Exploring the potential value of improved care for secondary hyperparathyroidism with a novel calcimimetic therapy

Aims: This study explored the use of a value-based pricing approach for the new calcimimetic etelcalcetide indicated for the treatment of secondary hyperparathyroidism (SHPT) in patients receiving hemodialysis. It used the US payer perspective and applied the cost-effectiveness framework. Because etelcalcetide is an intravenous therapy that can be titrated for individual patients, and because its utilization is yet to be assessed in real world settings, a range of plausible doses were estimated for etelcalcetide to define a range of prices. These were either in relation to the existing oral calcimimetic cinacalcet or compared to no calcimimetic treatment.

Materials and methods: The value-based price of etelcalcetide was determined via a Markov model. This model combined data from the etelcalcetide trials and previously published cost-effectiveness models in SHPT, and allowed extrapolation of treatment effects on mortality, cardiovascular events, fracture, and parathyroidectomy. Several dosing scenarios were explored covering the dose ranges of 30.0–64.18?mg per day for cinacalcet and 1.07–3.11?mg per day for etelcalcetide. These included the mean dose from the etelcalcetide trials, the preliminary defined daily dose, and the expected most common dose in real world. An acceptable price range for etelcalcetide was assessed by comparing the incremental cost-effectiveness ratios obtained with the willingness-to-pay threshold range of $100,000–$300,000/quality-adjusted life-years.

Results: Cost-effectiveness analysis supported value-based prices for etelcalcetide ranging from $21.15–$49.97/mg vs cinacalcet, and $13.79–$119.45/mg vs no calcimimetics.

Limitations: There is uncertainty around what the real-world dosing will be for etelcalcetide. Another important nuance is that no studies have examined etelcalcetide effects on hard outcomes and, therefore, this modeling exercise relied on an extrapolation approach.

Conclusions: This cost-effectiveness analysis, including scenarios to address uncertainties, allowed estimation of a value-based price range to aid reimbursement decisions in the US.     

Geographies of injustice: the socio-spatial determinants of energy poverty in Poland,the Czech Republic and Hungary

Falling real incomes, rising utility prices and the historically poor thermal quality of the housing stock are some of the main factors that have driven the rise of systemic injustices surrounding energy poverty in the post-communist states of Eastern and Central Europe (ECE). We undertake a socio-spatial and temporal assessment of energy poverty in Hungary, the Czech Republic and Poland, using Household Budget Survey micro-data and the consolidated national results of the EU Survey of Income and Living Conditions. Our results indicate that increases in domestic energy prices and expenditures during the last decade have not been offset by purchasing power gains or energy efficiency improvements, resulting in sustained and growing levels of energy poverty. Capital city regions have fared better than rural areas even if traditional macroeconomic performance indicators do not easily match domestic energy deprivation metrics. We thus question policy approaches that favour income-based solutions and fail to recognise housing- and demography-related vulnerabilities.     

Real options and cost-based access pricing: Model and methodology

This paper proposes a model and methodology for valuing the option to delay network investment decisions and calculating cost-based access prices. It argues that an option value multiple must be applied to the investment cost component of each network element in order to account for the value of the delay option that is extinguished at the time of investment. Option value multiples are calculated for the investment decision in three main network elements, each representing a different part of the Brazilian fixed telecommunications network, subject to different technological and demand uncertainties. After applying the markup factors, network costs must be assigned to network services on the basis of how much each service uses each network element.     

Low‐Carbon Gentrification: When Climate Change Encounters Residential Displacement

This article focuses on the emergence of ‘low‐carbon’ gentrification as a distinct urban phenomenon, a process that we see as the outcome of efforts to change the social and spatial composition of urban districts under the pretext of responding to climate change and energy efficiency imperatives. The article develops a conceptual framework for scrutinizing low‐carbon gentrification, predicated upon insights from literatures on ecological gentrification and displacement. It documents the existence of an ‘eco‐social paradox’ associated with new patterns of socio‐spatial segregation and energy efficiency retrofits. We interrogate the discursive and policy frameworks, socio‐spatial implications and political contestations of low‐carbon gentrification. Evidence is drawn from case study research in an inner‐city district of the Polish city of Gdańsk, where such processes have been unfolding since 2006 due to the implementation of a targeted urban regeneration programme. This investigation is positioned within a wider analysis of secondary written sources about similar developments in other geographical contexts across Europe and North America, where anecdotal evidence suggests that low‐carbon gentrification may be widespread and common.     

A fuzzy set theory based computational model to represent the quality of inter-rater agreement

In this paper we present a method to evaluate the quality of a rater’s judgement, which can integrate and enrich the use of inter-rater agreement as a reliability measure. Our proposal is an integrative one and evaluates the quality of a rater’s performance through an analysis of the profile of that individual rater’s performance. We discuss its rationale on the basis of the interpretation of inter-rater agreement, highlighting some critical issues. For this purpose, we adopt a computational model based on fuzzy set theory, demonstrating its main characteristics with an exemplary case study.     

The effect of output specification on the optimal policy design for electric utilities

We show that the manner in which the production process is seen when analyzing data on electricity production has an impact on the policy conclusions. In particular, we show that the different specifications of output found in the literature can generate quite diverse views regarding regulation and optimal industry structure, even when using the same data to estimate a cost function. To illustrate this we use information gathered from the Spanish Electric Industry and analyze electricity activities following three approaches: the traditional aggregate activity view, the multistage model and the multioutput-multistage approach. We estimate the degree of economies of scale S and derive marginal costs for all models, plus economies of vertical integration (EVI) for the last two ones. Then we compare these results and verify that the aggregate analysis can mislead policymaking.     

Derived versus full name brand extensions

Most brand extension studies follow the assumption that brand extensions use the full original parent brand name (e.g., Oral-B tooth brush may extend to Oral-B dental floss). However, some companies use derived brand names in their brand extension strategies (e.g. Nestea Iced Tea). This study explores the advantages and disadvantages of derived brand extensions compared to full name extensions. The study examines the importance of target market effects on the evaluation of both brand extension strategies. Findings support the idea that derived brand names leverage parent brand evaluations and protect parent brand from extension failures.     

Modeling public transport corridors with aggregate and disaggregate demand

Microeconomic public transport models aimed at maximizing social benefits usually consider demand in an aggregate manner. In this paper we examine the effect of this approach on the optimal values of frequency and vehicle size by comparison with models where demand is described in detail as a matrix of flows between every station in a single line service. The theoretical analysis and the numerical examples suggest that the spatially aggregated model underestimates optimal frequency and overestimates vehicle size.