Background
There have been growing evidence that both individual and neighborhood socioeconomic position (SEP) play a role in shaping health and health inequalities [
1]. However, despite the increasing interest in place effect to explain health inequalities [
2], there are currently no gold standard and no consensus on which kind of area-based socioeconomic measures researchers should use to assess neighborhood SEP [
3].
Various area-based socioeconomic measures, such as deprivation index (DI), have been developed using census data [
4]. For example, the Townsend deprivation index [
5], developed in England, has been widely applied mostly in Anglo-Saxon countries [
6] but also in French studies [
7]. It has been shown that the Townsend index was poorly adapted to the specific French social and economic context [
8] and more overall it is recognized that DIs are specific to their country of origin [
9]. Despite the growing development and used of DIs, they are rarely explicitly validated [
1,
10] and how DIs are built may have important impact on its explanatory power with respect to health [
11,
12]. In addition, some methodological limitations have been underlined [
4]. Composite area-based SEP may be sensitive to urban-rural differences according to the items included [
13]. DIs that are based on census-data often include variables related to the active population or male-centered [
14] and might not be suitable to specific populations, such as elderly [
15,
16] or women [
17].
Recently, French specific DIs have been developed, based on different statistical methods and following different objectives [
18‐
23]. Some of them were built as a proxy of individual SEP [
24] such as the French European Deprivation Index (FEDI) [
19] whereas others were built to capture health inequalities at ecological level such as the French Deprivation Index (FDep) [
22].
To the best of authors’ knowledge, no study had examined both agreement between DIs and their ability to detect well-established socially dependent outcomes in the French context and more specifically among elderly women as a check on external validity. At individual-level, smoking initiation is a well-established SEP related outcome among French elderly women (more smokers among those with higher educational level) [
25,
26] and a similar trend has been observed at area-level [
27]. In the same way, overweight status is a known SEP related outcome at individual level (less overweight women among those with higher educational level) [
28‐
30] with a consistent trend at area-level [
31]. In addition, Chaix et al. underlined that area-based SEP is associated in the same direction as individual SEP with smoking and overweight [
32].
The main objective of our study was to investigate indirectly the ability of different DIs to measure socioeconomic residential conditions of a large population of French elderly women [
33]. We tested the external validity of these DIs, as previously performed [
34,
35], by comparing their ability to demonstrate expected associations with smoking and overweight status.
Discussion
Our findings showed substantial agreement between the two French area-based DIs and between Townsend and FEDI but fair agreement between Townsend and FDep. We observed expected known associations among French elderly women between individual educational level and both smoking and overweight. At area-level, only FDep showed similar patterns for both smoking and overweight. Inconsistent associations were observed for the two others DIs. FDep seemed reliable to capture socioeconomic residential conditions of the E3N elderly women, highly educated and living mostly in urban areas.
Ability of FDep to predict outcomes with well-known social patterns
The ability of area-based indicators to predict known socially patterned outcomes have been previously studied mostly in Anglo-Saxon countries [
40]. Our study, the first one performed in a very large French epidemiological cohort, showed the reliability of FDep to capture socioeconomic residential conditions among E3N elderly women. Results for FDep were consistent with those of the literature for both smoking and overweight status with a clear gradient between the quintiles [
27,
31]. Moreover, we observed a stronger association among older women between FDep and smoking, similarly to what was observed with educational level. It has been suggested that commonly used DIs suited poorly to study inequalities in older people especially because they included variables related to the active population or male-centered (social class) [
14]. The use of a DI in our population could therefore be a limitation. However, interestingly, FDep did not varied according to age, contrary to FEDI and Townsend for which older women were classified as more deprived in average. Stronger associations were also observed with FDep when we restricted the analyses to women with precise geocoding linkage, which was expected in the case of non-differential geocoding errors regarding SEP. [
41] In French ecological studies, stronger associations were observed with FDep compared to Townsend, studying DIs and all-cause mortality at commune-level [
22], consistently to our results. In addition, FDep was found less sensitive to urban-rural differences than Townsend, studying associations with colorectal cancer screening attendance [
8] consistently to our results.
Interpretation of the differences between FDep, Townsend and FEDI
As expected, Townsend was not adapted to evaluate residential deprivation in a French context [
20,
22]. Although FDep and FEDI showed a substantial agreement in classifying the women across the range of deprivation, we observed discrepancies in predicting smoking and overweight social patterns in E3N. This discrepancy could be explained by their different mode of construction. FDep was constructed to maximize the heterogeneity of the components using a PCA [
22]. FEDI was composed of weighted variables identified to best represent individual experience of deprivation and based on average social deprivation [
19] and thus might be less adapted to capture the variety of socio-spatial situations that composed the French territory. Moreover, DIs we applied here have not been created in the same context. FEDI was constructed to proxy individual SEP whereas FDep was setup in the context of ecological approaches.
The different items included in the DIs may also explain the disagreement. For example, FEDI and Townsend included the "proportion of households not owner occupied", "primary residence with more than 1 person per room" and “without a car”. These items are known to vary according to the degree of urbanicity and specifically in rural vs. urban areas [
13,
42]. For example, in rural areas, not possessing cars could be an obstacle for mobility and though be a proxy of deprivation, whereas in urban areas, especially in large cities, it is common to have no car as public transport is particularly developed in France. Likewise, overcrowding and home-ownership are not comparable between urban and rural settings and could be a marker of deprivation in rural areas but not always in urban ones. We observed that the FDep index tended to decrease (i.e. less deprivation) with the increasing of urbanicity, especially for Paris and suburbs. On the contrary, FEDI and Townsend increase (i.e. more deprivation) with the increasing of urbanicity. This opposite trend which appeared clearly on the maps of the distribution of the IRIS (Additional file
1: Fig. S6), could ensue from these items. For example, the map with the Townsend index showed light shades because the majority of the French territory is rural. On the contrary, the map with the FDep index is darker because rural areas are classified as more deprived than urban ones. Furthermore, individual characteristics of the participants varied according to the degree of urbanicity of their place of residence, with higher prevalence of ever smokers and less prevalence of overweight in urban areas that might also explain the unexpected associations observed for Townsend and FEDI with smoking.
Strengths and limitations
Our study presented several strengths. We used a very large population sample homogeneously distributed across the French territory. At area-level, composite indicators were more effective to take into account the multidimensionality of the SEP than a single one [
4]. Our results confirmed that FDep, initially developed at commune-level [
22], was also able to capture the inter- and intra-urban socio-spatial divisions existing in France at IRIS level. In addition, we used the population-weighted approach to construct the DIs quintiles, which allowed a better classification of population and gave stronger associations between FDep and the outcomes. To the best of authors’ knowledge the present epidemiological study is the first one to compare weighted and un-weighted methods. We tested two different outcomes with established social pattern, smoking and overweight, to assess the robustness of the DIs [
3]. We used GEE models to control for clustering effects from participants within the same IRIS in a context of sparsely clustering data, as recommended [
39]. We used the finest spatial unit with socio-demographic data available in France to minimize misclassification and potential ecological bias as recommended [
43]. Associations observed between FDep and known SEP related outcomes fit within 2 a priori criteria described as external validity and robustness by Krieger et al. [
3].
The study nevertheless had some limitations. Models including both individual and area-level variables were not performed in the present study because the hypotheses were based on studies using either SEP indicators at individual- or at area-level separately. Therefore, we were not able to distinguish compositional from contextual effects. Women’s residential history were not taken into account. However, less than 30% of movers were identified between 1991 and 2005 in a sub-E3N population [
44]. E3N women were 45 years at baseline, thus we hypothesized that their social trajectory was already settled and did not change much during this period. In addition, census data were not available to calculate the DIs at baseline. However, French studies have shown that spatial distribution of deprivation did not change substantially since 1991 [
45,
46]. Nonetheless, we performed a sensitivity analysis including only women who did not move and the conclusion was similar.
Choosing the most appropriate contextual indicators to capture socioeconomic conditions in a specific population
Historically, area-based SEP has been used as a surrogate of individual-SEP in medical records [
47], but this strategy have been questioned particularly in Anglo-Saxon countries [
48]. Some methodological studies have compared the agreement between individual and area-based SEP and their ability in predicting health outcomes [
35,
49] with conflicting results. Poor agreement has been reported between self-reported individual income and area-based income [
49]. While, in others studies, area-based SEP was considered as a good proxy of individual-level SEP [
35] allowing the prediction of socially patterned outcomes. Finally, it has been underlined that area-based SEP indicators fairly classify socially homogenous areas (most and least deprived neighborhoods) but failed sometimes to classify the in-between situations that are more heterogeneous [
50]. In the relatively highly educated E3N population, we observed a clear gradient across the quintiles of FDep for both outcomes, whatever the strategy of analysis. The E3N population is not representative of the French elderly women. They have in average higher educational level than French elderly women and probably healthier conditions. However, even in this specific population, we found that social disparities in smoking and overweight do not affect only extreme social situations but rather the socioeconomic gradient [
43]. Our objective was to determine which area-based SEP could meaningfully be used to further study social disparities in health in an elderly women population. It has been underlined that DIs might not be suitable in specific populations, such as elderly [
15] or women [
15,
17]. FDep appeared to be a good indicator to capture inter- and intra-urban socio-spatial divisions existing in France and seemed reliable to capture socioeconomic residential conditions of the E3N elderly women population, mostly teachers living in urban areas.
Conclusion
In conclusion, we showed that associations might vary strongly according to DIs with unexpected results for some of them. Our results suggested that it is important to test external validity to found well known associations before studying social disparities in health in specific populations.
Acknowledgments
The authors would like to thank especially Guy Fagherazzi, Marie Fangon, Maryvonne Niravong, Lyan Hoang (Inserm, CESP Centre for Research in Epidemiology and Population Health, Mode de vie, gènes et santé: épidémiologie intégrée trans-générationnelle, Villejuif, France) for the implementation of the study. We are indebted to all the participants for their high involvement in the E3N study, and without whom the study would not have been possible. We thank Guy Launoy and the ERISC platform (U1086 INSERM – UCBN “Cancers & Preventions”, Caen, France) for providing us with the FEDI indicator. We thank the INSEE (French National Institute for Statistics and Economic Studies), IGN (French National Institute of Geography) for having provided contextual data. We further thank Estelle SEGUIN-CADICHE and Walid GHOSN for their contributions to the analyses and Annette LECLERC for her comments on the final version.
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