**Economic Crisis and Healthcare Services**

Editor

**Dimitris Zavras**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editor* Dimitris Zavras University of West Attica Greece

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### **Contents**

Amid the COVID-19 Pandemic, Unethical Behavior in the Name of the Company: The Role of Job Insecurity, Job Embeddedness, and Turnover Intention Reprinted from: *Int. J. Environ. Res. Public Health* **2022**, *19*, 247, doi:10.3390/ijerph19010247 . . . **145**

### **Ibrahim A. Elshaer, Marwa Ghanem and Alaa M. S. Azazz**

An Unethical Organizational Behavior for the Sake of the Family: Perceived Risk of Job Insecurity, Family Motivation and Financial Pressures Reprinted from: *Int. J. Environ. Res. Public Health* **2022**, *19*, 6541, doi:10.3390/ijerph19116541 . . . **161**

### **About the Editor**

### **Dimitris Zavras**

Dimitris Zavras was born in Athens in 1968 and studied Physics at the National and Kapodistrian University of Athens.

His postgraduate studies include an M.Sc. in Applied Physics from the University of Massachusetts, an M.Sc. in Statistics from Athens University of Economics and Business, an M.Sc. in Healthcare Management from the National School of Public Health, and a Doctorate (Ph.D.) in Health Services Research and Health Economics from the University of Thessaly (Department of Economics).

He is currently an Assistant Professor at the University of West Attica and a Faculty Member of the Hellenic Open University. In the past, he has taught at the University of Peloponnese, at the Neapolis University Pafos (Cyprus), and at the Athens University of Applied Sciences.

Dimitris Zavras' research interests focus on the utilization of healthcare services, healthcare provider choice, unmet healthcare needs, and health status.

He is a member of the Hellenic Scientific Society of Health Economics and Health Policy and of the Hellenic Society of Public Health. He is also a member of the Hellenic Society for Healthcare Services Management.

### *Editorial* **Access to the COVID-19 Vaccine**

**Dimitris Zavras**

Laboratory for Health Technology Assessment, Department of Public Health Policy, School of Public Health, University of West Attica, 11521 Athens, Greece<EMAIL_ADDRESS>
As of 31 August 2022, 599,825,400 confirmed coronavirus disease 2019 (COVID-19) cases and 6,469,458 deaths have been reported globally [1]. The pandemic has tested humanity for more than two years. What characterizes this time period is the high death toll, the suffering [2], and the high levels of uncertainty in every aspect of the crisis. In the early phase of the pandemic, the time needed to develop and deploy vaccines was one of the sources of such uncertainty [3].

Due to the importance of COVID-19 vaccination in saving lives and in contributing to global economic recovery, this Editorial attempts to study the access to the COVID-19 vaccine.

The world's first vaccination was made available in December 2020 [4]. Reducing the spread of the disease, protecting segments of the population that are not yet vaccinated as well as those for whom vaccines have limited efficacy, and reducing severe cases that burden healthcare systems are among the benefits of vaccination against COVID-19 [5]. In addition, economic recovery will be faster in countries with higher vaccination rates [6].

However, although healthcare access is a right, there are still more than two and a half billion people around the world who are unvaccinated [4], and access to vaccines in low- and middle-income countries (LMICs) is far lower than it is in high-income countries (HICs). Consequently, rates of COVID-19 infections and deaths are higher in LMICs [7].

It is evident that access to the COVID-19 vaccine is inequitable; such global inequity is due to the concentration of vaccine development and production in high-income countries, dose-hoarding by those countries, high vaccine prices, and challenges in deploying vaccines in resource-poor settings [8]. Beyond these reasons, the existence of barriers in terms of access to healthcare may undermine the achievement of health equity [9]. According to Mooney [10], "if we are taking access to mean opportunity to use or freedom to use, it is immediately apparent that it is the potential users' perspectives on what constitutes a barrier and its height that must be used in assessing differential access", while "freedom to use describes the social possibility and the individual ability to give direction to one's will to use health services" [11].

Given that "equal access" is defined in parallel with "equal need", a question is posed: how should need be approached? The answer is that need can be considered as an expression of the risk of contracting COVID-19 [12]; people aged 60 years and over, those living in long-term care facilities, and people with underlying health conditions such as hypertension, diabetes, cardiovascular disease, etc., are considered high-risk groups for COVID-19 [13].

On the other hand, access should be approached through its dimensions and through the barriers that individuals face.

Adopting Penchansky's and Thomas's [14] framework for the dimensions of access, i.e., the five As, namely, availability, affordability, accessibility, accommodation, and acceptability, the global picture regarding the COVID-19 vaccine is as follows: (a) the availability of COVID-19 vaccines differs significantly around the world [15], and specifically, in LMICs, availability is far lower than in wealthier countries [16]; (b) in several countries, COVID-19 vaccines are provided free of charge [17], and thus, their affordability may not be an issue in monetary terms, something that is not true in some less developed nations [18];

**Citation:** Zavras, D. Access to the COVID-19 Vaccine. *Int. J. Environ. Res. Public Health* **2022**, *19*, 11054. https://doi.org/10.3390/ ijerph191711054

Received: 31 August 2022 Accepted: 1 September 2022 Published: 3 September 2022

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

**Copyright:** © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/).

(c) for several communities around the world, accessibility is an issue [19,20]; (d) telehealth has been adopted on a large scale for vaccination registration and for relevant medical interventions, but knowing how to further improve vaccination accommodation through telehealth while bridging the digital divide is another matter that requires immediate investigation and implementation [21]; and (e) while developing countries mainly face affordability—and accessibility-related issues regarding vaccines, developed countries face acceptability issues [22,23].

With regard to the acceptance or refusal of the COVID-19 vaccine, vaccine hesitancy is a major obstacle to high vaccination rates [24]. According to Lin et al. [25], the most common reasons for vaccination hesitation or refusal are a fear of side effects; safety; and effectiveness, while the belief that the vaccines are unnecessary; inadequate information; the unknown/short duration of vaccines; and a general anti-vaccine stance are associated with lower acceptance. In addition, vaccine acceptance on a global scale is influenced by the speed of vaccine development [26].

A fear of side effects, safety or effectiveness concerns, and beliefs related to the COVID-19 vaccine are demand-side barriers; concerns related to vaccination timing, vaccination location, and the financial cost of getting vaccinated are also demand-side barriers. Vaccine delivery and administration barriers, such as inaccessible places and people, are supply side barriers [27].

Significant predictors of COVID-19 vaccine uptake intentions are education, the existence of insurance coverage, scoring high on subjective norms, a positive attitude toward the vaccine, high perceived susceptibility to COVID-19, high perceived benefits of the vaccine, and scoring low on barriers to the vaccine [28].

Holloman [29] defined access to healthcare as the freedom from barriers to healthcare, while barriers are defined as anything that constrains, deters, delays, denies, dissuades, discourages, handicaps, or prevents the acquisition or utilization of those services that are ultimately provided by society to its members individually and collectively for the maintenance, preservation, and improvement of health.

In the case of COVID-19 vaccination, the "freedom to receive a vaccination" has been undermined to a large extent by exposure to misinformation [30]. However, we should note that a belief in misinformation may be related to prevailing generalized uncertainty. COVID-19-related uncertainty arose from various aspects of the COVID-19 crisis, i.e., health-related, economic, and social. Uncertainties related to the nature of the virus, alternative treatment options, clinical outcomes, and prevention methods [31] all concern the health dimension of the crisis. Uncertainties related to impacts on personal finances and the effectiveness and impacts of the restriction measures [32] concern the economic dimension of the crisis.

As mentioned above, barriers play a decisive role in access to healthcare, and in the actual use of services [33]. In addition, since awareness, i.e., communication and information, may be considered as the sixth dimension of access [34], providing up-to-date information would be an effective strategy against barriers such as fear or concerns related to the safety and effectiveness of the vaccine [35]. Since vaccination rates have increased in numerous communities as populations have begun to see their friends, colleagues, and neighbors get vaccinated without adverse events [36], relative information could effectively motivate individuals to get the vaccine.

COVID-19 vaccination concerns all of us, and efforts to minimize barriers to receiving it should continue. Thus, understanding such barriers and the best way to influence individuals is crucial to achieve high vaccination rates. The affordability and accessibility issues facing developing countries and the acceptability issues facing developed countries must be eliminated. "Freedom to receive a vaccination" should be translated to freedom to fight the enemy that threatens one's life, health, and well-being, and the global economy.

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

### **References**

### **Factors Associated with the Implementation of an Improved Community Health Fund in the Ubungo Municipality Area, Dar es Salaam Region, Tanzania**

**Goodluck Mselle 1, Peter Nsanya 1, Kennedy Diema Konlan 2,3, Yuri Lee 4, Jongsoo Ryu 5 and Sunjoo Kang 5,\***

**Abstract:** Community-based health insurance schemes help households to afford healthcare services. This paper describes healthcare facilities and community factors that are associated with the Improved Community Health Fund (iCHF) scheme in the Ubungo district of Tanzania. A cross-sectional descriptive study was conducted using online questionnaires that were completed by healthcare providers and community members in public-owned healthcare facilities in the Ubungo Municipal Council district of Dar es Salaam, Tanzania, between October and November 2021. The data were analyzed using descriptive statistics and the chi-squared test of association. We found a statistically significant relationship between income level and satisfaction with the iCHF scheme. For community-related factors, income level was statistically significant in the level of involvement in iCHF implementation among local leaders. Further, income level was statistically significant in relation to community behavior/culture toward the iCHF. Occupation was statistically significant in iCHF implementation, iCHF premiums, and iCHF membership size. A statistically significant relationship was also found between income, iCHF membership size, and iCHF premiums. Moreover, people would be willing to pay the required premiums if the quality of the healthcare services under the iCHF scheme improves. Therefore, the government should allocate resources to reduce the challenges that are facing iCHF implementation, such as the preference for a user fee scheme over the iCHF, the issues that are faced by enrollment officers, and inadequate iCHF premiums and membership size.

**Keywords:** Improved Community Health Fund (iCHF); Ubungo municipality; healthcare providers; community health; health insurance

### **1. Introduction**

Health finance in low-income countries (LIC) is derived from various sources, including government allocations, out-of-pocket payments, and external aid from high-income countries [1,2]. Out-of-pocket payments are direct payments that are made by citizens for the use of healthcare services. The burden of out-of-pocket payments varies depending on household size, the nature of any relevant health insurance cover, and the presence of chronic diseases in the family [3]. Out-of-pocket healthcare expenditure is large in low-income countries and poses a major barrier for community access to healthcare services. In 2018, out-of-pocket healthcare expenditure accounted for 42% of the total government healthcare budget [1], whereas social health insurance accounted for only 7% of all healthcare-related expenditure [1]. In contrast, for high-income countries, social

**Citation:** Mselle, G.; Nsanya, P.; Konlan, K.D.; Lee, Y.; Ryu, J.; Kang, S. Factors Associated with the Implementation of an Improved Community Health Fund in the Ubungo Municipality Area, Dar es Salaam Region, Tanzania. *Int. J. Environ. Res. Public Health* **2022**, *19*, 5606. https://doi.org/10.3390/ ijerph19095606

Academic Editor: Dimitris Zavras

Received: 23 March 2022 Accepted: 3 May 2022 Published: 5 May 2022

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

**Copyright:** © 2022 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/).

health insurance funds accounted for 22% of their budget and only 21% was accounted for by out-of-pocket expenditure [1]. In the Universal Health 525 Coverage (UHC) report by the World Health Organization (WHO), out-of-pocket expenditure in Africa was primarily spent on medicines and medical supplies [2]. Moreover, it exceeded the 25% limit that was set by the UHC for the Sustainable Development Goal (SDG) indicator for out-of-pocket expenditure [4]. As per the Abuja Declaration, each nation must spend 15% of its total budget on healthcare, yet Tanzania only spent 8% of its budget on healthcare [4]. Tanzania also only spent USD 46 per capita on healthcare and 7.3% of the GDP on healthcare goods and services in 2014–2015, compared to 10% in Rwanda [5]. Out-of-pocket payments accounted for 26% of the total healthcare expenditure in Tanzania between 2014 and 2015. Tanzania is struggling to find the resources to create a robust healthcare budget and depends on external sources, which account for 37% of the country's total healthcare budget [6].

The Improved Community Health Fund (iCHF) is a community-based voluntary insurance program that was established in 2001 in Tanzania, which targets the populations that cannot access the National Health Insurance Fund (NHIF). In Tanzania, the NHIF primarily caters for the healthcare needs of government and private employees, who constitute only 7% of the population [7]. The NHIF covers families with up to eight children who are enrolled at dispensaries, health centers, and district hospitals for medical costs of between USD 12 and USD 65 per year. In addition, members of the NHIF benefit from outpatient care and referral systems to district or zonal hospitals. The central government, local government, and iCHF members also contribute to a matching grant [2]. The Community Health Fund (CHF) is an alternative healthcare financing measure for families who are not qualified for or cannot afford the NHIF. In 2015, the government of Tanzania set the target of enrolling 30% of the population under the CHF; however, only 16.4% were enrolled. In 2016, the government had to revise the CHF in order to increase enrollment and improve the quality of the healthcare services that were provided under the scheme. The new changes pertained to enrollment, premium amounts, collection mechanisms, and purchasing arrangements. The new version of the CHF was named the iCHF [8]. In 2018, iCHF coverage increased to 25%, with 13 million beneficiaries [7], although the number of beneficiaries was still limited according to the 2015 government projections [7]. In a study of the Mtwara region in Tanzania, healthcare facility factors, such as the absence of medicines and medical supplies, were found to cause dropouts from iCHF membership [9]. Additional challenges, such as the poor supervision of healthcare facilities by district health authorities and the mismanagement of iCHF funds, led to an insufficient enrollment of CHF members [10]. Therefore, iCHF implementation in Tanzania has faced many challenges.

To address the urgent need to examine the factors that are associated with the implementation of the iCHF, the present study of the Ubungo municipality in the Dar es Salaam region of Tanzania, where iCHF coverage is low, aimed to identify those factors. To address the factors that influence the implementation of the iCHF, specific focus needs to relate to the healthcare providers and community members who are involved in the service implementation and utilization, respectively. Therefore, this study aimed to describe the community and healthcare facility factors that influence the implementation of the iCHF in Tanzania.

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

### *2.1. Study Design and Setting*

Using a descriptive cross-sectional design, data were collected from healthcare facilities that are registered under the iCHF. Healthcare providers who were working in a total of 21 public-owned healthcare facilities (17 dispensaries, 3 health centers, and 1 district hospital) in the Ubungo municipality were identified for the study. The Ubungo district is an urban area with a population of 1,043,549 people. The district covers a total surface area of 210 square kilometers and has 91 streets (the lowest level administrative area) and 14 wards (the mid-level administrative area) [11].

### *2.2. Population and Sampling*

A multistage sample method was adopted for the selection of the facilities and respondents for this study. A total of 13 iCHF members were selected from each facility to participate in the study, using the simple random sample method. In using this sampling method, a sample framework of the iCHF members within each facility was first created and listed in a basket. Names were then drawn from the basket without any replacements. Those whose names were selected through this process were then contacted to respond to a questionnaire. A simple random sampling method was used to select 16 healthcare facilities out of the 21 public healthcare facilities that are under the Ubungo Municipal Council [12]. The sampling of the healthcare facilities was also performed by firstly creating a sample framework of the 21 healthcare facilities and then facilities were chosen without replacement until 16 facilities were selected. The respondents were required to provide consent prior to responding to the questionnaire. During the collection, 29 people did not return the questionnaire or did not initially provide consent to participate in the study. This represented a non-response rate of 13.9%. During data cleaning, five questionnaires were found to be completed inappropriately and hence, were also excluded from the analysis. Therefore, the effective sample size that was used for analysis was 174.

### *2.3. Measurements*

The design and content of the online questionnaires were obtained from the existing literature on the factors that affect community-based health insurance (CBHI) [13]. The questionnaire for the healthcare providers had three parts: questions regarding the demographic characteristics of the participants, closed-ended questions regarding the healthcare facility and community-related factors (independent variables), and questions on iCHF implementation The closed-ended questions were measured using a 5-point Likert-type scale (5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, and 1 = strongly disagree). The demographic factors that were assessed for healthcare providers were level of healthcare facility, age, sex, profession, and educational level, while those for community members were age, sex, marital status, family size, occupation, educational level, income level, and duration of iCHF enrollment. The questionnaire for the healthcare providers also elicited their rating of facility factors that influence the implementation of the iCHF on a scale of agree, neutral or disagree. Some of the healthcare facility factors that they rated included the availability of medical equipment and healthcare personnel, the effectiveness of the referral systems, and the awareness of promotion strategies. Community members were also asked to provide similar ratings on their level of satisfaction of the implementation of the iCHF, the involvement of community members in membership registration, and the influence of cultural factors on the implementation of the iCHF. Variables that were rated by both the community members and healthcare professionals included difficulty in paying iCHF premiums, membership size, and the availability of enrolment officers.

Before data collection, the questionnaire was pretested among healthcare providers and community members in the Ubungo district. The questions were uploaded via Google Forms and distributed to 20 healthcare providers at the district hospital and dispensaries and 10 community members. The questionnaire was reviewed by the Ubungo Municipal Council's Health Department Research Review Committee. The Cronbach's alpha was 0.785 for the community questionnaire and 0.707 for the healthcare provider questionnaire.

### *2.4. Data Analysis*

The data analysis was performed using the SPSS 25.0 software after downloading the data from Google Forms into Excel spreadsheets. A descriptive analysis of the sociodemographic data was conducted using frequencies, means (M), and standard deviations (SD). A chi-squared test was performed to measure the association between sociodemographic factors and iCHF implementation. The statistical significance was set at *p* < 0.05 for a 95% confidence interval.

### **3. Results**

### *3.1. Sociodemographic Characteristics*

Among the participants who were healthcare providers, 75.9% worked in healthcare centers/dispensaries, 55.8% were below 39 years of age, and 70.1% were female (Table 1).

**Table 1.** Sociodemographic characteristics of healthcare providers.

Among the participants from the community, 79.4% were aged over 29 years, 52.8% were male, and 77.4% were married. The main occupations of the community members were office work (55.8%) and business (44.2%). As shown in Table 2, additional sociodemographic factors that were reported by the community members included education level, income level, and health insurance enrollment status.

**Table 2.** Sociodemographic characteristics of community members.

### *3.2. Healthcare Facility-Related Characteristics That Affect the iCHF*

The findings of the present study showed that the level of the healthcare facility and the adequate availability of healthcare providers were statistically significant (χ2 = 15.70, *p* < 0.00). A total of 62.1% of the healthcare workers who were working in health centers and dispensaries agreed that there were enough workers in the healthcare facilities, as shown in Table 3. The educational level of the healthcare professionals was also statistically significant (χ2 = 12.56, *p* < 0.00) with an efficient referral system under the iCHF scheme. A total of 88.7% of the nurses and other workers agreed that the referral system

was working well under the iCHF scheme. The relationship between education level and the availability of healthcare providers in a healthcare facility was statistically significant (χ2 = 38.99, *p* = 0.00). A total of 55% of the community members with university-level education agreed that there were enough healthcare providers in the healthcare facilities. The relationship between income level and iCHF awareness and promotion strategies in the community was statistically significant (χ2 = 17.60, *p* < 0.00). A total of 83% of the community members from low-income groups agreed that iCHF awareness and promotion strategies were conducted in the community. The relationship between occupation and the availability of medical supplies/equipment in a healthcare facility was statistically significant (χ2 = 51.68, *p* < 0.00), as 81.5% of the community members who were running businesses agreed that there were enough medical supplies/equipment in the healthcare facilities. Additionally, there was a statistically significant (χ2 = 43.59, *p* < 0.00) relationship between occupation and the availability of healthcare providers in a health facility. A total of 81.8% of the community members who were running businesses or were self-employed agreed that there were enough healthcare providers in the healthcare facilities. A statistically significant (χ2 = 52.86, *p* < 0.00) relationship was also identified between occupation and an efficient referral system under the iCHF scheme, as 76% of the community members who were running businesses agreed that the referral system was working well. The relationship between occupation and the awareness and promotion strategies for the iCHF in the community was also statistically significant (χ2 = 37.91, *p* = 0.000), as 47.8% of the community members who were running businesses agreed that awareness and promotion strategies for the iCHF were conducted in the community.

**Table 3.** Healthcare facility-related factors that affect the iCHF scheme, according to healthcare providers and community members.

**Table 3.** *Cont.*

A statistically significant (χ2 = 46.02, *p* < 0.00) relationship was found between education level and the involvement of local leaders, as shown in Table 3. A total of 59% of the community members with secondary- or primary-level education agreed that community leaders were involved in iCHF implementation. In addition, the relationship between education level and community behavior/culture toward the iCHF was statistically significant (χ2 = 16.65, *p* < 0.00), as 41% of the community members with secondary- or primary-level education agreed that members of the community seek to register with the iCHF after they have had an illness (Table 3). A statistically significant (χ2 = 12.07, *p* < 0.000) relationship was also found between income level and the involvement of local leaders in iCHF implementation. A total of 85% of the community members with low income levels agreed that local leaders participated in iCHF implementation. Moreover, the relationship between income level and community behavior/culture toward the iCHF was statistically significant (χ2 = 16.92, *p* < 0.00). A total of 72% of the community members with low income levels agreed that members of the community seek to register with the iCHF after an illness.

### *3.3. Institution-Related Factors That Affect the iCHF*

A statistically significant (χ2 = 14.01, *p* = 0.001) relationship was identified between education level and ability to pay the iCHF premiums. A total of 50% of the community members with university-level education agreed that the iCHF premiums were affordable. The relationship between education level and iCHF membership size was also statistically significant (χ2 = 14.01, *p* < 0.00). A total of 46% (χ2 = 14.01, *p* < 0.001) of the community members with university-level education disagreed that iCHF membership should require the payment of premiums (Table 4). The relationship between income level and iCHF membership size was statistically significant (χ2 = 21.818, *p* = 0.000) as well. A total of 75% of the community members with low income levels disagreed regarding iCHF membership size. Finally, the relationship between income level and ability to pay the iCHF premiums was statistically significant (χ2 = 18.68, *p* = 0.00). A total of 68% of the community members with low income levels agreed that the iCHF premiums were affordable. A statistically significant (χ2 = 19.08, *p* < 0.001) relationship was found between occupation and ability to pay the iCHF premiums. A total of 61.4% of the community members who were running businesses agreed that the iCHF premiums were affordable. The relationship between occupation and iCHF membership size was statistically significant (χ2 = 21.289, *p* < 0.00) as well. A total of 65.9% of the community members who were running businesses disagreed that iCHF membership was acceptable.

**Table 4.** Institution-related factors that affect the iCHF, according to healthcare providers and community members.

**Table 4.** *Cont.*

### **4. Discussion**

The present study identified the factors that influenced the implementation of the Improved Community Health Fund project in the Ubungo municipality of Dar es Salaam, Tanzania. The results of this study confirmed the availability of sufficient healthcare providers at various levels of healthcare facilities, thereby demonstrating the increased healthcare service provision under the iCHF scheme. The role of healthcare providers was found to be essential for the quality of the services that were provided. Therefore, it is pertinent that government interventions are aimed at improving healthcare service provision in communities by enhancing the quantity and quality of healthcare workers. This would also help to increase the confidence of community members in adopting insurance schemes, such as the iCHF. To ensure this occurs, the government should invest more financial resources into improvements in healthcare service provision, which would automatically increase the coverage and enrollment of iCHF members. Our findings regarding the importance of the availability of qualified healthcare providers were consistent with studies that were conducted in the Iramba and Liwale districts of Tanzania, where the development of healthcare providers influenced the excellent performance of the iCHF scheme [14].

Additionally, the necessary medical equipment/supplies were considered to be essential for healthcare and iCHF provision, as members would receive better healthcare services under the scheme. This finding was consistent with a study that was conducted in Ethiopia, where the presence of medical equipment led to an increase in the enrollment of iCHF members [15]. With the increasing quantity and quality of equipment and personnel within healthcare facilities, community confidence in orthodox medicine is likely to improve, along with an increase in enrollment in health insurance schemes. This is particularly true for resource-strapped African countries, in which the use of traditional alternative medicine and treatment has been a barrier to service acquisition. Most alternative medical practices in poor countries lack scientific rigor and are conducted in unsanitary and unsafe environments. This study demonstrated that other factors that affect the implementation of the iCHF included insufficient supplies and a lack of trained professionals. The iCHF would perform better if healthcare facilities had sufficient medical supplies/equipment and healthcare providers. In Ethiopia, the community perceived a poor quality of healthcare under the community-based health insurance scheme due to the unavailability of

medicines/medical supplies, long wait times, and inadequate healthcare providers [13]. In Tanzania, poor healthcare outcomes under the iCHF were caused by a lack of healthcare providers, long wait times, and a lack of medications [16,17].

This study indicated that the referral systems under the iCHF were functioning well in the healthcare facilities that were included in the research. This could be due to the proximity of the hospitals in the municipality, the availability of ambulances, and the presence of several referral hospitals and a national hospital in the area. This finding contrasted with many studies that were conducted in other parts of Tanzania, where the referral systems under the iCHF have been reported to be not working well. This could be caused by the absence of referral or zonal hospitals in those areas and the limited number of ambulances, especially in rural areas [18,19]. Healthcare referral systems are essential for improving community perceptions of the benefits that are associated with insurance facilities in low-resource settings.

Community awareness and promotion strategies contributed to the implementation of the iCHF at the community level by increasing iCHF enrollment [20]. This finding was consistent with studies that were conducted in three Dodoma districts in Tanzania, which showed higher levels of community awareness [21,22]. However, the factors that influence enrollment in community-based health insurance schemes are numerous and interact in complex ways to influence individual decisions. In a study that was conducted in northwest Ethiopia, community awareness programs about saving and credit decisions contributed significantly to the implementation of the community health insurance scheme [21]. In another study in southern Ethiopia, community leaders played a crucial role in enrolling and implementing community-based health insurance [13]. A study in Tanzania indicated that most of the information on iCHF implementation was obtained from local leaders [21]. Another study in Dodoma, Tanzania, indicated that the implementation of the iCHF scheme improved after the involvement of community leaders. District authorities ensured that all local leaders were registered with the iCHF scheme and were informed as to its importance for the local community [23]. Therefore, in addition to an increased awareness of the health insurance scheme, other factors and barriers that influence enrollment need to be further investigated in future studies.

The income and education levels of the community members were important factors that influenced the adoption of iCHF, as the participants with high income and education levels perceived the healthcare services under the iCHF scheme to be of good quality, regardless of the iCHF premiums or iCHF membership size. The results of studies in northwest [24,25] and southwest Ethiopia [26] were consistent with the current findings that the iCHF premiums were acceptable to the community if the scheme could ensure quality healthcare services. However, a study in Rwanda opposed this finding because it was found that a change in the premiums could cause low enrollment rates, especially in rural areas [27]. In addition, high premium costs could contribute to differences in healthcare provision between urban and rural areas due to varying income levels. The results from a previous study in Tanzania were consistent with the current findings that the low levels of iCHF membership in three districts were caused by poor healthcare services and low enrollment. However, in another study, the iCHF premiums were influenced by the user fee and people who were not members of the CHF scheme preferred it because the quality of the healthcare services was good [27].

### *Strengths and Limitations*

The present study was successful in identifying factors that affect the implementation of the iCHF at both the community member and healthcare provider levels, unlike many of the other studies, which have focused on the factors that affect community health while ignoring the role of healthcare providers. However, a key limitation of this study was the inability to determine the causal relationship between the factors that impact iCHF adoption due to the cross-sectional nature of the study. The factors that were associated with the implementation of the iCHF were multiple dimensional and the level of impact of each variable could only be ascertained through longitudinal studies or those that implement experimental designs. Due to this, we could not determine the level of impact of each factor on the implementation of the iCHF in Tanzania. Therefore, it is important that experimental research designs are employed to test the contribution and efficacy of each influencing factor in terms of an individual's choice to use the iCHF. However, regardless of these limitations, we highlighted the factors that are associated with the implementation of the iCHF in Tanzania.

### **5. Conclusions**

This study demonstrated that multiple dimensional factors influence the implementation of the iCHF in Tanzania. These factors range from individual to community factors, which interact dynamically to influence the implementation of the iCHF. For healthcare providers, the factors that affect the implementation of the iCHF scheme that were identified in the present study included the level of the healthcare facility, the education level of the healthcare providers, and the referral system in the hospital. For community members, the factors included education level, age, and income level. All health, community, and institutional factors were affected at both the healthcare facility and community levels. It is important that future research uses experimental designs or longitudinal follow-up approaches to identify the level of influence of each factor on the implementation of the iCHF. To have an effective iCHF, stakeholders must focus on mitigating these factors while efforts are made to improve the education and awareness of the tenants of the fund. The education of community members about the iCHF must focus on those in the lower socioeconomic groups, which have limited formal education. Healthcare institutions must also be empowered to implement effective referral systems that can ensure prompt care for patients.

**Author Contributions:** This work was carried out in collaboration with all authors. G.M. conceptualized the study, developed the initial research question, conducted the literature review, and developed the study design in discussion with S.K. and Y.L. Data collection and data curation were supported by P.N. and K.D.K. G.M. wrote the original draft of this manuscript and all of the relevant data in the tables were collected under the supervision of S.K., Y.L. and J.R. All authors have read and agreed to the published version of the manuscript.

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

**Institutional Review Board Statement:** Permission was granted by Ubungo Municipal Council's Health and Social Welfare Department on 4 October 2021.

**Informed Consent Statement:** Informed consent was obtained from all subjects who were involved in the study. A researcher explained the aims and objectives of the study to the healthcare providers and community members before the questionnaire was distributed. A consent form was signed by all participants, which informed them of their voluntary participation, right to withdraw from the study at any time, and right to freedom of choice, expression, anonymity, and confidentiality during the study.

**Data Availability Statement:** The data are available upon reasonable request from the corresponding author.

**Acknowledgments:** We gratefully acknowledged the efforts of Myung-Ken Lee, former director, Whiejong M. Han, director of the Global Health Policy and Finance Program at the Graduate School of Public Health, Yonsei University, and the entire staff for their support of this study, as well as the scholarship support of the KOICA.

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

**Disclosure:** This article is a condensed form of the first author's master's degree thesis for the Graduate School of Public Health, Yonsei University, Seoul, South Korea.

### **References**

### *Article* **Exploring the Role of Health Expenditure and Maternal Mortality in South Asian Countries: An Approach towards Shaping Better Health Policy**

**Noshaba Aziz 1, Jun He 1,\*, Tanwne Sarker 2 and Hongguang Sui 3**

**Abstract:** Accomplishing unremitting favorable health outcomes, especially reducing maternal mortality, remains a challenge for South Asian countries. This study explores the relationship between health expenditure and maternal mortality by using data set consisting of 18 years from 2000 to 2017. Fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) models were employed for the empirical analysis. The outcomes revealed that a 1% rise in health expenditure increased the maternal mortality rate by 1.95% in the case of FMOLS estimator and 0.16% in the case of DOLS estimator. This reflects that the prevailing health care system is not adequate for reducing maternal mortality. Moreover, the meager system and the priorities established by an elitist system in which the powerless and poor are not considered may also lead to worsen the situation. In addition, the study also added population, economic growth, sanitation, and clean fuel technology in the empirical model. The findings revealed that population growth has a significant long-term effect on maternal mortality—an increase of 40% in the case of FMOLS and 10% in the case of DOLS—and infers that an increase in population growth has also dampened efforts towards reducing maternal mortality in the South Asian panel. Further, the results in the case of economic growth, sanitation, and clean fuel technologies showed significant long-term negative effects on maternal mortality by 94%, 7.2%, and 11%, respectively, in the case of the FMOLS estimator, and 18%, 1.9%, and 5%, respectively, in the case of the DOLS estimator. The findings imply that GDP and access to sanitation and clean fuel technologies are more nuanced in declining maternal mortality. In conclusion, the verdict shows that policymakers should formulate policies considering the fundamental South Asian aspects warranted to reduce maternal mortality.

**Keywords:** health expenditure; maternal mortality; sanitation; clean technologies; South Asian countries

### **1. Introduction**

Human capital is an imperative factor for attaining the desired economic growth and development of any country [1]. Under the concept of the neoclassical growth model, human capital, particularly good human health, markedly influences a country's per capita income in the long run [2]. The health status of any given country depends upon maternal and infant mortality rates, which play a crucial role in evaluating the country's population health, quality of care, socioeconomic status, and poverty [3]. Moreover, maternal health is likely to bring economic benefits for the whole family and society. But recently, mothers' poor health, especially maternal deaths, have remained an unavoidable phenomenon that occurs at relatively high rates in many developing countries despite a steep reduction in maternal mortality worldwide since 1990 [4–6]. The maternal mortality ratio numerically shows the maternal deaths during a given time period per 100,000 live births during the same time period. It illustrates the maternal death risks relative to the number of live births

Sui, H. Exploring the Role of Health Expenditure and Maternal Mortality in South Asian Countries: An Approach towards Shaping Better Health Policy. *Int. J. Environ. Res. Public Health* **2021**, *18*, 11514. https://doi.org/10.3390/ ijerph182111514

**Citation:** Aziz, N.; He, J.; Sarker, T.;

Academic Editor: Dimitris Zavras

Received: 6 September 2021 Accepted: 27 October 2021 Published: 2 November 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/).

and basically captures the death risk in a single pregnancy or a single live birth. According to the World Health Organization (WHO), around 810 women die during pregnancy every year, and about 94% of them belong to developing countries [7].

Globally, the maternal mortality ratio has declined from 342 to 311 (by nearly 38%) from 2000 to 2017 [7]. However, South Asian countries and sub-Saharan African countries face disproportionately higher maternal mortality ratios than other regions of the world, making up around 85% of all maternal-related deaths. The third Sustainable Development Goal (SDG 3) of reducing the deaths to 70 per 100,000 live births and ensuring that all human beings, at all ages, achieve healthy lives by 2030, has intensified their struggles to promote the health sector [8]. In this regard, a good health care system is focused, and has been is shown that the health care system comprises various sectors, of which the most important is health financing [3]. Evidence has confirmed that financing in the health sector considerably supports the economy [9]. This implies that spending in the health care system improves peoples' health and generates employment opportunities, increases social and political stability, and ultimately leads to the growth and development of the whole economy [10–12].

Despite significant investments in the health sector, expenditure on health in governments' financial plans in developing regions, including South Asian countries, is still under-represented due to resource scarcity [13]. Even if South Asian countries develop and progress at the same pace, the global SDG target of decreasing maternal deaths to 70 per 100,000 live births by 2030 is hard to accomplish. Over the past few decades, keeping in view the importance of the health of the people and its role in the national economy, scholars and researchers have devoted their attention to evaluating the link between health expenditure and health outcomes. In this vein, many studies have been conducted before (see, for example, [14–21]). Some studies showed that health expenditure leads to better health outcomes [22], while others found no such relationship [23,24]. The link between health expenditure with health outcomes is uncertain, as health expenditure and health care patterns alter considerably; therefore, the debate over health care expenditure and health outcomes remains questionable [25], which warrants more investigation. Moreover, the rise in health expenditure in developing countries has demonstrated that maternal mortality rates are still higher, which has prompted authors to identify the core cause of maternal mortality, so health expenditure is appropriately directed and utilized.

Based on the above discussion, the current study was designed to explore the following research question: "Does health expenditure contribute to decreased maternal mortality in South Asian countries?" Exploring this question will enable policymakers to prepare strategies accordingly to accomplish the sustainable development goal of reducing maternal mortality, as health expenditure is regarded as the clearest policy variable [26]. Investigating health care outcomes in South Asian countries is remarkably important, not only due to the relationship of health with economic growth but also poor health leads to disparity between actual income and real opportunities, as it decreases the degree to which a given income level can be transformed into the ability to live a life of an acceptable quality [27]. Perkins, Radelet, Lindauer, and Block [28], and Booth and Cammack [29] also pointed out that the health sector is among the most pervasive markets that may lead to failure because of the problems of principal agents, negative externalities, collective actions, and information failures, which require government interventions. Moreover, most preceding studies ignored other macro-level factors of maternal mortality, such as population growth, economic growth, sanitation, and access to clean fuel technologies. Thus, the current study may help to unpack the association of health expenditure with maternal mortality by incorporating these factors for the South Asian region.

As discussed above, many previous studies have explored the relationships between health outcomes (mortality) by using different datasets and have used different research methods and found diverse findings, particularly for the maternal mortality ratio [24,30–34]. However, a recent study pointed out that an investigation of this kind must account for "unobserved heterogeneity" [34]. Even so, many studies have ignored

the heterogeneity and even cross-sectional dependency within the panel data. Therefore, to fix the cross-sectional dependency and heterogeneity issues in the panel data and add a contribution to the health economics literature, the present study applied fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS). The recent study of Aziz et al. [35] also used this phenomenon to assess the determinants of undernourishment in a South Asian panel. As per the authors' knowledge, this is the first study attempting to scrutinize the long-run effects of health expenditure on maternal mortality in a South Asian panel via this empirical approach.

### **2. Literature Review**

This section is an attempt to review the previous efforts of scholars and researchers on the subject matter of the current study. This section concisely reviews the studies available in the literature.

According to previous studies, the health expenditure level in a nation is one of the key measures for determining the health investment level; hence, it is acknowledged as an imperative input factor, like diet and exercising, for enhancing health. An extensive body of literature has scrutinized the association between health expenditure and health outcomes across countries. Despite these efforts, the pivotal link between health expenditure and health outcomes is still not clear. It is generally presumed that increasing health expenditure will inevitably improve health outcomes. In this regard, Aldogan, Austill, and Kocakülâh [36] observed prominent health outcomes such as infant, under-five, and maternal mortality rates due to government spending in the MENA region from 1990 to 2010, by using pooled ordinary least regression, random effects, and Hausman–Taylor instrumental variable models. Betrán et al. [30] argued that with the exception of developed countries, variability of national maternal mortality estimates is large even within sub-regions. In another study, Anyanwu and Erhijakpor [9] used data from 47 African countries between 1999 and 2004, and statistically revealed that health expenditure significantly and negatively influenced the infant and under-five mortality rates. In developing countries, Bokhari, Gai, and Gottret [37], using the instrumental variable approach, implied that economic growth and government expenditure on health are important factors for boosting public health. Nketiah-Amponsah [38] studied 46 sub-Saharan African countries over the period 2000–2015 and unveiled that a 1% increase in health expenditure per capita reduced under-five mortality by 0.5% and maternal mortality by 0.35%, while improving life expectancy by 0.06%. Likewise, Arthur and Oaikhenan [39] also indicated that health expenditure meaningfully boosted the expectancy of life and lessened maternal mortality and under-five mortality.

On the other side, several other studies found no causal association between expenditure on health and health outcomes [24,40]. Ashiabi, Nketiah-Amponsah, and Senadza [41], in another study, investigated the impact of both public and private health expenditure on maternal and child health for 40 sub-Saharan African countries for the years 2000–2010. The study used a fixed effect model and indicated that there was no significant effect of health expenditure on maternal mortality. Bradley, Elkins, Herrin, and Elbel [42] used a pooled cross-sectional analysis of OECD countries from the year 1995 to 2005. The main outcomes were life expectancy at birth, infant mortality, low birth weight, maternal mortality and potential years of lost life. The findings revealed that health service expenditure adjusted for GDP and two health outcomes were significantly associated, while in the case of social services expenditure adjusted for GDP; only three of five indicators were found to be significant. Several other studies have also shown contrary findings in low- and highincome countries. For example, Self and Grabowski [43] revealed that health expenditure significantly influenced health only in low- to middle-income countries. The countries belongs to different income groups have differences in health expenditures as well which corresponds to different health outcomes. Likewise, Rana, Alam, and Gow [34] indicated that the association of health expenditure with health outcomes was resilient only for countries with a low income. Lower-income countries are more susceptible to poor health

due to negative shocks to health expenditure. Gupta, Verhoeven, and Tiongson [44], by using demographic health surveys from 44 countries, established that the poor experience worse health than the non-poor, and their empirical findings stated that public spending alone is not enough to improve health status. Gupta [45] argued that public spending enhances health and education in developing and emerging economies. Ullah et al. [46] findings confirm that public healthcare spending significantly impacts health outcomes in Pakistan both in the short-run and long-run. Using a fixed effect model, Nixon and Ulmann [19], in a panel of 15 European Union members over the period 1980–1995, showed that health care expenditure and infant mortality rates were strongly associated, while the results for life expectancy were found to be marginally associated. The findings indicated that factors such as lifestyle, diet, and the environment also influence health outcomes, as these factors vary significantly between low-income and high countries. Therefore, the relationship between health expenditure and health outcomes at different income levels was found to be inconclusive. In another study of Nigeria, Yaqub, Ojapinwa, and Yussuff [47] stated that public expenditure was significant for enhancing health unless corruption in the country was controlled. By using both ordinary least squares and two-stage least squares, the study proposed that lowering infant mortality and under-five mortality rate and raising the life expectancy in Nigeria would only be possible if corruption in the country reduced considerably.

Based on the literature review mentioned above, it is apparent that the results on heath expenditure and health outcome are inconclusive and there has been variance in the use of specific econometric methods. To explore the phenomenon empirically by using advanced econometric techniques, the current study utilized the data of South Asian countries. Finally, by using a more robust empirical strategy, the current study offers new empirical evidence concerning health expenditures and it impact on maternal mortality in the South Asian panel.

### **3. Materials and Methods**

### *3.1. Data Sources*

Based on the availability, the data set of eight South Asian countries including Pakistan, Bangladesh, Bhutan, Nepal, Sri Lanka, India, Maldives and Afghanistan was collected. The maternal mortality ratio (MMR) was used as an outcome variable; this numerically shows the women died during pregnancy or pregnancy-related causes or within 42 days of pregnancy termination per 100,000 live births, while the predictor variables included total health expenditure (HE), population (POP), economic growth (LNGDP), basic sanitation (BS), and access to clean technologies (CT). Health expenditure was measured in current USD. Economic growth (LNGDP) was measured in constant 2010 USD and was used in natural logarithmic form for empirical analysis. The variable population (POP) was measured as the annual percentage growth in the population. The other variables, basic sanitation (BS) and clean technologies (CT), measured the proportion of the population using at least basic sanitation services and clean fuel technologies for cooking.

The data for the desired variables used in the current study were sourced from the World Bank Development Indicators databank from 2000 to 2017. Furthermore, the quarterly data were used for applying the quadratic match sum method. Many previous studies have used this method [48]. This method is appropriate for altering low-frequency data into high-frequency data, as it allows adjustments for seasonal deviation by dropping endto-end data deviation. The trends of maternal mortality and health expenditure in South Asian countries are portrayed below. The Figure 1 below reveals that though maternal mortality has slightly declined in all regions, the most apparent decline is seen for Afghanistan compared with other regions, and the least were seen in Sri Lanka and Maldives.

**Figure 1.** Trends of Maternal Mortality in the South Asian Panel.

### *3.2. Model Specification*

It is a crucial and well recognized fact that a better understanding of various aspects in the economy leads to the formulation of effective economic policy, including the nexus among maternal mortality, health expenditure, economic growth, population, basic sanitation, and clean technologies. As the present study intended to explicate the long-run relationship of health expenditure effects on maternal mortality in South Asian regions while controlling for population, economic growth, basic sanitation, and access to clean technologies, the subsequent model was designed to analyze the relationship empirically:

$$\text{MMR}\_{i,t} = a\_0 + \beta\_1 \text{MMR}\_{i,t-1} + \beta\_2 \text{HE}\_{i,t} + \beta\_3 \text{LNGDP}\_{i,t} + \beta\_4 \text{POP}\_{i,t} + \beta\_5 \text{BS}\_{i,t} + \beta\_6 \text{CT}\_{i,t} + \mu\_{i,t} \tag{1}$$

where MMR, HE, LNGDP, POP, BS, and CT signify the maternal mortality ratio, health expenditure, economic growth, population, basic sanitation, and clean fuel technology, respectively. In this model, the maternal mortality ratio represents the dependent variable; *βk* (*k* = 1, 2, 3, 4, 5) are the coefficients of the lag of maternal mortality ratio, health expenditure, economic growth, population, basic sanitation, and clean fuel technology; and *μi,t* shows the error term. Our models remained confined to these variables to avoid over fitting the model and to allow our core variables of interest to show the association.

Determination of the long-run relationships among the study variables was undertaken via the following essential steps. Firstly, panel unit root tests were used to examine the stationarity properties of the variables. Testing stationarity is vital for both time and panel data because non-stationary data lead to spurious results [49]. Thus, Im, Pesaran, and Shin's test [50] for unit roots was applied to test the stationarity [51–53]. However, it has been argued that such unit root tests for panel data may not be accurate due to the probability of cross-sectional dependency. Therefore, second-order generation tests such as cross-sectional IPS (CIPS) were also used, along with a cross-dependency test (CD), as these tests can be executed under the notion of cross-sectional dependency as well

as heterogeneity [54], which was unnoticed by Im, Pesaran, and Shin [50]. Many earlier studies have used this method [55,56].

Once the stationarity had been checked, the next step was to test the variables' cointegration. Cointegration expresses the association among the studied variables in the long run. The cointegration test of [57] for panel data involves two stages. In the first stage, it monitors short-run parameters and individual deterministic trends to control the heterogeneity. On the basis of the estimated residuals, statistics of different tests can be derived, i.e., pooled (within-dimension) data to test the common process, and grouped (between-dimensions) data to test individual processes. The four statistics included in a within-dimensions approach were panel v, panel ρ, panel PP, and panel ADF. In contrast, the three statistics included in the between-dimensions approach were group O, group PP, and group ADF. The cointegration test only indicated the long-run associations among the variables and did not indicate the direction of the variables used in the study.

To indicate the direction, the study primarily used fixed effect and random effect models [58], but as the panel data suffered from cross-sectional dependency, FMOLS and DOLS were used, as in Kao and Chiang [59]. FMOLS and DOLS are appropriate for panel data.

### **4. Results and Discussion**

### *4.1. Descriptive Statistics*

The variables used in the current study and their descriptive statistics are presented in Table 1. The findings reveal that maternal mortality's maximum and minimum values are around 1450 and 36, respectively, for the sample countries. The sampled countries' health expenditure had a mean value of about 105, with a standard deviation of 184, a maximum of 946, and a minimum value of 8.36. The mean value of economic growth was USD 24.18; the mean value for population was nearly 1.83 and its standard deviation was about 1.067. Clean fuel technologies and basic sanitation had a mean value of 32.16 and 53.63, with a standard deviation of 20.24 and 23.83, respectively.

**Table 1.** Descriptive Statistics.

Source: Authors' estimations.

### *4.2. Unit Root Tests*

Before performing the regression analysis, the variables' stationarity was required to be checked. First, the variables' stationarity properties were checked using the conventional unit root tests of Im, Pesaran, and Shin [50]. In Table 2, the findings of the unit root test with individual intercepts and individual intercepts with trend terms are illustrated. The findings reveal that basic sanitation and clean technologies have a unit root problem at the level but become stationary at first difference. The study then proceeded to inspect the cointegration among variables.

**Table 2.** Results of the stationarity analysis.

Note: \*\*\*, \* show significance at the 1% and 10% levels, respectively.

Before checking cointegration, the present study used the cross-sectional dependency. It has been argued that orthodox unit root tests do not offer robust outcomes in data series with cross-sectional dependence. Therefore, according to the cross-section dependency test results, it was apparent that there was cross-sectional dependence, as the results were found to be significant for all variables at the 1% significance level (see Table 3).For panel data with cross-sectional dependence, it is recommended to use a cross-sectional unit root test IPS (CIPS), rather than the conventional Im, Pesaran, and Shin unit root test. Therefore, the current study used the CIPS results, which assumed cross-section dependency within the panel data and revealed that all variables became stationary at first difference.

**Table 3.** Cross-sectional dependence and CIPS unit root test results.

Note: \*\*\*, \* signifies significance at the 1% and 10% levels, respectively.

### *4.3. Panel Cointegration Test*

Once the stationarity features of all variables had been proven, panel data cointegration was valid to apply. To check the cointegration, the study used the panel cointegration test of Pedroni [57]. The results in Table 4 revealed that four tests out of seven were significant at the 1% level. Based on these test results, it is apparent that variables are co-integrated in the long run.

**Table 4.** Results of Pedroni co-integration (Engle-Granger based) in the South Asian panel.

Note: The panel cointegration null hypothesis is no cointegration.

### *4.4. Panel Estimation Results*

### 4.4.1. Fixed Effect and Random Effect Results

As a preliminary analysis, the conventional panel data models, i.e., fixed and random effects were applied. The results of the fixed effect model showed that health expenditure, economic growth, and population positively influenced maternal mortality in South Asian countries by 43%, 58%, and 71%, respectively, while in the case of random effects, the coefficients were similar: 42%, 44%, and 72%, respectively. The findings for health expenditure are unexpected and reveal that an increase in health expenditure increased maternal mortality in the South Asian panel. In contrast, access to sanitation and clean fuel technologies were proven to reduce maternal mortality. The random effect model also reported the same findings, with similar coefficients in the sample countries. Hausman's [60] test was also executed, and indicated that the fixed effect model was more appropriate for the current study (see Table 5). However, as the panel data suffered from cross-sectional dependency and heterogeneity, as the countries are diverse and are not identical in their demographic aspects, health infrastructure, medical technology, and the prevalence of diseases, relying on fixed effects may give biased results. Therefore, the study proceeded towards using fully modified ordinary least squares and dynamic ordinary least squares. These methods are beneficial for overcoming heteroscedasticity and autocorrelation in the panel data [61,62]

Source: Estimations by the authors.

### 4.4.2. FMOLS and DOLS Results

Table 6 reports the estimation results of FMOLS and DOLS and indicates that though the magnitude of the coefficients is diverse, the signs are almost similar. There is a consensus that increased health expenditure crucially reduces maternal mortality. However, in South Asian countries, the outcomes are quite surprising, showing that a 1% rise in health expenditure led to an upsurge of the mortality rate by 1.95% for FMOLS and 0.16% for the DOLS estimator. The results in the case of economic growth were, as expected, significant and vigorous, and negatively affected maternal mortality. The outcomes show that an increase in GDP by 1% negatively influenced maternal mortality by 94% in FMOLS. The results in the case of population were expected and revealed that a 1% increase in population growth increased maternal mortality by 40% in the case of FMOLS and 10% in the case of DOLS. In the context of basic sanitation and clean fuel technologies, the results were found to be negatively significant: 7% and 11% in the case of FMOLS and 1.9% and 5.6% in the case of the DOLS estimator.

**Table 6.** FMOLS and DOLS long-run results.

Note: \*\*\*, \*\* indicates significance at the 1% and 5% levels, respectively.

### *4.5. Discussion*

This study contributes to the rising debate regarding maternal mortality and health expenditure. The data from eight South Asian countries over 2000–2017 is used for the FMOLS and DOLS estimation techniques. The study findings revealed that there is rise in the maternal mortality ratio in developing countries and it is less likely to obtain better health facilities. The results further imply that majority of females in developing countries belong to resource-poor settings, and even when the females reach a health center, they face innumerable hurdles such as inappropriate and inadequate care, and also other factors such as failures in the delivery of health services, a shortage of equipment or personnel, and—worst of all—faulty management. Despite increasing health expenditure in the sampled region, the unsatisfactory and meager quality of management and substandard health care services may dampen efforts to reduce maternal mortality. The result further suggests that though funds allocated for health care have increased in South Asian countries, the funds might be poorly distributed and mismanaged. Previous studies also found that a large share of the health budget is spent on pharmaceuticals, which reduces the share for other health programs [63]. Various other studies have shown contradictory findings regarding healthcare expenditure and health outcomes. For instance, Rana et al. [34], and Anand and Ravallion [64] revealed a positive link between health expenditure and the health sector's performance. Nketiah-Amponsah [38] showed that an increase in expenditure on health of 1% resulted in reducing maternal mortality by 0.35% and under-five mortality by 0.5%, and improved life expectancy by 0.06%.

Regarding infant mortality, the study revealed that total expenditure on health significantly reduced infant mortality in South Asian countries [65]. In contrast, Filmer, Pritchett, and Musgrove [24,40] found no association between these variables. Likewise, Zakir and Wunnava [66], and Nolte and Mckee [67] also found no association between health expenditure and health outcomes. Moreover, the health expenditure effects in the sampled region can additionally be attributed to the fact that there are culturally significant beliefs regarding prenatal care services. Most of the women in South Asian countries perceive prenatal care to be worse for their offspring in the womb. Thus, on the basis of empirical estimations, it was concluded that health and betterment in maternal health are not associated with increased health expenditure. Still, the value, cost, reliability, and acceptability of health facilities also matter.

Similarly, the positive coefficient of population growth revealed the expected finding that an increase in population also leads to increased maternal mortality. South Asia comprises a considerable proportion of the global population and includes some of the peak child and maternal mortality rates internationally. The reason could be that when households have more family members, they may be less likely to provide adequate nutrition and healthcare services [68,69]. Such an alarming growth rate could lead to absolute scarcity of food, shelter, clothing, and, more importantly, health services. The exploitation of resources becomes worse due to population pressure and creates many social problems. Due to the large population size, most health services are required to be shared, and a large portion of the health expenditure is spent on curative medicines, which mostly reach the people of urban areas. Moreover, the majority of the population

in developing countries belongs to resource-poor settings and face hurdles in obtaining government of health care services due to the great mass of inhabitants, so maternal death prevention entails fundamental changes not only through increased health expenditure but also proper resource allocation and delivery of health services. People from resource-poor settings will have to be fought for to achieve equity and social justice [70–73].

Setting health expenditure and population growth aside, the significance of economic growth, access to sanitation, and clean fuel technologies in decreasing mortality rate cannot be disregarded. This suggests that maternal mortality can be reduced with an increase in income. Moreover, dietary preferences and lifestyle can also be enhanced with an increased share of income [74,75]. Many prior studies have stated a similar verdict and revealed that economic growth increases the average income, which intensifies accessibility and consumption of goods and services, and eventually improves peoples' health [76,77]. Increased income can help women seek experienced health experts and recognize the perils of not receiving satisfactory healthcare during pregnancy. The study of Pritchett and Summers [78] also showed that per capita GDP positively and significantly influenced health. Usually, in developing countries, people face financial constraints and cannot afford health expenses, so they prefer to spend on food commodities. Therefore, in this case, government spending can improve the health of poor people. Health is an essential human right, so economic growth can lessen maternal mortality in South Asian regions. Bloom, Canning, Kotschy, Prettner, and Schünemann [79] also showed that income positively affects health and improves life expectancy. The findings also showed that individual income plays a vital role in improving people's health in South Asian countries.

Further, several studies have shown a link between sanitation and improved health outcomes [80,81]. Many former researchers have considered access to water and sanitation for dropping the morbidity of diarrhea, and infants and under-five mortality [82–84]. The existing studies proposed that well-executed interventions in socioeconomic conditions, particularly in areas where primary settings are meager, can reduce maternal mortality. The results correspond well with those for sub-Saharan Africa found by Pickbourn and Ndikummana [85], where mortality rates and diarrhea morbidity were greater than in other regions. The individuals whose water and hygienic sanitation improved are among the lowest in the world. Thus, the findingsindicatethat investments in socioeconomic determinants such as sanitation and clean technologies are also likely to improve the maternal health status of the South Asian region.

### **5. Conclusions and Policy Recommendations**

The mother's health is crucial for a child's cognitive growth and time spent caring for children. Given the rising maternal mortality rates in South Asian countries, reducing maternal mortality remains a noteworthy challenge for human development in South Asian countries. The current study unpacked the myth of increased health expenditure and reduced maternal mortality in South Asian countries by utilizing panel data from 2000–2017. The current study applied heterogeneous panel estimation techniques to examine the overlooked time-invariant heterogeneity and cross-dependency across countries.

The outcomes revealed that increased health expenditure and population growth were associated with an increased maternal mortality ratio. Health expenditure is a vital matter that can govern policy decisions at the national and international levels. Nevertheless, as indicated above, merely increasing health expenditure is insufficient; access, price, and consistency may influence the extent to which they are used, as well as, most importantly, their accessibility by deprived women. Thus, it is essential to go beyond simply expanding. Though increasing governments' health care spending is essential for reducing mortality and enhancing health outcomes, the results for the sample countries indicate that governments should support the accountability and transparency of the amounts allocated for maternal health care services. In the nonexistence of transparent financial support, the possibility of achieving the SDG in South Asian countries will be difficult. This evidence should support officials in the health sector in countries with

rising rates of maternal mortality to lower the mortality rate, either directly by increasing access to better health amenities for underprivileged women or indirectly by increasing the financial capability of officials to allow sufficient funds to the maternal health sector in general and programs for the prevention of mortality in particular [86]. The government should allocate a larger portion of the health budget for maternal health. Health services should accompany a suitable allocation of funds for women's health at the community level. Women from resource-poor settings who are in utmost need of health advice and at the highest peril of death have limited health facilities. They can better avail these facilities if such services accessible adjacent to their home town, preferably in their neighborhoods. What is required is the placement of a number of health workers at the community level who are armed with proper training for maternal health care. This is possibly one of the best methods for taking advantage of resources. Additionally, our outcomes also focus on the significance of economic growth, and access to sanitation and clean fuel technologies in lowering maternal mortality. The results indicate that the governments of South Asian countries should improve the infrastructure and increase access to water and sanitation to end preventable deaths of mothers. The prevention of maternal deaths requires extensive socioeconomic modifications beyond the boundaries of the health care system alone, and these aspects can make childbirth and pregnancy safer naturally.

The findings of this study area new addition to the literature concerning health expenditure and maternal mortality in similar nations of South Asia. The findings show that each region has its challenges for health, and how sensibly expenditure on health is directed is the crux of the matter. Another area of action is improving the quality of health care facilities and competent people. This encompasses more than guaranteeing equipment availability; it also requires making the health services more publicly liable. The prevailing healthcare system adds to the rising maternal mortality due to meager and incompetent arrangements or meager administrative competencies. It also reflects the priorities established by an elitist system in which the powerless and poor do not count.

This study has also certain notable limitations. For example, the analysis only added a few control variables and ignored other potential confounding variables, such as the literacy rate, the physician to population ratio, and many other aspects of health. The reason for this was the high rate of missing data for these variables, which resulted in the exclusion of these variables from the model. Moreover, it is also likely that not all of the indicators of the World Bank databank are recounted each year. Thus the World Bank data might not remain consistent with the annually reported data of the Ministry of Health of each country. Further, the World Development data only give the data at the national level and, in this case, may not be helpful for showing the situation at the individual and community levels. Moreover, the present study utilized data from an South Asian panel, so the outcome may not be generalized to other developing countries. Regardless of these limitations, it is expected that the current study will provide impactful and meaningful results for policy makers to devise strategies, keeping in view the fundamental South Asian aspects warranted to reduce maternal mortality.

**Author Contributions:** Conceptualization, writing and methods, N.A.; supervision and funding acquisition, J.H.; review and editing, T.S.; review and analysis, H.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China, grant number 713111025 and Postdoctoral Science Foundation of China, grant number 2021M691610.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data will be available on request.

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

### **References**

### *Article* **The Impact of the SARS-CoV-19 Pandemic on the Global Gross Domestic Product**

**Piotr Korneta \* and Katarzyna Rostek**

Faculty of Management, Warsaw University of Technology, 02-524 Warszawa, Poland<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The rapid, unexpected, and large-scale expansion of the SARS-CoV-19 pandemic has led to a global health and economy crisis. However, although the crisis itself is a worldwide phenomenon, there have been considerable differences between respective countries in terms of SARS-CoV-19 morbidities and fatalities as well as the GDP impact. The object of this paper was to study the influence of the SARS-CoV-19 pandemic on global gross domestic product. We analyzed data relating to 176 countries in the 11-month period from February 2020 to December 2020. We employed SARS-CoV-19 morbidity and fatality rates reported by different countries as proxies for the development of the pandemic. The analysis employed in our study was based on moving median and quartiles, Kendall tau-b coefficients, and multi-segment piecewise-linear approximation with Theil–Sen trend lines. In the study, we empirically confirmed and measured the negative impact of the SARS-CoV-19 pandemic on the respective national economies. The relationship between the pandemic and the economy is not uniform and depends on the extent of the pandemic's development. The more intense the pandemic, the more adaptive the economies of specific countries become.

**Keywords:** SARS-CoV-19; pandemic; crisis management; GDP; gross domestic product; multisegment Theil–Sen

### **1. Introduction**

The SARS-CoV-19 pandemic has rapidly spread around the world and has posed significant threats to public health, as well as social and economic standing of many countries. The unexpected onset of the global health crisis caused many scholars to change their research domains towards pandemic-related areas. A significant number of studies have already been conducted in terms of the medical aspects of the coronavirus-2019, including the understanding of coronavirus-2019 pathogenic mechanism [1], its transmission routes [2], and different patterns of symptom development [3–6]. Several researchers have already identified obesity; ageing; and comorbidities such as cardiovascular diseases, cancers, and diabetes as risk factors in SARS-CoV-19 patients [7–10]. The SARS-CoV-19 fatality rate is estimated at around 4% of infected patients but varies between respective countries from 0 to 20% [11,12]. Such a large range of fatality discrepancies might result from different demographic, economic, and political variables specific to different countries [12]. Finally, it is noteworthy that thanks to the extensive work performed by scientists and pharmaceutical companies, the vaccination process is already underway around the world [13].

As research in the healthcare perspective is starting to yield significant effects, it is now time to focus our attention on the economy and the significant consequences the pandemic has had in this context. A number of scholars have already studied the impact of the global health crisis on financial markets whose sharp fluctuations during the pandemic have led to confusion and uncertainty among investors [14–16]. Results reported by other researchers indicate that the SARS-CoV-19 pandemic has changed consumer behaviors [17]. The first papers on the impact of the SARS-CoV-19 pandemic on the condition of the

**Citation:** Korneta, P.; Rostek, K. The Impact of the SARS-CoV-19 Pandemic on the Global Gross Domestic Product. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5246. https:// doi.org/10.3390/ijerph18105246

Academic Editors: Dimitris Zavras and Paul B. Tchounwou

Received: 29 March 2021 Accepted: 8 May 2021 Published: 14 May 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/).

healthcare industry have also already appeared. It has been claimed that the pandemic has had both positive and negative effects on respective branches of the healthcare industry, but the latter are nonetheless predominant [18]. The negative impact of the pandemic on the economies of various countries has already been acknowledged in both local [19–21] and global terms [22–24]. The first gross domestic product (GDP) forecasts have also been already provided [25]. It is believed that the decline of global economy stems from two primary factors [23]. The first relates to the various forms of isolation imposed by the governments, such as social distancing, shutdowns of events and corporate offices, and lockdowns. The second is the uncertainty about how bad the situation can get. According to information provided by the International Monetary Fund (IMF), the global gross domestic product is estimated to have declined by around −3.5%. We should note, however, that the data provided by IMF indicate significant discrepancies between economic effects observed in respective countries [26]. Such differences may result from two major factors. Firstly, the degree to which the economies of specific countries have been affected by the pandemic. This can be measured relative to SARS-CoV-19 morbidity and fatality rates. The second factor relates to the various restrictions introduced in particular countries [27,28], availability of public aid [29,30], and interconnections developed between the economies of various countries during the pandemic [31,32].

Since 2020 is already over and real data reflecting changes in the global GDP are now available, scientists can commence studies in this area. The objective of our paper was to consider the impact of the SARS-CoV-19 pandemic on national economies. We selected SARS-CoV-19 morbidity and fatality rates as viable proxies of pandemic development in specific countries, while GDP fluctuations served as a measure of the economy's condition. In order to achieve the objective of the paper, we formulated the following two hypotheses:

### **H1:** *There is a negative correlation between the SARS-CoV-19 pandemic development and GDP growth.*

### **H2:** *The relationship between the pandemic and GDP fluctuations is not a linear one.*

The first of the formulated hypotheses is based on the postulates of other scholars [22–24] who reported on the negative effects of the pandemic on the global economy. The second results from the assumption that the actions taken by governments might affect the severity of the impact that the pandemic has had on the economies of respective countries [27–32]. To our best knowledge, this study is the first to explore correlations between SARS-CoV-19 morbidity and fatality rates on the one hand, and the economies of specific countries on the other. Notably, the objective of this paper was not to analyze the sources of morbidity and fatality rates discrepancies between respective countries, but rather to study how such differences affect their economies.

The paper analyzed data relating to 176 countries from six different continents in a 11-month period from February 2020 to December 2020. Because of significant data dispersion, we had to use robust statistical methods in our analysis. We employed moving medians and quartiles to determine the form of the relationship between the degree of pandemic development and the change of gross domestic product in specific countries, in their respective ranges. We employed the Kendall tau-b to determine the significance of correlations. Next, we calculated multi-segment Theil–Sen linear approximations to fit the obtained relations.

The rest of the paper is structured in the following way: In Section 2, research methods and their different components such as the database and sample of observations, variables, and design of the study are provided. Section 3 presents results derived from empirical data. Section 4 provides the discussion and indicates limitations of our study. Finally, we provide conclusions and implications.

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

### *2.1. Database and Sample of Observations*

The data used in this study were obtained from two well recognized sources: the open access database "Our World in Data" [33] and the "International Monetary Fund" [26]. From the latter, information on the change of gross domestic product in 2020 as of 7 March 2021 as compared to 2019 by different countries was obtained. The "Our World in Data" database compiles official information relating to the SARS-CoV-19 pandemic, including the number of reported new SARS-CoV-19 cases and deaths and PCR testing around the world [33]. From this database, accessed on March 7, 2021, we obtained 59,787 daily observations relating to 176 countries in a 11-month period from February 2020 to December 2020. Since this database provided no information regarding other countries, we were not in a position to analyze all the countries worldwide. We aggregated the obtained daily observations into 176 yearly observations. As a result, the studied figures comprised 82.5 million and 1.8 million diagnosed SARS-CoV-19 cases and reported deaths, respectively, and related the same to the world population of 7.6 billion. Table 1 presents the breakdown of observation samples used in the study, divided by continent, with the numbers of SARS-COV-19 new cases and deaths reported and the respective number of countries considered.

### *2.2. Variables*

The variables used in this study, their acronyms, number of observations, and descriptions of calculations are presented in Table 2. The first two of the studied variables are SARS-CoV-19 case rate (CCR) and SARS-CoV-19 fatality rate (CFR). We calculated these variables for each country. Since the populations of respective countries differ, we divided newly diagnosed SARS-CoV-19 cases and deaths per 1000 residents. A similar approach has already been used by other scholars [34]. We employed CCR and CFR variables to measure the pandemic. The last of the variables selected for the study was the change in gross domestic product relative increase (GDP). This ratio indicates the market value of all goods and services produced in a specific period. Hence, the change in GDP relative increase is a good proxy of the change in the world's economy.

### *2.3. Design and Data Analysis*

Once the variables had been selected, we analyzed descriptive statistics. In the next step of the study, we plotted the relationships between respective variables on graphs. Visualization of the contemplated relationships was achieved by employing the moving median and first and third quartiles in order to determine different correlations between the considered variables in different ranges [35]. We calculated the median values for both variables in a moving window containing data from 50 observations. The division of our relations into segments helped us to identify ranges of pandemic intensity with different effects on the GDP. In practice, it is important to identify what pandemic intensity has a dominant effect on the change of global gross domestic product. The approach of dividing infected individuals according to the 80/20 rule has been suggested to explain transmission events during the SARS epidemic [36]. After identifying the relation between variables in different ranges, we calculated the Kendall tau-b [37–39] coefficients. Kendall tau-b rank correlation coefficient is widely used as a distribution-free measure of the strength and direction of association that exists between two variables. In order to analyze data using Kendall tau-b, one should measure two variables on an ordinal or continuous scale (interval or ratio variables), and data should follow a monotonic relationship. We calculated Kendall tau-b rank correlation coefficient in intervals where monotonic relationship was indicated by the moving median and quartiles. Our variables thus satisfy the requirements of Kendall tau-b, which can be used to determine the strength and direction of the association [40–43]. The values of Kendall's rank correlation coefficient around 0.1 are typically obtained in the analysis of meteorological data, where they vary between 0.012 and 0.159 [44]. Decision about the statistical significance was made using empirical significance level (*p*-value) compared to α = 5%. We used one-tailed Mann–Kendall test [41,42].

Next, we applied the multi-segment linear regression with the Theil–Sen robust lines to characterize different relations between our independent and dependent variables in different ranges [45]. This regression model was selected because it is resistant to the effects of outliers and non-normality in residuals. The multi-segment model is the most suitable choice if more than one regression relation is necessary to characterize different relations that may occur in different regimes of explanatory variables. The nonparametric estimate of the slope of the line, identified as the Theil–Sen (or Kendall–Theil or Sen) slope in the literature, is calculated as the median of all possible pairwise slopes between points [45–49]. The line intercept was calculated by using the median slope and the median of the independent and dependent variables [47,49]. The multi-segment Theil– Sen regression technique was comprehensively described by Granato [50] and has been used by other researchers [50,51]. Furthermore, the Theil–Sen estimator is considerably more accurate than non-robust simple linear regression, especially for heteroskedastic and skewed data. It should also be noted that the Theil–Sen estimator provides better results than the non-robust least squares method, even for normally distributed data [47]. Finally, we obtained multi-segment piecewise-linear approximation to the studied relationships between considered variables. The Theil–Sen procedure has already been used in other studies in the fields of economy [51] and health [52,53].

### **3. Results**

Presented in Table 3 are the descriptive statistics of the variables used in this study, calculated for each country in the relevant period. On average, 15.749 (CCR) per 1000 persons were infected with SARS-CoV-19 in 2020, and 76.819 was the maximum. Great differences in mortality between respective countries are revealed in the min, median, and max values of the SARS-CoV-19 fatality rate (CFR). The highest mortality of 1.739 deaths per 1000 people was reported in San Marino, closely followed by Belgium, where the fatality rate in the studied period reached 1.685 per 1000. The (arithmetic) mean of the GDP fluctuation was −5.694 (%) and was negative in 153 countries, with only 23 states reporting positive values.

**Table 3.** Descriptive statistics.

Table 4 presents the results of statistical tests for the relationships between CCR, CFR, and GDP in two identified ranges for each variable. We showed the Kendall tau-b values with their *p*-values (one side). In the selected ranges, we performed linear approximation of the studied relationships using the Theil–Sen procedure. The obtained values of Theil–Sen slopes (m) and intercepts (b) are given in Table 4. This two-segment piecewise-linear approximation well describes the considered relationships in the two identified ranges.

**Table 4.** Results of statistical tests using the Kendall tau-b of relations between CCR, CFR, and the change in GDP relative increase (GDP) with Theil–Sen slopes (m) and intercepts (b) of trend lines approximating these relations.

The scatterplot in Figure 1 was fitted with the multi-segment Theil–Sen regression model. The number of segments, coefficient of regression lines, and the convergence points were calculated and adjusted according to the procedure described by Granato [50]. The two-line model, without discontinuity plotted on the scatterplot, was a good fit. One can notice that this relationship differs considerably for the CCR variable up to 7 and above. It can thus be well represented by two straight lines, i.e., by two-segment approximation. The Kendall tau-b for the first of approximated lines (CCR < 7) amounts to −0.1274, which confirms the negative trend. The low *p*-value, below 0.05, confirms that these results are statistically significant. The slope of the approximated line is −0.4851. Such a high value of the approximated slope indicates that the GDP is very sensitive to any changes of the CCR variable in the studied range. The relationship between GDP and CCR in the second of the identified ranges (CCR > 7) was statistically insignificant as the *p*-value was above 0.3. These results are endorsed by the Theil–Sen trend line, which was flat (the slope was 0.008), indicating that there was no relationships between CCR and GDP in the second range, i.e., for CCR above 7.

Figure 2 shows the relation between SARS-CoV-19 fatality rate (CFR) and GDP. As can be seen, the contemplated relationship can be divided into two different ranges and approximated with two straight trend lines. The first range comprised CFR observations below 0.2. Here, the Theil–Sen trend line (m = −12.9928) was sharp and fast declining. The relationship between CFR and GDP was negative. The Kendall tau-b coefficient was −0.1401. This confirmed that this relationship was negative and statistically significant (*p*-values below 0.05). The relation between CFR and GDP for GDP> 0.2 was well fitted by the Theil–Sen straight line with the slope of m = - 1.3475 and the intercept b = −5.8339. This result was endorsed by the Kendall tau-b, which was −0.1421. The results are statistically significant with *p* = 0.0456.

**Figure 1.** The relation between CCR and GDP with the moving median, first and third quartiles, and two-segment linear Theil–Sen approximation.

**Figure 2.** The relation between CCR and GDP with the moving median, first and third quartiles, and two-segment linear Theil–Sen approximation.

### **4. Discussion**

As already discussed and confirmed in the literature, the global health crisis caused by coronavirus-2019 has negatively affected the world economy. This negative impact has already been discussed at the levels of both local economies [19–21] and globally [22–24]. Several researchers have provided the very first approximations of the global economic

costs of the SARS-CoV-19 pandemic, which might total at around USD 2.7 trillion [54]. Other scholars note that the negative impact of SARS-CoV-19 on the economies has not only caused GDPs to decline but has also increased the importing and exporting costs. At the same time, it must be added that this negative impact varies greatly between respective economy sectors, with tourism and domestic services losing the most, and natural resources and agriculture being only negligibly affected. Reports also confirm considerable differences between individual countries [55]. König and Winkler provided the initial empirical evidence on the relationship between the SARS-CoV-19 pandemic and the economic crisis. Their research was based on data from the first three quarters of 2020 relating to 42 countries, using simple ordinary least squares and panel fixed effects regressions. They explained the link between the pandemic and the GDP by highlighting two major factors. The first results from restrictive measures imposed by the governments of different countries. Their results indicate that governmental restrictions lowered the GDP growth in the same quarter, with noticeable improvement in GDP dynamics in the following one. The second link between the pandemic and GDP is the health risk that leads to voluntary social distancing, which, again, lowers the GDP. They measured the health risk on the basis of fatality rates and provided initial empirical evidence suggesting that high fatality rates contribute significantly to the negative growth rates [56]. In comparison with the study by König and Winkler, our research was based on a considerably larger sample (176 versus 42 countries), related to a longer period (full year instead of three quarters), and employed a different econometric approach and databases. Our results are aligned to postulates of other scholars and empirically confirm the negative impact of the SARS-CoV-19 pandemic on the global economy. In our calculations, Kendall tau-B coefficients confirmed the negative correlation between SARS-CoV-19 fatality rates and changes in the GDP relative increase. The very low *p*-value (below 0.05) indicates that the obtained results are robust. We also identified a negative relationship between SARS-CoV-19 infections rate (CCR variable) and the decrease of gross domestic product. This relationship, however, was only significant for CCR values below 7, i.e., up to 7 infections per 1000 people in 2020. Therefore, the first of the formulated hypotheses, stating that *there is a negative correlation between the SARS-CoV-19 pandemic and GDP growth*, has been corroborated.

The onset of the SARS-CoV-19 pandemic and efforts aimed at limiting its further development were approached differently by decision makers in various countries. The tools most frequently employed against the pandemic included isolation, quarantine, and lockdown. In general, the more rapidly the pandemic developed in a country, the more severe restrictions were introduced by the policymakers [27,28]. SARS-CoV-19 tests have also been widely used in an effort to halt the pandemic development. Several researchers have claimed that the expansion of the pandemic can be limited by increasing the testing rate [57]. However, high costs and financial restraints caused the countries employ different testing strategies [34,58,59]. The third tool employed to reduce the negative impact of the SARS-CoV-19 pandemic on the economy entailed government programs supporting the companies and citizens during periods of isolation and lockdowns [29,30]. Finally, several scholars have also noted the development of interactions between different countries during the pandemic [31,32]. Assuming that all the aforementioned actions might affect the expansion of the pandemic, we formulated the second hypothesis, namely, that *the correlation between the pandemic and GDP fluctuations is not a linear one*. We estimated that the relationship between SARS-CoV-19 fatality rates and changes in the gross domestic product relative increase can be well fitted with two straight lines, with slopes of −12.9928 and −1.3475 and in the ranges below 0.2 deaths per 1000 (or up to 200 per 1 million) of the population and above the same, respectively. This shows that the GDP falls by 0.013 percentage points with each death per 1 million people. Once the threshold of 0.2 deaths per 1000 people is exceeded, however, the gross domestic product becomes less sensitive to new SARS-CoV-19 deaths and falls by only 0.0013 percentage points with each new death per 1 million people. The relationship between gross domestic product and SARS-CoV-19 morbidity is similar, with the threshold of 7 new infections per 1000 people. Since the

slope of the approximated line was −0.4851, each new infected person per 1000 residents reduced the GDP by 0.4581. Once the threshold of seven new infections is exceeded, the contemplated relationship becomes statistically insignificant (very high *p*-value Kendall tau-B). Hence, the GDP change is considerably more sensitive to new SARS-CoV-19 deaths than to infections. The two distinctly identified ranges in the relationship between GDP and SARS-CoV-19 morbidity and fatality rates indicate that there is a threshold over which countries manage to adapt to the pandemic. Due to this observation, our second hypothesis must be accepted. The world economic crisis triggered by the SARS-CoV-19 pandemic is a complex and multifaceted issue and differs from other economic slowdowns in recent decades. This is because, additionally to the burden on the healthcare systems, the pandemic has directly resulted in the premature deaths of employees, large-scale workplace absenteeism, productivity reduction, negative supply shocks, manufacturing activity slowdown, global supply chain disruptions, and decimation of the tourism industry [60]. Given the complexity of this crisis, a fraction of researchers employed a high-level, comprehensive approach in their studies, while most of the others focused only on selected economy issues. Al-Thaqeb et al. claim economic policy uncertainty plays a key role in understanding the current crisis. They indicate that high uncertainty in terms of economic policy is associated with adverse effects on households, corporations, and governments, which tend to delay financial decisions due to the same reasons. This leads to lower consumption, lower loan issuance, fewer investments, and higher unemployment. They also note that the effects of political and regulatory uncertainty extend even to the commodity and crypto-currency markets. [61]. Song and Zhou also acknowledge the fact that uncertainty plays a key role in the current economic crisis. They draw attention to high periods prior to SARS-CoV-2, resulting primarily from Chinese economy problems and slow recent growth; synchronized global economy slowdown; de-globalization; and unfavorable macroeconomic settings, including deflation [62]. Stiifani´c et al. studied the impact of the SARS-CoV-19 pandemic on crude oil prices and selected stock indexes: DJI, S&P 500, and NASDAQ. They employed new SARS-CoV-19 infections as the independent variable. In terms of crude oil prices, they identified uncertainty and supply shock increase in the global stocks of crude oil, which led to price slumping. They also demonstrated the existence of a link between stock market prices and the pandemic, indicating, however, that the movement of stock indexes does not reflect the real situation in the economy but is mainly based on expectations and monetary and fiscal incentives. [63]. Finally, other researchers studied the impact of the SARS-CoV-19 pandemic on selected industries, namely, travel, hospitality, sports, events, entertainment, education, and finance. Their approximations were negative for each of the studied industries [64]. In our study, we focused on the question of how the SARS-CoV-19 pandemic has affected the economies of 176 selected countries as a whole. Therefore, unlike other authors, we did not study specific industries or selected economy ratios. Instead, we considered the change in gross domestic product relative increase throughout the pandemic period in the studied countries, which we considered to provide a good proxy for the condition of economy as a whole. Our sample of countries and studied period were considerably larger than those reported in previous studies. As a result of the above and due to the robust statistical results, our findings provide global evidence on how the SARS-CoV-19 pandemic has affected the economies of respective countries.

The presented study has several limitations. Firstly, the sample for this study comprised observations relating to 176 countries. Hence, not all countries could be considered in this study. Secondly, we calculated the change in the GDP for each country on the basis of the initial calculations of 2020 GDP provided by the International Monetary Fund as of 7 March 2021. These variables might require minor adjustments. The aforementioned two limitations might change the values of coefficients calculated in the study, but not our overall results. This is because our results were robust with low *p*-values (below 0.05). The final limitation of this study results from the analyzed period. Since we studied observations relating to only a single year, i.e., 2020, we are not in a position to conclude on the long-term impact of the SARS-CoV-19 pandemic on the economies of the analyzed countries.

In this study, we identified the economies of the countries with regards to the SARS-CoV-19 pandemic. We did not study the underlying reasons, which we consider to be rather multifaceted in nature. Hence, further research into adaptation mechanism in the context of the pandemic could prove interesting from the scientific point of view. In this paper, we analyzed and identified global trends related to the pandemic and GDP fluctuations. To this end, we ignored local differences, timing, and intensity of the pandemic. Although such an analysis was not required for our study, we consider it to be a good indication for further research. The final indication for the continuation of our research is to study the long-term impact of coronavirus-2019 on the global economy.

### **5. Conclusions**

The global health and economy crisis caused by coronavirus-2019 is unprecedented, both in terms of its global dimension and the depth of its impact on the economy, society, political and legal conditions, and even the environment. The severity of the aforementioned phenomena and various related factors continues to increase, while the pandemic's prolonged duration poses a very real risk of a global catastrophe. As a result, many researchers and state policymakers have shifted the domains of their professional interests towards the pandemic and its surrounding issues.

In this paper, we studied data relating to 176 countries in the pandemic period and in the previous year. The objective of this paper was to study the impact of SARS-CoV-19 pandemic on national economies. Given the fact that at the time of writing this article real data pertaining to GDP values worldwide have just been officially provided, our study is the first or one of the first to analyze the impact of SARS-CoV-19 on the economies of countries in the first year of the pandemic. We selected the SARS-CoV-19 morbidity and fatality rates as proxies for the pandemic's development and GDP fluctuations in 2020 and 2019 as representative of the state of economy. Firstly, we identified the negative and statistically significant correlations between the SARS-CoV-19 morbidity and fatality rates and GDP changes. Next, we determined that the studied relationships were not linear, with the economies growing increasingly immune to the pandemic. The more severe the development of the pandemic, the more adaptive to its negative effects the economies eventually became. This finding is especially interesting as it shows that the global economy can quickly adjust to fast-changing severe and negative conditions. Hence, our results also demonstrate how the economies of the respective countries have been developing immunity to the negative effects of coronavirus-2019. This has been due to both governmental action and agility of companies.

Our results might have practical implications for policymakers. We identified and econometrically measured the direct connection between the spread of the SARS-CoV-19 pandemic, measured with new infections and fatality rates, and the change in gross domestic product relative increase. Therefore, our results empirically confirm the intuitive approach of many policymakers, who were the first to assume that the spread of the virus should be contained and next the economic issues should follow.

Finally, it should be noted that our paper shows the possibility of applying the multisegment Theil–Sen model to disciplines such as healthcare and economics.

**Author Contributions:** Conceptualization, P.K. and K.R.; methodology, P.K.; validation, P.K. and K.R.; formal analysis, P.K. and K.R.; data curation, P.K. and K.R.; writing—original draft preparation, P.K. and K.R.; writing—review and editing, P.K. and K.R.; visualization, P.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded/by IDUB against COVID-19 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (IDUB).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used in this study were from the open access databases "Our World in Data" and "International Monetary Fund" and can be found there. Further information on data and materials used are available from the corresponding author on reasonable request.

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

### **References**

### *Article* **Performance Evaluation of the Chinese Healthcare System**

**Muhammad Umar 1,\*, Mário Nuno Mata 2,\*, Adnan Abbas 3, José Moleiro Martins 2,4, Rui Miguel Dantas 2 and Pedro Neves Mata 5**

**Abstract:** This study aims to evaluate the performance of the Chinese healthcare system. It uses sustainable development goal (SDG) 3, set by the United Nations to ensure healthy lives and promote well-being for all at all ages as a benchmark. It uses data of 17 variables ranging from the year 2000 to 2017 and uses a multistage methodology to evaluate the performance. In the first stage, it uses difference in mean test to know whether or not the indicators show an improvement in the second decade of the 21st century compared to the first decade. In the second phase, simple linear regression has been used to know the rate of change of performance of every indicator over the sample period. The third step compares the performance of the healthcare sector with the sustainable goals set by the UN and the fourth phase attempts to forecast performance for the next five years i.e., 2018 to 2022. As per the results, the Chinese healthcare sector has performed very well on many fronts except alcohol consumption in males, road accidents and the incidence of non-communicable diseases. Alcohol consumption by males is touching dangerous levels. Therefore, the policies should focus on educating males to lower their alcohol consumption to stay fit and healthy.

**Keywords:** Chinese healthcare system; sustainable development goals (SDGs); performance; evaluation

### **1. Introduction**

The current pandemic has highlighted the importance of healthcare in a society. China was the first country that was hit hard by COVID-19. So, this study aims to evaluate the performance of the Chinese healthcare sector. As performance is always measured by the comparison of data with a benchmark, this study uses health-related sustainable development goals (SDG) set by the United Nations as a reference point. All UN member states unanimously adopted "The 2030 Agenda for Sustainable Development" in 2015. At the heart of this agenda are 17 SDGs that are to be achieved by 2030 [1]. The third goal amongst 17 goals is related to primary healthcare. This goal aims at achieving "healthy lives and promote well-being for all at all ages". According to studies, there exists a connection between health-related indicators, health outcomes, environment and metabolic risks [2,3].

This new agenda replaces the Millennium development goals (MDG) framework, which expired in 2015 [1]. The SDGs were developed through a highly consultative and iterative process, including many meetings with area experts, civil society, and governments. The operation of developing the SDGs' goals, targets, and indicators has not been without criticism. The common goal of scientific meetings and news media was to create more and more SDGs and to achieve the best results [4–9]. Health takes a central position in the SDGs, e.g., improvement in maternal mortality rate, neonatal death rate, incidence of

**Citation:** Umar, M.; Mata, M.N.; Abbas, A.; Martins, J.M.; Dantas, R.M.; Mata, P.N. Performance Evaluation of the Chinese Healthcare System. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5193. https:// doi.org/10.3390/ijerph18105193

Academic Editor: Dimitris Zavras

Received: 12 April 2021 Accepted: 10 May 2021 Published: 13 May 2021

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malaria, tuberculosis, non-communicable diseases, rural health, equal access to treatment, reduction in non-communicable diseases, etc. The SDGs play an essential role in promoting public health through a proper approach to public policies across different sectors [3]. For example, for better education of girls, sound mental health is a pre-requisite. Burning coal harms the health of the general public. So, we may say that the ultimate objective of all the goals is to achieve better health and wellbeing for all people at all ages. Research on healthcare shows that a robust primary healthcare system forms a solid foundation to provide accessible and reasonable primary healthcare to the residents [10,11].

There are a number of studies that have evaluated the performance of global-, regionaland country-level healthcare systems. A comprehensive study conducted by using data from 173 countries concluded that the average efficiency of national health systems was 78.9% [12]. African countries had the lowest efficiency of 67% and countries in the west Pacific had the highest efficiency of 86%. Furthermore, the efficiency of national health systems depends on national economic status, the incidence of HIV/AIDS, governance and health insurance mechanisms. The study concluded that a 1% increase in social security expense as a percentage of total health expenditure results in a 1.9% increase in national health systems efficiency. Another study on 30 European countries concluded that both developed and developing countries lie on an efficient frontier and many of them are inefficient [13].

Another study conducted on 14 high-performing low- and middle-income countries (LMIC) found that most of these countries maintained or improved their performance in scope of service, quality, and access to health insurance [14]. Another study on seven high-income countries concluded that these countries face similar challenges regarding healthcare system performance but all have different policies to cope with these issues. There are some country-specific studies as well. A study on the Australian healthcare system concluded that healthcare services in Australia are among the best in the world [15]. The study highlighted resource allocation and performance in patient outcome improvements as two main challenges faced by the healthcare system. Lebanon is one of the countries that has opened its borders for Syrian refugees. The influx of the refugees has put pressure on the Lebanese healthcare system. A study conducted on the Lebanese healthcare system inferred that the healthcare system performed reasonably well despite being stressed by the influx of refugees [16].

There are some studies that have analyzed the healthcare sector of China. An editorial of The Lancet states that China has made great progress in providing equal access to healthcare and health insurance but the challenges of better quality, control of non-communicable disease and efficiency in healthcare services remains a challenge [17]. Another communication from the BMJ states that China has improved its primary healthcare, health insurance coverage and medicine policies; however, the challenges of better-quality primary healthcare, cost of medical care and inefficient use of resources remain [18]. A study conducted on healthcare system reform of Hubei province concluded that the reforms resulted in better healthcare services for Hubei province [19]. However, despite being the largest country on the planet as per population records, the comprehensive studies regarding the Chinese primary healthcare system are rare. Therefore, the current study bridges the above-mentioned gap by evaluating the performance of the Chinese healthcare system. It not only analyzes the performance of the healthcare system in the post-reforms era but entails the pre-reform period as well.

The question is, why it is important to study China's healthcare system? There are a number of reasons. First, China is the largest country by population. It is home to 1.4 billion people. Its huge population makes it an important country to study. Second, China has achieved marvelous progress in terms of economic development in the recent past. This led to many challenges for environmental and health-related development [20,21]. Third, in 2009, China's healthcare reforms objective shifted to achieve "Health for all" by expanding the underlying healthcare system [22]. The focus was to provide the best health and medical services to its citizens, which are affordable, safe, and useful. Using this National strategy, China started investing in this area and achieved excellent results from the primary

healthcare system [23–29]. To strengthen these efforts, China is currently working with the World Health Organization (WHO) framework to garner quick results [30]. This system will help to organize health needs and engagement.

Fourth, China is still a developing country with nearly 30 million people living below poverty [31]. So, China faces many challenges in achieving health-related SDGs. A study called Global Burden Disease [32] showed that China ranked 88th among the 195 countries and territories evaluated on health-related SDG index. A total number of health-related SDG indicators are 41 in this study [32]. China achieved 62 in health-related SDG, a little higher than the global median index of 59.4, but it is lower than the top three countries; the top countries achieved scores of 83 in this area. Fifth, China faces many health challenges as many middle-income countries, including the highest portion of hepatitis disease. A third of 240 million people are fighting with chronic hepatitis [33] and it is mainly due to the lack of an adequate and comprehensive hepatitis disease control program. Another of the biggest health-related issues in China is smoking; 300 million smokers are living in China, which is a third of the world total, with limited measures in place to control this problem. Other important challenges regarding basic healthcare include alcohol consumption by males, road traffic accidents and the incidence of non-communicable diseases. So, there still exists many significant health-related problems in reducing social and health disparities in the context of economic growth [34].

Sixth, the Chinese government has a strong commitment to achieving health-related SDG targets. To achieve the above-mentioned goals, the State Council of China issued the Healthy China 2030 Planning Outline in 2016 [35]. The purpose of this study is multifold: to compare the performance of China with its own past, comparison of performance with the SDGs, forecasting the performance on the basis of the past and suggestions to remedy the weak areas. Many health-related SDGs have already been achieved by the China but some of them still need a lot of work to be complete.

This study uses data of 17 primary healthcare-related variables which include data regarding maternal mortality, neonatal mortality, the incidence of lethal and non-communicable diseases, abuse of drugs and alcohol, road-side accidents, and spending on healthcare. It uses a multistage methodology to evaluate the performance of the Chinese healthcare sector with its own past and the SDGs. In the first step, the study compares the performance of the Chinese basic healthcare facilities in the second decade of the 21st century with the first decade by using the difference in mean test. The second stage runs regression with goals as dependent variables and years as independent to calculate the average rate of change in performance over the sample period. The third step compares the performance of Chinese healthcare with the SDGs and the final step forecasts the expected performance ranging from 2017 to 2022. The study proposes policy suggestions to improve basic healthcare and the well-being of people in China.

The rest of this study is as follows: Section 2 explains the data and methodology used to evaluate and forecast the performance of the Chinese healthcare sector. Section 3 presents the empirical results and Section 4 concludes by providing policy suggestions for improvement.

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

### *2.1. Data*

The data was obtained from the Health Nutrition and Population Statistics database of the World Bank, and it ranges from the year 2000 to 2017. The dataset consists of 17 variables, with each variable having 18 observations. The ultimate source of data for different variables extracted from the World Bank repository and used in this study is the WHO, UNICEF, UNFPA and United Nations Population Division and the UN. The World Bank repository is a consolidated and reliable source of data, so it has been accessed for data collection. Another reason for collecting data from the World Bank is its relative neutrality. Therefore, this study uses a very rich and reliable dataset spanning over almost two decades.

### *2.2. Study Variables*

Table 1 mentions the abbreviations and description of variables used in this study. The maternal mortality rate is estimated by the World Bank and the data is provided by the national statistics. National estimates define the maternal mortality rate as the ratio of women who die from pregnancy-related causes during pregnancy or within 42 days of pregnancy termination per 100,000 live births. The World Bank estimates this variable by using a regression model based on maternal deaths among non-AIDS deaths in women aged 15 to 49, birth attendants, fertility, and GDP. Neonatal deaths in numbers provide information about the number of deaths of newborns before reaching 28 days in a particular year, and the neonatal mortality rate is the number of neonates dying before reaching 28 days per 1000 live births in a specific year.