Effect of COVID-19 Pandemic Lockdowns on Body Mass Index of Primary School Children from Different Socioeconomic Backgrounds

Abstract

Background: Childhood obesity is associated with various health outcomes. Restrictive measures to contain the spread of the Coronavirus Disease 2019 (COVID-19) pandemic, like lockdowns and school closures, affected children's daily structure, physical activity, dietary habits, and sleep quality, possibly exacerbating risk factors for childhood obesity and higher body mass index (BMI) in children. Poor socioeconomic conditions may have led to relatively higher risk for elevated BMI levels following pandemic measures. In this study, the impact of measures related to the COVID-19 pandemic on the BMI of third graders was investigated regarding children's socioeconomic background (SEB).

Methods: Data from 41,728 children (8.84 ± 0.56 years, 20,431 female) were collected in the context of a cohort study. Children were tested either before the pandemic (pre_COVID: Sept2017-March2020, n = 26,314), or following the first (post_LDI: Aug2020-Dec2020, n = 6657) or second lockdown in Germany (post_LDII: Aug2021-Jan2022, n = 8757). SEB was based on the official school type classification of the state of Berlin. Outcome was BMI standard deviation scores (SDS).

Results: Significant effects of Time and SEB revealed elevated BMIs in post_LDI (M = 0.23, p = 0.011) and post_LDII (M = 0.22, p = 0.011) compared to pre_COVID (M = 0.17) cohorts and higher BMIs for children with lower SEB (b = - 0.13, p < 0.001). A significant Time × SEB interaction indicated that the effect of SEB on children's BMI increased in response to lockdowns, especially in post_LDII (b = - 0.05, p = 0.006). Results suggest that the COVID-19-related measures lead to increased BMI in children, and that children of lower SEB were at particular risk for higher BMIs following lockdowns.

Conclusions: These findings highlight the dependency of children's BMI on societal circumstances. Over the course of two lockdowns in Germany, children have experienced BMI increments, particularly in low socioeconomic areas. Authorities are called into action to counteract increasing rates of childhood weight by promoting physical activity of children and establishing related post-pandemic offers.

Keywords: BMI standard deviation score; BMI z-score; Childhood obesity; Childhood overweight; Physical literacy.

Fig. 1

Location of schools which participated in the ‘Berlin has Talent’ program within the socioeconomically diverse city of Berlin. Schools are displayed as geo tag icons in black. Planning areas of Berlin are color graded according to the Employment and Social Index (ESIx) of the city of Berlin. The ESIx is adapted from the ‘Health and Social Structure Atlas of Berlin’ and informs about the economic and social wealth of a region. For more information about the ESIx, see Berlin Senate [37]

Fig. 2

Flow diagram of excluded cases as well as demographic characteristics of the final sample by pandemic stage

Fig. 3

Data collection periods between September 2017 and January 2022. Measurements are displayed in grey by Time. Lockdowns are highlighted as red rectangles, the “lockdown light” (November 2020 until January 2021) is highlighted in light red. For more information on regulatory measures in Germany, see German Federal Statistical Office [Statistisches Bundesamt] [59].

Fig. 4

Main effects of Time and SEB on BMI SDS. a Post-hoc pairwise comparisons (contrasts) of estimated marginal mean BMI SDS with standard errors by Time (pre_COVID, post_LDI and post_LDII). Benjamini–Hochberg adjusted p-values from the estimated marginal mean analysis are given. b Effect of SEB on BMI SDS (Slope). Unstandardized beta values and p-values from multilevel model estimates are given

Fig. 5

Interaction effect of Time × SEB on BMI SDS. a Post-hoc pairwise comparison of slopes (estimated marginal trends with standard errors) in pre_COVID, post_LDI and post_LDII. Benjamini–Hochberg adjusted p-values from estimated marginal trends analysis are given. b Effect of SEB on BMI SDS (slopes) in pre_COVID, post_LDI and post_LDII according to estimated marginal trends analysis. Beta values (estimated marginal trends) are given