Psychological factors and Quality of Life in children with palliative needs: A predictive analysis

doi:10.21203/rs.3.rs-3997966/v1

Children with palliative needs present physical and psychological symptoms and it is important to be aware of their self-perception to improve their quality of life.

Purpose: Determine the predictive value of sociodemographic, disease and psychological variables in relation to the self-perceived Quality of Life (pQoL) of children with limiting and life-threatening diseases.

Methods: A cross-sectional study with children aged 8 and over, where sociodemographic and disease variables are collected through clinical history. Psychological evaluation is carried out using specific tools validated in the child population on Emotion Regulation, Cognitive Strategies and Risk of Depression and Anxiety, and on their pQoL using a visual analogue scale. Descriptive means and standard deviations are calculated, and three linear regression models are estimated.

Results: The sample was made up of 60 children with an average age of 16.0 (SD=4.2), 33.3% girls, with a mean pQoL of 7.0 (SD=1.8). 45% presented emotional symptoms; 46.7% relationship problems with peers; 33.3% behavioral problems; 22% risk of Depression and 30% of Anxiety; and 18.3% Emotion Regulation difficulties. The regression model explained 54.7% of the variance of pQoL, showing the following to negatively influence pQoL: aged 14 and over (p=0.03), exacerbated symptoms (p=0.01), Risk of Depression (p= 0.01) and the use of the Cognitive Rumination and Catastrophizing Strategy (p<0.01).

Conclusions: The holistic perspective, being a model that can identify psychological variables that influence QoL, best explains the variability of children's perception of quality of life. Psychological interventions should be aimed at improving depressive symptoms, cognitive strategies, and relational skills.

Quality of life

pediatric palliative care

psychological assessment

life-limiting conditions

children

cognitive strategies

Children with life-limiting conditions experience multiple psychological symptoms that affect their QoL. The child’s perspective is often overlooked.

We explored the results of the 60 children that respond about their perception of QoL and their psychological symptoms.

The holistic perspective, being a model that can identify psychological variables that influence QoL, best explains the variability of children's perception of quality of life.

Psychological interventions should be aimed at improving depressive symptoms, cognitive strategies, and relational skills.

Worldwide there are approximately 21 million children and young people aged 0–19 years (hereafter ‘children’) with life-limiting and life-threatening conditions (hereafter ‘LLTC) (1). These children have many complex symptoms and concerns that span health and social care domains (2) and their treatment requires a palliative approach. The World Health Organization (WHO) defines pediatric palliative care (PPC) as a holistic approach focused on improving the quality of life of Children with LLTC and their families (3). This is approached from four core domains: physical, psychological, social and spiritual (1,4–7).

Children with LLTC are substantially at risk of suffering physical and psychological symptoms (8), as well as pain, nervousness or irritability, poor quality of life, adaptive and behavioral disorders, post-traumatic stress, together with depression and anxiety and limitations in coping to mitigate multidimensional suffering (9). The psychosocial pressures that children with LLTC experience involve trying to maintain a normal life and finding ways to deal with the disruption to normality (10). Cognitive Strategies (CS) and Emotion Regulation (ER) are the most important strategies in relation to adaptation to the disease. The latter depends on the effort that the person makes to find stability and be able to cope with the stressful situation (11).

The key element to understanding the experience of the suffering of children and adolescents with complex chronic diseases and/or palliative needs is the meaning that the child attributes to these based on the degree of threat and discomfort that each situation generates, so it is important to be aware of the self-perception of well-being that the children themselves have. Nevertheless, this is a perspective that is often overlooked (12,13).

Accordingly, the aim of this study is to determine the predictive value of socio-demographic illness and psychological variables in relation to children’s perceived Quality of Life (pQoL). The following specific hypotheses are proposed: 1) A holistic view (bio-psycho-social-spiritual) is better to predict the pQoL; 2) Higher Emotion Regulation (ER) scores are associated with higher levels of pQoL;3) A worse Psychosocial Functioning (PSF) is associated with a worse pQoL; and 4) Maladaptive Cognitive Strategies are more likely present where there is a worse pQoL.

Study design and Setting

We present a cross-sectional study of the social, medical, and psychological factors that are related to the perceived quality of life as stated by the children themselves. In this study, a convenience sample of children being treated at the Palliative Care and Complex Chronic Patient Service (C2P2) of the Sant Joan de Déu Hospital (Barcelona, Spain) was used. We evaluated the QoL of all the children through assessments, attempting to gauge the opinions of the children from June 2021 to August 2023. The study was approved by the Medical Research Ethics Committee of the Sant Joan de Déu Hospital (reference code PIC-158-20 in 02/06/2020).

Study sample

Children attending C2P2, and their parents, were eligible to participate. Additional inclusion criteria were: 1) having a minimum age of 8 years, 2) the children selected were fluent in Spanish or Catalan language; 3) the child’s first multidisciplinary evaluation had been carried out by C2P2's team; 4) the parents or legal tutors and the child had signed the consent form. Children with moderate or severe neurological impairment or situation of imminent death were excluded.

Variables and Data Collection

Senior psychologists from the palliative care service collected data via interviews and self-reported scales with the children. Sociodemographic and disease variables were recorded from medical history and questions answered by health professionals, and psychological assessments were recorded using specific tools for reports related to children.

Sociodemographic information: age and sex were collected from the medical history.

Disease variables: data collected included diagnosis (we differentiate between oncohematological diseases and other types, such as dermatopathies, respiratory system diseases and others); adequacy of the therapeutic effort (the decision to withhold or withdraw diagnostic and therapeutic measures in response to the patient’s condition); presence of exacerbated symptoms, whether the disease has been present since birth or not; and the time elapsed since diagnosis, taken from the medical history and from specific questions answered by health professionals. Health professionals answered specific questions within a maximum of a 3-day period to ensure that the time elapsed since the child’s response did not result in a confounding variable, thus avoiding potential sources of bias.

Psychological variables: different studies provide evidence that most children over five are able to reliably self-report on their health (14) and were used self-report scales validated in children and adolescents to measure:

Child’s perceived Quality of Life (pQoL): The Distress Thermometer is a valid and reliable self-report measure used to assess emotional distress in adults (15) and for children and adolescents (16). This is a one-item instrument indicating a patient´s general distress level on a 0–10 visual analogue scale. We adapted it to ask about children's perception of their quality of life. Children were asked: “In general, how do you rate your quality of life (well-being) at the present time?” They responded by pressing directly on a screen, and the response ranged from 0 to 10, where 0 corresponded to very bad and 10 to very good.

Emotion Regulation (ER): we used the “mood repair sub-scale” of the Spanish version of Trait Meta-Mood Scale-23 (TMMS-23) in the child and adolescent population (17–19). It is a scale comprised of 8 items. The respondents are required to indicate their level of agreement with each item on a five-point Likert type scale from strongly disagree (1) to strongly agree (5).

Cognitive Strategies of Emotion Regulation (CS): we used a short Spanish version of the “Cognitive Emotion Regulation Questionnaire for Spanish Kids” (CERQ-Sk) (20,21). This is a questionnaire that contains 18 items on nine different subscales (self-blame, acceptance, rumination, positive focusing, refocus on planning: positive reappraisal, putting into perspective, catastrophizing, and other-blame) assessed on a 5-point Likert-type scale (1=almost never, 5 = almost always) of what children think after experiencing negative life events. The higher the score on each scale, the more pronounced the use of the respective cognitive coping strategy.

Psychosocial functioning (PSF): the self-report “Strengths and Difficulties Questionnaire” (SDQ) (22) is used to assess children’s emotional and behavioral problems. The SDQ includes a total of 25 items grouped in 5 factors: emotional symptoms, conduct problems, hyperactivity, peer problems, and prosocial behavior. Each item is scored at 3 levels of 0–2, with 0 points for inconformity, 1 point for slight conformity, and 2 points for complete compliance. Higher scores indicate more severe problems on every subscale, except for prosocial behavior.

Depression and Anxiety symptoms: the questionnaire includes 2 items to evaluate depression (PHQ-2) and another 2 for anxiety (GAD-2) (23) adapted in the child and adolescent population (24,25). It has a 4-point Likert-type response format (0-3) and the cut-off is ≥ 3.

Statistical analysis

The database is created and analyzed using the software R version 4.3.1. Basic descriptive statistics are obtained, as well as the mean and the standard deviation for quantitative variables and the frequency distribution for categorical variables. Mean differences between pQoL for different groups defined by the social, medical, and psychological variables are tested by the statistics based on Student’s t distribution (t-test). Additionally, rank-sum tests for non-normal data are used and the same inference results are obtained. Pearson linear correlations between the pQoL and the psychological variables are estimated. Three multivariate linear regression models are estimated, the dependent variable is the self-perceived QoL and the explanatory variables are different sub-sets of social, medical and psychological factors. The p-values are reported, and conclusions are drawn with a 95% confidence level.

Of the 94 children who met the inclusion criteria, 60 (63.83%) finally participated. Figure 1 is a flow chart representing the participants in the study.

Table 1 presents the characteristics of the sample. Regarding disease-related variables, 41 (68.3%) had a diagnosis of an oncohematological disease and 10 (16.7%) were children with dermopathies such as Epidermolysis Bullosa.

Table 2 presents the children's perception of their Quality of Life (pQoL) and the different psychological variables measured. Children rate their pQoL on average as 7 out of 10 (SD=1.8; R=3-10) and 18.3% of children have difficulties in Emotion Regulation. With regard to Psychosocial Functioning (PSF) evaluated with the SDQ questionnaire, the greatest risk problems identified are found in Problems with Peers (46.7%) and Emotional Symptoms (45.0%). Regarding the presence of anxious-depressive symptoms, we find that 30.0% of children are at risk of Anxiety and 22.0% of Depression. Regarding the Coping Strategies (CS) used, those strategies that obtain the highest score are `Positive Reappraisal´ (mean=7.5, SD= 2.3) and `Refocus on planning´ (mean=6.9, SD= 2.0), both considered “adaptive strategies”. Of the strategies considered “maladaptive”, the highest rated are `Rumination´ (mean=6.0, SD= 2.0) and `Catastrophizing´ (mean=5.6, SD= 2.5). We find that the strategies of `Catastrophizing´ and `Rumination´ correlated with 0.72 which suggests these may be measuring similar aspects.

The pQoL results with regard to the other variables are shown in Table 3. These show a difference in the group of children over 14 years of age (mean=6.4; SD=1.4), with the difference being statistically significant (p=0.003) compared to those under 14 (mean=7.8; SD=19).

In the disease variables, the presence of exacerbated symptoms shows statistically significant differences (p=0.001) between having them (mean=5.7; SD=1.3) or not having them (mean=7.3; SD=1. 7). When comparing having an oncohematological disease (mean=6.8 SD=1.7) or not (mean=7.5; SD=1.8), no statistically significant differences are found (p=0.159), as is the case for being classified as a patient with no adequacy of therapeutic effort (mean=7.5; SD=2.0) or adequacy of therapeutic effort(mean=6.8; SD=1.6). Having the diagnosis of the disease from birth (mean=7.1; SD=1.8) or not (mean=7.0; SD=1.8) presents statistically non-significant differences (p=0.830).

When we review the pQoL values in relation to the psychological variables evaluated, the existence of statistically significant differences is observed. Lower scores of pQoL are associated with higher scores of Emotional Symptoms (mean=6.2; SD=1.5; p=0.002), and with lower scores of Emotion Regulation (mean=5.6; SD=1.6; p=0.009). The results show that children at risk of hyperactivity have a better pQoL (mean=7.9; SD=1.8; p=0.047). This result is not representative because there are few children with hyperactivity (n=14) and the majority of them (n=12) indicate a pQoL equal to 8 out of 10. Higher scores of Depression (mean=5.2; SD=1.4; p<0.001) are associated with lower scores of pQoL. This does not occur with respect to the evaluation of the risk of having anxiety (mean=6.6; SD=1.8; p=0.296)

Figure 2 shows the correlations between pQoL and psychological variables. The pQoL decreases as risk of Depression (r=-0.55), Emotional Symptoms (r=-0.49), Catastrophizing (r=-0.50), or Rumination (r=-0.57) increases, and when the pQoL increases then Emotion Regulation (r=0.35) and cognitive strategy Positive Refocusing (r=0.25) also increase.

Finally, to analyze the predictive value of the variables that best explain the pQoL perceived by children, 3 regression models are applied, shown in Table 4. In Model I, the sociodemographic and disease variables are analyzed. These show that the variables aged over 14 years (coef=-0.98; p=0.03) and having exacerbated symptoms (coef=-1.46; p=0.01), together, explain 29.2% of the pQoL variability. In Model II, the psychological variables are analyzed, where the Cognitive Strategies, Rumination and Catastrophizing are added due to the high correlation explained above. The variables of risk of Depression (coef=-1.49; p<0.01) and Rumination and Catastrophizing (coef=-0.18; p<0.01) are seen to be statistically significant. This model explains the variability of pQoL with an R²=38.1%. For a more holistic view, it was decided to incorporate all the variables analyzed in Model III. This last model shows that the variables aged over 14 years (coef=-078; p=0.03), having exacerbated symptoms ( coef=-1.04; p=0.01), risk of Depression (coef=-1.14; p=0.01) and the cognitive strategies Rumination and Catastrophizing (coef=-0.17; p<0.01) explain the variability of pQoL with an R2=54.7%.

The main objective of this study was to analyze the predictive value of the sociodemographic, disease and psychological variables regarding the self-perception of the quality of life (pQoL) in children with LLTC. The results show the relationship with a worse pQoL in those children who are aged 14 and over, have exacerbated symptoms, risk of Depression, and use cognitive strategies of Rumination and Catastrophizing.

Our results indicate that children with LLTC who are aged 14 and over are those who perceive a worse pQoL than younger children. Other studies show similar results in those adolescents and young adults with palliative needs who demonstrate a worse quality of life compared to younger children, with higher rates of depression and anxiety, increased feelings of sadness, loss of independence, fear and guilt, and a higher prevalence of complex pain (26–29). Childhood and adolescence are life stages with many developmental changes and yet it remains unclear how these changes influence QoL in young people, particularly for those suffering chronic illness (30). On the one hand, it may be a reflection of the adolescent's greater cognitive capacity for logical and abstract thinking, which leads to greater questioning of one's own values and beliefs, and a better understanding of the disease may be confronted with more illness-related stressors than in children (31,32). Children aged 7 to 11 also use their reasoning skills more successfully with concrete information than with abstract concepts (33), such as pQoL. In relation to the other sociodemographic variables more studies are necessary because it is widely accepted that sociocultural factors can have an influence on children’s pQoL (34,35).

The presence of exacerbated symptoms is associated with lower scores of pQoL. Other studies also present these results, where disease progression is associated with higher physical scores (26,13,36). One of the main explanations that the research team gives to interpret these results is that children with LLTC do not see themselves as sick, except when they are ill with symptoms that cause limitations, disruption of their normality and associated discomfort. (36).

The risk of Depression and higher scores on the cognitive strategies of Rumination or Catastrophizing was associated with lower scores of pQoL. Other studies show that depressive symptoms are common in adolescents with LLTC (35,37) and that chronic medical conditions increase the risk of anxiety and depression symptoms in children by an estimated 20% to 50% (29). Different studies show a stronger relationship between Rumination or Catastrophizing with the reporting of symptoms of depression (21). In our study, 46.7%, express having problems with their peers, 45% of children show Emotional Symptoms and 18% of children have difficulty in Emotion Regulation. Children with LLTC describe many psychological and emotional impacts that increasing the risk of developing emotional and behavioral problems compared to their healthy peers (38). Emotional distress is known to be another psychological challenge faced by the children (35) and that higher levels of Emotion Regulation are associated with better psychological adjustment to their situation and better mental health (39).

In our study, the children scored their Quality of Life, on average, as 7 out of 10, this being a notably high level on the scale and implying a relatively high quality of life among participants. Some studies describe that children may express optimism to maintain a positive mindset (40) and that they find it difficult to evaluate negatively their well-being (41). Some of them pretend to be brave, perhaps to encourage themselves or to protect their parents from feeling guilty or sad (42), and that maintaining normality can reduce feelings of helplessness and despair (43). It is important children with LLTC can say what they think, answer for themselves and express their feelings, as well as assess their own QoL (44). The results of this study provide knowledge and greater understanding, from a holistic point of view, of the variables which influence the self-perception of the quality of life of children with LLTC. As a priority, good symptom management and psychological intervention regarding depressive symptoms, ruminative and catastrophic cognitive strategies, increased emotional regulation and improved relationships with peers must be integrated into action protocols.

Strengths and Limitations

Despite the importance of the findings reported here, this study has limitations that merit consideration. One limitation is related to the sample size. Ideally, studies that include a greater number of children are needed, although the inherent difficulty of making demands of children with LLTC is acknowledged. Nevertheless, a prospective study with a sample size of 60, such as this one, is also considered by the authors as a particularly strong representation of the population being treated. It is imperative to conduct a thorough exploration of the implications stemming from sociodemographic variables. The wide age range of the participants can be a confounding variable in the interpretation of the results, as well as in the use of the different self-report scales. The authors are aware that simplifying evaluation of the QoL into a single question is problematic. It is understood that health-related QoL measurement instruments must consist of physical, social and mental health dimensions as outlined by the World Health Organization. The tool used provides a simple evaluation of the QoL and offers a concrete and easily reproducible measure to be used continuously, which is useful to healthcare professionals in daily practice. Likewise, our study had a cross-sectional design. To confirm these results, it would be appropriate to carry out longitudinal studies with the participation of a greater number of health centres to avoid evaluative biases.

The holistic perspective integrating sociodemographic, disease and psychological variables is the model that best explains the variability of pQoL and allows us to better interpret the information reported by children and to be able to carry out specific interventions accordingly. Scores of a worse pQoL are seen in children who are over 14 years old, have exacerbated symptoms, risk of Depression, and use cognitive strategies of Rumination and Catastrophizing. Despite the difficulties inherent in managing advanced and end-of-life disease in the pediatric branch of healthcare, children with LLTC can assess their perception of quality of life and we can look into the underlying psychological mechanisms they use to cope with their ill-health.

Children: Children and young people

LLTC: Life-limiting and life-threatening condition

Parents: Fathers, mothers and careers

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript

Competing Interest

The authors have no relevant financial or non-financial interests to disclose. The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this paper.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all the authors. The first draft of the manuscript was written by Daniel Toro-Pérez and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics approval

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by the Medical Research Ethics Committee of the Sant Joan de Déu Hospital (reference code PIC-158-20 in 02/06/2020).

Consent to participate

Informed consent was obtained from all individual participants and their parents included in the study.

Acknowledgments

We would like to express our gratitude to all children and parents participating in the study who contributed to improving the assessment of children with chronic complex illness and life-limiting and life-threatening conditions. The authors are also grateful the members of the Palliative Care and Complex Chronic Patient Service (Sant Joan de Déu Hospital). We also would like to thank the ANJANA Working Group of the Catalan-Balearic Society of Palliative Care and the Catalan Society of Pediatrics for their support, and for including this study in their research priorities for the improvement of end-of-life health care.

Data Availability Statement

Data (45) are available in the following link https://data.mendeley.com/datasets/xt585dkhgt/1

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Table 1. Sociodemographic and disease characteristics of the patients (N = 60)

Sociodemographic Data Characteristic	n (%)
Age
Mean (SD)	16.0 (4.2)
Group age
Age>14	34 (56.7)
Age<=14	26 (43.3)
Sex
Male	40 (66.7)
Female	20 (33.3)

Disease Data Characteristic	n (%)
Diagnosis
Oncohematology	41 (68.3)
Dermopathies	10 (16.7)
Respiratory	4 (6.7)
Others	5 (8.3)
Adequacy of therapeutic effort
Yes	40 (66.7)
No	20 (33.3)
Exacerbated symptoms
No	47 (78.3)
Yes	13 (21.7)
Diagnosis at Birth
Yes	12 (20.0)
No	48 (80.0)
Time since Diagnosis (Years, SD, R)
Not Birth	3.24 (SD=2.89, R=0.06-10.03)
Birth	14.12 (SD=3.66, R=9.07-19.05)

Table 2. Statistical description of psychological variables (N = 60)

	Mean (SD)
Perception of Quality of life (pQoL)	7.0 (1.8)
Cognitive Strategies of Emotion Regulation (CS)
Positive reappraisal	7.5 (2.3)
Refocus on planning	6.9 (2.0)
Putting into perspective	6.6 (2.5)
Acceptance	6.4 (2.2)
Rumination	6.0 (2.0)
Positive focusing	5.6 (2.4)
Catastrophizing	5.6 (2.5)
Self.blame	4.1 (1.9)
Other blame	3.2 (1.6)
Psychosocial functioning (PSF)	Proportion Risk (n)
Peer problems	46.7% (28)
Emotional symptoms	45.0% (27)
Global Functioning	38.3% (23)
Conduct problems	33.3% (20)
Hyperactivity	23.3% (14)
Prosocial behavior	8.3% (5)
Anxiety (RA)	30.0% (18)
Depression (RD)	22.0% (13)
Emotional Regulation (ER)	18.3% (11)

Table 3. Quality of Life in relation with sociodemographic, disease and psychological variables (N=60)

	Perception Quality of Life (Mean (SD))
			Test	p-value
	Male	Female
Sex	7.2 (1.8)	6.7 (1.6)	1.1	0.283
	=<14	>14
Group age	7.8 (1.9)	6.4 (1.4)	3.1	0.003
	No	Yes
Exacerbated symptoms	7.3 (1.7)	5.7 (1.3)	3.8	0.001
	No	Yes
Adequacy of therapeutic effort	7.5 (2.0)	6.8 (1.6)	1.3	0.188
	No	Yes
Oncohematological disease	7.5 (1.8)	6.8 (1.7)	1.4	0.159
	No	Yes
Diagnosis at Birth	7.00 (1.8)	7.10 (1.8)	0.20	0.830
Psychosocial Functioning	No Difficulties	Difficulties
Emotional symptoms	7.6 (1.7)	6.2 (1.5)	3.3	0.002
Peer problems	6.9 (1.9)	7.0 (1.7)	0.2	0.830
Conduct problems	7.2 (1.9)	6.7 (1.3)	1.2	0.249
Hyperactivity	6.7 (1.7)	7.9 (1.8)	2.1	0.047
Prosocial behavior	7.6 (1.8)	6.9 (1.8)	0.8	0.465
Global difficulties	7.2 (1.8)	6.6 (1.8)	1.5	0.139
	No Difficulties	Difficulties
Emotion Regulation	7.3 (1.7)	5.6 (1.6)	3.0	0.009
	No Risk	Risk
Anxiety	7.1(1.7)	6.6 (1.8)	1.1	0.296
	No Risk	Risk
Depression	7.5(1.5)	5.2(1.4)	5.1	0.000

Table 4. Regression model for pQoL as a function of sociodemographic, disease and psychological variables.

	Model I: Sociodemographic and Disease	Modelo II: Psychological	Modelo III: Holistic view

Constant	8.43 (<0.01)	9.36 (<0.01)	9.81 (<0.01)
Age>14	-0.98 (0.03)	-	-0.78 (0.03)
Female	-0.47 (0.27)	-	-0.03 (0.92)
Adequacy of therapeutic effort	-0.07 (0.91)	-	-
Oncohematological diagnosis	-0.54 (0.40)	-	-
Exacerbated Symptoms	-1.46 (0.01)	-	-1.04 (0.01)
Rumination and Catastrophizing¹	-	-0.18 (<0.01)	-0.17 (<0.01)
Risk Depression	-	-1.49 (<0.01)	-1.14 (0.01)
R2	29.2%	38.1%	54.7%
N	60	60	60
¹These variables are compared together due to the high correlation they present

No competing interests reported.

Psychological factors and Quality of Life in children with palliative needs: A predictive analysis

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Abstract

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What is Known

What is New

Introduction

Methods

Results

Discussion

Conclusions

Abbreviations

Statements & Declarations

References

Tables

Additional Declarations

Status:

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