key: cord-0855762-l47frm50
authors: Monnier, Maëva; Moulin, Flore; Thierry, Xavier; Vandentorren, Stéphanie; Côté, Sylvana; Barbosa, Susana; Falissard, Bruno; Plancoulaine, Sabine; Charles, Marie-Aline; Simeon, Thierry; Geay, Bertrand; Marchand, Laetitia; Ancel, Pierre-Yves; Melchior, Maria; Rouquette, Alexandra; Galera, Cédric
title: Children's mental and behavioral health, schooling, and socioeconomic characteristics during school closure in France due to COVID-19: the SAPRIS project
date: 2021-11-17
journal: Sci Rep
DOI: 10.1038/s41598-021-01676-7
sha: 3f4e14bf117a06b4ce8c1522c27c0bcef0c0d187
doc_id: 855762
cord_uid: l47frm50

COVID-19 limitation strategies have led to widespread school closures around the world. The present study reports children’s mental health and associated factors during the COVID-19 school closure in France in the spring of 2020. We conducted a cross-sectional analysis using data from the SAPRIS project set up during the COVID-19 pandemic in France. Using multinomial logistic regression models, we estimated associations between children’s mental health, children’s health behaviors, schooling, and socioeconomic characteristics of the children’s families. The sample consisted of 5702 children aged 8–9 years, including 50.2% girls. In multivariate logistic regression models, children’s sleeping difficulties were associated with children’s abnormal symptoms of both hyperactivity-inattention (adjusted Odds Ratio (aOR) 2.05; 95% Confidence Interval 1.70–2.47) and emotional symptoms (aOR 5.34; 95% CI 4.16–6.86). Factors specifically associated with abnormal hyperactivity/inattention were: male sex (aOR 2.29; 95% CI 1.90–2.76), access to specialized care prior to the pandemic and its suspension during school closure (aOR 1.51; 95% CI 1.21–1.88), abnormal emotional symptoms (aOR 4.06; 95% CI 3.11–5.29), being unschooled or schooled with assistance before lockdown (aOR 2.13; 95% CI 1.43–3.17), and tutoring with difficulties or absence of a tutor (aOR 3.25; 95% CI 2.64–3.99; aOR 2.47; 95% CI 1.48–4.11, respectively). Factors associated with children’s emotional symptoms were the following: being born pre-term (aOR 1.34; 95% CI 1.03–1.73), COVID-19 cases among household members (aOR 1.72; 95% CI 1.08–2.73), abnormal symptoms of hyperactivity/inattention (aOR 4.18; 95% CI 3.27–5.34) and modest income (aOR 1.45; 95% CI 1.07–1.96; aOR 1.36; 95% CI 1.01–1.84). Multiple characteristics were associated with elevated levels of symptoms of hyperactivity-inattention and emotional symptoms in children during the period of school closure due to COVID-19. Further studies are needed to help policymakers to balance the pros and cons of closing schools, taking into consideration the educational and psychological consequences for children.

Covariates. Children's health behavior. Health behaviors included the following: sleeping difficulties (e.g., difficulty falling asleep, waking up frequently or too early at night without being able to go back to sleep) since the beginning of lockdown (yes: new occurrence, increase, or stability, no: decrease, disappearance or none); time per day spent by child on the following activities: (a) reading, drawing, board games; (b) screen for recreation, (c) social network, (d) physical activity (sports or walks outside; indoor and outdoor physical activity).

Children's schooling. Educational characteristics included the following: school situation before lockdown (normal; with assistance or unschooled); average time devoted to schoolwork per day (none or less than 1 h, 1-3 h, > 3 h); tutoring difficulty (tutoring with difficulty, tutoring without difficulty, no tutoring); average screen time for educational reasons per day. Tutoring refers to the presence of someone who can help the child with homework (e.g., parents, siblings, grandparents etc.). Screen time for educational reasons refers to time spent on television or other screens to follow school or educational programs.

Socioeconomic characteristics of children's families. Socioeconomic characteristics included the following: the parents' occupational category (executive, intermediate and executive, intermediate and employee, independent, laborer, 2 inactive or only one employee/laborer); change in parents' work situation (change at least for one parent, no change for either parent); parents' distance working (neither working, one teleworking and the other not working, at least one working outside, both teleworking); perceived financial situation (affluent and constant income; affluent and declining income; modest and constant income; modest and declining income); type of housing (rural house, urban house, flat with balcony/garden, flat without balcony/garden, other); region classified according to the prevalence of COVID-19 at the beginning of the epidemic (High: Ile de France and Grand Est vs Medium: Bourgogne-Franche-Comté, Auvergne-Rhône-Alpes and Hauts-de-France vs. Low: other regions); number of rooms per inhabitant and if the child lived with both parents or not.

Others. Additional data included the following: sex; chronic disease (yes, no); born pre-term (yes, no); children's access to specialized care prior to the COVID-19 epidemic (e.g., physiotherapist, speech therapist, psychologist, rehabilitation) and its continuation during school closures (yes and pursuit, yes but no pursuit, no); COVID-19 cases among household members (yes, no). Statistical analysis. We first described child and family sociodemographic characteristics, (i.e., weighted means with standard deviations for continuous variables; frequency with weighted percentages for categorical variables). Descriptive analyses were conducted after correcting the sample data to be representative of children born in France in 2011 36 . For this purpose, a weighting coefficient was calculated. For more information, see the online supplement.

Before performing multiple imputation, we removed variables with more than 30% missing data (screen time and physical activity) and observations with missing data for both outcomes (hyperactivity/inattention and emotional symptoms). Classification and Regression Tree methods (CART, imputation method for mixed data: both Continuous and Categorical) 37 for multiple imputation were used to handle missing data. Secondly, to test the association between each covariate and mental health outcomes (i.e., hyperactivity/inattention and emotional symptoms), we used multinomial logistic regressions adjusted on prematurity to estimate Odds Ratios (OR) and 95% confidence intervals (CI). Thirdly, to perform relevant covariate selection, we applied a penalized regression method, Elastic-Net, combining bootstrapping with multiple imputed data [38] [39] [40] . We used the variable inclusion probability with a 50% threshold to select variables associated with children's mental health. Finally, we www.nature.com/scientificreports/ used multinomial logistic regression to estimate adjusted OR and 95% CI for the association between variables selected by Elastic-Net, prematurity and both mental health outcomes. Data were analyzed using the nnet package in R (version 3.6.1) with multinomial logistic regressions specified using the multinom function. The mice package and its cart method were used to perform multiple imputation. Finally, the caret, glmnet, doParallel and dplyr packages were applied to implement the Elastic-Net method on imputed data.

Sample characteristics. The study included 5702 children (Table 1 ). In total, 2808 (50.2%) children were females, 1161 (19.4%) had access to specialized care prior to the COVID-19 epidemic, 289 (4.5%) had chronic disease, and 1122 (2.9%) were premature children. COVID-19 cases were reported in 235 (4.4%) households. Concerning the children's mental health, hyperactivity/inattention scores were abnormal for 672 (13.6%) and moderate for 452 (8.2%) children, respectively. Emotional difficulties scores were abnormal or moderate for 404 (7.5%) and 288 (5.8%) children, respectively. In terms of children's health behaviors, 39.9% had sleeping difficulties. Children spent an average of 2 h and 20 min reading, drawing, and playing board games per day, and 2 h and 45 min doing physical activities per day. Regarding screen time, children spent an average of 3 h and 10 min per day for recreation, and 5 min on social networks. Only 188 (2.8%) children were unschooled or in school with assistance before the lockdown, 2764 (62.8%) spent between 1 and 3 h per day on schoolwork and an average of 50 min on educational programs per day. 4202 (96.7%) children had a tutor at home to help them with their homework.

As shown in Table 2 , the pandemic situation led to a change in work situation in 1483 (43.9%) households, working from home was implemented in 2371 households (32.6%) and 1813 (41.6%) perceived financial difficulties. Children lived mostly in a house (67.4%) and with their two parents (84.5%). 9.0% and 17.3% of children lived in regions with high or moderate prevalence of COVID, respectively.

Factors associated with hyperactivity/inattention and/or emotional symptoms. The factors associated with hyperactivity/inattention and/or emotional symptoms when adjusting for prematurity are presented in Tables 3 and 4 . The factors associated with an increased risk of both hyperactivity/inattention and emotional symptoms in univariate analysis were the following: having chronic disease, being born pre-term, having access to specialized care prior to the COVID-19 epidemic, having difficulties in sleeping, being schooled with assistance or unschooled before the lockdown, having difficulties with tutoring or not having a tutor, low parents' dominant socio-professional category, presence of change in the parents' work situation, having modest and declining outcome during the lockdown, and not living with both parents. The factor associated with a lower risk of both hyperactivity/inattention and emotional symptoms adjusted on prematurity was having at least one parent who worked outside.

Factors exclusively associated with an increased risk of hyperactivity/inattention adjusted on prematurity were the following: being a boy; having emotional symptoms. Factors associated with a decreased risk of symptoms of hyperactivity/inattention were the following: spending 1-3 h or more than 3 h per day devoted to schoolwork, having parents working remotely, and living in an urban environment.

In contrast, factors only associated with an increased risk of emotional symptoms adjusted on prematurity were the following: presence of COVID-19 cases in the household, hyperactivity/inattention symptoms, having modest and constant income, and living in an urban flat without a balcony/garden. The only factor associated with a lower risk of emotional symptoms was being a boy.

Finally, the number of rooms per inhabitant and the French regions most affected by the COVID-19 epidemic were not associated with children's mental health.

Factors associated with children's mental health after Elastic Net selection. The factors most strongly associated with children's mental health after using Elastic Net selection are presented in Tables 5 and  6 . Elastic Net selected 10 factors as most associated with hyperactivity/inattention in children: sex, access to specialized care prior to the COVID-19 epidemic, having emotional symptoms, having sleeping difficulties, being schooled with assistance or unschooled before the lockdown, devoting 1 h or less to school work per day, having difficulties with tutoring or not having a tutor, low parents' dominant socio-professional category, living in an urban house, and not living with both parents (Table 5) .

After adjusting for these 10 factors most associated with hyperactivity/inattention symptoms and prematurity, boys remained at greater risk for abnormal hyperactivity-inattention than girls (aOR 2.29; 95% CI 1.90-2.76). Children who had regular care before the COVID-19 epidemic and could not continue the sessions during the lockdown had an increased risk of abnormal hyperactivity/inattention symptoms (aOR 1.51; 95% CI 1.21-1.88).

Concerning children's mental health, compared to normal children, children with abnormal emotional symptoms or at boundary state were associated with higher risk of abnormal hyperactivity/inattention symptoms (aOR 4.06; 95% CI 3.11-5.29 and aOR 2.14; 95% CI 1.54-2.97, respectively). Regarding the children's health behavior, risk of abnormal hyperactivity/inattention symptoms or at boundary state appeared to be higher for children with sleeping difficulties (OR 2.05; 95% CI 1.70-2.47, OR 1.71; 95% CI 1.39-2.10) than in those without sleeping difficulties (Table 5) .

Regarding children's schooling, being schooled with assistance or unschooled was associated with a higher risk of abnormal hyperactivity/inattention (aOR 2.13; 95% CI 1.43-3.17). Spending 1-3 h per day devoted to schoolwork or more than 3 h seemed to be associated with less risk of abnormal hyperactivity/inattention symptoms than spending no time or less than 1 h (aOR 0.55; 95% CI 0.34-0.91 and aOR 0.56; 95% CI 0.33-0.92, respectively). In this multivariate analysis, having difficulties with tutoring or not having a tutor were still associated For socioeconomic characteristics, the low parents' occupational category (laborer) was still associated with a higher risk of abnormal hyperactivity/inattention as compared to the executive category (aOR 1.66; 95% CI 1.01-2.71). Compared to living in a rural house, living in an urban house was associated with less risk of abnormal hyperactivity/inattention symptoms (aOR 0.79; 95% CI 0.64-0.97). Table 1 . Characteristics of children participating in SAPRIS project in France on weighted data (n = 5702). www.nature.com/scientificreports/ Concerning the factors most associated with emotional symptoms, Elastic Net selected the following seven variables: being born pre-term, COVID-19 cases in the household, hyperactivity-inattention symptoms, sleeping difficulties, parents' occupational category, change in parents' work situation, and financial situation ( Table 6 ). The results described below are adjusted on these seven variables.

Children born pre-term remained at a higher risk of abnormal emotional symptoms (aOR 1.34; 95% CI 1.03-1.73). The presence of COVID-19 cases in the household (aOR 1.72; 95% CI 1.08-2.73), abnormal symptoms of hyperactivity/inattention (aOR 4.18; 95% CI 3.27-5.34) and sleeping difficulties (aOR 5.34; 95% CI 4.16-6.86) remained risk factors of abnormal emotional symptoms (Table 6 ). In this multivariate analysis, only the "intermediate and executive" parents' occupational category was still associated with a higher risk of abnormal emotional symptoms as compared to the "executive" category (aOR 1.61; 95% CI 1. 16-2.21) . Having a modest income remained a risk factor of abnormal emotional symptoms (aOR 1.45; 95% CI 1.07-1.96; aOR 1.36; 95% Table 2 . Socioeconomic characteristics of children's families in SAPRIS project in France on weighted data (n = 5702). a In the absence of a spouse, the respondent who is executive refers to «intermediate and executive» category; which is intermediate corresponds to «intermediate and employee» category; and which is employee, worker or without profession refers to «both inactive or only one employee/labourer» category. b In the absence of a spouse, the respondent who has undergone a change in work situation during the lockdown refers to «change at least for one parent» category and the one who has not undergone any change to «no change for either parent» category. c In the absence of a spouse, the respondent who is not working is associated with the «neither working» category; the one who teleworks refers to «both teleworking» category; and the respondent who works outside to «at least one working outside» category. d Region classified according to the prevalence of COVID-19 at the beginning of the epidemic (High: Ile de France and Grand Est vs Medium: Bourgogne-Franche-Comté, Auvergne-Rhône-Alpes and Hauts-de-France vs Low: other regions). 

Main findings and interpretations. Our study showed high rates of hyperactivity/inattention and emotional symptoms during school closure due to COVID-19 in France. It also helped in identifying risk factors of hyperactivity/inattention and/or emotional symptoms in children. Difficulties in sleeping and parents' occupational category were the only factors independently associated in the multivariate models with both dimensions of mental disorder. Factors specifically associated with hyperactivity/inattention were the following: sex, presence of regular care and its pursuit during school closure, emotional symptoms, school situation, presence of tutoring and difficulties with it, and type of housing. Factors related to emotional symptoms were the following: being born pre-term, the presence of COVID-19 cases in the household, hyperactivity/inattention and financial difficulties.

Children's sex, chronic disease, specialized care and being born pre-term. In the context of the COVID-19 crisis, our results indicate a higher risk of hyperactivity/inattention among boys than girls, and a higher risk of emotional symptoms among girls than boys. Findings by others were similar outside the context of the COVID-19 pandemic 41,42 and our findings are consistent with those of recent studies on it 4, 43, 44 . A crosssectional online survey of 8079 Chinese adolescents aged 12-18 affected by the COVID-19 pandemic revealed that female gender was the most important risk factor for depressive and anxiety symptoms 4 . After adjustment on covariates, we found that children who had access to specialized care prior to the COVID-19 pandemic and who were unable to continue follow up during school closures had a higher risk of abnormal hyperactivity-inattention. Being born pre-term was associated with a higher risk of abnormal emotional symptoms during the COVID-19 pandemic. These results are coherent with epidemiological studies conducted outside the COVID-19 context which found that pre-and perinatal factors (e.g. prematurity, chronic disease) are associated with Attention-Deficit/Hyperactivity Disorder (ADHD) [45] [46] [47] and emotional problems 48, 49 , although the definite causes remain unknown. While being born pre-term is a known risk factor for hyperactivity/inattention disorder 50, 51 , it is the association between prematurity and emotional symptoms that is emphasized by our findings.

In our multivariate analyses, the presence of COVID-19 cases in the household seemed to be a risk factor of abnormal emotional symptoms in children. This was one of the strongest associations according to Elastic Net. These results are in line with a large cross-sectional online survey of 44,447 college students conducted in China 52 . Compared with students who reported not having infected or suspected cases in family members and relatives, those who reported having confirmed and suspected cases in family members and relatives had a higher risk of depressive symptoms (OR 4.06; 95% CI 1.62-10.19 and OR 2.11; 95% CI 1.11-4.00, respectively) 52 . Interestingly, several studies evidenced an association between emotional symptoms and COVID-19 epidemic-related factors such as potential exposure to the virus, fear of infection (i.e., selfand/or family members), and loss of loved ones 3,53,54 . Other studies found an increased risk of post-traumatic stress disorder (PTSD) symptoms 55 and moderate to high perceived stress scores 56 in isolated/quarantined individuals during the COVID-19 pandemic. Mental stress, anxiety, depressive symptoms, insomnia and fear are triggered by the pandemic itself, mandatory preventive measures, and COVID-19-related circumstances 53,54,57 .

Children's mental health. Our findings showed that during the school closure in France, hyperactivity/ inattention and emotional symptoms were associated. Elastic Net found this to be one of the strongest associations. Our results confirm those of another study conducted outside the epidemic context which showed a stronger correlation between hyperactivity/inattention and emotional symptoms in the SDQ 34 .

Our study is consistent with recent reports on the association between difficulties in sleeping and children's mental health during the COVID-19 pandemic 14 , in particular with hyperactivity/inattention and emotional symptoms. Difficulty in sleeping is one of the most predictive variables of hyperactivity/inattention in children, as well as emotional symptoms. Our findings confirm those of previous studies conducted outside the COVID-19 pandemic 58-60 . Hyperactivity/inattention and emo- Table 3 . Factors associated with hyperactivity/inattention: multinomial logistic regressions adjusted on prematurity (n = 5097). a Region classified according to prevalence of COVID-19 at beginning of epidemic (High: Ile de France and Grand Est vs Medium: Bourgogne-Franche-Comté, Auvergne-Rhône-Alpes and Hauts-de-France vs Low: other regions). www.nature.com/scientificreports/ tional symptoms are both characteristics of children with ADHD. A meta-analysis of 16 studies comparing sleep in ADHD children versus controls found that the former were significantly more impaired than the latter on most subjective and some objective sleep measures 60 . The relationship between ADHD and difficulties in sleeping is bidirectional 59, [61] [62] [63] . ADHD may cause sleep problems and sleep problems may cause or mimic ADHD 58, 59 . Moreover, a reciprocal relationship between sleep quality and anxiety problems seems very likely, although the specific mechanisms that contribute to these sleep disturbances remain unclear 64-67 . Children's schooling: school situation, time devoted to schoolwork, and tutoring. In multivariate analyses, being unschooled or schooled with assistance before the lockdown was associated with a higher risk of abnormal hyperactivity/inattention. Spending 1-3 h or more than 3 h per day on schoolwork seemed to be associated with a lower risk of abnormal hyperactivity/inattention symptoms than spending no time or less than 1 h. Furthermore, children without a tutor and those with tutoring difficulties were at greater risk for abnormal hyperactivity-inattention compared with those without tutoring difficulties. Our results are consistent with those of a previous study, which found that prolonged school closure regardless of the epidemic context can cause significant mental health problems in children, especially among economically disadvantaged groups 68 . While schools are essential for children's academic education, they also play an important role in addressing Table 5 . Associations between most predictive factors from elastic net selection and hyperactivity/inattention symptoms: multivariate multinomial logistic regressions (n = 5097). a Adjusted on sex, prematurity, access to specialized care prior to COVID-19 and its continuation during school closures, emotional symptoms, sleeping difficulties, school situation before lockdown, time devoted to schoolwork, tutoring, parents' occupational category, type of housing, and child living with both parents. Socioeconomic characteristics of children's families. Our multivariate analyses found an increased risk of abnormal emotional symptoms in children whose families experienced financial difficulties. Children of parents in a low socio-professional category were also the most affected in terms of hyperactivity/inattention. Similar results were recently reported in a French community-based sample of 432 parents and their children during the COVID-19 lockdown 14 . The authors showed that family socioeconomic factors such as financial difficulties and unemployed parents were significantly associated with a higher risk of children's mental health problems, such as hyperactivity/inattention and emotional symptoms 14 . Stability of family income was significantly associated with children's psychological difficulties during the COVID 19 pandemic 3 , due to economic decline and the stress that it causes 70, 71 .

Our adjusted analysis showed that, compared to living in a rural house, living in an urban house was associated with a lower risk of hyperactivity/inattention during school closure in France. The type of housing was not associated with emotional symptoms in multivariate analysis after an Elastic Net selection. These results are contrary to most studies showing a lower risk of ADHD in children living in rural rather than urban areas 72, 73 . A large web-based survey of 8177 Italian students found that poor housing (i.e., living in apartments < 60 m 2 with poor views and poor indoor quality) was associated with an increased risk of depressive symptoms during lockdown 74 . In addition, an Iranian study which evaluated people's preferences and priorities to choose healthy homes after the COVID-19 pandemic revealed that the most critical priorities for residents were natural light, having a view, acoustic protection, and an open or semi-open space 75 . Our conflicting results may be explained by the greater proximity to other children in urban than in rural areas.

Contrary to findings in the literature 18 , our adjusted analysis did not find a significant association between living in one of the regions of France most affected by COVID 19 and children's mental health. Finally, living with both parents was found to be protective for hyperactivity/inattention but not for emotional symptoms. Living with both parents has been shown to be a protective factor for children's mental health in the context of the COVID-19 pandemic, since a Chinese study of 7143 college students found that living with parents was a protective factor against anxiety (OR 0.75; 95% CI 0.60-0.95) 3 . A recent study on the mental health of children aged 9-18 in France also showed the protective effect of a two-parent family on the psychological distress of children during the COVID-19 pandemic 76 . Table 6 . Associations between most predictive factors from elastic net selection and emotional symptoms: multivariate multinomial logistic regressions (n = 5097). a Adjusted on prematurity, COVID cases in household, hyperactivity/inattention symptoms, sleeping difficulties, parents' occupational category, change in parents' work situation and perceived financial situation. www.nature.com/scientificreports/ Strengths and limitations. This study has some limitations. First, owing to too many missing data regarding screen time and physical activity, we were unable to estimate the associations between these variables and the presence of hyperactivity/inattention or emotional symptoms in children. The results could be different or even more marked in the general population. Second, only a few parents reported their alcohol and smoking consumption as well as their mental disorders, which prevented us from taking these variables into account in our analyses. Third, we did not have any objective measures of sleep quality but only a parent's self-reported measure of difficulty sleeping. Fourth, we had no data on the consumption of media information related to the pandemic in minutes per day and quality of information received. We did not have access to the children's temperament which has already been shown to be a predictor of emotional disorders and hyperactivity/inattention in children 77 . It could be interesting for future research to take this variable into account, especially since there is already a validated tool in French 78 . Therefore, we were not able to study the association between these environmental factors with children's mental health, even though significant associations have already been reported 44, 53, 79 . Fifth, our data were collected during a period of school closure in the first COVID-19 wave in France, so we could not analyze data on children's mental health prior to the lockdown. As a result, we were unable to assess the impact of school closure on children's mental health independently from that of the COVID-19 pandemic. Sixth, we could not re-evaluate the mental health of the children, yet it is possible that the reopening of schools in France has resulted in health benefits for them. Future study designs should include re-assessment of mental health and compare children's health scores between countries that implemented vs. did not implement school closure as a preventive measure. Future research should also investigate potential environmental factors that might influence children's mental health, such as parental history of mental illness, consumption of media information related to COVID-19, and domestic violence. In addition, the causal relationship between environmental factors related to COVID-19 and children's mental health should be explored.

The study also has some strengths. First, it includes the large number of parent respondents during school closures in France at the beginning of the COVID 19 pandemic. It also includes pre-term children, a population more at risk for hyperactivity/inattention, so it provides more power for studying risk and protective factors in a lockdown situation. Second, we were able to control a wide range of possible covariates from sociodemographic and socioeconomic characteristics of children's families to children's schooling. To our knowledge, this is the first study to consider a set of variables related to children's housing and schooling. Third, using validated measures of hyperactivity/inattention and emotional symptoms (SDQ) with satisfactory psychometric properties is a strength and novel aspect of this study 31, 34 . Overall, we believe that our findings contribute to the evolving debate concerning school closure and children's mental health. We suggest that school closure due to COVID-19 could increase the risk of mental health problems and exacerbate health inequalities 71 . Our findings provide important guidance for the development of children's psychological support strategies in France.

The current focus on measures to contain COVID-19 transmission around the world may distract attention from emerging mental health issues 57, 80 . The closure of schools due to COVID-19 and the lockdown could lead to an increase in mental disorders in children, especially among vulnerable populations. Efforts must be made to address the pandemic situation while considering children's mental health and to adapt the delivery of care to meet the demands of COVID-19 71 . Our findings have important clinical and policy implications. In this global public health emergency, identifying vulnerable groups of children and environmental risk factors of children's mental health is a critical step in developing and adapting mental health services and tools 81 . Health authorities and governments should implement services that improve the mental health of the population, particularly that of vulnerable children. Emerging digital applications such as telehealth, social media, and mobile health could serve to promote children's mental health 82 .

This study conducted in France explored several risk factors of hyperactivity/inattention and emotional symptoms in children during the school closure due to the COVID-19 pandemic. Some factors that may increase the risk of children developing mental health problems are described: sex, pre-existing health disorders, COVID cases in the household, difficulties in sleeping and in schooling, and sociodemographic and socioeconomic disparities. Policymakers need to balance the pros and cons of reopening school, taking into consideration the educational and psychological consequences for children. Therefore, it is essential to implement appropriate protective measures to manage the unprecedented repercussions of school closure on children's mental health while avoiding an increase in socio-economic inequalities.

In regards to data availability, data of the study are protected under the protection of health data regulation set by the French National Commission on Informatics and Liberty (Commission Nationale de l'Informatique et des Libertés, CNIL). The data can be available upon reasonable request after a consultation with the steering committee of the Sapris study. The French law forbids us to provide free access to Sapris data; access could however be given by the steering committee after legal verification of the use of the data.

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Institut de Recherche Interdisciplinaire sur les enjeux Sociaux -Sciences Sociales, Politique, Santé, IRIS (UMR 8156 CNRS -EHESS -U997 INSERM)

23 National Institute for Demographic Studies

We are grateful to the New-Aquitaine region and its AMI FLASH Recherche et Innovations COVID, without whom the project would never have started. The authors warmly thank all the volunteers of the ELFE, and EPI-PAGE2 cohorts. We thank the staff of the SAPRIS study group that have worked with dedication and engagement to collect and manage the data used for this study and to ensure continuing communication with the cohort participants.

M.M. contributed to the literature search, data analysis, and wrote the first draft of the manuscript. C.G., M.M., and A.R. contributed to the data interpretation and writing. S.B., L.M. and T.S. contributed to the data analysis.All authors reviewed the manuscript. C.G. was the senior author of the manuscript. M.M. and C.G. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Mrs Maeva Monnier has been funded by the New-Aquitaine region (AMI Flash Recherche et Innovations COVID). The other authors declare no competing interests related to this paper.

The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598-021-01676-7.Correspondence and requests for materials should be addressed to M.M. or C.G.

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