Ecosystem Services Mapping Uncertainty Assessment: A Case Study in the Fitzroy Basin Mining Region

Ecosystem services mapping is becoming increasingly popular through the use of various readily available mapping tools, however, uncertainties in assessment outputs are commonly ignored. Uncertainties from different sources have the potential to lower the accuracy of mapping outputs and reduce their reliability for decision-making. Using a case study in an Australian mining region, this paper assessed the impact of uncertainties on the modelling of the hydrological ecosystem service, water provision. Three types of uncertainty were modelled using multiple uncertainty scenarios: (1) spatial data sources; (2) modelling scales (temporal and spatial) and (3) parameterization and model selection. We found that the mapping scales can induce significant changes to the spatial pattern of outputs and annual totals of water provision. In addition, differences in parameterization using differing sources from the literature also led to obvious differences in base flow. However, the impact of each uncertainty associated with differences in spatial data sources were not so great. The results of this study demonstrate the importance of uncertainty assessment and highlight that any conclusions drawn from ecosystem services mapping, such as the impacts of mining, are likely to also be a property of the uncertainty in ecosystem services mapping methods

Fewer COVID-19 neurological complications with dexamethasone and remdesivir

OBJECTIVE: To assess the impact of treatment with dexamethasone, remdesivir or both on neurological complications in acute COVID-19. METHODS: We used observational data from the International Severe Acute and emerging Respiratory Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK). Hospital inpatients aged ≥18 years with laboratory-confirmed SARS-CoV-2 infection admitted between 31 January 2020 and 29 June 2021 were included. Treatment allocation was non-blinded and performed by reporting clinicians. A propensity scoring methodology was used to minimize confounding. Treatment with remdesivir, dexamethasone or both was assessed against standard of care. The primary outcome was a neurological complication occurring at the point of death, discharge, or resolution of the COVID-19 clinical episode. RESULTS: Out of 89,297 hospital inpatients, 64,088 had severe COVID-19 and 25,209 had non-hypoxic COVID-19. Neurological complications developed in 4.8% and 4.5% respectively. In both groups, neurological complications associated with increased mortality, ICU admission, worse self-care on discharge and time to recovery. In severe COVID-19, treatment with dexamethasone (n=21,129), remdesivir (n=1,428) and both combined (n=10,846) associated with a lower frequency of neurological complications: OR=0.76 (95% CI=0.69-0.83), OR 0.69 (95% CI=0.51-0.90) and OR=0.54, (95% CI=0.47-0.61) respectively. In non-hypoxic COVID-19, dexamethasone (n=2,580) associated with less neurological complications (OR=0.78, 95% CI 0.62-0.97), while the dexamethasone/remdesivir combination (n=460) showed a similar trend (OR=0.63, 95% CI=0.31-1.15). INTERPRETATION: Treatment with dexamethasone, remdesivir or both in patients hospitalised with COVID-19 associated with a lower frequency of neurological complications in an additive manner, such that the greatest benefit was observed in patients who received both drugs together. This article is protected by copyright. All rights reserved

Obesity, chronic disease, age, and in-hospital mortality in patients with covid-19: analysis of ISARIC clinical characterisation protocol UK cohort.

BACKGROUND: Although age, obesity and pre-existing chronic diseases are established risk factors for COVID-19 outcomes, their interactions have not been well researched. METHODS: We used data from the Clinical Characterisation Protocol UK (CCP-UK) for Severe Emerging Infection developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC). Patients admitted to hospital with COVID-19 from 6th February to 12th October 2020 were included where there was a coded outcome following hospital admission. Obesity was determined by an assessment from a clinician and chronic disease by medical records. Chronic diseases included: chronic cardiac disease, hypertension, chronic kidney disease, chronic pulmonary disease, diabetes and cancer. Mutually exclusive categories of obesity, with or without chronic disease, were created. Associations with in-hospital mortality were examined across sex and age categories. RESULTS: The analysis included 27,624 women with 6407 (23.2%) in-hospital deaths and 35,065 men with 10,001 (28.5%) in-hospital deaths. The prevalence of chronic disease in women and men was 66.3 and 68.5%, respectively, while that of obesity was 12.9 and 11.1%, respectively. Association of obesity and chronic disease status varied by age (p < 0.001). Under 50 years of age, obesity and chronic disease were associated with in-hospital mortality within 28 days of admission in a dose-response manner, such that patients with both obesity and chronic disease had the highest risk with a hazard ratio (HR) of in-hospital mortality of 2.99 (95% CI: 2.12, 4.21) in men and 2.16 (1.42, 3.26) in women compared to patients without obesity or chronic disease. Between the ages of 50-69 years, obesity and chronic disease remained associated with in-hospital COVID-19 mortality, but survival in those with obesity was similar to those with and without prevalent chronic disease. Beyond the age of 70 years in men and 80 years in women there was no meaningful difference between those with and without obesity and/or chronic disease. CONCLUSION: Obesity and chronic disease are important risk factors for in-hospital mortality in younger age groups, with the combination of chronic disease and obesity being particularly important in those under 50 years of age. These findings have implications for targeted public health interventions, vaccination strategies and in-hospital clinical decision making

Obesity, Ethnicity, and Risk of Critical Care, Mechanical Ventilation, and Mortality in Patients Admitted to Hospital with COVID-19: Analysis of the ISARIC CCP-UK Cohort.

OBJECTIVE: The aim of this study was to investigate the association of obesity with in-hospital coronavirus disease 2019 (COVID-19) outcomes in different ethnic groups. METHODS: Patients admitted to hospital with COVID-19 in the United Kingdom through the Clinical Characterisation Protocol UK (CCP-UK) developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) were included from February 6 to October 12, 2020. Ethnicity was classified as White, South Asian, Black, and other minority ethnic groups. Outcomes were admission to critical care, mechanical ventilation, and in-hospital mortality, adjusted for age, sex, and chronic diseases. RESULTS: Of the participants included, 54,254 (age = 76 years; 45.0% women) were White, 3,728 (57 years; 41.1% women) were South Asian, 2,523 (58 years; 44.9% women) were Black, and 5,427 (61 years; 40.8% women) were other ethnicities. Obesity was associated with all outcomes in all ethnic groups, with associations strongest for black ethnicities. When stratified by ethnicity and obesity status, the odds ratios for admission to critical care, mechanical ventilation, and mortality in black ethnicities with obesity were 3.91 (3.13-4.88), 5.03 (3.94-6.63), and 1.93 (1.49-2.51), respectively, compared with White ethnicities without obesity. CONCLUSIONS: Obesity was associated with an elevated risk of in-hospital COVID-19 outcomes in all ethnic groups, with associations strongest in Black ethnicities

Admission Blood Glucose Level and Its Association With Cardiovascular and Renal Complications in Patients Hospitalized With COVID-19

OBJECTIVE: To investigate the association between admission blood glucose levels and risk of in-hospital cardiovascular and renal complications. RESEARCH DESIGN AND METHODS: In this multicenter prospective study of 36,269 adults hospitalized with COVID-19 between 6 February 2020 and 16 March 2021 (N = 143,266), logistic regression models were used to explore associations between admission glucose level (mmol/L and mg/dL) and odds of in-hospital complications, including heart failure, arrhythmia, cardiac ischemia, cardiac arrest, coagulation complications, stroke, and renal injury. Nonlinearity was investigated using restricted cubic splines. Interaction models explored whether associations between glucose levels and complications were modified by clinically relevant factors. RESULTS: Cardiovascular and renal complications occurred in 10,421 (28.7%) patients; median admission glucose level was 6.7 mmol/L (interquartile range 5.8-8.7) (120.6 mg/dL [104.4-156.6]). While accounting for confounders, for all complications except cardiac ischemia and stroke, there was a nonlinear association between glucose and cardiovascular and renal complications. For example, odds of heart failure, arrhythmia, coagulation complications, and renal injury decreased to a nadir at 6.4 mmol/L (115 mg/dL), 4.9 mmol/L (88.2 mg/dL), 4.7 mmol/L (84.6 mg/dL), and 5.8 mmol/L (104.4 mg/dL), respectively, and increased thereafter until 26.0 mmol/L (468 mg/dL), 50.0 mmol/L (900 mg/dL), 8.5 mmol/L (153 mg/dL), and 32.4 mmol/L (583.2 mg/dL). Compared with 5 mmol/L (90 mg/dL), odds ratios at these glucose levels were 1.28 (95% CI 0.96, 1.69) for heart failure, 2.23 (1.03, 4.81) for arrhythmia, 1.59 (1.36, 1.86) for coagulation complications, and 2.42 (2.01, 2.92) for renal injury. For most complications, a modifying effect of age was observed, with higher odds of complications at higher glucose levels for patients age <69 years. Preexisting diabetes status had a similar modifying effect on odds of complications, but evidence was strongest for renal injury, cardiac ischemia, and any cardiovascular/renal complication. CONCLUSIONS: Increased odds of cardiovascular or renal complications were observed for admission glucose levels indicative of both hypo- and hyperglycemia. Admission glucose could be used as a marker for risk stratification of high-risk patients. Further research should evaluate interventions to optimize admission glucose on improving COVID-19 outcomes

Admission Blood Glucose Level and Its Association With Cardiovascular and Renal Complications in Patients Hospitalized With COVID-19

ObjectiveTo investigate the association between admission blood glucose levels and risk of in-hospital cardiovascular and renal complications.Research design and methodsIn this multicenter prospective study of 36,269 adults hospitalized with COVID-19 between 6 February 2020 and 16 March 2021 (N = 143,266), logistic regression models were used to explore associations between admission glucose level (mmol/L and mg/dL) and odds of in-hospital complications, including heart failure, arrhythmia, cardiac ischemia, cardiac arrest, coagulation complications, stroke, and renal injury. Nonlinearity was investigated using restricted cubic splines. Interaction models explored whether associations between glucose levels and complications were modified by clinically relevant factors.ResultsCardiovascular and renal complications occurred in 10,421 (28.7%) patients; median admission glucose level was 6.7 mmol/L (interquartile range 5.8-8.7) (120.6 mg/dL [104.4-156.6]). While accounting for confounders, for all complications except cardiac ischemia and stroke, there was a nonlinear association between glucose and cardiovascular and renal complications. For example, odds of heart failure, arrhythmia, coagulation complications, and renal injury decreased to a nadir at 6.4 mmol/L (115 mg/dL), 4.9 mmol/L (88.2 mg/dL), 4.7 mmol/L (84.6 mg/dL), and 5.8 mmol/L (104.4 mg/dL), respectively, and increased thereafter until 26.0 mmol/L (468 mg/dL), 50.0 mmol/L (900 mg/dL), 8.5 mmol/L (153 mg/dL), and 32.4 mmol/L (583.2 mg/dL). Compared with 5 mmol/L (90 mg/dL), odds ratios at these glucose levels were 1.28 (95% CI 0.96, 1.69) for heart failure, 2.23 (1.03, 4.81) for arrhythmia, 1.59 (1.36, 1.86) for coagulation complications, and 2.42 (2.01, 2.92) for renal injury. For most complications, a modifying effect of age was observed, with higher odds of complications at higher glucose levels for patients age ConclusionsIncreased odds of cardiovascular or renal complications were observed for admission glucose levels indicative of both hypo- and hyperglycemia. Admission glucose could be used as a marker for risk stratification of high-risk patients. Further research should evaluate interventions to optimize admission glucose on improving COVID-19 outcomes

Cluster detection methods applied to the Upper Cape Cod cancer data

BACKGROUND: A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. METHODS: We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. RESULTS: The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. CONCLUSION: The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component

Outcome of COVID-19 in hospitalised immunocompromised patients:An analysis of the WHO ISARIC CCP-UK prospective cohort study

Background: Immunocompromised patients may be at higher risk of mortality if hospitalised with Coronavirus Disease 2019 (COVID-19) compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death and how this risk changed over the pandemic. Methods and findings: We included patients &gt; = 19 years with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK prospective cohort study. We defined immunocompromise as immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination, and comorbidities. We used Bayesian logistic regression to explore mortality over time. Between 17 January 2020 and 28 February 2022, we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. Approximately 29% (n = 6,499) of immunocompromised and 21% (n = 28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjusted OR 1.44, 95% CI [1.39, 1.50], p &lt; 0.001). Not all immunocompromising conditions had the same risk, for example, patients on active cancer treatment were less likely to have their care escalated to intensive care (adjusted OR 0.77, 95% CI [0.7, 0.85], p &lt; 0.001) or ventilation (adjusted OR 0.65, 95% CI [0.56, 0.76], p &lt; 0.001). However, cancer patients were more likely to die (adjusted OR 2.0, 95% CI [1.87, 2.15], p &lt; 0.001). Analyses were adjusted for age, sex, socioeconomic deprivation, comorbidities, and vaccination status. As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50 to 69 years was 88% for men and 83% for women, and for those &gt;80 years was 99% for men and 98% for women. The study is limited by a lack of detailed drug data prior to admission, including steroid doses, meaning that we may have incorrectly categorised some immunocompromised patients as immunocompetent. Conclusions: Immunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses, monoclonal antibodies, and nonpharmaceutical preventive interventions should be continually encouraged for this patient group. Trial registration: ISRCTN 66726260