SSM - Population Health 19 (2022) 101152 Contents lists available at ScienceDirect SSM - Population Health journal homepage: www.elsevier.com/locate/ssmph Macro-level factors explaining inequalities in expected years lived free of and with chronic conditions across Spanish regions and over time (2006–2019) Elisenda Rentería a, *, Pilar Zueras a, b a Centre d’Estudis Demogràfics, Carrer de Ca n’Altayó, Edifici E2, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain b Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK A R T I C L E I N F O A B S T R A C T Keywords: Life expectancy has long been associated with macro-level factors, including health expenditures, but little Healthy life expectancy research has focused on the relationship with morbidity measures. This paper examines the relationship between Chronic conditions the expected years lived free of and with chronic conditions (YLFCC and YLCC) at age 50 and macroeconomic Health expenditures and social factors including, for the first time, several indicators of public health expenditure. We calculate Regional differences Spain YLFCC and YLCC for Spanish regions using the Sullivan method over a long period of time (2006–2019). Spain is a good case study due to two reasons. First, its national health system is decentralized among regional admin­ istrations since 2002. Second, the financial crisis of 2008 led to public health cuts in 2010–2014 that each region handled differently. We use fixed-effects models to assess the relationship between changes in macro-level regional indicators (socioeconomic factors, healthcare resources, health behavior and public health expendi­ tures) with YLFCC and YLCC across regions and over time. Results show that socioeconomic levels, public health expenditure, healthcare resources and health behaviors are associated with years lived free of and with chronic conditions when analyzing them independently. However, in the global model including all these dimensions only public health expenditure is associated with both YLFCC and YLCC for men and women, showing that a higher level of expenditures is correlated with more YLFCC and less YLCC. Therefore, regional authorities need to pay special attention to the level of investments on health services, as they are clearly associated with a better quality of living of the middle age and older population. 1. Introduction differ depending on the country under study (Groenewegen et al., 2003; Kondo et al., 2005; Laborde et al., 2021; Liu et al., 2010; Liu et al. 2010, Country and regional disparities in life expectancies (LE) have been 2010; Minagawa & Saito, 2018). long attached to income levels, and, more recently, to healthcare The case of Spain is of particular interest in the study of regional expenditure (Cremieux et al., 1999, 2005; Elola et al., 1995; Shaw et al., differences in health indicators because the country unfolded a decen­ 2002; Nixon & Ulman, 2006; Jain & Yuan, 2020; Martín-Cervantes, tralization of the national health system in favor of the regional ad­ Rueda López, & Cruz Rambaud, 2019). However, since LE are achieving ministrations that finished in 2002 (Bernal-Delgado et al., 2018). very high levels, researchers have turn into looking at the macro de­ Moreover, the 2008 financial crisis caused serious cuts on public pro­ terminants behind differences in morbidity measures, specifically on visions of health expenses which have been handled differently in each different indicators of healthy life expectancies (Gutierrez-Fisac et al., region (Gallo & Gené-Badia, 2013). Regional disparities in healthy life 2000; Groenewegen et al., 2003; Kondo et al., 2005; Jagger et al., 2008; expectancies of Spain have been observed for a long time (Gutierrez-­ Liu et al., 2010; Fourweather et al., 2015; Minagawa & Saito, 2018; Fisac et al., 2000; Gispert et al., 2007; Zueras & Rentería, 2020), but the Laborde et al., 2021). Here, the impact of socioeconomic variables on reasons behind these differences might have changed over time. In this healthy life expectancies is extensively observed, but the correlation vein, a recent study about trends in disease-free life expectancy at age 65 with healthcare supply indicators is not straightforward and results in Spain (Zueras & Rentería, 2020) found that regional differences * Corresponding author. E-mail address: erenteria@ced.uab.es (E. Rentería). https://doi.org/10.1016/j.ssmph.2022.101152 Received 25 January 2022; Received in revised form 15 June 2022; Accepted 17 June 2022 Available online 2 July 2022 2352-8273/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 decreased between 2006 and 2012 and widened again by 2017, sug­ correlated with DFLE at age 60 in China, jointly with GDP per capita, the gesting that not all regions recovered equally from the government cuts proportion of urban residents in the region and having fewer household to health services that occurred between 2009 and 2014. In addition, utilities (Liu et al., 2010). In the case of Japan, Kondo et al. (2005) found there were diverging trends by sex showing an expansion of morbidity that ordinary income was associated with DFLE at age 65 using data among men in most regions while women experienced a compression in from 1995, but only in the univariate correlation. In the multivariate about half of them. Whether these results are related to regional in­ analysis no socioeconomic indicator coefficient was significant. How­ equalities of socioeconomic levels or to changes in the supply of public ever, in another study for Japan with data from 2010 Minagawa and healthcare services and expenditure has not been explored yet. Saito (2018) showed that income per capita, unemployment levels, the This paper examines the factors explaining regional differences in percentage of workers older than 65 years old and social welfare health levels among the middle aged and older population, represented expenditure were strongly correlated to DFLE at age 65 for both men and by two health indicators based on LE at age 50 over the period from women. Finally, a recent study examined the correlation of the taxation 2006 until 2019 in Spain: the expected years lived free of chronic con­ potential, the ratio of manual workers to higher-level occupations, the ditions (YLFCC) and expected years lived with chronic conditions unemployment rate and the proportion of the population living in large (YLCC). All the health measures are based on the specific chronic con­ urban areas with the indicators of LE, DFLE and share of DFLE over LE in ditions that are the main causes of death and disability in Spain (Zueras France (Laborde et al., 2021). These indicators were associated inde­ & Rentería, 2020) with the exception of high cholesterol -to avoid biases pendently with these health measures, however, the correlation of the due to measurement changes over the period. We focus on the analysis of local taxation potential disappeared in the multivariate model. the prevalence of diseases over the LE because chronic conditions Studies that include healthcare supply measures are less common. determine quality of life, they are a cause of multiple health impair­ The study in Japan from Kondo et al. (2005) showed a positive associ­ ments and define health care needs, the level of utilization of healthcare ation between DFLE and the number of public health nurses and phy­ services as well as the demand of medical interventions. Using a healthy sicians, but no significant association with medical infrastructure such life expectancy measure is the best way to summarize both mortality and as hospitals, clinics, and beds. In Liu et al. (2010) with Chinese data they morbidity levels in one indicator (Robine et al., 1999). This type of in­ found the reversed results, with no correlation for the number of doctors dicators has been broadly used in public health because compared to life and nurses, but a strong correlation with the number of hospital beds per expectancy measures, they also provide information on the frailty level 10,000 residents. In the study of Groenewegen et al. (2003) in the and quality of life of individuals and allow to monitor trends of Netherlands, there were no significant correlations with any health compression and expansion of morbidity easily (Mathers et al., 2004; resource indicator. Lastly, in France, in the study of Laborde et al. GBD, 2020). In addition, we use the threshold of age 50, instead of the (2021), the density of nurses and physiotherapists were strongly asso­ more used age 65 for disability-free health expectancy, because chronic ciated with regional variations in health indicators but showed opposite diseases appear earlier in life than disability impairments (Verbrugge & directions. The density of physiotherapists had a positive association Jette, 1994). Among the factors explaining regional differences we with DFLE while the number of nurses per 1000 population had a include four groups of measures related to: (i) the socioeconomic level of negative relationship. Authors proposed that physiotherapists help the region, (ii) the level and type of health expenditure, (iii) health prevent functional deterioration of health problems whereas nurses services infrastructures, and (iv) individual behaviors. The advantage of respond to population needs. conducting regional analysis to examine these relationships is the higher Health behavior indicators are rarely included in these studies, and comparability of variables across regions and over time (Crémieux et al., when considered, they refer mainly to the proportion of smokers or 1999; Crémieux et al., 2005; Gutierrez-Fisac et al., 2000; Groenewegen heavy drinkers. In the research conducted in the Netherlands (Groene­ et al., 2003; Kondo et al., 2005; Liu et al., 2010; Minagawa & Saito, wegen et al., 2003) and Spain (Gutierrez-Fisac et al., 2000), they showed 2018; Laborde et al., 2021). the expected association with the number of estimated years lived in good health, that is to say, a higher proportion of smokers and heavy 2. Macro-level factors and healthy life expectancy drinkers was correlated with less years spent in good health, although the association was only relevant for men. Previous studies on the macro-level factors associated with healthy Finally, health expenditure indicators are present in many studies life expectancy have explored a broad range of indicators. Socioeco­ that try to explain country variations in LE over time (Linden & Ray, nomic factors such as Gross Domestic Product (GDP) per capita, un­ 2017; Mackenbach et al., 2017; Obrizan & Wehby, 2018), although they employment rates, material deprivation, several educational level are less used to understand healthy life expectancy differences. Mina­ indicators, as well as levels of urbanization show relevant associations gawa and Saito (2018) explored welfare expenditures in the long-term with health indicators. Nevertheless, the same indicators are not sig­ care insurance program in Japan and found a strong relationship be­ nificant for all studies. For example, Jagger et al. (2008) found that tween these expenditures and DFLE at age 65 for both men and women. Healthy Life Years (HLY) at age 50 (using activity limitation) was One of the reasons of not assessing health expenditure in health ex­ associated with GDP per capita for both men and women, and long-term pectancy studies could be that in unique country studies it is difficult to unemployment and educational level were also correlated with HLY for capture regional differences in health expenditures if those are not men, when comparing 25 European countries in 2005. However, in decentralized as in the case of Spain. Another reason is that health ex­ Fourweather et al. (2015), also with European countries, only material penditures are very much related to GDP, and therefore, it is difficult to deprivation and long-term unemployment was correlated with varia­ disentangle them. Nevertheless, health expenditures have shown to be a tions in HLY from 2005 to 2010 (also using activity limitations). No very powerful intervention, at least to improve life expectancy (Obrizan association was found for GDP, poverty risk for older population, & Wehby, 2018), and, therefore, it is worth analyzing if there would be inequality of income, life-long learning or low education attainment. similar effects on healthy life expectancies. This is even more important Results slightly differ in studies investigating within-country regional in a context of economic crisis, as in the case of 2008 in Spain, where it is disparities. A study in the Netherlands showed that unemployment rates crucial to assess the prioritization of health expenditure to ensure a explained regional disparities in Healthy Life Expectancy (HLE) (using higher quality of life of the population. self-perceived health) in 1992–1997 for both men and women, and Despite life expectancy with and without disability or activity limi­ educational level was also significant for women (Groenewegen et al., tations are the prominent indicators used in cross-country or regional 2003). In Spain, also unemployment was relevant to Disability Free Life comparative studies examining the above mentioned factors, chronic Expectancy (DFLE) at age 65, as well as illiteracy rate in 1996 (Gutier­ conditions are more often used to explore the relationship with health rez-FIsac et al., 2000). Illiteracy and unemployment were again expenditures. In an international study involving 17 European 2 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 countries, Mackenbach et al. (2017) found that relative increases in between health expenditure and population health (Joumard et al., healthcare expenditure was associated with lower mortality from 2008). Although uneven access to general practitioners could bias amenable causes, mostly based in chronic diseases. These results were prevalence of diagnosed diseases, the Spanish public healthcare system particularly significant regarding cerebrovascular and cardiovascular is universal and Spain is among the European countries with lowest diseases. On the contrary, healthcare expenditure was not associated share of unmet needs for medical examination or treatment (OECD, with a reduction of nonamenable mortality, except for low educated 2017). Proportions of missing information for each question and health men. In the case of Spain, regional disparities in years spent with and conditions included was very low among population aged 50 and over. without chronic conditions were increasing over time in recent years, Specifically, for all the health conditions included in each survey, less showing the importance to explore the reasons behind these trends than 0.05% individuals in our sample did not respond or did not know if (Zueras & Rentería, 2020), knowing the special economic circumstances they had ever suffered the specific condition (question 1). Similarly, the due to the financial crisis (2008–2014). Moreover, Spain is an ideal numbers of those who had missing information on all health conditions setting to study the association between regional disparities because it is here included were also below 0.05% in all years. a highly decentralized country. Large education and job outcomes di­ To estimate the expected years lived free of chronic conditions vergences translate into one of the European countries with higher (YLFCC) and the expected years lived with chronic conditions (YLCC) regional disparities in income and poverty (McGowan & San Millán, we used the Sullivan method (Sullivan, 1971). This method estimates 2019). Spain is also one of the most decentralized countries regarding the number of expected years that a life table cohort would live at a public spending, including health, where the provision of public services certain age in a healthy or unhealthy state, given that they experience and the regulation of human resources or number of centers is trans­ the same prevalence rates of disease as the population in that year. In ferred to regional governments (McGowan & San Millán, 2019). this case, YLFCC (free of chronic condition) is estimated using the Regional differences have already been found for DFLE (Gutiérrez-Fisac following formula: et al., 2000; Gispert et al., 2007), but when looking at years lived with 1 ∑ w and without chronic conditions only one study has been done for Cat­ YLFCCfx c = ⋅ Li (1 − Ci ) [1] alonia, a region of Spain (Sole-Auro & Alcaniz, 2015). Their findings lx i=x refer to an increase between 1994 and 2011 in the percentage of LE lived with disease, as well as an increase in functional limitations, for both where Li is the number of person-years lived in the age group (x, x + 5), men and women, however, they did not compare with other regions in lx corresponds to the number of survivors at age x (in this case age 50) Spain. Also, only one of this three studies (Gutiérrez-Fisac et al., 2000) and Ci is the proportion of people living with any of the conditions referred to regional characteristics to explain regional disparities. To our considered in the age group (x, x+5) (prevalence rate of having a knowledge, this is the first longitudinal analysis to assess the effects of chronic condition). YLCC is estimated by subtracting to LE at age 50, the changes in a broad range of macro-level indicators on changes in YLFCC YLFCC estimated with formula 1. and YLCC with high comparable data across entities and over a long Macro factors to explain the difference between regions were divided period of time. in four groups of variables. The first group measured socioeconomic characteristics and included: real GDP per capita in 2015 year constant prices, the level of inequality measured by the S80/S20 ratio, the per­ 3. Data and methods centage of rural population, the total unemployment rate, the older population (55+) unemployment rate, and the proportion of people over To conduct the analysis, we used all the information disaggregated 50 with primary education or less. The S80/S20 ratio represents the by Spanish regions. Life tables for Spanish regions are extracted from the relationship between the average income obtained by the 20% of the National Institute of Statistics (INE, 2019). Macro level factors related to population with the highest income (highest quintile), and the average socioeconomic level, health expenditure and healthcare supplies at the income obtained by the 20% of the population with the lowest income regional level come from the National Institute of Statistics, the Labor (lowest quintile). The percentage of rural population refers to the pro­ Force Survey, and the Public Healthcare Expenditure Statistics, avail­ portion of people living in municipalities smaller than 5000 inhabitants able from the Ministry of Health, Consumption and Social Welfare. in the region. The second group related to healthcare expenditures and is Prevalence data on diseases and chronic conditions, as well as represented by three measures: total expenditure on public healthcare, health-related behaviors come from six health surveys: The National expenditure on public primary care and public hospital expenditure. Health Surveys (ENS as per its Spanish acronym) of 2006, 2012 and These measures were included as real per capita measures over the 2017; and the European Health Interview Surveys (EHIS) of 2009, 2014 whole population in 2015 euros. We focused only on public health and 2019 (the latest available). Data from health surveys correspond to a expenditure due to their great importance in Spain. Public share of sample of the Spanish population that is representative for regions. We health expenditure represented 71% of total health expenditure in 2019, limited the study to 17 regions because we had to discard data from the but they were even more important in the past, and private health two autonomous cities of Ceuta and Melilla due to small sample. We expenditure are mainly devoted to co-payments for prescribed medi­ analyzed data for adults aged 50 and over. cines, dental care and optical care (OECD, 2017). Besides, previous The health conditions considered in this study are related to the research has shown that public health expenditure is especially crucial leading causes of disability, death or their main risk factors (Soriano to increase health levels, and private health expenditure increases are et al., 2018). These include asthma, back pain (including both low back mainly driven by rises in public health expenditure as well (Linden & and neck), chronic obstructive pulmonary disease (COPD), cancer, dia­ Ray, 2017). betes, heart disease, hypertension, myocardial infarction, and stroke. In the third group of variables, we considered healthcare services and The following set of questions addresses the presence of these health supplies and we used: number of hospital beds per 1000 population, the conditions: (1) Have you ever suffered from ‘this specific health condi­ number of general practitioner doctors and nurses per 1000 population tion’? And, for those who have, (2) Have you had it in the last 12 and the number of specialist doctors and nurses per 1000 population. months? (3) Has a doctor told you that you have it? We considered that Finally, in the last group we included measures that capture individual individuals have the chronic condition when they answered all the behaviors such as the percentage of overweight, the percentage of obese questions in the affirmative. Therefore, the prevalence rate of having a and the percentage of current and ever smokers. Given a moderate chronic condition was estimated for each year and region as the proportion of missing information on smoking, height and weight, we weighted proportion of individuals in each age group and sex having at used multiple imputation by sex using chained equations with age, ed­ least one of the chronic conditions under study. Equity in access to ucation and region as factors to impute these variables before deriving healthcare is definitely an issue when investigating the relationship 3 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 the proportion of smokers, overweight and obese. We created 20 im­ putations and used the random number seed 1234 for reproducibility. All these indicators were calculated among the 50+ population and separately for men and women. In a first step, we explored the Pearson’s correlation of each variable with each of the health measures (YLFCC and YLCC) for men and women separately. Secondly, we conducted fixed-effects multivariate models for each health outcome using all variables that turned out to be significant in the univariate analysis. We chose this model for panel data including regions because it allows to control for individual heterogeneity by controlling for unobserved time-invariant variables and variables that change over time but not across regions, such as national policies. Fixed- effect models were fit separately for each group of variables (socioeco­ nomic factors, health expenditures, health resources and health behav­ iors) and then we also run a final model including all variables together. The equation for the fixed effects is: Yit = αi + β1 Xit + μit [2] where αi (i = 1, …, n) is the unknown intercept for each entity (in this case 17 regions). Yit is the dependent variable (YLFCC or YLCC estimated Fig. 2. Expected years lived with chronic conditions (YLCC) at age 50 in with formula 1) where i = entity and t = time. Xit represents an inde­ Spanish regions over 2006–2019 by sex. pendent variable, β1 is its coefficient, and μit is the error term. Fixed- Source: Author’s calculations effects models were done separately for men and women, but to test if sex-differences along the variables used were significant we also con­ men and women, although women display a higher dispersion over re­ ducted a fixed-effects model for each health indicator with men and gions. YLFCC was higher in 2009 and decreased in many regions in 2012 women outcomes together. In this model, each entity referred to each to continue a more stable trend until 2019, when YLFCC increased again. sex and region. Also, since 2014, women experienced higher levels of YLFCC than men in more regions. Regarding YLCC, levels for women were higher than for 4. Results men all over the period, but trends were similar between sexes and followed what was found in YLFCC but in the opposite direction, as 4.1. Trends in health indicators and public health expenditure expected. Hence, in 2009 YLCC had the lower levels that increased in 2012. After 2012, trends turned more stable, and only in 2019 the level Figs. 1 and 2 show the box plots of the regional YLFCC and YLCC in of YLCC clearly diminished again. Spain between 2006 and 2019 for men and women. Therefore, these Macro factor levels used here refer to a wide range of socioeconomic, figures show the level of regional dispersion in each year, as well as the health expenditure, health services and human resources, and health- difference in levels over time. The maximum, minimum and median behavior indicators. Table S2 in the supplementary materials shows levels of YLFCC and YLCC can also be consulted in the supplementary the great range of disparities in each macro level factor. For more materials (Table S1). YLFCC indicators have similar trends and levels for detailed examination, Fig. 3 displays three-year moving averages of yearly values of per capita public health expenditure in the different regions of Spain. Levels of expenditure per capita differ greatly by re­ gion, where highest values surpass by 50% the lowest values. Trends Fig. 3. Total public health expenditure per capita (moving averages) from 2006 Fig. 1. Expected years lived free of chronic conditions (YLFCC) at age 50 in to 2019 in the 17 regions of Spain. Spanish regions over 2006–2019 by sex. Source: Author’s calculations from the Ministry of Health, Consumption and Source: Author’s calculations Social Welfare. Real values in 2015 euros. 4 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 look similar, although the onset of changes differ in some years for each correlated with the percentage of population with lower education, as region. In any case, there are two clear inflexion points as expenditure we saw in the individual correlation. Among health expenditure vari­ increased until around years 2008–2010, and then decreased until ables, public health expenditure per capita was positively correlated 2013–2014, where it increased again until nowadays. This applies to all with YLFCC and negatively with YLCC, and more hospital expenditure regions with two exceptions (La Rioja and Cantabria) that show a was also correlated with a higher YLFCC, but also with a higher YLCC. In continuous decrease in health expenditure since 2006. In 2012–2014 the the fixed-effects models of health resources and health behaviors, no majority of regions experienced the lowest levels of the period, as a variable turned significant for both YLFCC and YLCC. Finally, in the result of the public health cuts that started around 2008–2010. Since global model, only public health expenditure was significant for both 2013–2014, health expenditure recovered, but in 2017-2019 the health indicators, with a clear positive correlation with YLFCC and a 2008–2010 real values were not achieved yet. negative correlation with YLCC. In the case of YLFCC also hospital expenditure continued to have a positive correlation in the global model, and for YLCC, a higher percentage of individuals with primary 4.2. Macro-level factors and regional health trends education also implied a lower YLCC, in the same direction that in the univariate correlation. As explained in the methods, first of all we explored the univariate In models over women, some of the variables that were significant correlation between each variable and each health indicator, for men differed from those for men. In the socioeconomic variables models, only and women separately. The Pearson’s correlation coefficients of each the proportion of women with primary or less educational level was variable can be found in Table S3 of the supplementary materials. From negatively significant for YLFCC, but no correlation was found for YLCC. this analysis, we selected only variables that were significant in the In the model of health expenditure variables, only hospital expenditure correlation with at least three of the health indicators, to be sure that per capita was significant for both health indicators. As in the case of they were significant for both men and women. Hence, only real GDP men, none of the health resources variables was significant, and contrary per capita and percentage of population with primary education or less to them, the percentage of obese women was significant and negatively were significant for all health indicators. GDP per capita was positively correlated with YLFCC, and positively with YLCC. In the global model, correlated with YLFCC and negatively with YLCC, in the expected di­ though, total public health expenditure per capita turned out to be rection. However, the percentage of population with primary education significant for both health indicators, in the same direction than for men. or less was negatively correlated with YLFCC, but also negatively Hospital expenditure per capita continued to be significant in the model correlated with YLCC for men (not for women). This means that regions of YLFCC but reduced the coefficient and the level of significance. When with a higher proportion of men with lower education level are having a modelling YLCC, the number of specialist doctors per 1000 population lower YLFCC and YLCC at the same time. However, it is not the case for was significant as well, although the coefficient was positive, meaning women, and a higher proportion of women with lower education level in that a higher number of doctors was correlated with more years living the region was correlated with a lower YLFCC and a higher YLCC. Those with disease. variables that were significant for three health indicators are the un­ A final set of models (Tables 5 and 6) were applied to men and employment rate of older workers, public health expenditure per capita, women jointly in order to know if differences by sex were significant. hospital care expenditure per capita, the number of specialist doctors Therefore, we tested the interaction of each relevant variable with sex per 1000 population, the number of specialist nurses per 1000 popula­ for YLFCC and YLCC. In the case of YLFCC, only the interaction of sex tion, the percentage of individuals aged 50 or more that are overweight, with GDP per capita, unemployment rate of older workers and the and the percentage of individuals aged 50 or more that are obese. From percentage of individuals with primary education was significant. In the all these 9 indicators, we included 8 of them in the global fixed-effects case of GDP per capita and percentage of primary education, the inter­ model. We withdrew the variable of being overweight to avoid the action coefficient was negative, meaning that the lower correlation of intercorrelation effect with being obese. GDP per capita and the more negative correlation of primary education Tables 1–4 display the results from the fixed-effects models for each for women than men, was, in fact, significant. In the case of the unem­ health indicator and for men and women separately. All analyses ployment rate, the interaction coefficient was positive, therefore, the examined first the role of macro-level factors separately by groups and higher correlation between unemployment and YLFCC in the case of then all groups of macro-level indicators together, following the same women compared to men, was also significant, even if the unemploy­ model specifications. Model 1 includes socioeconomic variables, model ment rate was not significant in the model of YLFCC for only women. 2 health expenditure variables, model 3 health resources, model 4 health The no significance in the interaction of the other variables indicates behaviors and finally, model 5 includes all variables together. that differences by sex are not relevant. This is especially interesting in In models among men, GDP per capita was significant in both YLFCC the case of the prevalence of obesity, as this variable turned significant and YLCC socioeconomic models, denoting that a higher GDP implied a in the joint model of men and women, although when the interaction is higher YLFCC and a lower YLCC. Additionally, YLCC was also negatively Table 1 Fixed-effect models of expected years lived free of chronic conditions (YLFCC) at age 50 among men in 17 regions of Spain between 2006 and 2019. Socio-economic Health expenditures Health resources Health Behaviour Total GDP per capita 0.000359* 0.0000571 Unemployment older workers (55+) 0.0628 0.0635 % of primary educ or less − 1.099 0.363 Public health expenditure x capita 0.00485*** 0.00583** Hospital expenditure x capita 0.00380* 0.00461+ Specialist doctors per 1000 pop. 1.25 − 1.124 Specialist nurses per 1000 pop. 0.303 − 0.296 % of obesity (50+) − 6.982 − 6.162 Constant 2.24 0.942 7.708*** 12.77*** 1.147 Observations (N) 102 102 102 102 102 + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. 5 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 Table 2 Fixed-effect models of expected years lived with chronic conditions (YLCC) at age 50 among men in 17 regions of Spain between 2006 and 2019. Socio-economic Health expenditures Health resources Health Behaviour Total GDP per capita − 0.000371* − 0.0000975 Unemployment older workers (55+) − 0.044 − 0.05 % of primary educ or less − 5.101** − 4.744* Public health exp. per capita − 0.00804*** − 0.00609** Hospital expenditure per capita 0.00377+ − 0.00373 Specialist doctors per 1000 pop. 1.343 1.694 Specialist nurses per 1000 pop. 0.507 0.698 % of obesity (50+) 4.12 5.949 Constant 31.76*** 28.33*** 15.89*** 19.14*** 30.05*** Observations 102 102 102 102 102 + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. Table 3 Fixed-effect models of years lived free of chronic conditions (YLFCC) at age 50 among women in 17 regions of Spain between 2006 and 2019. Socio-economic Health expenditures Health resources Health Behaviour Total GDP per capita 0.000202 − 0.00014 Unemployment older workers (55+) 0.103 0.092 % of primary educ or less − 4.380** − 3.465 Public health expenditure per capita 0.000547 0.00581** Hospital expenditure per capita 0.00947*** 0.00464+ Specialist doctors per 1000 pop. 1.45 − 3.866+ Specialist nurses per 1000 pop. 1.366 0.856 % of obesity (50+) − 10.62* − 3.733 Constant 7.341 2.242 3.803+ 13.93*** 8.339 Observations (N) 102 102 102 102 102 + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. Table 4 Fixed-effect models of years lived with chronic conditions (YLCC) at age 50 among women in 17 regions of Spain between 2006 and 2019. Socio-economic Health expenditures Health resources Health Behaviour Total GDP per capita − 0.000156 0.000167 Unemployment older workers (55+) − 0.0563 − 0.057 % of primary educ or less 0.822 0.962 Public health expenditure per capita − 0.00275+ − 0.00627** Hospital expenditure per capita − 0.00453* − 0.0041 Specialist doctors per 1000 pop. 0.332 4.075* Specialist nurses per 1000 pop. − 0.867 − 0.54 % of obesity (50+) 6.355+ 1.593 Constant 29.22*** 33.01*** 27.51*** 23.55*** 27.56*** Observations (N) 102 102 102 102 102 + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. included, this is not significant, and the coefficient of the variable loses of specialist doctors was associated with less years of good health, and its significance, as well. with more years of bad health, which could be representing a response of In YLCC models, the interaction of sex with GDP per capita and the public health sector to a higher prevalence of chronic problems population with primary education were also significant, in a similar among the older population. way as in the YLFCC model. Also, the interaction with the number of specialist doctors per 1000 population was significant and negative, 5. Discussion meaning that the higher negative correlation of specialist doctors with YLCC for women than men was statistically significant. In this study we have assessed the macro factors explaining regional In both models (YLFCC and YLCC) for all sexes, public health differences in YLFCC and YLCC at age 50 in Spain from 2006 to 2019. expenditure per capita was significant and positively correlated with We have analyzed the relationship between these two morbidity mea­ YLFCC and negatively with YLCC. Hospital expenditure per capita was sures and a battery of macro factors that relate to the socioeconomic also significant, although with a lower level, and with the same sign of level of the region, the distribution of health resources and different correlation that public health expenditure. Specialist doctors per 1000 indicators of health-related behaviors of the population aged 50 and population were significant for both health indicators, but in the over, and for the first time we have examined the relationship with the opposite direction than the previous variables. Hence, a higher density level of public health expenditure at the regional level. This approach is 6 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 Table 5 Fixed-effect models of years lived free of chronic conditions (YLFCC) at age 50 among men and women in the 17 regions of Spain between 2006 and 2019. No Interactions of Sex with … interactions YLFCC GDP per cap Older % primary Public health Hospital Specialist % obesity unemployment education expenditure per expendiutres per doctors 1000 50+ cap cap pop GDP per capita − 0.0000384 0.0000982 − 0.0000433 − 0.0000361 − 0.0000392 − 0.0000384 − 0.0000385 − 0.0000386 Unemployment older 0.0754+ 0.0748+ 0.0204 0.0786* 0.0747+ 0.0757+ 0.0761+ 0.0757+ workers (55+) % of primary educ or − 1.366 − 1.541 − 1.663 0.797 − 1.43 − 1.358 − 1.356 − 1.333 less Public health exp. 0.00566*** 0.00574*** 0.00580*** 0.00570*** 0.00671*** 0.00567*** 0.00569*** 0.00567*** enditure per capita Hospital expenditure 0.00474* 0.00467* 0.00462* 0.00471* 0.00471* 0.00392+ 0.00474* 0.00474* per capita Specialist doctors per − 2.390+ − 2.447+ − 2.485+ − 2.483+ − 2.419+ − 2.384+ − 3.327* − 2.371+ 1000 pop. Specialist nurses per 0.273 0.262 0.255 0.295 0.268 0.274 0.275 0.274 1000 pop. % of obesity (50+) − 4.773+ − 4.570+ − 4.355+ − 4.823+ − 5.180* − 4.563+ − 4.229 − 5.163 Women X GDP cap − 0.000279* Women X 0.108** Unemployment 55+ Women X % primary − 4.408* educ. Women X Public − 0.00208 health exp. x cap. Women X Hosp. Exp. x 0.00164 cap. Women X Specialist 1.899 doctors 1000 pop. Women X % obesitiy 0.702 50+ Constant 4.444 4.646 4.761 4.56 4.697 4.354 4.227 4.381 Observations 204 204 204 204 204 204 204 204 + p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. especially important in a country like Spain where the public health and YLCC, but the relationship was not as persistent across models and, system is decentralized and, therefore, the regional government has the in some cases, differed by sex. For example, GDP per capita was corre­ power to decide, at some level, the amount of public health expenditure lated with YLFCC and YLCC for men, but not for women. The proportion and its management. Moreover, the recent economic crisis of 2008 of people aged 50 with primary education was significant for both men brought significant cuts in public transfers, including health provisions, and women but in the case of men, it was negative for both YLFCC and which each region managed differently (Bernal-Delgado et al., 2018) YLCC, and for women it was negative only in the YLFCC model. A and here we could preliminarily explore some of the health conse­ negative correlation with both the YLFCC and YLCC indicators implies quences of the different regional strategies. that having a higher proportion of the population with primary educa­ The overall results showed that levels of public health expenditure tion or less is associated with a lower LE, which could mean both less per capita were strongly related to the number of years lived with and YLFCC and YLCC. This has already been detected in other international without disease and health problems, for both men and women. comparative studies, where countries with a lower LE would also show a Therefore, a region with a higher level of public health expenditure per lower life expectancy with functional limitations (Mathers et al., 2004). capita would also relate to a higher number of expected years lived Regarding health resources, only the number of specialist doctors without disease for individuals over 50 years of age, and a lower number and nurses was significant in the univariate models, and in the fixed- of years lived with disease for both men and women. Most importantly, effects models, only specialist doctors was significant in the global public health expenditure per capita was the only significant variable for model of YLCC for women, and with a positive coefficient. Specialist all health measures and all sexes in all statistical models performed here, doctors were also significant in the model pooling male and female including the partial and global models, and the model with male and outcomes of YLFCC and YLCC. Here, again, the coefficient was negative female outcomes together. Moreover, in the latter model the interaction for YLFCC and positive for YLCC, meaning that a higher number of between men and women was not significant, meaning that the rela­ doctors is attached to a worse YLCC and a higher number of years with tionship between public health expenditure and the health indicators diseases. This was a result already observed in Laborde et al. (2021) for across regions and years does not differ by sex. This result suggests the the density level of nurses, and as proposed by these authors, this could importance that health cuts have had on health measures in the imme­ be a response of the public health sector to a higher demand of doctors diate aftermath of the economic crisis. This is not always observed for due to a higher prevalence of health problems in the population. This mortality measures. As noted by Ruhm (2000) and Ruhm (2012), re­ could suggest that, in the specific case of specialist doctors, there is a cessions can have a positive impact on mortality measures due to a strategy of adjusting the services offered to the healthcare demand, but reduction in road traffic accidents and work-related diseases and this should be further examined in the appropriate analytical model. injuries. Health-related behaviors were only important for women and in the Some of the other factors analyzed were also correlated with YLFCC case of the proportion of overweight and obese individuals. 7 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 Table 6 Fixed-effect models of years lived with chronic conditions (YLCC) at age 50 among men and women in the 17 regions of Spain between 2006 and 2019. YLCC No Interactions of Sex with … interactions GDP per cap % primary Public health Hospital expendiutres Specialist doctors % obesity education expenditure per cap per cap 1000 pop 50+ GDP per capita 0.0000321 − 0.000118 0.0000287 0.0000327 0.0000321 0.0000324 0.0000327 Unemployment older − 0.0489 − 0.0483 − 0.0538 − 0.0483 − 0.0495 − 0.0501 − 0.0494 workers (55+) % of primary educ or less − 1.981 − 1.789 − 5.283** − 1.93 − 1.997 − 1.998 − 2.05 Public health expenditure − 0.00599*** − 0.00607*** − 0.00604*** − 0.00682*** − 0.00601*** − 0.00604*** − 0.00601*** per capita Hospital expenditure per − 0.00402* − 0.00394* − 0.00397* − 0.00400* − 0.00224 − 0.00403* − 0.00403* capita Specialist doctors per 1000 2.804* 2.866* 2.946* 2.826* 2.790* 4.480** 2.763* pop. Specialist nurses per 1000 0.1 0.112 0.0665 0.104 0.0982 0.0971 0.0976 pop. % of obesity (50+) 3.656 3.434 3.733 3.98 3.199 2.685 4.481 Women X GDP cap 0.000306** Women X % primary educ. 6.730*** Women X Public health 0.00165 exp. x cap. Women X Hosp. Exp. x cap. − 0.00358 Women X Specialist doctors − 3.396** 1000 pop. Women X % obesitiy 50+ − 1.481 Constant 28.88*** 28.65*** 28.70*** 28.67*** 29.07*** 29.26*** 29.01*** Observations 204 204 204 204 204 204 204 Nevertheless, in the models with results for men and women together, health has also been reported when examining morbidity measures, the proportion of obesity remained significant, revealing that YLFCC and although often using educational measures (Solé-Auró et al., 2020), YLCC also differed for men according to the level of obesity in the re­ which is the only socioeconomic measure we founded relevant for gion, but this relationship might not be as strong as for women. women in this study. In the univariate models we tested a number of variables that were Conclusions drawn from an ecological study such as the one pre­ not significant, and we would have expected some of them to show some sented here are always subject to the possibility of ecological fallacies. level of correlation with health indicators. It is particularly intriguing However, this is a very effective way of analyzing whether macro fac­ that smoking behaviors did not show any correlation for either men or tors, such as health expenditure and healthcare resources, are, in fact, women. It is true that in the case of women, for whom the smoking linked to different performances among regional populations. Of course, epidemic is more recent and linked to regions where female labor force results should always be taken with caution, especially with data that participation is higher, there might be some confounding with other can be measured at the individual level such as socioeconomic factors socioeconomic variables that have positive impacts on health. However, and health-related behaviors. For example, the fact that a higher level of in the case of men, the non-existent correlation could be related to the unemployment is correlated with a higher YLFCC in the univariate fact that smoking levels after the age of 50 might be ascribed to more models, does not mean that an unemployed person is more likely to live severe stages of diseases that have a large impact on mortality rates, but more years in good health. Other limitations of the study refer to the not on chronic diseases that persist over time (Reuser, Bonneux, & information gathered. Health survey data does not include people living Willekens, 2009). Another variable that was not significant is public in long-term care institutions. This could bring bias to the study as health spending on primary care, while total public healthcare expen­ institutionalized people are presumably in poorer health. However, in diture and hospital expenditure were significant in the different models. Spain, the proportion of older people living in institutions is very low, This goes along with the fact that the density of general practitioner only 7% of individuals aged 85 to 89 in 2011 (Eurostat) and much lower doctors and non-specialist nurses was also non-significant. One expla­ for younger ages. Additionally, the measure used here refers to the ex­ nation could be that hospital expenditure increases sharply with age, istence of a chronic condition, but we don’t know the level of severity in while primary care expenditure is more stable over the life course and the disease. Therefore, although the presence of health problems might not so correlated with health problems at older ages (Kalseth & Hal­ be increasing in specific periods, we don’t know if they are affecting vorsen, 2020). more or less the quality of life of the individual. Another appealing result of the current study is the differences Finally, our results highlight the importance of such a study in Spain, observed for men and women in the partial models. Socioeconomic where the correlation of health expenditure measures with healthy life variables are more correlated with YLFCC and YLCC in men, and health- expectancies of the older population had not yet been assessed. It is also related behavior measures are significant in women. The association of the first time that measures of health expenditure are included in a socioeconomic variables in men is a result previously observed in Spain model to understand differences in morbidity levels on different mea­ (Gutierrez-Fisac et al., 2000), where unemployment levels were only sures, and therefore underlines the recommendation to use them in relevant for men’s morbidity measures. This is not new, and it is well further international comparisons of YLFCC and YLCC. known that socioeconomic status at the individual level is usually more influential on men’s mortality than on women’s (Permanyer et al., 2018). In contrast, the impact of socioeconomic status on women’s 8 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 6. Conclusion categories below. Category 1 This study comes to acknowledge the importance of public health Conception and design of study: E. Rentería, P. Zueras. investments on the health status of the adult and older population. This Acquisition of data: P. Zueras, E. Rentería. is a prominent result because public health expenditure can be directly Analysis and/or interpretation of data: E. Rentería, P.Zueras. controlled by public policies, with a great benefit on the quality of life of Category 2 the population. In the specific case of Spain, the paper also contributes to Drafting the manuscript: E. Rentería, P.Zueras. show that regional differences in YLFCC and YLCC are explained by Revising the manuscript critically for important intellectual content: different socioeconomic and behavioral factors, that might have a P. Zueras, E. Rentería. different impact on men and women’s health, but more importantly by Category 3 regional decisions on public health investments. Approval of the version of the manuscript to be published (the names of all authors must be listed): E. Rentería, P. Zueras. Ethical statement for SSM-population health Declarations of interests 1) This material is the authors’ own original work, which has not been previously published elsewhere. None. 2) The paper is not currently being considered for publication elsewhere. Acknowledgements 3) The paper reflects the authors’ own research and analysis in a truthful and complete manner. All persons who have made substantial contributions to the work 4) The paper properly credits the meaningful contributions of co- reported in the manuscript (e.g., technical help, writing and editing authors and co-researchers. assistance, general support), but who do not meet the criteria for 5) The results are appropriately placed in the context of prior and authorship, are named in the Acknowledgements and have given us their existing research. written permission to be named. If we have not included an Acknowl­ 6) All sources used are properly disclosed (correct citation). Literally edgements, then that indicates that we have not received substantial copying of text must be indicated as such by using quotation marks contributions from non-authors. and giving proper reference. 7) All authors have been personally and actively involved in substantial Appendix A. Supplementary data work leading to the paper, and will take public responsibility for its content. Supplementary data to this article can be found online at https://doi. org/10.1016/j.ssmph.2022.101152. Contribution of authors References Elisenda Rentería: Conceptualization, Methodology, Data analysis, Writing-Original draft preparation. Pilar Zueras: Conceptualization, Bernal-Delgado, E., García-Armesto, S., Oliva, J., Sánchez Martínez, F. I., Repullo, J. R., Peña-Longobardo, L. M., Ridao-López, M., & Hernández-Quevedo, C. (2018). Spain: Methodology, Data preparation, Writing- Reviewing and Editing. Health system review. Health Systems in Transition, 20(2), 1–179. Cremieux, P.-Y., Meilleur, M. C., Ouellette, P., Petit, P., Zelder, M., & Potvin, K. (2005). Funding Public and private pharmaceutical spending as determinants of health outcomes in Canada. Health Economics, 14, 107–116. Cremieux, P.-Y., Quellette, P., & Pilon, C. (1999). Health care spending as determinant of This work was supported by the "Plan Estatal de Investigación health outcomes. Health Economics, 8, 627–639. Científica y Técnica y de Innovación 2013–2016, Programa Estatal de Elola, J., Daponte, A., & Navarro, V. (1995). Health indicators and the organization of health care systems in Western Europe. American Journal of Public Health, 85(10), Promoción del Talento y su Empleabilidad, Subprograma Estatal de 1397. Formación y en el Subprograma Estatal de Incorporación: Ayudas para Eurostat. EUROSTAT the census hub: 2011 census database. Contratos Ramón y Cajal 2017 [grant number RYC-2017-22586]”; the Fourweather, T., Gillies, C., Wohland, P., Van Oyen, H., Nusselder, W., Robine, J.-M., Cambois, E., Jagger, C., & EHLEIS team. (2015). Comparison of socio-economic project of Spanish Ministry of Science and Innovation of the National indicators explaining inequalities in Healthy Life Years at age 50 in Europe:2005 and R&D&I Plan COMORHEALTHSES (PID2020-113934RB-I00); the Eco­ 2010. The European Journal of Public Health, 25(6), 978–983. nomic and Social Research Council (ESRC) through the Research Centre Gallo, P., & Gené-Badia, J. (2013). Cuts drive health systems reforms in Spain. Health on Micro-Social Change (MiSoC) at the University of Essex, grant Policy, 113, 1–7. GBD 2019 Demographics Collaborators. (2020). Global age-sex-specific fertility, number ES/S012486/1; and support from CERCA Programme Gen­ mortality, healthy life expectancy (HALE), and population estimates in 204 countries eralitat de Catalunya. and territories, 1950-2019: A comprehensive demographic analysis for the global burden of disease study 2019. The Lancet, 396(10258), 1160–1203. Gispert, R., Ruiz-Ramos, M., Barés, M. A., Viciana, F., & Clot-Razquin, G. (2007). Authorship statement Diferencias en la esperanza de vida libre de discapacidad por sexo y comunidades autónomas en España (Vol. 81, pp. 155–165). Revista Española de Salud Pública. All persons who meet authorship criteria are listed as authors, and all Groenewegen, P. P., Westert, G. P., & Boshuizen, H. C. (2003). Regional differences in healthy life expectancy in The Netherlands. Public Health, 1(17), 424–429. authors certify that they have participated sufficiently in the work to Gutiérrez-Fisac, J. L., Gispert, R., & Sola, J. (2000). Factors explaining the geographical take public responsibility for the content, including participation in the differences in disability free life expectancy in Spain. Journal of Epidemiology & concept, design, analysis, writing, or revision of the manuscript. Community Health, 54(4), 451–455. INE (Instituto Nacional de Estadistica). (2019). Indicadores de mortalidad. Esperanza de Furthermore, each author certifies that this material or similar material vida al nacimiento segun sexo. Downloaded at: https://www.ine.es/jaxiT3/Tabla. has not been and will not be submitted to or published in any other htm?t=1414. publication before its appearance in the SSM. Jagger, C., Gillies, C., Moscone, F., Cambois, E., Van Oyen, H., Nusselder, W., Robine, J.- M., & EHLEIS team. (2008). Inequalities in healthy life years in the 25 countries of the European union in 2005: A cross-national meta-regression analysis. The Lancet, Authorship contributions 372(9656), 2124–2131. Jain, V., & Yuan, J.-M. (2020). Systematic review and meta-analysis of predictive symptoms Please indicate the specific contributions made by each author (list and comorbidities for severe COVID-19 infection. medRxiv. Joumard, I., et al. (2008). “Health status determinants: Lifestyle, environment, health care the authors’ initials followed by their surnames, e.g., Y.L. Cheung). The resources and efficiency”, OECD economics department working papers. OECD name of each author must appear at least once in each of the three Publishing. https://doi.org/10.1787/240858500130. No. 627. 9 E. Rentería and P. Zueras SSM - Population Health 19 (2022) 101152 Kalseth, J., Halvorsen, T. Health and care service utilisation and cost over the life-span: A OECD/European Observatory on Health Systems and Policies. (2017). Spain: Country descriptive analysis of population data. BMC Health Services Research, 20:435. health profile 2017. State of health in the EU. Brussels: OECD Publishing, Paris/ Kondo, N., Mizutani, T., Minai, J., Kazama, M., Imai, H., Takeda, Y., & Yamagata, Z. European Observatory on Health Systems and Policies. (2005). Factors explaining disability-free life expectancy in Japan: The proportion of Permanyer, I., Spijker, J., Blanes, A., & Rentería, E. (2018). Longevity and lifespan older workers, self-reported health status, and the number of public health nurses. variation by educational attainment in Spain: 1960-2015. Demography, 55(6), Journal of Epidemiology, 15(6), 219–227. 2045–2070. Laborde, C., Crouzet, M., Carrère, A., & Cambois, E. (2021). Contextual factors Reuser, M., Bonneux, L. G., & Willekens, F. J. (2009). Smoking kills, obesity disables: A underpinning geographical inequalities in disability-free life expectancy in 100 multistate approach of the US health and retirement survey. Obesity, 17(4), 783–789. French departements. European Journal of Ageing, 18, 381–392. Robine, J.-M., Romieu, I., & Cambois, E. (1999). Health expectancy indicators. Bulletin of Linden, M., & Ray, D. (2017). Life expectancy effects of public and private health the World Health Organization, 77(2), 181–185. expenditures in OECD countries 1970-2012: Panel time series approach. Economic Ruhm, C. (2000). Are recessions good for your health? Quarterly Journal of Economics, Analysis and Policy, 56, 101–113. 115(2), 617–650. Liu, J., Chen, G., Chi, I., Pei, L., Song, X., Zhang, L., Pang, L., Han, Y., & Zheng, X. (2010). Ruhm, C. J. (2012). Understanding the relationship between macroeconomic conditions Regional variations in and correlates of disability-free life expectancy among older and health. In A. M. Jones (Ed.), Elgar companion to health economics (2nd ed., pp. adults in China. BMC Public Health, 10, 446. 5–14). Cheltenham, UK: Edward Elgar. Mackenbach, J. P., Hu, Y., Artnik, B., Bopp, M., Costa, G., Kalediene, R., … Shaw, J. W., Horrace, W. C., & Vogel, R. J. (2002). The productivity of pharmaceuticals Nusselder, W. J. (2017). Trends in inequalities in mortality amenable to health care in improving health: An analysis of the OECD health data. In WUSTL economics in 17 European countries. Health Affairs, 36(6), 1110–1118. working paper archive. HEW series 0206001:1–42. Martín-Cervantes, P. A., Rueda López, N., & Cruz Rambaud, S. (2019). A causal analysis Sole-Auro, A., & Alcaniz, M. (2015). Are we living longer but less healthy? Trends in of life expectancy at birth. Evidence from Spain. International journal of mortality and morbidity in Catalonia (Spain), 1994-2011. European Journal of Ageing, environmental research and public health. 16(13), 2367. https://doi.org/10. 12(1), 61–70. https://doi.org/10.1007/s10433-014-0317-9 3390/ijerph16132367. Sole-Auro, A., Martín, U., & Dominguez, A. (2020). Educational inequalities in life and Mathers, C., Iburg, K. M., Salomon, J. A., Tandon, A., Chatterji, S., Ustün, B., & healthy life expectancies among the 50-plus in Spain. International Journal of Murray, C. J. L. (2004). Global patterns of healthy life expectancy in the year 2002. Environmental Research and Public Health, 17(10), 3558. BMC Public Health, 4(6). Soriano, J. B., Rojas Rueda, D., Alonso, J., Anto, J. M., Cardona, P.-J., Fernandez, E., McGowan, M. A., & San Millán, J. A. (2019). Reducing regional disparities for inclusive et al. (2018). La carga de enfermedad en Espana: Resultados del Estudio de la Carga growth in Spain. Economic Department Working Paper, 1549. global de las Enfermedades 2016. Medicina Clínica, 151(5), 171–190. Minagawa, Y., & Saito, Y. (2018). An analysis of factors related to disability-free life Sullivan, D. F. (1971). A single index of mortality and morbidity. HSMHA Health Reports, expectancy at 65 years of age across Japanese prefectures in 2010. European Journal 86(4), 347. of Aging, 15, 15–22. Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Soc. Sci. Med., 38(1), Nixon, J., & Ulman, P. (2006). The relationship between health care expenditure and 1–14. https://doi.org/10.1016/0277-9536(94)90294-1 health outcomes. Evidence and caveats for a causal link. The European Journal of Zueras, P., & Rentería, E. (2020). Trends in disease-free life expectancy at age 65 in Health Economics, 7, 7–18. Spain. PLoS One, 16(3), Article e0249155. https://doi.org/10.1371/journal. Obrizan, M., & Wehby, G. (2018). Health expenditures and global inequalities in pone.0240923 longevity. World Development, 101, 28–36. 10