The objective of this study was to evaluate the scientific literature concerning asbestos and lung cancer, emphasizing low-level exposure. A literature search in PubMed and Embase resulted in 5,864 citations. Information from included studies was extracted using SIGN. Twenty-one statements were evidence graded. The results show that histology and location are not helpful in differentiating asbestos-related lung cancer. Pleural plaques, asbestos bodies, or asbestos fibers are useful as markers of asbestos exposure. The interaction between asbestos and smoking regarding lung cancer risk is between additive and multiplicative. The findings indicate that the association between asbestos exposure and lung cancer risk is basically linear, but may level off at very high exposures. The relative risk for lung cancer increases between 1% and 4% per fiber-year (f-y)/mL, corresponding to a doubling of risk at 25-100 f-y/mL. However, one high-quality case-control study showed a doubling at 4 f-y/mL.

AND KEYWORDS AbstractTraditional rolling stock rescheduling applications either treat passengers as static objects whose influence on the system is unchanged in a disrupted situation, or they treat passenger behavior as a given input. In case of disruptions however, we may expect the flow of passengers to change significantly. In this paper we present a model for passenger flows during disruptions and we describe an iterative heuristic for optimizing the rolling stock to the disrupted passenger flows. The model is tested on realistic problem instances of NS, the major operator of passenger trains in the Netherlands. FreeAbstract Traditional rolling stock rescheduling applications either treat passengers as static objects whose influence on the system is unchanged in a disrupted situation, or they treat passenger behavior as a given input. In case of disruptions however, we may expect the flow of passengers to change significantly.In this paper we present a model for passenger flows during disruptions and we describe an iterative heuristic for optimizing the rolling stock to the disrupted passenger flows. The model is tested on realistic problem instances of NS, the major operator of passenger trains in the Netherlands.

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Objectives: Multiple myeloma (MM) patients report high symptom burden and reduced health-related quality of life (HRQoL) compared to patients with other haematological malignancies. The aim of this review was to analyse published longitudinal studies including MM patients according to a change in HRQoL scores, which is perceived as beneficial to the patient according to two published guidelines. The background of these findings should be in focus in future studies, and HRQoL measurements should be integrated in maintenance studies. Methods: K E Y W O R D SHealth-related quality of life, multiple myeloma, longitudinal studies

Purpose Results of patient-reported outcome measures (PROMs) are increasingly used to inform healthcare decision-making. Research has shown that response shift can impact PROM results. As part of an international collaboration, our goal is to provide a framework regarding the implications of response shift at the level of patient care (micro), healthcare institute (meso), and healthcare policy (macro). Methods Empirical evidence of response shift that can influence patients’ self-reported health and preferences provided the foundation for development of the framework. Measurement validity theory, hermeneutic philosophy, and micro-, meso-, and macro-level healthcare decision-making informed our theoretical analysis. Results At the micro-level, patients’ self-reported health needs to be interpreted via dialogue with the clinician to avoid misinterpretation of PROM data due to response shift. It is also important to consider the potential impact of response shift on study results, when these are used to support decisions. At the meso-level, individual-level data should be examined for response shift before aggregating PROM data for decision-making related to quality improvement, performance monitoring, and accreditation. At the macro-level, critical reflection on the conceptualization of health is required to know whether response shift needs to be controlled for when PROM data are used to inform healthcare coverage. Conclusion Given empirical evidence of response shift, there is a critical need for guidelines and knowledge translation to avoid potential misinterpretations of PROM results and consequential biases in decision-making. Our framework with guiding questions provides a structure for developing strategies to address potential impacts of response shift at micro-, meso-, and macro-levels.