Educational inequalities in mortality amenable to healthcare. A comparison of European healthcare systems.

BACKGROUND: Educational inequalities in health and mortality in European countries have often been studied in the context of welfare regimes or political systems. We argue that the healthcare system is the national level feature most directly linkable to mortality amenable to healthcare. In this article, we ask to what extent the strength of educational differences in mortality amenable to healthcare vary among European countries and between European healthcare system types.
METHODS: This study uses data on mortality amenable to healthcare for 21 European populations, covering ages 35-79 and spanning from 1998 to 2006. ISCED education categories are used to calculate relative (RII) and absolute inequalities (SII) between the highest and lowest educated. The healthcare system typology is based on the latest available classification. Meta-analysis and ANOVA tests are used to see if and how they can explain between-country differences in inequalities and whether any healthcare system types have higher inequalities.
RESULTS: All countries and healthcare system types exhibited relative and absolute educational inequalities in mortality amenable to healthcare. The low-supply and low performance mixed healthcare system type had the highest inequality point estimate for the male (RII = 3.57; SII = 414) and female (RII = 3.18; SII = 209) population, while the regulation-oriented public healthcare systems had the overall lowest (male RII = 1.78; male SII = 123; female RII = 1.86; female SII = 78.5). Due to data limitations, results were not robust enough to make substantial claims about typology differences.
CONCLUSIONS: This article aims at discussing possible mechanisms connecting healthcare systems, social position, and health. Results indicate that factors located within the healthcare system are relevant for health inequalities, as inequalities in mortality amenable to medical care are present in all healthcare systems. Future research should aim at examining the role of specific characteristics of healthcare systems in more detail.

Introduction

Over the last few decades, many studies have shown that socioeconomic factors (such as educational attainment, occupational class, and income) are the leading determinants of population health in European countries, and their influence appears to have increased substantially (cf. [1-3]). Healthcare systems have been characterized as one of the key dimensions of modern welfare states, since welfare states constitute “a complex set of institutionalized citizenship rights”, shaping “the causes and consequences of health, illness and healing” [4]. Nevertheless, healthcare has been by and large absent from major welfare state theories [5-9]. In this article, we explore and discuss the associations between healthcare and social inequalities in health, on the empirical basis of mortality data from 21 European countries.

Educational level and health are related through numerous pathways, such as smaller risk of unemployment, higher income, good housing conditions, low financial hardship, lower levels of health damaging behavior, and feelings of mastery, control, and social support [10]. Educational attainment is also closely related to health literacy: the ability to use reading and numerical skills to understand health information provided by for instance physicians, nurses, and pharmacists [11]. Educational inequalities in health and mortality appear to vary across European countries, with the rank order of countries depending on the indicator of health and mortality that is used (cf. [12-15]). Education is a pragmatic measure of social position status which is reasonably comparable across contexts, and often used in cross-national studies where data on income or occupation is unavailable or considered too context-dependent–as is the case with this article [16]. Further, education is less sensitive to reverse causation–for adults, educational attainment does not change if one’s health deteriorates. Educational distribution in the study countries is available in S2 Table.

A common approach to comparative studies of and social inequalities in health has been to focus on the role of welfare regime types (e.g., [17]) or political systems (e.g., [18,19]). Welfare regime typologies have contributed to highlighting and comparing some of the principles underpinning welfare states, the generosity of social transfers, and entitlements and social rights, which all may affect the social distribution of health [20]. The results from this regime approach to health inequalities have been described as “a patchy picture with contradictory findings” [21].

A common criticism against the welfare state regime approach has been related to its crudeness–it has been argued that there is a need to specify which welfare state characteristics are of importance for public health outcomes [22]. Moreover, reviews of the regime approach to health inequalities have concluded that the empirical evidence does not consistently support the association between welfare regime and health outcomes proposed by welfare regime theory [21,23]. Most notably: The Nordic countries belonging to the Social Democratic welfare regime, committed to universality and equality, have exhibited high life expectancies in combination with comparatively large health inequalities–often described as the Nordic public health puzzle or paradox [15,20].

In order to further advance research on macro-level explanations for cross-national differences in socioeconomic health inequality, more detailed accounts of the specific aspects of welfare regimes or political systems most prone to influence health are needed. Further, there is a need to link specific country-level mechanisms to specific health outcomes rather than general indicators of health or mortality.

In this study, we aim to provide a novel contribution by exploring the variation of educational inequalities in mortality amenable to healthcare among European countries and healthcare system types. We argue that the healthcare system is a feature of welfare states that is most directly relevant and linkable to health outcomes, compared to for instance GDP per capita or indicators of healthcare spending. We further argue that mortality amenable to healthcare is a health outcome with a clearer and stronger connection to state or healthcare intervention than other measures of health and mortality [24]. Amenable mortality can be defined as deaths which are preventable through medical intervention and which should not occur in the presence of timely and effective healthcare, including prevention, diagnosis, and treatment [25-27]. From this perspective, we aim to explore variation across 1) European countries and 2) European healthcare system types.

Welfare and healthcare typologies

Several strategies to measure and classify healthcare systems have been proposed since the 1970s, often based on healthcare expenditure, healthcare financing, service provision, and access regulation and resulting in versions of three healthcare system ideal types closely connected to Esping-Andersens welfare state regimes: voluntary insurance, social health insurance, and national health service [7]. Reibling, Ariaans, and Wendt [28] used 13 country-level variables to construct a typology of healthcare systems across 29 high-income countries. Health expenditure per capita and the number of GPs per population indicated healthcare supply, the financial and human resources spent on health. The role of the state and the public/private mix in healthcare was indicated by the public share of health expenditure, the share out-of-pocket payments, and the remuneration of specialists as a measure of cost sharing. Access regulation was measured by indicators of healthcare coverage and choice restrictions. Expenditure on outpatient-care and their GP-to-specialist ratio indicated primary care orientation. Finally, healthcare performance was measured by indicators of tobacco and alcohol consumption and a quality sum index based on avoidable hospital admissions. Here, tobacco and alcohol consumption were used as proxies for the effectiveness of a healthcare system’s preventive efforts, as adequate data on regulatory and monitoring activities was not available. Factor analyses of these indicators resulted in a five-fold typology of healthcare systems (countries included in our data in bold):

Type 1 –Supply- and choice-oriented public systems (Australia, Austria, Belgium, Czech Republic, France, Germany, Iceland, Ireland, Luxembourg, Slovenia): Primarily public funded social insurance systems. Characterized by medium to high levels of financial and human resources, free choice, and access regulation only by cost sharing. Performance scores are mediocre with regards to both prevention and healthcare quality.

Type 2 –Performance- and primary-care-oriented public systems (Finland, Japan, New Zealand, Norway, Portugal, South Korea, Sweden): Public funded high-performing healthcare systems. The state has a strong role in regulating access and in the payment of medical specialists. Primary care has high priority.

Type 3 –Regulation-oriented public systems (Canada, Denmark, Italy, Netherlands, Spain, United Kingdom): Primarily public funded healthcare systems. Medium level of resources, low levels of out-of-pocket payments, and high level of access regulation and limitation of choice. Lower priority of primary care and lower performance than Type 2.

Type 4 –Low-supply and low performance mixed systems (Estonia, Hungary, Poland, Slovakia): Mostly public funded healthcare systems with low levels of financial and human resources, high levels of out-of-pocket spending, strong access regulations, and low performance on prevention and quality of care.

Type 5 –Supply- and performance-oriented private systems (Switzerland, United States): Healthcare systems with a strong role of private financing and out-of-pocket payments. Public resources are in the majority, with high supply and expenditures. Access is regulated by sharing regulations such as deductibles. This type shows high quality-of-care performance.

Since we wanted to utilize the full range of our data, and to avoid calculating with single-country clusters, we grouped Lithuania (which is not included in the data of Reibling et al. [28]) in Type 4, and Switzerland (which is the only Type 5 country in our data) in Type 1. This is done based on an assessment of key indicators used in the initial factor analysis. Subsequently, only four of the five healthcare systems types were included in our analysis. As results from research using welfare state regimes to compare health inequalities have been largely inconclusive, our contribution with this article is to use a validated and more specific health outcome–amenable mortality rather than self-reported health or limiting longstanding illness–and a typology more directly related to health–Reibling and colleagues’ [28] healthcare system types.

Expectations

Our study design is not suited for predicting inequality effects of specific health policies. However, we expect inequality rates to vary across countries and healthcare system types, and results from previous research allow us to formulate some modest expectations with regards to this variation. First, low education can be associated with poor health by being an indicator of material disadvantage. Financial strain due to e.g. unemployment or low income may matter more in a context with scarce healthcare resources and high out-of-pocket payments. Blom, Huijts, and Kraaykamp’s [29] analyses of repeated cross-sectional survey data revealed that high total and state provision of healthcare, measured as total and governmental healthcare expenditure, was associated with smaller educational inequalities in self-rated health, while specific inequality-reducing health policies had a less substantial effect. This leads us to expect that low public funding, as found in the low supply and low performance mixed systems (Type 4), is associated with higher levels of inequalities.

Second, the impact of strong access regulation and choice restriction, as found in the performance- and primary-care-oriented public systems (Type 1) and the regulation-oriented public systems (type 3), appears less clear. On the one hand, regulations may enhance health equality, ensuring equal access and preventing overconsumption of services. On the other hand, to maneuver a bureaucracy-governed healthcare system may (unintentionally) reward immaterial resources typically associated with high socioeconomic position, such as health literacy, social networks and the ability to “work the system” [30].

Third, people of low socioeconomic position have tended to be more intensive users of general practitioners, mainly due to a higher disease prevalence [31,32]. High priority of primary care, as found in the performance- and primary-care-oriented public systems (Type 2), could therefore also be associated with lower inequalities.

Data and methods

Data

The EURO-GBD-SE project collected and harmonized mortality data from the 21 European countries for which comparable data was available. This article utilizes all available data, covering time periods between 1998 and 2006, depending on country (see S1 Table). This data is to our knowledge the latest individual-level mortality dataset encompassing a majority of European countries. The datasets included four Nordic countries (Finland, Sweden, Norway, and Denmark), six Western European populations (England & Wales, Scotland, Belgium, France, Switzerland, and Austria), four Southern European populations (Barcelona, Basque Country and Madrid (Spain) and Turin (Italy)), four Central/Eastern European countries (Slovenia, Hungary, Czech Republic, and Poland) and two Baltic countries (Estonia and Lithuania). The data covered the entire national, regional (Madrid, the Basque Country) or urban (Barcelona and Turin) populations. The data from Spain and Italy only covers parts of the population, which prevents us from generalizing to the whole countries. These populations are therefore excluded when we estimated relative and absolute inequalities for the different healthcare system types but are displayed in tables and figures as a reference point.

Mortality data for Hungary, the Czech Republic, Poland and Estonia came from cross-sectional (CS) unlinked mortality studies. Data for Barcelona and Madrid was derived from a cross-sectional census linked studies. Data for other European countries has a longitudinal design. In the cross-sectional unlinked mortality studies, information on socioeconomic position was derived separately from death certificates and census records. In the longitudinal studies, mortality was linked to socioeconomic position determined during a census. An overview of the mortality data sources is displayed in S1 Table.

The Finnish dataset included only 80% of the Finns. The Swiss dataset excluded Non-Swiss nationals, the French dataset excluded those born outside mainland and the Dutch dataset excluded people from institutions. The 100% linkage between the population and death registries was achieved in most of the included populations. In countries where the default in linkage was lower than 5% no corrections were applied. In countries and areas such as Austria, Barcelona, the Basque Country, and Madrid, where a higher percentage of deaths that could not be matched with the mortality registry, we introduced a correction factor. In Austria, the correction factor was broken down by sex and 5-year age group. In Barcelona, the Basque Country and Madrid, there were no variations by age and sex for excluded deaths. The correction factor was therefore equal to 1.06 (1/0.946) for Barcelona and the Basque Country and 1.25 (1/0.8) for Madrid.

The causes of death amenable to healthcare were selected on basis of the publications by Stirbu et al. (2010) and the AMIEHS (2011) report from the European Union's Public Health Programme. In public health research, the terms “avoidable”, “amenable”, and “preventable” have been associated with some ambiguity, and often been used interchangeably [33]. Piers, Carson, Brown, and Ansari [34] have argued that avoidable mortality includes amenable and preventable conditions, where deaths can be averted from the former, while the latter can be prevented from occurring altogether. Others have attempted to classify mortality according to the relevant level of healthcare intervention: primary, secondary, and tertiary avoidable mortality [35], and health policy and medical care indicators of avoidable mortality [36]. For example, Perez and colleagues’ [37] analysis of avoidable mortality in Spain showed that figures on avoidable mortality could be affected by different processes such as healthcare interventions, prevention and promotion strategies, or by intersectoral policies. The authors argued that the concepts (and sub-concepts) of amenable and avoidable mortality have tended to blur the image of the prevalence and trends of specific causes of death. Nolte and McKee [33] have further questioned the underlying assumption of these classifications: that health outcomes can be attributed to specific elements of healthcare. For several conditions, there are discrepancies in the literature regarding the effect of public health and medical interventions, and thus also the nature of their preventability. Additionally, the classification of amenable mortality may to a certain extent suffer from systematic cross-national variation in diagnosis, death certification, and cause of death classification [27]. When assessing amenable mortality in the different healthcare system types, we will also contrast these estimates with inequalities in all-cause mortality.

Our classification leans on the precedence set by previous cross-national comparisons of amenable mortality (cf. [38-40]). One contested measure has been to classify ischemic heart disease and heart failure as non-amenable. It has been argued that the impact of medical treatment on these causes of death is unclear, while the association with lifestyle factors such as smoking, alcohol consumption and obesity is strong. Causes of death classified as amenable to healthcare are reported in Table 1. Other scholars have used different versions of the same data with similar classifications. Stirbu et al. [41] found educational inequalities in mortality amenable to medical care across all European countries, particularly pronounced in Central-/Eastern-, and Baltic European countries; Plug et al. [42] found that these inequalities were not associated with inequalities in healthcare use; Mackenbach et al. [15] compared mortality amenable to behavior change, amenable to medical intervention, amenable to injury prevention, and non-preventable mortality, finding the smallest inequalities in the latter category, and the steepest gradient in the former; Mackenbach et al. [43] found that mortality declined faster among the higher than among the lower educated and that educational inequalities in mortality decline were similar between causes of death amenable to behaviour change and medical care.

Table 1

Causes of death amenable to medical care according with ICD10 codes.

Cause of death	ICD10 codes
HIV/ AIDS	B20-B24
Tuberculosis	A15–A19, B90
Other infectious and parasitic diseases	A00-B99
Cancer of colon-rectum	C18–C21
Cancer of cervix uteri	C53
Cancer of testis	C62
Hodgkins lymphoma	C81
Leukemia	C91-C95
Rheumatic heart disease	I00–I09
Hypertension	I10–I15
Other heart disease	I30-I52
Cerebrovascular disease	I60–I69
Pneumonia/ influenza	J10–J18
Asthma	J45–J46
Appendicitis, hernia, cholecystitis and lithiasis	K11.5, K35-K38, K40-K46, K80, K81, N20,
Peptic ulcer	K27
Prostate hyperplasia	N40
Maternal deaths, conditions originating in the perinatal period	O00-O99
Congenital heart disease	Q20-Q28

We used educational attainment as a measure of socioeconomic position. This was categorized according to the International Standard Classification of Education as low (no or primary education and lower secondary education, ISCED 0–2), middle (upper secondary education, ISCED 3–4) and high (tertiary education, ISCED 5–6) education. In order to create comparability across countries, we needed the same educational grouping in all countries. These three groups were what national educational classifications allowed us to create, and this division is also utilized in the studies cited above. Table 2 displays the amenable mortality rates by educational level.

Table 2

Mortality rates by educational level standardized to the European Standard Population.

Country	Gender	Mortality rates, ISCED 0–2	Mortality rates, ISCED 3–4	Mortality rates, ISCED 5–6
Austria	Men	274.4	210.1	148.4
	Women	159.8	114.9	90.2
Belgium	Men	238.0	198.2	153.9
	Women	158.5	121.1	94.8
Czech Republic	Men	478.2	265.7	163.8
	Women	261.7	182.9	106.1
Denmark	Men	284.3	232.3	183.7
	Women	190.7	150.1	118.9
England/ Wales	Men	219.0	144.3	122.5
	Women	159.2	106.8	110.8
Estonia	Men	689.0	530.2	317.3
	Women	403.5	279.2	172.8
Finland	Men	242.7	184.1	138.6
	Women	144.9	102.8	74.7
France	Men	310.7	223.4	141.1
	Women	136.8	90.2	55.7
Hungary	Men	644.1	351.5	247.8
	Women	345.8	188.6	182.4
Italy (Turin)	Men	200.8	170.6	136.7
	Women	120.2	111.0	95.4
Lithuania	Men	405.4	270.4	155.2
	Women	235.6	130.3	73.9
Norway	Men	246.5	181.1	136.1
	Women	163.3	120.8	85.3
Poland	Men	248.6	134.6	84.1
	Women	130.5	78.6	48.2
Scotland	Men	223.2	163.3	148.7
	Women	158.2	72.8	99.4
Slovenia	Men	421.2	278.2	178.3
	Women	202.4	133.8	104.3
Spain (Barc.)	Men	239.3	193.2	151.6
	Women	119.5	94.3	78.1
Spain (Basque)	Men	206.5	162.6	158.1
	Women	95.1	77.6	67.8
Spain (Madrid)	Men	231.8	206.9	183.2
	Women	122.4	111.0	78.3
Sweden	Men	184.2	146.6	113.4
	Women	125.8	95.4	69.8
Switzerland	Men	183.6	113.8	83.5
	Women	88.4	61.4	46.5

Analyses

All analyses were conducted separately for women and men aged 35–79 years (age interval depending on country) and age-standardized with the European Standard Population as reference [44]. Individuals whose educational attainment was unknown were omitted from the analyses. The magnitude of relative educational inequalities in mortality amenable to healthcare across European countries and across healthcare systems was calculated by relative indices of inequality (RII) by means of Poisson regression. The RII is a regression-based measure that accounts for the distribution of the population by educational groups using rank of educational attainment as a dependent variable [45]. The educational rank was calculated over all three educational groups defined above. The resulted RII represents the risk of death at the lowest educational level as compared to the highest educational level in the population. Values larger than 1 indicate a disadvantage for the low educated, values smaller than 1 a disadvantage for the high educated. The magnitude of absolute educational inequalities was calculated by Slope Index of Inequality (SII), a regression-based measure that takes into consideration the entire distribution of education; its values indicates differences in predicted values between low and high educated. Positive values indicate a disadvantage for the low educated, negative values a disadvantage for the high educated.

To further test the applicability of the different typologies, meta-analyses and analysis of variance (ANOVA) was performed on RII and SII estimates. Meta-analyses are common in systematic reviews and aim to synthesize data from multiple studies [46]. In this article, pooled estimates were calculated for each healthcare system type through meta-analysis techniques; each country estimate was weighed with its inversed variance to calculate effect summary with standard errors and confidence intervals. Since the inequality rates were estimated from different populations, we calculated random effects models when heterogeneity was not too low. When performing ANOVA analyses, we used F-tests to compare the RII and SII means of the healthcare systems, and to determine whether between-group variance was larger than within-group variance. Meta- and ANOVA analyses utilize tests of statistical significance, but with a small country-level sample size, estimates are bound to be surrounded by uncertainty [47]. We therefore avoid using these analyses as tests of whether differences between healthcare system types are significant or non-significant. Fig 1 displays statistical uncertainty as 95% confidence intervals, while S3–S5 Tables includes the p-values from the ANOVA analyses.

Fig 1

RII and SII estimates (95% CIs).

Healthcare system types in parentheses.

Results

Relative and absolute inequality estimates are displayed in Table 3. In all countries, and subsequently in all healthcare system types, RII > 1 and SII > 0, meaning that mortality amenable to healthcare was higher for lower educated groups in all populations, both in relative and absolute measures. Among men, Poland (RII 4.67) and the Czech Republic (RII 4.60) showed higher relative inequalities, while Denmark (RII 1.81) and Sweden (RII 1.95) showed the lowest. The highest absolute inequalities were found in Hungary (683.3) and the Czech Republic (503.5), while the lowest inequalities were found in Sweden (SII 105.0) and Switzerland (SII 116.5). In the female population, Poland (3.66) and Hungary (3.65) showed the highest inequalities; Denmark (RII 2.0) and Austria (RII 2.0) has the lowest relative inequalities. Hungary (348.2) and Estonia 223.7) showed high absolute inequalities; Switzerland (55.2) and Sweden (82.9) had the lowest.

Table 3

RII and SII estimates.

Standard errors in parentheses.

	Men		Women
	RII	SII	RII	SII
Austria	1.91 (0.11)	141.4 (11.7)	2.0 (0.14)	92.2 (8.7)
Belgium	1.93 (0.08)	138.0 (7.8)	2.25 (0.12)	111.2 (6.8)
Czech Republic	4.60 (0.10)	503.5 (5.1)	2.67 (0.07)	217.4 (5.2)
Denmark	1.81 (0.06)	140.6 (7.2)	2.0 (0.08)	109.7 (6.5)
England/ Wales	2.66 (0.36)	171.6 (20.1)	2.06 (0.3)	100.6 (18.6)
Estonia	2.28 (0.10)	423.8 (20.5)	2.23 (0.11)	223.7 (12.7)
Finland	2.26 (0.08)	157.0 (6.0)	2.55 (0.12)	101.9 (4.6)
France	2.62 (0.28)	232.9 (22.9)	3.12 (0.57)	120.1 (16.7)
Hungary	4.5 (0.1)	686.3 (7.2)	3.65 (0.11)	348.2 (6.6)
Italy (Turin)	1.64 (0.14)	90.0 (14.5)	1.25 (0.14)	25.8 (12.4)
Lithuania	2.84 (0.12)	293.6 (10.1)	3.18 (0.18)	150.9 (6.3)
Norway	2.18 (0.09)	143.8 (6.7)	2.2 (0.1)	99.4 (5.4)
Poland	4.67 (0.11)	254.2 (3.0)	3.66 (0.11)	114.7 (2.0)
Scotland	1.81 (0.35)	162.6 (47.2)	2.52 (0.65)	164.6 (38.4)
Slovenia	2.85(0.13)	305.1 (11.7)	2.58 (0.16)	153.1 (9.0)
Spain (Barcelona)	1.95 (0.12)	134.4 (12.0)	2.0 (0.19)	71.7 (10)
Spain (Basque Country)	1.73 (0.12)	101.4 (11.4)	1.98 (0.25)	58.6 (9.6)
Spain (Madrid)	1.57 (0.11)	96.5 (13.8)	1.77 (0.19)	65.9 (11.8)
Sweden	1.95 (0.06)	105.0 (4.4)	2.22 (0.08)	82.9 (3.6)
Switzerland	2.72 (0.11)	116.5 (4.4)	2.17 (0.11)	55.2 (3.6)
Pooled estimate	2.53 (0.22)	220.1 (36.6)	2.39 (0.14)	123.0 (14.4)
1. Supply- and choice-oriented public systems	2.77 (0.48)	239.6 (84.7)	2.37 (0.14)	124.9 (31.6)
2. Performance- and primary care-oriented public systems	2.12 (0.10)	135.0 (17.2)	2.31 (0.10)	94.4 (6.6)
3. Regulation-oriented public systems	2.01 (0.24)	146.8 (8.89)	1.98 (0.08)	110.6 (8.23)
4. Low-supply and low performance mixed systems	3.57 (0.61)	414.5 (121.4)	3.18 (0.37)	209.3 (56.6)

The healthcare system typology estimates were associated with much uncertainty and few clear-cut differences could be detected. A general pattern was that type 4, the low-supply and low performance mixed systems, had the highest point estimate in all analyses, while types 2 and 3, the performance- and primary care-oriented and the regulation-oriented public systems, showed the lowest absolute and relative inequality estimates respectively.

Results from ANOVA tests (S3–S5 Tables) were mixed; for most combinations of inequality measure and gender, except from relative inequalities among women, results indicated that variation between healthcare system types was not smaller than variation within types. These results imply that healthcare system similarities were not reflected in health inequality outcomes.

Discussion

Few distinct conclusions can be drawn from our comparisons of European healthcare system types. As expected, Type 4 characterized by low supply in general showed the highest inequality rates, suggesting that high supply of healthcare services combined with focus on primary and preventive healthcare focus may moderate health inequalities. We outlined different mechanisms through which regulation of access and choice in a healthcare system could affect inequalities. The healthcare systems characterized by public financing and regulation of access had low point estimates of inequality. However, results were associated with uncertainty, demonstrated by the large confidence intervals. Type 4 scores low on both resources and the performance indicators, and it is thus difficult to distinguish any specific healthcare system characteristics affecting inequalities in amenable mortality. This inconclusiveness corresponds with the findings from Bergqvist, Yngwe, and Lundberg’s [21] review, leading the authors to suggest that the regime approach “is not a fruitful way forward”. In a sensitivity analysis (S6 Table), we calculated RII and SII estimates in total mortality for all countries and healthcare system types, finding similar patterns: The low-supply and -performance systems showed the largest relative and absolute inequalities, with indiscernible differences between the other types., results from ANOVA tests of all-cause mortality were, similar to those of amenable mortality, mixed. Greater variation was demonstrated between than among types only for relative inequalities among women and absolute inequalities among men. Analyses using all-cause mortality accounts for competing causes; when using amenable mortality and excluding some causes of death, we risk removing data points where multiple morbidities have affected death. Results from these sensitivity analyses suggest similar population health patterns in the countries within each typology, but potentially through other mechanisms than similar healthcare systems.

Inequalities were demonstrated also in systems emphasizing high supply and state control of access and choice, i.e. being close to what one could call universal healthcare systems. A common explanation of health inequalities in these systems has been to emphasize social patterns in background risk factors, for example in smoking, since these systems exhibit large social inequalities in such risk factors [12,48,49]. However, we have defined mortality directly related to tobacco and alcohol (cancer of larynx, trachea, bronchus, and lung; chronic obstructive pulmonary disease; alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy and cirrhosis of liver; and accidental poisoning by alcohol) as not amenable to healthcare, and thus excluded these causes of death from our analyses. This is not to say that smoking and drinking could not be indirectly related to other causes of death, for instance as cardiovascular-related mortality amenable to healthcare, but we have assumed them to only have a limited influence on the observed mortality inequalities, leaving the greatest explanatory power to factors located within the healthcare services.

Healthcare plays a key role in the social distribution of health, illness and death. Healthcare system arrangements may therefore function as mechanisms connecting social position to health outcomes. At the organizational level, a lack of access to good quality healthcare in lower socioeconomic groups could translate into larger educational inequalities in mortality. However, the evidence on this point is inconclusive, in particular for high-income countries with publicly financed healthcare systems [15,50]. A related, potentially inequality-producing, factor is unequal use of healthcare services by socioeconomic groups. Low socioeconomic position has been associated with more use of primary healthcare, while higher socioeconomic groups have reported significantly more specialist contact, even though they overall are in better health. These inequalities have been shown to vary across countries and welfare state regimes [31,32,51-53]. Some examples of suggested explanations are 1) that physicians could be more concerned about high-status patients; 2) that low-status patients are less able to “work the system” and pressure their physicians to prescribe more care; 3) that the interpretation of symptoms and perception of the need for healthcare, are closely associated with socioeconomic position; and 4) that patients with low education are more sensitive to a paternalistic doctor-patient relationship [30,54-56]. At the level concerning the specific treatment and the physician-patient relation, patients with low education and patients who in less affluent areas are more likely to receive shorter primary care consultations and to experience their physician as less empathic [57,58]. Similar to previous research, our results indicated that amenable mortality inequalities existed in all study countries and healthcare system types. The type characterized by low resources and access regulation showed signs of the overall largest inequalities, but some decoupling of the typologies is still needed. Further, our data did not allow us to determine whether these inequalities estimates stem from inequalities in access, in use, or in quality of healthcare services.

Limitations

The approach of classifying countries into typologies or regimes has been subject to debate. As Wendt [6] has demonstrated, several typologies with different healthcare system types and varying country classifications have been proposed during the last few decades (e.g. [7,59-61]). Although typologies inherently capture a broad range of interrelated dimensions, they also always depend on the extent to which dimensions are emphasized or de-emphasized in the operationalization. Apparently similar programs and policies may be differently organized, and indicators upon which a typology is based, for instance choice restrictions and funding, may be confounded. However, the healthcare system typologies first developed by Wendt [6] and later followed up by Reibling et al. [28] is to our knowledge the most comprehensive typology to our knowledge, aiming to intercept all important aspects of a healthcare system.

To adapt the Reibling et al. [28] typology to our available data material, we classified Lithuania and Switzerland as respectively Low-supply and low performance mixed systems and Supply- and choice-oriented public systems. Classification was done by key indicators from the initial factor analyses of Reibling et al. [28]. Additional meta-analyses and ANOVA tests showed that including these countries in their respective clusters affected meta-analysis estimates, but the overall differences between the estimates remained similar, while results from ANOVA tests excluding Switzerland and Lithuania indicated that the within-type variation was not lower than the between-type variation, similar to the analyses of amenable mortality.

Some compatibility issues occurred between the country-level healthcare system typology and the individual-level cause-specific mortality data. The Reibling et al. [28] typology is based on data from 2011 to 2014, while the mortality data covers the period 1998 to 2006 (depending on country, see S1 Table). Though the 2019 healthcare system types have similarities with earlier typologies (cf. [6,62]), this partial incompatibility weakens the link between our two data levels. Most all analyses combining data from the individual and country level face similar constraints; the influence of country-level variables on mortality is hard to narrow down in general, as numerous policies affect one’s health over the life course. In our discussion, we have met this limitation by using the typologies to describe variations rather than assigning direct effects to specific policies.

The 20% of Finns excluded from the data was a random sample and results should not be affected. Related is the exclusion of non-Swiss nationals from the Swiss data. The impact of this potential bias is unclear; our analyses may over- or underestimate the magnitude of inequalities in mortality in Switzerland as a whole, depending on inequalities in mortality in the excluded population compared to Swiss nationals. As aforementioned, meta-analyses and ANOVA with and without Switzerland returned similar results, but this exclusion nevertheless limits our conclusions. Non-linkage represents another limitation; applying the correction factor provides a more accurate result but will not remove a systematic non-linkage bias–we do not know the composition of the non-linked populations. Lastly, the “No education” and “Missing education data” categories may be heterogenous; Flanagan and McCartney [63] have demonstrated how differentiation across categories and missing data on educational attainment has varied between censuses in England and Wales from 1971 to 2001. The ISCED categories provides comparability across countries, but national differences in questioning, coding, and organization of the education system are still unaccounted for.

The applied definition of amenable mortality and the indicators used to construct a typology may also be conflicting. An apparent example is that consumption data on alcohol and tobacco are used to measure for healthcare prevention performance, while mortality directly related to lifestyle traits was excluded from the analyses. Variation in countries’ performance in preventing smoking and alcohol use may thus not be reflected in the mortality numbers. On the other hand, Reibling et al. [28] included these indicators as proxies; they are meant to indicate general preventive care performance. Further, only mortality directly attributed to smoking and alcohol use was excluded; we included causes of death indirectly associated with lifestyle, which again could be related to the performance of a country’s preventive services.

The concepts of amenable mortality and healthcare system types offers both the advantages and disadvantages associated with combining several dimensions in one encompassing classification. Originally, amenable mortality was intended to be useful in terms of policy intervention, with an aim to distinguish those forms of mortality that a more effective organization of the healthcare system could deal with. However, such classifications may also hide variation between the different causes of death–within and across countries. Though amenable mortality was originally proposed as an indicator of healthcare quality, Nolte and McKee [33] have suggested–on the basis of the ambiguous operationalisations and evidence–that it rather should be treated as a starting point for further research and an indicator of concern. Although our analysis may suffer from crude divisions of mortality, we argue that these were necessary steps for the cause of overview and comparison, and as a point of departure for discussing how healthcare systems may produce health inequalities. We urge future research to derive more specific policy recommendations based on empirical analyses focusing on specific aspects of healthcare systems and detailed forms of amenable mortality. This will require the availability of rich data at the individual level as well as the national level for a large number of countries to improve statistical power.

Conclusions

Many of the pathways connecting social position to health can potentially be found within the healthcare system. This article has combined a novel healthcare system typology with comprehensive individual-level mortality data. Our results demonstrated educational inequalities in mortality amenable to healthcare across 21 European populations. Meta-analyses suggested that higher inequalities were found in healthcare systems characterized by low healthcare supply, strong access regulation, and low scores on selected performance indicators.

All four healthcare system types exhibited inequalities in mortality amenable to medical care, and healthcare systems characterized by universality and high levels of provision did not show smaller inequalities. This paradox has previously been explained by pointing to inequalities in lifestyle traits, but our analyses indicated that inequalities are apparent in these systems also when mortality directly attributable to alcohol and tobacco is excluded, suggesting that organizational features of these healthcare systems also could be determinants of health inequalities, but the typology utilized may be a too crude measure. One purpose of our analyses was to provide an overview and discuss how healthcare systems may affect health. We further recommend future research on amenable mortality and morbidity to examine specific health policies and their impact on specific amenable health outcomes.

Data sources.

(DOCX)

Click here for additional data file.

Educational distribution.

(DOCX)

Click here for additional data file.

Analysis of variance, RII and SII estimates of healthcare system types (amenable mortality).

(DOCX)

Click here for additional data file.

Analysis of variance, RII and SII estimates of healthcare system types–excluding Switzerland and Lithuania (amenable mortality).

(DOCX)

Click here for additional data file.

Analysis of variance, RII and SII estimates of healthcare system types (all-cause mortality).

(DOCX)

Click here for additional data file.

RII and SII estimates in total (all-cause) mortality.

(DOCX)

Click here for additional data file.

3 Mar 2020

PONE-D-19-34940

Educational Inequalities in Mortality Amenable to Healthcare. A Comparison of European Healthcare Systems

PLOS ONE

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Reviewer #1: Thank you for the opportunity to review this manuscript. This is the latest in a series of articles which uses mortality data stratified by educational attainment for a selection of European countries to examine the relationship between relevant exposures and inequalities in mid-life mortality.

Detailed comments are provided below. Overall, it is a very interesting paper and addresses an important question. There are numerous limitations with the exposure and outcome measures, and with the potential for confounding. These all limit the inferences that can be made.

I am sorry there are so many comments. It is worthwhile research!

Abstract:

1. The use of 'tertiles' is misleading as the three educational groups are not equally sized. Instead the authors should just say three groups. It would be helpful (in the body of the main manuscript) if the authors could confirm that they have taken the difference in size of the educational groups within each country into account in calculating the RII and SII values.

2. The authors should mention the time period for which the mortality data pertain in the methods.

3. It would be useful to provide some numerical results in the abstract, perhaps summarising the meta-analytical results.

4. I will make further comments on the use of significance testing elsewhere, but I do not think the use of significant in the abstract is appropriate.

Introduction

1. In setting up the research question the authors use educational attainment as a pragmatic means of ranking the population to assess health inequalities. This should be made explicit that there is no attempt to use any theories of socioeconomic position or class to understand how educational attainment per se is related to the outcomes. This is okay for the purpose of this paper, but it would be helpful for this to be explicit.

2. The authors have misrepresented Julian Tudor Hart's seminal work on the inverse care law in the second paragraph. The full definition of this is, "The availability of good medical care tends to vary inversely with the need for it in the population served. This inverse care law operates more completely where medical care is most exposed to market forces, and less so where such exposure is reduced. The market distribution of medical care is a primitive and historically outdated social form, and any return to it would further exaggerate the maldistribution of medical resources." (https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(71)92410-X/fulltext). This is important because the inverse relationship is explicitly linked to the degree of marketisation of the healthcare system - something that is only superficially addressed in the composite index used for the analysis in this paper. I did not follow the description and application provided here and would urge the authors to reconsider and redraft this section.

3. I understand that the focus of the work here is in examining inequalities rather than mean outcomes. However, it would be helpful to acknowledge in paragraph three that the Nordic countries have comparably high life expectancy despite reasonably high inequality.

4. (Relevant to the methods and discussion section too.) The definitions of amenable and avoidable mortality have been developed in order to distinguish the role and contribution of healthcare interventions. There is some discussion in the paper about these and the authors have taken the decision to change the accepted definitions in order to reduce the role of some causes (e.g. those closely related to substance use) as they view these are less amenable to healthcare intervention. In doing so the authors are implicitly acknowledging that none of the definitions of amenable or avoidable mortality are very accurate in that almost all causes of death are both socially/economically caused, and, to some degree, amenable to some healthcare intervention. It would be helpful to have a fuller discussion of what the use of the selected mortality codes do and do not show, and the limitations of these. It would be particularly helpful to discuss how this fits with the concepts of primary, secondary and tertiary prevention.

5. The discussion on the different means of classifying healthcare systems is very informative and interesting. It does however highlight the limitations of this way of classifying (similar to the limitations of the classifications of welfare state types). I am not an expert in the variety of healthcare systems across Europe but I did think that some of the groupings were very surprising. The link between restricting choice (which is an important means of preventing ineffective spending on low impact pharmaceuticals and specialist input) and funding is confounded - as the former limits the latter all other things being equal. The very small number of countries for which data were used in each category very much limits the extent to which it can be said that any differences in outcomes are due to the systems or other unmeasured confounding factors. Bringing together categories seems to further blunt any nuance within the typology and is another limitation that needs acknowledged.

Hypotheses

1. Please edit the sentence with "...supply-healthcare systems Even through these systems...".

2. I wasn't clear about the logic of the hypothesis that "high public involvement in the healthcare system and of high supply, free access and choice does not result in high educational inequality in amenable mortality". This runs counter to Tudor Hart's work about marketisation of healthcare. This may depend what is meant by 'public involvement', as the meaning of this is not clear to me.

Methods

1. Please justify why the selection of countries was made. Were these the only countries for which data was available perhaps?

2. The data used in the analysis is now quite old. This needs to be acknowledged and justified.

3. The exclusion of 20% of Finns and non-Swiss nationals needs to be discussed further. To what degree do these create systematic biases?

4. The correction factor used will not correct for systematic biases in non-linkage. This needs to be discussed as a limitation.

5. I wasn't convinced by the claims that ischaemic heart disease and heart failure are not amenable to healthcare intervention. Clearly it has both social and economic causes as well as being amenable to treatment. This again highlights the limitations of the amenable mortality measure and the attempt to dissociate the healthcare effects from the social and economic effects.

6. Could the authors justify why a pre-analysis protocol was not produced and published online?

7. I was unclear as to why the ISCED categories where grouped into three groups rather than used across all available data to service the regression modelling. Can this be justified?

8. Omitting those without educational attainment recorded is problematic. For example, in some years in the UK this has been as high as 85% of the population and systematically different from the population mean (see https://www.ncbi.nlm.nih.gov/pubmed/25862252).

9. It is described that mortality was "controlled for 5-year age groups". Do the authors mean that the data were age-standardised? If so, to what standard population?

10. The approach to the ANOVA analysis is well-described and appropriate.

11. I found it somewhat implausible that cerebrovascular disease was classified as amenable but IHD was not. I worked as a GP at this time and if anything the opposite was true.

Results

11. The authors are over-reliant on statistical significance to make inferences (see https://www.bmj.com/content/322/7280/226.1). There is substantial confusion in the reporting about whether they are simply talking about the degree to which there could be random variation; small sample sizes and underpowering of the analysis; and the importance/policy relevance of the analysis. These are all conflated and strong conclusions are made about there being no differences in some cases when in fact this is simply likely to be due to small samples.

12. It would be best if the ANOVA results were simply put into a webappendix so that it can be readily available in the future and fully transparent. People move on and analyses get lost otherwise!

13. It is important that the mortality rates for each ISCED group, and the proportion of the population in each ISCED group, and the mean for the total population, are provided in a table.

14. The sensitivity analyses should be shown in a webappendix.

Discussion

1. In the first sentence the authors discuss equal access to healthcare. Do they mean equal or equitable? Being clearer about their definition of health inequality would also be helpful in this regard.

2. The use of Barcelona, Basque Country, Madrid and Turin is a major limitation and the authors should not make any generalisation to the countries overall in the use of these data. The points made about urban and rural inequalities are not relevant here. It is akin to generalising to the whole of the UK from London or Glasgow inequalities, either of which would be completely misleading. Within Spain the poorest regions are not included; the same is true for Italy.

3. At the end of the first paragraph ("In line with more agency-based approaches...") the formulation seems to be that regulation is about preventing the rich using their resources to access healthcare. This seems a little inconsistent with the countries in the typology where there is a private healthcare sector which regulation does not limit access to. I'd urge the authors to redraft this section.

4. The second paragraph discusses some aspects of the inverse care law. There is a large primary care literature on this (see Graham Watt, Stewart Mercer, Mhairi Mackenzie and others for example) which could usefully be integrated into this section.

5. I think the robustness of the results of the paper are overplayed and the limitations under-recognised in the discussion. There are numerous issues with the measures of the exposure and outcome, as well as limited data availability and a high risk of confounding. Taken together it is quite difficult to be sure that the relationship described is robust.

6. A limitation not recognised in the use of a selection of mortality codes is competing causes. By removing IHD etc., people with multiple morbidity in middle age will be removed from the analysis and underestimate the mortality rates that would have occurred had the IHD not intervened. Comparing the results with all-cause mortality inequality would allow this to be discussed in more detail.

Table 1

1. I was not clear what the numbers in the period column mean. Possibly months and years?

Figure 1

1. It is not clear what the vertical dotted lines represent.

Reviewer #2: Summary

Using data on mortality amenable to health care for 21 European populations, this manuscript presents estimates of relative and absolute educational inequalities and investigates them through the lens of European health systems. The study advances on previous research which focused primarily on welfare typologies and/or aggregates of political systems. The authors find considerable variation across the clusters of health systems. Further analyses suggest that health care budgets, rather than access regulation or choice control, may explain some of these differences.

There is a lot to like about this manuscript since it examines the role of health systems in one closely linked outcome, notably, educational inequalities in mortality amenable to healthcare. It draws on a recent typology and discusses a range of causal pathways. In short, the manuscript promises to be a valuable contribution to a growing literature on health inequalities. At the same time, I believe several important issues—pertaining to the typology, data, and design—need to be addressed before publication.

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Major issues

The motivation of the study, focusing on specific elements of welfare regimes (in this case, health systems) rather than broader categories, is first discussed in the fourth paragraph. I think it would be helpful to lead with health systems, and then conceptualize their role in the broader discussion on health inequalities.

While the authors do a good job describing the typology of health systems, I believe one key aspect is missing: temporal information on the underlying variables. That is, the typology is based on measures that vary considerably over time, e.g., health expenditure per capita, number of GPs, public share of health expenditure, tobacco and alcohol consumption, etc. However, it is unclear which years were used to construct the original typology which raises at least two concerns: First, the typology might be based on recent data, which means that educational inequalities based on mortality data from ca. 2000-2005 (depending on the country, according to Table 1, p. 22) are related to characteristics of ‘current’ health systems. Second, if the typology is identified in years around the Millennium, i.e., similar to the health inequality measures, the implications for current health governance are different. In any case, I find the reader is unable to evaluate the analysis and conclusion due to this uncertainty.

Data issues are another major concern for me. First, some of the data cover regional or urban populations. However, the hypotheses and typology of health systems are based on national characteristics (e.g., to what extent is the population of Madrid and/or its health system measures representative of Spain?, do these drive results?). Second, ischemic heart disease and heart failure were excluded due to their ‘strong association with life style factors such as smoking, alcohol consumption and obesity’ (p. 7). Yet for the typology of health systems, ‘healthcare performance was measured by indicators of tobacco and alcohol consumption …’ (p. 4). Thus, the performance measure does consider tobacco and alcohol, the mortality data does not. I think this inconsistency needs to be addressed.

The authors argue for the benefits of using health system typology as explanatory variable, while acknowledging several limitations (pp. 13-14). Particularly in view of the finding that health spending is important, I remain to be convinced why, under these circumstances, the typology is useful as an analytical category. In the concluding section, the authors ‘recommend future research on amenable mortality and morbidity to examine specific health policies’ (p. 16). Why does this study not already cover at least one of these aspects?

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Minor issues

In the opening paragraph (p. 2), the authors mention numerous pathways through which education affects health outcomes and behavior. I would encourage them to use these insights and studies, and discuss the hypotheses (pp. 5-6) with a stronger focus on educational inequalities.

In Figure 1, it would be helpful for the reader if it was indicated which clusters the countries belong to. A note should also explain how to interpret the estimates.

**********

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Reviewer #1: Yes: Gerry McCartney

Reviewer #2: No

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28 Apr 2020

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20 May 2020

Educational Inequalities in Mortality Amenable to Healthcare. A Comparison of European Healthcare Systems

PONE-D-19-34940R1

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Reviewer #1: Dear Authors

Thank you for the opportunity to reread your manuscript and comment again. It would have been very helpful if in your responses to the reviewers comments you had provided the quotes of the changed sections rather than just making general statements. Perhaps this is something you could do in the future to help reviewers.

I think you have generally understood the comments I made previously (I am reviewer 1), with the exception of the pre-analysis protocol (comment labelled Methods 6). It is precisely to avoid data dredging and post-hoc changes to categories and analysis in order to produce results that authors prefer that protocols should be published, and your paper is at risk of this. You say in the response to my comment that you did not publish a protocol to avoid data dredging. This cannot be true.

I am still not convinced that the tripartite classification of ISCED categories is the most appropriate. I accept that there is consideration precedent for this with the plethora of Mackenbach papers using it. However, this is very prone to differently sized groups meaning different things at different time points (as detailed in the Flanagan reference, which, incidently, is given in a very odd format and should be correct at proofing stage to make sure it is linked to the peer reviewed article).

Thank you for making the changes you have made to the paper, I think it is much improved as a result.

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Reviewer #1: Yes: Gerry McCartney

26 May 2020

PONE-D-19-34940R1

Educational Inequalities in Mortality Amenable to Healthcare. A Comparison of European Healthcare Systems

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