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Background The Health Economics Research Centre (HERC) Database of Mapping Studies was established in 2013, based on a systematic review of studies developing mapping algorithms predicting EQ-5D. The Mapping onto Preference-based measures reporting Standards (MAPS) statement was published in 2015 to improve reporting of mapping studies. We aimed to update the systematic review and assess the extent to which recently-published studies mapping condition-specific quality of life or clinical measures to the EQ-5D follow the guidelines published in the MAPS Reporting Statement. Methods A published systematic review was updated using the original inclusion criteria to include studies published by December 2016. We included studies reporting novel algorithms mapping from any clinical measure or patient-reported quality of life measure to either the EQ-5D-3L or EQ-5D-5L. Titles and abstracts of all identified studies and the full text of papers published in 2016 were assessed against the MAPS checklist. Results The systematic review identified 144 mapping studies reporting 190 algorithms mapping from 110 different source instruments to EQ-5D. Of the 17 studies published in 2016, nine (53%) had titles that followed the MAPS statement guidance, although only two (12%) had abstracts that fully addressed all MAPS items. When the full text of these papers was assessed against the complete MAPS checklist, only two studies (12%) were found to fulfil or partly fulfil all criteria. Of the 141 papers (across all years) that included abstracts, the items on the MAPS statement checklist that were fulfilled by the largest number of studies comprised having a structured abstract (95%) and describing target instruments (91%) and source instruments (88%). Conclusions The number of published mapping studies continues to increase. Our updated database provides a convenient way to identify mapping studies for use in cost-utility analysis. Most recent studies do not fully address all items on the MAPS checklist.

Soluble α-synuclein aggregates varying in size, structure, and morphology have been closely linked to neuronal death in Parkinson’s disease. However, the heterogeneity of different co-existing aggregate species makes it hard to isolate and study their individual toxic properties. Here, we show a reliable non-perturbative method to separate a heterogeneous mixture of protein aggregates by size. We find that aggregates of wild-type α-synuclein smaller than 200 nm in length, formed during an in vitro aggregation reaction, cause inflammation and permeabilization of single-liposome membranes and that larger aggregates are less toxic. Studying soluble aggregates extracted from post-mortem human brains also reveals that these aggregates are similar in size and structure to the smaller aggregates formed in aggregation reactions in the test tube. Furthermore, we find that the soluble aggregates present in Parkinson’s disease brains are smaller, largely less than 100 nm, and more inflammatory compared to the larger aggregates present in control brains. This study suggests that the small non-fibrillar α-synuclein aggregates are the critical species driving neuroinflammation and disease progression. α-synuclein aggregates cause neuronal damage, but their heterogeneity complicates studying their toxic properties. Here, the authors analyze α-synuclein aggregates in vitro and study post-mortem brain samples, providing evidence that small aggregates are the main culprit for neuronal death in Parkinson’s disease.

Background We aimed to assess the magnitude of interactions in costs, quality-adjusted life-years (QALYs) and net benefits within a sample of published economic evaluations of factorial randomised controlled trials (RCTs), evaluate the impact that different analytical methods would have had on the results and compare the performance of different criteria for identifying which interactions should be taken into account. Methods We conducted a systematic review of full economic evaluations conducted alongside factorial RCTs and reviewed the methods used in different studies, as well as the incidence, magnitude, statistical significance, and type of interactions observed within the trials. We developed the interaction-effect ratio as a measure of the magnitude of interactions relative to main effects. For those studies reporting sufficient data, we assessed whether changing the form of analysis to ignore or include interactions would have changed the conclusions. We evaluated how well different criteria for identifying which interactions should be taken into account in the analysis would perform in practice, using simulated data generated to match the summary statistics of the studies identified in the review. Results Large interactions for economic endpoints occurred frequently within the 40 studies identified in the review, although interactions rarely changed the conclusions. Conclusions Simulation work demonstrated that in analyses of factorial RCTs, taking account of all interactions or including interactions above a certain size (regardless of statistical significance) minimised the opportunity cost from adopting treatments that do not in fact have the highest true net benefit.

Systematic literature searches were conducted to identify studies that conducted statistical mapping to predict EQ-5D utilities or responses from any source instrument and reported the estimated algorithms in sufficient detail to allow other researchers to use them to predict EQ-5D in other studies. Ninety studies reporting 121 mapping algorithms met the inclusion criteria. The studies estimated EQ-5D utilities from 80 source instruments. All but two studies included direct utility mapping to predict EQ-5D utilities, while 20 studies (22%) conducted response mapping to predict responses to each EQ-5D domain. Seventy-two studies (80%) explored ordinary least squares regression and 16 (18%) used censored least absolute deviations (CLAD) models. The details of the studies identified are made available in an online database, which will be updated regularly to enable researchers to easily identify studies that can help them to estimate utilities for economic evaluation.

Objectives Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA 1c )]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates. Aims (1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations. Methods Using pooled contemporary data from two randomised trials EXSCEL and TECOS ( n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA 1c , haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations. Results All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations. Discussion Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients’ health, expenditure and quality of life. Trial Registration ClinicalTrials.gov NCT01144338 and NCT00790205

ObjectivesMany UK primary care trusts have recently introduced eligibility criteria restricting total knee replacement (TKR) to patients with low pre-operative Oxford Knee Scores (OKS) to cut expenditure. We evaluate these criteria by assessing the cost-effectiveness of TKR compared with no knee replacement for patients with different baseline characteristics from an NHS perspective.DesignThe cost-effectiveness of TKR in different patient subgroups was assessed using regression analyses of patient-level data from the Knee Arthroplasty Trial, a large, pragmatic randomised trial comparing knee prostheses.Setting34 UK hospitals.Participants2131 osteoarthritis patients undergoing TKR.Interventions and outcome measuresCosts and quality-adjusted life years (QALYs) observed in the Knee Arthroplasty Trial within 5 years of TKR were compared with conservative assumptions about the costs and outcomes that would have been accrued had TKR not been performed.ResultsOn average, primary TKR and 5 years of subsequent care cost £7458 per patient (SD: £4058), and patients gained an average of 1.33 (SD: 1.43) QALYs. As a result, TKR cost £5623/QALY gained. Although costs and health outcomes varied with age and sex, TKR cost <£20 000/QALY gained for patients with American Society of Anaesthesiologists grades 1–2 who had baseline OKS <40 and for American Society of Anaesthesiologists grade 3 patients with OKS <35, even with highly conservative assumptions about costs and outcomes without TKR. Body mass index had no significant effect on costs or outcomes. Restricting TKR to patients with pre-operative OKS <27 would inappropriately deny a highly cost-effective treatment to >10 000 patients annually.ConclusionsTKR is highly cost-effective for most current patients if the NHS is willing to pay £20 000–£30 000/QALY gained. At least 97% of TKR patients in England have more severe symptoms than the thresholds we have identified, suggesting that further rationing by OKS is probably unjustified.Trial registration numberISRCTN 45837371.

Objective Medical interventions used in pregnancy can affect the length and quality of life of both the pregnant person and fetus. The aim of this systematic review was to identify and describe the theoretical frameworks that underpin outcome measurement in cost-utility analyses of pregnancy interventions. Methods Searches were conducted in the Paediatric Economic Database Evaluation (PEDE) database (up to 2017), as well as Medline, Embase and EconLit (2017–2019). We included all cost-utility analyses of any intervention given during pregnancy, published in English. We conducted a narrative synthesis of: study design; outcome construction (life expectancy, quality adjustment, discount rate); and whether the Incremental Cost-Effectiveness Ratio (ICER) was constructed using maternal or fetal outcomes. Where both outcomes were included, methods for combining them were extracted. Results We identified 127 cost-utility analyses in pregnancy, of which 89 reported QALYs and 38 DALYs. Outcomes were considered solely for the fetus in 59 studies (47%), solely for the pregnant person in 13 studies (10%), and for both in 49 studies (39%). The choice to include or exclude one or both sets of outcomes was not consistent within particular clinical areas. Where outcomes for both mother and baby were included, methods for combining these outcomes varied. Twenty-nine studies summed QALYs/DALYs for maternal and fetal outcomes, with no adjustment. The remaining 20 took a variety of approaches designed to weigh maternal and fetal outcomes differently. These include (1) treating fetal outcomes as a component of maternal quality of life, rather than (or in addition to) an independent individual health outcome; (2) treating the maternal-fetal dyad as a single entity and applying a single utility value to each combination of outcomes; and (3) assigning a shorter time horizon to fetal outcomes to reduce the weight of lifetime fetal outcomes. Each approach made different assumptions about the relative value of maternal and fetal health outcomes, demonstrating a lack of consistency and the need for guidance. Conclusion Methods for capturing QALY/DALY outcomes in cost-utility analysis in pregnancy vary widely. This lack of consistency indicates a need for new methods to support the valuation of maternal and fetal health outcomes.

Which isoforms of apolipoprotein E (apoE) we inherit determine our risk of developing late-onset Alzheimer’s Disease (AD), but the mechanism underlying this link is poorly understood. In particular, the relevance of direct interactions between apoE and amyloid-β (Aβ) remains controversial. Here, single-molecule imaging shows that all isoforms of apoE associate with Aβ in the early stages of aggregation and then fall away as fibrillation happens. ApoE-Aβ co-aggregates account for ~50% of the mass of diffusible Aβ aggregates detected in the frontal cortices of homozygotes with the higher-risk APOE4 gene. We show how dynamic interactions between apoE and Aβ tune disease-related functions of Aβ aggregates throughout the course of aggregation. Our results connect inherited APOE genotype with the risk of developing AD by demonstrating how, in an isoform- and lipidation-specific way, apoE modulates the aggregation, clearance and toxicity of Aβ. Selectively removing non-lipidated apoE4-Aβ co-aggregates enhances clearance of toxic Aβ by glial cells, and reduces secretion of inflammatory markers and membrane damage, demonstrating a clear path to AD therapeutics. Here the authors connect inherited Apolipoprotein E genotype with the risk of developing Alzheimer’s disease by demonstrating how, in an isoform- and lipidation-specific way, apoE modulates the aggregation, clearance and toxicity of Amyloid-beta.

Background The process of “mapping” is increasingly being used to predict health utilities, for application within health economic evaluations, using data on other indicators or measures of health. Guidance for the reporting of mapping studies is currently lacking. Objective The overall objective of this research was to develop a checklist of essential items, which authors should consider when reporting mapping studies. The MAPS (MApping onto Preference-based measures reporting Standards) statement is a checklist, which aims to promote complete and transparent reporting by researchers. This paper provides a detailed explanation and elaboration of the items contained within the MAPS statement. Methods In the absence of previously published reporting checklists or reporting guidance documents, a de novo list of reporting items and accompanying explanations was created. A two-round, modified Delphi survey, with representatives from academia, consultancy, health technology assessment agencies and the biomedical journal editorial community, was used to identify a list of essential reporting items from this larger list. Results From the initial de novo list of 29 candidate items, a set of 23 essential reporting items was developed. The items are presented numerically and categorised within six sections, namely, (i) title and abstract, (ii) introduction, (iii) methods, (iv) results, (v) discussion and (vi) other. For each item, we summarise the recommendation, illustrate it using an exemplar of good reporting practice identified from the published literature, and provide a detailed explanation to accompany the recommendation. Conclusions It is anticipated that the MAPS statement will promote clarity, transparency and completeness of reporting of mapping studies. It is targeted at researchers developing mapping algorithms, peer reviewers and editors involved in the manuscript review process for mapping studies, and the funders of the research. The MAPS working group plans to assess the need for an update of the reporting checklist in 5 years’ time.

Background For patients with painful knee osteoarthritis, long-term symptomatic relief may improve quality of life. Cooled radiofrequency ablation (CRFA) has demonstrated significant improvements in pain, physical function and health-related quality of life compared with conservative therapy with intra-articular steroid (IAS) injections. This study aimed to establish the cost-effectiveness of CRFA compared with IAS for managing moderate to severe osteoarthritis-related knee pain, from the US Medicare system perspective. Methods We conducted a cost-effectiveness analysis utilizing efficacy data (Oxford Knee Scores) from a randomized, crossover trial on CRFA (NCT02343003), which compared CRFA with IAS out to 6 and 12 months, and with IAS patients who subsequently crossed over to receive CRFA after 6 months. Outcomes included health benefits (quality-adjusted life-years [QALYs]), costs and cost-effectiveness (expressed as cost per QALY gained). QALYs were estimated by mapping Oxford Knee Scores to the EQ-5D generic utility measure using a validated algorithm. Secondary analyses explored differences in the settings of care and procedures used in-trial versus real-world clinical practice. Results CRFA resulted in an incremental QALY gain of 0.091 at an incremental cost of $1711, equating to a cost of US$18,773 per QALY gained over a 6-month time horizon versus IAS. Over a 12-month time horizon, the incremental QALY gain was 0.229 at the same incremental cost, equating to a cost of US$7462 per QALY gained versus IAS. Real-world cost assumptions resulted in modest increases in the cost per QALY gained to a maximum of US$21,166 and US$8296 at 6 and 12 months, respectively. Sensitivity analyses demonstrated that findings were robust to variations in efficacy and cost parameters. Conclusions CRFA is a highly cost-effective treatment option for patients with osteoarthritis-related knee pain, compared with the US$100,000/QALY threshold typically used in the US.