Psychopathological risk stability and change in a sample of mothers and preschool children before, during and after the first wave of COVID-19 pandemic

doi:10.21203/rs.3.rs-1987856/v1

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Psychopathological risk stability and change in a sample of mothers and preschool children before, during and after the first wave of COVID-19 pandemic

https://doi.org/10.21203/rs.3.rs-1987856/v1

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Numerous studies have found that the COVID-19 epidemic and the measures to stop it have had a substantial impact on the mental health of the general population. Nevertheless, the majority of this research only looked at the variations in the degree of psychopathological symptoms in individuals before and after the first wave of the pandemic. In a sample of N = 380 preschoolers and their mothers assessed through the Symptom Check-List/90-R and of the Child Behavior Check-List, the present study aimed at exploring psychopathological risk in mothers and their offspring’s dysregulation levels before (T1), during (T2) and after (T3) the first wave of COVID-19 pandemic. Our main results showed that mothers’ relational distress increased from T1 to T2 and then increased again from T2 to T3. Moreover, maternal aggressiveness, hostility, and anxiety scores significantly decreased from T1 to T2 but slightly increased from T2 to T3. In children, dysregulation levels increased from T1 to T2 but decreased from T2 to T3. Children of mothers with clinical scores at the SCL-90/R showed significantly higher dysregulation problems at T1, T2 and T3 than children of mothers with scores below the clinical threshold. This study adds to previous literature in that it evaluates stability or change in maternal and offspring scores not only in the pre-pandemic period and during the first wave of the pandemic, but it also considers the subsequent months, focusing on a broad range of maternal symptoms, rather than assessing depressive and anxiety symptoms as most of previous research did.

COVID-19

psychopathological risk

dysregulation

mothers and children

In March 2020 the whole world faced the outbreak of the first wave of the COVID-19 infection. Rather than definitively decreasing and eventually disappearing after a few weeks (as hoped), infections, symptoms and (unfortunately) deaths continued, with periodic surges and declines of cases [1]. With some differences, researchers traced a general worldwide trend with four major waves associated with the appearance of new variants of the virus [2]. Although the number of cases was high in all the waves, the majority of countries adopted stringent precautions, quarantines, lockdowns and curfews especially during the first wave (which some authors have proposed to consider as the peak of the pandemic; [3, 4]) to diminish the transmission rate of the virus, progressively lifting them concurrently with the vaccination of the population. Despite the virus being prevalent in Italy since January 2020, the first COVID-19 case there was first verified on February 20, 2020 [5]. Since the discovery of "patient one," the virus has spread rapidly throughout the nation, resulting in both illnesses and fatalities. By the end of July, there were about 250,000 recorded cases, exceeding the number of cases in China on March 27. Between 19 March and 7 April, Italy recorded more Covid-19 fatalities than any other nation, topping 35,000 deaths at the start of August [6].

Several studies have now shown that the COVID-19 pandemic and the efforts put in place to stop it have had a significant influence on the general population’s mental health [7–9]. However, most of these studies considered possible differences in the levels of psychopathological symptoms in individuals only comparing their mental health before the pandemic and during the first wave [10, 11]. Although very informative, this research did not describe the situation in the subsequent months, when the pandemic concluded its first wave while being nonetheless present and virulent. A huge number of scientific articles have been published on this topic, although most of these studies focused on adults’ mental health and on depression, anxiety, and PTSD symptoms. Limited attention has been given to specific components of the population, such as, for instance, parents and their children (with the exception of some important papers; see for example the seminal review [12]) and to a broader variety of symptoms.

Indeed, children's emotional and behavioral functioning severely deteriorated during the epidemic, according to the World Health Organization (2020) [13]. Schools have been closed to reduce the spread of the COVID-19 virus, reducing opportunities of in-person socialization with peers, play outside, and exercise [14, 15], while screen time have increased [16]. After the school cancellations, parental anxiety increased rapidly [17], frequently in association with parents’ financial and health issues, and the disrupting of familial routines. This phenomenon was so disturbing that 27 percent of parents and 15 percent of children's mental health has been shown to have deteriorated in the United States during the first wave in 2020 [18]. Increased depressed and externalizing symptoms were found in a recent research on preschoolers when compared to pre-COVID levels [19], and although children with pre-existing emotional and behavioral difficulties had the most worrying outcomes [20], COVID-19 has had an influence even on non-at-risk individuals (those who had no clinical signs prior to the pandemic) [21], underlying the importance of research on the general population in this field.

According to the developmental psychopathology framework, a caregiver's emotional response to a stressful event and parental psychopathological risk can spillover on their children's emotional and behavioral functioning [22]. Even in the case of the COVID-19 pandemic, researchers have hypothesized an association between caregivers’ and their children’s psychological symptoms [23]. Parents and their offspring were compelled to be confined together at home during lockdowns, resulting in significant changes in everyday habits and routines [24], frequently associated with higher internalizing and externalizing symptoms in youths [21]. On the other hand, research has shown (in most cases) that parents reacted to the pandemic increasing their psychopathology symptoms, being less capable of perceiving their children emotional distress, frequently underestimating offspring’s symptoms [25]. Even caregivers with no previous indicators of psychopathology have been shown to be affected by the COVID-19 pandemic, particularly mothers, who are usually the primary caretakers [26]. As caregivers are the primary source of children’s security and emotional regulation [27], parents’ difficulties could have impaired offspring’s capacity to respond to distress during the pandemic and regulate their emotional and behavioral functioning [28]. Dysregulation in children can cause an internalized state of hypervigilance, inhibition and withdrawal, anxiety, melancholy, and attachment insecurity and it therefore important to investigate this specific variable [29]. High scores on the so-called AAA scales (forming the Dysregulated Profile - DP), which measure three distinct but equally significant aspects of children's emotional and behavioral functioning (behavioral area: subscale Anxious/Depressed; behavioral area: Aggressive Behavior; cognitive area: Attention Problems), can be used to identify the symptoms of dysregulation [30].

With these premises, this longitudinal study aimed at exploring: a) psychopathological risk stability and change over time in a sample of mothers of preschooler children; b) dysregulation profile’s stability and change over time in a sample of preschooler children; c) the differences in dysregulation profiles’ stability and change over time in preschoolers of mothers with and without psychopathological risk during pre-pandemic period.

Participants and procedure

This longitudinal study was conducted with three serial assessments, which took place six months apart from each other. Particularly, the initial assessment (T1) was administered during pre-pandemic period, the second assessment (T2) during the first wave of the first coronavirus pandemic wave, while the third assessment (T3) after this first wave, when contagions were declining.

A total of N = 380 mothers and their preschooler children were recruited from the general population by a team of psychologists in schools randomly selected in Italy according to the following inclusion criteria: a) no referred psychiatric diagnosis in the mothers or their children; b) no medical condition present in the subjects at the time of recruitment; c) no current medical and/or psychological treatment; d) no COVID-19 current contagion in any member of the family and no death of any close relative associated with COVID-19.

Most of the mothers (92%) were of middle socio-economic status (SES; household income between EUR 28.00 and 55.000 per year) and they have mainly attended high school (73.2%), followed by university (21.1%) and by secondary school (5.7%). Eighty-two percent of mothers were married to the biological father of the child and 94% of the subjects were Caucasian. Children were 50.8% females and 49.2% males and their mean age at the time was 4.5 (SD=.12) years at T1, 5 years (SD=1.4) at T2 and 5.5 (SD=1.3) T3. In 91% of the families, children were singleton. All the dyads involved in the study were connected by a consanguinity. Thanks to the great effort of the research team and to the collaboration of schools where the subjects were recruited, no attrition was found at T2 and T3.

Once mothers provided a written informed consent for all the procedures, they were asked to complete a self-report questionnaire to assess their psychological symptoms as well as a parent-report questionnaire to assess child behavioral and emotional characteristics at T1, T2 and T3. Following the Declaration of Helsinki, the Ethical Committee of the Department of SC accepted the study before its start (Protocol Number: 0000809).

Tools

The Symptom Checklist-90-Revised (SCL-90-R)

The SCL-90-R is a 90-item self-report questionnaire for assessing presence and intensity of psychological symptoms and distress experienced during the latest 7 days across nine subscales: Somatization, Obsessive–Compulsive, Interpersonal Sensitivity, Depression, Anxiety, Hostility, Phobic Anxiety, Paranoid Ideation and Psychoticism [31]. The measure has been well-validated in several countries both in clinical and non-clinical samples (e.g., [32-34]).

For this study, it has been used the Italian version [35] which, differently from the original one, is composed by 69-item exploring the psychological symptoms and distress across eight dimensions: Relational Distress, Physical Complaints, Anxiety, Aggressiveness/Hostility, Dysphoria, Depressive Ideation, Obsessive-Compulsive, and Sleep Difficulties. The internal coherence tested within a sample of adolescent and adults is good (alpha between .70 and .96), and the clinical cut-off =1.

The Child Behavior Checklist (CBCL)

The CBCL is a questionnaire filled out by parents and caregivers to assess a child’s abilities and his/her behavioral/emotional functioning over the last 2 months [36,37]. The CBCL/1.5-5 is composed by 100 items which provides scores for the Internalizing Problem Scale (the result of the sum of Emotionally Reactive, Anxious/Depressed, Somatic Complaints and Withdrawn subscales scores) and for the Externalizing Problem Scale (consisting in Attention Problems and Aggressive Behavior subscales. Also, the measure is useful for assessing specific abilities in regulating affect, behavior, and cognition providing a dysregulation profile score [29], resulting from the sum of AAA scales (that is, Anxious/Depressed, Attention Problems, and Aggressive Behavior subscales), to which we referred in the present study. The measure shows good psychometric properties, particularly a quite satisfactory internal consistency (alpha ranging from .65 to .96) and an excellent test-retest reliability (.85) [36].

Statistical analyses

As a preliminary step, a post hoc power analysis was conducted using G*Power software [38] to determine whether the current sample size of 380 was sufficient to detect a hypothesized incremental effect size of .02 (a small effect) and an alpha level of α=.05. Results indicated the current sample size was sufficient to achieve power of .99, with a critical F value of 3.00. Moreover, to identify relevant confounder variables to include in the subsequent analyses, Multivariate ANOVA was run for exploring the impact of socio-demographic variables (infant gender, maternal level of education, maternal socio-economic status) on independent and dependent variables, that is, on each maternal SCL-90-R dimensions’ mean scores (i.e., relational distress, physical complaints, anxiety, aggressiveness/hostility, dysphoria, depressive ideation, obsessive-compulsive and sleep difficulties) and on children dysregulation profile’ mean scores at T1, T2 and T3. Results did not detect any significant impact; therefore, no variables were included as confounder variables in the subsequent analyses.

In line with first and second aim, we performed two independent series of repeated measure analyses of variance for exploring the impact of Time, as a within-subject factor, on the dependent variables, that is, on mean scores of each maternal SCL-90-R dimensions, and of children dysregulation profile. Also, growth curve model (GCM) analysis was used to describe trajectories of these dependent variables as a function of time. With three repeated measures (i.e., T1, T2, and T3), GCM were limited to linear and quadratic models [39]: (1) a linear model with a random intercept and random slopes, which reflects linear change over time; (2) a quadratic model with a random intercept and random slopes, which reflects change that takes on a “U” or inverted “U” shape. These unconditional models —where only the intercept, linear slope, and curved slope were specified to determine the trajectories — were centered at the month during which the first data was collected (i.e., at T1) and, therefore, represented initial scores of mothers’ SCL-90-R and of children dysregulation profile. Model fit was evaluated using the — 2 log likelihood difference test (— 2LL).

Lastly, in line with third aim, we performed repeated measure analyses of variance for exploring the impact of specific factors, that is, of each maternal psychological dimensions at T1, along with its interaction with Time, on children dysregulation profile mean scores at T1, T2 and T3 (see supplementary material for graphical representation of results).

All statistical analyses were carried out using the IBM SPSS statistical package version 25.0.

Stability and change of maternal psychological SCL-90-R dimensions

In line with first aim, that is, exploring fluctuations in maternal SCL-90-R mean scores as a function of time, results showed that, with the exception obsessive-compulsive and sleep difficulties profiles, maternal psychological dimensions significantly change over time (Table 1). For clarity, for each SCL-90-R subscale, we will present first the results obtained with repeated measures ANOVA and then those obtained with GCM.

Table 1. Means (standard deviation) of mothers’ SCL-90-R subscales by time of assessment

	Time of Assessment			F
	T1	T2	T3	Time of Assessment
SCL-90 -R subscales
RD	.41 ± .57	.45 ± .55	.47 ± .61	6.98*
PC	.53 ± .72	.53 ± .74	.35 ± .54	204.84***
AN	.42 ± .62	.38 ± .61	.49 ± .74	4.83*
A/H	.45 ± .54	.35 ± .55	.37 ± .56	12.71***
DY	.60 ± .68	.63 ± .77	.32 ± .46	127.16***
DI	.50 ± .74	.50 ± .74	.34 ± .57	29.00***
O/C	.46 ± .64	.44 ± .64	.46 ± .73	. .03
SD	.40 ± .65	.37 ± .67	.39 ± .68	. 03

***p < .001

** p < .01

*p < .05

Particularly, relational distress scores (F _(1,380) = 6.98, p < .05) tended to significantly increase from T1 to T2 and to remain stable from T2 to T3; the — 2 log likelihood model comparison tests (χ²₍₁₎ = 1.46, p > .05) indicated that this pattern of change over time was best described by a significant positive linear change over time, indicating a linear and moderately increasing trajectory (F _(380.000) = 116.82, p < .001).

Aggressiveness/hostility scores (F _(1,380) = 12.71, p < .001) tended to significantly decrease from T1 to T2 and to remain stable from T2 to T3; according to the — 2 log likelihood model comparison tests (χ²₍₁₎ = 26.72, p < .01), this trajectory over time was best described by a U-shaped pattern, characterized by a significant negative linear slope, followed by a slight rebound, indicating a positive quadratic (curved) slope (F _(380.001) = 27.68, p < .001).

Anxiety scores (F _(1,380) = 4.83, p < .05) tended to significantly decrease from T1 to T2 and to increase from T2 to T3; the — 2 log likelihood model comparison tests suggested (χ2(1) = 39.33, p < .01) that, on average, their means scores over time were best described by a significant negative linear slope and a positive quadratic (curved) slope, indicating a U-shaped pattern (F _(380.000) = 21.94, p < .001).

Physical complaints (F _(1,380) = 204.84, p < .001), Dysphoria (F _(1,380) = 127.16, p < .001) and Depressive Ideation scores (F _(1,380) = 29.00, p < .001) tended to remain stable from T1 to T2 and to show a significantly decrease from T2 to T3; comparing the fit of the models with the — 2 log likelihood model comparison tests (χ²₍₁₎ = 40.82, p < .01, χ²₍₁₎ = 88.31, p < .01 and χ²₍₁₎ = 24.30, p < .01, respectively), these patterns of change over time were best described by a significant positive linear slope, followed by a negative quadratic (curved) slope, indicating an inverted U-shaped pattern (F _(380.000) = 41.83, p < .001, F _(380.000) = 95.10, p < .001 and F _(380.000) = 25.09, p < .001, respectively).

Lastly, as already mentioned, obsessive-compulsive (F _(1,380) = .03, p = .854) and sleep difficulties scores (F _(1,380) = .03, p = .954) were stable over time; coherently, the — 2 log likelihood model comparison tests (χ2(1) = 2.32, p > .05, χ2(1) = 1.49, p > .05) suggested that their pattern of change were best described by flat trajectories with no trend in their means scores, that is neither a significant increasing nor a significant decreasing over time (F _(380.000) = .01, p = .944, F _(380.000) = .03, p = .854, respectively).

Stability and change of children dysregulation profile

According to second aim, that is, exploring fluctuations in children dysregulation profile as a function of time, results reported a significantly change in its scores (Table 2). Also in this case, for clarity, we will present first the results obtained with repeated measures ANOVA and then those obtained with GCM.

Table 2. Means (standard deviation) of children’ dysregulation profile (PD) by time of assessment

Time of Assessment

means

scores

17.71

±9.58

18.97

±10.98

8.56

±8.71

459.15***

Particularly, results (F (1,379) = 459.15, p < .001) showed a significant scores’ increasing from T1 to T2 followed by a decreasing trend from T2 to T3; the — 2 log likelihood model comparison tests (χ²₍₁₎ = 27.74, p < .01) indicated this pattern of change over time was best described by an inverted U-shaped pattern, characterized by a significant positive linear slope, followed by a rebound, indicating a negative quadratic (curved) slope (F (760.000) = 238.90, p < .001).

Impact of SCL-90-R maternal psychological dimensions at T1 on children dysregulation profile over time

With respect to third aim, we explored the impact of each maternal psychological dimensions at T1, along with its interaction with time of assessment, on children dysregulation profile mean scores.

When the impact of maternal psychological dimensions at T1 was assessed (Table 3), results showed that children of mothers with scores above the clinical threshold on all SCL-90-R subscales considered, that is, relational distress (F _(1,378) = 312.18, p < .001), physical complaints (F _(1,378) = 361.12, p < .001), anxiety (F _(1,378) = 291.65, p < .001), aggressiveness/hostility (F _(1,378) = 248.55, p < .001), dysphoria (F _(1,378) = 531.30, p < .001), depressive ideation (F _(1,378) = 362.47, p < .001), obsessive-compulsive (F _(1,378) = 423.30, p < .001) and sleep difficulties (F _(1,378) = 276.19, p < .001), showed significantly higher dysregulation profile mean scores than children of mothers with scores below the clinical threshold.

Table 3. Means (standard deviation) of children’ dysregulation profile (PD) by mothers’ SCL-90-R subscales at T1

	DP means scores
SCL-90-R subscales at T1			F
RD	< 1	12.35 ±.36	312.18***
	> 1	26.95 ±.74
PC	< 1	12.32 ±.43	361.12***
	> 1	27.74 ±.74
AN	< 1	12.81 ±.35	291.65***
	> 1	28.80 ±.87
A/H	< 1	13.01 ±.36	248.55***
	> 1	28.77 ±.93
DY	< 1	11.03 ±.33	531.30***
	> 1	25.41 ±.53
DI	< 1	12.12 ±.35	362.47***
	> 1	26.92 ±.69
O/C	< 1	12.13 ±.33	423.30***
	> 1	27.91 ±.69
SD	< 1	12.77 ±.36	276.19***
	> 1	28.16 ±.85

When the interaction between maternal psychological dimensions at T1 and time of assessment was considered (Table 4), results reported, both between- and within-groups, significant differences in dysregulation profile mean scores in relation to the following subscales: relational distress (F _(1,378) = 9.17, p < .01), physical complaints (F _(1,378) = 21.96, p < .001), anxiety (F _(1,378) = 27.50, p < .001), aggressiveness/hostility (F _(1,378) = 7.44, p < .01), dysphoria (F _(1,378) = 28.12, p < .001), depressive ideation (F _(1,378) = 5.20, p < .05), and obsessive-compulsive (F _(1,378) = 4.03, p < .05).

Table 4. Means (standard deviation) of children’ dysregulation profile (PD) by mothers’ SCL-90-R subscales at T1 and time of assessment

Time of Assessment

SCL-90 scales at T1

Time of Assessment x

SCL-90 scales at T1

< 1

15.20

±.46

15.87

±.50

5.98

±.39

9.17**

> 1

28.61

±.95

32.46

±1.05

19.77

±.81

< 1

15.64

±.48

15.73

±.48

5.59

±.34

21.96***

> 1

27.20

±1.03

33.82

±.1.03

22.20

±.72

< 1

15.86

±.47

16.16

±.47

6.40

±.38

27.50***

> 1

28.87

±1.15

35.93

±1.16

21.59

±.94

A/H

< 1

16.04

±.47

16.54

±.50

6.44

±.37

7.44**

> 1

28.74

±1.21

35.00

±1.28

22.56

±.96

< 1

14.46

±.49

14.69

±.52

3.94

±.28

28.12***

> 1

25.99

±.78

29.89

±.83

20.34

±.45

< 1

15.19

±.47

15.60

±.50

5.56

±.36

5.20*

> 1

27.76

±.94

32.43

±1.00

20.55

±.73

O/C

< 1

15.15

±.45

15.65

±.48

5.59

±.35

4.03*

> 1

28.83

±.94

33.42

±1.01

21.46

±.73

< 1

15.33

±.43

16.63

±.53

6.35

±.39

.79

> 1

31.16

±1.03

32.23

±1.25

21.10

±.92

***p < .001

** p < .01

*p < .05

Particularly, regarding to between-groups differences, at T1, T2 and T3 children of mothers with scores above the clinical threshold showed significantly higher dysregulation profile mean scores than children of mothers with scores below the clinical threshold.

Regarding to within-groups differences, children of mothers with scores above the clinical threshold showed a significant increasing in dysregulation profile scores from T1 to T2 followed by a significant decreasing trend from T2 to T3. Differently, children of mothers with scores below the clinical threshold showed a stable trend from T1 to T2 followed by a significant decreasing at T3.

On the contrary, no significant differences, nor between neither within-groups, were observed in dysregulation profile mean scores according to the interaction between maternal sleep difficulties at T1 and time of assessment (F _(1,378) = .79, p = .374).

This study intended to verify whether parents’ psychopathological symptoms and children’s dysregulation problems were stable or changed from pre-COVID-19 period (T1) to the first wave period (T2) and then to the post-first wave period (T3) of the pandemic. We also aimed at understanding the impact of pre-pandemic clinical and non-clinical maternal psychological symptoms on children’s dysregulation profiles over time.

The first result we found was that mothers’ relational distress scores increased from the pre-pandemic period to the first wave period and then moderately increased again from T2 to T3. The relational distress sub-dimension of the SCL-90/R mainly taps symptoms such as paranoid ideation and social anxiety [35]. Social anxiety can be characterized by subjective distress experienced by the subject in crowded places, and feeling lonely and isolated [40]. We speculate that subjects could have felt the proximity of other people as particularly distressing during the pandemic, and especially during the first wave of it when individuals were asked to maintain social distancing and to avoid unnecessary contacts. Thus, preoccupation for possible contagion and physical problems may have exacerbated relational distress symptoms. Besides, the uncertainty about the dangerousness of the virus and its possible complications could have augmented the paranoid ideation about the menace represented by others. It is noteworthy that these possible effects were not confined to the first wave period of the pandemic but lasted afterwards, even when restrictions to social interactions were gradually lifted.

Moreover, maternal aggressiveness and hostility scores significantly decreased from T1 to T2 but to slightly increased from T2 to T3. The decrease of scores from T1 to T2 is consistent with previous literature that posited how the condition of necessary isolation from others during the lockdowns has frequently caused in the general population the experience of being protected from the day-to-day interpersonal conflicts, therefore reducing feelings of resentment and anger towards others [41]. Instead, the increase of the levels of aggressiveness after the first wave of the pandemic could be associated with the realization of its duration, which in someone’s perception could have been caused by the improper use of personal protective equipment (masks) and low rate of vaccination in part of the population [42].

The same trend characterized maternal anxiety scores, which significantly decreased from T1 to T2 and increased from T2 to T3. We hypothesize that, as elaborated above, the imposed restrictions could have resulted in a sense of safeness and protection from the outside dangers represented by the presence of the virus and its possible transmission from one to another person. This could have led to reduced anxiety levels; however, when the restrictions have been lifted and subjects were required to return to work in presence, travel and interact with others, they suffered an increase in these symptoms. After several months of “new normality” as it was frequently called by media, it is possible that some individuals found it difficult and distressing to go back to direct encounters with colleagues and relatives [43].

Physical complaints, Dysphoria and Depressive Ideation scores remained substantially stable from T1 to T2 and to show a significantly decrease from T2 to T3. Contrary to previous literature, in this study we did not find increased physical, dysphoric and depressive symptoms from the pre-COVID period to the first wave phase of the pandemic [44]. We instead found significantly less of these problems after the first wave. Specifically, subjects at T3 showed significantly less symptoms than T2 and even than T1, that is in our sample individuals experienced less problems in these areas than before the pandemic. As the SCL-90/R is a self-report questionnaire tapping perceived (not observed) symptoms, we speculate that the ending of the first wave period could have allowed a sense of enthusiasm in individuals, consistently with the studies of [45], thus reducing preoccupation, lack of hope for the future, and sadness.

Not in line with previous literature [46], besides, we did not find an increase in obsessive-compulsive, neither from T1 to T2 not form T2 to T3. In fact, scores in this area were stable over time, showing a flat trajectory. We hypothesize that this result could be due to the nature of the rules imposed to stop the spreading of the virus (for instance frequently washing hands and sanitizing surfaces). These behaviors, in the pre-pandemic world, had been associated with obsessive-compulsive functioning [47]. Now, in the new reality, they are socially accepted, or even strongly encouraged, with powerful media campaigns. This normalization of behaviors previously recognized as maladaptive may have caused a shift from an ego-dystonic to an ego-syntonic experience of these conducts. Therefore, although obsessive-compulsive behaviors could have increased, subjects could have not recognized them as such in the self-report measure SCL-90/R.

So far, we discussed the results concerning maternal psychological functioning over the three periods (T1, T2 and T3). Now, we will focus on the stability and change of children’s dysregulation scores.

Our results showed a significant increase of dysregulation levels from T1 to T2 followed by a decrease trend from T2 to T3. As noted in the tools section, the dysregulation profile is composed by internalizing and externalizing problems; a large amount of studies has now demonstrated that the pandemic has caused a strong increase in these difficulties in children, especially in offspring as young as in our sample (see for example: [48, 49]). Emotion regulation in preschoolers is crucial for positive outcomes in later life therefore this result rises serious concerns about possible negative consequences in the development of an entire generation of children (as every child in the world has been exposed, more or less directly, to the effects of the pandemic). Meanwhile, the decrease of dysregulation symptoms after the first wave period of the pandemic allows prudent hope about the fact that its effects could be acute but not chronic. Future studies should evaluate this hypothesis. The improvement in these problems from T2 to T3 could also be due to the enhancement in mothers’ emotional-behavioral functioning in the same period (particularly in term of a decreasing in physical complaints, depressive ideation, and mood alterations) that could have had the effect of a positive protecting factor on offspring regulation processes, as posited in previous literature [50].

After evaluating the individual characteristics of mothers and children’s psychological functioning at the three assessment points, we investigated the possible impact of pre-existing maternal psychopathological risk on their children dysregulation problems over time.

We found that offspring of mothers with scores above the clinical threshold at the SCL-90/R (especially relational distress, physical complaints, anxiety, and aggressiveness/hostility at T1) showed significantly higher dysregulation problems at T1, T2 and T3 than children of mothers with scores below the clinical threshold. This confirms consolidated results of previous studies, which had underlined the key role of clinically relevant maternal symptomatology in predicting negative outcomes in their children, especially when they must face distressing experiences (e.g. the pandemic). In fact, psychopathology in mothers is often associated with an impaired quality of caregiver-child interactions, which is indeed crucial for the development of an adaptive emotional-behavioral functioning in young children [51].

Noteworthy, children of mothers with scores above the clinical threshold showed a significant increase in dysregulation problems from T1 to T2 followed by a significant decrease from T2 to T3. Conversely, children of mothers with scores below the clinical threshold showed a stable trend from T1 to T2 followed by a significant decrease at T3. Regarding to the increasing observed at T2 in children of clinical mothers, we speculate that mothers with clinically relevant symptoms failed to protect children from the development of psychological problems subsequent the pandemic outbreak, due to their own impaired capacity of recognizing signs of distress in offspring and/or react contingently and sensitively to those cues [52]. In other words, it could be that mothers with relevant psychopathological symptoms could have exposed their children to an adjunct risk factor (their own psychopathology) besides the environmental risk represented by the pandemic. On the contrary, mothers without significant psychopathological risk seem to have protected their offspring from the potentially negative outcomes of the pandemic and from the consequences of the imposed restrictions (school closures, isolation, etc.). It could be that when mothers were not engaged in coping with their own problematic symptoms, they could hold back the potentially distressful influence of the pandemic protecting their children’s psychological well-being.

Finally, we found that in both children of mothers exceeding the clinical cut-off and in the offspring of non-clinical mothers, dysregulation problems at T3 (after the first wave of the pandemic) decreased. This could be due to the re-opening of schools and to the renewed possibility of interpersonal contacts with peers, which previous studies have shown as strong protective factors against psychopathological problems [53].

This study has some limitations. First, the tools adopted to measure maternal psychopathological symptoms and children’s dysregulation problems were respectively self-report and report form questionnaires. We did not use clinical interviews or observational procedures administered by an expert in the field, which could have guaranteed a more objective assessment and more robust results. On the other hand, the measures we chose are widely used in the scientific literature and are fully validated for the Italian population. Second, we did not consider the possible role of fathers as protecting or risk factors mediating or moderating the effect of maternal psychopathology on offspring dysregulation. Third, the sample of this study is relatively small and it is not representative of Italian population. Notwithstanding these limitations, this study has several strengths. It uses a longitudinal design, allowing hypothesizing causational effects rather than describing mere correlations or associations between variables. Moreover, it adds to previous literature in that it evaluates stability or change in maternal and offspring scores not only in the pre-pandemic period and during the first wave of the pandemic, but it also considers the subsequent months. Finally, it focuses on a broad range of maternal symptoms, rather than assessing depressive and anxiety symptoms as most of previous research did.

Data availability: data are available here: 10.17632/jwvrvhdkwv.1

Competing interests: The authors have no competing interests to declare that are relevant to the content of this article.

Funding information: This study received specific funds.

Acknowledgements: We thank all the mothers and children who accepted to be recruited for this study.

Vaishnav M, Dalal PK, Javed A. When will the pandemic end? Indian J Psychiatry. 2020; 62(Suppl 3): S330–4. http://dx.doi.org/10.4103/psychiatry.IndianJPsychiatry_1030_20
Cao Y, Wang J, Jian F, Xiao T, Song W, Yisimayi A, et al. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature. 2022; 602(7898): 657–63. http://dx.doi.org/10.1038/s41586-021-04385-3
Bhatla A, Ryskina KL. Hospital and ICU patient volume per physician at peak of COVID pandemic: State-level estimates. Healthc (Amst). 2020; 8(4): 100489. http://dx.doi.org/10.1016/j.hjdsi.2020.100489
Hoque A, Malek A, Zaman KMRA. Data analysis and prediction of the COVID-19 outbreak in the first and second waves for top 5 affected countries in the world. Nonlinear Dyn. 2022; 109(1): 77–90. http://dx.doi.org/10.1007/s11071-022-07473-9
Cereda D, Manica M, Tirani M, Rovida F, Demicheli V, Ajelli M, et al. The early phase of the COVID-19 epidemic in Lombardy, Italy. Epidemics. 2021; 37(100528): 100528. http://dx.doi.org/10.1016/j.epidem.2021.100528
Basellini U, Camarda CG. Explaining regional differences in mortality during the first wave of Covid-19 in Italy. Popul Stud (Camb). 2022; 76(1): 99–118. http://dx.doi.org/10.1080/00324728.2021.1984551
Cogan N, Archbold H, Deakin K, Griffith B, Sáez Berruga I, Smith S, et al. What have we learned about what works in sustaining mental health care and support services during a pandemic? Transferable insights from the COVID-19 response within the NHS Scottish context. Int J Ment Health. 2022; 51(2): 164–88. http://dx.doi.org/10.1080/00207411.2022.2056386
Forte G, Favieri F, Tambelli R, Casagrande M. The enemy which sealed the world: Effects of COVID-19 diffusion on the psychological state of the Italian population. J Clin Med. 2020; 9(6): 1802. http://dx.doi.org/10.3390/jcm9061802
Samji H, Wu J, Ladak A, Vossen C, Stewart E, Dove N, et al. Review: Mental health impacts of the COVID-19 pandemic on children and youth - a systematic review. Child Adolesc Ment Health. 2022; 27(2):173–89. http://dx.doi.org/10.1111/camh.12501
Jadaon JE, Ali-Saleh O, Halperin O, Karram J, Shlomo IB, Ore L. Stress, Anxiety and Depression among Pregnant Women during the first wave of the COVID-19 Pandemic in Israel. Open J Depress Anxiety. 2021; 1–15. http://dx.doi.org/10.36811/ojda.2021.110006
Bajo M, Gallego P, Stavraki M, Lamprinakos G, Luna P, Díaz D. Anxiety, trauma and well-being in health-care professionals during COVID-19 first wave in Spain: the moderating role of personal protection equipment availability. Health Qual Life Outcomes. 2021; 19(1): 207. http://dx.doi.org/10.1186/s12955-021-01845-2
Manchia M, Gathier AW, Yapici-Eser H, Schmidt MV, de Quervain D, van Amelsvoort T, et al. The impact of the prolonged COVID-19 pandemic on stress resilience and mental health: A critical review across waves. Eur Neuropsychopharmacol. 2022; 55:22–83. http://dx.doi.org/10.1016/j.euroneuro.2021.10.864
Liu T, Li S, Qiao X, Song X. Longitudinal change of mental health among active social media users in China during the COVID-19 outbreak. Healthcare (Basel). 2021; 9(7). http://dx.doi.org/10.3390/healthcare9070833
de Lannoy L, Rhodes RE, Moore SA, Faulkner G, Tremblay MS. Regional differences in access to the outdoors and outdoor play of Canadian children and youth during the COVID-19 outbreak. Can J Public Health. 2020; 111(6):988–94. http://dx.doi.org/10.17269/s41997-020-00412-4
Moore KJ, Sampson EL, Kupeli N, Davies N. Supporting families in end-of-life care and bereavement in the COVID-19 era. Int Psychogeriatr. 2020; 32(10):1245–8. http://dx.doi.org/10.1017/S1041610220000745
Orgilés M, Morales A, Delvecchio E, Francisco R, Mazzeschi C, Pedro M, et al. Coping behaviors and psychological disturbances in youth affected by the COVID-19 health crisis. PsyArXiv. 2020. http://dx.doi.org/10.31234/osf.io/2gnxb
Hiraoka D, Tomoda A. Relationship between parenting stress and school closures due to the COVID-19 pandemic. Psychiatry Clin Neurosci. 2020; 74(9):497–8. http://dx.doi.org/10.1111/pcn.13088
Patrick SW, Henkhaus LE, Zickafoose JS, Lovell K, Halvorson A, Loch S, et al. Well-being of parents and children during the COVID-19 pandemic: A national survey. Pediatrics. 2020; 146(4): e2020016824. http://dx.doi.org/10.1542/peds.2020-016824
Bignardi G, Dalmaijer ES, Anwyl-Irvine AL, Smith TA, Siugzdaite R, Uh S, et al. Longitudinal increases in childhood depression symptoms during the COVID-19 lockdown. Arch Dis Child. 2020; 106(8):791–7. http://dx.doi.org/10.1136/archdischild-2020-320372
Phelps C, Sperry LL. Children and the COVID-19 pandemic. Psychol Trauma. 2020; 12(S1): S73–5. http://dx.doi.org/10.1037/tra0000861
Cerniglia L, Tambelli R, Trombini E, Andrei F, Cimino S. The quality of mother-child feeding interactions during COVID-19 pandemic: An exploratory study on an Italian sample. Eur J Dev Psychol. 2022; 19(5): 679–95. http://dx.doi.org/10.1080/17405629.2021.1947233
Cummings EM, Cheung RYM, Davies PT. Prospective relations between parental depression, negative expressiveness, emotional insecurity, and children’s internalizing symptoms. Child Psychiatry Hum Dev. 2013; 44(6): 698–708. http://dx.doi.org/10.1007/s10578-013-0362-1
Cimino S, Cerniglia L. The Capacity to be alone moderates psychopathological symptoms and social networks use in adolescents during the COVID-19 pandemic. Int J Environ Res Public Health. 2021; 18(21): 11033. http://dx.doi.org/10.3390/ijerph182111033
Yue P, Gizem Korkmaz A, Zhou H. Household financial decision making amidst the COVID-19 pandemic. Emerg Mark Fin Trade. 2020; 56(10): 2363–77. http://dx.doi.org/10.1080/1540496x.2020.1784717
Taraban L, Shaw DS. Parenting in context: Revisiting Belsky’s classic process of parenting model in early childhood. Dev Rev. 2018; 48: 55–81. http://dx.doi.org/10.1016/j.dr.2018.03.006
Granek L, Rosenberg-Yunger ZRS, Dix D, Klaassen RJ, Sung L, Cairney J, et al. Caregiving, single parents and cumulative stresses when caring for a child with cancer: Caregiving, single parents and cumulative stresses. Child Care Health Dev. 2014; 40(2): 184–94. http://dx.doi.org/10.1111/cch.12008
Buckland NJ, Swinnerton LF, Ng K, Price M, Wilkinson LL, Myers A, et al. Susceptibility to increased high energy dense sweet and savoury food intake in response to the COVID-19 lockdown: The role of craving control and acceptance coping strategies. Appetite. 2021; 158(105017): 105017. http://dx.doi.org/10.1016/j.appet.2020.105017
Stein DJ, Ipser J, McAnda N. Pharmacotherapy of posttraumatic stress disorder: a review of meta-analyses and treatment guidelines. CNS Spectr. 2009; 14(1 Suppl 1): 25–31.
Geeraerts SB, Deutz MHF, Deković M, Bunte T, Schoemaker K, Espy KA, et al. The Child Behavior Checklist Dysregulation Profile in preschool children: A broad dysregulation syndrome. J Am Acad Child Adolesc Psychiatry. 2015; 54(7): 595–602.e2. http://dx.doi.org/10.1016/j.jaac.2015.04.012
Kim J, Carlson GA, Meyer SE, Bufferd SJ, Dougherty LR, Dyson MW, et al. Correlates of the CBCL-dysregulation profile in preschool-aged children: CBCL-dysregulation profile in preschoolers. J Child Psychol Psychiatry. 2012; 53(9): 918–26. http://dx.doi.org/10.1111/j.1469-7610.2012.02546.x
Derogatis LR. SCL-90-R: administration, scoring and procedures manual. Minneapolis; 1994.
Rauter UK, Leonard CE, Swett CP. SCL-90-R factor structure in an acute, involuntary, adult psychiatric inpatient sample. J Clin Psychol. 1996; 52(6): 625–9. http://dx.doi.org/10.1002/(SICI)1097-4679(199611)52:6<625::AID-JCLP4>3.0.CO; 2-J
Urbán R, Kun B, Farkas J, Paksi B, Kökönyei G, Unoka Z, et al. Bifactor structural model of symptom checklists: SCL-90-R and Brief Symptom Inventory (BSI) in a non-clinical community sample. Psychiatry Res. 2014; 216(1): 146–54. http://dx.doi.org/10.1016/j.psychres.2014.01.027
Schmitz N, Hartkamp N, Kiuse J, Franke GH, Reister G, Tress W. The Symptom Check-List-90-R (SCL-90-R): a German validation study. Qual Life Res. 2000; 9(2): 185–93. http://dx.doi.org/10.1023/a:1008931926181
Prunas A, Sarno I, Preti E, Madeddu F, Perugini M. Psychometric properties of the Italian version of the SCL-90-R: a study on a large community sample. Eur Psychiatry. 2012; 27(8): 591–7. http://dx.doi.org/10.1016/j.eurpsy.2010.12.006
Achenbach TM, Rescorla LA. Manual for the ASEBA school-age forms & profiles. Research Center for Children. 2001;
Frigerio A, Montirosso R. La valutazione su base empirica dei problemi emotivo-comportamentali in età evolutiva. Infanzia e Adolescenza. 2002; 1: 38–48.
Mayr S, Erdfelder E, Buchner A, Franz F. A short tutorial of G Power. Tutorials in Quantitative Methods for Psychology. 2007; 3(2): 51–9.
Field A. Discovering Statistic Using IBM SPSS Statistics. London: SAGE Publication; 2014.
Zhao SZ, Wong JYH, Wu Y, Choi EPH, Wang MP, Lam TH. Social distancing compliance under COVID-19 pandemic and mental health impacts: A population-based study. Int J Environ Res Public Health. 2020; 17(18): 6692. http://dx.doi.org/10.3390/ijerph17186692
Talbot CV, Briggs P. Getting back to normality seems as big of a step as going into lockdown’: the impact of the COVID-19 pandemic on people with early to middle stage dementia. Age and Ageing. 2021; 50(3): 657–63.
Morgenstern-Kaplan D, Buitano-Tang B, Martínez-Gil M, Zaldívar-Pérez Pavón A, Talavera JO. U-shaped-aggressiveness of SARS-CoV-2: Period between initial symptoms and clinical progression to COVID-19 suspicion. A population-based cohort study. PLoS One. 2020; 15(12): e0243268. http://dx.doi.org/10.1371/journal.pone.0243268
Fancourt D, Steptoe A, Bu F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19: longitudinal analyses of 36. MedRxiv. 2020.
Ubillos-Landa S, Puente-Martínez A, González-Castro JL. Psychological withdrawal and mental health during the COVID-19 pandemic: a longitudinal study. Psychol Health. 2021; 1–17. http://dx.doi.org/10.1080/08870446.2021.2019254
Kelly P, Hofbauer S, Gross B. Renegotiating the public good: Responding to the first wave of COVID-19 in England, Germany and Italy. Eur Educ Res J. 2021; 20(5): 584–609. http://dx.doi.org/10.1177/14749041211030065
Zaccari V, D’Arienzo MC, Caiazzo T, Magno A, Amico G, Mancini F. Narrative review of COVID-19 impact on obsessive-compulsive disorder in child, adolescent and adult clinical populations. Front Psychiatry. 2021; 12: 673161. http://dx.doi.org/10.3389/fpsyt.2021.673161
Ong CW, Graves K, Berry MS, Odum AL, Twohig MP. Obsessive-compulsive symptoms are associated with increased delay discounting in a novel hand-washing task. Behav Anal (Wash, DC). 2019; 19(2): 136–49. http://dx.doi.org/10.1037/bar0000096
Du F, He L, Francis MR, Forshaw M, Woolfall K, Lv Q, et al. Associations between parent-child relationship, and children’s externalizing and internalizing symptoms, and lifestyle behaviors in China during the COVID-19 epidemic. Sci Rep. 2021; 11(1):23375. http://dx.doi.org/10.1038/s41598-021-02672-7
Lionetti F, Spinelli M, Moscardino U, Ponzetti S, Garito MC, Dellagiulia A, et al. The interplay between parenting and environmental sensitivity in the prediction of children’s externalizing and internalizing behaviors during COVID-19. Dev Psychopathol. 2022; 1–14. http://dx.doi.org/10.1017/S0954579421001309
Cole PM, Michel MK, Teti LO. The development of emotion regulation and dysregulation: A clinical perspective. Monogr Soc Res Child Dev. 1994; 59(2/3): 73. http://dx.doi.org/10.2307/1166139
Calkins SD, Hill A. Caregiver influences on emerging emotion regulation. Handbook of emotion regulation. London, England: Guilford Press; 2007.
Licata M, Zietlow A-L, Träuble B, Sodian B, Reck C. Maternal emotional availability and its association with maternal psychopathology, attachment style insecurity and theory of mind. Psychopathology. 2016; 49(5): 334–40. http://dx.doi.org/10.1159/000447781
Ye Y, Wang C, Zhu Q, He M, Havawala M, Bai X, et al. Parenting and teacher–student relationship as protective factors for Chinese adolescent adjustment during COVID-19. School Psych Rev. 2022; 51(2): 187–205. e from: http://dx.doi.org/10.1080/2372966x.2021.1897478

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Psychopathological risk stability and change in a sample of mothers and preschool children before, during and after the first wave of COVID-19 pandemic

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