Real and predicted mortality under health spending constraints in Italy: a time trend analysis through artificial neural networks

BMC Health Services Research
Davide GolinelliMaria Pia Fantini

Abstract

After 2008 global economic crisis, Italian governments progressively reduced public healthcare financing. Describing the time trend of health outcomes and health expenditure may be helpful for policy makers during the resources' allocation decision making process. The aim of this paper is to analyze the trend of mortality and health spending in Italy and to investigate their correlation in consideration of the funding constraints experienced by the Italian national health system (SSN). We conducted a 20-year time-series study. Secondary data has been extracted from a national, institution based and publicly accessible retrospective database periodically released by the Italian Institute of Statistics. Age standardized all-cause mortality rate (MR) and health spending (Directly Provided Services - DPS, Agreed-Upon Services - TAUS, and private expenditure) were reviewed. Time trend analysis (1995-2014) through OLS and Multilayer Feed-forward Neural Networks (MFNN) models to forecast mortality and spending trend was performed. The association between healthcare expenditure and MR was analyzed through a fixed effect regression model. We then repeated MFNN time trend forecasting analyses on mortality by adding the spending item resu...Continue Reading

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Citations

May 23, 2020·International Journal of Environmental Research and Public Health·Robert StefkoMartin Rigelsky
Oct 17, 2019·International Journal of Environmental Research and Public Health·Viera IvankováMartin Rigelský

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Software Mentioned

TAUS
R
DPS
OLS
ISTAT
MFNN
R Development Core Team

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