EFFICIENCY OF HEALTHCARE EXPENDITURE IN THE PRE-PANDEMIC AND PANDEMIC PERIODS

: Via bibliometric analysis, the research identifies the contextual vectors of the healthcare financial provision effectiveness. Moreover, we broaden the empirical substantiation of panel data regression modelling for 34 European countries. In particular, we define performance of public (budgetary) and private financing to reduce the mortality rate and raise life expectancy in pre-pandemic and pandemic periods. The systematization of existing literature sources and approaches to solving the problem is implemented by means of bibliometric and monographic analysis. Consequently, there are 6 contextual clusters of scientific research on determining the healthcare financial provision effectiveness in the modern science. Within the analysed works, researchers mainly study general prerequisites of the healthcare financial provision effectiveness. The optimal cost formation of medical services for diagnosing and treating diseases is reproduced as well. The issue urgency consists in analysing the efficient patterns of spending various funds to decrease the mortality rate and increase life expectancy in the pre-pandemic and pandemic periods. Subsequently, we detect some general parameters of healthcare resistance to counter shocks similar to the COVID-19 pandemic. In this paper, we show statistical analysis of mortality indexes (total and COVID-19). Among 34 European countries, the highest and lowest efficiency levels were identified within these parameters. The study empirical block constructs 8 regression models on panel data. They differ in dependent (mortality rate or life expectancy) or independent variables (block 1: current and capital healthcare expenditures in GDP; block 2: current healthcare expenditures), and modelling period (pre-pandemic – 2000-2019, pandemic– 2020-2022 or the current period). The modelling results represent financial drivers and change inhibitors of the mortality rate and life expectancy during the pandemic and pre-pandemic periods. Therefore, we established the most effective groups of healthcare expenditures, which is based on the country epidemiological situation. The obtained results can be useful for scientists, representatives of state and local authorities.

Introduction. The coronavirus pandemic  launched some fundamental transformational processes to absorb the destructive disease impact on various parameters of the socio-economic life and health. Theory and practice require determining the empirical prerequisites and causal dependencies of healthcare expenditure influence on the mortality rate and life expectancy. Therefore, we may formalize certain affecting patterns in the pre-pandemic and pandemic periods. They can also be compared to define changes in the healthcare financial provision effectiveness.
Via bibliometric analysis, the research identifies the contextual vectors of the healthcare financial provision effectiveness. Moreover, we broaden the empirical substantiation of panel data regression modelling for 34 European countries. In particular, we define performance of public (budgetary) and private financing to reduce the mortality rate and raise life expectancy in pre-pandemic and pandemic periods.
Literature Review. The first research step is bibliometric analysis of efficient healthcare funding to set main directions of its study. Here, Scopus publications should be involved (Scopus, 2023). We apply the search query TITLE-ABS-KEY ("healthcare expenditure" OR "health care expenditure" AND efficiency OR effectiveness).
A sample of 1,198 Scopus search hits was generated. The dynamics of publication activity is show at Figure 1. The maximal annual publication rate (106 articles) was recorded during the COVID-19 pandemic. Geographically, the research rates of the analysed topic are represented at Figure 2. According to Figure 2, the highest publishing rate on healthcare financing effectiveness is traced in the USA, the United Kingdom and Canada. There are moderate publications in Australia, the Netherlands and Germany. The lowest values concern Italy, China, Switzerland and Japan.
The bibliometric analysis results are shown at Figure 3.  − Green cluster (188 items)evaluation of medical service costs to treat diseases; − Blue cluster (154 items)assessment of medical service costs to diagnose and treat cancer; − Yellow cluster (142 items)financial provision to treat mental illnesses (including various addictions: alcohol, narcotic, etc); − Purple cluster (108 items) -patients' hospitalisation costs as to age; emergency medical care provision; − Turquoise cluster (107 items)medical service costs to diagnose and treat infections or viruses (including . To secure the in-depth analysis of healthcare financing effectiveness, it is reasonable to read the TOP-10 most cited Scopus publications (Scopus, 2023) on the specified topic. Their full description is given in Table 1. Sources: developed by the author on the basis of (Scopus, 2023).
The above-mentioned papers can be combined into the following research blocks: − Treatment cost effectiveness for specific diseases: diabetes (Hogan et al., 2003), asthma (Barnes et al., 1996), neck pain (Borghouts et al., 1999); − New treatment methods for specific diseases: heart illnesses (Heidenreich et al., 2013), osteoporosis (Melton et al., 1997), urinary problems (Hoberman et al., 1999); − Educational roles in clinical decision-making (Soumerai and Avorn, 1990), information in health care (Kellermann and Jones, 2013); − Efficiency of palliative and hospital treatment to optimize health care (Kavalieratos et al., 2016); − Lifestyle impacts on health, medical system and budget (Cecchini et al., 2010). In terms of the researched topic, it is the diagnosing and treatment cost effectiveness for specific diseases that is investigated most often. Simultaneously, the optimal financing of general hospitalization, emergency and palliative care are focused on.
Methodology and research methods. The empirical part of this study tests the hypothesis if it is more effective to resist the destructive COVID-19 impact in countries with a higher pre-pandemic financing of the healthcare system. Dependent variables of the models are: death rate, crude (per 1,000 people) and life expectancy at birth, total (years). All indexes are selected from the collection "Health Nutrition and Population Statics" in the statistical database of the World Bank Group (World Bank DataBank, 2023).
The relationship between dependent and explanatory variables will be determined via the panel data regression models in the Stata 14.2/SE software product (Stata, 2023).
Results. Therefore, to identify the financial healthcare provision effectiveness during the pre-pandemic and pandemic periods, we should analyse dynamic indexes of the COVID-19 mortality rate. It is a ratio of two values values: total COVID-19 cases, per million, cumulative against total COVID-19 death, per million, cumulative (Worldometer, 2023). The 2020-2021 dynamics of the calculated index is represented at Figure 4. According to Figure 4, it is Iceland, Denmark, Estonia, Norway and Serbia that are the TOP-5 countries with the lowest mortality rate. Here, the COVID-19 mortality rate in 2020-2021 did not exceed 1%. We may preliminarily regard the healthcare system in these states as effective to counter the destructive pandemic impact.
As a part of statistical analysis, we should observe the mortality rate change in 34 European countries ( Figure 5). According to these data, the 2000-2020 mortality fall was recorded in Denmark, Estonia, Iceland, Ireland, Norway and Sweden. Here, the COVID-19 mortality is low, which also confirms the healthcare efficiency in these states. Meanwhile, there was a rapid death increase for 2000-2020 in Albania, Bosnia and Herzegovina, Lithuania, North Macedonia, Poland, and Moldova. Besides, the 2019-2020 mortality during the COVID-19 period was higher than that of 2000-2020 in Great Britain, Ukraine, Switzerland, Sweden, Estonia, Denmark, the Czech Republic, Belgium and Austria. It testifies the significant destructive COVID-19 impact on healthcare. Therefore, the system needs reforming to raise its resistance to future global threats. The next step of our research is the panel data regression modelling to determine influence of healthcare expenditures on the mortality and life expectancy shifts in the pre-pandemic and pandemic periods. We conducted a preliminary correlation analysis for multicollinearity between independent variables. Subsequently, we arranged some blocks of these variables as different models: a) Block 1 -Capital Health Expenditure (% of GDP) and Current Health Expenditure (% of GDP); b) Block 2 -Domestic General Government Health Expenditure (% of Current Health Expenditure), Domestic Private Health Expenditure (% of Current Health Expenditure), External Health Expenditure (% of Current Health Expenditure), Out-of-Pocket Expenditure (% of Current Health Expenditure).
Each block will be tested for causality with two dependent variables -Death Rate, crude (per 1,000 people) and Life Expectancy at birth, total (years).
At the same time, each model will be calculated for 2 time periods: pre-pandemic (2019-2000) and pandemic (2019-2022 or the last available observation period).
Totally, 8 variations of panel data regression models will be formed (Tables 2-9). Therefore, for 34 European countries in the pre-pandemic period, the impact of capital health expenditures on death rate is not statistically significant. However, a 1% current health expenditure increase (% of GDP) leads to a 0.19% mortality decrease. For the structure of current healthcare expenditures, Table 3 shows that only out-of-pocket expenditures have a statistically significant impact on performance. Thus, a 1% out-of-pocket expenditure increase (% of current health expenditure) causes a 0.07% mortality decrease with a 99% confidence probability. The remaining types of current health expenditures do not have a significant impact on the pre-pandemic mortality. Sources: developed by the author. Table 4 shows that in 34 European countries, current and capital expenditures have a positive and statistically significant influence on life expectancy in the pre-pandemic period. Thus, a 1% increase in the relevant types of healthcare expenditures contributes to an increase in life expectancy by 0.82 years and 1 year, respectively. Sources: developed by the author. Table 5 proves an increase in the specific weight of all expenditure types in the structure of current healthcare expenses with a 99% confidence probability. In the pre-pandemic period, it leads to a life expectancy rise. The impact on efficiency of the domestic public current healthcare expenditures, domestic private current healthcare expenditures and external current expenditures is almost the same and contributes to the life expectancy growth by 3.3-3.6 years.
However, the impact of out-of-pocket expenditures is weaker. A 1% independent variable increase causes a life expectancy increase by 0.24 years. Sources: developed by the author. Table 6 demonstrates that no current or capital healthcare expenditures have a statistically significant effect on the pandemic mortality. Sources: developed by the author.
In the overall structure of current expenditures, Table 7 shows that the specific weight growth of all types of healthcare expenditures has a statistically significant effect on the pandemic mortality rise. Simultaneously, external healthcare expenditures have the strongest influence while out-of-pocket expenditures have the smallest impact. Sources: developed by the author. Table 8 shows that in 34 European countries during the pandemic period, the current health expenditures contribute to the life expectancy increase. A 1% healthcare expenditure increase leads to a life expectancy increase by 1.32 years with a 99% confidence probability. However, capital health expenditure is not a relevant driver of the life expectancy increase during the COVID-19 pandemic. Sources: developed by the author.
In the structure of current healthcare expenditures, Table 9 demonstrates that domestic private expenditures and out-of-pocket expenditures have a statistically significant influence on life expectancy. At the same time, a 1% increase in the weight of domestic private healthcare expenditures leads to a life expectancy increase by 0.47 years. However, an out-of-pocket healthcare expenditure rise affects the pandemic life expectancy with a 99% confidence probability. It leads to a dependent variable reduction by 0.26 years.
Conclusions. Therefore, our research concerned the financial healthcare provision among 34 European countries in the pre-pandemic and pandemic periods. The obtained results make the following conclusions: 1) Bibliometric analysis of 1,198 Scopus publications confirms the topic study rise since 2007. The research leaders are the USA, the United Kingdom and Canada.
2) There are 6 contextual clusters in study of the financial healthcare provision effectiveness. They investigate healthcare expenses: costs for diagnosing and treating specific diseases, for age hospitalisation, for emergency care, etc.
3) Iceland, Denmark, Estonia, Norway and Serbia resist the pandemic most effectively (by the COVID-19 mortality rate). 4) For 34 studied countries, it is empirically proven that the pre-pandemic mortality rate fell due to the increase in the specific weight of current healthcare expenditures. Especially, that concerns the out-of-pocket health expenditures.
The pre-pandemic life expectancy was positively determined by the growth of both current and capital healthcare expenditures. The influence of domestic public current healthcare expenditures, domestic private current healthcare expenditures and external current expenditures was the strongest.
The pandemic mortality did not depend on current or capital expenditures in general. However, the increase in the specific weight of all types of current healthcare expenditures in the overall structure of current costs has a statistically significant effect on the mortality rise.
Meanwhile, the pandemic life expectancy is positively determined by the current expense growth, especially out-of-pocket expenditures (% of current costs) and other private healthcare expenditures.
Therefore, the ability of households to cover the medical care costs with their own funds allows to increase the pandemic life expectancy. On the contrary, none of state agents of healthcare support has proven their effectiveness in countering the pandemic.
The obtained results can be useful for scientists and authorities to optimize the financial healthcare provision. That is adjusted for the most efficient resilience to COVID-19 and other national or global threats.