Gac Sanit. 2022;36(6):520–525 Original article Inequalities in life expectancy by educational level and its decomposition in Barcelona, 2004-2018 Xavier Bartoll-Rocaa,b,∗ , Maica Rodríguez-Sanza,b,c,d , Esther Sánchez-Ledesmaa , Katherine Péreza,b,c , Carme Borrella,b,c,d a Agència de Salut Pública de Barcelona, Barcelona (ASPB), Spain b Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain c CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain d Departament de Cièncias Experimentales i de la Salut, Facultat de Ciències de la Salut i de la Vida, Universitat Pompeu Fabra, Barcelona, Spain a r t i c l e i n f o a b s t r a c t Article history: Objective: To analyse the gap in life expectancy by educational level in the city of Barcelona from 2004 Received 22 August 2021 to 2018 and to decompose this gap by age and causes of death. Accepted 29 November 2021 Method: We computed abridged life tables at the age of 25 years by sex from 2004 to 2018 using standard Available online 22 March 2022 methods. Educational level was categorised in two groups (lower secondary or less vs. upper secondary or higher education). The life expectancy gap was further decomposed by age and by causes of death Keywords: based in Arriaga’s method in 5-year age blocks up to the age of ≥ 90 years and broad causes of death Life expectancy using ICD-10 codes. Socioeconomic factors Mortality Results: The life expectancy gap at 25 years by educational level oscillated without trend at around 3.08 years for men and 1.93 years for women. Decomposition by age showed a favourable significant shift in the contribution to this gap from young to older ages for men, with few changes for women. Decomposition by causes of death showed that the diseases concentrating the largest share of the contribution were neoplasms and respiratory and circulatory disease. There was a significant downward trend in external causes for men and in infectious diseases for both men and women but a significant upward trend for respiratory disease for both sexes. Conclusions: The stability of the life expectancy gap by educational level during the period analysed resulted from a combination of divergent trends by age and causes of death among high and low educa- tional levels. © 2021 SESPAS. Published by Elsevier España, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Desigualdades en la esperanza de vida por nivel educativo y su descomposición en Barcelona, 2004-2018 r e s u m e n Palabras clave: Objetivo: Analizar la brecha en la esperanza de vida por nivel educativo en la ciudad de Barcelona desde Esperanza de vida 2004 hasta 2018, y descomponer esta brecha por edad y causas de muerte. Factores socioeconómicos Método: Se calcularon tablas de vida abreviadas a la edad de 25 años por sexo desde 2004 hasta 2018 Mortalidad utilizando métodos estándar. El nivel educativo se clasificó en dos grupos: secundaria inferior o menor frente a secundaria superior o educación superior. La brecha de la esperanza de vida se descompuso además por edad y por causas de muerte según el método de Arriaga en bloques de edad de 5 años hasta la edad de ≥90 años y causas amplias de muerte utilizando los códigos de la CIE-10. Resultados: La brecha de la esperanza de vida a los 25 años por nivel educativo osciló sin tendencia en torno a los 3,08 años para los hombres y 1,93 años para las mujeres. La descomposición por edad mostró un cambio favorable a esta brecha desde las edades jóvenes a las mayores en los hombres, con pocos cambios en las mujeres. La descomposición por causas de muerte mostró una mayor contribución de las neoplasias y las enfermedades respiratorias y circulatorias. Hubo una tendencia a la baja en las causas externas en los hombres, y en las enfermedades infecciosas tanto en los hombres como en las mujeres, pero al alza en las enfermedades respiratorias para ambos sexos. Conclusiones: La estabilidad de la brecha de la esperanza de vida por nivel educativo durante el periodo analizado fue el resultado de una combinación de tendencias divergentes por edad y causas de muerte entre los niveles educativos altos y bajos. © 2021 SESPAS. Publicado por Elsevier España, S.L.U. Este es un artı́culo Open Access bajo la licencia CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). ∗ Corresponding author at: Corresponding author. E-mail address: xbartoll@aspb.cat (X. Bartoll-Roca). https://doi.org/10.1016/j.gaceta.2021.11.008 0213-9111/© 2021 SESPAS. Published by Elsevier España, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc- nd/4.0/). X. Bartoll-Roca et al. / Gac Sanit. 2022;36(6):520–525 521 Introduction Barcelona city, for whom complete educational level was avail- able due to individual linkage between the mortality register and Determining the gap in life expectancy between the population the municipal census of Barcelona. The population aged ≥25 years with high and low educational levels is a useful way to summarise consisted of more than 1.2 million persons for each year, of which social inequalities in mortality in a single measure. Although mor- approximately 53% were women in each year. tality rates have followed a downward trend in western European Age-standardised mortality rates (with reference to the 2011 countries in the last few decades, inequalities by educational level population calculated by the direct method) by educational level, have tended to persist or even widen.1,2 Previous studies have sex and year are reported. Death counts were available in 5-year reported that the gap in life expectancy at age 35 years across age blocks from the mortality register of Barcelona. The underly- 14 European countries was 6.9 years for men and 3.2 years for ing cause of death was classified using International Classification women3 , with eastern European countries showing larger gaps.4,5 of Diseases, Tenth revision (ICD-10) codes. The analysis was per- For several decades, the Spanish population has experienced formed for broad causes of death (1 digit) and the results are overwhelming sociodemographic and institutional changes along reported for 11 groups concentrating 98% and 93% of deaths for men with increases of life expectancy of more than 40 years between and women. The remaining causes were collapsed into a category 1910 and 2009.6 Pioneering studies have found territorial inequal- termed “other”. ities in mortality.7–9 However, fewer studies have analysed the We included the highest level of education attained. To obtain gap in life expectancy by educational level in Spain.10–12 Looking reliable estimates of life expectancy and its decomposition by at long-term inequalities, one study found that between 1960 age and cause, educational level was categorised in two groups: and 2015 life expectancy increased for all education groups but medium and low level was defined as no formal education or particularly among the highly educated, thus widening inequalities pre-primary education, primary education and lower secondary between the populations with the lowest and highest educational education (ISCED 0 to 2), while high educational level was defined levels.13 as upper secondary education and university education (ISCED The life expectancy gap between two groups can be expressed from 3 to 8). To identify differences by educational attainment, we through the contribution, positive or negative, of the underlying restricted the analysis to the population aged 25 years and older, causes of death and age. Decomposition methods are ideal for as a person’s education is usually complete by that age. determining whether life expectancy gaps are the result of dif- ferent patterns. For instance, the decomposition of the gap across European countries shows much higher mortality rates from all the Life expectancy gap by education and its decomposition main causes of death among low-educated prime-age men, and half of the gap in mortality rate among men in this age group is Abridged (based on 5-year age groups) life tables at the age of due to higher death rates from circulatory diseases and cancer and 25 years by sex from 2004 to 2018 were computed using standard to external causes of death (e.g. accidents and suicide).4 In Spain, methods, except for 2009, for which deaths desegregated by edu- the long-term increase in life expectancy by age in 1910 and 2009 cational level were lacking. The cut-off age of 25 years was chosen has been attributed to the reduction in infant mortality until 1970; as the minimum age at which the educational level is sufficiently since then, the largest contribution has been found at older ages, stable and retains the most information on the cause of mortality. mainly due to the decrease in mortality from diseases of the cir- The tables were created using the numbers of deaths registered in culatory system.6 A 7-year follow-up study performed in Spain calendar years and mid-year population estimates. In the case of in 2001 on decomposition of inequalities in mortality patterns by small populations, as in our case, a common practice is to intro- educational level found that the main contributors for both sexes duce uncertainty in the number of deaths. Following Chiang, by were cardiovascular and respiratory diseases.14 The relevance of modelling the number of deaths as a binomial event for each age decomposition analysis is that, if the ages or causes of death are group, the standard error for life expectancy can be computed for sensitive to preventive interventions, decomposition approaches each year.18 To assess the stability and reliability of life expectancy can provide public policy initiatives with evidence on which age estimates, we calculated relative standard errors, defined as the groups or causes of death need to be targeted to reduce mortality.15 ratio of the standard error (RSE) and life expectancy. The higher Because the largest share of the population lives in urban areas, the RSE, the more unstable the estimate. An RSE of around 30% is there is increasing interest in the availability of official records of considered reliable. A z-score was computed to assess the signifi- inequalities in life expectancy at the city level, as reflected by sev- cance of the gaps in life expectancy by educational level for each eral international projects.16,17 In Spain, studies on socio-economic year.19 inequalities in life expectancy have been carried out at territorial Decomposition of the life expectancy gap is an extension of the level, for instance, the mortality atlas MEDEA3 in Spain analyses decomposition methods in life expectancy developed by Arriaga.20 26 cities at small area level including Barcelona city. However, a Briefly, decomposition of life expectancy between high and low decomposition by age and causes of death by area would represent educational levels consists of the sum of two effects: the first is the an excess of information that is difficult to handle and interpret. In direct number of years that an age group adds to the life expectancy contrast, the decomposition by educational level is easier to obtain gap, due to higher mortality in that specific age group in each edu- and meaningful. To our knowledge this is the first study that applies cational level; the second effect consists of the indirect effect on decomposition techniques in a city in Spain. In doing so, we aim the higher mortality in that age group, leaving fewer survivors and to contribute to a better understanding of the inequalities in life affecting all later age groups in the life table. To decompose by expectancy at city level. In this study, we analysed the gap in life cause of death, the total contribution of a given age group is further expectancy by educational level in the city of Barcelona from 2004 divided into the number of years contributed by each cause. The to 2018 and analysed the decomposition of this gap by age and total contribution by causes of death is simply obtained by adding broad causes of death. the contributions across age groups. We adapted the life tables and Method decomposition programmes from Auger et al.15 using Stata version 15 (code in the Supplementary Material). A linear regression was Population and variables performed to identify whether there was a linear tendency in life The study population consisted of deaths occurring from expectancy and to determine the contribution of age and cause of 2004–2018 among persons aged ≥25 years who were resident in death in the period analysed. 522 X. Bartoll-Roca et al. / Gac Sanit. 2022;36(6):520–525 Table 1 Population and standardised mortality rate per 100,000 persons at 25 years and older, by educational level and sex, in Barcelona 2004-2018a . Men Women Medium and low educational level High educational level Medium and low educational level High educational level Persons SMR Persons SMR Persons SMR Persons SMR 2004 288197 1940.2 283642 1654.5 385016 1079.3 279966 877.3 2005 289308 2048.2 290488 1683.7 381591 1145.1 289172 929.1 2006 290042 1858.2 297491 1525.3 377345 1038.4 298034 911.6 2007 283750 1898.0 297928 1612.5 369639 1053.9 301606 906.1 2008 286573 1812.1 305152 1500.1 367118 1044.2 311584 892.1 2010 276902 1698.2 314386 1429.8 353933 961.2 329441 798.8 2011 277398 1652.3 316616 1358.3 347512 940.5 334369 813.9 2012 268773 1724.8 324616 1443.8 336891 980.4 345405 812.0 2013 253308 1588.3 335804 1291.1 319517 896.4 361456 772.6 2014 253308 1585.0 335804 1303.5 319517 905.7 361456 776.1 2015 248183 1572.4 338190 1281.5 312482 909.7 366137 783.5 2016 240599 1526.3 342664 1267.4 303817 896.4 373841 750.5 2017 236082 1523.0 349163 1238.3 297280 877.0 383445 684.9 2018 234678 1570.3 360880 1235.9 291905 898.6 397318 719.1 SMR: standardised mortality rate. a Note that data for 2009 are missing. Results For instance, Table 3 shows that, in 2004, men aged 40-44 years made the largest contribution (13.5%) to the life expectancy gap Life expectancy gap by education (0.42 years) by educational level (3.1 years). The decomposition showed different pattern by gender. For men, the contribution to Data on the population showed a constant decrease over the the gap was more evenly distributed among age groups. The main years among men and women in the medium and low educational consistent finding was a shift in the contribution to older ages level and, conversely, an increase in the population with a high throughout the period. The contribution made by persons aged educational level (Table 1). A downward trend was observed in between 35 and 49 years (39.9% in 2004 to 26.6% in 2018) showed age-standardised mortality rates at 25 years. Mortality rates were a statistically significant shift to the group aged between 60 and 69 higher in men than in women and were also higher in both sexes years (17.7% in 2004 to 23.8% in 2018). For women, the pattern was with a lower educational level. quite different, with most of the contribution being concentrated Analogous to mortality rates, life expectancy was higher in at the age of 70 years and older and the contribution was stable women than in men across all years (Table 2). The life expectancy across the study period (around 60%). However, a downward trend for men and women increased throughout the period with a pos- was also observed among women aged between 30 and 39 years itive significant trend, except for 2005, 2012 and 2018, when life (14.7% in 2004 to 5.1% in 2018). expectancy slightly decreased for both sexes irrespective of edu- cational level. Life expectancy at 25 years increased between 2004 Decomposition by cause of death and 2018 by 2.98 years for men with a high educational level and by 3.27 years for those with a medium and low educational level. The There were also gender differences in the decomposition by increase for women was lower: 1.55 and 1.66 years, respectively. groups of causes of death in the life expectancy gap by educational The gap in life expectancy at 25 years by educational level for level (Table 4). Detailed information for each year is included in men and women was fairly stable across the years, oscillating with- Table II of the online Appendix. out trend around a mean value of 3.08 years for men and 1.93 years For men, the causes of death with the largest mean contribution for women. For men, however, the gap decreased from 3.27 years in to the life expectancy gap by educational level for the whole period 2005 to 2.70 years in 2008 and increased again to reach 3.42 years were neoplasms (28.8%), circulatory system diseases (18.2%), res- in 2013, and then decreased again until reaching a maximum of piratory system diseases (15.3%) and external causes (9.8%). There 3.43 years in 2018. For women, the gap in life expectancy by edu- was a significant upward trend in the contribution of respiratory cation also decreased from 2.26 years in 2005, increased again in diseases (14.2% in 2004 to 19% in 2018), but a significant down- 2010, decreased again until 2015, before reaching a new maximum ward trend in external causes (14.8% in 2004 to 7.7% in 2018) and of 2.70 in 2018. The z-scores derived from the ratio between the life infectious diseases (9.1% in 2004 to 0.1% in 2018). expectancy gap and the standard errors strongly rejected the null For women, the causes of death making the largest mean contri- hypothesis of the equality of life expectancies by education for all bution to the life expectancy gap by educational level for the whole years in men and women. The average RSE across years were in the period were circulatory diseases (28.6%), followed by mental and range of 26.5% to 36.0%, showing acceptably reliable life expectancy behavioural disorders (9.8%), gastrointestinal diseases (8.8%) and estimates (not shown). respiratory system diseases (8.2%). Among women, there was a sig- nificant upward trend in the contribution of respiratory diseases (3.4% in 2004 to 15.1% in 2018) and a downward trend for infec- tious diseases (8.2% in 2004 to 2.0% in 2018), with the other causes Decomposition by age group contributing around their mean values without significant trend. Decomposition by age group of the life expectancy gap at 25 years is shown in percentages in Table 3. For simplicity, the table Discussion shows only the contributions for 2004 and 2018 plus the mean values for the overall period with trend. Detailed information for Using linked data between mortality registers and the census each year is included in Table I of the online Appendix. for Barcelona from 2004 to 2018 (except for 2009), we found a X. Bartoll-Roca et al. / Gac Sanit. 2022;36(6):520–525 523 Table 2 Life expectancy gap at 25 years by educational level and sex in Barcelona (2004-2018)a . Men Women Medium and low High educational level Gap Medium and low High educational level Gap educational level educational level LE SE LE SE Years LE SE LE SE Years 2004 52.35 0.19 55.50 0.21 3.15 60.16 0.15 62.39 0.26 2.23 2005 52.01 0.19 55.27 0.21 3.27 59.52 0.15 61.78 0.25 2.26 2006 53.02 019 56.19 0.21 3.17 60.45 0.16 62.03 0.24 1.58 2007 53.04 0.19 55.92 0.20 2.88 60.34 0.16 62.01 0.24 1.67 2008 53.85 0.18 56.55 0.21 2.70 60.39 0.16 62.02 0.23 1.64 2010 54.24 0.19 57.13 0.19 2.89 61.04 0.16 63.24 0.22 2.20 2011 54.66 0.19 57.64 0.20 2.98 61.28 0.16 63.03 0.22 1.74 2012 54.21 0.19 57.31 0.19 3.09 60.95 0.16 62.87 0.21 1.92 2013 54.81 0.20 58.23 0.19 3.42 61.83 0.17 63.36 0.21 1.53 2014 55.01 0.20 57.96 0.19 2.95 61.71 0.17 63.44 0.20 1.73 2015 55.30 0.19 58.33 0.19 3.03 61.81 0.16 63.32 0.20 1.51 2016 55.63 0.20 58.75 0.18 3.11 61.85 0.16 63.80 0.20 1.96 2017 55.96 0.19 58.99 0.18 3.03 62.01 0.17 64.71 0.20 2.70 2018 55.33 0.20 58.76 0.18 3.43 61.72 0.17 64.05 0.19 2.34 Mean 3.08 1.93 Trendb (p-value) Yes (+ 0.001) Yes (+ 0.001) No (0.491) Yes (+ 0.001) Yes (+ 0.001) No (0.534) LE: life expectancy; SE: standard error. a Note that data for 2009 are missing. b Significance for a linear trend: No stands for no trends; Yes + for positive trend. Table 3 Contribution to the life expectancy gap at 25 years by educational level presented by age group in 2004 and 2018 and mean values, and trend for 2004-2018. Men Women 2004 2018 2004-2018a 2004 2018 2004-2018a b Mean Trend Mean Trendb Age group (years) % % % (p-value) % % % (p-value) 25-29 3.9 1.1 2.2 No (0.102) 1.6 0.2 2.7 No (0.488) 30-34 6.5 2.3 3.1 Yes –(0.048) 6.0 1.6 3.5 Yes – (0.044) 35-39 9.6 3.9 5.3 Yes –(0.001) 8.7 3.5 6.4 Yes – (0.010) 40-44 13.5 4.3 7.6 Yes –(0.000) 7.1 10.1 8.1 No (0.385) 45-49 10.3 6.6 10.6 Yes –(0.060) − 1.6 2.0 5.6 No (0.726) 50-54 3.9 8.9 10.4 No (0.218) 0.9 2.4 5.9 No (0.106) 55-59 11 14.3 10.7 No (0.833) 4.6 9.8 4.0 No (0.324) 60-64 8.3 11.2 11.2 Yes + (0.006) − 2.2 6.0 1.1 Yes + (0.034) 65-69 9.4 12.6 8.8 Yes + (0.049) 8.4 − 0.4 4.4 No (0.313) 70-74 6 11.8 9.1 No (0.138) 11.8 4.3 5.2 No (0.188) 75-79 12.8 5.2 8.3 No (0.661) 8.7 6.9 6.5 No (0.837) 80-84 4.6 6.4 4.7 No (0.339) 8.3 10.8 13.0 No (0.669) 85-89 − 0.3 7.2 4.4 Yes + (0.059) 16.7 11.9 12.4 No (0.834) ≥90 0.5 4.2 3.5 No (0.494) 21.0 30.9 21.2 No (0.206) 100 100 100 100 a Data for 2009 are missing. b Significance for linear trend at (p-value)< 0.1: No stands for no trends; Yes – for downward trend;Yes + for upward trend. Table 4 Contribution to the life expectancy gap at 25 years by educational level presented by broad causes of death in 2004 and 2018, and mean values and trend for 2004-2018. Men Women 2004 2018 2004-2018a 2004 2018 2004-2018a b Mean Trend Mean Trendb Broad causes of death (IDC-10) % % % (p-value) % % % (p-value) Circulatory system (I00-I99) 21.3 13.2 18.2 No (0.879) 22.8 29.5 28.6 No (0.766) Neoplasms (C00-D48) 19.5 34.4 28.8 No (0.141) 11.5 6.4 5.3 No (0.514) External causes (V01-Y98) 14.8 7.7 9.8 Yes – (0.007) 3.5 3.5 7.4 No (0.217) Respiratory system (J00-J99) 14.2 19 15.3 Yes + (0.071) 3.4 15.1 8.2 Yes + (0.003) Infectious & parasitic (S00-B99) 9.1 0.1 5.5 Yes – (0.001) 8.3 2.0 7.3 Yes – (0.060) Digestive system (K00-K93) 8.9 9.9 8.7 No (0.442) 11.4 8.6 8.8 No (0.514) Endocrine (E00-E90) 4.4 4.3 3.3 No (0.315) 5.9 4.4 8.0 No (0.234) Mental & health behav. (F00-F99) 3.9 4.2 3.6 No (0.156) 16.6 6.9 9.8 No (0.914) Nervous system (G00-G99) 1.6 3.9 3 Yes + (0.096) 2.8 6.5 7.6 No (0.211) Genitourinary system (N00-N99) 1.1 2 1.6 No (0.197) 8.5 5.1 4.6 No (0.638) Abnormal (R00-R99) 1.3 0.6 1.1 Yes – (0.025) 3.0 2.2 1.4 No (0.526) Others − 0.1 0.7 1.1 No (0.872) 2.3 9.8 3.0 No (0.202) 100 100 100 100 a Data for 2009 are missing. b Significance for linear trend at (p-value) < 0.1: No stands for no trends; Yes – for downward trend;Yes + for upward trend. 524 X. Bartoll-Roca et al. / Gac Sanit. 2022;36(6):520–525 statistically significant gap in life expectancy by educational level differences by sociodemographic groups and the phase of the epi- for men and women, of 3.08 and 1.93 years, respectively. Decom- demic wave.28 position of the gap by age group among men showed a larger The persistence of inequalities in life expectancy is a matter of contribution in the middle-aged group that shifted to the older age concern for public health policy to the extent that these inequali- group over time, while the contribution among women was con- ties can be reduced by improving the socioeconomic conditions in centrated at older ages and remained stable. Decomposition of the which people live and work, promote the adoptions of healthier gap by cause of death showed that the main contributor was neo- behaviours, facilitate access to appropriate health care and pro- plasms for men and circulatory diseases for women. There was a vide healthier urban environments. Decomposition analysis can downward contribution of external causes in men and of infectious provide an integrated framework for monitoring inequalities in life disease in men and women, but an upward trend in the contribu- expectancy that reflect partial targets for reducing mortality by age tion of respiratory diseases in both sexes, with a higher increase in and cause. women. Our research has several limitations. Official census registers Trends were not apparently affected by the potential adverse can under-report the immigrant population; in addition, a “healthy effect of the economic crisis since 2008. Other studies conducted migrant effect” has been reported for Barcelona,29 and this phe- in the Spanish population during the post-crisis period have also nomenon may have reduced the gap in life expectancy through found a constant downward trend in mortality from 2001 to 2011 lower mortality rates among the low-income population. Our and consequently an upward trend in life expectancy by educa- results should be interpreted as an initial approach but further tional level.21 Similarly, a study analysing inequalities in mortality analysis by cause-specific deaths is strongly recommended because through an index of deprivation in small areas in nine Spanish cities broad causes of death can hide divergent trends within specific between 2001 and 2012 found stable trends, despite some hetero- causes. The life expectancy gap reported in the presented study can geneity by causes of death, with traffic injuries accounting for the be interpreted as a conservative estimate as we conflated educa- only decrease in the post-crisis period for some cities, including tional levels in two categories in order to obtain reliable estimates Barcelona.22 For the longer period from 1996 to 2015 in Spain, instead of using the extreme categories. Nonetheless, the effect is cuts to medical staff and reductions in hospital beds appear to probably small, as the life expectancy of the conflated categories have significantly increased mortality rates from circulatory dis- is very close to our final classification. Finally, other methods have eases and external causes, but not mortality from other causes of been suggested to account for uncertainty in life expectancy in the death. These increases appear to be driven by deaths occurring final open-ended age30 . However, both methods produce almost during emergency treatment, suggesting accessibility problems,23 identical life expectancy estimates across a large range of popu- which have a limited impact in densely populated cities such as lation sizes31 , as is the case in the present study according to the Barcelona. reliability measures of the RSE. As already mentioned, prior research has also found similar To sum up, the stability in the life expectancy gap by educational results to our own in terms of a persistence of social inequal- level during the period analysed resulted from the combination of ities in life expectancy.1,2 However, even a small gap in years divergent trends by age and causes of death among high and low may imply different patterns by age and causes of death. In educational levels. Decomposition by age and cause of death may our study, decomposition methods by age showed an improve- help to compute contrafactual hypotheses and allow the design of ment due to a decreasing contribution to the gap by persons policies to reduce inequalities in life expectancy. aged less than 49 years and a shift to older ages between 2004 Statistical codes to generate life tables and decomposition by and 2018, especially for men. Decomposition by broad causes of cause and age are provided in the online Appendix. death showed a favourable downward trend in the contribution of external causes of death among men (mainly due to traffic Availability of databases and material for replicating injuries and overdoses). Official records show that road traffic injuries decreased by 49.2% for men and 43.2% for women dur- Data available to persons on request to xbartoll@aspb.cat from ing the period analysed, but data is not available by education the Institut d’Investigació Biomèdica Sant Pau, Barcelona (Spain), in level.24 Our results suggest that this downward trend was more accordance with the data policy applicable to the Barcelona health favourable among men with a low educational level, thus con- survey. tributing to a narrowing of the life expectancy gap, but not for women. Conversely, our finding of the relative importance and the adverse trend in the contribution of respiratory diseases to the life expectancy gap among men and women needs further investiga- What is known about the topic? tion in relation to risk factors. Obviously, tobacco smoking can affect several causes of death, especially cardiovascular and respiratory Although life expectancy has followed an upward trend in diseases and neoplasms. Cardiovascular and respiratory diseases the last decade, inequalities by educational level have tended are also the main contributors to inequalities in mortality by educa- to persist or even widen. tional level for men and women in Spain.14 In turn, tobacco smoking is influenced by social class. The international evidence indicates What does this study add to the literature? that tobacco smoking is the leading risk factor contributing to the gap in life expectancy by educational level (19.8% among men and In Barcelona city, the persistence of a life expectancy gap by educational level decomposes in divergent trends by age 18.9% among women),25 while relative inequalities in respiratory and causes of death among high and low educational levels. diseases are often larger in younger age groups.26 In Spain, tobacco smoking is also the leading risk factor in disability-adjusted years What are the implications of the results? lost attributable to neoplasms and cardiovascular and respiratory diseases among men and is the sixth among women, with high Monitoring and computing of contrafactual hypotheses is body mass index being the first.27 Despite the large reductions in recommended to allow the design of policies to reduce social smoking since the introduction of the law restricting smoking in inequalities in life expectancy. 2005, inequalities in tobacco consumption are still present, with X. Bartoll-Roca et al. / Gac Sanit. 2022;36(6):520–525 525 Editor in charge 8. Borrell C, Marí-Dell’Olmo M, Serral G, et al. Inequalities in mortality in small areas of eleven Spanish cities (the multicenter MEDEA project). Health Place. 2010;16:703–11. Miguel Ángel Negrín Hernández. 9. Marí-Dell’Olmo M, Gotsens M, Palència L, et al. Trends in socioeconomic inequal- ities in mortality in small areas of 33 Spanish cities. BMC Public Health. Transparency declaration 2016;29:663. 10. Martínez-Sánchez E, Gutiérrez-Fisac JL, Gispert R, et al. Educational differences in health expectancy in Madrid and Barcelona. Health Policy. 2001;55:227–31. The corresponding author on behalf of the other authors guar- 11. Solé-Auró A, Martín U, Domínguez A. Educational inequalities in life and healthy antee the accuracy, transparency and honesty of the data and life expectancies among the 50-plus in Spain. Int J Environ Res Public Health. 2020;17:3558. information contained in the study, that no relevant information 12. Requena M. UNED y Grupo de Estudios Población y Sociedad La desigualdad has been omitted and that all discrepancies between authors have ante la muerte: educación y esperanza de vida en España. Centre d’Estudis been adequately resolved and described. Demogràfics. Perspectives Demogràfiques. 2017. 13. Permanyer I, Spijker J, Blanes A, et al. Longevity and lifespan variation by edu- cational attainment in Spain: 1960-2015. Demography. 2018;55:2045–70. Authorship contributions 14. Reques L, Giráldez-García C, Miqueleiz E, et al. Educational differences in mortal- ity and the relative importance of different causes of death: a 7-year follow-up study of Spanish adults. J Epidemiol Community Health. 2014;68:1151–60. X. Bartoll-Roca designed the study and carried it out. M. 15. Auger N, Feuillet P, Martel S, et al. Mortality inequality in populations with equal Rodríguez-Sanz contributed to the methodological discussion. M. life expectancy: Arriaga’s decomposition method in SAS Stata, and Excel. Ann Rodríguez-Sanz, E. Sánchez, K. Pérez and C. Borrell have contributed Epidemiol. 2014;24:575–80. 16. Eurostat. Urban Audit. (Accessed 2021 May 9.) Available at: corrections and improvements in the writing and final presentation https://ec.europa.eu/eurostat/web/cities/background. of the study. 17. NVSS - National Vital Statistics Systems - CDC - United States Small-Area Life Expectancy Estimates Project. (Accessed 2021 May 9.) Available at: https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html. Funding 18. Chiang. Chin Long and World Health Organization. Life table and mortality anal- ysis. Geneva: World Health Organization; 1979. None. 19. Method changes to life and health state expectancies - Office for National Statistics. (Accessed 2021 May 9.) Available at: https://www.ons.gov.uk/people populationandcommunity/healthandsocialcare/healthandlifeexpectancies/ Conflicts of interest methodologies/methodchangestolifeandhealthstateexpectancies. 20. Arriaga EE. Measuring and explaining the change in life expectancies. Demog- None. raphy. 1984;21:83–96. 21. Regidor E, Vallejo F, Granados JAT, et al. Mortality decrease according to socioe- conomic groups during the economic crisis in Spain: a cohort study of 36 million Appendix A. Supplementary data people. Lancet. 2016;388:2642–52. 22. Gotsens M, Ferrando J, Marí-Dell’Olmo M, et al. Inequalities in mortality in small areas in seven Spanish cities. Int J Environ Res Public Health. 2020;17:958. Supplementary data associated with this article can be found, in 23. Borra B, Pons-Pons J, Vilar-Rodríguez M. Austerity, healthcare provision, and the online version, at doi:10.1016/j.gaceta.2021.11.008. health outcomes in Spain. Eur J Health Econ. 2020;21:409–23. 24. Valmayor S, Pérez C. 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