**2. Progress in UHC: socio-economic impact**

#### **2.1 Concept of UHC and surrounding economic trends**

Sustainable Development Goal (SDG) 3 comprises 13 targets related to "health and welfare for all." The other 16 goals were either related—or indirectly contributed—to health. The SDGs aim to "leave no one behind" and are international objectives applicable to developing and advanced countries. UHC is a concept that includes 1) protection from financial risks for all, 2) access to quality primary health services, and 3) access to essential medicines and effective, high-quality, and inexpensive vaccines. Target 3.8 SDG 3, which involves achieving UHC and health improvement worldwide, is considered the most crucial task of the WHO [17].

The measurement approaches and definitions of the UHC index evolved between 2015 and 2019, and the index is now used in every global monitoring report [18]. UHC progress between regions and countries can be compared. Additionally, the UHC service coverage index (SCI) has been calculated as a single number (i.e., score) since the late 2010s, thereby improving comparability between nations. Although the performance of different countries can now be compared, global monitoring alone is insufficient to guide policymaking [19]. Therefore, each country should be encouraged to develop a country-specific global framework. The relationship between the environmental factors surrounding medical care and progress toward UHC should be analyzed to achieve this.

Healthcare systems generally help improve clinical outcomes by increasing public financial investment [20, 21]. Meanwhile, declining birth rates, aging populations, and the maturation of medical systems generally tend to reduce the baseline performance of medical systems. Some reports mention that unemployment and poverty, which are distant causes of catastrophic health costs, are factors that reduce service coverage index levels [22]. Therefore, there is room for countermeasures, including population policies and economic measures. For example, future economic growth strategies could include the promotion of healthcare and life sciences industries. Improvements in health care programs include disease prevention and medical insurance policies.

Problems regarding medical financial systems constitute a significant challenge to achieving UHC. According to the WHO, a healthcare financial system that eliminates the financial constraints of access to health services is crucial [23, 24]. Several previous studies have suggested that UHC is more likely to be achieved when patients' outof-pocket medical costs are low [25]. As rational policy decision-making is imperative for discussing the financial burden, analytical aspects, such as UHC and socioeconomic factor relationships, are necessary. For example, CEA, a performance analysis of medical functions, is the most common approach for assessing the health benefits for each spent or the cost for each additional health unit. CEA is a tool used to enhance the sustainability of medical systems.

#### **2.2 Relationship between UHC and socio-economic factors**

This section introduces an example of the relationship between SCI and major socio-economic indicators to establish UHC levels and economic factors [25]. This study used SCI as a proxy for progress toward UHC in 11 Asian countries. A fixedeffects regression model was employed to analyze panel data from 2015 to 2017, and *Socio-Economic Considerations of Universal Health Coverage: Focus on the Concept… DOI: http://dx.doi.org/10.5772/intechopen.104798*

to explain the interrelationship between the SCI and major socio-economic indicators (health expenditure, unemployment, etc.) Performance analysis (to determine the ratio of the achieved SCI level to gross domestic product or health expenditure displacement) was also conducted. This analysis examines the balance between the degree of achievement related to UHC and a country's economic level.

The gross domestic product (GDP) and SCI had a significant positive correlation (Spearman's rank correlation coefficient [Rs] = 0.716, p < 0.01). Health expenditure and SCI were significantly and positively correlated (Rs = 0.743, p < 0.01). When both GDP and SCI indicators were transformed using logarithms, the abovementioned trend did not change significantly (Rs = 0.731, p < 0.01; **Figure 2**). The results of the panel data analysis showed that GDP per capita significantly contributed to SCI (standardized partial regression coefficient, 1.6129; partial regression coefficient, 0.0049; 95% Confidence interval [CI], 0.0025–0.0074; **Table 1**). The total population, governmental health expenditure, unemployment, and poverty rates were statistically significant, whereas health expenditure was not significant. The unemployment and poverty rates show a negative trend, and the entire model is statistically significant (R2 = 0.991, F-test: p < 0.001). The ROC curve for health expenditure per GDP for SCI showed a cutoff of 3.7% (p < 0.01) for the Youden index and 4.9% (p < 0.01) for the shortest distance (AUC = 0.8125, 95% CI: 0.6350–0.9899, p < 0.05; **Figure 3**).

**Figure 2.**

*Relationship between economic level (GDP) and SCI (logarithmic transformation, 2017). Note: UHC, universal health coverage; SCI, service coverage index [21].*


*Note: GDP, gross domestic product; UHC, universal health coverage; SCI, service coverage index; SE, standard error; CI, confidence interval [21].*

#### **Table 1.**

*Panel data analysis of the impact of economic level (GDP, health expenditure, unemployment, and poverty) on SCI.*

**Figure 3.** *ROC curve of health expenditure (per GDP: %) for SCI (criterion: Score 70) [21].*

*Socio-Economic Considerations of Universal Health Coverage: Focus on the Concept… DOI: http://dx.doi.org/10.5772/intechopen.104798*

#### **Figure 4.**

*Performance status by country (broad cost-effectiveness analysis based on displacement from 2015 to 2017). Note: SCI, service coverage index. \*1: Dominant is positioned in a more cost-effective dimension with increasing outcomes (SCI) even if the economy (GDP) declines. \*2: Performance was a cost-effectiveness analysis (difference in outcome "SCI" ÷ difference in the economy "GDP"; displacement from 2015 to 2017). Both indices were logarithmically transformed to consider the elasticity [21].*

#### **Figure 5.**

*Trends in SCI and performance (economic level: GDP) with respect to the aging rate (percentage of the population aged 65 years and above). Note: UHC, universal health coverage; SCI, service coverage index. (†) Myanmar has a different quadrant (dimension) because it is "dominant" [21].*

*Socio-Economic Considerations of Universal Health Coverage: Focus on the Concept… DOI: http://dx.doi.org/10.5772/intechopen.104798*

From the results of the performance analysis after the logarithmic transformation of each index, South Korea (high-income country: HIC) scored the lowest (GDP: 0.12 SCI score/USD per capita, health expenditure: 0.07 SCI score/USD per capita; **Figure 4**), followed by Vietnam (lower-middle-income country: LMIC) and India (LMIC). Japan's (HIC) performance was moderate, while Indonesia (UMIC), Thailand (UMIC), and Cambodia (LMIC) had relatively high performance. The Philippines (LMIC) had the highest performance (GDP: 1.84 SCI score/USD per capita, health expenditure: 1.04 SCI score/USD per capita). Myanmar (LMIC) was marked as the "dominant quadrant." The more effective but less expensive quadrant exhibited the best performance in the cost-effectiveness analysis. When the relationship between the proportion of the population aged 65 and above was organized without logarithmic conversion, the SCI score increased with age (Rs = 0.779, p < 0.01), and the performance value decreased (Rs = 0.830, p < 0.01; **Figure 5**).

Each of the four SCI components had a different level of achievement (**Figure 6**). LMICs were most countries with SCI levels of 60 or below (i.e., Bangladesh, India, Indonesia, and Cambodia), where "infectious diseases" and "service capacity and access" were more widely dispersed. This was compared to the group of countries with SCIs of more than 80 (i.e., South Korea, Japan, Thailand, and China), HIC, and UMIC. Multiple regression analysis used SCI's annual rate of change as the objective variable and SCI components as the explanatory variable. The results indicate that "service capacity and access" significantly contributed to the SCI level (standardized partial regression coefficient, 0.9209; partial regression coefficient, 0.3581; 95% CI, 0.3142–0.4019). Furthermore, when the GDP per capita and "service capacity and access" values of each country were relatively arranged, with Japan as the standard, a positive correlation was observed between the two indicators (i.e., single correlation: Rs = 0.901, p < 0.01) (**Figure A1**).

#### **2.3 Health economies necessary for the development of UHC**

The present study used SCI as a proxy for the progress of UHC. Currently available service coverage metrics focused on infectious diseases and reproductive, neonatal, maternal, and child health [26]. In this study, the indicators for SCI-related data (**Figure A2**) were "reproductive, maternal, newborn and child health," "infectious diseases," "noncommunicable diseases," and "service capacity and access." In addition, the country-by-country socio-economic indicators included "total population," "population aged 65 and above," "gross domestic product (GDP) per capita," "health expenditure per GDP/per capita," "government health expenditures," "unemployment rate," and "poverty rate." All data were converted into a panel from 2015 to 2017; SCI-related and socio-economic data were also compiled [27–29].

According to the analysis results derived by applying these data, UHC progress tends to increase as the share of the healthcare domain in government spending increases. Future studies on UHC development measures are important to discuss the appropriate form of resource allocation (public finance) according to sustainability-based productivity and efficiency or value evaluation (national consensus). Based on the statistical analysis results, some cases exist wherein SCI achievement levels differ even among countries at the same economic level. Furthermore, SCI improvement is small, even in countries with high economic investment levels. Exploring these factors and considering improvement measures are assumed to promote UHC progress. This study examined the influences of the maturity of the medical system as an additional countryspecific factor (rather than the social system, national character, and culture).

#### **Figure 6.**

*Distribution composition of SCI components according to SCI level (*≥ *60 and* ≥ *80). Note: SCI, service coverage index [21].*

*Socio-Economic Considerations of Universal Health Coverage: Focus on the Concept… DOI: http://dx.doi.org/10.5772/intechopen.104798*

The results showed that when aging and health expenditure exceed a certain level, UHC performance decreases as a country's need to raise its goal increases. Additionally, the weight of "service capacity and access" to SCI was considerable. This secondary index, which embodies the environment of the healthcare system, can be considered a surrogate index that predicts the maturity of social and medical care. The considerable impact of these factors on UHC implies that stable development cannot be expected simply by expanding the expenditure scale due to the mechanisms related to economic conditions. As a result, policymakers must implement countermeasures based on indicators that can estimate the economic status of the UHC approach, such as its cost-effectiveness.

CEA is often applied to medical-economic evaluations, such as high-priced medicines and health programs, but can also be applied to macro issues, such as medical systems [30]. Cost-effectiveness is an instrument widely used in Western health systems. The instrument provides the information needed to reach a consensus among stakeholders in allocating medical resources and setting medical prices. As UHC progress requires country-specific efforts, as discussed in the introduction, estimating the coefficients that define each country's UHC progress and socio-economic status is also necessary. Hence, a country-specific performance analysis (CEA: country-specific coefficient calculations) was conducted. In the present study, CEA was performed using economic level as a cost index and SCI level as an effective index.

This approach suggests that regardless of the maturity of the system or the size of the economy, the status of UHC activities in each country can be evaluated based on the displacement of economic and SCI levels achieved.
