Measurement error in time-series analysis: a simulation study comparing modelled and monitored data

Butland, Barbara K.; Armstrong, Ben; Atkinson, Richard W.; Wilkinson, Paul; Heal, Mathew R.; Doherty, Ruth M.; Vieno, Massimo ORCID: https://orcid.org/0000-0001-7741-9377. 2013 Measurement error in time-series analysis: a simulation study comparing modelled and monitored data. BMC Medical Research Methodology, 13, 136. 12, pp. https://doi.org/10.1186/1471-2288-13-136

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Abstract/Summary

Background: Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Methods: Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003–2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). Results: When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Conclusion: Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1186/1471-2288-13-136
Programmes:	CEH Topics & Objectives 2009 - 2012 > Biogeochemistry
UKCEH and CEH Sections/Science Areas:	Billett (to November 2013)
ISSN:	1471-2288
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - Official URL link provides full text
Additional Keywords:	measurement error, epidemiology, time-series, mortality, nitrogen dioxide, ozone
NORA Subject Terms:	Health Atmospheric Sciences
Date made live:	28 Nov 2013 16:48 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/503920

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