Infusion administration billing for vedolizumab and infliximab in inflammatory bowel disease

Abstract

Aims: Increasing use of biologics has led to interest in treatment components with potential for cost savings. This study was aimed at comparing administration times and associated costs of infliximab and vedolizumab infusions for inflammatory bowel disease (IBD).

Materials and methods: This study used claims data from the Symphony Health Integrated Dataverse to identify IBD patients using infliximab or vedolizumab between 20 May 2014 and 29 February 2016. Use of Current Procedural Terminology administration codes was evaluated and costs calculated using the 2016 Center for Medicare and Medicaid Services Physician Fee Schedule. Assessments included infusion times, associated costs, productivity loss using average wage estimates from the United States Bureau of Labor Statistics, and home infusion adoption.

Results: A total of 10,051 infliximab and 3114 vedolizumab patients with first-hour claims were identified; 52.0% were female and 64.5% had Crohn’s disease. There were 48,377 infliximab first-hour claims (mean 4.8 infusions per patient); 46,462 (96.0%) had a second-hour claim. In comparison, there were 14,717 vedolizumab claims (mean 4.7 infusions per patient), with only 411 (2.8%) second-hour claims, resulting in vedolizumab cost savings of approximately $1.27 million. The difference in second-hour infusions resulted in 46,051 additional hours of productivity loss with infliximab, and lost wages averaging $1.18 million (range $0.68–$1.77 million).

Limitations: Administration costs were inferred as charge costs and not directly assessed. Productivity loss assessed time spent on infusion only, and included a small proportion of patients beyond working age.

Conclusions: Second-hour infusion billing was significantly lower with vedolizumab than with infliximab, corresponding to cost savings and reduced productivity loss.     

New empirical evidence on the bid-ask spread

In this article, we model the determinants of spread for 734 firms listed on the NYSE over the period 1 January 1998 to 31 December 2008. We propose a panel data model of the determinants of spread. There are four main messages emerging from our work. We find a statistically significant effect of volume on spread inconsistent with the work of Johnson (2008). On price, we find mixed results, consistent with the literature. On the effect of price volatility on spread, our results are completely the opposite of the cross-sectional literature but sides with the relatively recent work of Chordia et al. (2001). We allow for persistence of spread as a determinant of spread and find significant evidence of spread persistence across all 16 sectors. Finally, we examine size effects and find statistically strong evidence of size effects based on the relationship between price and spread, persistence and spread, and volatility and spread.     

A New Keynesian IS curve for Australia: is it forward looking or backward looking?

This article estimates the forward looking, backward looking and an extended version of the New Keynesian IS curve for Australia. The validity of these models is investigated by imposing the constraint on real rate of interest as well as when the constraint is relaxed. Two measures of output gap, namely GAP1 (constructed using the unobserved components approach) and GAP2 (constructed using a quadratic trend) are utilized. Our results suggest that the baseline backward looking and forward looking models are overwhelmingly rejected by the data. This evidence strongly supports the extended backward looking model (with GAP2) being relevant for monetary policy analysis.     

Economies of scale in school operation–a reply

In addition to pointing out that Kumar (1983) omits Hough (1981) and Knight (1983) from its list of reference, Hough (1983) raises two issues of largely statistical nature. ^{1 1} Omissions are clearly inadvertant. The time lag between publication and the general availability makes ther reference list look more subjective than it actually is. These are:

1. the use of an average (AC) rather than a total (TC) cost curve as the correct statistical device for examining economies of scale, and

2. estimation problems arising from possible heteroscedasticity of the error term in the estimating relation.

Exactly the same issues are also raised in Hough (1981). Notwithstanding our earlier disclaimer (p. 324) that ‘no new grounds are broken on the methodological front’ as well as a clear mention of both problems (Footnotes 1 and 2), Hough is quite correct that the implications of these issues for our results were not spelled out. We take this opportunity to offer some clarifications with respect to the results in Kumar (1983).     

Revisiting the Satisfaction–Loyalty Relationship: Empirical Generalizations and Directions for Future Research

This extensive literature review highlights the state of the art regarding the relationship between customer satisfaction and loyalty, both attitudinal and behavioral. In particular, it brings to light several issues that should be carefully considered in analyzing the efficacy of customer satisfaction in explaining and predicting customer loyalty. In fact, for many years companies all around the world have heavily invested in customer satisfaction in the hope of increasing loyalty, and hence, consequently, profitability. But after having gone through a detailed analysis, it is clear that this link it is not as strong as it is believed to be and customer satisfaction is not enough to explain loyalty. In fact, the major findings of this review are captured in the form of a few empirical generalizations. We generalize that, while there is a positive relationship between customer satisfaction and loyalty, the variance explained by just satisfaction is rather small. Models that encompass other relevant variables as moderators, mediators, antecedent variables, or all three are better predictors of loyalty than just customer satisfaction. Further, the satisfaction–loyalty relationship has the potential to change over time. Similar weaker findings are uncovered and the study offers specific guidelines on who, when, and how much to satisfy. Finally, suggestions for future research to explore this domain are offered.     

Benchmarking Indian banks using DEA in post-reform period: a progressive time-weighted mean approach

The ranking of banks based on single period data could mislead the management as well as customers, as many one-time influences may wrongly portray banks with lasting strategy. This study uses data envelopment analysis (DEA) models to rank Indian banks based on their performance over 13 years of post-reform period by using the progressive time-weighted means of variable benchmarking super-efficiency scores. Furthermore, the relative performances of these banks are evaluated against the ‘best practice’ bank by using the fixed benchmarking DEA model. The results show no statistical evidence of dominance of either public sector banks over private sector banks or vice-versa when their efficiencies are evaluated with the common frontier. However, the variations in efficiency across the public sector banks are comparatively less as compared with private sector banks. The efficiency based on the ownership frontier further supports our argument that the public sector banks are relatively more consistent in their performance over the years as well as across the banks. The results show a huge difference in the ranking of some of the banks when the usual method of ranking is compared with progressive time-weighted mean approach, where the performance of the banks over the years is taken into account.     

An Empirical Comparison of Two Stochastic Volatility Models using Indian Market Data

We conduct an empirical comparison of hedging strategies for two different stochastic volatility models proposed in the literature. One is an asymptotic expansion approach and the other is the risk-minimizing approach applied to a Markov-switched geometric Brownian motion. We also compare these with the Black–Scholes delta hedging strategies using historical and implied volatilities. The derivatives we consider are European call options on the NIFTY index of the Indian National Stock Exchange. We compare a few cases with profit and loss data from a trading desk. We find that for the cases that we analyzed, by far the better results are obtained for the Markov-switched geometric Brownian motion.     

Impact of patenting on firms' performance: an empirical investigation based on manufacturing firms in India

The study aims to estimate the impact of R&D expenditure and patenting on the performance of firms using productivity, profitability and Tobin's q ratio as the performance indicators. The study uses firm-level data of 489 high- and medium-technology firms during the period of 2000–2010. We employ relatively a new source of data particularly in the context of India, firm-level patent granted, that has not been explored earlier. The study finds that firms patenting result in productivity improvement of firms, whereas R&D expenditure does not. The study further finds the evidence of positive impact of patenting on financial performance of firm with significant differences between foreign and domestic firms.     

Supply chain analysis of e-tailing versus retailing operation – a case study

The swift growth of e-commerce or e-tailing as a consumer retail channel has made it a serious competitor to traditional retail channels and is changing consumers’ purchasing behaviour. The purpose of this case study, based on Target and Amazon.com, is to analyse the attributes of traditional retailing, e-tailing, and hybrid supply chain models to form conclusions about the feasibility of an idealised supply chain model for the future. An integrated and generalised modelling framework is used that incorporates Six Sigma – define, measure, analyse, improve, control methodology leveraging various tools, including process flow maps, cause and effect diagram, performance efficiency metrics, failure mode and effects analysis (FMEA), and Monte Carlo simulation. Based on this analysis and research, the conclusion is that the idealised supply chain of the future may evolve into a hybrid supply chain, which includes both e-tail and retail channels. The main recommendations from this study include assessing the risks of migrating to such a hybrid supply chain and to leverage the recommended actions provided in the hybrid FMEA. To facilitate more effective and mature processes, this study can guide researchers in exhaustive empirical evaluations of hybrid supply chains, gather experiences and lessons learned for practitioners.     

Green supplier selection: a new genetic/immune strategy with industrial application

With the onset of the ‘climate change movement’, organisations are striving to include environmental criteria into the supplier selection process. This article hybridises a Green Data Envelopment Analysis (GDEA)-based approach with a new Genetic/Immune Strategy for Data Envelopment Analysis (GIS-DEA). A GIS-DEA approach provides a different view to solving multi-criteria decision making problems using data envelopment analysis (DEA) by considering DEA as a multi-objective optimisation problem with efficiency as one objective and proximity of solution to decision makers’ preferences as the other objective. The hybrid approach called GIS-GDEA is applied here to a well-known automobile spare parts manufacturer in India and the results presented. User validation developed based on specific set of criteria suggests that the supplier selection process with GIS-GDEA is more practical than other approaches in a current industrial scenario with multiple decision makers.