Fingolimod versus natalizumab in patients with relapsing remitting multiple sclerosis: a cost-effectiveness and cost-utility study in Iran

Aims: Multiple sclerosis (MS) is a chronic, autoimmune, and inflammatory disease. If the first-line medicines are not effective enough, specialists will prescribe second-line medicines, such as natalizumab and fingolimod. This study aimed to compare the cost-effectiveness and cost-utility of fingolimod with those of natalizumab in patients with relapsing-remitting multiple sclerosis (RRMS) in Iran, Fars province in 2016.

Materials and methods: This study was a cost-effectiveness and cost-utility study in which a Markov model was used. The study used the census method to evaluate 81 patients with MS in Iran, Fars province who were being treated with fingolimod and natalizumab. In this study, costs were collected from the societal perspective, and the outcomes were the mean of relapse avoided rate and QALY. The cost data collection form, Kurtzke Expanded Disability Status Scale, and EQ-5D-3L questionnaire were used to collect the required data.

Results: The results showed that, compared to natalizumab, patients who used fingolimod had decreased costs (58,087 vs 201,707), increased QALYs (8.09 vs 7.37), and a better relapse avoided rate (6.27 vs 5.83) per patient over the lifetime. The results of the sensitivity analysis showed that the results of the study were robust. Also, the results of the scatter plots showed that fingolimod was more cost-effective based on the QALY and relapse avoided rate in 62% and 56%, respectively, of the simulations for the thresholds below $15,657 for the studied patients.

Conclusions: According to the results of this study, the cost-effectiveness and cost-utility of fingolimod were higher than those of natalizumab. Therefore, it is recommended that treatment with fingolimod be the first priority of second-line treatment for MS patients, and policy-makers and health managers are encouraged to make efforts in order to increase insurance coverage and reduce the out-of-pocket payments of these patients.     

Developing a multiple-criteria decision analysis for green economy transition: a Canadian case study

ABSTRACT

Identifying planning strategies for the transition to a green economy is a formidable challenge. We proposed a novel multiple-criteria decision analysis model which can quantitatively identify the socio-economic and environmental impacts of various government and public policies. We applied the model to four practical scenarios in Canada for determining the optimal final demand that maximizes the country's GDP and employment while minimizing GHG emissions for small, short-term changes. As a result, the model suggested potential ways to simultaneously achieve a GDP growth of 2.5 billion CAD and creation of over 25,000 new jobs, and a saving of 2514 kt CO₂. As per the final demand, the electrification of domestic heating and transport should be more promoted. The proposed analysis tool will provide decision-makers with the ability to explore the design and effects of policy reforms, regulatory changes, and targeted public expenditure strategies, thereby overcoming barriers towards a green economy.     

Modified CADF and CIPS Panel Unit Root Statistics with Standard Chi‐squared and Normal Limiting Distributions

In an influential paper Pesaran (‘A simple panel unit root test in presence of cross‐section dependence’, Journal of Applied Econometrics, Vol. 22, pp. 265–312, 2007) proposes two unit root tests for panels with a common factor structure. These are the CADF and CIPS test statistics, which are amongst the most popular test statistics in the literature. One feature of these statistics is that their limiting distributions are highly non‐standard, making for relatively complicated implementation. In this paper, we take this feature as our starting point to develop modified CADF and CIPS test statistics that support standard chi‐squared and normal inference.     

Growth, development and natural resources: New evidence using a heterogeneous panel analysis

This paper explores whether natural resource abundance is a curse or a blessing. To do so, we firstly develop a theory consistent econometric model, in which we show that there is a long run relationship between real income, the investment rate, and the real value of oil production. Secondly, we investigate the long-run (level) impacts of natural resource abundance on domestic output as well as the short-run (growth) effects. Thirdly, we explicitly recognize that there is a substantial cross-sectional dependence and cross-country heterogeneity in our sample, which covers 53 oil exporting and importing countries with very different historical and institutional backgrounds, and adopt the non-stationary panel methodologies developed by Pesaran (2006) and Pedroni (2000) for estimation. Our results, using the real value of oil production, rent or reserves as a proxy for resource endowment, reveal that oil abundance has a positive effect on both income levels and economic growth. While we accept that oil rich countries could benefit more from their natural wealth by adopting growth and welfare enhancing policies and institutions, we challenge the common view that oil abundance affects economic growth negatively.     

Inflation and the Income Share of the Rich: Evidence for 14 OECD Countries

This paper examines the distributional implications of inflation on top income shares in 14 advanced economies using data over the period 1920–2016. We use local projections to analyze how top income shares respond to an inflation shock, and panel regressions in which all variables are defined as 5-year averages to examine the impact of inflation on the position of the top-one-percent in the long run. Our findings suggest that inflation reduces the share of national income held by the top 1 percent. Furthermore, we find that inflation shocks and long-run inflation have similar effects on top income shares.     

A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

Testing the Olson Hypothesis within the Australian Context

This article seeks to empirically test whether the Olson hypothesis is applicable in the Australian context. Both cointegration and regression analyses which are utilised for this purpose provide strong support for this hypothesis; that is, the existence of a statistically significant negative relationship between import protection and the proportion of manufactured goods exported. More specifically, it is revealed that falling levels of import protection have underpinned an increase in the proportion of manufacturing goods exported and hence assisted in improving the manufacturing sector's export orientation in the period 1980:1–1996:2. A particularly important dimension of the empirical results is the strengthening of this relationship in the more recent years to 1996:2. This phenomenon probably indicates the favourable impact of the accumulated reduction in the protection levels since the early 1970s.     

Information Collection Strategic Design in Experts-assisted Decision Making Paradigm

In an experts-assisted decision making paradigm, the information collection design becomes a strategic variable under a weak assumption that the final decision is dependent on the design used to collect information as well. As a result, the same information of the experts and the decision maker about the problem can potentially produce different final decisions for different information collection designs. The implication is that a decision maker can strategically select a design which serves his/her objective. This paper uses a Bayesian estimation methodology for combining experts' information with the decision maker's prior. An information collection process is designed by setting constraints on this model. Several designs are developed here using such controlled factors as a one-stage versus a two-stage decision process, experts' rank ordering, and group versus individual lobbying/consultation. An example is provided to illustrate the applicability of the concept. It is shown that the information produced in the process of producing a decision can also give insights into the impacts of the decision maker and the experts on the decision.