Factors associated with inappropriate prescribing among older adults with complex care needs who have undergone the interRAI assessment

Aim To identify factors associated with prescribing potentially inappropriate medications (PIMs) in older adults (≥ 65 years) with complex care needs, who have undertaken a comprehensive geriatric risk assessment.METHODS: A nationwide cross-sectional (retrospective, observational) study was performed. The national interRAI Home Care assessments conducted in New Zealand in 2015 for older adults were linked to the national pharmaceutical prescribing data (PHARMS). The 2015 Beers criteria were applied to the cross-matched data to identify the prevalence of PIMs. The factors influencing PIMs were analysed using a multinomial logistic regression model.RESULTS: 16,568 older adults were included in this study. Individuals diagnosed with cancer, dementia, insomnia, depression, anxiety, and who were hospitalized in the last 90 days, were more likely to be prescribed PIMs than those who were not diagnosed with the above disorders, and who were not hospitalized in the last 90 days. Individuals over 75 years of age, the Māori ethnic group among other ethnicities, individuals who were diagnosed with certain clinical conditions (diabetes, chronic obstructive pulmonary disease, stroke, or congestive cardiac failure), individuals requiring assistance with activities of daily living and better self-reported health, were associated with a lesser likelihood of being prescribed PIMs.CONCLUSION: The study emphasizes the identification of factors associated with the prescription of PIMs during the first completed comprehensive geriatric assessment. Targeted strategies to reduce modifiable factors associated with the prescription of PIMs in subsequent assessments has the potential to improve medication management in older adults.</p

Tear biomarkers for keratoconus

Keratoconus is a progressive corneal thinning, ectatic condition, which affects vision. Recent advances in corneal topography measurements has helped advance proper diagnosis of this condition and increased research and clinical interests in the disease etiopathogenesis. Considerable progress has been achieved in understanding the progression of the disease and tear fluid has played a major role in the progress. This review discusses the importance of tear fluid as a source of biomarker for keratoconus and how advances in technology have helped map the complexity of tears and thereby molecular readouts of the disease. Expanding knowledge of the tear proteome, lipidome and metabolome opened up new avenues to study keratoconus and to identify probable prognostic or diagnostic biomarkers for the disease. A multidimensional approach of analyzing tear fluid of patients layering on proteomics, lipidomics and metabolomics is necessary in effectively decoding keratoconus and thereby identifying targets for its treatment

Drug Burden Index is a Modifiable Predictor of 30-Day-Hospitalization in Community-Dwelling Older Adults with Complex Care Needs:Machine Learning Analysis of InterRAI Data

BACKGROUND: Older adults (≥ 65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimise ML models further. Accurately predicting hospitalization may tremendously impact the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.METHODS: In this retrospective cohort study, a dataset of 14,198 community-dwelling older adults (≥ 65 years) with complex care needs from the Inter-Resident Assessment Instrument database was used to develop and optimise three ML models to predict 30-day-hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all three models to identify key predictors of 30-day-hospitalization.RESULTS: The area under the receiver operating characteristics curve for the RF, XGB and LR models were 0.97, 0.90 and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day-hospitalization.CONCLUSIONS: Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day-hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.</p

Fastpass: A Centralized “Zero-Queue” Datacenter Network

An ideal datacenter network should provide several properties, including low median and tail latency, high utilization (throughput), fair allocation of network resources between users or applications, deadline-aware scheduling, and congestion (loss) avoidance. Current datacenter networks inherit the principles that went into the design of the Internet, where packet transmission and path selection decisions are distributed among the endpoints and routers. Instead, we propose that each sender should delegate control—to a centralized arbiter—of when each packet should be transmitted and what path it should follow. This paper describes Fastpass, a datacenter network architecture built using this principle. Fastpass incorporates two fast algorithms: the first determines the time at which each packet should be transmitted, while the second determines the path to use for that packet. In addition, Fastpass uses an efficient protocol between the endpoints and the arbiter and an arbiter replication strategy for fault-tolerant failover. We deployed and evaluated Fastpass in a portion of Facebook’s datacenter network. Our results show that Fastpass achieves high throughput comparable to current networks at a 240 reduction is queue lengths (4.35 Mbytes reducing to 18 Kbytes), achieves much fairer and consistent flow throughputs than the baseline TCP (5200 reduction in the standard deviation of per-flow throughput with five concurrent connections), scalability from 1 to 8 cores in the arbiter implementation with the ability to schedule 2.21 Terabits/s of traffic in software on eight cores, and a 2.5 reduction in the number of TCP retransmissions in a latency-sensitive service at Facebook.National Science Foundation (U.S.) (grant IIS-1065219)Irwin Mark Jacobs and Joan Klein Jacobs Presidential FellowshipHertz Foundation (Fellowship

Factors associated with inappropriate prescribing among older adults with complex care needs who have undergone the interRAI assessment

<p><b>Aim:</b> To identify factors associated with prescribing potentially inappropriate medications (PIMs) in older adults (≥65 years) with complex care needs, who have undertaken a comprehensive geriatric risk assessment.</p> <p><b>Methods:</b> A nationwide cross-sectional (retrospective, observational) study was performed. The national interRAI Home Care assessments conducted in New Zealand in 2015 for older adults were linked to the national pharmaceutical prescribing data (PHARMS). The 2015 Beers criteria were applied to the cross-matched data to identify the prevalence of PIMs. The factors influencing PIMs were analyzed using a multinomial logistic regression model.</p> <p><b>Results:</b> In total, 16,568 older adults were included in this study. Individuals diagnosed with cancer, dementia, insomnia, depression, anxiety, and who were hospitalized in the last 90 days were more likely to be prescribed PIMs than those who were not diagnosed with the above disorders, and who were not hospitalized in the last 90 days. Individuals over 75 years of age, the Māori ethnic group among other ethnicities, individuals who were diagnosed with certain clinical conditions (diabetes, chronic obstructive pulmonary disease, stroke, or congestive cardiac failure), individuals requiring assistance with activities of daily living, and better self-reported health, were associated with a lesser likelihood of being prescribed PIMs.</p> <p><b>Conclusion:</b> The study emphasizes the identification of factors associated with the prescription of PIMs during the first completed comprehensive geriatric assessment. Targeted strategies to reduce modifiable factors associated with the prescription of PIMs in subsequent assessments has the potential to improve medication management in older adults.</p

Weitz

ABSTRACT Technological advancements, environmental regulations, and emphasis on resource conservation and recovery have greatly reduced the environmental impacts of municipal solid waste (MSW) management, including emissions of greenhouse gases (GHGs). This study was conducted using a life-cycle methodology to track changes in GHG emissions during the past 25 years from the management of MSW in the United States. For the baseline year of 1974, MSW management consisted of limited recycling, combustion without energy recovery, and landfilling without gas collection or control. This was compared with data for 1980, 1990, and 1997, accounting for changes in MSW quantity, composition, management practices, and technology. Over time, the United States has moved toward increased recycling, composting, combustion (with energy recovery) and landfilling with gas recovery, control, and utilization. These changes were accounted for with historical data on MSW composition, quantities, management practices, and technological changes. Included in the analysis were the benefits of materials recycling and energy recovery to the extent that these displace virgin raw materials and fossil fuel electricity production, respectively. Carbon sinks associated with MSW management also were addressed. The results indicate that the MSW management actions taken by U.S. communities have significantly reduced potential GHG emissions despite an almost 2-fold increase in waste generation. GHG emissions from MSW management were estimated to be 36 million metric tons carbon equivalents (MMTCE) in 1974 and 8 MMTCE in 1997. If MSW were being managed today as it was in 1974, GHG emissions would be ~60 MMTCE. INTRODUCTION Solid waste management deals with the way resources are used as well as with end-of-life deposition of materials in the waste stream. 1 Often complex decisions are made regarding ways to collect, recycle, transport, and dispose of municipal solid waste (MSW) that affect cost and environmental releases. Prior to 1970, sanitary landfills were very rare. Wastes were &quot;dumped&quot; and organic materials in the dumps were burned to reduce volume. Waste incinerators with no pollution controls were common. 1 Today, solid waste management involves technologies that are more energy efficient and protective of human health and the environment. These technological changes and improvements are the result of decisions made by local communities and can impact residents directly. Selection of collection, transportation, recycling, treatment, and disposal systems can determine the number of recycling bins needed, the day people must place their garbage at the curb, the truck routes through residential streets, and the cost of waste services to households. Thus, MSW management can be a significant issue for municipalities. IMPLICATIONS Technology advancements and the movement toward integrated strategies for MSW management have resulted in reduced GHG emissions. GHG emissions from MSW management would be 52 MMTCE higher today if old strategies and technologies were still in use. Integrated strategies involving recycling, composting, waste-to-energy combustion, and landfills with gas collection and energy recovery play a significant role in reducing GHG emissions by recovering materials and energy from the MSW stream

Frailty of Māori, Pasifika, and non-Māori/non-Pasifika older people in New Zealand: a national population study of older people referred for home care services

Little is known about the prevalence of frailty in indigenous populations. We developed a frailty index for older New Zealand Māori and Pasifika who require publicly funded support services.A frailty index (FI) was developed for New Zealand adults aged ≥65 years who had an interRAI-Home Care assessment between 1 June 2012 and 30 October 2015. A frailty score for each participant was calculated by summing the number of deficits recorded and dividing by the total number of possible deficits. This created a FI with a potential range from 0 to 1. Linear regression models for FIs with ethnicity were adjusted for age and sex. Cox proportional hazards models were used to assess the association between the FI and mortality for Māori, Pasifika, and non-Māori/non-Pasifika.Of 54,345 participants, 3,096 (5.7%) identified as Māori, 1,846 (3.4%) were Pasifika, and 49,415 (86.7%) identified as neither Māori nor Pasifika. New Zealand Europeans (48,178, 97.5%) constituted most of the latter group. Within each sex, the mean FIs for Māori and Pasifika were greater than the mean FIs for non-Māori and non-Pasifika, with the difference being more pronounced in females. The FI was associated with mortality (Māori SHR 2.53, 95% CI 1.63 to 3.95; Pasifika SHR 6.03, 95% CI 3.06 to 11.90; non-Māori and non-Pasifika SHR 2.86, 95% 2.53 to 3.25).This study demonstrated differences in FI between the ethnicities in this select cohort. After adjustment for age and sex, increases in FI were associated with increased mortality. This suggests that FI is predictive of poor outcomes in these ethnic groups

Energy Proportionality and Workload Consolidation for Latency-Critical Applications

Energy proportionality and workload consolidation are important objectives towards increasing efficiency in large-scale datacenters. Our work focuses on achieving these goals in the presence of applications with microsecond-scale tail latency requirements. Such applications represent a growing subset of datacenter workloads and are typically deployed on dedicated servers, which is the simplest way to ensure low tail latency across all loads. Unfortunately, it also leads to low energy efficiency and low resource utilization during the frequent periods of medium or low load. We present the OS mechanisms and dynamic control needed to adjust core allocation and voltage/frequency settings based on the measured delays for latency-critical workloads. This allows for energy proportionality and frees the maximum amount of resources per server for other background applications, while respecting service-level objectives. The two key mechanism allow us to detect increases in queuing latencies and to re-assign flow groups between the threads of a latency-critical application in milliseconds without dropping or reordering packets. We compare the efficiency of our solution to the Pareto-optimal frontier of 224 distinct static configurations. Dynamic resource control saves 44%–54% of processor energy, which corresponds to 85%–93% of the Pareto-optimal upper bound. Dynamic resource control also allows background jobs to run at 32%–46% of their standalone throughput, which corresponds to 82%–92% of the Pareto bound

ESTIMA: Extrapolating ScalabiliTy of In-Memory Applications

This paper presents ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. ESTIMA is designed to perform a simple, yet important task: given the performance of an application on a small machine with a handful of cores, ESTIMA extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying ESTIMA is the use of stalled cycles (e.g. cycles that the processor spends waiting for various events, such as cache misses or waiting on a lock). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. ESTIMA can be effectively used to predict the scalability of in-memory applications. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation on a large number of in-memory benchmarks shows that ESTIMA has generally low prediction errors