[Turing-Southampton] S3RI seminar: Marta Blangiardo, Thursday 2-3pm

[Ogden H.E.]

Dear all,

On Thursday (6 February) at 2pm in 54 / 7033 (7C), we have an S3RI seminar from Marta Blangiardo (Imperial) on "A data integration approach to adjust for residual confounding in area-referenced environmental health studies". Details are given below.

The seminar will also be available via a live web-cast at
https://southampton.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=6498f6bd-d352-40bb-b639-ab5200e14a9d

The talk will be followed by tea and cake in the staff reading room on level 4 of building 54.

All are welcome!

Best wishes,

Helen

A data integration approach to adjust for residual confounding in area-referenced environmental health studies

Marta Blangiardo, Imperial College London

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological-level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological-level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).

For the current schedule of S3RI seminars, see https://tinyurl.com/s3riseminar<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftinyurl.com%2Fs3riseminar&data=01%7C01%7C%7C578a11f7517f410c8c3d08d7a88202cd%7C4a5378f929f44d3ebe89669d03ada9d8%7C0&sdata=xWLdxq2deumj1diyVlunARSMN8w5E4DgpRhgiZtaH7o%3D&reserved=0>
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