NDLI: 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

Content Provider	Springer Nature : BioMed Central
Author	Lamy, Sébastien Molinié, Florence Daubisse-Marliac, Laetitia Cowppli-Bony, Anne Ayrault-Piault, Stéphanie Fournier, Evelyne Woronoff, Anne-Sophie Delpierre, Cyrille Grosclaude, Pascale
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.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-019-7220-4.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-019-7220-4
Journal	BMC Public Health
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2019-07-02
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Social inequalities Methodology Cancer Population-based data Deprivation Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

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