Shifting determinants of health inequalities in unstable times: Portugal as a case study

Shifting determinants of health inequalities in unstable times: Portugal as a case study Abstract Background We explore how health inequalities (HI) changed in Portugal over the last decade, considering it is one of the most unequal European countries and has gone through major economic changes. We describe how inequalities in limitations changed considering different socioeconomic determinants, in order to understand what drove changes in HI. Methods We used cross-sectional waves from the European Survey on Income and Living Conditions database to determine how inequalities in health limitations changed between 2004 and 2014 in Portugal in residents aged 16 years and over. We calculated prevalence estimates of limitations and differences between income terciles, the concentration index for each year and its decomposition and multiple logistic regressions to estimate the association between socioeconomic determinants and limitations. Results The prevalence of health limitations increased in Portugal since 2004, especially after 2010, from 35 to 47%. But the difference between top and bottom income terciles decreased from 23 to 10 percentage points, as richer people experienced a steeper increase. This was driven by an increase in prevalence among economically active people, who, from 2011 onwards, had more limitations (OR and 95% CI were 2.42 [2.13–2.75] in 2004 and 0.71 [0.65–0.78] in 2014). Conclusion These results suggest worsening health in Portugal in the last decade, possibly connected to periods of economic instability. However, absolute HI decreased considerably in the same period. We discuss the possible role of diverse adaptation capacity of socioeconomic groups, and of high emigration rates of young, healthier people, reflecting another side of the ‘migrant health effect’. Introduction Socioeconomic health inequalities (HI) are ubiquitous. They have been observed worldwide as long as data have been available. It appears that, regardless of place and time, health tends to follow the patterning of socioeconomic differences (1). Various socioeconomic indicators—education, financial resources, employment or occupation—determine HI, operating through different pathways. Education leads to better information, cognitive abilities and determines preferences (2); financial resources, such as income or wealth, allow individuals to access health-producing resources, such as healthcare or housing. Employment not only provides income, but also a sense of control over one’s life, lack of which is strongly associated with important stress reactions, which can deteriorate health (3). People with higher occupational grades also tend to have a stronger sense of control over their health, their jobs and their lives (4), but occupation can also reflect an individual’s place in society, showing the effect of rank and subjective feelings towards one’s position in society (2). The simultaneous analysis of various socioeconomic determinants of HI can provide clues as to which processes are more important in the creation of HI (5). A better understanding of which processes shape HI will help to build a base to design policies that tackle them effectively. Portugal is a particularly interesting case study for HI. The country has had low economic growth (6), and despite substantial investments in social protection, education and healthcare (7, 8), remains one of the most unequal European Union countries in income distribution (9). This is reflected in health distribution: several analyses found Portugal to have some of the highest HI among European countries (10–12). Additionally, Portugal has gone through a period of economic crisis and implementation of austerity measures in the last years, that have led to a spike in emigration (13) and a deterioration of public social services (14). A recent review of the impact of economic crises found that they tended to aggravate HI in a variety of countries (15). However, the review noted that results were variable, perhaps due to differing welfare policies, or the diversity of health and socioeconomic variables. Poor understanding of how economic crises shape HI hinders the interpretation of these results. This work aims to support policy choices that attempt to mitigate the effect of economic crises or other contextual changes on HI. To do this, we describe how HI changed in Portugal over the last decade, in light of the important social and macroeconomic changes that the country has been through, and how the socioeconomic determinants of these inequalities changed. We used data from the cross-sectional waves of the European Survey on Income and Living Conditions (EU-SILC), from 2004 to 2014. Portugal is used as a case study, but this analysis is applicable to other countries as it describes how determinants of HI can be shaped by contextual transformations. This is particularly useful considering that many countries have recently gone through similar macroeconomic changes as Portugal. Methods This analysis was performed using data from the Portuguese cross-sectional waves of EU-SILC between 2004 and 2014 (provided by Eurostat in December 2015). EU-SILC is an annual survey carried out in several European countries with a mixed longitudinal and cross-sectional design. Despite this mixed design, cross-sectional samples are representative of the target population when appropriate weights are used (16). Portugal participates since 2004 using a stratified, multi-stage, household-based sample. The survey collects data on living conditions and includes three health related questions: limitations in daily activities due to health problems, self-reported health (SRH) and chronic conditions. We used ‘limitations’ as our health outcome. Individuals were asked if they were limited in activities they usually did because of health problems. Possible answers included ‘Yes, strongly limited’, ‘Yes, limited’ or ‘No’. The first two options were collapsed, creating a binary variable (1 = ‘with limitations’, 0 = ‘without limitations’). This health outcome was chosen as it provides an objective measure than SRH and should capture health status more accurately (17). The initial descriptive analysis was also done for the other two health variables: SRH and chronic conditions. SRH is a widely used survey measure in which respondents rate their overall health; we used SRH as a binary variable in which ‘bad’ and ‘very bar’ health were the outcome. ‘Chronic conditions’ is a self-assessed question in which respondents are asked whether they have a chronic condition; this was also used as a binary variable, in which having a chronic condition was the outcome. The following variables were included in the analysis: Age at interview (in years). Sex (male or female). Income: yearly household equivalised disposable income, in euros, deflated using the harmonised index of consumer prices (18). Education: defined by highest International Standard Classification of Education (ISCED) level attained (19), categorised into ‘primary or less’ or ‘more than primary’. Occupation: based on the International Standard Classification of Occupations (ISCO) used in EU-SILC, occupations were categorised in white or blue collar, following previous work (ISCO codes 1–5 were white collar, 6–9 blue collar and armed forces were excluded) (20). Activity: based on the EU-SILC variable ‘self-defined current economic status’, people were categorised as ‘active’ if they defined themselves as being employed (part or full time), in training or studying, or fulfilling domestic tasks; and ‘inactive’ if they were unemployed, retired, unfit to work or in the ‘other inactive’ category. Savings: EU-SILC further asks households about their capacity to face unexpected financial expenses and to afford one-week annual holiday away from home. These two variables were merged and transformed into a binary variable so that the value ‘0’ was attributed to households who could afford both and ‘1’ to the remaining households. We used the complete sample of residents aged 16 and over. The proportion of individuals who had limitations was calculated for each year in the overall sample, within each income tercile, and stratified by age groups. Income terciles were calculated according to the distribution of income for each year. The concentration index (CIx) for income-related inequalities in limitations was calculated for each year. The CIx is a measure of inequalities based on the health concentration curve. This curve is the result of plotting of the cumulative percentage of individuals, ranked by income, with the cumulative percentage of limitations. In this plot, perfect equality is represented by a diagonal line, showing an equal distribution of limitations among the population, regardless of income. The CIx is calculated as twice the area between the concentration curve and the line of perfect equality. When there is perfect equality, the CIx is zero. By convention, if all limitations are concentrated in the richest (poorest) person, the CIx is 1 (-1). However, with dichotomous outcome variables, the CIx is not within the [-1,1] range and between-year comparability may be limited; following Wagstaff (21), to minimise this limitation, we normalised the CIx by dividing it by 1 minus the proportion of respondents reporting limitations in each year. Wagstaff et al. (22) showed that the CIx can be decomposed into contributions of individual factors to the income-related HI. This analysis allows for the quantification of how each factor (i.e. each socioeconomic variable) contributes to the overall distribution of the health outcome among income ranks. The contribution of each factor is the product of the elasticity of that factor with respect to the health variable (i.e. the proportional change of a specific factor in relation to a proportional change in the health variable) and the CIx of that factor (i.e. the degree of income-related inequality of that factor). Finally, we performed a multiple logistic regression for each year, using the dichotomous health variable (limitations) as an outcome. We included all the demographic and socioeconomic variables listed above as explanatory variables: age, sex, income, education, occupation, activity and savings. These were all added to the model simultaneously. Analyses were weighed by a personal cross-sectional weight provided by the EU-SILC database, which controls for geographical, household size, gender, and age group distribution, and non-response within each household. Analyses were done on SPSS Statistics v21 and in ADePT Software v6.0 using a non-linear model for the CIx. Results Table 1 summarises the sample characteristics. Yearly sample size ranged from 9947 individuals in 2007 to 14 650 in 2014. Average age increased from 46.3 to 49.0-years-old from 2004 to 2014. The proportion of individuals with limitations also increased from 35.2 to 47.3%. Median income increased between 2004 and 2012, from 5869 to 8366 euros per year, and dropped to 8265 euros in 2014. There was also an increase in the proportion of people with secondary and tertiary education and in white-collar occupations, both representing approximately half the sample in 2014. The proportion of active people decreased from 70% in 2004 to 58.5% in 2014. Table 1 Sample characteristics per year Year  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  Sample size  11 690  10 706  10 148  9947  10 101  11 101  11 380  12 489  13 584  14 009  14 650  Age (mean)  46.3  46.6  46.8  47.0  47.2  47.4  47.7  47.9  48.8  48.7  49.0  Gender (women, %)  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.9  53.0  53.1  Limitations (%)  35.2  29.3  28.7  30.1  30.0  31.9  31.3  43.3  38.5  38.8  47.3  Income (€, median)  5869.50  6242.08  6512.14  6924.29  7672.91  7735.13  8198.83  8271.50  8366.97  8285.95  8265.94  Income (€, lowest tercile cut-off—P33)  4530.13  4841.51  5160.42  5465.58  6011.53  6163.88  6377.49  6537.98  6599.24  6592.84  6414.43  Income (€, highest tercile cut-off—P66)  7583.07  7956.39  8280.41  9215.01  9716.13  9821.59  10 372.96  10 581.76  10 571.86  10 606.75  10 411.02  Education (more than primary, %)  45.9  46.0  45.5  47.5  48.6  50.9  51.5  54.4  52.8  55.7  52.9  Occupation (white collar, %)  44.6  43.4  42.4  43.0  43.8  44.1  44.1  45.0  48.8  49.9  51.3  Activity (active, %)  70.2  69.8  69.4  68.8  68.6  64.0  62.8  62.3  58.8  57.6  58.5  Savings (can afford unexpected expenses and annual holiday, %)  38.3  39.6  39.1  37.8  34.6  35.5  34.4  40.0  37.7  34.1  37.1  Year  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  Sample size  11 690  10 706  10 148  9947  10 101  11 101  11 380  12 489  13 584  14 009  14 650  Age (mean)  46.3  46.6  46.8  47.0  47.2  47.4  47.7  47.9  48.8  48.7  49.0  Gender (women, %)  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.9  53.0  53.1  Limitations (%)  35.2  29.3  28.7  30.1  30.0  31.9  31.3  43.3  38.5  38.8  47.3  Income (€, median)  5869.50  6242.08  6512.14  6924.29  7672.91  7735.13  8198.83  8271.50  8366.97  8285.95  8265.94  Income (€, lowest tercile cut-off—P33)  4530.13  4841.51  5160.42  5465.58  6011.53  6163.88  6377.49  6537.98  6599.24  6592.84  6414.43  Income (€, highest tercile cut-off—P66)  7583.07  7956.39  8280.41  9215.01  9716.13  9821.59  10 372.96  10 581.76  10 571.86  10 606.75  10 411.02  Education (more than primary, %)  45.9  46.0  45.5  47.5  48.6  50.9  51.5  54.4  52.8  55.7  52.9  Occupation (white collar, %)  44.6  43.4  42.4  43.0  43.8  44.1  44.1  45.0  48.8  49.9  51.3  Activity (active, %)  70.2  69.8  69.4  68.8  68.6  64.0  62.8  62.3  58.8  57.6  58.5  Savings (can afford unexpected expenses and annual holiday, %)  38.3  39.6  39.1  37.8  34.6  35.5  34.4  40.0  37.7  34.1  37.1  Notes: Sample includes all individuals in the survey (16 years and older). Income refers to yearly household equivalised income, in euros, deflated using the harmonised index of consumer prices, base year 2015. ‘Active’ people include individuals who defined themselves as being employed, in training or studying, or fulfilling domestic tasks. ‘Savings’ is a variable build from two variables (capacity to face unexpected financial expenses and capacity to afford a one-week holiday per year). All values (except sample size) are calculated using sample weights for geographical, household size, gender and age group distribution, and non-response within each household. P33 and P66 are percentile 33 and 66, respectively. Figure 1 shows the proportion of individuals with limitations by year. This proportion was stable at around 30% until 2011, when it increased to 43%, and then increased again in 2014 to 47%. These changes occurred in all income terciles, but a few differences were noticeable: (i) in almost every year, the proportion of people with limitations was higher in the first tercile (the lowest-income population group), followed by the second, and lowest in the third; (ii) this difference was stable until 2011, when the proportion of limitations increased in all terciles, most markedly in the second and third; (iii) this led to a decrease in the absolute difference in limitations inequalities between the first and third income terciles. Figure 1 points out the absolute differences between the first and third income terciles in four years (23% age points in 2004 and 2010, 16 in 2011, and 10 in 2014). When stratified by age groups, the analysis presented in figure 1 shows that inequalities in limitations were highest in the older age groups, the increase in limitations in 2011 occurred in younger age groups, and the oldest age groups showed a decrease in limitations in 2012 (Supplementary figure S1A). Figure 1 View largeDownload slide Proportion of the sample with limitations per income tercile and in total. Notes: Income terciles are defined for the income distribution within that year. Values are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household, but do not control for compositional differences between income tercile groups. Absolute differences between the first and third terciles are presented for clarity for 2004, 2010, 2011 and 2014 Figure 1 View largeDownload slide Proportion of the sample with limitations per income tercile and in total. Notes: Income terciles are defined for the income distribution within that year. Values are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household, but do not control for compositional differences between income tercile groups. Absolute differences between the first and third terciles are presented for clarity for 2004, 2010, 2011 and 2014 The CIx was negative every year, as the prevalence of limitations was higher in poorer people (figure 2). The CIx ranged between 0.15 and 0.18 (in absolute values) until 2010 and dropped in 2011 to 0.09 and to 0.05 in 2014. Until 2010, every socioeconomic variable had a negative contribution to the CIx, meaning that they all contributed to pro-poor inequality in the distribution of limitations. However, after 2010 there were a few noticeable changes. Figure 2 View largeDownload slide Concentration index and decomposition of its components for income-related inequalities in limitations per year. Notes: For visual simplicity, residuals were excluded from the bar charts (but are included in the total concentration index). The analyses are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household Figure 2 View largeDownload slide Concentration index and decomposition of its components for income-related inequalities in limitations per year. Notes: For visual simplicity, residuals were excluded from the bar charts (but are included in the total concentration index). The analyses are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household First, activity now gave a positive contribution to the CIx. Detailed analysis of the contribution of each variable (Supplementary table S1A) showed that the elasticity of limitations with respect to activity changed in 2011, from positive to a negative contribution; the CIx of activity, on the other hand, remained stable. This means that, in all years, inactive people had lower incomes when compared with active people. However, while limitations were more prevalent in inactive people until 2010, they were more prevalent in active people after this year. Second, the contribution of education was considerably reduced from around 0.03 until 2010 to less than half from 2011. The CIx of education remained approximately the same through the years, but its elasticity changed, indicating differences in limitations between educational groups decreased over the years. Third, occupation, which had a small contribution up until 2010, became the most important component of the CIx from 2011 onwards. This was due to an increase in the elasticity of the occupation component, meaning that inequalities in limitations between occupational groups increased. These findings are further explored by yearly logistic regression analyses. Figure 3 shows the odds ratios (OR) for occupation, education and activity for multiple logistic regressions for each year, which controlled for socioeconomic and demographic variables. These show that, controlling for other variables, the odds of limitations were always higher in blue collar workers when compared with white collar, but especially so from 2011 onwards. People with primary education or less had higher odds of limitations when compared with people with more than primary education, although this decreased significantly from 2010 onwards. Finally, when compared with active people, inactive people showed significantly higher odds of limitations up until 2010, and from 2011 onwards this was inverted, as the OR was below 1. Figure 3 View largeDownload slide Odds ratios (and 95% confidence intervals) of limitations according to occupation, education and activity per year. Notes: Odds ratios (OR) are calculated from logistic regressions for each year, which controlled for sex, age, income and savings. OR of occupation are blue collar in relation to white collar; OR of education are primary or less in relation to more than primary; OR of activity are inactive in relation to active Figure 3 View largeDownload slide Odds ratios (and 95% confidence intervals) of limitations according to occupation, education and activity per year. Notes: Odds ratios (OR) are calculated from logistic regressions for each year, which controlled for sex, age, income and savings. OR of occupation are blue collar in relation to white collar; OR of education are primary or less in relation to more than primary; OR of activity are inactive in relation to active Analysis of the other two health outcomes provided by EU-SILC—SRH and chronic conditions—did not show such marked changes in the overall sample or in each income tercile over the years (Supplementary figures S1B and C). Discussion This analysis showed that health limitations increased in Portugal over the last decade, especially after 2010. This occurred in a time of socioeconomic instability, after the first announcement of austerity measures in 2010, followed by resignation of the ruling government and the beginning of external financial intervention (23). Immediate health impacts of recessions and economic crises were also reported in other European countries such as Spain (24) and Greece (25). A second finding was that the increase in prevalence of limitations was particularly marked in the richest terciles of the population, leading to a decrease in income-related inequalities. Between 2010 and 2011, the difference in the prevalence of limitations between the richest and the poorest income terciles decreased from 23 to 16 percentage points. Decomposition of the CIx and logistic regression analyses showed that the decrease in HI was driven by two important changes: (i) an inversion in the distribution of limitations among active and inactive people in 2011, whereby active people had more limitations than inactive people after 2010 and (ii) a decrease in the difference in prevalence of limitations among educational groups, such that people with less education always had higher prevalence of limitations, but less so after 2010. These findings are somewhat surprising, considering the deterioration of public social services in Portugal in the last years (14), negative growth rate of household disposable income in 2011 (26), and literature showing increases in HI after periods of economic crises and austerity measures (15, 27, 28). However, this literature is not consistent (15). In the case of Portugal, the decrease in HI may be a reflection of different adaptation capacities among socioeconomic groups. Research has shown that declining economies can lead to a deterioration of population health, which may be a consequence of economic circumstances forcing individuals to adapt to unexpected, health damaging events (29). In our analysis, richer individuals showed a steeper increase in limitations, which may reflect more difficulties in adapting to new economic circumstances than poorer people, possibly for inexperience in dealing with a less affluent lifestyle. The decrease in HI might also be understood in light of a process of selective migration. Emigration in Portugal increased markedly after 2009 (13), making it the 12th country with the highest emigration rate in 2010, most of which affecting the working age population, who also have the highest educational level (30). Migration is a selective process, as healthier people tend to migrate more easily (31); if healthier people migrated at a higher rate than their unhealthy counterparts, this might have led the richer tercile to become unhealthier, contributing to the decrease in HI after 2010. This explanation is compatible with the differences seen among age groups in our EU-SILC Portuguese sample, as the increasing HI were especially marked among the working age population. This would also explain the ‘inversion’ in the prevalence estimate of limitations between active and inactive people, as emigration would have happened pre-dominantly in people who were active and healthy, thus leaving behind an unhealthier group of active people. This would constitute the other side of the coin of the ‘healthy migrant effect’—a finding from multiple epidemiological studies that has shown that migrants are almost always healthier than the population they leave behind (31). Our results should be interpreted under some limitations. Firstly, data collection for the EU-SILC database might have excluded marginalised groups, as household survey non-respondents tend to have lower socioeconomic status and worse health (32). However, overall household response rates for the Portuguese cross-sectional sample have been around 90% every year (33), a measure that would be considered excellent by many researchers (34), and this analysis used weights that made the sample representative of the target population (35), making this a less likely explanation. Nevertheless, EU-SILC excludes institutionalised and homeless people, who may in the future be warranted as a research focus. A second limitation is that our selected health variable might be an inadequate measure of respondents’ health. Self-reported limitations have been used before as an objective measure of functional limitations (36, 37) and Eurostat uses it as an aspect of disability (38), making it the most objective measure available in EU-SILC. However, it is surprising that the other health variables show considerably less marked changed. On the other hand, studies have also shown that the association between functional limitations and SRH can be moderated by demographic and socioeconomic factors such as race (39), gender (40) or education (36). This may mean that, despite showing an increase in prevalence of limitations, richer people in this sample did not necessarily have worsening health status, but may have less capacity to deal with these limitations. Finally, it is important to note that we interpreted these results through time, but the analysis was based on cross-sectional samples. As such, despite the use of weights that made samples representative of the target population, there might be changes in the composition of the yearly samples populations that could impact the results. Our findings show that changes in HI may result from profound, sometimes counter-intuitive, changes in the determinants of health. Looking at how the determinants change can help in understanding the underlying mechanisms at work. In the case of Portugal, these might be explained by socioeconomic groups’ different ability to changing economic circumstances, or by a process of selective migration, although our analysis alone cannot confirm either. Further analyses should focus on a more detailed exploration of these changes in the determinants of health, perhaps using longitudinal data, in order to capture trajectories rather than compositional changes within socioeconomic groups. Supplementary data Supplementary data are available at EURPUB online. Acknowledgements I.C.M. is supported by a PhD grant from the Portuguese Foundation of Science and Technology and L.G. is supported by project UID/MAT/00006/2013 from the Centre of Statistics and its Applications from the University of Lisbon. Conflict of interest: None declared. Key points Portugal is a good case study for shifting health inequalities (HI), being one of Europe’s most unequal countries and having gone through substantial instability in the last decade. Prevalence of health limitations increased substantially in Portugal in conjunction with the start of socio-political instability. However, HI seem to have decreased over the same period, driven by an increase in limitations in active people. The impact of major economic changes in HI may occur through multiple mechanisms, such as migration trends and socio-economic groups’ different ability to adapt to changing circumstances. References 1 Glymour MM, Avendano M, Kawachi I. Socioeconomic status and health. Soc Epidemiol  Oxford University Press, New York, NY, 2014. pp. 17– 62. 2 Cutler DM, Lleras-Muney A, Vogl T. Socioeconomic status and health: dimensions and mechanisms. National Bureau of Economic Research; 2008. 3 Heckhausen J, Schulz R. A life-span theory of control. 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Shifting determinants of health inequalities in unstable times: Portugal as a case study

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Oxford University Press
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© The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
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1101-1262
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1464-360X
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10.1093/eurpub/ckx080
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Abstract

Abstract Background We explore how health inequalities (HI) changed in Portugal over the last decade, considering it is one of the most unequal European countries and has gone through major economic changes. We describe how inequalities in limitations changed considering different socioeconomic determinants, in order to understand what drove changes in HI. Methods We used cross-sectional waves from the European Survey on Income and Living Conditions database to determine how inequalities in health limitations changed between 2004 and 2014 in Portugal in residents aged 16 years and over. We calculated prevalence estimates of limitations and differences between income terciles, the concentration index for each year and its decomposition and multiple logistic regressions to estimate the association between socioeconomic determinants and limitations. Results The prevalence of health limitations increased in Portugal since 2004, especially after 2010, from 35 to 47%. But the difference between top and bottom income terciles decreased from 23 to 10 percentage points, as richer people experienced a steeper increase. This was driven by an increase in prevalence among economically active people, who, from 2011 onwards, had more limitations (OR and 95% CI were 2.42 [2.13–2.75] in 2004 and 0.71 [0.65–0.78] in 2014). Conclusion These results suggest worsening health in Portugal in the last decade, possibly connected to periods of economic instability. However, absolute HI decreased considerably in the same period. We discuss the possible role of diverse adaptation capacity of socioeconomic groups, and of high emigration rates of young, healthier people, reflecting another side of the ‘migrant health effect’. Introduction Socioeconomic health inequalities (HI) are ubiquitous. They have been observed worldwide as long as data have been available. It appears that, regardless of place and time, health tends to follow the patterning of socioeconomic differences (1). Various socioeconomic indicators—education, financial resources, employment or occupation—determine HI, operating through different pathways. Education leads to better information, cognitive abilities and determines preferences (2); financial resources, such as income or wealth, allow individuals to access health-producing resources, such as healthcare or housing. Employment not only provides income, but also a sense of control over one’s life, lack of which is strongly associated with important stress reactions, which can deteriorate health (3). People with higher occupational grades also tend to have a stronger sense of control over their health, their jobs and their lives (4), but occupation can also reflect an individual’s place in society, showing the effect of rank and subjective feelings towards one’s position in society (2). The simultaneous analysis of various socioeconomic determinants of HI can provide clues as to which processes are more important in the creation of HI (5). A better understanding of which processes shape HI will help to build a base to design policies that tackle them effectively. Portugal is a particularly interesting case study for HI. The country has had low economic growth (6), and despite substantial investments in social protection, education and healthcare (7, 8), remains one of the most unequal European Union countries in income distribution (9). This is reflected in health distribution: several analyses found Portugal to have some of the highest HI among European countries (10–12). Additionally, Portugal has gone through a period of economic crisis and implementation of austerity measures in the last years, that have led to a spike in emigration (13) and a deterioration of public social services (14). A recent review of the impact of economic crises found that they tended to aggravate HI in a variety of countries (15). However, the review noted that results were variable, perhaps due to differing welfare policies, or the diversity of health and socioeconomic variables. Poor understanding of how economic crises shape HI hinders the interpretation of these results. This work aims to support policy choices that attempt to mitigate the effect of economic crises or other contextual changes on HI. To do this, we describe how HI changed in Portugal over the last decade, in light of the important social and macroeconomic changes that the country has been through, and how the socioeconomic determinants of these inequalities changed. We used data from the cross-sectional waves of the European Survey on Income and Living Conditions (EU-SILC), from 2004 to 2014. Portugal is used as a case study, but this analysis is applicable to other countries as it describes how determinants of HI can be shaped by contextual transformations. This is particularly useful considering that many countries have recently gone through similar macroeconomic changes as Portugal. Methods This analysis was performed using data from the Portuguese cross-sectional waves of EU-SILC between 2004 and 2014 (provided by Eurostat in December 2015). EU-SILC is an annual survey carried out in several European countries with a mixed longitudinal and cross-sectional design. Despite this mixed design, cross-sectional samples are representative of the target population when appropriate weights are used (16). Portugal participates since 2004 using a stratified, multi-stage, household-based sample. The survey collects data on living conditions and includes three health related questions: limitations in daily activities due to health problems, self-reported health (SRH) and chronic conditions. We used ‘limitations’ as our health outcome. Individuals were asked if they were limited in activities they usually did because of health problems. Possible answers included ‘Yes, strongly limited’, ‘Yes, limited’ or ‘No’. The first two options were collapsed, creating a binary variable (1 = ‘with limitations’, 0 = ‘without limitations’). This health outcome was chosen as it provides an objective measure than SRH and should capture health status more accurately (17). The initial descriptive analysis was also done for the other two health variables: SRH and chronic conditions. SRH is a widely used survey measure in which respondents rate their overall health; we used SRH as a binary variable in which ‘bad’ and ‘very bar’ health were the outcome. ‘Chronic conditions’ is a self-assessed question in which respondents are asked whether they have a chronic condition; this was also used as a binary variable, in which having a chronic condition was the outcome. The following variables were included in the analysis: Age at interview (in years). Sex (male or female). Income: yearly household equivalised disposable income, in euros, deflated using the harmonised index of consumer prices (18). Education: defined by highest International Standard Classification of Education (ISCED) level attained (19), categorised into ‘primary or less’ or ‘more than primary’. Occupation: based on the International Standard Classification of Occupations (ISCO) used in EU-SILC, occupations were categorised in white or blue collar, following previous work (ISCO codes 1–5 were white collar, 6–9 blue collar and armed forces were excluded) (20). Activity: based on the EU-SILC variable ‘self-defined current economic status’, people were categorised as ‘active’ if they defined themselves as being employed (part or full time), in training or studying, or fulfilling domestic tasks; and ‘inactive’ if they were unemployed, retired, unfit to work or in the ‘other inactive’ category. Savings: EU-SILC further asks households about their capacity to face unexpected financial expenses and to afford one-week annual holiday away from home. These two variables were merged and transformed into a binary variable so that the value ‘0’ was attributed to households who could afford both and ‘1’ to the remaining households. We used the complete sample of residents aged 16 and over. The proportion of individuals who had limitations was calculated for each year in the overall sample, within each income tercile, and stratified by age groups. Income terciles were calculated according to the distribution of income for each year. The concentration index (CIx) for income-related inequalities in limitations was calculated for each year. The CIx is a measure of inequalities based on the health concentration curve. This curve is the result of plotting of the cumulative percentage of individuals, ranked by income, with the cumulative percentage of limitations. In this plot, perfect equality is represented by a diagonal line, showing an equal distribution of limitations among the population, regardless of income. The CIx is calculated as twice the area between the concentration curve and the line of perfect equality. When there is perfect equality, the CIx is zero. By convention, if all limitations are concentrated in the richest (poorest) person, the CIx is 1 (-1). However, with dichotomous outcome variables, the CIx is not within the [-1,1] range and between-year comparability may be limited; following Wagstaff (21), to minimise this limitation, we normalised the CIx by dividing it by 1 minus the proportion of respondents reporting limitations in each year. Wagstaff et al. (22) showed that the CIx can be decomposed into contributions of individual factors to the income-related HI. This analysis allows for the quantification of how each factor (i.e. each socioeconomic variable) contributes to the overall distribution of the health outcome among income ranks. The contribution of each factor is the product of the elasticity of that factor with respect to the health variable (i.e. the proportional change of a specific factor in relation to a proportional change in the health variable) and the CIx of that factor (i.e. the degree of income-related inequality of that factor). Finally, we performed a multiple logistic regression for each year, using the dichotomous health variable (limitations) as an outcome. We included all the demographic and socioeconomic variables listed above as explanatory variables: age, sex, income, education, occupation, activity and savings. These were all added to the model simultaneously. Analyses were weighed by a personal cross-sectional weight provided by the EU-SILC database, which controls for geographical, household size, gender, and age group distribution, and non-response within each household. Analyses were done on SPSS Statistics v21 and in ADePT Software v6.0 using a non-linear model for the CIx. Results Table 1 summarises the sample characteristics. Yearly sample size ranged from 9947 individuals in 2007 to 14 650 in 2014. Average age increased from 46.3 to 49.0-years-old from 2004 to 2014. The proportion of individuals with limitations also increased from 35.2 to 47.3%. Median income increased between 2004 and 2012, from 5869 to 8366 euros per year, and dropped to 8265 euros in 2014. There was also an increase in the proportion of people with secondary and tertiary education and in white-collar occupations, both representing approximately half the sample in 2014. The proportion of active people decreased from 70% in 2004 to 58.5% in 2014. Table 1 Sample characteristics per year Year  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  Sample size  11 690  10 706  10 148  9947  10 101  11 101  11 380  12 489  13 584  14 009  14 650  Age (mean)  46.3  46.6  46.8  47.0  47.2  47.4  47.7  47.9  48.8  48.7  49.0  Gender (women, %)  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.9  53.0  53.1  Limitations (%)  35.2  29.3  28.7  30.1  30.0  31.9  31.3  43.3  38.5  38.8  47.3  Income (€, median)  5869.50  6242.08  6512.14  6924.29  7672.91  7735.13  8198.83  8271.50  8366.97  8285.95  8265.94  Income (€, lowest tercile cut-off—P33)  4530.13  4841.51  5160.42  5465.58  6011.53  6163.88  6377.49  6537.98  6599.24  6592.84  6414.43  Income (€, highest tercile cut-off—P66)  7583.07  7956.39  8280.41  9215.01  9716.13  9821.59  10 372.96  10 581.76  10 571.86  10 606.75  10 411.02  Education (more than primary, %)  45.9  46.0  45.5  47.5  48.6  50.9  51.5  54.4  52.8  55.7  52.9  Occupation (white collar, %)  44.6  43.4  42.4  43.0  43.8  44.1  44.1  45.0  48.8  49.9  51.3  Activity (active, %)  70.2  69.8  69.4  68.8  68.6  64.0  62.8  62.3  58.8  57.6  58.5  Savings (can afford unexpected expenses and annual holiday, %)  38.3  39.6  39.1  37.8  34.6  35.5  34.4  40.0  37.7  34.1  37.1  Year  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  Sample size  11 690  10 706  10 148  9947  10 101  11 101  11 380  12 489  13 584  14 009  14 650  Age (mean)  46.3  46.6  46.8  47.0  47.2  47.4  47.7  47.9  48.8  48.7  49.0  Gender (women, %)  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.2  52.9  53.0  53.1  Limitations (%)  35.2  29.3  28.7  30.1  30.0  31.9  31.3  43.3  38.5  38.8  47.3  Income (€, median)  5869.50  6242.08  6512.14  6924.29  7672.91  7735.13  8198.83  8271.50  8366.97  8285.95  8265.94  Income (€, lowest tercile cut-off—P33)  4530.13  4841.51  5160.42  5465.58  6011.53  6163.88  6377.49  6537.98  6599.24  6592.84  6414.43  Income (€, highest tercile cut-off—P66)  7583.07  7956.39  8280.41  9215.01  9716.13  9821.59  10 372.96  10 581.76  10 571.86  10 606.75  10 411.02  Education (more than primary, %)  45.9  46.0  45.5  47.5  48.6  50.9  51.5  54.4  52.8  55.7  52.9  Occupation (white collar, %)  44.6  43.4  42.4  43.0  43.8  44.1  44.1  45.0  48.8  49.9  51.3  Activity (active, %)  70.2  69.8  69.4  68.8  68.6  64.0  62.8  62.3  58.8  57.6  58.5  Savings (can afford unexpected expenses and annual holiday, %)  38.3  39.6  39.1  37.8  34.6  35.5  34.4  40.0  37.7  34.1  37.1  Notes: Sample includes all individuals in the survey (16 years and older). Income refers to yearly household equivalised income, in euros, deflated using the harmonised index of consumer prices, base year 2015. ‘Active’ people include individuals who defined themselves as being employed, in training or studying, or fulfilling domestic tasks. ‘Savings’ is a variable build from two variables (capacity to face unexpected financial expenses and capacity to afford a one-week holiday per year). All values (except sample size) are calculated using sample weights for geographical, household size, gender and age group distribution, and non-response within each household. P33 and P66 are percentile 33 and 66, respectively. Figure 1 shows the proportion of individuals with limitations by year. This proportion was stable at around 30% until 2011, when it increased to 43%, and then increased again in 2014 to 47%. These changes occurred in all income terciles, but a few differences were noticeable: (i) in almost every year, the proportion of people with limitations was higher in the first tercile (the lowest-income population group), followed by the second, and lowest in the third; (ii) this difference was stable until 2011, when the proportion of limitations increased in all terciles, most markedly in the second and third; (iii) this led to a decrease in the absolute difference in limitations inequalities between the first and third income terciles. Figure 1 points out the absolute differences between the first and third income terciles in four years (23% age points in 2004 and 2010, 16 in 2011, and 10 in 2014). When stratified by age groups, the analysis presented in figure 1 shows that inequalities in limitations were highest in the older age groups, the increase in limitations in 2011 occurred in younger age groups, and the oldest age groups showed a decrease in limitations in 2012 (Supplementary figure S1A). Figure 1 View largeDownload slide Proportion of the sample with limitations per income tercile and in total. Notes: Income terciles are defined for the income distribution within that year. Values are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household, but do not control for compositional differences between income tercile groups. Absolute differences between the first and third terciles are presented for clarity for 2004, 2010, 2011 and 2014 Figure 1 View largeDownload slide Proportion of the sample with limitations per income tercile and in total. Notes: Income terciles are defined for the income distribution within that year. Values are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household, but do not control for compositional differences between income tercile groups. Absolute differences between the first and third terciles are presented for clarity for 2004, 2010, 2011 and 2014 The CIx was negative every year, as the prevalence of limitations was higher in poorer people (figure 2). The CIx ranged between 0.15 and 0.18 (in absolute values) until 2010 and dropped in 2011 to 0.09 and to 0.05 in 2014. Until 2010, every socioeconomic variable had a negative contribution to the CIx, meaning that they all contributed to pro-poor inequality in the distribution of limitations. However, after 2010 there were a few noticeable changes. Figure 2 View largeDownload slide Concentration index and decomposition of its components for income-related inequalities in limitations per year. Notes: For visual simplicity, residuals were excluded from the bar charts (but are included in the total concentration index). The analyses are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household Figure 2 View largeDownload slide Concentration index and decomposition of its components for income-related inequalities in limitations per year. Notes: For visual simplicity, residuals were excluded from the bar charts (but are included in the total concentration index). The analyses are weighed so as to reflect the geographical, gender, age group, household size distribution and the non-response rate within each household First, activity now gave a positive contribution to the CIx. Detailed analysis of the contribution of each variable (Supplementary table S1A) showed that the elasticity of limitations with respect to activity changed in 2011, from positive to a negative contribution; the CIx of activity, on the other hand, remained stable. This means that, in all years, inactive people had lower incomes when compared with active people. However, while limitations were more prevalent in inactive people until 2010, they were more prevalent in active people after this year. Second, the contribution of education was considerably reduced from around 0.03 until 2010 to less than half from 2011. The CIx of education remained approximately the same through the years, but its elasticity changed, indicating differences in limitations between educational groups decreased over the years. Third, occupation, which had a small contribution up until 2010, became the most important component of the CIx from 2011 onwards. This was due to an increase in the elasticity of the occupation component, meaning that inequalities in limitations between occupational groups increased. These findings are further explored by yearly logistic regression analyses. Figure 3 shows the odds ratios (OR) for occupation, education and activity for multiple logistic regressions for each year, which controlled for socioeconomic and demographic variables. These show that, controlling for other variables, the odds of limitations were always higher in blue collar workers when compared with white collar, but especially so from 2011 onwards. People with primary education or less had higher odds of limitations when compared with people with more than primary education, although this decreased significantly from 2010 onwards. Finally, when compared with active people, inactive people showed significantly higher odds of limitations up until 2010, and from 2011 onwards this was inverted, as the OR was below 1. Figure 3 View largeDownload slide Odds ratios (and 95% confidence intervals) of limitations according to occupation, education and activity per year. Notes: Odds ratios (OR) are calculated from logistic regressions for each year, which controlled for sex, age, income and savings. OR of occupation are blue collar in relation to white collar; OR of education are primary or less in relation to more than primary; OR of activity are inactive in relation to active Figure 3 View largeDownload slide Odds ratios (and 95% confidence intervals) of limitations according to occupation, education and activity per year. Notes: Odds ratios (OR) are calculated from logistic regressions for each year, which controlled for sex, age, income and savings. OR of occupation are blue collar in relation to white collar; OR of education are primary or less in relation to more than primary; OR of activity are inactive in relation to active Analysis of the other two health outcomes provided by EU-SILC—SRH and chronic conditions—did not show such marked changes in the overall sample or in each income tercile over the years (Supplementary figures S1B and C). Discussion This analysis showed that health limitations increased in Portugal over the last decade, especially after 2010. This occurred in a time of socioeconomic instability, after the first announcement of austerity measures in 2010, followed by resignation of the ruling government and the beginning of external financial intervention (23). Immediate health impacts of recessions and economic crises were also reported in other European countries such as Spain (24) and Greece (25). A second finding was that the increase in prevalence of limitations was particularly marked in the richest terciles of the population, leading to a decrease in income-related inequalities. Between 2010 and 2011, the difference in the prevalence of limitations between the richest and the poorest income terciles decreased from 23 to 16 percentage points. Decomposition of the CIx and logistic regression analyses showed that the decrease in HI was driven by two important changes: (i) an inversion in the distribution of limitations among active and inactive people in 2011, whereby active people had more limitations than inactive people after 2010 and (ii) a decrease in the difference in prevalence of limitations among educational groups, such that people with less education always had higher prevalence of limitations, but less so after 2010. These findings are somewhat surprising, considering the deterioration of public social services in Portugal in the last years (14), negative growth rate of household disposable income in 2011 (26), and literature showing increases in HI after periods of economic crises and austerity measures (15, 27, 28). However, this literature is not consistent (15). In the case of Portugal, the decrease in HI may be a reflection of different adaptation capacities among socioeconomic groups. Research has shown that declining economies can lead to a deterioration of population health, which may be a consequence of economic circumstances forcing individuals to adapt to unexpected, health damaging events (29). In our analysis, richer individuals showed a steeper increase in limitations, which may reflect more difficulties in adapting to new economic circumstances than poorer people, possibly for inexperience in dealing with a less affluent lifestyle. The decrease in HI might also be understood in light of a process of selective migration. Emigration in Portugal increased markedly after 2009 (13), making it the 12th country with the highest emigration rate in 2010, most of which affecting the working age population, who also have the highest educational level (30). Migration is a selective process, as healthier people tend to migrate more easily (31); if healthier people migrated at a higher rate than their unhealthy counterparts, this might have led the richer tercile to become unhealthier, contributing to the decrease in HI after 2010. This explanation is compatible with the differences seen among age groups in our EU-SILC Portuguese sample, as the increasing HI were especially marked among the working age population. This would also explain the ‘inversion’ in the prevalence estimate of limitations between active and inactive people, as emigration would have happened pre-dominantly in people who were active and healthy, thus leaving behind an unhealthier group of active people. This would constitute the other side of the coin of the ‘healthy migrant effect’—a finding from multiple epidemiological studies that has shown that migrants are almost always healthier than the population they leave behind (31). Our results should be interpreted under some limitations. Firstly, data collection for the EU-SILC database might have excluded marginalised groups, as household survey non-respondents tend to have lower socioeconomic status and worse health (32). However, overall household response rates for the Portuguese cross-sectional sample have been around 90% every year (33), a measure that would be considered excellent by many researchers (34), and this analysis used weights that made the sample representative of the target population (35), making this a less likely explanation. Nevertheless, EU-SILC excludes institutionalised and homeless people, who may in the future be warranted as a research focus. A second limitation is that our selected health variable might be an inadequate measure of respondents’ health. Self-reported limitations have been used before as an objective measure of functional limitations (36, 37) and Eurostat uses it as an aspect of disability (38), making it the most objective measure available in EU-SILC. However, it is surprising that the other health variables show considerably less marked changed. On the other hand, studies have also shown that the association between functional limitations and SRH can be moderated by demographic and socioeconomic factors such as race (39), gender (40) or education (36). This may mean that, despite showing an increase in prevalence of limitations, richer people in this sample did not necessarily have worsening health status, but may have less capacity to deal with these limitations. Finally, it is important to note that we interpreted these results through time, but the analysis was based on cross-sectional samples. As such, despite the use of weights that made samples representative of the target population, there might be changes in the composition of the yearly samples populations that could impact the results. Our findings show that changes in HI may result from profound, sometimes counter-intuitive, changes in the determinants of health. Looking at how the determinants change can help in understanding the underlying mechanisms at work. In the case of Portugal, these might be explained by socioeconomic groups’ different ability to changing economic circumstances, or by a process of selective migration, although our analysis alone cannot confirm either. Further analyses should focus on a more detailed exploration of these changes in the determinants of health, perhaps using longitudinal data, in order to capture trajectories rather than compositional changes within socioeconomic groups. Supplementary data Supplementary data are available at EURPUB online. Acknowledgements I.C.M. is supported by a PhD grant from the Portuguese Foundation of Science and Technology and L.G. is supported by project UID/MAT/00006/2013 from the Centre of Statistics and its Applications from the University of Lisbon. Conflict of interest: None declared. Key points Portugal is a good case study for shifting health inequalities (HI), being one of Europe’s most unequal countries and having gone through substantial instability in the last decade. Prevalence of health limitations increased substantially in Portugal in conjunction with the start of socio-political instability. However, HI seem to have decreased over the same period, driven by an increase in limitations in active people. The impact of major economic changes in HI may occur through multiple mechanisms, such as migration trends and socio-economic groups’ different ability to adapt to changing circumstances. References 1 Glymour MM, Avendano M, Kawachi I. Socioeconomic status and health. Soc Epidemiol  Oxford University Press, New York, NY, 2014. pp. 17– 62. 2 Cutler DM, Lleras-Muney A, Vogl T. Socioeconomic status and health: dimensions and mechanisms. National Bureau of Economic Research; 2008. 3 Heckhausen J, Schulz R. A life-span theory of control. 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The European Journal of Public HealthOxford University Press

Published: Feb 1, 2018

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