Abolishing user fees for children and pregnant women trebled uptake of malaria-related interventions in Kangaba, Mali.

Malaria is the most common cause of morbidity and mortality in children under 5 in Mali. Health centres provide primary care, including malaria treatment, under a system of cost recovery. In 2005, Médecins sans Frontieres (MSF) started supporting health centres in Kangaba with the provision of rapid malaria diagnostic tests and artemisinin-based combination therapy. Initially MSF subsidized malaria tests and drugs to reduce the overall cost for patients. In a second phase, MSF abolished fees for all children under 5 irrespective of their illness and for pregnant women with fever. This second phase was associated with a trebling of both primary health care utilization and malaria treatment coverage for these groups. MSF's experience in Mali suggests that removing user fees for vulnerable groups significantly improves utilization and coverage of essential health services, including for malaria interventions. This effect is far more marked than simply subsidizing or providing malaria drugs and diagnostic tests free of charge. Following the free care strategy, utilization of services increased significantly and under-5 mortality was reduced. Fee removal also allowed for more efficient use of existing resources, reducing average cost per patient treated. These results are particularly relevant for the context of Mali and other countries with ambitious malaria treatment coverage objectives, in accordance with the United Nations Millennium Development Goals. This article questions the effectiveness of the current national policy, and the effectiveness of reducing the cost of drugs only (i.e. partial subsidies) or providing malaria tests and drugs free for under-5s, without abolishing other related fees. National and international budgets, in particular those that target health systems strengthening, could be used to complement existing subsidies and be directed towards effective abolition of user fees. This would contribute to increasing the impact of interventions on population health and, in turn, the effectiveness of aid

Studying complex interventions : reflections from the FEMHealth project on evaluating fee exemption policies in West Africa and Morocco

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I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

The open streets initiative "opens" streets to pedestrians and bicyclists by closing them to cars and trucks. The initiative, adopted by many cities across North America, increases community space in urban environments. But could open streets also make cities safer and less congested? We study this question by framing the choice of which streets to open as a reinforcement learning problem. In order to simulate the impact of opening streets, we first compare models for predicting vehicle collisions given network and temporal data. We find that a recurrent graph neural network, leveraging the graph structure and the short-term temporal dependence of the data, gives the best predictive performance. Then, with the ability to simulate collisions and traffic, we frame a reinforcement learning problem to find which streets to open. We compare the streets in the NYC Open Streets program to those proposed by a Q-learning algorithm. We find that the streets proposed by the Q-learning algorithm have reliably better outcomes, while streets in the program have similar outcomes to randomly selected streets. We present our work as a step toward principally choosing which streets to open for safer and less congested cities. All our code and data are available on Github.Comment: camera ready for AAAI 202

Quality Improvement Analysis of the Air Force Aircraft Maintenance and Munitions Officers Course

Previous research concerning aircraft maintenance officer training focused on the content of the training curriculum of the Aircraft Maintenance and Munitions Officers Course AMMOC. Conversely, this study sought improvement in aircraft maintenance officer training by evaluating the guidance and support provided to AMMOC. Two methods were employed for identifying such opportunities. First, a descriptive model of the training system supporting AMMOC was developed and analyzed. Second, feedback was solicited from AMMOC instructors through the use of a two-round Delphi. The Delphi was employed to develop a consensus among the instructors regarding what improvement opportunities existed and potential means for taking advantage of these opportunities. Findings indicate that AMMOC may be improved by facilitating better communication between AMMOC, its customers, and other organizations empowering AMMOC instructors with more control over the course training standard CTS, student scheduling, and customer feedback and providing instructors more time for course development by assessing and satisfying AMMOCs manpower requirements and refining training development and manning policies

Applications of the Quantum Algorithm for st-Connectivity

We present quantum algorithms for various problems related to graph connectivity. We give simple and query-optimal algorithms for cycle detection and odd-length cycle detection (bipartiteness) using a reduction to st-connectivity. Furthermore, we show that our algorithm for cycle detection has improved performance under the promise of large circuit rank or a small number of edges. We also provide algorithms for detecting even-length cycles and for estimating the circuit rank of a graph. All of our algorithms have logarithmic space complexity

A Local Search Algorithm for the Min-Sum Submodular Cover Problem

We consider the problem of solving the Min-Sum Submodular Cover problem using local search. The Min-Sum Submodular Cover problem generalizes the NP-complete Min-Sum Set Cover problem, replacing the input set cover instance with a monotone submodular set function. A simple greedy algorithm achieves an approximation factor of 4, which is tight unless P=NP [Streeter and Golovin, NeurIPS, 2008]. We complement the greedy algorithm with analysis of a local search algorithm. Building on work of Munagala et al. [ICDT, 2005], we show that, using simple initialization, a straightforward local search algorithm achieves a $(4+\epsilon)$-approximate solution in time $O(n^3\log(n/\epsilon))$, provided that the monotone submodular set function is also second-order supermodular. Second-order supermodularity has been shown to hold for a number of submodular functions of practical interest, including functions associated with set cover, matching, and facility location. We present experiments on two special cases of Min-Sum Submodular Cover and find that the local search algorithm can outperform the greedy algorithm on small data sets

Robust and Space-Efficient Dual Adversary Quantum Query Algorithms

The general adversary dual is a powerful tool in quantum computing because it gives a query-optimal bounded-error quantum algorithm for deciding any Boolean function. Unfortunately, the algorithm uses linear qubits in the worst case, and only works if the constraints of the general adversary dual are exactly satisfied. The challenge of improving the algorithm is that it is brittle to arbitrarily small errors since it relies on a reflection over a span of vectors. We overcome this challenge and build a robust dual adversary algorithm that can handle approximately satisfied constraints. As one application of our robust algorithm, we prove that for any Boolean function with polynomially many 1-valued inputs (or in fact a slightly weaker condition) there is a query-optimal algorithm that uses logarithmic qubits. As another application, we prove that numerically derived, approximate solutions to the general adversary dual give a bounded-error quantum algorithm under certain conditions. Further, we show that these conditions empirically hold with reasonable iterations for Boolean functions with small domains. We also develop several tools that may be of independent interest, including a robust approximate spectral gap lemma, a method to compress a general adversary dual solution using the Johnson-Lindenstrauss lemma, and open-source code to find solutions to the general adversary dual