The SARS-CoV-2 epidemic has had little medical consequences for children and adolescents, as incidence of severe forms of COVID-19 in the pediatric population was low and symptoms of infection were generally mild (1, 2). However, young people were deeply affected by the restrictive measures imposed globally to reduce transmission, such as quarantine, lockdown, and social distancing, often referred to as Non-Pharmaceutical Interventions (NPIs), which considerably changed their daily lives (3). They were confined at home for long periods, with limited opportunity for learning and reduced peer contact, together with adults who were often anxious or psychologically stressed by the circumstances, which added to their own discomfort (3, 4). School closure, enforced in many countries with different durations, was particularly relevant for these age groups, as school is where children and adolescents spend most of their time, and have opportunity for both social interactions and intellectual stimulation (5).

The debate on the pros and cons of population-wide restrictions enacted during the COVID-19 pandemic is ongoing. On the one hand, data seems to support the positive effects of NPIs (6–10), particularly in terms of control of virus spread and consequent reduction in mortality (10). On the other hand, some authors emphasize a range of “side effects” of NPIs, including economic, educational, and health repercussions, disproportionately affecting more vulnerable populations, including children, with little health benefits (11). To manage future health crises, therefore, it is crucial that these strategies are further assessed to inform future pandemic policy and avoid past mistakes (12).

The timing and intensity of NPIs against COVID-19 all over the world varied greatly according to local situations (7). Italy, starting from the Northern regions, was the first European country to be affected by the pandemic (13), and enacted very aggressive restrictive policies, including one of the longest school closures in the world (14).

The analysis of hospitalization trends can provide valuable insights into the repercussions of different restrictions adopted over time, needed to prepare for future pandemics. In particular, to estimate the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time, Interrupted Time Series (ITS) regression analysis is the recommended method (15). However, the majority of research on this topic is monocentric (16–19), is restricted to specific pediatric age classes or considers all ages including adults (17, 19–26), focuses on specific diagnoses (9, 16, 17, 19, 21, 25, 27), only looks at Emergency Department (ED) visits (16, 17, 19, 21, 23, 26, 28, 29), or addresses the time period immediately following the pandemic onset without evaluating ongoing effects (18, 25, 30).

We therefore aimed to quantify the impact of NPIs adopted to prevent or control COVID-19 transmission on the trend of hospitalizations, in 12 hospitals in the Emilia-Romagna Region, Northern Italy, during the 2 years following the start of the pandemic, compared with the previous 3 years, considering two pandemic phases according to the type of adopted NPIs.

Overall, in the 5 years of the study and in the 12 participating centers, there were 84,368 cases. Case demographics are shown in Table 1 for each of the three phases: PC, SC, and MM. The sample of admissions was made up of 57.0% males, with the predominant age group being between 0 and 1 year (38.0%). As expected, the hospitalization rate decreased considerably when school closure was enforced with respect to the pre-pandemic time period (2,548 vs. 3,915 × 100,000 person-year). Supplementary Table S1 shows the standardized hospitalization rates by type of primary diagnosis, from highest to lowest.

Figure 1 shows the comparisons in terms of SHRR, overall and for individual ICD9-CM categories, between SC, MM and PC. Overall, a statistically significant decrease in hospitalizations with respect to pre-pandemic rates was observed both in SC (−35%, SHRR 0.65, 95%CI: 0.64–0.67) and MM (−19%, SHRR 0.81, 0.80–0.83), while a 25% increase (SHRR 1.25, 1.22–1.28) was recorded in MM with respect to SC.

Considering individual ICD-9-CM diagnoses, a generalized reduction was detected during SC for all categories. The greatest reduction (−73%, SHRR 0.27, 95%CI 0.25–0.29) occurred in the “Respiratory Diseases” category, which exhibited the highest frequency of hospitalizations (approximately 4,000 cases/year in the 3 years before the pandemic). In MM, the reduction compared to PC persisted, although less prominent. Only the Mental Disorders category showed a large increase (51%, SHRR 1.51, 95%CI 1.30–1.75).

To measure NPI effects, ITS regression analysis was carried out on overall hospital admissions and on the two categories which stood out for the greatest variation (Respiratory Diseases and Mental Disorders).

Results of the ITS analysis are presented in the following paragraphs.

This is the first European study on the impact of COVID-19 NPIs on the trend of pediatric hospitalizations conducted in a wide area severely hit by the pandemic, covering an extended pandemic period (24 months). The use of appropriate analysis through ITS regression makes our findings and corresponding conclusions reliable. In fact, existing research on the effects of NPIs mostly consists of modeling studies, implying a lack of empirical, real-world data, or uses descriptive statistics on admission trends (34).

Overall, our results showed that the number of pediatric hospital admissions dropped by more than 50% in the first months of the lockdown period, and then began to rise, achieving pre-pandemic hospitalization levels only 2 years later. This considerable, long-lasting reduction appears to be mainly determined by a decrease in the occurrence of infectious diseases (the most frequent cause for hospitalization in children), mainly affecting the respiratory system. However, these results may also be due to a change in health-seeking behaviors of parents, who might have chosen not to attend hospital with their sick children for fear of contagion (9, 35). Moreover, the decrease may be attributed to a tendency to avoid hospitalizing children with minor health problems. Supporting this hypothesis is the fact that admissions for childhood neoplasms remained constant, suggesting that healthcare services were maintained for severe illnesses. A similar observation was made by Wang et al. (9), who found a 55% reduction in admissions for all-cause respiratory diseases, in line with our finding, and a smaller reduction in admissions for childhood neoplasms.

Interesting results emerged from ITS analyses conducted on the two disease categories exhibiting the largest variation, which recorded opposite trends. For Respiratory Diseases, we observed a marked reduction of hospitalizations which persisted throughout school closure and for the most part of the subsequent time period when less stringent mitigation measures were enforced, in the absence of typical seasonal epidemic peaks. Conversely, for Mental Disorders an immediate decline of admissions was detected in the first 2 months of lockdown, followed by an incremental trend, on average by 11% monthly. These trends need to be further investigated using hospitalization data recorded in the following years, to understand whether the effects persist, or whether at the end of the pandemic hospitalizations return to pre-pandemic levels.

Some plausible reasons for these results exist. Regarding Respiratory Diseases, the drop in admissions is likely to be related to the impact of mask-wearing, hand washing, and social distancing on the interruption of person-to-person viral or bacterial transmission, as also discussed by Wang et al. (9). The reduction may also be partly due to a “virus interference phenomenon” among respiratory viruses, whereby the infection of one virus can partially prevent or inhibit the infection of another virus in the same host (36). The contribution of this factor is however likely to be marginal compared to the absence of influenza epidemics and other respiratory infections following social distancing, which has been reported and commented in the literature (37, 38). Concerning mental health, the negative effects may have taken longer to manifest, but once developed they may not resolve easily even if restrictions are lifted, instead requiring much time and specific care to be removed (3).

The results of this study can contribute to the current debate on benefits and harms of individual NPIs, which is not a simple one, also because it is hard to separate the impact of one measure from that of other interventions introduced simultaneously. Concerning the pediatric population in particular, it would be essential to elucidate the role of school closures on the control of pandemic spread (39). Recent reviews (12, 34, 40) suggested that measures implemented in the school setting may have limited the number or proportion of cases and deaths among adults, and delayed the progression of the pandemic. This seems to contrast with a report on data from Sweden, where school closure was only reserved for upper secondary schools, indicating that the number of deaths per population unit was lower than most other high-income countries that applied stringent school closure policies (41). On the other hand, the literature also highlights negative consequences of these measures on children’s health and education. As reported by UNICEF (42), school closures disrupted the provision of educational (and in some cases health and nutritional) services, increased emotional distress and mental health problems, an prevented access to a wide range of school-provided services, including school meals, monitoring of health and welfare, social skills training, and services targeted to children with special needs. Furthermore, as schools moved online, impoverished children experienced dramatic educational setbacks contributing to inequalities and long-term hardship (42).

Within the current debate, our findings also highlight that evaluating the trade-offs between positive and negative consequences of NPI implementation during pandemics is a complex task. In particular, as commented above, the decrease in hospitalizations for Respiratory Diseases after the beginning of the outbreak may be due both to the hesitancy in attending hospitals, certainly an undesired effect, and to the reduction of respiratory infections due to lockdown measures, a welcome benefit.

One of the main strengths of this research lies in the use of ITS analysis, one of the strongest evaluative designs when randomization is not possible (15). Furthermore, the study involves numerous hospitals, which makes results robust and increases their generalizability. Also, analyzed data concern the first European area hit by the pandemic, where aggressive restrictive measures were immediately adopted since the start of the outbreak and maintained for an extended period, are restricted to one endpoint (pediatric hospitalizations) and include COVID and non-COVID hospitalizations. Finally, the study covers a wide timeframe, longer than most similar research, which enabled to verify the impact of NPIs in the long-term.

This study has some limitations. Firstly, data were taken from hospital administrative databases and were not collected prospectively for this research. However, the data quality is supposed to be similar in the years we compared; thus, this aspect should not impact interpretation. Secondly, we did not attempt to discriminate between new versus recurrent hospitalizations. Such discrimination would be important to understand whether the observed changes were due to the onset of a new condition or to the exacerbation of existing problems. Thirdly, since the analysis used data collected retrospectively without formal power analysis, we cannot exclude the risk of false negative findings in the case of low-prevalence diagnoses. Lastly, we did not attempt to investigate the potential role of different waves of variants of the SARS-CoV-2 virus which were predominant in the 2 years covered by the study, because it was not an objective of our research. This may have led to an overestimation of the effect of NPIs on hospital admissions.

The results of this and other studies on the impact of COVID-19 NPIs on children provide information needed to guide and target interventions in the event of future pandemics, and to plan the allocation of resources where they are needed most. However, the different plausible interpretations of our findings make it difficult to inform about the trade-offs between benefits and negative consequences of NPI strategies during pandemics. Rigorous research should be conducted to understand whether the reduction in pediatric hospital admissions we observed over a two-year period has affected child and adolescent health. Meta-analyses are needed to quantify the contribution to observed effects of individual mitigation actions, to better determine the appropriateness of their introduction, timing and intensity.

The data analyzed in this study is subject to the following licenses/restrictions: the raw data supporting the conclusions of this article will be made available by the authors upon a motivated request to the corresponding author. Requests to access these datasets should be directed to ccaminiti@ao.pr.it.

The studies involving humans were approved by AVEN (Area Vasta Emilia Nord) Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because waiver for informed consent was obtained from the Italian Data Protection Authority (Garante della Privacy), because of feasibility issues (more than 80,000 subjects should have been contacted).

Collective authors who fulfill all four ICMJE authorship criteria.

Francesca Diodati, Chiara Maria Palo: Clinical and Epidemiological Research Unit, University Hospital of Parma, Parma, Italy; Angela Miniaci, Luca Bertelli: Pediatric Clinic, IRCCS Azienda Ospedaliera Universitaria di Bologna, Bologna, Italy; Giovanni Biserni: Pediatric Emergency Unit, IRCCS Azienda Ospedaliera Universitaria di Bologna, Bologna, Italy; Angela Troisi, Alessandra Iacono: Pediatrics and Neonatology Unit, Ravenna Hospital, AUSL Romagna, Ravenna, Italy; Federico Bonvicini, Domenico Bartolomeo, Andrea Trombetta: Paediatrics Unit, Santa Maria Nuova Hospital, AUSL-IRCCS of Reggio Emilia, Reggio Emilia, Italy; Tommaso Zini: Pediatrics Unit, Department of Medical and Surgical Sciences of Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy; Nicoletta de Paulis: Pediatrics and Neonatology Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy; Cristina Forest: Pediatric Clinic, University of Ferrara, Ferrara, Italy; Battista Guidi: Pediatric Unit, Pavullo Hospital, AUSL Modena, Pavullo, Italy; Francesca Di Florio: Paediatrics Unit, Maggiore Hospital, Bologna, Italy; Enrico Valletta, Francesco Accomando: Pediatric Unit, G.B. Morgagni - L. Pierantoni Hospital, AUSL Romagna, Forlì; Greta Ramundo, Alberto Argentiero, Valentina Fainardi, Michela Deolmi: Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy.

GM: Formal analysis, Writing – original draft, Writing – review & editing, Data curation, Methodology. MP: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. CC: Formal analysis, Writing – original draft, Writing – review & editing, Conceptualization, Validation. AP: Investigation, Resources, Resources, Writing – review & editing. ML: Investigation, Resources, Writing – review & editing. FC: Investigation, Resources, Writing – review & editing. FM: Investigation, Resources, Writing – review & editing. AF: Investigation, Resources, Writing – review & editing. LI: Investigation, Resources, Writing – review & editing. GB: Investigation, Resources, Writing – review & editing. AS: Investigation, Resources, Writing – review & editing. AM: Investigation, Resources, Writing – review & editing. CG: Investigation, Resources, Writing – review & editing. GV: Investigation, Resources, Writing – review & editing. MA: Investigation, Resources, Writing – review & editing. MS: Investigation, Resources, Writing – review & editing. SE: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing.