3 recherche sur le mot-clé 'Population register'

Low but Increasing Prevalence of Autism Spectrum Disorders in a French Area from Register-Based Data / Marit Maria Elisabeth VAN BAKEL in Journal of Autism and Developmental Disorders, 45-10 (October 2015)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 45-10 (October 2015) . - p.3255-3261 Titre : Low but Increasing Prevalence of Autism Spectrum Disorders in a French Area from Register-Based Data Type de document : texte imprimé Auteurs : Marit Maria Elisabeth VAN BAKEL, Auteur ; Malika DELOBEL-AYOUB, Auteur ; Christine CANS, Auteur ; Brigitte ASSOULINE, Auteur ; Pierre-Simon JOUK, Auteur ; Jean-Philippe RAYNAUD, Auteur ; Catherine ARNAUD, Auteur Article en page(s) : p.3255-3261 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Population register  Prevalence  Comorbidities Index. décimale : PER Périodiques Résumé : Register-based prevalence rates of childhood autism (CA), Asperger’s syndrome (AS) and other autism spectrum disorders (ASD) were calculated among children aged 7 years old of the 1997–2003 birth cohorts, living in four counties in France. The proportion of children presenting comorbidities was reported. 1123 children with ASD were recorded (M/F ratio: 4.1), representing an overall prevalence rate of 36.5/10,000 children (95 % CI 34.4–38.7): 8.8/10,000 for CA (95 % CI 7.8–9.9), 1.7/10,000 for AS (95 % CI 1.3–2.3) and 25.9/10,000 for other ASD (95 % CI 24.2–27.8). ASD prevalence significantly increased (p < 0.0001) during the period under study. The proportion of children with an intellectual disability was 47.3 %, all other comorbidities were present in less than 5 % of the cases. En ligne : http://dx.doi.org/10.1007/s10803-015-2486-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=267 [article] Low but Increasing Prevalence of Autism Spectrum Disorders in a French Area from Register-Based Data [texte imprimé] / Marit Maria Elisabeth VAN BAKEL, Auteur ; Malika DELOBEL-AYOUB, Auteur ; Christine CANS, Auteur ; Brigitte ASSOULINE, Auteur ; Pierre-Simon JOUK, Auteur ; Jean-Philippe RAYNAUD, Auteur ; Catherine ARNAUD, Auteur . - p.3255-3261. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 45-10 (October 2015) . - p.3255-3261 Mots-clés : Autism spectrum disorder  Population register  Prevalence  Comorbidities Index. décimale : PER Périodiques Résumé : Register-based prevalence rates of childhood autism (CA), Asperger’s syndrome (AS) and other autism spectrum disorders (ASD) were calculated among children aged 7 years old of the 1997–2003 birth cohorts, living in four counties in France. The proportion of children presenting comorbidities was reported. 1123 children with ASD were recorded (M/F ratio: 4.1), representing an overall prevalence rate of 36.5/10,000 children (95 % CI 34.4–38.7): 8.8/10,000 for CA (95 % CI 7.8–9.9), 1.7/10,000 for AS (95 % CI 1.3–2.3) and 25.9/10,000 for other ASD (95 % CI 24.2–27.8). ASD prevalence significantly increased (p < 0.0001) during the period under study. The proportion of children with an intellectual disability was 47.3 %, all other comorbidities were present in less than 5 % of the cases. En ligne : http://dx.doi.org/10.1007/s10803-015-2486-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=267

Brief Report: Maternal Smoking During Pregnancy and Autism Spectrum Disorders / Brian K. LEE in Journal of Autism and Developmental Disorders, 42-9 (September 2012)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 42-9 (September 2012) . - p.2000-2005 Titre : Brief Report: Maternal Smoking During Pregnancy and Autism Spectrum Disorders Type de document : texte imprimé Auteurs : Brian K. LEE, Auteur ; Renee M. GARDNER, Auteur ; Henrik DAL, Auteur ; Anna SVENSSON, Auteur ; Maria Rosaria GALANTI, Auteur ; Dheeraj RAI, Auteur ; Christina DALMAN, Auteur ; Cecilia MAGNUSSON, Auteur Année de publication : 2012 Article en page(s) : p.2000-2005 Langues : Anglais (eng) Mots-clés : Autism  Population register  Smoking  Sweden  Tobacco Index. décimale : PER Périodiques Résumé : Prenatal exposure to tobacco smoke is suggested as a potential risk factor for autism spectrum disorders (ASD). Previous epidemiological studies of this topic have yielded mixed findings. We performed a case–control study of 3,958 ASD cases and 38,983 controls nested in a large register-based cohort in Sweden. ASD case status was measured using a multisource case ascertainment system. In adjusted results, we found that maternal smoking during pregnancy is not associated with increased risk of ASD regardless of presence or absence of comorbid intellectual disability. Apparent associations were attributable to confounding by sociodemographic characteristics of parents such as education, income, and occupation. En ligne : http://dx.doi.org/10.1007/s10803-011-1425-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=180 [article] Brief Report: Maternal Smoking During Pregnancy and Autism Spectrum Disorders [texte imprimé] / Brian K. LEE, Auteur ; Renee M. GARDNER, Auteur ; Henrik DAL, Auteur ; Anna SVENSSON, Auteur ; Maria Rosaria GALANTI, Auteur ; Dheeraj RAI, Auteur ; Christina DALMAN, Auteur ; Cecilia MAGNUSSON, Auteur . - 2012 . - p.2000-2005. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 42-9 (September 2012) . - p.2000-2005 Mots-clés : Autism  Population register  Smoking  Sweden  Tobacco Index. décimale : PER Périodiques Résumé : Prenatal exposure to tobacco smoke is suggested as a potential risk factor for autism spectrum disorders (ASD). Previous epidemiological studies of this topic have yielded mixed findings. We performed a case–control study of 3,958 ASD cases and 38,983 controls nested in a large register-based cohort in Sweden. ASD case status was measured using a multisource case ascertainment system. In adjusted results, we found that maternal smoking during pregnancy is not associated with increased risk of ASD regardless of presence or absence of comorbid intellectual disability. Apparent associations were attributable to confounding by sociodemographic characteristics of parents such as education, income, and occupation. En ligne : http://dx.doi.org/10.1007/s10803-011-1425-4 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=180

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field / Shyam Sundar RAJAGOPALAN in Journal of Neurodevelopmental Disorders, 16 (2024)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 16 (2024) Titre : Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field Type de document : texte imprimé Auteurs : Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur Langues : Anglais (eng) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576 [article] Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field [texte imprimé] / Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 16 (2024) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576