Instruments to assess secondhand smoke exposure in large cohorts of never smokers: The smoke scales

© 2014 The Authors. Published by PLOS. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1371/journal.pone.0085809The objectives of this study were to: (i) to develop questionnaires that can identify never-smoking children and adults experiencing increased exposure to secondhand smoke (SHS+), (ii) to determine their validity against hair nicotine, and (iii) assess their reliability. A sample of 191 children (85 males; 106 females; 7-18 years) and 95 adult (23 males; 72 females; 18- 62 years) never-smokers consented to hair nicotine analysis and answered a large number of questions assessing all sources of SHS. A randomly-selected 30% answered the questions again after 20-30 days. Prevalence of SHS+ in children and adults was 0.52±0.07 and 0.67±0.10, respectively (p16.5 and >16, respectively. Significant Kappa agreement (p0.05). Area under the curve and McNemar's Chi-square showed no pair-wise differences in sensitivity and specificity at the cutoff point between the two different days for SS-C and SS-A (p>0.05). We conclude that the SS-C and the SS-A represent valid, reliable, practical, and inexpensive instruments to identify children and adult never-smokers exposed to increased SHS. Future research should aim to further increase the validity of the two questionnaires. © 2014 Misailidi et al.Published versio

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Incorporating Mobile Energy Resources in Optimal Power Flow Models Considering Geographical and Road Network Data

<p>Mobile energy resources (MERs) are becoming an increasingly popular asset in modern power systems with several potential benefits. While the incorporation of MERs in optimal power flow (OPF) models can enhance both reliability and resilience of the system, they add additional levels of complexity to the optimization problem. To properly determine optimal dispatch of MERs, electrical power grid constraints must be incorporated with and associated to those posed by geography and road networks. In this paper, a mixed-integer programming (MIP) model is proposed which incorporates MER dispatch into OPF. The computational implementation using Python utilizes real-world geographical and road network data to provide an optimal dispatch of MERs in the OPF solution considering actual driving and dispatching times. The implemented model is demonstrated and validated using a 24-bus test system in Portugal. A day-ahead operation planning scenario is considered with overloading and loss of renewable generation, showcasing how load shedding can be mitigated through proper dispatch of MERs. Several scenarios are tested, including the definition of critical loads, varying individual MER capacities and numbers, and MER dispatch origins or depots. Finally, an N-1 contingency analysis is performed to study the effect the different MER dispatch scenarios on overall system reliability.</p&gt

Quantifying the Difference Between Resilience and Reliability in the Operation Planning of Mobile Resources for Power Distribution Grids

<p>Modern power grids have high levels of distributed energy resources, automation, and inherent flexibility. Those characteristics have been proven to be favorable from an environmental, social and economic perspective. Despite the increased versatility, modern grids are becoming more vulnerable to high-impact low-probability (HILP) threats, particularly for the distribution networks. On one hand, this is due to the increasing frequency and severity of weather events and natural disasters. On the other hand, it is aggravated by the increased complexity of smart grids. Resilience is broadly defined as the capability of a system to mitigate the effects of and recover from HILP events, which is often confused with reliability that is concerned with low-impact high-probability (LIHP) ones. In this paper, a distribution system in Portugal is simulated to showcase how the utilization of flexibility and mobile energy resources (MERs) should be considered differently relative to HILP vs LIHP threats.</p&gt

Energy trade tempers Nile water conflict

The demand for energy, water and food in Africa continues to increase, resulting in growing pressure on contentious multisector resource systems like the River Nile. The ongoing dispute over Nile resources could become a zero-sum game if addressed from a water-centric viewpoint. Understanding how energy system management impacts water infrastructure introduces new opportunities to solve water conflicts. Although benefit-sharing of water resources in the Nile Basin has been promoted to counteract water volume disputes, it has not yielded actionable solutions to the toughest negotiations over the past two decades. Here we develop a detailed and integrated energy–river basin system simulator of 13 East African countries, including the Nile Basin, and show how new electricity trade agreements between Ethiopia, Sudan and Egypt could help resolve the ongoing water dispute over the Grand Ethiopian Renaissance Dam. The results show that increasing energy trade can reduce Egyptian water deficits, reduce regional greenhouse gas emissions, increase hydropower generation in all three countries, reduce energy curtailment in Sudan and increase Ethiopia’s financial returns from electricity. This study underscores how spatial quantification of river–energy system interdependencies can help decision-makers find actionable multisector benefit-sharing solutions

Results (median ± interquartile range) for hair nicotine and prevalence rates (±95% confidence interval) for SHS+ and SHS- in children and adult never-smokers.

<p>Note: * = χ² significant difference (p<0.05) between SHS+ and SHS–.</p><p> = χ² significant difference (p<0.05) between children and adults.</p><p>Key: SHS+ = positive diagnosis of SHS exposure using the forms; SHS– = negative diagnosis of SHS exposure using the forms.</p

Reliability results for SS-C and SS-A.

<p>Note: * = AUC test statistically significant (p<0.05) from 0.5 (i.e., no diagnostic ability).</p><p>Key: IR = interquartile range; 95%LoA = 95% limits of agreement; %CV = percent coefficient of variation; SE = sensitivity; SP = specificity; PPV = positive predicted value; NPV = negative predicted value; LR = likelihood ratio; AUC = area under the ROC curve; CI95% = 95% confidence interval; SE = standard error.</p

Receiver operating characteristics (ROC) curves for SS-C (A) and SS-A (B) indicating the designated cut off points at 16.5 and 16, respectively.

<p>The points at the ROC curve at the designated cut offs for SS-C (C) and SS-A (D) forms 1 and 2 are also illustrated.</p