Versión publicada del artículo: Rueda-Salazar, Sarahí; Devolder, Daniel; Spijker, Jeroen (2022) “Functional Limitations among European Older Adults: Cross-Country Differences in Health Improvements and Social Environment Influence.” The Journal of Frailty & Aging, 11 (4). (ISSN 2273-4309) https://doi.org/10.14283/jfa.2022.55. J Frailty Aging 2022;11(4)378-386 Original Research Published online October 4, 2022, http://dx.doi.org/10.14283/jfa.2022.55 Functional Limitations among European Older Adults: Cross-Country Differences in Health Improvements and Social Environment Influence S. Rueda-Salazar1, J. Spijker2, D. Devolder2 1. University of Southampton (UoS), Department of Social Statistics and Demography, Southampton, United Kingdom; 2. Centre d’Estudis Demogràfics (CED), Barcelona, Spain Corresponding Author: Sarahí Rueda-Salazar, Department of Social Statistics and Demography, School of Social Sciences, University of Southampton, Highfield Campus, Building 58, Southampton, UK, SO17 1BJ, email: s.c.rueda-salazar@soton.ac.uk Abstract Health status can be measured by a quite range of disability This study explores differences on health status transitions based on measures as health conditions and diseases (chronic condition functional health and dead records among older population in Europe. and long term illness), self-perceived health (self-report about We also study the influence of residence pattern on health changes over individuals perception about their health status: very good, time. Method: Data used came from EU-SILC registers on individuals good, fear, bad, very bad), functional limitation (which captures aged 50 + from 19 countries. Fixed and Mixed effect Cox Proportional functioning on six life domains as cognition, mobility, self- Hazards Models are used to ascertain any country differences on care, getting along, life activities and participation (9, 14)). health outcomes and then by co-residence pattern. Results: There are remarkable country heterogeneity among older people to experience Among them, World Health Organization (WHO) recommends changes on their health status, specially, for health improvements and functional based approach to assess health status across mortality risk. As expected, Baltic and Eastern European countries have population following the disability process as it is closely a higher propensity for health deterioration and Southern European related to disability and dependence. In addition, it makes countries for health improvements on their functional capacities. possible internationally comparable health outcomes and However, there are exceptions as Bulgaria and Romania, which have designing and monitoring health related interventions (15, 16). a lower risk of deterioration and death transition than average. Overall, In this sense, functional limitation is defined in the International living with partner and adult children-rather than living only with a Classification of Functioning, Disability and Health (ICF) as couple shown positive effects for older European to experience health limitation in performing physical and cognitive actions, that improvements with notably differences in Southern countries as in Italy and Spain. Discussion: country framework differences play a can be affected by environmental and personal factors that may fundamental role to understand changes on health status at older ages, in exacerbate predisposing risk factors in the disablement process particular, how health care support toward older people is managed by (17). health systems within European countries and the availability of close From all indicators measuring functioning, Global Activity relatives among older adult population. Limitation Index (GALI) has shown great advantages as it satisfactorily assess the health status of the population in a Key words: Functional limitation, European countries, living worldwide framework (18–20) .It was designed for healthy arrangements, cox mixed effects models, social environmental factors. years comparisons across European countries, particularly for the estimation of Healthy Life Expectancy (HLE) (21). And, this measure is one of the three questions of the Minimal Background European Health Modules (MEHM) in European surveys to H monitor health status across countries since 2005 (18, 22). ealth dynamics of older adult population are quite Studies have shown that GALI is significantly associated with diverse in European countries (1, 2). Mortality has other function measures as Activities Daily Life (ADLs) and declined with varying intensities among regions, Instrumental Activities Daily Life limitation (IADLs) (19, 23). countries and by socioeconomic gradients (3–6). While the Trends in functional limitations at older ages in European increase in extra years of remaining life expectancy were due countries reflect pronounced inequalities. Yet, studies to improvements in older adults’ health and functional status don’t show a clear picture about health improvements and between 1970 and 2000 (7–9), nowadays Life Expectancy (LE) deterioration in functioning based in longitudinal data over is reported to be stagnating in some countries and in specific time. Evidence available reported similar speed of heath ages (10, 11) when compared with the improvements observed deterioration by gender across countries, being older women in the 2000s. the group with higher proportion of severe activity limitation However, there is still a debate with regard to health trends (24). Eastern countries as Poland and the Czech Rep have over time, as this depends on the health dimensions used, the shown highest functional limitation incidence (64% and time period under study and the population characteristics 49%, respectively). While most countries shown 35-40%. In covered (12). Some studies illustrate the existence of complex severe limitations, remarkable figures were found in Southern interacting mechanisms for mortality and morbidity trends, countries as Greece and Spain with lowest rates (around 5%) in including different dimensions of health (13). contrast to Poland (19%) (3). Received April 5, 2022 Accepted for publication August 3, 2022 378 JFA - Volume 11, Number 4, 2022 Figure 1. Intrinsic capacity and environmental factors Source: Own elaboration based on Cesari De Carvalho, Thiyagarajan et al. (2018) and Rudnicka, Napierala and Podfigurna et al. (2020) Studies led by Vaupel and colleagues introduced the using wheel chairs, etc.) (27). Social environmental factors analysis of heterogeneity in health and mortality dynamics. are considered as relationships at home (living arrangements), Their main finding (which is of interest to this study) was that community and society as a whole. Contextual factors, individuals experience different mortality risks based on their including the social environmental ones do impact functional characteristics. They used the term “frailty” to study and model ability of adult people differently according to personal´s unobserved heterogeneity, particularly for older adults (24, 25). attributes such as sex, genetics, ethnicity, educational level Their work also showed large dissimilarities among countries and life style (28). For instance, the cognitive and physical in relation with the processes of compression or expansion capacity of a 70-year-old could be better or similar than a of mortality and health conditions. In this line, transition 50-year old person due to differential influences of a quite probabilities between health statuses based on longitudinal range of factors as health related behaviour or early living data are useful indicators to assess improvements or declines in conditions. Circumstances that are considered as diversity functional capacities that allow to identify divergent pattern on and also inequalities. Evidence has shown that environmental the epidemiological trend among different population groups factors can present barriers in engagement to healthy lifestyle as over time. infrastructure deprivation is positive associated to risky health Moving the focus to ageing adult health, recently novel behaviours, loss of physical function, increased incidence of studies emphasise the importance of the functional-based chronic diseases, between others (29). approach for measuring health status at old ages. In this sense, In this framework, co-residence pattern is part of functional ability in older population is based on the intrinsic the immediate social environment in later life and it plays capacity, the environment and the interactions between them. a fundamental role in preserving health at older ages (30) Intrinsic capacity (see Fig.1) involves five core domains; as social environmental has been considered both facilitator cognitive, psycho-emotional, sensory function, vitality and and constrain in mental and physical functioning in this locomotion. Domains that are fundamental for developing population group (27). The lack of a partner at older ages could and maintaining wellbeing and an autonomous life free of be detrimental for health, in particular for men as having a dependency in later life (26). couple is beneficial for their wellbeing at older ages (31, 32). All these factors surrounding intrinsic capacity are the Yet, analysing health transitions by living arrangements has framework of the individual´s life and these can be material not been previously done including the effect of living with or external environmental conditions as public areas, including children. Furthermore, there is not concrete proof whether environmental barriers in and surrounding home (e.g. street living arrangements are also associated with differences in crossing, social and leisure facilities, public transport features, (the rate of) health transitions, neither, whether those changes etc.) living and housing conditions (e.g. closeness to grocery on health status are experienced in the same way for the older shops or healthcare facilities, living in rural or urban areas, lift adults across European countries. or stairs at home, appropriate walking areas considering people 379 FUNCTIONAL LIMITATIONS AMONG EUROPEAN OLDER ADULTS Hence, this study follows the central aim to provide evidence 36, 37). Therefore, GALI measures health changes related of the variation of health improvements and health deterioration to limitation in performing activities people usually do due at older ages among 19 European countries and concurrently to health problems experienced during at least the past six ascertain the effect that living arrangements groups have on months. Answer categories are: 1- Yes, severely limited, 2- those health transitions. By health transition we mean health Yes, limited but not severely and 3- Not limited (21, 38). This deterioration (from healthy to unhealthy), health improvements indicator is considered to be a good proxy of impairment and (from unhealthy to healthy) and death (from any of these two dependence as it is used to calculate Healthy Life Expectancy statuses: healthy or unhealthy). To analyse the effect of living (“HLE”, expected years to live in a healthy state) by Eurostat. arrangements on health status transitions, we apply a multilevel We also choose GALI instead of self-perceived health or approach —also known as mixed effects survival modelling or long standing illness indicator as the former allows us to shared frailty model (33)— to all three transitions separately. compare our results with previous research (19, 20, 39) (see We further contribute to the current body of knowledge by also supplementary materials eData2 for further explanation of including countries from Eastern Europe and the former Soviet limitation). Categories 1 and 2 were grouped into one category States where studies on health transition are rare. for identifying “Unhealthy state” and “Not limited” as a “Healthy state”. For the analysis of the transition to death, Data and Method personal register (Membership status: RB110) and household register (Household status: DB110) variables were used to Data identify the respondents who had died since the previous survey (40). The data source used for this study is Eurostat’s European With GALI indicator and death register, three health states Union Statistics on Income and Living Conditions (EU-SILC) were identified: Healthy, Unhealthy and Death, from which panel survey, which provides up-to-date comparative four types of transitions were calculated: health deterioration information of income and living conditions across countries (transition from healthy to unhealthy state, health improvement from all parts of Europe. The source combines the use of (from unhealthy to healthy state), death from the unhealthy registers with interviews in household sample surveys, state and death from the healthy state. However, we excluded providing information at individual and household level of non- the transition to death from a healthy state from the analysis, institutionalised household members aged 16 and over since as there were too few respondents who experienced this for the 2004. For this study we use information from respondents aged results to be reliable. There are other limitations explained in 50+ in panel data files, period (2004 to 2014) who were at least supplemental material (eData2). observed twice (see Table 1). The EU-SILC survey has a rotational design that defines the Control variables used sample in cross-sectional and longitudinal form (panel data). Sex, year of birth (grouped into 1924-33, 1934-43, 1944-53 Specifically, the sample selection is based on a number of sub- and 1954-64), educational attainment (grouped into “primary or samples or ‘replications’. For this study, the longitudinal design less”, “lower and upper secondary”, “post-secondary vocational is used, which, permits health transitions to be estimated across and tertiary”) and country of residence. four-year spells. Although Eurostat provides harmonized data Living arrangements indicator was based on the following across countries, variations in data collection procedures do variables: Household size, marital status and information on the exist among EU-SILC countries (34) (see eData1). For instance, IDs of the spouse, father and mother. Living arrangements were there are countries where data collection relies on surveys with grouped into four categories: 1) Living with a partner (2-people personal and proxy interviews while others also use data from household); 2) living with partner and children (which also administrative registers (35). Large percentages of missing include other co-residents in the same household, but their link and truncated data persist in EU-SILC countries that only could not be attained); 3) living alone (1-person household); 4) provided register data (see eData1). Therefore, we removed and living with others, (includes other relatives or people who countries with register data and those which reported high shared the same household (e.g. single-parent households)). rates of attrition and missing data from our analysis. Countries Finally, for comparative purposes and to ease the description selected were: Austria, Belgium, Bulgaria, Cyprus, the Czech of the results we have grouped the selected 19 countries into Rep., Estonia, Greece, Spain, Hungary, Ireland, Italy, Latvia, four geographical-historical regions: Eastern Europe, Baltic Lithuania, Luxemburg, Malta, Poland, Portugal, Romania and countries, Southern Europe, and Western Europe. Slovakia Indicators used in the analysis Modelling strategy Dependent variable We applied Mixed Effects Survival Modelling (MESM) with a Cox approach for modelling transition probability among GALI indicator was used to identify the health status of health states (41). Methodology widely used in Epidemiology the target population: population aged 50+. It is a summary and clinical trials as treatment effects on specific diseases as measure of functioning and is also considered a self-reported cancers or progression of disease stages and recurrent infections measure of participation restriction by severity level (17, (42, 43). Furthermore, this type of models allows to understand 380 JFA - Volume 11, Number 4, 2022 Table 1. EU-SILC Sample Population description and demographic indicators EU-SILC Survey sample description: 19 countries Demographic indicators Total Population 258,824 Population 50+ Follow up Sex Countries Base 2 3 4 Female 143,524 55.5% AT 13,443 10,671 9,121 7,989 Male 115,300 44.5% BE 12,389 10,211 8,766 7,967 Age Groups BG 11,032 10,260 9,321 8,474 [50,60) 96,351 37.2% CY 7,200 6,671 6,165 5,798 [60,70) 78,295 30.3% CZ 19,733 17,007 16,850 16,441 [70,85) 84,178 32.5% EE 8,990 8,079 7,296 6,696 Cohort EL 15,586 13,508 11,891 10,458 [1.924,1.933) 55,196 21.3% ES 32,083 27,042 22,930 20,528 [1.934,1.943) 69,865 27.0% HU 20,661 18,079 15,765 13,942 [1.944,1.953) 89,523 34.6% IE 6,361 5,785 3,898 2,212 [1.954,1.963) 44,240 17.1% IT 49,866 42,474 36,259 31,859 Educational Attaiment LT 11,101 10,408 9,633 9,043 Primary 131,048 50.6% LU 8,376 6,800 5,751 5,039 Secondary 88,855 34.3% LV 12,463 10,796 9,458 8,599 Tertiary 31,951 12.3% MT 7,492 6,572 5,939 5,418 Missing 6,970 2.7% PL 27,523 24,141 22,788 21,317 Living Arrangements PT 12,817 12,038 10,954 10,352 L.w/ partner 108,743 42.0% RO 10,754 10,554 10,272 10,034 L.w/ partner+Ch 68,874 26.6% SK 8,172 7,728 7,230 6,719 L.w/others 35,907 13.9% Total 296,042 258,824 230,287 208,885 L. alone 45,300 17.5% Source: EU-SILC, Panel Data from 19 countries, Period 2004-2014 the association between environment factors and specific health country-specific random effects. conditions over time (44). The risk of health deterioration is statistically the same for We started by estimating Cox Proportional Hazards models older adults who live only with their partner compared with (PH) for measuring the effects of the different kinds of those who also co-reside with adult children according to living arrangements on our three types of health transitions. the simple Cox PH model. However, the difference in these As individuals appear in different waves, we considered risks becomes significant when the country random intercepts possible inter-person correlation. Also, even when individuals are included (see Model 2 in Table 2). Living alone, on the reside in different countries, they may share common and other hand, does lead to a significantly higher risk of health unobserved characteristics that violate the Cox assumption deterioration (in all models), as they experience a 6% higher of proportionality within categories of covariates (45). This chance of worsening their health status, and living with others is why we also estimated Mixed Effects Models (with more leads to a 3% higher risk of deterioration than older people than one random effect), which allows modelling statistical living with their partners. dependence (46), and, simultaneously, consider individual- For health improvement the picture is different. Adults aged and country-level intra-correlations (see further explanation in 50 and over living with a partner and adult children are 13% supplementary material: eMethods1 and eMethods2). more likely to experience an improvement in health than those who only live with a partner. Conversely, older adults living Results with others and living alone are 14% and 8%, respectively, less likely to have improvements in their health status than the The results show that all models with random effects fit reference category. significantly better than the simple Cox PH model for each For the transition to death, in comparison with older adults transition type considering the log-likelihood and chi square living with their partners, those who exclusively live with values (see eMethods2, model description and eTable1). In others have a remarkably 86% higher mortality risk and those general terms, Cox PH ratios for living arrangements are very who live with their partner and children a 19% higher risk. similar in the simplest Model (see Table 2), to the values we All coefficients are highly significant. As there are insufficient estimated with the models that also include random effects events in the living alone category, we discarded this transition Model 2 y 3, but with one important difference. The main from the multilevel model analysis. difference lies in the changes in the significance of the risks for Additionally, we pointed out high differences by birth cohort the living arrangements categories when we take into account groups in all health transitions types, while the results by sex 381 FUNCTIONAL LIMITATIONS AMONG EUROPEAN OLDER ADULTS Table 2. Models 1-3. Cox PH Model by transition type and Mixed Effects Models with random intercept (by country) and random slopes (by living arrangements) Health Deterioration Model= 1 Model= 2 Model= 3 Cox Random intercept= Country Random intercept/ Random Slope Country/Living A. Fixed coefficients Living arrangements RR Sig. RR Sig. RR Sig. Living with partner(ref) Living with partner+ch 0.99 0.98 . 0.98 Living alone 1.06 *** 1.04 *** 1.04 * Living with others 1.03 ** 1.01 1.01 Mixed Effects Random Intercept Country - 0.32 37% 0.31 36% Random slopes Living A. - 0.07 8% Loglikelihood -676245 -672273 -672232 Health Improvements Model= 1 Model= 2 Model= 3 Cox Random intercept= Random intercept/ Country Random Slope Country/Living A. Fixed coefficients Living arrangements RR Sig. RR Sig. RR Sig. Living with partner(ref) Living with partner+ch 1.13 *** 1.10 *** 1.10 *** Living alone 0.92 *** 0.93 *** 0.94 ** Living with others 0.86 *** 0.87 *** 0.88 *** Mixed Effects Random Intercept Country - 0.38 46% 0.38 46% Random slopes Living A. - 0.05 5% Loglikelihood -543539 -539354 -539323 Death from unhealthy Model= 1 Model= 2 Model= 3 Cox Random intercept= Random intercept/ Country Random Slope Country/Living A. Fixed coefficients Living arrangements RR Sign. RR Sign. RR Sign. Living with partner(ref) Living with partner+ch 1.19 *** 1.19 *** 1.19 *** Living alone 0.01 *** 0.01 *** 0.01 *** Living with others 1.86 *** 1.75 *** 1.75 *** Mixed Effects Random Intercept Country - 0.34 41% 0.34 41% Random slopes Living A. - 0.01 1% Loglikelihood -61130 -60795 -60795 Signification level: *** p < 0.001; ** p < 0.01; * p < 0.05. Relative Risk (exponentiated coef.); Note. All models control for sex, education and birth cohort at the individual level (mean effect). Fixed coefficients calculated with “survival” R package, “coxph” function.Mixed coefficients calculated with “survival” and “coxME” R packages; Source: Own calculation based on EU-SILC from 19 countries, Panel Data, Period 2004-2014 382 JFA - Volume 11, Number 4, 2022 Figure 2. Model 3. Random intercept by countries compared with sample transitions counts Note: the red line represents the reference risk for each transition type (which is unknown in Cox PH) and the estimated excess risk reported by the (exponentiated) standard deviations for each country. The model controls for sex, education and birth cohort in the fixed coefficients. Mixed coefficients calculated with “survival” and “coxME” R packages; Source: Own calculation based on EU-SILC, Panel Data from 19 countries, Period 2004-2014 and educational level are as expected; women are more likely For health improvements, country variation in the risk is to experience health deterioration (RR:1.13, p< 0.001) and slightly greater, as country effects vary from 0.5 to over twice less likely to improve their health status (RR: 0.98, p<0.01) the average. Countries with higher estimated risk of health but they have better survival (RR:0.49, p<0.001) than their improvements are mainly Southern European countries: Malta male counterparts. Regarding the educational level, higher (2.1), followed by Luxemburg, Cyprus and Spain with excess educational attainment is associated with a lower probability of risks estimated close to 50% more than the reference risk. On health deterioration and mortality risk and a higher probability the contrary, Eastern European and Baltic countries have lower of health improvement. propensities for improvements than the mean. For instance, an individual living in Slovakia has approximately half the risk of Random effects among countries experimenting health improvements than the sample average. For death transition from unhealthy status, country effects Figure 2 illustrates the differences in the risks for the three range from less than 0.5 to 1.9 times the average risk. The transitions at the country level, according to the country sample model results show Baltic and Eastern European countries to size. The latter was included to show the effects of smaller and have the highest excess of risk of death (Bulgaria 1.9 times the larger country size (e.g., Luxembourg and Spain, respectively) mean, Latvia and Hungary 1.5 times) and Belgium, Romania which could shrink or inflate the estimated risk closer to the and Italy the lowest risk. overall mean (47). Regarding country effects on health transitions, Western For health deterioration transitions, 37% of the total variance European countries tend to report values close to the average is explained by the variation between countries. For health risk for three health transition types, although Belgium exhibits improvements and death, the average between-countries the lowest mortality risk. On the other hand, Baltic and Eastern variation is even higher (respectively, 46% y 41%). There are European countries have a higher propensity for deterioration country effects in the risk score for deterioration transition, and Southern European countries for health improvements. ranging from less than 0.7 to over 1.8 times the average risk Several countries do not follow this regional pattern, such of deterioration. As expected, older adults living in Baltic and as Bulgaria and Romania which have a lower risk of health Eastern European countries observe the highest excess of risk deterioration and death transition than average. to experience a deterioration in their health status. Slovakia Spain has the average risk for mortality and health and Latvia have 1.75 times (75% higher) and Estonia a 1.45 deterioration, but regarding health improvements its risk is 50% times (45% higher) risk than the mean. Conversely, health higher than the sample mean. For health improvements there deterioration is lowest in older people living in Bulgaria and are no Eastern European and Baltic countries above the mean Greece (50% lower risk than the mean). risk, indicating a lower likelihood for older adults to improve 383 FUNCTIONAL LIMITATIONS AMONG EUROPEAN OLDER ADULTS their health status. Only Bulgaria and Latvia are close to the mean effect, while in Romania and the Czech Republic the risk average risk. is about 5% lower. For health deterioration, most Eastern European and Baltic In the case of health deterioration, the only significant older adults experience a higher risk (more than double in difference is regarding older people living alone in comparison Slovakia and 50% more likely than the average in Estonia), with those living with a partner (+4%), whereby differences although Bulgaria, the Czech Republic and Poland are with the reference are particularly high in the case of the two exceptions with risks that are 20-35% lower. Conversely, the Southern countries, Malta and Greece (+10%). On the opposite worst situation is shown for Hungary, Lithuania and Estonia. end of the scale, Romanians and Latvians living alone show the Countries which report lower health improvement and higher lowest risk (10% less than the average). health deterioration and mortality risk, as well. Meanwhile striking values are reported by Slovakia and Latvia; Slovakia Discussion with the lowest risk to improve for older adults their health This study analysis health status in 19 European countries status (less than 50% the mean risk) and the highest risk for using the European Union Statistics on Income and Living deterioration (more than double mean risk). Latvia with 50% Conditions (EU-SILC) survey, through the implementation of higher relative risk than the average for deterioration and hierarchical or mixed effects (multi-level) models on survival mortality risk. analysis, with the aim to describe the heterogeneity of the Lastly, if we compare these results with the simple Cox PH health changes health improvement, health deterioration and model (see Table 2), the mean effects of living arrangements mortality among the population aged 50 years and over in observed few changes after including the random country 19 European countries. We also analysed the influence of intercepts. In the case of health improvements, it declined living arrangements on these health dynamics and its variation slightly, most noteworthy for living with children and partner between countries, with the intention of illustrating health (from 13% to 10%). patterns in the European context. This also applies to living with others in the case of the The results show a diverse pattern in health transitions transition to death (from 1.9% to 1.8%), while living with both at older ages across countries, which is slightly greater for children and partner became significant in health deterioration health improvements and mortality (around 40 %) than for (from 1% to 2%, with p<0.05). This implies that the effect health deterioration (30%). In particular, Eastern European of living with both children and partner contains significant and Baltic countries tend to perform worse than Western and country-specific differences in comparison with the reference Southern European countries as they have higher risk of health risk (living with a partner), while living with others doesn’t. deterioration and mortality and, as expected, are less likely to experience improvement in health. This pattern reflects trends Random effects by living arrangements within in LE and healthy LE as reported by previous studies and countries national statistics (48–50). Reasons why older people in Eastern Europe generally have There are more country differences (level 2) in the a lower chance for health to improve and a higher change to average risk of health deterioration (8%) according to living deteriorate compared to the rest of Europe is well established. arrangements (level 1) than is the case for health improvements While prior to the fall of communism, health differences (5%), while there is almost no variation in the mean effect of already existed as the East did not experience the onset of living arrangements on mortality risk for older adults between renewed mortality decline in the 1970s and 1980s, especially countries. from cardiovascular mortality, as the West did, the abrupt Turning now to the differences between type of living political and economic changes that led to the collapse of its arrangement, they are significant for health improvement social welfare system, strained its health care system and higher and mortality risk, although associations are not in the same levels of unhealthy behaviour, had devastating repercussions direction. Living with both partner and children favours health on population health (51). At the same time, most Southern improvement for older adults, as they are 10% more likely to do European countries converged and even surpassed the levels of so than those who only live with a partner. Nevertheless, there LE recorded in Western and Northern Europe. are country-differences as this effect is more than 5% higher Our results, however, did show some notable exceptions in Slovakia, Italy, Ireland and Greece but 5% less likely in for the period under study (2004-14). In particular, the 50+ Luxemburg, the Czech Republic and Spain. in the Czech Republic, Bulgaria and Poland observed a lower Conversely, older people living alone and living with others propensity of health deterioration than the European average, are 6% and 12%, respectively, less likely to improve their with levels similar to Belgium and Greece. Likewise, the risk of health status. The negative mean effect for living alone is less death in Romania is lower than average and similar to Italy and pronounced (7%) in the Czech Republic and Portugal, meaning Portugal. These results are in line with recent mortality reversal that in those countries, older adults living alone doesn’t in Central Europe driven by improvements in health behaviour represent a disadvantage in health status when compared and health care (52, 53). The Czech Republic pioneered this to those living with a partner. In Italy, Greece and Latvia trend, followed by Poland and Slovenia Slovakia, Hungary and health improvement among those living alone are the lowest. Estonia and more recently (but still lagging behind by a great Concerning the 50+ living with others, in Poland, Malta and margin) by Russia, Ukraine and the remaining Baltic States (52, Bulgaria health improvements are more than 5% higher than the 384 JFA - Volume 11, Number 4, 2022 54). health improvements and deterioration rather than mortality Health transition probabilities –whether it pertains to an risk at older ages, as more co-residence-variation was reported improvement or a deterioration or death– differ not only in the transition to health deterioration (8%) and for health between but also within countries. We therefore examined improvement (5%). This also reflects country differences in the differences according to living arrangement, controlling for composition by living arrangement (57, 58), how health care age, sex and educational attainment, as there are still notable policies are addressed to older population (59), availability of country differences in intergenerational co-residence in Europe. children and the type of help that is provided to older adults, Results show that older adults living with a couple and living which is likely related to the age of adult children (60). with partner and children have the same probability of health While current official statistics show increases in life deterioration, while in the case of health improvements, expectancy in European countries (48), major challenges are the latter group has a higher chance to improve their health concentrated in improving functional abilities at older ages. condition. Others living arrangements show a lower chance Policy strategies across European countries for a healthy of health improvements compared to those only living with ageing should prioritize friendly environments and integration a couple. These findings therefore support others who found initiatives with an ecological approach in later life to improve that the influence of family resources, in particular living with the intrinsic capacities of older individuals and mitigate partner and older children, is beneficial in the wellbeing of social inequalities (28, 61). Strategies should also consider: older people with health problems (55). Nevertheless, there lifelong prevention programmes promoting healthy behaviours is possibility of a selection effect on the health status of those (exercising, social participation, learning activities focused who live with their partner and children as they may have on third age, etc.), interventions for reducing barriers to get chosen this living arrangement because of bad health. Although access to healthcare including enough geriatrics and physicians this could lead to an overestimation of the risk of health according to the population size of older people at community deterioration, this is not observed. On the contrary, results show level, and implementing health-related social initiatives that that the presence of a co-residing partner and children leads to integrate older adults, their families and community. a greater probability of recovery and an equal probability of Funding: Sarahí Rueda-Salazar received funding from the Spanish Ministry of health deterioration compared to those who live only with a Economy and Competitiveness. FPI scholarship I+D+I Project “With whom will I live and partner. Therefore, we can rule out a selection effect on the state who will take care of me? Present and future determinants of co-residence and dependence of the old-age population” (Reference Project CSO2014-60113-R). This work was also of health in this living arrangements group. supported by the Spanish ‘Ramón y Cajal’ programme (JS, RYC-2013-14851) and the Surprisingly, Southern European countries showed R&D projects CSO2017-89721-R (Will future elderly have any relative available to care for them) and PID2020-113934RB-I00 (COMORHEALTHSES) as well as the European the most diverse pattern of health changes according to the Research Council (ERC-2019-CoG-864616, HEALIN project; and the Catalan government co-residence pattern. This is perhaps unexpected given certain under the CERCA programme). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. cultural, economic and political similarities between the countries, including a similar profile of (multigenerational) Conflict of interest: The authors have declared that no competing interests exist. living arrangements, proportion of older adults people in the Open Access: This article is distributed under the terms of the Creative Commons population and a lack of state support regarding long-term Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium older adults care (56). 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