Utilization indicators of balneotherapy in Hungary

Abstract

The role of spa therapy is well defined and its importance has significantly increased in the healthcare but the utilization indicators of the implemented treatments are less known. The objective of our study was to analyze the utilization and the social insurance indicators of the healthcare publicly financed by health insurance in spa institutions. The data used for the analysis were derived from the funding database of the National Health Insurance Fund of Hungary. The period examined covered the years between 2009 and 2016. The spa treatment counts, social insurance expenses, the territorial inequalities in utilization, sex and age distribution of the treatments were examined. The treatment counts were the highest (7349587) in 2009 and they gradually decreased with 6558 204 treatments by 2012. 'Spa pool of medicinal water' treatment was the most common care in each year which incidence showed a downward trend during the past years: 2544 617 treatments were performed in 2009 but 2016 showed only 1898 338 treatments. We found the highest health insurance expenditures in 2016: 4.261 billion HUF or 13.8 EUR. In the previous years, there was a lower health insurance expenditure: in 2010 3.928 billion HUF (14.3 million EUR), in 2011 3.921 billion HUF (14.0 million EUR) and in 2012 3.875 billion HUF (13.4 million EUR). The utilization made the highest incidence of treatments in Csongrád county with 13174/10000 inhabitants and 8160 thousand HUF/10000 inhabitants of social security subsidy in 2016. The lowest utilization counts for treatments were found in Nógrád county with 3233/10000 inhabitants and 2192 thousand HUF/10000 inhabitants of social security subsidy. The highest utilization indicators were found in the age group between 60 and 69 in the distribution of population and genders. In the utilization of spa therapy funded by health insurance fund, no significant change has occurred during the past years but territorial discrepancies can be seen in sex, age, and county breakdown. Orv Hetil. 2019; 160(Suppl 1): 22-28.