A flexible hierarchical framework for improving inference in area-referenced environmental health studies

Monica Pirani*, Alexina J. Mason, Anna L. Hansell, Sylvia Richardson, Marta Blangiardo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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).

Original languageEnglish
Pages (from-to)1650-1669
Number of pages20
JournalBiometrical Journal
Volume62
Issue number7
DOIs
Publication statusPublished - 1 Nov 2020
Externally publishedYes

Bibliographical note

Funding Information:
The study is supported by the UK Medical Research Council (MRC) Methodology (grant number MR/M025195/1). We thank Yingbo Wang for the insights on the methodology and Gianluca Baio for the advice in structuring the analysis stage of the proposed approach. We acknowledge Hima Daby for the lung cancer cases extraction, Daniela Fecht for providing the LUR concentrations, and James Bennett for providing the population data estimates. The work of the UK SAHSU is funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, funded also by the UK MRC. This paper does not necessarily reflect the views of Public Health England or the Department of Health. SAHSU holds approvals both from the London - South East Research Ethics Committee - reference 17/LO/0846 and from the Health Research Authority - Confidentiality Advisory Group - HRA CAG reference: 14/CAG/1039.

Funding Information:
The study is supported by the UK Medical Research Council (MRC) Methodology (grant number MR/M025195/1). We thank Yingbo Wang for the insights on the methodology and Gianluca Baio for the advice in structuring the analysis stage of the proposed approach. We acknowledge Hima Daby for the lung cancer cases extraction, Daniela Fecht for providing the LUR concentrations, and James Bennett for providing the population data estimates. The work of the UK SAHSU is funded by Public Health England as part of the MRC‐PHE Centre for Environment and Health, funded also by the UK MRC. This paper does not necessarily reflect the views of Public Health England or the Department of Health. SAHSU holds approvals both from the London ‐ South East Research Ethics Committee ‐ reference 17/LO/0846 and from the Health Research Authority ‐ Confidentiality Advisory Group ‐ HRA CAG reference: 14/CAG/1039.

Publisher Copyright:
© 2020 The Authors. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords

  • area-referenced studies
  • Bayesian inference
  • data integration
  • missing data
  • uncertainty

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