Conceptual frameworks and empirical approaches used to assess the impact of health research: an overview of reviews

<p>Abstract</p> <p>Background</p> <p>How to assess the impact of research is of growing interest to funders, policy makers and researchers mainly to understand the value of investments and to increase accountability. Broadly speaking the term "research impact" refers to the contribution of research activities to achieve desired societal outcomes. The aim of this overview is to identify the most common approaches to research impact assessment, categories of impact and their respective indicators.</p> <p>Methods</p> <p>We systematically searched the relevant literature (PubMed, The Cochrane Library (1990-2009)) and funding agency websites. We included systematic reviews, theoretical and methodological papers, and empirical case-studies on how to evaluate research impact. We qualitatively summarised the included reports, as well the conceptual frameworks.</p> <p>Results</p> <p>We identified twenty-two reports belonging to four systematic reviews and 14 primary studies. These publications reported several theoretical frameworks and methodological approaches (bibliometrics, econometrics, ad hoc case studies). The "payback model" emerged as the most frequently used. Five broad categories of impact were identified: a) advancing knowledge, b) capacity building, c) informing decision-making, d) health benefits, e) broad socio-economic benefits. For each proposed category of impact we summarized a set of indicators whose pros and cons are presented and briefly discussed.</p> <p>Conclusions</p> <p>This overview is a comprehensive, yet descriptive, contribution to summarize the conceptual framework and taxonomy of an heterogeneous and evolving area of research. A shared and comprehensive conceptual framework does not seem to be available yet and its single components (epidemiologic, economic, and social) are often valued differently in different models.</p

Glycoproteins of the aspartyl proteinase gene family secreted by the developing placenta

peer reviewedaudience: researcher, professionalPregnancy in cattle and sheep can be diagnosed by the presence of placentally-derived antigens (pregnancy-associated glycoproteins or PAG-1) in maternal serum soon after implantation begins at about Day 20 following conception. Molecular cloning of their cDNA has revealed that PAG-1 belong to the aspartic proteinase gene family and have about 50% amino acid sequence identity to pepsin. However, critical amino acid substitutions at the active site regions suggest that both bovine and ovine PAG-1 are enzymatically inactive. PAG-1 expression has been shown by in situ hybridization and immunocytochemistry to be localized to the trophoblast binucleate cells, which invade maternal uterine endometrium during implantation. The glycoproteins are concentrated in dense cytoplasmic granules that are discharged after the binucleate cells have migrated to the maternal side of the placental barrier. We suggest, therefore, that the PAG-1 might have an endocrine function either as carriers of other bioactive peptides or by acting as hormones themselves. Recently screening of placental libraries with nucleic acid probes has identified additional cDNA that are very abundant and code for polypeptides (PAG-2 and PAG-3) related to, but antigenically and structurally distinct from PAG-1 described above. These molecules have sequences of amino acids at their catalytic centers that are consistent with their being potentially functional proteinases but their role during pregnancy, like that of PAG-1, is unclear

How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy and practice

This article has been made available through the Brunel Open Access Publishing Fund.Background: The time taken, or ‘time lags’, between biomedical/health research and its translation into health improvements is receiving growing attention. Reducing time lags should increase rates of return to such research. However, ways to measure time lags are under-developed, with little attention on where time lags arise within overall timelines. The process marker model has been proposed as a better way forward than the current focus on an increasingly complex series of translation ‘gaps’. Starting from that model, we aimed to develop better methods to measure and understand time lags and develop ways to identify policy options and produce recommendations for future studies. Methods: Following reviews of the literature on time lags and of relevant policy documents, we developed a new approach to conduct case studies of time lags. We built on the process marker model, including developing a matrix with a series of overlapping tracks to allow us to present and measure elements within any overall time lag. We identified a reduced number of key markers or calibration points and tested our new approach in seven case studies of research leading to interventions in cardiovascular disease and mental health. Finally, we analysed the data to address our study’s key aims. Results: The literature review illustrated the lack of agreement on starting points for measuring time lags. We mapped points from policy documents onto our matrix and thus highlighted key areas of concern, for example around delays before new therapies become widely available. Our seven completed case studies demonstrate we have made considerable progress in developing methods to measure and understand time lags. The matrix of overlapping tracks of activity in the research and implementation processes facilitated analysis of time lags along each track, and at the cross-over points where the next track started. We identified some factors that speed up translation through the actions of companies, researchers, funders, policymakers, and regulators. Recommendations for further work are built on progress made, limitations identified and revised terminology. Conclusions: Our advances identify complexities, provide a firm basis for further methodological work along and between tracks, and begin to indicate potential ways of reducing lags

Animal Models for Prenatal Gene Therapy: Choosing the Right Model

Testing in animal models is an essential requirement during development of prenatal gene therapy for -clinical application. Some information can be derived from cell lines or cultured fetal cells, such as the efficiency of gene transfer and the vector dose that might be required. Fetal tissues can also be maintained in culture for short periods of time and transduced ex vivo. Ultimately, however, the use of animals is unavoidable since in vivo experiments allow the length and level of transgene expression to be measured, and provide an assessment of the effect of the delivery procedure and the gene therapy on fetal and neonatal development. The choice of animal model is determined by the nature of the disease and characteristics of the animal, such as its size, lifespan, and immunology, the number of fetuses and their development, parturition, and the length of gestation and the placentation. The availability of a disease model is also critical. In this chapter, we discuss the various animal models that can be used and consider how their characteristics can affect the results obtained. The projection to human application and the regulatory hurdles are also presented

How culture shaped the human genome:bringing genetics and the human sciences together

Researchers from diverse backgrounds are converging on the view that human evolution has been shaped by gene-culture interactions. Theoretical biologists have used population genetic models to demonstrate that cultural processes can have a profound effect on human evolution, and anthropologists are investigating cultural practices that modify current selection. These findings are supported by recent analyses of human genetic variation, which reveal that hundreds of genes have been subject to recent positive selection, often in response to human activities. Here, we collate these data, highlighting the considerable potential for cross-disciplinary exchange to provide novel insights into how culture has shaped the human genome.</p