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      Estimating the returns to United Kingdom publicly funded musculoskeletal disease research in terms of net value of improved health outcomes

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          Abstract

          Background

          Building on an approach applied to cardiovascular and cancer research, we estimated the economic returns from United Kingdom public- and charitable-funded musculoskeletal disease (MSD) research that arise from the net value of the improved health outcomes in the United Kingdom.

          Methods

          To calculate the economic returns from MSD-related research in the United Kingdom, we estimated (1) the public and charitable expenditure on MSD-related research in the United Kingdom between 1970 and 2013; (2) the net monetary benefit (NMB), derived from the health benefit in quality adjusted life years (QALYs) valued in monetary terms (using a base-case value of a QALY of £25,000) minus the cost of delivering that benefit, for a prioritised list of interventions from 1994 to 2013; (3) the proportion of NMB attributable to United Kingdom research; and (4) the elapsed time between research funding and health gain. The data collected from these four key elements were used to estimate the internal rate of return (IRR) from MSD-related research investments on health benefits. We analysed the uncertainties in the IRR estimate using a one-way sensitivity analysis.

          Results

          Expressed in 2013 prices, total expenditure on MSD-related research from 1970 to 2013 was £3.5 billion, and for the period used to estimate the rate of return, 1978-1997, was £1.4 billion. Over the period 1994–2013 the key interventions analysed produced 871,000 QALYs with a NMB of £16 billion, allowing for the net NHS costs resulting from them and valuing a QALY at £25,000. The proportion of benefit attributable to United Kingdom research was 30% and the elapsed time between funding and impact of MSD treatments was 16 years. Our best estimate of the IRR from MSD-related research was 7%, which is similar to the 9% for CVD and 10% for cancer research.

          Conclusions

          Our estimate of the IRR from the net health gain to public and charitable funding of MSD-related research in the United Kingdom is substantial, and justifies the research investments made between 1978 and 1997. We also demonstrated the applicability of the approach previously used in assessing the returns from cardiovascular and cancer research. Inevitably, with a study of this kind, there are a number of important assumptions and caveats that we highlight, and these can inform future research.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12961-017-0276-7) contains supplementary material, which is available to authorized users.

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          Most cited references43

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          Health effects of light and intermittent smoking: a review.

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

            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. Electronic supplementary material The 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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              Burden of major musculoskeletal conditions

              Musculoskeletal conditions are a major burden on individuals, health systems, and social care systems, with indirect costs being predominant. This burden has been recognized by the United Nations and WHO, by endorsing the Bone and Joint Decade 2000-2010. This paper describes the burden of four major musculoskeletal conditions: osteoarthritis, rheumatoid arthritis, osteoporosis, and low back pain. Osteoarthritis, which is characterized by loss of joint cartilage that leads to pain and loss of function primarily in the knees and hips, affects 9.6% of men and 18% of women aged >60 years. Increases in life expectancy and ageing populations are expected to make osteoarthritis the fourth leading cause of disability by the year 2020. Joint replacement surgery, where available, provides effective relief. Rheumatoid arthritis is an inflammatory condition that usually affects multiple joints. It affects 0.3-1.0% of the general population and is more prevalent among women and in developed countries. Persistent inflammation leads to joint destruction, but the disease can be controlled with drugs. The incidence may be on the decline, but the increase in the number of older people in some regions makes it difficult to estimate future prevalence. Osteoporosis, which is characterized by low bone mass and microarchitectural deterioration, is a major risk factor for fractures of the hip, vertebrae, and distal forearm. Hip fracture is the most detrimental fracture, being associated with 20% mortality and 50% permanent loss in function. Low back pain is the most prevalent of musculoskeletal conditions; it affects nearly everyone at some point in time and about 4-33% of the population at any given point. Cultural factors greatly influence the prevalence and prognosis of low back pain.
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                Author and article information

                Contributors
                matthew.glover@brunel.ac.uk
                erin.montague@kcl.ac.uk
                alexandra.pollitt@kcl.ac.uk
                sguthrie@rand.org
                stephen.hanney@brunel.ac.uk
                martin.buxton@brunel.ac.uk
                jonathan.grant@kcl.ac.uk
                Journal
                Health Res Policy Syst
                Health Res Policy Syst
                Health Research Policy and Systems
                BioMed Central (London )
                1478-4505
                10 January 2018
                10 January 2018
                2018
                : 16
                : 1
                Affiliations
                [1 ]ISNI 0000 0001 0724 6933, GRID grid.7728.a, Health Economics Research Group, , Brunel University London, ; Uxbridge, United Kingdom
                [2 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Policy Institute at King’s, , King’s College London, ; Virginia Woolf Building, 22 Kingsway, London, WC2B 6LE United Kingdom
                [3 ]ISNI 0000 0004 0623 2013, GRID grid.425785.9, RAND Europe, ; Cambridge, United Kingdom
                Author information
                http://orcid.org/0000-0002-1646-3486
                Article
                276
                10.1186/s12961-017-0276-7
                5761203
                29316935
                27b61417-14d2-4cc3-932e-a7d77d7dd9f2
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 24 October 2017
                : 12 December 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000341, Arthritis Research UK;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000691, Academy of Medical Sciences;
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Health & Social care
                medical research investment,qalys,musculoskeletal disease,medical research charities,value of health,rate of return,elapsed time,research payback

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