Implications of Advancing Paternal Age: Does It Affect Offspring School Performance?

Average paternal age is increasing in many high income countries, but the implications of this demographic shift for child health and welfare are poorly understood. There is equivocal evidence that children of older fathers are at increased risk of neurodevelopmental disorders and reduced IQ. We therefore report here on the relationship between paternal age and a composite indicator of scholastic achievement during adolescence, i.e. compulsory school leaving grades, among recent birth cohorts in Stockholm County where delayed paternity is notably common. We performed a record-linkage study comprising all individuals in Stockholm County who finished 9 years of compulsory school from 2000 through 2007 (n = 155,875). Data on school leaving grades and parental characteristics were retrieved from administrative and health service registers and analyzed using multiple linear regression. Advancing paternal age at birth was not associated with a decrease in school leaving grades in adolescent offspring. After adjustment for year of graduation, maternal age and parental education, country of birth and parental mental health service use, offspring of fathers aged 50 years or older had on average 0.3 (95% CI −3.8, 4.4) points higher grades than those of fathers aged 30–34 years. In conclusion, advancing paternal age is not associated with poorer school performance in adolescence. Adverse effects of delayed paternity on offspring cognitive function, if any, may be counterbalanced by other potential advantages for children born to older fathers

Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses

BACKGROUND: There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of other available data. METHODS: We searched PubMed and EMBASE for studies comparing routinely collected mental health diagnosis data to a reference standard. We produced diagnostic category-specific positive predictive values (PPV) and Cohen’s kappa for each study. RESULTS: We found 39 eligible studies. Studies were heterogeneous in design, with a wide range of outcomes. Administrative error was small compared to diagnostic error. PPV was related to base rate of the respective condition, with overall median of 76 %. Kappa results on average showed a moderate agreement between source data and reference standard for most diagnostic categories (median kappa = 0.45–0.55); anxiety disorders and schizoaffective disorder showed poorer agreement. There was no significant benefit in accuracy for diagnoses made in inpatients. CONCLUSIONS: The current evidence partly answered our questions. There was wide variation in the quality of source data, with a risk of publication bias. For some diagnoses, especially psychotic categories, administrative data were generally predictive of true diagnosis. For others, such as anxiety disorders, the data were less satisfactory. We discuss the implications of our findings, and the need for researchers to validate routine diagnostic data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12888-016-0963-x) contains supplementary material, which is available to authorized users

Arrow plot for selecting genes in a microarray experiment: An explorative study

Genetic expression analysis is essential for the identification of gene functions and take even more importance when they are directly related with diseases. For the performing of a large-scale study of changes in gene expression it is necessary to find a method to do it with precision and accuracy. Thus, the analysis by the microarray technology is an important tool in the diagnosis of diseases. An important role of the analysis of microarray data involves the determination of which genes could be differentially expressed (DE) across two or more kind of tissue samples. The traditional methods to detect DE genes are generally based on simple measures of distances and could failed in this classification. In this work it is explored a new tool proposed by Silva-Fortes [21] that overcome this difficulty, the arrow plot. This tool is also compared with other methods mostly used to this purpose. The arrow plot is a graphical tool based on two measures of distances between two probability density functions: the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve (AUC), for each gene of a microarray experience. For illustrative purpose we will use a dataset of pancreatic adenocar-cinoma. All computation will be done in R software.This work was supported by FCT - (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio