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It has been suggested that psychosocial factors are related to survival time of inpatients with cancer. However, there are not many studies examining the relationship between spiritual well-being (SWB) and survival time among countries. This study investigated the relationship between SWB and survival time among three East Asian countries.
This international multicenter cohort study is a secondary analysis involving newly admitted inpatients with advanced cancer in palliative care units in Japan, South Korea, and Taiwan. SWB was measured using the Integrated Palliative Outcome Scale (IPOS) at admission. We performed multivariate analysis using the Cox proportional hazards model to identify independent prognostic factors.
A total of 2,638 patients treated at 37 palliative care units from January 2017 to September 2018 were analyzed. The median survival time was 18.0 days (95% confidence interval [CI] 16.5–19.5) in Japan, 23.0 days (95% CI 19.9–26.1) in Korea, and 15.0 days (95% CI 13.0–17.0) in Taiwan. SWB was a significant factor correlated with survival in Taiwan (hazard ratio [HR] 1.27; 95% CI 1.01–1.59; p = 0.04), while it was insignificant in Japan (HR 1.10; 95% CI 1.00–1.22; p = 0.06), and Korea (HR 1.02; 95% CI 0.77–1.35; p = 0.89).
Significance of results
SWB on admission was associated with survival in patients with advanced cancer in Taiwan but not Japan or Korea. The findings suggest the possibility of a positive relationship between spiritual care and survival time in patients with far advanced cancer.
Maps of global biomes or ecoregions show geographical clusters – unique assemblages of plants and animals that are spatially tied with associated geomorphologic and climatic features. Biomes are typically defined on the basis of broad vegetation types and the biophysical features that impose fundamental controls on the distribution of plants (Cox and Moore 2000). The concept of biomes has a deep history in ecology and has experienced waves of knowledge synthesis, reaching a recent consensus of seven points (Mucina 2019), one of which caught our attention: ‘A biome incorporates a complex of fine-scale biotic communities; it has its characteristic flora and fauna and it is home to characteristic vegetation types and animal communities.’ Macro-scale biodiversity patterns, therefore, reflect the overarching geophysical structures of the globe such as the well-known latitudinal gradients of biodiversity (Willig et al. 2003) and associated ecosystem functioning (e.g., litter decomposition in streams via detritivores; Boyero et al. 2015). Nevertheless, within constantly changing environments, the species composition and geographical boundaries of biomes (called ecotones) are not fixed, but are fluid over evolutionary timescales (Haywood et al. 2019). This biodiversity–environment coupling has been disrupted by agriculture and urbanisation, and the appetite of humans for resources and raw materials and their carelessness in handling waste. Humans are steadily altering land cover and modifying ecological processes across the globe, creating a new ecological order of anthropogenic biomes (anthromes; sensu Ellis and Ramankutty 2008). Natural biomes are facing unprecedented pressures to change, shift, dissolve, merge and emerge, at a pace on par with the most tumultuous periods of the biosphere’s history.
At the time of writing this book, we have witnessed an extreme case of biological invasion. A virus, through an evolutionary leap, has jumped onto a new host species, Homo sapiens, and has taken advantage of the new host’s ambitions and mobility in the zealous phase of globalisation, causing a worldwide pandemic and economic meltdown. The 2019 coronavirus outbreak (COVID-19) is a showcase of the core of invasion science. A list of questions spring to mind. Why this particular virus, and not others? Why now? How fast can it spread? How is its spread mediated by climatic and other environmental factors? What are its vectors and pathways of transmission? Which regions and populations are most susceptible? How much damage can it cause to public health and economies? What factors cause substantial variation in mortality between human populations in different countries? How can we control it? Can we forecast and prevent future outbreaks of emerging infectious diseases? While the whole world scrambles to make sense of COVID-19 and to combat the biggest crisis for humanity since World War II (WWII), we embark on a journey to address these questions to cover many more taxa and situations – the invasion of any biological organism into novel environments.
To assess community assembly via natural colonisation and the potential ceiling of species richness in local communities, Wilson and Simberloff (1969) fumigated nine red mangrove (Rhizophora mangle) islands in Florida Bay, United States. This exemplifies the need in ecology to elucidate the concepts regarding community succession and assembly. New species arrive at a site predominantly via chance and dispersal, while resident species interact with each other via eco-evolutionary games (Chapter 2). Biotic interactions act as engineers to form ecological networks. Together with filters and forces from environmental and disturbance gradients, these ecological interaction networks define realised ecological niches and mediate community assembly rules and trajectories, thereby building an ecological house on the hill. With limited space and resource and the inevitable minimum sustainable size required for a viable population to survive stochasticity and disturbance, there must be an upper bound on the number and kinds of species that can be accommodated in a community, either via natural or human-mediated colonisation of both regional endemics and alien species. For this reason, questions pertaining to the ways in which an ecological community absorbs new arrivals have been on the agenda of community ecology since its inception. Despite progress on that front, making precise predictions about the trajectory of community assembly, the characteristics of the eventual resident species and the realised number of resident species in a local community remains a formidable challenge.
Before diving into a discussion of open adaptive systems, we need to revisit the definition of an ecological network. Material covered in Chapters 2 and 3 showed that ecological networks are webs of co-evolving and co-fitting interactions among species residing in an ecosystem. Such networks subjected to regular incursions of new members in the form of biological invasions are a good example of Open Adaptive Systems (OASs). OASs are different from Clements’ (1916) superorganism metaphor that was further developed and scaled up into the concept of Lovelock’s (1972) Gaia theory, which posits that organisms interact to form a synergistic and self-regulating complex system. The reason for considering an ecological network (or its embedded ecological community) a system, rather than an organism or an organisation (sensu Keller 2005), lies with the type of its boundaries. A system can have either permeable or closed boundaries, while an organism cannot survive with a closed boundary. More importantly, a system has more flexible and tenuous boundaries, the positions of which are often set by the beholder. Boundaries drawn around sampling areas based on what we call an ecological community or an ecosystem are largely subjective. In contrast, the boundary of an organism is clear-cut and plays important physiological and metabolic roles. The value of a system’s boundary, albeit usually subjectively defined, is to identify and differentiate its residents from alien visitors, thereby providing the foundation for labelling entities for management purposes. In contrast, the organic boundary is inseparable from the organism; they belong to an irreducible whole.
Astrologists have predicted the occurrence of solar eclipses with increasing precision through the ages. Predicting celestial motions invokes the dynamics of a relatively simple and rigid system; it is straightforward and akin to identifying regularities in recurrent records. Discovering regularities, however, does not necessarily impart true comprehension. While we can speculate about the mechanisms and forces at work to fill gaps as we edge towards comprehension, such conjectured theories are often misleading. In early 2020, epidemiologists were confronted with a once-in-a-lifetime challenge: forecasting the number of infections of COVID-19 both regionally and globally. With little understanding of the viral transmission at the time, most forecasts failed miserably. Failed forecasts abound, especially for systems that are complex and adaptive; the bet between ecologist Paul Ehrlich and economist Julian Simon on the swings of metal price anticipated from socioeconomic impacts of overpopulation (Sabin 2013) is a good example. The forecasting conundrum is both typical and perplexing to ecologists and invasion scientists; hindsight is an exact science, while forecasting is no easier than catching the Cheshire Cat.
Until now, biological invasions have been conceptualised and studied mainly as a linear process: from introduction to establishment to spread. This volume charts a new course for the field, drawing on key developments in network ecology and complexity science. It defines an agenda for Invasion Science 2.0 by providing new framings and classification of research topics and by offering tentative solutions to vexing problems. In particular, it conceptualises a transformative ecosystem as an open adaptive network with critical transitions and turnover, with resident species heuristically learning and fine-tuning their niches and roles in a multiplayer eco-evolutionary game. It erects signposts pertaining to network interactions, structures, stability, dynamics, scaling, and invasibility. It is not a recipe book or a road map, but an atlas of possibilities: a 'hitchhiker's guide'.
This book deals with the roles and impacts of the entangled web of biotic interactions that an alien species partakes in as it infiltrates ecological networks. We partition related issues into six topics (network interactions, structures, stability, dynamics, scaling and invasibility). We start unpacking these issues here and will dive deeper into each in subsequent chapters. To embrace the complexity of ecological networks we need to introduce a few simple mathematical models and associated concepts that are fundamental to network analyses, visualisation and the ideas we develop. We keep the mathematical details to a minimum and provide intuitive explanation of their meaning and rationale; we also discuss, using simple terminology wherever possible, key procedures that lead to deductive conclusions. Most of the models we cite have been elucidated in great detail elsewhere and can be implemented in any computational language. Although we will not provide technical details, readers will be able to design their models and conduct analyses based on what is provided here to suit their own needs. Although we have tried to determine consensus views in the literature, the transdisciplinary nature of this field makes the knowledge landscape rugged and fluid. Answers are often not definite but contextualised. Let our journey begin.
Humanity’s rise is rapidly moulding the structure and functioning of the biosphere over the surface of our planet, while human-mediated translocations of organisms – an inevitable consequence of this rise – is driving further transformation (Pyšek et al. 2020b). Drawing inspiration and concepts from population ecology, Invasion Science 1.0 (see Chapter 1) has explored the myriad ways a focal alien species can negotiate geographical, ecological and environmental barriers to establish and potentially invade in new novel environments. Coordinated efforts have been made to classify introduction pathways (Hulme et al. 2008; Wilson et al. 2009); forecast invasion risks (Kumschick and Richardson 2013) and impacts (Jeschke et al. 2014); model invasive spread (Hui and Richardson 2017); unify invasion frameworks (Wilson et al. 2020); and prescribe management strategies such as early detection and rapid response to prevent, contain and eradicate problematic species (Wilson et al. 2017). However, the phenomenon of biological invasions involves all types of organisms, ecosystems and a wide range of contexts and framings; this has given rise to a plethora of invasion hypotheses and theories that seek to explain and ultimately predict aspects of invasion dynamics and the expected outcome of specific management actions (Jeschke and Heger 2018). Most invasion hypotheses are relevant in specific contexts and often fail when faced with the reality of contextual complexity. This has led to a wave of syntheses that have attempted to classify invasion cases and hypotheses based largely on three aspects – invasive traits, site characteristics and invasion pathways (Pyšek et al. 2020a). To embrace considerations that arise when attempting to merge insights from all these perspectives, a paradigm shift began emerging at the turn of the millennium, together with the rise of network science. It embraces the complexity of biotic interaction networks (Figure 7.1; e.g., Segar et al. 2020), the trait paradigm in community ecology (Figure 7.2; e.g., McGill et al. 2006; Salguero-Gómez et al. 2018), and considers how functional traits of species dictate their ecology and roles in networks (Figure 7.3; e.g., Mello et al. 2019). This new lens for drawing together threads pertaining to all facets of biological invasions (Invasion Science 2.0) seeks to elucidate the structure and function of an ecological network facing biological invasions. This book has laid out a road map of signposts, hazard warnings and shortcuts for the journey to Invasion Science 2.0, framing and classifying research topics and offering tentative solutions and travel advisories.
Cancer-related dyspnea is a common symptom in patients with cancer. It has also been reported to be a predictor of poorer prognosis, which can then change clinical treatment and advance care planning. Currently, no definitive recommendation for pharmacologic agents for cancer-related dyspnea exists. The aim of this systematic review and network meta-analysis is to compare pharmacologic agents for the prophylaxis and treatment of cancer-related dyspnea.
A search was conducted in the databases of PubMed, Embase, and Cochrane CENTRAL through May 2021. Standardized mean differences (SMDs), as reported by studies or calculated from baseline and follow-up dyspnea scores, were amalgamated into a summary SMD and 95% confidence interval (CI) using a restricted maximum likelihood multivariate network meta-analysis.
Twelve studies were included in this review; six reported on prophylaxis of exertional dyspnea, five on treatment of everyday dyspnea, and one on treatment of episodic dyspnea. Morphine sulfate was better at controlling everyday dyspnea than placebo (SMD 1.210; 95% CI: 0.415–2.005). Heterogeneity in study design and comparisons, however, led to some concerns with the underlying consistency assumption in network meta-analysis design.
Optimal pharmacologic interventions for cancer-related dyspnea could not be determined based on this analysis. Further trials are needed to report on the efficacy of pharmacologic interventions for the prophylaxis and treatment of cancer-related dyspnea.
Measurement-based care (MBC) in mental health improves patient outcomes and is a component of many national guidelines for mental healthcare delivery. Nevertheless, MBC is not routinely integrated into clinical practice. Several known reasons for the lack of integration exist but one lesser explored variable is the subjective perspectives of providers and patients about MBC. Such perspectives are critical to understand facilitators and barriers to improve the integration of MBC into routine clinical practice.
This study aimed to uncover the perspectives of various stakeholders towards MBC within a single treatment centre.
Researchers conducted qualitative semi-structured interviews with patients (n = 15), family members (n = 7), case managers (n = 8) and psychiatrists (n = 6) engaged in an early-psychosis intervention programme. Data were analysed using thematic analysis, informed by critical realist theory.
Analysis converged on several themes. These include (a) implicit negative assumptions; (b) relevance and utility to practice; (c) equity versus flexibility; and (d) shared decision-making. Providers assumed patients’ perspectives of MBC were negative. Patients’ perspectives of MBC were actually favourable, particularly if MBC was used as an instrument to engage patients in shared decision-making and communication rather than as a dogmatic and rigid clinical decision tool.
This qualitative study presents the views of various stakeholders towards MBC, providing an in-depth examination of the barriers and facilitators to MBC through qualitative investigation. The findings from this study should be used to address the challenges organisations have experienced in implementing MBC.
Several studies supported the usefulness of “the surprise question” in terms of 1-year mortality of patients. “The surprise question” requires a “Yes” or “No” answer to the question “Would I be surprised if this patient died in [specific time frame].” However, the 1-year time frame is often too long for advanced cancer patients seen by palliative care personnel. “The surprise question” with shorter time frames is needed for decision making. We examined the accuracy of “the surprise question” for 7-day, 21-day, and 42-day survival in hospitalized patients admitted to palliative care units (PCUs).
This was a prospective multicenter cohort study of 130 adult patients with advanced cancer admitted to 7 hospital-based PCUs in South Korea. The accuracy of “the surprise question” was compared with that of the temporal question for clinician's prediction of survival.
We analyzed 130 inpatients who died in PCUs during the study period. The median survival was 21.0 days. The sensitivity, specificity, and overall accuracy for the 7-day “the surprise question” were 46.7, 88.7, and 83.9%, respectively. The sensitivity, specificity, and overall accuracy for the 7-day temporal question were 6.7, 98.3, and 87.7%, respectively. The c-indices of the 7-day “the surprise question” and 7-day temporal question were 0.662 (95% CI: 0.539–0.785) and 0.521 (95% CI: 0.464–0.579), respectively. The c-indices of the 42-day “the surprise question” and 42-day temporal question were 0.554 (95% CI: 0.509–0.599) and 0.616 (95% CI: 0.569–0.663), respectively.
Significance of results
Surprisingly, “the surprise questions” and temporal questions had similar accuracies. The high specificities for the 7-day “the surprise question” and 7- and 21-day temporal question suggest they may be useful to rule in death if positive.