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Background Existing evidence on long-term ambient air pollution (AAP) exposure and risk of cardio-respiratory diseases in China is mainly on mortality, and based on area average concentrations from fixed-site monitors for individual exposures. Substantial uncertainty persists, therefore, about the shape and strength of the relationship when assessed using more personalised individual exposure data. We aimed to examine the relationships between AAP exposure and risk of cardio-respiratory diseases using predicted local levels of AAP. Methods A prospective study included 50,407 participants aged 30–79 years from Suzhou, China, with concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), fine (PM2.5), and inhalable (PM10) particulate matter, ozone (O3) and carbon monoxide (CO) and incident cases of cardiovascular disease (CVD) (n = 2,563) and respiratory disease (n = 1,764) recorded during 2013–2015. Cox regression models with time-dependent covariates were used to estimate adjusted hazard ratios (HRs) for diseases associated with local-level concentrations of AAP exposure, estimated using Bayesian spatio–temporal modelling. Results The study period of 2013–2015 included a total of 135,199 person-years of follow-up for CVD. There was a positive association of AAP, particularly SO2 and O3, with risk of major cardiovascular and respiratory diseases. Each 10 µg/m3 increase in SO2 was associated with adjusted hazard ratios (HRs) of 1.07 (95% CI: 1.02, 1.12) for CVD, 1.25 (1.08, 1.44) for COPD and 1.12 (1.02, 1.23) for pneumonia. Similarly, each 10 µg/m3 increase in O3 was associated with adjusted HR of 1.02 (1.01, 1.03) for CVD, 1.03 (1.02, 1.05) for all stroke, and 1.04 (1.02, 1.06) for pneumonia. Conclusions Among adults in urban China, long-term exposure to ambient air pollution is associated with a higher risk of cardio-respiratory disease.

Background There is an increasing demand by non-commercial funders that trialists should provide access to trial data once the primary analysis is completed. This has to take into account concerns about identifying individual trial participants, and the legal and regulatory requirements. Methods Using the good practice guideline laid out by the work funded by the Medical Research Council Hubs for Trials Methodology Research (MRC HTMR), we anonymised a dataset from a recently completed trial. Using this example, we present practical guidance on how to anonymise a dataset, and describe rules that could be used on other trial datasets. We describe how these might differ if the trial was to be made freely available to all, or if the data could only be accessed with specific permission and data usage agreements in place. Results Following the good practice guidelines, we successfully created a controlled access model for trial data sharing. The data were assessed on a case-by-case basis classifying variables as direct, indirect and superfluous identifiers with differing methods of anonymisation assigned depending on the type of identifier. A final dataset was created and checks of the anonymised dataset were applied. Lastly, a procedure for release of the data was implemented to complete the process. Conclusions We have implemented a practical solution to the data anonymisation process resulting in a bespoke anonymised dataset for a recently completed trial. We have gained useful learnings in terms of efficiency of the process going forward, the need to balance anonymity with data utilisation and future work that should be undertaken. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-2382-9) contains supplementary material, which is available to authorized users.

Reports of clinical trials often include adjusted analyses, which incorporate covariate data into the analysis model. Adjusting for covariates can increase the precision of treatment effect estimates and increase the power of statistical tests, without the need to increase sample size. In individually randomised trials, the main reason to adjust for a particular covariate is that it is expected to be strongly associated with the primary outcome. The larger the association between covariate and outcome, the greater the increase in power achieved from an adjusted analysis. A valid analysis of a cluster randomised trial (CRT) must take into account the clustered structure of the data, for example by using a mixed effects model. The selection of covariates for an adjusted analysis of a CRT is more complicated because covariates exist at both the cluster level and individual level. Further, adjustment for an individual level covariate can affect the residual variance of the outcome at both the cluster and individual levels. Using results from simulations, and some analytic investigation, I show how the size of clusters and the intra-cluster correlations of the covariate and outcome affect power and precision in adjusted analyses of CRTs using linear mixed effects models. I also consider logistic mixed effects models and show how adjusting for individual level or cluster level covariates affect what treatment effect is being estimated.

Ab initio molecular orbital calculations using the STO3-21G basis set has been carried out for the cluster series Na + , Na n , and Na − (wheren=2–7). The basis set is shown to be reliable compared with more extensive basis sets at the Hartree-Fock level. Thirty-one optimized structures are reported and discussed, many of which (especially for the anions) have not been considered. The STO3-21G//STO3-21G calculations suggest that for most of the species the optimum geometries are planar. In particular, the optimized structures for the anionic species should provide a starting point for more sophisticated configuration interaction calculations.

Ab initio molecular orbital calculations using the STO-6G and STO6-21G basis sets have been performed for the cluster series Li n + , Li n , and Li − (wheren=2–7). Thirty-two optimized structures are discussed and reported, many of which (especially for the anionic structures) have not yet been considered. The calculations suggest that for all three species the optimum geometries are planar. Of the two levels of theories that were investigated, STO-6G//STO-6G and STO6-21G//STO-6G, the latter hybrid theory was found to be less reliable. In particular, for the anionic structures these calculations should provide a platform from which more sophisticated, i.e., configuration interaction, geometry optimization can be performed.