Measuring change in prescription drug utilization in Australia

• Published in: Pharmacoepidemiology and drug safety 2006-07, Vol.15 (7), p.477-484
• Main Authors: Mandryk, John A., Mackson, Judith M., Horn, Fiona E., Wutzke, Sonia E., Badcock, Caro-Anne, Hyndman, Rob J., Weekes, Lynn M.
• Format: Article
• Language: English
• Subjects: antibiotics; Australia; claims data; Drug Prescriptions; Drug Utilization - trends; evaluation; Humans; Linear Models; longitudinal study; Physicians, Family; regression methods; seasonality; Seasons; time series
• ISSN: 1053-8569; 1099-1557
• DOI: 10.1002/pds.1247

Purpose The National Prescribing Service Ltd (NPS) aims to improve prescribing and use of medicines consistent with evidence‐based best practice. This report compares two statistical methods used to determine whether multiple educational interventions influenced antibiotic prescription in Australia. Methods Monthly data (July 1996 to June 2003) were obtained from a national claims database. The outcome measures were the median number of antibiotic prescriptions per 1000 consultations for each general practitioner (GP) each month, and the mean proportion (across GPs) of each subgroup of antibiotics (e.g. roxithromycin) out of nine antibiotics having primary use for upper respiratory tract infection. Two approaches were used to investigate shifts in prescribing: augmented regression, which included seasonality, autocorrelation and one intervention; and seasonally adjusted piecewise linear dynamic regression, which removed seasonality prior to modelling, included several interventions, GP participation and autocorrelated errors. Both methods are variations of piecewise linear regression modelling. Results Both approaches described a similar decrease in rates, with a non‐significant change after the first intervention. The inclusion of more interventions and GP participation made no difference. Using roxithromycin as an example of the analyses of proportions, both approaches implied that after the first intervention the proportion decreased significantly. The statistical significance of this intervention disappears when other interventions are included. Conclusions The two analyses provide results which agree regarding the possible impact of the NPS interventions, but raise questions about what is the best way to model drug utilization, particularly regarding whether to include all intervention terms when they belong to an extended roll‐out of related interventions. Copyright © 2006 John Wiley & Sons, Ltd.