Aïna Chalabaev
email: aina.chalabaev@univ-grenoble-alpes.fr

Stefan Sieber

David Sander

Stéphane Cullati

Silvio Maltagliati

Philippe Sarrazin

Matthieu P Boisgontier

Boris Cheval
email: boris.cheval@unige.ch

EARLY-LIFE DISADVANTAGE AND WOMEN'S PHYSICAL ACTIVITY 1

Keywords: sex, early-life socioeconomic circumstances, health, physical activity, aging

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Early-life Socioeconomic Circumstances and Physical Activity in Older Age: Women Pay the Price

Health and health behaviors are patterned by socioeconomic circumstances (SEC) [START_REF] Cutler | Socioeconomic status and health: dimensions and mechanisms[END_REF] and sex [START_REF] Macintyre | Gender differences in health: are things really as simple as they seem?[END_REF]: people from disadvantaged SEC and women have poorer health than people from advantaged SEC and men. However, how SEC and sex interact has been under-researched [START_REF] Eagly | Feminism and psychology: Analysis of a half-century of research on women and gender[END_REF][START_REF] Parker | The intersection of gender, social class, and cultural context: a meta-analysis[END_REF], although many aspects of daily life are structured by sex, suggesting that men and women respond differently to adverse SEC [START_REF] Macintyre | Socio-economic position, gender and health: how do they interact[END_REF]. For example, living in disadvantaged SEC is associated with smoking and excessive alcohol consumption in men, and unhealthy eating and physical inactivity in women [START_REF] Mackenbach | Socioeconomic inequalities in mortality among women and among men: an international study[END_REF]. This suggests the social patterning of health behaviors differs for men and women. This study pursues this line of research by further investigating the impact of the SEC-by-sex interaction on one major health factor: physical activity.

A few studies conducted on children confirmed the social patterning of physical activity by sex [START_REF] Cairney | Children's participation in organized sport and physical activities and active free play: Exploring the impact of time, gender and neighbourhood household income using longitudinal data[END_REF][START_REF] Gorely | Family circumstance, sedentary behaviour and physical activity in adolescents living in England: project STIL[END_REF][START_REF] Seabra | Gender, weight status and socioeconomic differences in psychosocial correlates of physical activity in schoolchildren[END_REF]. Compared with highsocioeconomic-status girls, low-socioeconomic-status ones report lower preference for physical activity, while no such effect is observed in boys [START_REF] Anokye | Physical activity in England: who is meeting the recommended level of participation through sports and exercise?[END_REF][START_REF] Seabra | Gender, weight status and socioeconomic differences in psychosocial correlates of physical activity in schoolchildren[END_REF]. In addition, participation in active free play decreases in girls from lower-income neighborhoods at a faster rate from ages 9 to 13 relative to girls from high-income neighborhoods [START_REF] Cairney | Children's participation in organized sport and physical activities and active free play: Exploring the impact of time, gender and neighbourhood household income using longitudinal data[END_REF]. In contrast, boys' participation rates are similar across neighborhood income levels.

However, why and how SEC interact with sex remains largely unknown. The cultural hypothesis proposes the role of sex roles and social norms, which might operate through at least two mechanisms [START_REF] Bourdieu | Sport and social class[END_REF][START_REF] Macintyre | Socio-economic position, gender and health: how do they interact[END_REF][START_REF] Mackenbach | Socioeconomic inequalities in mortality among women and among men: an international study[END_REF]. First, sex roles may create differences in the access to coping mechanisms against the stress associated with disadvantaged SEC. For example, smoking, alcohol consumption, and physical activity, are more socially acceptable in men, while unhealthy eating and physical inactivity are more socially acceptable in women [START_REF] Macintyre | Socio-economic position, gender and health: how do they interact[END_REF]. Second, the social norms associated with physical activity could also play a role. According to [START_REF] Bourdieu | Sport and social class[END_REF], while aesthetics, absence of physical contact, and education are promoted in an advantaged socioeconomic environment, strength, physical contacts, and competition are instead valued in disadvantaged socioeconomic environments. Of particular relevance here, activities that are valued in an advantaged milieu are often perceived as appropriate for both men and women (e.g., horseback riding, tennis). In contrast, activities valued in a disadvantaged milieu are more often typically masculine (e.g., boxing, soccer) (for a review see [START_REF] Chalabaev | The influence of sex stereotypes and gender roles on participation and performance in sport and exercise: Review and future directions[END_REF]. Consequently, access to physical activities is restricted to "masculine" sports (e.g., rugby, cycling) for women living in disadvantaged SEC, while women living in advantaged ones have access to "feminine" (e.g., dance, synchronized swimming) or "sex-neutral" (e.g., volleyball, table tennis) activities. In contrast, men have access to activities that are congruent with a masculine sex role both in advantaged (e.g., fencing, golf) and disadvantaged (e.g., weightlifting, cycling) SEC. This could explain why the social patterning of physical activity is gendered.

Although cultural explanations of the SEC-by-sex interaction have been put forward by several authors, empirical support is lacking. We propose to test this hypothesis by distinguishing the cultural and material dimensions of SEC. This may allow to disentangle whether disadvantaged early-life SEC are a risk factor for physical inactivity because families lack the economic resources necessary to facilitate their children's participation in physical activity, or because their cultural background does not encourage involvement in physical activities, especially for girls [START_REF] Johnston | Sports participation and physical education in American secondary schools: current levels and racial/ethnic and socioeconomic disparities[END_REF][START_REF] Stalsberg | Effects of socioeconomic status on the physical activity in adolescents: a systematic review of the evidence[END_REF]. Until now, this cultural hypothesis has been overlooked, as most research explained SEC differences in physical activities using mechanisms related to material, or economic, resources, such as lack of access to transportation to facilities for physical activity, lack of economic resources to pay for leisure-time activities and sports, and lack of time [START_REF] Chinn | Barriers to physical activity and socioeconomic position: implications for health promotion[END_REF][START_REF] Sherwood | The behavioral determinants of exercise: implications for physical activity interventions[END_REF], or in terms of the increased biopsychosocial stress linked with low material socioeconomic conditions [START_REF] Mcneill | Social environment and physical activity: a review of concepts and evidence[END_REF].

In addition, while SEC and sex seem to interact during childhood, whether this interaction persists through the lifespan remains an open question, as their effects have usually been examined separately [START_REF] Anokye | Physical activity in England: who is meeting the recommended level of participation through sports and exercise?[END_REF]. A recent study showed that women who grew up in disadvantaged SEC were less physically active later in life, compared with women who grew up in advantaged SEC (Cheval, Sieber, et al., 2018). Crucially, from a statistical standpoint, the association between early-life SEC and physical inactivity was fully mediated by adulthood SEC, especially by the level of education. This suggests that the influence of the socioeconomic milieu in which women grew up can be fully compensated for their adult-life socioeconomic trajectories. Moreover, past research suggests that the cultural dimension of SEC may have stronger effects through the lifespan than the material one because of its impact on health behaviors [START_REF] Cutler | Socioeconomic status and health: dimensions and mechanisms[END_REF]. However, no study to our knowledge has investigated whether the early-life SEC-by-sex interaction persists in later life.

To fill these gaps, this study investigated whether the early-life SEC-by-sex interaction on physical activity in later life depends on the SEC dimension (material or cultural). This question was examined in a 13-year large-scale cohort of adults aged 50 and older. Indicators of material early-life disadvantages included overcrowding and low housing quality at age 10, and indicators of cultural ones included low occupational position of the household's main breadwinner and low number of books at home at age 10. Finally, to further understand the potential long-lasting effect of early-life SEC, we assessed whether the associations between early-life disadvantage and physical activity in later life were mediated by adulthood SEC, as in Cheval, Sieber, et al. (2018).

First, we predicted that, compared to men, women would be less physically active (H1). Second, we expected that people would be less physically active when they grew up in disadvantaged early-life SEC than in advantaged ones. Based on previous studies showing the importance of the cultural dimension in explaining physical activity, we hypothesized that the cultural aspect of disadvantage would be more strongly associated with lower physical activity than the material one (H2). Third, we hypothesized that the associations between early-life SEC and physical activity would be particularly pronounced in women (H3). Fourth, we hypothesized that the associations of sex and early-life SEC with physical activity could be, at least partly, explained by adulthood SEC (H4). Finally, we explored these effects on the evolution of physical activity over time.

Method

Study population and design

We used the Survey of Health Ageing and Retirement in Europe (SHARE; Börsch-Supan et al., 2013), a 13-year large-scale population-based European cohort of adults aged 50 and older, with repeated measurements in seven waves every two years between 2004 and 2017.

Physical activity was assessed at wave 1, 2, 4, 5, 6 and 7. Early-life SEC were assessed once, either at wave 3 or 7. Participants were eligible for the study if they participated either in the third wave or seventh wave and had at least one measure of physical activity. The relevant local research ethics committees in the participating countries approved SHARE. All participants provided written informed consent.

Measures

Physical activity. Physical activity was derived from the following two questions: "How often do you engage in activities that require a low or moderate level of energy such as gardening, cleaning the car, or going for a walk?" and "How often do you engage in vigorous physical activity, such as sports, heavy housework, or a job that involves physical labor?" [START_REF] Cheval | Relationship between decline in cognitive resources and physical activity[END_REF][START_REF] Cheval | Cognitive resources moderate the adverse impact of poor neighborhood conditions on physical activity[END_REF][START_REF] De Souto Barreto | Physical activity and incident chronic diseases: a longitudinal observational study in 16 European countries[END_REF]. Participants answered using a 4-point scale: 1: more than once a week; 2: once a week; 3: one to three times a month; 4: hardly ever or never. Participants who did not answer "more than once a week" to either item were classified as physically inactive. As described in previous research (Cheval, Sieber, et al., 2018), this strategy reduces the potential misclassification bias which would lead to physically inactive participants being incorrectly classified as physically active. Yet, a robustness analysis was conducted to test the models with a different cut point (see the Robustness Analysis section for more details) and showed results consistent with the main analyses.

Early-life SEC. Early-life SEC included four binary indicators reflecting cultural and material dimensions of participants' SEC at the age of 10 [START_REF] Wahrendorf | Does labour market disadvantage help to explain why childhood circumstances are related to quality of life at older ages? Results from SHARE[END_REF]. The cultural dimension of early-life SEC included the number of books at home, and the occupational position of the household's main breadwinner (first and second vs. higher skill levels of the International Standard Classification of Occupations). The number of books was used as a proxy of a focus on education [START_REF] Evans | Family scholarly culture and educational success: Books and schooling in 27 nations[END_REF][START_REF] Van Bergen | Why are home literacy environment and children's reading skills associated? What parental skills reveal[END_REF] and was dichotomized into 0-10 vs. more books at home. Occupational position was built according to a reclassification of the main occupational groups of the International Standard Classification of Occupations based on skill levels (Wahrendorf et al., 2013). The first and second skill levels were grouped as "disadvantaged" occupational position, whereas the third and fourth levels were grouped as "advantaged" occupational position. The material dimension of SEC included a measure of overcrowding (more vs. less than one person per room in the household) and housing quality (absence vs. presence of either fixed bath, cold running-water supply, hot running water supply, inside toilet, or central heating) (Cheval, Boisgontier, et al., 2018;[START_REF] Der Linden | Life-course circumstances and frailty in old age within different European welfare regimes: a longitudinal study with SHARE[END_REF]. Overcrowding [START_REF] Marsh | Home sweet home?: the impact of poor housing on health[END_REF] and housing quality [START_REF] Dedman | Childhood housing conditions and later mortality in the Boyd Orr cohort[END_REF][START_REF] Stafford | Neighbourhoods, housing, and health[END_REF] are independent social characteristics related to the household that have been described as a determinant of health. Previous studies have supported the validity of the operationalization of the aforementioned SEC indicators in distinguishing advantaged and disadvantaged individuals [START_REF] Pinto Pereira | Early life factors and adult leisure time physical inactivity stability and change[END_REF][START_REF] Pinto Pereira | Early-life predictors of leisure-time physical inactivity in midadulthood: findings from a prospective British birth cohort[END_REF]. However, whether this categorization can be generalized across countries still needs to be examined (see the Limitations section).

Sex. Sex was derived from the question: "What is your sex?". Participants could answer either "male" or "female". To be consistent with the terminology used by SHARE, we used sex rather than gender here. However, the present study focused on socially constructed roles and behaviors associated with a person's biological sex, which reflect gender according to the American Psychological Association (APA) guidelines (American Psychological Association, 2012).

Adult-life SEC mediators. The following variables were included as potential adultlife SEC mediators: participants' highest educational attainment (primary, secondary, or tertiary) during the follow-up, main occupational position based on the skill classification of the main job over the life course (low skill, high skill, or no paid work), and satisfaction with current household income, using the question, ''Is the household able to make ends meet?'', with a scale ranging from 1 (with great difficulty) to 4 (easily). Primary, secondary and tertiary categories corresponded to the codes 0 and 1 (less than primary and primary education), 2, 3, and 4 (lower and upper secondary and post-secondary non-tertiary education), 5 and 6 (shortcycle tertiary education and bachelor's or equivalent degree) of the International Standard Classification of Education 97 classification. We computed the mode of this variable over the follow-up to retain as many observations as possible [START_REF] Aartsen | Advantaged socioeconomic conditions in childhood are associated with higher cognitive functioning but stronger cognitive decline in older age[END_REF]Cheval, Chabert, Orsholits, et al., 2019;[START_REF] Sieber | Welfare regimes modify the association of disadvantaged adult-life socioeconomic circumstances with self-rated health in old age[END_REF].

Covariates and potential confounders. All analyses were adjusted for age, body mass index, attrition [no dropout, dropout (participants who responded to neither wave 6 nor wave 7), death], birth cohort [war (between 1914and 1918and between 1939and 1945), Great Depression (between 1929and 1938), no war and no economic crisis (before 1913, between 1919 and 1928, and after 1945)], and for country of residence (Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovenia, Spain, Sweden, Switzerland). All these variables have been found to be related to later-life health (Cheval, Chabert, Sieber, et al., 2019;[START_REF] Sieber | Do welfare regimes moderate cumulative dis/advantages over the life course? Cross-national evidence from longitudinal SHARE data[END_REF].

Statistical analysis

Logistic mixed-effects models were used to account for the nested structure of the data (i.e., multiple observations within a single participant) [START_REF] Boisgontier | The anova to mixed model transition[END_REF]. The final random structure included random intercepts for participants and random linear slopes for age at the participant's level. These random effects estimated each participant's probability of engagement in physical activity and the rate of change of this probability of engagement over time. Age was centered on the midpoint of the sample's age range (73 years) and was then divided by 10. This approach allows a simpler interpretation of the parameters and reduces the risk of model convergence issues. The coefficients yielded the effects of the physical activity rate of change over a 10-year period. For time-varying covariates, we used the mode to reduce observation loss. Odds Ratios (OR) were reported as a measure of effect size by exponentiating the values of the estimates obtained in the logistic mixed-effects model, with 1.5, 2.5, and 4.3 being used as cut-offs for small, medium, and large effect size, respectively [START_REF] Cohen | Statistical power analysis for the behavioral sciences[END_REF].

Model 1 tested the association between sex and the probability of physical activity. In addition, this model included interaction terms between sex and age (both linear and quadratic) to examine whether sex was associated with (both linear and non-linear) change in the probability of physical activity over the years. A statistically significant interaction would indicate that the evolution of the probability of physical activity over the years depended on the participant's sex. In model 2, we tested the association between early-life SEC indicators and the probability of physical activity and change over the years. All early-life SEC indicators were included in model 2 as well as in the subsequent models. In Model 3, we added interaction terms between sex and the early-life SEC indicators, as well as a three-way interaction between sex, early-life SEC indicators, and age (linear and quadratic) to examine whether the effects of early-life SEC on the probability of physical activity and of its change over the years were moderated by the participant's sex. In Model 4, we tested whether the effects of early-life SEC and sex on the probability of physical activity and of its change over the years were explained by adult-life SEC. Statistical assumptions associated with MEM (i.e., normality of the residuals, linearity, multicollinearity, and undue influence) were checked and met for all models.

Sensitivity analyses

We performed a series of sensitivity analyses excluding 1) participants with dementia, 2) participants who died during the survey, and 3) participants who dropped out during the survey. Table S3 provides more details on all covariates used in the sensitivity analyses.

Robustness analysis

A robustness analysis was conducted to test the models with a different cut-off point for the measure of physical activity. Specifically, participants who answered "hardly ever or never" to at least one of the physical-activity items were classified as physically inactive, whereas participants who answered "more than once a week" or "once a week" to at least one of the physical-activity items were classified as physically active. The objective of this classification was to reduce the potential misclassification bias, which could lead to physically active participants being erroneously classified as physically inactive.

Results

After the descriptive statistics, the results are reported in three sections: The first describes analyses of the association between sex and physical activity; the second describes analyses of the association between early-life SEC and physical activity, and the third describes results concerning the moderation of the association between early-life SEC and physical activity by sex. Table S2 provides the results of the mixed-effects models with full details.

Descriptive results

Table 1 shows the characteristics of the participants by sex at baseline. Briefly, our study sample consisted of 56,331 people (56.3% women), aged between 50 and 96. During the course of the study 3,013 (5.3%) participants died, and 7,234 (12.8%) dropped out for other reasons. The average number of observations per respondent was 2.91 (total number of observations was 164,022). Tables S1 present additional descriptive statistics stratified by physical activity levels and early-life SEC.

Main analyses

Sex and moderate physical activity. As hypothesized (H1), results showed that at 73 years (because age was centered at 73 years, the midpoint of the sample's age range) women were less likely to be physically active than men (adjusted odds ratio [OR] = .69, 95% confidence interval [CI] = .66-.72, p < .001). In addition, compared to men, women demonstrated a steeper decrease in the probability of being physically active over the years (OR = .75, 95%CI = .72-.79, p < .001, for linear effect of age; OR = .95, 95%CI = .92-.98, p < .001, for quadratic effect of age (Table S2;Table 2,Model 1).

Early-life SEC and physical activity. As hypothesized (H2), results showed that, at 73 years, cultural aspects of disadvantaged early-life SEC were associated with lower probability of physical activity (OR = .76, 95%CI = .72-.80, p < .001, for low number of books; OR = .87, 95%CI = .81-.93, p < .001, for occupational position of the main breadwinner associated with low skill). By contrast, material aspects of disadvantaged early-life SEC were not associated with the probability of physical activity (OR = .96, 95%CI = .90-1.02, p = .188, for overcrowding; OR = 1.04, 95%CI = .98-1.10, p = .173, for housing quality). In addition, low occupational skill of the main breadwinner (OR = .88, 95%CI = .82-.94, p < .001) and poor housing quality (OR = 94, 95%CI = .90-1.00, p = .033) were associated with a steeper linear decrease in the probability of physical activity over the years. Number of books and overcrowding were, however, not associated with the probability of physical activity change over aging (Table S2;Table 2,Model 2).

The moderating role of sex in the associations between early-life SEC and physical activity. As predicted (H3), interactions between the cultural aspects of early-life SEC disadvantage and sex on the probability of physical activity were significant (OR = .88, 95%CI = .79-.98, p = .019, for number of books; OR = .84, 95%CI = .73-.97, p = .014, for occupational position of the main breadwinner). When these interactions were further analyzed, results revealed that the occupational position of the main breadwinner was not significantly associated with the probability of physical activity in men (OR = .95, 95%CI = .86-1.06, p = .383), while this variable was associated with lower probability of physical activity in women (OR = .81, 95%CI = .74-.88, p < .001). Moreover, the association between the number of books and physical activity was significantly stronger in women (OR = .72, 95%CI = .67-.78, p < .001) than in men (OR = .82, 95%CI = .76-.89, p < .001). By contrast, no significant interactions were found between the material aspects of disadvantaged early-life SEC and sex (OR = .97, 95%CI = .86-1.09, p = .633, for overcrowding and OR = .99, 95%CI = .89-1.11, p = .891, for housing quality). Finally, no significant three-way interactions between early-life SEC, sex, and age (linear and quadratic) were found (ps > .167), thereby suggesting that sex did not significantly moderate the effect of early-life SEC on the evolution of the probability of physical activity over the years (Table S2; Table 2, Model 3: Figure 1).

Mediating role of adulthood SEC in the associations between early-life SEC, sex, and physical activity. The associations of sex and early-life SEC with the probability of physical activity were only partially attenuated by the life-course socioeconomic conditions. Specifically, sex and number of books, but not the occupational skill of the main breadwinner, remained associated with the probability of physical activity, and the occupational skill of the main breadwinner remained associated with a steeper linear decrease in the probability of physical activity over the years (Table S2; Table 2, Model 4a). Moreover, housing quality turned out to be associated with the probability of physical activity (OR = 1.12, 95%CI = 1.05-1.18, p < .001), while its association with the linear decrease in the probability of physical activity over the years became marginal (p = .075). The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity remained more pronounced in women than in men (although the effect of the number of books became marginal, p = .091; Table S2; Table 2,Model 4b). In this fully adjusted model, number of books was associated with lower probability of physical activity in both men and women, while the association between main breadwinner and the probability of physical activity was only observed in women.

Sensitivity and robustness analyses. Results of the sensitivity and robustness analyses were consistent with those of the main analyses (Table S3).

Discussion

Main findings

The effects of sex and early-life SEC on health in older age have been largely examined separately [START_REF] Eagly | Feminism and psychology: Analysis of a half-century of research on women and gender[END_REF]. Here, we examined their interaction on physical activity, and tested a cultural explanation of this interaction. Results showed that the cultural dimension of early-life disadvantaged SEC was more strongly associated with physical activity in women than in men. This suggests that these barriers act in a multiplicative manner, rather than in an additive way, calling into question the widespread assertion that SEC are a well-established risk factor for physical inactivity. In fact, SEC may be an important risk factor in women, but less in men.

In addition, the association between early-life SEC and physical activity was only partially attenuated by life-course SEC, suggesting that early-life SEC have a long-lasting impact on health behaviors, which cannot be fully compensated for by socioeconomic mobility during adulthood. Taken together, these results further support the need for adopting an intersectional perspective [START_REF] Eagly | Feminism and psychology: Analysis of a half-century of research on women and gender[END_REF] when examining health inequalities. However, the size of the association was small (ORs < 1.5). This suggests that other variables (e.g., motivation) could affect physical activity.

Comparison with other studies

Observing SEC-by-sex interactions only for the cultural aspects of SEC provides support to the cultural hypothesis. Cultural factors, such as social norms and sex roles, may lead women and men to have a differential access to health behaviors, with physical activity being more socially acceptable in men than in women, especially in disadvantaged SEC [START_REF] Bourdieu | Sport and social class[END_REF][START_REF] Macintyre | Socio-economic position, gender and health: how do they interact[END_REF][START_REF] Mackenbach | Socioeconomic inequalities in mortality among women and among men: an international study[END_REF] Moreover, observing that the social patterning of physical activity is sex dependent is in line with past studies conducted on children [START_REF] Cairney | Children's participation in organized sport and physical activities and active free play: Exploring the impact of time, gender and neighbourhood household income using longitudinal data[END_REF][START_REF] Seabra | Gender, weight status and socioeconomic differences in psychosocial correlates of physical activity in schoolchildren[END_REF]. It adds to the existing literature by showing that cultural early-life SEC have long-lasting effects. A potential explanation is that sex roles are learnt very early, during childhood, and that their effects are implicit [START_REF] Boiché | Social antecedents and consequences of gender-sport stereotypes during adolescence[END_REF]. Therefore, girls' tendency to avoid physical activities may turn into habits that may be difficult to change in adulthood. These findings stand in contrast with Cheval, Sieber, et al. (2018)'s study, which found that the association between early-life SEC and physical inactivity was fully mediated by adulthood SEC. This difference can be explained by the fact that our study disaggregated the material and cultural aspects of early-life disadvantage, while the previous one did not. Therefore, consistent with recent suggestions (Cheval, Orsholits, et al., 2019), results confirm the need to differentiate various aspects of early-life disadvantage to better gauge their specific links with health in later life.

Strengths and limiting conditions

This study has several strengths. First, the repeated measurement of physical activity allowed to investigate its evolution over 46 years (from age 50 to 96). Second, our study was based on large-scale data (N = 56,331). Third, we measured four different indicators of SEC (two cultural and two material), allowing a more fine-grained measure of this early-life disadvantage. Fourth, we applied an analytical approach well-suited to examine not only level differences, but also change as a person ages.

However, this study also has limitations. First, we used self-reported measures of physical activity, which may generate a misclassification bias [START_REF] Prince | A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review[END_REF], be subject to lack of granularity, and include examples that may prime individuals' response toward activities related to house chores and recreational activities. Nevertheless, we believe the potential measurement errors associated with self-reports are unlikely to explain the observed associations between SEC, sex, and physical activity. Moreover, the observed associations remained consistent across the multiple ways of modelling the dependent variable (i.e., three different cut points) and across the different population samples (i.e., three sensitivity analyses using different samples). Finally, applying a cut-off to both the moderate and vigorous-intensity items addresses some limits of our measure (e.g., difficulty for participants to categorize their physical activities according to the intensity). Second, early and adult-life SEC were assessed using self-reported retrospective data, which is subject to memory bias or social desirability. However, previous studies suggest sufficient validity of this self-reported socioeconomic information to discriminate between advantaged and disadvantaged individuals based on the specific cut-off points [START_REF] Pinto Pereira | Early life factors and adult leisure time physical inactivity stability and change[END_REF][START_REF] Pinto Pereira | Early-life predictors of leisure-time physical inactivity in midadulthood: findings from a prospective British birth cohort[END_REF][START_REF] Solís | Adverse childhood experiences and physiological wear-and-tear in midlife: Findings from the 1958 British birth cohort[END_REF]. In addition, and unlike previous literature relying on crude assessments of early-life SEC (e.g., using a limited number of early-life indicators) [START_REF] Stringhini | Association of lifecourse socioeconomic status with chronic inflammation and type 2 diabetes risk: the Whitehall II prospective cohort study[END_REF]Wannamethee et al., 1996), we used a comprehensive and rich measure of early-life SEC. However, these indicators were not exhaustive and may still have been insufficient to accurately distinguish between the different SEC groups. Consequently, we cannot fully exclude potential misclassification bias. Third, the SHARE design has two selection biases, related to the recruitment procedure that occurs at age 50+ (respondents may be more likely to be in better health than non-respondents) and to the loss of participants during the follow-up. This latter bias was limited by adjusting for attrition in the analyses, and by excluding participants who died or dropped out during the follow-up in the sensitivity analyses. Fourth, the cultural interpretation of the SEC-by-sex interaction remains speculative as individual motivations for physical activity were not measured. Fifth, the cut-off scores used in SHARE to operationalize early-life SEC are the same across countries, which may limit their validity in distinguishing advantaged and disadvantaged SEC. Likewise, although a discussion of how the findings observed can be moderated by the country is outside of the scope of the current paper, we believe that future studies need to investigate this question. This will allow to determine whether and how the influence of sex and SEC can be increased vs. decreased depending on the type of country type or welfare regime [START_REF] Sieber | Welfare regimes modify the association of disadvantaged adult-life socioeconomic circumstances with self-rated health in old age[END_REF][START_REF] Sieber | Do welfare regimes moderate cumulative dis/advantages over the life course? Cross-national evidence from longitudinal SHARE data[END_REF].

Conclusions

This is one of the first studies that provide empirical support to a cultural explanation of the SEC-by-sex interaction on health behaviors. In women, but not in men, the number of books in the household remained significantly associated with a lower probability of physical activity after adjustments for life-course socioeconomic trajectories, suggesting that habits developed within the social milieu during childhood may partly determine behavior on a lifelong basis.

Figure 1. Association of cultural and material aspects of early-life SEC disadvantage and physical activity depending on sex

Note. Odds ratios (OR) and 95% confidence interval steaming from Model 3 are reported. Note. SEC = socioeconomic conditions. All models were adjusted for age, body max index, attrition, birth cohort, and country of residence. Models 4a and 4b were also adjusted for adult-life SEC mediators -i.e., participants' highest educational attainment during the follow-up, main occupational position, and satisfaction with current household income. Models 4b included interaction terms between sex and early-life SEC. Table S2 provides the full table.

Wahrendorf, M., Blane, D., Bartley, M., Dragano, N., & Siegrist, J. (2013). Working conditions in mid-life and mental health in older ages. Advances in Life Course Research, 18(1), 16-25. Wannamethee, S. G., Whincup, P. H., Shaper, G., & Walker, M. (1996) Age was centred on the midpoint of the sample's age range (73 years). "Age (10-year follow-up)" and "Age2 (10-year follow-up)" estimated the linear and quadratic changes in the engagement in physical activity over a 10-year period.

Main results

Results were consistent with those of the main analysis.

In brief, 1) Women were less likely to be physically active than men, and were more likely to show a steeper decline across aging than men. 2) Cultural aspects of disadvantaged early-life SEC, but not material aspects of disadvantaged early-life SEC (although p =.056 for housing quality), were associated with a lower probability of physical activity and with a steeper decline of this probability across aging.

3) The associations between one dimension of cultural aspects of disadvantaged early-life SEC (i.e., main breadwinner), but not material aspects of disadvantaged early-life SEC, and the probability of physical activity were more pronounced in women than in men (although p = .079). The association between the other dimension of cultural aspects of disadvantaged early-life SEC (i.e., number of books) was not significantly moderated by sex (p = .119) 4) The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity were attenuated after adjustment for adult-life SEC. 5) In the fully adjusted model, one cultural aspect of disadvantaged early-life SEC (number of books) remained significantly associated with the probability of physical activity in women, while these associations were not significant in men (although p = .074 for number of books in men). Excluding 3,013 participants who died during follow-up

Rationale

Respondents who died during the follow-up were likely to have poorer health status (selective attrition).

Main results

Results were consistent with those of the main analysis.

1) Women were less likely to be physically active than men, and were more likely to show a steeper decline across aging than men. 2) Cultural aspects of disadvantaged early-life SEC were associated with a lower probability of physical activity and with a steeper decline of this probability across aging. Poor housing quality, but not overcrowding, was associated with a higher probability of physical activity and with a steeper decline of this probability across aging 3) The associations between one dimension of cultural aspects of disadvantaged early-life SEC (i.e., main breadwinner), but not material aspects of disadvantaged early-life SEC, and the probability of physical activity were more pronounced in women than in men. The association between the other dimension of cultural aspects of disadvantaged early-life SEC (i.e., number of books) was not significantly moderated by sex (p = .173) 4) The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity were attenuated after adjustment for adult-life SEC. 5) In the fully adjusted model, one cultural aspect of disadvantaged early-life SEC (number of books) remained significantly associated with the probability of physical activity in women, while in men only the association between number of book and physical activity was significant.

3 Excluding 7,234 participants who dropped out during follow-up

Rationale

Respondents who dropped during the follow-up were likely to have specific characteristics that may bias the associations observed (selective attrition).

Main results

Results were consistent with those of the main analysis. 1) Women were less likely to be physically active than men, and were more likely to show a steeper decline across aging than men.

2) Cultural aspects of disadvantaged early-life SEC, but not material aspects of disadvantaged early-life SEC, were associated with a lower probability of physical activity and with a steeper decline of this probability across aging. Poor housing quality was associated with a steeper decline of this probability across aging 3) The associations between Cultural aspects of disadvantaged early-life SEC, but not material aspects of disadvantaged early-life SEC and the probability of physical activity were more pronounced in women than in men (although p =.076 for number of books). 4) The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity were attenuated after adjustment for adult-life SEC. 5) In the fully adjusted model, one cultural aspect of disadvantaged early-life SEC (number of books) remained significantly associated with the probability of physical activity in women, but not in men.

Robustness analysis Rationale

To ensure the robustness of the main results, we tested the models with a different cut-point of the physical activity scale.

Cut-point:

Participants who answered "hardly ever or never" were classified as ''physically inactive'', whereas the other participants were classified as "physically active".

The objective of this cut-off was to reduce a potential misclassification bias in which physically active participants would be classified as physically inactive (i.e., the opposite bias to the one minimized in the main analysis).

Main results

Results were consistent with those of the main analysis. Women were less physically active than men, and showed a steeper decline of the probability of physical activity across aging. Cultural aspects of disadvantaged early-life SEC were associated with lower probability of physical activity and with a steeper linear decrease in the probability of physical activity across aging. Material aspects of disadvantaged early-life SEC were not associated with the probability of physical activity.

Table 2 .

 2 Summary of the coefficients obtained from the mixed-effects models for sex, early-life SEC, and the interaction between sex and early-life SEC on physical activity level

		Number of books
		(ref. more than 10)
	Cultural aspect
	of early-life SEC	Occupational position
		main breadwinner
		(ref. high skills) Overcrowding	Odd Ratios
	Material aspect of early-life SEC	(ref. no) Housing quality	Men Women
		(ref. high)
			0.7	0.8	0.9	1.0
					Odds of physical activity x

Additional descriptive statistics stratified by physical activity levels and early- life SEC

  . Influence of fathers' social class on cardiovascular disease in middle-aged men. TheLancet, 348(9037), 1259Lancet, 348(9037), -1263. .

		Denmark Sample characteristics by cultural aspects of early-life SEC 400 2.8% Sample characteristics by material aspects of early-life SEC Tables S1. Sample characteristics by physical activity levels France 913 6.4% Germany 725 5.1% Number of books Overcrowding Greece 1067 7.5% Israel 353 2.5% Less than 10 books More than 10 books Yes No	6.1% 6.0% Main ccupational position Housing quality 7.6% 5.7% 2.5% Low skills High skills Low quality High skills
	Sex Italy Netherlands Women Sex Sex Spain Women Men Sweden Men Physical activity outcomes	23726 12230 18183 9977	56.6% 55.1% 43.4% 44.9%	7891 19387 6531 14737	Physically inactive 1827 12.9% 277 2.0% 54.7% 8618 1284 9.1% 56.8% 25727 45.3% 6582 404 2.8% 43.2% 20063	Physically active 7.1% 56.7% 22999 3.9% 56.2% 43.3% 18132 8.1% 7.4% 43.8%	P value 55.9% 55.9% 44.1% 44.1%
	Women Switzerland Physical activity outcomes Physically inactive	10983	26.2%	3204	8415 59.3% 409 2.9% 22.2% 4454	23202 55.1% 4.7% 29.3% 9733	23.7%
	Men Czech Republic Physically inactive Physically active	6543 30926	29.5% 73.8%	7644 11218	5772 40.7% 1370 9.7% 22.4% 11830 77.8% 10746	18942 44.9% <0.001 6.7% 25.8% 22.4% 70.7% 31398 76.3%
	Early-life SEC Ireland Physically active Early-life SEC	15664	70.5%	26480	134 0.9% 77.6%	33960	74.2%	1.1%	77.6%
	No of book Poland Early-life SEC No of book				757 5.3%			2.9%
	Less than 10 Estonia No of book Less than 10	18900	45.1%	3307	6543 46.1% 774 5.5% 77.1% 10298	67.7%	37.2% 6.1% 11909	29.0%
	More than 10 Hungary Less than 10 More than 10	23009	54.9%	11115	7644 53.9% 140 1.0% 20384 22.9% 4902	44.5% 32.2%	62.8% <0.001 0.7% 17.3% 29222 71.0%
	Occupational position of the main breadwinner Portugal More than 10 Occupational position of the	168 1.2%	25406	55.5%	0.4%	82.7%
	Low skills High skills Overcrowding Slovenia Occupational position of the main breadwinner Luxembourg main breadwinner Low skills Croatia Low skills High skills	35491 20384 6418	84.7% 91.8% 15.3%	10299 25406 4123	11830 83.4% 2357 16.6% 514 3.6% 178 1.3% 74.4% 13816 451 3.2% 74.5% 28.6% 1384	90.9% 9.1%	80.6% 19.4% <0.001 4.9% 31974 77.7% 1.6% 2.9% <0.001 9157 22.3%
	Yes Birth cohort High skills Overcrowding	1823	8.2%	8718	10983 77.4% 25.5%		73.4%
	No After 1945 Overcrowding Yes				3204 22.6% 6828 48.1% 13648	89.8%	26.6% <0.001 57.7% 28261 68.7%
	Yes No	Housing quality between 1939 and 1945 18900	85.1%	23009	2924 20.6% 67.4% 35491 1552	77.5% 10.2%	20.8% 12870	60.9% 31.3%
	Low quality between 1929 and 1938 3307 Housing quality No	14.9%	11115	4454 31.4% 3163 22.3% 32.6% 10299	22.5%	25.5% 17.1%	39.1%
	High quality between 1919 and 1928 Housing quality Low quality 13648	32.6%	1552	9733 68.6% 1272 9.0% 10.8%		74.5% <0.001 4.4% <0.001
	Adult-life SEC Attrition Low quality High quality	10298 28261	46.4% 67.4%	4902 12870	14.4% 89.2%	13816	30.2%		13.1%
	Education No drop out High quality Adult-life SEC	11909	53.6%	29222	11434 80.6% 85.6% 31974	69.8%	82.2%	86.9%
	Primary Drop out Adult-life SEC Education				4528 31.9% 1582 11.3%		20.9% 13.4%
	Secondary Death Education Primary	11378	27.2%	1964	7267 51.2% 1171 8.3% 13.6% 6148	40.4%	55.9% 4.4% 7194	<0.001 17.5%
	Tertiary Note. SD = standard deviation; SEC = socioeconomic circumstances. 2392 16.9% Primary 9328 42.0% 4014 11.8% 12486 Secondary 23273 55.5% 7547 52.3% 7568	27.3% 49.8%	23.2% <0.001 856 8.1% 23252 56.5%
	Main occupational position Secondary 11085 Tertiary 7258	49.9% 17.3%	19735 4911	57.8% 34.1%	25664 1484	56.0% 9.8%	10685	48.9% 26.0%
	Low skills Main occupational position Tertiary	1794	8.1%	10375	8991 63.4% 30.4% 7640	16.7%	62.6%	43.0%
	High skills Never worked Main occupational position Low skills Low skills High skills Ability to make ends meet 27463 16113 11142 With great difficulty High skills 3401 Never worked 3304 With some difficulty Never worked 2693 Satisfaction with Income Fairly easily Satisfaction with Income With great difficulty 5511 Easily With great difficulty 3871 With some difficulty 9944 With some difficulty 5804 Fairly easily 12851 Covariates Age at baseline (years), SD Fairly easily 6602 Easily 13603 Easily 5930 Covariates Countries Belgium Covariates Age at baseline (years), SD 63.2 Austria Countries Age at baseline (years), SD 64.9 Countries	65.5% 72.6% 26.6% 15.3% 7.9% 12.1% 13.1% 17.4% 23.7% 26.1% 30.7% 29.7% 32.5% 26.7% 9.2 9.4	7920 19270 5763 13504 739 1350 806 2446 1978 6118 4057 10306 7581 15254 61.8 61.5	3620 25.5% 1576 11.1% 54.9% 10686 56.5% 31103 40.0% 2840 2255 15.9% 39.6% 11095 5.1% 1674 3563 25.1% 4.0% 3592 4333 30.5% 5.6% 2629 4036 28.4% 7.2% 5606 13.7% 4360 17.9% 10215 28.1% 4829 64.9 10.0 30.2% 13935 52.6% 3382 44.7% 16034 1277 9.0% 9.1 66.7 765 5.4% 8.80 63.1	70.3% 67.9% 18.7% 24.2% 11.0% 7.8% 17.3% 12.2% 28.7% 22.3% 31.8% 62.2 30.4% 22.2% 35.0% 9.2 9.2	31.5% 5.9% 24697 14065 2369 9.6% 451 19.8% 3688 29.9% 711 7562 40.7% <0.001 60.0% <0.001 40.6% 34.2% 55.1% 5.8% 4.3% 9.0% 6.7% 18.4% 16.2% 12079 29.4% 8.8 28.2% 17802 43.3% <0.001 48.9% 61.4 8.8 8.5% 5.2% 62.0 9.0

Table S2 . Full table for the mixed effects models

 S2

Table S3 . Summary of the sensitivity and robustness analyses

 S3

				Model 1		Model 2		Model 3		Model 4a		Model 4b
	Row	Variables	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
		1	Intercept	3.06 (2.81-3.32)	<0.001	3.99 (3.60-4.42)	<0.001	3.50 (3.09-3.98)	<0.001	1.30 (1.12-1.50)	<0.001	1.12 (0.95-1.32)	0.164
		2	Age (10y follow-up)	0.47 (0.44-0.49)	<0.001	0.55 (0.51-0.60)	<0.001	0.54 (0.49-0.60)	<0.001	0.48 (0.43-0.55)	<0.001	0.46 (0.40-0.54)	<0.001
		3	Age2 (10y follow-up)	0.81 (0.79-0.83)	<0.001	0.79 (0.75-0.83)	<0.001	0.78 (0.73-0.84)	<0.001	0.83 (0.77-0.90)	<0.001	0.82 (0.75-0.91)	<0.001
		4	Sex (ref. Men)
			Women	0.69 (0.66-0.72)	<0.001	0.69 (0.66-0.72)	<0.001	0.87 (0.76-1.01)	0.061	0.78 (0.74-0.82)	<0.001	0.98 (0.85-1.13)	0.769
		5	Age x sex (ref. men)
		6	Age x women Age2 x women x low Poland Age2 x sex (ref. men) Education (ref. primary) Estonia	0.75 (0.72-0.79) 0.48 (0.43-0.54) 1.38 (1.25-1.52)	<0.001 <0.001 <0.001	0.76 (0.73-0.80) 0.50 (0.45-0.56) 1.33 (1.20-1.47)	<0.001 <0.001 <0.001	0.78 (0.68-0.89) 0.95 (0.88-1.02) 0.49 (0.44-0.55) 1.30 (1.18-1.44)	<0.001 0.167 <0.001 <0.001	0.80 (0.76-0.84) 0.63 (0.56-0.70) 1.49 (1.34-1.65)	<0.001 <0.001 <0.001	0.84 (0.73-0.96) 0.94 (0.87-1.02) 0.62 (0.56-0.70) 1.49 (1.34-1.65)	0.014 0.111 <0.001 <0.001
			Age2 x women Secondary Hungary	0.95 (0.92-0.98) 0.61 (0.46-0.82)	0.001 0.001	0.96 (0.93-0.99) 0.59 (0.44-0.78)	0.004 <0.001	0.97 (0.89-1.05) 0.58 (0.43-0.77)	0.433 <0.001	0.95 (0.92-0.98) 1.09 (1.02-1.16) 0.80 (0.60-1.06)	0.002 0.008 0.12	0.96 (0.88-1.05) 1.08 (1.01-1.15) 0.79 (0.60-1.06)	0.391 0.023 0.115
		7	Early-life SEC Tertiary Portugal Number of book (ref. more than 10) Age x Education (ref. primary) Slovenia	0.38 (0.29-0.49) 1.96 (1.74-2.20)	<0.001 <0.001	0.40 (0.31-0.52) 2.01 (1.78-2.26)	<0.001 <0.001	0.40 (0.31-0.51) 1.98 (1.76-2.22)	<0.001 <0.001	1.32 (1.20-1.44) 0.56 (0.43-0.72) 2.36 (2.10-2.66)	<0.001 <0.001 <0.001	1.31 (1.19-1.43) 0.55 (0.43-0.71) 2.37 (2.11-2.68)	<0.001 <0.001 <0.001
	1	8 9	Less than 10 Age x Number of book (ref. more than 10) Age x Less than 10 Age x Secondary Age x Tertiary Luxembourg 1.66 (1.37-2.01) Croatia 1.02 (0.87-1.19) Description Age2 x Number of book (ref. more than 10) Age2 x Less than 10 Age2 x Education (ref. primary) Age2 x Secondary Sensitivity analyses Occupational position of the main breadwinner (ref. high skills) Low skills Never worked Age x Occupational position of the main breadwinner (ref. high skills) <0.001 0.802 Age2 x Tertiary Excluding 2,504 participants with dementia Main occupational position (ref. low skill) High skill	0.76 (0.72-0.80) 0.96 (0.91-1.01) 1.65 (1.36-2.00) 1.12 (0.95-1.31) Rationale and main results <0.001 0.82 (0.76-0.89) 0.129 0.96 (0.89-1.04) <0.001 1.64 (1.36-1.99) 0.166 1.02 (0.88-1.20) 1.03 (0.99-1.06) 0.119 1.02 (0.97-1.08) 0.87 (0.81-0.93) <0.001 0.95 (0.86-1.06) Rationale Respondents with dementia may bias the observed associations. <0.001 0.87 (0.82-0.92) <0.001 0.314 0.97 (0.92-1.03) 0.350 1.01 (0.96-1.07) 0.719 1.03 (0.95-1.13) 0.449 <0.001 1.65 (1.36-1.99) <0.001 0.760 1.49 (1.27-1.75) <0.001 0.420 1.01 (0.98-1.05) 0.551 1.06 (1.02-1.10) 0.006 0383 0.97 (0.90-1.04) 0.332 0.72 (0.65-0.79) <0.001 1.01 (0.95-1.07) .694 1.15 (1.08-1.22) <0.001	0.92 (0.85-1.00) 0.98 (0.90-1.07) 1.02 (0.96-1.08) 1.05 (0.96-1.15) 1.65 (1.36-2.00) 1.51 (1.28-1.77) 1.01 (0.95-1.07) 1.07 (1.03-1.11) 1.07 (0.97-1.19) 0.73 (0.67-0.80) 1.02 (0.96-1.09) 1.16 (1.08-1.23)	0.044 0.586 0.318 <0.001 <0.001 0.709 0.001 0.701 0.184 <0.001 0.433 <0.001
			Age x Low skills Age x Main occupational position (ref. low skill)		0.88 (0.82-0.94)	<0.001	0.85 (0.77-0.95)	0.003	0.95 (0.90-1.00)	0.075	0.89 (0.80-0.99)	0.034
			Age2 x Occupational position of the main breadwinner (ref. high skills) Age2 x Low skills Age x High skill Age x Never worked	1.00 (0.95-1.04)	0.858	0.99 (0.93-1.06)	0.847	1.00 (0.96-1.04) 1.03 (0.97-1.10) 0.96 (0.89-1.04)	0.966 0.329 0.317	0.97 (0.91-1.04) 1.05 (0.98-1.12) 0.96 (0.88-1.04)	0.187 0.427 0.327
			Overcrowding (ref. no) Yes Age2 x Main occupational position (ref. low skill) Age2 x High skill		0.96 (0.90-1.02)	0.188	0.99 (0.90-1.08)	0.805	1.02 (0.96-1.08) 0.93 (0.89-0.97)	0.608 0.001	1.03 (0.95-1.13) 0.93 (0.89-0.98)	0.465 0.002
			Age x Overcrowding (ref. no) Age2 x Never worked							1.01 (0.95-1.07)	0.759	1.01 (0.95-1.07)	0.822
			Age x Yes Satisfaction with Income (ref. with great difficulty) Age2 x Overcrowding (ref. no) With some difficulty		0.98 (0.93-1.04)	0.541	1.01 (0.92-1.10)	0.866	0.99 (0.94-1.05) 1.45 (1.32-1.58)	0.856 <0.001	1.02 (0.93-1.12) 1.45 (1.32-1.58)	0.675 <0.001
			Age2 x Yes Fairly easily Housing quality (ref. high) Low Easily Age x Satisfaction with Income (ref. with great difficulty)		1.00 (0.97-1.04) 1.04 (0.98-1.10)	0.800 0.173	1.01 (0.95-1.07) 1.05 (0.96-1.14)	0.746 0.289	1.00 (0.96-1.03) 1.85 (1.69-2.02) 1.12 (1.05-1.18) 1.45 (1.32-1.58)	0.806 <0.001 <0.001 <0.001	1.00 (0.94-1.06) 1.84 (1.69-2.01) 1.12 (1.03-1.22) 2.53 (2.31-2.77)	0.985 <0.001 <0.001 0.011
			Age x Housing quality (ref. high) Age x Low Age x With some difficulty Age x Fairly easily			0.94 (0.90-1.00)	0.033	0.98 (0.90-1.06)	0.583	0.95 (0.90-1.00) 1.06 (0.97-1.16) 1.04 (0.96-1.14)	0.075 0.231 0.338	0.99 (0.91-1.08) 1.05 (0.96-1.16) 1.04 (0.95-1.14)	0.283 0.786 0.378
			Age2 x Housing quality (ref. high) Age x Easily							1.08 (0.99-1.18)	0.094	1.08 (0.99-1.18)	0.101
			Age2 x Low Age2 x Satisfaction with Income (ref. with great difficulty) Age x sex (ref. Women) Age2 x With some difficulty		1.01 (0.97-1.05)	0.723	1.04 (0.98-1.11)	0.182	1.00 (0.96-1.04) 1.00 (0.94-1.06)	0.966 0.977	1.03 (0.97-1.10) 1.00 (0.94-1.06)	0.280 0.965
			Sex x early-life SEC Age2 x Fairly easily Sex (ref. men) x Number of book (ref. more than 10) Age2 x Easily						0.97 (0.92-1.03) 0.95 (0.90-1.01)	0.298 0.084	0.97 (0.92-1.03) 0.96 (0.90-1.01)	0.350 0.144
			Women x less than 10 Attrition (ref. no drop out)					0.88 (0.79-0.98)	0.019			0.91 (0.82-1.01)	0.091
			Age x sex (ref. men) x Number of book (ref. more than 10) Age x women x less than 10 Drop out 0.83 (0.78-0.90) Death 0.43 (0.39-0.47)	<0.001 <0.001	0.85 (0.79-0.91) 0.44 (0.40-0.48)	<0.001 <0.001	0.99 (0.90-1.10) 0.85 (0.79-0.91) 0.44 (0.40-0.48)	0.913 <0.001 <0.001	0.88 (0.81-0.94) 0.47 (0.43-0.51)	<0.001 <0.001	0.98 (0.88-1.09) 0.87 (0.81-0.94) 0.47 (0.43-0.51)	<0.001 0.715 <0.001
			Age x sex (ref. men) x Number of book (ref. more than 10) Birth cohort (ref. born after 1945)
			Age2 x women x less than 10 1939 to 1945 Sex (ref. men) x Occupational position of the main breadwinner (ref. high skills) 1.12 (1.05-1.18) <0.001 1929 to 1938 1.34 (1.24-1.45) <0.001	1.12 (1.05-1.18) 1.36 (1.25-1.46)	<0.001 <0.001	1.00 (0.93-1.08) 1.12 (1.05-1.18) 1.35 (1.25-1.46)	0.953 <0.001 <0.001	1.12 (1.06-1.19) 1.39 (1.29-1.51)	<0.001 <0.001	0.99 (0.92-1.07) 1.89 (1.66-2.15) 1.42 (1.31-1.54)	0.876 <0.001 <0.001
			Women x low skills 1919 to 1928 Age x sex (ref. men) x Occupational position of the main breadwinner (ref. high skills) 1.77 (1.56-2.02) <0.001 1.79 (1.57-2.03) Countries (ref. Belgium)	<0.001	0.84 (0.73-0.97) 1.79 (1.57-2.03)	0.014 <0.001	1.87 (1.64-2.12)	<0.001	0.83 (0.73-0.95) 1.89 (1.66-2.15)	0.009 <0.001
			Age x women x low skills Austria	1.31 (1.19-1.45)	<0.001	1.31 (1.19-1.44)	<0.001	1.05 (0.92-1.21) 1.29 (1.17-1.42)	0.442 <0.001	1.24 (1.12-1.37)	<0.001	1.03 (0.90-1.19) 1.24 (1.13-1.37)	0.639 <0.001
			Age x sex (ref. men) x Number of book Occupational position of the main breadwinner (ref. high skills) Age2 x women x low skills Denmark 2.94 (2.65-3.26) <0.001 2.82 (2.55-3.13) <0.001 France 1.03 (0.94-1.13) 0.527 1.04 (0.95-1.14) 0.403	1.01 (0.92-1.10) 2.79 (2.52-3.09) 1.02 (0.93-1.12)	0.849 <0.001 0.624	2.48 (2.24-2.74) 1.11 (1.01-1.21)	<0.001 0.03	1.01 (0.92-1.10) 2.50 (2.25-2.77) 1.11 (1.01-1.22)	<0.001 0.846 0.023
			Sex (ref. men) x Overcrowding (ref. no) Germany	1.79 (1.63-1.97)	<0.001	1.77 (1.61-1.94)	<0.001	1.75 (1.59-1.93)	<0.001	1.68 (1.53-1.84)	<0.001	1.69 (1.54-1.86)	<0.001
			Women x yes Greece Age x sex (ref. men) x Overcrowding (ref. no) 0.69 (0.63-0.76) Israel 1.00 (0.88-1.13)	<0.001 0.978	0.74 (0.67-0.81) 0.98 (0.86-1.12)	<0.001 0.78	0.97 (0.86-1.09) 0.72 (0.66-0.80) 0.96 (0.85-1.09)	0.633 <0.001 0.552	1.10 (0.99-1.21) 1.15 (1.01-1.31)	0.071 0.032	0.96 (0.86-1.08) 1.09 (0.99-1.21) 1.14 (1.00-1.30)	0.544 0.086 0.042
			Age x women x yes Italy Age x sex (ref. men) x Overcrowding (ref. no) 0.45 (0.42-0.49) Netherlands 2.54 (2.22-2.91)	<0.001 <0.001	0.49 (0.45-0.53) 2.50 (2.18-2.85)	<0.001 <0.001	0.96 (0.86-1.08) 0.48 (0.44-0.52) 2.46 (2.16-2.82)	0.531 <0.001 <0.001	0.64 (0.59-0.70) 2.34 (2.05-2.67)	<0.001 <0.001	0.96 (0.85-1.08) 0.64 (0.59-0.70) 2.35 (2.06-2.69)	0.453 <0.001 <0.001
			Age2 x women x yes Spain	1.04 (0.95-1.13)	0.421	1.08 (0.99-1.18)	0.07	0.99 (0.92-1.07) 1.07 (0.98-1.16)	0.849 0.144	1.33 (1.22-1.45)	<0.001	1.00 (0.92-1.08) 1.34 (1.22-1.46)	0.936 <0.001
			Sex (ref. men) x Housing quality (ref. high) Women x low Sweden 3.63 (3.28-4.01) Switzerland 2.14 (1.93-2.38)	<0.001 <0.001	3.45 (3.12-3.82) 2.10 (1.89-2.33)	<0.001 <0.001	0.99 (0.89-1.11) 3.41 (3.09-3.77) 2.08 (1.87-2.31)	0.891 <0.001 <0.001	3.23 (2.92-3.57) 1.99 (1.79-2.21)	<0.001 <0.001	1.00 (0.89-1.12) 3.25 (2.93-3.59) 2.00 (1.79-2.22)	<0.001 0.996 <0.001
			Age x sex (ref. men) x Housing quality (ref. high) Czech Republic 0.81 (0.75-0.89)	<0.001	0.79 (0.72-0.86)	<0.001	0.77 (0.70-0.84)	<0.001	0.87 (0.79-0.95)	0.002	0.86 (0.79-0.95)	0.002
			Age x women x low Age x sex (ref. men) x Housing quality (ref. high) Ireland 1.57 (1.20-2.04)	0.001	1.53 (1.18-1.99)	0.002	0.95 (0.85-1.06) 1.50 (1.15-1.94)	0.342 0.003	1.63 (1.25-2.11)	<0.001	0.94 (0.84-1.04) 1.63 (1.25-2.13)	0.234 <0.001

Acknowledgments

This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (DOIs: 10.6103/SHARE.w1.600, 10.6103/SHARE.w2.600, 10.6103/SHARE.w3.600, 10.6103/SHARE.w4.600, 10.6103/SHARE.w5.600, 10.6103/SHARE.w6.600), The SHARE data collection was primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: no.211909, SHARE-LEAP: no.227822, SHARE M4: no.261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org).

Funding B.C. is supported by an Ambizione grant (PZ00P1_180040) from the Swiss National Science Foundation (SNSF). M.P.B. is supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2021-03153) and by a grant from The Banting Research Foundation.

Ethical approval

This study was part of the SHARE study, approved by the relevant research ethics committees in the participating countries, and all participants provided written informed consent.

Data sharing

This SHARE dataset is available at http://www.share-project.org/data-access.html.

Author

All the authors critically appraised the manuscript, worked on its content, and approved the final version for submission.

Conflict of interest

The authors declare no conflict of interest.

Supplemental Material

-Tables S1. Additional descriptive statistics stratified by physical activity levels and early-life SEC -Table S2. Full table for the mixed effects models -Table S3. Summary of the sensitivity analyses and robustness analyses

The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity were more pronounced in women than in men (although the interaction was marginal for number of books, p=.083). As in the main analysis, number of books was associated with lower probability of physical activity in both men and women (OR=.74, p < .001 in men and OR=.64, p < .001 in women), while the negative association between occupation of the main breadwinner and the probability of physical activity was only observed in women (OR=.96, p = .602 in men and OR=.79, p < .001 in women).

Sex and number of books, but not occupation of the main breadwinner, remained associated with probability of physical activity, after adjustments for adult-life SEC. The associations between cultural aspects of disadvantaged early-life SEC and the probability of physical activity remained more pronounced in women than in men (although the effect of the number of books remained marginal, p = .091). In this fully adjusted model, both occupation of the main breadwinner (OR=.89, p = .014) and number of books (OR=.84, p < .001) remained significantly associated with the probability of physical activity in women, while only the number of books (OR=.92, p = .044) remained associated with the probability of physical activity in men.


Adrien Maire
email: adrien219.maire@laposte.net

Nicolas Chapet
email: chapet@chu-montpellier.fr

Sylvain Aguilhon

Marie-Lucie Laugier
email: lmarielucie@yahoo.fr

Paul Laffont-Lozes
email: paul.laffontlozes@chu-nimes.fr

Mélinda Rigoni
email: melinda.rigoni@gmail.com

Betty Mathieu
email: betty-mathieu@chu-montpellier.fr

Yohan Audurier
email: yaudurier@chicacr.fr

Cyril Breuker
email: c-breuker@chu-montpellier.fr

Gaëlle De Barry
email: de_barry@chu-montpellier.fr

Anne Jalabert
email: jalabert@chu-montpellier.fr

Florence Leclercq
email: f-leclercq@chu-montpellier.fr

Jean-Luc Pasquié
email: jl-pasquie@chu-montpellier.fr

François Roubille
email: f-roubille@chu-montpellier.fr

Audrey Castet-Nicolas

Evaluation of vaccination coverage in heart failure patients in a tertiary center

Keywords: Heart failure, Vaccination coverage, Influenza vaccination, Pneumococcal vaccination, Clinical pharmacy 13PCV, 13-valent pneumococcal conjugate vaccine, 23PPV, 23-valent pneumococcal polysaccharide vaccine, COPD, chronic obstructive pulmonary disease, ESC, European Society of Cardiology, GP, General Practitioner, HF, Heart Failure, HFpEF, Heart failure with pre-served ejection fraction, HFrEF, Heart failure with reduced ejection fraction, IQR, Inter-Quartile Ranges, LVEF, Left Ventricular Ejection Fraction, OR, Odds Ratio, SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2

Background:

Despite current recommendation, vaccination coverage (VC) for patients with heart failure (HF) remains far too limited.

Aims:

To evaluate the VC of HF patients followed in our hospital center and investigate the barriers to vaccination and the ways to address them.

Methods:

This was a cross-sectional monocentric descriptive study conducted between December 2019 and January 2021 at the University Hospital of Montpellier, France. Patients with HF history hospitalized in cardiology unit (CU) and patients in a HF telemonitoring program (TP) were included. An interview was conducted by a pharmacist to find out the patient's vaccination status against influenza and pneumococcus. For non-vaccinated patients, opinion and willingness to be vaccinated were also obtained.

Results:

Data from 335 patients were collected (185 in CU, 150 in TP). The mean age was 69.3 years and the proportion of males was 72%. About 65% were vaccinated against influenza in the last year (60% in CU, 72% in TP, p = 0.022) and 22% were up to date with pneumococcal vaccination (11% in CU, 35% in TP, p < 0.001). Among patients not vaccinated, 17% refused vaccination. Among unvaccinated patients who consider vaccination, 69% wanted to be vaccinated by their general practitioner (GP).

Conclusions:

The VC of HF patients remains insufficient. Patients in TP are more vaccinated than patients in CU, which could involve better management. The low rate of vaccinated patients is mainly explained by a lack of awareness. The medical team, including the clinical pharmacist by his dedicated time during medication reconciliation may play a major role in the management of hospitalized patients as well as GP's as local actors --

Introduction

The prevalence of heart failure (HF) is estimated at 1-2% of the general population in developed countries and is over 10% in people aged 70 years or older [START_REF] Mosterd | Clinical epidemiology of heart failure[END_REF]. Despite marked pharmacologic and device-based advances for HF with reduced ejection fraction (HFrEF) in recent years, HF is associated with significant morbidity, mortality, and a heavy economic burden.

Recent European data (ESC-HF pilot study) show that the one-year all-cause mortality rates for inpatients and outpatients with HF were 17% and 7% respectively, and the one-year hospitalisation rates were 44% and 32% respectively [START_REF] Maggioni | EURObservationalResearch programme : regional differences and 1-year follow-up results of the heart failure pilot survey (ESC-HF pilot)[END_REF].

Infections can be responsible for acute HF exacerbation and increased hospitalisation in people with HF [START_REF] Bhatt | Can vaccinations improve heart failure outcomes[END_REF]. Indeed, in a large cohort of 9,335 HF patients with a mean followed-up period of 2.8 ± 2.6 years, 38% of the study cohort experienced at least one infection-related hospital admission. Among infections, pneumonia and chronic obstructive pulmonary disease (COPD) exacerbations were the most common symptoms [START_REF] Alon | Predictors and outcomes of infection-related hospital admissions of heart failure patients[END_REF]. Moreover, large HF registry data have shown that lung infections are the leading cause of hospitalisation (15.3%) ahead of ischaemic causes (14.7%) and rhythm disorders (13.5%). In addition, they are also associated with higher hospital mortality [START_REF] Fonarow | Factors identified as precipitating hospital admissions for heart failure and clinical outcomes[END_REF]. Vaccination may reduce the incidence and/or severity of respiratory infection, and thereby, reduce the risk of HF exacerbation.

Three vaccines are recommended (but not mandatory) for patients with HF: influenza vaccine, pneumococcal vaccine and now, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine [START_REF]Calendrier des vaccinations et recommandations vaccinales[END_REF][START_REF]HAS -Guide du parcours de soins/Insuffisance cardiaque -Juin[END_REF][START_REF]Strat´egie de vaccination contre le Sars-Cov-2 -Actualisation des facteurs de risque de formes graves de la Covid-19 et des recommandations sur la stratégie de priorisation des populations `a vacciner, 2021, 2 mars)[END_REF][START_REF] Mcdonagh | ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure[END_REF].

Recent evidence from PARADIGM-HF trial found that influenza vaccination was associated with a reduced risk of all-cause mortality in a cohort of patients with HFrEF [START_REF] Vardeny | Influenza vaccination in patients with chronic heart failure[END_REF]. Even more recently, the randomised, double-blind IVVE trial compared an inactivated influenza vaccine with a placebo, given annually for 3 influenza seasons in HF patients. No statistically significant difference was observed between the two groups in the two primary endpoints: cardiovascular deaths, non-fatal heart attacks, non-fatal strokes, and hospitalisations for HF. However, influenza vaccination was associated with less pneumonia ( 42%) and all causes hospitalisations in HF patients ( 16%), and fewer deaths ( 21%) and cardiovascular events ( 18%), particularly during periods of viral circulation. A reduction in the risk of hospitalisation for HF was observed with vaccination but outside the period of virus circulation ( 24%) [START_REF]Influenza Vaccine to Prevent Adverse Vascular Events -IVVE[END_REF].

By contrast, the effectiveness of pneumococcal vaccine seems less reliable as the protective effect evidenced relies on observational studies only [START_REF] Girerd | Vaccination for respiratory infections in patients with heart failure[END_REF], although the risk of pneumococcal infection is 4 times higher in patients with HF [START_REF] Lipsky | Risk factors for acquiring pneumococcal infections[END_REF].

Despite the potential benefits, vaccination coverage for patients with HF remains far too limited, as illustrated in the PARADIGM-HF trial, where the influenza vaccination rate for winter 2014/2015 was estimated at 39%, and only 20% of patients had at least one pneumococcal vaccination in a cohort of 200 HF patients in a Belgian hospital [START_REF] Boey | Vaccination coverage of recommended vaccines and determinants of vaccination in at-risk groups[END_REF].

The reasons for such a huge gap between guidelines and their application remains to be investigated. To our knowledge, there are currently no data on the causes of non-vaccination in HF patients.

The aim of our study was first to evaluate the vaccination coverage of HF patients followed in our tertiary hospital center and then investigate the barriers to vaccination and the ways to address them.

Methods

Study design, setting and participants

This was a cross-sectional monocentric descriptive study conducted between December 2019 and January 2021. Participation to this study was proposed by a clinical resident pharmacist to adult patients (>18 years) with HF history admitted in the two complete hospitalisation (32 beds each) cardiology units University Hospital of Montpellier and to patients included in our telemonitoring program (connected scale with daily weight tracking), permitting the inclusion of patients followed by cardiologists from other centers in a large region. Patients hospitalized in these units are only admitted for acute reasons or interventional cardiology procedures.

Depending on the need, they are reviewed later by their usual cardiologist, or in our outpatient department. Patients who were newly diagnoses HF were not included in this study. Patients receiving immunosuppressive treatment, people whose physical and/or psychological health is severely impaired (whose health condition does not allow them to give consent to participate in the study or to answer questions), people living in semi-liberty or institutionalised, people under guardianship or curatorship, and people who do not speak French were also not included. The investigation conforms to the principles outlined in the Declaration of Helsinki and was approved by the Hospital Institutional Review Board (IRB-MTP_2020_07_202000541).

Pharmaceutical intervention

In our clinical practice, a pharmaceutical team, including a senior pharmacist and/or a resident and pharmacy students, conducted a medication reconciliation process within 24 h of admission or on the first working day following admission to the unit. During the patient's hospitalisation, the pharmacist or resident pharmacist participates in optimising the management of patients with HF and in the reviewing of drug prescriptions (issuing pharmaceutical advice, proposing therapeutic optimisation in collaboration with the medical team).

As part of this practice, the pharmaceutical team assesses vaccination coverage with the patient, but also with the community pharmacist and the general practitioner (GP).

The interview with the patient included a 4-questions knowledge questionnaire: a question on the principles of vaccination (4 items), a question on the content of vaccines (4 items), a question on influenza vaccination (5 items) and a question on pneumococcal vaccination (4 items) ( Appendix A). An answer was considered correct if all the items in a question were correct. A 4-point score was established for all patients. A score of 0 or 1 on the knowledge questionnaire was defined as a low score, while a score ≥2 was considered as a high score. The responses to the questionnaire were given to all patients after they had completed it. The pharmaceutical team also answered any questions the patients had at the end of the interview. For all patients, medication compliance was determined by the Girerd score [START_REF] Girerd | Evaluation de l'observance du traitement antihypertenseur par un questionnaire : mise au point et utilisation dans un service spécialisé[END_REF], and for nonvaccinated patients, opinion and willingness to be vaccinated were also obtained during the interview. After each pharmaceutical interview, the patients were given an information leaflet on how the vaccination works and on the usefulness of carrying out these vaccinations for patients with HF.

If patients are not up to date with their vaccinations, a vaccination strategy has been set up with the patients, their cardiologist or their general practitioner.

Study outcomes

The primary outcome was to determine the proportion of patients who were up to date with influenza, pneumococcal vaccination during the study period.

The secondary outcomes were to assess the proportion of patients who were favorable, had mixed opinion or who were against vaccination, to study the factors influencing vaccination, to collect the proportion of patient with a high knowledge score about vaccination, the kinds of methods desired by unvaccinated patients to achieve vaccination and the kind of methods in which patients were informed of the need of vaccination.

Data collection

Socio-demographic (age, sex), clinical (vaccination status, reason for hospitalisation, left ventricular ejection fraction (LVEF)), compliance score, knowledge score and biological (NT proBNP at admission) data were collected. Biological data and transthoracic echocardiography were all performed during the patient's stay, or at the last visit for telemonitored patients. Patients were considered with HFrEF if LVEF was ≤40% and with HF with preserved ejection fraction (HFpEF) if LVEF was >40% associated with elevated BNP and echocardiographic criteria according to ESC 2016 guidelines [START_REF] Ponikowski | ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure[END_REF].

The vaccination status was obtained from patient self-reporting, and by systematically checking all patients with their pharmacist and general practitioner. It was collected for all patients during the hospital interview for patients in cardiology units, and by a phone interview for telemonitored patients. In case of discrepancies between the patients' declaration and the pharmacists/general practitioners, or in the absence of recollection by the patient, we took into consideration the information obtained from the pharmacists and doctors. Patients were considered to be unvaccinated against pneumococcus if they have had only a 13-valent pneumococcal conjugate vaccine (13PCV) or a 23-valent pneumococcal polysaccharide vaccine (23PPV), in accordance with French vaccination recommendations. On the other hand, they were considered vaccinated if they received a 13PCV followed within a minimum of two-month intervals by a 23PPV in the last five years. Patients vaccinated first with 23PPV were also considered vaccinated if they were subsequently vaccinated with 13PCV at least one year later [START_REF]Calendrier des vaccinations et recommandations vaccinales[END_REF]. For influenza vaccination, patients were considered vaccinated if they received the vaccine during the last vaccination campaign. Patients were considered unvaccinated even if they informed us that they wanted to be vaccinated soon. Patients' opinions on vaccination, the way in which they were informed of the vaccination and the method of vaccination for those who are not vaccinated and who wish to be vaccinated were also collected. The questions regarding opinion on covid-19 vaccination were asked before the vaccines were available in France i.e. from July 2020.

Statistical analysis

Patients' characteristics, vaccination status and patient answers were described with numbers and proportions for categorical variables and with medians and inter-quartile ranges (IQR25-75) for quantitative variables because they did not follow a normal distribution.

The vaccination status (for influenza, pneumococcal) of patients in the cardiology unit and telemonitored patients, the rate of vaccination between patient with HFrEF and HFpEF, the relationship between the opinion on vaccination and the knowledge score as well as the difference between the number of correct answers concerning influenza vaccination and pneumococcal vaccination were analysed using a bilateral Chi square test.

Multivariable logistic regression analysis was performed between influenza vaccine status and gender, age, pneumococcal vaccination, medication compliance, knowledge score, patient's group (cardiology unit or telemonitoring) and between pneumococcal vaccination status and influenza vaccination, knowledge score and patient's group. The variables used for the multivariable logistic regression were those with a p-value <0.1 in univariate analysis (Appendix B and C). Finally, variables retained were selected using a backward stepwise selection procedure (Akaike Information Criterion).

Statistical analyses were performed at the conventional two-tailed α level of 0.05 using R version 4.0.4.3.

Results

Population description

Data from 335 patients were collected (185 in cardiology units and 150 in telemonitoring program). The proportion of males was 71.9% (n = 241) and the sex ratio was 2.6 (Table 1). The median age was 71 years with a minimum age of 28 years and a maximum age of 97 years. Median LVEF and median Nt-pro-BNP were respectively 42% and 2,296 ng/L (n =267 patients). 11.3% of patients (n =34) had a history of hospitalisation for pulmonary infection (on 300 patients, based on reporting by GP's and notifications in medical records). Medication compliance was good for 49.6% of patients (n = 166), moderate for 37.9% (n = 127), bad for 3.9% (n = 13) and unknown for 8.6% (n = 29, including 3 patients with no treatments).

Vaccination coverage

20.0% of the patients (n = 67) were vaccinated against both influenza and pneumococcus, 10.3% (n = 19) in cardiology units' group and 32.0% (n = 48) in the telemonitoring program group (p < 0.001). 65.4% of the patients (n = 219) were vaccinated against influenza in the last year, 60.0% (n = 111) in the cardiology units' group and 72.0% (n = 108) in the telemonitoring program group (p = 0.022) (Table 2).

Regarding pneumococcal vaccination, 22.1% of patients (n = 74) were up to date, 11.4% (n = 21) in the cardiology units' group and 35.3% (n = 53) in the telemonitoring program group (p < 0.001) (Table 2). No patients were in the process of completing their immunization schedule.

Opinion on vaccination

Regardless of vaccination status and type of vaccination, 68.0% of the patients (n = 204) were in favour of the vaccination, 23.0% (n = 69) had a mixed opinion and 9.0% (n = 27) were against it. In cardiology units and in telemonitoring program they were respectively 70.0% and 66.0% in favour (n = 105, n = 99), 22.0% and 24.0% with mixed opinion (n = 33, n = 36), 8% and 10.0% against vaccination (n = 12, n = 15) (p = 0.73).

Among the patients asked about vaccination against covid-19 (n = 198, 106 in cardiology units and 92 in telemonitoring program), 59.6% (n = 118) were in favour, 29.8% (n = 59) were still hesitating and 10.6% (n = 21) were against it. Among the unvaccinated patients who refused to be vaccinated against influenza or pneumococcus (n = 24), 58.3% (n = 14) were still considering vaccination against covid-19. Among the patients considering or wishing to be vaccinated against influenza or pneumococcus (n = 134), only 6.7% (n = 9) refused to be vaccinated against covid-19.

Questionaries' analyses

Concerning the answers to the knowledge questionnaire, only 1.0% of patients had good response to all items (n = 3). 11.0% got a score of 3/4 (n = 33) and 34.3% had 2/4 (n = 103). More than half of the patients had a low questionnaire score, with 32.0% scoring 1/4 (n = 96) and about 21.7% scoring 0/4 (n = 65).

They were respectively 45.3% (n = 68) and 47.3% (n = 71) with high knowledge score in cardiology units and in telemonitoring program (p = 0.73).

We noticed that 52.0% (n = 156) of patients had a good answer to the influenza question, compared to 19.0% (n = 57) to pneumococcal question (p < 0.001).

Among patients with a low knowledge score (0-1) and with high knowledge score (2-3-4), respectively 65.8% (n = 106) and 70.5% (n = 98) were in favour to vaccination (p = 0.39).

Factors influencing vaccination

Being male (OR 2.55, p = 0.003) or older patients (OR 2.46, p = 0.011 for patients aged 61 to 74 years; OR 4.27, p < 0.001 for patients over 74 years), having completed the pneumococcal vaccination (OR 4.34, p = 0.001) and a high score on the knowledge questionnaire (OR 1.62, p = 0.001) are factors associated with a better rate of vaccination against influenza (Fig. 1). The telemonitoring program also appears to be a positive factor for influenza vaccination, although it is not significant (OR 1.71, p = 0.074). By contrast, a good medication compliance (OR 1.90, p = 0.369) does not seem to influence influenza vaccination. Being vaccinated against influenza (OR 4.59, p < 0.001), having a high knowledge score (OR 1.64, p = 0.002) and being telemonitored (OR 4.28, p < 0.001) are associated with a better vaccination against pneumococcus (Fig. 2).

Ways of patients getting information on vaccination

Among the patients who were informed about the necessity of vaccination, information had been provided to them mostly by health insurance (73.3%, n = 200) and their GP (19.0%, n=52) for influenza and by their cardiologist (54.8%, n = 46) and GP (32.1%, n = 27) for the pneumococcal vaccination (Fig. 3). 3.7. Ways of achieving vaccination Considering all nonvaccinated patients against influenza or pneumococcus (n = 233), 50.6% patients wishing to be vaccinated (n = 118), 32.2% patients considering the possibility of being vaccinated (n=75) and 17.2% refused to be vaccinated after the failure (n = 40). The main reasons for refusal were lack of interest in vaccines due to ineffectiveness (34.8%), fear of adverse effects (31.9%) and vaccine excipients (15.9%).

Among unvaccinated patients who consider vaccination (n = 194), 68.6% wanted to be vaccinated by their GP (n = 133) and 7.2% wanted to be vaccinated by their cardiologist (n = 14) (Fig. 4A). 32.1% of patients in the cardiology units' group wanted a prescription at discharge after pharmaceutical intervention during their hospital stay (n = 36) (Fig. 4B). More than 3/4 of patients in telemonitoring program wanted a prescription by their GP (76.9%, n = 63) (Fig. 4C).

Discussion

Vaccination is still the most effective way to reduce morbidity and mortality from infections, especially for patients with chronic diseases. Indeed, similarly to HF, vaccination against influenza is recommended in patients with COPD, to decrease all-cause mortality [START_REF] Schembri | Influenza but not pneumococcal vaccination protects against all-cause mortality in patients with COPD[END_REF] and in diabetic patients, to decrease hospitalisations and deaths [START_REF] Vamos | Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes[END_REF][START_REF] Lau | Effectiveness of influenza vaccination in working-age adults with diabetes : a population-based cohort study[END_REF]. Importantly, both influenza and pneumococcal vaccination have been shown to reduce mortality in haemodialysis patients [START_REF] Bond | Mortality of dialysis patients according to influenza and pneumococcal vaccination status[END_REF]. Despite this benefit, this study shows that the vaccination coverage of HF patients 1/remains insufficient, 2/is associated with some characteristics of the patients such as the vaccination knowledge or telemonitoring programs, 3/could be improved if more often integrated in the systematic management program.

Vaccination coverage is largely insufficient. This is particularly true regarding pneumococcus with only 22.1% of vaccination, as underlined in another French study, with only 9.1% (on 673 patients) of HF outpatients vaccinated against pneumococcus [START_REF] Kopp | Pneumococcal vaccination coverage in France by general practitioners in adults with a high risk of pneumococcal disease[END_REF]. Patients in telemonitoring program seem to be more vaccinated for influenza (OR 1.71, p = 0.074) and are significantly more vaccinated for pneumococcus (OR 4.28, p < 0.001) than patients in cardiology units, which could involve better management of HF. Indeed, these patients benefit from therapeutic education interviews and a follow-up by a caregiver team dedicated to this telemonitoring program who monitored the patients and thus allowing a better global management. Also, other physicians including GP but also other cardiologists could be involved in the program, allowing various ways of education. Consistently, these patients are more aware of the factors of HF exacerbation, such as infections, which can be prevented by vaccination. However, there was no difference in the knowledge score about vaccination between the two groups. During the educational interviews, we presume that the importance of vaccinations to avoid HF complications is more frequently discussed than more technical and specific information about vaccines.

The impact of telemonitoring remains a matter of debate. In a meta-analysis including 15 studies conducted between 2003 and 2013, home telemonitoring interventions (especially automated device-based telemonitoring and mobile telemonitoring) reduced the relative risk of all-cause mortality and HF-related hospitalisations compared with usual care [START_REF] Kitsiou | Effects of home telemonitoring interventions on patients with chronic heart failure : an overview of systematic reviews[END_REF]. Here, our telemonitoring system is close to the program developed in the OSICAT trial [START_REF] Galinier | Telemonitoring versus standard care in heart failure : a randomised multicentre trial[END_REF]. The primary endpoint was negative in this trial (preventing all-cause deaths or unplanned hospitalisations), but the analyses suggested that some subgroups of patients could draw more benefit, especially the patients with more symptoms, the patients more adherent to the program or the most isolated. New ESC guidelines considered non-invasive telemonitoring for patients with HF in order to reduce the risk of recurrent cardiovascular and HF hospitalisations and cardiovascular death [START_REF] Mcdonagh | ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure[END_REF]. Lack of awareness among health professionals also leads to a lack of awareness among patients regarding the need to be vaccinated. Actually, only 17% of patients refuse to be vaccinated after being informed of the benefits of vaccination for them. The higher vaccination rate for influenza than for pneumococcus may be explained first by better knowledge about this infection. Indeed, almost half of the patients answered correctly to the question about influenza, compared to only 19% for the question about pneumococcus. Knowledge was found to be the only factor positively influencing both influenza and pneumococcal vaccination. This demonstrates the importance of patient education in their own care.

The better vaccination coverage for influenza can also be attributed to the presence of an annual vaccination campaign from the French medical insurance for HF patients or over 65 years old that does not exist for pneumococcus. Approximately three quarters of patients who were informed of the need for influenza vaccination were informed by the French national health insurance. Older patients tend to be more vaccinated against influenza (OR 2.46, p = 0.011 for patients aged 61 to 74 years; OR 4.27, p < 0.001 for patients over 74 years), which can also be explained by the vaccination campaign, as patients over 65 years of age receive an annual influenza vaccination voucher. Thus, simple patient information could be enough to improve vaccination coverage. It therefore seems more relevant to focus on awareness of vaccination and the need to carry out vaccination rather than on technical aspects and the way vaccines work. Similar measures used for pneumococcal vaccination could drastically increase pneumococcal vaccination coverage and thus reduce the number of hospitalisations or deaths. Indeed, communication campaigns to promote vaccines and the multiplication of means to do this, increase vaccination coverage [START_REF] Yoo | Effects of mass media coverage on timing and annual receipt of influenza vaccination among medicare elderly[END_REF], and it is estimated that about 2000 deaths are avoided each year in France thanks to flu vaccination [START_REF] Ameli | Vaccination contre la grippe saisonni`ere[END_REF]. There is however no data for HF patients.

Other options can also be considered to improve vaccination coverage, such as the use of a computerised vaccination management application for general practitioners (electronic shared medical records with notifications for GP's), the simplification of the vaccination process (diversification of the locations of vaccination and the professionals authorised to vaccinate) and the updating of health professionals' knowledge on vaccination guidelines [START_REF] Berrada | Comment améliorer la couverture vaccinale en France ? ´Étude qualitative auprès de professionnels de santé[END_REF].

We notice that patients who received either the influenza or pneumococcal vaccine are more likely to have received the other vaccine. This seems to demonstrate that patients vaccinated against a disease are generally in favour of any type of vaccination and supports the idea that better information on the need for vaccination would improve vaccination coverage. We can therefore also assume that patients vaccinated against influenza and pneumococcus would be more susceptible to receive the Sars-Cov-2 vaccine and inversely. About two thirds of patients wanted to be vaccinated by their GP, who thus play a major role in their global care. According to a study investigating the patients' point of view about preventive care in Switzerland, preventive care should be the mission of their GP (84.1%) and they consider him/her to be the best source of information in this domain (83.4%). About 72% of them consider they are very well or well informed about immunization [START_REF] Cohidon | Patients' and general practitioners' views about preventive care in family medicine in Switzerland : a crosssectional study[END_REF].

Each healthcare professional has a role to play in promoting vaccination in order to inform the patient. The hospital admission, specifically when a patient has a complication of HF, is an opportunity to consider the prevention of acute HF and to prevent it through vaccination. Hospital health professionals (doctors, nurses, pharmacists) can play an important role in the vaccination of HF patients. Indeed, among unvaccinated patients, more than 80% wanted or were considering vaccination and approximately one-third of hospitalized patients wish to have a vaccine prescription at discharge on the advice of the pharmaceutical team. In a US study including patients vaccinated against influenza, pneumococcal vaccination coverage was better when influenza vaccination was performed by a pharmacist compared to standard care. The pharmacist asked patients about their risk of pneumococcal disease (e.g., age, smoking status, co-morbid conditions) and recommended vaccination if any risk was identified [START_REF] Taitel | Pharmacists as providers : targeting pneumococcal vaccinations to high risk populations[END_REF]. In another study looking at the influenza vaccination coverage of patients with rheumatoid arthritis, it was demonstrated that a multimodal implementation strategy including patient recalls, a nurse providing vaccines, and physician reminders resulted in an increase in the vaccination coverage from 48.5% to 62.6% [START_REF] Valerio | A multimodal intervention increases influenza vaccine uptake in rheumatoid arthritis[END_REF]. This demonstrates once again that the more sources of information on vaccination there are, the better the vaccination coverage. As such, the promotion of vaccination can be carried out by all health professionals in charge of a patient in order to increase the chances of its implementation. Different measures can be derived from the data of this study that will presumably help to increase vaccination coverage of patients with HF. Presumably, these will include inclusion in a managed care program (as recommended by the guidelines), increasing awareness of vaccination in the hospital and during ambulatory care, and providing easy access to vaccination through the general practitioner as well as all other health professionals.

In summary, this study shows that it's crucial to improve educational and organisational aspects in order to reinforce awareness and accessibility to vaccination.

Study limitations

Our observational study also has some limitations. This was a monocentric study, so conducting the study in other hospitals would have provided a larger sample size and avoided possible prescriber bias. This is limited by inclusions of patients followed in the telemonitoring program which cover a large part of our geographical region.

The age and the stage of the HF were not collected in this study because of the difficulty to collect this information, but we didn't recruit patients admitted for the first HF exacerbation.

The rate of hospitalisation for pulmonary infection appears to be low compared with the literature [START_REF] Fonarow | Factors identified as precipitating hospital admissions for heart failure and clinical outcomes[END_REF]. This may be explained by the difficulty in collecting the etiology of the cardiac exacerbation responsible for the hospitalisation from the medical records.

Finally, the knowledge score was set up arbitrarily according to our experience as there is no standardised knowledge questionnaire on vaccination. When a question was confusing and the patient had questions about it, the pharmacist conducting the interview guided the patient by giving him explanations. This study was conducted prior to the availability of COVID vaccines in France. It is possible that patients' perception of vaccination has changed since the introduction of the COVID vaccines.

Conclusion

Influenza and pneumococcal vaccination coverage of HF patients are still largely insufficient. This remains essential to improve, as patients with HF are likely to draw major benefits (fewer exacerbations lead to less admissions and readmissions and lower mortality).

Improving patients' knowledge about vaccination is an independent factor for better vaccination coverage and could then be a powerful approach to improve the situation. To this purpose, it is crucial to educate health professionals and to carry out public health campaigns about the benefits of vaccinations. The medical team, including the clinical pharmacist by his dedicated time during medication reconciliation may play a major role in the management of hospitalized patients as well as GP's as local actors.

Appendices.

Author contribution statement

Adrien Maire, Nicolas Chapet: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. S. Aguilhon, M. Rigoni, Betty Mathieu, Yohan Audurier, Cyril Breuker, Ga¨elle de Barry, Anne Jalabert, Florence Leclercq, Jean-Luc Pasquié: Analyzed and interpreted the data.

Laugier Marie Lucie, P. Laffont-Lozes: Performed the experiments. François Roubille, Audrey Castet-Nicolas: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Data availability statement

Data will be made available on request.

Institutional review board statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Montpellier University Hospital (protocol code IRB-MTP_2020_07_202000541 approved on July 16, 2020).

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Roubille Francois reports a relationship with Pfizer Inc that includes: consulting or advisory. CHAPET Nicolas reports a relationship with Pfizer Inc that includes: consulting or advisory.