Since the onset of the COVID-19 pandemic in late 2019, its far-reaching effects have significantly altered global economies, social structures and healthcare systems(Reference Temesgen, DeSimone and Mahmood1,Reference Zhang, Chen and Wang2) . In response to the outbreak, many countries enforced lockdowns and restrictions on social activities, which dramatically impacted the lifestyles of pregnant women(Reference Hao, Jin and Hao3,Reference Garabedian, Dupuis and Vayssière4) . Foetuses and newborns, being particularly vulnerable, faced not only the direct risks associated with COVID-19 infection but also the indirect consequences stemming from disrupted healthcare services and increased social isolation. These disruptions have had lasting repercussions on infant health, potentially leading to delays in growth and development, malnutrition, immune dysfunction and mental health challenges.
Furthermore, lockdowns and supply chain disruptions during the pandemic could have significantly altered household food accessibility and dietary patterns. Such shifts are particularly critical for infants and toddlers, whose nutrient requirements are high for growth and development and whose diets are reliant on adult provision. Investigating nutritional intake during this period is therefore crucial to understanding the pandemic’s full impact on child health.
COVID-19 infection exerts its effects primarily through dysregulated immune responses, rather than through direct viral damage to organs(Reference Marcuzzi, Melloni and Zauli5). The virus interferes with the innate immune system and alters the host’s immune response, with severe cases often progressing to multisystem inflammatory syndrome in neonates(Reference Ramaswamy, Abiramalatha and Pullattayil S6). Additionally, viral infections like COVID-19 have been linked to various autoimmune diseases, including systemic rheumatoid arthritis, lupus erythematosus and multiple sclerosis(Reference Winchester, Calabrese and Calabrese7,Reference Smatti, Cyprian and Nasrallah8) . However, it remains unclear whether exposure to COVID-19 during critical periods of foetal or neonatal development may have long-term consequences on immune system function(Reference Bradley, Tucker and Sampath9).
Neutrophils, monocytes, lymphocytes and platelets play crucial roles in immune responses by releasing inflammatory cytokines and autoantibodies. Inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) have gained recognition as reliable indicators of inflammation and immune status(Reference Hua, Long and Zhang10–Reference Kosidło, Wolszczak-Biedrzycka and Matowicka-Karna13).
This study aims to explore two primary questions: (1) How has the nutritional intake and physical growth of children born during the COVID-19 pandemic been affected? and (2) Do their immune systems function normally, or do they show signs of dysfunction, as indicated by inflammatory markers?
Methods
Study population
This analysis draws on data from the National Health and Nutrition Examination Survey (NHANES), an ongoing, nationally representative surveillance programme initiated in the early 1960s to monitor the health and nutritional status of the U.S. population. Its complex survey design ensures generalisability to the broader U.S. public. The publicly released data, organised in sequential cross-sectional cycles, encompass demographic information, dietary recalls, physical examination findings, laboratory tests and responses to health questionnaires. For comprehensive methodological details, please refer to the official NHANES website(14).
Given that the first confirmed case of COVID-19 in the U.S. was reported in January 2020(15), we defined the period from 2020 to 2021 as the COVID-19 pandemic period. This analysis included data from two NHANES cycles: 2017–2018 (pre-pandemic) and 2021–2023 (pandemic period). Specifically, we selected children aged 12–24 months from the 2021–2023 NHANES cycle who were born during the pandemic (i.e. between 2020 and 2021) and raised during this period. As the 2019–2020 NHANES cycle was disrupted by the pandemic, it was excluded from the analysis. The reference group consisted of children aged 12–24 months from the 2017–2018 cycle, representing those born prior to the pandemic. Although NHANES did not systematically collect COVID-19 infection status for all participants during the 2021–2023 cycle, the dataset remains representative of the U.S. paediatric population during the pandemic period, capturing a broad range of health and nutritional outcomes.
Study variables
Covariates
Demographic variables included age, race, sex, maternal education level and the family income-to-poverty ratio (used as a proxy for socioeconomic status, categorised as < 1·85, 1·85–3·5 or > 3·5)(Reference Johnson, Paulose-Ram and Ogden16). Other covariates included parental marital status, household size, birth weight (categorised as < 2·5 kg and ≥ 2·5 kg) and breastfeeding history, as reported in NHANES reproductive history questionnaires. Breastfeeding data were derived from the question: ‘Was {Child Name} ever breastfed or fed breast milk?’. This question specifically pertained to the study child, ensuring the accuracy of the breastfeeding history variable used in our analysis.
Dietary intake assessment
Dietary intake data were collected by trained interviewers using the Automated Multiple-Pass Method, a standardised five-step 24-hour dietary recall protocol designed to enhance completeness and accuracy. Interviewers used standard measuring cups, spoons, rulers and a set of two- and three-dimensional food models to assist participants in estimating portion sizes, in accordance with NHANES protocol. Nutrient intakes were calculated using the USDA Food and Nutrient Database for Dietary Studies linked to the survey cycle(17). A minimum reportable intake amount was not specified; all consumed foods and beverages were recorded. Nutritional intake variables included total energy, macronutrients (protein, carbohydrate, total fat, SFA, MUFA and PUFA and cholesterol), dietary fibre and key micronutrients (including vitamins A, C, D, E, K, B1, B2, B6, B12, total folate, calcium, phosphorus, magnesium, iron, zinc, copper, sodium, potassium, selenium and carotenoids).
Laboratory assessment
Laboratory measurements encompassed complete blood count, C-reactive protein, ferritin, transferrin receptor, RBC folate and blood levels of cadmium, mercury, selenium, manganese and 25-hydroxyvitamin D2 + D3. Blood cell counts (lymphocytes, neutrophils, platelets, monocytes, basophils, eosinophils), red cell distribution width, mean platelet volume (MPV) and Hb levels were measured using automated haematology analysers following standardised NHANES protocols, with blood cell counts expressed as ×103/µL and Hb levels in g/dl(18). Laboratory data with missing values were excluded from the statistical analysis.
Outcome definitions
The primary outcomes included birth weight, weight, length and BMI, calculated using the formula: BMI = weight (kg)/(height (m))2. Weight and length/height were directly measured by trained health technicians following standardised NHANES protocols, rather than being self-reported, ensuring high data quality. To enable valid comparisons across ages and cohorts, weight, length and BMI were converted to Z-scores based on the WHO Child Growth Standards. Z-scores for weight, length and BMI were calculated based on the WHO Child Growth Standards for children born during the pandemic(19).
Inflammatory markers were calculated as follows: the SII was derived by multiplying platelet count by neutrophil count and dividing by lymphocyte count. The NLR was calculated as the neutrophil count divided by the lymphocyte count, while the PLR was calculated as the platelet count divided by the lymphocyte count. The LMR was derived by dividing the lymphocyte count by the monocyte count.
Statistical analysis
All statistical analyses were conducted in accordance with the guidelines established by the Centers for Disease Control and Prevention (CDC), which account for NHANES’ complex, multistage cluster survey design. Each participant in NHANES is assigned a sample weight that reflects the number of individuals in the U.S. population represented by that participant. This weight adjusts for the unequal probability of selection, nonresponse and population control(20).
To address missing data, we employed multiple imputation using a multivariate logistic regression model, iterating the process for ten cycles to generate a final imputed dataset. Statistical comparisons were performed employing t tests for continuous variables and chi-squared tests for categorical variables. Non-normally distributed continuous data are reported as median (IQR; p25–p75), while categorical data are expressed as proportions. The Kruskal–Wallis test was applied to evaluate differences across multiple groups.
Additionally, we compared physiological indicators (including weight, length, BMI, NLR, PLR, LMR and SII) between pre-pandemic and pandemic-born cohorts, stratified by sex. Data were transformed into a long format, and mean values with 95 % CI were calculated to illustrate differences. Local polynomial regression (LOESS) curves were used to examine trends over time. t tests were conducted to assess differences between birth cohorts and sexes, with P-values reported for each comparison.
All statistical analyses were conducted using R software (https://www.R-project.org, The R Foundation), with a two-sided P-value of less than 0·05 defining statistical significance.
Results
Demographic and socioeconomic characteristics
Table 1 summarises the demographic and socioeconomic characteristics of a weighted cohort of 7 401 768 children, divided into two birth cohorts: pre-pandemic (3 904 863 children) and pandemic (3 496 904 children). The cohort was weighted to ensure accurate representation of the sample distribution. The median age of the children was 18 months, with no significant difference between the pre-pandemic and pandemic groups (P = 0·060). Gender distribution was also similar between the cohorts, with 52 % boys and 48 % girls overall (P = 0·208).
Demographic, socioeconomic and birth characteristics of the study population, stratified by birth cohort (pre-pandemic v. pandemic)
Table 1. Long description
The table presents demographic and socioeconomic characteristics of a weighted cohort of 7401768 children, divided into pre-pandemic (3904863 children) and pandemic (3496904 children) birth cohorts. The table has 428 rows and 10 columns. Column headers are Characteristic, n, Overall n, Overall percent, Pre-pandemic n, Pre-pandemic percent, Pandemic n, Pandemic percent, and P-value. Row labels include Age (months), Gender, Race/ethnicity, Household size, Parental education level, Parents’ marital status, Poverty income ratio, Breastfeeding, and Birth weight (kilogram). Each row provides specific data points for each characteristic across the overall, pre-pandemic, and pandemic cohorts, along with corresponding P-values indicating statistical significance. Notable trends include similar gender distribution, with 52% boys and 48% girls overall, and a median age of 18 months with no significant difference between cohorts. Race/ethnicity distribution shows Non-Hispanic White at 49%, Non-Hispanic Black at 16%, Multi-Racial at 10%, Mexican American at 13%, and Other Hispanic at 12%. Household size has a median of 4. Parental education level is predominantly high school (57%), followed by college degree or higher (30%), and below high school (12%). Parents’ marital status shows 85% married/partnered, 6.8% lost partner, and 8.4% single. Poverty income ratio indicates 43% below 1.85, 28% between 1.85-3.50, and 29% above 3.50. Breastfeeding is reported in 84.2% of cases. Birth weight is predominantly between 2.5-4.0 kilograms (84%).
* n not missing (unweighted).
† Median (IQR) for continuous; n (%) for categorical.
‡ Design-based Kruskal–Wallis test; Pearson’s X^2: Rao & Scott adjustment.
Regarding race/ethnicity, the cohort included 49 % Non-Hispanic White, 16 % Non-Hispanic Black, 10 % Multi-Racial, 13 % Mexican American and 12 % Other Hispanic children, with no significant differences observed between the two cohorts (P = 0·224). Household size remained consistent, with a median of four members in both groups. Parental education levels (P = 0·286) and marital status (P = 0·570) showed no significant differences. However, the poverty income ratio differed significantly between the cohorts (P = 0·020), with a higher proportion of children in the pre-pandemic cohort living below the poverty line (52 %) compared to the pandemic cohort (34 %). Breastfeeding rates were high across both cohorts, with 84 % of children being breastfed, and birth weight distributions showed no significant differences (P = 0·269 and P = 0·861, respectively). These findings suggest that while most demographic and socioeconomic characteristics remained stable, there was a notable reduction in income disparities during the pandemic.
Dietary nutrient intake
There were no significant differences in energy, protein, carbohydrate, total sugar or fat intake between the two cohorts. The median energy intake was approximately 1035 kcal/d, with protein and carbohydrate intakes averaging 36·3 g/d and 125·2 g/d, respectively. However, specific nutrient intakes revealed significant differences. Vitamin A intake was significantly lower in the pandemic cohort (437·0 mcg/d) compared to the pre-pandemic cohort (540·0 mcg/d, P = 0·006). Conversely, vitamin C intake was higher during the pandemic (87·0 mg/d v. 62·8 mg/d, P < 0·001). Vitamin B12 intake also decreased significantly in the pandemic cohort (2·7 mcg/d v. 3·4 mcg/d, P = 0·009), and zinc intake was slightly lower (5·9 mg/d v. 6·3 mg/d, P = 0·045). In contrast, dietary iron intake did not differ between cohorts (P = 0·172), which is consistent with the absence of differences in iron status biomarkers (ferritin, transferrin receptor and Hb). No statistically significant differences were observed for any other nutrients (Table 2).
Dietary nutrient intakes of children, stratified by birth cohort (pre-pandemic v. pandemic)
* n not missing (unweighted).
† Median (IQR) for continuous; n (%) for categorical.
‡ Design-based Kruskal–Wallis test.
Laboratory results
Most laboratory parameters, including leukocyte counts, lymphocytes, neutrophils, basophils, eosinophils and monocytes, showed no significant differences between the two cohorts. Hb levels were slightly lower in the pandemic cohort, but this difference was not statistically significant (P = 0·086). Platelet count and red cell distribution width also did not differ significantly between the cohorts.
Notably, environmental toxin exposure was significantly reduced in the pandemic cohort. Blood cadmium levels were lower in the pandemic cohort (0·05 µg/l) compared to the pre-pandemic cohort (0·07 µg/l, P < 0·001), and blood mercury levels were similarly reduced (0·12 µg/l v. 0·20 µg/l, P = 0·002). Other biomarkers, such as blood selenium, manganese and vitamin D, did not show significant differences between the cohorts (Table 3).
Laboratory indicators of nutritional and health status, stratified by birth cohort (pre-pandemic v. pandemic)
* n not missing (unweighted).
† Median (IQR) for continuous; n (%) for categorical.
‡ Design-based Kruskal–Wallis test.
Growth metrics
Figures 1 and 2 illustrate Z-scores for growth metrics, including weight-for-age, length-for-age and BMI-for-age, for boys and girls across age groups in the pre-pandemic and pandemic birth cohorts. The overall mean (sd) Z-scores for the pandemic cohort were as follows: weight-for-age: (0·52 (sd 1·00)), length-for-age: (0·37 (sd 1·10)) and BMI-for-age: (0·44 (sd 1·00)). All mean Z-scores were within the normal range (–2 to +2), suggesting that growth trajectories in both cohorts adhered to expected developmental patterns, with only minor fluctuations.
Z-Score comparison of weight, length and BMI by age and gender in pandemic birth cohorts.
Figure 1. Long description
The image contains six line graphs comparing Z-scores of weight, length, and BMI by age and gender in pandemic birth cohorts. The graphs are divided into two rows, with the top row representing boys and the bottom row representing girls. Each row contains three graphs: weight Z-score, length Z-score, and BMI Z-score. The x-axis for all graphs represents age groups in months, ranging from 12 to 24 months. The y-axis represents the Z-score, ranging from -2 to 3. Each graph includes data points with error bars and a trend line. Panel A (top left) shows the weight Z-score for boys, Panel B (top middle) shows the length Z-score for boys, and Panel C (top right) shows the BMI Z-score for boys. Panel D (bottom left) shows the weight Z-score for girls, Panel E (bottom middle) shows the length Z-score for girls, and Panel F (bottom right) shows the BMI Z-score for girls. The trend lines indicate variations in Z-scores over time for each gender and measurement type.
Comparison of weight, length and BMI by age group, gender and birth cohort (pre-pandemic v. pandemic).
Figure 2 provides a direct comparison of growth metrics between the two cohorts. Boys born during the pandemic exhibited significantly lower BMI at 15–17 months (P = 0·045), while no significant differences were observed in weight or length across other age groups. For girls, BMI was significantly lower at 18–20 months (P = 0·035), with no notable differences in weight or length. Overall, most P-values indicated minimal differences between the cohorts, suggesting that the pandemic had a limited impact on growth metrics.
Inflammatory markers
Figure 3 compares inflammatory markers, including the LMR, NLR, PLR and SII, across age groups, gender and birth cohorts.
Comparison of NLR, PLR, LMR and SII by age group, gender and birth cohort (pre-pandemic v. pandemic).
Figure 3. Long description
The image contains eight scatter plots comparing NLR, PLR, LMR, and SII by age group, gender, and birth cohort during pre-pandemic and pandemic periods. Each plot is divided into two rows for boys and girls, with four columns representing different measures: LMR, NLR, PLR, and SII. The x-axis of each plot represents age groups in months (12-14, 15-17, 18-20, 21-24), and the y-axis represents the measure value. Each plot includes data points for pre-pandemic (blue) and during the pandemic (red) cohorts. Panel A (Boys LMR) shows LMR values with p-values indicating statistical significance. Panel B (Boys NLR) shows NLR values with corresponding p-values. Panel C (Boys PLR) shows PLR values with p-values. Panel D (Boys SII) shows SII values with p-values. Panel E (Girls LMR) shows LMR values with p-values. Panel F (Girls NLR) shows NLR values with p-values. Panel G (Girls PLR) shows PLR values with p-values. Panel H (Girls SII) shows SII values with p-values. Each plot includes error bars representing variability in the data.
For boys, no significant differences were observed between the two cohorts for most inflammatory markers, except for PLR at 15–17 months (P = 0·017) and SII at 21–24 months (P = 0·038). In girls, significant differences were observed for PLR at 12–14 months (P = 0·014) and 18–20 months (P = 0·038). NLR at 18–20 months also approached significance (P = 0·045). These findings suggest some variations in immune profiles between the two cohorts. However, most values remained within the normal range, indicating that the overall immune status of children in both cohorts was largely unaffected by the pandemic.
Discussion
The present analysis utilised data from the NHANES, a large, diverse dataset that is representative of the U.S. population(Reference Johnson, Paulose-Ram and Ogden16,Reference Terry, Chiappa and McAllister21) . By including newborns from community settings, the findings can be generalised to the broader U.S. population(Reference Chen, Clark and Riddles22). Our study demonstrated that, overall, most demographic, nutritional, laboratory and growth indicators for children remained stable before and during the COVID-19 pandemic. While certain micronutrient intakes and a few inflammatory markers exhibited notable differences, the overall development and health status of children were largely within normal and expected ranges.
COVID-19 infection has the potential to directly impact neonatal growth and development, primarily through systemic inflammation. Severe COVID-19 cases are associated with cytokine storms, which lead to elevated levels of pro-inflammatory cytokines, such as IL-6 and tumour necrosis factor-alpha(Reference Fajgenbaum and June23,Reference Merad and Martin24) . These cytokines can suppress the function of insulin-like growth factor-1, a critical regulator of bone and muscle growth(Reference Clemmons25). Disruption of insulin-like growth factor-1 activity may result in growth delays, particularly in cases of severe or prolonged inflammation(Reference Wong, Dobie and Altowati26).
Additionally, symptoms associated with COVID-19 – such as fever, respiratory distress and feeding difficulties – can adversely affect nutritional intake, further exacerbating metabolic stress. Neonates, in a critical phase of rapid growth, require adequate energy and nutrient intake to support weight gain and linear growth. Insufficient energy intake during this period can lead to negative energy balance, ultimately suppressing growth. Deficiencies in essential micronutrients, such as zinc and iron, during key growth periods are linked to impaired cognitive and motor development(Reference Abdelhaleim, Abdo Soliman and Amer27–Reference Jáuregui-Lobera31). Bone development may also be compromised, as infections can disrupt calcium and phosphorus metabolism, both of which are essential for bone mineralisation(Reference Ciosek, Kot and Kosik-Bogacka32,Reference Friedman, Bailey and Rondon33) . Chronic infections may further impair vitamin D status, delaying skeletal growth and potentially contributing to long-term developmental challenges(Reference Holmlund-Suila, Koskivirta and Metso34).
Beyond the direct effects of SARS-CoV-2 infection, the broader context of the pandemic – including lockdowns, work-from-home policies and reduced outdoor activities – may have exerted indirect influences on child health. On one hand, restricted access to healthcare services, disruptions in food supply chains and diminished availability of nutritional support programmes may have negatively impacted maternal and neonatal health outcomes(Reference Roberton, Carter and Chou35,Reference Chmielewska, Barratt and Townsend36) . On the other hand, increased parental presence at home could have facilitated more attentive caregiving and closer monitoring of dietary intake, potentially mitigating adverse nutritional outcomes. Similarly, the significant reduction in exposure to environmental toxins such as cadmium and mercury – likely resulting from decreased industrial and traffic-related pollution during the pandemic – may have contributed to a less pro-inflammatory environment. Vulnerable populations, particularly those in resource-limited settings, may have been disproportionately impacted by these disruptions(Reference Headey, Heidkamp and Osendarp37,Reference Kc, Gurung and Kinney38) .
Despite these challenges, our study found no significant differences in most nutrient intakes between children born before and during the pandemic. Macronutrient intake, including energy, protein and fat, as well as trace elements and vitamins, remained stable. Similarly, laboratory indicators of nutritional and health status, such as Hb, ferritin and blood levels of 25-hydroxyvitamin D2 + D3, showed no significant differences. The observed shift towards higher vitamin C intake coupled with lower intakes of vitamin A and B12 (Table 2) may reflect changes in dietary patterns and food accessibility during the pandemic. The elevated vitamin C intake likely indicates increased parental awareness of, or preference for, foods perceived to support immune function. Conversely, the decrease in vitamins A and B12 could be attributable to disruptions in the supply chain for certain fortified foods or animal products or to broader shifts in household dietary habits. While these differences are noteworthy, they did not significantly alter overall growth trends, as both groups maintained normal Z-scores for weight, length and BMI.
Although physical growth was not significantly affected, increasing evidence suggests that COVID-19 may induce autoimmune phenomena through immune system dysregulation(Reference Gracia-Ramos, Martin-Nares and Hernández-Molina39). Overactivation of the immune system can lead to autoimmune inflammation, which disrupts normal immune responses and results in immune system imbalance(Reference Marcuzzi, Melloni and Zauli5,Reference Hedrich40) . This dysregulation not only impairs the body’s ability to defend against infections but may also increase the risk of developing various chronic conditions, such as autoimmune diseases(Reference Ramaswamy, Abiramalatha and Pullattayil S6), cancer(Reference Terzić, Grivennikov and Karin41,Reference Elinav, Nowarski and Thaiss42) , diabetes(Reference Oguntibeju43), CVD(Reference Pearson, Mensah and Alexander44,Reference Danesh, Collins and Appleby45) and metabolic syndrome(Reference Welty, Alfaddagh and Elajami46,Reference Syauqy, Hsu and Rau47) . To assess changes in immune function, inflammatory markers such as the NLR, LMR, PLR and the SII are widely recognised as reliable indicators of inflammation and immune status(Reference Hua, Long and Zhang10).
In interpreting the inflammatory markers in our study, it is essential to consider the likelihood of widespread SARS-CoV-2 exposure within the pandemic cohort. Although individual-level infection data were not available in NHANES, the high community transmission during the study period – especially with the dominance of the highly transmissible Omicron variant – suggests that most U.S. children, including those in our sample, had been infected. National seroprevalence studies support this, estimating that over 90 % of children had acquired SARS-CoV-2 antibodies by the end of 2022(Reference Clarke48). Against this backdrop of substantial viral exposure, the only minor and clinically non-significant fluctuations observed in NLR, PLR, LMR and SII in our study are particularly reassuring. These findings suggest that, at a population level, the innate and adaptive immune systems in infants and toddlers exhibited considerable resilience, effectively re-establishing homeostasis after widespread viral exposure without evidence of persistent inflammatory dysregulation.
Moreover, a substantial body of evidence indicates that inflammatory markers are closely associated with growth and development in children. Studies have linked these markers to malnutrition(Reference Can and Can49), overweight(Reference Rodríguez-Rodríguez, Salas-González and Ortega50,Reference Marra, Bondesan and Caroli51) , bronchopulmonary dysplasia(Reference Jiang, Mao and Wu52) and retinopathy of prematurity(Reference Kıran Yenice, Kara and Karsli Türkoglu53) – all common issues in neonates. In our study, inflammatory markers showed minor variations in certain age groups of pandemic-born children but remained within normal ranges overall. These findings suggest that the immune function of pandemic-born children was minimally impacted, and the likelihood of future growth abnormalities remains low.
While these findings are encouraging, the study has some limitations. First, its cross-sectional design does not allow for the establishment of causal relationships. To simulate a longitudinal approach, we compared growth indicators across children of different ages, but the lack of repeated measurements on the same individuals prevents the evaluation of individual changes over time. Second, the age range of the study participants was relatively narrow, and the absence of long-term follow-up data limits the ability to assess future growth and developmental trajectories. Third, laboratory data used in this study were derived from a single blood test. Serial measurements could provide a more comprehensive understanding of changes in nutritional and inflammatory markers over time. Fourth, data on individual COVID-19 infection status (including maternal infection during pregnancy) were not collected in NHANES, precluding a sub-analysis comparing directly infected and non-infected children. However, given the high seroprevalence in the paediatric population during the study period, the pandemic cohort overall represents a group with substantial viral exposure. Future prospective studies with serial serological testing would be valuable to correlate the timing and severity of infection with long-term growth and immune outcomes.
Conclusion
In conclusion, the COVID-19 pandemic did not appear to have a significant negative impact on the physical growth, nutritional intake, or immune function of children born during this period. Although minor variations were observed in certain inflammatory markers and nutrient intakes, the overall health status of these children remained within normal ranges. Future studies, particularly prospective research, are needed to evaluate the long-term effects of the pandemic on child development and health outcomes.
Acknowledgements
The authors thank the National Health and Nutrition Examination Survey for providing publicly available data.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interests
The authors declare no conflicts of interest.
Authorship
Conception and design: Y.L., Q.C. Data collection: Y.L., D.K., S.C. Analysis and interpretation: Y.L., D.K. Manuscript drafting: All authors. Critical revision and final approval: All authors.
Ethics of human subject participation
This study analysed de-identified, publicly available data from NHANES. NHANES protocols were approved by the National Center for Health Statistics (NCHS) Ethics Review Board. All participants provided written informed consent. The study complies with the ethical standards of the Declaration of Helsinki.
Open access
