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Health Policy. 2013 May;110(2-3):131-40. doi: 10.1016/j.healthpol.2012.12.004. Epub 2013 Feb 1.

Trends and income related differences in out-of-pocket costs for prescription and over-the-counter medicines in Finland from 1985 to 2006.

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Social Insurance Institution, Research Department, P.O. Box 450, FIN-00101 Helsinki, Finland.



To explore trends and income related differences in out-of-pocket (OOP) costs for prescription and over-the-counter medicines in Finland in 1985-2006.


Cross-sectional data collected in Household Budget Surveys conducted in 1985, 1990, 1995, 2001 and 2006 were used to calculate trends in household OOP payments in absolute and relative terms. Covariance analyses were used to evaluate age-adjusted OOP costs across income groups.


Mean OOP costs per household increased 2.7 fold over inflation from 1985 to 2006. The growth was steepest (60%) in 1990-1995 and slowest (10%) in 1995-2001. The mean costs, in 2006 currency value, increased from €138 to €373 and the average share of household total consumption spent on medicines increased from 0.8% to 1.6%. After adjusting by age, the lowest income quintile had the lowest mean OOP costs for all types of medicines at every time point, although the overall differences were small. In 1985/2006, the age-adjusted estimated marginal means for household medicinal costs were €121/€332 for the lowest income quintile and €138/€449 for the highest quintile, and for the share of household consumption 1.1%/2.2% for the lowest and 0.5%/1.1% for the highest quintile.


All patients faced increasing OOP payments for medicines throughout the study period, but the relative growth was largest for the lowest income groups. Our results suggest that savings achieved by increasing the patients' share of costs coincided with steep growth in OOP costs and wider differences between income groups. Cost containment measures targeted at prices, on the other hand, coincided with stabilised OOP costs and decreasing dispersion between the income quintiles. More research is needed to evaluate whether differences in OOP costs reflect differences in patterns of use.

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