BackgroundThe Pharmaceutical Benefits Scheme (PBS) is Australia’s national drug subsidy program. This paper provides a practical guide to researchers using PBS data to examine prescribed medicine use.FindingsExcerpts of the PBS data collection are available in a variety of formats. We describe the core components of four publicly available extracts (the Australian Statistics on Medicines, PBS statistics online, section 85 extract, under co-payment extract). We also detail common analytical challenges and key issues regarding the interpretation of utilisation using the PBS collection and its various extracts.ConclusionsResearch using routinely collected data is increasing internationally. PBS data are a valuable resource for Australian pharmacoepidemiological and pharmaceutical policy research. A detailed knowledge of the PBS, the nuances of data capture, and the extracts available for research purposes are necessary to ensure robust methodology, interpretation, and translation of study findings into policy and practice.

Background Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues. Methods We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results. Results We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data. Conclusions ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

BackgroundFrom January 30-February 6, 2011, New South Wales was affected by an exceptional heat wave, which broke numerous records. Near real-time Emergency Department (ED) and ambulance surveillance allowed rapid detection of an increase in the number of heat-related ED visits and ambulance calls during this period. The purpose of this study was to quantify the excess heat-related and all-cause ED visits and ambulance calls, and excess all-cause mortality, associated with the heat wave.MethodsED and ambulance data were obtained from surveillance and administrative databases, while mortality data were obtained from the state death registry. The observed counts were compared with the average counts from the same period from 2006/07 through 2009/10, and a Poisson regression model was constructed to calculate the number of excess ED visits, ambulance and deaths after adjusting for calendar and lag effects.ResultsDuring the heat wave there were 104 and 236 ED visits for heat effects and dehydration respectively, and 116 ambulance calls for heat exposure. From the regression model, all-cause ED visits increased by 2% (95% CI 1.01-1.03), all-cause ambulance calls increased by 14% (95% CI 1.11-1.16), and all-cause mortality increased by 13% (95% CI 1.06-1.22). Those aged 75 years and older had the highest excess rates of all outcomes.ConclusionsThe 2011 heat wave resulted in an increase in the number of ED visits and ambulance calls, especially in older persons, as well as an increase in all-cause mortality. Rapid surveillance systems provide markers of heat wave impacts that have fatal outcomes.

There has been a dramatic increase in pregabalin use, poisonings and deaths in Australia since it became subsidized publicly in 2013. One in seven Australians dispensed pregabalin appears to be at high risk of misuse.

Background and aims Globally, codeine is the most‐used opioid. In December 2016, Australia announced that low‐strength codeine (≤ 15 mg) would be re‐scheduled and no longer available for purchase over‐the‐counter; this was implemented in February 2018. We aimed to evaluate the effect of this scheduling change on codeine misuse and use and misuse of other opioids. Design and setting Interrupted time–series analysis of monthly opioid exposure calls to New South Wales Poisons Information Centre (NSWPIC, captures 50% of Australia's poisoning calls), January 2015– January 2019 and monthly national codeine sales, March 2015–March 2019. We incorporated a washout period (January 2017 – January 2018) between the announcement and implementation, when prescriber/consumer behaviour may have been influenced. Participants Intentional opioid overdoses resulting in a call to NSWPIC. Measurements We used linear segmented regression to identify abrupt changes in level and slope of fitted lines. Codeine poisonings and sales were stratified into high strength (> 15 mg per dose unit) and low strength (≤ 15 mg). Only low‐strength formulations were re‐scheduled. Findings We observed an abrupt −50.8 percentage [95% confidence interval (CI) = −79.0 to −22.6%] level change in monthly codeine‐related poisonings and no change in slope in the 12 months after February 2018. There was no increase in calls to the NSWPIC for high‐strength products, level change: –37.2% (95% CI = −82.3 to 8%) or non‐codeine opioids, level change: –4.4% (95% CI = −33.3 to 24.4%). Overall, the re‐scheduling resulted in a level change in opioid calls of −35.8% calls/month (95% CI = −51.2 to −20.4%). Low‐strength codeine sales decreased by 87.3% (95% CI = −88.5 to −85.9%), with no increase in high‐strength codeine sales in the 14 months following re‐scheduling, −4.0% (95% CI = −19.6 to 14.6%). Conclusions Codeine re‐scheduling in Australia appears to have reduced codeine misuse and sales.