Earth, formed 4.6 billion years ago, is divided into seven continents: Asia, Africa, North America, South America, Antarctica, Europe, and Australia (1). Asia is the largest and most populous continent (2). Asia’s vast geographical area offers a population advantage, comprising 4.6 billion out of the global population of 7.7 billion (3). The Asian region comprises a diverse array of countries, including some of the world’s least and most developed nations (4). The East Asia and Pacific region, with over two billion people, is the most populous globally, home to fast-growing economies and the second-largest number of fragile states after Africa (5).

Healthcare systems in Asia are diverse (6–8), with Japan, Singapore, South Korea, and Taiwan renowned for advanced systems, high care standards, and UHC (9–11). Governments in some Asian countries ensure healthcare access through public funding and provider regulation (12), while other Asian countries face challenges like limited access, inadequate infrastructure, and disparities in healthcare delivery (9). As a result, Asian countries are prioritizing healthcare infrastructure investment, quality access, and addressing non-communicable diseases (13, 14), while promoting collaboration and knowledge-sharing to improve health outcomes (15). Hence, Asia’s healthcare systems are undergoing significant transformations and reforms to improve access, quality, and affordability of services for their unique challenges (16, 17).

Africa is the world’s second-largest and second-most populous continent, after Asia. It spans approximately 30.3 million km², including adjacent islands, accounting for 6.0% of Earth’s surface area and 20.0% of its land area. With a 2021 population of 1.4 billion, it comprises about 18.0% of the global human population (18). The majority of the African population, comprising 53.3%, is rural, with a median age of 18.8 years (19), the youngest population worldwide (18).

Healthcare systems in Africa encounter multiple challenges, such as institutional, human resources, financial, technical, and political issues (20). Africa is grappling with a significant number of both communicable and non-communicable diseases (21). The African continent was home to a particularly diverse and deadly set of tropical diseases (20). Cost-effective interventions to prevent disease burden are available, but their coverage is limited by weaknesses in health systems (21).

The primary objective of an efficient health system is to improve public health (22, 23), which critically requires UHC (22). The concept of UHC was introduced in 2005 with the aim of addressing disparities in access to healthcare services (24). The post-2015 sustainable development agenda proposes UHC as an umbrella health goal, aiming for universal, equitable, and effective delivery of comprehensive health services (25), which is at the heart of contemporary efforts to strengthen health systems (26).

However, financing UHC is a challenging task, requiring countries to consider all revenue sources for healthcare system reform (27). On the other hand, while increased spending can improve health outcomes, improving the efficiency of these expenditures is even more critical (28). Health financing involves the collection of revenue, pooling of risk, and purchasing of goods and services to enhance population health, primarily funded by individuals and households through the tax system (29). Not only revenue collection but also effective healthcare purchasing and proper regulation of healthcare providers are crucial for the sustainability of healthcare financing (30). Health purchasing is the transfer of funds to health providers, either passively or strategically, to deliver services (31).

As mentioned before, one of the targets of the Sustainable Development Goals (SDGs) is the promise to work toward achieving UHC by 2030 (32). UHC not only contributes to achieving SDG 3, good health and wellbeing, but also poverty eradication, work and economic growth, and reduced inequalities, which represent the targets of SDGs 1, 8, and 10, respectively (33). It should shield households from financial risk, especially the poorest who find it difficult to pay for services (34), which include access to high-quality, vital medical care as well as safe, effective, and reasonably priced medicines and vaccinations (32). The World Health Organization (WHO) emphasizes the significance of equitable access to safe and affordable medicines for optimal health, aligning with the SDGs, particularly SDG 3.8, which aims for UHC, and SDG 3.b, which focuses on the accessibility of medicines to address existing treatment gaps (35).

Yet, 2 billion people worldwide have no access to essential medicines (35) and face catastrophic or impoverished health spending (36), and at least 400 million individuals do not have access to essential health services (32), because the most common method of paying for health services globally is OOP spending (37). On average, each nation spends roughly 32.0% of its total health budget on OOP expenses. Due to OOP medical expenses, 150 million individuals each year experience financial catastrophe (32), and 100 million are forced into poverty (32, 38). These individuals reside in developing countries where the health systems are plagued by inefficiency, unequal access, insufficient funding, and substandard services and account for 92.0, 68.0, and 80.0% of the world’s annual deaths from communicable diseases, non-communicable conditions, and injuries, respectively (39).

The burden of the lack of UHC is highest in LMICs, particularly in Africa and Asia (Figure 1) (40), where 97.0% of the population is impoverished by OOP health spending (33). In 2023, 75.0% of the 3.1 billion people worldwide without effective UHC are from LMICs in South Asia, Southeast Asia, East Asia, and Sub-Saharan Africa (SSA) (41). For example, by using their household income to access healthcare services, medications, and other products, an estimated 11 million Africans fall into poverty each year (33), which is a concerning issue considering that Asia and Africa together constitute over 75% of the global population (41).

Nevertheless, a system that generates net savings by eliminating profit and waste can be used to address the rapidly rising health expenditures (42), because up to one fifth of health expenditures could be directed towards better use by avoiding waste (43). To ensure equitable access through such a mechanism, health care must be funded, managed, and provided in a way that puts the needs of individuals and communities first (32), which is embedded in national, regional, and international contexts (44). This implies that improving health system performance to attain UHC requires actions at national, regional, and global levels (45). In fact, the dedication to collaborating with the global health community enhances access to quality healthcare and makes UHC a reality for patients, families, and communities worldwide. Together, this effort will lead to healthier communities and stronger economies (46).

Accordingly, to overcome the challenge to achieve UHC, the two regions, Africa and Asia, have recognized the importance of collaboration among governments, civil society, and the private sector. Consequently, they have started working together to use a global south perspective. Kenya and Egypt, for instance, are looking for guidance on free basic healthcare through health insurance from Thailand and Japan, respectively (33), which is, undoubtedly, the most significant sort of insurance (47).

Insurance is a contract where the subscriber pays the insurer on a regular basis in exchange for the assurance of indemnification against specific risks. The specific risks covered by health insurance are the financial burden of the treatments required following an illness or injury (48). There are several reasons to introduce health insurance, including the removal of financial barriers to healthcare access, providing financial protection against high medical costs, and negotiating better-quality healthcare with providers (49), which dictate that the way of funding health services is a crucial aspect of UHC (50). As a result, health policymakers must prioritize selecting the appropriate financing mechanisms for health services to achieve broader health policy goals, as this decision impacts both providers and consumers, particularly in low-income countries (LICs) where service usage rates across income groups are a significant issue (51).

Though there are various healthcare financing mechanisms to choose from, national, social, private, and community-based health insurance programs are the four main categories of health insurance programs (52, 53), dictating that health insurance programs can be privately or publicly run, cover various population subgroups, and provide different premium costs and benefit packages. The two primary types of government health insurance programs are NHI and Social Health Insurance (SHI), which are based on the Beveridge and Bismarck healthcare systems, respectively. Health insurance programs in many nations include components from both of these models, and the design varies across countries. However, in the majority of government health insurance schemes, enrollment, contributions, and payments are managed by a fully or partially independent government body (52).

Public health insurance models provide benefits through either a national health insurance fund, a national social security fund, or branches of the central government (54). NHI, as a public health insurance scheme, is thus provided by the federal government through general taxation (55), usually with mandatory coverage for all citizens (56). It is a single-payer scheme that covers all citizens and residents, with eligibility based on citizenship and residency status (57), indicating that it is the best option to ensure equity, fairness, and priorities aligned with medical needs. This strategy improves public health by providing universal access to desirable care with treatment options tailored to the patient’s needs (42).

Therefore, as health care is a human right and requires system-wide changes in financing to achieve UHC (58), a public, single-payer system is the best, most efficient, and most equitable health-care system. This is because, through partnerships with provider organizations and the use of taxes for everyone, single-payer systems enable people to serve as their own insurers. The best care to satisfy needs is then selected by the consumer, with minimal or no OOP expenses. That is, patients are partners in their care, obtaining diagnosis, treatment, and prevention services without financial restrictions (42). Hence, it is important to bear in mind that a nation’s financial resources primarily originate from its population, with the exception of external aid and natural resources (56).

Thus, NHI is funded through income-based premiums (59). The premium is the cost of insurance coverage, which is typically paid monthly or yearly (52). This cost of a health insurance plan is a key factor in its viability, which is determined by the members’ WTP (60), i.e., a stated preference that involves assigning a monetary value to the benefits of health-related goods or services (61). However, LICs face constraints in raising revenue to finance health and health insurance, as government tax revenues are only 15% of gross domestic product (GDP), compared to over 20% in higher-income countries (HICs) (56), and the tax structure is often regressive (62).

The WTP is the utmost amount of money that an individual is WTP for a service or product (52). It is a proxy measure of cost–benefit trade-offs in health insurance (63) and is one of two popular approaches for estimating the monetary value of health benefits, the other being the Human Capital (HC) approach (64). WTP is a widely-used concept in the health sector to guide policy decisions (65). The assessment of WTP can be conducted through evaluating historical healthcare utilization and expenditure data or by employing a contingent valuation (CV) approach (66), which is a survey methodology to assess the benefit or worth of a program to individuals (67). When employing the CV method to determine WTP, two general elements should be included: a hypothetical scenario and a bidding vehicle. The goal of the hypothetical scenario is to give the respondents a detailed explanation of the good or service they are being asked to pay for. Bids can be obtained in a number of ways, including open-ended or closed-ended questions, a bidding game, or a payment card (64).

Estimating the WTP is the best way to assess the expected income of health insurance schemes. This estimate is required to ensure that the cost of benefit packages does not surpass available resources to minimize the risk of bankruptcy. WTP data are, therefore, crucial for informing the design of tailored benefit packages for consumers, particularly groups or communities (68). Cross-country studies are decisive in assessing such data and the impacts of common elements of reforms adopted by many countries, as they provide a comprehensive understanding of which reforms have been successful or not (22). Thus, this systematic review and meta-analysis aimed to determine the pooled WTP for NHI and its influencing factors from the available literature in Africa and Asia.

The protocol for this review was registered on PROSPERO, accessible at https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023411411. We used the PRISMA 2020 Statement as a frame for the review (69) (Supplementary file S1). However, to pictorially present the screening process of the studies, because of its ease and clarity, we used the PRISMA 2009 flow diagram (70), while we sufficiently discussed the screening process in words in line with the PRISMA 2020 flow diagram.

All original and published cross-sectional studies that report the prevalence of WTP for NHI and/or factors influencing it were deemed eligible for the systematic review. We considered all English-language studies conducted in both community and institutional settings on WTP for NHI in Africa and Asia. The selection of studies was also based on several parameters including outcome variables, study population, year of the study, regional context, sample size, and response rate.

Database searches were conducted on Scopus, HINARI, PubMed, Google Scholar, and Semantic Scholar from March 31 to April 4, 2023 (Supplementary file S2). Manual searches were performed on PubMed and HINARI. Conversely, Scopus, Google Scholar, and Semantic Scholar were searched using the “Publish or Perish” database searching tool, version 8 (71).

After excluding duplicate studies using Zotero reference manager version 6, two reviewers, EMB and HNT, independently screened the remaining studies. The selection process was meticulously conducted by these researchers. Initially, articles were refined based on their title and abstract; subsequently, a full-text revision was performed independently and then collaboratively until a consensus was reached. In cases of disagreement, a third reviewer was consulted for resolution.

A Microsoft Excel 2019 spreadsheet was utilized for data extraction. The outcome variables - population (study units), year of study, context, sample size, response rate, and proportions - were extracted using this spreadsheet. Two independent reviewers, EMB and HNT, extracted the data, compared their findings and reached a consensus. In cases where agreement could not be reached, a third reviewer was invited to help achieve consensus. Additionally, we reached out to the authors of the study to gather any missing information. The primary outcome of this systematic review and meta-analysis was the WTP for NHI. An additional outcome was the factors influencing the WTP for NHI.

Two reviewers, EMB and HNT, independently assessed the risk of bias in the included studies using JBI tools. The assessment focused on several criteria: inclusion in the sample, descriptions of study subjects and settings, validity and reliability of measurements, confounding factors and strategies to address them, and appropriateness of the outcome measure. The JBI’s tool with eight criteria was used to evaluate the risk of bias within the articles. Scores of 7 or higher were classified as low risk, 5–6 as medium risk, and 4 or lower as high risk. Studies identified as low and medium risk were included in the review. All inconsistencies were addressed through discussion and, if necessary, the involvement of a third reviewer.

For the qualitative synthesis, thematic strategies were utilized to categorically conceptualize the outcome variables. Preliminary effect measures were then calculated for the quantitative synthesis based on the qualitative synthesis using a Microsoft Excel 2019 spreadsheet. STATA 17 was employed to determine the effect estimates (proportions and odds ratios—ORs) of the WTP for NHI. Sub-group analyses were subsequently conducted to compare these effect estimates across studies focusing on the outcome variable. The overall level of statistical significance was set at a p-value less than 0.05 with a 95% confidence interval (CI).

The study authors were contacted to obtain missing or incomplete data. Studies with incomplete data were excluded from the analysis. Heterogeneity between studies was assessed using the I² statistic. The influence of each study on the overall meta-analysis was measured using percentages of weights, and subgroup analysis comparing the WTP for NHI in Asia and Africa was conducted. A sensitivity analysis was also conducted to determine the outlier studies.

Moreover, Doi plots were used to examine potential inter-study bias, also known as publication bias, which helps to calculate asymmetry using the LFK index (72). The Doi plot, which is a folded normal quantile (z-score) versus effect plot, replaces the conventional scatter (funnel) plot of precision versus effect. It provides a quick overview of study symmetry and heterogeneity when combined with the Galbraith plot (73). The visual examination involves observing the dots representing specific studies and their arrangement (74). In addition, the plots comprise the LFK index and p-value of Egger’s test (73). The LFK index is used to identify and measure the asymmetry of study effects (75). The closer the LFK index value is to zero, the greater the symmetry in the Doi plot. Values of the LFK index that fall outside the range of −1 to +1 suggest asymmetry, indicative of publication bias (76).

We conducted a comprehensive search of Google Scholar (n = 200), HINARI (n = 50), Scopus (n = 19), and PubMed (n = 22) from March 31 to April 4, 2023. An additional 23 records were identified through other sources, resulting in a total of 314 resources (Figure 2). After eliminating duplicates, we were left with 225 articles. Out of these, we screened 157 papers for relevance and discarded 68. Subsequently, 25 studies were chosen based on title and abstract evaluation. After a full-text evaluation, six studies were excluded due to reasons such as language other than English (77), high risk of bias (78–81), and inaccessibility of the full text (82). In conclusion, 19 resources were identified for inclusion, out of which 15 were deemed suitable for the quantitative meta-analysis.

Ten of the studies included in the systematic review were conducted in Asia, and nine were conducted in Africa. The Asian studies took place in Indonesia (n = 5), Malaysia (n = 3), and Saudi Arabia (n = 2). The African studies were carried out in Nigeria (n = 4), Gambia, South Sudan, Sierra Leone, Côte d’Ivoire, and Ghana. Each study was evaluated based on its design, context, year of study, sample size, non-response and response rates, and primary outcome. The individual characteristics of each study are summarized in Table 1.

The risk of bias in the included studies was evaluated using JBI’s critical appraisal tool. Subsequently, studies with low and medium risk were incorporated into the review. As depicted in (Figure 3), the average risk of bias across the studies was 6.0 (75%).

The I² statistic was used to measure between-study heterogeneity, which showed that the overall I² value was greater than 50% (99.51%). To control the influence of each study on the combined result, measured as percentages of weights, a random-effects model was used to calculate the pooled WTP for NHI. To identify the source of heterogeneity, we conducted a test for the subgroup difference based on continents, which was significant (p = 0.039). To determine if there was publication bias between studies (asymmetry or the effect of small studies), we drew a Doi plot (Figure 12) that provided an LFK index of −1.67 and showed minor asymmetry.

A sensitivity analysis was also conducted to identify outlier studies. Accordingly, seven studies were found to be outliers (88, 91, 93, 94, 96, 99, 101), and the pooled estimate (effect size) was influenced by the inclusion and exclusion of these studies. Nevertheless, as shown in Figure 13, when we adjusted for outliers using a random effects model, all of the included studies (n = 12) were found not to be outliers. Therefore, we combined the data using a random effects model without excluding any outliers. Yet, the WTP for NHI was higher in Africa compared to Asia.

This systematic review and meta-analysis found that the pooled WTP for NHI in Africa and Asia was 71.0% (95% CI: 68–75%). Though the difference was not significant (p = 0.680), the WTP for the scheme in Africa (73.0, 95% CI: 65.0–80.0%) was higher than that in Asia (71.0, 95% CI: 66–75%). This is comparable to the findings of a primary study conducted in St. Vincent and the Grenadines, a Caribbean country, which reported that the WTP for NHI was 72.3% (108). From this evidence, one can easily extrapolate that the mean of the three percentages (73.0% in Africa, 71.0% in Asia, and 72.0% in Latin America) was 72.0%, which was approximately equivalent to the pooled WTP data mentioned above. This, in turn, indicates that the WTP for the scheme across the three continents was noteworthy, with minimal differences. Despite this appreciable WTP for NHI, the UHC on these continents, particularly in Africa and Asia, is still the lowest, and access to essential health services remains at a worrying level (109).

Chen et al. (110) noted that attaining UHC is difficult due to various factors globally and on these continents. Primarily, LMICs lack the funds required for UHC (110). As a result, OOP payments remained the single most common healthcare financing option in LICs, which can create a health protection gap (HPG) (111), a shortfall in finances to fund health expenditures (38). HPG is the portion of uninsured losses in total losses, or it is the sum of financially stressful OOP expenditures and the estimated cost of non-treatment due to unaffordability (111). In areas with weak financial protection, HPG, or catastrophic spending, is primarily driven by OOP payments (112). For instance, in SSA, the pooled annual incidence of catastrophic health expenditure (CHE) was 16.5%, with countries in central SSA having the highest incidence while those in southern SSA had the lowest (113). Additionally, health insurance coverage is both insufficient and unbalanced (110). These two reasons dictate that development partners should align financial and technical assistance with national health priorities, establish accountability mechanisms for resource efficiency, and strengthen national health accounts to monitor allocations and expenditures, as health financing is a global responsibility that requires solidarity and collective effort (114).

However, without controlling for outliers, the combined WTP for the scheme was 66.0% (95% CI: 54.0–77.0%), 77.0% (95% CI: 63–91%) in Africa, and 56.0% (95% CI: 41–70%) in Asia, which indicated a significant difference in the WTP for the scheme between the two continents (p = 0.039). In both scenarios, with and without outlier studies, the review indicated that the WTP for the scheme was higher in Africa than in Asia, which might be due to the heterogeneity in health insurance preference between the two continents’ populations, as reported by a study in Australia (115). The lower WTP for NHI in Asia might be attributable to the fact that the included studies for meta-analysis were from only three countries, as opposed to six nations in Africa. This slight lower WTP for the scheme in Asia contrasted with the evidence that consumers bear a disproportionate share of healthcare costs as OOP expenses due to inadequate UHC and the middle class’s demand for high-quality care, which in turn is expected to significantly increase their demand for health insurance (116). In Africa, even higher aggregate proportions of WTP for health insurance were reported. For instance, a cross-sectional study in seven East and West African communities revealed a 78.8% WTP for health insurance, exceeding the results of our review (60).

In fact, several African countries have recently made significant progress in extending UHC (117), as highlighted by the Africa Health Strategy 2016–2030, which aimed to strengthen national health systems as a crucial step towards achieving UHC (114). However, the rate of UHC varies significantly among countries (117). Cashin et al. (118) state that SSA is increasingly adopting public contributory health insurance for UHC, with eight countries having NHI systems and seven more considering or enacting laws. Gabon, Ghana, and Rwanda have notably extended NHI to cover large population segments, demonstrating that clear goals, equity-focused designs, strong political will, and ongoing adaptation are key to facilitating UHC through NHI (118). To that end, the WHO is collaborating with the African Union and the Africa Centers for Disease Control (CDC) to establish the African Medicines Agency (AMA). This important regulatory authority aims to improve access to medicines in Africa, where many nations struggle with pharmaceutical regulation. A vital part of this effort is guaranteeing a reliable supply of safe, effective, and high-quality medicines across the continent (119).

Nonetheless, due to the inherent drawbacks of WTP studies, it cannot necessarily be said that the demand for health insurance is higher in Africa than in Asia (120). For instance, even in Asia, an original study conducted in Indonesia reported that the ability to pay (ATP) was greater than the WTP (121). Another original study conducted in the same country showed that about 57.6% of the participants were able to pay for NHI, but only 17.4% of them were willing to pay the required premium (96). This indicates that an individual’s WTP may not align with their ability to pay (ATP), despite WTP being directly related to household income or the ATP (122). This in turn might be due to the fact that an ATP approach assumes all resources are available for healthcare, while a capacity to pay (CTP) approach considers some resources for basic needs. The CTP for healthcare is defined as a household’s consumption minus a standard amount for basic needs, known as the poverty line or basic needs line (123).

Accordingly, ATP could be a more accurate predictor of enrollment, continuity, and sustainability than WTP (122), whereas, since it is the level of contribution that would prevent an individual from falling below the poverty line, CTP could be a better indicator of the WTP (set premium amount). As a result, placing all participants together in a single pool and mandating contributions based on their CTP instead of individual or average pool risk can enable cross-subsidization and, depending on the extent of pooled resources, greatly improve financial protection for all members (124). Moreover, governments should not rely solely on household premiums as a primary source of financing but should enhance their fiscal capacity through alternative means to support an equitable healthcare system (125). This is because resilient and equitable health systems with strong population coverage are crucial for preparing for health emergencies and ensuring equity, the right to health, and justice for all people (126).

The other reason that the WTP might not be as accurate as ATP and CTP is that the responses can be biased because respondents either want to please the interviewer, indicating a high WTP, or they aim to ensure the intervention is offered at a low cost, reflecting a low WTP (127); consequently, what people say they are willing to pay may not correspond to what they actually would pay (120). The former bias is termed social desirability bias (SDB), which is a pervasive measurement challenge, particularly in the social sciences and survey research (128). This bias can increase the WTP by approximately 23–29% if surveys are administered through face-to-face interviews (129). It is more common in collectivist cultures, which tend to engage more in deception and socially desirable responses (128). That is because collectivistic cultures prioritize social harmony over personal goals, promoting forbearance and enduring adversity as coping mechanisms to maintain positive social relations (130).

Inversely, this social cohesion can have invisible benefits and can serve as a strategic tool in community-based healthcare interventions. Evidence shows that collectivistic individuals are more likely to adhere to public health interventions than their less collectivistic counterparts (131), with collectivistic leadership interventions have shown positive results (132). Hence, because healthcare spending is a critical expense for most nations and their citizens to ensure they stay healthy and receive proper care (133), and collectivism is common in the traditional societies (Asia, Latin America, and Africa) than in Western Europe, North America, and Australia (134), healthcare leaders in Africa and Asia may consider collective leadership as a strategy to achieve UHC through NHI.

Though our finding showed a higher WTP for NHI in Africa, the population generally exhibits low satisfaction levels with their healthcare systems compared to other regions globally. This persists despite significant investments from international donors and African governments, as the healthcare sector continues to struggle with issues such as limited access, corruption, infrastructural deficiencies, and medication and personnel shortages (135). On the contrary, beside the health workforce, the implementation of comprehensive NHI itself requires human resources, all of which contribute to the promotion of noble goals for everyone’s well-being (136). The primary challenge caused by funding shortages in many African countries is that their health financing systems’ strategies and mechanisms are problematic. In nearly half of these nations, household OOP payments make up 40% or more of total health expenditure, representing the least equitable method of healthcare funding (137). For instance, the health insurance coverage in the SSA is limited and favors the wealthy (138). This indicated that the majority of health care funding in SSA comes from direct OOP payments, predominantly from lower-income households (139). Donor funding is another common source of finance (139). As a result, less than half of Africa’s population (48%) has access to necessary healthcare, with the quality of services being substandard. Every year, 97 million Africans, or 8.2% of the population, face CHE, and these OOP costs drive 15 million people into poverty annually (119).

In contrast to the slightly lower WTP for NHI than Africa, estimates for Asian people were the lowest globally for delaying or foregoing care due to cost (4%) (140), because Asian individuals possess the lowest rate of being uninsured (141). Though UHC is thought to be a system that offers coverage to a vast majority of the population, the exact percentage required for it to be considered universal is debated. Some experts argue that coverage must extend to 99% of citizens and residents, whereas others believe a 90% threshold is sufficient (140). On the other hand, progress varies by country. Indonesia has advanced in health service coverage and financing, yet faces implementation challenges. Ghana’s health funding has decreased, with under 50% achievement in the UHC service coverage index. India still contends with high OOP expenses and low public health financing. Kenya still faces challenges in using public financing to enhance coverage for the informal sector, while South Africa has made little progress in strategic purchasing (142).

The disparity in population coverage could stem from the fact that many African countries adopt CBHI, characterized by fragmented coverage, whereas Asian countries prioritize SHI, which is typically more integrated (143). This demonstrates that the fragmentation and inequity caused by targeting specific population groups with various prepayment mechanisms can be addressed by harmonizing these schemes early on. Thus, it is advisable to implement strong public accountability and participation when formulating the UHC strategy (144) through equitable NHI schemes (139). In fact, most countries now prefer mixed NHI systems, learning from past experiences. These systems are not primarily funded by contributions but rather by significant tax-financed subsidies, resulting in a blend of contribution payments and government revenues. NHI schemes are thought to improve equity and reduce barriers to access in LMICs through two features: pooling, whereby financial risk is spread across the population, and pre-payment, the collection of financial resources in anticipation of service use rather than out-of-pocket payments when health care is consumed. Accordingly, a growing number of LMICs are considering rolling out NHI schemes (145).

However, there are various challenges with the implementation of NHI schemes in LMICs. Friebel et al. (145) identified several obstacles to implementing SHI or NHI programs in LMICs. Firstly, LMICs often have limited fiscal capacity compared to wealthier nations. Secondly, countries with smaller workforces may struggle to generate adequate funds for UHC. Lastly, the administrative expenses of NHI plans and the costs associated with collecting contributions are frequently overlooked (145). According to a systematic review by Lim et al. (146), the health financing challenges in Southeast Asian countries for UHC are unsustainable revenue-raising methods, fragmented health insurance schemes, a mismatch between insurance benefits and the needs of the population, political and legislative apathy, unmanageable and swiftly escalating healthcare costs, and unethical behaviors (146). A study by Oleribe et al. (147) reported that inadequate human resources, insufficient budgetary allocation to health, and poor leadership and management are key challenges facing healthcare systems in Africa on the path to UHC through health insurance (147). According to a qualitative study in Nepal, the major bottlenecks for the implementation of the NHI program included difficulty enrolling insurees, non-competitive selection of health providers, and failure to act as a prudent purchaser, leading to high dropout rates and low coverage of poor households, potentially jeopardizing the program’s sustainability if these problems persist (148).

The factors affecting the WTP for NHI were thematically identified as demographic variables, income and economic issues, information level and information sources, illness and illness expenditure, health service factors, factors related to financing schemes, and social network and social solidarity. According to systematic reviews conducted in the context of LMICs on the uptake of CBHI, these factors not only affect the WTP for health insurance but also its uptake (149, 150). Another piece of evidence also showed that the choice of health insurance is influenced by a variety of factors, which can be broadly categorized into personal, economic, and external factors. Personal factors include age, health status, and income, as well as awareness, financial security, lifestyle, and risk cover. Economic factors encompass income and the cost of insurance packages, while external factors involve awareness, company-related information, risk, promotion, tax benefits, and advertising. Additionally, personality traits and clients’ preferences play a role in their choice of health insurance, often driven by social and behavioral factors (151). A systematic review of the WTP for SHI in Ethiopia also found that sociodemographic factors, health status, health service-related factors, awareness, perception, and scheme-related factors were significant determinants of the WTP (152).

The combination of social insurance and taxes in healthcare financing systems in LMICs is known to be effective when supported by political commitment and family-based membership, contributing to rapid population coverage and leading to UHC. Effective healthcare purchasing and provider regulation are also crucial for sustainability (30), as successful universal healthcare schemes follow standard country-wide rules, combining decentralized financial management with centralized oversight and risk pooling (117). Based on the review results, it is strongly recommended to consider economic factors, particularly income levels and age stratification, in the design characteristics of the scheme and implementation strategies. Since all successful schemes offer free health coverage for the poorest segments (117), considering these factors in designing, implementing, monitoring, and evaluating is highly recommended to minimize adverse selection when determining eligibility for indigent services among poor families.

The data was pooled despite the high heterogeneity between the reports of the included studies. We could not be able to calculate and pool the exact amount of the WTP in monetary value due to the differences in the exchange rates between the countries. As a result, we only pooled the percentage of participants who were found to be willing to pay. Additionally, since the included studies were few in number, the result could not necessarily be generalizable to all other countries on the continents other than the countries from which the studies were included.

Since the WTP for a health intervention before any actual health adversity or experience is highly influenced by an individual’s feelings and intentions, comprehensive exploratory studies regarding the perceptions and sociocultural beliefs of NHI in Africa and Asia may be important. It might also be important to conduct actuarial analyses to measure the success of NHI implementation, which may help evaluate the effectiveness of the scheme and revise the design and implementation strategies.