Major bleeding risk and healthcare economic outcomes of non-valvular atrial fibrillation patients newly-initiated with oral anticoagulant therapy in the real-world setting

Aims: This study compared the risk for major bleeding (MB) and healthcare economic outcomes of patients with non-valvular atrial fibrillation (NVAF) after initiating treatment with apixaban vs rivaroxaban, dabigatran, or warfarin.

Methods: NVAF patients who initiated apixaban, rivaroxaban, dabigatran, or warfarin were identified from the IMS Pharmetrics Plus database (January 1, 2013–September 30, 2015). Propensity score matching (PSM) was used to balance differences in patient characteristics between study cohorts: patients treated with apixaban vs rivaroxaban, apixaban vs dabigatran, and apixaban vs warfarin. Risk of hospitalization and healthcare costs (all-cause and MB-related) were compared between matched cohorts during the follow-up.

Results: During the follow-up, risks for all-cause (hazard ratio [HR]?=?1.44, 95% confidence interval [CI]?=?1.2–1.7) and MB-related (HR?=?1.57, 95% CI?=?1.0–2.4) hospitalizations were significantly greater for patients treated with rivaroxaban vs apixaban. Adjusted total all-cause healthcare costs were significantly lower for patients treated with apixaban vs rivaroxaban ($3,950 vs $4,333 per patient per month [PPPM], p?=?.002) and MB-related medical costs were not statistically significantly different ($100 vs $233 PPPM, p?=?.096). Risk for all-cause hospitalization (HR?=?1.98, 95% CI?=?1.6–2.4) was significantly greater for patients treated with dabigatran vs apixaban, although total all-cause healthcare costs were not statistically different. Risks for all-cause (HR?=?2.22, 95% CI?=?1.9–2.5) and MB-related (HR?=?2.05, 95% CI?=?1.4–3.0) hospitalizations were significantly greater for patients treated with warfarin vs apixaban. Total all-cause healthcare costs ($3,919 vs $4,177 PPPM, p?=?.025) and MB-related medical costs ($96 vs $212 PPPM, p?=?.026) were significantly lower for patients treated with apixaban vs warfarin.

Limitations: This retrospective database analysis does not establish causation.

Conclusions: In the real-world setting, compared with rivaroxaban and warfarin, apixaban is associated with reduced risk of hospitalization and lower healthcare costs. Compared with dabigatran, apixaban is associated with lower risk of hospitalizations.     

Conceptualizing the Evolution and Future of Advertising

Technology has irreversibly changed the way that firms identify who is more likely to buy, what they are more likely to buy, when they are more likely to buy, why they are likely to buy, and how they are likely to buy, repurchase, and recommend. It is now easier than ever for firms to have a body of evidence in the form of actual insights, as opposed to having mere guestimates, on consumers' apparent intentions to buy. Power dynamics have changed in the marketing climate, and firms have to factor in not only aggressive competitors but also empowered customers with limited attention spans. In such a climate, what lies in the future for advertising? And how can marketing managers make the most of the changing climate and maximize their returns on advertising? How can academics advance research related to maximizing the effectiveness and efficiency of advertising? These are the questions that this article addresses via an integrated framework that expounds all the factors related to customers, firms, technological environment, and data resources, as well as the contextual factors, including product life cycle, customer life cycle, and so on, and their collective impact on advertising strategy, which includes advertising content, media selection, message, and targeting.     

Does conditionality in IMF-supported programs promote revenue reform?

This paper studies whether revenue conditionality in Fund programs had any impact on the revenue performance of 126 low- and middle-income countries during 1993–2013. The results indicate that such conditionality had a positive impact on tax revenue, with strongest improvement felt on taxes on goods and services, including the VAT. Revenue conditionality matters more for low-income countries, particularly those where revenue ratios are below the group average. Moreover, revenue conditionality appears to be more effective when targeted to a specific tax. These results hold after controlling for potential endogeneity, sample selection bias, and when revenues are adjusted for economic cycle.     

An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa

This paper tests whether housing prices in the five segments of the South African housing market, namely large-middle, medium-middle, small-middle, luxury and affordable, exhibit non-linearity based on smooth transition autoregressive (STAR) models estimated using quarterly data from 1970:Q2 to 2009:Q3. Findings point to an overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of the linear and non-linear models. We next provide further support for non-linearity by comparing one- to four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical and Bayesian versions of the linear autoregressive (AR) models for each of these segments, for the out-of-sample horizon 2001:Q1 to 2009:Q3, using the in-sample period 1970:Q2 to 2000:Q4. Our results indicate that barring the one-, two and four-step(s)-ahead forecasts of the small segment, the non-linear model always outperforms the linear models. In addition, given the existence of strong causal relationship amongst the house prices of the five segments, the multivariate versions of the linear (classical and Bayesian) and STAR (MSTAR) models were also estimated. The MSTAR always outperformed the best performing univariate and multivariate linear models. Thus, our results highlight the importance of accounting for non-linearity, as well as the possible interrelationship amongst the variables under consideration, especially for forecasting.     

Accountability and legitimacy in earth system governance: A research framework

Along with concerns over the effectiveness of earth system governance, ways of enhancing its accountability and legitimacy are increasingly coming to the fore in both scholarly debate and political practice. Concerns over accountability and legitimacy pertain to all levels of governance, from the local to the global, and cover the spectrum of public and private governance arrangements. This conceptual article elaborates on the sources, mechanisms and reform options relating to more accountable, legitimate and democratic earth system governance. We proceed in four steps. First, we conceptualize accountability and legitimacy in earth system governance. Second, we place questions of accountability and legitimacy within the larger context of earth system transformation, which, we argue, poses special challenges to the pursuit of accountability and legitimacy. Third, drawing on the contributions to this special section, we analyze different sources and mechanisms of accountability and legitimacy and their effects on the democratic potential and effectiveness of governance. Fourth, in concluding, we outline reform options that may help alleviate persisting deficits in the democratic potential of earth system governance.     

Researches in Corporate Social Responsibility: A Review of Shifting Focus,Paradigms, and Methodologies

Owing to the growing academic and practitioner’s interest in the field of Corporate Social Responsibility, there is a need to do a comprehensive assessment and synthesis of research activities. This article addresses this need and examines the academic literature on Corporate Social Responsibility and Performance using a paradigmatic and methodological lens. The objective of this article is fourfold. First, it examines the status of CSR research from its beginning especially after 1970 to year 2008 in leading academic journals and reports to assess the focus areas of research on CSR so far. Second, it analyzes the research paradigms adopted in these research articles using the Operations Research Paradigm framework. Third, it compares and contrasts various kinds of research articles, methodologies, and research designs used in various researches in literature. Finally, it uncovers the implications of this study and directions for future research.     

Supply-side investments: An international analysis of the return and risk relationship in the Travel & Leisure sector

This study investigates supply-side investments in the tourism sector, analysing the return and risk relationship of investments in tourism across several countries. The performance of tourism sector companies listed on the stock exchange, for the top one dozen countries according to the World Economic Forum (WEF) tourism rankings, is investigated by region, across these countries and within each country by comparison to other sectors. Several metrics are used to compare the risk-adjusted returns, over a 15-year period ending March 2007 and sub-periods to check the persistence of performance over time. The results present a strikingly different ranking than those of the WEF. The implications for the flow of investment funds are of importance in a global capital market. Money will flow to those investments with the highest expected returns for a given risk level and this has consequential impacts for economic growth and employment in the tourism sector.     

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.     

Sustainability as corporate culture of a brand for superior performance

Sustainability research highlights new challenges and opportunities for businesses. This paper reviews the literature to understand the ability of sustainable green initiatives when practiced as a corporate culture to individually create new opportunities for operations, management and marketing. According to current research, business opportunities exclusively available to different functions of a firm can drive its performance. The role of marketing in the achievement of superior performance by virtue of sustainability practices is also explained by the existing literature. Branding literature, however, fails to explain the influence of a brand on sustainability-driven opportunities available to a firm for superior performance. The objective of this study is to explore if a brand can strengthen the ability of sustainability-based green initiatives of managers to drive opportunities available to a firm for superior performance. A conceptual framework grounded in the triple bottom line theory is presented based on the assumption that brand as a stimulating factor can accelerate the conversion of opportunities available to a business into superior performance. Academic and managerial perspectives have been used to draw upon the implications of the model. Both practitioners and academic researchers will benefit from future research on this topic.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.