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The COVID-19 pandemic disrupted family life and raised concerns about child development, but a new multicenter study suggests some toddlers demonstrated resilience. Researchers analyzing data from more than 3,400 children found that toddlers assessed during the pandemic had slightly fewer emotional and behavioral problems compared with those assessed before the pandemic.^1,2

The study, published in JAMA Network Open, used data from the Environmental Influences on Child Health Outcomes (ECHO) Cohort Consortium, collected between September 2009 and July 2023.

Investigators evaluated 3,438 children (average age, 2 years) from 9 sites across the United States and Puerto Rico. Families were divided into 3 groups: those born and assessed before March 2020, those born before but assessed after March 2020, and those both born and assessed during the pandemic.

Behavior was measured using the Preschool Child Behavior Checklist (CBCL), a validated parent-report tool assessing internalizing symptoms such as anxiety and sadness, and externalizing symptoms such as aggression or hyperactivity.

Key findings

Compared with children assessed prior to March 2020, both groups of toddlers with pandemic exposure had lower mean CBCL scores. Internalizing scores were approximately 1.5 to 2 points lower, and externalizing scores were 1.7 to 3.2 points lower, depending on exposure group. While differences were modest, results were consistent across analyses.

Notably, associations appeared stronger among children of mothers without a bachelor’s degree. In adjusted models, children of mothers with less education had larger reductions in both internalizing and externalizing problems compared with their prepandemic peers.

“Our findings suggest that even during periods of widespread pandemic-related disruption, toddler-aged children may have experienced opportunities for resilience,” said study author Anahid Akbaryan, BS, research associate at NYU Grossman School of Medicine. “Future research should explore within-home factors that may have supported child mental health outcomes during this time, such as consistent caregiving routines, household stability, and positive parental coping strategies to identify potential protective influences during times of instability.”

Study coauthor Lauren Shuffrey, PhD, also of NYU Grossman School of Medicine, added, “These findings suggest that some families may have adapted in ways that buffered young children from behavioral challenges during the pandemic, highlighting the importance of identifying and strengthening those protective supports.”

Prior research raised concerns about infants born during the pandemic, citing possible risks for developmental delays. However, findings across studies have been mixed. Some reports suggested increased vulnerability, while others noted minimal or even positive differences in certain developmental areas.

The authors emphasized that although caregiver-reported outcomes can be influenced by parental perceptions, the large, diverse cohort and consistency of findings strengthen the conclusions. They acknowledged limitations, including reliance on parent-report measures, lack of assessment of parental mental health, and differences in regional pandemic experiences.

Implications for practice

According to the authors, these results suggest that pandemic exposure was not linked to higher rates of behavioral problems in toddlers. Instead, some families may have adapted in ways that provided protective benefits. The study highlights the importance of investigating household and caregiving factors that supported resilience during a period of global disruption.

Further research is needed to identify which elements of the home environment—such as stable routines, caregiver coping strategies, or family cohesion—contributed most strongly to positive outcomes. Insights from such work may help clinicians and policymakers develop interventions to support families during future crises.

References

1. Environmental influences on Child Health Outcomes. Toddlers showed slightly fewer behavioral problems during COVID-19 pandemic, NIH study finds. Eurekalert. September 3, 2025. Accessed September 8, 2025. https://www.eurekalert.org/news-releases/1096955
2. Akbaryan A, Churchill ML, McGrath M, et al. COVID-19 Pandemic Exposure and Toddler Behavioral Health in the ECHO Program. JAMA Netw Open. 2025;8(9):e2530346. doi:10.1001/jamanetworkopen.2025.30346

An improved risk-prediction tool has been developed, providing enhanced accuracy for predicting patients’ future risk of invasive melanoma, new findings suggest.¹

This tool, comprised of 7 predictors and referred to as MP7, was evaluated by a series of investigators in an analysis authored by such researchers as David C. Whiteman, MBBS, PhD, of the Department of Population Health at QIMR Berghofer Medical Research Institute in Australia.

In their analysis’s introduction, Whiteman and colleagues highlighted that target approaches are increasingly being used to systematically detect melanomas, and those at highest risk are prioritized for screenings.²

“Moreover, the number of newly diagnosed invasive melanomas in the QSkin cohort has almost tripled, providing substantially greater statistical power than previously,” Whiteman and coauthors wrote.¹ “Herein, we describe the development and validation of a new prediction model for invasive melanoma in the QSkin cohort over 10 years to inform risk-stratification strategies.”

Cohort Study Design

The investigative team implemented data for their analysis drawn from the QSkin Sun and Health Study. QSkin was a large population-based prospective cohort based in Queensland, Australia. QSkin had recruited individuals of adult age, specifically aged 40–69 years, between November 2010 – December 2011 using a random sampling of their state’s population.

Those who were shown to have a registry-confirmed history of melanoma (either in situ or invasive) before their enrollment were not included by Whiteman et al. Ethical approval was granted by the Human Research Ethics Committee of the QIMR Berghofer Medical Research Institute, with all subjects providing their written informed consent.

For the investigation, participants had been followed for a decade after the 2011 baseline survey. Only individuals who had been listed as melanoma-free at recruitment and who completed the comprehensive baseline risk factor questionnaire were included in this analysis. Whiteman and colleagues’ nalyses were carried out in the period between October 2024 – April 2025.

At the study’s entry, 31 baseline variables were prespecified as potential predictors of subsequent occurrences of invasive melanoma. Incident cases of invasive melanoma among participants, determined histologically, were found by the investigators via record linkage with the Queensland Cancer Register for diagnoses. Censoring of participants at the time of melanoma took place in situ diagnosis or death. To build the prediction model, Whiteman and collagues used Cox proportional hazards regression with both forward and backward variable selection strategies.

Melanoma Prediction Accuracy Findings

Among the 41,919 participants deemed eligible for inclusion, Whiteman and coauthors noted that 55% were female and that the mean age at baseline was 55.4 years. Across 401,356 person-years of observation, there were 706 individuals who developed a new invasive melanoma.

In the final model, there were 14 baseline predictors: sex, age, patient ancestry, nevus density, hair color, freckling density, tanning response, history of sunburns in adulthood, family melanoma history, previous diagnosis of another cancer, skin cancer excision history, smoking status, previous actinic keratoses, and height. There were also 2 additional terms: age squared and an age-by-sex interaction.

The investigative team concluded that the prediction tool attained a discrimination index (C statistic) of 0.74 (95% CI, 0.73–0.76).¹ Through the use of the Youden index, the optimal screening threshold was noted as having corresponded to targeting the top 40% at predicted risk. This would identify 74% of future melanoma cases, with a number needed to screen of 32.

Overall, the team’s cohort study identified the tool as an option allowing for enhanced accuracy for the prediction of one’s future risk of invasive melanoma compared with other current tools.

“In future research, we will assess the benefit of adding genetic information to the tool, and we have recently recruited a second, independent cohort in which to validate the tool externally in the years ahead,” Whiteman and colleagues concluded.¹ “In the meantime, we see strong merit in assessing the performance of this tool independently in other settings.”

References

1. Whiteman DC, Olsen CM, Pandeya N, et al. A Risk Prediction Tool for Invasive Melanoma. JAMA Dermatol. Published online September 10, 2025. doi:10.1001/jamadermatol.2025.3028.

2. Collins GS, Dhiman P, Ma J, et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024;384:e074819. doi:10.1136/bmj-2023-074819.