Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: A longitudinal analysis from the SHARE study.

Despite advances in the healthcare system, cardiovascular diseases (CVDs) are still an important public health problem with disparities in the burden within and between countries. Studies among the adult population documented that socioeconomic and environmental factors play a role in the incidence and progression of CVDs. However, evidence is scarce on the socioeconomic determinants and the interplay with behavioral risks among older adults. Therefore, we identified socioeconomic and behavioral determinants of CVDs among older adults. Our sample consisted of 14,322 people aged 50 years and above from Belgium and France who responded to the waves 4, 5, 6 and/or 7 of the Survey of Health Ageing and Retirement in Europe. The effect of determinants on the occurrence of CVD was examined using a Generalized Estimating Equation (GEE) approach for binary longitudinal data. The overall rate of heart attack was 8.3%, which is 7.6% in Belgium and 9.1% in France. Whereas, 2.6% and 2.3% in Belgium and France, respectively, had experienced stroke. In the multivariable GEE model, older age [AOR: 1.057, 95%CI: 1.055-1.060], living in large cities [AOR: 1.14, 95%CI: 1.07-1.18], and retirement [AOR: 1.21, 95%CI: 1.16-1.31] were associated with higher risk of CVD. Furthermore, higher level of education [AOR: 0.82, 95%CI: 0.79-0.90], upper wealth quantile [AOR: 0.82, 95%CI: 0.76-0.86] and having social support [AOR: 0.81, 95%CI: 0.77-0.84] significantly lowers the odds of having CVD. A higher hand grip strength was also significantly associated with lower risk of CVD [AOR: 0.987, 95%CI: 0.984-0.990]. This study demonstrated that older adults who do not have social support, live in big cities, belong to the lowest wealth quantile, and have a low level of education have a higher likelihood of CVD. Therefore, community-based interventions aimed at reducing cardiovascular risks need to give more emphasis to high-risk retired older adults with lower education, no social support and those who live in large cities.

Introduction

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide and constitute a major burden on the healthcare system in all countries [1]. In 2017, an estimated 422.7 million prevalent cases, 17.9 million deaths, and 366 million disability adjusted life years (DALYs) were attributed to CVDs [2]. In Europe, nearly half of all deaths are related to CVDs so being responsible for more deaths than any other condition [3, 4]. In Belgium and France, CVDs account for 28% and 24% of all deaths respectively [5].

Despite advances in the primary and secondary prevention, there are still disparities in the CVD burden within and across countries, some segment of the population being at a higher risk [6-8]. Previously, epidemiological studies have focused on identifying and modifying individual risk factors. As a result, several health conditions and behavioral factors such as tobacco use, unhealthy diet, alcohol consumption and physical inactivity were identified to be related with CVDs. The reduction of such unhealthy lifestyles and early detection and treatment of risk factors are effective to reduce the burden. Hence, improvements have been observed in the reduction of CVD morbidities and its associated premature mortality among adults. However, many cardiovascular risks still remain as a major public health issue at different rates globally. The burden of CVDs highly varied across segments of the population, older age, the least affluent and deprived communities are disproportionately affected [9-11]. Socioeconomic and environmental factors, including income, living condition, level of education also play an important role in the development, progression and outcomes of CVDs [12-15].

As part of the efforts to narrow disparities, the World Health Organization (WHO) established a Commission on Social Determinants of Health and developed a conceptual framework for action [16]. The framework emphasizes that social determinants as well as resources to prevent illness, are not distributed randomly throughout human society and such disparities need to be identified. Albeit some studies assessed the socioeconomic determinants of CVDs in the general population, the evidence specific to older adults is scant. Older adults have a different lifestyle and social dynamics compared to younger counterparts [17]. As age increases, the shrinkage of social networks through deaths of friends puts older people at risk of social isolation and loneliness, which might be associated with the occurrence of CVDs in those individuals [18].

Identifying the socioeconomic and behavioral factors of CVDs and evaluating the moderation effect in between could help in improving intervention strategies to reduce the burden of CVD in the older population. The EU Horizon 2020 funded project named SPICES–Scaling-up Packages of Interventions for cardiovascular diseases in Europe and Sub-Saharan Africa, aims to reduce CVD risks among vulnerable populations through risk profiling and coaching intermediate-risk participants. The current study supports the identification of target population and area of intervention among high-risk older adults in Belgium and France. Hence, in this study, we identified the socioeconomic determinants and behavioral risks of CVDs among older adults in Belgium and France, using a longitudinal data collected in the Survey of Health, Ageing and Retirement in Europe (SHARE) study. We also assessed the moderation and interplay of socioeconomic, behavioral and clinical risk factors on CVD, particularly heart attack and stroke.

Methods and materials

Study design and participants

We used the longitudinal dataset from the SHARE survey, which collects information on health status, behavioral risks, socioeconomic condition, and social networks of individuals aged 50 years and above and their partners. Details about the sampling design, methodology, and questionnaires of the SHARE survey have been presented elsewhere [19]. Methods specific to this study are briefly summarized here. A population-based sample of the non-institutionalized population is used for this survey, with data collected via multi-stage sampling. We used data from wave 4 (2011–12), wave 5 (2013), wave 6 (2015) and wave 7 (2017) for both Belgium and France. Data from wave 3 (2008) were not included, as these data assessed the life histories of participants and did not provide the necessary information regarding health and behavioral risks. Moreover, due to the wide time interval between waves 1 (2004) and 2 (2006) on the one hand and wave 4 (2011) and subsequent waves on the other hand (five years), thereby resulting in interruption of the longitudinal nature of the data, we relied on data derived from waves 4 to 7.

Participants fulfilling the following criteria were included to the analysis: i) aged 50 years or above and ii) having information on the main outcome variables, heart attack, stroke or atherosclerosis at least in one wave. Data were not analyzed for participants i) < 50 years of age and ii) people who were reported as having CVD during enrollment, and iii) with no CVD information at enrollment. Overall, 7,914 adults in Belgium and 6,408 in France participated in at least one wave of the survey and were eligible, resulting in a total number of person-observations of 20,555 and 16,305 for Belgium and France, respectively.

Outcome and exposure assessment

Cardiovascular diseases

The presence or absence of cardiovascular diseases was measured in the SHARE study using the question ‘Has a doctor told you that you had/Do you currently have any of the conditions on this card?’. The possible answers were: yes or no; if yes, the time of first diagnosis was asked. In this study, we defined a person as having CVD if he/she has at least one of the following problems, or both; i) a heart attack including myocardial infarction or coronary thrombosis; or ii) a stroke or cerebrovascular disease diagnosed between waves.

Socioeconomic determinants

Socioeconomic variables, including age, sex, household income, retirement status, marital status, educational status, and social support were collected in the SHARE study. In our study, we categorized marital status as a dichotomous variable as living with a partner or living alone, where the latter included those who were widowed, divorced or never married. Level of education was assessed with the International Standard Classification of Education (ISCED) 1997 [20], and was categorized as low (level 0–2), medium (levels 3 and 4), and high (levels 5 and 6). We categorized wealth using three quantiles for each country into upper, middle and lower one-third. We categorized living areas in three groups: i) large town or city, ii) small town, and iii) rural or village. Social support was measured using two questions: (1) ‘In the past twelve months, has any family member from outside the household, friend, or neighbor given you any type of support?’ and, if ‘yes’, (2) ‘Is there someone living in this household who has helped you regularly during the past twelve months?’. We dichotomized this information by defining social support as having positive responses on both of the above questions, and no social support otherwise.

Behavioral determinants

Smoking status in the SHARE survey was assessed by asking ‘Do you smoke at the present time?’. Regular alcohol consumption was assessed with the question ‘In the last three months, how often did you have six or more units of alcoholic beverages on one occasion?’. The possible response options were: i) daily or almost daily, ii) 5 or 6 days a week, iii) 3 or 4 days a week, iv) once or twice a week, v) once or twice a month, vi) less than once a month and vii) not at all in the last 3 months. We dichotomized this variable into regular alcohol consumption (i-iv) and no regular alcohol consumption (v-vii). The frequency of engaging in vigorous and moderate physical activity (PA) was assessed asking: ‘How often do you engage in vigorous/moderate PA, such as sports, heavy housework, or a job that involves physical labor?’ The possible responses were: i) more than once a week, ii) once a week, iii) once to three times a month, and iv) hardly ever or never. We dichotomized this variable into regular PA (i and ii), and no regular PA (iii and iv). Fruit and vegetable intake was assessed by asking: ‘In a regular week, how often do you have a serving of fruit and vegetables?’. The possible responses were: i) every day, ii) three to six times a week, iii) twice a week, iv) once a week, v) less than once a week. We dichotomized into adequate (i) and inadequate intake (ii-v).

Physical measurements and clinical characteristics

We computed body mass index (BMI) using the self-reported values of body weight and height in each wave. Hand-grip strength was measured by trained interviewers using a handheld dynamometer. Self-reported long-term illnesses were assessed with the question ‘Do you have any long-term health problems, illness, disability or infirmity such as chronic respiratory, kidney disease, cancer, etc.?’. We categorized comorbidities into; i) no comorbidity, ii) one comorbidity, and iii) two and more comorbidities. Depression status was assessed using a EURO-12, a 12-item geriatric depression scale and those who scored below 4 were considered as not having depression and those above 4 as having depression. Moreover, whether the participant ever had CVD risks, including high blood pressure or hypertension; high blood cholesterol; and diabetes and/or high blood sugar were assessed.

Statistical analysis

Descriptive statistics including absolute and relative frequencies for categorical variables, mean with Standard Deviation (SD) or median with Inter Quartile Range (IQR) for continuous variables were employed to summarize the characteristics of participants. We compared socioeconomic characteristics between individuals included in the analysis (n = 14,322) and those excluded as a result of missing CVD outcome values at enrollment (n = 117), using two sample t-tests and chi-square tests for numerical and categorical variables, respectively. The results show that individuals who were included are comparable with the excluded ones in terms of socioeconomic characteristics (Table 1 in S1 Annex). To estimate the population averaged effect of socioeconomic, behavioral and physical determinants on the occurrence of CVD, we developed two independent marginal models using multivariable Generalized Estimating Equations (GEEs) with the binomial distribution and logit link function [21]. GEE accounts for within-subject association and allows to estimate the between-subject effects of the relationships of determinants with the occurrence of CVD over the observation period. The unequal selection probabilities and unequal numbers of follow-up interviews among study participants was taken into consideration in the GEE analysis using individual survey weights. We assumed that the within-subject association among the vectors of repeated outcomes would have an exchangeable working correlation structure. Albeit other correlation structures were considered, goodness-of-fit indices were found under the exchangeable correlation structure (Table 3 in S1 Annex). As a goodness-of-fit indices, we used the quasi-likelihood under the Independence Model Criterion (QIC), the Wald χ2 test, and we performed a residual analysis to assess the presence of outliers and their random distribution [22, 23]. To compare the models, we used both the QIC and the Wald-test between the null, non-adjusted model and the adjusted models. Hence, the model with an exchangeable correlation structure was selected as it provided the smallest QIC. To evaluate whether the determinants vary between Belgium and France, we also performed a stratified analysis fitting a model for each country separately, however, there was no significant difference between countries in terms of covariate effects and the results are shown in Tables 7 and 8 in S1 Annex. We performed the GEE analyses using the ‘geepack’ package of the free statistical software, R version 3.6.1 [24].

The percentage of missing values across all the variables varied between 0 and 37.6%. Missing results were imputed for all missing variables evaluated in the GEE model. Assuming the missing data to be missing at random (MAR), we performed Multivariate Imputation by Chained Equations (MICE) using the ‘mice’ package in R [25]. We imputed the dataset to have M = 100 complete datasets using all the variables included in the model and some additional auxiliary variables (detailed description on the imputation model is available in the S1 Annex). For more details regarding the MICE approach we used, we refer the reader to a book by Van Buuren [26]. The parameters of interest were estimated in each imputed dataset separately, and combined using Rubin’s rules [27]. We performed a sensitivity analysis to compare the results obtained from a multiple imputation approach with those from a complete case analysis. The results were comparable between the two approaches, except in terms of precision of the estimated model parameters. More specifically, multiple imputation was found to lead to more precise estimates which can be seen from the shorter confidence intervals (Tables 4 and 5 in S1 Annex). We assessed the distributional similarity of the observed and imputed observations using summary statistics and showed that the distributions of the imputed and observed values are comparable (Table 6 in S1 Annex).

Ethics statement

We used publicly available data (available via the website http://www.share-project.org/data-documentation/share-data-releases.html) for a secondary data analysis. SHARE underwent a review of ethical standards by the University of Mannheim's internal review board. Ethical considerations including the written informed consent has been taken care of by another institution. Details on the conduct of the study including the ethical approval can be found elsewhere [28].

Results

Socioeconomic characteristics participants

Table 1 displays the socioeconomic characteristics of participants at their first enrollment. The mean age of participants was 64.4 (SD: 10.9) and 65.6 (SD: 10.9) in Belgium and France, respectively. Overall, 55.7% were females and 41.8% had low level of education. Majorities (67.0%) were living with their partners and one-third (33.1%) were living in large towns or cities. More than half (55.4%) were retired and nearly one-third (31.8%) had regular social support from family members or any other person. The median household annual net income was 32.6 and 28.6 thousand Euro in Belgium and France, respectively.

Table 1

Socioeconomic characteristics of adults aged 50 years or older in Belgium and France (n = 14,322) during enrollment in the Survey of Health, Ageing and Retirement in Europe, 2011 to 2017.

Participant characteristics	Total	Belgium	France
Age (years)	64.9 (10.9)	64.4 (10.9)	65.6 (10.9)
Sex (female)	7975 (55.7)	4340 (54.8)	3635 (56.7)
Level of education a (n = 14,130)
Low	5902 (41.8)	3135 (40.0)	2767 (44.0)
Medium	4327 (30.6)	2121 (27.0)	2206 (35.0)
High	3901 (27.6)	2579 (32.9)	1322 (21.0)
Marital status (n = 14,250)
With partner	9547 (67.0)	5338 (67.6)	4209 (66.2)
Alone	4703 (33.0)	2553 (32.4)	2150 (33.8)
Living area (n = 13,793)
Rural	4965 (36.0)	2088 (27.5)	2877 (46.5)
Small town	4263 (30.9)	2632 (34.6)	1631 (26.3)
Large town or city	4565 (33.1)	1631 (26.3)	1683 (27.2)
Family size (no of persons)
One	3869 (27.0)	2071 (26.2)	1798 (28.1)
Two	7989 (55.8)	4326 (54.7)	3663 (57.2)
Three	1493 (10.4)	923 (11.7)	570 (8.9)
Four and above	971 (6.8)	594 (7.5)	377 (5.9)
Estimated household income (€) (median(IQR)) (n = 14,109)	30463.2 (31030.7)	32640.3 (35174.8)	28621.0 (26953.5)
Lower	4404 (31.2)	2377 (30.6)	2027 (32.0)
Middle	4452 (31.6)	2421 (31.1)	2031 (32.1)
Upper	5253 (37.2)	2984 (38.3)	2269 (35.9)
Social support (n = 11,275)
Yes	3589 (31.8)	2166 (34.7)	1423 (28.3)
No	7686 (68.2)	4083 (65.3)	3603 (71.7)
Retirement (n = 13,994)
Yes	7939 (56.7)	3971 (51.7)	3968 (62.9)
No	6055 (43.3)	3717 (48.3)	2338 (37.1)

a-Based on ISCED 1997 (level 0–2).

Behavioral, physical measurements and other health related characteristics

The trend of behavioral, physical and other health related characteristics of participants at each wave of data collection is presented in detail in Table 2 in S1 Annex. Overall, 87.6% of participants reported adequate fruit and vegetable intake, i.e., at least one serving per day and the trend was consistent from 2011 to 2017. Above half (50.9%) hardly ever or never involved in vigorous physical activities, whereas, 65.6% reported that they performed moderate physical activities more than once a week. Similarly, the frequency of vigorous and moderate physical activity remains relatively constant across time. Overall, only 7.8% have a history of regular alcohol consumption within 6 months prior to data collection. Overall, one-fifth (19.9%) were smokers at the time of each interview and the proportion was highest in 2017 (29.2%). The rate of obesity is relatively consistent throughout the observation period, ranging 18.6% (2011) to 20.7% (2015). The overall mean of hand grip strength was 33.6 (SD: 11.8) and it was consistent across the waves. The rate of depression ranged from 29.9% in 2017 to 31.5% in 2015, with an overall rate of 30.9%.

The prevalence of cardiovascular risks

Table 2 summarizes the prevalence of CVD risks in Belgium and France. One-third (33.2%) and 31.0% of older adults in Belgium and France, respectively, had a history of hypertension at least once in their life time. The prevalence of high blood cholesterol in Belgium (30.6%) is higher than in France (23.3%). Whereas, the prevalence of high blood sugar in Belgium (10.8%) is nearly equal to the prevalence in France (10.9%).

Table 2

Prevalence of cardiovascular disease risks among adults aged 50 years or older in Belgium and France (n = 14,276), from the Survey of Health, Ageing and Retirement in Europe, 2011 to 2017.

Cardiovascular risks	Total n(%)	Belgium n(%)	France n(%)
Ever had hypertension (Yes)	4596 (32.2)	2617 (33.2)	1979 (31.0)
Ever had high blood cholesterol (Yes)	3898 (27.3)	2410 (30.6)	1488 (23.3)
Ever had high blood sugar (Yes)	1546 (10.8)	848 (10.8)	698 (10.9)
Total	14276	7888	6388

The occurrence of cardiovascular events

The overall rate of heart attack was 8.3%, which is 7.6% in Belgium and 9.1% in France. The rate of stroke was 2.6% and 2.3% in Belgium and France, respectively. Throughout the observation period, the overall rate of CVD was 9.5% and 10.7% in Belgium and France, respectively. (Table 3)

Table 3

Occurrence of cardiovascular diseases among adults aged 50 years or older in Belgium and France (n = 36,781 person observation points), from the Survey of Health, Ageing and Retirement in Europe, 2011 to 2017.

CVD events	Overall percentage	Belgium N (%)	France N (%)
Heart attack	8.3	1563 (7.6)	1483 (9.1)
Stroke	2.4	526 (2.6)	369 (2.3)
Heart attack or stroke	10.0	1,954 (9.5)	1,738 (10.7)

CVD: cardiovascular diseases.

Socioeconomic determinants of cardiovascular diseases among older adults

In the multivariable GEE model, the odds of having CVD was 5.7% higher for a one year increase in age [AOR: 1.057, 95%CI: 1.055–1.060]. The risk of CVD is significantly higher among those who were living in large cities [AOR: 1.14, 95%CI: 1.07–1.18] than those living in rural areas. Similarly, retired older adults showed a higher odds of CVD than those who were not retired [AOR: 1.21, 95%CI: 1.16–1.31]. Being female [AOR: 0.54, 95%CI: 0.51–0.56], high level of education [AOR: 0.82, 95%CI: 0.79–0.90], higher income [AOR: 0.82, 95%CI: 0.76–0.86] and social support [AOR: 0.81, 95%CI: 0.77–0.84] imply a significantly lower risk of CVD as compared to their respective reference categories (Table 4).

Table 4

Multivariable GEE models estimating the effect of socioeconomic characteristics on cardiovascular diseases among adults aged 50 years or older in Belgium and France.

Variables	Percent with outcome	COR [95%CI]	AOR [95%CI]
Age	Cont.	1.063 [1.059–1.067]***	1.057 [1.055–1.060]***
Sex
Male	12.6	1	1
Female	8.0	0.60 [0.55–0.65]***	0.54 [0.51–0.56]***
Living area
Rural	9.6	1	1
Small	10.4	1.08 [0.99–1.18]	1.08 [0.99–1.14]
Large city/town	10.5	1.14 [1.01–1.22]*	1.14 [1.07–1.18]*
Level of education
Primary	12.7	1	1
Secondary	8.6	0.64 [0.58–0.72]**	0.91 [0.86–0.97]*
Higher	7.8	0.58 [0.52–0.65]***	0.82 [0.79–0.90]**
Marital status
Partner	9.2	1	1
Alone	11.5	1.30 [1.19–1.41]*	1.04 [0.99–1.10]
Net income
Lower	12.1	1	1
Middle	10.3	0.86 [0.79–0.94]*	0.99 [0.94–1.03]
Upper	7.0	0.60 [0.55–0.67]***	0.82 [0.76–0.86]**
Social support
No	12.2	1	1
Yes	7.0	0.63 [0.58–0.69]***	0.81 [0.77–0.84]***
Retirement
No	5.8	1	1
Yes	13.0	2.10 [1.92–2.28]***	1.21 [1.16–1.31]**

* p–value<0.05

** p<0.01

*** p <0.001.

AOR: Adjusted odds ratio; COR: Crude odds ratio; GEE: Generalized Estimating Equation.

Multivariate multiple imputations were performed (n = 36,860).

• Interaction of age and level of education with living area, income and social support was assessed but statistically not significant.

Behavioral and physical determinants of cardiovascular diseases among older adults

In the multivariable GEE model (Table 5), on average the odds of CVD is 31% lower for those who do regular physical activity than those who do not [AOR: 0.69, 95%CI: 0.64–0.73]. Similarly, adequate fruit and vegetable intake is also associated with lower odds of CVD among older adults [AOR: 0.93, 95%CI: 0.87–0.99]. The odds of having CVD is 1.5 times higher for those who are obese than for those not obese [AOR: 1.49, 95%CI: 1.44–1.55]. Those who have one comorbidity and two or more multimorbidity are 2.2 and 4.5 times respectively, higher likelihood of having CVD than those with no comorbidity. The odds of having CVD is also 1.3 times higher for those who have depression than their counterparts [AOR: 1.27, 95%CI: 1.21–1.33]. Moreover, hand grip strength is significantly associated with lower CVD [AOR: 0.987, 95%CI: 0.984–0.990].

Table 5

Multivariable GEE model estimating the effect of behavioral and physical determinants of cardiovascular diseases among older adults in Belgium and France.

Variables	Percent with outcome	COR [95%CI]	AOR [95%CI]
Age	Cont.	1.063 [1.059–1.067]***	1.045 [1.042–1.048]***
Sex (female)	8.0	0.60 [0.55–0.65]***	0.42 [0.39–0.45]***
Physical activity
No regular PA	12.6	1	1
Regular PA	6.1	0.55 [0.51–0.60]***	0.69 [0.64–0.73]**
Fruit and vegetable intake
Not adequate	10.2	1	1
Adequate	9.3	0.93 [0.82–1.06]	0.93 [0.87–0.99]*
Smoking
No	9.1	1	1
Yes	7.9	1.16 [1.02–1.31]*	1.19 [1.13–1.25]*
Regular alcohol consumption
No	11.4	1	1
Yes	10.8	0.99 [0.80–1.21]	1.04 [0.96–1.13]
BMI
Normal	8.2	1	1
Overweight	10.0	1.20 [1.10–1.31]***	1.08 [1.03–1.14]*
Obesity	13.9	1.67 [1.50–1.85]***	1.49 [1.44–1.55]***
Chronic comorbidities
No comorbidity	2.1	1	1
One comorbidity	8.1	2.84 [2.52–3.21]***	2.23 [2.10–2.37]***
> = 2 comorbidities	21.9	6.98 [6.20–7.87]***	4.54 [4.27–4.83]***
Grip strength	cont.	0.984 [0.980–0.988]***	0.987 [0.984–0.990]*
Depression
No	8.2	1	1
Yes	13.0	1.51 [1.40–1.63]***	1.27 [1.21–1.33]**

* p–value<0.05

** p<0.01

*** p <0.001.

AOR: Adjusted odds ratio; COR: Crude odds ratio; GEE: Generalized Estimating Equation; BMI: Body Mass Index.

Multivariate multiple imputations were performed (n = 36,860).

Interaction of physical activity level with fruit and vegetable intake and smoking with alcohol consumption was examined but statistically not significant.

Discussion

Based on a large sample of adults aged 50 years and above in Belgium and France, this study aimed to identify the socioeconomic, behavioral and physical determinants of cardiovascular diseases and the interplay in between longitudinally. Whilst several studies evidenced the role of socioeconomic factors on CVD among adults, the role of these factors on older people were not well documented. This study showed that socioeconomic characteristics, including having social support, level of education, living area, retirement and income level were independent determinants of CVD among older adults.

After adjustment for the socioeconomic and other related variables, the risk of having CVD was significantly higher among older adults who live in large cities than those who live in villages. A lower green area in urban settings could be a possible reason as indicated by Seo S. and colleagues who found adults living in areas with greater amounts of green space found to have a lower risk of CVD [29]. Consistently, various studies showed that exposure to rural green space is associated with a reduction in indicators of CVD risk factors compared to urban streets [30-34]. The higher risk of CVD in the most deprived groups is more pronounced in urban areas with low amounts of green space coverage [35, 36]. Furthermore, the higher prevalence of smoking, physical inactivity, and unhealthy diet in urban areas could also be the possible reasons for higher risk of CVD. Thus, interventions aimed at reduction of CVD risks need to integrate with other sectors for optimal intervention effect.

In our study, older adults with regular social support from family or anyone else have a lower risk of CVD. This finding is similar to a study by Rosengren AL. et al, which indicate low social support is associated with coronary heart diseases [37]. Several studies also documented the role of social support in CVD incidence and mortality [38-40]. Both objective social isolation and the subjective perception of being isolated have been shown to be associated with a higher rate of CVD [41, 42]. Orth-Gomer explained the psychological mechanisms of social support leading to CVD morbidity and mortality [43]. Therefore, it is essential to elucidate the molecular mechanisms of loneliness leading to CVDs. Moreover, community based CVD prevention strategies need to consider the role of social support to prevent the detrimental effects of isolation.

The present study showed, retired older adults have a higher risk of CVD after adjusted for age, sex and other socioeconomic characteristics. A recent systematic review indicated the impact of retirement on the rate of CVDs and risk factors varies across countries, in which studies in the European countries showed a detrimental effect of retirement on CVDs [44]. Pedron and colleagues identified male and low-educated retirees as potential high-risk groups for worsening CVD risk factors after retirement [45]. Retirement has been linked to increased leisure time activities but, it may reduce transport and work related activities. The effect of retirement also varied with the type of job a person retired from. A study by Godard and associates showed retirement caused an increase in the likelihood of being obese among men retiring from strenuous jobs [46]. On the other hand Stenholm and colleagues indicated retirement was associated with slight weight loss in men retiring from sedentary jobs [47]. Hence, policies concerning the retirement age need to focus on ensuring whether they are suited to individuals and contexts.

In this study, higher income and higher level of education are associated with lower risk of CVDs. A multi-country study showed cardiovascular events were more common among those with low levels of education [48]. Dégano and her colleagues also indicated the rate of CVD events is 50% lower for those with university education compared to primary or lower education [49] The variation in CVD incidence and mortality based on the socioeconomic status has also been documented elsewhere [12, 14, 15, 50].

Various physical factors were also found significant in our study. Grip strength is associated with CVD after adjusting for socioeconomic and behavioral factors. Previous studies showed the association of hand grip strength with heart failure and cardiovascular risks such as high blood pressure, high blood sugar and lipid levels [51, 52]. Hand grip strength in patients with type 2 diabetes is inversely associated with CVD independently from well-established cardiovascular risks [53]. A prospective study from the UK also showed hand grip strength has inverse associations with incidence of cardiovascular events [54]. This implies hand grip strength could be a valuable CVD risk assessment tool for older adults in combination with the already available indicators.

Our findings also showed behavioral factors, including physical inactivity, inadequate fruit and vegetable intake, obesity, smoking and depression are associated with higher risk of cardiovascular events, as it is evidenced from several studies [55, 56], indicating older adults are not an exception in this regard.

The findings from this study need to be interpreted in the context of the following limitations. First, we used the existing measures from the SHARE study and were not able to include more measures of determinants that might be more appropriate to estimate the effect of behavioral determinants on CVD. For instance, we could not explore the amount of serving of fruit and vegetable intake, the duration and intensity of vigorous and moderate physical activity level, and the amount of alcohol consumption. Secondly, the measurement of socioeconomic characteristics, CVDs and risks was self-reported. We did not assess it using physical or laboratory measurements. Participants in low socioeconomic status (SES) could under-report their CVD status and risks [57], which might nullify the effect size of SES on CVD. This indicates the effect could even be more than what we found. Further studies with a more objective assessment of CVDs and risks might provide a larger effect size. Nevertheless, SHARE used cards with a list of disease conditions to probe participants, which could minimize the reporting bias. Thirdly, as our aim was not to identify a single determinant, we did not perform a comprehensive mediation analysis for each determinant-mediator-outcome relationship. We recommend future studies to identify independent socioeconomic and behavioral determinants of CVDs with extensive mediation analysis.

Conclusions

This study addressed several aspects of cardiovascular disease prevention areas among older adults. Our research demonstrated that older adults who are retired, do not have social support, live in big cities, belong to the lowest wealth quantile and have a low educational level have a higher likelihood of CVD. It also showed that behavioral risks are prevalent in older adults living in Belgium and France and associated with an increase in CVD as documented from our study and several other studies. Physical factors such as a better hand grip strength is associated with lower incidence of CVD. The findings from this study underlined the fact that socioeconomic disparities affect the occurrence of CVDs in older adults. Therefore, community based interventions aimed at improving cardiovascular risks need to give more emphasis to high-risk older adults to get optimal benefit from the interventions. Older adults who live in large cities with no social support need to gain more emphasis to halt the continued problem of CVD and associated premature mortality.

Supplementary material.

The supplementary material contains detailed information on comparison of included and excluded participants, behavioral risks overtime, sensitivity analysis of multiple imputation and complete case analysis, checking for imputation model fit, comparison of models with various correlation structure, and stratified GEE analysis for Belgium and France.

(PDF)

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23 Sep 2020

PONE-D-20-21259

Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: a longitudinal analysis from the SHARE study

PLOS ONE

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Reviewer #2: Partly

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Reviewer #2: Yes

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Reviewer #1: The revised work is very well conducted, the ideas are original and relevant. The results are very interesting and will be very beneficial for the scientific community. Additionally, I find it well written and easy to understand. I would just like to comment that in table 1 in the Sex (Female) variable, I noticed that there is a typo: you need to close the parentheses.

Reviewer #2: The authors conducted a large cohort study evaluating socioeconomic determinants and cardiovascular disease using data collected from several waves of the SHARE study. By using marginal GEE models the investigators were able to account for between- and within- subject effects of social determinants. The paper was well thought out in many ways but there are some areas for improvement. Suggested edits/comments could be found below:

1. Please provide some analysis of individuals for which the outcomes of interest were missing. The authors state in line 108-109 that only persons with outcome data were contained in the sample but it is not clear how not including these persons may have biased the results. Even some descriptive analyses among persons with missing outcome data would be helpful. This is exceptionally important for the outcome which may be missing a large proportion of individuals who are deceased and therefore were not included in the sample.

2. Related to this point, proportions of missingness in tables 3-4 were helpful, however, in general it would be helpful to know more about the multiple imputations models. How were variables selected and were there auxillary variables used to specify the missing model?

3. Please clarify if any of the "multivariate". This appears to be used interchangeably but it seems as though "multivariable" may be the correct wording. The different types of models have very different implications.

4. While the authors allude to some of the limitations of using self-reported data, it might be helpful to include some more discussion of how measurement error may have biased some of these findings (e.g., low SES underreporting certain social behaviors such as alcohol/tobacco use/abuse, how does self-reporting CV events limit this analysis?).

5. The mediation analysis was well thought out but since this contains so many similar social constructs, the authors may benefit from removing it from the main text and supplement. It can be explored in future work when developing a causal model rather than the present predictive one. Collinearity also may be an issue between some of the crudely defined socioeconomic factors.

6. A similar analysis looking at incident CVD cases would be interesting either for this study or in the future. Is there a way to exclude persons with prevalent CVD events from earlier waves and at baseline of this study to understand if one of these factors (i.e., retirement status, depression) cause CVD in this population?

7. Reporting variability within and between countries is an advantage of using hierarchical models such as the one employed in this study but there is no mention of it in the text. Only descriptive results were presented between the countries. Please include more information on how these countries differ.

8. It will be generally important to state whether this is a predictive model that identifies risk factors and not a causal model which may be limited to confounding and selection biases.

**********

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Reviewer #1: Yes: MIREYA MARTÍNEZ-GARCÍA

Reviewer #2: No

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20 Oct 2020

Response to Reviewers

PONE-D-20-21259

Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: a longitudinal analysis from the SHARE study

PLOS ONE

First of all, I wish to thank the editor and the reviewers, also on behalf of all authors, for their valuable and constructive comments to improve our manuscript. We revised the manuscript based on the comments and issues raised by the editor and the reviewers. We are confident that we have addressed all these comments adequately and we therefore hope that the manuscript is accepted for publication. Find hereunder a point by point reply to the comments and questions raised by the editor and the two reviewers.

Editor

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: Thank you for your suggestions. We have reviewed the manuscript to meet the journal requirements and we hope that the manuscript in its current form does so.

2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

Response: Thank you for your suggestions. We used publicly available data (available via the website http://www.share-project.org/data-documentation/data-documentation-tool.html) for a secondary data analysis. In order to clarify this, we have added the following sentences in line 208 - 214 of the revised version of the manuscript:

“We used publicly available data (available via the website http://www.share-project.org/data-documentation/data-documentation-tool.html) for a secondary data analysis. SHARE underwent a review of ethical standards by the University of Mannheim's internal review board (IRB). Ethical considerations including written informed consent has been taken care of by another institution. Details on the conduct of the study including the ethical approval can be found elsewhere (Alcser, Benson et al. 2005).”

Reviewer # 1

1. The work is very well conducted, the ideas are original and relevant. The results are very interesting and will be very beneficial for the scientific community. Additionally, I find it well written and easy to understand. I would just like to comment that in table 1 in the Sex (Female) variable, I noticed that there is a typo: you need to close the parentheses.

Response: Thank you for your feedback and appreciation for our work. We corrected the typo accordingly.

Reviewer # 2

1. The authors conducted a large cohort study evaluating socioeconomic determinants and cardiovascular disease using data collected from several waves of the SHARE study. By using marginal GEE models the investigators were able to account for between- and within- subject effects of social determinants. The paper was well thought out in many ways but there are some areas for improvement. Suggested edits/comments could be found below.

Response: Thank you for your feedback. The comments were very helpful and we revised the manuscript thoroughly. We hope that we addressed all your concerns in the revised manuscript.

2. Please provide some analysis of individuals for which the outcomes of interest were missing. The authors state in line 108-109 that only persons with outcome data were contained in the sample but it is not clear how not including these persons may have biased the results. Even some descriptive analyses among persons with missing outcome data would be helpful. This is exceptionally important for the outcome which may be missing a large proportion of individuals who are deceased and therefore were not included in the sample.

Response: Thank you for your feedback. As we mentioned in line 108 to 110, we excluded individuals who are aged < 50 years, individuals that already have CVD at enrollment and those that did not have information on the CVD outcome variables at enrollment. Participants with intermediate missingness in terms of the outcome variables are included in the analysis and managed using the MI technique. The main reason to exclude individuals with no CVD outcome information at enrollment is to make sure that the individuals that are included are free from CVD events prior to study initiation. In total, 467 participants were excluded (108 being aged below 50 years, 117 with missing outcome values at enrollment and 242 individuals that already had a history of CVD at enrollment). Although we assume missingness of CVD outcomes at enrollment to be completely at random (MCAR) by not including those 117 participants, the percentage of missing outcome data is below 0.8% and we therefore believe that the impact on the inference is limited when the underlying missingness mechanism is mis-specified. Nevertheless, we do agree with the reviewer that a descriptive analysis to deal with the baseline missingness could be explored. As recommended by the reviewer, we performed a descriptive analysis to compare the included individuals and the individuals that were excluded due to missing outcome values (n=117) in terms of a set of covariates. The descriptive results thereof are presented in table 1 of the supplementary material. The results show that individuals who were included are comparable with the excluded ones in terms of socioeconomic characteristics. We also added a description in line 166 to 171 of the revised manuscript.

3. Related to this point, proportions of missingness in tables 3-4 were helpful, however, in general it would be helpful to know more about the multiple imputations models. How were variables selected and were there auxiliary variables used to specify the missing model?

Response: Thank you for this comment. Indeed, we used auxiliary variables in combination with the covariate information used in the substantive model(s) in order to specify the imputation models. Examples of such auxiliary variables include family size, activities of daily living, limitation of activities, self-perceived health, among other variables. The perspective taken in the Multiple Imputation by Chained Equations (MICE) approach, also referred to as full conditional specification, is one of a specification of several imputation models which sequentially imputed missing values for a given variable when regressed against all other covariates available. For more details regarding this approach and the justification of full conditional specification, we refer the reader to (Van Buuren 2018). We revised the description in line 194 to 200 of the analysis section in order to clarify this. Moreover, more details concerning the imputation models and the covariates included therein are included in page 6 of the supplementary material. In general, the inclusion of auxiliary variables in the imputation models, next to the covariates present in the substantive model(s), albeit potentially unimportant from an explanatory perspective, leads to a more precision in terms of estimation of the model parameters in the final substantive model.

4. Please clarify if any of the "multivariate". This appears to be used interchangeably but it seems as though "multivariable" may be the correct wording. The different types of models have very different implications.

Response: We agree with the reviewer that a multivariate analysis is different from a multiple or multivariable one, the latter being the analysis that has been conducted in our manuscript. Therefore, we revised the manuscript to consistently use ‘multivariable’ throughout the manuscript.

5. While the authors allude to some of the limitations of using self-reported data, it might be helpful to include some more discussion of how measurement error may have biased some of these findings (e.g., low SES underreporting certain social behaviors such as alcohol/tobacco use/abuse, how does self-reporting CV events limit this analysis?).

Response: We kindly agree with the reviewer that those in low socioeconomic status usually have a higher likelihood of under-reporting CVD status, risks and other health related issues, which might nullify the effect size of SES on CVD. This indicates the effect of SES on CVD could be even more than what we found. We elaborated more extensively on the limitation of the self-reported data and discussed this in view of the reviewer’s comment in line 367 to 374 of the revised manuscript.

6. The mediation analysis was well thought out but since this contains so many similar social constructs, the authors may benefit from removing it from the main text and supplement. It can be explored in future work when developing a causal model rather than the present predictive one. Collinearity also may be an issue between some of the crudely defined socioeconomic factors.

Response: We agree that the mediation analysis needs more exploration. We accept that this study and more importantly its design did not fully comply with nor allow for a formal and in-depth mediation analysis as we have a number of related determinants and mediators. Separate mediation analyses are needed, principally for each determinant-mediator-outcome relationship. We will consider to perform an extensive mediation analysis in the continuing analysis. Initially, we believed that this mediation analysis would provide some indications for future studies. However, to avoid confusion and mis-understanding for readers, we removed it from the main text and the supplement.

7. A similar analysis looking at incident CVD cases would be interesting either for this study or in the future. Is there a way to exclude persons with prevalent CVD events from earlier waves and at baseline of this study to understand if one of these factors (i.e., retirement status, depression) cause CVD in this population?

Response: Thank you for your feedback. In fact, we excluded those individuals with CVD during the first wave of their enrollment as we mentioned in line 108 to 110 of the methods section. However, in this study, as the survey is every two years, we could not ascertain the exact number of CVD events (MI, stroke, etc..) and the exact date of such an event in between the waves. This made the incidence analysis somewhat complicated. Nevertheless, we do believe that this is very interesting and a potential avenue for further research. As suggested, we will consider to perform incidence analysis in the future.

8. Reporting variability within and between countries is an advantage of using hierarchical models such as the one employed in this study but there is no mention of it in the text. Only descriptive results were presented between the countries. Please include more information on how these countries differ.

Response: Thank you for your comment. We explored the difference between two countries extensively in the revised version. Besides the descriptive statistics, we also compared the difference between countries in several ways. Firstly, we compared both countries using differences in socioeconomic as well as the outcome variable. Secondly, as we have mentioned in line 187 to 190, we developed a separate GEE model for the two countries to assess whether the determinants vary across countries. However, the results showed no significant difference in the effect sizes of determinants between countries (detailed results are indicated in table 7 and 8 of the supplementary material). Thirdly, we performed a combined analysis without the inclusion of country-specific determinants under the assumption that such differences were unimportant based on our earlier stratified analysis. Hence, we finally preferred to present the results of the (combined) GEE model, for both countries combined.

9. It will be generally important to state whether this is a predictive model that identifies risk factors and not a causal model which may be limited to confounding and selection biases.

Response: Thank you for your feedback. We do agree that the aim of this study is to identify risk factors for the occurrence of CVD, i.e. a so-called risk factor identification study. We stated this explicitly in the discussion section and indicated that our model is not a causal one, rather one that focuses on association and not on (the establishment of) causality. We believe that this study would provide overall insight for further individual causal study.

References

Alcser, K. H., G. Benson, A. Börsch-Supan, A. Brugiavini, D. Christelis, E. Croda, M. Das, G. de Luca, J. Harkness and P. Hesselius (2005). "The survey of health, aging, and retirement in Europe—Methodology." Mannheim Mannheim Research Institute for the Economics of Aging (MEA).

Van Buuren, S. (2018). Flexible imputation of missing data, CRC press.

Hamid Y. Hassen

Hamid.hassen@uantwerpen.be

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

23 Nov 2020

Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: a longitudinal analysis from the SHARE study

PONE-D-20-21259R1

Dear Dr. Hassen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Luisa N. Borrell, DDS, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

You have addressed the reviewer's comments. However, I have some suggestions for the tables as they should stand alone.

Reviewers' comments:

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Reviewer #2: All comments have been addressed

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

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Reviewer #2: No

Submitted filename: PONE-D-20-21259_R1 LNB.pdf

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26 Nov 2020

PONE-D-20-21259R1

Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: a longitudinal analysis from the SHARE study

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