Long-term benefits and harms of early colorectal cancer screening in German individuals with familial cancer risk.

Individuals with a family history of colorectal cancer (CRC) may benefit from early screening with colonoscopy or immunologic fecal occult blood testing (iFOBT). We systematically evaluated the benefit-harm trade-offs of various screening strategies differing by screening test (colonoscopy or iFOBT), interval (iFOBT: annual/biennial; colonoscopy: 10-yearly) and age at start (30, 35, 40, 45, 50 and 55 years) and end of screening (65, 70 and 75 years) offered to individuals identified with familial CRC risk in Germany. A Markov-state-transition model was developed and used to estimate health benefits (CRC-related deaths avoided, life-years gained [LYG]), potential harms (eg, associated with additional colonoscopies) and incremental harm-benefit ratios (IHBR) for each strategy. Both benefits and harms increased with earlier start and shorter intervals of screening. When screening started before age 50, 32-36 CRC-related deaths per 1000 persons were avoided with colonoscopy and 29-34 with iFOBT screening, compared to 29-31 (colonoscopy) and 28-30 (iFOBT) CRC-related deaths per 1000 persons when starting age 50 or older, respectively. For iFOBT screening, the IHBRs expressed as additional colonoscopies per LYG were one (biennial, age 45-65 vs no screening), four (biennial, age 35-65), six (biennial, age 30-70) and 34 (annual, age 30-54; biennial, age 55-75). Corresponding IHBRs for 10-yearly colonoscopy were four (age 55-65), 10 (age 45-65), 15 (age 35-65) and 29 (age 30-70). Offering screening with colonoscopy or iFOBT to individuals with familial CRC risk before age 50 is expected to be beneficial. Depending on the accepted IHBR threshold, 10-yearly colonoscopy or alternatively biennial iFOBT from age 30 to 70 should be recommended for this target group.

Methodological guidelines and publications of benefit-risk assessment for health technology assessment: a scoping review protocol.

BACKGROUND: Benefit-risk assessment (BRA) is used in multiple phases along the health technology's life-cycle to evaluate the balance between the benefits and risks, as it is fundamental to all stakeholders. BRA and its methodological approaches have been applied primarily in the context of regulatory agencies. However, BRA's application and extent in the context of health technology assessment (HTA) bodies remain less clear. Our goal is to perform a scoping review to identify and map methodological guidelines and publications on methods of BRA. This will be done considering the different phases of the life-cycle of health technologies to underline both the depth and extent of research concerning BRA, especially in the context of HTA. METHODS AND ANALYSIS: This scoping review protocol was developed following the framework proposed by Arksey and O'Malley, and the updated guidelines by the Joanna Briggs Institute. We will include methodological publications that provide recommendations or guidelines on methods for BRA. We will conduct electronic searches on Medline (PubMed) and EMBASE (Ovid) databases; manual searches on the main websites of HTA bodies and drug regulatory organisations; and contact experts in the field. Systematic extraction forms will be used to screen and assess the identified publications by independent assessors. We will provide a qualitative synthesis using descriptive statistics and visual tools. Results will be summarised in systematic evidence tables and comparative evidence scoping charts. ETHICS AND DISSEMINATION: This review will use data publicly available and does not require ethics approval. The results of this scoping review will contribute to scientific knowledge and act as a basis for methodologists, guideline developers and researchers for the development of BRA to inform regulatory decisions, reimbursement and coverage decision making. The results will be disseminated through peer-reviewed articles, conferences, policy briefs and workshops. TRIAL REGISTRATION NUMBER: Open Science Framework (https://doi.org/10.17605/OSF.IO/69T3V).

Colorectal Cancer Screening for Persons With a Positive Family History­Evaluation of the FARKOR Program for the Secondary Prevention of Colorectal Cancer in Persons Aged 25 to 50.

BACKGROUND: Persons with a positive family history of colorectal cancer (CRC) are more likely than others to develop CRC and are also younger at the onset of the disease. Nonetheless, the German Federal Joint Committee (G-BA, Gemeinsamer Bundes - ausschuss) recommends screening all persons aged 50 and above regardless of their family history. FARKOR was a project supported by the Innovation Fund of the G-BA to study the feasibility, efficacy, and safety of a risk-adapted early detection program for CRC among persons aged 25 to 50 without any specific past medical history. METHODS: Physicians in private practice in Bavaria documented their activities relating to FARKOR online. The FARKOR process comprised a declaration of consent, a simplified family history for CRC, an optional, more comprehensive family history, a counseling session for participatory decision-making on further measures, and various modalities of screening (an immunological fecal occult blood test [iFOBT], colonoscopy, or no screening). Related physician activities outside the FARKOR process were assessed by record linkage between study data and data of the patients' health insurance carriers. RESULTS: The simplified family history was documented in 25 847 persons and positive for CRC in 5769 (22.3%). 3232 persons had a more comprehensive family history, among whom 2054 (63.6%) participated in screening measures. 1595 underwent colonoscopy; 278 persons who had already undergone colonoscopy in the preceding five years were excluded from the analysis. Colonoscopy revealed adenoma in 232 persons (17,6 %), advanced adenoma in 78 (5.9%) and carcinoma in 4 (0.3%). There were no serious complications. CONCLUSION: The detection rates in this study corresponded to those of persons aged 55 to 59 in the current early detection program. Despite numerous problems in the performance of the study (inconsistencies in documentation, external performance of screening measures on program participants), the results support the feasibility of a risk-adapted early detection program in the young target population with a family history of CRC.

Stool DNA testing for early detection of colorectal cancer: systematic review using the HTA Core Model® for Rapid Relative Effectiveness Assessment.

Background: Stool DNA testing for early detection of colorectal cancer (CRC) is a non-invasive technology with the potential to supplement established CRC screening tests. The aim of this health technology assessment was to evaluate effectiveness and safety of currently CE-marked stool DNA tests, compared to other CRC tests in CRC screening strategies in an asymptomatic screening population. Methods: The assessment was carried out following the guidelines of the European Network for Health Technology Assessment (EUnetHTA). This included a systematic literature search in MED-LINE, Cochrane and EMBASE in 2018. Manufacturers were asked to provide additional data. Five patient interviews helped assessing potential ethical or social aspects and patients' experiences and preferences. We assessed the risk of bias using QUADAS-2, and the quality of the body of evidence using GRADE. Results: We identified three test accuracy studies, two of which investigated a multitarget stool DNA test (Cologuard®, compared fecal immunochemical test (FIT)) and one a combined DNA stool assay (ColoAlert®, compared to guaiac-based fecal occult blood test (gFOBT), Pyruvate Kinase Isoenzyme Type M2 (M2-PK) and combined gFOBT/M2-PK). We found five published surveys on patient satisfaction. No primary study investigating screening effects on CRC incidence or on overall mortality was found. Both stool DNA tests showed in direct comparison higher sensitivity for the detection of CRC and (advanced) adenoma compared to FIT, or gFOBT, respectively, but had lower specificity. However, these comparative results may depend on the exact type of FIT used. The reported test failure rates were higher for stool DNA testing than for FIT. The certainty of evidence was moderate to high for Cologuard® studies, and low to very low for the ColoAlert® study which refers to a former version of the product and yielded no direct evidence on the test accuracy for ad-vanced versus non-advanced adenoma. Conclusions: ColoAlert® is the only stool DNA test currently sold in Europe and is available at a lower price than Cologuard®, but reliable evidence is lacking. A screening study including the current product version of ColoAlert® and suitable comparators would, therefore, help evaluate the effectiveness of this screening option in a European context.

Cost-effectiveness of testing for CYP2C19 loss-of-function carriers following transient ischemic attack/minor stroke: A Canadian perspective.

BACKGROUND: The CHANCE-2 study compared 3 weeks of aspirin-ticagrelor to aspirin-clopidogrel in CYP2C19 loss-of-function (LOF) allele carriers following a transient ischemic attack (TIA)/minor stroke and demonstrated a modestly lower risk of stroke recurrence with aspirin-ticagrelor. This stroke protection was largely for minor stroke and came at an increased risk of bleeding. The cost-effectiveness of implementing testing for LOF allele status to personalize antiplatelet regimen for secondary stroke prevention after a TIA/minor stroke in the Canadian health care context is unknown. METHODS: Cost-effectiveness analysis using a decision-analytic Markov cohort model with a lifetime horizon was performed to determine the costs and health benefits of testing for LOF allele status compared with no testing (current standard of care). The population of interest was patients living in Canada who suffered a TIA/minor stroke. Outcomes of interest were life-years gained (LYG), quality-adjusted life years (QALY) gained, costs (reported in 2022 Canadian dollars), and the incremental cost-effectiveness ratio (ICER). We adopted the perspective of the Federal, Provincial, and Territorial Ministries of Health and used a 1.5% annual discount rate. Sensitivity analyses were performed to assess uncertainty. RESULTS: Compared to standard of care, LOF allele testing leads to 0.14 LYG (undiscounted), 0.12 QALYs gained (undiscounted), and additional lifetime costs of CAD$432 (discounted) per patient. The ICER of the LOF allele testing strategy is CAD$4310 per QALY gained compared with standard of care. The probabilistic sensitivity analyses demonstrated that LOF allele testing was cost-effective in more than 99.99% of simulations using a willingness-to-pay threshold of CAD$50,000 per QALY. CONCLUSION: Based on available evidence, testing for LOF allele followed by short duration 3 weeks of aspirin-ticagrelor compared to standard-of-care aspirin-clopidogrel can lead to prolonged life and improved quality of life and can be considered very cost-effective when compared with other well-accepted technologies in health and medicine.

Familial hypercholesterolemia: A systematic review of modeling studies on screening interventions.

BACKGROUND AND AIMS: FH is still underdiagnosed. Cost-effectiveness results of preventive screening strategies vary. We aimed at systematically assessing the benefits, harms and cost effectiveness of screening for familial hypercholesterolemia (FH) and at providing an overview of the main characteristics and methodological approaches of applied decision-analytic models. METHODS: A systematic literature search was conducted in MEDLINE, EconLit, CRD-databases and the CEA-registry for FH screening starting 2012. Earlier studies were included from a published systematic review. Results were reported in standardized semi-quantitative evidence tables. Costs were converted to current euros. Incremental cost-effectiveness ratios (ICERs) were recalculated according to economic guidelines. RESULTS: Out of our 211 retrieved studies, eight were included in the review in addition to six studies from an earlier review. Studies were conducted in Europe (UK, The Netherlands, Spain, Poland), USA and Australia evaluating cascade (CS), opportunistic (OS), universal screening (UniS), or combinations using genetic testing, clinical criteria or combinations. Studies evaluating only CS identified strategies with an ICER of up to 37,100 EUR/quality-adjusted life-year (QALY) but some strategies were dominated depending on test combinations. UniS of newborns in combination with CS had an ICER≤15,000 EUR/QALY for sequential cholesterol-genetic screening. In other studies, UniS was dominated by OS/CS. CONCLUSIONS: Our systematic review demonstrates the values of FH screening and provides an overview of potentially relevant screening strategies to be tested using a decision-analytic model for the respective country or region. Future research is needed on the transferability of results to other countries and modeling spillover effects to newborns.

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply.

BACKGROUND AND OBJECTIVE: The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply. METHODS: We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model. RESULTS: We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden. DISCUSSION: The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review.

Objective: The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques. Methods: For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary. Results: Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data. Conclusion: Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science.

Objectives: Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience. Methods: To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends. Results: Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased. Conclusions: Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies.

OBJECTIVES: Benefit and cost effectiveness of breast cancer screening are still matters of controversy. Risk-adapted strategies are proposed to improve its benefit-harm and cost-benefit relations. Our objective was to perform a systematic review on economic breast cancer models evaluating primary and secondary prevention strategies in the European health care setting, with specific focus on model results, model characteristics, and risk-adapted strategies. METHODS: Literature databases were systematically searched for economic breast cancer models evaluating the cost effectiveness of breast cancer screening and prevention strategies in the European health care context. Characteristics, methodological details and results of the identified studies are reported in evidence tables. Economic model outputs are standardized to achieve comparable cost-effectiveness ratios. RESULTS: Thirty-two economic evaluations of breast cancer screening and seven evaluations of primary breast cancer prevention were included. Five screening studies and none of the prevention studies considered risk-adapted strategies. Studies differed in methodologic features. Only about half of the screening studies modeled overdiagnosis-related harms, most often indirectly and without reporting their magnitude. All models predict gains in life expectancy and/or quality-adjusted life expectancy at acceptable costs. However, risk-adapted screening was shown to be more effective and efficient than conventional screening. CONCLUSIONS: Economic models suggest that breast cancer screening and prevention are cost effective in the European setting. All screening models predict gains in life expectancy, which has not yet been confirmed by trials. European models evaluating risk-adapted screening strategies are rare, but suggest that risk-adapted screening is more effective and efficient than conventional screening.

Effects of 3D image-guided brachytherapy compared to 2D conventional brachytherapy on clinical outcomes in patients with cervical cancer: A systematic review and meta-analyses.

PURPOSE: To assess the effects of three-dimensional image-guided brachytherapy (3D BT) compared to bi-dimensional BT (2D BT) on clinical outcomes in patients with cervical cancer. METHODS AND MATERIALS: We searched PubMed/MEDLINE, EMBASE, Scopus, CENTRAL, Web of Science, and LILACS for studies assessing the effects of 3D BT versus 2D BT on clinical outcomes. Two reviewers independently screened retrieved citations, extracted data and assessed risk of bias from eligible studies. Hazard ratios (HR) were calculated from Kaplan-Meier curves considering the number of events, their timing and the followup of censored patients. We conducted meta-analyses of HR using the inverse-variance random-effects method. Risk Difference (RD) for toxicities were pooled using the Mantel-Haenszel random-effects method. We used the GRADE system to rate the certainty of evidence. RESULTS: Twenty observational studies involving 4287 patients were included. The meta-analyses assessing the effect of 3D BT versus 2D BT on overall survival resulted in a HR of 0.78 (95%CI 0.62-0.98), HR of 0.75 (95%CI 0.62-0.90) for pelvic disease-free survival, HR of 0.93 (95%CI 0.81-1.06) for metastatic disease-free survival, and HR of 0.77 (95%CI 0.59-0.99) for local control. Grade 3-4 global and gastrointestinal toxicities were, respectively, 9% lower (95%CI 6% to 11%) and 5% lower (95%CI 2% to 8%) in patients receiving 3D BT versus 2D BT. Certainty of evidence was very low for all assessed outcomes. CONCLUSIONS: Our study may suggest a benefit of 3D BT over conventional 2D BT on important clinical outcomes.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation.

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Long-Term Effectiveness and Cost Effectiveness of Multiple Myeloma Treatment Strategies for Elderly Transplant-Ineligible Patients in Serbia.

INTRODUCTION: Evidence on long-term effectiveness and cost effectiveness of treatment sequences for multiple myeloma (MM) is sparse. We used published data and country-specific data to assess the cost effectiveness of four-line treatment sequences for elderly transplant-ineligible patients with MM in Serbia. METHOD: We developed a Markov cohort model to compare long-term effectiveness and cost effectiveness of five sequential MM treatment alternatives from the perspective of the national healthcare provider. Effectiveness parameters on progression, mortality and adverse events were extracted from published clinical trials. Costs were based on price lists of the National Health Insurance Fund. We compared life expectancy, costs, and incremental cost-effectiveness ratios among alternative courses of action. The model was analyzed over a lifelong time horizon applying a 3% annual discount rate for effectiveness outcomes and costs. Robustness of the model was tested in multiple deterministic sensitivity analyses. RESULTS: The sequences were defined by the frontline treatment: MPT (melphalan-prednisone-thalidomide), MPV (melphalanprednisone-bortezomib), CTD (cyclophosphamide-thalidomide-dexamethasone), VCD (bortezomib-cyclophosphamidedexamethasone) and BP (bendamustine-prednisone). MPV sequence resulted in the highest remaining life expectancy (4.76 life years). Cost-effectiveness analysis resulted in three non-dominated strategies: MPT, VCD, and MPV sequences, with an incremental cost-effectiveness ratio of EUR 35,300 per life-year gained (LYG) for VCD and EUR 47,200/LYG for MPV relative to MPT. CONCLUSION: MPV sequence was the most effective in terms of life expectancy for elderly transplant-ineligible MM patients in Serbia. Bortezomib-based strategies would be recommended for the frontline treatment of patients with MM in Serbia if the willingness-to-pay threshold is around EUR 35,000-60,000/LYG.

Decision-Analytic Modeling Studies in Prevention and Treatment of Iodine Deficiency and Thyroid Disorders: A Systematic Overview.

Background: Prevention and treatment of iodine deficiency-related diseases remain an important public health challenge. Iodine deficiency can have severe health consequences, such as cretinism, goiter, or other thyroid disorders, and it has economic implications. Our aim was to give an overview of studies applying decision-analytic modeling to evaluate the effectiveness and/or cost-effectiveness of iodine deficiency-related prevention strategies or treatments related to thyroid disorders. Methods: We performed a systematic literature search in PubMed/MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica Database), Tuft's Cost-Effectiveness Analysis Registry, and National Health System Economic Evaluation Database (NHS EED) to identify studies published between 1985 and 2018 comparing different prevention or treatment strategies for iodine deficiency and thyroid disorders by applying a mathematical decision-analytic model. Studies were required to evaluate patient-relevant health outcomes (e.g., remaining life years, quality-adjusted life years [QALYs]). Results: Overall, we found 3950 studies. After removal of duplicates, abstract/title, and full-text screening, 17 studies were included. Eleven studies evaluated screening programs (mainly newborns and pregnant women), five studies focused on treatment approaches (Graves' disease, toxic thyroid adenoma), and one study was about primary prevention (consequences of iodine supplementation on offspring). Most of the studies were conducted within the U.S. health care context (n = 7). Seven studies were based on a Markov state-transition model, nine studies on a decision tree model, and in one study, an initial decision tree and a long-term Markov state-transition model were combined. The analytic time horizon ranged from 1 year to lifetime. QALYs were evaluated as health outcome measure in 15 of the included studies. In all studies, a cost-effectiveness analysis was performed. None of the models reported a formal model validation. In most cases, the authors of the modeling studies concluded that screening is potentially cost-effective or even cost-saving. The recommendations for treatment approaches were rather heterogeneous and depending on the specific research question, population, and setting. Conclusions: Overall, we predominantly identified decision-analytic modeling studies evaluating specific screening programs or treatment approaches; however, there was no model evaluating primary prevention programs on a population basis. Conclusions deriving from these studies, for example, that prevention is cost-saving, need to be carefully interpreted as they rely on many assumptions.

Universal screening for SARS-CoV-2 infection: a rapid review.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by the novel betacoronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Most people infected with SARS-CoV-2 have mild disease with unspecific symptoms, but about 5% become critically ill with respiratory failure, septic shock and multiple organ failure. An unknown proportion of infected individuals never experience COVID-19 symptoms although they are infectious, that is, they remain asymptomatic. Those who develop the disease, go through a presymptomatic period during which they are infectious. Universal screening for SARS-CoV-2 infections to detect individuals who are infected before they present clinically, could therefore be an important measure to contain the spread of the disease. OBJECTIVES: We conducted a rapid review to assess (1) the effectiveness of universal screening for SARS-CoV-2 infection compared with no screening and (2) the accuracy of universal screening in people who have not presented to clinical care for symptoms of COVID-19. SEARCH METHODS: An information specialist searched Ovid MEDLINE and the Centers for Disease Control (CDC) COVID-19 Research Articles Downloadable Database up to 26 May 2020. We searched Embase.com, the CENTRAL, and the Cochrane Covid-19 Study Register on 14 April 2020. We searched LitCovid to 4 April 2020. The World Health Organization (WHO) provided records from daily searches in Chinese databases and in PubMed up to 15 April 2020. We also searched three model repositories (Covid-Analytics, Models of Infectious Disease Agent Study [MIDAS], and Society for Medical Decision Making) on 8 April 2020. SELECTION CRITERIA: Trials, observational studies, or mathematical modelling studies assessing screening effectiveness or screening accuracy among general populations in which the prevalence of SARS-CoV2 is unknown. DATA COLLECTION AND ANALYSIS: After pilot testing review forms, one review author screened titles and abstracts. Two review authors independently screened the full text of studies and resolved any disagreements by discussion with a third review author. Abstracts excluded by a first review author were dually reviewed by a second review author prior to exclusion. One review author independently extracted data, which was checked by a second review author for completeness and accuracy. Two review authors independently rated the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for diagnostic accuracy studies and a modified form designed originally for economic evaluations for modelling studies. We resolved differences by consensus. We synthesized the evidence in narrative and tabular formats. We rated the certainty of evidence for days to outbreak, transmission, cases missed and detected, diagnostic accuracy (i.e. true positives, false positives, true negatives, false negatives) using the GRADE approach. MAIN RESULTS: We included 22 publications. Two modelling studies reported on effectiveness of universal screening. Twenty studies (17 cohort studies and 3 modelling studies) reported on screening test accuracy. Effectiveness of screening We included two modelling studies. One study suggests that symptom screening at travel hubs, such as airports, may slightly slow but not stop the importation of infected cases (assuming 10 or 100 infected travellers per week reduced the delay in a local outbreak to 8 days or 1 day, respectively). We assessed risk of bias as minor or no concerns, and certainty of evidence was low, downgraded for very serious indirectness. The second modelling study provides very low-certainty evidence that screening of healthcare workers in emergency departments using laboratory tests may reduce transmission to patients and other healthcare workers (assuming a transmission constant of 1.2 new infections per 10,000 people, weekly screening reduced infections by 5.1% within 30 days). The certainty of evidence was very low, downgraded for high risk of bias (major concerns) and indirectness. No modelling studies reported on harms of screening. Screening test accuracy All 17 cohort studies compared an index screening strategy to a reference reverse transcriptase polymerase chain reaction (RT-PCR) test. All but one study reported on the accuracy of single point-in-time screening and varied widely in prevalence of SARS-CoV-2, settings, and methods of measurement. We assessed the overall risk of bias as unclear in 16 out of 17 studies, mainly due to limited information on the index test and reference standard. We rated one study as being at high risk of bias due to the inclusion of two separate populations with likely different prevalences. For several screening strategies, the estimates of sensitivity came from small samples. For single point-in-time strategies, for symptom assessment, the sensitivity from 12 cohorts (524 people) ranged from 0.00 to 0.60 (very low-certainty evidence) and the specificity from 12 cohorts (16,165 people) ranged from 0.66 to 1.00 (low-certainty evidence). For screening using direct temperature measurement (3 cohorts, 822 people), international travel history (2 cohorts, 13,080 people), or exposure to known infected people (3 cohorts, 13,205 people) or suspected infected people (2 cohorts, 954 people), sensitivity ranged from 0.00 to 0.23 (very low- to low-certainty evidence) and specificity ranged from 0.90 to 1.00 (low- to moderate-certainty evidence). For symptom assessment plus direct temperature measurement (2 cohorts, 779 people), sensitivity ranged from 0.12 to 0.69 (very low-certainty evidence) and specificity from 0.90 to 1.00 (low-certainty evidence). For rapid PCR test (1 cohort, 21 people), sensitivity was 0.80 (95% confidence interval (CI) 0.44 to 0.96; very low-certainty evidence) and specificity was 0.73 (95% CI 0.39 to 0.94; very low-certainty evidence). One cohort (76 people) reported on repeated screening with symptom assessment and demonstrates a sensitivity of 0.44 (95% CI 0.29 to 0.59; very low-certainty evidence) and specificity of 0.62 (95% CI 0.42 to 0.79; low-certainty evidence). Three modelling studies evaluated the accuracy of screening at airports. The main outcomes measured were cases missed or detected by entry or exit screening, or both, at airports. One study suggests very low sensitivity at 0.30 (95% CI 0.1 to 0.53), missing 70% of infected travellers. Another study described an unrealistic scenario to achieve a 90% detection rate, requiring 0% asymptomatic infections. The final study provides very uncertain evidence due to low methodological quality. AUTHORS' CONCLUSIONS: The evidence base for the effectiveness of screening comes from two mathematical modelling studies and is limited by their assumptions. Low-certainty evidence suggests that screening at travel hubs may slightly slow the importation of infected cases. This review highlights the uncertainty and variation in accuracy of screening strategies. A high proportion of infected individuals may be missed and go on to infect others, and some healthy individuals may be falsely identified as positive, requiring confirmatory testing and potentially leading to the unnecessary isolation of these individuals. Further studies need to evaluate the utility of rapid laboratory tests, combined screening, and repeated screening. More research is also needed on reference standards with greater accuracy than RT-PCR. Given the poor sensitivity of existing approaches, our findings point to the need for greater emphasis on other ways that may prevent transmission such as face coverings, physical distancing, quarantine, and adequate personal protective equipment for frontline workers.

Effectiveness, benefit harm and cost effectiveness of colorectal cancer screening in Austria.

BACKGROUND: Clear evidence on the benefit-harm balance and cost effectiveness of population-based screening for colorectal cancer (CRC) is missing. We aim to systematically evaluate the long-term effectiveness, harms and cost effectiveness of different organized CRC screening strategies in Austria. METHODS: A decision-analytic cohort simulation model for colorectal adenoma and cancer with a lifelong time horizon was developed, calibrated to the Austrian epidemiological setting and validated against observed data. We compared four strategies: 1) No Screening, 2) FIT: annual immunochemical fecal occult blood test age 40-75 years, 3) gFOBT: annual guaiac-based fecal occult blood test age 40-75 years, and 4) COL: 10-yearly colonoscopy age 50-70 years. Predicted outcomes included: benefits expressed as life-years gained [LYG], CRC-related deaths avoided and CRC cases avoided; harms as additional complications due to colonoscopy (physical harm) and positive test results (psychological harm); and lifetime costs. Tradeoffs were expressed as incremental harm-benefit ratios (IHBR, incremental positive test results per LYG) and incremental cost-effectiveness ratios [ICER]. The perspective of the Austrian public health care system was adopted. Comprehensive sensitivity analyses were performed to assess uncertainty. RESULTS: The most effective strategies were FIT and COL. gFOBT was less effective and more costly than FIT. Moving from COL to FIT results in an incremental unintended psychological harm of 16 additional positive test results to gain one life-year. COL was cost saving compared to No Screening. Moving from COL to FIT has an ICER of 15,000 EUR per LYG. CONCLUSIONS: Organized CRC-screening with annual FIT or 10-yearly colonoscopy is most effective. The choice between these two options depends on the individual preferences and benefit-harm tradeoffs of screening candidates.

Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available. METHODS: A systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables. RESULTS: In total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy. CONCLUSIONS: A variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.

Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment.

Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the 'target trial' approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.