Evaluating and Measuring Exactly the Distances between Aggregate Health Performances: A Global Health Data and Welfare Regime Analysis

The Trinity of Methodology, More Data, and More Theory

For comparative social policy, and comparative health policy, which here is seen as an integral part of social policy (in the wider sense, i.e., the only meaningful sense), the issue of how to compare and how to interpret data, and health care systems and health outcomes as a whole, is monumentally important.

Health development and health policy are wicked problems, as they are utterly complex and intertwining (cf. Blackman et al, 2006; Navarro, 2009). Assuming one has been traveling the roads of comparative health policy and health care system analysis for some time (and fruitfully so), one can hope to have chosen the right variables (that are hopefully fully available for all countries, and close to all countries that are on earth). This is not a big problem. Bigger problems yet come with the choice of methodology, the amount of data one is being able to handle (calculate and analyze) and present (any given space and/or attention limitations), and what theories one is using and/or trying to develop over the course of this research study or one’s longer-term research program/project. This can be (quite rightfully) dubbed the trinity of research design problems when it comes to global health comparison, which is of particular interest in our case at hand.

The methodology chosen is usually a make-or-break decision when it comes to more complex and difficult research problems and problem constellations, which usually aims to and/or will end up leading to, or lead the way toward, new breakthroughs in knowledge creation and knowledge application.

In all shortness, it can be said that qualitative data analyses are great (Aspalter, 2020a). Simple and “reductionist” quantitative data analyses are the worst, like, for example, cluster analysis, which by design reduces the “quality”/“information” of the data used (Aspalter, Forthcoming c). Deep and/or comprehensive quantitative data analyses are also great. Best are, hence, by way of logic (and experience), a combination, if possible, of qualitative and deep/comprehensive quantitative methodologies, that is, mixed methodologies (Brannen, 1992, 2005, 2021).

Comparative rich qualitative health studies deal with great and hence complex systems of health care provision and its financing, together with complexities of health policies and health/social/economic development, and last and certainly not least, health cultures (culinary preferences/non-preferences, exercise preferences/nonpreferences, etc.). On top, the additional power of additional, rich, and meaningful quantitative numerical data reflecting the quantified achievements and nonachievements in health development and health care development is also vital. Therefore, the author predicts that in the future comparative health analysis, be it in medical science or in policy science, will develop fast through the use of deep data and/or comprehensive analysis (first without and then with the help of data science and artificial intelligence).

The more data the better, generally speaking, that is true. However, still, theories are also desperately needed to guide the researcher to understand and being able to work with the data in meaningful and scientific ways—that is, the ways that help further the science one is working in, and the theory/subject/problem one is working on (cf. Aspalter 2023, Forthcoming b,c; Brady, Finnigan, & Hübgen, 2017; Remington, Forthcoming a,b).

Having taken to the so-called trinity of research design problems for data analysis to heart, this study proceeds with the methodology of health indicator analysis that is enhanced with the method of using the Standardized Relative Performance (SRP) Index developed by Aspalter (2006) (cf. Appendix). The advantage is the exact representativeness of the results given by Aspalter’s SRP Index, and to be more specific, every value of Aspalter’s SRP Index says a lot not just about the research subject under scrutiny but also, and this is of utmost importance, it says even much more in relation to all other subjects (the group of subjects) included in the comparison. This is due to the fact that each value of the SRP is a standardized value, ranging from 0 to 10 (or 100, or 1,000, if one wishes to do so). The smallest SRP value in each column is always 0, and the highest is always 10 (or 100, or 1,000, in case one changes the formula). Therefore, the value of 5 always expresses the midway of performance between the worst and the best, or the lowest and the highest performing member of the group of subjects under scrutiny. Each time one combines variables into dimensions, or add up the variables or dimensions, one has to re-standardize the values in that column. Negative values have to be transformed into positive values. One has to make sure that bad (negative) indicators are not mixed with good (positive) performance indicators, without transforming, that is, inversing the good performance or the bad performance indicators’ SRP values first (by simply calculating: 10 – current SRP value; cf. Aspalter, 2023, Forthcoming b). In doing so, thus, standardization, in general, brings in additional levels and certainties of comparability and objectivity at the same time (cf. also Kumar & Ozdamar, 2004).

A maximum number of countries and a larger number of health indicators have been included to boost the comparability and analytical output of the study. Furthermore, in the following, this study applies the Theory of Ten Worlds of Welfare Capitalism, or in short the Ten Worlds Theory, to strengthen, that is, widen the range of conclusions to be drawn, and the depth of their salience, and hence their significance and validity.

Therefore, in general, consequential choices need to be made, for which solutions need to be found, each time, for each subject and the case of research investigation under way.

Global Data Analysis Itself: Negative Health Performance Around the World

Choices that look simple are not always simple, or simple at all. The selection of health care and health indicators need to be carefully performed. Cultures and environments (e.g., living in a desert, or Himalayan mountains) do change health care realities and health outcomes. The very best health care indicator is infant mortality rate, or neonatal mortality rate, or a combination of both. Why? Because we measure a whole range of numerous health care system aspects with this indicator, not only babies dying, but what led to it, and what did not prevent it from happening. Thus, when we compare mortality rates of babies, we are, in fact, comparing the availability of the choice of giving birth in a clinic with trained health care professionals; and thus the availability of hospitals and clinics, and doctors, midwives/nurses altogether, their training levels, their equipment; the availability of emergency operating theatres, and the like. We also measure the health policy of the country, and social services in general, plus health education and health information of the population (cf. the decisive impact of Integrated Child Development Services in India on women’s fertility choices etc.). Of course, availability of ambulance service and even distribution of health care facilities, and especially urban versus rural inequalities, are also being automatically measured when one looks at infant mortality or neonatal mortality rates.

On the other hand, child mortality rates are a perfect measurement of poverty. Poverty related to children in fact serves as a good proxy of poverty for all of the population. Under value 5 mortality, for the most part, is related to shortage of food, and proper food (vitamins etc.), and only in second instance it is an additional (very good) indicator of all of the health care system’s performance (almost as “good” an indicator as infant mortality, as 3- or 4-year-old children are already exposed to more geographically distinguished risks, and hence mortality factors, such as traffic accidents, drowning, etc.). Other poverty indicators—and hence inequality indicators—that affect health of the population are tuberculosis (TB) rate (a major poverty disease indicator, as lack of food and lack of proper food is the key factor behind high rate of TB), and of course there is the rate of children that suffer from stunting. Globally speaking, wasting is less common; therefore, it is not a good indicator to use, as this indicator suffers from the problem of bimodal distribution (cf. Esping-Andersen, 2000 as well as Künzler & Nollert, 2017). Stunting, still, is required to be combined with other factors, a larger group of factors, as, for example, in this global data analysis, as it mostly disaggregates (“spreads out”) the poorest and the poor countries only.

With hypertension, one has a very accurate and convenient proxy indicator for diabetes (as large shares of diabetic cases are not diagnosed, especially in developing countries, and there again in rural areas). Noncommunicable diseases (NCDs), or better modern-mass diseases (MMDs), are extremely widespread all over the earth. The overwhelming majority of preventable deaths in developing countries are from NCDs/MMDs; therefore, our study includes hypertension. Hypertension is the beginning of diabetes and metabolic syndrome complex, that is, twin evils, which form mostly two sides of the very same coin, so to speak. To boot, the diabetes and metabolic syndrome complex is the cornerstone and beginning of cardiovascular disease pandemic, the cancer pandemic as well as the chronic respiratory disease pandemic. Of course, it is worth mentioning that Alzheimer’s disease has been not long time ago identified as constituting type 3 diabetes, or stage 3 diabetes (hence being all the same disease, just forming its later stage¹) (cf. Aspalter, 2020b; Health Line, 2019). A great deal of modern mass diseases, including about 100 different kinds of arthritis, and a number of other most common neurodegenerative diseases, plus irritate bowl syndrome, Crohn’s disease, among others, are also the outcome of chronic inflammation, which is the main cause of hypertension and diabetes and all the other described follow-on diseases (cf Time, 2004). In order to capture modern mass diseases, the World Health Statistics database, on which our global data analysis is based, also offers the very great health indicator on the probability of dying from any of cardiovascular diseases, cancers, diabetes, chronic respiratory diseases between 30 and exact 70 years of age (in percent). Obesity indicators are also important indicators for modern mass diseases, as they are also chiefly responsible for diabetes, cardiovascular disease, cancer and arthritis pandemics.

The problem of bimodal distribution (Esping-Andersen, 2000), where one indicator is great to show problems of disease etc. in some parts of the world, but not in others, is the main reason for communicable diseases not to be included in this study. Another is that communicable diseases, by their very nature, vary a great deal over time, and, of course, geographically.

This study, therefore, has chosen positive indicators (that have been reversed in calculations of the SRPs used for total comparison of health care and health outcomes, i.e., performances)—of life expectancy at birth, healthy life expectancy at birth, and the number of doctors and nurses per population (variables 1 to 4). In addition, the following negative indicators have been added to the overall performance indicator (Aspalter’s SRP Index) of each country: maternal mortality rates and neonatal mortality rates (variables 5 and 6) as primary indicators of the health care system performance; plus child mortality rate (under 5 years mortality rate), the rate of stunting in children aged less than 5 years, and rate of tuberculosis as indicators of diseases of poverty (variables 7 to 9); and last but not least, hypertension, the probability of dying from major NCDs/MMDs between 30 and 70 years of age (see above), and obesity (and here again obesity rate in 5–19-year old persons, and obesity rate of the age-standardized adult population) as indicators of modern mass diseases (variables 10 to 12). Each variable has been given equal weight, apart from the two obesity variables, which have been combined (used as a single variable) to depict obesity of the entire population as such.

When looking at (or first glancing at) the overall results (Table 1), it may be not too surprising that a larger number of African countries, globally speaking, fare the worst in terms of health inequalities, i.e., their extremely bad performance in terms of health care development and health outcomes. Moreover, the same holds for the opposite end of the overall health performance spectrum in the rich developed countries of Europe, which fared best. When looking closer, really closer, though, a great deal of revealing findings can be discovered. First, the performance of Belgium is remarkable, given the fact that Belgium in comparative social policy, and of course also health policy, has for the most part been either forgotten or ignored. Among the Northern European countries, Denmark stands out, relatively negatively because it does not provide enough health care personnel, compared to the other four countries of Northern Europe, that are, Sweden, Norway, Finland, and Iceland. On the other end of the world, in China, the same problem is even more accentuated than in Denmark. This problem, however, would be a relatively easy fix by training and hiring more doctors and nurses, and of course adding a bit more to the overall health care budget of the respective governments, which both countries can easily afford to do. The very rich city of Hong Kong suffers the same problem as China as a whole, which is a political problem of not choosing the right choice. The stubbornness of not hiring (much) much more doctors and nurses in both places is remarkable, but points at the essential problem of public choice-making (as explained by the public choice theory) and that of general cause for super-inequality across the world, the super-super-rich and the super-powerful do not care about the health care and the health of normal people, the working and the middle classes alike (Aspalter, 2022, Forthcoming b).

Developing countries are also affected by policy decision-making, or better non-policy decision-making, by their governing and ruling elites. Oil-rich countries in particular (that is OPEC countries) have the relative worst performance of health care system outcomes (Aspalter, Forthcoming b), compared to their relative levels of economic development (i.e., GDP per capita).

This is what is called distorted health development. Hence, the aggregate sum of resources (natural or other economic resources) [that] a country has is not the causal variable, or chief causal variable, for the determination of a country’s health care and health performance. But politics is.

The same is true for poverty (cf. Brady, 2009; Brady & Burton, 2019), which in return affects health outcomes to a great degree, of course in a negative manner. Other variables, such as, for example, competitive democracy and levels of inequality (which causes poverty, that is, makes it possible and maintains/worsens it), are better predictors of overall positive health care performance/outcomes when seen from a global comparative perspective (cf. above-mentioned data as well as data in Aspalter, Forthcoming b).

Global Health Data Analysis and the “Ten Worlds Theory” in Welfare Regime Comparison

When taking a closer look at the findings, and taking on the perspective of ideal-typical welfare regime theory (Table 2), one can arrive at a greater number of (meaningful) additional conclusions and findings in analyzing the above data results.

The top results of the North European countries are not surprising at all, as they are considered to deliver one of the best model of welfare state systems in terms of people’s well-being and welfare, and, of course, overall social development. The Social Democratic Welfare Regime is marked by high levels of equality in many respects, and is paramount to the highest levels of health outcomes and development of health care system. Generally, higher levels of gender equality, educational equality, and economic equality are engineered with a combination of high horizontal redistribution plus very high rates of individual taxation and social security taxation alike.

In addition, the outstanding results, including the very best results for Switzerland and Belgium, for all of the member countries of Christian Democratic Welfare Regime come as a confirmation to many who are very familiar with these countries and their health care systems, which are, generally speaking, among the very best in the world. What is particularly interesting, and useful, is the fact that our health data comparison fully supports not only the internal consistency of the grouping of ideal-typical welfare state systems in most of “Western” and “Central” Continental Europe but also, and perhaps more importantly, it confirms yet again the non-membership of neighboring countries of the Christian Democratic Welfare Regime, all countries south of Hungary, east and southeast of Croatia, and north of Greece as well as west of Ukraine and Moldova. Therefore, this group of countries—that includes Bulgaria, Rumania, Serbia, Bosnia and Herzegovina, Northern Macedonia, and Albania—share the same health and health care fate, and not just historical fate and social development fate. These are, in essence, transition countries from socialism to capitalism that have not yet managed to get on their feet in terms of economic, social, and health development (cf. e.g., the case of Serbia: Bjegović-Mikanović et al., 2019; Vidojević & Žarkovic´, Forthcoming; Vukovic´ & Perišic´, 2011). If these countries manage, they most likely end up in the same group of countries that have made a successful transition to capitalism with high levels of social and health development, such as Slovenia and Czechia, but also Slovakia, Croatia, Hungary, and Poland, that is, they will join, or are in transition toward joining the Christian Democratic Welfare Regime. Aspalter, Kim, & Park (2009) have explained that the first rim of successful transition countries from the former Eastern Block have joined their former neighbors and/or mother countries in historical terms, as their law systems and government/administrative systems are strongly rooted and firmly based on that of the Austro-Hungarian Empire, and Prussia in the north. There are also strong cultural and ethnic ties among the follow-on countries of the Austro-Hungarian Empire, which survived the historical onslaught that has been brought about by decades of authoritarian communism and the Iron Curtain.

As to the new largely enlarged membership of the Christian Democratic Welfare Regime (cf. Table 2), the center of the very same has, as a consequence of the enlargement of the overall group, shifted to the southeast, from Germany to Austria and Slovenia and their neighboring countries (cf. Aspalter, 2023). This observation has been largely built on the greater degree of universalism in these countries, plus the fact that the boundaries of the European Union that it inherited from the fall of the Berlin Wall, and the economic division between the South and the North of the European Union have finally started to disappear. Hence, the project of European Union has finally, one can say, succeeded in breaking boundaries and inequalities on a larger European scale. There is neither East versus West, nor South versus North anymore. This is obvious, especially when one applies the high-flying-bird’s perspective as developed and enabled by ideal-typical welfare regime analysis, its ideal-typical comparison and theory.

If one were to look only at the old group of Christian Democratic Welfare Regime members, the one looked at by Esping-Andersen in 1990 (where he used the data from the year 1980), Belgium today would be, arguably, the best country to represent that particular smaller group of countries excluding all former Eastern Block countries. However, this view does have several problems. First, time, that is, excluding countries today (in the year 2023) based on how they were 43 years ago (in 1980). Second, it would (is) be utterly unscientific to exclude countries based on exclusionary practices that now—and after so many decades—had time to settle in and permeate not only people’s stereotypes but also mainstream theoretical thinking, and thus delay the progress of the very same (a Kuhnian trap of “normal” science, so to speak; cf. Kuhn, 1971).

It is difficult for many to accept that Poland and Hungary are part of the Christian Democratic family of ideal-typical welfare regimes, and the same applies to Croatia, Slovenia, Slovakia, and Czechia. However, the data only speak the scientific language. The quantitative data given by Aspalter (2023) fully support the inclusion of not only the former Eastern Block countries but also all so-called Southern European countries on south of the Alps and south of the Pyrenees. Here, again, the stereotypical ways of thinking have been ingrained so deep that many cannot cope to accept Portugal, Spain, Italy, Greece, and Cyprus to be part of the Christian Democratic Welfare Regime family. However, here, we apply expressively, and distinctively, the view of a high-flying birds’ view, that is, the ideal-typical approach.

And there is a plenty of room for the low-flying bird perspective to be applied by real-typical studies, using real-typical methodology in forming and shaping real-typical welfare regime theory, which is a very different thing, with altogether different purpose and scientific design. Thus, a Chinese Wall must be kept in between the two, while learning from one another is ok, comparing one another on a one-to-one basis is fatal, and only causes confusion and loss of scientific progress and waste of time. That is to say, decades were lost to the confusing, ignorance, and mixing up of different theories that served different purposes and used very different levels of analysis.

Aspalter et al. (2009) extended the Christian Democratic Welfare Regime in the world of ideal-typical welfare regime theory, and this was recently confirmed and new countries, Croatia and Slovakia, were added to the rim of countries that made up this group/family of welfare state systems. Greece and Cyprus have been confirmed as regime members as well. And, more importantly, perhaps (for analytical clarity and validity), the exclusion (at this moment in time) of Serbia, Bosnia and Herzegovina, North Macedonia, Romania, and Bulgaria has also been confirmed twice, first by Aspalter’s (2023) global data analysis that looked comparative levels of inequality and povertization (processes of impovertization and their outcomes), and then again in the above global health performance data analysis. New signs of convergence in the center of the European Union are mounting, with the publication of new empirical-based research reports in recent years (cf. esp. Leichsenring, 2020; Poławski, 2021; Røkkum, Parton, & Heggem Kojan, 2022).

As scientists, we can see that data are evidence and stereotypes are ghosts that blind and twist our thinking and thus make rational thinking impossible. Alternatively, what we think is rational, in fact becomes utterly emotional, unscientific, and hence irrational, “objectively” speaking, as much that—“pure objectivity”—is possible in the first place, as we all are always caught in our own life experiences, plus our linguistic, cultural, and historical boundaries that limit rationality (cf. Foucault, 1976; Myrdal, 1965, 1969; Nietzsche, 2008 [1878]; Weber, 2012, as well as Freud, 1921; Vygotsky, 1978).

As for the Neoliberal Welfare Regime, which are, in fact, only possible to be grouped together when applying a high-flying bird’s view (i.e., ideal-typical perspective and methodology), the relative inferior position of the United States in terms of social development, well-being, and welfare, has now been proven to be also the case with regard health development. The leading position of Australia is indeed noteworthy, as is the relative lacking performance of New Zealand; both of which call for further investigation and (detailed, and well-founded) explanation; here, real-typical additional analysis is the right instrument to get this job done.

For all of East Asia, the Pro-Welfare Conservative Welfare Regime, from Indonesia in the south to Mongolia and Japan in the north, Japan continues to show remarkable positive outcomes in terms of health development as well as on the whole range of social indicators earlier (Aspalter, 2006, 2023). South Korea, being almost virtually, on equal position with Japan, is extremely remarkable. More comparative research is warranted in case of the top performing Asian countries and regions, including Hong Kong, Macao, and Taiwan. The case of Singapore, however, points to the decisive role of inequality and poverty as health deteriorating determinants (cf. the problem of poverty in Singapore, especially; Teo, 2017, 2018). Noteworthy is the positive situation in terms of the number of doctors per population in Mongolia. It shows that despite its poor economic conditions, it invests a lot in social welfare in general, as well as in health care, at least in relative terms.

In Latin America, the Anti-Welfare Conservative Welfare Regime, the ideal-typical welfare regime family identified is rather large, after greater inclusions made recently by Aspalter (2023). The extremely poor performance of Brazil in terms of inequality and poverty warranted the lowering of the bar, in terms of ‘positive’ performance needed to qualify for regime membership. Hence, this facilitated a much larger inclusion of the number of countries in the ideal-typical anti-welfare conservative welfare regime. Ideal types are word pictures, or mental images (as noted by Weber, 2012). Hence, they can be (and shall be) adapted over time, if necessary.

In the case of Latin America, the picture one faces when looking at welfare and social development outcomes and policies is the opposite of being rosy, put politely. Even some extreme or rather strong overestimation of Brazil and Costa Rica did not change the facts (data and situation) on the ground. Aspalter (2017b) and Lima de Farias (2003) have not only pointed out and analyzed the overall dire situation of lack of poverty reduction and the regressive nature of the Brazilian welfare state system but also the positive effects of its universal health care system and the universal right to health care in Brazil.

These qualitative studies, on the one hand, have been confirmed as well as qualified that poverty has indeed been stronger than expected, also within Latin American comparison if looking at the quantitative data, which point to the necessity of conducting deep quantitative data analyses, in addition to strong in-depth qualitative case study analyses. The same happened with Germany and Russia; both of which strongly underperformed their formerly believed middle-of-the-road performance among their respective welfare regimes. However, this is not the case, they both were bottom performers of their respective ideal-typical welfare regimes (in terms of inequality and poverty that is); and so is Brazil.

When it comes to health outcomes and health care development, the picture seems to be less bad than looking at inequality and poverty dimensions in all three generally (across the board) underperforming countries—Germany, Russia, and Brazil. The thing one needs to factor in here is time. There may be, and it would be odd if there were not (logically speaking), a time delay between cause and effect, inequality and poverty on the one hand and health deterioration on the other. Therefore, the present study, and the data above, warrants a continuous closed-up monitoring of the situation of health deterioration on the ground, especially among poorer segments of the population, including migrants (as in the case of Germany, including the usage of age-standardized data, as many migrants are of younger age and working age!).

When the concerns the Slightly Universal Welfare Regime, i.e. South Asia, and India in particular, general expectations and in-depth qualitative case studies alike, pointed to a very dire health care situation on the ground, and anyone who traveled to and/or lived in India, is well aware of this. The surprise, this time again, a negative surprise, was caused by the relative performance of Mauritius, which supports the evaluation of local experts on the ground (Peeroo, 2020; Phaahla, 2017, 2018). Fiji is negatively outperforming the rest of the group, that is, members of the Slightly Universal Welfare Regime, while Mauritius falls right in the middle of the group, when it comes to health care and health performance of welfare state systems in question. The closeness of Fiji to the Philippines and Indonesia is caused by their common lack of doctors and nurses, in relation to the overall population. Nepal and Bhutan are almost equal in terms of relative health and health care performance, compared to India. Bangladesh is significantly ahead of India, which is an intriguing and indeed encouraging fact, the causal factors and the trend of which need to be analyzed further—hence, more in-depth research is warranted. Sri Lanka, confirming earlier findings, is performing rather well, in comparative terms (cf. Ranaweera, 2008). The super good performance of Maldives could serve well normative studies that also include other group members of this ideal-typical welfare regime.

For the group members of the Selective Rudimentary Welfare Regime, in most former countries of the Soviet Union, a number of conclusions could be drawn. First, the lead position of Belarus, not only in terms of social development and overall welfare but also in terms of health development is confirmed. Not long time ago, i.e. before Russia invaded Ukraine, Russia was still able to perform equally with Kazakhstan; this is noteworthy because Kazakhstan chose to focus more on health care investment as a main policy goal (given its extremely poor performance in terms of especially rock-bottom low male life expectancy; cf. Amagoh, 2017). The relatively much poorer performance of Tajikistan and Turkmenistan in this rim of countries, in terms of life expectancy, healthy life expectancy, the relative number of doctors and nurses, and NCD/MMD prevalence, needs to be investigated and explained further; especially with the relatively strong performance of Kyrgyzstan (geography and economy are not the key factors, perhaps cultural/ethnic ties, or just politics, this needs to be explained by in-depth case studies, and more in-depth analytical, e.g., district-level, data analyses).

For Cuba, the only representative of the Socialist/Communist Welfare Regime, the health care data vindicate, in general, its universal approach, given the tremendous problems caused by economic and financial deprivation due to Western (US-imposed) sanctions and lack of economic development (also chiefly because of authoritarian communist plan economy and governance style). However, here again, the performance could be much better if Cuba were to implement smart universalism, instead of, as now, blind universalism, especially regarding its health care system financing, to establish individual incentives for better and healthier (and healthy) lifestyle choices, including food and drink choices and exercise choices—to cut down its very high obesity and hypertension levels (cf. Aspalter, 2021, 2023).

For the ideal-typical Exclusion-Based Welfare Regime in the rich countries of the Middle East, that is, the six gulf states plus Israel, it becomes clear that when it comes to health care and health outcomes, Arabic countries are far apart from Israel. Ideal-typical welfare regime theory, by its very nature and design (i.e., fully on purpose), zooms out a lot in order to achieve its goal to localize commonalities, where all other means (methods) cannot find or paint the greater picture. Real-typical welfare regime analyses are supposed to fill this gap. Hence, the world as such still has to wait for a great deal more real-typical studies, not only with regard to whole welfare state systems but also with its sub-systems, for instance, health care or long-term care, or gender policies (outside the Western world, in particular), and so forth.

Hence, the comparison of welfare state systems, and the corresponding ideal-typical theories, do not need to and cannot be expected to match “all” (or any random) sub-levels of welfare state systems, e.g., health policy and health care policy-making—as these are entirely different levels of analysis, and hence entirely different research subjects under observation. (see Aspalter, 2014, pp. 1–2)

This needs to be kept in mind when looking at health care outcomes (i.e., different research objects other than whole welfare state systems) on the one hand, and bridging our findings to the findings of ideal-typical welfare state system theory on the other.² Hence, it can be concluded merely that the Gulf cooperation council states are very close together regarding their respective relative levels of health and health care performances.

Concerning the largest group of welfare state systems, and their health care systems, as well as their health care outcomes, the Extreme Rudimentary Welfare Regime in all former non-British colonies in Africa, including Liberia (former more-or-less American colony but influenced by its neighboring countries, when it comes to welfare state system policies), plus Haiti (as geography is not important, but political/cultural/historical realities are), a greater stretch of results (spread of results due to the size of the group itself per se, apart from other additional reasons in case) is expected. Still, remarkable is the exclusive concentration of a large group of this welfare regime members among the very worst performing countries in the world in terms of health outcomes and health care indicators. This is more than what one could have expected. As for the major reason(s) for this, again, this needs to be determined and confirmed by forthcoming in-depth studies through comparative historical and political economy case and other in-depth studies. Botswana, for one, was an early leader in health care provision extension, including government financing thereof (cf. Bar-On, 1999; Rankopo & Diraditsile, 2018). In addition, also, all cases in the Southern African region demand higher levels of international and comparative scrutiny.

For the very northwest of Africa, four countries have been identified by welfare regime theory to have left the realm of the Extreme Rudimentary Welfare Regime a long time ago. The data from the global data analysis on the dimensions of societal inequality and different forms of povertization (Aspalter, 2023) have provided quantitative evidence to support the conclusion that these four countries, Tunisia, Cabo Verde, Algeria, and Morocco, have left behind African welfare reality a long time ago, and are in fact moving much closer to their former colonial mother countries, France and Portugal. This fact has been once again strongly validated by the new global set and comparison of health care and health outcome indicators as presented above (cf. Table 1).

Parts Unknown: By Way of Conclusion

The global data analysis included in this study looked at the world of health and health care inequalities for all of humanity, and not just the rich and developed countries. In addition, a truly global, all-inclusive ideal-typical welfare regime theory, the “ten worlds theory,” was used to analyze and evaluate the results of the global data analysis.

There is plenty of room and plenty of need for conducting a myriad of global data analyses with the guiding and interpretive support of strong theories that can either be applied and strengthen in doing so or devised and developed along the way. Aspalter’s SRP Index can help facilitate more precise and a greater volume, and hence a greater quality, of comparative data analysis on local as well as global scale.

This becomes ever more important as we, the world, and science as a whole, are entering a new era that is marked by the development and applications of data science (with the very good and extremely worrisome consequences that it brings with). It is important to be able to analyze different kinds of data in bulk, in groups, and analyze these groups in a truly comparative manner. The author hopes that this will, in due course, help to include the world of human beings that are so far being left out from scientific analysis and evaluation altogether due to down-trodden paths, practices, and habits of global human exclusion of the poor and the poorest as well as the not so rich. As of today, that is, in 2023), the WHO (2022), the OECD (2022), and the World Bank have yet to use such a method and index developed by Aspalter back in the year 2006 (cf. Baland, Cassan, & Decerf, 2022; Decerf, 2022; and esp. Aspalter, 2006, 2023).

In general, it can be concluded that most of the world is still extremely unexplored, and that this is not due to lack of data, and lack of access to local studies or government sources, but rather this is, by and large, the result of a Western-centered world of social science itself.

We are still bound by our own history of Colonization of a majority of countries, by a privileged few, based on military domination in the past. Academia too is still in the hands of the anglophone Western world, as virtually all SSCI journals are in their possession, not to mention a good majority of global publishing giants as a whole. In social policy, we are mirroring our history of domination by thinking that only developed countries have it good, and only developed countries have a welfare state. That is why, learning from Prof. B. Vivekanandan a long time ago, back in the year 2001, the author has applied, in the last two or so decades, the most-inclusive approach by theorizing and analyzing all sorts of welfare state systems. As Prof. Vivekanandan had put it, and as he told the author, India too has a welfare state system, it is (was) just very small (in terms of population coverage and financing, back in the year 2001 that was). The interview with Prof. Vivekanandan at Jawaharlal Nehru University in New Delhi, India, has changed the author’s perspective on the welfare state for decades. It is hoped here that this new perspective though could and would catch on over the passage of time on to all researchers and students across the globe. Thus, it would be able to dissipate quickly, rather than slowly, and abandon post-Colonialist points of views and a deeply entrenched load of stealthy and sneaky stereotypes on all developing countries (as they are called now), that is, the poor, the poorest, and the not so poor countries of the world. This is especially a problem in European-centered science of social policy, and particularly also the Western-centered practices in welfare regime theory, be it ideal-typical or real-typical welfare regime theory, and comparative social policy and health policy in general. That is to say, a great majority of social policy and health policy scientists are still trapped or bound to old ways of thinking and old ways of looking and not looking. Yet, a growing number of seasoned and young scientists have already left the ranks of the inactive, and started to include remote parts of the world.

Dixon (1987, 2016; cf. also Dixon & Macarov, 1992, 2002) was the first to encircle fully and intensively the globe in the name of social policy exploration. Many others have since followed in these footsteps (cf. Aspalter, 2023; Biehl & Petryna, 2013; Cerami, 2013; Gough & Wood, 2004; Leon & Walt, 2001; Mohan, 2011; Mohan & Bäckmann, 2020). While data on, for instance, health and health care are available for all countries, their regional and local district-level data are for the very most part not available for international global researchers. All in all, the number of qualitative and quantitative studies on remote and poor countries’ health outcomes and health care systems and policies are extremely rare. Virtually all poor countries, including all transition countries, are still parts of the world that are to a great extent unknown, utterly neglected, and/or utterly ignored.