Background
In Europe and North America, low socioeconomic status groups have a higher prevalence of chronic disease risk factors such as smoking, physical inactivity, obesity, hypertension, hypercholesterolemia, and diabetes [
1‐
3], and a higher cardiovascular risk score [
2,
4].
Associations are well established between various categorizations of occupation and related social class and cardiovascular risk factors [
5‐
9]. Based on occupational classifications that distinguish blue-collar from white-collar workers or professional, technical, and manual workers [
5,
9], there is substantial evidence for an inverse relationship between the occupational social level and cardiovascular disease risk factors [
10] such as high blood pressure [
5,
9,
11,
12], smoking [
13‐
16], cholesterol [
17], hemostatic factors [
18], and obesity [
19].
Compared to occupational social class, very few studies have focused on work economic sectors. Work economic sectors, even considered on a broad scale, may be of importance, because numerous exposures may vary in prevalence from one sector to the other. Studies have suggested that occupational exposures such as psychosocial factors [
20,
21], work hardness, high physical demand, noise at work [
21,
22], working rhythm [
23,
24], and the prevalence of health behavior such as tobacco [
25], alcohol, and drug consumption [
26] show a different prevalence in the various work economic sectors [
27].
The present work is grounded on the idea that, in addition to this flourishing literature investigating the relationship between specific occupational exposures and health; it is relevant to consider the broad economic sectors where people work. In Social epidemiology, it is very common to examine causes that are distant from the health outcomes investigated, with a distinction between the so-called upstream determinants (e.g., social class, work economic sectors) and downstream determinants of diseases that mediate the effects of the former. Considering work economic sectors in addition to social class and other broad characteristics of populations may be useful to identify an additional marker of populations in poor health, in order to target interventions to populations who critically need them. The operational interest of work economic sectors for targeting interventions is also related to the fact that this information is commonly reported and available in a number of databases. Finally, work economic sectors are also connected to a potential approach to develop interventions, through the occupational medicine departments represented in each company. Overall, documenting relationships between work economic sectors and cardiovascular risk factors is potentially important to develop Public health interventions.
Regarding cardiovascular health as the focus of the present article, one study investigated work economic sectors in relation to hypertension. Takashima et al. found that the age-adjusted prevalence of hypertension among transportation and communication workers was four times higher than that of service workers, who had the lowest prevalence of hypertension [
11]. In another study, occupation-based social class and work economic sectors were combined into one classification, not allowing the authors to disentangle their independent effect on hypertension [
7].
One study examined work economic sectors in relation to obesity in a Dutch working population [
28]. The study found that there were three sectors with a relatively low body mass index (BMI): the catering industry, the healthcare sector, and the culture, sport, and recreation sector. On the opposite, workers of the transportation sector showed a high BMI and a high prevalence of overweight and obesity.
Only one study conducted among women examined work economic sectors in relation to multiple cardiovascular risk factors including cholesterol, blood pressure, heart rate, and anthropometric variables aggregated in order to reflect allostatic load [
29]. The study found that working in the health care sector was associated with a higher allostatic load than working in the information technology and media sector. However, this study only compared two work economic sectors.
To our knowledge, no study investigated the association between a panel of different work economic sectors and multiple cardiovascular risks factors. Moreover, no study adjusted the relationships between work economic sectors and health for individual and neighborhood socioeconomic characteristics or compared the associations between men and women. Overall, our aim was to investigate whether and how a large panel of cardiovascular risk factors including weight status and fat, blood pressure (BP), cholesterol, glycaemia, and resting heart rate varied between 11 work economic sectors after adjustment for individual and neighborhood sociodemographic characteristics in a large working population.
Results
The associations between individual or neighborhood sociodemographic variables and cardiovascular risk factors are shown in Additional files
2,
3,
4 and
5. The most consistent associations were documented with age, individual education, and neighborhood education. All risk factor variables increased with age, and HDL cholesterol also increased with age. In men and/or women, a low personal education was associated with a higher BMI, waist circumference, SBP, DPB, and pulse pressure, and with a lower HDL cholesterol. After mutual adjustment, in men and/or women, living in a low education neighborhood was related to a higher BMI, waist circumference, SBP, DBP, and resting heart rate, and to a lower HDL cholesterol. Additionally, men living alone had a lower BMI, waist circumference, total cholesterol, and glycaemia than men living in couple.
Anthropometric risk factors
Men working in the Construction sector and in the Transport and communications sector had a higher BMI and tended to have a larger waist circumference (Table
1). On the opposite, men working in the Health and social work sector (the reference group) and in the Collective, social, and personal services sector had the lowest BMI and waist circumference.
Table 1
Associations with BMI and waist circumference estimated from multilevel regression models among men and women*
Work economic sector
| | | | |
Ref.: Health and social work | | | | |
Manufacturing industry | 0.32 -0.65 – 1.30 | -1.43 -2.82 – -0.03 | -0.17 -3.01 – 2.66 | -2.59 -5.87 – 0.69 |
Construction | 1.50 0.43 – 2.57 | -2.12 -5.49 – 1.24 | 3.62 0.52 – 6.73 | -3.85 -11.76 – 4.05 |
Commercial, repair of motor vehicles and motorcycles | 0.64 -0.34 – 1.62 | -1.55 -2.85 – -0.26 | 1.39 -1.46 – 4.24 | -3.47 -6.53 – -0.42 |
Hotels and restaurants | 0.31 -0.73 – 1.34 | -0.67 -2.26 – 0.92 | 0.23 -2.77 – 3.24 | -0.02 -3.76 – 3.71 |
Transport and communications | 1.15 0.13 – 2.18 | -1.89 -3.46 – -0.33 | 2.44 -0.53 – 5.42 | -4.45 -8.14 – -0.77 |
Financial activities | 0.50 -0.49 – 1.50 | -0.98 -2.37 – 0.41 | 0.20 -2.70 – 3.10 | -3.08 -6.36 – 0.19 |
Real estate, renting and business services | 0.58 -0.35 – 1.51 | -1.17 -2.32 – -0.01 | 1.05 -1.65 – 3.75 | -2.36 -5.10 – 0.36 |
Public administration | 0.61 -0.58 – 1.81 | -0.12 -1.55 – 1.31 | 1.45 -2.02 – 4.93 | -0.96 -4.34 – 2.42 |
Education | 0.59 -0.53 – 1.72 | -0.95 -2.42 – 0.52 | 0.85 -2.40 – 4.11 | -1.64 -5.13 – 1.83 |
Collective, social, and personal services | -0.13 -1.07 – 0.79 | -1.99 -3.17 – -0.82 | -1.04 -3.75 – 1.67 | -4.35 -7.12 – -1.58 |
Private (vs. public)
| 0.09 -0.46 – 0.66 | -0.68 -1.57 – 0.22 | 0.88 -0.74 – 2.52 | -0.94 -3.04 – 1.16 |
For women, the associations between work economic sectors and anthropometric variables were strikingly different. Women working in the Health and social work sector had the highest, not the lowest, BMI and waist circumference. Contrary to men, women working in the Transport and communications sector had among the lowest BMI and smallest waist circumference. Other work economic sectors associated with a lower BMI and waist circumference among women included the following sectors: Collective, social and personal services; Commercial and repair of vehicles; and Real estate, renting and business service.
Blood pressure
Both men and women working in the Health and social work sector had the highest SBP (Table
2). On the opposite, both men and women working in the Collective, social and personal service sector had a relatively low SBP. Men working in the Hotels and restaurants sector also had a relatively low SBP.
Table 2
Associations with SBP, DBP, and pulse pressure estimated from multilevel regression models among men and women*
Work economic sector
| | | | | | |
Ref.: Health and social work | | | | | | |
Manufacturing industry | -1.97 -6.02 – 2.08 | -0.71 -5.89 – 4.46 | 1.37 -1.38 – 4.12 | -3.09 -6.20 – 0.01 | -3.33 -5.97 – -0.69 | 2.38 -1.11 – 5.88 |
Construction | -2.52 -6.96 – 1.91 | -8.92 -21.33 – 3.48 | 1.39 -1.61 – 4.41 | -5.20 -12.65 – 2.24 | -3.91 -6.80 – -1.02 | -3.71 -12.09 – 4.65 |
Commercial, repair of motor vehicles and motorcycles | -2.20 -6.27 – 1.87 | -4.14 -8.96 – 0.67 | 0.87 -1.89 – 3.64 | -5.16 -8.06 – -2.27 | -2.98 -5.63 – -0.33 | 1.02 -2.23 – 4.27 |
Hotels and restaurants | -4.85 -9.15 – -0.56 | -2.48 -8.36 – 3.40 | 0.29 -2.62 – 3.21 | -2.08 -5.61 – 1.44 | -5.15 -7.94 – -2.39 | -0.39 -4.36 – 3.57 |
Transport and communications | -1.62 -5.87 – 2.61 | -1.60 -7.42 – 4.21 | 1.43 -1.45 – 4.31 | -4.74 -8.24 – -1.26 | -3.05 -5.82 – -0.29 | 3.14 -0.78 – 7.07 |
Financial activities | -2.89 -7.03 – 1.25 | -1.90 -7.05 – 3.24 | 0.96 -1.85 – 3.77 | -2.02 -5.11 – 1.06 | -3.86 -6.56 – -1.16 | 0.11 -3.35 – 3.58 |
Real estate, renting and business services | -3.24 -7.09 – 0.62 | -2.01 -6.30 – 2.28 | 0.40 -2.21 – 3.02 | -3.53 -6.10 – -0.95 | -3.61 -6.13 – -1.10 | 1.52 -1.37 – 4.42 |
Public administration | -1.25 -6.22 – 3.72 | -5.07 -10.37 – 0.23 | 0.31 -3.06 – 3.69 | -2.79 -5.97 – 0.39 | -1.62 -4.87 – 1.61 | -2.27 -5.85 – 1.30 |
Education | -2.92 -7.57 – 1.72 | -1.90 -7.39 – 3.59 | 0.34 -2.81 – 3.49 | -1.87 -5.17 – 1.42 | -3.29 -6.31 – -0.26 | -0.02 -3.73 – 3.68 |
Collective, social and personal services | -3.88 -7.75 – -0.01 | -6.17 -10.56 – -1.79 | -0.12 -2.75 – 2.51 | -4.90 -7.53 – -2.27 | -3.77 -6.29 – -1.25 | -1.27 -4.23 – 1.68 |
Private (vs. public)
| 0.60 -1.71 – 2.92 | -0.92 -4.23 – 2.40 | 0.08 -1.49 – 1.65 | 1.32 -0.67 – 3.31 | 0.48 -1.02 – 1.99 | -2.24 -4.48 – -0.01 |
No associations were documented between work economic sectors and DBP among men. Among women, a number of work economic sectors (especially Commercial and repair of vehicle; Collective, social, and personal services; Transport and communications; and Real estate, renting and business services) were associated with a lower DBP than in the Health and social work sector.
As a result of the different patterns of associations with SBP and DBP among men and women, no association was documented between work economic sectors and pulse pressure among women, while all economic sectors (especially Hotels and restaurants) were related to a lower pulse pressure than in the Health and social work sector among men. Women working in the private sector had a lower pulse pressure than those working in the public sector (private – public sector differences were documented for no other risk factor).
Cholesterol
Work economic sectors were not associated with total cholesterol or LDL cholesterol among men and women (Table
3). Associations were documented between work economic sectors and HDL cholesterol, but only among men. Men working in the Health and social work sector had the highest HDL cholesterol, while the lowest HDL cholesterol levels were observed in the Hotels and restaurants sector and in the Education sector.
Table 3
Associations with total cholesterol, LDL cholesterol, and HDL cholesterol estimated from multilevel regression models among men and women*
Work economic sector
| | | | | | |
Ref.: Health and social work | | | | | | |
Manufacturing industry | 2.27 -9.03 – 13.58 | -1.12 -12.28 – 10.03 | 3.71 -6.16 – 13.58 | -3.08 -13.05 – 6.89 | -1.74 -5.12 – 1.65 | 0.22 -3.82 – 4.26 |
Construction | 8.05 -4.35 – 20.46 | 12.04 -14.48 – 38.55 | 9.00 -1.84 – 19.84 | 12.87 -10.75 – 36.50 | -1.86 -5.58 – 1.85 | 0.89 -8.69 – 10.48 |
Commercial, repair of motor vehicles and motorcycles | 1.26 -10.09 – 12.61 | 4.41 -5.95 – 14.79 | 3.03 -6.88 – 12.94 | 1.28 -8.00 – 10.57 | -2.28 -5.68 – 1.12 | 2.53 -1.23 – 6.30 |
Hotels and restaurants | 0.69 -11.33 – 12.72 | 12.84 -0.26 – 25.93 | 3.98 -6.52 – 14.49 | 8.83 -2.90 – 20.57 | -4.98 -8.58 – -1.37 | 3.04 -1.72 – 7.80 |
Transport and communications | -4.53 -16.40 – 7.34 | -0.45 -13.06 – 12.17 | -1.64 -12.03 – 8.75 | -3.25 -14.57 – 8.07 | -3.52 -7.08 – 0.04 | 2.59 -1.99 – 7.18 |
Financial activities | 4.41 -7.13 – 15.95 | 1.56 -9.67 – 12.81 | 6.87 -3.20 – 16.95 | 1.74 -8.31 – 11.80 | -2.30 -5.76 – 1.15 | -0.50 -4.58 – 3.58 |
Real estate, renting and business services | 0.70 -10.06 – 11.46 | 4.84 -4.43 – 14.13 | 3.06 -6.34 – 12.45 | 2.45 -5.87 – 10.78 | -2.61 -5.84 – 0.61 | 1.83 -1.54 – 5.21 |
Public administration | 1.92 -12.09 – 15.92 | -5.20 -16.69 – 6.29 | 1.38 -10.85 – 13.61 | -2.01 -12.28 – 8.26 | -2.55 -6.75 – 1.64 | -2.46 -6.63 – 1.70 |
Education | -3.54 -16.43 – 9.33 | 4.04 -7.87 – 15.95 | 1.52 -9.72 – 12.77 | 3.70 -6.94 – 14.34 | -3.87 -7.74 – -0.01 | -0.77 -5.09 – 3.54 |
Collective, social and personal services | 5.20 -5.57 – 15.97 | -1.55 -11.01 – 7.89 | 7.30 -2.11 – 16.70 | -2.51 -10.97 – 5.94 | -2.89 -6.11 – 0.33 | 1.28 -2.14 – 4.72 |
Private (vs. public)
| 3.17 -3.29 – 9.64 | -1.89 -9.11 – 5.32 | 2.16 -3.49 – 7.81 | 0.34 -6.10 – 6.77 | -0.41 -2.35 – 1.52 | -0.79 -3.40 – 1.82 |
Glycaemia
No association was documented with glycaemia among women (Table
4). Among men, those working in the Transportation and communications sector and in the Hotels and restaurants sector had the highest glycaemia levels while those working in the Health and social work sector had the lowest level.
Table 4
Associations with glycaemia and resting heart rate estimated from multilevel regression models among men and women
*
Work economic sector
| | | | |
Ref.: Health and social work | | | | |
Manufacturing industry | 2.55 -1.32 – 6.43 | 0.65 -3.58 – 4.89 | -1.36 -4.16 – 1.44 | -2.22 -5.15 – 0.70 |
Construction | 4.08 -0.17 – 8.33 | 1.28 -8.80 – 11.37 | -1.84 -4.90 – 1.22 | 5.47 -1.59 – 12.53 |
Commercial, repair of motor vehicles and motorcycle | 3.13 -0.75 – 7.02 | 1.56 -2.37 – 5.50 | -0.83 -3.64 – 1.98 | -2.14 -4.88 – 0.60 |
Hotels and restaurants | 4.98 0.85 – 9.11 | -0.66 -5.64 – 4.33 | -1.39 -4.36 – 1.58 | -3.68 -7.02 – -0.34 |
Transport and communications | 6.30 2.23 – 10.37 | -0.47 -5.24 – 4.28 | -1.13 -4.07 – 1.80 | -0.85 -4.14 – 2.43 |
Financial activities | 3.34 -0.61 – 7.30 | -0.25 -4.53 – 4.02 | -1.08 -3.95 – 1.77 | 0.09 -2.83 – 3.02 |
Real estate, renting and business services | 2.92 -0.76 – 6.61 | 0.26 -3.26 – 3.78 | -0.95 -3.61 – 1.71 | -0.22 -2.66 – 2.22 |
Public administration | 0.87 -3.90 – 5.64 | 1.81 -2.55 – 6.18 | 0.32 -3.12 – 3.76 | 0.40 -2.62 – 3.42 |
Education | 0.56 -3.88 – 4.99 | -2.05 -6.58 – 2.47 | -1.51 -4.73 – 1.70 | 0.72 -2.35 – 3.79 |
Collective, social and personal services | 3.11 -0.57 – 6.80 | -0.57 -4.16 – 3.01 | -1.71 -4.38 – 0.96 | -2.49 -4.96 – -0.02 |
Private (vs. public)
| -0.39 -2.62 – 1.84 | -1.31 -4.04 – 1.41 | 1.19 -0.42 – 2.80 | 0.36 -1.54 – 2.25 |
Resting heart rate
No association was documented between work economic sectors and resting heart rate among men. As the only associations that were observed among women, those working in Hotels or restaurants and in the Collective, social, and personal services sector had the lowest resting heart rate (Table
4).
When work economic sectors were combined to distinguish between the secondary sector and the tertiary sector, no association was found with any of the cardiovascular risk factors among men and women (Additional file
6).
Discussion
Because, as social class, work economic sectors may be seen as an upstream cause of diseases, and because knowledge on the relationships between work economic sectors and health may be useful to target Public health interventions, our aim was to investigate disparities between work economic sectors and most of the basic anthropometric and biological risk factors of cardiovascular diseases. In order to assess whether work economic sectors truly contributed to the variations in cardiovascular risk, our analyses were carefully adjusted for individual and neighborhood socioeconomic factors.
Main findings
We found disparities in certain (but not all) cardiovascular risk factors between work economics sectors, and the patterns of such disparities were different in men and women and according to the risk factor.
Regarding similarities observed from one risk factor to the other, the Health and social work sector was found to be the most protective sector for BMI and waist circumference but also for glycaemia among men, while unfavorable profiles for these three risk factors among men were relatively consistently observed in the Construction sector and in the Transport and communications sector. Among men, the HDL cholesterol was also found to be the highest for those who were working in the Health and social work sector. However, this pattern did not apply to the other risk factors since no clear relationships were documented among men for total cholesterol, LDL cholesterol, and resting heart rate, and since, on the opposite, men working in the Health and social work sector showed the highest systolic BP and pulse pressure (rather than the lowest as for BMI, waist circumference, and glycaemia).
There were also discrepancies in the patterns of disparities between men and women. While the Health and social work sector was the most protective among men for different risk factors, women working in the Health and social work sector had the highest BMI, the largest waist circumference, and the most elevated systolic but also diastolic BP. On the opposite, the Commercial and repair of vehicles sector, the Transport and communication sector, and the Collective, social, and personal services sector were fairly consistently associated with a more favorable profile for these risk factors among women. Overall, it is not clear whether the higher SBP (and higher DBP among women) documented in the Health and social work sector may be due to the specific occupational risks of the sector, including strong constraints in the schedule and a high physical demand and job strain [
22,
31].
Some of the disparities between work economic sectors may be due to differences in total energy intake or energy expenditure between work economic sectors. Regarding energy intake, a Japanese study [
49] based on food records indicated that the daily average energy intake was higher in Craftsmen, in the Production process and construction personnel, and in Laborers (2432 kcal), in the service personnel (2444 kcal), in the professional and technical personnel (2174 kcal), and in the transport and communication personnel (2200 kcal). These data derived from selected individuals of a different country with markedly different dietary habits likely not represent the actual energy intake of the French subjects in the present study. If these findings on dietary habits are unable to explain, e.g., the higher BMI and larger waist circumference of men working in the Construction sector and Transportation and communications sector documented in our study, at least they show that energy intake can vary to a large extent between work economic sectors, as a potential explanation for the observed disparities. Similarly, our study does not empirically confirm that the trend of relationship between working in hotels and restaurants and higher total cholesterol and the relationship between working in this sector and lower HDL cholesterol documented among women is attributable to the specific dietary habits of these women.
Because work can be a major contributor to the total daily physical activity [
50], an hypothesis may be that differences in physical activity levels between work economic sectors contribute to the reported economic disparities in cardiovascular risk factors. Based on this hypothesis, one would expect work economic sectors typically related with heavy manual work to be associated with a lower prevalence of cardiovascular risk factors, especially with a lower BMI and thinner waist circumference. However, we did not validate empirically this hypothesis, since for example men working in the Construction sector were found to have the highest BMI and largest waist circumference. This is in line with a previous study that indicated that, unlike recreational physical activity, occupational physical activity was not beneficial for cardiovascular risk factors, including those related to excess weight [
51].
It should be noted that in addition to the social and physical characteristics of work conditions, the characteristics of the geographic environment around the typical locations of the worksites of each work economic sector may contribute to the reported associations.
Finally, also as an indication of the relevance of our examination of the different work economic sectors, we found that associations were lost when contrasting the secondary sector to the tertiary sector, which distinction was therefore not meaningful for cardiovascular risk factors.
Strengths and limitations
Regarding study strengths, first, unlike previous studies on work economic sectors and cardiovascular risk factors, we examined in a comparative way a large panel of cardiovascular risk factors including BMI, waist circumference, SBP and DBP, pulse pressure, total cholesterol, HDL cholesterol, LDL cholesterol, glycaemia, and resting heart rate. While a large number of studies [
5‐
9] have focused on differences in cardiovascular risk factors between occupational groups (as a
social category), much less studies [
7,
11,
28] have analyzed cardiovascular risk according to the work economic sector (as an
economic variable). Our study is the very first to examine the relationships between work economic sectors and a large set of cardiovascular risk factors after controlling for individual and residential neighborhood socioeconomic variables. Another strength of our study is that information on the work economic sector was obtained through the linkage of administrative data to a population sample, leading to the presence of a very large number of workplaces (n = 3553 work establishments) and companies (n = 3005) in the sample, thus improving the generalizability of the findings.
Regarding study limitations, a first shortcoming of the work is related to the notion of work economic sector and operationalization of it that were used. It may be difficult to build a universal classification of work economic sectors because of the large difference in socioeconomic background between the countries [
49]. In addition, it is sometimes problematic to classify a subject into only one work economic sector, since a number of companies and work establishments may be related to different economic sectors. Moreover, as the work life of workers is complex to assess and is the combination of a number of activities and circumstances, even within the same work economic sector, there may be notable variations in the lifestyles, in the magnitude of psychological stress, and in the intensity of physical activity (for a given occupation) between different territories, especially between different countries [
11].
As a second limitation, the cross-sectional design of the study made it impossible to determine the direction of the causal effects involved in the associations reported (a poor cardiovascular health may differentially encourage to withdraw from professional activity according to the work economic sector, or may lead to seek for a job in specific sectors). Third, our sample recruited in preventive healthcare centers was not representative of the Paris Ile-de-France region [
32]. However, a large panel of municipalities from the region was
a priori selected to ensure the presence in the sample of people from all socioeconomic backgrounds. Moreover, the present analysis controlled for the individual and neighborhood factors that were found to influence participation in the study [
52]. Fourth, the workplace was retrieved through the linkage of administrative information and we could not be formally sure that all the participants were still employed in the company and in the related work economic sector at the recruitment in the study. It may be expected, however, that participants changing from one company to another would tend to remain in the same work economic sector. Moreover, even if the participants were working in another work economic sector or were no longer working when they were enrolled in the study, they had been exposed to the work economic sector taken into account in the study the year prior to their recruitment (exposure to work economic sectors was measured homogeneously in the whole sample). Fifth and finally, the duration of employment in the work economic sector was not taken into account in the present study.
Conclusion
In conclusion, the results of this study suggest that work economic sectors contribute to shape metabolic and cardiovascular parameters, even after adjustment for individual and neighborhood sociodemographic characteristics. The patterns of associations were found to vary, however, according to the risk factor examined and between men and women.
Overall, even if our study did not consider specific characteristics of work environments, the examination of the relationships between broad work economic sectors and a panel of cardiovascular risk factors is useful, in addition to the consideration of individual and residential characteristics, to determine profiles of populations to target Public health interventions and prevention efforts. As an example, while our previous work showed that having a low education, residing in neighborhoods with a low education and a low urbanicity degree, and shopping in hard discount supermarkets were all associated with a higher BMI and larger waist circumference [
30,
36,
38], the present study further suggests that working in the Construction and in the Transport and communication sectors among males was also related to a larger body weight and fat, thus refining our knowledge on the profile of people at risk.
Acknowledgements
We are grateful to INPES (and Pierre Arwidson) for its continued support since the beginning of the study. We also thank Mélani Alberto from the ARS of Ile-de-France. We are grateful to Insee, the French National Institute of Statistics and Economic Studies, which provided support for the geocoding of the RECORD participants and allowed us to access to relevant geographical data (with special thanks to Pascale Breuil). We thank Geoconcept for allowing us to access to the Universal Geocoder software. We also thank CNAM-TS and the Caisse Primaire d’Assurance Maladie de Paris (CPAM-P, France) for helping make this study possible. We thank Isabelle Bridenne and Vincent Poubelle who allowed us to access to the data from the National Old Age Insurance System (Caisse Nationale d’Assurance Vieillesse, CNAV). Regarding the data used the present study, we also thank the National Geographic Institute (IGN). This work was supported by a doctoral grant of Région Île-de-France (CORDDIM) attributed to Antoine Lewin. The RECORD study is funded by the Institute for Public Health Research (IReSP, Institut de Recherche en Santé Publique); the National Institute for Prevention and Health Education (INPES, Institut National de Prévention et d’Education pour la Santé); the National Institute of Public Health Surveillance (InVS, Institut de Veille Sanitaire); the French Ministries of Research and Health (Epidemiologic Cohorts Grant 2008); the National Health Insurance Office for Salaried Workers (CNAM-TS, Caisse Nationale d’Assurance Maladie des Travailleurs Salariés); the Ile-de-France Regional Health Agency (ARS, Agence Régionale de Santé); the National Research Agency (ANR, Agence Nationale de la Recherche); the City of Paris (Ville de Paris); and the Ile-de-France Youth, Sports, and Social Cohesion Regional Direction (DRJSCS, Direction Régionale de la Jeunesse, des Sports et de la Cohésion Sociale).
Competing interest
The authors declare that they have no competing interests.
Authors’ contributions
BC designed the overall research project; BC and AL designed the analytical strategy of the present study; AL analyzed the data; AL wrote the paper, and BC revised the first draft of the manuscript; all other authors, including BP and FT, critically revised the manuscript for important intellectual content. All authors have read and approved the final manuscript. BC and AL have primary responsibility for the final content of the article.