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This article explores the phenomenon of the sedition trial in the early history of the Spanish American republics, focusing on sedition trials that occurred in Santiago de Chile from the late 1820s to the early 1850s. Sedition trials were governed by laws enacted in the wake of Chile's political independence to protect and regulate the freedom of the press. Because of the way press laws were written, sedition trials were conducted in front of juries composed of active citizens. As such, they constituted a dramatic break with the judicial tradition of Spanish colonial rule. The article argues that sedition trials were instrumental in the dynamics of political conflict, but only when the national government allowed them to operate without interference, which was not always the case. When sedition trials had integrity, as they did for a period in the 1840s, they became a public space in which citizen-journalists and publishers participated in establishing the boundaries of political speech. However, as one might expect, government officials also used the charge of sedition to silence their opponents. Sedition trials can thus be seen as a form of political warfare that has not been fully appreciated by scholars.
Background: Healthcare facilities have experienced many challenges during the COVID-19 pandemic, including limited personal protective equipment (PPE) supplies. Healthcare personnel (HCP) rely on PPE, vaccines, and other infection control measures to prevent SARS-CoV-2 infections. We describe PPE concerns reported by HCP who had close contact with COVID-19 patients in the workplace and tested positive for SARS-CoV-2. Method: The CDC collaborated with Emerging Infections Program (EIP) sites in 10 states to conduct surveillance for SARS-CoV-2 infections in HCP. EIP staff interviewed HCP with positive SARS-CoV-2 viral tests (ie, cases) to collect data on demographics, healthcare roles, exposures, PPE use, and concerns about their PPE use during COVID-19 patient care in the 14 days before the HCP’s SARS-CoV-2 positive test. PPE concerns were qualitatively coded as being related to supply (eg, low quality, shortages); use (eg, extended use, reuse, lack of fit test); or facility policy (eg, lack of guidance). We calculated and compared the percentages of cases reporting each concern type during the initial phase of the pandemic (April–May 2020), during the first US peak of daily COVID-19 cases (June–August 2020), and during the second US peak (September 2020–January 2021). We compared percentages using mid-P or Fisher exact tests (α = 0.05). Results: Among 1,998 HCP cases occurring during April 2020–January 2021 who had close contact with COVID-19 patients, 613 (30.7%) reported ≥1 PPE concern (Table 1). The percentage of cases reporting supply or use concerns was higher during the first peak period than the second peak period (supply concerns: 12.5% vs 7.5%; use concerns: 25.5% vs 18.2%; p Conclusions: Although lower percentages of HCP cases overall reported PPE concerns after the first US peak, our results highlight the importance of developing capacity to produce and distribute PPE during times of increased demand. The difference we observed among selected groups of cases may indicate that PPE access and use were more challenging for some, such as nonphysicians and nursing home HCP. These findings underscore the need to ensure that PPE is accessible and used correctly by HCP for whom use is recommended.
We present new experiments of particle-driven turbulent plumes issuing from a constant source of dense particle-laden fluid, with buoyancy flux, $B$, in a uniform horizontal current, $u$. Experiments show that a turbulent, well-mixed plume develops, in which the downward vertical speed $w$ decreases with depth $z$ according to $w = 0.76 (B/uz)^{1/2}$ while the horizontal speed rapidly asymptotes to the current speed $u$, provided that the Stokes settling speed of the particles $v<0.92 w$. For $v > 0.92 w$, the particles separate from the plume fluid, and their depth $z$ increases according to the simple sedimentation trajectory $\textrm {d}z/{\textrm {d}\kern0.7pt x} = v/u$. As the particles sediment, they form clusters of particles, which lead to fluctuations in the particle load with position, but do not appear to change the time-average sedimentation speed. We explore the impact of these results for deep-sea mining, in which the fate of the plume water as well as the particles is key for assessing potential environmental impacts.
This chapter discusses a grammar of realism that calls upon a variety of prose effects, including the management of time effects and the presence of abundant or telling detail, to show what is at stake in realist writing and how much happens in the prose despite its alleged retreat from artifice. Although realism has found itself assailed periodically, often for its artifice, this chapter testifies to the enduring value of realist prose while offering insights into its evolution.
Background: Blood cultures are fundamental in the diagnosis and treatment of sepsis. Culture practices vary widely, and overuse can lead to false-positive results and unnecessary antibiotics. Our objective was to describe the implementation of a multisite quality improvement collaborative to reduce unnecessary blood cultures in pediatric intensive care unit (PICU) patients, and its 18-month impact on blood culture rates and safety metrics. Methods: In 2018, 14 PICUs joined the Blood Culture Improvement Guidelines and Diagnostic Stewardship for Antibiotic Reduction in Critically Ill Children (Bright STAR) Collaborative, designed to understand and improve blood culture practices in critically ill children. Guided by a centralized multidisciplinary study team, sites first reviewed existing evidence for safe reduction of unnecessary blood cultures and assessed local practices and barriers to change. Subsequently, local champions developed and implemented clinical decision-support tools informed by local patient needs to guide new blood-culture practices. The coordinating study team facilitated regular evaluations and discussions of project progress through monthly phone calls, site visits if requested by sites or the study team, and collaborative-wide teleconferences. The study team collected monthly blood culture rates and monitored for possible delays in obtaining blood cultures using a standardized review process as a safety balancing metric. We compared 24 months of baseline data to 18 months of postimplementation using a Poisson regression model accounting for the site-specific patient days and correlation of culture use within a site over time. Results: Across the 14 sites, before implementation, 41,768 blood cultures were collected over 259,701 PICU patient days. The mean preimplementation site-specific blood culture rate was 15.7 cultures per 100 patient days (rate range, 9.6–48.2 cultures per 100 patient days). After implementation, 22,397 blood cultures were collected over 208,171 PICU patient days. The mean postimplementation rate was 10.4 cultures per 100 patient days (rate range, 4.7–28.3 cultures per 100 patient days), which was 33.6% lower than the preimplementation (relative rate 0.66; 95% CI, 0.65–0.68 p <0.01). In 18 months post-implementation, sites reviewed 793 positive blood cultures, and identified only one suspected delay in culture collection possibly attributable to the site’s blood culture reduction program. Conclusions: Multidisciplinary quality improvement teams safely facilitated a 33.6% average reduction in blood culture use in critically ill children at 14 hospitals. Future collaborative work will determine the impact of blood culture diagnostic stewardship on antibiotic use and other important patient safety outcomes.
Healthcare personnel with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection were interviewed to describe activities and practices in and outside the workplace. Among 2,625 healthcare personnel, workplace-related factors that may increase infection risk were more common among nursing-home personnel than hospital personnel, whereas selected factors outside the workplace were more common among hospital personnel.
The availability of colonizable substrate is an important driver of the temporal dynamics of sessile invertebrates on coral reefs. Increased dominance of algae and, in some cases, sponges has been documented on many coral reefs around the world, but how these organisms benefit from non-colonized substrate on the reef is unclear. In this study, we described the temporal dynamics of benthic organisms on an Indonesian coral reef across two time periods between 2006 and 2017 (2006–2008 and 2014–2017), and investigated the effects of colonizable substrate on benthic cover of coral reef organisms at subsequent sampling events. In contrast with other Indonesian reefs where corals have been declining, corals were dominant and stable over time at this location (mean ± SE percentage cover 42.7 ± 1.9%). Percentage cover of turf algae and sponges showed larger interannual variability than corals and crustose coralline algae (CCA) (P < 0.001), indicating that these groups are more dynamic over short temporal scales. Bare substrate was a good predictor of turf cover in the following year (mean effect 0.2, 95% CI: 0–0.4). Algal cover combined with bare space was a good predictor of CCA cover the following year generally, and of sponge cover the following year but only at one of the three sites. These results indicate that turf algae on some Indonesian reefs can rapidly occupy free space when this becomes available, and that other benthic groups are probably not limited by the availability of bare substrate, but may overgrow already fouled substrates.
Background: Blood cultures are essential diagnostic tools used to identify bloodstream infections and to guide antimicrobial therapy. However, collecting cultures without clear indications or that do not inform management can lead to false-positive results and unnecessary use of antibiotics. Blood culture practices vary significantly in critically ill children. Our objective was to create a consensus guideline focusing on when to safely avoid blood cultures in pediatric intensive care unit (PICU) patients. Methods: A panel of multidisciplinary experts, many participating in the Blood Culture Improvement Guidelines and Diagnostic Stewardship for Antibiotic Reduction in Critically Ill Children (Bright STAR) Collaborative, engaged in a 2-part modified Delphi process. Round 1 consisted of a preparatory literature summary and an electronic survey sent to subject matter experts (SMEs). In the survey, SMEs rated a series of recommendations about when to avoid blood cultures on a 5-point Likert scale, 1 being the lowest score and 5 being the highest score. Consensus was achieved for each recommendation if 75% of respondents chose a score of 4 or 5, and these were included in the final guideline. Any recommendations that did not meet these a priori criteria for consensus were set aside for discussion during the in-person expert panel review (round 2). An outside expert in consensus methodology facilitated round 2. After a review of the survey results and comments from round 1 and group discussion, the SMEs voted on these recommendations in real time. Voting was blinded. Participants included Bright STAR site leads, national content experts, and representatives from relevant national societies. Results: We received 29 completed surveys from 34 invited participants for an 85% response rate. Of the 27 round 1 recommendations, 18 met predetermined criteria for consensus. Round 2 included 26 in-person voting participants who (1) discussed and modified the 9 recommendations that had not met round 1 consensus, and (2) modified for clarity or condensed from multiple into single recommendations the 18 recommendations that had met the round 1 consensus. The final document contains 19 recommendations that provide guidance on how to safely improve blood culture use in PICU patients (Table 1). Also, 8 recommendations discussed did not reach consensus for inclusion. Conclusions: Using a modified Delphi process, we created consensus recommendations on when to avoid blood cultures and prevent overuse in critically ill children. These guidelines are a critical step in disseminating diagnostic stewardship and reducing unnecessary testing on a wider scale.
Funding: Agency for Healthcare Research and Quality, R18 HS025642-01, 9/2017 – 9/2020 (Aaron Milstone, PI)
Viewing the subsistence farm as primarily a 'demographic enterprise' to create and support a family, this book offers an integrated view of the demography and ecology of preindustrial farming. Taking an interdisciplinary perspective, it examines how traditional farming practices interact with demographic processes such as childbearing, death, and family formation. It includes topics such as household nutrition, physiological work capacity, health and resistance to infectious diseases, as well as reproductive performance and mortality. The book argues that the farming household is the most informative scale at which to study the biodemography and physiological ecology of preindustrial, non-commercial agriculture. It offers a balanced appraisal of the farming system, considering its strengths and limitations, as well as the implications of viewing it as a 'demographic enterprise' rather than an economic one. A valuable resource for graduate students and researchers in biological and physical anthropology, cultural anthropology, natural resource management, agriculture and ecology.
In the previous chapter we examined models of the influence of fertility and mortality on the size of households. Two major (and quite deliberate) simplifying assumptions underlying all these models are (i) that fertility and mortality are exogenous to the household and (ii) that all households in a community are exposed to exactly the same demographic conditions. This assumption is patently false, even if it is convenient for some analytical purposes. Starting in the present chapter, we reverse the causality, asking whether mortality, fertility and migration differ among households owing to material conditions (especially those influencing food availability) that are peculiar to each individual household. Do all households in a community have the same level of, for example, early childhood mortality – and, if not, why not? I will argue that an important part of the answer is that feedbacks operate within the household involving food production and the household’s demographic life-cycle, and these feedbacks are powerful enough on their own to differentiate households even in the absence of clear social and political differences dividing the community as a whole. The critical linkage in this feedback, I believe, involves the relationship between dietary adequacy and various aspects of human physiology. If I am right about the importance of this relationship, it would point to a basic (and perhaps ever-present) form of population regulation operating at the level of the subsistence farming household. But before we can examine this claim, we need to review what is currently known about the relationship between dietary adequacy and the basic forces of demography – fertility, mortality and migration. And before we do that, we need to think more carefully about what we mean by “dietary adequacy” and how to measure it. This chapter does both. Because little of the evidence I marshal in this chapter is explicitly organized at the household level, households will play a somewhat covert role in this treatment. But, while contemplating the relationship between under-nutrition and, say, fertility or mortality, the reader should really be thinking about the role that under-nutrition plays as a potential impediment to the household demographic enterprise. Subsequent chapters return the household to center stage.
In discussing Richard Longhurst’s model of the energy trap in the previous chapter, we speculated that differences among households in nutritional status could be amplified, via their effects on work capacity, into semipermanent, inter-generational differences in a family’s material well-being. But the energy trap, if it is to operate at all, must act on preexisting differences in household well-being. Thus far, we have said nothing about where those initial differences come from. A large part of the answer, of course, is that traditional farmers, like all people, vary among themselves in such things as industriousness, cleverness, prudence, fecklessness, even sheer dumb luck – not to mention the political nous to turn a temporary advantage into something more enduring. In addition, environmental unpredictability can be a potent source of differential household success, even in communities that appear to us to be egalitarian. In this chapter, however, we focus on one possible source of economic differentiation that is inherent in the household demographic enterprise – the household’s built-in demographic life cycle, the ebb and flow of household size and age–sex composition resulting from births, deaths, marriages and other forms of inter-household migration (see Chapter 7). More specifically, inter-household differentiation arises from two facts: (i) that the life cycles of all the households in a community are never in perfect synchrony, and (ii) that an element of randomness always plays a part in every household’s life cycle. No two life cycles are ever quite the same in their intensity and timing – and these dissimilarities can have important economic consequences.
Traditional farming – farming in the absence of fossil fuels, electricity, commercial seed, tractors and combines and other industrial inputs such as inorganic fertilizers, pesticides and herbicides – has sustained much of the human species for ten millennia. And not just sustained: the global emergence of farming led to a thousand-fold increase in the size of the human population by the beginning of the Industrial Revolution in the late eighteenth century (Cohen, 1995: 96). Traditional farming provided the foundation for early civilizations, cities and states, all of which evolved along with it. By some estimates, traditional farming or something very like it was still feeding a third of the world’s people in the second half of the twentieth century (Haswell, 1973).
In every preindustrial farming operation, some of the most important and insurmountable limits to a household’s food supply are imposed by the size and demographic makeup of the work force it can muster to produce it. Apart from the energy for photosynthesis provided by the Sun, the energy for food production in subsistence farming is almost entirely biological in nature – mostly human and secondarily animal (in some places), with wind or water power very occasionally playing a minor role. Almost universally, the labor needed for household food production is organized by the household itself and is indeed made up overwhelmingly of its own members. Granted, as will be discussed in more detail in Chapter 12, voluntary work groups that draw in members of several neighboring households may be mobilized at certain times of year for particularly heavy tasks that need to be done quickly, often in return for some kind of payment in kind (perhaps a part of the harvest, a feast, or a beer-drinking party) or a promise of reciprocation; in addition, individual labor contributions by close relatives living in nearby households may be requested on a day-to-day basis. In this chapter and the next, however, I shall make the model assumption – one of those tactical “lies” discussed in Chapter 1 – that labor on the household farm is provided exclusively by the household itself. I do this to show some of the built-in limitations to household-based food production, limitations that require special social arrangements – the expenditure of “social capital” – if they are to be overcome. The idea that households are wholly on their own when it comes to farm labor may be a tactical lie, but the truth is that most labor in traditional farming is provided by the household that runs the farm (Figure 9.1). The subsistence-oriented household provides by far the largest contribution of nonsolar energy for its own food production.
In 1975). Keyfitz suggested that few of the most firmly established facts of demography are based on observations alone – reality is far too messy for that. Instead, it is the interplay of theoretical models and observations that underpins what we know about population – and sometimes it is theory alone that provides the most reliable pointers to the nature of reality. As Keyfitz famously put it, “no model, no understanding” – perhaps the most fundamental of all the facts of demography.
A major goal of this book has been to reframe the scientific debate over the relationship between population and preindustrial farming. One way to see how successful I have been in meeting that goal is to turn back to the section on “Rethinking the relationship between human population and traditional agriculture” in Chapter 1, which posed a series of questions that needed to be answered before further progress could be made. It is now time to revisit those questions in order to judge how far, if at all, we have advanced toward answering them.