System-related Variables Research Articles

Machine Learning System Aware Optimization

ABSTRACT New computer systems have emerged in response to the increasing size and complexity of modern data sets. In order to ensure optimum performance, software approaches have to be closely matched with the basic features of systems. This research demonstrates the impact of system-sensitive machine learning using an optimizer lens, a crucial design and solution factor in the majority of machine learning problems. The exactness and convergence rates are traditionally measured for the optimization method. In contrast, a number of system-related variables are crucial to modern computing systems' overall efficiency. Specifications such as data or parameters for the device and higher-level meanings, such as communication and computer interconnections may be included. We propose CoCoA, an overall learning method that closely reviews and incorporates device parameters into the process and theory. We have shown the impact of CoCoA on two conventional distributed systems, that being the traditional cluster environment and the increasingly (founded) machine learning environment. Our results show that we get orders of magnitude quick, by combining system parameters and optimization techniques, to solve current machine learning difficulties. These empirical findings support the assumption that device parameters give more knowledge about the scientific performance.

A novel fractional multivariate GM(1,N) model with interaction effects and its application in forecasting carbon emissions from China's civil aviation

PurposeThe purpose is to forecasting carbon emissions from China's civil aviation more accurately, a novel fractional multivariate GM(1,N) model with interaction effects is developed in this paper.Design/methodology/approachFirst, the interaction term, the trend terms are introduced in the grey action term to reflect the influence of the interaction between the system-related variables on the change of the system characteristic variables and the time trend of the system development. Then fractional cumulative generating sequence is used as the modeling sequence to reduce the perturbation of the original data. Finally, in order to effectively find the optimal fraction accumulation generation coefficient, the particle swarm optimization (PSO) is used to determine the emerging coefficient.FindingsExperimental results show that FIEGM(1, N) outperforms other grey prediction models in predicting the carbon emissions of CAAC, which can better solve the problem of multivariate system prediction of small samples with trend interaction effect.Originality/valueBy considering the influence of interactions in the system and the trend of system development in combination with fractional accumulation theory, a new method to improve the prediction performance of the GM(1, N) model is proposed. The model is first applied to the prediction of carbon emission of civil aviation in China.

Inequality in COVID-19 vaccination in Africa.

The COVID-19 pandemic has spread rapidly to all countries with significant health, socioeconomic, and political consequences. Several safe and effective vaccines have been developed. However, it is not certain that all African countries have successfully vaccinated their populations. To study the distribution and determinants of COVID-19 vaccination in Africa from March 2021 to June 2022. Using reliable open-access data, we used the proportion of fully vaccinated people with a complete schedule as a reference variable. To analyze the level of inequality in COVID-19 vaccination, we computed common inequality indicators including two percentile ratios, the Generalized Entropy index, the Gini coefficient, and the Atkinson index. We also estimated the Lorenz curve. To identify drivers of COVID-19 vaccination, we estimated univariate and multivariate regression models as a function of COVID-19-related variables, demographic, epidemiologic, socioeconomic, and health system-related variables. To overcome a potential endogeneity bias, we checked our results using simultaneous equation models. 53 African countries with available data were included in the study. The proportion of fully vaccinated people increased during the study period. However, this increase remained unequal across African countries. Based on the inequality indicators and the Lorenz curve, inequalities in COVID-19 vaccination across African countries were high, although they have decreased in recent months. Total COVID-19 cases and human development index were identified as significant determinant factors that were independently associated with COVID-19 vaccination. Inequality in COVID-19 vaccination in Africa was high. Promoting adequate information to the general population and providing financial and logistical support to low-income countries can help expand COVID-19 vaccination in Africa.

Prevalence and prognosis of seizures among patients undergoing mechanical thrombectomy for acute ischemic stroke: A look at pre-2015 aha/asa guidelines update regarding endovascular treatment

BackgroundMechanical Thrombectomy (MT) is standard of care for eligible patients with Acute Ischemic Stroke (AIS) due to large vessel occlusion (LVO). With increasing use of MT, clinicians are more likely to encounter seizures, a potential complication of AIS treated with MT. Tracking future trends in the burden of post-stroke seizure associated with MT will require baseline pre-approval benchmark estimates of its frequency and outcomes. MethodsAll patients with AIS who underwent MT (International Classification of Diseases, Ninth Revision, Clinical Modification; ICD-9-CM procedure code: 39.74) were identified from the National Inpatient Sample (NIS) 2006-2014, using appropriate ICD-9-CM codes. We identified a subset of patients with seizures using ICD-9-CM secondary discharge diagnoses codes 780.3x and 345.x. We computed the rate of seizures overall and across pre-specified demographic, clinical, and healthcare system-related variables. Finally, we assessed the independent association of mortality with seizures using a multivariable logistic regression model. ResultsOf 30137 (weighted) patients with AIS who underwent MT, 1,363 (4.5%) had seizures. Patients who had seizures were younger, privately insured, or Medicaid beneficiaries, and frequently died in the hospital. There were no statistically significant differences between the seizures and no-seizures groups by race, sex, IV thrombolysis with recombinant tissue plasminogen activator, length of stay, and the number of medical comorbidities. However, patients who underwent MT and developed seizures had 75% higher odds of in-hospital mortality (adjusted OR 95% CI 1.75; 1.22-2.49). ConclusionIn this nationwide sample, prior to the 2015 AHA/ASA guidelines update supporting MT use, seizures occurred in one of twenty patients with AIS treated with MT, and occurrence of seizure was independently associated with a nearly two-fold increase in the odds of in-hospitality death.

Abstract WS1-2: How to measure and improve drug adherence in clinical trials

Abstract The past decade has seen a dramatic increase in the number of Food and Drug Administration approval of oral anti-cancer drugs (OACDs). In 2018 alone, OACDs comprised ten of the sixteen newly approved oncology therapeutics in the United States. Despite the clinical benefits and convenience of OACDs, these new oral options present potential challenges, including polypharmacy, and can result in increased toxicities, drug–drug interactions (DDIs), inappropriate consumption of medications, and medication non-adherence. Adverse drug events have the potential to affect quality of life, healthcare utilization, treatment response, and survival. For the treatment of breast cancer, Endocrine therapies have remained a critical component of care for early stage and advance disease. Despite the proven efficacy adherence to therapy is suboptimal. Issues related to non-adherence are increasingly important, as analyses from prospective randomized trials show compliance to endocrine therapy is associated with improved disease-free survival. The reasons for non-adherence to hormonal therapy are multifactorial. Barriers to compliance include patient, physician, medication and system related variables and poor compliance is usually associated with a combination of these factors. Characteristics associated with non-adherence include very high and low age, minority race, being single, increased number of comorbidities, lack of knowledge about efficacy of AI therapy, history of non-adherence to other chronic medications, limited insurance status, and higher out of pocket costs. Side effects from hormonal therapy is the most common reason for early discontinuation. An understanding of factors that contribute to non-adherence and interventions that have been tested to improve adherence will be discussed. Medication compliance has become a more salient issue in cancer care with the increased availability of oral antineoplastic therapies for breast cancer. The treatment of BC now routinely encompasses targeted therapies such as oral poly(ADP-ribose) polymerase (PARP) inhibitors and cyclin-dependent kinase 4 (CDK4) and CDK6 inhibitors, PI3 Kinase(Pi3K) Inhibitors, in addition to traditional ET. Furthermore, as advances in BC treatment have improved long-term prognosis, the management of other chronic conditions has become an essential component of BC survivorship care. It remains unclear how this increased oral medication burden on BC survivors affects compliance with both ET and other chronic medications. Issues related to adherence measurement will be discussed. Citation Format: Dawn Hershman. How to measure and improve drug adherence in clinical trials [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr WS1-2.

Psoriasis care in Germany: do patients who receive better care travel longer?

BackgroundLarge variations in the quality of psoriasis care lead to patients being willing to bypass the nearest physician to receive higher quality of care. However, it remains unknown whether actual travel time is associated with quality of care. This study aimed to identify perceived quality of care determinants for travel time to the physician among patients with psoriasis in Germany. Furthermore, differences in access and perceived quality of care between urban and rural areas in Germany were analyzed.MethodsThis cross-sectional observational study based on patient-level healthcare data. Perceived quality of care and treatment satisfaction were assessed from the patients’ perspective. Travel time was estimated by the patients. Multiple regression analysis with the predictors patient characteristics, system-related variables, urbanity, and patient satisfaction with treatment, was applied to identify determinants of travel time with subgroup analyses for rural and urban areas.ResultsWe included 497 patients from 29 dermatological practices in Germany. There were significant differences in psoriasis care between urban and rural areas. Longer travel time was associated with lower age, higher income, higher number of consulted dermatologists since diagnosis, rural residence, more waiting time for the first appointment, lower dermatologist density, and higher patient reported treatment satisfaction.DiscussionThe results indicate an association between actual travel time and treatment satisfaction. Patients with higher perceived quality of care travel longer for their dermatological treatment. The results are also relevant to needs related planning.

System and technoeconomic analysis of solar thermochemical hydrogen production

Hydrogen is a promising energy carrier that can be obtained from various feedstocks using renewable energy sources. Direct solar thermochemical hydrogen (STCH) production by water splitting can utilize the full spectrum of solar radiation and has the potential to achieve high solar energy conversion efficiencies. Currently STCH research areas focus on material discovery. This paper evaluates the performance of various STCH materials in the context of a system platform to assess techno-economic benefits and gaps in the path to STCH scale-up. To analyze the hydrogen production cost, a concentrating solar thermal (CST) system is introduced as a platform for integrating STCH materials and accommodating generalized thermochemical processes. The thermochemical process is based on a two-step STCH cycle using metal oxide that consists of a high temperature step for metal oxide reduction, followed by an oxidation step for water splitting at a lower temperature. A preferred configuration is to have the high temperature step occurring in a directly irradiated solar receiver reactor. To this end, we conceptualized a receiver design and associated solar field layout and investigated STCH operational boundaries, component costs and sensitivity parameters on the $2/kgH2 goal of hydrogen production. The study explored system-related variables and factors associated with scaling up. The CST platform allows more comprehensive studies that encompass aspects of STCH materials and systems such as cost, hydrogen productivity and replacement frequency, alongside other system components like heliostat field, tower, and potential receiver costs.

Machine learning-based cost predictive model for better operating expenditure estimations of U.S. light rail transit projects

Inaccurate forecasts of operating expenditures (OPEX) during the planning phase for new Light Rail Transit (LRT) projects in the United States underestimated future costs by up to 45% ( Harmatuck, 2008 ). When OPEX exceeded projected levels, local transit agencies often reduced public transit services to operate within their respective annual budgets. Therefore, it is imperative for transit agencies to produce reasonably accurate planning estimates to secure sufficient funding to support future operations, maintenance, and service delivery associated with LRT systems. The research aimed to develop a more accurate LRT OPEX predictive model to be used during the planning stage. Traditional statistical analysis and various machine learning-based algorithms were utilized with input from 22 LRT systems in the United States spanning between 2008 and 2018 from various U.S. governmental public databases. This study extended the current state of practice that relied primarily on sum of unit-cost estimates (also known as the unit-cost method) which generally failed to produce accurate forecasts due to lack of engineering details at the planning stage. Existing research attempted to develop regression-based methodologies using system-based attributes but did not substantially increase prediction accuracy from using the unit-cost method. The research improved current practices and research by having developed a more accurate and replicable machine learning-based predictive model using available geographic, socio-economic and LRT system-related variables at the planning stage.

How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom

Driven by the necessity to increase utilisation of the existing networks and accommodation of volatility in renewable energy generation, the energy sector is undergoing a shift from an unconstrained infrastructure expansion to accommodate growth in demand towards demand management strategies. Such strategies, for example nudging demand using incentives such as price signals, or Demand Side Response (DSR), rely on the ability to accurately understand and harness flexibility in demand. Activity-based demand modelling frameworks can provide this capability, as they enable the detailed modelling and simulation of individuals and their activities. However, to date, no modelling approach has been proposed that can link energy consumption of a household to the activities undertaken, heterogeneity of the household residents, presence and use of household appliances and devices as well as weather and energy system-related variables. This paper addresses the gap by proposing a log-linear mixed-effects model of energy consumption based on reported household activities alongside a comprehensive set of attributes and contextual variables that might influence household energy consumption. Application of the model is demonstrated using joint time-use and residential electricity consumption data from 160 households, collected between 2016 and 2018 in the UK. The modelling results prove the value of incorporating time-use (activities) in modelling residential electricity demand, when compared against modelling without such considerations. Furthermore, the model provides (semi-)elasticities of demand and marginal changes in electricity consumption due to activities, which are of direct policy value or serve as inputs into activity-based energy demand simulation.

Factors Associated With Prolonged Length of Stay in Patients Hospitalized With Generalized Convulsive Status Epilepticus in the United States.

Generalized convulsive status epilepticus (GCSE) is a severe complication of epilepsy, which typically requires extended hospitalization, resulting in substantial resource utilization, hospital expenditures, and patient costs. In this nationwide analysis, we examined hospital length of stay (LOS) patterns for GCSE, and the factors that influence prolonged LOS. We extracted data for adult patients (age 18 years and above) with a primary discharge diagnosis of GCSE from the National Inpatient Sample (NIS) from 2006-2014, the largest all-payer inpatient care database in the United States. We computed LOS (≤1, 2-6, and ≥7 days), overall, and across pre-specified patient-related, hospital-related, and healthcare system-related variables available in the NIS. We identified factors independently associated with prolonged hospitalization (2 or more days), using a multivariable logistic regression model. Of 57,832 discharged with a primary diagnosis of GCSE, 6,133 (10.7%) had a LOS ≤1 day, 27,327 (7.3%) stayed for 2-6 days, and 24,372 (42.1%) stayed for ≥7 days. After adjusting for confounders, patients who were older, female, Black, and Hispanic, who underwent continuous EEG video monitoring, were Medicare beneficiaries, had medical comorbidities, or were admitted to large/urban hospitals, were all significantly more likely to have prolonged LOS. Over 40% of patients hospitalized for GCSE in the United States spend at least a week in the hospital. Efforts to shorten hospitalization for GCSE may need to primarily focus on patient groups with select sociodemographic and clinical characteristics.

Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

BackgroundPopulation segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients.MethodsThe literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed.ResultsOf 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients’ race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies.ConclusionsPopulation segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.

Temporal trends and regional disparities in cancer screening utilization: an observational Swiss claims-based study

BackgroundWe examined colorectal, breast, and prostate cancer screening utilization in eligible populations within three data cross-sections, and identified factors potentially modifying cancer screening utilization in Swiss adults.MethodsThe study is based on health insurance claims data of the Helsana Group. The Helsana Group is one of the largest health insurers in Switzerland, insuring approximately 15% of the entire Swiss population across all regions and age groups. We assessed proportions of the eligible populations receiving colonoscopy/fecal occult blood testing (FOBT), mammography, or prostate-specific antigen (PSA) testing in the years 2014, 2016, and 2018, and calculated average marginal effects of individual, temporal, regional, insurance-, supply-, and system-related variables on testing utilization using logistic regression.ResultsOverall, 8.3% of the eligible population received colonoscopy/FOBT in 2014, 8.9% in 2016, and 9.2% in 2018. In these years, 20.9, 21.2, and 20.4% of the eligible female population received mammography, and 30.5, 31.1, and 31.8% of the eligible male population had PSA testing. Adjusted testing utilization varied little between 2014 and 2018; there was an increasing trend of 0.8% (0.6–1.0%) for colonoscopy/FOBT and of 0.5% (0.2–0.8%) for PSA testing, while mammography use decreased by 1.5% (1.2–1.7%). Generally, testing utilization was higher in French-speaking and Italian-speaking compared to German-speaking region for all screening types. Cantonal programs for breast cancer screening were associated with an increase of 7.1% in mammography utilization. In contrast, a high density of relevant specialist physicians showed null or even negative associations with screening utilization.ConclusionsVariation in cancer screening utilization was modest over time, but considerable between regions. Regional variation was highest for mammography use where recommendations are debated most controversially, and the implementation of programs differed the most.

Understanding Public Transport Ridership in Developing Countries to Promote Sustainable Urban Mobility: A Case Study of Mexicali, Mexico

Some small- and medium-sized Global South cities have unsustainable transport systems and no information to plan interventions in addition to having limited resources for data collection. This study proposes a method to understand Public Transport (PT) ridership in cities of these characteristics, based on previous studies and by analysing available indicators related to Manheim’s macro-variables, to identify their influence on the PT ridership. The method was applied in the city of Mexicali, Mexico. The results help to understand the causes of the low PT ridership and have implications for achieving sustainable urban mobility in the city. Findings reveal that mobility planning in Mexicali has been occurring without properly considering activity system related variables, so it is necessary to integrate urban and transport administration. Moreover, to increase PT demand in Mexicali, mobility strategies to discourage the use of private cars are necessary. The proposed method can be applied in other cities of the Global South with characteristics similar to the case study to understand the causes of PT ridership, so these can be considered by the agencies responsible for the planning of the city’s transportation system to promote a sustainable urban mobility.

Patterns of Primary, Specialty, Urgent Care, and Emergency Department Care in Children With Inflammatory Bowel Diseases.

Pediatric patients with inflammatory bowel diseases (IBD) require treatment, monitoring, and health maintenance services. We described patterns of primary, specialty, emergency department (ED) and urgent care delivery, and explored patient- and system-related variables that impact ED/urgent care utilization. We conducted a cross sectional survey of parents of children with IBD at a large tertiary children's hospital. One hundred sixty-one parents completed the survey (75% response). Mean patient age 13.9 years (51% boys); 80% Crohn disease, 16% ulcerative colitis, 4% IBD-unspecified. Mean disease duration 4 years (standard deviation [SD] 2.7). Thirty percent had at least 1 other chronic disease, 31% had a history of IBD-related surgery. Parents were predominantly Caucasian (94%), well-educated (61% bachelor's degree/higher), part of a 2-parent household (79%) living in a suburban setting (57%). Seventy-seven percent of patients had private insurance. In the past year, most children had 1 to 2 IBD-related office visits (54%) with their gastroenterology (GI) doctor and no IBD-related hospitalizations (79%). Eighty-eight percent (N = 141) had a primary care provider (PCP), and most (70%) saw their PCP 1 to 2 times. Even so, 86% (N = 139) received medical care from places other than their PCP or GI doctor; 27% in the ED and 45% at urgent care. Children of parents with less than a bachelor's degree, families that lived further from their GI doctor, and children who saw their PCP more often were more likely to utilize ED/urgent care. ED/urgent care utilization in pediatric patients with IBD was greater than expected, potentially contributing to fragmented, costly care and worse outcomes.

Abstract ES8-3: Everything Comes at a Price - Toxicity, Cost and Adherence to Endocrine Therapy

Abstract Despite the proven efficacy of aromatase inhibitors (AIs) for the treatment of hormone-sensitive breast cancer (BC) adherence to therapy for the full duration of 5-years is suboptimal. Adherence is likely even lower for extended adjuvant therapy for up to 10-years. Issues related to non-adherence are increasingly important, as analyses from prospective randomized trials show compliance to endocrine therapy is associated with improved disease-free survival. The reasons for non-adherence to hormonal therapy are multifactorial. Barriers to compliance include patient, physician, medication and system related variables and poor compliance is usually associated with a combination of these factors. Characteristics associated with non-adherence include very high and low age, minority race, being single, increased number of comorbidities, lack of knowledge about efficacy of AI therapy, history of non-adherence to other chronic medications, limited insurance status, and higher out of pocket costs. Side effects from hormonal therapy is the most common reason for early discontinuation. AI arthralgia has been estimated to affect 45-60% of patients, many of whom report severe or continuous pain. One study reported an overall AI discontinuation rate of 32.4% within 2 years due to adverse effects, 24.3% specifically due to musculoskeletal symptoms, which was the primary reason for discontinuation. There is no consensus on the best treatment for AI arthralgia. The most commonly used therapy is NSAIDs, although there are no formal data testing their efficacy for AI arthralgia, and they are associated with potentially severe adverse effects. Randomized controlled trials have shown benefits from exercise (aerobic and strength training exercises), acupuncture, omega-3-fatty acids and duloxetine compared to placebo for AI arthralgia. Studies have also demonstrated improvement in symptoms over time even among patients receiving placebo. Various educational interventions have also been studied to improve adherence. A systematic review of behavioral interventions to enhance adherence to hormone therapy found five articles that met inclusion criteria and none reported the interventions significantly enhanced adherence compared to usual care. All evaluated educational interventions with or without reminders or navigation. In addition, a multicenter randomized clinical trial of patients with early stage breast cancer on AI’s found that bi-weekly unidirectional text messages for 36 months focused on overcoming potential barriers to medication adherence did not result in improved adherence to hormonal therapy. It is likely that improving long-term adherence will likely require sustained behavioral interventions and support. Citation Format: DL Hershman. Everything Comes at a Price - Toxicity, Cost and Adherence to Endocrine Therapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES8-3.

In-hospital mortality among incident hemodialysis older patients in Peru.

BackgroundUnderstanding the pattern of mortality linked to end stage renal disease (ESRD) is important given the increasing ageing population in low- and middle-income countries.MethodsWe analyzed older patients with ESRD with incident hemodialysis, from January 2012 to August 2017 in one large general hospital in Peru. Individual and health system-related variables were analyzed using Generalized Linear Models (GLM) to estimate their association with in-hospital all-cause mortality. Relative risk (RR) with their 95% confidence intervals (95% CI) were calculated.ResultsWe evaluated 312 patients; mean age 69 years, 93.6% started hemodialysis with a transient central venous catheter, 1.7% had previous hemodialysis indication and 24.7% died during hospital stay. The mean length of stay was 16.1 days (SD 13.5). In the adjusted multivariate models, we found higher in-hospital mortality among those with encephalopathy (aRR 1.85, 95% CI 1.21-2.82 vs. without encephalopathy) and a lower in-hospital mortality among those with eGFR ≤7 mL/min (aRR 0.45, 95% CI 0.31-0.67 vs. eGFR>7 mL/min).ConclusionsThere is a high in-hospital mortality among older hemodialysis patients in Peru. The presence of uremic encephalopathy was associated with higher mortality and a lower estimated glomerular filtration rate with lower mortality.

Inequalities and outcomes: end stage kidney disease in ethnic minorities

BackgroundThe international evidence about outcomes of End Stage Kidney Disease (ESKD) for ethnic minorities was reviewed to identify gaps and make recommendations for researchers and policy makers.MethodsNine databases were searched systematically with 112 studies from 14 different countries included and analysed to produce a thematic map of the literature.ResultsReviews (n = 26) highlighted different mortality rates and specific causes between ethnic groups and by stage of kidney disease associated with individual, genetic, social and environmental factors. Primary studies focussing on uptake of treatment modalities (n = 19) found ethnic differences in access. Research evaluating intermediate outcomes and quality of care in different treatment phases (n = 35) e.g. dialysis adequacy, transplant evaluation and immunosuppression showed ethnic minorities were disadvantaged. This is despite a survival paradox for some ethnic minorities on dialysis seen in studies of longer term outcomes (n = 29) e.g. in survival time post-transplant and mortality. There were few studies which focussed on end of life care (n = 3) and ethnicity.Gaps identified were: limited evidence from all stages of the ESKD pathway, particularly end of life care; a lack of system oriented studies with a reliance on national routine datasets which are limited in scope; a dearth of qualitative studies; and a lack studies from many countries with limited cross country comparison and learning.ConclusionsDifferences between ethnic groups occur at various points and in a variety of outcomes throughout the kidney care system. The combination of individual factors and system related variables affect ethnic groups differently indicating a need for culturally intelligent policy informed by research to prevent disadvantage.

Predictors of engagement in post-discharge quitline counseling among hospitalized smokers.

Quitlines provide evidence-based tobacco treatment and multiple calls yield higher quit rates. This study aimed to identify subgroups of smokers with greater quitline engagement following referral during hospitalization. Data were from a randomized clinical trial assessing the effectiveness of fax referral (referral faxed to proactive quitline) versus warm handoff (patient connected to quitline at bedside) (n = 1054). Classification and regression trees analyses evaluated individual and treatment/health system-related variables and their interactions. Among all participants, warm handoff, higher ratings of the tobacco treatment care transition, and being older predicted completing more quitline calls. Among patients enrolled in the quitline, higher transition of care ratings, being older, and use of cessation medication post-discharge predicted completing more calls. Three of the four factors influencing engagement were characteristics of treatment within the hospital (quality of tobacco treatment care transition and referral method) and therapy (use of cessation medications), suggesting potential targets to increase quitline engagement post-discharge.

Why do 'fast track' patients stay more than four hours in the emergency department? An investigation of factors that predict length of stay.

Low-acuity 'fast track' patients represent a large portion of Australian EDs' workload and must be managed efficiently to meet the National Emergency Access Target. The current study determined the relative importance and estimated marginal effects of patient and system-related variables in predicting ED fast track patients who stayed longer than 4 h in the ED. Data for ED presentations between 1 July 2014 and 30 June 2015 were collected from a large regional Australian public hospital. Only 'fast track' patients were included in the analysis. A gradient boosting machine was used to predict which patients would have an ED length of stay greater or less than 4 h. The performance of the final model was tested using a validation data set that was withheld from the initial analysis. A total of 27 variables were analysed. The model's performance was very good (area under receiver operating characteristic curve 0.89, where 1.0 is perfect prediction). The five most important variables for predicting length of stay were time-dependent and system-related (not patient-related); these were the amount of time taken from when the patient arrived at the ED to: (i) order imaging; (ii) order pathology; (iii) request admission to hospital; (iv) allocate a clinician to care for the patient; and (v) handover a patient between ED clinicians. We identified the most important variables for predicting length of stay greater than 4 h for fast track patients in our ED. Identifying factors that influence length of stay is a necessary step towards understanding ED patient flow and identifying improvement opportunities.

Healthcare renunciation among young adults in French higher education: A population-based study.

Healthcare renunciation has been a recent focus of the public health debate, but large-scale investigations of healthcare renunciation are scarce. The goal of this study was to examine healthcare renunciation among young adults at French universities. It investigated prevalence of and reasons for healthcare renunciation and identified subgroups of vulnerable youths. Data were collected in the 2013 triennial study conducted by the French National Observatory of Student Life. That study had a sample of 35,810 18- to 25-year-old participants. Measures included healthcare renunciation, reasons for healthcare renunciation, demographics, cultural and socioeconomic background, health and social outcomes, healthcare system-related variables, and markers of adulthood. The results showed that the prevalence of healthcare renunciation was 27.2%. The most common reasons for healthcare renunciation were refusal renunciation (self-care, 12.7%; wait for improvement, 15.5%) and barrier renunciation (financial reasons, 12.1%; lack of time, 10.2%). Therefore, healthcare renunciation is linked not only to financial constraints but also to individuals' subjective needs and ways of facing illness. Overall, disadvantaged youths and youths transitioning to adulthood were likelier to renounce care, especially for financial reasons. To conclude, there are social inequalities in the use of healthcare by emerging adults in higher education in France. Emerging adulthood appears to be a risky period in which people are likely to renounce healthcare for multiple reasons. Support should be provided for vulnerable youths to prevent them from becoming increasingly vulnerable.