This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.

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BackgroundThe 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.MethodsFollowing 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.ResultsThe 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.ConclusionsOur advances identify complexities, provide a firm basis for further methodological work along and between tracks, and begin to indicate potential ways of reducing lags.Electronic supplementary materialThe online version of this article (doi:10.1186/1478-4505-13-1) contains supplementary material, which is available to authorized users.

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Background: External and internal factors are increasingly encouraging research funding bodies to demonstrate the outcomes of their research. Traditional methods of assessing research are still important, but can be merged into broader multi-dimensional categorisations of research benefits. The onus has hitherto been on public sector funding bodies, but in the UK the role of medical charities in funding research is particularly important and the Arthritis Research Campaign, the leading medical charity in its field in the UK, commissioned a study to identify the outcomes from research that it funds. This article describes the methods to be used.

Global investment in biomedical research has grown significantly over the last decades, reaching approximately a quarter of a trillion US dollars in 2010. However, not all of this investment is distributed evenly by gender. It follows, arguably, that scarce research resources may not be optimally invested (by either not supporting the best science or by failing to investigate topics that benefit women and men equitably). Women across the world tend to be significantly underrepresented in research both as researchers and research participants, receive less research funding, and appear less frequently than men as authors on research publications. There is also some evidence that women are relatively disadvantaged as the beneficiaries of research, in terms of its health, societal and economic impacts. Historical gender biases may have created a path dependency that means that the research system and the impacts of research are biased towards male researchers and male beneficiaries, making it inherently difficult (though not impossible) to eliminate gender bias. In this commentary, we – a group of scholars and practitioners from Africa, America, Asia and Europe – argue that gender-sensitive research impact assessment could become a force for good in moving science policy and practice towards gender equity. Research impact assessment is the multidisciplinary field of scientific inquiry that examines the research process to maximise scientific, societal and economic returns on investment in research. It encompasses many theoretical and methodological approaches that can be used to investigate gender bias and recommend actions for change to maximise research impact. We offer a set of recommendations to research funders, research institutions and research evaluators who conduct impact assessment on how to include and strengthen analysis of gender equity in research impact assessment and issue a global call for action.

The combined influences of intensive mussel aquaculture and watershed nutrient inputs on nitrogen dynamics in Tracadie Bay, Prince Edward Island, Canada, were examined using a nitrogen budget and an ecosystem model. Budget calculations, and inputs and parameters for the model were based on extensive field data. Both approaches showed that mussel aquaculture has a dominant influence on all aspects of the nitrogen cycle and dramatically alters pathways by which nitrogen reaches the phytoplankton and benthos. A large proportion of phytoplankton production is supported by land-derived nitrogen and this anthropogenic input is important for sustaining existing levels of mussel production. The amount of nitrogen removed in the mussel harvest is small compared with agricultural nitrogen inputs and the amounts excreted and biodeposited on the seabed. Mussel biodeposition greatly increases the flux of nitrogen to the benthos, with potentially serious eutrophication impacts. Results from the observation-based nitrogen budget and dynamic model were compared and both support the above conclusions. However, the ability of the model to test different scenarios and to provide additional information (e.g. fluxes) over a finer spatial scale led to insights unattainable with a nitrogen budget. For example, food appears to be less available to mussels at the head of the Bay than at the mouth, despite the lower density of grow-out sites in the former location. The number of fundamental ecosystem processes influenced by the mussels and the complexity of their interactions make it difficult to predict the effects of mussels on many ecosystem properties without resorting to a model.

Bedload sediment transport, clam transport across the sediment surface, clam population density, and spat settlement were measured daily for 10 months to determine the magnitude and frequency of clam transport and its d.ependency on bedload transport and to evaluate the relative importance of this phenomenon to population growth of Mya arenaria. From July to April, the transport of juvenile clams was observed frequently on a sheltered and an exposed intertidal sandflat. The maximum rate of clam transport on the sheltered sandflat (790 ind. m-l d-l) and on the exposed site (2,600 ind. m I d-l) coincided with peaks of bedload sediment transport (-35 kg m-l d-l). At both sites, bedload transport was positively correlated with clam transport (r = 0.33 and 0.5 1; sheltered and exposed sites, P < 0.001); on the sheltered site, clam transport was negatively correlated with clam density (Y = -0.47, P < 0.001). Cross-spectral analysis showed that bedload and clam transport time series were significantly coherent with zero lag at periods of < 10 d. Clam transport on the high-energy sandllat accounted for an order-of-magnitude increase in clam density in early September, a precipitous decline 2 months later, and the complete removal of recently settled spat. A net population increase on this sandflat was most likely a result of clam import during bedload events.