Department of Epidemiology, Municipal Health Service The Hague, PO Box 12652, 2500 DP The Hague, The Netherlands.
Correspondence: Barend Jacob Cornelis Middelkoop, GGD (Municipal Health Service) The Hague, PO Box 12652, 2500 DP The Hague, The Netherlands. E-mail: b.j.c.middelkoop{at}ocw.denhaag.nl
Abstract
Background Cause-specific information on socioeconomic differences in health is necessary for a rational public health policy. At the local level, the Municipal Health Service studies these differences in order to support the authorities in policy making.
Methods Mortality data of the under 65 age group in The Hague were analysed (19821991) at residential area level.
Results Causes of death with a high socioeconomic gradient among males were: homicide, chronic liver disease, other external causes of injury, diabetes, bronchitis, emphysema and asthma, and motor vehicle accidents; and among females: diabetes, ischaemic heart disease, other diseases of the circulatory system, signs, symptoms and ill-defined conditions, malignant neoplasm of cervix, and other diseases. Main contributors to the mortality differences between the highest and lowest socioeconomic quartiles among males were: ischaemic heart disease (17.3%), other diseases of the circulatory system (10.2%), signs, symptoms and ill-defined conditions (9.0%), other external causes of injury (8.6%), and chronic liver disease (7.2%); and among females: ischaemic heart disease (25.5%), other diseases (20.1%), signs, symptoms and ill-defined conditions (18.6%), other diseases of the circulatory system (11.0%), and diabetes (9.1%). Among females the contributions of malignant neoplasms of breast (16.3%) and colon (5.5%) and suicide (4.3%) were negative.
Conclusions The diseases that are the main contributors to urban socioeconomic mortality differences can be influenced by public health policy.
Keywords Cause of death, differential mortality, mortality, small area analysis, socioeconomic factors, urban health
Accepted 24 May 2000
In western society an association has often been found between the socioeconomic position of an individual and mortality risk. On average, people with a low socioeconomic position die at a younger age than people with higher socioeconomic status.114
A method frequently used to study socioeconomic differences in mortality is comparison of geographic areas. In the Netherlands, various studies have already been carried out to examine the differences in mortality between residential areas with different levels of socioeconomic deprivation.1519 The way in which this deprivation was measured differed in the various studies, but a higher mortality rate was always found in the areas with a high level of deprivation. In a joint project carried out by the municipal health services of the four major cities in the Netherlands (Amsterdam, Rotterdam, The Hague and Utrecht), a deprivation score was uniformly allocated to the various areas of each city, based on the percentage of unemployed and the average income per breadwinner.20 In this study the relationship between mortality and deprivation score appeared to be comparable in the four cities. The Hague was characterized by the largest variation in deprivation scores and confirmed its status as (in terms of socioeconomic position) the city with the highest level of segregation in the Netherlands. In The Hague socioeconomic differences in all-cause mortality did not decrease in the period 19771997.21
Studying socioeconomic differences in mortality for all causes of death together would provide insufficient indications for policy-making. Therefore, the Municipal Health Service (GGD) in The Hague initiated a research project to determine the extent, for males and females separately, to which individual causes of death contributed to the total socioeconomic difference in mortality between residential districts in The Hague.
Methods
The present study was restricted to mortality under the age of 65 years. Earlier research in The Hague has shown that the socioeconomic differences in mortality between districts are far less pronounced in the over 65 age group, and survival of the fittest in the districts with a high deprivation score and selective migration probably act as confounding factors.15,17,22 In the Netherlands, many aged people with deteriorating health move to nursing and rest homes. In The Hague, there are disproportionately many nursing and rest homes in the prosperous areas. A recent study showed that, if restricted to non-institutionalized inhabitants of The Hague, the relationship between deprivation score of the residential area and all-cause mortality in the over 65s was comparable to that in the under 65 age group.23 There were no cause-specific data.
For the present study use has been made of data which were available from the Municipality of The Hague and from the Central Bureau for Statistics (CBS), which collects mortality data at national level. However, neither of these organizations provide data on the socioeconomic position of the deceased at individual level. Therefore, an ecological approach was chosen for this study, and the method of aggregation was based on the geographical layout of the city of The Hague.
The following data were provided by the Registry Office of the Municipality of The Hague for all individuals who died in the years 1982 through 1991 and who were registered in the municipality at the time of death: date of birth, gender, date of death, residential district (according to the division of the municipality into 93 districts), number of the death certificate.
In the Netherlands, the physician who certifies a deathin most cases this is the attending physicianis bound by the Burial Act to enter the cause of death on a so-called cause-of-death statement. This statement is subsequently sent by the authorities of the municipality in which the death took place to the CBS, however, the content of the statement is confidential. Death certificates from all municipalities in the Netherlands are also sent to the CBS.
On the basis of the numbers of the death certificates, the CBS was able to retrieve all the relevant primary causes of death from the cause-of-death statements. The primary cause of death was determined as the starting point in the sequence of events leading to death.24
For reasons of privacy, it was only possible to obtain data from the CBS at the level of residential area. Per area, data were therefore available on the number of deaths per cause of death, according to age group (0, 124, 2544, 4554 and 5564 years) and gender.
Causes of death
The classification of the causes of death was based on the so-called Adapted Mortality List (the AM List; Table 1) which was used by the CBS during the study period and consisted of 52 categories (based on the Ninth Revision of the International Classification of Diseases of the World Health Organization).25 With regard to seven AM numbers, no deaths from these causes occurred in the under 65 age group during the study period (Table 1
). The AM numbers 48 (motor vehicle traffic accident) and 49 (motor vehicle non-traffic accident; only one death) were combined in the analysis. Further, the AM numbers 28 (acute myocardial infarction) and 29 (other ischaemic heart disease) were combined.
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In this way 28 residential areas were formed all of which met the first and second criteria. Of these, three areas did not completely meet the third criterion as the number in the municipal socioeconomic order differed more than 2 (maximum: 4) between two districts within one area. However, subdivision of these areas into socioeconomically more homogeneous areas was impossible, given the first criterion.
Each area was allocated a deprivation score. The municipality of The Hague uses several deprivation scores for local policy purposes. These scores consist of a number of indicators like the percentage of voters during elections, indicators of the quality of the residential environment, non-attendance at school, the percentage of migrants, the average level of income, and the percentage of inhabitants unemployed and receiving income support.
In the present study the deprivation score consisted of the variables average income per breadwinner in 1984 and percentage unemployed between the ages of 15 and 64 in 1990. Both variables were first normalized to a Z-score, after which these two scores were averaged, resulting in a deprivation score which varied from 1.93 for the area with the greatest prosperity to +1.83 for the area in the lowest socioeconomic position.
The variables included in the deprivation score were chosen on the basis of the following considerations. First, in public health research these variables are current indicators of socioeconomic position at area level.26 Second, application of the combination of these two variables in the above-mentioned study in the four major cities in the Netherlands had led to practically identical regression coefficients, which supports the validity of the score.20 Third, the measure comprised information from the first as well as the second half of the study period. Fourth, adding other available indicators to the deprivation score appeared to have little influence on the classification of the areas.22 To illustrate, there was a strong correlation between the chosen deprivation score and the percentage of inhabitants belonging to an ethnic minority group: people from Surinam (Pearson's R = 0.90), Turkey (Pearson's R = 0.71) and Morocco (Pearson's R = 0.84).
During the study period the classification of the areas according to socioeconomic level remained almost constant. For example, the correlation between the average income per breadwinner in 1984 and in 1989 was high (Pearson's R = 0.94). Also, no significant changes in the number of inhabitants in the areas or in the composition of age and gender occurred in this period. There was one exception: an area in which the number of inhabitants tripled and also the average income decreased. The inhabitants of this newly built area had moved from socioeconomiccally very divergent areas, which makes a measure at area level less meaningful.27 Therefore, this area was not included in the analysis. Table 2 presents some characteristics of the remaining 27 residential areas.
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Thus, a total of 27 areas resulted in 17 male and 12 female groups of 27 SMR for individual causes of death; plus for both genders three groups of 27 SMR for aggregated causes of death, namely non-external, external, and all-cause mortality. Each of these groups of SMR were analysed by means of linear regression (weighting factor being the size of the population per area) in order to determine the association between the SMR and the deprivation score of the area. For 25 SMR there was a significant assessment of homogeneity (P < 0.01) with a maximum of two per AM number.
High versus low deprivation score
The resulting regression coefficients provide inadequate insight into the contribution of the individual causes of death to the total socioeconomic difference in mortality. This contribution is also influenced by the percentage of cause-specific mortality in the total mortality.
In order to determine the impact of the individual causes of death on the total socioeconomic difference in mortality, a comparison was made of the quartiles with the lowest and the highest deprivation scores. The most prosperous quartile includes eight areas with, during the study period, an average of 92 729 inhabitants under the age of 65 years; the poorest quartile includes seven areas with an average of 93 749 inhabitants under the age of 65 years.
A calculation was made, for both males and females, of what the mortality (as a result of all causes of death together) would be in the region with the high deprivation score if the age-specific mortality rate of the region with the low deprivation score was applied. The difference between this mortality and the actual mortality observed in the region with the high deprivation score is the total difference in mortality between the two regions, corrected for age differences. The same calculation was subsequently made for the individual causes of death, after which the percentage per cause of death was calculated.
Results
Deaths per cause of death
Table 1 presents, for males and females separately, crude data per cause of death. The number of people under the age of 65 who died during the study period was 6263 males and 3661 females. Among the males, diseases of the circulatory system (AM numbers 2632; 32.5%) and malignant neoplasms (AM numbers 1219; 29.7%) were responsible for the majority of deaths, followed by external causes (12.1%). Among the females, malignant neoplasms scored highest (44.0%), followed by diseases of the circulatory system (21.3%) and external causes (9.6%).
With regard to the individual causes of death, among males ischaemic heart disease scored highest (19.3%), followed by other malignant neoplasms (13.4%) and malignant neoplasm of trachea, bronchus and lung (10.7%). These were followed by other diseases (8.0%), other diseases of the circulatory system (7.9%), signs, symptoms and ill-defined conditions (7.0%) and suicide (5.0%).
Among females, other malignant neoplasms scored highest (17.1%), followed by malignant neoplasm of breast (12.8%) and ischaemic heart disease (10.4%). These were followed by other diseases (8.6%) and malignant neoplasm of trachea, bronchus and lung (5.8%). Almost identically high scores were found for signs, symptoms and ill-defined conditions and other diseases of the circulatory system (both 5.6%). Suicide also occurred fairly often among females (5.1%).
Association between SMR and deprivation score
From the linear regression analysis it appears that for total mortality, the increase in SMR associated with an increase in deprivation score was equally pronounced for males and for females (regression coefficient ß = 18 and 16, respectively; Table 3).
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Among females the following causes of death had considerably higher point estimates for the regression coefficient compared to total mortality: diabetes mellitus, ischaemic heart disease, other diseases of the circulatory system, signs, symptoms and ill-defined conditions, malignant neoplasm of cervix, and other diseases.
A negative regression coefficient was found among females for malignant neoplasm of colon and malignant neoplasm of the breast. More females die from these causes of death in the region with a low deprivation score.
Excluding SMR with a significant assessment of homogeneity from the analysis led to practically the same regression coefficients.
Relative percentage of total mortality difference per cause of death
Table 4 presents the contributions of the major causes of death to the total difference in mortality between regions with a low and a high deprivation score, respectively. For both males and females, ischaemic heart disease had the highest contribution.
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Among females, after ischaemic heart disease, other diseases took second place, followed by signs, symptoms and ill-defined conditions, other diseases of the circulatory system, and diabetes mellitus. The entire socioeconomic difference in mortality among females was found to be due to non-external causes of death. All diseases of the circulatory system together were responsible for 42.9% of the difference in mortality. The contribution of the combined malignant neoplasms was negative among females (12.1%), in particular as a result of the negative contribution of the malignant neoplasms of the breast and colon. Suicide also had a negative contribution.
Discussion
This is the first study in which a quantitative estimate is made of the contribution of individual causes of death to the socioeconomic differences in mortality within a city, categorized according to gender. The results offer a rational basis for a public health policy which is aimed at reducing these differences.
Recent comparable studies did not differentiate with respect to gender28 or specific causes of death.29 In a recently published Italian study relative risks have been presented, but no relative contributions to mortality differences.13
The general concept is that for most of the individual causes of death, the mortality risk increases with an increase in the deprivation score of the residential area. The main exceptions are found for malignant neoplasm of the female breast and malignant neoplasm of colon; in fact, more females die from these causes in the more prosperous districts. Indications for the existence of a positive relationship between the occurrence of these two diseases and prosperity have long been acknowledged in the US,3033 and have also been confirmed in research carried out in other industrialized countries.3441
Differences in mortality are caused by differences in incidence and differences in case-fatality. Differences in case-fatality can be the result of differences in the quality of health care (including differences in the care-seeking behaviour of patients). In the Netherlands, a list has recently been compiled of the diseases for which death is considered avoidable if optimal quality health care existed for everyone.42 Almost none of the diseases on this list made a substantial contribution to the socioeconomic differences in mortality found in this study. It is therefore unlikely that a difference in the quality of health care would make an important contribution to these mortality differences. Apparently, they reflect socioeconomic differences in incidence.
In this study a comparison is made of residential areas with different deprivation scores. However, these areas also differ in various other aspects. Methods described a strong correlation between deprivation score and the percentage of inhabitants belonging to an ethnic minority group, in particular those from Surinam. It is therefore possible that some of the socioeconomic differences found in this study can be attributed to autochthonous Dutch inhabitants, on the one hand, and representatives of ethnic minorities, on the other. In an earlier study a high prevalence of diabetes mellitus was found among Surinam South Asian inhabitants of The Hague.43 This finding could be associated with the increased mortality due to diabetes mellitus, ischaemic heart disease, cerebrovascular disease and other diseases of the circulatory system in districts with a high percentage of Surinamese inhabitants.
It is difficult to determine the extent to which differences in cause-specific mortality are influenced by differences in the way in which the causes of death are established and registered. It is possible that diagnoses made by physicians in poor districts are different to those made in districts with a low deprivation score, for instance due to a lack of information. This is plausible with regard to mortality from signs, symptoms and ill-defined conditions, which is clearly associated with the deprivation score in females (ß = 36) and scores highly for both males and females on the list of causes of death which make a considerable contribution to the total socioeconomic difference in mortality (9.0% and 18.6%, respectively). This diagnosis will, in general, more often be established for people who die abroad, which occurs more frequently in districts with a high deprivation score, where many of the inhabitants make regular lengthy visits to their country of origin. Moreover, districts with a high deprivation score probably have more inhabitants who are isolated and/or live on the fringe of society. In these cases, too, the physician will often have too little information to be able to establish a definite cause of death. These reflections might also apply to the category of other diseases.
If other diseases and signs, symptoms and ill-defined conditions are not taken into consideration, it becomes apparent that for both males and females the diseases which make a large, positive contribution to the total socioeconomic mortality difference show a relationship with physical environmental factors (such as the quality of the home) and with lifestyle factors (such as nutrition, physical activity, alcohol consumption and smoking). Among others, this applies to deaths caused by ischaemic heart disease, chronic liver disease and cirrhosis, diabetes mellitus, malignant neoplasm of trachea, bronchus and lung, cerebrovascular disease, and bronchitis, emphysema and asthma. Therefore, the results of this study provide excellent indications on which to base priorities and evaluation of the national and local government policies aimed at reducing socioeconomic differences in health.
Acknowledgments
The authors wish to thank Dr PD Bezemer (Institute for Research in Extramural Medicine, Vrije Universiteit Amsterdam) for his statistical advice and Prof. G van der Wal (Department of Social Medicine, Vrije Universiteit Amsterdam) for his critical comments on this paper.
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