Title: “The answer is 17 years, what is the question: understanding time lags in translational research”
Published: December, 2011 | 🔗 View this study here | Authors: Zoë Slote Morris, Steven Wooding, and Jonathan Grant
- It’s often reported it takes “an average of 17 years” for research evidence to reach the patient in clinical practice
- Balas, Bohen, Grant and Wratschko all estimate a time lag of 17 years
- However, this “average” hides a number of complexities relevant to policy and practice
This study looked at how long it takes for health research to make its way from the lab to real-life application. i.e. Treatments and medications that will benefit patients.
It identified 23 papers that try to quantify time lags in the development of health interventions.
While Balas, Bohen, Grant and Wratschko have all estimated a time lag of 17 years there are many other measures and estimations and it tries to unpack this.
The researchers found it difficult to compare papers, as they frequently use different measures and timescales.
Despite the limitations, this study provides some insights and offers suggestions for future research policy and practice.
Editors Note: While this study reveals discrepancies in identifying the time it takes for research to filter down to patient care, it shows that delays are typically extremely long.
This highlights the importance of reading up on the latest findings. If it is safe to do so there may be opportunities to try therapies and interventions (with the consent of your healthcare professional) which could help.
Here are some direct quotes from the study:
“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.”
“Timely realization of the benefits of expensive medical research is an international concern attracting considerable policy effort around ‘translation’.
Policy interventions to improve translation respond to a vast empirical literature on the difficulties of getting research across research phases and into practice.
Both literature and policy tend to assume that speedy translation of research into practice is a good thing. Delays are seen as a waste of scarce resources and a sacrifice of potential patient benefit.
Although some lag will be necessary to ensure the safety and efficacy of new interventions or advances, in essence we should aim to optimize lags.
One recent study (of which JG and SW were co-authors) estimating the economic benefit of cardiovascular disease (CVD) research in the UK between 1975 and 2005, found an internal rate of return (IRR) of CVD research of 39%.
In other words, a £1.00 investment in public/charitable CVD research produced a stream of benefits equivalent to earning £0.39 per year in perpetuity.
Of this, 9% was attributable to the benefit from health improvements, which is the focus of this paper. (The remaining 30% arise from ‘spillovers’ benefiting the wider economy.)
This level of benefit was calculated using an estimated lag of 17 years. Varying the lag time from 10 to 25 years produced rates of return of 13% and 6%, respectively, illustrating that shortening the lag between bench and bedside improves the overall benefit of cardiovascular research.
What is notable is that all the above calculations depended upon an estimated time lag; estimated because, despite longstanding concerns about them,14 time lags in health research are little understood.
It is frequently stated that it takes an average of 17 years for research evidence to reach clinical practice. Balas and Bohen,Grant and Wratschko all estimated a time lag of 17 years measuring different points of the process.
Such convergence around an ‘average’ time lag of 17 years hides complexities that are relevant to policy and practice which would benefit from greater understanding.
Despite longstanding concerns about delays in getting research into practice, the literature on time lags seems surprisingly under-developed.
To help address this gap, this paper aims to synthesize existing knowledge and to offer a conceptual model that can be used to standardize measurement and thus help to quantify lags in future. This would allow efforts to reduce lags to be focused on areas of particular concern or value, or on areas where interventions might be expected to have best effect.
It would also provide the potential for evaluating the cost-effectiveness of translation interventions if their impact on lags can be measured. The aim was to overlay empirical lag data onto the conceptual model of translational research to provide an overview of estimated time lags and where they occur. The first part of the paper explores conceptual models of the translation pipeline in order to provide context.
The second part of the paper presents a review of the literature on time lags to present current estimates and issues. This leads to a discussion on the current state of understanding about time lags and considers the implications for future practice and policy.”
“Translating scientific discoveries into patient benefit more quickly is a policy priority of many health research systems. Despite their policy salience, little is known about time lags and how they should be managed.
This lack of knowledge puts those responsible for enabling translational research at a disadvantage. An ambitious reason for being able to accurately measure lags is that it would be possible to look at their distribution to identify research that is both slow and fast in its translation.
Further investigation of the characteristics of research at both ends of a distribution could help identify actionable policy interventions that could speed up the translation process, where appropriate, and thus increase the return on research investment.”