Using ecological socioeconomic position (SEP) measures to deal with sample bias introduced by incomplete individual-level measures: inequalities in breast cancer stage at diagnosis as an example

Sébastien Lamy 1, * Florence Molinié 2, 3, 4 Laetitia Daubisse-Marliac 1, 2, 5 Anne Cowppli-Bony 2, 3, 4 Stéphanie Ayrault-Piault 2, 3, 4 Evelyne Fournier 2, 6, 7 Anne-Sophie Woronoff 2, 6, 7 Cyrille Delpierre 1 Pascale Grosclaude 1, 2, 5
* Corresponding author
1 Equipe 5 - EQUITY - Embodiment, social ineQualities, lifecoUrse epidemiology, cancer and chronIc diseases, intervenTions, methodologY
LEASP - Epidémiologie et analyses en santé publique : risques, maladies chroniques et handicaps
2 Francim - French network of cancer registries [Toulouse]
3 Registre des cancers de Loire‐Atlantique et Vendée
4 ILIAD - Imaging and Longitudinal Investigations to Ameliorate Decision-making [Nantes]
5 Tarn Cancer Registry [Toulouse]
6 Doubs and Belfort territory Cancer Registry [Besançon]
7 CEF2P / CARCINO - Carcinogénèse épithéliale : facteurs prédictifs et pronostiques - UFC (EA 3181)
Abstract : BACKGROUND: When studying the influence of socioeconomic position (SEP) on health from data where individual-level SEP measures may be missing, ecological measures of SEP may prove helpful. In this paper, we illustrate the best use of ecological-level measures of SEP to deal with incomplete individual level data. To do this we have taken the example of a study examining the relationship between SEP and breast cancer (BC) stage at diagnosis. METHODS: Using population based-registry data, all women over 18 years newly diagnosed with a primary BC in 2007 were included. We compared the association between advanced stage at diagnosis and individual SEP containing missing data with an ecological level SEP measure without missing data. We used three modelling strategies, 1/ based on patients with complete data for individual-SEP (n = 1218), or 2/ on all patients (n = 1644) using an ecological-level SEP as proxy for individual SEP and 3/ individual-SEP after imputation of missing data using an ecological-level SEP. RESULTS: The results obtained from these models demonstrate that selection bias was introduced in the sample where only patients with complete individual SEP were included. This bias is redressed by using ecological-level SEP to impute missing data for individual SEP on all patients. Such a strategy helps to avoid an ecological bias due to the use of aggregated data to infer to individual level. CONCLUSION: When individual data are incomplete, we demonstrate the usefulness of an ecological index to assess and redress potential selection bias by using it to impute missing individual SEP.
Keywords : Cancer Deprivation Methodology Population-based data Social inequalities
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Journal articles
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Contributor : Myriam Bodescot <>
Submitted on : Friday, September 27, 2019 - 2:07:46 PM
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Sébastien Lamy, Florence Molinié, Laetitia Daubisse-Marliac, Anne Cowppli-Bony, Stéphanie Ayrault-Piault, et al.. Using ecological socioeconomic position (SEP) measures to deal with sample bias introduced by incomplete individual-level measures: inequalities in breast cancer stage at diagnosis as an example. BMC Public Health, BioMed Central, 2019, 19 (1), pp.857. ⟨10.1186/s12889-019-7220-4⟩. ⟨inserm-02299053⟩

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