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Background As a core component of harm-reduction strategies to address the opioid crisis, several countries have instituted publicly funded programs to distribute naloxone for lay administration in the community. The effectiveness in reducing mortality from opioid overdose has been demonstrated in multiple systematic reviews. However, the economic impact of community naloxone distribution programs is not fully understood. Objectives Our objective was to conduct a review of economic evaluations of community distribution of naloxone, assessing for quality and applicability to diverse contexts and settings. Data Sources The search strategy was performed on MEDLINE, Embase, and EconLit databases. Study Eligibility Criteria and Interventions Search criteria were developed based on two themes: (1) papers involving naloxone or narcan and (2) any form of economic evaluation. A focused search of the grey literature was also conducted. Studies exploring the intervention of community distribution of naloxone were selected. Study Appraisal and Synthesis Methods Data extraction was done using the British Medical Journal guidelines for economic submissions, assigning quality levels based on the impact of the missing or unclear components on the strength of the conclusions. Results A total of nine articles matched our inclusion criteria: one cost-effectiveness analysis, eight cost-utility analyses, and one cost-benefit analysis. Overall, the quality of the studies was good (six of high quality, two of moderate quality, and one of low quality). All studies concluded that community distribution of naloxone was cost effective, with an incremental cost-utility ratio range of $US111–58,738 (year 2020 values) per quality-adjusted life-year gained. Limitations Our search strategy was developed iteratively, rather than following an a priori design. Additionally, our search was limited to English terms. Conclusions and Implications of Key Findings Based on this review, community distribution of naloxone is a worthwhile investment and should be considered by other countries dealing with the opioid epidemic. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-021-00309-z.

The use of population averages in cost-effectiveness analysis may hide important differences across subgroups, potentially resulting in suboptimal resource allocation, reduced population health and/or increased health inequalities. We discuss the factors that limit subgroup analysis in cost-effectiveness analysis and propose more thorough and transparent reporting. There are many issues that may limit whether subgroup analysis can be robustly included in cost-effectiveness analysis, including challenges with prespecifying and justifying subgroup analysis, identifying subgroups that can be implemented (identified and targeted) in practice, resource and data requirements, and statistical and ethical concerns. These affect every stage of the design, development and reporting of cost-effectiveness analyses. It may not always be possible to include and report relevant subgroups in cost effectiveness, e.g. due to data limitations. Reasons for not conducting subgroup analysis may be heterogeneous, and the consequences of not acknowledging patient heterogeneity can be substantial. We recommend that when potentially relevant subgroups have not been included in a cost-effectiveness analysis, authors report this and discuss their rationale and the limitations of this. Greater transparency of subgroup reporting should provide a starting point to overcoming these challenges in future research.

Background Primary care in England is facing increasing pressure due to the increasing number and complexity of consultations and the declining number of doctors per head of population. The improvement of primary care efficiency and productivity should be a priority, to ensure that future investments in the medical workforce can cope with the increasingly large and complex demand for care. Objectives This paper presents a systematic literature review of studies that define or measure efficiency in primary care in high-income settings. The review of the existing definitions of primary care efficiency and their limitations will inform future research on the measurement of efficiency in primary care in England and its determinants. Methods Literature searches were performed on Embase, Medline, and EconLit in January 2020. The records that passed the screening were reviewed in full text, and data on the study settings, the efficiency definition, and the efficiency analysis were extracted. Results Of the 2590 non-duplicate records retrieved from the searches, 38 papers were included in the analysis. The volume of the literature on primary care efficiency has evolved significantly from the 1980s, with the majority of the published studies focussing on European health systems. The setting most often analysed was primary care centres. Output was usually expressed using measures of primary care utilisation, with or without quality adjustments. Reference to the health outcomes achieved was, however, limited. Inputs were more commonly expressed in labour terms, while the exogenous variables related either to the characteristics of the patient population or the organisation of primary care. While all studies included an analysis of technical efficiency, consideration of allocative or cost efficiency or the determinants of productivity (e.g. technological change, skill mix) was rare. Conclusions The main limitations that future research on primary care efficiency should address relate to the definition of output. Current approaches to measure the impact on health and the multiple dimensions of output are not sufficient to represent the valued output of primary care. In light of the recent changes in the model of primary care delivery in England, future research should also investigate the impact of technological change on productivity and the scope for substitution across staff roles. Supplementary Information The online version contains supplementary material available at 10.1007/s40258-021-00669-x.

Background Debate over the viability of the current commercial research and development (R&D) model is ongoing. A controversial theme is the cost of bringing a new molecular entity (NME) to market. Objective Our aim was to evaluate the range and suitability of published R&D cost estimates as to the degree to which they represent the actual costs of industry. Methods We provided a systematic literature review based on articles found in the Pubmed, Embase and EconLit electronic databases, and in a previously published review. Articles published before March 2020 that estimated the total R&D costs were included (22 articles with 45 unique cost estimates). We included only literature in which the methods used to collect the information and to estimate the R&D costs were clearly described; therefore, three reports were excluded. We extracted average pre-launch R&D costs per NME and converted the values to 2019 US dollars (US$) using the gross domestic product (GDP) price deflator. We appraised the suitability of the R&D estimated costs by using a scoring system that captures three domains: (1) how success rates and development time used for cost estimation were obtained; (2) whether the study considered potential sources contributing to the variation in R&D costs; and (3) what the components of the cost estimation were. Results Estimates of total average capitalized pre-launch R&D costs varied widely, ranging from $161 million to $4.54 billion (2019 US$). Therapeutic area-specific estimates were highest for anticancer drugs (between $944 million and $4.54 billion). Our analysis identified a trend of increasing R&D costs per NME over time but did not reveal a relation between cost estimates and study ranking when the suitability scores were assessed. We found no evidence of an increase in suitability scores over time. Conclusion There is no universally correct answer regarding how much it costs, on average, to research and develop an NME. Future studies should explicitly address previously neglected variables, which likely explain some variability in estimates, and consider the trade-off between the transparency and public accessibility of data and their specificity. Use of our proposed suitability scoring system may assist in addressing such issues. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01065-y.

Background Decisions on funding new healthcare technologies assume that all health improvements are valued equally. However, public reaction to health technology assessment (HTA) decisions suggests there are health attributes that matter deeply to them but are not currently accounted for in the assessment process. We aimed to determine the relative importance of attributes of illness that influence the value placed on alleviating that illness. Method We conducted a discrete choice experiment survey that presented general public respondents with 15 funding decisions between hypothetical health conditions. The conditions were defined by five attributes that characterise serious illnesses, plus the health gain from treatment. Respondent preferences were modelled using conditional logistic regression and latent class analysis. Results 905 members of the UK public completed the survey in November 2017. Respondents generally preferred to provide treatments for conditions with ‘better’ characteristics. The exception was treatment availability, where respondents preferred to provide treatments for conditions where there is no current treatment, and were prepared to accept lower overall health gain to do so. A subgroup of respondents preferred to prioritise ‘worse’ health states. Conclusion This study suggests a preference among the UK public for treating an unmet need; however, it does not suggest a preference for prioritising other distressing aspects of health conditions, such as limited life expectancy, or where patients are reliant on care. Our results are not consistent with the features currently prioritised in UK HTA processes, and the preference heterogeneity we identify presents a major challenge for developing broadly acceptable policy.

Background and Objective Enthusiasm for the use of outcomes-based managed entry agreements (OBMEAs) to manage uncertainties apparent at the time of appraisal/pricing and reimbursement of new medicines has waned over the past decade, as challenges in establishment, implementation and re-appraisal have been identified. With the recent advent of innovative treatments for rare diseases that have uncertainties in the clinical evidence base, but which could meet a high unmet need, there has been renewed interest in the potential of OBMEAs. The objective of this research was to review the implementation of OBMEAs for two case studies across countries in the European Union, Australia and Canada, to identify good practices that could inform development of tools to support implementation of OBMEAs. Methods To investigate how OBMEAs are being implemented with rare disease treatments, we collected information from health technology assessment/payer experts in countries that had implemented OBMEAs for either nusinersen in spinal muscular atrophy or tisagenlecleucel in two cancer indications. Operational characteristics of the OBMEAs that were publicly available were documented. Then, the experts discussed issues in implementing these OBMEAs and specific approaches taken to overcome challenges. Results The OBMEAs identified were based on individual outcomes to ensure appropriate use, manage continuation of treatment and in two cases linked to payment schedules, or they were population based, coverage with evidence development. For nusinersen, population-based OBMEAs are documented in Belgium, England and the Netherlands and individual-based schemes in Bulgaria, Ireland, Italy and Lithuania. For tisagenlecleucel, there were population-based schemes in Australia, Belgium, England and France and individual-based schemes in Italy and Spain. Comparison of the OBMEA constructs showed some clear published frameworks and clarity of the uncertainties to be addressed that were similar across countries. Agreements were generally made between the marketing authorisation holder and the payer with involvement of expert physicians. Only England and the Netherlands involved patients. Italy used its long-established, national, web-based, treatment-specific data collection system linked to reimbursement and Spain has just developed such a national treatment registry system. Other countries relied on a variety of data collection systems (including clinical registries) and administrative data. Durations of agreements varied for these treatments as did processes for interim reporting. The processes to ensure data quality, completeness and sufficiency for re-analysis after coverage with evidence development were not always clear, neither were analysis plans. Conclusions These case studies have shown that important information about the constructs of OBMEAs for rare disease treatments are publicly available, and for some jurisdictions, interim reports of progress. Outcomes-based managed entry agreements can play an important role not only in reimbursement, but also in treatment optimisation. However, they are complex to implement and should be the exception and not the rule. More recent OBMEAs have developed document covenants among stakeholders or electronic systems to provide assurances about data sufficiency. For coverage with evidence development, there is an opportunity for greater collaboration among jurisdictions to share processes, develop common data collection agreements, and share interim and final reports. The establishment of an international public portal to host such reports would be particularly valuable for rare disease treatments. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01050-5.

Background and Objective During the COVID-19 pandemic, resources in intensive care units (ICUs) have the potential to be inadequate to treat all those who might benefit. Therefore, it is paramount to identify the views of the community regarding how to allocate such resources. This study aims to quantify Australian community preferences for ventilation allocation. Methods A discrete choice experiment was designed and administrated to an adult Australian online panel. Each survey respondent answered 12 choice sets from a total design of 120. Each choice set placed the respondent in the role of hypothetical decision maker, prioritising care between two patients. Conditional logit, mixed logit regression and latent class analysis were used to analyse the data. Additionally, we asked a series of attitudinal questions about different methods of making such decisions in practice, focusing on who should be responsible. Results A total of 1050 community members completed the survey and responded to each choice. Dimensions considered most important were age, likely effectiveness, smoking status, whether the person has dependents, whether they are a healthcare worker, and whether they have a disability or not. Estimating marginal rates of substitution between patient characteristics and chance of survival if ventilated yielded values of up to 30 percentage points if the patient was 70 years old relative to being 30. However, respondents typically said they would prefer such decisions to be made by medical professionals. Conclusion This study demonstrated the preferences of the community to allocation of ventilators during the COVID-19 pandemic. The use of such information should be treated with some caution as the underlying reason for such preferences are unclear, and respondents themselves preferred the decision to be made by others. Supplementary Information The online version contains supplementary material available at 10.1007/s40271-021-00498-z.

Background As Nigeria prepares to introduce a rotavirus vaccine, the Gavi board has approved the extension of the transition period for the country until 2028. The current position of the country on Gavi’s funding profile calls for a pragmatic step in planning and implementation so that sustainability at the fully self-financing phase will be feasible. Objective This study aimed to inform the decisions of the country’s health policymakers on the costs, benefits, and implications of the introduction of rotavirus vaccine. Methods This study was an economic evaluation using a simulation-based Markov model. It compared four approaches: ‘no vaccination’ and vaccination with ROTARIX, ROTAVAC, or ROTASIIL. Ten cohorts from the year 2021 to 2030 were used in the analysis. Primary measures were the benefit-cost ratio (BCR) and the incremental cost-effectiveness ratio (ICER). Future costs and outcomes were discounted to 2019 values. Results The adjusted vaccine cost of ROTARIX was the highest, followed by ROTAVAC and ROTASIIL, whereas the immunization delivery cost was in the reverse order. All the vaccines were very cost effective, with ROTARIX being the optimal choice for the 10-year period, having a BCR of 27 and an ICER of $US100 (95% confidence interval [CI] 71–130)/disability-adjusted life-year averted. Adopting ROTARIX was the optimal choice from 2021 to 2027, whereas ROTAVAC was optimal from 2028 to 2030. The net budget impact of the programme was $US76.9 million for the 10-year period. The opportunity cost of a late introduction was about $US8 million per annum from 2021 to 2028. Conclusions The rotavirus vaccine ROTARIX should be implemented in Nigeria at the earliest opportunity. A switch to ROTAVAC should be considered from the year 2028. Cost-minimization measures are imperative to ensure the sustainability of the programme after the transition out of Gavi support. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-020-00251-6.

Due to the risks involved in not achieving desired health outcomes for the dollar spent on drugs, healthcare decision makers, including payers, providers, drug manufacturers, and patients, need a mechanism to share this financial risk among the involved parties. Performance-based risk-sharing arrangements (PBRSAs) are agreements that can potentially reduce the 'drug lag' in which patients wait for an unknown amount of time until a particular drug is covered under their health plan. In addition, PBRSAs can mitigate the risk of investing heavily in drugs that are ineffective or do not deliver good value or "bang for the buck". This review describes and evaluates PBRSAs for drugs in the USA and juxtaposes to other developed nations (i.e. Germany) that adopted PBRSAs in their healthcare model. There are different types of outcomes-based health schemes, namely conditional coverage, which can be further broken down into coverage with evidence development (CED), conditional treatment continuation (CTC), and performance-linked reimbursement, which includes outcomes guarantees. Both CED and CTC are 'conditional' on the collected evidence of the new drug's effectiveness, offering discount only if the drug delivers desirable results. The outcomes guarantee scheme offers discount or even a full refund if the outcome is less than expected, forcing the drug to meet the expected effectiveness. The USA can follow the German reference pricing model in which the assessment of new drugs is centralized and done collectively by representatives from a group of healthcare decision makers. In any shape or form, PBRSA is a clever mechanism to cope with uncertainty if drug price is scaled appropriately based on value.

Background Limited empirical evidence exists regarding the effect of price changes on hospital behavior and, ultimately, the quality of care. Additionally, an overview of the results of prior literature is lacking. Objective This study aims to provide a synthesis of existing research concerning the relationship between hospital cost/price and the quality of care. Methods Searches for literature related to the effect of hospital cost and price on the quality of care, including studies published between 1990 and March 2019, were carried out using four electronic databases. In total, 47 studies were identified, and the data were extracted and summarized in different tables to identify the patterns of the relationships between hospital costs/prices and the quality of care. Results The study findings are highly heterogenous. The proportion of studies detecting a significant positive association between price/cost and the quality of care is higher when (a) price/reimbursement is used (instead of cost); (b) process measures are used (instead of outcome measures); (c) the focus is on acute myocardial infarction, congestive heart failure, and stroke patients (instead of patients with other clinical conditions or all patients); and (d) the methodological approach used to address confounding is more sophisticated. Conclusion Our results suggest that there is no general relationship between cost/price and the quality of care. However, the relationship seems to depend on the condition and specific resource utilization. Policy makers should be prudent with the measures used to reduce hospital costs to avoid endangering the quality of care, especially in resource-sensitive settings. Electronic supplementary material The online version of this article (10.1007/s40258-020-00577-6) contains supplementary material, which is available to authorized users.

The intrinsic value of a firm’s equity is determined by the present value of future payoffs to equity-holders. Thus, to estimate equity value, one needs to identify and process a series of information that is relevant to the present value of expected future payoffs. Among the value-relevant information, earnings pertain to a summary measure of firm performance, and thus play a key role in the equity valuation. Nevertheless, it is not uncommon in practice that firm managers either intentionally or unintentionally misreport earnings, making valuation challenging for outsiders. This chapter aims to explore the role earnings management plays in equity valuation, and how market participants may detect and adjust for misreported earnings, if any, in their forecasts and valuation for a firm. The chapter is organized to comprise the following sections: (i) varied managerial incentives for earnings management; (ii) various tactics of earnings management via accruals manipulation; (iii) various tactics of earnings management via real activities manipulation; (iv) consequences and determinants of earnings management; (v) trade-off between accruals manipulation and real activities manipulation to manage earnings; (vi) how to discern and measure earnings management; (vii) introduction of various equity-valuation models; (viii) how to adjust for the effects of accruals-based and real earnings management in equity valuation.

A forecast is merely a prediction about the future values of data. Financial forecasts span a broad range of areas, and each of the forecasts is of interest to a number of people and departments in a firm. A sales manager may wish to forecast sales (either in units sold or revenues generated). This prediction is of interest to the operations (manufacturing) department in order to predict the materials and time needed to create the product. The corporate financial officer is interested in the amount of cash required to support the projected level of sales and how much available cash inflow he can eventually expect to pay financial costs, cover expansion programs, and provide cash payouts to investors. In short, good forecasting underlies the construction of an operational cash budget.

US GAAP takes an asset-liability approach to accounting for income taxes and thus records deferred tax assets and liabilities. A deferred tax liability or asset generally should be recognized for the future tax effects of all temporary differences and carryforward. Deferred taxes are calculated using the asset or liability approach, which is intended to recognize, in the balance sheet, the future tax consequences of events that have been either recognized in the financial statements or the tax return. Accounting Standards Update 2015–2017 requires that deferred tax liabilities and assets be classified as non-current in a classified statement of financial position (Balance sheet).

A company should measure an intangible asset at its fair value at the time of acquisition. The costs of internally developing, maintaining, or restoring intangible assets generally should be expensed as incurred (with some exceptions). Intangible assets other than goodwill may or may not be amortized depending on their useful lives to the entity: Assets with finite lives are amortized; assets with indefinite lives are not. Goodwill is not amortized.

An intangible is a non-monetary asset that manifests itself by its economic properties. It does not have physical substance but grants rights and economic benefits to its owner. The examination of the general approaches of the valuation of companies is preliminary to the estimation of assets such as the intangibles. Intangibles are more specific than other assets and incorporate higher information asymmetries, linked to higher risk profiles and lower collateral value. Their accounting is controversial, privileging prudence over substance. The most widely used approaches of assessing intangibles are based on market, income, or cost-related metrics. Hard-to-value intangibles are based on innovative business models, whose value drivers are difficult to analyze and compare.

There are two types of income statement: (i) the single-step income statement and (ii) the multiple-step income statement. A single-step income statement uses just one subtraction. This is done by subtotaling all the revenues and gains together at the top of income statement and subtotaling all the expenses and losses together below revenues. The sum of expenses and losses is then subtracted from the sum of revenues and gains to arrive at net income. Multi-step income statement involves more than one subtraction to arrive to a net income and provides more information than a single-step income statement.

Biosimilar medicines have shown similarity with the originator biologic and offer a similar clinical outcome generally at a lower cost. This paper identifies benefits of off-patent biologics and biosimilars, and illustrates these benefits with empirical data from Europe. We provide a narrative review of published literature on values and benefits of biosimilars in Europe. The results describe cost savings as the key driver stemming from the lower price of biosimilars, than that of originator products, and from price competition between biosimilar(s), originator, and next-generation products. Cost savings may then translate into a number of other associated benefits. The lower price of biosimilars and similar effectiveness to the originator biologics improve cost effectiveness, implying that reimbursement can be granted or extended to other patient groups, or that the biologic therapy can be moved to an earlier line of treatment. Cost savings from biosimilars can be used to increase patient access to therapy or to increase the number of healthcare professionals. Finally, competition between off-patent biologics and biosimilars may stimulate an innovation in the formulation and development of next-generation biologics. Our paper illustrates that the benefit of off-patent biologics and biosimilars is not restricted to cost savings, but that these medicines may contribute to an expansion of medical treatment options for patients, hence concomitantly contributing to the long-term sustainability of the healthcare system. This review provides a broader view for clinical and economic decision makers and healthcare professionals on the added benefits of off-patent biologics and their use in clinical practice. ispartof: BIODRUGS vol:34 issue:2 pages:159-170 ispartof: location:New Zealand status: published

A great deal of information about property elevation and potential flood risk is available to homebuyers. Most Florida homebuyers are moving from another Florida residence, and therefore are at least somewhat familiar with both storms and tidal flooding. Flood insurance requirements also are a source of information about flood risk. Part of the flood insurance purchase process requires the home buyer to acquire a certificate of elevation that indicates the point at which wave action, tidal action or storm surge will flood the first floor of the house. In 2020, the National Association of Realtors® added two more indicators of flood risk to their realtor.com listings: information about the FEMA insurance classification of the property as well as maps and text from Flood Factor presenting a numerical score representing the flood risk factor of the property on a scale from 1 to 10. At the time of our survey, two to three months after this change, Realtors® reported little response to this additional information from either buyers or sellers. The respondents to the survey indicated that some homebuyers are avoiding low-lying property susceptible to flooding, but most real estate agents stated that it is not the flood risk itself but instead the cost of flood insurance that drives buyer response to coastal property.

Background: Occupational noise is still a significant issue having severe impacts on the worker’s health. This problem is relevant in the extractive industry since it endangers the worker and the surrounding communities. Objective: This short review aimed to identify the occupational exposure to noise setting within the extractive sector and earthworks. Methodology: The Preferred reporting items for systematic reviews and meta-analyses were used to conduct the search. “Noise” was combined with “extractive industry” (and similar terms) and “earthworks” in multidisciplinary databases and journals. A set of exclusion criteria was determined to filter the information of this first phase, and the results were fully appraised on a narrative basis. Results and discussion: From the 1148 papers primarily identified, only 15 met all the criteria and were included in this work. The data were categorised and divided into activity, equipment, job category and working area. Despite being able to provide some insight into each category, no detail related to experimental methodology was reported, being the main limitation in analysing the results. Conclusions: Although this research met the proposed topic, further work needs to be carried out, namely the inclusion of more relevant articles and statistical analysis.

The recent COVID-19 pandemic has affected the world in many ways, economically, socially and culturally. One of the most important ways to recover from the devastating effects of COVID-19 is identification of ideal vaccination strategy. The fact that it is not possible to vaccinate everyone at the same time in the world leads to vaccinations by giving priority to certain groups. Therefore, governments should determine priority groups for allocating COVID-19 vaccines. In this study, four main criteria (age, people’s health status, woman status, and job kinds), fifteen sub-criteria, and six possible vaccine alternatives are identified. First, the main and sub-criteria are evaluated using the Interval-Valued Pythagorean fuzzy Analytical Hierarchy Process (IVPF-AHP). Then, possible COVID-19 vaccine alternatives are ranked using Interval-Valued Pythagorean fuzzy WASPAS (IVPF-WASPAS). In addition, the results show that the most suitable vaccine for people with risky health problems and the elderly have priority over other alternative vaccines.

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections, and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously been applied to forecast COVID-19 confirmed cases for profoundly affected countries. Future predictions of daily COVID-19 cases are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. However, due to the presence of extreme uncertainty in these time series datasets, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. Still, nowcasting and forecasting of COVID-19 cases are difficult due to insufficient input data, flaw in modeling assumptions, lack of epidemiological features, inadequate past evidence on effects of available interventions, and lack of transparency. This chapter focuses on assessing different short-term forecasting models that are popularly used to forecast the daily COVID-19 cases for various countries. This chapter provides strong empirical evidence that there is no universal method available that can accurately forecast pandemic data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 has raised, and continues to raise, questions about the traditional approach to insurance cover. For instance, business interruption insurance covering “pandemics” under all risks insurance policies are likely to be a thing of the past. With tensions between businesses and the insurance industry on the rise, what can be done to offer businesses some protection at a premium they can afford, without emptying insurers’ reserves? In this chapter we talk about legal challenges related to traditional insurance against the risk of losses caused by a pandemic, and whether parametric insurance is the solution.

We discuss in this chapter how the insights and methods presented in the previous chapters can be effectively and practically implemented to manage and mitigate pandemics. The findings are not only analysed for the current COVID-19 crisis, but we also present some insights that could be gained for future pandemics and other extreme events. Coming from an actuarial background, the main focus lies on practical and technical aspects of the presented articles, namely on data, models and possible risk mitigation through (re)insurance, capital markets and similar approaches.

Our society’s efforts to fight pandemics rely heavily on our ability to understand, model and predict the transmission dynamics of infectious diseases. Compartmental models are among the most commonly used mathematical tools to explain reported infections and deaths. This chapter offers a brief overview of basic compartmental models as well as several actuarial applications, ranging from product design and reserving of epidemic insurance, to the projection of healthcare demand and the allocation of scarce resources. The intent is to bridge classical epidemiological models with actuarial and financial applications that provide healthcare coverage and utilise limited healthcare resources during pandemics.

Motivated by macroeconomic risks, such as the COVID-19 pandemic, we consider different risk management setups and study efficient insurance schemes in the presence of low probability shock events that trigger losses for all participants. More precisely, we consider three platforms: the risk-sharing, insurance and market platform. First, we show that under a non-discriminatory insurance assumption, it is optimal for everybody to equally share all risk in the market. This gives rise to a new concept of a contingent premium which collects the premia ex-post after the losses are realised. Insurance is then a mechanism to redistribute wealth, and we call this a risk-sharing solution. Second, we show that in an insurance platform, where the insurance is regulated, the tail events are not shared, but borne by the government. Third, in a competitive market we see how a classical solution can raise the risk of insolvency. Moreover, in a decentralised market, the equilibrium cannot be reached if there is adequate sensitivity to the common shock events. In addition, we have applied our theory to a case where the losses are calibrated based on the UK Coronavirus Job Retention Scheme.

Road accidents have claimed many lives with approximately 1.35 million deaths worldwide and deemed as a critical issue in most countries. Understanding the common factors that contribute to road accidents is not enough, and it is essential to assess the risk involved to prepare for precautionary actions. In this study, we utilize the autoregressive model and value at risk approach in predicting Malaysian road accidents occurrences and assessing the respective risk. We construct a current risk analysis theoretical framework pertain to the vehicle’s condition, forecast and investigate the relationship between the involved variables, and obtain the value at risk for road accidents. From our findings, the road accidents will increase 1.12% higher than the year 2019, along with a 2.77% increase in the number of transportations in 2020. In addition, there is a 95% confidence that in year 2020, the number of road accidents will reduce not more than 3.81%. Coherent from the analysis, there is a potential to adopt these approaches more extensively for this issue as the quantitative analysis are feasible. In addition, a strong positive relationship is found between the likelihood of road accidents and the number of transportations. Thus, prediction of road accidents and identification of its value at risk on yearly basis are beneficial to project the best course of action to deal with road accidents occurrences in the country.

The article investigates the modeling of forecasting financial statement, which will facilitate the procedure of assessing the risks and prospects for improving the financial activity of the enterprise in the digital age. The views of world famous scientists on the essence of forecasting the activity of companies using Excel are reflected. The procedure of modeling the methodology of forecasting the financial report of the company is proposed. According to the yearly financial report of the National Joint Stock Company «Naftogaz of Ukraine» shows an example of the implementation of the proposed procedure for modeling steps for forecasting financial reports for the coming year. The considered forms of forecasting tables will promote unification of analytical calculations and will increase clearness and informativeness of forecast indicators in the conditions of digital technologies. The results of the study are useful for users in order to make the right management decisions about the provision of financial resources to the entity.

In this competitive world, companies need to manage their arrears of debtors to survive. Big companies monthly face thousands of debt cases from their customers and if these debtor customers do not pay their debts within the specified period, these cases will enter the legal stage as legal debt collection that is handled by experienced lawyers. At the legal stage, the cases are sent to the contracted legal debt collection agencies to start a lawsuit. Therefore, assigning which case to which legal debt collection agency is a significant and critical issue in the company’s success in getting its debts in the legal stage. So, this study aims to find the legal debt collection agency, which has more capability to close the assigned debt case by predicting case closing probability with machine learning techniques based on past historical data. To predict case closing probability, 8 machine learning algorithms, Catboost Classifier, Extreme Gradient Boosting Classifier, Gradient Boosting Classifier etc., are applied to the processed dataset. The results show us Catboost Classifier has the best accuracy performance 0.87 accuracy. Also, the results show us boosting type ensemble learning algorithms have better performance than other algorithms. Finally, we tune hyper-parameters of Catboost classifier to get better accuracy in the modeling and applied k-fold cross-validation for testing the model’s testing stability.

Risk management is one of the critical elements in financial institutions, in which early detection of financial crises can be monitored. Literature suggests that there are many types of risks in financial management such as liquidity risk, interest rate risk, equity risk, commodity risk, just to name a few. However, authentic risk that can be associated to financial crises are very inconclusive and unpredictable. This study aims to identify the financial risks that affecting financial management using the proposed ranking trapezoidal fuzzy numbers method that based on circumcenter. Five decision makers which are experts in financial management were requested to provide evaluation against a set of eight financial risks. The proposed method of circumcenter, which considers area, spread, and distance of trapezoidal fuzzy numbers are applied to financial risk management. The result shows that ‘credit risk’ is the highest risk value compared to other seven risks. It is also suggested that ‘commodity risk’ is the lowest risk in financial management.

PURPOSE: Healthcare out-of-pocket (OOP) costs consist of the annual expenses paid by individuals or families that are not reimbursed by insurance. In the U.S, broadening healthcare disparities are caused by the rapid increase in OOP costs. With a precise forecast of the OOP costs, governments can improve the design of healthcare policies to better control the OOP costs. This study designs a purely data-driven ensemble learning procedure to achieve a collection of factors that best predict OOP costs. METHODS: We propose a voting ensemble learning procedure to rank and select factors of OOP costs based on the Medical Expenditure Panel Survey dataset. The method involves utilizing votes from the base learners forward subset selection, backward subset selection, random forest, and LASSO. RESULTS: The top-ranking factors selected by our proposed method are insurance type, age, asthma, family size, race, and number of physician office visits. The predictive models using these factors outperform the models that employ the factors commonly considered by the literature through improving the prediction error (test MSE of the OOP costs’ log-odds) from 0.462 to 0.382. CONCLUSION: Our results indicate a set of factors which best explain the OOP costs behavior based on a purely data-driven solution. These findings contribute to the discussions regarding demand-side needs for containing rapidly rising OOP costs. Instead of estimating the impact of a single factor on OOP costs, our proposed method allows for the selection of arbitrary-sized factors to best explain OOP costs.

Cost-effectiveness analysis provides information on the potential value of new cancer treatments, which is particularly pertinent for decision makers as demand for treatment grows while healthcare budgets remain fixed. A range of decision-analytic modelling approaches can be used to estimate cost effectiveness. This study summarises the key modelling approaches considered in oncology, alongside their advantages and limitations. A review was conducted to identify single technology appraisals (STAs) submitted to the National Institute for Health and Care Excellence (NICE) and published papers reporting full economic evaluations of cancer treatments published within the last 5 years. The review was supplemented with the existing methods literature discussing cancer modelling. In total, 100 NICE STAs and 124 published studies were included. Partitioned-survival analysis (n = 54) and discrete-time state transition structures (n = 41) were the main structures submitted to NICE. Conversely, the published studies reported greater use of discrete-time state transition models (n = 102). Limited justification of model structure was provided by authors, despite an awareness in the existing literature that the model structure should be considered thoroughly and can greatly influence cost-effectiveness results. Justification for the choice of model structure was limited and studies would be improved with a thorough rationale for this choice. The strengths and weaknesses of each approach should be considered by future researchers. Alternative methods (such as multi-state modelling) are likely to be utilised more frequently in the future, and so justification of these more advanced methods is paramount to their acceptability to inform healthcare decision making. Electronic supplementary material The online version of this article (10.1007/s40258-019-00513-3) contains supplementary material, which is available to authorized users.

We conducted a scoping review with the objective of synthesizing available literature and mapping what designs and methods have been used to evaluate health insurance reforms in sub-Saharan Africa. We systematically searched for scientific and grey literature in English and French published between 1980 and 2017 using a combination of three key concepts: “Insurance” and “Impact evaluation” and “sub-Saharan Africa”. The search led to the inclusion of 66 articles with half of the studies pertaining to the evaluation of National Health Insurance schemes, especially the Ghanaian one, and one quarter pertaining to Community-Based Health Insurance and Mutual Health Organization schemes. Sixty-one out of the 66 studies (92%) included were quantitative studies, while only five (8%) were defined as mixed methods. Most studies included applied an observational design (n = 37; 56%), followed by a quasi-experimental (n = 27; 41%) design; only two studies (3%) applied an experimental design. The findings of our scoping review are in line with the observation emerging from prior reviews focused on content in pointing at the fact that evidence on the impact of health insurance is still relatively weak as it is derived primarily from studies relying on observational designs. Our review did identify an increase in the use of quasi-experimental designs in more recent studies, suggesting that we could observe a broadening and deepening of the evidence base on health insurance in Africa over the next few years. Electronic supplementary material The online version of this article (10.1007/s40258-019-00499-y) contains supplementary material, which is available to authorized users.

Objective Our objective was to review, compare and gain insight into economic evaluations in primary open-angle glaucoma (POAG) with a focus on existing decision analytic models. Methods A literature review was performed using clinical and specialized databases following best practices. Relevant inclusion criteria included the development of a decision analytic model, the assessment of POAG interventions, and a full economic evaluation in terms of costs and health-related outcomes. Model inputs and settings were extracted, compared and analyzed. Main study incremental outcomes were also reported. Results The literature review identified 22 full articles in alignment with the eligibility criteria for a total of 15 countries and a wide range of years from 1983 to 2018. Interventions included as competing alternatives in the eligible studies were topical medications (33%), screening or diagnosis (33%), surgical interventions (10%), laser trabeculoplasty (10%) and minimally invasive surgeries (3%). Markov models using transition states were the most common type of modeling approach. Cost-utility models using a mid- to long-term time horizon with a national payer perspective were the most frequent type of economic evaluation identified. Model states commonly included disease severity levels, as defined by glaucoma staging systems, and other relevant events such as blindness and death. Authors did not sufficiently justify key modeling assumptions, inputs or the robustness of their findings. Conclusions Decision analytic models in POAG can reasonably guide future modeling research by revealing common practices, inputs and assumptions. Furthermore, this review revealed evidence gaps in terms of unexplored interventions and treatment sequences. Electronic supplementary material The online version of this article (10.1007/s41669-019-0141-4) contains supplementary material, which is available to authorized users.

Spillover effects on the welfare of family members may refer to caregiver health effects, informal care time costs, or both. This review focuses on methods that have been used to measure and value informal care time and makes suggestions for their appropriate use in cost-of-illness and cost-effectiveness analyses. It highlights the importance of methods to value informal care time that are independent of caregiver health effects in order to minimize double counting of spillover effects. Although the concept of including caregiver time costs in economic evaluations is not new, relatively few societal perspective cost-effectiveness analyses have included informal care, with the exception of dementia. This is due in part to challenges in measuring and valuing time costs. Analysts can collect information on time spent in informal care or can assess its impact in displacing other time use, notably time in paid employment. A key challenge is to ensure appropriate comparison groups that do not require informal care to be able to correctly estimate attributable informal care time or foregone market work. To value informal care time, analysts can use estimates of hourly earnings in either opportunity cost or replacement cost approaches. Researchers have used widely varying estimates of hourly earnings. Alternatively, stated-preference methods (i.e. contingent valuation, conjoint analysis) can be used to value the effect of informal care on utility, but this can entail double counting with health effects. Lack of consensus and standardization of methods makes it difficult to compare estimates of informal care costs. Electronic supplementary material The online version of this article (10.1007/s40273-019-00782-9) contains supplementary material, which is available to authorized users.

Third-party funding is a risky investment and, therefore, third-party funders strive to be very careful and thorough when assessing the risks of each claim that needs funding and the impact those risks may have on the cost, duration and quantum of the claim. In this chapter, we explore the main case-related and context-related risks that may be present in investment arbitration, which has certain specificities. We look at this from the practitioner’s standpoint, with the view to making practitioners aware of third-party funders’ motivation and priorities. This may be useful to counsel when they present their clients’ application for funding to third-party funders and also, generally, to gain a better understanding of third-party funders’ stance, so that they are better equipped to interact with them and represent their clients’ interest.

Having a concept and understanding of coding and billing for procedural services is important to the advanced practice provider (APP). Reimbursement for services delivered is important to the financial stability of practices and organizations. Billing and coding also hold legal obligations to the provider of such services and require understanding of concepts of waste, abuse, and fraud. Understanding reimbursement practices to local regions and the nuances of billing and coding is crucial to showing the value of the APP to the practice.

The importance and significance of accurately and clearly communicating COVID-19 risks should receive more attention. At the present time, many of those who provide us with statistics and facts are, unfortunately, confusing us and making us unsure and nervous. We will explain why use of the BRCT graphic can provide information on COVID-19 risks in a way which is accurate and clearly understood. It allows for engagement of members of the community thereby, ensuring participation for concerned individuals. We present examples for different age groups and explain how these BRCTs can demonstrate why certain information is deceptive and of no real value.

An adequate risk allocation is a distinctive feature of PPP contracts. This chapter illustrates the main risks that could affect a PPP contract, the main rules to secure a correct risks allocation, and how and why to estimate the cost of risks. Risks allocation is also fundamental for the accounting treatment of PPP contracts, especially those based on the payment of the availability fees by the authorities. Therefore, this chapter presents and discusses the sophisticated accounting system for PPP contracts in place in Europe. The chapter also includes a risk matrix (see Appendix), which, per each of the main relevant risks, shows the subject, public or private, that should bear it and the implications of such decision.

Covid-19 has affected the lives of people in different ways. A number of models are available in the literature to study the dynamics of a pandemic. However, there are very few models that study the impact of vaccine distribution among different population groups. In this paper, we use a modified epidemiological model which incorporates relationships between different populations to incorporate the impact of vaccine distribution and present results for different vaccine distribution strategies. We have presented how a sharing-based vaccine distribution strategy could be implemented in the face of the spread of the virus. The main finding of our work is that an effective vaccine distribution may differ depending upon the level of interactions among different population groups and a sharing-based vaccine distribution strategy is the most effective strategy. © 2021, IFIP International Federation for Information Processing.