The diversity of measures used to assess early childhood development complicates comparison across studies, populations, and programs. The Global Scales for Early Development (GSED) address this challenge by providing interoperable long and short forms (GSED-LF and GSED-SF) linked through a shared measurement model to a common developmental metric, the D-score. This article presents an updated GSED core model based on Rasch analysis of data from seven sites (n = 9,287 children aged 0-41 months). The final model includes 281 items selected through item- and person-fit evaluation and shows improved measurement precision compared with earlier keys. Descriptive age-conditional reference curves derived from combined GSED-LF and GSED-SF data provide a methodological benchmark for typical development. We further demonstrate how external measures, including the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III), can be mapped onto the D-score scale by anchoring new items to the core model. D-scores and Development-for-Age Z-scores (DAZ) derived from GSED-LF, GSED-SF, and BSID-III show strong agreement, supporting the validity of the extension. The resulting GSED2510 key, implemented in the open-source dscore R package, enables standardized, measure-independent assessment of early child development.

In preventive Youth Health Care (YHC), professionals increasingly need flexible, data-driven tools to monitor child development in a personalized way. The Dutch Developmental Instrument (DDI) screens at fixed ages, limiting adaptability and parental engagement. We developed the DDI-Continuous (DDI-C), a digital tool that integrates developmental data for individualized monitoring. Using published D-score calibrations and Dutch reference curves, percentile ages were computed for all 75 DDI milestones and visualized in an interactive interface. This interface enables selection of milestones that are easy, average, or challenging for a given age, matching a child's developmental level, rather than focusing solely on delay. The prototype was iteratively refined through three pilot cycles (Plan-Do-Check-Act). Pilot evaluations indicated high usability, deeper insight, and clinical relevance. By operationalizing developmental data in an accessible format, DDI-C provides decision support and fosters meaningful conversations with parents, contributing to more personalized and engaging preventive YHC.

Achieving and maintaining weight loss are challenging and require sustained lifestyle changes. This study examined which behavior change techniques (BCTs) support long-term weight outcomes among adults with overweight or obesity. We synthesized evidence from randomized controlled trials of behavioral interventions versus control or alternative interventions, focusing on nonclinical adults (BMI > 25) with ≥ 6-month follow-up, published between 2010 and 2025, that reported weight outcomes. Sixty-five studies (approximately 10,000 participants) evaluating 87 behavioral interventions targeting physical activity, diet, or both were included. BCTs were coded using the BCT Taxonomy v1. Meta-analysis estimated pooled effects on total weight loss (baseline to follow-up) and weight maintenance (posttreatment to follow-up). Behavioral interventions produced a mean effect size for total weight loss of Hedges' g = -0.19 (95% CI = -0.30 to -0.13), equating to an average additional weight loss of approximately 3.25 kg compared to controls. No significant effect was found for weight maintenance after initial loss, indicating no statistically significant difference in weight change between intervention and control groups during follow-up. Meta-regression examined the number of BCTs, intervention type, and follow-up duration, but none significantly moderated effects. Subgroup analyses identified the following effective BCTs: goal setting, feedback on behavior and outcome(s) of behavior, self-monitoring of behavior and outcome(s) of behavior, instruction on how to perform the behavior, demonstration of the behavior, conserving mental resources and incentives. These BCTs appear to support relative long-term weight outcomes and warrant further investigation.

To ensure the continued provision of high-quality care, the Dutch Child and Youth Healthcare (CYH) is shifting towards more personalized approaches. Data-driven innovations, e.g., decision support systems and automated personalized advice, are crucial in this shift. However, the decentralized nature of the CYH, similar to other healthcare domains, provides significant challenges at the level of finance, operational demands, and IT-system, in the adoption and implementation of data-driven innovations. The I-JGZ platform aims to address these challenges by offering a modular architecture, developed in collaboration with healthcare organizations and IT-suppliers. This study documents how the I-JGZ platform can improve care quality, professional efficiency, and parental empowerment within a fragmented healthcare landscape, such as CYH. By enabling seamless integration of data-driven innovations, the platform aligns local variations in care processes with national guidelines, facilitating consistency in service delivery. Through real-time decision support and personalized advice, I-JGZ enhances professional efficiency and parent engagement, ensuring high-quality and accessible care. Continued development, alongside addressing financial hurdles, will unlock the platform's full potential to transform youth healthcare across the Netherlands and beyond.

To assess the neurodevelopment of children under three years, a multinational team of subject matter experts (SMEs) led by the World Health Organization (WHO) developed the Global Scales for Early Development (GSED). The measures include (1) a caregiver-reported short form (SF), (2) a directly administered long form (LF), and (3) a caregiver-reported psychosocial form (PF). The feasibility objectives of this study in Bangladesh, Pakistan, and the United Republic of Tanzania were to assess (1) the study implementation processes, including translation, training, reliability testing, and scheduling of visits and (2) the comprehensibility, cultural relevance, and acceptability of the GSED measures and the related GSED tablet-based application (app) for data collection for caregivers, children, and assessors. In preparation for a large-scale validation study, we implemented several procedures to ensure that study processes were feasible during the main data collection and that the GSED was culturally appropriate, including translation and back translation of the GSED measures and country-specific training packages on study measures and procedures. Data were collected from at least 32 child-caregiver dyads, stratified by age and sex, in each country. Two methods of collecting inter-rater reliability data were tested: live in-person versus video-based assessment. Each country planned two participant visits: the first to gain consent, assess eligibility, and begin administration of the caregiver-reported GSED SF, PF, and other study measures and the second to administer the GSED LF directly to the child. Feedback on the implementation processes was evaluated by in-country assessors through focus group discussions (FGDs). Feedback on the comprehensibility, relevance, and acceptability of the GSED measures from caregivers was obtained through exit interviews in addition to the FGD of assessors. Additional cognitive interviews were conducted during administration to ensure comprehension and cultural relevance for several GSED PF items. The translation-back translation process identified items with words and phrases that were either mistranslated or did not have a literal matching translation in the local languages, requiring rewording or rephrasing. Implementation challenges reiterated the need to develop a more comprehensive training module covering GSED administration and other topics, including the consent process, rapport building, techniques for maintaining privacy and preventing distraction, and using didactic and interactive learning modes. Additionally, it suggested some modifications in the order of administration of measures. Assessor/supervisor concurrent scoring of assessments proved to be the most cost-effective and straightforward method for evaluating inter-rater reliability. Administration of measures using the app was considered culturally acceptable and easy to understand by most caregivers and assessors. Some mothers felt anxious about a few GSED LF items assessing motor skills. Additionally, some objects from the GSED LF kit (a set of props to test specific skills and behaviors) were unfamiliar to the children, and hence, it took extra time for them to familiarize themselves with the materials and understand the task. This study generated invaluable information regarding the implementation of the GSED, including where improvements should be made and where the administered measures’ comprehensibility, relevance, and acceptability needed revisions. These results have implications both for the main GSED validation study and the broader assessment of children’s development in global settings, providing insights into the opportunities and challenges of assessing young children in diverse cultural settings.

Objective To assess contributions of 22 risk factors (each related to life style, diet, reproduction, environment or infection) to the incidence of all cancer cases.Design Secondary data analysis, reference year 2019. Independence of risk factors was assumed.Setting The Netherlands, nationwide.Population Dutch men and women, ages >30 years.Main outcome measures Population attributable fractions and numbers of newly diagnosed cancers by gender.Results Of all newly diagnosed cancers, an estimated 34% (40 054 out of 119 728 cancers, excluding basal cell carcinoma of the skin) was attributable to the evaluated risk factors (35% in men, 32% in women). Among these factors, smoking was by far the largest contributor, accounting for 16% of all cancers (19 095 cases), followed by the combined impact of dietary factors (5%, 6 452 cases) and overweight and obesity (4%, 4995 cases). Limited data on basal and squamous cell carcinoma led to an underestimation of the burden of ultraviolet radiation.Conclusions A substantial proportion of cancer cases arises from potentially modifiable risk factors. Implementation of effective public health strategies to reduce exposures is crucial to alleviate the future burden of cancer.

Reliability and measurement error are related but distinct measurement properties. They are connected because both can be evaluated using the same data, typically collected from studies involving repeated measurements in individuals who are stable on the outcome of interest. However, they are calculated using different statistical methods and refer to different quality aspects of measurement instruments.We explain that a measurement error refers to the precision of a measurement, that is, how similar or close the scores are across repeated measurements in a stable individual (variation within individuals). In contrast, reliability indicates an instrument’s ability to distinguish between individuals, which depends both on the variation between individuals (i.e. heterogeneity in the outcome being measured in the population) and the precision of the score, i.e. the measurement error. Evaluating reliability helps to understand if a particular source of variation (e.g. occasion, type of machine, or rater) influences the score, and whether the measurement can be improved by better standardizing this source. Intraclass-correlation coefficients, standards error of measurement and variance components are explained and illustrated with an example.

BACKGROUND:The proliferation of instruments that define instrument-specific metrics impedes progress in comparative assessment across populations. This paper explores a method to extract a common metric from related but different instruments and transform the original measurements into scores with a standard unit of measurement. METHODS:Existing data from four assessment instruments of child development, collected from three different samples of children, were used to create "equate clusters" of items that measure the same behaviour in (slightly) different ways. A probability model was formulated to identify best items and groups to serve as anchors linking the instruments, assuming that items in an anchoring or "active" equate cluster are psychometrically equivalent. Quantification and inspection of item characteristic curves were used to resolve which equate clusters should be active. We simulated the impact of various analytic choices. RESULTS:Simulation confirmed the feasibility of creating a common metric from data collected with different instruments from respondent samples with different abilities. The method performed as expected in an application in early childhood development. CONCLUSIONS:The use of equate clusters is an intuitive and flexible way to establish a common metric across instruments and facilitates the transformation of measurements obtained to a standardized scale. Standardizing instrument scores to a common metric allows for population-level comparisons on a global scale.

Rubin's Rules are commonly used to pool the results of statistical analyses across imputed samples when using multiple imputation. Rubin's Rules cannot be used when the result of an analysis in an imputed dataset is not a statistic and its associated standard error, but a test statistic (e.g. Student's t-test). While complex methods have been proposed for pooling test statistics across imputed samples, these methods have not been implemented in many popular statistical software packages. The median p-value method has been proposed for pooling test statistics. The statistical significance level of the pooled test statistic is the median of the associated p-values across the imputed samples. We evaluated the performance of this method with nine statistical tests: Student's t-test, Wilcoxon Rank Sum test, Analysis of Variance, Kruskal-Wallis test, the test of significance for Pearson's and Spearman's correlation coefficient, the Chi-squared test, the test of significance for a regression coefficient from a linear regression and from a logistic regression. For each test, the empirical type I error rate was higher than the advertised rate. The magnitude of inflation increased as the prevalence of missing data increased. The median p-value method should not be used to assess statistical significance across imputed datasets.

AimThe Strengths and Difficulties Questionnaire self-report (SDQ-SR) is a valid instrument for detection of emotional and behavioral problems. The aim of this study was to compare the psychometric properties of the SDQ-SR for low and higher educated adolescents, and to explore its suitability.MethodsWe included 426 adolescents. We compared internal consistency for low-educated, i.e., at maximum pre-vocational secondary education, and higher educated adolescents and assessed whether the five-factor structure of the SDQ holds across educational levels. We also interviewed 24 low-educated adolescents, and 17 professionals.ResultsOn most SDQ subscales the low-educated adolescents had more problematic mean scores than the higher educated adolescents. Findings on the invariance factor analyses were inconsistent, with some measures showing a bad fit of the five factor model, and this occurring relatively more for the low-educated adolescents. Professionals and adolescents reported that the SDQ included difficult wordings.DiscussionOur findings imply that the scale structure of the SDQ-SR is slightly poorer for low educated adolescents. Given this caveat, psychometric properties of the SDQ-SR are generally sufficient for use, regardless of educational level.

Childhood overweight and psychosocial issues remain significant public health concerns. Schools worldwide implement health promotion programs to address these issues and to support the physical and psychosocial health of children. However, more insight is needed into the relation between these health-promoting programs and the Body Mass Index (BMI) z-score and psychosocial health of children, while taking into account how school factors might influence this relation. Therefore, we examined whether the variation between primary schools regarding the BMI z-score and psychosocial health of students could be explained by school health promotion, operationalized as Healthy School (HS) certification, general school characteristics, and the school population; we also examined to what extent the characteristics interact. The current study had a repeated cross-sectional design. Multilevel analyses were performed to calculate the variation between schools, and to examine the association between HS certification and our outcomes. Existing data of multiple school years on 1698 schools were used for the BMI z-score and on 841 schools for psychosocial health. The school level explained 2.41% of the variation in the BMI z-score and 2.45% of the variation in psychosocial health, and differences were mostly explained by parental socioeconomic status. Additionally, HS certification was associated with slightly lower BMI z-scores, but not with psychosocial health. Therefore, obtaining HS certification might contribute to the better physical health of primary school students in general. This might indicate that HS certification also relates to healthier lifestyles in primary schools, but further research should examine this.

Background: Interventions, targeting youth, are necessary to prevent obesity later in life. Especially youth with low socioeconomic status (SES) are vulnerable to develop obesity. This meta-analysis examines the effectiveness of behavioral change techniques (BCTs) to prevent or reduce obesity among 0 to 18-year-olds with a low SES in developed countries. Method: Intervention studies were identified from systematic reviews or meta-analyses published between 2010 and 2020 and retrieved from PsycInfo, Cochrane systematic review, and PubMed. The main outcome was body mass index (BMI), and we coded the BCTs. Results: Data from 30 studies were included in the meta-analysis. The pooled postintervention effects of these studies indicated a nonsignificant decrease in BMI for the intervention group. Longer follow-up (≥12 months) showed favorable differences for intervention studies, although that BMI change was small. Subgroup analyses showed larger effects for studies with six or more BCTs. Furthermore, subgroup analyses showed a significant pooled effect in favor of the intervention for the presence of a specific BCT (problem-solving, social support, instruction on how to perform the behavior, identification of self as role model, and demonstration of the behavior), or absence of a specific BCT (information about health consequences). The intervention program duration and age group of the study population did not significantly influence the studies' effect sizes. Conclusions: Generally, the effects of interventions on BMI change among youth with low SES are small to neglectable. Studies with more than six BCTs and/or specific BCTs had a higher likelihood of decreasing BMI of youth with low SES.

Many children in the Netherlands do not adhere to dietary guidelines. Therefore, the Healthy School (HS) program stimulates healthier dietary intake of students through schools. However, evaluating the effectiveness of school health promotion in improving dietary intake is challenging due to the influence of contextual factors. Qualitative Comparative Analysis (QCA) considers these contextual factors. Therefore, we performed a QCA to examine which (combinations of) contextual factors contribute to the healthier dietary intake of students during school hours in primary schools (approximate age range children 4-12 years) and secondary schools (age range 12-18 years) when implementing the HS program for nutrition. Data were collected mainly through interviewing school staff and a school-level questionnaire in fifteen primary schools and twelve secondary schools. We included five factors for primary schools: implementation of the HS program for nutrition, degree of implementation, socioeconomic status, parental support, and student support. For secondary schools, we included school environment instead of parental and student support. For primary schools, the best results were obtained if the HS program for nutrition was implemented in high socioeconomic status schools with a combination of high implementation, parental support, and student support. Findings indicate that if secondary schools have an impeding environment and low socioeconomic status, implementation of the HS program for nutrition can result in healthier dietary intake.

Abstract Introduction Type 2 diabetes mellitus is a common disease with increasing global burden. Although multiple lifestyle risk factors for type 2 diabetes have been reported, little is known regarding occupational risk factors for the disease. Methods Respondents of the cross-sectional Netherlands Working Conditions Survey (NWCS) from years 2013 to 2021 were linked to prescription medication registration from Statistics Netherlands using unique national personal identifiers. Type 2 diabetes cases were identified in the population via initiation of diabetes medication at age 30 or above. Occupational risk factors will be assessed using multiple methods, including self-reports from the NWCS, job-exposure matrices (JEMs), and agnostic analyses by occupational group. Important individual characteristics such as sex, age, and education were directly available from the NWCS survey; important lifestyle characteristics such as smoking were modeled based on smoking behaviors in the Dutch population. Results A total of 382,946 workers participated in the NEA surveys from 2013 to 2021. Linkage to national prescription medication registry resulted in 2,784 type 2 diabetes cases. Cox proportional hazard models will be used to assess the relationships between type 2 diabetes and various occupational exposures. Discussion and conclusion Little is known regarding how type 2 diabetes is related to occupation and work exposures. Insight on occupational factors associated with type 2 diabetes adds to the existing knowledge base around this important metabolic disease, and may enable prevention and intervention strategies for high-risk groups.

The lack of a valid and interpretable score to track early child development over time is a primary reason for neglecting child development in policymaking. Many instruments exist, but there is no accepted method for comparing their scores across different ages, samples, and instruments. This paper aims (1) to enhance the Development Score (D-score), a unidimensional scale for early child development, to compare measurements across ages, samples, and instruments, (2) to develop a conversion key that enables the transformation of measurements obtained from existing instruments into a D-score, and (3) to investigate two new measures designed to optimize the quantification of the D-score. Study 1 gathered data from 51 sources in 32 countries among 66,075 children using 18 instruments with 2,211 items. Subject matter experts used the output of the Study-1 true score equating model to create the Global Scales for Early Development Short Form (GSED SF) and Long Form (GSED LF). Study 2 collected additional data on the GSED LF and GSED SF in three countries among 4,374 children. The Study-2 model enables the conversion of measurements into a D-score for 20 different instruments. We propose the D-score as a unifying evaluation unit to reduce fragmentation, simplify measurement, and enhance comparability.

Little information is available regarding the influence of the interplay between the school context and school health promotion on educational performance. Therefore, we examined whether the variation between primary and secondary schools regarding the educational performance of students could be explained by general school characteristics, school population characteristics, and school health promotion and to what extent these factors interact. We performed multilevel analyses using existing data on 7021 primary schools and 1315 secondary schools in the Netherlands from the school years 2010–2011 till 2018–2019. Our outcomes were the final test score from primary education and the average grade of standardized final exams from secondary education. School health promotion was operationalized as having obtained Healthy School (HS) certification. For the test score, 7.17% of the total variation was accounted for by differences at the school level and 4.02% for the average grade. For both outcomes, the percentage of disadvantaged students in a school explained most variation. HS certification did not explain variation, but moderated some associations. We found small to moderate differences between schools regarding educational performance. Compositional differences of school populations, especially socioeconomic status, seemed more important in explaining variation in educational performance than general school characteristics and HS certification. Some associations were moderated by HS certification, but differences remained small in most cases.

BACKGROUND:Worldwide, recommendations for fruit and vegetable consumption are not met, which can cause chronic diseases. Especially adolescence is an important phase for the development of health behaviours. Therefore, in the Netherlands, the Healthy School program was established to aid schools in promoting healthy lifestyles among their students. We examined to what extent the variation between secondary schools regarding students' fruit and vegetable consumption could be explained by differences between schools regarding Healthy School certification, general school characteristics, and the school population. Additionally, we examined whether Healthy School certification was related to the outcomes, and whether the association differed for subgroups.METHODS:We performed a repeated cross-sectional multilevel study. We used data from multiple school years from the national Youth Health Monitor on secondary schools (grades 2 and 4, age ranged from approximately 12 to 18 years) of seven Public Health Services, and added data with regard to Healthy School certification, general school characteristics and school population characteristics. We included two outcomes: the number of days a student consumed fruit and vegetables per week. In total, we analysed data on 168,127 students from 256 secondary schools in the Netherlands.RESULTS:Results indicated that 2.87% of the variation in fruit consumption and 5.57% of the variation in vegetable consumption could be attributed to differences at the school-level. Characteristics related to high parental educational attainment, household income, and educational track of the students explained most of the variance between schools. Additionally, we found a small favourable association between Healthy School certification and the number of days secondary school students consumed fruit and vegetables.CONCLUSIONS:School population characteristics explained more variation between schools than Healthy School certification and general school characteristics, especially indicators of parental socioeconomic status. Nevertheless, Healthy School certification seemed to be slightly related to fruit and vegetable consumption, and might contribute to healthier dietary intake. We found small differences for some subgroups, but future research should focus on the impact in different school contexts, since we were restricted in the characteristics that could be included in this study.