Health and social inequities in The Netherlands

so<. Sr,i. .Mcv/. Vol. 31. No. 3. pp. 319-329. 1990 02’7.9536 90 s3 00 + 0.00 PrInted in Great Briuin. All rights reserved Copyright C 1990 Pergpmon Press plc

HEALTH AND SOCIAL INEQUITIES IN THE NETHERLANDS

INGEBORG P. SPRUIT

N.I.A.D.. P.O. Box 725, 3500 AS Utrecht. The Netherlands

Abstract-Although there is some knowledge of inequity in mortality due to socioeconomic differences in the late nineteenth and early twentieth century. we know very little about present mortality differentiated by socioeconomic status. However, at present there are signs that interest in this theme may be increasing, particularly among government authorities. An overview will be presented of the few, rather scattered. existing data on regional and socioeconomic differences, mortality and other indicators of ill-health. This includes results of a secondary analysis of mortality differences among middle-aged Dutch men in a rural town. In this analysis the central question was whether mortality differences related to socioeconomic status could be explained by different risk factors. Socioeconomic status and risk factors turned out to be independent predictors of mortality. Finally, a short description will be given of the awakening interest for such data among health policymakers.

Key Icords-socioeconomic status, mortality, risk factors, health policy

INTRODUCTION

Before 1900, when sociology, epidemiology and social medicine were in their infancy, there were several early studies on social and economic aspects of health in the Netherlands. These are, for instance, examples of studies on socioeconomic differences and the mortality of infants in various Dutch towns and cities.

In the Netherlands Coronel (one of the founding fathers of Dutch social medicine) tried repeatedly, like Chadwick in the United Kingdom, to gain policy attention for his findings on inequities in mortality related to urban and socioeconomic characteristics. Although these early studies varied in quality and comparability, van Poppel [I] constructed a graph (Fig. 1) showing a decline through the years in total infant mortality, as well as in social differences. In this graph he indicated the mortality levels of four social classes in four Dutch cities, compared to the general temporal sequence in infant mortality. Classes are based in the fathers’ occupations and were seen as prosperity levels, I representing the lowest and V the highest.

To further illustrate the substantial socioeconomic and urban (and urban-overall) differences, a few elaborations are required. In the cities of Dordrecht and Rotterdam (now a substantial part of the largest industrialized urban conglomerate in the country) the overall infant mortality from 1877 to 1897 was already as low as the overall Dutch infant mortality from 1895 to 1899. However, the lowest economic category was still at the same level as five years earlier, while the level of the highest economic category was as low as the overall Dutch level of 1915-1919. So the mortality of this highest category is 20 years ahead in development, compared to the lowest category. Another city, the Hague (in the same urban conglomerate), in 1908 reached the overall Dutch level of 1915-1919, but the lowest economic category was still at the 1890-1894 level,

while the highest was already at a level that the Netherlands in general had reached at the end of the 1940s.

In the twentieth century (but before World War II) the absolute differences in infant mortality had decreased strongly (in the 1980s perinatal mortality was no more than one-quarter of the 1940 figure) but the relative differences were still substantial. General (crude) mortality decreased strongly over time, but after 1970 it stopped falling. Per 10,000 population, crude mortality was 84 in 1970, 83 in 1980 and 80 in 1984. However, age adjusted mortality is still decreasing in both sexes [2]. Compared to other European countries, mortality in the Netherlands contrasts favourably, as shown in Fig. 2, constructed by van Poppel [4]. This shows male longevity in some Eu- ropean countries, compared to the development of male longevity in Sweden. In the 197Os, male longevity in the Netherlands was comparable to that of Sweden and Norway, which on average have the highest longevity. Portugal in the 1970s was at the level reached by Sweden in the mid 1930s; France at the level reached in the early 1950s. There is regional variation in the Netherlands, with the least favoured region being comparable to Swedish levels in the 195Os, but the most favoured having greater longevity than the Swedish average.

Maybe because of this development (or for other unknown reasons) there is no strong tradition of studying mortality related to socioeconomic characteristics. In any case data-especially recent data- are incidental. However, the Dutch Ministry of Health recently (1986) published its policy intentions for the future [S], in which socioeconomic (in)equity is explicitly mentioned. To stimulate interest and data gathering, the Ministry organized a small but important symposium on the subject, supported by the Scientific Council for Government Policy.

In the present article, postwar Dutch data on health inequality and recent trends in data collection will be reviewed. Some results of analyses on

319

320 INGEBORG P. SPt7mr

24 r

I. Toddlers mortality (per 100 live births) in the Netherlands: time trend and social differences. Source: Van Poppel [I, p. 2721.

relationships between socioeconomic status, smoking habits, risk factors and mortality among middle-aged males in a small Dutch town will also be presented. Finally, recent (1987) policy on the subject will be discussed.

GEOGRAPHICAL MORTALITY DIFFERENCES

Existing information on regional mortality differences is variable in source and quality, as will be illustrated, but it does yield relevant information.

For many years, the Netherlands Central Bureau of Statistics has published mortality data by province. This is an administrative (as well as a historical and cultural) unit between the municipal and the national level, covering populations from 350,000 to 3,000,OOO people. Provinces vary substantially according to demography, degree of urbanization, population density, unemployment rate, etc. Standardized mortality ratios (but also other health indicators) vary

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c 1 I I I I I I I

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from I. I I to 0.88, as shown in Table I, constructed by Mackenbach [6].

There is some relationship between the health differences and demographic factors, but not a fully explanatory one. Van Poppel [7]. who analysed provincial differences over time, concluded that the pattern is rather constant over 20 years for both men and women. So it is highly probable that existing differences are not merely a coincidence, but real differences between provinces, according to various- but unknown-structural factors. Inspection of the figures does not give us many clues. Both the histor- ically poorest provinces in the south (Limburg and Brabant), which are also the only two catholic ones, have the highest mortality. However. another (histor- ically) poor area in the north (Drenthe) has a relatively very low mortality rate. Degrees of unemployment (highest in Limburg and Groningen) do not correlate. Zeeland and Drenthe, which have consistently low to lowest mortality ratios, are weakly

Sweden I 1 The Netherlands

Fig. 2. Regional (countries) differences in average longevity, compared to the temporal development of longevity in Sweden (male). Source: Van Poppel [4. p. 177).

Health and social inequities in the Netherlands 321

Table I. Standxdized mortality ratios (SMRs). standardized hospitalization ratios (SHRs) and

Standardized disability incidence ratios (SDIRs) all causes, by province and sex (1977-78) combined

SMR SMR SHR SMR SDIR

(males) (females) (males) (females) (males)

industrialized and have the lowest population den-

Groningen 0.98 0.98 0.83 0.85 1.36

Friesland 0.9Ii 0.99 0.90 0.95 1.04

Drenthe 0.94 0.98 0.83 0.88 I.22 - Overijssel 0.99 I .02 0.93 0.95 1.06

Gelderland I .oo 1.02 l.Gll 1.00 1.03 Utrecht I .oo 0.99 1.0s 1.05 I .02 Noord-Holland 1.00 0.97 1.04 1.05 1.03

Zuid-Holland 0.99 0.99 0.96 0.96 0.74

Zeeland 0.88 0.92 0.95 0.94 0.73

Noord-Brabant 1.04 1.08 J.icJ 1.04 1.04

Limburg 1.II 1.09 1.II !.l_i 1.44

xi, 205.31 172.45 8119.05 6245.52 6816.85

Underlined values have standardized normal deviates > 1.96 or < - 1.96 (P < 0.05).

Source: Mackenbach [6, p. 2501.

sity, but there is no consistent relationship between population density and mortality. The most densely populated (and industrialized) provinces (Noord- Holland 865 inhabitants per km’ and Zuid-Holland 1080 per km’) have middle-range mortality ratios, despite the fact that in terms of self-reported illness the largest communities, which are mainly situated in these provinces, have the most unhealthy and chron- ically sick people (Table 2).

Differences between big cities and rural areas have been researched only for chronic illnesses in persons over 55, and significant differences were not found [S].

Attempts were not made to study the relationship of provincial differences in mortality to socioeconomic structural factors. The real and structural nature of the mortality differences legitimates such research, but finding relevant factors (including migration and demography) may turn out to be difficult. Another problem may be that the influences of diverse socioeconomic factors (e.g. unemployment) may vary by sub-region within the provinces but, despite being diverse, they may be jointly responsible for mortality differences.

One smaller scale study, on health differences between Amsterdam neighbourhoods [9], is more informative about factors associated with mortality inequity. In Table 3 some important findings are summarized. This table shows the variance of three health indicators and six groups of neighbourhood characteristics. The numbers in each column give the percentage of variance explained by specified neighbourhood characteristics. Percentages can exceed 100 because the clusters of neighbourhood characteristics are not independent of each other.

Housing, socioeconomic and demographic characteristics are quite strongly related to mortality whereas the density of general practitioners is not. This, however, shows a major problem in such research designs: they do not provide insight into processes or causality. One important factor of differences in this study, for example, is migration of the elderly into an institutionalized form of living in another neighbourhood, because of health or physical mobility problems. In a previous Rotterdam neighbourhood study, Herzog et al. [I I] found positive relationships between death rate, overcrowding and migration. On the other hand, detailed analysis in the Amsterdam study did show a positive relation between socioeconomic status and infant mortality.

In this study ethnic differences are not explicitly mentioned, despite concentrated living patterns of labour and other ethnic immigrants in Amsterdam since 1960-1970. For a sensitive mortality and socioeconomic status inequity indicator such as perinatal and infant mortality, Nordbeck [I21 found within (a part of) this same city a relatively high rate for some ethnic categories. Differences between three ethnic categories and the Dutch population are shown in Table 4. In this clinically oriented study, low maternal weight (50 kg or less) proved to be a main factor in showing a strong relationship with perinatal mortality.

What can we conclude from these scanty and heterogeneous observations?

1. There are probably structural differences between regions in mortality-and also in such a sensitive indicator as perinatal and infant mortality.

Table 2. State of health of the population of I5 years and over. per category of municipalities. 1977

III for a III for a Number of

Healthy short spell long period Total respondents

Municipalities with (%) (%) (%) (%) (abs.)

5000 inhab.

5000 to lO.OOIl inhab.

10.000 to 20.000 inhab.

20.000 to 5O.C@0 inhab.

50.000 to 100.000 inhab.

100.000 to 400.000 inhab.

) 400.000 inhab.

Total

43 44

47

45

43

38

36

43

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II

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II

45

46

44

43

46

48

54

46

I00 243

IO0 558

IO0 774

100 IO51

100 538

100 560

100 435

100 4159

Source: Compendium Health Statistics [3. p. 1861.

INGEBORG P. SPRUIT

Table 3. Relationship (variance) between six groups of netghbourhood characteristics and three

health indicators

Neighbourhood Hospital Disability

characteristics Morrality admissions pension

I. Demographic variables e.g. migration. percenl- I3 9 15 age of persons living alone. percentage aliens. age

composition of populalion

2. Socioeconomic characteristics. e.g. socioeco- II 31 64

nomic status; political, religious and occupational

diversity; mean rental values of living quarters

-socioeconomic slatus separately 20 42 83

3. Housing characteristics. e.g. equipment available. I9 28 44

age of building

4. Population density IO 21 34

5. Physio-chemical environmental characteristics 10 18 22

6. Density of general praclilioners 4 I3 I7

Sources: Habbema ct al. 19. p. 104]. van der Bos cl al. [IO. p. 3871.

2. We do not know from empirical research what factors are responsible for these differences. The hypothesis that social. cultural and economic factors are of importance is justified, but needs research.

3. Aggregate level research can quickly identify general tendencies, but individual level research is needed to find operating factors (mechanisms) and (multi)causal relationships of (clusters of) factors. Important difficulties in research are: the multicausality and inter- relationships (and interdependency) of various factors hypothetically responsible for mortality inequity. Another important problem (especially at aggregate level studies) is migration and social mobility.

SOClOECONOMIC DIFFERENCES:

HEALTH AND MORTALITY

There is one reliable post-war study designed to identify socioeconomic related mortality. Frinking [ 131 discusses this study originally done by Meerdink and De Wolff. It is an analysis of mortality according to occupational category in Amsterdam, 1947-1952. In this study significant differences are only found for category III (non-government administrative personnel; Table 5). There is no satisfactory hypothesis explaining the results.

Two studies dealt with morbidity in the dependent labour force. Groothoff [I41 found the mortality risk

Table 4. Perinatal mortality rate by ethnic category. in percentages

Dutch Negroid Asian Mediterranean

Stillbirths I .4 2.6 1.5 I .4

First week deaths 0.8 1.4 1.0 1.0

Total 2.2 4.0 2.5 2.4

Source: Nordbeck [IZ. p. 1701.

Table 5. Mortality of men aged 25-64 years. per 1000 (standardized)

1947-1952

I Free and higher occupations. government em- 3.5

ployees. teaching personnel. etc.

II All self-employed 3.92

III Administrative personnel (non-government). 5.1 I shop-servants. commercial travellers

IV Managerial personnel. foreman 3.34

V Skilled workers (manual) 4.16

VI Unskilled workers (manual) 4.15

Source: Frinking. referring IO Meerdink and De Wolff [l3].

of disability beneficiaries to be three times as high as that of the population in general, independent of diagnostic category. This is indirectly important because of the unequal risk of work-related handicaps. Allegro [ 151 found in a “non-representative but pos- itively indicative study” important differences between socioeconomic strata and sick leave as well as work disability. The higher the status, the less (especially long-term) sick leave and disability benefits. independent of age. People in manual work have many more sick days than non-manuals. This difference in volume is not explained so much by the frequency of sick leave as by a longer duration. According to ‘executive’ level, the gap is greatest between people at ‘no level at all’ (i.e. unskilled and with minimum skills) and the other levels (Table 6).

Recently also the Netherlands Central Bureau of Statistics has begun to publish data on health and other relevant data, related to occupational category [l6]. In Scheme I data are compiled from their 1983 questionnaire data among a 4000 representative Dutch sample of men and women > I8 years of age. To this table are added the only existing data from comparable Central Bureau of Statistics research on two ethnic categories: Turks and Moroccans [17].

The most remarkable differences are between categories I-VI and VII. The relatively deprived situation of the ethnic categories is also noticeable. There are, however, important difficulties in interpreting these figures. related to the construction of socioeconomic categories and their indices. So, let us first go into details of the construction of these categories. One of the questions in the interviews concerned the main source of income in the household. For 65% of all households this was paid labour. They were classified in categories I-VI. All other households were placed in category VII, being: retired/benefit level (15% of

Table 6. Sick-leave and ‘executive’ level in some firms, registered by

C.C.O.Z.*. indices

Sick-leave indices

Level

None

Lower

Middle

Management

of firm

Number %

19.256 107

I502 74

I805 66

725 41

Freq. Length

105 102

82 94

69 89

53 86

l C.C.O.Z. is a scientifically-oriented organization which collects

sick-leave data from various organizations and firms.

Source: Allegro (IS. p. 1171.

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324 INGEFIORG P. SPRCIT

total sample), otherwise retired (5%). unemployed (6%) and on disability benefits (6%). Another com- plicating factor is that category VII people more often live in a big city (Noord- and Zuid-Holland provinces). So, for the interpretation of socioeconomic characteristics influencing health status. figures like these are relatively useless. However. it does show the reverse, namely that older, less healthy and the least wealthy people more often live in deprived situations.

This occupation based construction of social categories brings us to a fundamental problem of cross- sectional research on socioeconomic status and health indicators. In the Central Bureau of Statistics study the age and gender composition within the occupational groups is relatively unequal. Among industrial workers, for example, there are relatively few aged 55-64 years; among the lower grade employees relatively many young people (aged 25-44 years) and especially very young women (aged 18-24 years). This is not merely a sampling and;‘or non- response problem, but to a certain extent reflects real societal differences, due to social mobility (upward occupational mobility partly coinciding with age) and health selection processes (more and ‘younge; work disability in blue-collar professions). Accordingly, even controlling for age would not reveal the full scope of socioeconomic status health hazards. This is important for interpreting data presented in the next study.

Another Central Bureau of Statistics study. per- formed annually by means of questioning a sample of the Dutch population, is the “continuous cross-sectional health survey”. Van Ginneken and Appelboom [I81 analysed data from several years (1981-1984) of questioning a total of 3000 people of > I6 years. Comparing people according to educational level and controlling for age, they found increasing/decreasing risks according to socioeconomic level as indicated by edtrcarion (Table 7a). From the same sample van Sonsbeek [19], after controlling for age and taking only persons aged 20 years or over, found similar tendencies for body height (Table 7b). Among men, the difference between the lowest and the highest class is 5 cm; among women the differences are smaller.

From these, again scanty and heterogeneous, research observation a few conclusions are:

I. Structural differences in health according to socioeconomic status are highly probable, but knowledge on the proportions of these differences is inadequate. Special attention should be given to a hypothetical ‘semi-dichotomy’ between lower blue-collar professions, people without paid labour, and the other categories, and there may be even another ‘semi- dichotomy’ between ethnic minorities and the other categories.

2. Cross-sectional research will inevitably suffer from three potentially misleading features or fallacies: social/occupational mobility, geographical mobility and ‘age-composition’ (a fallacy interrelated to both the former ones).

*Presently coordinated by Professor D. Kromhout. tInternationally coordinated by Professor A. Keys.

Table 7a. Health indutors by level of education and xx (and

controlled by age). per 100 persons (1981-1984)

Percaved No. of chronic

health stiltus conditions

Lerel of _____-

educatron M F M F

Primary education

Junior traintng

Secondary education

Vocational college

Universtty

Total

Beta

235 235 44 46

215 219 35 39

204 213 32 42

199 209 29 I95 (210) 22 (:z,

214 222 35 42

0.15 0.1 I 0.08 0.04

Figures in pxcnthcses are based on small number of cases (80-158).

Source: Van Ginneken and Appelboom [IS].

Table 7b. Differences in average body height (1981-1983) in cm

controlled for see

Body height

Level of education M F

Primary education 175.1 165.4

Junior training 176.5 166.0

Secondary education 178.0 166.4

Vocational college 179.0 167.5

University 180.0 167.7

Eta 0.17 0.10

Source: Van Sonsbeek [l9, p. 51

Retrospective questioning on former geographical and social mobility processes may somewhat decrease this disadvantage.

3. Two research-design fallacies (existence of divergent indicators for socioeconomic status and the separate handling of people without paid labour) can be solved to a great extent in research especially designed for the study of inequity and health.

4. As to cultural, social or economic circumstances which differ by socioeconomic category. there are hardly any clues on those factors which might be considered to have more or less influences on morbidity/ mortality. At this (empirically) superficial level of knowledge, mention can be made of work, ethnicity and housing conditions.

OCCUPATIONAL CATEGORY, SMOKING.

RISK FACTORS AND MORTALITY:

REPORT OF A STUDY

One recent study, based on secondary analysis of existing data, focused on hypothesized interrelation- ships between occupational category, smoking and 25-year mortality of middle-aged (40-59 years) men [20]. Since such studies are scarce, we will go further into detail on this one. The study was done for the Ministry of Health, looking (for policy reasons) for quickly attainable results from existing datasets. A primary objective was to find out whether mortality inequity by socioeconomic status could be explained by different smoking behaviour.

The data used in the analysis were originally collected for studying risk factors for coronary heart disease in the ‘Zutphen Study’.* This project is part of the Seven countries study’.t In this study, 878 men, who were aged 40-59 years in 1960, were longitudinally followed in 1960, 1965, 1970, 1977-78


and 1985. Among the data gathered was information about their occupation and smoking behaviour. The men are a representative sample of (aged 40-59 years) men in one small Dutch town, Zutphen. By 1985,430 (49%) had died. From 1960 onwards mortality was continuously registered; the causes of death were according to the eighth revision of the International Classification of Diseases [21]. For the purpose of the analysis the only data used were those relating to men participating in the (first) 1960 part of the study, and of their cigarette and shag smoking behaviour in and before 1960. The latter was done because of interpre- tative problems arising from people who quit smoking because of health complaints [22]. Survival analysis was done using the proportional hazards model (Cox’s regression).

The occupational categories constructed were:

I (16%)

II (21%)

III (25%;)

IV (37%‘)

High and secondary management level and teaching professions. Self-employed (predominantly small tradesmen, and a small percentage of farmers). Lower administrative professions and employees. Industrial (predominantly) and agri- cultural (a very small percentage) workers.

Only 1% of the sample was unemployed or on disability benefits in 1960. They were left out of the analysis.

Mortality by socioeconomic and smoking behauiour by socioeconomic status

Before studying the relationship between mortality and occupation, social mobility (between 1960 and 1985) was traced. Only 23 men had made a down- ward move (of at least one category) and 47 made an upward move. These numbers were too small to construct extra categories for analysis. Crude figures gave significant differences between the occupational categories in the expected direction, with one excep- tion: the highest mortality was found among the self-employed (category II). However, since age was different at the beginning of the study, correction for age is essential. Relative risks (category I = 1) are presented in Table 8. The trend found in the crude figures still existed, but the differences were not statistically significant.

In Table 9, however, it is clear that mortality differences are significant for the younger IO-year category (the ones that were 4&59 years in 1960). There is an increasing risk from categories 1 to III to IV. and again the highest risk is found for category II (self-employed).

Table 8. Cox’s regression of relation between age. occupational calegory (OX.) and mortality (N = 856) in Zutphen

Variable

Regression Relative

coefficient risk P

Age 0.10 1.10 0.0 I O.C. III 0.07 I .07 NS

O.C. IV 0. I7 I.19 NS

O.C. II 0.19 I.21 NS

Source: Spruil er ul. [20. pp. I I. 121.

Table 9. Morrality and occupational category of the

‘younger’ Zutphen men (40-49 yews in 1960) N = 457

Regression Relative

Variable coefficient risk P

As 0.10 I.10 0.01

O.C. III 0.49 I .63 NS

O.C. IV 0.73 2.07 0.01

O.C. II 0.8 I 2.25 0.01

If one supposes that socioeconomic status mortality inequity is influenced by smoking behaviour, one logical hypothesis is that smoking behaviour differs more among the younger categories than among the older categories. However, the reverse is true. Smok- ing behaviour between the ‘young’ occupational categories varies from 81.4 to 82.0% (not significant of course). On the other hand we can see from Table 10 that smoking behaviour between the ‘old’ occupational categories varies considerably, with only the lowest category smoking about as much as the younger ones.

Smoking was strongly related to mortality: 1.012 per packyear. Within the occupational categories there also is significant influence of smoking on mortality. However, from retrospective and from prospective data analysis, it turned out that differences in smoking behaviour among the older men are the result of quitting smoking when older, and. the higher the category, the more often they quit smoking.

The independent relationship of socioeconomic status as well as smoking on mortality

Two final questions have to be answered before coming to conclusions. The first one is whether the relation between occupational category and mortality is in fact due to differences in smoking behaviour. For this purpose a combined analysis is made of the relative risk of age, occupational category, smoking and mortality. The results for the overall sample and the ‘young’ subsample are presented in Tables II and 12.

Comparing Tables 1 I and 12 with Tables 8 and 9, it is clear that relative risks and regression coefficients are more or less alike for the occupational category mortality risks unadjusted and adjusted for smoking. Also the relative risks for smoking were similar. So it can be concluded that the mortality risk of belonging to an occupational category cannot be explained by smoking behaviour.

The second question to be answered is whether the occupational category mortality risk is interrelated to the packyears mortality risk. For that purpose an estimation was made of the terms of interaction between occupational category and packyears.

Table IO. Percentqe of ‘old’

(50-59 years in 1960) smokers

and occupational category

Category % Smokers

I 55.0

II 56. I

111 61.7

IV 79.4

P = 0.03

Source: Spruit t-t (11. [ZO, p. 71.

316 INGEBORG P. SPRUIT

Table I I. Relawmshlp between age. occupol~~~~l category.

smoking and mortality: whole

P

As 0.096 I.092 0.01

O.C. 111 0.084 I.088 NS

O.C. IV 0.146 I.156 NS

OS. II 0.160 I.173 NS

Packyears 0.012 I.012 0.01

Source: Spruit PI ol. [20. pp. 14, 151.

Such an interaction was not found. So occupational category and smoking do influence mortality inde- pendently.

The independent contribution of blood pressure, serum cholesterol, height and weight

Not only data on smoking behaviour were available in this sample. During the physical examination also height, weight and blood pressure were meas- ured, blood samples were taken and fasting serum cholesterol determinations were done. Analysing the influence of these risk factors and smoking the Cox proportional hazard model was used to estimate the effects of socioeconomic status and risk factors on mortality. Firstly the full model including risk factors and socioeconomic status as well as their interactions were estimated in order to establish whether interactions were present. The maximum likelihood ratio test was used to test the significance of these interactions. Thereafter a reduced model without interaction terms was compared to a model including only socioceonomic status and age to analyse whether risk factors were confounding the effect of socioeconomic status on mortality (231.

The risk factors were not equally distributed. When the self-employed were removed they were, however, rather small. Only the Quetelet Index for body fat was significantly higher in group I. The self- employed, however, had significantly higher levels for all risk factors except serum cholesterol. Cox’s regression model was applied including socioeconomic status, packyears, systolic blood pressure and their interaction terms and age. Serum cholesterol and the Quetelet Index (height and weight) were not included, because they turned out not to be significant predictors of all cause mortality. Next the full model, including interaction terms was compared to a reduced model without interaction terms. The maximum likelihood ratio test statistics between the models was not significant, so the interaction of risk factors and socioeconomic status did not contribute to mortality in this analysis either. Also the effect of socioeconomic status on mortality remained significant after adjustment for the effect of risk factors.

Table 12. Relalionship between age. occupational calegory.

smoking and mortality for ‘young’ subsample (N = 457).

Cox‘s regression of relative risks

Regression Relative

Variable coefficient risk P

A&- 0.093 I .089 0.01

O.C. 111 0.484 I.622 NS

O.C. IV 0.700 2.014 0.01

O.C. II 0.754 2.126 0.01

Packyears 0.016 I.016 0.01

In both these analyses socioeconomic status appears to be an independent risk factor.

In interpreting these findings a few observations are relevant. The first observation is that it is difficult to discuss further the finding that the relationship between occupational category and mortality disap- pears above 50 years of age. Although a weakening relationship by increasing age may be expected to be normal [24], such relatively ‘young’ disappearance has not yet been so explicitly reported for socioeconomic status. It may be a coincidence that this has been found only in this period and in this small town, but it may also be a yet undiscovered trend. depend- ing on specific (prosperous) circumstances, or it may be due to relative ‘excessive’ mortality in the preced- ing years of very high risk groups.

As for highest risk found in category II. a few observations are relevant. The first one is that here too we may face a coincidence. On the other hand, Mackenbach and van der Maas [25] reviewing the international literature, call attention to weaker and more variable tendencies in socioceonomic status related mortality in studies outside the United Kingdom than in United Kingdom studies. They also cautiously mention: “By the way, it recently came into the open that some manipulation has been used in constructing the socioeconomic classification. In order to achieve more or less trendy gradients of mortality figures, for example, the designer of this classification in the twenties, contrary to scientific morality included the level of infant mortality into his classification criteria” [26, p. 71. Referring to Abrahamson et al. [27], they confirm that classification construction to a certain extent influences corre- lations with health indicators. In the ‘Zutphen’ analysis classification criteria may play a role too. In this context, therefore, one other result which is somewhat similar, is interesting. This is a finding of the Amsterdam Statistics Bureau, which in 1953 published the results of a study on infant mortality in Amsterdam 1939-1940 [28]. They used the same classification criteria as in Zutphen 1960. Per 100 live births they found mortality figures to be: occupational category I, 1.89; occupational category II, 3.20; occupational category III, 2.35; occupational category IV, 3.28. So we see a more or less equal risk in categories II and IV, but in a different period and an entirely different population. However, without further and systematic research, discussion can only be guesswork.

With reference to the main outcome, the independent influence of occupational category and of smoking and systolic blood pressure on mortality, if such an outcome is repeated in other prospective studies it will have policy consequences. For example, incen- tives to quit smoking will influence overall mortality, but may not be expected to decrease mortality inequities by social class.

However, since the study was done to find out to what extent risk factors affected the socioeconomic status mortality relationship, the study did not find a confirmative answer. Discovery of (isolated or configurations of) factors responsible for the relationship between socioeconomic status and mortality is, however, important for the awakening policy attention in the Netherlands. This brings us to the last


subject: recent health policy intentions and attention paid to socioeconomic equity factors.

MORTALI’TY INEQLITY AND HEALTH POLICY

In health policy, interest in inequity questions is beginning to manifest itself but, where data are scarce, specific policy measures are difficult to formulate. Since a regular census appears to be politically unattainable, and privacy protection legislation is such that it constitutes an important impediment to aggregate level research, data collection is not easy either. One way to collect national data on the very existence of socioeconomic status inequity in mortality can very well be the addition of data on socioeconomic characteristics to the existing national mortality registration. Indeed, in 1979, the former Secretary of Public Health requested the Central Bureau of Statistics to explore such a possibility. Despite their positive advice [29] in 1983, further steps to execute proposals have not yet been taken.

In 1986 the Ministry of Welfare, Health and Cul- tural Affairs published its policy goals for the future [S], an impressive document explicitly inspired by WHO’s health for all by the year 200 targets. This Note 2000 intentionally ushers in a reorientation from focusing on a provisions-oriented policy to a health policy. Such a change may not only be wise- as maximization turns out to be infinite and is not necessarily related to improved public health-but will also be (or most probably has to be) cheaper. Among comments on this Note, one repeatedly en- counters: the analyses of the health and health care situations are of good quality, the goals are noble, but there is little sign of concrete steps or tangible reorganization plans. The latter is even more necessary when in the near future unpleasant choices have to be made because of budget cuts or other lack of material resources. To a certain extent this applies to the subject of inequity too.

The paragraph on ‘differences in health’ gives a good and rather comprehensive description of the international body of knowledge on socioeconomic related mortality, morbidity and use of health services. So for scientists/researchers it is not so much what is in the document that is interesting, but what is lacking. Some of this information may be more interesting to scientists than to politicians, but some reflects the general inadequacy of the document concerning policy steps and measures.

We lack an evaluation of existing knowledge in terms of a ‘state of the art’ level, which would indicate what we really know for sure, what we may very well hypothesize and what we do not know but (by priority or not) need to know. There is, for example, a lack of attention to time-trends and to persistence of regional and social class related mortality differences. For policy reasons the detection of regionally concentrated risk factors can be of importance, because intervention at such a level is relatively easy. Another policy consequence is that the persistence of mortality inequity raises questions as to how much inequality is justifiable. since total equity cannot be expected.

Another notable omission is the ‘cause and effect question’, which is far from being solved by research

and, with the existence of high unemployment and work disability rates. it should nowadays be even more relevant for policy.

A third very notable omission is the question of interaction and cumulative (or the reverse, diminish- ing) effects of empirical clusters of heterogeneous factors related to socioeconomic categories. This is especially important for three reasons:

I. For the development of policy instruments, knowledge is necessary of the empirical occur- rence of (constellations of) risk factors related to socioeconomic classes and their relative risk attribution(s) (e.g. labour, housing, traffic and other physical safety factors, physical and social environment, lifestyle and health habits, public insurance and socioeconomic security, ‘minimum’ subsistence levels etc.).

3 _. Most of the (hypothetical) structural factors (e.g. labour, housing, poverty) are not easily influenced by the Ministry of Health, nor is policy in such domains predominantly guided by public health motives. Intersectoral or interdepartmental policy is difficult and requires special efforts.

3. The government’s absolute priority to reduce public expenditure (including the costs of health care, social insurance and security systems), the managerial difficulties in interdepartmental issues and the increasing reluc- tance to promote welfare state objectives, may very well enhance the risk of an unbalanced focus on (randomly chosen) lifestyle factors to ‘prevent’ health inequity and victim-blaming of people living in structural and chronic stressful conditions who have unhealthy habits. Early signs of such public victim- blaming are detectable already. However, as Slater et al. [30 p. 3771 concisely summarize, we do not know at all: “To what extent [health practices/lifestyles] are determined by social and biological structures beyond the control of the individual and to what extent they are a matter of individual rational choice.”

The Ministry, well aware of various difficulties yet to overcome before being able to formulate specified policy measures, points out the necessity of longitudi- nal research and further analysis of present research in search for policy-relevant information. A seminar to awaken interest in these questions, which was participated in by people involved in policy, politics, health care, trade and industry, financing and science, was organized early in 1987.

SL’MMMARY AND DISCUSSION

The scarcity of well qualified data prevents distinct conclusions on the very existence of socioeconomic health inequities, but even more on its substantiality. However, indicative studies do justify the hypothesis of structural socioeconomic differences as well as regional ones. Virtually nothing is known about factors influencing existing differences, nor about cause and effect. Such knowledge appears indispensable for the identification of vulnerable groups, as

328 1~0~~0~0 P. SPRUIT

well as knowledge of and the contribution of occupation and educational level.

Several studies indicate differential high risk groups andlor ‘semi-dichotomies’ in society. One hypothesized semi-dichotomy is between the unskilled and the rest, another semi-dichotomy may exist between ethnic minorities and native Dutch and yet another semi-dichotomy is supposed to exist between the ‘active’ (employed) and the ‘non-active’ (retired. unemployed. disabled, etc.). Formulated this way, categories at risk are poorly defined and ‘dichotomies’ partially overlapping, for example in ethnic minorities, dependency of unskilled labour and prevalence of unemployment. Another vulnerable group might be the self-employed, but data are not yet sufficient. In general there is however a tendency to overlook this category. However, instead of randomly constructing vulnerable groups categorized by statistical attributions. it will be more efficient and policy-relevant to tackle processes establishing the link between socioeconomic status and health, constructing an empirical basis for vulnerability within or throughout social classes. Special attention should be given to socioeconomic as well as geographical mobility (also influenced by unemployment and disability), new population categories in society (like ethnic minorities), new heavy risk behaviour patterns constituting high risk groups (e.g. drug addiction and related prostitution) and structural developments like the worsening of work conditions and the social security system, creating new poverty and hazardous labour.

Aggregate level research is not suited to answer cause and effect questions, nor for the identification of mechanisms. However, it is very suited to discover and monitor tendencies and the very fact of mortality inequity. The development of a registration system for socioeconomic status appropriately linkable to the existent mortality registration of the Central Statistics Bureau should have higher priority than it presently has.

Recently (1987:88), the Ministry of Health, inspired by the WHO health for all by the year 2000 targets. showed awakening interest in the subject of health inequity, but the state of the art of the national as well as the international body of knowledge on mortality inequity is such that specified policy measures are difficult to formulate. One central policy difficulty might turn out to be that structural inequity in various societal domains requires an interdepartmental government approach. However, analysis and monitoring of socioeconomic health differences and identification of vulnerable groups are indispensable to further specify general policy intentions and to evaluate measures.

Research will also be necessary for another equally relevant question, namely what mechanisms are at stake in the relationship between socioeconomic status and health. Risk factors can be deduced to exist at different levels: the structural, the behavioural and the psychosocial level. Such factors do not occur singly in their actual empirical existence, but neither the very empirical existence of constellations of factors nor their risk attribution is known. Neither do we know how structural. behaviour and psychosocial factors are related to each other, and what risks they

constitute in which constellations. Although for some risk factors it is already possible to discriminate between higher and lower risk factors, in general three questions can be formulated:

I. What is the relative weight of different material and immaterial ‘life circumstances’ (structural factors) separately and in what empirical constellation are people exposed by socioeconomic status to such circumstances? What is the (increased or decreased) risk of specific empirical constellations of such factors? An example of such constellations, as occurring in certain provincial or neighbourhood regions, is: heavy irregular and shift work, periodical or permanent unemployment, poor housing quality, high neighbourhood population density, longer distance from facilities and recre- ation, more aggression and violence in the primary environment, less material resources, etc.

2. What is the relative weight of different habits, customs, beliefs, norms and values (as expressed in lifestyle, health habits. use of services and prevention) separately. In what empirical constellation do people of different socioeconomic status express particular behaviour and adapt lifestyles in response to various life stresses and circumstances. What is the (increasing or decreasing) risk of specific empirical constellations of such factors? An example of such a hypothetical constellation is: the experience of stressful life events, a limited social network, regular drinking and smoking, fat food, daily suppressed aggression in traffic queues, etc.

3. Probably the most important but difficult question is: What are the relative risks result- ing from interaction between structural/ cultural, behavioural and psychosocial risk factors? This is important because we do not know which interaction can be controlled or changed and which cannot.

However, there is a tendency to stimulate changes of behaviour, neglecting the importance of, or interrelation with, other factors. Such a choice may not always be right, just or efficacious, but may also increase the risk of ‘unintended hazardous side effects’ of erroneous policy actions. A somewhat simplified example is the stimulation, or rewarding, of physical exercise for everybody. There are a few examples that the people most inclined to such exercise are those who make their living by (heavy) physical labour. Another example is the condemna- tion of traffic accidents. While circumstances force some people to participate daily in traffic queues, thereby increasing their chances of becoming involved in accidents, others who live nearer to their work, have more convenient working hours and/or live in areas with better public transport. In an atmosphere of sanctions or rewards for healthy or hazardous behaviour, related (to an unknown extent) to structural factors, the negative side-effects on behaviour may eliminate or even reverse the positive ones [31]. Besides much behaviour has multiple functions and does not only serve health. This also


calls for consideration of arguments of justice and 16.

ethics in measures involving behavioural changes by a system of government forwarded rewards and sanctions.

17.

Ackno~~ledg~n?t,nr-I wish to express my gratitude to Professor Dr Ir. D. Kromhout for his critical comments on an earlier draft. of this paper

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Health and social inequities in The Netherlands