A process evaluation of user fees abolition for pregnant women and children under five years in two districts in Niger (West Africa)

<p>Abstract</p> <p>Background</p> <p>African policy-makers are increasingly considering abolishing user fees as a solution to improve access to health care systems. There is little evidence on this subject in West Africa, and particularly in countries that have organized their healthcare system on the basis of the Bamako Initiative. This article presents a process evaluation of an NGO intervention to abolish user fees in Niger for children under five years and pregnant women.</p> <p>Methods</p> <p>The intervention was launched in 2006 in two health districts and 43 health centres. The intervention consisted of abolishing user fees and improving the quality of services (drugs, ambulance, etc.). We carried out a process evaluation in April 2007 using qualitative and quantitative data. Three data collection methods were used: i) individual in-depth interviews (n = 85) and focus groups (n = 8); ii) participant observation in 12 health centres; and iii) self-administered structured questionnaires (n = 51 health staff).</p> <p>Results</p> <p>The population favoured abolition; health officials and local decision-makers were in favour, but they worried about its sustainability. Among health workers, opposition to providing free services was more widespread. The strengths of the process were: a top-down phase of information and raising community awareness; appropriate incentive measures; a good drug supply system; and the organization of a medical evacuation system. The major weaknesses of the process were: the perverse effects of incentive bonuses; the lack of community-based management committees' involvement in the management; the creation of a system running in parallel with the BI system; the lack of action to support the service offer; and the poor coordination of the availability of free services at different levels of the health pyramid. Some unintended outcomes are also documented.</p> <p>Conclusion</p> <p>The linkages between systems in which some patients pay (Bamako Initiative) and some do not should be carefully considered and organized in accordance with the local reality. For the poorest patients to really benefit, it is essential that, at the same time, the quality of services be improved and mechanisms be put in place to prevent abuses. Much remains to be done to generate knowledge on the processes for abolishing fees in West Africa.</p

Prediction and Analysis of Ground Stops with Machine Learning

A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common type of delays. Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops (GS), issued at affected airports. Ground Stops are implemented to control air traffic volume to specific airports where the projected traffic demand is expected to exceed the airports’ acceptance rate over a short period of time due to conditions such as inclement weather, volume constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic Management Initiative (TMI), particularly because all flights destined to affected airports are grounded until conditions improve. Efforts have been made over the years to reduce the impact of Traffic Management Initiatives on airports and flight operations. However, these efforts have largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP), due to their frequency and duration compared to Ground Stops. Limited work has also been carried out on Ground Stops because of the limited amount of time that traffic management personnel often have between planning and implementing Ground Stops and external factors that influence decisions of traffic management personnel. Consequently, this research primarily focuses on the prediction of weather-related Ground Stops at Newark Liberty International (EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors that influence their occurrence. It is expected that this research will provide stakeholders with further insights into factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by benchmarking Machine Learning algorithms in order to identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning algorithms to predict the occurrence of weather-related Ground Stops. The performance of these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR and LGA. Further analysis also revealed that model performance is significantly better when using balanced datasets compared to imbalanced datasets

The Summer Heat Balance of the Oregon Inner Shelf Over 2 Decades: Intraseasonal Variability

This study examines the heat balance of the inner shelf along the US West Coast using 14 years of summer temperature and velocity observations in 15 m water depth. Previous work at this site found no year-to-year variability in the observed mean summer temperature change despite significant warming due to surface heat flux and variable cooling due to across-shelf heat flux. Here, the processes that affect coastal water temperatures over time scales of days and months are investigated. Synoptic temperature variability (on time scales of several days) was predominantly due to the across-shelf heat flux, as there was little synoptic variability in the surface heating and no evidence that an along-shelf heat flux would close the two-dimensional heat budget when the residual was large. The analyses suggest the across-shelf heat flux was influenced by multiple mechanisms in addition to Ekman transport by along-shelf winds. For example, there was a three-layer vertical across-shelf velocity structure 40% ± 10% of the time and during downwelling-favorable winds there was an upwelling circulation 80% of the time. The across-shelf heat flux was generally highest near the surface and bottom boundaries, where the velocity observations were most limited. The heat budget residual was positive most of the time, likely resulting from uncertainty due to the observational limitations. An improved understanding of the processes that control the across-shelf heat flux and buffer temperature variability in the coastal ocean has the potential to improve predictions of coastal temperatures under climate change scenarios