Challenges to the delivery of clinical diabetes services in Ghana created by the COVID-19 pandemic.

OBJECTIVE: The barriers to delivering clinical non-communicable disease services in low- and middle-income countries have risen with the onset of COVID-19. Using Ghana as a case study, this article examines the changes COVID-19 has brought to diabetes service delivery and considers policy responses to deal with future such outbreaks. METHODS: We conducted 18 interviews between November 2020 and February 2021 with health professionals and administrators from primary, secondary and tertiary facilities within the Ghana Health Service. The analysis was performed using deductive and inductive methods. RESULTS: There were six general themes in interviewees' responses: (1) COVID-19 had exacerbated the problems of high medicine and service costs and medicine shortages, (2) the pandemic had exacerbated problems of poor patient record keeping, (3) COVID-19 had reduced the availability of suitably trained health providers, (4) staff had become demoralized by management's unwillingness to make innovative changes to cope with the pandemic, (5) COVID-19 led to a reorganization of diabetes services, and (6) the country's national health insurance scheme lacked flexibility in dealing with the pandemic. CONCLUSIONS: Access to resources is limited in LMICs. However, our study highlights practical policy responses that can improve health providers' response to COVID-19 and future pandemics.

United Kingdom Early Detection Initiative (UK-EDI): protocol for establishing a national multicentre cohort of individuals with new-onset diabetes for early detection of pancreatic cancer.

INTRODUCTION: Pancreatic cancer is a leading cause of cancer deaths worldwide. Screening for this disease has potential to improve survival. It is not feasible, with current screening modalities, to screen the asymptomatic adult population. However, screening of individuals in high-risk groups is recommended. Our study aims to provide resources and data that will inform strategies to screen individuals with new-onset diabetes (NOD) for pancreatic cancer. METHODS AND ANALYSIS: The United Kingdom Early Detection Initiative (UK-EDI) for pancreatic cancer is a national, prospective, observational cohort study that aims to recruit 2500 individuals with NOD (<6 months postdiagnosis) aged 50 years and over, with follow-up every 6 months, over a 3-year period. For study eligibility, diagnosis of diabetes is considered to be clinical measurement of haemoglobin A1c ≥48 mmol/mol. Detailed clinical information and biospecimens will be collected at baseline and follow-up to support the development of molecular, epidemiological and demographic biomarkers for earlier detection of pancreatic cancer in the high-risk NOD group. Socioeconomic impacts and cost-effectiveness of earlier detection of pancreatic cancer in individuals with NOD will be evaluated. The UK-EDI NOD cohort will provide a bioresource for future early detection research to be conducted. ETHICS AND DISSEMINATION: The UK-EDI study has been reviewed and approved by the London-West London and GTAC Research Ethics Committee (Ref 20/LO/0058). Study results will be disseminated through presentations at national and international symposia and publication in peer-reviewed, Open Access journals.

Impact of visitation and cohorting policies to shield residents from covid-19 spread in care homes: an agent-based model.

BACKGROUND: This study examines the impact of visitation and cohorting policies as well as the care home population size upon the spread of COVID-19 and the risk of outbreak occurrence in this setting. METHODS: Agent-based modelling RESULTS: The likelihood of the presence of an outbreak in a care home is associated with the care home population size. Cohorting of residents and staff into smaller, self-contained units reduces the spread of COVID-19. Restricting the number of visitors to the care home to shield its residents does not significantly impact the cumulative number of infected residents and risk of outbreak occurrence in most scenarios. Only when the community prevalence where staff live is considerably lower than the prevalence where visitors live (the former prevalence is less than or equal to 30% of the latter), relaxing visitation increases predicted infections much more significantly than it does in other scenarios. Maintaining a low infection probability per resident-visitor contact helps reduce the effect of allowing more visitors into care homes. CONCLUSIONS: Our model predictions suggest that cohorting is effective in controlling the spread of COVID-19 in care homes. However, according to predictions shielding residents in care homes is not as effective as predicted in a number of studies that have modelled shielding of vulnerable population in the wider communities.

Collaborative critical care prediction and resource planning during the COVID-19 pandemic using computer simulation modelling: future urgent planning lessons.

In March 2020, epidemiological modelling of COVID-19 predicted overwhelming demand on healthcare resources, yet data that emerged painted a different picture. Our management science health systems team at the University of Strathclyde collaborated with one NHS organisation to contextualise national policy and predict local resource needs before the pandemic took hold. Using action research, we combined organisational expertise, local and international data, and healthcare systems expertise to create a discrete event simulation model that predicted concurrent resource use over the first 10 weeks of the pandemic with realistic estimates of uncertainty. This allowed the organisation to create an effective strategy for resource planning. Had they followed national guidance, the costs would have been unwieldy and futile. Our decentralised approach delivered valuable information in a timely manner. This case study is unique in healthcare literature and serves as an example of successful methodology for similar crises.

Health systems, population and patient challenges for achieving universal health coverage for hypertension in Ghana.

Ghana has signed on to the United Nations Sustainable Development Goal to achieve universal health coverage (UHC), ensuring that all individuals receive the health care they require without financial hardship. Achieving that goal is a difficult task in any setting. The challenges are further exacerbated by a changing disease landscape, as the burden of non-communicable diseases (NCDs) is increasing and creating a dual burden along with infectious diseases. This study explores the existing health system for delivering hypertension care and the challenges of delivering UHC for hypertension in Ghana. Document analysis of national health reports, policies and legislations along with a review of research articles was conducted to explore the challenges of delivering UHC for NCDs in Ghana, and hypertension in particular. The main themes and indicators related to the challenges of delivering UHC for hypertension were mapped and analysed. The main challenges to delivering UHC for hypertension can be grouped into population and patient, on the one hand, and health system factors, on the other. Population and patient factors include (1) unhealthy lifestyles overburdening the health system, (2) poor health-seeking behaviour and (3) poor adherence to medication, which has led to uncontrolled cases and poor clinical outcomes even among treated patients with hypertension. Health system factors include (1) inadequate health system capacity for early diagnosis due to an increasing number of patients, (2) inequitable distribution of health care facilities affecting access, (3) financial sustainability of the National Health Insurance Scheme and delays in reimbursement of claims to facilities that affect the health system's ability to provide timely management of hypertension and (4) health care facilities and practitioners' use of non-standardized and uncalibrated blood pressure measuring equipment. Ghana therefore will need to make important decisions to overcome operational and financial challenges on its path to UHC.

Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model.

BACKGROUND: Care homes are vulnerable to widespread transmission of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) with poor outcomes for staff and residents. Infection control interventions in care homes need to not only be effective in containing the spread of coronavirus disease 2019 (COVID-19) but also feasible to implement in this special setting which is both a healthcare institution and a home. METHODS: We developed an agent-based model that simulates the transmission dynamics of COVID-19 via contacts between individuals, including residents, staff members, and visitors in a care home setting. We explored a representative care home in Scotland in our base case and explore other care home setups in an uncertainty analysis. We evaluated the effectiveness of a range of intervention strategies in controlling the spread of COVID-19. RESULTS: In the presence of the reference interventions that have been implemented in many care homes, including testing of new admissions, isolation of symptomatic residents, and restricted public visiting, routine testing of staff appears to be the most effective and practical approach. Routine testing of residents is no more effective as a reference strategy while routine testing of both staff and residents only shows a negligible additive effect. Modeling results are very sensitive to transmission probability per contact, but the qualitative finding is robust to varying parameter values in our uncertainty analysis. CONCLUSIONS: Our model predictions suggest that routine testing should target staff in care homes in conjunction with adherence to strict hand hygiene and using personal protective equipment to reduce risk of transmission per contact.

A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinoma.

BACKGROUND: The narrative surrounding the management of potentially resectable pancreatic cancer is complex. Surgical resection is the only potentially curative treatment. However resection rates are low, the risk of operative morbidity and mortality are high, and survival outcomes remain poor. The aim of this study was to create a prognostic Bayesian network that pre-operatively makes personalized predictions of post-resection survival time of 12months or less and also performs post-operative prognostic updating. METHODS: A Bayesian network was created by synthesizing data from PubMed post-resection survival analysis studies through a two-stage weighting process. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology results and adjuvant therapy. RESULTS: 77 studies (n = 31,214) were used to create the Bayesian network, which was validated against a prospectively maintained tertiary referral centre database (n = 387). For pre-operative predictions an Area Under the Curve (AUC) of 0.7 (P value: 0.001; 95% CI 0.589-0.801) was achieved accepting up to 4 missing data-points in the dataset. For prognostic updating an AUC 0.8 (P value: 0.000; 95% CI:0.710-0.870) was achieved when validated against a dataset with up to 6 missing pre-operative, and 0 missing post-operative data-points. This dropped to AUC: 0.7 (P value: 0.000; 95% CI:0.667-0.818) when the post-operative validation dataset had up to 2 missing data-points. CONCLUSION: This Bayesian network is currently unique in the way it utilizes PubMed and patient level data to translate the existing empirical evidence surrounding potentially resectable pancreatic cancer to make personalized prognostic predictions. We believe such a tool is vital in facilitating better shared decision-making in clinical practice and could be further developed to offer a vehicle for delivering personalized precision medicine in the future.

A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer.

OBJECTIVES: To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC). DESIGN/SETTING: A narrative systematic review of international peer reviewed journals DATA SOURCE: Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models. RESULTS: 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2). CONCLUSIONS: Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias. TRIAL REGISTRATION NUMBER: CRD42018105942.

Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed, and Cochrane Database were undertaken. Studies were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1) and artificial neural network (n = 1), and one study explored machine learning algorithms including Bayesian network, decision trees, k-nearest neighbor, and artificial neural networks. The main methodological issues identified were limited data sources, which limits generalizability and potentiates bias; lack of external validation; and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision-making.

Upfront Surgery versus Neoadjuvant Therapy for Resectable Pancreatic Cancer: Systematic Review and Bayesian Network Meta-analysis.

Current treatment recommendations for resectable pancreatic cancer support upfront resection and adjuvant therapy. Randomized controlled trials offering comparison with the emerging neoadjuvant approach are lacking. This review aims to compare both treatment strategies for resectable pancreatic cancer. PubMed, MEDLINE, Embase, Cochrane Database and Cochrane Databases were searched for studies comparing neoadjuvant and surgery-first with adjuvant therapy for resectable pancreatic cancer. A Bayesian network meta-analysis was conducted using the Markov chain Monte Carlo method. Cochrane Collaboration's risk of bias, ROBINS-I and GRADE tools were used to assess quality and risk of bias of included trials. 9 studies compared neoadjuvant therapy and surgery-first with adjuvant therapy (n = 22,285). Aggregate rate (AR) of R0 resection for neoadjuvant therapy was 0.8008 (0.3636-0.9144) versus 0.7515 (0.2026-0.8611) odds ratio (O.R.) 1.27 (95% CI 0.60-1.96). 1-year survival AR for neoadjuvant therapy was 0.7969 (0.6061-0.9500) versus 0.7481 (0.4848-0.8500) O.R. 1.38 (95% CI 0.69-2.96). 2-year survival AR for neoadjuvant therapy was 0.5178 (0.3000-0.5970) versus 0.5131 (0.2727-0.5346) O.R. 1.26 (95% CI 0.94-1.74). 5-year AR survival for neoadjuvant therapy was 0.2069 (0.0323-0.3300) versus 0.1783 (0.0606-0.2300) O.R. 1.19 (95% CI 0.65-1.73). In conclusion neoadjuvant therapy may offer benefit over surgery-first and adjuvant therapy. However, further randomized controlled trials are needed.

Neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic cancer: A Markov decision analysis.

BACKGROUND: Neoadjuvant therapy has emerged as an alternative treatment strategy for potentially resectable pancreatic cancer. In the absence of large randomized controlled trials offering a direct comparison, this study aims to use Markov decision analysis to compare efficacy of traditional surgery first (SF) and neoadjuvant treatment (NAT) pathways for potentially resectable pancreatic cancer. METHODS: An advanced Markov decision analysis model was constructed to compare SF and NAT pathways for potentially resectable pancreatic cancer. Transition probabilities were calculated from randomized control and Phase II/III trials after comprehensive literature search. Utility outcomes were measured in overall and quality-adjusted life months (QALMs) on an intention-to-treat basis as the primary outcome. Markov cohort analysis of treatment received was the secondary outcome. Model uncertainties were tested with one and two-way deterministic and probabilistic Monte Carlo sensitivity analysis. RESULTS: SF gave 23.72 months (18.51 QALMs) versus 20.22 months (16.26 QALMs). Markov Cohort Analysis showed that where all treatment modalities were received NAT gave 35.05 months (29.87 QALMs) versus 30.96 months (24.86QALMs) for R0 resection and 34.08 months (29.87 QALMs) versus 25.85 months (20.72 QALMs) for R1 resection. One-way deterministic sensitivity analysis showed that NAT was superior if the resection rate was greater than 51.04% or below 75.68% in SF pathway. Two-way sensitivity analysis showed that pathway superiority depended on obtaining multimodal treatment in either pathway. CONCLUSION: Whilst NAT is a viable alternative to traditional SF approach, superior pathway selection depends on the individual patient's likelihood of receiving multimodal treatment in either pathway.

Economic costs of hospitalized diarrheal disease in Bangladesh: a societal perspective.

BACKGROUND: Diarrheal diseases are a major threat to human health and still represent a leading cause of morbidity and mortality worldwide. Although the burden of the diarrheal diseases is much lower in developed countries, it is a significant public health problem in low and middle-income countries like Bangladesh. Though diarrhea is preventable and managed with low-cost interventions, it is still the leading cause of morbidity according to the patient who sought care from public hospitals in Bangladesh indicating that significant resources are consumed in treating those patients. The aim of the study is to capture the inpatients and outpatient treatment cost of diarrheal disease and to measure the cost burden and coping mechanisms associated with diarrheal illness. METHODS: This study was conducted in six randomly selected district hospitals from six divisions (larger administrative units) in Bangladesh. The study was performed from the societal perspective which means all types of costs were identified, measured and valued no matter who incurred them. Cost analysis was estimated using the guideline proposed by the World Health Organization for estimating the economic burden of diarrheal diseases. The study adopted quantitative techniques to collect the household and hospital level data including structured and semi-structured questionnaires, observation checklists, analysis of hospital database, telephone interviews and compilation of service statistics. RESULTS: The average total societal cost of illness per episode was BDT 5274.02 (US $ 67.18) whereas the average inpatient and outpatient costs were BDT 8675.09 (US $ 110.51) and BDT 1853.96 (US $ 23.62) respectively. The cost burden was significantly highest for poorest households, 21.45% of household income, compared to 4.21% of the richest quintile. CONCLUSIONS: Diarrheal diseases continue to be an overwhelming problem in Bangladesh. The economic impact of any public health interventions (either preventive or promotive) that can reduce the prevalence of diarrheal diseases can be estimated from the data generated from this study.

Modeling the critical care pathway for cardiothoracic surgery.

The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type's pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique with minor capacity reallocations resulted in more than 60% drop in cancellations.

Cost-effectiveness analysis of introducing universal childhood rotavirus vaccination in Bangladesh.

Diarrhea is one of the world's leading killers of children, and globally, rotavirus is the most common cause of severe diarrhea among under 5 children. In Bangladesh, rotavirus kills nearly 6,000 under 5 children in each year. To reduce the burden of childhood rotavirus diseases, universal rotavirus vaccination is recommended by World Health Organization. The objective of this study is to assess the cost-effectiveness of introducing universal childhood rotavirus vaccination with the newly developed ROTAVAC vaccine in national Expanded Programme of Immunization in Bangladesh. We developed a decision model to examine the potential impact of vaccination in Bangladesh and to examine the effect if the vaccination is applied in the nationwide immunization program schedule. Introduction of childhood universal rotavirus vaccination in Bangladesh scenario appears as highly cost-effective and would offer substantial future benefits for the young population if vaccinated today. The cost per DALY averted of introducing the rotavirus vaccine compared with status quo is approximately US$ 740.27 and US$ 728.67 per DALY averted from the health system and societal perspective respectively which is "very cost-effective" using GDP threshold level according to World Health Organization definition. The results of this analysis seek to contribute to an evidence-based recommendation about the introduction of universal rotavirus vaccination in national Expanded Programme of Immunization (EPI) in Bangladesh.

Treatment log files as a tool to identify treatment plan sensitivity to inaccuracies in scanned proton beam delivery.

Dose distributions delivered at Gantry 2 at the Paul Scherrer Institut (PSI) can be reconstructed on the patient anatomy based on machine log files. With the present work, the dependency of the log file calculation on the planning optimization technique and on other planning parameters, such as field direction and tumour size, has been investigated. Interestingly, and despite the typically higher modulation of Intensity Modulated Proton Therapy (IMPT) plans, the results for both Single Field Uniform Distribution and IMPT approaches have been found to be similar. In addition, complex fields with steep in-field dose gradients, such as Simultaneous Integrated Boost, and with couch movements in between the delivery, also resulted in good agreement between planned and reconstructed doses. Nevertheless, highly modulated plans can have regions of larger local dose deviations and attention should therefore be paid during the planning stage to the location of isolated, highly weighted pencil beams. We propose also, that further effort should be invested in order to predict field robustness to delivery fluctuations before the clinical delivery of the plan as part of the plan specific Quality Assurance.

Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital.

OBJECTIVE: Healthcare faces the continual challenge of improving outcome while aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. DESIGN: Discrete event simulation was used to model and analyse cost and resource utilisation with an activity-based costing approach. Data for a full comparison before the process change was unavailable so we used a modelling approach, comparing a virtual fracture clinic (VFC) with a simulated traditional fracture clinic (TFC). SETTING: The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. OUTCOME MEASURES: Our study focused exclusively on patients with non-operative trauma attending emergency department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries are presented in association with activity costs from the models. RESULTS: Patients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face (f2f) attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p≤0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI 21.74 to 23.92) per patient compared with £36.81 (95% CI 35.65 to 37.97) for the TFC pathway. CONCLUSIONS: Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional f2f clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings.

Measurements and simulations of boron carbide as degrader material for proton therapy.

We report on test measurements using boron carbide (B4C) as degrader material in comparison with the conventional graphite, which is currently used in many proton therapy degraders. Boron carbide is a material of lower average atomic weight and higher density than graphite. Calculations predict that, compared to graphite, the use of boron carbide results in a lower emittance behind the degrader due to the shorter degrader length. Downstream of the acceptance defining collimation system we expect a higher beam transmission, especially at low beam energies. This is of great interest in proton therapy applications as it allows either a reduction of the beam intensity extracted from the cyclotron leading to lower activation or a reduction of the treatment time. This paper summarizes the results of simulations and experiments carried out at the PROSCAN facility at the Paul Scherrer Institute(1). The simulations predict an increase in the transmitted beam current after the collimation system of approx. 30.5% for beam degradation from 250 to 84 MeV for a boron carbide degrader compared to graphite. The experiment carried out with a boron carbide block reducing the energy to 84 MeV yielded a transmission improvement of 37% compared with the graphite degrader set to that energy.

Prevalence and Health Care-Seeking Behavior for Childhood Diarrheal Disease in Bangladesh.

In Bangladesh, the burden of diarrheal diseases is significant among children <5 years old. The objective of this study is to capture the prevalence of and health care-seeking behavior for childhood diarrheal diseases (CDDs) and to identify the factors associated with CDDs at a population level in Bangladesh. We use a logistic regression approach to model careseeking based on individual characteristics. The overall diarrhea prevalence among children <5 years old was found to be 5.71%. Some factors found to significantly influence the health care-seeking pattern were age and sex of the children, nutritional score, age and education of mothers, wealth index, and access to electronic media. The health care service could be improved through working in partnership with public facilities, private health care practitioners, and community-based organizations, so that all strata of the population get equitable access in cases of childhood diarrhoea.

High efficiency structured EUV multilayer mirror for spectral filtering of long wavelengths.

High spectral purity at longer wavelength side is demanded in many extreme ultraviolet (EUV) and soft X-ray (together also referred to as XUV) optical systems. It is usually obtained at the expense of a high loss of XUV efficiency. We proposed and developed a new method based on a periodic, tapered structure integrated with an EUV multilayer. The longer wavelength radiation is scattered/diffracted away by the tapered multilayer structure while the EUV light is reflected. The first proof-of-principle showed a broadband suppression from λ = 100-400 nm with an average factor of 14. Moreover, a high EUV reflectance of 64.7% was achieved, which corresponds to 94% of the efficiency of a regular EUV multilayer mirror.