Agreement between administrative data and the Resident Assessment Instrument Minimum Dataset (RAI-MDS) for medication use in long-term care facilities: a population-based study

Background: Prescription medication use, which is common among long-term care facility (LTCF) residents, is routinely used to describe quality of care and predict health outcomes. Data sources that capture medication information, which include surveys, medical charts, administrative health databases, and clinical assessment records, may not collect concordant information, which can result in comparable prevalence and effect size estimates. The purpose of this research was to estimate agreement between two population-based electronic data sources for measuring use of several medication classes among LTCF residents: outpatient prescription drug administrative data and the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0. Methods: Prescription drug and RAI-MDS data from the province of Saskatchewan, Canada (population 1.1 million) were linked for 2010/11 in this cross-sectional study. Agreement for anti-psychotic, anti-depressant, and anti-anxiety/hypnotic medication classes was examined using prevalence estimates, Cohen’s κ, and positive and negative agreement. Mixed-effects logistic regression models tested resident and facility characteristics associated with disagreement. Results: The cohort was comprised of 8,866 LTCF residents. In the RAI-MDS data, prevalence of anti-psychotics was 35.7%, while for anti-depressants it was 37.9% and for hypnotics it was 27.1%. Prevalence was similar in prescription drug data for anti-psychotics and anti-depressants, but lower for hypnotics (18.0%). Cohen’s κ ranged from 0.39 to 0.85 and was highest for the first two medication classes. Diagnosis of a mood disorder and facility affiliation was associated with disagreement for hypnotics. Conclusions: Agreement between prescription drug administrative data and RAI-MDS assessment data was influenced by the type of medication class, as well as selected patient and facility characteristics. Researchers should carefully consider the purpose of their study, whether it is to capture medication that are dispensed or medications that are currently used by residents, when selecting a data source for research on LTCF populations

ER stress-regulated translation increases tolerance to extreme hypoxia and promotes tumor growth

Tumor cell adaptation to hypoxic stress is an important determinant of malignant progression. While much emphasis has been placed on the role of HIF-1 in this context, the role of additional mechanisms has not been adequately explored. Here we demonstrate that cells cultured under hypoxic/anoxic conditions and transformed cells in hypoxic areas of tumors activate a translational control program known as the integrated stress response (ISR), which adapts cells to endoplasmic reticulum (ER) stress. Inactivation of ISR signaling by mutations in the ER kinase PERK and the translation initiation factor eIF2α or by a dominant-negative PERK impairs cell survival under extreme hypoxia. Tumors derived from these mutant cell lines are smaller and exhibit higher levels of apoptosis in hypoxic areas compared to tumors with an intact ISR. Moreover, expression of the ISR targets ATF4 and CHOP was noted in hypoxic areas of human tumor biopsy samples. Collectively, these findings demonstrate that activation of the ISR is required for tumor cell adaptation to hypoxia, and suggest that this pathway is an attractive target for antitumor modalities

How do urban residents use energy for winter heating at home? A large-scale survey in the hot summer and cold winter climate zone in the Yangtze River region

The increasing demand for improving indoor thermal environment in the hot summer and cold winter climate zone (HSCW) in the Yangtze River region in China poses enormous challenges in terms of energy policy and design solutions for this unique region. A comprehensive understanding of people’s habits and behaviors involving winter heating is imperative for decision making for urban heating infrastructure investment strategies that significantly impact the decarbonization of heating. However, there are little studies of a large-scale survey to gain such knowledge acrose the region. The aim of this study is to develop a rigorous survey method in order to obtain reliable data for analysis. Five municipal/capital cities across the upper, middle and downstream Yangtze River were surveyed based on 30 randomly generated locations in each city. A total of 8481 valuable samples were obtained in the survey conducted in the winter from November 2017 to March 2018. It is revealed that air conditioning/air source heat pumps are the predominant systems, accounting for 63% and 58% for bedroom and living room heating respectively. The use patterns of heating are diverse featuring ‘part-time-part-space’ systems in accordance with the occupancy patterns. There is significant evidence of the habit of opening a window to provide a gap for fresh air irrespective of whether the heating is in use. Two-step cluster analysis is employed to subdivide occupants’ heating-related behaviors into three clusters to characterize households. This study fills the knowledge gap of winter-heating-related behaviors. The research outcomes will benefit building energy simulations for energy prediction and help policy makers making decisions on providing strategic guidance in terms of winter heating solutions in this region

Modeling and Optimization of a CoolingTower-Assisted Heat Pump System

To minimize the total energy consumption of a cooling tower-assisted heat pump (CTAHP) system in cooling mode, a model-based control strategy with hybrid optimization algorithm for the system is presented in this paper. An existing experimental device, which mainly contains a closed wet cooling tower with counter flow construction, a condenser water loop and a water-to-water heat pump unit, is selected as the study object. Theoretical and empirical models of the related components and their interactions are developed. The four variables, viz. desired cooling load, ambient wet-bulb temperature, temperature and flow rate of chilled water at the inlet of evaporator, are set to independent variables. The system power consumption can be minimized by optimizing input powers of cooling tower fan, spray water pump, condenser water pump and compressor. The optimal input power of spray water pump is determined experimentally. Implemented on MATLAB, a hybrid optimization algorithm, which combines the Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with the greedy diffusion search (GDS) algorithm, is incorporated to solve the minimization problem of energy consumption and predict the system’s optimal set-points under quasi-steady-state conditions. The integrated simulation tool is validated against experimental data. The results obtained demonstrate the proposed operation strategy is reliable, and can save energy by 20.8% as compared to an uncontrolled system under certain testing conditions

Performance Analyses of Counter-Flow Closed Wet Cooling Towers Based on a Simplified Calculation Method

As one of the most widely used units in water cooling systems, the closed wet cooling towers (CWCTs) have two typical counter-flow constructions, in which the spray water flows from the top to the bottom, and the moist air and cooling water flow in the opposite direction vertically (parallel) or horizontally (cross), respectively. This study aims to present a simplified calculation method for conveniently and accurately analyzing the thermal performance of the two types of counter-flow CWCTs, viz. the parallel counter-flow CWCT (PCFCWCT) and the cross counter-flow CWCT (CCFCWCT). A simplified cooling capacity model that just includes two characteristic parameters is developed. The Levenberg–Marquardt method is employed to determine the model parameters by curve fitting of experimental data. Based on the proposed model, the predicted outlet temperatures of the process water are compared with the measurements of a PCFCWCT and a CCFCWCT, respectively, reported in the literature. The results indicate that the predicted values agree well with the experimental data in previous studies. The maximum absolute errors in predicting the process water outlet temperatures are 0.20 and 0.24 °C for the PCFCWCT and CCFCWCT, respectively. These results indicate that the simplified method is reliable for performance prediction of counter-flow CWCTs. Although the flow patterns of the two towers are different, the variation trends of thermal performance are similar to each other under various operating conditions. The inlet air wet-bulb temperature, inlet cooling water temperature, air flow rate, and cooling water flow rate are crucial for determining the cooling capacity of a counter-flow CWCT, while the cooling tower effectiveness is mainly determined by the flow rates of air and cooling water. Compared with the CCFCWCT, the PCFCWCT is much more applicable in a large-scale cooling water system, and the superiority would be amplified when the scale of water distribution system increases. Without multiple iterative calculations and extensive experimental data, the simplified method could be used to effectively analyze the thermal performance of counter-flow CWCTs in operation. It is useful for optimization operation of counter-flow CWCTs such that to improve the energy efficiency of the overall cooling water system

Investigation on Indoor Air Pollution and Childhood Allergies in Households in Six Chinese Cities by Subjective Survey and Field Measurements

Greater attention is currently being paid to the relationship between indoor environment and childhood allergies, however, the lack of reliable data and the disparity among different areas hinders reliable assessment of the relationship. This study focuses on the effect of indoor pollution on Chinese schoolchildren and the relationship between specific household and health problems suffered. The epidemiological questionnaire survey and the field measurement of the indoor thermal environment and primary air pollutants including CO2, fine particulate matter (PM2.5), chemical pollutants and fungi were performed in six Chinese cities. A total of 912 questionnaires were eligible for statistical analyses and sixty houses with schoolchildren aged 9–12 were selected for field investigation. Compared with Chinese national standards, inappropriate indoor relative humidity (<30% or >70%), CO2 concentration exceeding 1000 ppm and high PM2.5 levels were found in some monitored houses. Di(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were the most frequently detected semi-volatile organic compounds (SVOCs) in house dust. Cladosporium, Aspergillus and Penicillium were detected in both indoor air and house dust. This study indicates that a thermal environment with CO2 exceeding 1000 ppm, DEHP and DBP exceeding 1000 μg/g, and high level of PM2.5, Cladosporium, Aspergillus and Penicillium increases the risk of children’s allergies

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance