Mapping research activity on mental health disorders in Europe: study protocol for the Mapping_NCD project

Background: Mental health disorders (MHDs) constitute a large and growing disease burden in Europe, although they typically receive less attention and research funding than other non-communicable diseases (NCDs). This study protocol describes a methodology for the mapping of MHD research in Europe as part of Mapping_NCD, a 2-year project funded by the European Commission which seeks to map European research funding and impact for five NCDs in order to identify potential gaps, overlaps, synergies and opportunities, and to develop evidence-based policies for future research. Methods: The project aims to develop a multi-focal view of the MHD research landscape across the 28 European Union Member States, plus Iceland, Norway and Switzerland, through a survey of European funding entities, analysis of research initiatives undertaken in the public, voluntary/not-for-profit and commercial sectors, and expert interviews to contextualize the gathered data. The impact of MHD research will be explored using bibliometric analyses of scientific publications, clinical guidelines and newspaper stories reporting on research initiatives. Finally, these research inputs and outputs will be considered in light of various metrics that have been proposed to inform priorities for the allocation of research funds, including burden of disease, treatment gaps and cost of illness. Discussion: Given the growing burden of MHDs, a clear and broad view of the current state of MHD research is needed to ensure that limited resources are directed to evidence-based priority areas. MHDs pose a particular challenge in mapping the research landscape due to their complex nature, high co-morbidity and varying diagnostic criteria. Undertaking such an effort across 31 countries is further challenged by differences in data collection, healthcare systems, reimbursement rates and clinical practices, as well as cultural and socioeconomic diversity. Using multiple methods to explore the spectrum of MHD research funding activity across Europe, this project aims to develop a broad, high-level perspective to inform priority setting for future research

Updating our understanding of health-related quality of life issues in children with cancer: a systematic review of patient-reported outcome measures and qualitative studies

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Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Effect of Inflation on Pension Fund Investment in Federal Government Securities in Nigeria

This study investigated the effect of inflation on pension fund investment in federal government securities in Nigeria utilizing time series data spanning from 2007 to 2019. Ex-post facto was the research design used in the study. Secondary data for the period were collected from the National Pension Commission Annual Reports and Central Bank of Nigeria Statistical Bulletin. Data collected were analyzed and tested for unit root, using the Augmented Dickey-Fuller test. While Ordinary Least Square (OLS) estimation technique was used to test the hypothesis. The result revealed that inflation rate did not significantly impact on pension fund investment in federal Government Securities in Nigeria. This result implies that pension funds invested in federal government Securities yield adequate return on investment capable of withstanding the adverse effect of rising rate of inflation in Nigeria. This could be that, the volumes of monthly pension contributions channeled into Federal Government Securities actually cover the effect of inflation on pension fund investment in federal government securities in Nigeria. Based on the findings of the study, it was concluded that inflation did not significantly impact on pension fund investment in federal government securities in Nigeria. In line with the findings of the study, it was recommended that Nigerian Pension Industry should continue to advocate that policy makers target reduction in the monetary policy rate and also stabilize the value of Naira in order to enhance actual value of pension benefits in the long run. Also, the National Pension Commission should differentiate the monthly contributions that come into the pension fund investment in the annual report so as to determine the actual return on investment of various securities where pension fund is invested.</jats:p