The Health and Productivity Burden of Migraines in Australia.

OBJECTIVE: This study aimed to quantify the health and productivity burden of migraines in Australia, measured by quality-adjusted life years (QALYs), productivity-adjusted life years (PALYs, a novel measure of productivity), and associated health-care and broader economic costs. METHODS: A Markov state-transition model was constructed to simulate follow-up of Australians aged 20-64 years over the next 10 years. The model was first run using current prevalence estimates of migraine. It was then rerun assuming that people with migraine hypothetically did not have the condition. Differences in outcomes between the 2 model simulations represented the health and productivity burden attributable to migraine. All data inputs were obtained from published sources. Gross domestic product (GDP) per equivalent full-time worker in Australia was used to reflect the cost of each PALY (AU$177,092). Future costs and outcomes were discounted by 5% annually. RESULTS: Currently, 1,274,319 million (8.5%) Australians aged 20-64 years have migraine. Over the next 10 years, migraine was predicted to lead to a loss of 2,577,783 (95% confidence interval [CI] 2,054,980 to 3,000,784) QALYs among this cohort (2.02 per person and 2.43% of total QALYs), and AU$1.67 (95% CI $1.16 to $2.37) billion in health-care costs (AU$1313 per person, 95% CI $914 to $1862). There would also be 384,740 (95% CI 299,102 to 479,803) PALYs lost (0.30 per person and 0.53% of total PALYs), resulting in AU$68.13 (95% CI $44.42 to $98.25) billion of lost GDP (AU$53,467 per person, 95% CI $34,855 to $77,102). CONCLUSION: Migraines impose a substantial health and economic burden on Australians of working age. Funding interventions that reduce the prevalence of migraines and/or its effects are likely to provide sound return on investment.

The role of Abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humans.

BACKGROUND: We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study. METHODS AND FINDINGS: A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered. CONCLUSIONS: ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity.

EmptyHeaded: A Relational Engine for Graph Processing.

There are two types of high-performance graph processing engines: low- and high-level engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures and computation models but require users to write low-level imperative code, hence ensuring that efficiency is the burden of the user. In high-level engines, users write in query languages like datalog (SociaLite) or SQL (Grail). High-level engines are easier to use but are orders of magnitude slower than the low-level graph engines. We present EmptyHeaded, a high-level engine that supports a rich datalog-like query language and achieves performance comparable to that of low-level engines. At the core of EmptyHeaded's design is a new class of join algorithms that satisfy strong theoretical guarantees but have thus far not achieved performance comparable to that of specialized graph processing engines. To achieve high performance, EmptyHeaded introduces a new join engine architecture, including a novel query optimizer and data layouts that leverage single-instruction multiple data (SIMD) parallelism. With this architecture, EmptyHeaded outperforms high-level approaches by up to three orders of magnitude on graph pattern queries, PageRank, and Single-Source Shortest Paths (SSSP) and is an order of magnitude faster than many low-level baselines. We validate that EmptyHeaded competes with the best-of-breed low-level engine (Galois), achieving comparable performance on PageRank and at most 3× worse performance on SSSP.