Patient and cost data were obtained directly from participating local (ASL) and regional (ASSR) healthcare authorities collecting computerised prescription records for a two year period, from January 2004 to December 2005. In particular, the demographic database provided information on subjects' date of birth, sex and healthcare identification number, while prescription data (including patient's name and healthcare identification number, date of issue, name of prescribing physician, brand name of the drug(s) prescribed, generic name, ATC classification, cost and patient's co-payment) was extracted from the territorial pharmaceutical database.
All personal data (name and identification number) were replaced by a univocal numerical code, making both databases anonymous at source in strict compliance with the Italian Privacy Law (Decree 196, 30/06/2003). The study design, observational and retrospective in nature, did not require a previous informed consent from the subjects included (Decree 196/03, art. 110).
The univocal numerical code, attributed to all subjects included in the analysis, made it possible to retrospectively match demographic patient's data with individual prescription costs.
This study was based on data from a 12 month period, as complete patient and cost data from all sources were available from October 2004 to September 2005. No major change in pharmaceutical demand or supply took place during the observation period. The total public expenditure for pharmaceuticals was reported to be €12.4 billion in 2005 and €12.6 in 2004 
The ASSET sample, collected from the ASL of Monza, the ASSR Marche and the ASSR Basilicata, totalled 3,175,691 residents.
shows the number of subjects and the percentage in each age/sex group for the sample and for the 2005 population estimates provided by the Italian National Institute of Statistics (ISTAT) 
Distribution of ASSET clusters compared to the distribution of Italian population (ISTAT, 2005)
Using the 2005 ISTAT figures as a base (see ), the difference between the sample and the expected distribution of population could be calculated using a chi-square goodness of fit test. The statistic was 17,064 with 15 degrees of freedom, which was highly significant (p
0.000). The null hypothesis that the ASSET sample and the Italian population had a similar age/sex distribution could be rejected. The most significant differences were in both tails of the distribution: the ASSET model had a larger number of subjects aged <14 and >75 compared to the distribution of population. Although the differences in are highly statistically significant, this is largely a function of the number of patients in each cell of the table. The absolute differences between the ASSET population and the Italian population are small: similarly to STAR-PUs, since the ARTE weightings were derived from the cost and number of items per patient, rather than from the total costs and number of items, this should not necessarily affect the quality of the measures 
Individual cost data referred to the pharmaceutical spending over the 12-month period between October 2004 and September 2005. Pharmaceutical spending was defined as the total individual cost for reimbursed drugs only (class A), dispensed by retail pharmacies (not including hospital consumption), at actual prices including co-payment. During the observation period, co-payment was limited to a fixed prescription fee amounting to €1/€2 depending on the number of items per prescription. In 2005, total co-payments amounted to 3.8% of total prescribing costs 
. Excluded from the analysis were special drugs dispensed from hospital pharmacies and out-of-pocket expenses for non-reimbursed drugs (class C).
The average prescribing costs by age group were calculated simply by dividing the total pharmaceutical cost per age group by the total number of subjects in the same age bracket. Note that the number of subjects was the number registered in the demographic database and not the number of persons receiving prescriptions. Weights were obtained by dividing each average prescribing cost by the total average cost (€195.6). Let i
be the age/sex group,
The ASSET weighting method was different from the one adopted by the British STAR/ASTRO-PUs, where each average cost by age group was divided by the average cost of the 0–4 age group. The choice of total average cost as a weighting constant was recommended by those Healthcare Administrators who presently do not have access to computerised patient records. While they cannot derive the average cost by individual age groups, they can still calculate the total average prescribing cost (total pharmaceutical expenditure divided by total assisted population). The constant known, they can derive a pro-forma age/sex weighted budget for each individual practice based on prior year prescribing costs.
The cost data reported in allow to easily calculating the weights consistently to the STAR/ASTRO-PUs formula. A strong caveat to any direct comparison between the two sets of weightings cannot be overemphasized, as they reflect different reimbursement and prescribing policies, drug prices, prescribing behaviours and observation periods.
ASSET's mean values by age group and standardised weights for overall prescribing.
The ASSET model grouped all patients into16 age groups, differently from the 18 weightings used by the STAR/ASTRO-PUs. The 0–4 and 5–14 age groups were aggregated into a single 0–14 cluster, since in Italy children under 15 years are mandatory seen by Paediatricians. reports the breakdown of the 0–14 age cluster into 0–4 and 5–14 separate groups (in italic), but the relative weightings were not utilised in the prescribing costs analysis.