headaches, lowered ability to work, and other psychiatric disorders than depression.
Being frequently intoxicated had a very weak but statistically significant association
with nightmares. Of socio-demographic factors, nightmares were weakly associated
with female gender, lower household income level, and being unemployed, and very
weakly but statistically significantly with lower education level, advancing age, and
being divorced. The findings on these associations were expected, as Sandman et al.
(2015) found that all these factors were associated with nightmares in their data that
included the dataset of this study.
In the present study, the odds of having frequent nightmares in the sparsely
populated rural areas were no longer statistically significant after adjusting for
symptoms of insomnia, symptoms of depression, and other psychiatric disorders than
depression, but also after adjusting for other health-related factors. Thus, poorer mental

23
and physical health in the sparsely populated rural areas compared to the semi-rural
areas explained some of the disparity in frequent nightmares between these areas.
Between the rural centers and the semi-rural areas, the socio-demographic factors
explained largely their small difference in the prevalence of frequent nightmares. For
example, household income and education level in the rural centers were generally
much lower than in the semi-rural areas. The difference in frequent nightmares between
the outer urban areas and the semi-rural areas, however, remained significant even after
adjusting for all the nightmare-associated factors, which was an unexpected finding.
Urban–rural differences in nightmare prevalence were also examined between
municipalities categorized according to their population size in order to compare the
findings with the findings of Schredl (2013) who used the same population size
categories in his study of nightmares. There was a statistically significant difference in
nightmare prevalence between the population size categories, but the size of the effect
was very small. The highest prevalence of frequent nightmares, 4.3%, was found in the
most populated city of Finland, Helsinki, which had a population of 596,200 inhabitants
in January 2012 (“Municipalities sorted by population 31.01.2012,” 2012). The lowest
prevalence of 3.3% was discovered in middle-sized municipalities of 20,000 to 49,999
inhabitants. Occasional nightmares were reported the most in the smallest municipalities
with up to 4,999 inhabitants (53.0%) and the least in cities with 144,200 to 203,700
inhabitants (42.2%).
Despite the strong correlation between the urban–rural classification and the
categories of population size of a municipality, differing urban–rural findings on
nightmares were obtained. The inconsistency between the results is probably explained
by the urban–rural variance in the middle-sized municipalities. For example, in the
municipalities of 20,000 to 49,999 inhabitants, 22% of the participants lived in the inner
urban areas, 40% in the outer urban areas, 22% in the semi-rural areas, 14% in the rural
centers, and 2% in the sparsely populated rural areas. Because a wide array of factors
was taken into consideration in creating the urban–rural classification, and defining the
areas were not restricted by municipal boundaries, the urban–rural classification was
considered to be a more reliable measure of urbanicity and rurality than population size
of a municipality.
Hence, the results obtained with the urban–rural classification supported the
hypothesis that frequent nightmares are more prevalent in the most rural areas than in
the semi-rural areas. However, it was also hypothesized that frequent nightmares would

24
be more prevalent in the most urban areas than in the semi-rural areas, which was not
the case as the rate of frequent nightmares in the inner urban areas was equal to average.
Although a slightly higher prevalence of frequent nightmares was detected in the largest
city of Helsinki, it was only 9% (0.4 percentage points) higher than average. In a further
analysis, the inner urban area of Helsinki did not significantly differ from the inner
urban areas of other cities in nightmare prevalence. This would indicate that the
explanation for the marginally higher nightmare prevalence in Helsinki compared to
less populated municipalities is not its possibly higher degree of urbanicity. The
hypothesis that factors previously associated with nightmares partly confound the
urban–rural differences in nightmare prevalence was supported; however, the difference
between the outer urban areas and the semi-rural areas remained significant after
controlling for other factors. The findings of this study have not been previously
observed, thus providing new information on the prevalence of nightmares.
4.1 Comparison with previous findings
The present results are not in line with the previous findings: the odds of having
nightmares were observed to be higher in more populated areas in a German study
(Schredl, 2013), and suffering from nightmares was more common in areas with over
50,000 inhabitants in an Austrian study (Stepansky et al., 1998). The most crucial issue
in the comparability of the studies is that they have been carried out in different
countries, which vary geographically, demographically, economically, socially, and
culturally. For example, according to the definition of a functional urban area of the
Organisation for Economic Co-operation and Development (OECD), there are 24
metropolitan areas in Germany with a population over 500,000, and six of them have a
population above 1.5 million (“Functional urban areas by country,” n.d.). By
comparison, Finland is among the most sparsely populated countries in Europe
(“Population density: Persons per km2
,” 2015), and no other city than Helsinki has over
500,000 inhabitants in Finland. Also, in these data, the second largest city had only
203,200 inhabitants in January 2012 (“Municipalities sorted by population 31.01.2012,”
2012). If nightmares were to be associated with urban environment, it could be possible
that such a high degree of urbanicity as in the metropolitan areas in Europe or elsewhere
does not exist in Finland, which is why the results of this study may apply only to
Finland.

25
Some methodological restrictions of the previous studies should also be noted.
Schredl (2013) reported only the results of the logistic regression analyses, yet the cross
tabulations of the raw data of his study (personal communication, August 24, 2015)
illustrate that his conclusion of urban living being associated with heightened nightmare
frequency is problematic. Because Schredl assessed nightmare frequency with eight
categories and population size with six categories, the large number of the cells resulted
in small cell sizes. Only four participants altogether reported having nightmares several
times a week, and 12 participants about once a week. This is why in all the population
size categories except from one, less than five participants reported nightmares once or
more per week, which could have caused some problems in the statistical analyses.
Schredl’s interpretation of the results on logistic regression may thus be based on data
that has low statistical power due to the scarcity of participants with weekly nightmares.
This is also reflected on the small effect size of the association between nightmare
frequency and population size reported in the study (SE = .0743, p = .0375). Hence, it is
concluded here that evidence for nightmare prevalence being higher in the largest cities
than in other areas is inconclusive based on Schredl’s data.
Stepansky et al. (1998) did not directly investigate the frequency of nightmares
but assessed suffering from nightmares, which is why the results of the present study are
not fully comparable to their findings. Even though they reported that 4% of the total
sample suffered from nightmares, which corresponds to the proportion of people
reporting frequent nightmares in this study, nightmare-induced distress and nightmare
frequency have not correlated perfectly (Belicki, 1992a, 1992b; Miró & Martínez, 2005;
Wood & Bootzin, 1990). Also, the interpretation of their results is restricted by the fact
that the category of the most populated areas was “more than 50,000 inhabitants,”
which might have included people living in very diverse environments. For example,
suffering from nightmares could as well be more common in areas with 50,000 to
99,999 inhabitants than in areas with over 500,000 inhabitants, meaning that suffering
from nightmares would not increase by the degree of urbanicity.
4.2 The poor well-being in the sparsely populated rural areas
In this study, mental and physical health were poorer in the sparsely populated rural
areas than in the semi-rural areas, and these differences explained partly the difference
in nightmare prevalence between the areas. Especially the symptoms of insomnia and

26
depression were more prevalent in the former areas than the latter ones. There were also
major differences in socioeconomic factors; household income, education level, and the
employment rate were considerably higher in the semi-rural than in the sparsely
populated rural areas. Surprisingly, this disparity did not considerably confound the
differences in nightmare prevalence between the areas. Thus, although low
socioeconomic status has been a risk factor for psychiatric disorders (Hudson, 2005),
there might be also other factors in the sparsely populated rural areas that could
predispose to insomnia and depression, and consequently, to nightmares.
These other factors could be related to the population but also to the physical or
social environment (Caracci, 2008; Philo et al., 2003). For example, regarding the
physical environment in the sparsely populated rural areas, the distance to the nearest
health care center was 22.6 kilometers on average in these areas in 2012 (“Households’
consumption 2012,” 2014). In one study, longer travel time to the healthcare provider
from whom the patient had received treatment for depression was significantly
associated with the patient making fewer visits to the healthcare provider (Fortney, Rost,
Zhang, & Warren, 1999). It could be speculated that if a depressed person living in a
sparsely populated rural area had frequent nightmares, a long distance to a healthcare
provider could decrease his or her odds on seeking help also for nightmares. However,
further investigating of the poorer mental or physical health in the sparsely populated
rural areas was beyond the scope of this study.
4.3 The unexpected findings in the urban areas
The inner urban areas did not have higher odds for frequent nightmares compared to the
semi-rural areas near cities, which was an unexpected finding. Namely, there seemed to
be several risk factors in the inner urban areas that could have increased their odds of
frequent nightmares. Frequent symptoms of insomnia, severe and moderate symptoms
of depression, and being intoxicated weekly or monthly were all reported more in the
inner urban than in the semi-rural areas. Moreover, having suicidal thoughts, which has
previously been associated with nightmares (Li et al., 2012; Pigeon et al., 2012), has
been observed to be more common in the inner urban than in other areas (Kaikkonen et
al., 2014). Also the proportion of people who have been a target of a violent or
threatening behavior, that is, experiences that could cause post-traumatic nightmares,
has been higher in the inner urban than in other areas. Hence, as there seem to be many

27
adverse characteristics in the inner urban areas in Finland, these areas might also have
some factors protecting against nightmares, which could not be detected in the present
study.
The higher odds of frequent nightmares in the outer urban areas compared to the
semi-rural areas were not significantly confounded by nightmare-associated factors.
Thus, there may truly be some factors related to the physical or social environment or to
the population structure that could increase the nightmare prevalence in the outer urban
areas, yet in these data, no major differences between the outer urban areas and the
semi-rural areas could not be detected. In general, the semi-rural areas had the most in
common with the outer urban areas in socio-demographic characteristics, in mental
health, and in physical health. However, although the areas could have some regional
differences in factors predisposing to nightmares, it should be noted that after adjusting
for socio-demographic factors and after adjusting for symptoms of depression and
insomnia, and other psychiatric disorders than depression, the higher odds of having
frequent nightmares in the outer urban areas was barely statistically significant.
4.4 Strengths and limitations
This has been a large-scale population study representative of the Finnish adult
population. Urban–rural differences in nightmare prevalence have been explored for the
first time using a modified version of the Finnish Environment Institute’s urban–rural
classification that is based on information on population, workforce, commuting, and
buildings from 250×250-meter grid squares (Helminen et al., 2014). To use grid squares
instead of administrative regions, such as municipalities, is of great value.
Administrative regions can cover vast areas, and therefore, provide inaccurate
information on the degree of urbanicity or rurality in a specific area. Furthermore, in
this study, the urban areas have been divided into inner and outer urban areas, rural
areas into rural centers and sparsely populated rural areas, and most importantly, semi-
rural areas between urban and rural areas have been recognized. Hence, the
categorization of the areas has been more sophisticated than in the previous studies
where only population size of areas has been used as an indicator of urbanicity and
rurality (Schredl, 2013; Stepansky et al., 1998).
A major methodological limitation of the study is that the data do not cover all
areas of Finland. For example, the most northern regions of Finland and Central Finland

28
have not been included in these data. Most of the study areas were located in eastern
Finland, which may have skewed the results. For example, the use of prescribed drugs
for depression has been high especially in the providences of North Karelia, Northern
Savonia, North Ostrobothnia, and Kainuu (Lehtonen & Kauronen, 2013), which were
all included in the FINRISK 2012 sample. Additionally, regional clustering of
schizophrenia has been detected in eastern and northeastern Finland, which could be
caused by genetic isolation (Haukka, Suvisaari, Varilo, & Lönnqvist, 2001); hence,
there could be a genetic risk for nightmares as well in these areas.
Another limitation to the interpretation of the results is that the FINRISK study
is cross-sectional, and it remains unknown how long the participants have lived in their
places of residence or where they have been living before. A high nightmare prevalence
in a specific area could be caused by migration if participants experiencing nightmares
or susceptible to nightmares are prone to move to that area or stay in that area (“drift
hypothesis”; Verheij, 1996). The retrospective nature of the study should also be noted.
Participants may have been susceptible to forgetting and memory bias and, thus, may
have underestimated their nightmare prevalence (Zadra & Donderi, 2000). It is not
impossible that in the rural areas with more elderly people, forgetting and memory bias
could be higher than in areas with a greater proportion of younger people. If this were
true, the comparability of the regional prevalence rates would be slightly unreliable. The
use of prospective dream logs could produce more reliable estimates (e.g., Zadra &
Donderi, 2000), yet a large population-based study would be very challenging to
execute with daily logs.
Some of the original categories of Helminen et al. (2014) have been combined in
this study to have adequate cell sizes in statistical analyses. This may bring some minor
challenges to the interpretations of the results of these categories. Peri-urban areas and
rural areas near cities can be combined for analyses according to Helminen et al. (2014),
but the combination of local centers in rural areas and rural heartland areas is not as
simple. Namely, the local centers in rural areas may include more densely populated
areas than the rural heartland areas. Nevertheless, the local centers in rural areas and
rural heartland areas were both defined as rural areas and they have mainly been located
close to each other in these data. Moreover, neither of these areas are located next to the
urban areas. This proximity of the urban areas has been an important factor in defining
the areas in the urban–rural classification as the urban areas are considered to have a
substantial influence on their surrounding areas.

29
In the present study, participants have not estimated their nightmare prevalence
with a numeric scale but with the options of having nightmares “frequently,”
“occasionally,” or “not at all” during the previous month. It is not entirely impossible
that there could be urban–rural differences in evaluating what is frequent or occasional.
Also, no definition for nightmares has been provided for the participants in this study.
There might be urban–rural differences in understanding the concept of a nightmare but
since nightmares are a well-known phenomenon, it can be assumed that subjective
definitions for a nightmare do not depend on such factors as education.
Despite the lack of the definition and not using a numeric scale, the prevalence
of frequent nightmares in the whole sample, 3.9%, is comparable to the prevalence of
weekly nightmares that has been about 2%–5% in the general population (Bjorvatn et
al., 2010; Hublin et al., 1999; Li et al., 2010; Schredl, 2010). In most of the studies of
nightmare prevalence, only 9%–17% of subjects have reported nightmares at least once
a month (Hublin et al., 1999; Li et al., 2010; Schredl, 2010). In this study, the
proportion of occasional nightmares during the previous month has been as much as
44.7%, which is in accordance with other FINRISK studies (e.g., Sandman et al., 2013).
The explanation for the higher prevalence of monthly nightmares in FINRISK may lie
in differences in the assessment of nightmares or in the study areas.
4.5 Future research
The present study has provided basic information on the urban–rural differences in
nightmare prevalence that can be utilized in future research. Follow-up studies could be
conducted in the sparsely populated rural areas in order to further explore nightmare
problems in these areas. Nightmare-induced distress should be assessed along with
nightmare frequency to achieve a better estimate of clinical nightmare problems.
Longitudinal data would enable researchers to examine variation in environmental
factors and demographical changes of the areas over time. If some environmental
factors predisposing to nightmares were to be found in the sparsely populated rural
aresa, interventional studies aiming to reduce the frequency of nightmares could be
conducted. Also the inner urban areas of Finland could be further examined to gain
information on the possibility of previously unknown protective factors for nightmares.
This information in particular could be valuable in other countries as well.

30
In order to examine the effect of the environment specifically on idiopathic
nightmares, they should be distinguished from post-traumatic ones. For example, the
prevalence of post-traumatic nightmares in some areas could be high if environmental
disasters or other traumatic events have occurred in these areas. Also, there could be a
high proportion of war veterans or war refugees with war-related nightmares in some
areas. The origin of the nightmares could be evaluated by asking about their content,
and nightmares related to a traumatic event could then be defined as post-traumatic. In
this way, regional differences in post-traumatic nightmares could be controlled for.
In regard to all future studies in which urban–rural differences are examined,
this study has showed the value of using a more detailed approach to the urban–rural
definitions and abandoning the urban–rural classifications based on administrative
boundaries. The dichotomization of areas to either urban or rural fails to recognize the
areas in-between, the “peri-urban” or “semi-rural” areas. Semi-rural areas cannot be
viewed as either urban or rural as they are tightly connected to urban areas both
functionally and geographically while simultaneously sharing a lot of characteristics
with rural areas, such as buildings more dispersed than in urban areas. Furthermore,
based on the results of this study, it would be important to separate inner and outer
urban areas, or “city centers” and “the suburbs.”
4.6 Conclusion
In the present study, both frequent and occasional nightmares were found to be the most
prevalent in the sparsely populated rural areas and the least prevalent in the semi-rural
areas near cities. The difference was partly explained by poorer mental and physical
health in the sparsely populated rural areas. Thus, in light of these results, more research
attention to nightmares and general well-being is needed in the most rural areas of
Finland. As nightmares have been associated with a variety of psychiatric symptoms in
the present study and in previous studies, and nightmares may even heighten the risk of
suicide, they should be carefully assessed in the rural healthcare centers, especially
among depressed people and insomniacs. Because of the long distances in the sparsely
populated rural areas, developing virtual mental health services could be valuable in
these areas.

31
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