Learning from health information challenges in the Central African Republic: where documenting health and humanitarian needs requires fresh approaches

The Central African Republic (CAR) is one of the world’s poorest and most fragile countries. Maybe there is no nation on the planet where the official health statistics are so poor. Evidence presented in this Conflict and Health themed collection to document humanitarian needs in CAR, suggests that UN statistics dramatically under-estimate the birth and death rates in conflict settings. To be current and valid, health indicator data in violent settings require more frequent measurement, more triangulation and granular exploration, and creative approaches based on few assumptions. In a world increasingly dependent on model driven data—data often inaccurate in conflict settings—we hope that this collection will allow those service providers and researchers operating in CAR to share their work and help us better learn how to learn. We particularly invite research from professionals working in CAR that documents humanitarian needs and presents indicators of population health where official estimates might not articulate the true extent of the health crisis.

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

Nurses’ Knowledge and Attitudes towards Prevention of Pressure Ulcers

Background: Pressure ulcers (PU) remain a serious complication of immobile patients and a burden for healthcare professionals. The incidence and prevalence remain alarming. Knowledge and attitudes of nurses play a fundamental role in prevention. The aim of this study was to determine the knowledge and attitudes of nurses towards the prevention of PU in selected Slovak hospitals and find relationships and differences among selected variables. Methods: A quantitative exploratory cross-sectional design was chosen. Validated instruments were used. From the 460 randomly selected nurses, 225 (49%) participated in this research. Results: Results showed insufficient knowledge (45.5%) and attitudes (67.9%) of nurses towards PU prevention. There was a significant positive correlation found between the knowledge and attitudes (ρ = 0.300; p = 0.000). Nurses´ knowledge was significantly different within the level of education (p = 0.031) and work department (p = 0.048). Conclusions: Results showed insufficiencies in the knowledge and attitudes of nurses towards PU prevention. Therefore, it is essential to focus on general education and continuing education and practice of nurses. Further development of educational programs and frequent measurement of these two parameters can lead to a significant improvement in the quality of care provided.

Changes in knowledge, perceptions and use of JUUL among a cohort of young adults

ObjectiveE-cigarettes have gained popularity, most recently with pod-style devices, such as JUUL. We examined changes in JUUL awareness, use, perceptions, nicotine content knowledge, number of days a pod lasts and exposure to JUUL retail advertising over a 6-month period in a cohort of young adults.MethodsIn spring and fall 2018, 1836 young adults completed online surveys on tobacco use, including JUUL perceptions and use behaviours. Demographics, tobacco use and JUUL advertising exposure in spring 2018 were examined as predictors of current JUUL use in fall 2018.ResultsEver and current JUUL use doubled in 6 months (5.9% vs 12.7%, p<0.001; 1.6% vs 3.4%, p<0.001). The number of days a JUUL pod lasts significantly changed (p=0.049). Although there was an increase in those reporting JUUL has as much or more nicotine than a pack of cigarettes, 58% are ‘not sure’ of JUUL’s nicotine content. Exposure to JUUL’s advertising significantly increased (31.8% to 46.4%; p<0.001). In multivariable models, those perceiving JUUL as or more harmful than cigarettes, and former and never cigarette smokers had significantly lower odds of current JUUL use at 6 months compared with their respective counterparts (p<0.0001). Those reporting exposure to JUUL’s advertising had significantly increased odds of current JUUL use 6 months later (p<0.03).ConclusionsFindings demonstrate changes in knowledge of JUUL’s nicotine content, perceptions and use over a short period of time, suggesting frequent measurement is necessary. Additionally, efforts are needed to regulate retail advertising and ensure consumer education about product risks as they are associated with current use.

Losing the wait: improving patient cycle time in primary care

Inefficient clinic systems leading to prolonged wait times at primary care clinics are a source of frustration for patients, physicians, staff and administration. Measuring and shortening cycle time has the potential to improve patient experience, staff satisfaction and patient access by moving more patients through in a shorter cycle time. Limited studies have demonstrated that improvements can be made to cycle time and may result in improved patient satisfaction. Most of these studies have focused their efforts on improving efficiency at the front end of the cycle. Our aim was to improve cycle time for the whole visit to less than 60 min within 1 year by engaging our team in brainstorming solutions, presenting regular measurements to our team for review and holding regular meetings to plan rapid improvement cycles. Over the course of 1 year (2017), we were able to reduce cycle time by 12% from 71 to 65 min and to improve patient satisfaction with care. Despite the reduction in cycle time, we maintained high satisfaction scores from patients who felt that the doctor spent enough time with them. We learnt the value of engaging our team, frequent measurement for reporting, adequate staffing at the beginning of clinic, and the value of MA staff acting in a flow coordinator role. We have not only maintained this improvement but also made further small gains over the subsequent 2 years, and by April 2019, our cycle time is at 60 min, despite a marked increase in patient volume. Additional work on the time after the patient is roomed and waiting for a doctor, and further analysis of the physician workflow would be important next steps to drive further improvement.

The German Job Search Panel

This report introduces the German Job Search Panel, a longitudinal survey that follows people who register as job seeking over the course of up to two years. The focus of the survey is on job seekers’ well-being and health. An innovative survey app is used to allow for frequent measurement every month and for conducting the experience sampling method. The collected data may be linked to administrative records of the Federal Employment Agency, provided that people give their consent. A subsample of surveyed job seekers took part in hair sampling to measure their cortisol levels. In this report, we describe the sampling procedure, adjustments over the recruitment period and the collected data. We moreover examine selective participation in the panel. It turns out that high-skilled workers, young individuals and women were more likely to sign up. Age increases the probability to take part in the hair sampling. People working in East Germany were more likely to consent to the linkage of survey data and administrative records.

Assessment of Salivary Adipokines Resistin, Visfatin, and Ghrelin as Type 2 Diabetes Mellitus Biomarkers

Type 2 diabetes mellitus (T2DM) is emerging as a metabolic epidemic worldwide. Pathologically, dysregulation of many biological pathways precedes hyperglycemia and the clinical diagnosis of T2DM. Changing trajectories along the process of T2DM development necessitates frequent measurement of biomarkers for early identification of at-risk individuals and successful prevention. Increase in circulating inflammatory adipokines has been suggested as predictive of T2DM. Human saliva is an easily accessible biospecimen amenable for painless frequent collection and possesses nearly 50% of serum proteome. In this study, we measured the adipokines resistin, visfatin, TNF-α, and ghrelin as markers for T2DM in unstimulated whole saliva (UWS) using specific assay kits. Resistin and visfatin concentrations were significantly higher in T2DM saliva. Although the concentration of acylated or unacylated ghrelin was lower in diabetic saliva, the decrease was not significant. Since resistin and visfatin are biomarkers integral to T2DM pathology, their salivary assessments may receive clinical acceptance.

Utilizing Time Redundancy for Particle Filter-Based Transfer Alignment

Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter.