Approaches to Health Efficiency across the European Space through the Lens of the Health Budget Effort
Abstract
:1. Introduction
2. Literature Review
3. Methodology
4. Results and Discussion
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- There is a direct correlation between the health expenditure in the budget year (HLTH_SHA11_HC (year)) and the social protection expenditure (SPR_REC_SUMT_ESC). This has been the case since 2010, when European policy underwent a paradigm shift from a conservative to a forward-looking orientation, focusing on disease prevention rather than treatment. This validates hypothesis H2: In the case of financial policy stability for health, the main deflator of the allocation efficiency is the social protection expenditure as a percentage of GDP (see Table 4). The approach is congruent with other papers [4,5,34].
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- There is a direct correlation between the expenditure allocated to health in the budget year (HLTH_SHA11_HC (year) and the social protection scheme approved at government level through national strategies and at European level through Community strategies (SPR_REC_SUMT_GOV (year)). This validates hypothesis H1: The health status of the population varies in direct proportion to the health budget allocations, and changes in the government policies are likely to have a direct proportional influence on the medium and long-term health balance (see Table 4). The same approach was used by [26,34].
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- There is a direct correlation between the expenditure allocated to health in the budget year (HLTH_SHA11_HC (year)) and the strategy to protect the health of the population in the long term (HLTH_SHA11_LT (year)), i.e., to populate health units with specialists and technical equipment to ensure the sustainability of the health services and the continuous increase in the level of health of the population.
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- The fluctuating dynamics of the Pearson correlation of the dependent variable in relation to social protection and health security expenditure suggests that the trend is polynomial, with the forecast line being affected (with an inflection point) during the pandemic period.
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- The dynamics of the social protection expenditure represented by administrative costs has a polynomial evolution on the Pearson correlation coefficients with the dependent variable defined by the equation:y = −0.0771x + 1.3205
5. Conclusions
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- Rebalancing financial allocations for health in relation to strategic objectives at national and European levels;
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- Refocusing health police on prevention and health screening;
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- Linking short-term and long-term health policies;
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- Ensuring sustainability of the health sector through the sustainability of the resources used.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ANMCS | National Authority of Quality Management in Health |
ANOVA | Analysis of variance is a collection of statistical models and their associated estimation procedures used to analyze the differences among means |
COVID-19 | Coronavirus disease 2019 |
DEA-BCC model | Data Envelopment Analysis-Banker, Charnes and Cooper Model |
EU | European Union |
EUROSTAT | is the statistical office of the European Union |
GDP | Gross Domestic Product |
IBM SPSS | software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open source extensibility, integration with big data and seamless deployment into applications |
ISQua | International Society for Quality in Health Care |
PPP | Purchasing power parity |
OECD | Organisation for Economic Co-operation and Development |
SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
VOSviewer | software tool for constructing and visualizing bibliometric networks |
Appendix A
Publication Type | Authors | Article Title | Source Title | ISSN | eISSN | ISBN | DOI | UT (Unique WOS ID) |
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Indicators’ Names | Symbol | Unit of Measurement | Source |
---|---|---|---|
Expenditure on social protection | SPR_EXP_SUM | Percentage of GDP | [37] |
Social benefits, in cash or in kind, to households and individuals | SPR_EXP_BNF | Percentage of GDP | [37] |
Administration costs to the scheme for health management and administration | SPR_ADM | Percentage of GDP | [37] |
Other expenditure by social protection schemes (payment of property income and other) | SPR_OTHER | Percentage of GDP | [37] |
Receipts of social protection schemes (social contributions, general government contributions and other receipts) | SPR_REC_SUMT_GOV | Percentage of GDP | [37] |
Employers’ social contributions | SPR_REC_SUMT_ESC- | Percentage of GDP | [37] |
Total health care expenditure by EU27 average expenditure (Country average/EU27 average) | HLTH_SHA11_HC | Percentage of EU27 average expenditure | [37] |
Long-term care (health) expenditure by EU27 average long-term expenditure (Country average/EU27 average) | HLTH_SHA11_LT | Percentage of EU27 average expenditure. | [37] |
The rate of illness | COVID_ILL | Cases per 1 M people | [38] |
The rate of vaccinations | COVID_VACC | % of population fully vaccinated | [38] |
Model Year | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | ||||
---|---|---|---|---|---|---|---|---|---|---|
R Square Change | F Change | df1 | df2 | Sig. F Change | ||||||
2009 | 0.999 | 0.998 | 0.996 | 2.365 | 0.998 | 734.070 | 10 | 16 | 0.000 | 2.059 |
2010 | 0.998 | 0.996 | 0.994 | 2.928 | 0.996 | 452.810 | 10 | 16 | 0.000 | 2.061 |
2011 | 0.998 | 0.996 | 0.994 | 3.013 | 0.996 | 443.429 | 10 | 16 | 0.000 | 2.131 |
2012 | 0.999 | 0.998 | 0.996 | 2.468 | 0.998 | 645.618 | 10 | 16 | 0.000 | 2.146 |
2013 | 0.999 | 0.998 | 0.997 | 2.075 | 0.998 | 863.058 | 10 | 16 | 0.000 | 2.303 |
2014 | 0.999 | 0.998 | 0.997 | 2.049 | 0.998 | 889.853 | 10 | 16 | 0.000 | 2.028 |
2015 | 1.000 | 0.999 | 0.999 | 1.253 | 0.999 | 2266.115 | 10 | 16 | 0.000 | 2.339 |
2016 | 0.999 | 0.999 | 0.998 | 1.569 | 0.999 | 1352.670 | 10 | 16 | 0.000 | 1.452 |
2017 | 0.999 | 0.999 | 0.998 | 1.454 | 0.999 | 1523.163 | 10 | 16 | 0.000 | 2.453 |
2018 | 1.000 | 0.999 | 0.998 | 1.365 | 0.999 | 1662.286 | 10 | 16 | 0.000 | 1.495 |
2019 | 0.999 | 0.999 | 0.998 | 1.423 | 0.999 | 1498.636 | 10 | 16 | 0.000 | 1.959 |
2020 | 0.998 | 0.996 | 0.994 | 2.649 | 0.996 | 443.106 | 10 | 16 | 0.000 | 1.910 |
Model Year | Sum of Squares Regression | Sum of Squares Residual | Sum of Squares Total | df Rg. | df Res | df Tot | Mean Square Regression | Mean Square Residual | F | Sig. |
---|---|---|---|---|---|---|---|---|---|---|
2009 | 41,072.167 | 89.522 | 41,161.689 | 10 | 16 | 26 | 4107.217 | 5.595 | 734.070 | 0.000 |
2010 | 38,825.678 | 137.190 | 38,962.868 | 10 | 16 | 26 | 3882.568 | 8.574 | 452.810 | 0.000 |
2011 | 40,249.734 | 145.231 | 40,394.965 | 10 | 16 | 26 | 4024.973 | 9.077 | 443.429 | 0.000 |
2012 | 39,328.383 | 97.465 | 39,425.848 | 10 | 16 | 26 | 3932.838 | 6.092 | 645.618 | 0.000 |
2013 | 37,149.809 | 68.871 | 37,218.680 | 10 | 16 | 26 | 3714.981 | 4.304 | 863.058 | 0.000 |
2014 | 37,373.159 | 67.199 | 37,440.357 | 10 | 16 | 26 | 3737.316 | 4.200 | 889.853 | 0.000 |
2015 | 35,586.873 | 25.126 | 35,611.999 | 10 | 16 | 26 | 3558.687 | 1.570 | 2266.115 | 0.000 |
2016 | 33,294.597 | 39.382 | 33,333.980 | 10 | 16 | 26 | 3329.460 | 2.461 | 1352.670 | 0.000 |
2017 | 32,190.421 | 33.814 | 32,224.236 | 10 | 16 | 26 | 3219.042 | 2.113 | 1523.163 | 0.000 |
2018 | 30,952.398 | 29.793 | 30,982.191 | 10 | 16 | 26 | 3095.240 | 1.862 | 1662.286 | 0.000 |
2019 | 30,344.785 | 32.397 | 30,377.182 | 10 | 16 | 26 | 3034.479 | 2.025 | 1498.636 | 0.000 |
2020 | 31,092.351 | 112.271 | 31,204.621 | 10 | 16 | 26 | 3109.235 | 7.017 | 443.106 | 0.000 |
Year | HLTH_SHA11_HC (Year-1) | SPR_EXP_SUM (Year) | SPR_EXP_BNF (Year) | SPR_ADM (Year) | SPR_OTHER (Year) | SPR_REC_SUMT_ESC (Year) | SPR_REC_SUMT_GOV (Year) | HLTH_SHA11_LT (Year) | HLTH_SHA11_LT (Year-1) |
---|---|---|---|---|---|---|---|---|---|
2009 | 0.999 | 0.840 | −0.546 | 0.581 | 0.272 | 0.306 | 0.580 | 0.854 | 0.850 |
2010 | 0.828 | −0.497 | 0.572 | 0.180 | 0.308 | 0.566 | 0.998 | 0.864 | 0.868 |
2011 | 0.800 | −0.397 | 0.558 | 0.038 | 0.376 | 0.574 | 0.987 | 0.856 | 0.896 |
2012 | 0.810 | −0.405 | 0.610 | −0.001 | 0.379 | 0.593 | 0.995 | 0.897 | 0.899 |
2013 | 0.797 | −0.233 | 0.573 | −0.090 | 0.410 | 0.558 | 0.998 | 0.895 | 0.899 |
2014 | 0.785 | −0.428 | 0.578 | 0.054 | 0.403 | 0.559 | 0.998 | 0.898 | 0.903 |
2015 | 0.735 | −0.439 | 0.546 | 0.097 | 0.346 | 0.568 | 0.999 | 0.902 | 0.907 |
2016 | 0.722 | −0.461 | 0.587 | 0.100 | 0.288 | 0.582 | 0.999 | 0.900 | 0.905 |
2017 | 0.718 | −0.493 | 0.576 | 0.158 | 0.277 | 0.598 | 0.999 | 0.904 | 0.911 |
2018 | 0.710 | −0.632 | 0.576 | 0.318 | 0.334 | 0.597 | 0.999 | 0.912 | 0.917 |
2019 | 0.658 | −0.551 | 0.570 | 0.187 | 0.334 | 0.574 | 0.999 | 0.915 | 0.918 |
2020 | 0.645 | −0.527 | 0.569 | 0.149 | 0.314 | 0.560 | 0.996 | 0.919 | 0.905 |
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Antohi, V.M.; Ionescu, R.V.; Zlati, M.L.; Mirica, C.; Cristache, N. Approaches to Health Efficiency across the European Space through the Lens of the Health Budget Effort. Int. J. Environ. Res. Public Health 2022, 19, 3063. https://doi.org/10.3390/ijerph19053063
Antohi VM, Ionescu RV, Zlati ML, Mirica C, Cristache N. Approaches to Health Efficiency across the European Space through the Lens of the Health Budget Effort. International Journal of Environmental Research and Public Health. 2022; 19(5):3063. https://doi.org/10.3390/ijerph19053063
Chicago/Turabian StyleAntohi, Valentin Marian, Romeo Victor Ionescu, Monica Laura Zlati, Cristian Mirica, and Nicoleta Cristache. 2022. "Approaches to Health Efficiency across the European Space through the Lens of the Health Budget Effort" International Journal of Environmental Research and Public Health 19, no. 5: 3063. https://doi.org/10.3390/ijerph19053063