**2.1 Study area**

A landlocked country Nepal situated between 80°and 88° E longitude and from 26° to 31° N latitude which covers an area of 147,181 Sq.km measuring 880 kilometers (547 mi) along its Himalayan axis by 150 to 250 kilometers (93 to 155 mi) across. The new

**Figure 1.** *Landuse pattern of Nepal.*

map showing Kalapani, Limpiyadhura and Lipulekh as Nepali territories, Government of Nepal has further declared as an extended area of 147, 516km2 (56,956 sq. mi) [20]. Nepal resembles five physiographic zones and seven bio-climatic zones identified by Dobremez [21] extending from East to West, including the High Himal, High Mountains, Middle Mountains (or Middle Hills), Siwalik (or Chure), and Terai [22] (**Figure 1**). The genetic, species and ecosystem level biodiversity of Nepal spreads over tropical forests from Terai (67-1000 masl), mid hill (1000-2000 masl) subtropical and temperate regions (2000–3000 masl) to Himalayas sub-alpine and alpine pastures and snow-covered peaks of the Himalayas (> 3000 masl). As shown in **Figure 1**, land cover in Nepal includes different types of forests (broadleaf, needle leaf, mixed), agriculture, shrub lands, grasslands, bare lands, river and lakes, and glacier/snow [23, 24].


*Edited by Arshad M. Malik*

This book watches out for the issues on making moves for chest radiology in carcinoma of the chest. It focuses on all parts of radiological approaches to manage the breast illness, be it light (optical), sound (ultrasound), interest, microwave, electrical impedance, blend of these modalities, and a section of the incredibly intense issues on computer-aided detection. The dedication of the eminent analysts in this book has incorporated a lot of energy for the people who are adequately drawn in with the clinical organization of this ailment and also for the students of radiology and surgery alike. This book will definitely be appreciated and well taken by the surgeons, radiologists, and other professionals involved in this field. The contributions are excellent in terms of diagnostic approach by radiological means and would certainly be a step forward in making it possible to reach to a conclusive diagnosis of breast cancer much before it becomes inoperable. The chapters included will further our knowledge and to the best of my belief will make things easier and definable in terms of diagnosis of breast cancer.

Photo by akesak / iStock

New Perspectives in Breast Imaging

New Perspectives in

Breast Imaging

*Edited by Arshad M. Malik*


E. LOCUS OF CONTROL

1.Illness is a matter of bad luck.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

2.If it is in your fate, you cannot avoid sickness.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

3.Keeping good health is a matter of good luck.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

4.I think I can control my illness.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

5.I do not have detail information about my illness.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

6.I think one can maintain good health by paying attention to nutrition, exercise etc.

**171**

*The Role of Lifestyle in Development of Coronary Heart Disease*

10.I know the steps to get out of this illness.

1.Give your priority to values given below.

Money, People, Carrier, Religion.

3.Money should be saved for old age.

Agree/Disagree/Can't say

11.Good doctors can cure most illness.

Strongly Disagree/Disagree/Undecided/Agree/Strongly Agree

4.Earning money only through labour and honestly appears descent.

Strongly Disagree/Disagree/Undecided/Agree/Strongly Agree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Very much Quite/Much Quite/Not much/Very little/Never

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

8.Keeping fast is necessary on main religious occasions.

5.Participation in social ceremonies is useless. Yes/No

6.Man should behave according to religious beliefs?

If yes, Always/Occasionally/Rarely/Never

7.To what extent are you religious?

9.There is no use worrying about illness, it will get cured when the time

8.People can help each other in dealing with many diseases.

*DOI: http://dx.doi.org/10.5772/intechopen.86866*

comes.

(F) VALUES

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

7.God helps us to keep healthy.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

8.People can help each other in dealing with many diseases.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

9.There is no use worrying about illness, it will get cured when the time comes.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

10.I know the steps to get out of this illness.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

11.Good doctors can cure most illness.

Strongly Disagree/Disagree/Undecided/Agree/Strongly Agree

Whether you feel successful/unsuccessful in your life, give at least five reasons:

(F) VALUES

*Inflammatory Heart Diseases*

3.Do you feel that your partner understands you?

Yes, always/Sometimes/Rarely/Never

Always/Occasionally/Rarely/Never

Always/Occasionally/Rarely/Never

Always/Occasionally/Rarely/Never

Always/Occasionally/Rarely/Never

advice about family matters. Yes/No.

1.Illness is a matter of bad luck.

4.I think I can control my illness.

7.God helps us to keep healthy.

If yes, Always/Occasionally/Rarely/Never

2.If it is in your fate, you cannot avoid sickness.

3.Keeping good health is a matter of good luck.

5.I do not have detail information about my illness.

child/children?

E. LOCUS OF CONTROL

exercise etc.

4.Do you visit friends and relatives together?

5.Do you engage in outside hobbies and interests together?

6.Do you share with your partner the responsibility of looking after your

8.Do you feel that your partner depends on you rather than her relatives for

7.Do you confide in your partner about your personal problems?

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

6.I think one can maintain good health by paying attention to nutrition,

**170**

1.Give your priority to values given below.

Money, People, Carrier, Religion.

Agree/Disagree/Can't say

If yes, Always/Occasionally/Rarely/Never

6.Man should behave according to religious beliefs?

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

7.To what extent are you religious?

Very much Quite/Much Quite/Not much/Very little/Never

8.Keeping fast is necessary on main religious occasions.

Strongly Agree/Agree/Undecided/Disagree/Strongly Disagree

*Inflammatory Heart Diseases*


**1. Introduction**

### **1.1 Diagnostic systems**

Anxiety disorders (ADs) are one of the most important conditions in child and adolescent psychiatry with a heterogeneous spectrum of clinical manifestations and variable levels of severity. ADs are commonly found at all levels of mental health services and involve the use of large resources for the appropriate assessment and therapeutic approach.

Anxiety disorders (ADs) are classified in *DSM-5* (APA) [1] or *ICD-10* (WHO) [2] diagnostic systems. Although developmental pathway features or a variety of other factors can significantly influence clinical presentation, both classification systems describe common clinical features for AD [3] and use roughly the same diagnosis criteria for children and adolescents as compared to adults. *ICD-10*, in contrast to *DSM-5*, uses several separate diagnostic codes for children. The new proposed disorder categories in *ICD-11* are largely the same as those in the equivalent section of *DSM-5* and recognize that the same disorders occur across the life span with developmentally distinct presentations [4]. Therefore, diagnoses in *ICD* are made based on essential features, with the expectation that the clinicians will use their clinical judgment on exact symptom counts and duration in a manner that is consistent with the diagnostic guidance provided [5]. Although this descriptive diagnosis criteria facilitated the development of diagnostic instruments for AD assessment [6–10], clinicians should be aware of their limitations, particularly related to developmental issues in obtaining self-reports from children and adolescents [11]. Due to these considerations, the core diagnostic criteria might present differently in the young, requiring special assessment strategies.

### **1.2 Prevalence**

Mental disorders affect a significant number of children and adolescents worldwide [12]. Among the children and adolescent psychiatric disorders, ADs are a representative category. Prevalence rates reported in studies are varying considerably sometimes, depending on the sample, assessment methods, the assessed period, or study design, between 6.5 [12] and 8.3% [13] and up to 27% [14]. In a meta-analysis of multiple data sets, Costello et al. have found the prevalence of 10.2% for any AD, 5.4% for specific phobia (SPEC), 3.6% for social phobia (SOC), 2.6% for separation anxiety disorder (SAD), 1.7% for generalized anxiety disorder (GAD), and 0.8% for panic disorder (PD) [15]. In the National Comorbidity Survey Replication and Adolescent Supplement, Kessler et al. reported a lifetime prevalence of any anxiety disorders of 32.4%, for the ages of 3–17 [16]. Spence et al.'s results are slightly different, with reported prevalence rates of 2.3% for social phobia (SOC), 4.3% for separation anxiety (SAD), and 2.2% for generalized anxiety disorder (GAD) [17]. Previous studies reported similar prevalence rates in diverse cultures [18–20].

In terms of onset age, Merikangas et al. found a prevalence of 31.4% for the age group of 13–14 years, 32.1% for the age group of 15–16 years, and 32.3% for the age group of 17–18 years, with particularly high rates of specific phobia (SPEC) [13]. Generalized anxiety disorder and social anxiety disorder are among the most common AD in youth [21]. Panic disorder is rare and occurs in adolescents rather than in young children [15, 21]. Findings from different studies have demonstrated that, in the general population, anxiety symptoms first decrease during early adolescence and subsequently increase from middle to late adolescence [22]. Separation anxiety disorder and specific phobias tend to emerge and predominate during childhood, whereas the initial onset of generalized anxiety disorder, panic disorder, and social anxiety disorder most often occurs during adolescence [16, 23].

The literature describes the theory on distinct latent developmental trajectories for different anxiety disorder symptoms, emphasizing the importance of examining separate anxiety dimensions rather than considering anxiety as a general construct. For example, girls were significantly more likely than boys to be in numerous latent generalized anxiety disorder symptom trajectory classes, including those distinguished by very high initial symptoms that decrease rapidly, high initial symptoms that decrease less markedly over time, and moderate initial symptoms that decrease slightly over time [24]. The results from Crocetti et al.'s study regarding latent

**77**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

growth trajectory classes found that adolescent population was best typified by a low-anxiety class, characterized by a low initial anxiety level that decreased over time and a high-anxiety class characterized by a higher initial anxiety level that increased over time [25]. In a 5-year study of Dutch, youth aged between 10 and 12 years at baseline, generalized anxiety disorder, panic disorder, and social anxiety disorder symptoms slightly decreased and then leveled off from early to middle adolescence, followed by a slight increase in generalized anxiety disorder and social anxiety disorder symptoms during middle adolescence and in panic disorder symptoms during late adolescence [22, 26]. Similar results have been reported by

There are mixed data regarding the differential gender prevalence rates of anxiety. Similar to gender ratio for adults, girls tend to have more of all subtypes of anxiety disorders, but there is no significant difference between boys and girls in the mean age at onset of anxiety [28]. Some studies showed that girls have a higher risk to develop AD [13, 16], are more affected [13, 24], and report higher cross-sectional anxiety symptom levels [29–31], and these differences remain stable

Generally, in child and adolescent psychiatry, the comorbidities are the rule, and anxiety disorders fit into this pattern. In the clinical practice, AD comorbidities in internalization symptom area are commonly identified and closely monitored compared with the externalization symptoms. However, there are very few papers focused on the comorbidity between specific anxiety disorders and other psychiat-

Comorbidity occurs frequently, both within the anxiety disorders and also with other psychiatric disorders. At least one third of the children and adolescents diagnosed with anxiety disorders meet the criteria for two or more anxiety disorders [32]. Children with a primary anxiety disorder were significantly more likely to be diagnosed with separation anxiety disorder than adolescents. Adolescents with a primary anxiety disorder received more frequently a primary diagnosis of social anxiety disorder, mood disorders, and irregular school attendance [33]. Anxiety disorders are associated with all the other major classes of disorders, including mood disorders, disruptive behaviors, ADHD, eating disorders, and substance use disorders [19, 28, 34–36]. Merikangas et al. reported in their national survey among adolescents aged 13–18 years that the anxiety disorders were the most common condition (31.9%), with approximately 40% of those with one class of disorder also meeting criteria for another class of lifetime disorder [13]. Costello et al. found that comorbidity with other psychiatric disorders was common, ranging from 53% of the generalized anxiety disorder cases to 100% of specific phobia cases. The most common type of comorbidity with non-anxiety disorders was with depression [15, 20, 37, 38]. Adolescents with high levels of depressive symptoms experienced less significant decline over time in symptoms of physical, social, and separation anxiety [30]. The Oregon Adolescent Depression Study, looking at lifetime diagnoses, revealed that depression was significantly associated with each of the anxiety disorders except obsessive-compulsive disorder. Other lifetime associations found were ADHD with specific phobia and bipolar disorder with separation anxiety in males [39]. In preschool children, Sterba et al. used confirmatory factor analysis to show that the best-fitting model for the emotional disorders had three factors (social phobia, separation anxiety, and a factor that combined GAD and depression) with significant correlations between these three factors and conduct disorder, oppositional disorder, and

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

recent studies [24, 27].

during adolescence [20].

**1.3 Comorbidities**

ric diagnoses.

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

growth trajectory classes found that adolescent population was best typified by a low-anxiety class, characterized by a low initial anxiety level that decreased over time and a high-anxiety class characterized by a higher initial anxiety level that increased over time [25]. In a 5-year study of Dutch, youth aged between 10 and 12 years at baseline, generalized anxiety disorder, panic disorder, and social anxiety disorder symptoms slightly decreased and then leveled off from early to middle adolescence, followed by a slight increase in generalized anxiety disorder and social anxiety disorder symptoms during middle adolescence and in panic disorder symptoms during late adolescence [22, 26]. Similar results have been reported by recent studies [24, 27].

There are mixed data regarding the differential gender prevalence rates of anxiety. Similar to gender ratio for adults, girls tend to have more of all subtypes of anxiety disorders, but there is no significant difference between boys and girls in the mean age at onset of anxiety [28]. Some studies showed that girls have a higher risk to develop AD [13, 16], are more affected [13, 24], and report higher cross-sectional anxiety symptom levels [29–31], and these differences remain stable during adolescence [20].

#### **1.3 Comorbidities**

*Psychopathology - An International and Interdisciplinary Perspective*

differently in the young, requiring special assessment strategies.

anxiety disorder most often occurs during adolescence [16, 23].

**1.2 Prevalence**

Anxiety disorders (ADs) are classified in *DSM-5* (APA) [1] or *ICD-10* (WHO) [2] diagnostic systems. Although developmental pathway features or a variety of other factors can significantly influence clinical presentation, both classification systems describe common clinical features for AD [3] and use roughly the same diagnosis criteria for children and adolescents as compared to adults. *ICD-10*, in contrast to *DSM-5*, uses several separate diagnostic codes for children. The new proposed disorder categories in *ICD-11* are largely the same as those in the equivalent section of *DSM-5* and recognize that the same disorders occur across the life span with developmentally distinct presentations [4]. Therefore, diagnoses in *ICD* are made based on essential features, with the expectation that the clinicians will use their clinical judgment on exact symptom counts and duration in a manner that is consistent with the diagnostic guidance provided [5]. Although this descriptive diagnosis criteria facilitated the development of diagnostic instruments for AD assessment [6–10], clinicians should be aware of their limitations, particularly related to developmental issues in obtaining self-reports from children and adolescents [11]. Due to these considerations, the core diagnostic criteria might present

Mental disorders affect a significant number of children and adolescents worldwide [12]. Among the children and adolescent psychiatric disorders, ADs are a representative category. Prevalence rates reported in studies are varying considerably sometimes, depending on the sample, assessment methods, the assessed period, or study design, between 6.5 [12] and 8.3% [13] and up to 27% [14]. In a meta-analysis of multiple data sets, Costello et al. have found the prevalence of 10.2% for any AD, 5.4% for specific phobia (SPEC), 3.6% for social phobia (SOC), 2.6% for separation anxiety disorder (SAD), 1.7% for generalized anxiety disorder (GAD), and 0.8% for panic disorder (PD) [15]. In the National Comorbidity Survey Replication and Adolescent Supplement, Kessler et al. reported a lifetime prevalence of any anxiety disorders of 32.4%, for the ages of 3–17 [16]. Spence et al.'s results are slightly different, with reported prevalence rates of 2.3% for social phobia (SOC), 4.3% for separation anxiety (SAD), and 2.2% for generalized anxiety disorder (GAD) [17]. Previous studies reported similar prevalence rates in diverse cultures [18–20].

In terms of onset age, Merikangas et al. found a prevalence of 31.4% for the age group of 13–14 years, 32.1% for the age group of 15–16 years, and 32.3% for the age group of 17–18 years, with particularly high rates of specific phobia (SPEC) [13]. Generalized anxiety disorder and social anxiety disorder are among the most common AD in youth [21]. Panic disorder is rare and occurs in adolescents rather than in young children [15, 21]. Findings from different studies have demonstrated that, in the general population, anxiety symptoms first decrease during early adolescence and subsequently increase from middle to late adolescence [22]. Separation anxiety disorder and specific phobias tend to emerge and predominate during childhood, whereas the initial onset of generalized anxiety disorder, panic disorder, and social

The literature describes the theory on distinct latent developmental trajectories for different anxiety disorder symptoms, emphasizing the importance of examining separate anxiety dimensions rather than considering anxiety as a general construct. For example, girls were significantly more likely than boys to be in numerous latent generalized anxiety disorder symptom trajectory classes, including those distinguished by very high initial symptoms that decrease rapidly, high initial symptoms that decrease less markedly over time, and moderate initial symptoms that decrease slightly over time [24]. The results from Crocetti et al.'s study regarding latent

**76**

Generally, in child and adolescent psychiatry, the comorbidities are the rule, and anxiety disorders fit into this pattern. In the clinical practice, AD comorbidities in internalization symptom area are commonly identified and closely monitored compared with the externalization symptoms. However, there are very few papers focused on the comorbidity between specific anxiety disorders and other psychiatric diagnoses.

Comorbidity occurs frequently, both within the anxiety disorders and also with other psychiatric disorders. At least one third of the children and adolescents diagnosed with anxiety disorders meet the criteria for two or more anxiety disorders [32]. Children with a primary anxiety disorder were significantly more likely to be diagnosed with separation anxiety disorder than adolescents. Adolescents with a primary anxiety disorder received more frequently a primary diagnosis of social anxiety disorder, mood disorders, and irregular school attendance [33]. Anxiety disorders are associated with all the other major classes of disorders, including mood disorders, disruptive behaviors, ADHD, eating disorders, and substance use disorders [19, 28, 34–36]. Merikangas et al. reported in their national survey among adolescents aged 13–18 years that the anxiety disorders were the most common condition (31.9%), with approximately 40% of those with one class of disorder also meeting criteria for another class of lifetime disorder [13]. Costello et al. found that comorbidity with other psychiatric disorders was common, ranging from 53% of the generalized anxiety disorder cases to 100% of specific phobia cases. The most common type of comorbidity with non-anxiety disorders was with depression [15, 20, 37, 38]. Adolescents with high levels of depressive symptoms experienced less significant decline over time in symptoms of physical, social, and separation anxiety [30]. The Oregon Adolescent Depression Study, looking at lifetime diagnoses, revealed that depression was significantly associated with each of the anxiety disorders except obsessive-compulsive disorder. Other lifetime associations found were ADHD with specific phobia and bipolar disorder with separation anxiety in males [39]. In preschool children, Sterba et al. used confirmatory factor analysis to show that the best-fitting model for the emotional disorders had three factors (social phobia, separation anxiety, and a factor that combined GAD and depression) with significant correlations between these three factors and conduct disorder, oppositional disorder, and

ADHD [40–42]. Social, separation, and generalized anxiety disorders in young people are relatively common and with a high level of comorbidity. All three ADs have a relatively high level of comorbidity with depression and a moderate degree of comorbidity with ADHD [17].

The major goal of the present study was to assess the prevalence of anxiety disorders among children and adolescents in a clinical setting and to describe the comorbidity and pharmacological treatment patterns.

### **2. Method**

#### **2.1 Participant selection**

We conducted a retrospective study on the admitted patients in the Clinic of Pediatric Psychiatry from Cluj-Napoca, Romania, between January and December 2017. A clinical sample of 2471 patients aged between 3 and 18 years, with different psychiatric disorders, assessed and/or treated in the clinic, was included in the study. 9.88% (N = 244) of the clinical sample were children and adolescents diagnosed with an anxiety disorder via the 10th revision of the *International Statistical Classification of Diseases and Related Health Problems (ICD 10*)-based clinical interviews. Participants were grouped according to the "primary" diagnosis, the primary diagnosis referring to the severity of current diagnoses and not the order of onset (the reason for admission). In accordance with the removal of OCD and PTSD from the broad anxiety disorders category in *DSM-5* (American Psychiatric Association, 2013), the patients with a primary diagnosis of OCD and PTSD were excluded from this study.

We included in the study: boys or girls aged 3–18 with a diagnosis of anxiety disorders (agoraphobia; social phobias; specific phobias; other/unspecified phobic anxiety disorders; panic disorder; generalized anxiety disorder; mixed anxiety and depressive disorder; other mixed, specified, or unspecified anxiety disorders; separation anxiety disorder of childhood; social anxiety disorder of childhood), according to *ICD-10* international diagnosis criteria, admitted for assessment or treatment; agreement (children/adolescent and caregiver) to participate after the purpose and protocol of the study was explained. We excluded from the study: children and adolescents admitted with other diagnosis than anxiety disorders and the patients receiving services in outpatient settings.

Data were collected from the patients' medical records (sociodemographic, clinical evaluations, and treatment recommendations). From the total of the 2471 patients selected, 244 children and adolescents were diagnosed with a type of anxiety disorder as a primary diagnosis. The patients come from all the countries and are diverse in terms of socioeconomic status. The mean age of participants was 12.75 (SD = 3.90), their age ranging from 3 to 18 years. All the participants were Caucasian. Data were used ensuring the privacy and subject's identity protection.

#### **2.2 Statistical analysis**

The Statistical Program for Social Sciences (SPSS) v. 17 was used for data analysis. To describe and assess the selected population, we used univariate statistical analysis (mean, standard deviation, frequencies). Pearson chi-square test was used to verify the association among categorical variables, and t-test was used to compare the mean age. The level of significance was set at 5%, and a confidence interval (CI) of 95% was used in all tests.

**79**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

Two hundred forty-four patients with a primary diagnosis of anxiety disorder were included in the study of which 130 (53.3%) were males and 114 (46.7%) were females. The boy-girl ratio was 1.14:1. The mean age of boys and girls differs significantly (mean (SD) boys = 12.02 (4.08), girls = 13.60 (3.52), t = −3.24, p = 0.001).

We analized the prevalence data for all anxiety disorder diagnosis of the patients included in the study, with current ICD 10 principal diagnosis are presented. As seen in **Table 1**, the most commonly occurring diagnosis were generalized anxiety disorder (GAD) 35.7% of the sample, social phobias (SOC) 12.3%, mixed anxiety and depressive (MAD) disorder 11.5%, other specified anxiety disorders (OSA) 9%, and separation anxiety disorder (SAD) of childhood 8.6%. The other anxiety disorders were less represented in our sample: specific phobias (SPEC) 6.1%, other phobic anxiety disorders (NOS) 4.9%, anxiety disorder, unspecified (ADU) 3.7%, panic disorder (PD) 3.3%, other mixed anxiety disorders (OMAD) 1.6%, phobic anxiety disorder, unspecified (UNS) 0.8%, and social anxiety disorder of childhood (SADC) 0.8%. About 20.5% of the sample (N = 50) had only 1 diagnosis at admission, 46.7% (N = 114) had 2 diagnoses, and 32.8% (N = 80) had 3 or more clinical diagnoses. Comorbidity is defined as having two or more co-occurring current diagnoses. **Table 1** presents the prevalence of the additional diagnosis for the participants grouped by the principal diagnosis. 34.4% of the participants had a diagnosis of anxiety or depressive disorder in addition to their principal anxiety disorder diagnosis. The other frequent comorbidities were attention deficit hyperactivity disorder (ADHD) (28.7%) and autism spectrum disorder (ASD) (8.6%).

The participants were divided into two groups by age: children (N = 88; 3–11 years of age) and adolescents (N = 156; 12–18 years of age). Among participants who met diagnostic criteria for the targeted anxiety disorders (N = 244), adolescents were significantly more likely to receive a principal diagnosis of GAD than children (39.7 vs. 28.4%, chi-square = 13.11, p < 0.001) or MAD (13.5 vs. 8%, chi-square = 9.33, p = 0.02). The prevalences of generalized anxiety disorder (GAD) and mixed anxiety and depressive (MAD) disorder were found higher in adolescents than in children (**Table 1**), but with a higher prevalence in adolescent girls, the proportion of boys diagnosed with GAD and MAD being relatively stable with age. Children were more likely to receive a principal diagnosis of SPEC than adolescents (11.4 vs. 3.2%, chi-square = 3.75, p = 0.05). For specific phobias (SPEC), the prevalence was higher in children than in adolescents, but while the proportion of boys remained relatively stable, the diagnosis wasn't present in adolescent girls. No significant differences by age were found for the other anxiety disorders. The odds for boys to have a primary diagnosis of anxiety disorder was 2.63 (95% CI 1.52, 4.55), with a risk of 1.87 in children (95% CI 1.29, 2.72) and 0.71 in adolescents

Among the participants that met the diagnosis criteria for at least one comorbidity (N = 194), adolescents were more likely than children to meet criteria for other anxiety disorder (14.7 vs. 13.6%, chi-square = 5.41; p = 0.02) or MDD (25.6 vs. 10.2%, chi-square = 5.49; p = 0.019). When looking at the age and gender trends for these comorbidities, we can observe some differences. While for the comorbidity with other anxiety disorder, the prevalence in adolescent boys is decreasing; in

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

*2.3.1 Primary disorder prevalence*

*2.3.2 Age and gender*

(95% CI 0.59, 0.86).

**2.3 Results**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

#### **2.3 Results**

*Psychopathology - An International and Interdisciplinary Perspective*

comorbidity and pharmacological treatment patterns.

the patients receiving services in outpatient settings.

of comorbidity with ADHD [17].

**2. Method**

**2.1 Participant selection**

excluded from this study.

ADHD [40–42]. Social, separation, and generalized anxiety disorders in young people are relatively common and with a high level of comorbidity. All three ADs have a relatively high level of comorbidity with depression and a moderate degree

The major goal of the present study was to assess the prevalence of anxiety disorders among children and adolescents in a clinical setting and to describe the

We conducted a retrospective study on the admitted patients in the Clinic of Pediatric Psychiatry from Cluj-Napoca, Romania, between January and December 2017. A clinical sample of 2471 patients aged between 3 and 18 years, with different psychiatric disorders, assessed and/or treated in the clinic, was included in the study. 9.88% (N = 244) of the clinical sample were children and adolescents diagnosed with an anxiety disorder via the 10th revision of the *International Statistical Classification of Diseases and Related Health Problems (ICD 10*)-based clinical interviews. Participants were grouped according to the "primary" diagnosis, the primary diagnosis referring to the severity of current diagnoses and not the order of onset (the reason for admission). In accordance with the removal of OCD and PTSD from the broad anxiety disorders category in *DSM-5* (American Psychiatric Association, 2013), the patients with a primary diagnosis of OCD and PTSD were

We included in the study: boys or girls aged 3–18 with a diagnosis of anxiety disorders (agoraphobia; social phobias; specific phobias; other/unspecified phobic anxiety disorders; panic disorder; generalized anxiety disorder; mixed anxiety and depressive disorder; other mixed, specified, or unspecified anxiety disorders; separation anxiety disorder of childhood; social anxiety disorder of childhood), according to *ICD-10* international diagnosis criteria, admitted for assessment or treatment; agreement (children/adolescent and caregiver) to participate after the purpose and protocol of the study was explained. We excluded from the study: children and adolescents admitted with other diagnosis than anxiety disorders and

Data were collected from the patients' medical records (sociodemographic, clinical evaluations, and treatment recommendations). From the total of the 2471 patients selected, 244 children and adolescents were diagnosed with a type of anxiety disorder as a primary diagnosis. The patients come from all the countries and are diverse in terms of socioeconomic status. The mean age of participants was 12.75 (SD = 3.90), their age ranging from 3 to 18 years. All the participants were Caucasian. Data were used ensuring the privacy and subject's identity

The Statistical Program for Social Sciences (SPSS) v. 17 was used for data analysis. To describe and assess the selected population, we used univariate statistical analysis (mean, standard deviation, frequencies). Pearson chi-square test was used to verify the association among categorical variables, and t-test was used to compare the mean age. The level of significance was set at 5%, and a confidence interval

**78**

protection.

**2.2 Statistical analysis**

(CI) of 95% was used in all tests.

Two hundred forty-four patients with a primary diagnosis of anxiety disorder were included in the study of which 130 (53.3%) were males and 114 (46.7%) were females. The boy-girl ratio was 1.14:1. The mean age of boys and girls differs significantly (mean (SD) boys = 12.02 (4.08), girls = 13.60 (3.52), t = −3.24, p = 0.001).

#### *2.3.1 Primary disorder prevalence*

We analized the prevalence data for all anxiety disorder diagnosis of the patients included in the study, with current ICD 10 principal diagnosis are presented. As seen in **Table 1**, the most commonly occurring diagnosis were generalized anxiety disorder (GAD) 35.7% of the sample, social phobias (SOC) 12.3%, mixed anxiety and depressive (MAD) disorder 11.5%, other specified anxiety disorders (OSA) 9%, and separation anxiety disorder (SAD) of childhood 8.6%. The other anxiety disorders were less represented in our sample: specific phobias (SPEC) 6.1%, other phobic anxiety disorders (NOS) 4.9%, anxiety disorder, unspecified (ADU) 3.7%, panic disorder (PD) 3.3%, other mixed anxiety disorders (OMAD) 1.6%, phobic anxiety disorder, unspecified (UNS) 0.8%, and social anxiety disorder of childhood (SADC) 0.8%. About 20.5% of the sample (N = 50) had only 1 diagnosis at admission, 46.7% (N = 114) had 2 diagnoses, and 32.8% (N = 80) had 3 or more clinical diagnoses. Comorbidity is defined as having two or more co-occurring current diagnoses. **Table 1** presents the prevalence of the additional diagnosis for the participants grouped by the principal diagnosis. 34.4% of the participants had a diagnosis of anxiety or depressive disorder in addition to their principal anxiety disorder diagnosis. The other frequent comorbidities were attention deficit hyperactivity disorder (ADHD) (28.7%) and autism spectrum disorder (ASD) (8.6%).

#### *2.3.2 Age and gender*

The participants were divided into two groups by age: children (N = 88; 3–11 years of age) and adolescents (N = 156; 12–18 years of age). Among participants who met diagnostic criteria for the targeted anxiety disorders (N = 244), adolescents were significantly more likely to receive a principal diagnosis of GAD than children (39.7 vs. 28.4%, chi-square = 13.11, p < 0.001) or MAD (13.5 vs. 8%, chi-square = 9.33, p = 0.02). The prevalences of generalized anxiety disorder (GAD) and mixed anxiety and depressive (MAD) disorder were found higher in adolescents than in children (**Table 1**), but with a higher prevalence in adolescent girls, the proportion of boys diagnosed with GAD and MAD being relatively stable with age. Children were more likely to receive a principal diagnosis of SPEC than adolescents (11.4 vs. 3.2%, chi-square = 3.75, p = 0.05). For specific phobias (SPEC), the prevalence was higher in children than in adolescents, but while the proportion of boys remained relatively stable, the diagnosis wasn't present in adolescent girls.

No significant differences by age were found for the other anxiety disorders. The odds for boys to have a primary diagnosis of anxiety disorder was 2.63 (95% CI 1.52, 4.55), with a risk of 1.87 in children (95% CI 1.29, 2.72) and 0.71 in adolescents (95% CI 0.59, 0.86).

Among the participants that met the diagnosis criteria for at least one comorbidity (N = 194), adolescents were more likely than children to meet criteria for other anxiety disorder (14.7 vs. 13.6%, chi-square = 5.41; p = 0.02) or MDD (25.6 vs. 10.2%, chi-square = 5.49; p = 0.019). When looking at the age and gender trends for these comorbidities, we can observe some differences. While for the comorbidity with other anxiety disorder, the prevalence in adolescent boys is decreasing; in

**81**

**Children**

**Male (**

Mood disorder

Other

MDD ADHD

ASD MR OCD Conduct disorder

ODD ATP

> No disorder

**Table 1.**

*Child/adolescent primary anxiety disorder and comorbidities.*

0 4 (6.7%)

3 (10.7%)

7 *Note: AG = agoraphobia; SOC = social phobias; SPEC = specific phobias; NOS = other phobic anxiety disorders; UNS = phobic anxiety disorder, unspecified; PD = panic disorder; GAD = generalized anxiety*

*disorder; MAD = mixed anxiety and depressive disorder; OMAD = other mixed anxiety disorders; OSA = other specified anxiety disorders; ADU = anxiety disorder, unspecified; SAD = separation anxiety*

*disorder of childhood; SADC = social anxiety disorder of childhood; OAD = other anxiety disorders; MDD = major depressive disorder; ADHD = attention deficit hyperactivity disorder; CD = conduct disorder;*

*ASD = autism spectrum disorder; MR = mental retardation; OCD = obsessive-compulsive disorder; ODD = oppositional defiant disorder; ATP = acute and transient psychotic disorders.*

15 (21.4%)

28 (32.6%)

43

*χ*

2(1) = 1.26, *p* = .261

0

0

0

1 (1.2%)

1

1 (1.7%)

0

1

0

1 (1.2%)

1

0

0

0

1 (1.4%)

1 (1.2%)

2

0

1 (3.6%)

1

1 (1.4%)

2 (2.3%)

3

—

—

—

3 (5%)

1 (3.6%)

4

4 (5.7%)

1 (1.2%)

5

—

6 (10%)

4 (14.3%)

10

10 (14.3%)

1 (1.2%)

11

*χ*

2(1) = 2.75, *p* = .097

31 (51.7%)

13 (46.4%)

44

18 (25.7%)

8 (9.3%)

26

*χ*

*2*(1) = 0.012, *p* = .91

7 (11.7%)

2 (7.1%)

9

14 (20%)

26 (30.2%)

40

*χ*

*2*(1) = 5.49, *p* = .019

*N* **= 60)**

**Female (**

*N* **=28)**

**All (**

*N* **= 88)**

**Male (**

*N* **= 70)**

**Female (**

*N* **= 86)**

**All (**

*N* **= 156)**

**(Children vs.**

**adolescents)**

**Adolescents**

**Statistic**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

**Table 1.**

*Child/adolescent primary anxiety disorder and comorbidities.*

#### *Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

*Psychopathology - An International and Interdisciplinary Perspective*

**80**

**Children**

**Male (**

Anxiety disorders

F40.0—(AG) F40.1—(SOC) F40.2—(SPEC)

F40.8—(NOS) F40.9—(UNS)

F41.0—(PD) F41.1—(GAD) F41.2—(MAD)

F41.3—(OMAD)

F41.8—(OSA) F41.9—(ADU)

F93.0—(SAD) F93.2—(SADC)

Comorbid anxiety

disorders

0 8 (13.3%)

4 (14.3%)

12

6 (8.6%)

17 (19.8%)

23

*χ*

2*(1) = 5.41, p = .02*

0

0

1 (1.4%)

1 (1.7%)

2

—

11 (18.3%)

8

19

1 (1.4%)

1 (1.7%)

2

*χ*

2(1) = 0.046, *p* = .83

2 (3.3%)

0

2

5 (7.1%)

2 (2.3%)

7

*χ*

2(1) = 0.73, *p* = .39

3 (5%)

0

3

8 (11.4%)

11 (12.8%)

19

*χ*

2(1) = 3.47, *p* = .06

0

1 (3.6%)

1

0

3 (3.5%)

3

—

7 (11.7%)

0

7

7 (10%)

14 (16.3%)

21

*χ*

2(1) = 9.33, *p* = .002

20 (33.3%)

5 (17.9%)

25

23 (32.9%)

39 (45.3%)

62

*χ*

2(1) = 13.11, *p* = .000

1 (1.7%)

0

1

3 (4.3%)

4 (4.7%)

7

*χ*

2(1) = 1.14, *p* = .28

0

1 (3.6%)

1

0

1 (1.2%)

1

—

3 (5%)

4 (14.3%)

7

2 (2.9%)

3 (3.5%)

5

*χ*

2(1) = 0.01, *p* = .92

5 (8.3%)

5 (17.9%)

10

5 (7.1%)

0

5

*χ*

2 (1) = 3.75, *p* = .05

7 (11.7%)

3 (10.7%)

10

13 (18.6%)

7 (8.1%)

20

*χ*²(1) = 0.75, *p* = .78

1 (1.7%)

1 (3.6%)

2

2 (2.9%)

0

2

*N* **= 60)**

**Female (**

*N* **=28)**

**All (**

*N* **= 88)**

**Male (**

*N* **= 70)**

**Female (**

*N* **= 86)**

**All (**

*N* **= 156)**

**(Children vs.**

**adolescents)**

*χ*²(1) = 1.33, *p* = .24

**Adolescents**

**Statistic**

**Table 2.**

**83**

**Anxiety disorders** F40.0—(AG)

**F40.1**

—**(SOC)** F40.2—(SPEC)

F40.8—(NOS) F40.9—(UNS)

F41.0—(PD)

**F41.1**

**F41.2**

—**(MAD)** F41.3—(OMAD)

**F41.8**

—**(OSA)** F41.9—(ADU)

**F93.0**

—**(SAD)** F93.2—(SADC)

0 *disorder of childhood; SADC = social anxiety disorder of childhood.*

**Table 3.**

*Pharmacological treatment patterns by principal diagnosis (percentage).*

1 (50%)

0

1 (4.8%)

1 (4.8%)

2 (9.5%)

1 (4.8%) 1 (50%) *Note: AG = agoraphobia; SOC = social phobias; SPEC = specific phobias; NOS = other phobic anxiety disorders; UNS = phobic anxiety disorder, unspecified; PD = panic disorder; GAD = generalized anxiety*

*disorder; MAD = mixed anxiety and depressive disorder; OMAD = other mixed anxiety disorders; OSA = other specified anxiety disorders; ADU = anxiety disorder, unspecified; SAD = separation anxiety*

1 (11.1)

2 (22.2%)

2 (22.2%)

0

8 (36.4%)

3 (13.6%)

5 (22.7%)

0

0

0

0

2 (7.1%)

7 (25%)

3 (10.7%)

1 (3.6%) 1 (25%)

—**(GAD)**

12 (13.8%)

30 (34.5%)

7 (8%)

19 (21.8%)

3 (37.5%)

4 (50%)

1 (12.5%)

4 (50%)

0

1 (50%)

0

0

4 (33.3%)

2 (16.7%)

1 (8.3%)

0

0

1 (6.7%)

3 (20%)

2 (13.3%)

2 (6.7%)

5 (16.7)

2 (6.7%)

2 (6.7%)

2 (50%)

1 (25%)

0

**Fluoxetine**

**Sertraline**

**Risperidone**

**Benzodiazepine**

**Other antipsychotics**

**Mood stabilizers**

**No pharmacological**

**treatment (**

1 (25%)

21 (70%)

10 (66.7%)

4 (33.3%)

1 (50%)

0

32 (36.8%)

13 (46.4%)

3 (75%)

10 (45.5%)

4 (44.4%)

17 (81%)

0

*N* **= 116)**

**(***N* **= 18)**

0 0 1 (6.7%) 5 (41.7%)

0 0 6 (6.9%) 4 (14.2%)

0 0 0 0 2 (100%)

**(***N* **= 22)**

0 1 (3.3%) 1 (6.7%) 2 (16.6%)

0 1 (12.5%) 13 (14.9%)

1 (3.6%)

0 2 (9%) 1 (11.1%)

0 0

**(***N* **= 38)**

1 (25%)

**(***N* **= 25)**

**(***N* **= 65)**

**(***N* **= 23)**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

*Comorbidity patterns by principal diagnosis [percentage].*

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

> **Table 3.**

*Pharmacological treatment patterns by principal diagnosis (percentage).*

*Psychopathology - An International and Interdisciplinary Perspective*

**82**

**Anxiety disorders**

F40.0—(AG)

**F40.1**

—**(SOC)** F40.2—(SPEC)

F40.8—(NOS) F40.9—(UNS)

F41.0—(PD)

**F41.1**

**F41.2**

—**(MAD)** F41.3—(OMAD)

**F41.8**

—**(OSA)** F41.9—(ADU)

**F93.0**

—**(SAD)** F93.2—(SADC)

—**(GAD)**

1 (25%) 4 (13.3%)

1 (6.7%) 2 (16.7%)

0 1 (12.5%) 21 (24.1%) 3 (10.7%)

1 (25%)

0 0 1 (4.8%)

0

0

0 *Note: AG = agoraphobia; SOC = social phobias; SPEC = specific phobias; NOS = other phobic anxiety disorders; UNS = phobic anxiety disorder, unspecified; PD = panic disorder; GAD = generalized anxiety*

*disorder; MAD = mixed anxiety and depressive disorder; OMAD = other mixed anxiety disorders; OSA = other specified anxiety disorders; ADU = anxiety disorder, unspecified; SAD = separation anxiety*

*disorder of childhood; SADC = social anxiety disorder of childhood; OAD = other anxiety disorders; MDD = major depressive disorder; ADHD = attention deficit hyperactivity disorder; CD = conduct disorder;*

*ASD = autism spectrum disorder; MR = mental retardation; ODD = oppositional defiant disorder.*

**Table 2.**

*Comorbidity patterns by principal diagnosis [percentage].*

1 (25%) 7 (31.8%) 4 (44.4%)

0

12 (57.1%)

1 (11.1%)

3 (13.6%)

1 (4.5%) 3 (33.3%)

2 (9.5%)

0

3 (14.3%)

0

0

1 (4.8%)

3 (13.6%)

0

0

0

1 (25%)

2 (7.1%)

8 (28.6%)

22 (25.3%)

18 (20.7%)

9 (10.3%)

1 (3.6%)

0

0

0

3 (37.5%)

0

1 (50%)

0

1 (50%)

0

1 (12.5%)

0 0

1 (3.6%)

2 (2.2%)

0

1 (6.7%)

0

7 (58.3%)

7 (46.7%)

9 (30%)

10 (33.3%)

1 (3.3%) 3 (20%)

0

2 (13.3%)

0 0

0

0

0

1 (3.3%)

1 (3.3%)

**OAD (**

*N* **= 35)**

**MDD**

**ADHD (**

*N* **= 70)**

**ASD (**

*N* **= 21)**

**MD (**

*N* **= 10)**

**ODD/CD (**

*N* **= 5)**

**No disorder (**

*N* **= 50)**

**(***N* **= 49)**

0

2 (50%)

0

0

0

1 (25%)

4 (13.3%)

1 (6.7%)

2 (16.7%)

0

3 (37.5%)

14 (16.1%)

13 (45.4%)

1 (25%)

7 (31.8%)

1 (11.1%)

1 (4.8%)

2 (100%)

adolescent girls it rises, being accountable for the significant difference between children and adolescents. For the comorbidity with MDD, the prevalence trends regarding age and sex are different than those for anxiety disorders. The comorbidity with MDD is rising with age, disregarding the gender, mentioning that the prevalence of MDD is higher in boys and the increase with age is milder than in girls.

There were no statistically significant relationships between the primary anxiety disorder diagnosis and gender. Regarding the comorbid diagnosis, the girls were more likely to receive an additional diagnosis of other anxiety disorder (18.4 vs. 10.8%), MDD (24.6 vs. 16.2%), while the boys were more likely to receive an additional diagnosis of ADHD (37.7 vs. 18.4%), ASD (12.3 vs. 4.4%), or MR (6.2 vs. 1.8%).

A chi-square analysis demonstrated that there was a significant difference in the presence of comorbidities between the principal diagnosis categories (chisquare = 132.46; p = .08). The principal anxiety disorders diagnosis with a higher number of comorbidities were GAD, SOC, MAD, OSA, and SAD, as seen in **Table 2**. Comorbidities associated with anxiety disorders are very common, especially among them. Patients with anxiety disorders had also high rates of depression and ADHD.

About 92% of the children and 72.4% of the adolescents with a primary diagnosis of anxiety disorder had at least one comorbidity, underlining that in our sample, the children had more comorbidities than the adolescents. The pattern of comorbidities was different for the most frequent anxiety disorders in our sample. For GAD, the most common comorbidities were other anxiety disorders and MDD, counting for almost half of the patients. Another 20.7% of them had comorbid ADHD. For MAD, the most frequent association was with ADHD, 28.6%, but the diagnosis includes specific symptoms of anxiety and depression, with neither type of symptom severe enough to justify a diagnosis if considered separately, so symptoms of depression are necessary for diagnosis. For SOC, the most frequent comorbidities were ADHD and MDD (~30%), followed by other anxiety disorders. SAD, which is a diagnosis specific for children, had comorbid ADHD in 57.1% of the cases.

From the 70 patients with ADHD comorbid diagnosis, 20 (28.6%) were treated with atomoxetine and 10 (14.3%) with methylphenidate. One hundred twentyone (49.6%) patients in our sample didn't receive pharmacological treatment, 49 (55.7%) children and 72 (46.2%) adolescents, 65 (50%) boys and 56 (49.1%) girls (see **Table 3**). Seventy-five (30.7%) patients received only one medication, of which 30 (34.1%) children and 45 (28.8%) adolescents, 41 (31.5%) boys and 34 (29.8%) girls. Forty-eight patients received more than one pharmacological treatment, of which 9 (10.2%) were children and 39 (25%) were adolescents, 24 (18.5%) boys and 24 (21.1%) girls. There were no statistically significant relationships between the recommended treatment and gender, but there were significant differences regarding the age group (children vs. adolescents). A chi-square analysis demonstrated that there was a significant difference in mono and without versus more pharmacological treatments between children and adolescent categories (chi-square = 7.77; p = .02), the adolescents being more likely to receive more medication.

#### **3. Discussion and conclusions**

Anxiety disorders are considered to be the most common psychiatric disorder in children and adolescents. In our study out of 2471 patients assessed and/or treated in the Clinic of Pediatric Psychiatry from Cluj-Napoca, Romania, between January and December 2017, only 244 (9.88%) patients received a primary diagnosis of different anxiety disorders, meaning that the anxiety disorders were the reason for admission. This low frequency of anxiety disorders in a clinical sample of children and adolescents can be explained by the selection criteria but also by the fact that

**85**

disorders.

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

children internalization problems are less recognized by parents and referred for treatment. Merikangas et al. reported an overall prevalence of anxiety disorders with severe impairment and/or distress of 8.3%, which is close to the prevalence reported in our study [13]. In a meta-analysis of multiple data sets, Costello and Egger have found the prevalence of 10.2% for any AD, 5.4% for specific phobia (SPEC), 3.6% for social phobia (SOC), 2.6% for separation anxiety disorder (SAD), 1.7% for generalized anxiety disorder (GAD), and 0.8% for panic disorder (PD) [15]. In our sample, the prevalences for these disorders were 35.6% for GAD, 12.3%

The study results showed that 79.5% of the selected sample had a comorbid disorder, and 34.4% had an anxiety or mood comorbidity which is similar to the findings from other studies on comorbidity rates of anxiety disorders in children. Previous studies found constantly elevated comorbidity rates in people with anxiety disorders [43], associated with increased symptom severity and greater functional impairment and worse outcome [15, 44]. The Child/Adolescent Anxiety Multimodal Study (CAMS) identified children and adolescents with social phobia, GAD, or SAD. They found that 78.6% of the sample had two or more of those disorders, and 35.9% met criteria for all three diagnoses simultaneously [36]. Kendall et al. find a comorbidity rate of 55.3% in a sample of youth with anxiety disorders [45]. Costello et al. found that comorbidity of AD with other psychiatric disorders was common, varying from 53% for GAD to 100% for SPEC. The most common type of comorbidity with non-anxiety disorders was with depression [15]. Also, the association between GAD and depression was found by Moffitt et al. in the Dunedin, New Zealand, longitudinal study with GAD predicting depression and depression predicting GAD across the life course [46]. In our study, 83.9% of the patients diagnosed with GAD had comorbidities, and 50% of them were diagnosed

Comorbidity patterns varied by *ICD-10* diagnosis. The principal diagnoses associated with significantly elevated risk for a comorbid diagnosis were GAD, MAD, SOC, OSA, and SAD. The association between GAD and other anxiety/depressive disorders raised many questions regarding the need for more accurate description in order to increase the reliability and validity of this disorder [47]. For SOC, the highest rate of comorbidities was with depression and ADHD. OSA had the highest comorbidity rate with depression, MAD with other anxiety disorders and ADHD

In our study, the prevalence of anxiety disorders primary diagnosis was 9.88%, with slightly different rates in females (46.7%) and males (53.3%). Although there were a greater number of boys than girls included in the study, the situation was different on age groups, more boys than girls in children group and more girls than boys in adolescent group; gender was not significantly associated with any of the anxiety diagnosis or comorbidity. The most common disorders in both males and females were GAD, SOC, MAD, other specified anxiety disorders (OSA), and separation anxiety disorder of childhood (SAD). Adolescents were more likely to receive a principal diagnosis of GAD or MAD than children. The prevalences of generalized anxiety disorder (GAD) and mixed anxiety and depressive (MAD) disorder were found higher in adolescents than in children (see **Table 1**), but with a higher prevalence in adolescent girls, the proportion of boys diagnosed with GAD and MAD being relatively stable with age. Children were more likely to receive a principal diagnosis of SPEC than adolescents. For specific phobias (SPEC), the prevalence was higher in children than in adolescents, but while the proportion of boys remained relatively stable, the diagnosis wasn't present in adolescent girls. No significant differences by age were found for the other anxiety

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

for SOC, 8.6% for SAD, 6.1% for SPEC, and 3.3% for PD.

also with other anxiety disorder or depression.

and SAD with ADHD.

#### *Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

children internalization problems are less recognized by parents and referred for treatment. Merikangas et al. reported an overall prevalence of anxiety disorders with severe impairment and/or distress of 8.3%, which is close to the prevalence reported in our study [13]. In a meta-analysis of multiple data sets, Costello and Egger have found the prevalence of 10.2% for any AD, 5.4% for specific phobia (SPEC), 3.6% for social phobia (SOC), 2.6% for separation anxiety disorder (SAD), 1.7% for generalized anxiety disorder (GAD), and 0.8% for panic disorder (PD) [15]. In our sample, the prevalences for these disorders were 35.6% for GAD, 12.3% for SOC, 8.6% for SAD, 6.1% for SPEC, and 3.3% for PD.

The study results showed that 79.5% of the selected sample had a comorbid disorder, and 34.4% had an anxiety or mood comorbidity which is similar to the findings from other studies on comorbidity rates of anxiety disorders in children. Previous studies found constantly elevated comorbidity rates in people with anxiety disorders [43], associated with increased symptom severity and greater functional impairment and worse outcome [15, 44]. The Child/Adolescent Anxiety Multimodal Study (CAMS) identified children and adolescents with social phobia, GAD, or SAD. They found that 78.6% of the sample had two or more of those disorders, and 35.9% met criteria for all three diagnoses simultaneously [36]. Kendall et al. find a comorbidity rate of 55.3% in a sample of youth with anxiety disorders [45]. Costello et al. found that comorbidity of AD with other psychiatric disorders was common, varying from 53% for GAD to 100% for SPEC. The most common type of comorbidity with non-anxiety disorders was with depression [15]. Also, the association between GAD and depression was found by Moffitt et al. in the Dunedin, New Zealand, longitudinal study with GAD predicting depression and depression predicting GAD across the life course [46]. In our study, 83.9% of the patients diagnosed with GAD had comorbidities, and 50% of them were diagnosed also with other anxiety disorder or depression.

Comorbidity patterns varied by *ICD-10* diagnosis. The principal diagnoses associated with significantly elevated risk for a comorbid diagnosis were GAD, MAD, SOC, OSA, and SAD. The association between GAD and other anxiety/depressive disorders raised many questions regarding the need for more accurate description in order to increase the reliability and validity of this disorder [47]. For SOC, the highest rate of comorbidities was with depression and ADHD. OSA had the highest comorbidity rate with depression, MAD with other anxiety disorders and ADHD and SAD with ADHD.

In our study, the prevalence of anxiety disorders primary diagnosis was 9.88%, with slightly different rates in females (46.7%) and males (53.3%). Although there were a greater number of boys than girls included in the study, the situation was different on age groups, more boys than girls in children group and more girls than boys in adolescent group; gender was not significantly associated with any of the anxiety diagnosis or comorbidity. The most common disorders in both males and females were GAD, SOC, MAD, other specified anxiety disorders (OSA), and separation anxiety disorder of childhood (SAD). Adolescents were more likely to receive a principal diagnosis of GAD or MAD than children. The prevalences of generalized anxiety disorder (GAD) and mixed anxiety and depressive (MAD) disorder were found higher in adolescents than in children (see **Table 1**), but with a higher prevalence in adolescent girls, the proportion of boys diagnosed with GAD and MAD being relatively stable with age. Children were more likely to receive a principal diagnosis of SPEC than adolescents. For specific phobias (SPEC), the prevalence was higher in children than in adolescents, but while the proportion of boys remained relatively stable, the diagnosis wasn't present in adolescent girls. No significant differences by age were found for the other anxiety disorders.

*Psychopathology - An International and Interdisciplinary Perspective*

adolescent girls it rises, being accountable for the significant difference between children and adolescents. For the comorbidity with MDD, the prevalence trends regarding age and sex are different than those for anxiety disorders. The comorbidity with MDD is rising with age, disregarding the gender, mentioning that the prevalence of MDD is higher in boys and the increase with age is milder than in girls.

a diagnosis specific for children, had comorbid ADHD in 57.1% of the cases.

p = .02), the adolescents being more likely to receive more medication.

**3. Discussion and conclusions**

From the 70 patients with ADHD comorbid diagnosis, 20 (28.6%) were treated with atomoxetine and 10 (14.3%) with methylphenidate. One hundred twentyone (49.6%) patients in our sample didn't receive pharmacological treatment, 49 (55.7%) children and 72 (46.2%) adolescents, 65 (50%) boys and 56 (49.1%) girls (see **Table 3**). Seventy-five (30.7%) patients received only one medication, of which 30 (34.1%) children and 45 (28.8%) adolescents, 41 (31.5%) boys and 34 (29.8%) girls. Forty-eight patients received more than one pharmacological treatment, of which 9 (10.2%) were children and 39 (25%) were adolescents, 24 (18.5%) boys and 24 (21.1%) girls. There were no statistically significant relationships between the recommended treatment and gender, but there were significant differences regarding the age group (children vs. adolescents). A chi-square analysis demonstrated that there was a significant difference in mono and without versus more pharmacological treatments between children and adolescent categories (chi-square = 7.77;

Anxiety disorders are considered to be the most common psychiatric disorder in children and adolescents. In our study out of 2471 patients assessed and/or treated in the Clinic of Pediatric Psychiatry from Cluj-Napoca, Romania, between January and December 2017, only 244 (9.88%) patients received a primary diagnosis of different anxiety disorders, meaning that the anxiety disorders were the reason for admission. This low frequency of anxiety disorders in a clinical sample of children and adolescents can be explained by the selection criteria but also by the fact that

There were no statistically significant relationships between the primary anxiety disorder diagnosis and gender. Regarding the comorbid diagnosis, the girls were more likely to receive an additional diagnosis of other anxiety disorder (18.4 vs. 10.8%), MDD (24.6 vs. 16.2%), while the boys were more likely to receive an additional diagnosis of ADHD (37.7 vs. 18.4%), ASD (12.3 vs. 4.4%), or MR (6.2 vs. 1.8%). A chi-square analysis demonstrated that there was a significant difference in the presence of comorbidities between the principal diagnosis categories (chisquare = 132.46; p = .08). The principal anxiety disorders diagnosis with a higher number of comorbidities were GAD, SOC, MAD, OSA, and SAD, as seen in **Table 2**. Comorbidities associated with anxiety disorders are very common, especially among them. Patients with anxiety disorders had also high rates of depression and ADHD. About 92% of the children and 72.4% of the adolescents with a primary diagnosis of anxiety disorder had at least one comorbidity, underlining that in our sample, the children had more comorbidities than the adolescents. The pattern of comorbidities was different for the most frequent anxiety disorders in our sample. For GAD, the most common comorbidities were other anxiety disorders and MDD, counting for almost half of the patients. Another 20.7% of them had comorbid ADHD. For MAD, the most frequent association was with ADHD, 28.6%, but the diagnosis includes specific symptoms of anxiety and depression, with neither type of symptom severe enough to justify a diagnosis if considered separately, so symptoms of depression are necessary for diagnosis. For SOC, the most frequent comorbidities were ADHD and MDD (~30%), followed by other anxiety disorders. SAD, which is

**84**

Different anxiety disorders have different age and gender distributions during childhood and adolescence. Separation anxiety disorder (SAD) and specific phobias (SPEC) are more common in children, while panic disorder and social phobia are more common in adolescents. Recent studies showed that, in the general population, anxiety symptoms first decrease during early adolescence and subsequently increase from middle to late adolescence [22]. SAD and SPEC tend to emerge and predominate during childhood, whereas the initial onset of generalized anxiety disorder (GAD), panic disorder (PD), and social anxiety disorder (SAD) most often occurs during adolescence [24, 46].

According to the findings of our study, gender and age contributed to the presence of comorbidity. Adolescents were more likely than children to meet criteria for other anxiety disorder or MDD. The age and gender trends for these comorbidities were different. While for the comorbidity with other anxiety disorder, the prevalence in adolescent boys is decreasing; in adolescent girls, it rises, being accountable for the significant difference between children and adolescents. For the comorbidity with MDD, the prevalence trends regarding age and sex are different. The comorbidity with MDD is rising with age, disregarding the gender, mentioning that the prevalence of MDD is higher in boys and the increase with age is milder than in girls. The girls were more likely to receive an additional diagnosis of other anxiety disorder and MDD, while the boys were more likely to receive an additional diagnosis of ADHD, ASD, or MR. This pattern is consistent with other studies which have found either no difference between the sexes or greater rates of comorbidity in males [48]. When examined separately, no difference was found in the presence of comorbidity by sex, for the principal diagnoses, but there were significant differences by age. The fact that adolescents experience higher levels of comorbid depressive disorders is consistent with findings from a clinical population [49].

Regarding the treatment patterns, in our study, the most frequently recommended pharmacological treatment was SSRIs, 36% of the patients receiving fluoxetine or sertraline. Other recommended treatments were benzodiazepines recommended to 15.6% of the patients, antipsychotics recommended to 19.3%, and mood stabilizers recommended to 7.4% of them. About 49.6% of the patients in our sample did not receive pharmacological treatment, 30.7% patients received only one medication, and 19.7% received more than one pharmacological treatment. There were no statistically significant relationships between the recommended treatment and gender, but there were significant differences regarding the age group (children vs. adolescents), the adolescents being more likely to receive more medication. These results should be interpreted keeping in mind that the sample had a high rate of comorbidities (79.5%) and that the pharmacological treatment may target those disorders. There is some evidence that medication can be effective in treating anxiety in children and adolescents, at least on the short term. A recent meta-analysis showed anxiolytic medication to be associated with a significantly greater clinical response than placebo (58.1 vs. 31.5%). Selective serotonin reuptake inhibitors (SSRIs) are regarded as the pharmacological treatment of choice for anxiety disorders in children and adolescents because of their effectiveness and safety profile. It is important to note that benzodiazepines have not been systematically assessed in children and adolescents, and, in view of concerns about dependency and side effects, their use is not recommended. It is unclear if there is an age below which medication would be contraindicated and what the duration of treatment should be [36]. Understanding the common patterns of anxiety disorders comorbidities and its treatments has important implications for child anxiety disorders treatment planning. Whether comorbid conditions might increase the need for treatment or cause patients to respond more poorly to psychological or psychiatric interventions is an important research area. Future treatments might need to be

**87**

provided the original work is properly cited.

Elena Predescu1,2\*, Anna Boglarka Asztalos2

Psychiatry, Cluj-Napoca, Romania

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Department of Neuroscience, Discipline of Psychiatry and Pediatric Psychiatry, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania

2 Clinical Emergency Hospital for Children Cluj-Napoca, Clinic of Pediatric

\*Address all correspondence to<EMAIL_ADDRESS>
and Roxana Șipoș

1,2

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample…*

adapted to better meet the needs of patients with commonly occurring comorbid conditions. This will help facilitate a better choice of existing treatments and may

It is important to consider the methodological limitations of the study which may impact the conclusions drawn based on this data. The age was considered within two categories, based on childhood and adolescence that can be seen as distinct developmental periods, and future studies should look at narrower age bands. Additionally, the sample included only admitted patients and excluded those

*DOI: http://dx.doi.org/10.5772/intechopen.82083*

receiving services in outpatient settings.

The authors declare that they have no conflict of interest.

improve treatment outcomes.

**Conflict of interest**

**Author details**

*Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample… DOI: http://dx.doi.org/10.5772/intechopen.82083*

adapted to better meet the needs of patients with commonly occurring comorbid conditions. This will help facilitate a better choice of existing treatments and may improve treatment outcomes.

It is important to consider the methodological limitations of the study which may impact the conclusions drawn based on this data. The age was considered within two categories, based on childhood and adolescence that can be seen as distinct developmental periods, and future studies should look at narrower age bands. Additionally, the sample included only admitted patients and excluded those receiving services in outpatient settings.


**5. Management**

#### **5.1 Current management strategies**

Early treatment in LN has been shown to improve outcome; however, effective management remains a challenge. It requires a multidisciplinary team approach (MDT), ideally by rheumatologists, nephrologists and nephropathologists. The cornerstone of treatment entails corticosteroids, antimalarial, and steroid-sparing agents (conventional immunomodulators and/or biological therapies) tailored to individual patients based upon histological class and severity to achieve rapid resolution of inflammation, proteinuria <0.5–0.7 g/day by 12 months (or up to 24 months in baseline nephrotic range proteinuria) [47] and prevention of relapsing episodes.

#### *5.1.1 Induction phase*

While there is little agreement for class II LN, in active proliferative class III, IV and pure membranous class V (with nephrotic range proteinuria or proteinuria >1 g/ day despite optimal use of renin-angiotensin-aldosterone system (RAAS) blockers), the current recommendation for initial induction treatment options include either low-dose intravenous cyclophosphamide (CYCi; 500 mg fortnightly infusions for 3 months) or mycophenolate mofetil (MMF; 2-3 g/day or mycophenolic acid (MPA) at equivalent dose) [47, 65–68]. This is combined with high-dose pulsed intravenous methylprednisolone followed by oral corticosteroid taper. High-dose CYCi is reserved for patients with severe LN due to its' various unfavourable side effects (mainly severe cytopenias and infection, cystitis, ovarian failure, cervical dysplasia and malignancy).

The use of calcineurin inhibitors (CNIs) namely tacrolimus (TAC) and cyclosporin (Cys) either as monotherapy or as part of a multitarget regimen therapy (with MMF/MPA and glucocorticoid) may have a favourable efficacy to induce remission. Meta-analysis in 2017 which included 45 induction trials of diverse participant background confirmed superior efficacy in induction by multitarget

**43**

*Lupus Nephritis: Current Updates*

*5.1.2 Maintenance phase*

ophthalmological screening.

*5.1.3 Refractory lupus nephritis*

response or recurrent LN flares [47, 65].

**5.2 Future novel therapeutic options**

*DOI: http://dx.doi.org/10.5772/intechopen.96891*

therapy compared to CYCi [69]; however, safety concern with its long term use mainly of chronic progressive irreversible nephrotoxicity remains an issue [70].

In the maintenance phase of treatment where less intensive therapy is required, MMF (1-2 g/day or MPA at equivalent dose) or azathioprine (AZA) are the drugs of choice [47, 71, 72] (with or without low dose <7.5 mg/day corticosteroid), depending on the induction regimen and plan for pregnancy. Hydroxychloroquine (HCQ ) is recommended for all LN patients in the absence of contraindications [47]. Due to possible ocular toxicity, the dose should not exceed 5 mg/kg body weight and should be adjusted in patients with renal and liver disease, with regular

Rituximab (RTX), although off-label, is not only indicated in patients refractory to conventional therapy or after great cumulative dose of CYCi, but also in patients of child bearing age [47, 73, 74]. Another B-cell targeting therapy which inhibits BlyS, Belimumab has recently been proven to be beneficial as add-on to the standard of care (SOC) therapy (mainly in the MMF subgroup) with primary efficacy

It is recommended not to discontinue immunosuppression too early as most renal flares occurs during this period. Treatment withrawal can be considered in patients with sustained complete remission for 3–5 years, with treatment deescalation prior to complete withrawal of therapy [47]. Close monitoring of patients and management of co-morbidities including blood pressure (BP) control, treatment of hyperlipidaemia with statins and proteinuria with RAAS blockers are important, while vaccination against influenza and *Streptococcus pneumoniae* are strongly recommended. Repeat renal biopsy may be considered to guide the duration of maintenance immunotherapy and may be required in patients with incomplete

Developing more effective treatment strategies in LN remains a priority among

There is gathering evidence, especially in more recent times, documenting the successful safe use of Belimumab, a monoclonal antibody (mAb) directed against BlyS as an add-on therapy in LN, especially in patients with low complement levels and high anti-DNA antibodies [75, 77]. It is the first targeted therapy and currently the only biological agent approved specifically for LN. There is also increasing interest in the sequential use of two B-cell targeting agents, RTX and Belimumab in active LN [78, 79] with a phase III trial already underway [80]. The rationale for this approach is due to the hypothesis that their co-administration may enhance deple-

clinicians and researchers across the globe; however, major challenges exist in its advancement due to the complex pathophysiology and heterogeneity, which directly impact on clinical trial design and overall outcome. Moreover, most trials are conducted with background therapy, which is difficult to control during the study and its subsequent analysis, as there is no clear definition in the SOC [76]. Notwithstanding this, extensive therapeutic strategies have emerged with wide array of novel treatments to improve patient outcomes. Major trend in current

treatment landscape for LN focuses on reduction of steroid use.

tion of circulating and tissue-resident autoreactive B cells.

renal response seen by week 24 and sustained through week 104 [75].

therapy compared to CYCi [69]; however, safety concern with its long term use mainly of chronic progressive irreversible nephrotoxicity remains an issue [70].


**1. Introduction**

Infant milk formula (IMF) is intended to serve as a functional substitute for infants under 12 months of age. The World Health Organization (WHO) recommends absolute breastfeeding of infants for the first 6 months of age [1], whereas the American Society of Paediatrics suggests the same for at least 12 months [2]. However, only 38% of infants are being breastfed globally [3], which indicates the common use of IMF, a \$41 billion USD market [4]. IMF is available in three forms—powdered, liquid concentrate, and liquid ready-to-feed (RTF). Among them, RTF, the most convenient form, is currently manufactured as a sterilized product to ensure safety using UHT (Ultra High Temperature, 135–145 ◦C for a short time). However, UHT causes the nutritional profile of RTF formula to deteriorate, especially vitamin A, B, and D, along with protein denaturation, which requires supplementation of micronutrients (e.g., vitamins and minerals) [5]. Although every possible effort is being made to bring IMF closer to human milk (HM), there is still a gap between them nutritionally, which governs neurological, physiological, and immunological growth and development of infants [6–8]. Such a

difference could be explained by the nutritional balance that remains fixed in an IMF, while it varies in HM throughout the lactation period and even between individuals [9].

Moreover, beta-lactoglobulin (β-Lg) represents about 50% of total whey as a major whey protein in cow milk. It is also considered a major allergen to infants despite having numerous functional and nutritional roles in adult human health. However, interestingly, this β-Lg is absent in HM. To address the cow's milk allergy (CMA), researchers and manufacturers introduced partially hydrolyzed formula (pHF) and extensively hydrolyzed formula (eHF), which are also recommended by paediatricians to reduce early allergy manifestation [10]. In pHF and eHF, enzymatic hydrolysis of proteins with digestive enzymes reduces the allergic properties by breaking them into small peptides and free amino acids, which are not allergenic [11]. However, these hydrolyzed formulas often exhibit bitter taste, poor flavour, reduced lipid emulsifiability, and elevated osmolality, which limit their application in general IMF [12].

On the other hand, alpha lactalbumin (α-Lac) is a bioactive protein present in all mammal milk, which is regarded as a component of lactose synthesis with antimicrobial, prebiotic, and Ca-binding capacity [13]. The α-lac content in human milk is 3–4 g/L, while it is only 1 g/L in mature cow milk. It contains a high level of different essential amino acids like tryptophan, lysine, and cysteine, and hence, fortification of IMF with α-Lac is recommended [13,14].

The standard protein content of infant formula is still a matter of controversy since formula production aims to mimic HM [14]. HM contains 9–11 g/L protein [15], while conventional infant formula provides 15 g/L [16]. Burgeoning demand for low-protein infant formula, especially in Asia, resulted mainly from paediatric obesity [17]. Moreover, excess protein also induces unnecessary strain on immature metabolic organs [18,19]. Thus, the possible alternative could be reducing the protein content and adding free amino acid into infant formula, which would be unphysiological since metabolic consequences of free amino acids are mostly unknown [14]. Thereupon, a logical approach would be to modify the protein profile of formula to make it closer to HM by increasing the ratio of α-Lac to β-Lg, which will yield a formula that has lower total protein, but retains the necessary balance of essential amino acids [14,16,20,21]. Moreover, this would also be an imperative strategy because complete removal of allergenic β-Lg would deteriorate the function of milk proteins since it is necessary for whey proteins to be associated with casein micelles [22]. High pressure processing (HPP) defragments casein micelles into smaller particles and splits them into more soluble components like αs1-, αs2-, β-, and k-caseins [23–25]. More than a 50% reduction in the size of casein micelle was observed after HPP at >300 MPa at 40 ◦C [24]. In powdered IMF, reducing the protein content to as low as 9.77 g protein/100 g did not cause a significant impact on physical stability and shelf-life [26]. However, in RTF liquid IMF, a higher ratio of α-Lac to β-Lg reduces the viscosity and induces rapid sedimentation during storage, as observed in UHT-treated products; and hence, the addition of thickener and stabilizer is suggested to manage the viscosity and sedimentation, respectively [27–29]. Furthermore, Crowley et al. [27] reported increased α-Lac reduced heat-induced coagulation in a model whey protein-dominant IMF. They further observed lower protein–protein interactions in the model IMF due to the fortification of IMF with α-Lac. Additionally, Kamarei [30] patented his invention in manufacturing refrigeration-shelf-stable pasteurized IMF with required quantities of nutrients. In this patent, he reported that UHT-treated RTF formula provides a different and unknown amount of degradable micro nutrients due to the high-heat treatment and subsequent storage, which also affects the nutritional value and sensory attributes. These outcomes evidence the possibility of developing an IMF with a higher ratio of α-Lac to β-Lg.

Previously, Huppertz et al. [31] described the mechanism of HPP-induced denaturation of α-Lac and β-Lg at 200–800 MPa/20 ◦C in whole milk and reported α-Lac as a pressure-resistant protein, unlike β-Lg. Furthermore, Mazri et al. [32] also investigated the denaturation kinetics of these two bioactive proteins in skim milk under HPP of 450–700 MPa at 20 ◦C and confirmed the baroresistance of α-Lac in comparison to β-Lg. Recently, HPP has been recommended to preserve human milk, due to its efficient inactivation of microbial pathogens, along with the retention of unique components [33]. Wesolowska et al. [34] also reviewed the effect of HPP to pasteurize human milk and referred to HPP as superior to the conventional holder pasteurization (63 ◦C/30 min) in maintaining bioactivity of protein components. To the best of our knowledge, the synergistic effect of HPP and heat to achieve a higher ratio of α-Lac to β-Lg has not been explored in IMF, which fundamentally pioneered this work.

Therefore, the aim of this work was to achieve a higher ratio of α-Lac to β-Lg through investigating their retention after HPP at different pressure–temperature–time combinations in reconstituted IMF, fortified with α-Lac. For comparison, we also performed high-temperature short-time (HTST) pasteurization at 72 ◦C for 15 and 30 s, and thereafter measured the concentration of α-Lac and β-Lg. The combined effect of pressure and temperature on the kinetics of denaturation of both proteins was also analyzed.


**2. Literature Review**

Because of its importance, students' physical activity has been drawing the attention for many years. The researches were conducted in different countries, among different departments, and usually were focused on examining significant physical activity determinants.

For example, physical activity, exercise and sedentary behaviors were analyzed, taking into account demographic variables of students enrolled in conditioning activity classes [30]. Among the others, their results concluded that men had greater levels of exercise and sedentary (TV/videos and computer) activities than women. Considering age, significant relationships between this variable and variables related to sedentary behaviors and physical activities were found as well. Hereby, younger students were more active when it comes to stretching and vigorous intensity activities, while older students spent more time with computers. In addition, the age analysis was also conducted separately for men and women.

Physical activity levels have also been investigated among medicine and nursing students [31]. The results have shown that 48% and 38% of surveyed nursing and medicine students, respectively, did not meet recommended physical activity levels. When comparing groups of students with low, moderate and high physical activity levels, significant differences were recorded in age, self-efficiency, benefits to barriers ratio, perceived support, and years of study. On the other hand, significant differences were not detected in perceived stress and gender ratio. In the same research, the application of the linear regression model showed that among eight factors (perceived stress, subject discipline, self-efficiency,

perceived support, benefits/barriers, gender, age, and year of the degree), only self-efficiency and perceived support had significant positive relationships with the reported level of physical activity.

In another study [32], the physical activity and quality of life (QoL) were examined for sports (SDS) and other department students (ODS). The scores of physical activity and QoL significantly differed between those departments, both in favor to SDS. In regard to gender-specific physical activity levels, significantly higher scores were recorded for men in total, high, medium, and low activities. When it comes to QoL, a significant difference between genders was detected in social relationships, while in other parameters, (physical health, environment, and psychological health) it wasn't the case. Furthermore, the results of the study pointed to the existence of positive relationship between physical activity and QoL levels.

The overall health behavior and its six dimensions, including physical activity, were also examined among Mexican University students at the Psychology School [33]. The subject of the analysis also included their relations with socio-demographic variables. As a separate dimension, physical activity was significantly related to student status, gender, age, marital status, socio-economical level, and mother's education. Hereby, full time students, men, and younger students were more active than part time students, women and students with 25 years or above, respectively. A higher level of physical activity was also recorded for single students, students with a medium-high socio-economic level, and students whose mothers had college education. In addition, when it comes to predicting, the multiple regression model included three variables (gender, marital status, and mother's education), which explained 11.1% of the total variance of physical activity.

Among the others, the influence of socio-demographic factors (gender, age, mother tongue, marital status, and the mother's and father's educational level) on physical activity was examined as one of the lifestyle behavior indicators [34]. According to the results of the research, which involved Swedish university students, significant relations were reported between physical activity and three out of six analyzed factors (gender, father's, and mother's educational level). When considering gender, male students had a higher physical activity score than female students, while in the case of two other factors, physical activity score was positively related to mother's education and it was highest for students whose fathers had a high school diploma.

Similar to previous research, physical activity was considered in the context of lifestyle behavior [35]. The study, realized among university students in Lebanon, showed that a larger percentage of respondents was physically active. In regard to the type of physical activity, more than a half of them did regular walking or running, while the others did exercise (playing games, aerobics, swimming, weight lifting etc.). As expected, male students had higher physical activity rate opposite to female. Moreover, authors analyzed relations between socio-demographic, environmental and lifestyle behavioral characteristics on one side, and health related quality of life (HRQoL), on the other. Hereby, they pointed to the importance of physical activity as a significant determinant of HRQoL.

Socio-demographic and lifestyle determinants of physical activity practice were also investigated among Spanish university students [36]. Following the obtained results, a significantly larger percent of men engaged in sport or physical activities compared to women. When it comes to food, male students who reported to be physically active ate more fruits, potatoes, rice, pasta, meat, and poultry opposite to physically inactive men. On the other hand, in the case of female students, those who were physically active, in addition to fruits, ate more vegetables and legumes. Body Mass Index (BMI), hours of PC, smoking habit and mother's educational level were important determinants of men's physical activity, while age, hours of TV, smoking habit, alcohol consumption, and mother's physical activity practice were significant determinants of women's physical activity. Besides determinants, the authors analyzed physical activity patterns and reasons for not practicing. For both genders, the main motive to practice physical activity was related to working out, i.e., maintaining their fitness. Other reported motives referred to health, enjoyment, affiliation, and others. Additionally, starting university and lack of time were the main reasons for having stopped practicing and not practicing physical activity, respectively.

Physical activity was analyzed among Portuguese university students as well [19]. In the study, the attention was dedicated to the effects of day of a week and gender on daily physical activity levels. For recording the number of steps and time spent in sedentary and physical activities, students were carrying an ActiGraph wGT3X-BT accelerometer during seven consecutive days. The results revealed that both male and female students were physically more active during weekdays in comparison to weekend days. From the aspect of gender, statistical differences in physical activity patterns were found only during weekdays in a way that male students spent more time in physical activities than female students, who, on the other hand, spent more sedentary time.

Undergraduate students from one of the U.S. universities were studied when it comes to changes in eating and physical activity behavior during a period of seven semesters [37]. The minority of students exercised at optimal levels during that period, and within it, there is also a significant decrease. The results pointed to the existence of gender differences since men were more likely to engage in physical activity. However, it should be noticed that men were more likely to engage in sedentary activities as well. Furthermore, the research included into consideration students' accommodation. Hereby, living outside of the campus showed negative influence. Nevertheless, it should be stated that students living with parents were excluded from the research.

In another study, students' physical activity motivation was examined by focusing on two types of physical activity: exercise and sport participation [38]. The analysis of the responses of United States college students revealed that intensity and frequency of exercise engaging were greater compared with sport, while adherence and duration of these two physical activity forms were similar. The authors analyzed 14 motivational variables: strength and endurance, appearance, weight management, stress management, positive health, Ill-health avoidance, health pressure, social recognition, competition, challenge, enjoyment, affiliation, nimbleness, and revitalization. When ranking them, positive health was the most important motive for exercise, and competition was the most important for sport participation. The respondents' rates for all motivational variables were examined in the context of mentioned types of physical activity and gender as well.

A number of socio-demographic characteristics are noticed in previous research [19,30–38]—gender, age, living standard, year of study, accommodation, marital status, student status, mother's and father's education, and mother's tongue. In this paper, students' physical activity was analyzed in relation to several socio-demographic variables: gender, age, living standard, emotional status, household size, year of study, and accommodation. Some of the variables listed in studies abroad were not found as appropriate for domestic context—student status, marital status, and mother's tongue, since almost all students are regular, not in a marriage, and with common culture. Parents' education was also not included since their fundamental education was unified in the period of their growing-up. However, we added some variables as well: emotional status and household-size. Having all previously stated in mind, the following model can be presented (Figure 1).

**Figure 1.** Conceptual model.

The influences of all the independent variables were considered from the gender aspect as well.


Author details

Shuye Zhang1 \*, Tiesong Lin1 , Peng He1 \* and Kyung-Wook Paik2

1 State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, China

2 Department of Materials Science and Engineering, KAIST, Daejeon, South Korea

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\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**2.5 Energy supply**

118 The Future of Humanoid Robots – Research and Applications

Copyright T. Bock, Picture taken at Hanyang University, Laboratory of Prof. Han.

University, Korea, 2011.

Electric Motors

Air Pressure

Series Elastic Actuators

 Electroactive Polymers Piezoelectric Actuators

**2.4 Actuator and control system technology**

Muscle Wire (e.g. Shape Memory Alloy)

construction and building technology.

power, motion/sensing and cognition augmentation:

Fig. 5. Fully functioning system for tele-operation of robotic excavators, the excavators can operate on a high level of autonomy; the excavation process is monitored by separate laser module (picture right side) providing information to the robotic excavator. Hanyang

Complex systems of actuators, joints and links are controlled based on information sensed and interpreted by internal and external sensor systems. Actuators create the activity and movement within robotic systems. Today following actuation systems are used in a robotic

Besides the increasing ability to downsize motors it is by now possible to improve precision and speed. Advances in robot kinematics and robot dynamics are important for developing robust and save control system technology for more complex man-robot systems in Energy Supply is a crucial issue in developing exoskeleton and humanoid robotic applications for construction and building technology. Unlike to robotic applications in other industries, many tools and assistive devices need to be independent from connecting cables. However, battery packs necessary to supply energy for the actuators represent heavy load. Thus, on the one hand the battery systems need to be developed so that they support mobility and wear-ability of robotic systems but on the other hand robotic applications and systems have to be designed to be highly energy efficient.


**2. HEVs configurations and reviews**

The first concept of HEVs was initiated by Ferdinand Porsche at Lohner Coach Factory when he designed the "Mixte", a series hybrid vehicle based on his earlier electrical vehicle. This hybrid vehicle was installed with a gasoline ICE connected to an electrical generator and an EM propels the vehicle with a small battery for more reliability. This serial HEV concept grew until the late of 1960s. In 1972, Viktor Wouk developed his first prototype for parallel hybrid powertrain in General Motors namely "Godfather of the Hybrid". This turning point ignited the very fast growing progress in HEVs as we can see nowadays and branched in many different aspects such as regenerative braking issues, fuel consumption, emission, battery problems and so on. Worldwide sales of hybrid vehicles reached more than four (4) million units by December 2010 and sold over 80 countries and regions, led by the United States with more 2.5 million units. Regenerative braking, shutting down the engine at idle have reduced the fuel consumption and emissions. The main problem for HEVs now is relying on the cost and the weight of the large batteries and the starter/generator motors.

The recent technology of HEVs is the development of the diesel HEVs since diesel prices are cheaper than gasoline while diesels produce more energy, suffer less wear while operating at higher efficiency. Diesel ICE produces higher torque and offer longer mileage. Most diesel ICE can use 100% pure biodiesel and they don't need petroleum. Diesel ICEs are 20% to 40% more efficient and produce less carbon-dioxide emissions than gasoline ICEs. Diesels are widely popular in Europe, accounting for more than 50% of the car market there. If the diesel HEVs were in use, higher benefits from this system can be achieved.