Provider issues related to patient controlled analgesia and nurse controlled analgesia errors in a pediatric hospital

Background: Medical errors are a danger to patient safety and a significant cause of morbidity and mortality. Additionally, they increase expenditures in an already significantly indebted U.S. health care system. Much confusion exists about definitions of medical errors, which include medication errors and adverse drug events (ADEs). Several federal and international organizations have attempted to standardize definitions in order to streamline data collection, but until these standards are universally adopted, error reports and trends are still subject to questions of validity. Reporting errors, in general, has become a more socially acceptable practice in health care with the advent of several anonymous reporting databases. There have also been several initiatives aimed at reducing the incidence of errors, which range from national programs to intrafacility guidelines. Several pieces of health information technology (HIT) have made an impact on error incidence and data collection, although there is much room for improvement. Patient controlled analgesia (PCA) pumps for pain management have been in existence for decades, and "smart pump" software has improved their safety and ease of programming. PCA use in children presents challenges to clinicians, and the characteristics of providers who write PCA orders and those who program PCA pumps may play a role in the incidence of events related to PCA. This study seeks to elucidate trends in errors as they related to these different PCA providers in a pediatric hospital in the northeastern U.S. and provide recommendations for how PCA practice can be improved in this facility. Methods: Safety Event Reporting System (SERS) reports of PCA events (n = 117) during the period of 2004 - 2012 were analyzed retrospectively to determine several key variables for data analysis. The main focus of this analysis was those variable trends related to providers, including: proportion of events caused by human error, proportion of events related to subcategories of human error, proportion of types of prescribers involved in PCA events, proportion of errors in medical and surgical patients, proportion of errors occurring on day and night shifts for the nursing staff, and proportion of events that were dosing mistakes. Statistical analysis was performed for these results when possible to determine significance. Results: Human errors were implicated in 84.1% of events, whereas PCA pump mechanical errors and software errors were implicated in 7.1% and 7.9% of events, respectively. Statistically significant differences were found in all variables tested, including the proportion of nursing errors (60.9%) versus prescriber errors (28.7%) (p < 0.0002). For types of prescribers, the proportion of PCA events occurring when a M.D. wrote the PCA order (56.41%) was statistically different than when a N.P. wrote the PCA order (39.32%) (p = 0.0129). More surgical patients (61.5%) were affected by PCA events than medical patients (36.8%) (p < 0.0002). There were more events occurring on the nursing staff day shift (59.8%) than the night shift (36.8%) (p = 0.0004). Finally, dosing mistakes (66.7%) were implicated in significantly more PCA events than any other error type (33.3%) (p < 0.0002). Conclusion: Several recommendations for improving the safety of PCA in pediatric pain management are justified by the results of this data analysis. First, further education and simulation for entering PCA orders into the CPOE system is needed for all prescribers. Secondly, further education and simulation in PCA pump programming and system set-up is needed for all nursing staff members. In regard to prescriber credentials, it is recommended that Pain Treatment Service (PTS) staff members train M.D. residents in writing PCA orders and entering them into the CPOE system. Finally, it is recommended that the SERS management team publish standardized error report content and entry format in order to streamline data analysis for quality improvement (QI) purposes

The ghosts of forests past and future : deforestation and botanical sampling in the Brazilian Amazon

The remarkable biodiversity of the Brazilian Amazon is poorly documented and threatened by deforestation. When undocumented areas become deforested, in addition to losing the fauna and flora, we lose the opportunity to know which unique species had occupied a habitat. Here we quantify such knowledge loss by calculating how much of the Brazilian Amazon has been deforested and will likely be deforested until 2050 without having its tree flora sufficiently documented. To this end, we analysed 399 147 digital specimens of nearly 6000 tree species in relation to official deforestation statistics and future deforestation scenarios. We find that by 2017, 30% of all the localities where tree specimens had been collected were mostly deforested. Some 300 000 km(2)(12%; 485 25 x 25 km grid cells) of the Brazilian Amazon had been deforested by 2017, without having a single tree specimen recorded. An additional 250 000-900 000 km(2)of severely under-collected rainforest will likely become deforested by 2050. If future tree sampling is to cover this area, sampling effort has to increase two- to six-fold. Nearly 255 000 km(2)or 7% of rainforest in the Brazilian Amazon is easily accessible but does yet but remain under-collected. Our study highlights how progressing deforestation increases the risk of losing undocumented species of a hyper-diverse tree flora.Peer reviewe

Methods to estimate aboveground wood productivity from long-term forest inventory plots

Forest inventory plots are widely used to estimate biomass carbon storage and its change over time. While there has been much debate and exploration of the analytical methods for calculating biomass, the methods used to determine rates of wood production have not been evaluated to the same degree. This affects assessment of ecosystem fluxes and may have wider implications if inventory data are used to parameterise biospheric models, or scaled to large areas in assessments of carbon sequestration. Here we use a dataset of 35 long-term Amazonian forest inventory plots to test different methods of calculating wood production rates. These address potential biases associated with three issues that routinely impact the interpretation of tree measurement data: (1) changes in the point of measurement (POM) of stem diameter as trees grow over time; (2) unequal length of time between censuses; and (3) the treatment of trees that pass the minimum diameter threshold (“recruits”). We derive corrections that control for changing POM height, that account for the unobserved growth of trees that die within census intervals, and that explore different assumptions regarding the growth of recruits during the previous census interval. For our dataset we find that annual aboveground coarse wood production (AGWP; in Mg ha−1 year−1 of dry matter) is underestimated on average by 9.2% if corrections are not made to control for changes in POM height. Failure to control for the length of sampling intervals results in a mean underestimation of 2.7% in annual AGWP in our plots for a mean interval length of 3.6 years. Different methods for treating recruits result in mean differences of up to 8.1% in AGWP. In general, the greater the length of time a plot is sampled for and the greater the time elapsed between censuses, the greater the tendency to underestimate wood production. We recommend that POM changes, census interval length, and the contribution of recruits should all be accounted for when estimating productivity rates, and suggest methods for doing this.European UnionUK Natural Environment Research CouncilGordon and Betty Moore FoundationCASE sponsorship from UNEP-WCMCRoyal Society University Research FellowshipERC Advanced Grant “Tropical Forests in the Changing Earth System”Royal Society Wolfson Research Merit Awar

Primary modes of tree mortality in southwestern Amazon forests

Tree mortality rates and the modes of tree death have recently been extensively investigated in the Amazon. However, efforts to describe these processes have not been well distributed across the basin. No study has yet investigated in depth tree mortality process in the unique low, open, bamboo-dominated forests of southwestern Amazonia, a region with a distinct climate and the epicenter of recent severe drought events. Here, we investigated the leading ways that trees die in the terra-firme forests of the southwestern Brazilian Amazon, to understand whether the dynamics of mortality differ from those recorded in other parts of the basin. Using data from six permanent plots located in southwestern Amazonia, we calculated the mortality rate for three main modes of tree death: standing, broken and uprooted. We thus identified the predominant mode of death over a 14 year period (2002–2016). We found that trees in the southwestern Amazon died mainly standing (325 trees, 0.8% year−1) and broken (362 trees, 0.8% year−1); significantly fewer trees died uprooted (156 trees, 0.4% year−1, equivalent to less than one in five of all trees dying). During the study period, the tree mode of death with the greatest proportion in the region alternated between standing and broken trees. Forest characteristics of the southwestern Amazon, like presence and high density of bamboo culms, and the fact that the region was subject to severe droughts in 2005 and 2010, may be affecting how trees die in southwestern Amazon. The presence of these factors makes the forest dynamics of the southwestern Amazon different from other regions of the Amazon basin

Relationships between species richness and ecosystem services in Amazonian forests strongly influenced by biogeographical strata and forest types

Despite increasing attention for relationships between species richness and ecosystem services, for tropical forests such relationships are still under discussion. Contradicting relationships have been reported concerning carbon stock, while little is known about relationships concerning timber stock and the abundance of non-timber forest product producing plant species (NTFP abundance). Using 151 1-ha plots, we related tree and arborescent palm species richness to carbon stock, timber stock and NTFP abundance across the Guiana Shield, and using 283 1-ha plots, to carbon stock across all of Amazonia. We analysed how environmental heterogeneity influenced these relationships, assessing differences across and within multiple forest types, biogeographic regions and subregions. Species richness showed significant relationships with all three ecosystem services, but relationships differed between forest types and among biogeographical strata. We found that species richness was positively associated to carbon stock in all biogeographical strata. This association became obscured by variation across biogeographical regions at the scale of Amazonia, resembling a Simpson’s paradox. By contrast, species richness was weakly or not significantly related to timber stock and NTFP abundance, suggesting that species richness is not a good predictor for these ecosystem services. Our findings illustrate the importance of environmental stratification in analysing biodiversity-ecosystem services relationships

Macroecological links between the Linnean, Wallacean, and Darwinian shortfalls

Species are the currency of most biodiversity studies. However, many shortfalls and biases remain in our biodiversity estimates, preventing a comprehensive understanding of the eco-evolutionary processes that have shaped the biodiversity currently available on Earth. Biased biodiversity estimates also jeopardize the effective implementation of data-driven conservation strategies, ultimately leading to biodiversity loss. Here, we delve into the concept of the Latitudinal Taxonomy Gradient (LTG) and show how this new idea provides an interesting conceptual link between the Linnean (i.e., our ignorance of how many species there are on Earth), Darwinian (i.e., our ignorance of species evolutionary relationships), and Wallacean (i.e., our ignorance on species distribution) shortfalls. More specifically, we contribute to an improved understanding of LTGs and establish the basis for the development of new methods that allow us to: (i) better account for the integration between different shortfalls and, (ii) estimate how these interactions may affect our understanding about the evolutionary components of richness gradients at macroecological scales.This manuscript is partially derived from a working group on “Biodiversity Shortfalls” held in November 2019 and sponsored by the National Institutes for Science and Technology (INCT) in Ecology, Evolution, and Biodiversity Conservation (CNPq proc. 465610/2014-5 and FAPEG proc. 201810267000023). JJMG and LEF are supported by Ph.D. and M.Sc. scholarships from CAPES, while LM and RBP are supported by postdoctoral fellowships from CAPES (PNPD). JS was funded by the funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Action (grant agreement #843234; project: TAXON-TIME) and by the Spanish Council for Scientific Research (IF_ERC). GT and LJ are supported by a DTI fellowships from CNPq, while JAFD-F, LGL, and CJBC are supported by Productivity Grants from CNPq.Peer reviewe

Tree mode of death and mortality risk factors across Amazon forests

The&nbsp;carbon sink capacity of tropical forests&nbsp;is substantially affected by tree mortality. However, the main drivers of tropical&nbsp;tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing &gt; 3800 species from 189 long-term&nbsp;RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted—modes of death with different ecological consequences. Species-level growth rate is the single&nbsp;most important predictor of tree death in Amazonia, with faster-growing species being at&nbsp;higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region&nbsp;species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a&nbsp;holistic pan-Amazonian picture of tree death but large-scale&nbsp;evidence for the overarching importance of the growth–survival trade-off in driving tropical&nbsp;tree mortality

Tree mode of death and mortality risk factors across Amazon forests

The carbon sink capacity of tropical forests is substantially affected by tree mortality. However, the main drivers of tropical tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing > 3800 species from 189 long-term RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted—modes of death with different ecological consequences. Species-level growth rate is the single most important predictor of tree death in Amazonia, with faster-growing species being at higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a holistic pan-Amazonian picture of tree death but large-scale evidence for the overarching importance of the growth–survival trade-off in driving tropical tree mortality