Patients with multiple sclerosis acquire disability either through: (1) Relapse-associated worsening (RAW), or (2) progression independent of relapse activity (PIRA). This study addresses the relative contribution of relapses to disability worsening over the course of the disease, how early progression begins, and the extent to which multiple sclerosis therapies delay disability accumulation. Using the Novartis-Oxford MS (NO.MS) data pool spanning all multiple sclerosis phenotypes and pediatric multiple sclerosis, we evaluated ∼200,000 EDSS transitions from >27,000 patients with ≤15 years follow-up. We analyzed three datasets: (A) A full analysis dataset containing all observational and randomized controlled clinical trials in which disability and relapses were assessed (N = 27,328); (B) All phase 3 clinical trials (N = 8364); and (C) All placebo-controlled phase 3 clinical trials (N = 4970). We determined the relative importance of RAW and PIRA, investigated the role of relapses on all-cause disability worsening using Andersen-Gill models, and observed the impact of the mechanism of worsening and disease modifying therapies (DMTs) on the time to reach milestone disability levels using time continuous Markov models. PIRA started early in multiple sclerosis, occurred in all phenotypes, and became the principal driver of disability accumulation in the progressive phase of the disease. Relapses significantly increased the hazard of all-cause disability worsening events: Following a year in which relapses occurred (vs a year without relapses), the hazard increased by 31–48%; all p < 0.001. Pre-existing disability and older age were the principal risk factors for incomplete relapse recovery. For placebo-treated patients with minimal disability (EDSS 1) it took 8.95 years until increased limitation in walking ability (EDSS 4) and 18.48 years to require walking assistance (EDSS 6). Treating patients with DMTs delayed these times significantly by 3.51 years (95% confidence limit: 3.19, 3.96) and by 3.09 years (2.60, 3.72), respectively. In relapsing-remitting multiple sclerosis (RRMS), patients who worsened exclusively due to RAW events took a similar time to reach milestone EDSS values compared with those with PIRA events; the fastest transitions were observed in patients with PIRA and superimposed relapses. Our data confirm relapses contribute to the accumulation of disability, primarily early in multiple sclerosis. PIRA starts already in RRMS and becomes the dominant driver of disability accumulation as the disease evolves. Pre-existing disability and older age are the principal risk factors for further disability accumulation. Using DMTs delays disability accrual by years, with the potential to gain time being highest in the earliest stages of multiple sclerosis.

BackgroundInternet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region.MethodsOfficial Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models’ accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC).ResultsEach model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand.ConclusionsModels built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005–2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.

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