Anton Schreuder

In thoracic photon-counting CT, virtual noncontrast images demonstrated close agreement with true noncontrast images for attenuation measurements within the lung parenchyma, supporting accurate lung densitometry; inspiratory depth, rather than reconstruction method, was the dominant source of attenuation variability.

Inleiding Effectief Kansrijke Start-beleid vereist inzicht in de verdeling van ongunstige geboorte-uitkomsten binnen de bevolking. Deze studie beschrijft ongelijkheid in ongunstige geboorte-uitkomsten naar opleidingsachtergrond van de moeder. Methode Landelijke registratiedata (Perined, Centraal Bureau van de Statistiek) van alle eenlinggeboorten in Nederland (2016–2019; n=639.007) werden gekoppeld. Ongunstige uitkomsten (doodgeboorte, neonatale sterfte, vroeggeboorte, te klein voor zwangerschapsduur, Apgar-score op 5 minuten <7, opname op neonatale intensive care, ernstige aangeboren afwijkingen) werden geanalyseerd voor vijf opleidingscategorieën. Resultaten Eén op de zes geboorten had een ongunstige uitkomst. Risico’s namen trapsgewijs toe in relatie tot genoten opleiding, van 13,5% (master) tot 21,1% (basisonderwijs). Het absolute aantal ongunstige geboorte-uitkomsten was het hoogst in de omvangrijke middengroep met een gematigd risico. Jaarlijks zouden 3703 (95%BI: 3357-4049) ongunstige uitkomsten (14,7% [95%BI: 13,3-16,0]) vermeden worden bij een populatie-breed risico gelijk aan dat van aan de hoogstopgeleiden. Neonatale sterfte was 2,25 keer (95%BI: 1,71-2,79) en doodgeboorten 2,94 keer (95%BI: 2,33-3,55) zo hoog in de laagste versus hoogste opleidingsgroep. Sterfte zou met circa een derde afnemen als de gehele bevolking de sterftecijfers had van de hoogstopgeleiden (populatie attributieve fractie [PAF]doodgeboorte=35,0% [24,4-45,6%]; PAFneonatale sterfte=27,1% [14,4-39,7%]). Ongelijkheden waren kleiner voor de overige uitkomsten (RR-bereik=1,32-1,77; PAF-bereik=13,8-17,7%). Beschouwing Ongelijkheid in geboorte-uitkomsten raakt de hele Nederlandse samenleving. Ondersteuning voor de groepen met het hoogste risico is belangrijk, maar niet genoeg. De grootste gezondheidswinst op populatieniveau is te behalen door het verkleinen van gezondheidsverschillen tussen álle opleidingsgroepen. Dit vraagt om verbreding van het Kansrijke Start programma, door inbedding van populatie-brede primaire preventie.

Doel Jeugdzorg ondersteunt gezinnen met opvoedproblemen. Jeugdzorggebruik kan mogelijk deels voorkomen worden door vroegtijdige preventieve ondersteuning. Wij ontwikkelden risicomodellen voor het voorspellen van jeugdzorggebruik bij kinderen van 0-5 jaar ter ondersteuning van vroeg-signalering en gerichte preventie. Methoden We gebruikten registratiedata voor alle geboortes in Nederland (2015-2019). Predictoren omvatten buurt- en huishoudkenmerken, sociaaleconomische status, gezondheid en gedrag van ouders, en geboorte-uitkomsten. We ontwikkelden Cox-regressiemodellen voor vier beoordelingsmomenten: één jaar vóór geboorte, bij geboorte, één jaar en twee jaar na geboorte. Resultaten Van 776.559 geboortes, ontvingen 32.290 kinderen minstens één jeugdzorgtraject (4,2%). De modellen hadden een redelijk goed onderscheidend vermogen. Een jaar vóór geboorte was de oppervlakte onder de ROC-curve (AUC) 0,760. De AUC verbeterde minimaal bij elk volgend beoordelingsmoment, tot 0,798 op twee jaar na geboorte. De sterkste voorspellers waren eerder jeugdzorggebruik, opleidingsniveau (ouders), gebruik van psychiatrische medicatie (moeder), en arbeidsstatus (moeder). Wanneer 10% van het cohort met het hoogste risico op jeugdzorg als positief wordt geclassificeerd, was de negatief voorspellende waarde hoog (≥0,972) en de positief voorspellende waarde laag (≤0,164). Conclusie Vroeg-signalering op basis van onze modellen zou gezinnen kunnen ondersteunen alvorens de overbelaste jeugdzorg wordt ingeschakeld, mits de gevolgen van vals-positieve testen kunnen worden beperkt.

To evaluate whether distribution measures of CT-based attenuation histograms in inspiration and expiration can indicate alveolar collapse and serve as a predictive marker in patients with idiopathic pulmonary fibrosis (IPF). This single-center retrospective longitudinal study analyzed CT scans of IPF patients in inspiration and expiration. The patient population was divided into two subgroups based on their status 3 years after baseline CT (death or transplantation versus clinical surveillance). Attenuation histograms in inspiration and expiration were created and analyzed. A Mann-Whitney U test was conducted to assess the difference of CT-derived histogram measures (including skewness) between the two subgroups. Logistic regression was applied to model the ability to distinguish between subgroups using baseline forced vital capacity (FVC%) and CT-derived histogram measures. The study included 66 patients (mean age 69.5 ± 10.9 years, 58 males). After the individual three-year observation period, 37 patients were still alive while 29 had either died or received a transplantation. The two patient subgroups were significantly different in terms of all CT-derived histogram measures and the baseline FVC%. A logistic regression model that only included the CT-derived histogram measure skewness had a better predictive performance (AUC = 0.793, 95% CI = 0.685–0.900) compared to the FVC% model alone (0.708, 0.581–0.836). Whereas further evaluation is needed, paired inspiratory/expiratory attenuation histogram analysis offers a promising approach as a prognostic imaging marker to improve outcome prediction and assess alveolar collapse in IPF.

We aimed to describe the magnitude and population burden of inequality in adverse birth outcomes by maternal education (five categories). Data from 639,007 singleton births of gestational age ≥ 24 weeks or, if missing, birthweight ≥ 500 g were included from the National registry of the Netherlands, 2016–2019. One in six Dutch births had an adverse outcome. Each step down the educational ladder was associated with higher adverse outcome rates. Inequalities in stillbirth and neonatal mortality rates were large between the highest (Master’s or higher) and lowest (primary) educated groups (rate ratio [RR] = 2.94 [95%CI: 2.33–3.55] and 2.25 [1.71–2.79], respectively). Inequalities were smaller for preterm birth, small-for-gestational-age, low Apgar score, neonatal intensive care unit admission, and severe congenital anomalies (RR range = 1.32–1.77; PAF range = 13.8–17.7%). Mortality would reduce by a third if the entire population had the mortality rates of the highest educated (population attributable fraction [PAF] _stillbirth =35.0% [24.4–45.6%]; PAF _{neonatal mortality} =27.1% [14.4–39.7%]). The middle education category (higher secondary) was the largest group, contributing most to the population burden of inequality despite only having a moderately elevated risk of adverse outcomes. Population health gains would be largest if addressed through preventive approaches targeting the entire health gradient rather than by only focussing on those at highest risk.

Summary: Objective: To investigate population trajectories of behavioural risk factors of obesity from childhood to adolescence and their associations with body mass index (BMI) in children across European regions.Methods: Data were harmonised between the European multi‐centre IDEFICS/I.Family and the Amsterdam Born Children and their Development Cohort. Participants were aged 2.0–9.9 and 5.0–7.5 years at baseline, respectively, and were followed until age 18 years. Behavioural risk factors of interest included diet, physical activity, media use and sleep. Mixed effects models were used for statistical analyses to account for repeated measurements taken from the same child.Results: The study included a total of 14 328 individuals: 4114, 4582, 3220 and 2412 participants from Northern, Southern, Eastern Europe and Amsterdam, respectively. Risk factor means and prevalences changed with age, but the trajectories were mostly similar across regions. Almost no associations between behavioural factors and BMI were found at the age of 6 years. At 11 years, daily sugar‐sweetened foods consumption, use of active transport, sports club membership and longer nocturnal sleep duration were negatively associated with BMI in most regions; positive associations were found with media use. Most associations at 11 years of age persisted to 15 years.Conclusions: Whilst population trajectories of media use and nocturnal sleep duration are similar across European regions, those of other behavioural risk factors like active transport and daily vegetable consumption differ. Also, associations between behavioural risk factors and BMI become stronger with age and show similar patterns across regions.

Background: Excessive weight gain during childhood is a strong predictor for adult overweight, but it remains unknown which growth measures in infancy (0–2 years of age), besides predictors known at birth, are the strongest predictors for excessive weight gain between 2 and 5–7 years of age. Methods: The Amsterdam Born Children and their Development (ABCD) study formed the derivation cohort, and the Groningen Expert Center for Kids with Obesity (GECKO) Drenthe study formed the validation cohort. Change (Δ) in body mass index (BMI) z-score between 2 and 5–7 years was the outcome of interest. The growth measures considered were weight, weight-for-length (WfL), and body mass index (BMI). Formats considered for each growth measure were values at 1, 6, 12, and 24 months, at the BMI peak, the change between aforementioned ages, and prepeak velocity. 10 model structures combining different variable formats and including predictors at birth were derived for each growth measure, resulting in 30 linear regression models. A Parsimonious Model considering all growth measures and a Birth Model considering none were also derived. Results: The derivation cohort consisted of 3139 infants of which 373 (11.9%) had excessive gain in BMI z-score (> 0.67). The validation cohort contained 2201 infants of which 592 (26.9%) had excessive gain. Across the 3 growth measures, 5 model structures which included measures related to the BMI peak and prepeak velocity (derivation cohort area under the curve [AUC] range = 0.765–0.855) achieved more accurate estimates than 3 model structures which included growth measure change over time (0.706–0.795). All model structures which used BMI were superior to those using weight or WfL. The AUC across all models was on average 0.126 lower in the validation cohort. The Parsimonious Model’s AUCs in the derivation and validation cohorts were 0.856 and 0.766, respectively, compared to 0.690 and 0.491, respectively, for the Birth Model. The respective false positive rates were 28.2% and 20.1% for the Parsimonious Model and 70.0% and 74.6% for the Birth Model. Conclusion: Models’ performances varied significantly across model structures and growth measures. Developing the optimal model requires extensive testing of the many possibilities.

Background: A baseline computed tomography (CT) scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC risk and a high CD risk.Methods: Participant demographics and quantitative CT measures of LC, cardiovascular disease and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting 5-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data were used to perform external validation (n=2287).Results: Our final CD model outperformed an external pre-scan model (CD Risk Assessment Tool) in both the derivation (area under the curve (AUC) 0.744 (95% CI 0.727–0.761) and 0.677 (95% CI 0.658–0.695), respectively) and validation cohorts (AUC 0.744 (95% CI 0.652–0.835) and 0.725 (95% CI 0.633–0.816), respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096 (27%)) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287 (34%)) were 129 (versus 29) and 1.67 (versus 0.43).Conclusions: Evaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.