Revisions of the International Classification of Diseases (ICD) can lead to biases in cause-specific mortality levels and trends. We propose a novel time series approach to bridge ICD coding changes which provides a consistent solution across causes of death. Using a state space model with interventions, we performed time series analysis to cause-proportional mortality for ICD9 and ICD10 in the Netherlands (1979–2010), Canada (1979–2007) and Italy (1990–2007) on chapter level. A constraint was used to keep the sum of cause-specific interventions zero. Comparability ratios (CRs) were estimated and compared to existing bridge coding CRs for Italy and Canada. A significant ICD9 to ICD10 transition occurred among 13 cause of death groups in Italy, 7 in Canada and 3 in the Netherlands. Without the constraint, all-cause mortality after the classification change would be overestimated by 0.4 % (NL), 0.03 % (Canada) and 0.2 % (Italy). The time series CRs were in the same direction as the bridge coding CRs but deviated more from 1. A smooth corrected trend over the ICD-transition resulted from applying the time series approach. Comparing the time series CRs for Italy (2003), Canada (1999) and the Netherlands (1995) revealed interesting commonalities and differences. We demonstrated the importance of adding the constraint, the validity of our methodology and its advantages above earlier methods. Applying the method to more specific causes of death and integrating medical content to a larger extent is advocated.

The allergological relevance of storage mites has been under discussion for the last 25 years. In humid homes, these mites will feed almost exclusively on fungi and may produce allergenic or irritating substances different from those arising on protein-rich laboratory media used in allergen extract production or present in carpets, bedding and furniture.

One hundred years after the discovery of acetylcholine (ACh) by Otto Lowei, ACh receptors, transporters and synthesizing and degrading enzymes became well-recognized contributors to cognition, neuromuscular, metabolic and immune processes. However, newer technologies identified unexpected molecular controllers over ACh signaling, including the SLEEPLESS, Isl1 and Lynx1 genes. These regulators are responsible, among other effects to the fine-tuned identity, functioning modes, dynamics and inter-cellular interactions of cholinergic cell types in and out of the brain, changing our understanding of ACh’s roles in human health and wellbeing. Furthermore, Genome-Wide Association Studies identify new disease-associated mutations and single nucleotide polymorphisms in coding and non-coding sequences within these genes. These discoveries add autism, amyotrophic lateral sclerosis, acute cardiac events, narcolepsy and obesity to the established acquired and inherited neuromuscular, stress-induced, dementia and epilepsy disorders that were traditionally associated with impaired ACh functioning. At the molecular level, cholinergic signaling involves both up- and down-regulation events of transcription, epigenetic modulations, alternative splicing and microRNA suppression that together coordinate the multi-targeted ACh signaling in brain and body functions and are also responsible to the reactions of patients to anti-cholinesterase therapeutics of Alzheimer’s disease as well as to global exposure to agricultural pesticides and to individual tendencies for nicotine addiction, calling for new basic and translational research venues for regulating ACh signaling. Integrating these molecular ACh regulators into every discussion of cholinergic issues, should incorporate data obtained by clinicians and molecular geneticists, neuroscientists and structural biochemists over the past decades into a refreshed look at the intricate checks and balances over cholinergic signaling. Our understanding of the cholinergic regulators is incomplete, but time is ripe to summarize the recent reports on checks and balances of cholinergic signaling and their implications in health and disease.

Drivers are factors that have the potential to directly or indirectly influence the likelihood of infectious diseases emerging or re-emerging. It is likely that an emerging infectious disease (EID) rarely occurs as the result of only one driver; rather, a network of sub-drivers (factors that can influence a driver) are likely to provide conditions that allow a pathogen to (re-)emerge and become established. Data on sub-drivers have therefore been used by modellers to identify hotspots where EIDs may next occur, or to estimate which sub-drivers have the greatest influence on the likelihood of their occurrence. To minimise error and bias when modelling how sub-drivers interact, and thus aid in predicting the likelihood of infectious disease emergence, researchers need good-quality data to describe these sub-drivers.This study assesses the quality of the available data on sub-drivers of West Nile virus against various criteria as a case study. The data were found to be of varying quality with regard to fulfilling the criteria.The characteristic with the lowest score was completeness, i.e. where sufficient data are available to fulfil all the requirements for the model. This is an important characteristic as an incomplete data set could lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good-quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and identifying the points on the risk pathway where preventive measures may be taken.