Abstract:

Although mortality from ischemic heart disease has declined over the past decades in Argentina, ischemic heart disease remains one of the most frequent causes of death. This study aimed to describe the role of individual and contextual factors on premature ischemic heart disease mortality and to analyze how educational differentials in premature ischemic heart disease mortality changed during economic fluctuations in two provinces of Argentina from 1990 to 2018. To test the relationship between individual (age, sex, and educational level) and contextual (urbanization, poverty, and macroeconomic variations) factors, a multilevel Poisson model was estimated. When controlling for the level of poverty at the departmental level, we observed inequalities in premature ischemic heart disease mortality according to the educational level of individuals, affecting population of low educational level. Moreover, economic expansion was related to an increase in ischemic heart disease mortality, however, expansion years were not associated with increasing educational inequalities in ischemic heart disease mortality. At the departmental level, we found no contextual association beween area-related socioeconomic level and the risk of ischemic heart disease mortality. Despite the continuing decline in ischemic heart disease mortality in Argentina, this study highlighted that social inequalities in mortality risk increased over time. Therefore, prevention policies should be more focused on populations of lower socioeconomic status in Argentina.

Keywords:
Myocardial Ischemia; Economic Recession; Socioeconomic Disparities in Health; Mortality; Multilevel Analysis

Resumen:

Si bien la mortalidad por cardiopatía isquémica ha disminuido en las últimas décadas en Argentina, la cardiopatía isquémica sigue siendo una de las causas más frecuentes de muerte. Los objetivos de este estudio fueron describir el papel de los factores individuales y contextuales en la mortalidad prematura por cardiopatía isquémica y analizar cómo estos cambiaron las diferencias educativas en la mortalidad prematura por cardiopatía isquémica durante las variaciones económicas en dos provincias de Argentina durante el periodo 1990-2018. Para probar la relación entre los factores individuales (edad, género y nivel de educación) y contextuales (urbanización, pobreza y variaciones macroeconómicas), se estimó un modelo de Poisson multinivel. Controlando el nivel de pobreza en el ámbito departamental, se observaron desigualdades en la mortalidad prematura por cardiopatía isquémica según el nivel de educación de los individuos, lo que afecta a la población con bajo nivel de educación; la expansión económica se relacionó con el aumento de la mortalidad por cardiopatía isquémica; sin embargo, el periodo de expansión no estuvo asociado a aumentos de las desigualdades educativas en la mortalidad por cardiopatía isquémica. En el ámbito departamental no se detectó asociación entre el nivel socioeconómico de la área y el riesgo de mortalidad por cardiopatía isquémica. A pesar de la disminución continua de la mortalidad por cardiopatía isquémica en Argentina, este estudio destaca que las desigualdades sociales con relación al riesgo de mortalidad tuvieron un aumento con el tiempo. Por lo tanto, las políticas de prevención deberán dirigirse más a las poblaciones de menor nivel socioeconómico en Argentina.

Palabras-clave:
Isquemia Miocárdica; Recesión Económica; Disparidades Socioeconómicas en Salud; Mortalidad; Análisis Multinivel

Resumo:

Embora a mortalidade por doença isquêmica do coração tenha diminuído nas últimas décadas na Argentina, a doença isquêmica do coração continua sendo uma das causas mais frequentes de morte. Os objetivos deste estudo foram descrever o papel de fatores individuais e contextuais na mortalidade prematura por doença isquêmica do coração e analisar como as diferenças educacionais na mortalidade prematura por doença isquêmica do coração mudaram durante as flutuações econômicas em duas províncias da Argentina durante o período 1990-2018. Para testar a relação entre fatores individuais (idade, sexo e escolaridade) e contextuais (urbanização, pobreza e variações macroeconômicas), estimou-se um modelo de Poisson multinível. Controlando o nível de pobreza no nível departamental, observaram-se desigualdades na mortalidade prematura por doença isquêmica do coração de acordo com o nível educacional dos indivíduos, afetando a população de baixa escolaridade; a expansão econômica esteve relacionada ao aumento da mortalidade por doença isquêmica do coração; no entanto, os anos de expansão não foram associados a aumentos nas desigualdades educacionais na mortalidade por doença isquêmica do coração. No nível departamental, não foi detectada uma associação contextual entre nível socioeconômico da área e risco de mortalidade por doença isquêmica do coração. Apesar do contínuo declínio da mortalidade por doença isquêmica do coração na Argentina, este estudo destaca que as desigualdades sociais em relação ao risco de mortalidade aumentaram ao longo do tempo. Portanto, as políticas de prevenção devem ser mais focadas nas populações de menor nível socioeconômico na Argentina.

Palavras-chave:
Isquemia Miocárdica; Recessão Econômica; Disparidades Socioeconômicas em Saúde; Mortalidade; Análise Multinível

Introduction

Ischemic heart disease has become the leading cause of death in large parts of the developing world ¹1. Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S, Murphy A. Growing epidemic of coronary heart disease in low-and middle-income countries. Curr Probl Cardiol 2010; 35:72-115.. Although ischemic heart disease mortality has declined over the past decades in Argentina ²2. Rodríguez T, Malvezzi M, Chatenoud L, Bosetti C, Levi F, Negri E, et al. Trends in mortality from coronary heart and cerebrovascular diseases in the Americas: 1970-2000. Heart 2006; 92:453-60., it remains one of the most frequent causes of death. The decrease in ischemic heart disease mortality over the past 20 years has occurred during a period of major economic fluctuations. From 1999 to 2002 Argentina’s economy, the third largest in Latin America after Brazil and Mexico, experienced a fall in Gross Domestic Product (GDP) annually, with the most severe drop in 2002. Unemployment reached 20% in the metropolitan area of Buenos Aires ³3. Kosacoff B. Marchas y contramarchas de la industria argentina. Santiago de Chile: Comisión Económica para América Latina y el Caribe; 2010.. Then, the economy grew steadily since 2003 until the global crisis hit Argentina in 2009.

Social and economic circumstances have been shown to be strongly related to ischemic heart disease risk. Many studies from high-income countries have reported inverse social trends of ischemic heart disease, that is, higher ischemic heart disease morbidity and mortality in lower social classes ⁴4. Avendano M, Kunst AE, Huisman M, Lenthe FV, Bopp M, Regidor E, et al. Socioeconomic status and ischaemic heart disease mortality in 10 western European populations during the 1990s. Heart 2006; 92:461-7.^,55. Leinsalu M, Vågerö D, Kunst AE. Estonia 1989-2000: enormous increase in mortality differences by education. Int J Epidemiol 2003; 32:1081-7.. Efforts to understand such trend and its macrosocial determinants in Latin American countries remain limited ⁶6. Mallinson PAC, Luhar S, Williamson E, Barreto ML, Kinra S. Socioeconomic position and cardiovascular mortality in 63 million adults from Brazil. Heart 2021; 107:822-7.. In particular, the role of the macroeconomic context, which seems to be essential in the temporal variation of deaths from cardiovascular diseases (CVDs), remains unclear outside high-income countries. On the other hand, most studies from high-income countries found a positive, procyclical relationship between economic growth and CVD mortality ⁷7. Gerdtham UG, Ruhm CJ. Deaths rise in good economic times: evidence from the OECD. Econ Hum Biol 2006; 4:298-316.^,88. Neumayer E. Recessions lower (some) mortality rates: evidence from Germany. Soc Sci Med 2004; 58:1037-47.^,99. Tapia Granados JA. Increasing mortality during the expansions of the US economy, 1900-1996. Int J Epidemiol 2005; 34:1194-202., although studies in Latin America showed mixed results ¹⁰10. Hone T, Mirelman AJ, Rasella D, Paes-Sousa R, Barreto ML, Rocha R, et al. Effect of economic recession and impact of health and social protection expenditures on adult mortality: a longitudinal analysis of 5,565 Brazilian municipalities. Lancet Glob Health 2019; 7:e1575-83.^,1111. González F, Quast T. Mortality and business cycles by level of development: evidence from Mexico. Soc Sci Med 2010; 71:2066-73.. In the United States, for example, Ruhm ¹²12. Ruhm CJ. A healthy economy can break your heart. Demography 2007; 44:829-48. found that the reduction in unemployment (during economic expansions) is associated with an increase in ischemic heart disease mortality, while Svensson ¹³13. Svensson M. Do not go breaking your heart: do economic upturns really increase heart attack mortality? Soc Sci Med 2007; 65:833-41. and Tapia Granados & Ionides ¹⁴14. Tapia Granados JA, Ionides EL. Mortality and macroeconomic fluctuations in contemporary Sweden. Eur J Popul Eur Démographie 2011; 27:157-84. examined the relation between macroeconomic fluctuations and mortality in Sweden, reaching different conclusions. It has been hypothesized that the relation between economic recessions and lower ischemic heart disease risk is due to improved health behaviors and environmental conditions and less job related stress during recessions ¹²12. Ruhm CJ. A healthy economy can break your heart. Demography 2007; 44:829-48.. A study in Brazil ¹⁰10. Hone T, Mirelman AJ, Rasella D, Paes-Sousa R, Barreto ML, Rocha R, et al. Effect of economic recession and impact of health and social protection expenditures on adult mortality: a longitudinal analysis of 5,565 Brazilian municipalities. Lancet Glob Health 2019; 7:e1575-83. associated the increase in unemployment with an increase in CVD mortality; whereas a study in Mexico ¹¹11. González F, Quast T. Mortality and business cycles by level of development: evidence from Mexico. Soc Sci Med 2010; 71:2066-73. found a negative, counter-cyclical relationship between economic growth and CVD mortality in states with the lowest Human Development Index (HDI), and a procyclical relationship in female population in states with the highest HDI.

However, these findings about the fall (or rise) of mortality during economic downturns do not provide clear evidence about changes in socioeconomic inequality in mortality. For example, does mortality decline more considerably among lower or higher socioeconomic groups during economic recessions? What mechanisms might underlie such change? Lower socioeconomic groups would experience greater unemployment and job insecurity but lower consumption of alcohol and tobacco compared to groups with a higher socioeconomic level. On the other hand, the collapse of the Argentine financial system during 2001 and 2002, which affected the free availability of saving accounts, could have greatly increased the level of psychosocial stress in the population with a high socioeconomic level. Overall, there is little information on how macroeconomic changes modify socioeconomic inequality in ischemic heart disease mortality. The few studies on the subject show mixed results. For example, Valkonen et al. ¹⁵15. Valkonen T, Martikainen P, Jalovaara M, Koskinen S, Martelin T, Mäkelä P. Changes in socioeconomic inequalities in mortality during an economic boom and recession among middle-aged men and women in Finland. Eur J Public Health 2000; 10:274-80. found a decrease in the absolute inequality between male manual and non-manual workers in Finland during recessions. A study in Japan found an increased risk of male ischemic heart disease mortality in managers and some other categories of workers generally considered of lower status during economic stagnation ¹⁶16. Wada K, Kondo N, Gilmour S, Ichida Y, Fujino Y, Satoh T, et al. Trends in cause specific mortality across occupations in Japanese men of working age during period of economic stagnation, 1980-2005: retrospective cohort study. BMJ 2012; 344:e1191.. These studies in Finland and Japan may indicate a reduction of ischemic heart disease mortality during recessions. Another study in Finland suggested unchanged inequality levels during economic recession ¹⁷17. Lammintausta A, Immonen-Räihä P, Airaksinen JKE, Torppa J, Harald K, Ketonen M, et al. Socioeconomic inequalities in the morbidity and mortality of acute coronary events in Finland: 1988 to 2002. Ann Epidemiol 2012; 22:87-93.. To the best of our knowledge, no study have examined the impact of macroeconomic conditions on inequalities in ischemic heart disease among working-age population of a developing country ¹⁸18. Benach J, Padilla-Pozo A, Martínez-Herrera E, Molina-Betancur JC, Gutiérrez M, Pericàs JM, et al. What do we know about the impact of economic recessions on mortality inequalities? A critical review. Soc Sci Med 2022; 296:114733..

Inequality in ischemic heart disease mortality by individual-level socioeconomic status, as well as the extent to which macroeconomic conditions could influence such inequality, may also depend on the pre-existing social-spatial distributions of ischemic heart disease mortality across local contexts, including local economic contexts and level of urbanization. Several studies in high-income countries have reported increased risk of death from ischemic heart disease in lower socioeconomic status areas, after adjusting for other individual characteristics ¹⁹19. Borrell LN, Diez Roux AV, Rose K, Catellier D, Clark BL. Neighbourhood characteristics and mortality in the Atherosclerosis Risk in Communities Study. Int J Epidemiol 2004; 33:398-407.^,2020. Chaix B, Rosvall M, Merlo J. Assessment of the magnitude of geographical variations and socioeconomic contextual effects on ischaemic heart disease mortality: a multilevel survival analysis of a large Swedish cohort. J Epidemiol Community Health 2007; 61:349-55.^,2121. Petrelli A, Gnavi R, Marinacci C, Costa G. Socioeconomic inequalities in coronary heart disease in Italy: a multilevel population-based study. Soc Sci Med 2006; 63:446-56.^,2222. Smith GD, Hart C, Watt G, Hole D, Hawthorne V. Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley Study. J Epidemiol Community Health 1998; 52:399-405.. However, in Latin American countries, the understanding of the impact of the socioeconomic characteristics of an area on ischemic heart disease mortality is still limited, since only a few ecological studies have investigated this association with conflicting outcomes ²³23. Duarte EC, Schneider MC, Sousa RP, Ramalho WM, Sardinha LMV, Silva Júnior JB, et al. Epidemologia das desigualdades em saúde no Brasil: um estudo exploratório. Brasília: Organização Pan-Americana da Saúde; 2002.^,2424. Farias NSO. Mortalidade cardiovascular e desigualdades sociais no município de São Paulo, Brasil, 1996-1998 e 2008-2010. Epidemiol Serv Saúde 2014; 23:57-66.^,2525. Ishitani LH, Franco GC, Perpétuo IHO, França E. Desigualdade social e mortalidade precoce por doenças cardiovasculares no Brasil. Rev Saúde Pública 2006; 40:684-91.^,2626. Diez Roux AV, Franklin TG, Alazraqui M, Spinelli H. Intraurban variations in adult mortality in a large Latin American city. J Urban Health 2007; 84:319-33.^,2727. Sánchez-Barriga JJ. Comportamiento de la mortalidad por cardiopatía isquémica en México en el periodo 2000-2007. Gac Méd Méx 2009; 145:375-82.. Urbanization, which has developed rapidly in Latin America ²⁸28. United Nations Human Settlements Programme. Urbanization and development: emerging futures. World Cities Report 2016. Nairobi: United Nations Human Settlements Programme; 2016. has also been associated to ischemic heart disease mortality. Although some studies reported higher ischemic heart disease mortality rates in urban areas compared to rural areas for at least the mid-20th century in developed countries ²⁹29. Barnett E, Halverson J. Disparities in premature coronary heart disease mortality by region and urbanicity among black and white adults ages 35-64, 1985-1995. Public Health Rep 2000; 115:52., recent studies showed mixed results. Recently, developed countries experienced a reversal, with higher mortality rates in rural areas and a convergence of both types of areas ³⁰30. Krüger O, Aase A, Westin S. Ischaemic heart disease mortality among men in Norway: reversal of urban-rural difference between 1966 and 1989. J Epidemiol Community Health 1995; 49:271-6.^,3131. Kulshreshtha A, Goyal A, Dabhadkar K, Veledar E, Vaccarino V. Urban-rural differences in coronary heart disease mortality in the United States: 1999-2009. Public Health Rep 2014; 129:19-29.. On the other hand, data from developing countries generally suggest higher ischemic heart disease mortality rates in urban areas ³²32. Araya MR, Padilla SG. Evolución de la mortalidad por enfermedad isquémica del corazón e infarto agudo del miocardio en Costa Rica, 1970-2001. Rev Panam Salud Pública 2004; 16:295-301.^,3333. Jiang G, Wang D, Li W, Pan Y, Zheng W, Zhang H, et al. Coronary heart disease mortality in China: age, gender, and urban-rural gaps during epidemiological transition. Rev Panam Salud Pública 2012; 31:317-24.^,3434. Lin JD, Zhang L, Xu ZZ, Xu LC. Research on burden of chronic diseases among rural-urban residents in Xuzhou. Public Health 2010; 124:345-9.^,3535. Yusuf S, Reddy S, Ôunpuu S, Anand S. Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation 2001; 104:2746-53..

In this study, we sought: to (1) describe the role of individual and contextual factors in premature ischemic heart disease mortality; and (2) to analyze how educational differentials in premature ischemic heart disease mortality changed during economic fluctuations in two provinces of Argentina from 1990 to 2018, a period of significant macroeconomic fluctuations and growing urbanization, while adjusting for background individual and contextual factors. Considering previous investigations, we hypothesized that: (a) premature ischemic heart disease mortality is higher among individuals with low socioeconomic status (education) compared to those with high socioeconomic status; (b) premature ischemic heart disease mortality is lower during economic recessions; and (c) educational inequality in premature mortality is lower during economic recessions than during economic expansions.

Methods

Data and measures

Mortality data by year of death, age at death, sex, department and province of residence, and educational level were obtained from the Argentine Ministry of Health for the years 1990 to 2018. Deaths from ischemic heart disease were identified based on the 9^th and 10^th revisions of the International Classification of Diseases (ICD-9 codes: 410-414, and ICD-10 codes: I20-I25, respectively). Age was divided into two groups (25-44 and 45-64 years). Educational level (low and medium-high level) was used as an indicator for socioeconomic status at the individual level. Low educational level included those up to incomplete secondary education. Medium-high educational level included individuals who completed secondary school (6 or more years of primary school, 5 or more years of secondary school) or had incomplete or complete tertiary education (1 or more years of tertiary education or no tertiary education after secondary education).

Departments were the main spatial unit for residential context: the smallest sub-provincial territory with mortality data available. The departments located in the provinces of Mendoza (18 departments) and San Juan (19 departments) were included in the study area. These provinces were chosen since, compared to the other provinces of Argentina, they have a low percentage of ill-defined deaths (ICD-10 codes: R001-R99X) ³⁶36. Ribotta BS. Causas de defunción mal definidas en las provincias de Argentina, 2001-2013. Mem Inst Invest Cienc Salud 2016; 14:86-95.^,3737. Ribotta BS. Hacia el seguimiento de los determinantes sociales de la salud: alcances y limitaciones de las estadísticas de defunción en la Argentina (2001-2009). Rev Fac Nac Salud Pública 2013; 31:139-48. and also the most complete data on both deaths and educational level at the department level. Unfortunately, the poor quality of data on educational level in deaths for the rest of Argentina, makes impossible to conduct a nation-wide study of all departments. For example, in Argentina in 2018, 69% of all deaths of individuals aged 25 or over had unknown information on the educational level of them.

Departments with less than 5 deaths in any year were aggregated to another department. Firstly, the department with less than 5 deaths in any year was aggregated to another contiguous department with the same situation (< 5 deaths). Secondly, if the resulting aggregation increases the number of annual deaths to 5 or more per year in the new area, both departments form a new spatial unit. Otherwise, both departments are aggregated to another contiguous area with the same situation (< 5 deaths in any year), until the set of departments forms a new spatial unit with five deaths or more per year. This procedure was carried out within each province and, as a result, 16 spatial units remained. Thus, in Mendoza there were three new spatial units. In the western part of the province, a spatial unit comprised the departments of Rivadavia, San Carlos, Tunuyán, and Tupungato. The second spatial unit comprised the departments in the south of Mendoza (General Alvear, Malargüe, and San Rafael), while the third spatial unit comprised departments in the northeast of the province (Junín, La Paz, Lavalle, and Santa Rosa). In the province of San Juan there were three new spatial units. Two spatial units were located in the western part (departments of Calingasta, Iglesia, Jáchal, Pocito, Sarmiento, Ullum, and Zonda) and eastern part of the province (Alabardón, Angaco, Caucete, San Martín, Valle Fertil, and 25 de Mayo). The third spatial unit comprised departments in the western metropolitan area of San Juan (Chimbas, 9 de Julio, and Santa Lucía). Virtually all spatial units had information on educational level in at least 80% of deaths annually for 1990 to 2018, with very few, negligible exceptions. For example, the Rivadavia department of the San Juan province had some missing data on educational level in 1990 (in 4 deaths out of 14) and in 1993 (in 2 deaths out of 8).

Two variables were used to measure local-level of socioeconomic status and urbanization. The percentage of households with unsatisfied basic needs was used as an indicator of socioeconomic status of each department. Population density (individuals per square kilometer) was used as an indicator of the urbanization level of the department. Population density and unsatisfied basic needs were estimated for each year from 1990 to 2018 by a linear projection using census data from 1991, 2001, and 2010. This estimation was also performed using population at the department level separately by sex, age group, and educational level.

To measure macroeconomic conditions, the annual change in the Gross Geographic Product (GGP, province output) of each province from 1994 to 2018 was used. This was a province-level factor, assigned to each department, with two categories indicating whether the year is one of economic expansion (positive GGP growth) or recession (null or negative GGP growth). These data were obtained from the Argentine Ministry of Economy ³⁸38. Ministerio de Hacienda. Mendoza: informe productivo provincial. Buenos Aires: Ministerio de Hacienda; 2019.^,3939. Ministerio de Hacienda. Mendoza: informe sintético de caracterización socio-productiva. Buenos Aires: Ministerio de Hacienda; 2014.^,4040. Ministerio de Hacienda. San Juan: informe sintético de caracterización socio-productiva. Buenos Aires: Ministerio de Hacienda; 2014. and the Ministry of Economy and Finance of the Province of San Juan ⁴¹41. Instituto de Investigaciones Económicas y Estadísticas. Producto geográfico bruto de la Provincia de San Juan. San Juan: Ministerio de Hacienda y Finanzas; 2019..

Statistical analysis

To test the associations of premature ischemic heart disease mortality with individual and contextual factors, a multilevel Poisson model ⁴²42. Subramanian SV, Duncan C, Jones K. Multilevel perspectives on modeling census data. Environ Plan A 2001; 33:399-417. was estimated with cells at level 1, consisting of individuals in numerators and denominators cross-tabulated by age group, sex, and educational level, which were nested within 16 departments (or aggregated departments) at level 2. The model additionally adjusts for time (calendar year), province of residence, and a random intercept for the department. This base model (Model 1) estimates relative educational inequality in premature ischemic heart disease mortality from 1990 to 2018, averaged over years and adjusted for the included covariates. Model 2 subsumes Model 1 and further includes a dichotomous variable indicating whether the year is one of economic expansion (positive GGP growth) or recession (null or negative GGP growth) to assess educational inequality in premature ischemic heart disease mortality from 1994 to 2018. Model 3 subsumes Model 2 and further includes an interaction between the recession/expansion binary variable and educational level. Model 4 is similar to Model 1, however, it includes an interaction between year and educational level to allow for the possibility of differential evolution in mortality rates by educational level from 1990 to 2018. Finally, Model 5 subsumes Model 4 and further includes the recession/expansion binary variable, restricted to the period 1994 to 2018. Model 5 allows investigating annual changes in educational level in ischemic heart disease mortality net of changes in macroeconomic conditions. As a measure of relative inequality, the ratio of mortality rates between the two educational levels were used and adjusted for the rest of the covariates. To interpret the results, values of population density and unsatisfied basic needs were transformed into z-scores. Data were analyzed using Stata version 13.1 (https://www.stata.com).

Results

Table 1 shows the characteristics of premature deaths from ischemic heart disease. We observed 9,630 deaths, with most of them occurring in Mendoza (6,865; 71%). Unadjusted death rates were higher in men compared with women, in the 45-64 age group compared with the 25-44 age group, and in lower than in higher educated individuals (Table 1). A declining time trend was generally observed in men, in the 45-64 age group, and in individuals with a high educational level (Table 1). The unadjusted rates were higher in San Juan than in Mendoza. The median percentage of households with unsatisfied basic needs was 14 (standard deviation - SD = 4; interquartile range - IQR: 11-16; not shown in Table 1), while the median population density was 158 inhabitants per km² (SD = 1,145; IQR: 6-998; not shown in Table 1).

In the base model (Model 1), we observed greater premature ischemic heart disease mortality among the low-education group, males, and older individuals (Table 2). However, there were no independent relationships between premature ischemic heart disease mortality and either poverty levels or population density of departments (Table 2). Overall, those with low education had 80% higher premature mortality than those with high-medium education (incidence-rate ratio - IRR = 1.8; 95% confidence intervals - 95%CI: 1.7-1.9). In Model 2, which further adjusts for economic recession/expansion, there were only small differences when compared to Model 1, except for the province of residence variable. Furthermore, recessions were associated with 15% lower premature ischemic heart disease mortality, compared to years of economic growth (IRR = 0.85; 95%CI: 0.77-0.95) (Table 2).

Table 3 shows the changes in educational inequality in ischemic heart disease mortality throughout years of expansion and recession. These estimates are derived from Model 3, where we associated educational level with years categorized as expansion or recession. While the mortality IRR by education during expansions is slightly lower than during recessions (1.78 vs. 1.84), the two quantities were not statistically different (Table 3): during recessions relative education inequality might have grown by only a 4% (ratio of IRRs = 1.04; 95%CI: 0.93-1.16).

Figure 1 and Figure 2 show education-adjusted ischemic heart disease mortality rates as well as mortality IRRs by education, respectively, based on marginal predicted rates obtained following multilevel Poisson model including all explanatory variables and an interaction between year and educational level (Model 4). Although adjusting for whether years had economic expansion or recession, a general trend of increasing educational inequalities in ischemic heart disease mortality was always observed (figures available upon request, for Model 5 restricted from 1994 to 2018).

Discussion

Using mortality data linked to geographic data from two Argentine provinces observed over 29 years, we found the following: (1) lower education is associated with higher premature ischemic heart disease mortality regardless of key individual and contextual factors, including department-level poverty and urbanization; (2) during our 29-year period there was a constant trend of increasing educational inequalities in premature ischemic heart disease mortality; (3) premature ischemic heart disease mortality was lower in years of economic recession compared with years of economic growth; (4) however, there were no significant differences in educational inequalities in premature ischemic heart disease mortality between both periods of recession and growth. To our knowledge, this is the first Latin American study that analyzed differences in premature ischemic heart disease mortality by educational level during periods of recession and economic expansion.

Similar to another study that investigated socioeconomic differences in risk factors for chronic diseases in Argentina ⁴³43. Fleischer NL, Diez Roux AV, Alazraqui M, Spinelli H, De Maio F. Socioeconomic gradients in chronic disease risk factors in middle-income countries: evidence of effect modification by urbanicity in Argentina. Am J Public Health 2011; 101:294-301., our study found an increased risk of ischemic heart disease deaths in individuals with low educational level compared with individuals with medium-high educational level. These data corroborate other studies that found differences in ischemic heart disease mortality according to education level in higher-income countries ⁴4. Avendano M, Kunst AE, Huisman M, Lenthe FV, Bopp M, Regidor E, et al. Socioeconomic status and ischaemic heart disease mortality in 10 western European populations during the 1990s. Heart 2006; 92:461-7.^,55. Leinsalu M, Vågerö D, Kunst AE. Estonia 1989-2000: enormous increase in mortality differences by education. Int J Epidemiol 2003; 32:1081-7.^,4444. Chen L, Tan Y, Yu C, Guo Y, Pei P, Yang L, et al. Educational disparities in ischaemic heart disease among 0.5 million Chinese adults: a cohort study. J Epidemiol Community Health 2021; 75:1033-43.^,4545. Ernstsen L, Bjerkeset O, Krokstad S. Educational inequalities in ischaemic heart disease mortality in 44,000 Norwegian women and men: the influence of psychosocial and behavioural factors. The HUNT Study. Scand J Public Health 2010; 38:678-85.^,4646. Kulhánová I, Menvielle G, Hoffmann R, Eikemo TA, Kulik MC, Toch-Marquardt M, et al. The role of three lifestyle risk factors in reducing educational differences in ischaemic heart disease mortality in Europe. Eur J Public Health 2017; 27:203-10..

A trend of increasing educational inequalities in premature ischemic heart disease mortality was observed from 1990 to 2018, as a result of a decrease in mortality in the population with a high-medium educational level and an increase in the population with low educational level. This trend could be associated with a decrease in the prevalence of smoking from 2005 to 2013 in Argentina ⁴⁷47. Ferrante D, Jörgensen N, Langsam M, Marchioni C, Torales S, Torres R. Inequalities in the distribution of cardiovascular disease risk factors in Argentina. A study from the 2005, 2009 and 2013 National Risk Factor Survey (NRFS). Rev Argent Cardiol 2016; 84:133-9.^,4848. Rodríguez López S, Bilal U, Ortigoza AF, Diez-Roux AV. Educational inequalities, urbanicity and levels of non-communicable diseases risk factors: evaluating trends in Argentina (2005-2013). BMC Public Health 2021; 21:1572., but with a parallel increase in educational inequalities in smoking during this period ⁴⁷47. Ferrante D, Jörgensen N, Langsam M, Marchioni C, Torales S, Torres R. Inequalities in the distribution of cardiovascular disease risk factors in Argentina. A study from the 2005, 2009 and 2013 National Risk Factor Survey (NRFS). Rev Argent Cardiol 2016; 84:133-9.. Obesity, also linked to ischemic heart disease deaths, also revealed increasing educational inequalities in women from 2005 to 2009, a period of high economic growth ⁴⁹49. Linetzky B, De Maio F, Ferrante D, Konfino J, Boissonnet C. Sex-stratified socio-economic gradients in physical inactivity, obesity, and diabetes: evidence of short-term changes in Argentina. Int J Public Health 2013; 58:277-84.. Another study found an increase in these inequalities among men from 2005 to 2013 ⁴⁸48. Rodríguez López S, Bilal U, Ortigoza AF, Diez-Roux AV. Educational inequalities, urbanicity and levels of non-communicable diseases risk factors: evaluating trends in Argentina (2005-2013). BMC Public Health 2021; 21:1572..

The risk of premature mortality due to ischemic heart disease was lower during years with negative GDP growth, i.e., recession years, compared with years of economic expansion. This finding, which corroborates the existing literature from higher-income countries, suggests that during recession years factors such as decreased smoking, reduced fat intake, and less exposure to air pollution may be more influential in lowering ischemic heart disease mortality ¹²12. Ruhm CJ. A healthy economy can break your heart. Demography 2007; 44:829-48.^,5050. Franco M, Ordunez P, Caballero B, Tapia Granados JA, Lazo M, Bernal JL, et al. Impact of energy intake, physical activity, and population-wide weight loss on cardiovascular disease and diabetes mortality in Cuba, 1980-2005. Am J Epidemiol 2007; 166:1374-80. than the stress caused by the loss of employment and the financial crisis that Argentina experienced in 2001.

Despite the decline in premature ischemic heart disease mortality during recession years, educational disparities in mortality remained stable between years of recession and years of economic growth. This finding is in line with what was found in Finland ¹⁷17. Lammintausta A, Immonen-Räihä P, Airaksinen JKE, Torppa J, Harald K, Ketonen M, et al. Socioeconomic inequalities in the morbidity and mortality of acute coronary events in Finland: 1988 to 2002. Ann Epidemiol 2012; 22:87-93. and Spain ⁵¹51. Bartoll X, Gotsens M, Marí-Dell'Olmo M, Palència L, Calvo M, Esnaola S, et al. Stable socioeconomic inequalities in ischaemic heart disease mortality during the economic crisis: a time trend analysis in 2 Spanish settings. Arch Public Health 2019; 77:12.. This may indicate that more general factors, such as the decrease in air pollution during recession years ⁵²52. Li Z, Chen WT, Chang IC, Hung CC. Dynamic relationship between air pollution and economic growth in Taiwan deduced from mathematical models. Clean (Weinh) 2021; 49:2100081., due to less traffic and decreased industrial activity, could reduce the risk of premature ischemic heart disease mortality in both educational groups by similar proportions. A decrease in smoking during recession years has been observed for a long time ⁵³53. Mitchell WC. What happens during business cycles: a progress report. New York: National Bureau of Economic Research; 1951. and could be also causing the decrease in premature ischemic heart disease mortality.

This study has several limitations. Firstly, we could not introduce explanatory variables related to lifestyles, such as smoking, physical activity, and alcohol consumption. Data on these variables are not available together with mortality data. Secondly, the spatial units used in this study may reflect a broad level of generalization, suppressing significant variations within the spatial units. This is part of the modifiable area unit problem. As previously observed in other studies, using smaller areas than departments (e.g., census tracts), may show positive relationships between area poverty levels and premature ischemic heart disease mortality. Finally, the findings of this study correspond only to the provinces of Mendoza and San Juan, two areas that cannot be considered as representative, in demographic and socioeconomic terms, of the rest of the 24 jurisdictions (23 provinces and the city of Buenos Aires) of Argentina. However, the GGPs of Mendoza and San Juan have a medium-high and medium position compared with other jurisdictions, respectively ⁵⁴54. Muñoz F, Trombetta M. Indicador Sintético de Actividad Provincial (ISAP): un aporte al análisis de las economías regionales argentinas. Investigaciones Regionales - Journal of Regional Research 2015; (33):71-96.. Demographically, both provinces are similar to Argentina. The percentage of men and women is the same (49% in Mendoza, San Juan and Argentina), while the percentage of economically active individuals is very similar (15-64 years: 64% in Mendoza, 63% in San Juan, and 64% in Argentina) ⁵⁵55. Instituto Nacional de Estadística y Censos de la República Argentina. Estructura de la población: composición y distribución. https://www.indec.gob.ar/indec/web/Nivel4-Tema-2-18-77 (accessed on 08/Feb/2023).
https://www.indec.gob.ar/indec/web/Nivel... .

Despite the continuing decline in ischemic heart disease mortality in Argentina ⁵⁶56. Ministerio de Salud de la Nación. Enfermedades no transmisibles y factores de riesgo. Buenos Aires: Ministerio de Salud de la Nación; 2011., this study highlights that social inequalities in premature mortality risk have increased over time. Therefore, prevention policies should be more focused on populations of lower socioeconomic status. Finally, the years of economic recession were associated with decreases in the risk of ischemic heart disease mortality. Therefore, it could be appropriate to take advantage of the years of economic growth, with greater availability of resources, to improve transport infrastructure in a more sustainable way, focusing in the use of public transportation, bicycles, and walking as a way of exercising, as well as to provide better access to healthy foods.

Acknowledgments

The authors acknowledge the valuable contribution of Ana Diez Roux. C. M. Leveau was supported by a short term scholarship from Drexel University (United States).

References

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