522556 research-article2014
SJP0010.1177/1403494814522556C. Borrell et al.Short title
Scandinavian Journal of Public Health, 2014; 42: 245–254
Original Article
Socioeconomic inequalities in mortality in 16 European cities
CARME BORRELL1–4, MARC MARÍ-DELL’OLMO1–3, LAIA PALÈNCIA1–3, MERCÈ GOTSENS1–3, BO BURSTRÖM5, FELICITAS DOMÍNGUEZ-BERJÓN6, MAICA RODRÍGUEZ-SANZ1–4, DAGMAR DZÚROVÁ7, ANA GANDARILLAS6, RASMUS HOFFMANN8, KATALIN KOVACS9, CHIARA MARINACCI10, PEKKA MARTIKAINEN11, HYNEK PIKHART12, DIANA CORMAN5, KATARINA ROSICOVA13, MARC SAEZ2,14, PAULA SANTANA15, LASSE TARKIAINEN11, ROSA PUIGPINÓS1–3, JOANA MORRISON1,2,12, M ISABEL PASARÍN1–4 & ÈLIA DÍEZ1–3 1Agència
de Salut Pública de Barcelona, Barcelona, Spain, 2CIBER Epidemiología y Salud Pública (CIBERESP), Spain, d’Investigació Biomèdica Sant Pau (IIB Sant Pau), Barcelona, Spain, 4Universitat Pompeu Fabra, Barcelona, Spain, 5Karolinska Institutet, Department of Public Health Sciences, Division of Social Medicine, Stockholm, Sweden, 6Subdirección de Promoción de la Salud y Prevención, Consejería de Sanidad, Comunidad de Madrid, Spain, 7Department of Social Geography and Regional Development, Faculty of Science, Charles University in Prague, Prague, Czech Republic, 8Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands, 9Demographic Research Institute, Budapest, Hungary, 10Epidemiology Unit, Local Health Unit TO3, Via, Turin, Italy, 11Department of Social Research, University of Helsinki, Helsinki, Finland, 12Department of Epidemiology and Public Health, University College London, London, UK, 13Graduate School Kosice Institute for Society and Health, Safarik University, Kosice, Slovakia, 14Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain, and 15Centro de Estudos de Geografia e de Ordenamento do Territorio (CEGOT), Departamento de Geografia, Colégio de S. Jerónimo, Universidade de Coimbra, Coimbra, Portugal 3Institut
Abstract Aims: To explore inequalities in total mortality between small areas of 16 European cities for men and women, as well as to analyse the relationship between these geographical inequalities and their socioeconomic indicators. Methods: A crosssectional ecological design was used to analyse small areas in 16 European cities (26,229,104 inhabitants). Most cities had mortality data for a period between 2000 and 2008 and population size data for the same period. Socioeconomic indicators included an index of socioeconomic deprivation, unemployment, and educational level. We estimated standardised mortality ratios and controlled for their variability using Bayesian models. We estimated relative risk of mortality and excess number of deaths according to socioeconomic indicators. Results: We observed a consistent pattern of inequality in mortality in almost all cities, with mortality increasing in parallel with socioeconomic deprivation. Socioeconomic inequalities in mortality were more pronounced for men than women, and relative inequalities were greater in Eastern and Northern European cities, and lower in some Western (men) and Southern (women) European cities. The pattern of excess number of deaths was slightly different, with greater inequality in some Western and Northern European cities and also in Budapest, and lower among women in Madrid and Barcelona. Conclusions: In this study, we report a consistent pattern of socioeconomic inequalities in mortality in 16 European cities. Future studies should further explore specific causes of death, in order to determine whether the general pattern observed is consistent for each cause of death. Key Words: Cities, Europe, mortality, socioeconomic factors, small areas
Introduction The special characteristics of urban areas and the social processes that occur in these areas can have an important role in the health of the population and
must be taken into account when implementing public health policies [1] and when exploring the economic, social, political, and health transformations
Correspondence: Carme Borrell, Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023 Barcelona, Spain. E-mail: [email protected] (Accepted 10 January 2014) © 2014 the Nordic Societies of Public Health DOI: 10.1177/1403494814522556
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246 C. Borrell et al. that take place in a country. It is important to understand these processes because the majority of Europeans live in cities. The special characteristics of cities include the following [2]: (i) they have high population density, usually higher in inner city areas, and higher national, cultural, and religious diversity; (ii) cities have a rich array of human resources, such as community organisations or unions; (iii) cities provide services for the population such as health care, education, or social services which are usually quite accessible; (iv) governments in urban areas are connected with those of other political levels such as metropolitan areas, regions, or countries with many stakeholders involved; and (v) socioeconomic inequalities in health tend to be greater in urban areas, with poor and disadvantaged groups being concentrated in marginalised neighbourhoods, usually in the outskirts of the city or in inner city areas [3]. Research interest in geographical area as a determinant of health has increased in last years due to increased interest in the social determinants of health [4], and because it is becoming increasingly clear that the social and physical characteristics of geographical areas contribute to health outcomes (contextual effect) independently of the profile of the individuals who live in those areas (compositional effect) [5–8]. In other words, variation in disease risk is not fully captured by individual characteristics, and thus it is important to account for the context in which people live [6]. As a result, area-based studies have identified geographical patterns of health and its contextual socioeconomic determinants. While several studies have analysed intra-urban inequality in mortality in cities [9–11], the INEQCITIES project [12] provides the opportunity to compare a large number of European cities in several countries, combining the data from this comprehensive project with a powerful analytical method in geospatial analysis that can generate new insights into the field of socioeconomic inequalities in health in urban areas. Therefore, the objectives of this study were to examine inequalities in total mortality between small areas of 16 European cities for men and women and to analyse the relationship between these inequalities and geographical socioeconomic indicators. Methods This study uses a cross-sectional ecological design to analyse socioeconomic inequalities in mortality at the small area level in 16 European cities (Table I). The selection of cities was related with the availability of
data through INEQ-CITIES partners. The study sample consisted of the residents of these cities (26,229,104 European inhabitants). Most cities had mortality data for a period around 2000–2008, population data for the same period or for at least a single year (see Table A1 in Supplementary Material A, available online) and socioeconomic indicators for 2001. The sources of information were mortality registers and registers and/ or census data for population data and socioeconomic indicators. Note that socioeconomic data for the Netherlands are based on a sample. Sex-stratified numbers of deaths were obtained for each small area of residence, or through the postal address or parish (Lisbon Metropolitan Area) of the deceased. Due to technical problems, geo-referencing of place of residence was not possible for some deaths in eight cities (varying from 0.24% in Brussels to 2.75% in Helsinki). As a socioeconomic indicator of each small area, we used an index of socioeconomic deprivation based on the following socioeconomic indicators (percentage): unemployment among the active population aged ≥16 years; manual workers; 16–25-year-olds with primary education or lower; 25–34-year-olds with university education; and foreigners from lowincome countries. The index of socioeconomic deprivation was constructed using the DP2 method, an iterative procedure that weights indicators depending on their correlation with the global index [13]; the final index was directly proportional to deprivation. In order to describe associations with simple socioeconomic indicators, we also used the percentage of unemployment in the active population aged ≥16 years, and the percentage of 25–64-year-olds with primary education or first stage of basic education, or lower (levels 0–1 of the International Standard Classification of Education – ISCED-97, and levels 0–2 in Helsinki and Budapest). We calculated indirect mortality rates for each city, standardised on the basis of WHO population data for 5-year age groups in the 25 countries of the European Union in 2004 [14]. For each small area, we estimated standardised mortality ratios (SMR), the ratio between observed and expected deaths. To control for variability due to small numbers we used Bayesian models proposed by Besag, York and Mollié (BYM) [15] taking spatial and heterogeneous random effects into account. Smoothed SMR (sSMR) was estimated for each sex and city. Relative risk (RR) estimates along with the corresponding 95% credible intervals (95% CI) for the socioeconomic indicators were computed assuming a 1% change in unemployment and low educational level, and a 1-unit change in the deprivation index (Supplementary Material B).
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Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Neighbourhood
2004 2004 2001 2007 2001 2001 2001 2004 2004
Year Men
Population
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Hungary Italy Portugal Spain
Slovakia
Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
17 22 23 2666 207 1491 2358
Neighbourhood Neighbourhood District Census tract Parisht Census tract Census tract
P25: First quartile; P50: Second quartile (median); P75: Third quartile.
South
UK Switzerland Czech Republic 2004 2004 2004 2004 2001 2004 2005
198,778 112,275 776,834 424,872 1,275,659 750,998 1,481,721
250,567 914,257 464,364 1,010,423 363,877 294,398 3,468,738 177,970 559,108
94 1172 118 987 94 88 633 212 57
No. of areas Type of area
North West Central–East
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague
City
Total
Finland Sweden Belgium France Netherlands
Country
Region
1138 598 26,010 87 1959 364 459
1410 249 2604 943 1630 417 4835 497 912
P25
8927 1632 35,590 130 4558 457 576
2351 596 3763 1102 3768 3276 5460 801 1875
P50
16,230 10,370 41,690 196 8278 578 724
3642 1070 5089 1362 5551 5466 6194 1119 13,320
P75
226,378 122,966 928,475 467,275 1,386,191 837,406 1,667,894
292,134 950,102 505,673 1,137,936 374,448 305,624 3,703,293 187,007 611,463
Total
Women
1216 647 32,380 96 2135 421 531
1524 257 2958 1063 1674 414 5177 489 906
P25
9795 1741 41,140 144 5065 517 663
2681 628 4020 1253 3826 3260 5827 842 1575
P50
18,360 11,820 49,410 215 9428 648 807
4368 1132 5742 1525 5766 5273 6582 1214 13,890
P75
Table I. Description of the 16 European cities: number and type of small areas, population year, total population, and first, second (median), and third quartiles of the population for each small area for men and women.
Socioeconomic inequalities in mortality in cities 247
248 C. Borrell et al. As a measure of impact, we calculated the excess number of deaths associated with changes in socioeconomic indicators (i.e. the excess number of deaths in each small area comparing observed and expected deaths, the latter obtained by assuming that the socioeconomic indicator of each area was equal to the average of the 10% of areas with higher socioeconomic level within each city in terms of their values for each socioeconomic indicator). The total excess number of deaths was obtained by summing the excess deaths across all small areas, and the proportion was calculated by dividing this figure by the observed number of deaths. The geographical distributions of socioeconomic indicators and sSMR derived from the BYM models are displayed using septile maps, where green and brown areas represent low and high sSMR values and better and worse socioeconomic indicators, respectively. Results Results for the number of small areas in each of the 16 cities and their population sizes are shown in Table I (data shown for a single year only, for descriptive purposes). The number of small areas varies markedly between cities, ranging from 17 (Bratislava) to 2666 (Turin), as did the population sizes, with London having the largest population and Kosice the smallest. The median population of small areas ranged from 130 for men and 144 for women (Turin) to 35,590 for men and 41,140 for women (Budapest), although the majority of cities had a median below 5000 for both sexes. The distribution of mortality is shown in Table II, where the number of deaths was correlated with the size of the areas; for example, the city with smallest areas, Turin, had the lowest median of number of deaths per area (men, 12; women, 13). Agestandardised mortality rates were higher among men than women in all cities. Data on the distribution of socioeconomic indicators in small areas in each city are shown in Table A2 of Supplementary material A. Figure 1 shows maps of sex-stratified sSMR and the index of deprivation in four cities (Helsinki, London, Prague, and Turin), one from each European area. In general, the geographical pattern of sSMR is similar to that of socioeconomic deprivation, although this correlation is least clear in Prague. Data showing the association between socioeconomic indicators and mortality are presented in Figure 2. A significant association between socioeconomic indicators and mortality was observed among men in all cities, with the exception of the three socioeconomic indicators in Bratislava, primary education
and deprivation index in Lisbon, and primary education in Prague. The highest RR of mortality due to deprivation was observed in two Eastern European cities (Prague RRdeprivation index (RRdi)=1.227, 95% CI 1.020– 1.455; Budapest RRdi=1.137, 95% CI 1.103–1.174) and two North European cities (Stockholm RRdi=1.144, 95% CI 1.108−1.181 and Helsinki RRdi=1.194, 95% CI 1.170−1.219). The lowest RRdi was observed in Brussels and Amsterdam. The association between socioeconomic indicators and mortality was less pronounced among women, and more likely to be statistically nonsignificant and the patterns were similar with the three socioeconomic indicators. Among women, the strongest association between the deprivation index and mortality in Eastern European countries was observed for Budapest (RRdi=1.098, 95% CI 1.053–1.144) and Kosice (RRdi=1.070, 95% CI 1.022–1.120), and in Northern European countries, Stockholm (RRdi=1.097, 95% CI 1.073–1.121) and Helsinki (Helsinki RRdi=1.080, 95% CI 1.038– 1.122). The lowest RRs were observed in Madrid (nonsignificant) and Barcelona. We observed an excess number and percentage of deaths in areas with poorer socioeconomic indicators in all cities, with the exception of women in Madrid (Table III). Excess mortality rates were generally higher among men than women, with the highest percentages among men observed in Budapest and Helsinki, and the highest among women observed in Budapest and Rotterdam. The lowest percentages of excess mortality were observed in two Southern European cities, Madrid and Barcelona. Discussion In this paper, we have analysed total mortality in 16 cities in diverse geographical cities of Europe, and have observed that patterns of inequality in mortality are consistent across all of the cities studied, and that mortality increases with increasing socioeconomic deprivation. This increase is generally more pronounced among men than women. These patterns are consistent despite the diverse geographical locations of these cities, and the fact that they are located in countries with distinct histories and political traditions. Relative inequalities were greater in Eastern and Northern European cities, and smaller in some Western (in men) and Southern (in women) European cities. The distribution of excess mortality was slightly different, with the highest inequalities in some Western and Northern European cities and in Budapest, and the lowest in Madrid and Barcelona (in women).
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Hungary Italy Portugal Spain
UK Switzerland Czech Republic Slovakia
Finland Sweden Belgium France Netherlands
Country
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
City
2000–2009 2000–2007 2001–2004 2004–2009 1996–2008 1996–2008 2000–2008 2000–2008 2003–2007 2000–2008 2000–2008 2001–2008 2000–2008 1995–2008 2000–2008 2000–2007
Period
21,624 58,565 14,737 38,600 40,335 38,456 237,624 23,832 30,034 18,056 9300 86,088 64,702 183,955 70,263 107,236
86 11 39 32 192 43 308 32 34 112 70 2930 7 341 34 34
179 31 99 42 392 323 368 58 70 415 142 3565 12 650 44 44
334 67 158 54 634 609 440 92 512 1488 608 4281 19 1236 56 55
P75 26,906 66,186 18,006 42,525 47,458 45,120 248,053 29,419 33,458 18,137 8333 100,332 68,459 171,408 73,689 106,004
59 8 32 32 151 45 288 28 40 122 58 3285 8 274 34 30
P25 188 25 112 42 418 316 362 53 66 375 118 4160 13 613 45 41
P50
412 72 196 58 739 694 481 94 458 1515 539 4966 21 1156 59 55
P75
1265.5 998.1 1020.1 804.4 1387.6 1388.8 1138.0 1034.5 1357.0 1543.1 1753.5 1620.0 1009.9 1417.4 1047.4 1091.8
Rate
Total
P50
Total
P25
Men
Women
Men
757.5 708.2 663.2 500.1 938.5 903.7 765.1 717.2 918.1 962.5 1016.9 1010.7 640.4 837.9 613.2 609.8
Rate
Women
Indirect age-standardised mortality rates per 100,000 inhabitants
Total number of deaths and quartiles of number of deaths in each small area
P25: First quartile; P50: Second quartile (median); P75: Third quartile.
North West Central–East South
Region
Table II. Description of mortality for the 16 European cities: period of study, total number of deaths and first, second (median), and third quartiles of the number of deaths in each small area, and indirect age-standardised mortality rates by 100,000 inhabitants, for men and women.
Socioeconomic inequalities in mortality in cities 249
Figure 1. Maps of socioeconomic deprivation and smoothed standardised mortality ratios (sSMR) for men and women in four European cities.
250 C. Borrell et al.
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Figure 2. Association between mortality and socioeconomic indicators, relative risk (RR) and 95% credible intervals (CI) for men and women in 16 European cities.
Socioeconomic inequalities in mortality in cities 251
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North West Central–East South
Region
Hungary Italy Portugal Spain
UK Switzerland Czech Republic Slovakia
Finland Sweden Belgium France Netherlands
Country
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
Cities
5111 (23.60) 11,503 (19.57) 1076 (7.29) 2784 (7.16) 5500 (13.62) 6398 (16.62) 34,944 (14.69) 2283 (15.65) 4860 (16.18) 1521 (8.42) 630 (6.77) 16,802 (19.51) 5272 (12.86) 35,890 (19.50) 4236 (6.02) 9369 (8.71)
4875 (22.51) 11,080 (18.84) 1256 (8.52) 2683 (6.90) 6138 (15.20) 5486 (14.26) 48,579 (20.43) 2000 (13.70) 203 (0.67) 1314 (7.28) 306 (3.29) 23,363 (27.14) 7045 (17.19) 3878 (2.11) 9231 (13.12) 15,142 (14.08)
%
%
4732 (21.86) 15,201 (25.86) 1190 (8.06) 3175 (8.17) 6208 (15.38) 8339 (21.67) 40,759 (17.14) 1996 (13.68) 1629 (5.42) 1304 (7.22) 357 (3.83) 24,333 (28.26) 7003 (17.08) 20,232 (10.99) 10,531 (14.97) 16,828 (15.65)
n
%
3067 (11.39) 8658 (13.03) 1207 (6.70) 1594 (3.72) 4056 (8.54) 7750 (17.17) 22,141 (8.82) 1349 (7.27) 1913 (5.72) 1010 (5.57) 911 (10.92) 12,174 (12.13) 2540 (5.77) 13,864 (8.08) 1176 (1.59) –114 (–0.11)
n
n
n
%
Unemployment
Primary education
Unemployment
Deprivation index
Women
Men
% 3952 (14.67) 6122 (9.21) 1401 (7.77) 1482 (3.46) 6241 (13.14) 6908 (15.30) 29,708 (11.83) 1603 (8.64) 240 (0.72) 282 (1.55) 407 (4.88) 18,894 (18.83) 4432 (10.08) 13,058 (7.61) 1199 (1.62) 1193 (1.12)
n
Primary education
% 3323 (12.34) 9389 (14.13) 1376 (7.63) 2024 (4.73) 6445 (13.57) 10,115 (22.40) 25,435 (10.13) 1405 (7.58) 866 (2.59) 325 (1.79) 483 (5.80) 21,114 (21.04) 3534 (8.03) 17,573 (10.25) 1445 (1.96) –423 (–0.40)
n
Deprivation index
Table III. Number (n) and percentage (%) of excess deaths in 16 European cities under the assumption that the socioeconomic indicator of each area was equal to the average of the 10% of areas with higher socioeconomic level within each city in terms of their values for each socioeconomic indicator for men and women.
252 C. Borrell et al.
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Socioeconomic inequalities in mortality in cities 253 The results shown are similar to the patterns of inequalities described in country-level studies. They had found the highest relative socioeconomic inequalities to occur in Northern and Eastern European countries [16, 17] although a recent study did not find this to be true in Northern European countries [18]. Various hypotheses have been proposed to explain the lowest inequalities found in Northern countries, such as: (i) that it is an artefact of relative measures or that it is not clear whether the deprived groups are the same in each country; (ii) that is due to higher socioeconomic inequalities in health behaviours such as smoking; or (iii) psychosocial factors as for example “relative deprivation” [17,18]. It is worth mentioning that the cities studied are in different phases of the epidemiological transition, which affects their causes of death. As shown in Table A3 (Supplementary Material A), the percentage of deaths due to circulatory diseases is higher in Eastern European cities and lower in some Western and Southern European cities. Conversely, the proportion of cancer deaths, which have less pronounced social patterning, tends to be higher in Southern European cities. The differences observed in Eastern European cities could be explained by political changes that occurred after the collapse of communism which led to inequalities in various health-related behaviours, such as alcohol consumption or differences in the effectiveness of or access to healthcare [19]. At an individual level, Leinsalu et al. [20] described an increase in mortality related with low educational level after the collapse of communism in four countries (Estonia, Lithuania, Poland, and Hungary), due to the rapid changes that these countries experienced in their economic and social systems. Note that the areas analysed in Eastern European cities in our study are bigger than those in other countries, and therefore our results could be explained by various factors related to population composition of the different areas as well as by contextual factors. The nonexistence of inequalities in mortality in Madrid differs from the results of a previous study carried out in small areas of Spanish cities [11], although we note that different socioeconomic indicators were used in that study. Inequalities in mortality were greater among men than women, as described in previous studies [11, 21] although Huisman et al. [22] did not observe this effect in their review of mortality in the elderly population. These differences may be partly due to differences in the distribution of causes of death between men and women [23]. For example, most studies have shown that there is little socioeconomic inequality in breast cancer mortality [24] the most common cause
of cancer death among women, while lung cancer, which is more common among men, is more socially patterned [25]. Moreover, the distribution of risk factors also differs between men and women [26]. It is also worth mentioning that the root gender inequalities that existing in society may partly account for the results we have observed. In this sense, masculinity is related to health behaviours because maintaining a heterosexual male identity may involve taking risks that are seriously hazardous to health [27]. We note that various factors that may influence our results. First, the number of small areas, and both their demographic and geographic sizes differ between cities; some have small areas, while others are larger neighbourhoods. In larger areas, individual and contextual effects are mixed to a greater extent. Although the effect of area size on inequalities in mortality has been found to be modest, in some cities smaller or more homogenous areas may show slightly greater between-area socioeconomic inequalities in health and in mortality [28, 29]. Second, we highlight various issues related to comparability between cities: some cities include metropolitan areas (e.g. Lisbon, which includes inner city parishes and suburban and relatively rural areas); mortality data do not cover the same years for all cities, although the majority of cities have data for the first years of the 21st century, the cities of the Netherlands and Portugal also include data from the 1990s; socioeconomic data from Amsterdam and Rotterdam include only a sample from a labour force survey, which results in more unstable socioeconomic indicators; and finally the method used to calculate population denominators differed between countries because not all had population data for the whole study period. The results of this study may be useful for designing public health policies because city areas with greater socioeconomic deprivation and mortality are specific areas to tailor interventions to reduce social inequalities in health or to make greater investments when implementing universal strategies to reduce inequalities in health. For this reason, the researchers of INEQ-CITIES project have disseminated the results [30] and have produced an atlas of inequalities in mortality in European cities [12], which is freely available online. In this study, it has been possible to analyse inequalities in mortality in 16 European cities thanks to the availability of spatial methods capable of analysing geographical information at the level of small areas. Future studies should further investigate specific causes of death in order to determine if the general patterns observed in this study persist for each cause of death.
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254 C. Borrell et al. Conflict of interest There is no conflict of interest. Funding This article has been partially funded by the project INEQ-CITIES, “Socioeconomic inequalities in mortality: evidence and policies of cities of Europe”; project funded by the Executive Agency for Health and Consumers (Commission of the European Union) (project no. 2008 12 13). References [1] United Nations Human Settlements Programme/UNHABITAT. State of the world’s cities 2010/2011 – cities for all: bridging the urban divide. London: Earthscan, 2010. [2] Vlahov D, Freudenberg N, Proietti F, et al. Urban as a determinant of health. J Urban Health 2007;84(3 Suppl):16–26. [3] Diez Roux AV, Green Franklin T, Alazraqui M, et al. Intraurban variations in adult mortality in a large Latin American city. J Urban Health 2007;84:319–33. [4] Marmot M, Friel S, Bell R, et al. Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet 2008;372:1661–9. [5] Macintyre S, Ellaway A and Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 2002;55:125–39. [6] Diez Roux AV and Mair C. Neighborhoods and health. Ann N Y Acad Sci 2010;1186:125–45. [7] Macintyre S and Ellaway A. Neighborhoods and health: an overview. In: Kawachi I, Berkman LF. Neighborhoods and health. Oxford: Oxford University Press, 2003. pp.20–42. [8] Borrell C, Pons-Vigués M, Morrison J, et al. Factors and processes influencing health inequalities in urban areas. J Epidemiol Community Health 2013;67:389–91. [9] Stafford M, Martikainen P, Lahelma E, et al. Neighbourhoods and self rated health: a comparison of public sector employees in London and Helsinki. J Epidemiol Community Health 2004;58:772–8. [10] Nolasco A, Melchor I, Pina JA, et al. Preventable avoidable mortality: evolution of socioeconomic inequalities in urban areas in Spain, 1996–2003. Health Place 2009;15:702–11. [11] Borrell C, Marí-Dell’Olmo M, Serral G, et al.; MEDEA Members. Inequalities in mortality in small areas of eleven Spanish cities (the multicenter MEDEA project). Health Place 2010;16:703–11. [12] Ineq-cities project. Socioeconomic inequalities in mortality in cities of Europe. University College London. Available at: www.ucl.ac.uk/ineqcities/ (consulted July 2013). [13] Salcedo N, Saez M, Bragulat B, et al. Does the effect of gender modify the relationship between deprivation and mortality? BMC Public Health 2012;12:574. [14] WHO Regional Office for Europe. European detailed mortality database [online database, July 2010 version]. Copenhagen: WHO Regional Office for Europe. Available at: http://data.euro.who.int/dmdb/ (2011, consulted December 2011).
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SJP0010.1177/1403494814522556C. Borrell et al.Short title
Scandinavian Journal of Public Health, 2014; 42: 245–254
Original Article
Socioeconomic inequalities in mortality in 16 European cities
CARME BORRELL1–4, MARC MARÍ-DELL’OLMO1–3, LAIA PALÈNCIA1–3, MERCÈ GOTSENS1–3, BO BURSTRÖM5, FELICITAS DOMÍNGUEZ-BERJÓN6, MAICA RODRÍGUEZ-SANZ1–4, DAGMAR DZÚROVÁ7, ANA GANDARILLAS6, RASMUS HOFFMANN8, KATALIN KOVACS9, CHIARA MARINACCI10, PEKKA MARTIKAINEN11, HYNEK PIKHART12, DIANA CORMAN5, KATARINA ROSICOVA13, MARC SAEZ2,14, PAULA SANTANA15, LASSE TARKIAINEN11, ROSA PUIGPINÓS1–3, JOANA MORRISON1,2,12, M ISABEL PASARÍN1–4 & ÈLIA DÍEZ1–3 1Agència
de Salut Pública de Barcelona, Barcelona, Spain, 2CIBER Epidemiología y Salud Pública (CIBERESP), Spain, d’Investigació Biomèdica Sant Pau (IIB Sant Pau), Barcelona, Spain, 4Universitat Pompeu Fabra, Barcelona, Spain, 5Karolinska Institutet, Department of Public Health Sciences, Division of Social Medicine, Stockholm, Sweden, 6Subdirección de Promoción de la Salud y Prevención, Consejería de Sanidad, Comunidad de Madrid, Spain, 7Department of Social Geography and Regional Development, Faculty of Science, Charles University in Prague, Prague, Czech Republic, 8Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands, 9Demographic Research Institute, Budapest, Hungary, 10Epidemiology Unit, Local Health Unit TO3, Via, Turin, Italy, 11Department of Social Research, University of Helsinki, Helsinki, Finland, 12Department of Epidemiology and Public Health, University College London, London, UK, 13Graduate School Kosice Institute for Society and Health, Safarik University, Kosice, Slovakia, 14Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain, and 15Centro de Estudos de Geografia e de Ordenamento do Territorio (CEGOT), Departamento de Geografia, Colégio de S. Jerónimo, Universidade de Coimbra, Coimbra, Portugal 3Institut
Abstract Aims: To explore inequalities in total mortality between small areas of 16 European cities for men and women, as well as to analyse the relationship between these geographical inequalities and their socioeconomic indicators. Methods: A crosssectional ecological design was used to analyse small areas in 16 European cities (26,229,104 inhabitants). Most cities had mortality data for a period between 2000 and 2008 and population size data for the same period. Socioeconomic indicators included an index of socioeconomic deprivation, unemployment, and educational level. We estimated standardised mortality ratios and controlled for their variability using Bayesian models. We estimated relative risk of mortality and excess number of deaths according to socioeconomic indicators. Results: We observed a consistent pattern of inequality in mortality in almost all cities, with mortality increasing in parallel with socioeconomic deprivation. Socioeconomic inequalities in mortality were more pronounced for men than women, and relative inequalities were greater in Eastern and Northern European cities, and lower in some Western (men) and Southern (women) European cities. The pattern of excess number of deaths was slightly different, with greater inequality in some Western and Northern European cities and also in Budapest, and lower among women in Madrid and Barcelona. Conclusions: In this study, we report a consistent pattern of socioeconomic inequalities in mortality in 16 European cities. Future studies should further explore specific causes of death, in order to determine whether the general pattern observed is consistent for each cause of death. Key Words: Cities, Europe, mortality, socioeconomic factors, small areas
Introduction The special characteristics of urban areas and the social processes that occur in these areas can have an important role in the health of the population and
must be taken into account when implementing public health policies [1] and when exploring the economic, social, political, and health transformations
Correspondence: Carme Borrell, Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023 Barcelona, Spain. E-mail: [email protected] (Accepted 10 January 2014) © 2014 the Nordic Societies of Public Health DOI: 10.1177/1403494814522556
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246 C. Borrell et al. that take place in a country. It is important to understand these processes because the majority of Europeans live in cities. The special characteristics of cities include the following [2]: (i) they have high population density, usually higher in inner city areas, and higher national, cultural, and religious diversity; (ii) cities have a rich array of human resources, such as community organisations or unions; (iii) cities provide services for the population such as health care, education, or social services which are usually quite accessible; (iv) governments in urban areas are connected with those of other political levels such as metropolitan areas, regions, or countries with many stakeholders involved; and (v) socioeconomic inequalities in health tend to be greater in urban areas, with poor and disadvantaged groups being concentrated in marginalised neighbourhoods, usually in the outskirts of the city or in inner city areas [3]. Research interest in geographical area as a determinant of health has increased in last years due to increased interest in the social determinants of health [4], and because it is becoming increasingly clear that the social and physical characteristics of geographical areas contribute to health outcomes (contextual effect) independently of the profile of the individuals who live in those areas (compositional effect) [5–8]. In other words, variation in disease risk is not fully captured by individual characteristics, and thus it is important to account for the context in which people live [6]. As a result, area-based studies have identified geographical patterns of health and its contextual socioeconomic determinants. While several studies have analysed intra-urban inequality in mortality in cities [9–11], the INEQCITIES project [12] provides the opportunity to compare a large number of European cities in several countries, combining the data from this comprehensive project with a powerful analytical method in geospatial analysis that can generate new insights into the field of socioeconomic inequalities in health in urban areas. Therefore, the objectives of this study were to examine inequalities in total mortality between small areas of 16 European cities for men and women and to analyse the relationship between these inequalities and geographical socioeconomic indicators. Methods This study uses a cross-sectional ecological design to analyse socioeconomic inequalities in mortality at the small area level in 16 European cities (Table I). The selection of cities was related with the availability of
data through INEQ-CITIES partners. The study sample consisted of the residents of these cities (26,229,104 European inhabitants). Most cities had mortality data for a period around 2000–2008, population data for the same period or for at least a single year (see Table A1 in Supplementary Material A, available online) and socioeconomic indicators for 2001. The sources of information were mortality registers and registers and/ or census data for population data and socioeconomic indicators. Note that socioeconomic data for the Netherlands are based on a sample. Sex-stratified numbers of deaths were obtained for each small area of residence, or through the postal address or parish (Lisbon Metropolitan Area) of the deceased. Due to technical problems, geo-referencing of place of residence was not possible for some deaths in eight cities (varying from 0.24% in Brussels to 2.75% in Helsinki). As a socioeconomic indicator of each small area, we used an index of socioeconomic deprivation based on the following socioeconomic indicators (percentage): unemployment among the active population aged ≥16 years; manual workers; 16–25-year-olds with primary education or lower; 25–34-year-olds with university education; and foreigners from lowincome countries. The index of socioeconomic deprivation was constructed using the DP2 method, an iterative procedure that weights indicators depending on their correlation with the global index [13]; the final index was directly proportional to deprivation. In order to describe associations with simple socioeconomic indicators, we also used the percentage of unemployment in the active population aged ≥16 years, and the percentage of 25–64-year-olds with primary education or first stage of basic education, or lower (levels 0–1 of the International Standard Classification of Education – ISCED-97, and levels 0–2 in Helsinki and Budapest). We calculated indirect mortality rates for each city, standardised on the basis of WHO population data for 5-year age groups in the 25 countries of the European Union in 2004 [14]. For each small area, we estimated standardised mortality ratios (SMR), the ratio between observed and expected deaths. To control for variability due to small numbers we used Bayesian models proposed by Besag, York and Mollié (BYM) [15] taking spatial and heterogeneous random effects into account. Smoothed SMR (sSMR) was estimated for each sex and city. Relative risk (RR) estimates along with the corresponding 95% credible intervals (95% CI) for the socioeconomic indicators were computed assuming a 1% change in unemployment and low educational level, and a 1-unit change in the deprivation index (Supplementary Material B).
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Census tract Census tract Census tract Census tract Census tract Census tract Census tract Census tract Neighbourhood
2004 2004 2001 2007 2001 2001 2001 2004 2004
Year Men
Population
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Hungary Italy Portugal Spain
Slovakia
Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
17 22 23 2666 207 1491 2358
Neighbourhood Neighbourhood District Census tract Parisht Census tract Census tract
P25: First quartile; P50: Second quartile (median); P75: Third quartile.
South
UK Switzerland Czech Republic 2004 2004 2004 2004 2001 2004 2005
198,778 112,275 776,834 424,872 1,275,659 750,998 1,481,721
250,567 914,257 464,364 1,010,423 363,877 294,398 3,468,738 177,970 559,108
94 1172 118 987 94 88 633 212 57
No. of areas Type of area
North West Central–East
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague
City
Total
Finland Sweden Belgium France Netherlands
Country
Region
1138 598 26,010 87 1959 364 459
1410 249 2604 943 1630 417 4835 497 912
P25
8927 1632 35,590 130 4558 457 576
2351 596 3763 1102 3768 3276 5460 801 1875
P50
16,230 10,370 41,690 196 8278 578 724
3642 1070 5089 1362 5551 5466 6194 1119 13,320
P75
226,378 122,966 928,475 467,275 1,386,191 837,406 1,667,894
292,134 950,102 505,673 1,137,936 374,448 305,624 3,703,293 187,007 611,463
Total
Women
1216 647 32,380 96 2135 421 531
1524 257 2958 1063 1674 414 5177 489 906
P25
9795 1741 41,140 144 5065 517 663
2681 628 4020 1253 3826 3260 5827 842 1575
P50
18,360 11,820 49,410 215 9428 648 807
4368 1132 5742 1525 5766 5273 6582 1214 13,890
P75
Table I. Description of the 16 European cities: number and type of small areas, population year, total population, and first, second (median), and third quartiles of the population for each small area for men and women.
Socioeconomic inequalities in mortality in cities 247
248 C. Borrell et al. As a measure of impact, we calculated the excess number of deaths associated with changes in socioeconomic indicators (i.e. the excess number of deaths in each small area comparing observed and expected deaths, the latter obtained by assuming that the socioeconomic indicator of each area was equal to the average of the 10% of areas with higher socioeconomic level within each city in terms of their values for each socioeconomic indicator). The total excess number of deaths was obtained by summing the excess deaths across all small areas, and the proportion was calculated by dividing this figure by the observed number of deaths. The geographical distributions of socioeconomic indicators and sSMR derived from the BYM models are displayed using septile maps, where green and brown areas represent low and high sSMR values and better and worse socioeconomic indicators, respectively. Results Results for the number of small areas in each of the 16 cities and their population sizes are shown in Table I (data shown for a single year only, for descriptive purposes). The number of small areas varies markedly between cities, ranging from 17 (Bratislava) to 2666 (Turin), as did the population sizes, with London having the largest population and Kosice the smallest. The median population of small areas ranged from 130 for men and 144 for women (Turin) to 35,590 for men and 41,140 for women (Budapest), although the majority of cities had a median below 5000 for both sexes. The distribution of mortality is shown in Table II, where the number of deaths was correlated with the size of the areas; for example, the city with smallest areas, Turin, had the lowest median of number of deaths per area (men, 12; women, 13). Agestandardised mortality rates were higher among men than women in all cities. Data on the distribution of socioeconomic indicators in small areas in each city are shown in Table A2 of Supplementary material A. Figure 1 shows maps of sex-stratified sSMR and the index of deprivation in four cities (Helsinki, London, Prague, and Turin), one from each European area. In general, the geographical pattern of sSMR is similar to that of socioeconomic deprivation, although this correlation is least clear in Prague. Data showing the association between socioeconomic indicators and mortality are presented in Figure 2. A significant association between socioeconomic indicators and mortality was observed among men in all cities, with the exception of the three socioeconomic indicators in Bratislava, primary education
and deprivation index in Lisbon, and primary education in Prague. The highest RR of mortality due to deprivation was observed in two Eastern European cities (Prague RRdeprivation index (RRdi)=1.227, 95% CI 1.020– 1.455; Budapest RRdi=1.137, 95% CI 1.103–1.174) and two North European cities (Stockholm RRdi=1.144, 95% CI 1.108−1.181 and Helsinki RRdi=1.194, 95% CI 1.170−1.219). The lowest RRdi was observed in Brussels and Amsterdam. The association between socioeconomic indicators and mortality was less pronounced among women, and more likely to be statistically nonsignificant and the patterns were similar with the three socioeconomic indicators. Among women, the strongest association between the deprivation index and mortality in Eastern European countries was observed for Budapest (RRdi=1.098, 95% CI 1.053–1.144) and Kosice (RRdi=1.070, 95% CI 1.022–1.120), and in Northern European countries, Stockholm (RRdi=1.097, 95% CI 1.073–1.121) and Helsinki (Helsinki RRdi=1.080, 95% CI 1.038– 1.122). The lowest RRs were observed in Madrid (nonsignificant) and Barcelona. We observed an excess number and percentage of deaths in areas with poorer socioeconomic indicators in all cities, with the exception of women in Madrid (Table III). Excess mortality rates were generally higher among men than women, with the highest percentages among men observed in Budapest and Helsinki, and the highest among women observed in Budapest and Rotterdam. The lowest percentages of excess mortality were observed in two Southern European cities, Madrid and Barcelona. Discussion In this paper, we have analysed total mortality in 16 cities in diverse geographical cities of Europe, and have observed that patterns of inequality in mortality are consistent across all of the cities studied, and that mortality increases with increasing socioeconomic deprivation. This increase is generally more pronounced among men than women. These patterns are consistent despite the diverse geographical locations of these cities, and the fact that they are located in countries with distinct histories and political traditions. Relative inequalities were greater in Eastern and Northern European cities, and smaller in some Western (in men) and Southern (in women) European cities. The distribution of excess mortality was slightly different, with the highest inequalities in some Western and Northern European cities and in Budapest, and the lowest in Madrid and Barcelona (in women).
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Hungary Italy Portugal Spain
UK Switzerland Czech Republic Slovakia
Finland Sweden Belgium France Netherlands
Country
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
City
2000–2009 2000–2007 2001–2004 2004–2009 1996–2008 1996–2008 2000–2008 2000–2008 2003–2007 2000–2008 2000–2008 2001–2008 2000–2008 1995–2008 2000–2008 2000–2007
Period
21,624 58,565 14,737 38,600 40,335 38,456 237,624 23,832 30,034 18,056 9300 86,088 64,702 183,955 70,263 107,236
86 11 39 32 192 43 308 32 34 112 70 2930 7 341 34 34
179 31 99 42 392 323 368 58 70 415 142 3565 12 650 44 44
334 67 158 54 634 609 440 92 512 1488 608 4281 19 1236 56 55
P75 26,906 66,186 18,006 42,525 47,458 45,120 248,053 29,419 33,458 18,137 8333 100,332 68,459 171,408 73,689 106,004
59 8 32 32 151 45 288 28 40 122 58 3285 8 274 34 30
P25 188 25 112 42 418 316 362 53 66 375 118 4160 13 613 45 41
P50
412 72 196 58 739 694 481 94 458 1515 539 4966 21 1156 59 55
P75
1265.5 998.1 1020.1 804.4 1387.6 1388.8 1138.0 1034.5 1357.0 1543.1 1753.5 1620.0 1009.9 1417.4 1047.4 1091.8
Rate
Total
P50
Total
P25
Men
Women
Men
757.5 708.2 663.2 500.1 938.5 903.7 765.1 717.2 918.1 962.5 1016.9 1010.7 640.4 837.9 613.2 609.8
Rate
Women
Indirect age-standardised mortality rates per 100,000 inhabitants
Total number of deaths and quartiles of number of deaths in each small area
P25: First quartile; P50: Second quartile (median); P75: Third quartile.
North West Central–East South
Region
Table II. Description of mortality for the 16 European cities: period of study, total number of deaths and first, second (median), and third quartiles of the number of deaths in each small area, and indirect age-standardised mortality rates by 100,000 inhabitants, for men and women.
Socioeconomic inequalities in mortality in cities 249
Figure 1. Maps of socioeconomic deprivation and smoothed standardised mortality ratios (sSMR) for men and women in four European cities.
250 C. Borrell et al.
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Figure 2. Association between mortality and socioeconomic indicators, relative risk (RR) and 95% credible intervals (CI) for men and women in 16 European cities.
Socioeconomic inequalities in mortality in cities 251
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North West Central–East South
Region
Hungary Italy Portugal Spain
UK Switzerland Czech Republic Slovakia
Finland Sweden Belgium France Netherlands
Country
Helsinki Stockholm Brussels Paris Amsterdam Rotterdam London Zurich Prague Bratislava Kosice Budapest Turin Lisbon Barcelona Madrid
Cities
5111 (23.60) 11,503 (19.57) 1076 (7.29) 2784 (7.16) 5500 (13.62) 6398 (16.62) 34,944 (14.69) 2283 (15.65) 4860 (16.18) 1521 (8.42) 630 (6.77) 16,802 (19.51) 5272 (12.86) 35,890 (19.50) 4236 (6.02) 9369 (8.71)
4875 (22.51) 11,080 (18.84) 1256 (8.52) 2683 (6.90) 6138 (15.20) 5486 (14.26) 48,579 (20.43) 2000 (13.70) 203 (0.67) 1314 (7.28) 306 (3.29) 23,363 (27.14) 7045 (17.19) 3878 (2.11) 9231 (13.12) 15,142 (14.08)
%
%
4732 (21.86) 15,201 (25.86) 1190 (8.06) 3175 (8.17) 6208 (15.38) 8339 (21.67) 40,759 (17.14) 1996 (13.68) 1629 (5.42) 1304 (7.22) 357 (3.83) 24,333 (28.26) 7003 (17.08) 20,232 (10.99) 10,531 (14.97) 16,828 (15.65)
n
%
3067 (11.39) 8658 (13.03) 1207 (6.70) 1594 (3.72) 4056 (8.54) 7750 (17.17) 22,141 (8.82) 1349 (7.27) 1913 (5.72) 1010 (5.57) 911 (10.92) 12,174 (12.13) 2540 (5.77) 13,864 (8.08) 1176 (1.59) –114 (–0.11)
n
n
n
%
Unemployment
Primary education
Unemployment
Deprivation index
Women
Men
% 3952 (14.67) 6122 (9.21) 1401 (7.77) 1482 (3.46) 6241 (13.14) 6908 (15.30) 29,708 (11.83) 1603 (8.64) 240 (0.72) 282 (1.55) 407 (4.88) 18,894 (18.83) 4432 (10.08) 13,058 (7.61) 1199 (1.62) 1193 (1.12)
n
Primary education
% 3323 (12.34) 9389 (14.13) 1376 (7.63) 2024 (4.73) 6445 (13.57) 10,115 (22.40) 25,435 (10.13) 1405 (7.58) 866 (2.59) 325 (1.79) 483 (5.80) 21,114 (21.04) 3534 (8.03) 17,573 (10.25) 1445 (1.96) –423 (–0.40)
n
Deprivation index
Table III. Number (n) and percentage (%) of excess deaths in 16 European cities under the assumption that the socioeconomic indicator of each area was equal to the average of the 10% of areas with higher socioeconomic level within each city in terms of their values for each socioeconomic indicator for men and women.
252 C. Borrell et al.
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Socioeconomic inequalities in mortality in cities 253 The results shown are similar to the patterns of inequalities described in country-level studies. They had found the highest relative socioeconomic inequalities to occur in Northern and Eastern European countries [16, 17] although a recent study did not find this to be true in Northern European countries [18]. Various hypotheses have been proposed to explain the lowest inequalities found in Northern countries, such as: (i) that it is an artefact of relative measures or that it is not clear whether the deprived groups are the same in each country; (ii) that is due to higher socioeconomic inequalities in health behaviours such as smoking; or (iii) psychosocial factors as for example “relative deprivation” [17,18]. It is worth mentioning that the cities studied are in different phases of the epidemiological transition, which affects their causes of death. As shown in Table A3 (Supplementary Material A), the percentage of deaths due to circulatory diseases is higher in Eastern European cities and lower in some Western and Southern European cities. Conversely, the proportion of cancer deaths, which have less pronounced social patterning, tends to be higher in Southern European cities. The differences observed in Eastern European cities could be explained by political changes that occurred after the collapse of communism which led to inequalities in various health-related behaviours, such as alcohol consumption or differences in the effectiveness of or access to healthcare [19]. At an individual level, Leinsalu et al. [20] described an increase in mortality related with low educational level after the collapse of communism in four countries (Estonia, Lithuania, Poland, and Hungary), due to the rapid changes that these countries experienced in their economic and social systems. Note that the areas analysed in Eastern European cities in our study are bigger than those in other countries, and therefore our results could be explained by various factors related to population composition of the different areas as well as by contextual factors. The nonexistence of inequalities in mortality in Madrid differs from the results of a previous study carried out in small areas of Spanish cities [11], although we note that different socioeconomic indicators were used in that study. Inequalities in mortality were greater among men than women, as described in previous studies [11, 21] although Huisman et al. [22] did not observe this effect in their review of mortality in the elderly population. These differences may be partly due to differences in the distribution of causes of death between men and women [23]. For example, most studies have shown that there is little socioeconomic inequality in breast cancer mortality [24] the most common cause
of cancer death among women, while lung cancer, which is more common among men, is more socially patterned [25]. Moreover, the distribution of risk factors also differs between men and women [26]. It is also worth mentioning that the root gender inequalities that existing in society may partly account for the results we have observed. In this sense, masculinity is related to health behaviours because maintaining a heterosexual male identity may involve taking risks that are seriously hazardous to health [27]. We note that various factors that may influence our results. First, the number of small areas, and both their demographic and geographic sizes differ between cities; some have small areas, while others are larger neighbourhoods. In larger areas, individual and contextual effects are mixed to a greater extent. Although the effect of area size on inequalities in mortality has been found to be modest, in some cities smaller or more homogenous areas may show slightly greater between-area socioeconomic inequalities in health and in mortality [28, 29]. Second, we highlight various issues related to comparability between cities: some cities include metropolitan areas (e.g. Lisbon, which includes inner city parishes and suburban and relatively rural areas); mortality data do not cover the same years for all cities, although the majority of cities have data for the first years of the 21st century, the cities of the Netherlands and Portugal also include data from the 1990s; socioeconomic data from Amsterdam and Rotterdam include only a sample from a labour force survey, which results in more unstable socioeconomic indicators; and finally the method used to calculate population denominators differed between countries because not all had population data for the whole study period. The results of this study may be useful for designing public health policies because city areas with greater socioeconomic deprivation and mortality are specific areas to tailor interventions to reduce social inequalities in health or to make greater investments when implementing universal strategies to reduce inequalities in health. For this reason, the researchers of INEQ-CITIES project have disseminated the results [30] and have produced an atlas of inequalities in mortality in European cities [12], which is freely available online. In this study, it has been possible to analyse inequalities in mortality in 16 European cities thanks to the availability of spatial methods capable of analysing geographical information at the level of small areas. Future studies should further investigate specific causes of death in order to determine if the general patterns observed in this study persist for each cause of death.
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