Bayesian spatial modelling for quasi-experimental designs: An interrupted time series study of the opening of Municipal Waste Incinerators in relation to infant mortality and sex ratio

A. Freni-Sterrantino*, R. E. Ghosh, D. Fecht, M. B. Toledano, P. Elliott, A. L. Hansell, M. Blangiardo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: There is limited evidence on potential health risks from Municipal Waste Incinerators (MWIs), and previous studies on birth outcomes show inconsistent results. Here, we evaluate whether the opening of MWIs is associated with infant mortality and sex ratio in the surrounding areas, extending the Interrupted Time Series (ITS)methodological approach to account for spatial dependencies at the small area level. Methods: We specified a Bayesian hierarchical model to investigate the annual risks of infant mortality and sex-ratio (female relative to male)within 10 km of eight MWIs in England and Wales, during the period 1996–2012. We included comparative areas matched one-to-one of similar size and area characteristics. Results: During the study period, infant mortality rates decreased overall by 2.5% per year in England. The opening of an incinerator in the MWI area was associated with −8 deaths per 100,000 infants (95% CI −62, 40)and with a difference in sex ratio of −0.004 (95% CI −0.02, 0.01), comparing the period after opening with that before, corrected for before-after trends in the comparator areas. Conclusion: Our method is suitable for the analysis of quasi-experimental time series studies in the presence of spatial structure and when there are global time trends in the outcome variable. Based on our approach, we do not find evidence of an association of MWI opening with changes in risks of infant mortality or sex ratio in comparison with control areas.

Original languageEnglish
Pages (from-to)109-115
Number of pages7
JournalEnvironment International
Volume128
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
The study was funded by a grant from Public Health England (PHE), by a grant from the Scottish Government , and funding from the MRC-PHE Centre for Environment and Health and from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards at King's College London and Imperial College London in partnership with Public Health England (PHE). The work of the UK Small Area Health Statistics Unit is funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, also funded by the UK Medical Research Council (MR/L01341X/1). PE is Director of the MRC-PHE Centre for Environment and Health and acknowledges support from the NIHR Imperial Biomedical Research Centre. This work used the computing resources of the UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the Medical Research Council (MR/L01632X/1).

Funding Information:
The study was funded by a grant from Public Health England (PHE), by a grant from the Scottish Government, and funding from the MRC-PHE Centre for Environment and Health and from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU)in Health Impact of Environmental Hazards at King's College London and Imperial College London in partnership with Public Health England (PHE). The work of the UK Small Area Health Statistics Unit is funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, also funded by the UK Medical Research Council (MR/L01341X/1). PE is Director of the MRC-PHE Centre for Environment and Health and acknowledges support from the NIHR Imperial Biomedical Research Centre. This work used the computing resources of the UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO)which is supported by the Medical Research Council (MR/L01632X/1). We thank Margaret Douglass, Peter Hambly and the UK Small Area Health Statistics Unit (SAHSU)database team for technical support. We thank the SAHSU incinerators study Scientific Advisory Group, Prof Tanja Pless-Mulloli, Dr. Mathew Heal, Dr. Sylvaine Cordier and Dr. Duncan Lee for their valuable comments and advice throughout this study.

Publisher Copyright:
© 2019 The Authors

Keywords

  • Bayesian models
  • Controls
  • Incinerators
  • Infant mortality
  • Interrupted time series
  • Spatial random effect

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