Abstract
Background
Appropriate use of available inpatient beds is an ongoing challenge for US hospitals. Historical capacity goals of 80% to 85% may no longer serve the intended purpose of ...maximizing the resources of space, staff, and equipment. Numerous variables affect the input, throughput, and output of a hospital. Some of these variables include patient demand, regulatory requirements, coordination of patient flow between various systems, coordination of processes such as bed management and patient transfers, and the diversity of departments (both inpatient and outpatient) in an organization.
Methods
Mayo Clinic Health System in the Southwest Minnesota region of the US, a community-based hospital system primarily serving patients in rural southwestern Minnesota and part of Iowa, consists of 2 postacute care and 3 critical access hospitals. Our inpatient bed usage rates had exceeded 85%, and patient transfers from the region to other hospitals in the state (including Mayo Clinic in Rochester, Minnesota) had increased. To address these quality gaps, we used a blend of Agile project management methodology, rapid Plan-Do-Study-Act cycles, and a proactive approach to patient placement in the medical-surgical units as a quality improvement initiative.
Results
During 2 trial periods of the initiative, the main hub hospital (Mayo Clinic Health System hospital in Mankato) and other hospitals in the region increased inpatient bed usage while reducing total out-of-region transfers.
Conclusion
Our novel approach to proactively managing bed capacity in the hospital allowed the region’s only tertiary medical center to increase capacity for more complex and acute cases by optimizing the use of historically underused partner hospital beds.
Artemisinin combination treatments (ACT) are recommended as first line treatment for falciparum malaria throughout the malaria affected world. We reviewed the efficacy of a 3-day regimen of ...mefloquine and artesunate regimen (MAS(3)), over a 13 year period of continuous deployment as first-line treatment in camps for displaced persons and in clinics for migrant population along the Thai-Myanmar border.
3,264 patients were enrolled in prospective treatment trials between 1995 and 2007 and treated with MAS(3). The proportion of patients with parasitaemia persisting on day-2 increased significantly from 4.5% before 2001 to 21.9% since 2002 (p<0.001). Delayed parasite clearance was associated with increased risk of developing gametocytaemia (AOR = 2.29; 95% CI, 2.00-2.69, p = 0.002). Gametocytaemia on admission and carriage also increased over the years (p = 0.001, test for trend, for both). MAS(3) efficacy has declined slightly but significantly (Hazards ratio 1.13; 95% CI, 1.07-1.19, p<0.001), although efficacy in 2007 remained well within acceptable limits: 96.5% (95% CI, 91.0-98.7). The in vitro susceptibility of P. falciparum to artesunate increased significantly until 2002, but thereafter declined to levels close to those of 13 years ago (geometric mean in 2007: 4.2 nM/l; 95% CI, 3.2-5.5). The proportion of infections caused by parasites with increased pfmdr1 copy number rose from 30% (12/40) in 1996 to 53% (24/45) in 2006 (p = 0.012, test for trend).
Artesunate-mefloquine remains a highly efficacious antimalarial treatment in this area despite 13 years of widespread intense deployment, but there is evidence of a modest increase in resistance. Of particular concern is the slowing of parasitological response to artesunate and the associated increase in gametocyte carriage.
Management of human activities which impact the seafloor in the deep ocean is becoming increasingly important as bottom trawling and exploration for minerals, oil, and gas continue to extend into ...regions where fragile ecosystems containing habitat-forming deep-sea corals and sponges may be found. Spatial management of these vulnerable marine ecosystems requires accurate knowledge of their distribution. Predictive habitat suitability modelling, using species presence data and a suite of environmental predictor variables, has emerged as a useful tool for inferring distributions outside of known areas. However, validation of model predictions is typically performed with non-independent data. In this study, we describe the results of habitat suitability models constructed for four deep-sea reef-forming coral species across a large region of the South Pacific Ocean using MaxEnt and Boosted Regression Tree modelling approaches. In order to validate model predictions we conducted a photographic survey on a set of seamounts in an un-sampled area east of New Zealand. The likelihood of habitat suitable for reef-forming corals on these seamounts was predicted to be variable, but very high in some regions, particularly where levels of aragonite saturation, dissolved oxygen, and particulate organic carbon were optimal. However, the observed frequency of coral occurrence in analyses of survey photographic data was much lower than expected, and patterns of observed versus predicted coral distribution were not highly correlated. The poor performance of these broad-scale models is attributed to lack of recorded species absences to inform the models, low precision of global bathymetry models, and lack of data on the geomorphology and substrate of the seamounts at scales appropriate to the modelled taxa. This demonstrates the need to use caution when interpreting and applying broad-scale, presence-only model results for fisheries management and conservation planning in data poor areas of the deep sea. Future improvements in the predictive performance of broad-scale models will rely on the continued advancement in modelling of environmental predictor variables, refinements in modelling approaches to deal with missing or biased inputs, and incorporation of true absence data.
•Field validation reveals poor performance of deep-sea habitat suitability models.•Low precision of global bathymetry data contributed to model overestimation.•High resolution substrate data are required to improve model performance.•Caution is advised when using such broad-scale models for management decisions.
Abstract
Vulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, making them likely to be substantially altered by disturbance. In the High Seas, regional fishery management ...organizations (RFMOs) are required to implement measures to prevent significant adverse impacts on VMEs. The objectives of the present study were to: update distribution models of VME indicator taxa in the South Pacific RFMO Convention Area; evaluate these against newly-collated independent field data to test the reliability of the presence-only habitat suitability models; and assess how well the updated models were able to predict into unsampled space. Ensemble habitat suitability models of 10 VME indicator taxa performed well using the newly collated data (AUC > 0.95, TSS > 0.76, and RMSE < 0.34). There were no obvious patterns of decreasing model performance with decreasing environmental coverage; areas with few samples underpinning model predictions still had AUC > 0.93, TSS > 0.71, and RMSE < 0.43. Despite these encouraging results, we also identify some important inherent issues with presence-only models that have profound implications for their use in management of VMEs. Future modelling efforts for VME management purposes should be based ideally on the use of survey presence-absence data and, preferably, abundance data.
In the high seas, regional fishery management organisations are required to implement measures to prevent significant adverse impacts on vulnerable marine ecosystems (VMEs). Our objectives were to ...develop habitat suitability models for use in the spatial management of bottom fisheries in the South Pacific and to evaluate these and existing models using independent data from high‐quality seafloor imagery. Presence‐only models for seven VME indictor taxa were developed to complement previous modelling. Evaluation of habitat suitability models using withheld data indicated high mean True Skill Statistic scores of 0.44–0.64. Most habitat suitability models performed adequately when assessed with independent data on taxon presence and absence but were poor surrogates for abundance. We therefore advocate caution when using presence‐only models for spatial management and call for more systematically collected data to develop abundance models.
Vulnerable marine ecosystems (VMEs) are ecosystems at risk from the effects of fishing or other kinds of disturbance, as determined by the vulnerability of their components (e.g., habitats, ...communities or species). Habitat suitability modelling is being used increasingly to predict distribution patterns of VME indicator taxa in the deep sea, where data are particularly sparse, and the models are considered useful for marine ecosystem management. The Louisville Seamount Chain is located within the South Pacific Regional Fishery Management Organisation (SPRFMO) Convention Area, and some seamounts are the subject of bottom trawling for orange roughy by the New Zealand fishery. The aim of the present study was to produce high-resolution habitat suitability maps for VME indicator taxa and VME habitat on these seamounts, in order to evaluate the feasibility of designing within-seamount spatial closures to protect VMEs. We used a multi-model habitat suitability mapping approach, based on bathymetric and backscatter data collected by multibeam echo sounder survey, and data collected by towed underwater camera for the stony coral and habitat-forming VME indicator species Solenosmilia variabilis, as well as two taxa indicative of stony coral habitat (Brisingida, Crinoidea). Model performance varied among the different model types used (Boosted Regression Tree, Random Forest, Generalized Additive Models), but abundance-based models consistently out-performed models based on presence-absence data. Uncertainty for ensemble models (combination of all models) was lower overall compared to the other models. Maps resulting from our models showed that suitable habitat for Solenosmilia variabilis is distributed around the summit-slope break of seamounts, and along ridges that extend down the seamount flanks. Only the flat, soft sediment summits are predicted to be unsuitable habitat for this stony coral species. We translated a definition for stony coral-reef habitat into a Solenosmilia variabilis abundance-based threshold in order to use our models to map this VME habitat. These maps showed that coral-reef occurred in small and isolated patches, and that most of the seabed on these seamounts is predicted to be unsuitable habitat for this VME. We discuss the implications of these results for spatial management closures on the Louisville Seamount Chain seamounts and the wider SPRFMO.
Methods that predict the distributions of species and habitats by developing statistical relationships between observed occurrences and environmental gradients have become common tools in ...environmental research, resource management, and conservation. The uptake of model predictions in practical applications remains limited, however, because validation against independent sample data is rarely practical, especially at larger spatial scales and in poorly sampled environments. Here, we use a quantitative dataset of benthic invertebrate faunal distributions from seabed photographic surveys of an important fisheries area in New Zealand as independent data against which to assess the usefulness of 47 habitat suitability models from eight published studies in the region. When assessed against the independent data, model performance was lower than in published cross-validation values, a trend of increasing performance over time seen in published metrics was not supported, and while 74% of the models were potentially useful for predicting presence or absence, correlations with prevalence and density were weak. We investigate the reasons underlying these results, using recently proposed standards to identify areas in which improvements can best be made. We conclude that commonly used cross-validation methods can yield inflated values of prediction success even when spatial structure in the input data is allowed for, and that the main impediments to prediction success are likely to include unquantified uncertainty in available predictor variables, lack of some ecologically important variables, lack of confirmed absence data for most taxa, and modeling at coarse taxonomic resolution.
Artemisinin resistance in Plasmodium falciparum threatens global malaria elimination efforts. To contain and then eliminate artemisinin resistance in Eastern Myanmar a network of community-based ...malaria posts was instituted and targeted mass drug administration (MDA) with dihydroartemisinin-piperaquine (three rounds at monthly intervals) was conducted. The prevalence of artemisinin resistance during the elimination campaign (2013-2019) was characterized.
Throughout the six-year campaign Plasmodium falciparum positive blood samples from symptomatic patients and from cross-sectional surveys were genotyped for mutations in kelch-13-a molecular marker of artemisinin resistance.
The program resulted in near elimination of falciparum malaria. Of 5162 P. falciparum positive blood samples genotyped, 3281 (63.6%) had K13 mutations. The prevalence of K13 mutations was 73.9% in 2013 and 64.4% in 2019. Overall, there was a small but significant decline in the proportion of K13 mutants (p < 0.001). In the MDA villages there was no significant change in the K13 proportions before and after MDA. The distribution of different K13 mutations changed substantially; F446I and P441L mutations increased in both MDA and non-MDA villages, while most other K13 mutations decreased. The proportion of C580Y mutations fell from 9.2% (43/467) before MDA to 2.3% (19/813) after MDA (p < 0.001). Similar changes occurred in the 487 villages where MDA was not conducted.
The malaria elimination program in Kayin state, eastern Myanmar, led to a substantial reduction in falciparum malaria. Despite the intense use of artemisinin-based combination therapies, both in treatment and MDA, this did not select for artemisinin resistance.
In this article, we focus on a key strategic objective of scientific organizations: maintaining the trust of the public. Using data from a nationally representative survey of American adults (n = ...1510), we assess the extent to which demographic factors and political ideology are associated with citizens’ trust in general science and climate science research conducted by US federal agencies. Finally, we test whether priming individuals to first consider agencies’ general science research influences trust in their climate science research, and vice versa. We found that federal agencies’ general science research is more trusted than their climate science research—although a large minority of respondents did not have an opinion—and that political ideology has a strong influence on public trust in federal scientific research. We also found that priming participants to consider general scientific research does not increase trust in climate scientific research. Implications for theory and practice are discussed.
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