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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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

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

Working practices and incomes of health workers : evidence from an evaluation of a delivery fee exemption scheme in Ghana

Background: This article describes a survey of health workers and traditional birth attendants (TBAs) which was carried out in 2005 in two regions of Ghana. The objective of the survey was to ascertain the impact of the introduction of a delivery fee exemption scheme on both health workers and those providers who were excluded from the scheme (TBAs). This formed part of an overall evaluation of the delivery fee exemption scheme. The results shed light not only on the scheme itself but also on the general productivity of a range of health workers in Ghana. Methods: A structured questionnaire was developed, covering individual and household characteristics, working hours and practices, sources of income, and views of the exemptions scheme and general motivation. After field testing, this was administered to 374 respondents in 12 districts of Central and Volta regions. The respondents included doctors, medical assistants (MAs), public and private midwives, nurses, community health nurses (CHNs), and traditional birth attendants, both trained and untrained. Results: Health workers were well informed about the delivery fee exemptions scheme and their responses on its impact suggest a realistic view that it was a good scheme, but one that faces serious challenges regarding financial sustainability. Concerning its impact on their morale and working conditions, the responses were broadly neutral. Most public sector workers have seen an increased workload, but counterbalanced by increased pay. TBAs have suffered, in terms of client numbers and income, while the picture for private midwives is mixed. The survey also sheds light on pay and productivity. The respondents report long working hours, with a mean of 54 hours per week for community nurses and up to 129 hours per week for MAs. Weekly reported client loads in the public sector range from a mean of 86 for nurses to 269 for doctors. Over the past two years, reported working hours have been increasing, but so have pay and allowances (for doctors, allowances now make up 66% of their total pay). The lowest paid public health worker now earns almost ten times the average gross national income (GNI) per capita, while the doctors earn 38.5 times GNI per capita. This compares well with average government pay of four times GNI per capita. Comparing pay with outputs, the relatively high number of clients reported by doctors reduces their pay differential, so that the cost per client – $1.09 – is similar to a nurse's (and lower than a private midwife's). Conclusion: These findings show that a scheme which increases demand for public health services while also sustaining health worker income and morale, is workable, if well managed, even within the relatively constrained human resources environment of countries like Ghana. This may be linked to the fact that internal comparisons reveal Ghana's health workers to be well paid from public sector sources.This work was undertaken as part of an international research programme – IMMPACT (Initiative for Maternal Mortality Programme Assessment) – funded by the Bill & Melinda Gates Foundation, the Department for International Development, the European Commission and USAID