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Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015, and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies. In this study, the authors present an analysis of the malaria burden in sub-Saharan Africa between 2000 and 2015, and quantify the effects of the interventions that have been implemented to combat the disease; they find that the prevalence of Plasmodium falciparum infection has been reduced by 50% since 2000 and the incidence of clinical disease by 40%, and that interventions have averted approximately 663 million clinical cases since 2000, with insecticide-treated bed nets being the largest contributor. Malaria control measures assessed In one of the largest public health campaigns in history, a concerted malaria control campaign has been under way in sub-Saharan Africa for the past 15 years. Billions of dollars have been invested to provide interventions such as bed nets and antimalarial drugs but the overall effect on malaria burden remains unclear. This study uses field data from 30,000 population clusters in a sophisticated space–time modelling framework to quantify the changing Plasmodium falciparum risk (a 40% decline in case incidence since 2000) and the role of malaria interventions (around 700 million cases averted). Although below target levels, the current campaign has substantially reduced the incidence of malaria across the continent. Continued success will depend upon increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance.

This paper describes (i). how a national health information System was designed, tested and implemented in Papua New Guinea, (ii). how the system was integrated with other management information systems, and (iii). how information has been used to support decision-making. It concludes that central coordination of systems design is essential to make sure that information systems are aligned with government priorities and can deliver the information required by managers. While there is often scope for improving the performance of existing information systems, too much emphasis can be placed on revising data collection procedures and creating the perfect information system. Data analysis, even from imperfect systems, can stimulate greater interest in information, which can improve the quality and completeness of reporting and encourage a more methodical approach to planning and monitoring services. Our experience suggests that senior decision-makers and political leaders can play an important role in creating a culture of information use. By demanding health information, using it to formulate policy, and disseminating it through the channels open to them, they can exert greater influence in negotiations with donors and other government departments, encourage a more rational approach to decision-making that will improve the operation of health services, and stimulate greater use of information at lower levels of the health system. The ability of information systems to deliver these benefits is critical to their sustainability.

[...] confirmation bias might have led to overdiagnosis of malaria among fever deaths in endemic areas; coding physicians were aware of the state in which each death occurred. [...] although N K Shah and colleagues suggest a case-fatality rate of only 0.1-0.3% for P falciparum, the true risk of death in rural untreated individuals with fever due to P falciparum could well be an order of magnitude higher.

Mark DePristo and colleagues report an analytical framework to discover and genotype variation using whole exome and genome resequencing data from next-generation sequencing technologies. They apply these methods to low-pass population sequencing data from the 1000 Genomes Project. Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

Mutations in the genes encoding isocitrate dehydrogenase, IDH1 and IDH2 , have been reported in gliomas, myeloid leukemias, chondrosarcomas and thyroid cancer. We discovered IDH1 and IDH2 mutations in 34 of 326 (10%) intrahepatic cholangiocarcinomas. Tumor with mutations in IDH1 or IDH2 had lower 5-hydroxymethylcytosine and higher 5-methylcytosine levels, as well as increased dimethylation of histone H3 lysine 79 (H3K79). Mutations in IDH1 or IDH2 were associated with longer overall survival ( P =0.028) and were independently associated with a longer time to tumor recurrence after intrahepatic cholangiocarcinoma resection in multivariate analysis ( P =0.021). IDH1 and IDH2 mutations were significantly associated with increased levels of p53 in intrahepatic cholangiocarcinomas, but no mutations in the p53 gene were found, suggesting that mutations in IDH1 and IDH2 may cause a stress that leads to p53 activation. We identified 2309 genes that were significantly hypermethylated in 19 cholangiocarcinomas with mutations in IDH1 or IDH2 , compared with cholangiocarcinomas without these mutations. Hypermethylated CpG sites were significantly enriched in CpG shores and upstream of transcription start sites, suggesting a global regulation of transcriptional potential. Half of the hypermethylated genes overlapped with DNA hypermethylation in IDH1 -mutant gliobastomas, suggesting the existence of a common set of genes whose expression may be affected by mutations in IDH1 or IDH2 in different types of tumors.

CLL is a heterogeneous disease with a variable clinical course and response to therapy. New genetic lesions have been noted in subgroups of patients through whole-exome and whole-genome sequencing. An abnormality in RNA splicing has been found in 15% of patients. Chronic lymphocytic leukemia is an incurable disease characterized by extensive clinical heterogeneity despite a common diagnostic immunophenotype (surface expression of CD19+, CD20+dim, CD5+, CD23+, and sIgMdim). Whereas the course of disease is indolent in some patients, it is steadily progressive in approximately half of patients, leading to substantial morbidity and mortality. 1 Our ability to predict a more aggressive disease course has improved with the use of tests for biologic markers (degree of somatic hypermutation in the variable region of the immunoglobulin heavy chain [ IGHV ] gene and expression of ZAP70) and the detection of cytogenetic abnormalities (deletions . . .

Genomic analysis of tumours has led to the identification of hundreds of cancer genes on the basis of the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here we perform deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters. Clear signals are found in the promoters of three genes. FOXA1 , a known driver of hormone-receptor positive breast cancer, harbours a mutational hotspot in its promoter leading to overexpression through increased E2F binding. RMRP and NEAT1 , two non-coding RNA genes, carry mutations that affect protein binding to their promoters and alter expression levels. Our study shows that promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions. Power analyses indicate that more such regions remain to be discovered through deep sequencing of adequately sized cohorts of patients. High-depth sequencing of targeted regions in primary breast cancer identifies mutated promoter elements with recurrent mutations at specific and/or nearby bases, suggesting selection of certain non-coding events. Non-coding driver mutations for breast cancer Many cancer genomic studies have characterized the landscape of driver mutations in protein-coding regions, but there has been limited exploration of non-coding regions. Gad Getz and colleagues searched for significantly mutated regulatory regions with high-depth sequencing of targeted regions in 360 primary breast cancers. They identified significantly mutated promoter elements associated with nine genes, and found recurrent mutations at specific and/or nearby bases, suggesting targeting of a specific element. For three of these genes, FOXA1 , RMRP and NEAT1 , they show that these recurrent promoter-proximal mutations influence gene expression and protein binding affinity. The authors suggest that further deep sequencing studies in larger cohorts could identify more as yet undiscovered promoter regions.