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Longitudinal data are required to characterise and measure the dynamics of income-related health inequalities (IRHI). This chapter develops a framework to evaluate the impact of population changes on the level of cross-sectional IRHI over time and thereby provides further insight into how health inequalities develop or perpetuate themselves in a society. The approach is illustrated by an empirical analysis of the increase in IRHI in Great Britain between 1999 and 2004 using the British Household Panel Survey. The results imply that levels of IRHI would have been even higher in 2004 but for the entry of youths into the adult population and deaths, with these natural processes of population turnover serving to partially mask the increase in IRHI among the resident adult population over the five-year period. We conclude that a failure to take demographic changes into account may lead to erroneous conclusions on the effectiveness of policies designed to tackle health inequalities.

Long-term care (LTC) homes are highly regulated settings that provide care to people living with complex health conditions who are often at the end of their lives. Mental health and quality of life are important concepts in LTC given the inherent poor health and diminished autonomy of residents living in this setting. The COVID-19 pandemic had the potential to further compound these issues through lockdowns limiting movement within and outside of LTC homes, increased fear of severe COVID-19 infections, staff shortages, and impaired communication through personal protective equipment. However, the evidence describing the effect of the pandemic on mental health and quality of life is mixed, with some studies describing increased rates of mental health concerns and others presenting modest increases or decreases. Creative strategies to mitigate negative mental health consequences of lockdown included technology supported and window or outdoor visits, increased access to volunteers, and supports for families. However, the evidence in this area continues to evolve as subsequent waves of the pandemic progress. Future research may present new evidence about other strategies that became important in different stages of the pandemic.

Purpose: In this chapter, we develop a conceptual model, the relational anger model (RAM). The model aims to better understand the receivers' attributions and emotion regulation strategies used in the face of intense workplace anger. We also report a test of this model in a workplace setting. Study Design/Methodology/Approach: The data were collected through a survey using a split administration design conducted in various industries. The analysis used PROCESS based on data gathered from 122 employees. Findings: The results indicated that perceptions of greater anger intensity are associated with lower target positive health (e.g., lowered work functionality). When attributions of higher sender anger intensity are viewed as appropriate, targets experience better health outcomes. Targets' attribution of lower sender anger intensity appropriateness is also associated with targets' reporting higher negative health outcomes (e.g., lowered self-esteem). Support for the full moderated mediation model of the effects of the ER strategies is not found. However, separate paths within the model are significant as outlined in the analysis throughout this chapter. Originality/Value: Overall, the RAM increases our understanding of a receivers' internal cognitive and affective processes in the face of workplace anger manifestations in organizations. Research Limitations: There is a possibility of common method variance affecting the study results, but a split administration design was used to minimize this effect. The study may also be affected by memory of the anger incident, which we tried to overcome using the Day Reconstruction Method.

This study aims to identify the determinants of transport mode choice and the constraints on shifting freight in New Zealand (NZ) from road to rail and/or coastal shipping, and to quantify the trade-off between factors affecting shippers’ perceptions, to assist in increasing the share of freight moved by non-road transport modes.

A revealed preference survey of 183 freight shippers, including small and medium enterprises and freight agents in NZ, is used to investigate whether freight shippers’ characteristics affect their ranked preference for attributes related to mode choice and modal shift. Additionally, a rank-ordered logistic (ROL) model is estimated using the ranking data.

The results reveal several distinct types of transport mode choice behaviour within the sample and show how the preferences for timeliness, cost, accessibility, damage and loss, customer service, and suitability vary between industry groups and business types. Also, the ROL method allows us to identify heterogeneity in preferences for mode choice and mode shift factors for freight within NZ.

The results imply that NZ shippers ranked transport time as the most significant constraint upon distributing goods by rail, while accessibility and load size were the most significant constraints upon using coastal shipping. The study also identifies how NZ shippers’ modal shift constraints vary according to the firm’s individual or logistical characteristics.

This study informs freight transport policy makers about the needs of NZ shippers by providing quantitative measures of the intensity of preference for the various mode choice factors.

Those involved in freight transport have a better basis for formulating transport policy.

This chapter examines self-assessed health (SAH) data of 29 European countries using Eurostat data for the years 2009 and 2018. It first computes the indices recently introduced by Seth and Yalonetzky (2020) and provides confidence intervals for these indices. The ranking of these countries for the year 2018 is then summarized by Hasse diagrams. The chapter then examines first- and second-order stochastic dominance, based again on the recent work of Seth and Yalonetzky. Here also bootstrap confidence intervals were computed. The ranking of the countries in 2018 is then translated again into Hasse diagrams. It appears that Hungary and Latvia are respectively the poorest and least poor countries, both in 2009 and 2018, in terms of their SAH condition. While countries like Ireland, Luxembourg, Romania and Portugal are in the poorer segment of the distribution of countries, Norway, the United Kingdom, Denmark, the Netherlands and Austria are located on the less poor portion. As expected, the Hasse diagrams show also that there are quite a few instances where some countries cannot be ranked.