The study also uses the data regarding the mortality rate of children who die before reaching their sixth birthday. The mortality rate under the age of five is measured as the probability per 1000 that a child will die before reaching the age of five. This study also uses the mortality rate of female and male children under the age of five. The incidence of malaria describes the number of new cases per 1000 of the population at risk in a year, and the impact of tuberculosis is estimated as several new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. Estimates include all forms of TB. Non-communicable diseases as a cause of death represent the percentage of deaths caused by cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies to the people of all ages.

The study also uses data regarding alcohol consumption. It is measured as the liters of pure alcohol consumed per capita (15 years or older) in a calendar year adjusted for tourist consumption. The data for female and male consumption have also been used for detailed analysis. Mortality as a result of road accidents is measured as the deaths caused by traffic injury per 100,000 of population. This variable informs us about road safety. Finally, the study also uses data regarding healthcare expenditure, which provides an idea about the overall healthcare of the people. Healthcare expenditure includes healthcare goods and services consumed during each year, and it does not include capital expenditure on a building, machinery, IT, or vaccines for emergencies or outbreaks. Health expenditure per capita includes current healthcare expenditure per capita in US dollars on a theoretical basis, and health expenditure per capita purchasing power basis is the current health expenditure per capita in terms of purchasing power parity (PPP).

### *2.3. Statistical Analysis*

This study uses a multiple-step approach for the analysis. In the first step, all the variables were divided into two segments. The first portion ranges from the year 2000 to 2009, and the other section covers 2010 to 2017. So, half of the data depicts the Chinese performance regarding health-related sustainable goals in the first decade of the 21st century, and the other half shows the performance of the Chinese healthcare sector in the second decade of the 21st century. The difference in the mean test for paired observations was run to know whether or not the performance has significantly improved over the period. As the number of observations for each variable was less than 30, the rejection or acceptance of the null hypothesis of no difference in performance was decided on the basis of *t* distribution.

Simple linear regression (SLR) was used in the second step to find the rate of change of performance regarding different targets over the years. The mathematical expression for our regression model is given below.

$$\mathbf{Y}\_{\mathbf{i}} = \alpha\_0 + \beta\_{\mathbf{i}} \,\, \mathbf{X}\_{\mathbf{i}} + \varepsilon\_{\mathbf{i}}$$

where Y represents the dependent variable, and subscript i represents the ith variable. α0 shows *y* intercept; X shows time, which ranges from year 2000 to 2017, and ε represents the error term.

In the third step, we used two different mechanisms for forecasting. In the first mechanism, we forecasted the values for all the variables using the beta coefficient β measured by the above model and by replacing X with year 2018 to 2022. In the second mechanism, we used extrapolation to forecast out of the sample the performance of the Chinese healthcare sector. We compared the results of both methods and drew forecasts mostly based on extrapolation to incorporate nonlinearities in the forecasts.

### **3. Results and Discussion**

Table 2 provides descriptive statistics for all the variables used in this study. As per the national estimates, the average maternal mortality rate over the sample period is 35 per 100,000 live births, far below the global target of 70 set under sustainable goals (SDG). The mean neonatal mortality rate is 11 per 1000 live births. This number is also below the global target of 12 per 1000, which is to be achieved by 2030. The under-five mortality rate of 19.84 is far below the target of 27 per 1000 live births. The statistics reveal that the incidence of TB and malaria is negligibly small with only 0.007 patients of malaria per 1000 of the population and 83.28 patients of TB per 100,000 of the population. Although malaria and TB have been curtailed, the government must keep up with its strategies to combat these diseases so that they may not emerge again.

As per the statistics, non-communicable diseases are the cause of 85.95% of deaths. As far as alcohol consumption is concerned, on average, people in China drink 7.075 L of pure alcohol each in a year. The average consumption of pure alcohol for females per capita is just 2.525 L, but this number for males is 11.45 L per capita. The alcohol consumption by males is on the rise and expected to surpass the binge drinking level of 12.98 L per capita soon. The statistics regarding alcohol consumption are based on the drinking levels defined by the National Institute on Alcohol Abuse and Alcoholism of the United States [Appendix A]. As per the statistics, the road accident mortality rate is declining and the average number of deaths per 100,000 of the population is 20.60. The average current healthcare expenditure as a percentage of GDP is 4.39%. The average health expenditure per capita is \$188.77 in nominal terms and \$385.33 per capita in purchasing power parity terms. Table 3 provides the pairwise correlation matrix.

Table 4 provides the results for the difference in the means test for the performance of the Chinese healthcare sector in the first decade of the 21st century and the second decade. As per the World Bank estimates, the average maternal mortality rate over the first decade was 48.40 per 100,000 live births, but the average maternal mortality rate over the second decade is only 29.25 per 100,000 live births. It shows a significant decline in

maternal mortality rate over the sample period. National estimates regarding maternal mortality rate provide an even better picture. The maternal mortality rate is 50% lesser in the second decade compared to the first. The average neonatal mortality rate was 14.87 per 1000 live births in the first decade, and it dropped to 6.37 in the second decade, which shows a significant improvement over the period.

**Table 2.** Descriptive statistics.

The mortality rate of children under the age of five has declined from 25.91 per 1000 in the first decade to 12.26 only in the second decade, a significant improvement on this front as well. The improvement in the under-five mortality rate for female children is slightly better compared to male children. The incidence of malaria has also declined from 0.01 per 1000 of the population to 0.003, from the first decade to the second. We may say that China has almost eradicated malaria. The incidence of tuberculosis has also declined from 94.1 to 69.75 per 100,000 of the population, a significant decline of 26% over two decades. However, further efforts are needed to eliminate these diseases from the country.

The mortality rate by non-communicable diseases including cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies has significantly increased from 84.03 percent in the first decade to 88.36 percent in the second decade. The numbers show that the incidence of deaths by infectious diseases has declined from 16% to 12%. According to descriptive statistics, the consumption of alcohol is one of the weak areas. The numbers show a deteriorating situation. Pure alcohol consumption has significantly increased from 7.008 to 7.16 L per capita.

Interestingly, the alcohol consumption has declined from 2.69 L to 2.54 L for females but for the males it has significantly increased from 11.32 L in first decade to 11.61 L of pure alcohol per capita in the second decade. This is the area which requires government attention because if the present trend continues, the alcohol consumption by males may surpass safe levels and reach the binge level. This will result in alcohol-related diseases and the deteriorating health of men.

The mortality rate from road accidents has significantly declined from 21.25 in the first decade to 19.79 per 100,000 population in the second decade. More efforts are needed to halve the number as per the SDGs by 2030. With the improvement in the economy, spending on current health expenditure has significantly increased from 4.16% of GDP to 4.69% over the period of two decades. Percentage increase in current healthcare expenditure relative to GDP is more vivid in health expenditure per capita which have increased from \$80.38 to 324.26, a rise of 303% in nominal terms and current healthcare expenditure per capita in terms of PPP has increased from \$211.46 to 602.66, an increase of 185%.

\*\*\* Represent statistical significance at 1% level

*Int. J. Environ. Res. Public Health* **2021**, *18*, 5193

Table 5 presents the results of a simple linear regression model mentioned in the methodology section. Time is a significant determinant for all the variables used in the study, which implies that different variables that measure the performance of the Chinese healthcare sector have significantly changed over time. The maternal mortality rate, neonatal mortality rate, mortality rate of children under the age of five, the incidence of Malaria, the incidence of tuberculosis, and the road accident mortality rate has declined while deaths as a result of non-communicable diseases and current healthcare expenditure have significantly increased over the sample period. Overall, the healthcare sector of China has shown improvements in the recent past.

**Table 5.** Simple linear regression.

\*\*\* Represent statistical significance at 1% level. *p* values are given in parentheses.

Beta coefficients of the variables given in Table 5 have been used for extrapolation to forecast. The graphical depiction of the performance of the Chinese healthcare sector and its comparison with the SDGs is presented in Figures 1–7. All the figures provide ex-post and ex-ante information. The ex-post period ranges from 2000 to 2017, and the ex-ante period is from 2018 to 2022. There are 17 SDGs, and goal 3 is about 'Good Health and Well-Being'. Like all other goals, goal 3 also has some targets that the participating nations should achieve before 2030 to make this planet a healthy place. Every figure provides information about the target and how the Chinese healthcare sector has performed against that target.

Figure 1 is about target 3.1, which aims to reduce the maternal mortality ratio below 70 per 100,000 live births. The maternal mortality rate was far below the target of 70 even in the year 2000, both according to national as well as World Bank estimates. As per the national estimates, only 18 women died of maternity-related problems per 100,000 live births. As per the World Bank estimates, the number was only 25 in the year 2017. If the current trend prevails, the maternal mortality rate is expected to drop to 12 as per the national estimates and 14 according to World Bank estimates.

Figure 2 relates to target 3.2, which states that the nations should reduce the neonatal mortality rate to at most 12 per 1000 live births, and the under-five mortality rate should be at least reduced to 25 per 1000 live births. China achieved the target of at most 12 neonatal deaths per 1000 way back in year 2007, and a target of less than 25 deaths of children under the age of five per 1000 live births was achieved in 2005. China recorded the lowest neonatal mortality rate of only 4.7 per 1000 live births in the year 2017, and the mortality rate of children under the age of five per 1000 live births was recorded to be only 9.3. If the current trend continues, the neonatal mortality rate is expected to drop to 2.7 per 1000 live births in the year 2022, and the mortality rate for children under the age of five is expected to drop to 5.8 by the year 2022.

**Figure 1.** Sustainable development goal 3, target 3.1 (maternal mortality) ex-post and ex-ante.

**Figure 2.** Sustainable development goal 3, target 3.2 (neonatal mortality) ex-post and ex-ante.

Figure 3 is about target 3.3, which states that the countries should end epidemics such as tuberculosis, malaria, and neglected tropical diseases by the year 2030. China has almost succeeded in eliminating malaria and tuberculosis. The incidence of malaria was recorded to be 0.000 patients per 1000 in 2017, and the incidence of tuberculosis was recorded to be only 63 per 100,000 population in 2017. If the current trend continues, malaria will be eliminated by 2022, and only 58 people out of 100,000 are expected to have TB in 2022. The results related to the incidence of tuberculosis seem to contradict the findings of [36] but it is not so. Our findings suggest that tuberculosis will still exist in the future and its incidence rate will be 0.058%, which is low enough to be neglected. The difference between our study and theirs is that their study only focuses on tuberculosis but ours is broader in context as we have studied the healthcare sector as a whole and not only a single disease.

Figure 4 is related to target 3.4, which aims to lower mortality from non-communicable diseases through prevention and treatment. More and more patients are dying of noncommunicable diseases rather than communicable diseases. In 2017, 89.6% of deaths were caused by non-communicable diseases, and only 10.4 deaths were caused by communicable diseases. If the current trend continues, 91.1% of deaths will be caused by non-communicable diseases in 2022. This area has been constantly neglected by Chinese health authorities, but it needs immediate consideration by policy-makers [37].

Figure 5 relates to target 3.5, which states that nations should strengthen the prevention and treatment of narcotics use and the harmful use of alcohol. One of the areas that need to be managed carefully in China is alcohol consumption. Pure alcohol consumption in China is on the rise. Each person in China drank 7.22 L of pure alcohol on average in 2017, and pure alcohol consumption has recorded an upward trend. Careful analysis of the numbers reveals that male and female sections of society are behaving totally differently. Alcohol consumption by females is on the decline, and alcohol consumption by males is surging at a very high rate. Females only drank 2.48 L of pure alcohol in the year 2017, while the males drank 11.73 L on average in 2017. Our results still validate the findings of [38] that males' alcohol consumption is many times more than females' consumption. If the current trend continues, females are expected to drink only 2.4 L of pure alcohol in the year 2022. On the other hand, males expected to drink 11.9 L on average; i.e., males will be touching the binge-drinking level by 2022. The binge-drinking level of 6.49 L per year for females and 12.98 L for males is calculated based on standards set by the National Institute on Alcohol Abuse and Alcoholism. This finding supports the results of [39] that the production and consumption of alcohol in China has increased manyfold. Therefore, the government should take measures to encourage the male population to drink alcohol within their limits. Otherwise, the harmful use of alcohol will lead to medical, social, and legal problems.

Figure 6 is about target 3.6, which states that the number of deaths and injuries from road traffic accidents should be halved by the year 2020. Unlike many other targets, this target is to be achieved by 2020 and not 2030, which shows the urgency of the problem. According to statistics, 19.4 deaths are caused by road traffic injuries per 100,000 of the population. The number is comparatively high due to rapid economic growth and increased motorization [40]. So, as per the target, the number should decrease to 9.7 deaths per 100,000 of the population. However, the number only dropped to 18.88 in the year 2017, and if the current trend continues, the number will only drop to 7.58 per 100,000 of population. Therefore, the government must take stringent actions to make roads safer for travelers to halve the number of deaths from traffic injuries by the year 2020. Special attention is needed to improve road safety because according to [41], the death rate from road accidents is three times higher than reported by the police.

**Figure 3.** Sustainable development goal 3, target 3.3 (end of tuberculosis, malaria, etc.) ex-post and ex-ante.

**Figure 4.** Strategic development goal 3, target 3.4 (mortality from non-communicable diseases) ex-post and ex-ante.

**Figure 5.** Sustainable development goal 3, target 3.5 (prevention of drugs and alcohol abuse) ex-post and ex-ante.

**Figure 6.** Sustainable development goal 3, target 3.6 (deaths by road accidents) ex-post and ex-ante.

Figure 7 relates to target 3.8, which stresses the importance of healthcare finance. This target is qualitative in nature as it does not provide any target in the form of numbers to achieve. This target suggests that governments should develop the systems so that everyone can have access to quality and affordable healthcare services and medicines. Chinese healthcare expenditure is increasing at a very rapid pace. The current healthcare expenditure is recorded to be 5.08% of the GDP, the highest over the sample period. If the trend continues, this number is expected to reach 5.54% of GDP in 2022. Current healthcare expenditure in terms of nominal as well as PPP terms is also on the rise. If the current trend continues, expenditure on healthcare per capita is expected to reach \$431.25 in nominal terms and \$1109.44 in PPP terms.

**Figure 7.** Sustainable development goal 3, target 3.8 (universal health coverage), ex-post and ex-ante.

Based on all the results mentioned above, we may state that China is doing very well regarding improvements in healthcare services as the current healthcare expenditure is increasing, thanks to the higher economic growth over the last three decades. The healthcare expenditure as a percentage of GDP dropped from 4.47% in 2000 to 3.67% in 2007 and rebounded to 4.98% in 2017. At this pace, healthcare expenditure is expected to grow to 5.54% by 2022. The current healthcare expenditure does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for an emergency or outbreak.

One of the most important areas where the government of China should pay attention is alcohol consumption in the male population of China. The government must take measures to encourage the male population to drink less alcohol so that many medical, social, and legal problems may be avoided. The second area that needs urgent attention is traffic safety. The authorities must make every possible effort to reduce traffic accidents as soon as possible. The government may do it by educating people and introducing severe

penalties for violations. Another area that needs immediate attention is to control the incidence of non-communicable diseases.

### **4. Conclusions**

The study concludes that the Chinese healthcare system has performed very well on many fronts of the sustainable development goals (SDGs). The statistics show significant improvement in healthcare performance in the second decade of the 21st century compared to the first decade. The areas that still need immediate attention include alcohol consumption, road traffic accidents and the incidence of non-communicable diseases. The government should spend resources to curtail the spread of non-communicable diseases along with the prevention of communicable diseases. The government should heavily focus on educating the male population about the harms of alcohol abuse so that they lower alcohol consumption to stay fit and healthy and to protect the society from harm overall. Road accidents are also an area where the government should focus its attention. The number of deaths by road accidents is declining but a lot more is needed to reduce the deaths by half by the year 2020. This could be achieved by educating people about road safety and issuing hefty fines to violators.

**Author Contributions:** All the authors have contributed substantially to the work reported as follow: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing original draft preparation, writing—review and editing, visualization, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by Instituto Politécnico de Lisboa.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data regarding this study can be assessed at https://databank. worldbank.org/source/health-nutrition-and-population-statistics (accessed on 13 May 2021).

**Acknowledgments:** We thank Instituto Politécnico de Lisboa for providing funding for this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Appendix A**

Calculations regarding calculation of alcohol consumption level of Chinese males and females

One alcoholic drink in America = 14 g = 0.01783 × 5 × 52 L= 4.6358 L/year for men Female 52 × 7 × 0.01783 = 6.49

Male 52 × 14 × 0.01783 = 12. 98

https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderatebinge-drinking (accessed on 12 May 2018)

### **References**

### *Article* **Competency Model for the Middle Nurse Manager (MCGE-Logistic Level)**

**Alberto González-García 1, Arrate Pinto-Carral 1,\*, Jesús Sanz Villorejo 2 and Pilar Marqués-Sánchez 1**

**Abstract:** Healthcare systems are immersed in transformative processes, influenced by economic changes, together with social and health instability. The middle nurse manager plays a fundamental role, since he or she is responsible for translating the strategic vision, values and objectives of the organization. The objective of this study was to propose the model of competencies to be developed by the middle nurse manager in the Spanish healthcare system. Our methodology consisted in the application of the Delphi method in order to reach an agreement on the necessary competencies, and principal component analysis (PCA) was used to determine the construct validity, reducing the dimensionality of the set of data. Fifty-one competencies were identified for the definition of the model, highlighting decision-making, leadership and communication. The PCA pointed out the structural validity of the proposed model through the saturation of the main components (α Cronbach > 0.631). The results show the model of competencies which the middle nurse manager in the Spanish healthcare system must develop. Middle nurse managers may use these as criteria to plan their professional strategies in the context of management. This model of competencies can be applied to establishing selection processes or training programs for the role of middle nurse manager.

**Keywords:** model of competencies; competency; middle nurse manager; nurse manager; logistic level; health policy; healthcare services; healthcare affordability; Hospital efficiency

### **1. Introduction**

Healthcare systems are immersed in processes of global transformation, influenced by economic and social changes, changes in health technology and structural alterations in systems for the provision of healthcare [1–5]. In this uncertain context, nurses are under pressure to improve quality of care [6]. It thus seems logical that nurses should form part of the nucleus of healthcare so that organizations are able to deal with these changes successfully [7–9]. Thus, as highlighted by Witt et al. [10], when the nurse takes part in the healthcare process (management and nursing care) organizations achieve better performances [11,12]. Within health organizations, nurse managers are a key part of any healthcare attention team. Nurse managers are responsible for introducing changes and creating environments in which nurses are able to provide quality attention, at the same time as guaranteeing the achievement of the objectives of the organization under sustainability and efficiency criteria [13–15].

The relationship between economic and sustainability policies with respect to offering quality care in health systems is the starting point and is of interest in justifying the development of managerial competencies, which are related to a higher degree of performance and results [16–19]. In this sense, Yoder-Wise [20] states that that the development of an advanced level of managerial competency is fundamental in achieving the objectives of the organization. Warshawsky [21] highlights that one of the key strategies for the success of health organizations currently resides in the capacity of the nurse manager to

**Citation:** González-García, A.; Pinto-Carral, A.; Villorejo, J.S.; Marqués-Sánchez, P. Competency Model for the Middle Nurse Manager (MCGE-Logistic Level). *Int. J. Environ. Res. Public Health* **2021**, *18*, 3898. https://doi.org/10.3390/ijerph1808 3898

Academic Editor: Dimitris Zavras

Received: 8 March 2021 Accepted: 7 April 2021 Published: 8 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

develop advanced management skills. This development is achieved through carrying out postgraduate university studies [22]. In this sense, West [23] states that it is possible to observe a difference between nurse managers who have undertaken university programs in management compared to others who have not completed this type of training program. For his part, Herrin [24] points out how master's degree training empowers nurse managers, enabling the effective management of the healthcare process.

Therefore, in order to identify, orient and train nurse managers, managerial competencies are an essential resource [25]. This competency training in management must go beyond the ambit of nursing, for example, including business management, artificial intelligence, technology, etc. [26–28].

Although there is no single definition of management competency [29], we can define it as the correct combination and application of the knowledge, attitudes and skills of middle nurse managers in specific management functions, which are observed and measured as behaviors [30]. New [31] defines managerial competencies as those in which the nurses are able to collaborate with other people, whereast Hudak et al. [32] define them as the skills, knowledge and capacities necessary to achieve quality healthcare.

In healthcare organizations, there is a chain of authority from upper management to the assistance level (Figure 1) [33]. A middle nurse manager (known in Spain as a "supervisora de área de enfermería" or "jefa de área de enfermería") is the person in the intermediate position between the operational level of the nurse manager and the nurse executive [34]. The middle nurse manager is responsible for translating the culture and strategy of the organization to the operational level, as well as managing resources, coordinating nursing care and planning and contributing to the evaluation of services provided, together with supporting and encouraging teamwork in the attention units and implementing innovative practices [33,35–38]. Therefore, the middle nurse manager plays a key position, since they do not only carry out clinical leadership and management, but are also responsible for translating the strategic vision and the values and objectives of the organization's care actions [39,40]. In the Spanish healthcare system, the middle nurse manager is responsible for the management and coordination of a functional area of nursing in a healthcare organization, for example, the surgical area [41].

**Figure 1.** Management levels. Source: own elaboration.

Managerial competencies have been researched from various angles. For example, Chase [41] identified technical, human and conceptual skills, as well as leadership and financial management skills. The American Organization of Nurse Executives (AONE) [36] identified the management of the relationships, communication, leadership, knowledge of the health environment and financial skills as strategic areas in the development of

competencies. González García et al. [42] highlighted the management of relationships, communication skills, listening, leadership, conflict management, ethical principles and skills for managing teams as core competencies for nurse managers. Finally, in a brief summary, Pillay [43] highlighted the management of people and organizational capacity, together with strategic thinking, as key competencies.

On the other hand, based on the literature review, it can be deduced that it is necessary to improve the state of knowledge about the role of the middle nurse manager [35,44,45]. The competencies necessary are not usually clearly defined, which would explain this gap in the understanding of the middle nurse managers. This same absence is evident in the Spanish context, as there is no model of competencies for the carrying out of management functions at the logistical level.

For this reason, the main objective of this study was to propose a model of competencies which should be developed by a middle nurse manager in the Spanish healthcare system. For this reason, the following specific objectives were proposed:

### **2. Materials and Methods**

### *2.1. Revision of the Literature*

Based on the scoping review [46] of the literature carried out during 2010–2019 to determine the competencies associated with nursing management, electronic databases were used (Web of Science, Scopus, PubMed and CINAHL) to carry out the search, identifying 56 competencies for middle nurse managers. The results of this review provided the basis for carrying out this Delphi study, evaluating the competencies of the positions of the middle nurse manager.

### *2.2. Delphi Methodology*

A four-round Delphi method was employed. The Delphi method focuses on the identification of expert opinion to reach a consensus [47]. The Delphi methodology has been considered the most convenient method when there is a lack of knowledge on a topic. [47,48].

The aim of the first Delphi round was to generate a list of competencies required for middle nurse managers. In the second round, experts were provided with feedback from the first iteration. All participants were invited to reconsider their opinion. In the third round, the experts were asked their opinion on the competencies of the middle nurse manager, in order to reach a consensus. The expert panel also was asked to indicate the training necessary to achieve specific levels of competency (expert, very competent, competent, advanced novice and novice). During round four, experts were provided with feedback from the third round and they were encouraged to rethink their original answers after reviewing the report of the third Delphi round.

### 2.2.1. Consensus

For this research, consensus was set at 80% or greater agreement (defined as somewhat in agreement–total agreement [score 4–5]) with regard to (I) the proposed competencies; (II) the level of development of the competencies; (III) the training for each competency. Items with less than an 80% response rate were eliminated for the following Delphi round.

### 2.2.2. Participants

Two categories of experts, consisting of a combined total of 50 experts (Table 1), were established based on the Delphi Technique:

**Table 1.** Sociodemographic characteristics of the panel experts.

Source: own elaboration.

Experts in healthcare management. This category of experts represented healthcare from the different hospitals and institutions, and performed management and leadership functions.

Experts in the health environment. This category of experts represented the different fields involved in healthcare and were selected for their specific views on healthcare practices, university training, students and healthcare research.

### 2.2.3. Variables

The following variables were used:

Sociodemographic variables: to determine the characteristics of the participants, information was collected on age, gender, university degree, university training, postgraduate training, professional role, location of the study, years of national activity, years of management experience, management functions carried out and international experience. Sociodemographic variables were used to establish the profile of the expert panel.

Competencies: the competencies suggested to the experts came from the literature review. The competencies were used to establish the competency model for the middle nurse manager.

### 2.2.4. Delphi Surveys

Two surveys were developed for the specific purpose of the study.

Competencies necessary for middle nurse managers: Every participant quantified their level of agreement or disagreement with each competence in accordance with the Likert scale from 1 to 5 (1 = disagree totally, 5 = completely in agreement).

Degree of development of the competencies of middle nurse managers: In order to achieve an agreement on the competencies required for each functional level of nurse manager, the level of consensus with each competency was registered in accordance with the Likert scale from 1 to 5 (1 = beginner, 5 = expert), and the type of training required to develop the competencies, in accordance with the Likert scale of 1 to 6 (1 = university extension, 2 = continuous training, 3 = university expert, 4 = diploma in university specialization, 5 = master's degree, 6 = PhD).

### 2.2.5. Degree of Development

The term "degree of development" was used to indicate the degree of proficiency shown by middle nurse managers in the performance of each competency. Based on Bernner's theory [49], therefore, the level of development was expressed as follows.

The degree of development must be understood in a progressive and exclusive manner, starting at the level of novice and ending at the level of expert. The degree of development is achieved through assigning the appropriate training to each level.

### *2.3. Principal Component Analysis*

Principal component analysis (PCA) is a technique for the transformation of data [50]. The main purpose is to reduce the dimensionality of a data set by reducing the number of variables and preserving as much relevant information as possible [50]. Factor analyses were performed according to Thurstone's theory [51,52] (3 phases): first, determining if the data are suitable for factor analysis; second, performing the extraction of the factors and, finally, carrying out the rotation and interpretation of the factors.

The Kaiser–Meyer–Olkin (KMO) method was used to determine the suitability of the data for the factorial analysis. The following step was the extraction of the data, using the Kaiser criterion, making the decision based on values higher than 1 [53], and a scree plot, which is a graphical representation transit value [54]. Finally, the rotation interpretation of the factors was made by means of the varimax rotation method and Kaiser standardization to obtain the simplest possible structure that was easy to interpret.

### **3. Results**

### *3.1. Demographic Data of the Panel of Experts*

Fifty experts responded to our invitation to participate and take part in the Delphi study. All of them completed the questionnaires of the Delphi study (100% response rate). The characteristics of the full 50-member Delphi panel are listed in Table 1.

### *3.2. Model of Competencies for the Middle Nurse Manager*

During the first and second Delphi rounds, a consensus was reached (more than 80%) for 51 competencies from the proposed list. The consensus of round 2 details the competencies that make up the model (Table 2) structured into six dimensions, according to the following definitive characteristics: management, communication and technology, leadership and teamwork, knowledge of the healthcare system, nursing knowledge and personality (Figure 2). In round 2, competencies with a consensus of less than 80% were eliminated.

**Table 2.** Model of competencies for a middle nurse manager.

Source: own elaboration. Legend of the table: EXP = expert. V. COMP = very competent. COMP = competent.

**Figure 2.** Dimensions of the middle nurse manager executive competency model. Source: own elaboration.

The third and fourth Delphi rounds demonstrated that 51 competencies are necessary for middle nurse managers, with a consensus that the development of these competencies should be at the level of "Expert", "Very competent" and "Competent". A consensus was also reached during rounds 3 and 4 to achieve the level of development of each competency. The final consensus as detailed in Table 2 and makes up the competency model for the middle nurse manager in the Spanish healthcare system.

As regards the training necessary for the development of each of the levels of competency, in Table 3, we can observe the consensus reached for each of the levels of development of the competency.

**Table 3.** Level of competency development and training.

Source: own elaboration. Note: Univ. Ext. = university extension diploma; Cont. Ed = continuing education; Univ. Exp. = university expert; Univ. Spec. D. = university specialization diploma; Master = master's degree; Ph.D. = Ph.D. degree.

### *3.3. Principal Component Analysis*

For the principal component analysis, the competencies were grouped together in dimensions, in accordance with their definitive characteristics. The dimensions of management, communication and technology and leadership and teamwork made up four principal components, the dimensions of knowledge of the healthcare system and the personality dimension comprised two principal components, whereas the nursing knowledge dimension was designated as a single main component (Table 4). The factorial loads of each

of the items integrated into each dimension widely exceeded the lower level of 0.4, and the α Cronbach demonstrates the quality of the adjustment (Table 4). From these results it can be deduced that the proposed model is structurally sound.

**Table 4.** Factor structure of the proposed competency model.

**Table 4.** *Cont.*

Source: own elaboration.

### **4. Discussion**

In this study, a competency model for the role of middle nurse manager was developed and validated (In Spain, the middle nurse manager is known as the "supervisora de área de enfermería" or "jefa de área de enfermería") in the context of the Spanish health system. The model of competencies for middle nurse managers in Spain is made up of 51 competencies, structured into six dimensions, according to their defining characteristics—(1) Management, (2) Communication and Technology, (3) Leadership and Teamwork, (4) Knowledge of the Healthcare System, (5) Nursing Knowledge and (6) Personality. These findings are in line with the arguments of McCarthy and Fitzpatrick [55], who state that the competencies of the middle nurse manager should be oriented towards negotiation, the coordination of resources, the monitoring of the activity, negotiation and empowerment. The AONE in its models suggested the need to develop 35 competencies [56], and Pillay described 51 competencies, building on the research carried out by AONE [43]. However, these models differ from our proposal in terms of the weight that has been awarded to some of the competencies that coincide within the models, such as, for example, the relationships with the management of the business.

During the third and fourth Delphi rounds, agreement was achieved among our experts on the development of the competencies. Agreement was reached for the following levels: "competent" levels (this level is achieved when there is a robust demonstration of competency), "very competent" (this level is considered to be achieved when there is

a meaningful demonstration of competency) and "expert" (this level is considered to be achieved when the knowledge and skills of the competency model are demonstrated). This proposal coincides with that of AONE, which uses the levels of competent, proficient and expert for the development of competencies, highlighting how these levels are achieved by means of a master's degree or PhD studies [57,58]. For the assessment of its relevance, we must highlight that the studies carried out by Chase [40] are different in some ways compared to our research, in that the levels of development of the competencies are not indicated, centering on the degree (minimally, moderately, significantly and essentially for management) to which they contribute to the role of a nursing manager. The results of our research highlight the importance of the strong development of competencies, in the same manner as Crawford et al. [58] emphasized the need for a high level of expertise and the development of a set of competencies to cope with the functions of a logistics-level manager.

During rounds 3 and 4 of the Delphi method, the panel of experts reached a consensus on the training that the nurse manager should develop at the logistic level in three levels of competency ("expert", "very competent" and "competent"). The "competent" level is achieved by completing continuing education, university expertise and a diploma in university specialization. As regards the "very competent" level, the experts agreed that this is achieved with university expertise, a diploma in university specialization or a master's degree. Finally, the "expert" level is reached by completing a master's degree program and PhD studies. It must be borne in mind that work experience does not prepare the nurse to assume management functions, with training being the factor that most significantly influences the development of the competencies of middle nurse managers [59]. Warshawsky et al. [21] warn of the risks to the organization if the nurses assume management responsibilities without the suitable knowledge and training. In the same way, Rizani et al. [60] and Herrin et al. [24] point out that that the competency of the nurse manager is greater when they have carried out advanced studies (a master's degree or PhD), increasing their level of competency over time to a higher degree than that of the managers who have not carried out advanced training. In this sense, the American Nurses Credentialing Center has made adjustments to the standards of training recommended for all nurse managers, elevating the degree of exigency [61].

The PCA verified the model of competencies for the middle nurse manager, highlighting that the importance of competencies can be defined by three principal components communication (communication skills, relationship management, conflict management), leadership (leadership skills and team management) and decision-making (decisionmaking and ethical principles). The eigenvalues demonstrated that the decision-making and ethical principles indicate a strong and significant relationship between these competencies [62]. Furthermore, the eigenvalues also point to the relationship between leadership and work teams [63], and between communication and conflict resolution.

The development of communication skills expected from the middle nurse manager must include the ability to provide critical thinking and stimulate reflection before taking action in nursing teams [35]. It should also provide, for instance, conflict resolution and shared decision-making, which is also associated with team management [64].

### **5. Conclusions**

In this study, consensus was reached on the competencies necessary for establishing a model of competencies for middle nurse managers (MCGE-logistic level) (In Spain: "supervisora de área de enfermería" or "jefa de área de enfermería") adapted to current health policies, economic necessities and the sustainability of the organizations and healthcare in an uncertain environment. In conclusion, this study developed a consensus on 51 competencies necessary for middle nurse managers in Spain, of which the following can be highlighted: communication, leadership and decision-making. The middle nurse manager is accountable for one of the most critical divisions of a healthcare organization, and is essential in the management of nurses and material resources. The quality of the final care provided will depend on their management style. Therefore, a nurse or a nurse

manager should not be promoted to the role of middle nurse manager without undertaking advanced programs in management.

The results of our research show the accurate levels of development for each competency for a middle nurse manager. It would be recommendable for the nurse to achieve these competencies before performing the functions of a middle nurse manager.

Any nurse who aspires to carry out the role of a middle nurse manager would be advised to develop the competencies that are set out in the proposed model beforehand.

Furthermore, this study sets out the training necessary to acquire the development of the competencies necessary for the logistical level. Both nurses who wish to be promoted to middle nurse managers and nurse managers who presently work at this level would be advised to follow the education programs that have emerged from our research, in order to adapt their knowledge to the requirements of this role.

### *Implications for Nursing Management*

This model has implications for the Spanish healthcare system, healthcare policies, and for the practices and education related to middle nurse management. The proposed model contributes to the design of the function of the middle nurse manager, to the selection processes and the design of the study plans of the nurse managers in traditional academic institutions and in programs for continuous professional development within organizations. It is probable that a greater understanding of these competencies can serve as the basis for developing interventions, which could improve the working environment of nurses and patient care, as well as ensuring the safety and the productivity of the organization.

**Author Contributions:** Conceptualization, A.G.-G., P.M.-S. and A.P.-C.; methodology, A.G.-G. and A.P.-C.; software, A.G.-G. validation, A.G.-G., P.M.-S., J.S.V. and A.P.-C.; formal analysis, A.G.-G. and P.M.-S.; investigation, A.G.-G. resources A.G.-G., P.M.-S., J.S.V. and A.P.-C. data curation, A.G.-G., P.M.-S., J.S.V. and A.P.-C.; writing—original draft preparation, A.G.-G.; writing—review and editing, A.G.-G., P.M.-S. and A.P.-C.; visualization, A.G.-G., P.M.-S., J.S.V. and A.P.-C.; supervision, A.G.-G., P.M.-S., J.S.V. and A.P.-C.; project administration, A.G.-G., P.M.-S. and A.P.-C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Article* **How Do Pharmaceutical Companies Overcome a Corporate Productivity Crisis? Business Diversification into Medical Devices for Growth Potential**

**Yoonje Euh and Daeho Lee \***

Department of Interaction Science, Sungkyunkwan University, Seoul 03063, Korea<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The purpose of this study was to analyze the performance of pharmaceutical companies' business diversification into medical devices in terms of their technical efficiency (TE) as compared to that of traditional pharmaceutical companies. For a total of 174 externally audited pharmaceutical companies engaged in the drug product business between 2008 and 2019, pharmaceutical companies were classified into two groups according to medical device business diversification. The TE of pharmaceutical companies that diversify the medical device business was lower than that of traditional pharmaceutical companies. However, in terms of the meta-technology ratio (MTR) calculated using meta-frontier analysis, pharmaceutical companies diversified into medical devices showed higher MTR than the traditional pharmaceutical company group. The results imply that the corporate performance growth potential of traditional pharmaceutical companies is lower than that of pharmaceutical companies that have diversified into the medical device business.

**Keywords:** pharmaceutical company; business diversification; medical device; technical efficiency; meta-frontier analysis

### **1. Introduction**

Health issues caused by cancer, diabetes, population aging with low birth rate, infectious diseases, etc. are topics of interest to many people. The importance of the medical device and pharmaceutical industries has been emphasized due to infectious diseases such as influenza, Middle East respiratory syndrome, and coronavirus viral disease 2019 (COVID-19). In the case of COVID-19, the virus has serious adverse effects not only on human health but also the social economy. Thus, a diagnostic test device and a vaccine for the disease are being devised and demanded in countries around the world [1–3]. This is because medical devices and medicine for patient treatment are directly connected to each other. However, despite the importance of these industries, pharmaceutical companies are under significant pressure between the consumer associations that demand inexpensive products with good efficacy and investors who pursue high performance and profit [4]

Interestingly, research and projects related to the pharmaceutical industry over the past two decades have been invested in discovering new drugs to generate profit [5]. However, as the cost for basic research and development (R&D) is gradually increased, much research and many projects at pharmaceutical companies were stopped [6]. Furthermore, poor R&D led to a decrease in the pharmaceutical industry's productivity. Specifically, changes in the industrial structure such as the rise of the biotechnology field contributed to the increase in R&D costs of pharmaceutical companies [7]. As productivity declines continue, the pharmaceutical industry is facing unprecedented industrial challenges and surveillance with the extinction of monopoly products and the reduction of pipelines caused by patent expiration [8].

To overcome the crisis of productivity decline, pharmaceutical companies first made an effort to improve the process for finding new drug candidates [9]. When productivity

**Citation:** Euh, Y.; Lee, D. How Do Pharmaceutical Companies Overcome a Corporate Productivity Crisis? Business Diversification into Medical Devices for Growth Potential. *Int. J. Environ. Res. Public Health* **2021**, *18*, 1045. https://doi.org/10.3390/ ijerph18031045

Received: 7 December 2020 Accepted: 20 January 2021 Published: 25 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

was significantly lowered, R&D through outsourcing was boldly chosen as an alternative to reduce costs [10]. Second, pharmaceutical companies aimed at increasing overall corporate productivity by improving the pharmaceutical manufacturing process [11] and optimizing the supply chain with inventory management [12]. These efforts in internal processes did not lead to improved R&D process for a new drug or higher profit that could raise corporate performance, because the causes of productivity decline in pharmaceutical companies are diverse and complex [13].

Another effort of pharmaceutical companies to overcome the crisis was business diversification [14]. For example, the number of mergers and acquisitions for the pharmaceutical and biotechnology industry in 2018 recorded a total of 1438 and a total volume of \$339.6 billion as the highest in the last decade. The medical device industry, which is indicated as a major factor in the decline of the pharmaceutical industry [15], has become a major target of business diversification from pharmaceutical companies. Interestingly, at this time, the number of acquisitions in the healthcare industry (131 cases), including medical devices, was the second highest after acquisitions of the homogeneous sector (449 cases) followed by distribution/logistics (57 cases) and the information and communication sector (30 cases) [16]. Pharmaceutical companies are highly interested in diversifying the medical device business because both medical device and drug are used by patients or doctors as end users for clinical purpose, and the distribution environment of products looks the same. The phenomenon by which multinational medical device companies are gradually developing and launching medical devices that contain medicines can also be attributed to the fact that the business environment is similar [17]. For these reasons, many pharmaceutical companies are expecting to raise overall corporate performance by improving their productivity through the business diversification of medical devices because of the productivity crisis.

However, there are many discussions on how merge and acquisition (M&A) or business diversification will affect corporate performance due to complex factors such as market characteristics of industry and understanding of product, conflicting regulations, organization, etc. as well as additional cost in the process [18–20]. In terms of the pharmaceutical industry, the studies for pharmaceutical companies mainly focused on R&D synergy of M&A with the biotechnology industry [21–23]. The integration of biotechnology with a similar R&D environment also presents various uncertainties regarding the improvement of pharmaceutical companies' performance [23]. When considering these studies, there is still an absence of research on whether the business diversification of pharmaceutical companies into medical devices positively affects their corporate performance in the crisis of productivity.

The pharmaceutical business differs from medical device in the characteristics of the entire business cycle from product R&D through sales. In the traditional pharmaceutical business, when a product is released through a large R&D investment, it continuously generates high profits with improving the supply of raw materials and promoting sales within the protection of patent rights [24]. When compared to pharmaceuticals, the medical device business has a short product life cycle and risk about easy product duplication, so the market competition is overheated due to the low entry barrier and product profits are not very high. Due to these differences, when a pharmaceutical company diversifies its medical device business, the total sales of the company might increase, but the overall performance of the company might decrease. Furthermore, considering additional costs and time, such as new production line, labor, preparation for medical device regulations, and expenses from sales and management, manufacturers should be aware that pharmaceutical and medical device businesses differ not only in their product development and life cycles, but also in the nature of the business and legal factors [25].

This study attempted to determine whether diversifying the medical device business can increase the performance of pharmaceutical companies by analyzing financial data from South Korea. According to the Ministry of Food and Drug Safety (MFDS), the Korean pharmaceutical market in 2019 increased by 5.2% from 2018 (21.24 billion USD) to 22.33

billion USD, ranking 12th in the world (1.6%). Although the Korean pharmaceutical market is growing at a high level as a mature market, overall pharmaceutical companies are facing a decline in corporate performance and a productivity crisis due to the cost of new drug development, patent expiration, regulation, and competition. The phenomenon is common among pharmaceutical companies around the world. For this reason, many pharmaceutical companies in South Korea are expecting to improve corporate performance through diversification of medical device business. South Korea became a country where the trend and phenomenon of pharmaceutical companies that are representatively diversifying medical device business is gradually expanded to overcome the decline in corporate performance and a productivity crisis. For this purpose, this study measured the performance of companies using technical efficiency (TE) indicators. Where sales or productivity is affected by the company size, technical efficiency has the advantage of not being affected by the firm size because it estimates the firm's production function first and measures technical efficiency according to the distance from the production function. Recently Chung et al. [26], Jo et al. [27], Na et al. [28], and many others measured firm performance using technical efficiency in accordance. Because conventional TE has the disadvantage of not being available for comparison between companies using different production functions, we compared pharmaceutical companies that have expanded their business to medical devices with traditional pharmaceutical companies in terms of a firm's TE.

### **2. Methodology**

### *2.1. Stochastic Frontier Analysis (SFA)*

SFA expresses the relationship between input and output factors as a production function and estimates the TE using the frontier production function representing the maximum output from the input. At this time, the TE of a company indicates where the company's technology level is relatively located, when compared to the efficiency technology level of the frontier production function. The farther the technology level of a company is from the frontier production function represents a lower level of efficiency. The production frontier can be estimated through both nonparametric and parametric methods. In this study, the production frontier for the parametric method was estimated using SFA. Also, FRONTIER *Version 4.1* software provided by Coelli was used for estimation.

According to Battesse and Coelli [29], the model of the stochastic production frontier is assumed as the following Equation (1) to reflect the change of time in efficiency.

$$Y\_{it} = f(\mathbf{x}\_{it}, \boldsymbol{\beta})e^{V\_{it} - L\_{it}}, \; i = 1, 2, \dots, N, \; t = 1, 2, \dots, T \tag{1}$$

At this time, *Yit* is the output of company *i* at time t, *xit* is the input vector of company *i* at time *t*, *f* is the production function, *β* is a vector containing the parameters of the production function, *Vit* is independent of *Uit* with a random error with a distribution of N- 0, *σ*2 *v* , and *Uit* is a nonnegative random variable representing the TE of company *i* at time *t*. If *Vit* is the general random error of the regression equation, then *Uit* represents the company's inefficiency. To show that it is always inefficient, *Uit* itself is not negative and this study assumed that *Uit* follows a half-normal distribution. Because data from 2008 to 2019 were used, *T* is 12.

From Equation (1), the efficiency, *TEit*, of company *i* at time *t* is given by Equation (2) below.

$$TE\_{it} = e^{-lL\_{it}} = \frac{Y\_{it}}{f(X\_{it}, \beta)e^{V\_{it}}}, \ t = 1, 2, \dots, N, \ t = 1, 2, \dots, T \tag{2}$$

In general, the Cobb–Douglas function and the translog function are the most widely used SFA production functions. However, in the case of Cobb and Glass, there is a tendency to oversimplify it because the output variable is seen as a linear combination of input variables. Therefore, in this study, we used the translog function. In particular, a randomeffects, time-varying production model was used. When assuming a translog type of production function, Equation (1) can be expressed as Equation (3) below.

$$\ln Y\_{it} = \beta\_0 + \sum\_{m=1}^{3} \beta\_m \ln \mathbf{x}\_{mit} + \sum\_{m=1}^{3} \sum\_{k \ge m}^{3} \beta\_{mk} \ln \mathbf{x}\_{mit} \ln \mathbf{x}\_{kit} + V\_{it} - \mathcal{U}\_{it} \tag{3}$$

where *x*1*it* represents the amount of capital (K) of the *i*-th company at time *t*, *x*2*it* represents the cost (M) of the *i*-th company at time *t*, and *x*3*it* is the number of workers (L) who receive a salary from the *i*-th company at time *t*. The total assets for K, the number of employees for L, and the cost of revenue for M are used as input variables, and net sales (Y) are used as an output variable in this study.

### *2.2. Meta-Frontier Analysis*

Since the TE of a specific company is difficult to compare with other companies using different technologies, comparisons of technological efficiency between each group cannot be performed through traditional SFA. Therefore, to compare the efficiency levels of different groups operating under different technical conditions, we used the meta-frontier production function that wraps the production functions of all groups [30]. From Battese, Rao, and O'Donnell [31], the meta-frontier production function model is defined as follows.

$$Y\_{\rm it}^{\*} = f\left(\mathbf{x}\_{\rm it}, \boldsymbol{\beta}^{\*}\right) = e^{\mathbf{x}\_{\rm i}\boldsymbol{\beta}^{\*}}, \ i = 1, 2, \dots, N, \ N = \sum\_{i=1}^{R} N\_{\rm j}, \ t = 1, 2, \dots, T, \ \text{s.t.} \ \mathbf{x}\_{\rm it}\boldsymbol{\beta}^{\*} \ge \mathbf{x}\_{\rm it}\boldsymbol{\beta}\_{\rm \left(j\right)} \text{ for all } j \tag{4}$$

In this case, *β*(*j*) is a vector composed of the parameters of the j-th group's production function and j indicates each group. In this study, the two groups are traditional pharmaceutical companies that have only produced medicines (*j* = 1) and diversified pharmaceutical companies that also produce medical devices (*j* = 2). *β*∗ is a vector of unknown variables of the meta-frontier function that satisfies the following equation. From Equation (4) above, the graph of the meta-frontier production function is positioned above the graph of the production frontier function of each group for all periods. The meta-frontier production function becomes an envelope of the frontier functions of each group based on the same technology. For simplicity, if we assume that function *f* in Equation (1) is *e Xitβ*(*j*), Equation (1) can be divided as shown in Equation (5).

$$\mathcal{Y}\_{it} = \mathfrak{e}^{-\mathcal{U}\_{it(j)}} \times \frac{\mathfrak{e}^{\mathbf{x}\_{it}\mathfrak{f}\_{(j)}}}{\mathfrak{e}^{\mathbf{x}\_{it}\mathfrak{f}^{s}}} \times \mathfrak{e}^{\mathbf{x}\_{it}\mathfrak{f}^{s} + \mathcal{V}\_{it(j)}} \tag{5}$$

Dividing both sides of Equation (5) by *e xitβ*∗+*Vit*(*j*) yields the following.

$$\frac{Y\_{it}}{\varepsilon^{x\_{it}\beta^\* + V\_{it(j)}}} = \varepsilon^{-\mathcal{U}\_{it(j)}} \times \frac{\varepsilon^{x\_{it}\beta\_{(j)}}}{\varepsilon^{x\_{it}\beta^\*}} \tag{6}$$

In Equation (6) above, the right side, *e* −*Uit*(*j*) is the technical efficiency (TE) of Group j and the second is the j group frontier for the meta-frontier function. It is expressed as a ratio of a function, which is called the Technical Gap Ratio or Meta-Technology Ratio (MTR). *TE*\*, representing TE in the meta-frontier function, is calculated by multiplying TE by MTR and can be expressed as Equation (7).

$$TE\_{it}^{\*} = \frac{Y\_{it}}{e^{x\_{it}\beta^\* + V\_{it(j)}}} = TE\_{it} \times TGR\_{it} \tag{7}$$

There are two methods of measuring the parameters of a meta-frontier function: Linear Programming (LP) and Quadratic Programming (QP). LP is a method of minimizing the sum of the absolute deviation values, and QP is a method of minimizing the sum of

the squares of deviations. According to Battese, Rao, and O'Donnell [31], LP and QP are defined as following Equations (8) and (9).

$$\text{LP}: \min\_{\beta^\*} L^\* = \sum\_{t=1}^T \sum\_{i=1}^N \left| \mathbf{x}\_{it} \beta^\* - \mathbf{x}\_{it} \boldsymbol{\hat{\beta}}\_{(j)} \right|, \text{ x}\_{it} \boldsymbol{\beta}^\* \ge \mathbf{x}\_{it} \boldsymbol{\hat{\beta}}\_{(j)} \tag{8}$$

$$\text{QP}: \min\_{\beta^\*} L^\* = \sum\_{t=1}^T \sum\_{i=1}^N \left( \mathbf{x}\_{it} \beta^\* - \mathbf{x}\_{it} \hat{\boldsymbol{\beta}}\_{(j)} \right)^2, \ \mathbf{x}\_{it} \beta^\* \ge \mathbf{x}\_{it} \hat{\boldsymbol{\beta}}\_{(j)} \tag{9}$$

Matlab 7.1 software was used to measure the parameters of the meta-frontier function using LP and QP.

### **3. Estimation Results**

In this study, actual corporate financial data were secured from the KIS-VALUE database of the National Information and Credit Evaluation. Based on the information from the South Korean Ministry of Food and Drug Safety (MFDS), pharmaceutical companies are divided into traditional pharmaceutical companies and diversified pharmaceutical companies for medical devices according to their import certifications and licenses to manufacture medical device products. Specifically, the number of externally audited Korean pharmaceutical companies that acquire approval to manufacture and import medical devices for general treatment and surgery from the MFDS has increased rapidly since 2008. Because of a novel influenza outbreak in 2009, Korean pharmaceutical companies expanded to new businesses that manufacture, import, and distribute various types of medical devices, from advanced medical devices to diagnostic test kits and instrument. Thus, a total of 174 externally audited pharmaceutical companies in South Korea were identified for the period between 2008 and 2019.

One hundred three traditional pharmaceutical companies that only conducted pharmaceutical business and 71 pharmaceutical companies that diversified into the medical device business were separated into two groups. The total number of samples with fiscal data used in the study was 1028 for traditional pharmaceutical companies and 728 for pharmaceutical companies diversified into medical devices. Table 1 contains details regarding the samples.

**Table 1.** Sample statistics.

Note: Numbers in the parenthesis are standard deviations. 1 USD is 1,105.0 KRW (Korean Won) as of 25 January 2021.

FRONTIER 4.1 was used for SFA and meta-frontier analysis (MFA) with MATLAB 7.1 was carried out to verify corporate efficiency. Table 2 shows the estimated frontier production function for each group with meta-frontier production function parameters optimized through LP and QP methods.

**Table 2.** Estimation results of group and meta-frontier production functions.

Note: \*, \*\*, and \*\*\* mean *p* < 0.1, *p* < 0.05, *p* < 0.01 respectively.

Table 3 shows the results of the TE for each group and the value of TE\* in the metafrontier production function with the MTR by using the estimates of the group frontier production function and meta-frontier production function.

**Table 3.** SFA estimates of technical efficiencies and meta-technology ratios.

As a result, traditional pharmaceutical companies (76.28%) showed higher TE values when compared to diversified pharmaceutical companies for medical devices (59.11%). However, as mentioned earlier, comparisons between groups using different production functions are meaningless. Therefore, the TE of the two groups using different production functions should be compared through MTR. Conversely, a group of diversified pharmaceutical companies for medical devices showed a higher MTR value both with LP and QP (MTR\_LP: 94.54%, MTR\_QP: 88.82%) when compared to the group of traditional pharmaceutical companies (MTR\_LP: 80.39%, MTR\_QP: 76.67%).

### **4. Discussion**

Traditional pharmaceutical companies have pursued high corporate efficiency through continuous research on pharmaceutical business models in diverse fields such as patents, regulations, distribution, R&D, and manufacturing innovation. With recent advances in marketing strategies and technologies, the pharmaceutical industry has been trying to overcome the decline in productivity by maximizing profits for many years [32–34].

Nevertheless, unlike increasing their market size, the problem of decreasing productivity has not yet been solved. For the pharmaceutical industry, this is expected to be a complex cause of the already mature business model, excessive market competition, regulation, and competition with similar businesses such as the biotechnology and medical device industries. Companies might seek to integrate with homogeneous firms to improve performance and expect corporate synergy through the combination and expansion of heterogeneous industries. The pharmaceutical industry is also making an effort to reinforce their business portfolio through mergers and acquisitions and business diversification due to the efficiency decline and low productivity. As mentioned in the introduction, in 2018, a total of 1438 transactions with a total volume of \$339.6 billion in global mergers and acquisitions in the pharmaceutical and bio-industry industries were recorded. The number of acquisitions in the healthcare industry including medical devices (131 cases) was the highest after homogeneous industry acquisitions (449) [16].

As mentioned earlier, pharmaceutical companies expect to improve overall productivity by reinforcing R&D pipelines between homogeneous industries [35], while expecting to improve corporate performance through business expansion into different industries. Globally, pharmaceutical companies' business expansion of the healthcare business including medical devices has gradually increased, and Korean pharmaceutical companies, which are diversifying into the medical device business, also had a total of 31 (17.8%) in 2008. Currently, a total of 71 (40.8%) pharmaceutical companies have gradually increased their number to diversify their medical device business. Thus, the importance of research on how the medical device business affects the performance of pharmaceutical companies has been raised in South Korea.

As a result of this study, the MTR of the pharmaceutical company group that conducted business diversification was higher than that of the traditional pharmaceutical company group. This higher MTR of the diversified group means that the group's frontier production function is located closer to the meta-frontier production function. The frontier production function is determined by the technology used by the companies in the group and is the set of maximum outputs that the companies can produce. Therefore, the fact that the MTR of the pharmaceutical group that diversified into the medical device business is higher than the MTR of the traditional pharmaceutical group means that the maximum output that can be produced through the same input is higher, that is, it has a higher potential. Interestingly, in the MFA, which is the same production function, both TE\* calculated with LP and QP showed higher results than traditional pharmaceutical companies diversifying their medical device business. As described above, TE\* can be calculated as the multiplication of TE and MTR. In traditional pharmaceutical companies, although the MTR was lower than that of the pharmaceutical company group that diversified into the medical device business, the TE was much higher than that of the group. Thus, the TE\* was higher. This means that traditional pharmaceutical companies are exhibiting higher efficiency under the current production function, but their potential is lower than that of pharmaceutical companies that have diversified into the medical device business.

Based on research results, policy makers and corporate decision makers as well as future study should consider the following implications of this study for sustainable management in the pharmaceutical industry and the medical device industry.

First, in terms of theoretical implications, while most of the existing studies for decades focused mainly on improving the R&D process of the pharmaceutical industry, the industry has been establishing diverse strategies to grow the productivity and performance potential of companies such as the business diversification of medical device. Future studies should be able to fill the blanks in these relevant research topics and provide new perspectives to improve the performance of pharmaceutical companies.

Second, in terms of practical implications, the interaction between doctors and pharmaceutical companies is known to influence the prescription of medicines [36]. Although doctors are users, they have a large portion of the patents for medical devices [37]. Since they have already been involved from the earliest stages of development in clinical trials [38], doctors are interested in not only medicines but also medical devices. Furthermore, since the trend of the treatment process is rapidly changing due to the development of medical devices, the direction of drug development and productivity can be flexibly modified. This is the reason why medicines and medical technology are complementary to each other and technology advancement is simultaneously progressing. Therefore, business diversification into medical devices seems to increase the potential for corporate performance by improving the technological efficiency of pharmaceutical companies.

Third, in terms of policy implications, policy makers considering and reviewing business promotion of the pharmaceutical industry should be encouraged to review diversified business models to improve corporate performance and potential. Furthermore, R&D for new drugs must be continuously conducted by promoting corporate policy with business portfolio expansion of medical device with complicated crisis of productivity.

### **5. Conclusions**

The medical device business clearly differs from the pharmaceutical business in terms of the product production cycle from R&D to product launch and follow-up management. However, when we examined the two businesses from the perspective of healthcare providers and users, not from manufacturers or regulatory agencies, medical devices and medicines in treatment are complementarily connected. In other words, both products are developed and used for the purpose of treatment based on the interaction between the doctor and patient. In conclusion, when compared to traditional pharmaceutical companies, pharmaceutical companies diversified into the medical device business are dominating the traditional pharmaceutical companies in the trends of treatment process and the contact point of customers with market changes. Therefore, these advantages appear to improve overall corporate performance growth potential.

As the pharmaceutical industry may experience a decline affected by major developments in the medical device industry and the biotechnology industry, it is necessary to take insights on multiple businesses and have a view of political decision for pharmaceutical industry promotion. Policy makers should not encourage companies to carry out one business and a similar industry as their conservative general policy.

This study has the following limitations. First, since this study used data from externally audited companies for the period between 2008 and 2019, smaller companies with low total assets were excluded from the group. Second, different variables that could affect the TE of pharmaceutical companies were not considered as well as business diversification. Third, the study is insufficient to provide global implications because it analyzed data only from Korean pharmaceutical companies. Therefore, future research could provide additional implications for the pharmaceutical industry if it continuously expands its importance by using worldwide data and considering other variables that can affect technological efficiency.

**Author Contributions:** Conceptualization, Y.E. and D.L.; Analysis, D.L.; Writing, Y.E. and D.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Ministry of Education of the Republic of Korea and the NRF (No. 2020S1A5A8045556, 2020R1F1A1048202).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from KIS-VALUE and are available from https://www.kisvalue.com/web/index.jsp with the permission of KIS-VALUE.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

International Journal of *Environmental Research and Public Health*

### *Article* **Studying Healthcare A**ff**ordability during an Economic Recession: The Case of Greece**

### **Dimitris Zavras**

Department of Public Health Policy, School of Public Health, University of West Attica, 11521 Athens, Greece<EMAIL_ADDRESS>
Received: 18 August 2020; Accepted: 22 October 2020; Published: 24 October 2020

**Abstract:** The significant deterioration of economic prosperity in Greece during the economic crisis decreased patients' ability to pay. Thus, the objective of this study is to determine the factors affecting healthcare affordability in Greece during an economic recession. This study used data from the European Union Statistics on Income and Living Conditions (EU-SILC) 2016. The sample consisted of 18,255 households. Healthcare affordability was regressed on geographic characteristics as well as several variables that refer to the households' financial condition. Region of residence, ability to make ends meet, and capacity to cope with unexpected financial expenses were found to be statistically significant. Using sample sizes of 1000 and 1096 adults, respectively, the European Quality of Life Surveys (EQLS) of 2007 and 2016 were also used as data sources. Economic crisis was expressed with a dummy variable: (1) 0: 2007, and (2) 1: 2016. Difficulty in responding to healthcare costs was regressed on survey year and several demographic, socioeconomic, and health characteristics, revealing that individuals were more likely to face difficulties in responding to healthcare costs during the economic crisis. These results confirm the mechanism on the basis of which economic crises affect healthcare access: primarily through the effects of demand-side barriers.

**Keywords:** healthcare affordability; economic crisis; ability to make ends meet; capacity to cope with unexpected financial expenses; geographic characteristics; socioeconomic characteristics; epidemiologic characteristics; demand-side barriers; supply-side barriers

### **1. Introduction**

Healthcare affordability, or, in other terms, "financial access", is among the main dimensions of healthcare access and relates the healthcare services' prices and providers' insurance or deposit demands to users' income, capacity to pay, and existing health insurance coverage [1]. Healthcare affordability may depend on a ratio of healthcare expenditure to non-healthcare expenditure with a consideration for budget constraints and other sourced elements of coverage [2]; the term "affordability" has no clear meaning in economics [3].

Thus, the operationalization of the concept of affordability requires: (i) informative data on household incomes; (ii) knowledge of the commodity price, and (iii) a definition of "unreasonable burden" [4]. However, there is a consensus that affordability is a subjective concept [5,6].

Healthcare affordability refers to financial and incidental costs [7] imposing barriers to access [8,9] and resulting in unmet healthcare needs [10], i.e., situations in which an individual needs healthcare but does not receive it [11]. Because both supply factors and demand factors result in unmet healthcare needs [12], unmet healthcare needs are a function of the healthcare system features or associated with the personal circumstances of those seeking healthcare [13]. In other words, unmet healthcare needs may be a consequence of limited availability of health services but may also arise, among others, from individual accessibility issues, such as cost [14].

The variables presented below are the key demand-side determinants of healthcare affordability, as they have been identified in the international bibliography.

By definition, healthcare affordability looks at prices of services as they relate to patients' income and capacity to pay [15]. As such, affordability is associated with dimensions of poverty [16], as poverty directly negates access to material resources [17].

Since income and, to a lesser extent, occupation determine the resources of individuals [18], limited income, unemployment and lack of health insurance coverage are strongly related to affordability barriers [19]. That is, higher income as an indicator of the disposable financial means of individuals [20] directly increases healthcare affordability [21], while those with lower incomes often have restricted access to healthcare services [22]. Additionally, unemployment puts individuals at risk of poverty and because of its influence on material circumstances, it can impair financial healthcare access [23]. In addition to drastic income reduction, job loss is often linked to a loss of health insurance [24], especially in Bismarck-type social security schemes, although this is not the case in all healthcare systems. On the other hand, the existence of health insurance coverage can nearly eliminate the negative influence of limited income on healthcare access and by decreasing out-of-pocket payments, makes healthcare more affordable for lower income individuals [25], providing access that would be otherwise unaffordable [26].

Research shows that healthcare expenditures often compete with other expenses in family budgets. According to Kushel et al. (2006) [27], housing instability, i.e., difficulties in paying rent, mortgage, or utility bills, is considered a risk factor for reduced access to healthcare services. Thus, because high housing costs reduce disposable income and impede families' capacity to account for other necessities [28], they constitute a risk factor for the postponement of healthcare services [29,30], which in essence translates to less overall investment in healthcare [31].

The relationship between health status, poverty and access to care is complex and confounded by several biologic as well as social determinants of health. Individuals with poor health often face unaffordable healthcare costs [32] due to their greater need for healthcare (more intense and more frequent use of healthcare), but also due to their low socioeconomic status. Poor health can result in fewer labor market opportunities and lower income as a result of lower productivity [33]. It is also well documented in social epidemiology that lower socioeconomic status and less disposable income force individuals to make life choices that lead to lower health status, creating a vicious cycle of increasingly poor health that leads to poor income and vice versa.

Age is another important variable in this discussion. In general, younger individuals are healthier than older adults but are more likely to be uninsured [34], and thus they face financial barriers in accessing healthcare [35] when they need it. However, the effect of age on healthcare affordability requires special analysis, since the probability of an individual being in poverty decreases from childhood to adulthood and then increases again with advanced age over one's own life cycle [36].

Research on gender differences shows that females have more difficulty affording healthcare than males [37], because females are more vulnerable to gaps in medical insurance coverage [38], but also because they have less access to material resources as compared to males [39]. In addition, females are more likely to develop numerous chronic health conditions resulting in high costs [38], but also are more vulnerable to reduced incomes and unemployment, as mentioned above.

Place of residence is one more variable that is important in the analysis between income, healthcare affordability and access. Healthcare affordability is found to be reduced for residents of rural areas [40], perhaps because rural populations are poorer, earn less at work, and work in industries with lower levels of employer-sponsored healthcare insurance coverage [41].

In summary, healthcare affordability depends on health systems features, such as financing [42], but it also depends on the population characteristics. Specifically, demographic (age and gender) and socioeconomic characteristics (income and occupation), health-related characteristics (health status, existence of a chronic health disease) and structural characteristics (health insurance coverage) affect healthcare affordability (Figure 1). Although demand-side factors such as those mentioned above are the main determinants of healthcare affordability at the individual level, the interrelation of these factors with the healthcare system should not be neglected. However, the causal relationship between

healthcare systems with health and wealth-two of the most important determinants of healthcare affordability-is not clear cut and not easily measurable [43].

**Figure 1.** Main demand-side factors affecting healthcare affordability.

As mentioned above, healthcare access depends not only on individual or population characteristics, but also on healthcare system characteristics [44]. Access to care issues as a function of the characteristics of particular healthcare systems must also be studied. For example, exemptions and low-income protection in several healthcare systems guarantee access to certain vulnerable groups of the population, such as those in poor health, older individuals, children or adolescents, women, and low-income individuals [45]. That is, the factors that affect access are related to healthcare system design.

Although in theory most of the European countries provide universal or nearly universal population coverage, research shows that low-income individuals, those in poor health, those between 20 and 30 years old, the unemployed, and women have a higher probability of feeling unable to access care. That is, the most financial disadvantaged groups of the population are the most likely to feel that they will be unable to access needed care [46].

Indeed, it is evident that people in the lowest income quintile are almost three times more likely to forgo healthcare for financial reasons versus richer individuals. In most member countries of the Organization for Economic Co-operation and Development (OECD), healthcare is less affordable for households with a low income than those with a high income [47]. The same figure also stands for developing countries: the poor face more financial difficulties to access healthcare [48].

Among the countries facing the economic crisis of 2008, Greece experienced the most severe economic recession. Several fiscal measures and structural reforms were implemented after May 2010 and triggered high unemployment and a considerable reduction in disposable income.

Greece's healthcare system is a mixed system in terms of both funding and provision-that is, a national health service type of system that coexists with a compulsory work-related social insurance system and a private healthcare sector. The National Organization for the Provision of Health Services (EOPYY) was established in 2011 after merging four large health insurance funds and was both a provider and purchaser of care. It covers the insured and their dependents. Since 2014, EOPYY has acted only as purchaser of care for the vast majority of the insured.

Insured individuals can access all public primary and secondary healthcare services free of charge; they also have access, on a case-by-case cost-sharing basis, to certain private providers contracted with EOPYY. In the case of private healthcare providers not contracted with EOPYY, insured individuals have to pay the entire cost by themselves (out-of-pocket payments) or through private insurance. Public healthcare coverage by EOPYY has been ensured since 2016 for uninsured citizens [49].

The significant deterioration of economic prosperity in Greece during the economic crisis decreased patients' ability to pay [50]. Thus, a substantial percentage of the Greek population could not afford healthcare [51]. The abovementioned evidence reflects the fact that private spending is one of the main sources of healthcare funding in Greece; the other two are taxation and social insurance. That is, not only could few Greeks afford private healthcare [52], but a significant percentage of insured individuals also limited their use of the EOPYY units because they could not afford co-payments [53].

Based on the previous points, the objective of this study is to determine the factors affecting the affordability of healthcare services in Greece during an economic recession. A second goal is to determine the characteristics of those considering healthcare affordability as a reason for unmet healthcare needs. A third goal is to study the impact of the economic crisis on individuals' ability to respond to their healthcare costs.

### **2. Materials and Methods**

### *2.1. Data Sources*

In this study, healthcare affordability was operationalized as households' perceived ability to respond to the healthcare costs.

In order to study healthcare affordability, three different models were fitted. The first model, which attempts to determine the factors affecting the affordability of healthcare services in Greece during an economic recession, used data at the household level from the EU-SILC 2016. The second model, which attempts to determine the characteristics of those considering healthcare affordability as a reason for unmet healthcare needs, used data at the individual level from the EU-SILC 2016. The third model, which attempts to study the impact of the economic crisis on individuals' ability to respond to the healthcare costs, used data at the individual level from the EQLS 2007 and 2016.

As mentioned above, in the first model and second model, data from the EU-SILC 2016 were used (Source: Hellenic Statistical Authority, EU-SICL 2016). The survey used a two-stage stratified sampling. The sample selection strata were based on the 2011 Census of the Hellenic Statistical Authority and were defined by region based on the Nomenclature of Territorial Units for Statistics II (NUTS II) and urbanity status. The sample size was 18,255 households. A total of 37,850 interviews were completed. All household members aged 16 years and over were selected for an interview. A personal interviewing technique was used for data collection. The data collection took place between May 2016 and November 2016.

In addition, in order to study the effect of the economic crisis on individuals' ability to respond to the healthcare costs (third model), we used data from the EQLS of 2007 and 2016 (Source: United Kingdom Data Archive (UKDA), EQLS 2003–2016) [54]. EQLS 2007 used a multi-stage, stratified, and clustered design with a "random walk" procedure for the selection of the households at the last stage. The sample size was 1000 adults. A face-to-face interviewing technique was used for the data collection. The data collection took place between September 2007 and November 2007. EQLS 2016 used a multi-stage, stratified, random sampling design. The sample size was 1096 adults. A face-to-face interviewing technique was used for the data collection. The data collection took place between September 2016 and February 2017. Economic crisis was expressed with a dummy variable: (1) 0: 2007, and 1: 2016. The analysis was not based on weighted data because the weighting methods of EQLS 2007 and EQLS 2016 were different (Eurofound, 2020).

### *2.2. Dependent and Independent Variables Used in the Three Models*

Potential predictors in the analysis were considered that: (a) are dependent on the economic crisis; and (b) have been identified in the international bibliography as determinants of healthcare affordability, unmet healthcare needs, and individuals' ability to respond to the healthcare costs. An additional criterion of independent variable selection was variable availability. For example, health insurance coverage was not available in the EU-SILC 2016, EQLS 2007, and EQLS 2016 surveys.

### 2.2.1. Variables Used in the First Model

The dependent variable in the first model was households' perceived ability to afford to pay for healthcare or households' perceived ability to respond to the healthcare costs; the question under study was asked as follows: "is your household able to afford to pay for health care services provided for all household members?", with answers: (1) with great difficulty; (2) with difficulty; (3) with some difficulty; (4) fairly easily; (5) easily, and (6) very easily. That is, perceived ability to afford to pay for healthcare corresponds to a single item concerning the household as a whole. The question was asked to the person responding to the household questionnaire.

In several studies, the ability to pay for healthcare is used as a synonym of healthcare affordability [55–58]. This specific ability interacts with direct, indirect, and opportunity costs in order to generate access. Thus, affordability reflects the economic capacity for people to spend resources and time to use services [59].

The outcome was dichotomized [60]: 0 to denote difficulty (great difficulty; difficulty; or some difficulty; and 1 ease (fairly easily; easily; or very easily) [61]. Health care spending is identified as unaffordable if costs exceed a relative threshold determined by a family's income or, more appropriately, a fraction of a family's available resources [62]. The existence of the affordability threshold justifies the use of a dichotomous variable. In addition, several studies have focused on the presence or absence of financial barriers [63–65]. Furthermore, the ability to pay may be approached by a dichotomous variable [66]. Thus, a logistic regression model was fitted to determine the factors associated with the degree of cost-related difficulty facing Greek households with regard to healthcare use. The potential predictors used in the first model are presented in Table 1.

Densely populated areas are defined as those areas with at least 50% of people living in contiguous grid cells of 1 km2 with a density of at least 1500 inhabitants per km2 and a minimum population of 50,000. Intermediate density areas are defined as areas with clusters of contiguous grid cells of 1k2 with a density of at least 300 inhabitants per k2 and a minimum population of 5000. Thinly-populated areas are defined as areas with more than 50% of the population living in rural grid cells outside urban clusters (Eurostat, 2020).

### 2.2.2. Variables Used in the Second Model

As mentioned above, data at the individual level (data source: EU-SILC 2006) were also analyzed. Specifically, each household member was asked two questions in terms of their healthcare use. The first question was, "was there any time during the past 12 months when you really needed medical examination or treatment (excluding dental) for yourself?" The potential answers to the first question were (1) no and (2) yes, at least one occasion. The 2nd question was "did you have a medical examination or treatment each time you really needed?" The potential answers to the second question were (1) yes (I had a medical examination or treatment each time I needed) and (2) no (there was at least one occasion when I did not have a medical examination or treatment). The respondents were then asked the question: "what was the main reason for not having a medical examination or treatment? with answers: (1) could not afford it (too expensive or there was no insurance coverage; (2) waiting list; (3) could not take time because of work, care for children, or for others, etc.; (4) too far to travel or no means of transportation; (5) fear of medical doctors, hospitals, examination, or treatment; (6) wanted to wait and see if the problem got better on its own; (7) did not know any good medical doctor; and (8) other reasons. This was dichotomized as: (a) 1: could not afford it (too expensive or there was no insurance coverage) and (b) 0: all the remaining reasons. Thus, a logistic regression model was performed, using the derived variable as the outcome. The potential predictors used in the second model are presented in Table 2.

**Table 2.** Potential predictors (2nd model).

**Table 2.** *Cont*.

### 2.2.3. Variables Used in the Third Model

In order to study the effect of the economic crisis on individuals' ability to respond to the healthcare costs, we used data from the EQLS of 2007 and 2016. The respondents were asked the question, "thinking about the last time you needed to see or be treated by a General Practitioner (GP), family doctor, or health center, to what extent did any of the following make it difficult or not for you to do so?" With regard to the option "cost of seeing the doctor", the answers were: (1) very difficult; (2) a little difficult, and (3) not difficult at all. This variable (the outcome) was dichotomized as follows: (1) 0 was not difficult at all, and 1 was a little difficult and very difficult. The rationale of dichotomization is as mentioned above, but it also was driven by the need for comparability with previous results. Since the outcome was dichotomized, a logistic regression model was fitted. The potential predictors used in the third model are presented in Table 3. Income was not used as a potential predictor due to the high percentage of missing values (19.37%).

**Table 3.** Potential predictors (3rd model).

**Table 3.** *Cont*.

### *2.3. Independent Variables' Coding*

Helmert coding was applied to the ordinal variables (a) 1st model: population density, ability to make ends meet, being in arrears on utility bills during the last 12 months, financial burden induced by total housing cost; (b) 2nd model: age, self-rated health, education and (c) 3rd model: degree of urbanity(subjective), self-rated health, education, ability to make ends meet). Helmert contrast compares each category (except the last) with the differences from the balanced mean of subsequent levels. Indicator coding was applied to the nominal variables (a) 1st model: region; (b) 2nd model: occupation and (c) 3rd model: occupation). Indicator contrast compares the reference category of a nominal variable with the remaining categories. Binary variables (a) 1st model: capacity to cope with unexpected financial expenses; (b) 2nd model: gender, existence of a chronic health condition and (c) 3rd model: year, gender, existence of a chronic health condition) were treated as such.

### *2.4. Multicollinearity*

Multicollinearity was tested through the variance inflation factor (VIF) and the tolerance. The VIF should not be greater than 10, and the tolerance should not be less than 0.1. Multicollinearity was not present (in each model, VIF < 10 for all predictors, and tolerance > 0.1 for all predictors).

### *2.5. Fitting Process*

The fitting process for all three models is as follows: although, the sample sizes of both EU-SILC 2016 and EQLS 2007 and 2016 surveys were large, the variable selection was based on a univariate model for each explanatory variable. Any variable with *p* < 0.25 was considered to be a candidate for the multivariate model. Once the variables were identified, a model containing all of the selected variables was fitted [67]. The healthcare affordability model had *p* < 0.25 for all potential predictors in the univariate analysis. For the unmet need due to affordability issues model, the existence of a chronic health disease had *p* > 0.25 (*p* = 0.873) in the univariate analysis. The remaining variables had *p* < 0.25. In the individuals' ability to respond to the healthcare costs model, age (*p* = 0.371), occupation (*p* = 0.526), and subjective urbanity status (*p* = 0.336) had *p* > 0.25 in the univariate analysis. The other variables had *p* < 0.25. The models only included the statistically significant covariates (significance level was set to a = 0.05).

The calibration of the models was tested with the calibration belt test [68]. In addition, the models were tested for specification error through the link test [69]. McFadden's *R*2 [70] was then calculated.

The STATA 14 statistical software package was used for the analysis. More specifically, the commands desmat [71], collin (Author: Ender P. B.), logistic, linktest, calibrationbelt [72], and fitstat [73] tests were used.

### **3. Results**

### *3.1. First Model*

Most (71.25%) Greek households used healthcare services during the last 12 months. In addition, 89.52% of the Greek households made a payment (either fully or partially) for healthcare services during the last 12 months. Furthermore, a substantial percentage of the Greek population (31.97%) reported facing great difficulty in responding to healthcare costs, while a similar percentage (34.70%) reported facing difficulty (Table 4).

**Table 4.** Healthcare affordability.

According to the logistic regression model (first model), healthcare affordability depends on the region of residence, the ability to make ends meet, and the capacity to face unexpected financial expenses. Residents of Attica found it easier to respond to the cost of healthcare versus residents of other regions. In the same vein, financially comfortable households and households that could face unexpected financial expenses reported a lower likelihood of cost-related difficulties with regard to healthcare use (Table 5).

**Table 5.** First model (logistic regression model).

The link test indicates that the model does not suffer from specification error (Table 6).

In addition, McFadden's *R*2 = 0.30 indicates a perfect fit [70]. Furthermore, the calibration belt test (*p* = 0.096) indicates good calibration.

**Table 6.** Link test (first model).

### *3.2. Second Model*

With respect to unmet healthcare needs, 14.40% of the respondents did not receive needed healthcare during the last 12 months at least once. Most respondents (83.15%) reported unmet healthcare needs due to affordability issues (Table 7).

**Table 7.** Reasons for unmet medical need.

According to the logistic regression model (second model), the unmet healthcare need due to affordability issues depends on self-rated health, education, and occupation. Specifically, individuals with lower education are more likely to report unmet healthcare needs due to affordability issues. The same also holds for the unemployed, pupils, students, those in compulsory military or community service, those in further training or in unpaid work experience, and those fulfilling domestic tasks and care responsibilities relative to those working full-time. However, those rating their health as very bad and those rating their health as good are less likely to report an unmet healthcare need due to affordability issues versus the subsequent levels. Those rating their health as fair are more likely to report an unmet healthcare need due to affordability issues (Table 8).

**Table 8.** Second model (logistic regression model).

**Table 8.** *Cont*.

The link test indicates that the model does not suffer from a specification error (Table 9).

**Table 9.** Link test (second model).

McFadden's *R*2 = 0.066 indicates a poor fit [70]. However, the calibration belt test (*p* = 0.083) indicates good calibration. Both models have a good fit based on the diagnostic tests.

### *3.3. Third Model*

According to the EQLS 2007 and 2016 data, in 2007, the percentage of those facing difficulty in responding to the healthcare costs was 46.22%. This percentage was 64.85% in 2016 (Table 10).

**Table 10.** Difficulty in responding to healthcare cost.

According to the logistic regression model (third model) (Table 11), individuals were more likely to face difficulties in responding to the healthcare costs during the crisis (year = 2016). In addition, women were more likely to face difficulties in responding to the healthcare costs. However, those making ends meet very easily, easily, fairly easily, and with some difficulty were less likely to face difficulties in responding to the healthcare costs than those making ends meet with higher levels of difficulty.

**Table 11.** Third model (logistic regression model).

According to the link test (Table 12), the model does not suffer from specification error. McFadden's *R*2 = 0.057 indicates a poor fit [70]. However, the calibration belt test (*p* = 0.192) indicates good calibration.

### **4. Discussion**

Affordability is commonly referred to as the "degree of fit" between the full costs (the price of service at the point of delivery as well as other direct or indirect costs) and the users' capacity to pay within the framework of their budget and other demands on that budget [74].

As mentioned in the introduction, healthcare affordability is a subjective concept [75]. We note that uncertainty constitutes a source of subjectivity with regard to healthcare affordability because prices in the healthcare market are uncertain [76,77], and the same holds for both the health and the effectiveness of medical treatment [78]; thus, the price and quantity of medical services are not initially known [79]. The uncertainty surrounding the decision-making process-the process with regard to healthcare use [80]-refers to limited knowledge and involves subjectivity [81]. Furthermore, self-perceived affordability constitutes a subjective assessment [82].

At this point, we note that affordability is considered a subjective concept not only at the level of individual judgment but also at the level of political judgment [83].

Although several demographic, socioeconomic, structural, and health-related factors influence healthcare affordability, interpreting the effect of geographic characteristics in a consistent health economics framework requires considering both the economic characteristics of the areas under study and the regional distribution of healthcare services.

Thus, the area of residence can have a negative effect on healthcare affordability and may be partially explained by the fact that, in Greece, the best situation with regard to the risk of poverty is observed in Attica (Source: Eurostat, 2020). On the other hand, Greek resources are largely concentrated in Athens [84]. Because supply-side issues play an important role in limiting access [85], it is obvious that people who live further from healthcare services are likely to face higher costs related to the use of medical care than those living closer [86]. It is also evident that a substantial percentage of the Greek population cannot even afford the cost of transportation to healthcare services [87]; thus, accessibility influences the choice of services available and the costs-both monetary and non-monetary-that users must pay to gain access [88]. These findings justify the results of a survey conducted in northern and western Greece indicating that the financial means of a substantial percentage of these areas' residents were not sufficient for them to afford healthcare [89].

The improvements in affordability in Greece requires interventions at the national level in terms of resource allocation, because accessibility overlaps with availability and includes affordability [90]; poor geographic availability of healthcare services impacts affordability [91].

The effect of the remaining variables that were found to be statistically significant is somewhat obvious. The ability to make ends meet is one of the dimensions of living standards [92] and is directly related to healthcare affordability [93]. It is obvious that the inability to make ends meet constitutes the primary determinant of relinquished healthcare due to cost in many countries [94]. That is, greater household capacity to meet economic needs lowers forgone medical care due to cost [95]. Those struggling to make ends meet do not have extra money to pay for unexpected illnesses [96]. Thus, access to affordable healthcare is a great concern for those struggling to make ends meet [97].

According to the bibliography, which is limited, individuals that cannot cope with unexpected expenses face higher healthcare costs [98]. A probable reason is that this group is likely to be in poor health [99]. Because households' poor economic condition implies a burden in terms of healthcare access, those with limited capacity to face unexpected financial expenses are more inclined to report unmet healthcare needs due to cost [100]. On the other hand, the unpredictability of illness exposes individuals to unexpected expenses [101,102].

According the analysis, the results obtained from EU-SILC 2016 justify the results obtained from EQLS 2007 and 2016.

The empirical findings demonstrate a dramatic worsening of the living and welfare standards of many Greeks over the years of recession and austerity [103]. The reduction in economic prosperity during the crisis decreased the patients' capacity to pay and therefore negatively impacted their ability to afford healthcare services [104,105]. On the other hand, as the state reduced its overall financial responsibility in healthcare, a substantial part of the cost was shifted to households and individuals [53]. Thus, while the Greek population is universally covered, healthcare remains unaffordable [106]. This situation justifies the effect of both demand-side and supply-side barriers to healthcare access [107].

What was described in the results section is quite similar to subsequent years, with the exemption of 2017. In 2017, among households that reported unmet healthcare needs, 74.66% cited cost as the main reason for not using needed healthcare. This percentage was 81.65% and 81.40% in 2018 and 2019, respectively. Nevertheless, the percentage of unmet healthcare needs increased compared to that of 2016 (2017: 24.50%; 2018: 23.34%, and 2019: 21.28%) (Source: Hellenic Statistical Authority, EU-SILC 2017, EU-SILC 2018, EU-SILC 2019). These findings confirm the argument that cost is a critical factor driving healthcare-seeking behavior [108].

The results of this study are consistent with a significant strand of the literature, not only with regard to the factors affecting healthcare affordability but also with regard to the characteristics of those reporting unmet healthcare needs due to affordability issues. Indeed, females (OR = 2.20), individuals suffering from at least one chronic disease (OR = 1.41), individuals with poor health (OR = 2.90), individuals with low income (OR for the first quintile = 2.67 and OR for the second quintile = 2.02), people facing economic difficulties (OR for the frequency of economic problems = 1.99 and OR for the degree of economic difficulties = 1.86) and individuals without a paid job (OR = 1.02) are more likely to report unmet healthcare needs for financial reasons. In addition, individuals of higher education (OR = 0.77), and older individuals (OR = 0.97) are less likely to report unmet healthcare needs for financial reasons [109–111].

Based on the results of this study, healthcare costs are a significant barrier for healthcare access in Greece.

This finding is also justified by the data of Eurobarometer 91.2 [112]. According to this survey, 42% of Greek respondents identified more affordable treatments as a way to improve healthcare access for all Europeans. This percentage is much higher than the European Union average (33%).

However, the results of this survey are somewhat surprising because this percentage is too high in countries such as the Netherlands (69%), where affordability-especially for those in a more financially difficult position-is considered to be very good [113]. In addition, this percentage is 29% in United

Kingdom, operating a Beveridge healthcare system, while France's Bismarck healthcare model has 30%. However, with regard to affordability, the United Kingdom is ranked first, while France is ranked second [114]. A similar percentage was reported in Germany (29%), where healthcare is considered to be quite affordable [115].

A probable reason for these inconsistencies may be that the question was not country-specific. That is, according to Mooney (2009) [116], the community's judgement with regard to access barriers is based on putting themselves in the position of those facing these barriers. However, Mooney does not refer to financial barriers, because they apply to all. We argue that that this is not the case in this specific question because the respondents should consider the whole European population.

In the case of Greece, the results are in line with the fact that 48% of the respondents declared unemployment as one of the two essential issues dealing with the country, while 45% declared the economic situation to be one of the two essential issues dealing with the country. Nevertheless, only 12% declared health and social security to be an issue.

However, the EU-SILC 2016 data confirm findings from the literature. Specifically, in the Netherlands, 4.3% of respondents reported great difficulty affording healthcare costs, while 10.4% and 12.5% reported difficulty and some difficulty affording healthcare costs, respectively. Furthermore, in the United Kingdom, the percentage of respondents reporting great difficulty, difficulty, and some difficulty affording healthcare costs was 1.9%, 3.2%, and 7.4%, respectively. In addition, in France, 2.3% of respondents reported great difficulty affording healthcare costs, while 7% and 12.5% reported difficulty and some difficulty affording healthcare costs, respectively. Finally, in Germany, the percentage of respondents reporting great difficulty, difficulty, and some difficulty affording healthcare costs was 2.4%, 4.3%, and 9.3%, respectively [117].

The economic crisis adversely affected a substantial percentage of the Greek population and negatively affected their capacity to satisfy healthcare needs. Indeed, the data from EU-SILC 2007 (Source: Hellenic Statistical Authority) indicate that the percentage of unmet health needs before starting the economic crisis, i.e., 2007, was 6.69%. In addition, among those reporting an unmet healthcare need, 68.98% declared affordability issues.

The economic crisis of 2008 affected the EU Member States in different ways. Taxation revenue decreased and borrowing costs increased in several member states. In some countries where healthcare is financed primarily through health insurance contributions from employees and employers, resources also decreased because of reduced wages and increasing unemployment rates. In addition, public expenditure increased because of an increase in take-up benefits such as unemployment benefits. In order to balance their budgets, many EU governments cut public expenditure including health expenditures. However, the crisis adversely impacted access to healthcare services not only because of budget cuts. Access to healthcare for households also reduced because disposable income decreased [118].

The mechanism on the basis of which economic crisis affected healthcare access is as follows: in combination with budget cuts, household income and wealth reduction led to a reduced ability to pay out of pocket. In countries with a work-related insurance system, job losses led to a loss of coverage. These in turn led to limited healthcare access [119].

Several high-and low-income countries in Europe, North America and Africa constitute examples of the abovementioned point [120–124].

Identifying the factors influencing healthcare affordability is particularly important because certain policies based on these factors can be applied during a period of recession especially for the more disadvantaged. However, this is not an easy task because affordability depends not only on the individuals' characteristics but also on the characteristics of the healthcare system and their interrelation. In addition, as previously mentioned, healthcare affordability is a subjective concept.

The main limitations of this study are: (1) the lack of information with regard to insurance coverage; (2) the fact that the analysis of EQLS 2007 and EQLS 2016 was not based on weighted data, and (3) that the study almost exclusively focused on househlods' and individuals' characteristics. Except for the information on geographic characteristics, no information is available with regard to the health system (type of services, availability, etc.).

### **5. Conclusions**

Access to healthcare in Greece presents challenges in terms of affordability, leading to high levels of unmet healthcare needs particularly among financially disadvantaged groups [125]. This point is of great importance because "the proof of access is use of service not simply the presence of a facility" [126].

However, because a large part of the world's population has limited access to affordable healthcare [127], we argue that facing cost-related difficulties with regard to healthcare use is a global phenomenon. All people should have equal financial access to medical care [128], because the accessibility of appropriate and affordable healthcare is vital to improving health outcomes and reducing health disparities [129].

The provision of affordable healthcare services is definitely a challenge that is increasingly difficult. Due to the healthcare system complexities, investigating the utilization and costs of healthcare services is key to informed decision-making [130]. Policy makers must address a diverse array of issues to reduce health disparities by making healthcare more affordable and accessible [131] because achieving equity in health access and outcomes is one of the goals of health policy [132].

If we accept that access may be defined as freedom from barriers to healthcare [133], then understanding the full range of barriers for vulnerable populations such as the poor is important to optimizing healthcare and health outcomes [134].

Thus, there is a need for more research while considering the influence of healthcare systems and patient characteristics [59].

The results of this study confirm that healthcare affordability is a critical issue for the Greek population. Access improvement in Greece requires interventions with regard to healthcare system inefficiencies, but also improvements in living standards, meaning that several factors should be addressed. That is, a multi-factorial health, social, and economic policy approach is required.

The well-documented negative effect of the economic crisis on health outcomes can be partially attributed to the unaffordability of the healthcare services and the healthcare system characteristics.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to thank Jason Zavras, 3rd year college student at Dartmouth College, for his work as research assistant in this project.

**Conflicts of Interest:** The author declares no conflict of interest.

### **References**

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Global Challenges vs. the Need for Regional Performance Models under the Present Pandemic Crisis**

**Romeo-Victor Ionescu 1, Monica Laura Zlati 2 and Valentin Marian Antohi 3,4,\***

**Abstract:** The present study uses the analysis of the EU's regional performance structure based on clusters to test the versatility of the regional administrative capacity in relation to three disruptive global phenomena: the economic crisis, the coronavirus epidemic and the phenomenon of refugee migration to Europe. We defined a regional performance model based on maintaining sustainability indicators in the 240 EU regions. The objectives of the study are aimed primarily at a structured assessment of regional administrative capacity in the initial version, based on statistical indicators, and in the current version, after the outbreak of the pandemic, based on quantifying the impact of the disturbing factors. Secondly, the objectives of the study are to evaluate the reaction of the administrative units according to their ability to respond to the economic problems in the region, in the sense of improving the performance of the regional economies. The methods used in this paper will be empirical (the study of the specialized literature), analytical and will contain econometric modelling and statistical processing of the data. The results of the study will allow the identification of the necessary traits to train a leader in regional performance, traits that will be useful to European decision makers in adjusting the EU regional policy. Moreover, the need to redefine the EU in terms of performance will be substantiated once again. The study is current and is based on the latest Eurostat information, pertinent tables and diagrams.

**Keywords:** European administrative capacity; econometric model; European regional development; COVID-19; population migration; economic crisis

### **1. General Approach**

The current context is an atypical one, in which disturbing factors are manifested both from the social and economic point of view. From the social point of view, regional development is directly influenced by the spread of the new virus (COVID-19), whose effects have manifested in all EU Member States. To this end, we have built a statistical database for the period 2006–2002, using Eurostat information [1].

From the regional point of view, the evolution in the most-affected Member States reflects that the regional character is typical of this disturbing phenomenon. Additionally, the regional specificity of the spread of the disease is manifested in Romania.

Overall, it is noted that three EU-representative economies are placed in the top 12 worldwide in terms of total COVID cases' impact in September 2021: France (6th place), Spain (11th place) and Italy (12th place). The UK has left the EU, but is ranked 4th overall. All European countries are currently facing social and economic crises amid dissent over anti-counterfeiting protective measures [2].

**Citation:** Ionescu, R.-V.; Zlati, M.L.; Antohi, V.M. Global Challenges vs. the Need for Regional Performance Models under the Present Pandemic Crisis. *Int. J. Environ. Res. Public Health* **2021**, *18*, 10254. https:// doi.org/10.3390/ijerph181910254

Academic Editor: Dimitris Zavras

Received: 24 August 2021 Accepted: 23 September 2021 Published: 29 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

From the economic point of view, the direct impact estimated by the national statistical systems at the level of the four above analysed countries consists of a decrease in GDP.

In this context, the population from the other EU Member States established in areas strongly affected by COVID-19, such as France, Italy or Germany, had the possibility to return massively to their countries of origin, favouring the spread of the virus and generating social problems and economic difficulties for their country of origin. In addition to the restriction of the health system and the increasing need for equipment, medicines and medical staff compared to the planned need, this phenomenon had to provide measures to quarantine the population that returned to the country, as well as to ensure the social protection of the population that lost their places of work during the pandemic. This migration phenomenon is strictly induced by the global pandemic, but Europe is also facing another migration phenomenon of the African, Arab and East Asian populations, which raises issues for European forums regarding ensuring social protection and integrating migrants into a system already incapacitated by the outbreak of the pandemic. The traffic restrictions imposed as a measure to control of the spread of the virus reduced the EU's economic activity and generated financial blockages of those companies which were virtually taken into account when forecasting the budgets of the EU Member States.

These three aggravating factors (pandemic, migration under different aspects and overstretching of economic activity) are significant premises for estimating a serious economic crisis, which will affect global society.

In this context, we aim to develop a regional performance model in the sense of the regional administrative capacity's versatility in relation to these aforementioned phenomena.

The novelty of the present scientific approach lies in identifying and managing a regional leader in terms of socio-economic performance. Moreover, our approach achieves a unified vision of EU regional policy, by standardizing and building on the literature on the subject. Another new element is the emphasis on the need to redefine the EU in performance-related terms.

The paper proceeds as follows. Section 2, Literature Review, discusses relevant models for the topic. It is followed by Section 3, Data and Methodology, in which we highlight the NUTS 2 regional efficiency indicators able to evaluate on a 5-step scale the administrative efficiency data, models and results. This is followed by an analysis of the results in Section 4. An overview in Sections 5 and 6 discusses the results and concludes the paper.

### **2. Literature Review**

Since December 2019, global society has been facing unfavourable conditions regarding economic growth against the background of the COVID-19 pandemic and the necessary global measures to limit the spread of the pandemic.