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

Overview of attention for article published in BMC Health Services Research, August 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Citations

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46 Mendeley
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Title
Real and predicted mortality under health spending constraints in Italy: a time trend analysis through artificial neural networks
Published in
BMC Health Services Research, August 2018
DOI 10.1186/s12913-018-3473-3
Pubmed ID
Authors

Davide Golinelli, Andrea Bucci, Fabrizio Toscano, Filippo Filicori, Maria 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 resulted significantly related with MR in the fixed effect analyses. DPS and TAUS decreased since 2011. There was a mismatch in mortality rates between real and predicted values. DPS resulted significantly associated to mortality (p < 0.05). In repeated mortality forecasting analysis, predicted MR was found to be lower when considering the pre-constraints health spending trend. Between 2011 and 2014, Italian public health spending items showed a reduction when compared to prior years. Spending on services directly provided free of charge appears to be the financial driving force of the Italian public health system. The overall mortality was found to be higher than the predicted trend and this scenario may be partially attributable to the healthcare funding constraints experienced by the SSN.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 22%
Student > Ph. D. Student 6 13%
Other 3 7%
Researcher 3 7%
Student > Bachelor 2 4%
Other 9 20%
Unknown 13 28%
Readers by discipline Count As %
Medicine and Dentistry 11 24%
Economics, Econometrics and Finance 4 9%
Social Sciences 3 7%
Psychology 2 4%
Engineering 2 4%
Other 7 15%
Unknown 17 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 August 2018.
All research outputs
#5,832,615
of 23,102,082 outputs
Outputs from BMC Health Services Research
#2,581
of 7,743 outputs
Outputs of similar age
#101,003
of 335,220 outputs
Outputs of similar age from BMC Health Services Research
#88
of 176 outputs
Altmetric has tracked 23,102,082 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,743 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,220 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 176 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.