Policies to Enhance Prescribing Efficiency in Europe: Findings and Future Implications

Abstract

Introduction: European countries need to learn from each other to address unsustainable increases in pharmaceutical expenditures. Objective: To assess the influence of the many supply and demand-side initiatives introduced across Europe to enhance prescribing efficiency in ambulatory care. As a result provide future guidance to countries. Methods: Cross national retrospective observational study of utilization (DDDs – defined daily doses) and expenditure (Euros and local currency) of proton pump inhibitors (PPIs) and statins among 19 European countries and regions principally from 2001 to 2007. Demand-side measures categorized under the “4Es” – education engineering, economics, and enforcement. Results: Instigating supply side initiatives to lower the price of generics combined with demand-side measures to enhance their prescribing is important to maximize prescribing efficiency. Just addressing one component will limit potential efficiency gains. The influence of demand-side reforms appears additive, with multiple initiatives typically having a greater influence on increasing prescribing efficiency than single measures apart from potentially “enforcement.” There are also appreciable differences in expenditure (€/1000 inhabitants/year) between countries. Countries that have not introduced multiple demand side measures to counteract commercial pressures to enhance the prescribing of generics have seen considerably higher expenditures than those that have instigated a range of measures. Conclusions: There are considerable opportunities for European countries to enhance their prescribing efficiency, with countries already learning from each other. The 4E methodology allows European countries to concisely capture the range of current demand-side measures and plan for the future knowing that initiatives can be additive to further enhance their prescribing efficiency.

Differences in utilization rates between commercial and administrative databases: implications for future health-economic and cross-national studies

“…Data sources for drug utilization (DU) studies include administrative databases, patient registries, electronic health records, and commercial databases [2,6]. However, CNC studies can be challenging as there can be differences in the data collected, e.g.…”

“…Is the data collected confined to a particular region or sociodemographic group that can make comparisons difficult? Second, whether there are factors that can appreciably affect utilization patterns, which have not been discussed, casting doubts on the figures [6,7]. For instance, atorvastatin was removed from the reimbursement list in Germany once generic simvastatin became available and patented angiotensin receptor blockers (ARBs) were removed from the reimbursement list in Denmark once generic losartan became available [8,9].…”

“…Prescribing restrictions and high co-payments for statins in Lithuania limited their reimbursed utilization in 2007 to just 0.8 defined daily doses per 1000 inhabitants per day (DIDs) versus for instance 114.7 in Scotland with no such restrictions [6], despite the prevalence of coronary vascular disease higher in Lithuania than among Western European countries [15]. In addition, prescribing restrictions and high co-payments limited the reimbursed utilization of proton pump inhibitors (PPIs) in Lithuania in 2007 to just 2.3 DIDs in 2007 compared with for instance 76.9 in Scotland with no such restrictions [6].…”

“…In addition, prescribing restrictions and high co-payments limited the reimbursed utilization of proton pump inhibitors (PPIs) in Lithuania in 2007 to just 2.3 DIDs in 2007 compared with for instance 76.9 in Scotland with no such restrictions [6]. Other authors have also shown that co-payments can appreciably affect subsequent utilization patterns [16][17][18].…”

“…To date, studies comparing differences in utilization rates between different databases have typically only included single classes apart from the study in France [6,19,20]. This has now been extended to investigate utilization differences between different databases in Lithuania among four high volume classes seen in Western European countries [6,10,19,[21][22][23], where there are differences in patient co-payment levels and other measures ( Table 1).…”