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There has historically been very little data on cycling in Australia. This lack of data has made it difficult to track whether cycling has changed over a long period of time. The number of cycling trips per day per person increased by 25.1% from the Day-to-Day Travel in Australia 1985/86 Survey to the 2011 National Cycling Participation Survey, while the Australian population 9 years of age and older has increased by 58.5%. The crude rate estimates a 20% reduction in cycling relative to population; however, this analysis does not account for changing Australian demographics during that time. When the rates of cycling are age-sex standardised, cycling trips in Australia increased by an estimated 11.0% (95% CI: 10.8%, 11.1%). The estimated increases in cycling trips, both in raw numbers and age-sex adjusted rates, support increased investments in cycling in Australia.

AimsThe primary aims were to determine the rate of potential drug-drug interactions (pDDIs) in patients with nasally placed feeding tubes (NPFT) and the factors significantly associated with pDDIs. The secondary aim was to assess the change in pDDIs for patients between admission and discharge.Material and methodsThis multicentre study applied a cross-sectional design and was conducted in six Brazilian hospitals, from October 2016 to July 2018. Data from patients with NPFT were collected through electronic forms. All regular medications prescribed were recorded. Medications were classified according to the World Health Organization (WHO) Anatomical Therapeutic Chemical code. Drug-drug interaction screening software was used to screen patients' medications for pDDIs. Negative binomial regression was used to account for the over dispersed nature of the pDDI count. Since the number of pDDIs was closely related to the number of prescribed medications, we modelled the rate of pDDIs with the count of pDDIs as the numerator and the number of prescribed medications as the denominator; six variables were considered for inclusion: time (admission or discharge), patient age, patient gender, age-adjusted Charlson Comorbidity Index (CCI) score, type of prescription (electronic or handwritten) and patient care complexity. To account for correlation within the two time points (admission and discharge) for each patient a generalised estimating equations approach was used to adjust the standard error estimates. To test the change in pDDI rate between admission and discharge a full model of six variables was fitted to generate an adjusted estimate.ResultsIn this study, 327 patients were included. At least one pDDI was found in more than 91% of patients on admission and discharge and most of these pDDIs were classified as major severity. Three factors were significantly associated with the rate of pDDIs per medication: patient age, patient care complexity and prescription type (handwritten vs electronic). There was no evidence of a difference in pDDI rate between admission and discharge.ConclusionPatients with a NPFT are at high risk of pDDIs. Drug interaction screening tools and computerized clinical decision support systems could be effective risk mitigation strategies for this patient group.