Effects of the 2016 pharmaceutical reimbursement scheme changes – a microsimulation study
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Effects of the 2016 pharmaceutical reimbursement scheme changes – a microsimulation study K Aaltonen, P Heino, E Ahola, JE Martikainen Social Insurance Institution (Kela), Finland
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Transcript of Effects of the 2016 pharmaceutical reimbursement scheme changes – a microsimulation study
Effects of the 2016 pharmaceutical reimbursement scheme changes – a microsimulation study
K Aaltonen, P Heino, E Ahola, JE MartikainenSocial Insurance Institution (Kela), Finland
Background
• The global financial crisis has led to consecutive initiatives to reduce public pharmaceutical spending.
• The government objectives for the current changes were• Budget cuts.• Targeting reimbursements to patients with highest
health needs.
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Reimbursements for outpatient medicines* in 2016 and the simulated changes
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* Only medicines on the positive list can be reimbursed. Reimbursements may be restricted to specific patients by priorauthorisation.
Aims
• Estimate the financial effects of the 2016 reimbursement scheme changes to patients and the public payer, the National Health Insurance (NHI).
• Describe and evaluate the used microsimulation method.
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Methods
• Microsimulation = a modelling techniquewhere legislative changes are applied to patient-level data.
• Static microsimulation• Does not take into account behavioral changes.
• Microsimulation was conducted using SAS software (SAS Institute Inc.).
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Simulation data
• 10% random sample (N=384,807) of people in the 2014 NHI prescription register (3.8 Million people)• All purchases for reimbursed medicines • Entitlements to disease based / restricted
reimbursements • Age, gender, personal income (tax register)
• Errors and discrepancies in the raw data solved in baseline
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Results
• Estimated savings for the NHI were €44.5 Million/year (−3% of pharmaceutical budget).
• Median change in out-of-pocket costs was +€11/patient/year (IQR +€3−+€17).
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Annual change in out-of-pocket (OOP) costs by OOP costs paid before change
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Annual change in out-of-pocket (OOP) costs byentitlement based on chronic/severe illness
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Annual change in out-of-pocket (OOP) costs by income
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Annual change in out-of-pocket (OOP) costs by age
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Conclusions
• As aimed, the changes increased patient’s share of costs for a large majority of people with relatively low prior costs.
• However, largest increases affected• high need people who previously had relatively low
out-of-pocket costs due to 100% reimbursements, and
• a small number of people with a very high numberof purchases after exceeding the annual ceiling.
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Conclusions
• Microsimulation allowed factoring in interactions between simultaneous changes and the spectrum of individual situations among affected patients.
• Ex post analyses are needed to confirm the effects and to evaluate the sensitivity and accuracy of the used method.
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