Impact of parental lifestyle patterns in the preconception and pregnancy periods on childhood obesity

International audienceIntroduction High prevalence of overweight and obesity already observed in preschool children suggests the involvement of early-life risk factors. Preconception period and pregnancy are crucial windows for the implementation of child obesity prevention interventions with parental lifestyle factors as relevant targets. So far, most studies have evaluated their role separately, with only a few having investigated their potential synergistic effect on childhood obesity. Our objective was to investigate parental lifestyle patterns in the preconception and pregnancy periods and their association with the risk of child overweight after 5 years. Materials and methods We harmonized and interpreted results from four European mother-offspring cohorts participating in the EndObesity Consortium [EDEN, France; Elfe, France; Lifeways, Ireland; and Generation R, Netherlands] with data available for 1,900, 18,000, 1,100, and 9,500 families, respectively. Lifestyle factors were collected using questionnaires and included parental smoking, body mass index (BMI), gestational weight gain, diet, physical activity, and sedentary behavior. We applied principal component analyses to identify parental lifestyle patterns in preconception and pregnancy. Their association with risk of overweight (including obesity; OW-OB) and BMI z -scores between 5 and 12 years were assessed using cohort-specific multivariable logistic and linear and regression models (adjusted for potential confounders including parental age, education level, employment status, geographic origin, parity, and household income). Results Among the various lifestyle patterns derived in all cohorts, the two explaining the most variance were characterized by (1) “high parental smoking, low maternal diet quality (and high maternal sedentary behavior in some cohorts)” and, (2) “high parental BMI and low gestational weight gain.” Patterns characterized by high parental BMI, smoking, low diet quality or high sedentary lifestyle before or during pregnancy were associated with higher risk of OW-OB in children, and BMI z -score at any age, with consistent strengths of associations in the main cohorts, except for lifeways. Conclusion This project provides insight into how combined parental lifestyle factors in the preconception and pregnancy periods are associated with the future risk of child obesity. These findings are valuable to inform family-based and multi-behavioural child obesity prevention strategies in early life

Estimation of attributable risk and prevented fraction in cohort studies

Le risque attribuable (RA) mesure la proportion de cas de maladie qui peuvent être attribués à une exposition au niveau de la population. Plusieurs définitions et méthodes d'estimation du RA ont été proposées pour des données de survie. En utilisant des simulations, nous comparons quatre méthodes d'estimation du RA dans le contexte de l'analyse de survie : deux méthodes non paramétriques basées sur l'estimateur de Kaplan-Meier, une méthode semi-paramétrique basée sur le modèle de Cox à risques proportionnels et une méthode paramétrique basée sur un modèle à risques proportionnels avec un risque de base constant par morceaux. Nos travaux suggèrent d'utiliser les approches semi-paramétrique et paramétrique pour l'estimation du RA lorsque l'hypothèse des risques proportionnels est vérifiée. Nous appliquons nos méthodes aux données de la cohorte E3N pour estimer la proportion de cas de cancer du sein invasif attribuables à l'utilisation de traitements hormonaux de la ménopause (THM). Nous estimons qu'environ 9 % des cas de cancer du sein sont attribuables à l'utilisation des THM à l'inclusion. Dans le cas d'une exposition protectrice, une alternative au RA est la fraction préventive (FP) qui mesure la proportion de cas de maladie évités. Cette mesure n'a pas été considérée dans le contexte de l'analyse de survie. Nous proposons une définition de la FP dans ce contexte et des méthodes d'estimation en utilisant des approches semi-paramétrique et paramétrique avec une extension permettant de prendre en compte les risques concurrents. L'application aux données de la cohorte des Trois Cités (3C) estime qu'environ 9 % de cas d'accident vasculaire cérébral peuvent être évités chez les personnes âgées par l'utilisation des hypolipémiants. Notre étude montre que la FP peut être utilisée pour évaluer l'impact des médicaments bénéfiques dans les études de cohorte tout en tenant compte des facteurs de confusion potentiels et des risques concurrents.The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier's estimator, one semiparametric based on Cox's model, and one parametric based on the piecewise constant hazards model. Our results suggest to use the semiparametric or parametric approaches to estimate AR if the proportional hazards assumption appears appropriate. These methods were applied to the E3N women cohort data to estimate the AR of breast cancer due to menopausal hormone therapy (MHT). We showed that about 9% of cases of breast cancer were attributable to MHT use at baseline. In case of a protective exposure, an alternative to the AR is the prevented fraction (PF) which measures the proportion of disease cases that could be avoided in the presence of a protective exposure in the population. The definition and estimation of PF have never been considered for cohort studies in the survival analysis context. We defined the PF in cohort studies with survival data and proposed two estimation methods: a semiparametric method based on Cox’s proportional hazards model and a parametric method based on a piecewise constant hazards model with an extension to competing risks. Using data of the Three-City (3C) cohort study, we found that approximately 9% of cases of stroke could be avoided using lipid-lowering drugs (statins or fibrates) in the elderly population. Our study shows that the PF can be estimated to evaluate the impact of beneficial drugs in observational cohort studies while taking potential confounding factors and competing risks into account

Estimation du risque attribuable et de la fraction préventive dans les études de cohorte

The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier's estimator, one semiparametric based on Cox's model, and one parametric based on the piecewise constant hazards model. Our results suggest to use the semiparametric or parametric approaches to estimate AR if the proportional hazards assumption appears appropriate. These methods were applied to the E3N women cohort data to estimate the AR of breast cancer due to menopausal hormone therapy (MHT). We showed that about 9% of cases of breast cancer were attributable to MHT use at baseline. In case of a protective exposure, an alternative to the AR is the prevented fraction (PF) which measures the proportion of disease cases that could be avoided in the presence of a protective exposure in the population. The definition and estimation of PF have never been considered for cohort studies in the survival analysis context. We defined the PF in cohort studies with survival data and proposed two estimation methods: a semiparametric method based on Cox’s proportional hazards model and a parametric method based on a piecewise constant hazards model with an extension to competing risks. Using data of the Three-City (3C) cohort study, we found that approximately 9% of cases of stroke could be avoided using lipid-lowering drugs (statins or fibrates) in the elderly population. Our study shows that the PF can be estimated to evaluate the impact of beneficial drugs in observational cohort studies while taking potential confounding factors and competing risks into account.Le risque attribuable (RA) mesure la proportion de cas de maladie qui peuvent être attribués à une exposition au niveau de la population. Plusieurs définitions et méthodes d'estimation du RA ont été proposées pour des données de survie. En utilisant des simulations, nous comparons quatre méthodes d'estimation du RA dans le contexte de l'analyse de survie : deux méthodes non paramétriques basées sur l'estimateur de Kaplan-Meier, une méthode semi-paramétrique basée sur le modèle de Cox à risques proportionnels et une méthode paramétrique basée sur un modèle à risques proportionnels avec un risque de base constant par morceaux. Nos travaux suggèrent d'utiliser les approches semi-paramétrique et paramétrique pour l'estimation du RA lorsque l'hypothèse des risques proportionnels est vérifiée. Nous appliquons nos méthodes aux données de la cohorte E3N pour estimer la proportion de cas de cancer du sein invasif attribuables à l'utilisation de traitements hormonaux de la ménopause (THM). Nous estimons qu'environ 9 % des cas de cancer du sein sont attribuables à l'utilisation des THM à l'inclusion. Dans le cas d'une exposition protectrice, une alternative au RA est la fraction préventive (FP) qui mesure la proportion de cas de maladie évités. Cette mesure n'a pas été considérée dans le contexte de l'analyse de survie. Nous proposons une définition de la FP dans ce contexte et des méthodes d'estimation en utilisant des approches semi-paramétrique et paramétrique avec une extension permettant de prendre en compte les risques concurrents. L'application aux données de la cohorte des Trois Cités (3C) estime qu'environ 9 % de cas d'accident vasculaire cérébral peuvent être évités chez les personnes âgées par l'utilisation des hypolipémiants. Notre étude montre que la FP peut être utilisée pour évaluer l'impact des médicaments bénéfiques dans les études de cohorte tout en tenant compte des facteurs de confusion potentiels et des risques concurrents

Comparison of methods for estimating the attributable risk in the context of survival analysis

International audienceAbstractBackgroundThe attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data.MethodsUsing simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier’s estimator, one semiparametric based on Cox’s model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox’s model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points.ResultsUnder proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort.ConclusionIn practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of time in cohort studies if the proportional hazards assumption appears appropriate

Additional file 1 of Comparison of methods for estimating the attributable risk in the context of survival analysis

Simulation results for the estimation of attributable risk A(.) under proportional hazards, constant baseline hazard (γ=1) with regression parameter β= ln(2) and probability of exposure q=0.25. (PDF 20.3 kb

Temps d’écran de 2 à 5 ans et demi chez les enfants de la cohorte nationale Elfe

National audienceChildren’s screen time has increased in recent years in France, as shown by repeated cross-sectional surveys. However, no national-scale longitudinal study had been published and data on children aged under 3 years is particularly scarce. We used data from the birth cohort study Elfe to describe total and device-specific screen time of followed children at 2 years, 3-and-a-half years and 5-and-a-half years of age. We also describe differences according to the family’s region of residence, migration history and origin, maternal education level and child sex. After weighting the data, total daily screen time was on average 56 min (95% confidence interval: [55 min-58 min]) at 2 years of age; 1 hr 20 min [1 hr 18 min-1 hr 22 min] at 3-and-a-half years of age; 1 hr 34 min [1 hr 32 min-1 hr 36 min] at 5-and-a-half years of age. Screen time increased substantially between 2 and 3-and-a-half years of age (Spearman’s correlation: 0.50) and between 3-and-a-half and 5-and-a-half years of age (0.67). Overall, screen time was greater in families with a history of migration and a lower level of maternal education. Regional disparities are also noted. Finally, there was no difference by sex at 2 years of age; however, boys spent 10 min longer watching screens than girls did at 5-and-a-half years of age. This is the first study to describe at national level the time children spend watching screens. It will help target families and contexts where screen time exceeds current guidelinesLe temps passé par les enfants devant les écrans a augmenté ces dernières années en France, comme en témoignent diverses enquêtes transversales répétées. Cependant, il n’existe à l’échelle nationale aucune donnée longitudinale, en particulier pour les moins de 3 ans. À partir des données de la cohorte Elfe, nous décrivons le temps d’écran, total et par type d’écran, des enfants suivis à 2 ans, 3 ans et demi et 5 ans et demi. Nous mettons également en avant des disparités selon la région d’habitation de la famille, son histoire et son origine migratoires, le niveau d’études de la mère et le sexe de l’enfant. Après pondération des données, le temps d’écran quotidien était en moyenne de 56 min (intervalle de confiance à 95%: [55-58]) à 2 ans, 1h20 [1h18-1h22] à 3 ans et demi et 1h34 [1h32-1h36] à 5 ans et demi. Ces temps étaient corrélés positivement (0,50 entre 2 et 3 ans et demi ; 0,67 entre 3 ans et demi et 5 ans et demi), démontrant une persistance individuelle de l’utilisation au cours du temps. Dans l’ensemble, les temps d’écran étaient plus élevés chez les familles ayant des origines immigrées, ou un niveau d’études de la mère faible. Des disparités régionales étaient aussi observées. Enfin, si aucune différence entre garçons et filles n’était observée à 2 ans, les garçons utilisaient les écrans 10 minutes de plus que les filles à 5 ans et demi. Cette étude décrit pour la première fois à l’échelle nationale et de façon longitudinale le temps passé par les jeunes enfants devant les écrans. Elle permettra de mieux cibler les familles et les contextes où ce temps excède les recommandations

TV, computer, tablet and smartphone use and autism spectrum disorder risk in early childhood: a nationally-representative study

International audienceBackground: Screen media use in early childhood has largely increased in recent years, even more so during the COVID-19 epidemic, and there is much discussion regarding its influence on neurodevelopment, including Autism Spectrum Disorder (ASD). Methods: We examined the relationship between use of TV, computer, tablet and smartphone at age 2 years and risk of ASD assessed in telephone-based questionnaires among 12,950 children participating in the nationally representative ELFE ('Etude Longitudinale Française sur les Enfants') birth cohort study in France. Results: In inverse-probability weighted (IPW) multinomial regression analyses, children's weekly or daily screen media use was associated with an increased likelihood of an intermediate risk of ASD (IPW-controlled OR for weekly use:1.07, 95% CI 1.02-1.12; IPW-controlled OR for daily use:1.05, 95% CI 1.02-1.08) but inversely associated with a high risk (IPW-controlled OR for weekly use: 0.60, 95% CI 0.50-0.73; IPW-controlled OR for daily use: 0.75, 95% CI 0.62-0.91), as ascertained by the M-CHAT. This was confirmed when studying TV as well as computer/tablet exposure separately. Conclusions: Overall, our nationally-representative study conducted among a large sample of 2-year-old children, indicates a complex relationship between screen exposure and ASD risk

Usage des écrans aux âges de 2 et 3,5 ans : associations avec le sommeil, les apprentissages préscolaires, le comportement et les troubles du spectre de l’autisme

Rapport à la demande de la Direction Générale de la Sant

Sociodemographic and behavioural factors of adherence to the no-screen guideline for toddlers among parents from the French nationwide Elfe birth cohort

International audienceAbstract Background Excessive screen time in infancy and childhood has been associated with consequences on children’s development and health. International guidelines call for no screen time before age 2 years, whereas in France, the most prominent guidelines recommend no screen before age 3 years. However, data are lacking on parental adherence to the no-screen guideline for toddlers and factors of adherence in France. Using data from the French nationwide Elfe birth cohort, we estimated adherence to the no-screen guideline at age 2 years and examined related factors, including sociodemographic characteristics, parental leisure activities and screen time. Methods In 2011, 18,329 newborns and their parents were enrolled in 349 randomly selected maternity units across mainland France. At age 2 years, screen exposure of 13,117 toddlers was reported by parents in phone interviews. Data on sociodemographic characteristics, parental leisure activities and screen time were collected from both parents. Three patterns of parental leisure activities were derived by principal component analysis: literate (e.g.,reading), screen-based, and physical/artistic activities. Multivariable logistic regression models were used to examine the associations of sociodemographic characteristics, parental leisure activities and parental screen time with adherence to the no-screen guideline for toddlers. Results Overall, 1809/13,117 (13.5%) families adhered to the no-screen guideline for toddlers. Adherence was reduced with maternal age < 40 years, low parental education, single-parent household and parental migration status. After adjusting for sociodemographic characteristics, adherence to the guideline was positively associated with a parental literate activity pattern (mothers: odds ratio [95% confidence interval]: 1.15 [1.08, 1.22]); fathers: 1.15 [1.07, 1.23]) and negatively with a screen-based activity pattern (mothers: 0.73 [0.69, 0.77]; fathers: 0.81 [0.76, 0.87]). With each additional hour of parental screen time, mothers and fathers were less likely to adhere to the guideline (mothers: adjusted odds ratio 0.80 [0.77, 0.83]; fathers: 0.88 [0.85, 0.91]). Conclusions Adherence to the no-screen guideline for toddlers in France was low. Parental leisure activities and parental screen time are major factors of adherence to the no-screen guideline and could be considered in targeted public health interventions