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.

BackgroundGovernment- and charity-funded medical research and private sector research and development (R&D) are widely held to be complements. The only attempts to measure this complementarity so far have used data from the United States of America and are inevitably increasingly out of date. This study estimates the magnitude of the effect of government and charity biomedical and health research expenditure in the United Kingdom (UK), separately and in total, on subsequent private pharmaceutical sector R&D expenditure in the UK.MethodsThe results for this study are obtained by fitting an econometric vector error correction model (VECM) to time series for biomedical and health R&D expenditure in the UK for ten disease areas (including ‘other’) for the government, charity and private sectors. The VECM model describes the relationship between public (i.e. government and charities combined) sector expenditure, private sector expenditure and global pharmaceutical sales as a combination of a long-term equilibrium and short-term movements.ResultsThere is a statistically significant complementary relationship between public biomedical and health research expenditure and private pharmaceutical R&D expenditure. A 1 % increase in public sector expenditure is associated in the best-fit model with a 0.81 % increase in private sector expenditure. Sensitivity analysis produces a similar and statistically significant result with a slightly smaller positive elasticity of 0.68. Overall, every additional £1 of public research expenditure is associated with an additional £0.83–£1.07 of private sector R&D spend in the UK; 44 % of that additional private sector expenditure occurs within 1 year, with the remainder accumulating over decades. This spillover effect implies a real annual rate of return (in terms of economic impact) to public biomedical and health research in the UK of 15–18 %. When combined with previous estimates of the health gain that results from public medical research in cancer and cardiovascular disease, the total rate of return would be around 24–28 %.ConclusionOverall, this suggests that government and charity funded research in the UK crowds in additional private sector R&D in the UK. The implied historical returns from UK government and charity funded investment in medical research in the UK compare favourably with the rates of return achieved on investments in the rest of the UK economy and are greatly in excess of the 3.5 % real annual rate of return required by the UK government to public investments generally.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0564-z) contains supplementary material, which is available to authorized users.

Differential pricing has been considered extensively for its potential to increase access to medicines in low-and middle-income countries. A differential pricing system applied within an economic union (such as the European Union [EU]) comprising high-income and middle-income countries would also increase access and provide stronger incentives to invest in the R&D of innovative medicines. Access to innovative medicines is limited in EU markets with relatively low GDP per capita, indicating that the current pricing system does not promote efficient access. This article looks at how theory could be put into practice suggesting ways to implement a differential pricing system in the EU that can enhance overall welfare.

Objectives: This study aims to determine whether patients receiving antiretroviral prescription medications to treat HIV/AIDS through a 340B program exhibited differences in medication adherence rates as compared to comparable patients whose medications were not received through a 340B program. MethOds: Using data from Walgreens, 2008 -2014, we conducted a retrospective propensity matched cohort study comparing medication adherence rates for a national sample of HIV/ AIDS patients who received medications through a 340B program to a matched cohort of HIV/AIDS patients whose antiretroviral medications were not received through a 340B program. Patients were matched by age, gender, initial dispensing year, length of HIV therapy, length of medication therapy, and 91 therapeutic classes. Adherence to antiretroviral medications was measured using the proportion of days covered (PDC) metric. One-to-one matching resulted in 9,437 matched pairs. We used mixed modelling methods to assess statistical differences. Results: Mean PDC was higher for the 340B cohort versus the comparison cohort (84% vs. 77%, p< .001). Forty-five percent of the 340B cohort had medication adherence rates (PDC) greater than or equal to the 95% adherence threshold for HIV/AIDS patients compared to the comparison cohort where 41% of patients were adherent according to this threshold (p< .001). cOnclusiOns: These results show that patients with HIV/AIDS who receive antiretroviral medications through a 340B program had higher medication adherence rates, which have been associated with better clinical outcomes. Various factors, such as lower patient costs or improved access to pharmacies, may impact patient adherence, and we will explore such factors on adherence in future research on the 340B Program.

ObjectivesTo assess the association between market concentration of hospitals (as a proxy for competition) and patient-reported health gains after elective primary hip replacement surgery.MethodsPatient Reported Outcome Measures data linked to NHS Hospital Episode Statistics in England in 2011/12 were used to analyse the association between market concentration of hospitals measured by the Herfindahl-Hirschman Index (HHI) and health gains for 337 hospitals.ResultsThe association between market concentration and patient gain in health status measured by the change in Oxford Hip Score (OHS) after primary hip replacement surgery was not statistically significant at the 5% level both for the average patient and for those with more than average severity of hip disease (OHS worse than average). For 12,583 (49.1%) patients with an OHS before hip replacement surgery better than the mean, a one standard deviation increase in the HHI, equivalent to a reduction of about one hospital in the local market, was associated with a 0.104 decrease in patients’ self-reported improvement in OHS after surgery, but this was not statistically significant at the 5% level.ConclusionsHospital market concentration (as a proxy for competition) appears to have no significant influence (at the 5% level) on the outcome of elective primary hip replacement. The generalizability of this finding needs to be investigated.

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Background: Relative effectiveness has become a key concern of health policy. In Europe, this is because of the need for early information to guide reimbursement and funding decisions about new medical technologies. However, ways that effectiveness (does it work?) and efficacy (can it work?) might differ across health systems are poorly understood.Methods: This study proposes an analytical framework, drawing on production function theory, to systematically identify and quantify the determinants of relative effectiveness and sources of variation between populations and healthcare systems. We consider how methods such as stochastic frontier analysis and data envelopment analysis using a Malmquist productivity index could in principle be used to generate evidence on, and improve understanding about, the sources of variation in relative effectiveness between countries and over time.Results: Better evidence on factors driving relative effectiveness could: inform decisions on how to best use a new technology to maximum effectiveness; establish the need if any for follow-up post-launch studies, and provide evidence of the impact of new health technologies on outcomes in different healthcare systems.Conclusions: The health production function approach for assessment of relative effectiveness is complementary to traditional experimental and observational studies, focusing on identifying, collecting, and analyzing data at the national level, enabling comparisons to take place. There is a strong case for exploring the use of this approach to better understand the impact of new medicines and devices for improvements in health outcomes.

Despite a large number of people globally being affected by rare kidney diseases, research support and health care policy programs usually focus on the management of the broad spectrum of CKD without particular attention to rare causes that would require a targeted approach for proper cure. Hence, specific curative approaches for rare kidney diseases are scarce, and these diseases are not treated optimally, with implications on the patients' health and quality of life, on the cost for the health care system, and society. There is therefore a need for rare kidney diseases and their mechanisms to receive the appropriate scientific, political, and policy attention to develop specific corrective approaches. A wide range of policies are required to address the various challenges that target care for rare kidney diseases, including the need to increase awareness, improve and accelerate diagnosis, support and implement therapeutic advances, and inform the management of the diseases. In this article, we provide specific policy recommendations to address the challenges hindering the provision of targeted care for rare kidney diseases, focusing on awareness and prioritization, diagnosis, management, and therapeutic innovation. In combination, the recommendations provide a holistic approach aiming for all aspects of rare kidney disease care to improve health outcomes, reduce the economic effect, and deliver benefits to society. Greater commitment from all the key stakeholders is now needed, and a central role should be assigned to patients with rare kidney disease to partner in the design and implementation of potential solutions.