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Transplantation Without Overimmunosuppression (TWO) study protocol: a phase 2b randomised controlled single-centre trial of regulatory T cell therapy to facilitate immunosuppression reduction in living donor kidney transplant recipients

Affiliations.

1 Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK [email protected].
2 Oxford Transplant Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
3 Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
4 Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
5 NIHR Biomedical Research Centre GMP unit, Guy's Hospital, London, UK.
6 Peter Gorer Department of Immunobiology, King's College London Faculty of Life Sciences and Medicine, London, UK.
PMID: 35428650
PMCID: PMC9014059
DOI: 10.1136/bmjopen-2022-061864

Introduction: Regulatory T cell (Treg) therapy has been demonstrated to facilitate long-term allograft survival in preclinical models of transplantation and may permit reduction of immunosuppression and its associated complications in the clinical setting. Phase 1 clinical trials have shown Treg therapy to be safe and feasible in clinical practice. Here we describe a protocol for the TWO study, a phase 2b randomised control trial of Treg therapy in living donor kidney transplant recipients that will confirm safety and explore efficacy of this novel treatment strategy.

Methods and analysis: 60 patients will be randomised on a 1:1 basis to Treg therapy (TR001) or standard clinical care (control). Patients in the TR001 arm will receive an infusion of autologous polyclonal ex vivo expanded Tregs 5 days after transplantation instead of standard monoclonal antibody induction. Maintenance immunosuppression will be reduced over the course of the post-transplant period to low-dose tacrolimus monotherapy. Control participants will receive a standard basiliximab-based immunosuppression regimen with long-term tacrolimus and mycophenolate mofetil immunosuppression. The primary endpoint is biopsy proven acute rejection over 18 months; secondary endpoints include immunosuppression burden, chronic graft dysfunction and drug-related complications.

Ethics and dissemination: Ethical approval has been provided by the National Health Service Health Research Authority South Central-Oxford A Research Ethics Committee (reference 18/SC/0054). The study also received authorisation from the UK Medicines and Healthcare products Regulatory Agency and is being run in accordance with the principles of Good Clinical Practice, in collaboration with the registered trials unit Oxford Clinical Trials Research Unit. Results from the TWO study will be published in peer-reviewed scientific/medical journals and presented at scientific/clinical symposia and congresses.

Trial registration number: ISRCTN: 11038572; Pre-results.

Keywords: Clinical trials; IMMUNOLOGY; Nephrology; TRANSPLANT MEDICINE; Transplant surgery.

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Conflict of interest statement

Competing interests: PH is an advisor to Sangamo Therapeutics. Envarsus long-acting sustained release tacrolimus is provided by Chiesi on a cost-neutral basis. MJB has received an educational grant and speakers fees from Astellas UK.

Diagrammatic representation of the immunosuppressive…

Diagrammatic representation of the immunosuppressive regimen used in The TWO study.

Overview of maintenance immunosuppression dosing…

Overview of maintenance immunosuppression dosing with minimisation in the TR001 (cell therapy) arm.

Key time points alongside clinical…

Key time points alongside clinical and immune monitoring plans. EQ-5D-5L, EuroQol 5 Dimensions…

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Open access
Published: 27 September 2020

Cost-effectiveness of adrenaline for out-of-hospital cardiac arrest

Felix Achana 1 , 2 ,
Stavros Petrou 1 , 2 na1 ,
Jason Madan 1 ,
Kamran Khan 1 ,
Chen Ji 1 ,
Anower Hossain 3 ,
Ranjit Lall 1 ,
Anne-Marie Slowther 1 ,
Charles D. Deakin 4 ,
Tom Quinn 5 ,
Jerry P. Nolan 1 , 6 ,
Helen Pocock 1 , 7 ,
Nigel Rees 8 ,
Michael Smyth 1 , 9 ,
Simon Gates 10 ,
Dale Gardiner 11 &
Gavin D. Perkins ORCID: orcid.org/0000-0003-3027-7548 1

for the PARAMEDIC2 Collaborators

Critical Care volume 24 , Article number: 579 ( 2020 ) Cite this article

4441 Accesses

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The ‘Prehospital Assessment of the Role of Adrenaline: Measuring the Effectiveness of Drug Administration In Cardiac Arrest’ (PARAMEDIC2) trial showed that adrenaline improves overall survival, but not neurological outcomes. We sought to determine the within-trial and lifetime health and social care costs and benefits associated with adrenaline, including secondary benefits from organ donation.

We estimated the costs, benefits (quality-adjusted life years (QALYs)) and incremental cost-effectiveness ratios (ICERs) associated with adrenaline during the 6-month trial follow-up. Model-based analyses explored how results altered when the time horizon was extended beyond 6 months and the scope extended to include recipients of donated organs.

The within-trial (6 months) and lifetime horizon economic evaluations focussed on the trial population produced ICERs of £1,693,003 (€1,946,953) and £81,070 (€93,231) per QALY gained in 2017 prices, respectively, reflecting significantly higher mean costs and only marginally higher mean QALYs in the adrenaline group. The probability that adrenaline is cost-effective was less than 1% across a range of cost-effectiveness thresholds. Combined direct economic effects over the lifetimes of survivors and indirect economic effects in organ recipients produced an ICER of £16,086 (€18,499) per QALY gained for adrenaline with the probability that adrenaline is cost-effective increasing to 90% at a £30,000 (€34,500) per QALY cost-effectiveness threshold.

Conclusions

Adrenaline was not cost-effective when only directly related costs and consequences are considered. However, incorporating the indirect economic effects associated with transplanted organs substantially alters cost-effectiveness, suggesting decision-makers should consider the complexity of direct and indirect economic impacts of adrenaline.

Trial registration

ISRCTN73485024 . Registered on 13 March 2014.

Introduction

Although survival rates following adult out-of-hospital cardiac arrest have increased in recent years, they generally remain below 10% [ 1 , 2 ]. Survivors are at increased risk of cognitive and functional impairment and often take significant time to attain an acceptable quality of life or to return to work [ 3 ].

In some countries, when a patient dies after cardiac arrest, families can be asked to agree to the patient’s organs being donated for the benefit of others. A meta-analysis of 26 studies reported that, on average, 5.8% (95% CI 2.1–10.9%) of patients admitted to intensive care donate their organs for transplantation after brain stem death [ 4 ]. Observational studies suggest that cardiac arrest before organ donation does not adversely affect long-term graft function [ 5 ] and that such transplant programmes are cost-effective [ 6 , 7 ].

The PARAMEDIC2 (Prehospital Assessment of the Role of Adrenaline: Measuring the Effectiveness of Drug Administration In Cardiac Arrest) trial showed that adrenaline improves overall survival, but not long term neurological outcome [ 8 , 9 ]. In this paper, we explore the costs and benefits of adrenaline within the PARAMEDIC2 trial across within-trial and lifetime horizons and model the contribution of organ donation after a cardiac arrest on cost-effectiveness.

Trial background

PARAMEDIC2 (ISRCTN73485024) was a pragmatic, individually randomised, double-blind, controlled trial of 8014 adult patients with out-of-hospital cardiac arrest in the UK [ 9 ]. Patients, treated by five National Health Service (NHS) ambulance services between December 2014 and October 2017, were randomised to either parenteral adrenaline or saline placebo, along with standard care. The primary clinical outcome was the rate of survival at 30 days. Survival, neurological outcomes as indicated by the modified Rankin Scale (mRS) (ranging from 0 [no symptoms] to 6 [death]) [ 10 ] and economic outcomes were followed up from randomisation to 6 months. The South Central–Oxford C Research Ethics Committee (REC: 14/SC/0157) and the Medicines and Healthcare Products Regulatory Authority (EudraCT: 2014-000792-11) approved the study protocol. The trial was funded by the UK National Institute for Health Research Health Technology Assessment Programme (12/127/126). Further details of the trial are reported elsewhere [ 9 ].

Overview of economic evaluation

A cost-utility analysis was conducted, with results expressed in terms of incremental cost per quality-adjusted life year (QALY) gained through the use of adrenaline. The baseline economic evaluation was conducted from the perspective of the UK NHS and personal social services (PSS) [ 11 ], with a time horizon of 6 months post-randomisation. A decision-analytic model was used to extrapolate outcomes beyond trial follow-up and assess the cost-effectiveness of adrenaline over a lifetime horizon. All costs were expressed in British pounds sterling and valued at 2017 prices (with commensurate values in Euros estimated using purchasing power parities). Costs and QALYs accrued beyond the first year of follow-up were discounted to present values at 3.5% per annum in accordance with recommendations from the National Institute for Health and Care Excellence (NICE) [ 11 ]. The economic evaluation adhered to an approved pre-specified analytical plan which is available in the supplementary material (Additional file 1 ).

Measurement and valuation of resource use

Resource inputs associated with the pre-hospital emergency response, until the point of hospital admission or death, were extracted from the trial case report forms completed by research paramedics. These data included the number of emergency response staff/ambulance crew and vehicles in attendance, duration of emergency response, and cumulative doses of adrenaline administered. Unit costs for these resource inputs were drawn from national tariffs (Additional file 2 ).

For patients surviving to hospital admission, the economic costs associated with hospital care were extracted through data linkage with two national population-wide databases: Hospital Episode Statistics (HES) for England and the Patient Episode Database for Wales (PEDW). This provided patient-level profiles of resource use associated with hospital episodes for study patients over the trial time horizon, including emergency department attendances, critical care and other inpatient admissions, covering procedures undertaken including percutaneous coronary intervention, by-pass surgery, implantable cardioverter defibrillators (ICDs), cardiac pacemakers and lengths of stay, day case admissions and outpatient attendances. HealthCare Resource Group (HRG) codes were derived using the NHS HRG4 2016–17 Reference Cost Grouper software version [ 12 ]. The Department of Health and Social Care’s Reference Costs 2016–17 schedule was used to assign costs to derived HRG codes [ 12 ]. For patients without linkage to routine hospital records, hospital resource inputs were extracted from data contained within the trial case report forms and valued using national tariffs.

Surviving patients or their proxies were asked to complete questionnaires at 3 and 6 months post-randomisation, reporting hospital admissions and use of hospital outpatient services following initial discharge, by type and volume, and use of community health and social services, medications and aids and equipment. Further questions captured wider societal attributable resource use, with data collected on time off work, over-the-counter medications, aids and adaptations purchased privately and any additional costs borne by study patients or informal carers. Unit costs for these resource inputs were drawn from national tariffs (Additional file 2 ).

Calculation of health utilities and QALYs

Health-related quality of life was assessed using the EuroQol EQ-5D-5L [ 13 ], completed at 3 and 6 months post-cardiac arrest. Responses to the EQ-5D-5L descriptive system were converted into health utility values, based on the UK EQ-5D-3L tariff using the van Hout interim cross-walk algorithm [ 14 ], in line with current recommendations [ 15 ]. mRS scores collected at hospital discharge were also converted into health utility values, based on the UK EQ-5D-3L tariff, using a published mapping algorithm [ 16 ]. A QALY profile was generated for each trial participant using the area-under-the-curve method, assuming a baseline health utility value of zero [ 17 ] and linear interpolation between utility measurements at hospital discharge and at 3 and 6 months assessment points, accounting for survival [ 18 ]. We used 3- or 6-month mRS-derived health utilities in place of EQ-5D-5L-derived values if the latter were missing.

Analytical methods

Analyses of clinical outcomes, resource use and costs.

Survival outcomes were analysed through the use of fixed-effect regression models with adjustment for age, sex, interval between the emergency call and the ambulance arrival at the scene, interval between the ambulance arrival and the administration of the study drug, cause of cardiac arrest, initial cardiac rhythm, whether the cardiac arrest was witnessed and whether CPR was performed by a bystander [ 9 ]. Between treatment-group differences in mean resource use and mean costs were estimated using two-sample Student’s t tests. Estimates of 95% confidence intervals (CIs) surrounding between-group differences in mean costs were obtained using nonparametric bootstrapping with replacement, based on 1000 replications [ 19 ].

Handling missing data

Multiple imputations using chained equations with predictive mean matching [ 20 ] was used to predict values for missing observations, assuming data were missing at random. Missing costs and health utility values were imputed at the level of resource category, health-related quality of life measure and assessment point, stratified by survival status at hospital discharge and treatment allocation in accordance with current recommendations [ 21 ]. Twenty imputed datasets were generated and used to inform the base-case and subsequent sensitivity and subgroup analyses. Parameter estimates were pooled across the 20 imputed datasets using Rubin’s rule to account for between and within-imputation components of variance terms associated with parameter estimates.

Within-trial economic evaluation

Bivariate linear regressions that take into account the correlation between each patient’s costs and effects were used to model total costs and total QALYs over the 6-month follow-up period. By specifying the treatment group as an indicator within each equation, the incremental costs and QALYs attributable to adrenaline were estimated, while controlling for baseline covariates (age, sex, time to first dose administration, cause of cardiac arrest, whether the cardiac arrest was witnessed, whether a bystander performed CPR and initial cardiac rhythm). Cost-effectiveness was expressed in terms of an incremental cost-effectiveness ratio (ICER), defined as the incremental adjusted cost of adrenaline divided by the incremental adjusted QALYs of adrenaline. The ICER was then compared with a range of cost-effectiveness threshold values for an additional QALY. ICERs falling below or in the region of £20,000 (€23,000) to £30,000 (€34,500) per QALY gained would, in general, be considered as representing a cost-effective use of UK NHS resources [ 11 , 22 ]. Confidence intervals surrounding mean ICER values were not calculated as they are problematic to interpret if the ICER denominator is estimated close to zero and the interval extends to cover negative values [ 23 ]. This is because a negative ICER might equally imply lower costs and more effective treatment or greater costs and less effective treatment compared with placebo. There is no way of differentiating between these two qualitatively different scenarios from a confidence interval alone. Thus, we followed standard practice in economic evaluations to present mean ICER values without accompanying confidence intervals or standard errors. Instead, uncertainty surrounding mean cost-effectiveness estimates were characterised through a Monte Carlo method involving simulating 1000 replicates of the ICER from a joint distribution of incremental costs and incremental QALYs [ 24 ]. By calculating net monetary benefits for each of these simulated ICER values at alternative cost-effectiveness thresholds varying from £0 to £100,000 (€115,000) per QALY gained, the probability of cost-effectiveness of adrenaline (defined as the proportion of positive net monetary benefits at a given threshold level) was calculated and plotted as a cost-effectiveness acceptability curve [ 25 ].

Sensitivity analyses

A series of sensitivity analyses, summarised in Additional file 3 , assessed the impact of varying features of the within-trial economic evaluation on cost-effectiveness. These included extending the perspective of analysis to cover broader societal resource use and costs and extending the time horizon to 12 months after the cardiac arrest event. Further economic modelling (described in the sections that follow) explored how results altered when the time horizon was extended to cover the lifetimes of cardiac arrest survivors and the scope extended to include recipients of donated organs.

Sub-group analyses

A series of pre-specified subgroup analyses, summarised in Additional file 4 , explored whether the cost-effectiveness estimates based on the within-trial data altered by age, gender, time to first dose administration, cause of cardiac arrest, whether the cardiac arrest was witnessed, whether bystander performed CPR or initial cardiac rhythm.

Extrapolations of cost-effectiveness

Controlled organ donation after neurological or cardiac death is available to families for patients who sustain un-survivable severe brain injury. Decision-analytic modelling was used to estimate the incremental cost-effectiveness of adrenaline beyond the parameters of the PARAMEDIC2 trial to include the costs and benefits of organ donation. A Markov state transition model was developed to extrapolate lifetime costs and benefits for out-of-hospital cardiac arrest survivors (Fig. 1 , plot a). We separately adapted a previous economic model of adult lung transplantation [ 26 ] to simulate the impact on the cost-effectiveness of adrenaline resulting from changes in organ donation from deceased PARAMEDIC2 donors who had experienced out-of-hospital cardiac arrest. The model (Fig. 1 , plot b) simulated pre and post-transplantation survival for individuals on the active national transplant waiting list. Epidemiological data for the model were informed by a separate linkage of the PARAMEDIC2 patients to the UK National Transplant Registry. Further details of the model structures, data inputs, analytical methods and description of methods for integrating the direct and indirect effects of adrenaline generated by the two models are provided in Additional file 5 .

Model diagrams. Plot a : Model to extrapolate cost-effectiveness beyond PARAMEDIC2 trial follow-up. OHCA represents health state for out-of-hospital cardiac arrest patients. Good represents survival with good neurological function. Poor represents survival with poor neurological function. Plot b : Organ donor model adapted from Fisher et al. [ 26 ]

A total of 8014 patients were randomised to either parenteral adrenaline (4015 patients) or saline placebo (3999 patients). In the adrenaline group, 130 patients (3.2%) were alive at 30 days, compared with 94 patients (2.4%) in the placebo group (unadjusted odds of survival, 1.39; 95% confidence interval [CI], 1.06 to 1.82; P = 0.02) [ 9 ]. Severe neurologic impairment at hospital discharge (mRS score of 4 or 5) was more common amongst survivors in the adrenaline group than in the placebo group (39 of 126 patients [31.0%] vs. 16 of 90 patients [17.8%]) [ 9 ].

Figure 2 summarises the flow and completeness of data sources used to inform the economic parameters of the trial. Overall, between 1 and 2% of costs (at the component level) and approximately 1% of QALY data were missing (and subsequently imputed) for the primary analysis.

Flow chart of data sources used to inform resource use and costs. S2HD indicates survivors to hospital discharge, HES/PEDW indicates Hospital Episode Statistics/Patient Episode Database for Wales

Resource use reported by the trial participants and or their proxies by treatment arm is summarised in Additional file 6 . More patients were admitted to intensive care in the adrenaline arm (2016 vs 1209, P < 0.001), creating a larger group for assessment for donation after neurological or cardiac death. Linkage of the PARAMEDIC2 patients to the UK National Transplant Registry revealed that there were 99 recipients of organs donated by the adrenaline group compared with 67 recipients of organs donated by the placebo group (from 40 donors in the adrenaline group vs 24 donors in the placebo group). Breakdown by organ type is provided in Table 1 .

Amongst all participants, mean total NHS and PSS costs over the 6-month trial time horizon was £3789 (€4357) in the adrenaline group vs. £2698 (€3103) in the placebo group [unadjusted mean cost difference £1091 (€1255) (bootstrap 95% CI £807 (€928) to £1398 (€1608)); P < 0.001] (Table 2 ). There was also a significant difference in mean total societal costs: £3829 (€4403) in the adrenaline group vs. £2687 (€3090) in the placebo group [unadjusted mean cost difference £1143 (€1314) (bootstrap 95% CI £861 (€990) to £1451 (€1669)); P < 0.001]. Amongst those who survived to hospital discharge, there were no significant differences in mean total NHS and PSS costs. However, amongst survivors to hospital discharge, mean utility values were significantly lower at hospital discharge in the adrenaline group [0.48 vs 0.60, unadjusted mean difference − 0.118 (95% CI − 0.196 to − 0.032); P = 0.002].

The within-trial economic evaluation results are summarised in Table 3 . The base case analysis, over a 6-month time horizon, produced an ICER of £1,693,003 (€1,946,953) per QALY gained, reflecting significantly higher mean costs and only marginally higher mean QALYs in the adrenaline group. Extrapolating the within-trial analysis produced ICERs of £644,308 (€740,954) per QALY gained over 1 year and £81,070 (€93,231) per QALY gained over the lifetimes of survivors. The probability that adrenaline is cost-effective remained below 1% across a range of cost-effectiveness thresholds (plots a and b of Fig. 3 ), while mean net monetary benefits were negative at all thresholds. The cost-effectiveness estimates remained robust to a range of sensitivity and subgroup analyses (Additional file 7 ).

The three graphs on the left-hand side represent the cost-effectiveness plane for the within-trial, lifetime and the combined cardiac arrest and organ donor analyses. The three graphs on the right-hand side represent cost-effectiveness acceptability curves and give the probability that adrenaline is cost-effective compared with placebo at a specified cost-effectiveness threshold. Each cost-effectiveness plane is divided into four quadrants (North-West, North-East, South-West and South-East) by the intersection of the horizontal and vertical axis. South-East quadrant implies adrenaline is less costly and more effective than placebo, North-West quadrant implies adrenaline is less effective and more costly, the North-East quadrant implies adrenaline is more effective but also more costly and the South-West quadrant implies adrenaline is less effective but also less costly

The decision-analytic modelling that estimated combined direct economic effects over the lifetimes of survivors and indirect economic effects in organ recipients revealed that routine use of adrenaline at a UK-wide level in this patient group would result in incremental costs of £49,759,481 (€57,223,403) and 3093 additional QALYs per annum (Table 4 ). The mean ICER at a patient-level was estimated at £16,086 (€18,499) per QALY gained for adrenaline. The probability that adrenaline is cost-effective compared with placebo was 39% at a £15,000 (€17,250) per QALY cost-effectiveness threshold, increasing to 90% at a £30,000 (€34,500) threshold (Table 4 and plot c of Fig. 3 ). Sensitivity analyses revealed that these cost-effectiveness estimates remained robust to a range of modelling assumptions and data inputs with the exception of the estimated impact of the number of organs donated for transplantation on the cost-effectiveness of adrenaline (Additional file 5 ).

This paper presents the first economic evaluation of adrenaline in adults with out-of-hospital cardiac arrest. The study revealed that when the assessment is restricted to the first 6 months that follow out-of-hospital cardiac arrest, adrenaline is associated with significantly higher mean costs, largely driven by the additional costs associated with hospital admissions, and only marginally higher mean QALYs. The base case analysis generated a mean ICER of £1,693,003 (€1,946,953) per QALY gained at 6 months post-randomisation, well in excess of accepted cost-effectiveness thresholds [ 11 ]. Consequently, the probability that adrenaline is cost-effective approximated to zero in the base-case analysis. Moreover, this conclusion remained robust after extensive sensitivity analyses that accounted for different sources of uncertainty. Notably, however, separate decision-analytic modelling that extrapolated cost-effectiveness over the lifetimes of survivors and incorporated additional costs and health consequences for organ recipients significantly altered the cost-effectiveness calculus. The probability that adrenaline is cost-effective increased to 90% at a £30,000 (€34,500) per QALY cost-effectiveness threshold, arguably within the bounds of acceptance by health technology agencies tasked with reimbursement decisions [ 11 ]. The cost-effectiveness conclusions were particularly sensitive to the inclusion of recipients of donated organs into economic modelling. The economic value of the additional survival and QALY benefits to organ recipients associated with adrenaline clearly outweigh the additional costs that result from organ transplantation. Similar findings have been reported for other interventions. In a study evaluating the costs and benefits of traumatic cardiopulmonary arrest resuscitation [ 27 ], the authors concluded that the financial burden associated with the procedure could be offset by expanding the benefits to include organ donation.

Previous economic evaluations of adrenaline were conducted in clinical contexts outside the management of out-of-hospital cardiac arrest [ 28 , 29 , 30 ], and therefore, a comparative assessment of cost-effectiveness evidence is not possible. Similarly, other pharmacological interventions for cardiac arrest, such as anti-arrythmics, have yet to be evaluated from an economic perspective. Our data should, therefore, be of relevance to clinical decision-makers and service planners tasked with the care and management of a condition that causes significant death and disability. Moreover, our economic evaluation is the first, to our knowledge, that has modelled the indirect economic benefits of organ donation arising from an intervention that increases the likelihood of admission to intensive care following an acute out-of-hospital clinical event. It may, therefore, alter methodological thinking on the incorporation of benefits other than directly to the patient into economic evaluations of interventions that affect the likelihood for organ donation. Such a methodological shift will require ethical consideration of how to balance benefits to others with benefits or harms to patients receiving the intervention.

Readers should consider caveats when interpreting the study results. First, our findings may not be applicable outside of the UK as the models of prehospital care, and healthcare costs may differ in other countries. Second, our adapted model of adult lung transplantation excluded the effects of adrenaline on pancreas and heart transplants. A paucity of epidemiological and economic evidence meant that we were unable to parameterise extensions to our decision-analytic model that considered effects beyond lung, liver and kidney transplants. Nevertheless, the number of pancreases and hearts transplanted to recipients of organs donated by the PARAMEDIC2 patients was small (< 10) and therefore unlikely to alter the balance of overall conclusions. Third, as with any health economic evaluation, there are likely to be potentially relevant effects that are not captured by the tools available for measurement and valuation. Specifically, we did not value other potential consequences of adrenaline, for example, the potential benefits to family members arising from the increased likelihood of being able to say goodbye and be present at the time of death [ 31 ]. Finally, in the modelling that extended our assessments of cost-effectiveness of adrenaline over the lifetimes of cardiac arrest survivors and incorporated the indirect economic effects of organ transplantation, a number of assumptions were required to simplify the parameterisation and address data limitations. Details of these assumptions are described in additional file 5 . These included assuming that functional health state does not change over extended periods of survival and that resource use captured in the 3–6 month post-randomisation period can be extrapolated to cover the 6–12 month post-randomisation period. The organ donor modelling required a set of steady or equilibrium-state assumptions (number of organs available each year equals the number of transplantations with no wastage; the probability of death or removal on the waiting list are independent of organ supply) that captured the impact of intervention on the probability of transplantation. Nevertheless, our approach was consistent with current methodological guidance for decision-analytic modelling [ 32 ]. Furthermore, extensive sensitivity analyses were conducted that found that the conclusions from the base-case analyses were robust to doubling and halving input parameter values and alternative specifications of modelling assumptions.

In conclusion, adrenaline use in adults with out-of-hospital cardiac arrest does not represent a cost-effective use of resources when only directly related costs and consequences are considered. However, incorporating the indirect economic effects associated with transplanted organs substantially alters the cost-effectiveness calculus, suggesting decision-makers should consider the complexity of direct and indirect economic impacts of adrenaline.

Availability of data and materials

Not applicable

Abbreviations

Donation after brain death

Donation after circulatory death

Ex vivo lung perfusion

Ex vivo machine perfusion

Hospital Episode Statistics

HealthCare Resource Group

Implantable cardioverter defibrillator

Incremental cost-effectiveness ratio

National Health Service

NHS and Personal Social Services

National Institute for Care Excellence

Patient Episode Database for Wales

Quality-adjusted life year

United Kingdom

United Kingdom Blood and Transplant

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Acknowledgements

The PARAMEDIC 2 trial team and the University of Warwick acknowledge the support of the National Institute for Health Research Clinical Research Network (NIHR CRN) and the Applied Research Centre (ARC) West Midlands. This project was funded by the National Institute for Health Research’s Health Technology Assessment Programme (project number 12/127/126).

We acknowledge NHS Blood and Transplant for access to data held on the UK Transplant Registry held by the Organ Donation and Transplantation Directorate and all those who have contributed data to the Registry.

The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Health Technology Assessment Programme, NIHR, NHS or the Department of Health.

PARAMEDIC 2 collaborators: Stavros Petrou, Jason Madan, Kamran Khan, Chen Ji, Anower Hossain, Ranjit Lall, Anne-Marie Slowther, Charles D. Deakin, Tom Quinn, Jerry P. Nolan, Helen Pocock, Nigel Rees, Michael Smyth, Simon Gates, Dale Gardiner, Gavin D. Perkins, Matthew Cooke, Sarah Lamb, Andrew Carson (RIP), Ian Jacobs (RIP), Ed England, John Black, Nicola Brock, Claire Godfrey, Sarah Taylor, Michelle Thomson, Isabel Rodriguez-Bachiller, Claire King, Marie Stevens, Johanna Lazarus, Helen Werts, Joshua Golding, Rachel Fothergill, Fionna Moore, Alex Boda, Richard Whitfield, Laura Galligan, Rob Lovett, Jennifer Bradley, Lyndsay O’Shea, Mark Docherty, Imogen Gunsen, Gill Price, Andy Rosser, Garry Parcell, Mindy Jhamat, Josh Miller, Jenny Sears Brown, Alice Pretty, Madison Larden, Emma Harris, Jenny Lumley-Holmes, Rhiannon Boldy, Prudence Horwood, Kyee Han, Karl Charlton, Sonia Byers, Gary Shaw, Matt Limmer, Craig Wynne, Michelle Jackson, Emma Bell, Oliver Gupta, Rima Gupta, Charlotte Scomparin, Susie Hennings, Jessica Horton, James Buck, Sarah Rumble, Hayley Johnson, Eva Kritzer, Chockalingham Muthiah, Adrian Willis, Claire Daffern, Louise Clarkson, Felix Achana, Nicola Cashin, Emma Skilton, Malvenia Richmond, Martin Underwood, Natalie Strickland, Sarah Duggan, Scott Regan, Marie Stevens, Jill Wood; Trial Steering Committee (Jon Nicholl, Neil Bayliss, Helen Snooks, Jonathan Benger, Robert Andrews, David Pitcher, William Lee, Matt Wise); Data Monitoring Committee (Marion Campbell, Jasmeet Soar, Kathy Rowan, Sue Mason). We would also like to thank collaborators at all receiving hospitals, all staff involved at participating ambulance services and our patient and public partners.

This project was funded by the National Institute for Health Research’s Health Technology Assessment Programme (project number 12/127/126).

Author information

Stavros Petrou and Gavin D Perkins are joint senior authors.

Authors and Affiliations

Warwick Clinical Trials Unit, Warwick Medical School, The University of Warwick, Coventry, UK

Felix Achana, Stavros Petrou, Jason Madan, Kamran Khan, Chen Ji, Ranjit Lall, Anne-Marie Slowther, Jerry P. Nolan, Helen Pocock, Michael Smyth, Gavin D. Perkins, Stavros Petrou, Jason Madan, Kamran Khan, Chen Ji, Ranjit Lall, Anne-Marie Slowther, Jerry P. Nolan, Helen Pocock, Michael Smyth & Gavin D. Perkins

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Felix Achana, Stavros Petrou & Stavros Petrou

Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka, Bangladesh

Anower Hossain & Anower Hossain

Southampton Respiratory Biomedical Research Unit, National Institute for Health Research, Southampton, UK

Charles D. Deakin & Charles D. Deakin

Kingston University and St. George’s, University of London, London, UK

Tom Quinn & Tom Quinn

Critical Care Unit, Royal United Hospital, Bath, UK

Jerry P. Nolan & Jerry P. Nolan

South Central Ambulance Service NHS Foundation Trust, Otterbourne, UK

Helen Pocock & Helen Pocock

Welsh Ambulance Services NHS Trust, Swansea, UK

Nigel Rees & Nigel Rees

West Midlands Ambulance NHS Foundation Trust, Dudley, UK

Michael Smyth & Michael Smyth

Cancer Clinical Trials Unit, University of Birmingham, Birmingham, UK

Simon Gates & Simon Gates

National Clinical Lead for Organ Donation, NHS Blood and Transplant, Bristol, UK

Dale Gardiner & Dale Gardiner

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Stavros Petrou
, Jason Madan
, Kamran Khan
, Anower Hossain
, Ranjit Lall
, Anne-Marie Slowther
, Charles D. Deakin
, Tom Quinn
, Jerry P. Nolan
, Helen Pocock
, Nigel Rees
, Michael Smyth
, Simon Gates
, Dale Gardiner
, Gavin D. Perkins
, Matthew Cooke
, Sarah Lamb
, Andrew Carson
, Ian Jacobs
, Ed England
, John Black
, Nicola Brock
, Claire Godfrey
, Sarah Taylor
, Michelle Thomson
, Isabel Rodriguez-Bachiller
, Claire King
, Marie Stevens
, Johanna Lazarus
, Helen Werts
, Joshua Golding
, Rachel Fothergill
, Fionna Moore
, Alex Boda
, Richard Whitfield
, Laura Galligan
, Rob Lovett
, Jennifer Bradley
, Lyndsay O’Shea
, Mark Docherty
, Imogen Gunsen
, Gill Price
, Andy Rosser
, Garry Parcell
, Mindy Jhamat
, Josh Miller
, Jenny Sears Brown
, Alice Pretty
, Madison Larden
, Emma Harris
, Jenny Lumley-Holmes
, Rhiannon Boldy
, Prudence Horwood
, Karl Charlton
, Sonia Byers
, Gary Shaw
, Matt Limmer
, Craig Wynne
, Michelle Jackson
, Emma Bell
, Oliver Gupta
, Rima Gupta
, Charlotte Scomparin
, Susie Hennings
, Jessica Horton
, James Buck
, Sarah Rumble
, Hayley Johnson
, Eva Kritzer
, Chockalingham Muthiah
, Adrian Willis
, Claire Daffern
, Louise Clarkson
, Felix Achana
, Nicola Cashin
, Emma Skilton
, Malvenia Richmond
, Martin Underwood
, Natalie Strickland
, Sarah Duggan
, Scott Regan
, Jill Wood
, Jon Nicholl
, Neil Bayliss
, Helen Snooks
, Jonathan Benger
, Robert Andrews
, David Pitcher
, William Lee
, Matt Wise
, Marion Campbell
, Jasmeet Soar
, Kathy Rowan
& Sue Mason

Contributions

FA, SP, KK, JM and DG contributed to the design of the within-trial cost-effectiveness analysis and the economic modelling. CJ, AH, RL and SG contributed to statistical design and analysed the data. AS, CD, TQ, JN, HP, NR, MS and GP contributed to the design of the study and data collection and provided clinical oversight. All authors contributed to drafting and proof-reading of the manuscript for high-quality intellectual work and that the findings and conclusions are consistent with the data. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Gavin D. Perkins .

Ethics declarations

Ethics approval and consent to participate.

The South Central–Oxford C Research Ethics Committee (REC: 14/SC/0157) and the Medicines and Healthcare Products Regulatory Authority (EudraCT: 2014-000792-11) approved the study protocol.

Consent for publication

Competing interests.

MAS reports funding from the National Institute for Health Research. MAS has volunteer roles with the International Liaison Committee on Resuscitation, European and Resuscitation Council UK.

TQ reports grant funding paid to his institution by the National Institute for Health Research and British Heart Foundation. TQ has volunteer roles with the European Society of Cardiology.

GDP reports grant funding paid to his institution from the National Institute for Health Research, Resuscitation Council UK and British Heart Foundation related to this work. GDP has volunteer roles with the International Liaison Committee on Resuscitation, European and Resuscitation Council UK and Intensive Care Foundation. GDP is supported as a NIHR Senior Investigator and serves as an Editor for Resuscitation.

SP is supported by a Senior Investigator award from the UK National Institute of Health Research.

NR reports grants from National Institute for Health Research and Health and Care Research Wales during the conduct of the study.

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Supplementary information

Additional file 1..

PARAMEDIC2 HEALTH ECONOMICS ANALYSIS PLAN. Details of the PARAMEDIC2 health economics analysis plan.

Additional file 2.

Unit costs for NHS, non-NHS and personal and social service resource inputs (£ sterling, 2017 prices). Unit costs data used to inform the within trial economic evaluation.

Additional file 3.

Pre-specified and post-hoc sensitivity analyses exploring varying features of the within-trial economic evaluation on the cost-effectiveness results. List of sensitivity analyses.

Additional file 4.

Subgroup analyses conducted that explored potential heterogeneity in the within-trial incremental cost-effectiveness of adrenaline.

Additional file 5.

Economic modelling to extrapolate cost-effectiveness beyond trial follow-up and incorporate the indirect effects of organ donation. List of subgroup analyses.

Additional file 6.

Resources use for NHS and personal and social services by trial arm in the 6 month post-randomisation period; all patients. Resource use results table, within-trial economic evaluation.

Additional file 7.

Cost-effectiveness (£, 2017 prices) of adrenaline based on within-trial economic evaluation; sensitivity analyses and subgroup analyses results. Additional cost-effectiveness results.

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Achana, F., Petrou, S., Madan, J. et al. Cost-effectiveness of adrenaline for out-of-hospital cardiac arrest. Crit Care 24 , 579 (2020). https://doi.org/10.1186/s13054-020-03271-0

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DOI : https://doi.org/10.1186/s13054-020-03271-0

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Methods and participant characteristics in the Cancer Risk in Vegetarians Consortium: A cross-sectional analysis across 11 prospective studies

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Background The associations of vegetarian diets with risks for site-specific cancers have not been estimated reliably due to the low number of vegetarians in previous studies. Therefore, the Cancer Risk in Vegetarians Consortium was established.

Objective To describe and compare the baseline characteristics between non-vegetarian and vegetarian diet groups and between the collaborating studies.

Methods We harmonised individual-level data from 11 prospective cohort studies in the UK, US, India, China, and Taiwan. Comparisons of food intakes, sociodemographic and lifestyle factors were made between diet groups and between cohorts using descriptive statistics.

Results 2.3 million participants were included; 66% women and 34% men, with mean ages at recruitment of 57 (SD: 7.8) and 57 (8.6) years, respectively. There were 2.1 million meat eaters, 60,903 poultry eaters, 44,780 pescatarians, 81,165 vegetarians, and 14,167 vegans. Food intake differences between the diet groups varied across the cohorts; for example, fruit and vegetable intakes were generally higher in vegetarians than in meat eaters in all the cohorts except in China. BMI was generally lower in vegetarians, particularly vegans, except for the cohorts in India and China. In general, but with some exceptions, vegetarians were also more likely to be highly educated and physically active and less likely to smoke. In the available resurveys, stability of diet groups was high in all the cohorts except in China.

Conclusions Food intakes and lifestyle factors of both non-vegetarians and vegetarians varied markedly across the individual cohorts, which may be due to differences in both culture and socioeconomic status, as well as differences in questionnaire design. Therefore, care is needed in the interpretation of the impacts of vegetarian diets on cancer risk.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work has been funded by the World Cancer Research Fund UK (grant 2019/1953). The funder was not involved in any of the following: study design, collection, analysis, interpretation of data, and writing of the report.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Each study in this consortium received ethical clearance from their respective ethical committees which covered future research. The Loma Linda University Institutional Review Board gave ethical approval for the Adventist Health Study 2. Ethical approval for the CARRS study was provided by Institutional Review Boards of PHFI, AIIMS, MDRF Chennai, India, AKU and Emory University. In addition, the study has received regulatory approval from the Health Ministry Screening Committee of India. The EPIC Oxford study was given ethical approval by Royal College of General Practitioners Clinical Research Ethics Committee, the Central Oxford Research Ethics Committee and local research ethics committees. The South Central Oxford C Research Ethics Committee gave ethical approval for the Oxford Vegetarian Study. The Institutional Review Board for ethics in the Buddhist Dalin Tzu Chi Hospital waived ethical approval for the Tzu Chi Health Study. The National Research Ethics Service Committee for Yorkshire and the Humber, Leeds East provided ethical approval for the UKWCS. The Ethical Review Committee of the Chinese Centre for Disease Control and Prevention, Oxford Tropical Research Ethics Committee, University of Oxford gave ethical approval for the China Kadoorie Biobank. The NHS Health Research Authority (approval provided by Anglia and Oxford Multi centre Research Ethics Committee) gave ethical approval for the Million Women Study. The Special Studies Institutional Review Board of the U.S. National Cancer Institute provided ethical clearance for the NIH AARP study. The North West Multi Centre Research Ethics Committee provided ethical approval for the UK Biobank study.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

List of abbreviations: BMI, Body Mass Index; CARRS, Centre for cArdiometabolic Risk Reduction in South Asia; EPIC, European Prospective Investigation into Cancer and Nutrition; FFQ, Food Frequency Questionnaire; MET, metabolic equivalent of task; NIH-AARP, National Institutes of Health-AARP Diet and Health Study.

Data described in the manuscript will not be made available because studies pooled by the Cancer Risk in Vegetarians Consortium are not owned by the writing group and so are not available from this consortium. Individual studies may be contacted to request access to their data.

Conflict of interest: None declared.

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Informed consent

Information and guidance for researchers, what is informed consent.

Informed consent is one of the founding principles of research ethics. Its intent is that human participants can enter research freely (voluntarily) with full information about what it means for them to take part, and that they give consent before they enter the research.

Consent should be obtained before the participant enters the research (prospectively), and there must be no undue influence on participants to consent. The minimum requirements for consent to be informed are that the participant understands what the research is and what they are consenting to.

There are two distinct stages to a standard consent process for competent adults:

Stage 1 (giving information) : the person reflects on the information given; they are under no pressure to respond to the researcher immediately.
Stage 2 (obtaining consent): the researcher reiterates the terms of the research, often as separate bullet points or clauses; the person agrees to each term (giving explicit consent) before agreeing to take part in the project as a whole. Consent has been obtained.

Researchers should ensure that they comply with the General Data Protection Regulation (GDPR) during and after the consent process, especially if they will be collecting 'special category' (ie sensitive) data or personal data in the course of their research (also refer to the advice on consent in research involving children ). See also the guidance on data protection and research and the data protection checklist for use when preparing an application for ethical review.

Where your research includes filming or photography, you should refer to specific guidance in the Photography and GDPR toolkit .

Written or oral consent – which process suits your project?

Which process to use depends on the research project (its context, design and participants), though an oral process is usually only appropriate where a written process is not feasible. Any consent process must be understandable to the participants concerned. Please see the sections below to find out about different processes which may be used depending on the context, as well as informed consent templates for each process.

Written informed consent process

A written process is used where:

Reading and signing forms is not problematic.
The research is complex or has multiple stages.
First access to the research participants is by providing written information.

Though opinions differ about the legal force of signed consent forms, they provide extra proof that the terms of consent have been understood. This can be especially important when seeking consent for copyright over data, or for future uses of data. Also, future funders or regulators may want written proof of the terms of original consent.

For literate participants who are not put off by written information, a written process is often a straightforward way of communicating the 'research contract'.

Between the provision of information and obtaining consent, the participant should be given a reasonable amount of time to consider whether to consent and to ask questions, though the time given depends on the project design, the context of the research and the participants.

The written consent templates below can be adapted to suit your study.

Oral informed consent process

An oral consent process is where researcher and participant have a conversation to give information and obtain consent. There is no paper form to sign. It is normally used:

where literacy is a problem
where there are cultural or political concerns with signing contract-like documents
where either the researcher and/or the participant could be put at risk by existence of a paper record
where time for consent is limited, eg a chance interaction between researcher and participant (although you should not use an oral process merely to correct poor planning of research)
for research conducted via remote video conferencing software

It may also be more appropriate when interviewing elite participants as part of the research.

For all other research, how you arrange the oral process depends on how you will encounter your participants (for example email, phone, an on-the-street-meeting by chance). Between the information-giving and consent stage the participant should be given a reasonable amount of time to consider whether to consent, though this depends on the project design, the type of participants and the context of the research.

When obtaining oral consent, please ensure you are recording the consent process either using a recording device (for example audio recorder if you are conducting an interview that needs to be recorded) or, if participants do not agree to audio recording or if using or keeping audio records is unsafe, by using a researcher record of oral consent template or completing a written consent form on their behalf.

The oral consent templates below can be adapted to suit your study, but careful consideration is required to ensure that these are appropriate for the research and the participants.

Informed consent templates

Written informed consent process (including online surveys).

Description	File download
Any relevant advertising or recruiting material (poster, email text, social media advert)
Template information sheet
Template written consent form
Template information sheet for online research For online tasks only, where there is no face-to-face contact with human participants
Template information sheet for research involving children
Template assent form for research involving children
Participant information sheet for CUREC 3 studies
Consent form for CUREC 3 studies
Template email consent form – for low-risk research in the social sciences and humanities that does not involve face-to-face contact with participants

Description	File download
Any relevant advertising or recruiting material (poster, email text, social media advert)
Oral script
Written information sheet
Assent template for studies recruiting children
Alongside your script, the use of an oral consent record form is recommended. This is a form only you fill in, and helps you keep track of any specific conditions attached to an oral consent

Template agreements

Description		File download
This legal contribution template should be signed by research participants and Oxford researchers if the latter wish to upload participants’ photographs, videos, films, podcasts, audio recordings, etc to University of Oxford archives. This should be completed in addition to the above informed consent templates
This legal confidentiality agreement template can be adapted for research assistants/ interpreters/ translators/ transcribers/ local fieldworkers who are non-University employees
This legal confidentiality agreement template can be adapted for research assistants/ interpreters/ translators/ transcribers/ local fieldworkers who are University employees

Cases where 'implied consent' may be acceptable (for example online surveys)

Researchers should always aim to inform people fully and obtain appropriate consent. However, in some cases the research may be straightforward enough that a separate, deliberate process for obtaining consent is not needed. In these cases participants, by their actions, imply consent. This is seen most often in research:

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Template information sheet for online research

where no participant personal details are obtained
where the topic of research is very low risk and no special category data will be collected
where participation is confined to one small task, eg completing a survey or simple pencil or computer task

Please note: consent cannot be inferred from inaction (eg failure to move away from a camera).

When consent works against the aims of research

If your research employs deception, you will not be able to inform participants fully about your project’s true aims. In this case please check if you can fully apply the CUREC approved procedure on research involving the deception of participants . If the deception raises ethical concerns such that the application is not covered by this procedure please complete a CUREC 2 application form.

Some research settings evolve very rapidly (for example in conflict studies). Similarly, some research participants may only be revealed in time-poor or emergency settings (eg heart attack patients). This infringes on the standard information-giving stage of research. The vulnerability of participants in those settings may justify an expedited or fully waived consent process. Again it is important to describe the research setting clearly. You may need to complete a CUREC 2 application to the relevant committee in these cases: please check with your DREC or your IDREC .

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In 1708, by decree of Peter the Great, Moskovskaya gubernia (province) was established. It included most of the territory of present Moscow oblast. In 1712, St. Petersburg became the capital of the Russian Empire and the significance of the Moscow region as the country’s economic center began to decrease.

In 1812, the Battle of Borodino took place near Moscow. It was the biggest battle of the Russian-French War of 1812. In the second half of the 19th century, especially after the peasant reform of 1861, the Moscow province experienced economic growth. In 1851, the first railway connected Moscow and St. Petersburg; in 1862 - Nizhny Novgorod.

The population of the Moscow region increased significantly (in 1847 - 1.13 million people, in 1905 - 2.65 million). On the eve of the First World War, Moscow was a city with a population of more than one million people.

In November, 1917, the Soviet power was established in the region. In 1918, the country’s capital was moved from St. Petersburg to Moscow that contributed to economic recovery of the province. In the 1920s-1930s, a lot of churches located near Moscow were closed, a large number of cultural monuments were destroyed. On January 14, 1929, Moscow Oblast was formed.

In 1941-1942, one of the most important battles of the Second World War took place on the territory of the region - the Battle for Moscow. In the postwar years, the growth of economic potential of the region continued; several science cities were founded (Dubna, Troitsk, Pushchino, Chernogolovka).

In the 1990s, the economy of Moscow Oblast experienced a deep crisis. Since the 1990s, due to the motorization of the population and commuting, road traffic situation in the Moscow region significantly deteriorated. Traffic jams have become commonplace.

Pictures of Moscow Oblast

Moscow Oblast scenery

Author: Mikhail Grizly

At the airport in the Moscow region

Author: Evgeny Davydov

Nature of Moscow Oblast

Author: Alexander Khmelkov

Moscow Oblast - Features

Moscow Oblast is located in the central part of the East European Plain, in the basin of the rivers of Volga, Oka, Klyazma, Moskva. The region stretches from north to south for 310 km, from west to east - 340 km. It was named after the city of Moscow, which however is not part of the region. Part of the administrative authorities of the region is located in Krasnogorsk.

On the territory of the Moscow region, there are 77 cities and towns, 19 of them have a population of more than 100 thousand people. The largest cities are Balashikha (518,300), Podolsk (309,600), Mytishchi (262,700), Khimky (256,300), Korolyov (225,300), Lubertsy (209,600), Krasnogorsk (174,900), Elektrostal (149,000), Odintsovo (138,900), Kolomna (136,800), Domodedovo (136,100).

The climate is temperate continental. Summers are warm, winters are moderately cold. The average temperature in January is minus 10 degrees Celsius, in July - plus 19 degrees Celsius.

One of the most important features of the local economy is its proximity to Moscow. Some of the cities (Odintsovo, Krasnogorsk, Mytishchi) have become in fact the “sleeping districts” of Moscow. The region is in second place in terms of industrial production among the regions of Russia (after Moscow).

The leading industries are food processing, engineering, chemical, metallurgy, construction. Moscow oblast has one of the largest in Russia scientific and technological complexes. Handicrafts are well developed (Gzhel ceramics, Zhostov trays, Fedoskino lacquered miniatures, toy-making).

Moscow railway hub is the largest in Russia (11 radial directions, 2,700 km of railways, the density of railways is the highest in Russia). There are two large international airports - Sheremetyevo and Domodedovo. Vnukovo airport is used for the flights within the country.

Attractions of Moscow Oblast

Moscow Oblast has more than 6,400 objects of cultural heritage:

famous estate complexes,
ancient towns with architectural monuments (Vereya, Volokolamsk, Dmitrov, Zaraysk, Zvenigorod, Istra, Kolomna, Sergiev Posad, Serpukhov),
churches and monasteries-museums (the Trinity Lavra of St. Sergius, Joseph-Volokolamsk monastery, Pokrovsky Khotkov monastery, Savvino Storozhevsky monastery, Nikolo Ugresha monastery).

The most famous estate complexes:

Arkhangelskoye - a large museum with a rich collection of Western European and Russian art of the 17th-19th centuries,
Abramtsevo - a literary and artistic center,
Melikhovo - an estate owned by A.P. Chekhov at the end of the 19th century,
Zakharovo and Bolshiye Vyazyomy included in the History and Literature Museum-Reserve of Alexander Pushkin,
House-Museum of the composer P.I. Tchaikovsky in Klin,
Muranovo that belonged to the poet F.I. Tyutchev,
Shakhmatovo - the estate of the poet Alexander Blok.

The architectural ensemble of the Trinity Sergius Lavra is a UNESCO World Heritage Site. The largest museum of the Moscow region is located in Serpukhov - Serpukhov Historical and Art Museum.

The places of traditional arts and crafts are the basis of the souvenir industry of Russia:

Fedoskino - lacquer miniature painting,
Bogorodskoe - traditional manufacture of wooden toys,
Gzhel - unique tradition of creating ceramics,
Zhostovo - painted metal crafts,
Pavlovsky Posad - fabrics with traditional printed pattern.

Some of these settlements have museums dedicated to traditional crafts (for example, a toy museum in Bogorodskoe), as well as centers of learning arts and crafts.

Moskovskaya oblast of Russia photos

Landscapes of moscow oblast.

Nature of the Moscow region

Country road in the Moscow region

Moscow Oblast landscape

Author: Mikhail Kurtsev

Moscow Oblast views

Author: Asedach Alexander

Country life in Moscow Oblast

Author: Andrey Zakharov

Church in Moscow Oblast

Author: Groshev Dmitrii

Churches of Moscow Oblast

Church in the Moscow region

Cathedral in Moscow Oblast

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Geographic coordinates of Elektrostal, Moscow Oblast, Russia

Coordinates of elektrostal in decimal degrees, coordinates of elektrostal in degrees and decimal minutes, utm coordinates of elektrostal, geographic coordinate systems.

WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).

Geographic coordinates (latitude and longitude) define a position on the Earth’s surface. Coordinates are angular units. The canonical form of latitude and longitude representation uses degrees (°), minutes (′), and seconds (″). GPS systems widely use coordinates in degrees and decimal minutes, or in decimal degrees.

Latitude varies from −90° to 90°. The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. N). Negative latitude values correspond to the geographic locations south of the Equator (abbrev. S).

Longitude is counted from the prime meridian ( IERS Reference Meridian for WGS 84) and varies from −180° to 180°. Positive longitude values correspond to the geographic locations east of the prime meridian (abbrev. E). Negative longitude values correspond to the geographic locations west of the prime meridian (abbrev. W).

UTM or Universal Transverse Mercator coordinate system divides the Earth’s surface into 60 longitudinal zones. The coordinates of a location within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone’s central meridian, and measured in meters.

Elevation above sea level is a measure of a geographic location’s height. We are using the global digital elevation model GTOPO30 .

Elektrostal , Moscow Oblast, Russia

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Moscow Oblast - Features. Moscow Oblast is located in the central part of the East European Plain, in the basin of the rivers of Volga, Oka, Klyazma, Moskva. The region stretches from north to south for 310 km, from west to east - 340 km. It was named after the city of Moscow, which however is not part of the region.
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Geographic coordinates of Elektrostal, Moscow Oblast, Russia
The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. ... within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone's central ...
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