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Modelling exposure opportunities: estimating relative risk for motor neurone disease in Finland

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  • Sabel, Clive E.
  • Gatrell, Anthony C.
  • Löytönen, Markku
  • Maasilta, Paula
  • Jokelainen, Matti

Abstract

This paper addresses the issues surrounding an individual's exposure to potential environmental risk factors, which can be implicated in the aetiology of a disease. We hope to further elucidate the 'lag' or latency period between the initial exposure to potential pathogens and the physical emergence of the disease, with specific reference to the rare neurological condition, motor neurone disease (MND), using a dataset obtained from the Finnish Death Certificate registry, for MND deaths between the period 1985-1995. A space-time approach is adopted, whereby patterns in both time and space are considered. No prior assumptions about the aetiology of MND are adopted. By using methods for the analysis of point processes, which preserve the continuous nature of the data, we resolve some of the problems of analysis that are often based on arbitrary areal units, such as postcode boundaries, or political boundaries. We use kernel estimation to model space-time patterns. Raised relative risk is assessed by adopting appropriate adjustments for the underlying population at risk, with the use of controls. Significance of the results is assessed using Monte Carlo simulation, and comparisons are made with results obtained from Openshaw's geographical analysis machine (GAM). Our results demonstrate the utility of kernel estimation as a visualisation tool. Small areas of elevated risk are identified, which need to be more closely examined before any firm conclusions can be drawn. We highlight a number of issues concerning the inadequacies of the data, and possibly of the techniques themselves.

Suggested Citation

  • Sabel, Clive E. & Gatrell, Anthony C. & Löytönen, Markku & Maasilta, Paula & Jokelainen, Matti, 2000. "Modelling exposure opportunities: estimating relative risk for motor neurone disease in Finland," Social Science & Medicine, Elsevier, vol. 50(7-8), pages 1121-1137, April.
  • Handle: RePEc:eee:socmed:v:50:y:2000:i:7-8:p:1121-1137
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    Cited by:

    1. Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    2. Jeffery Caroline & Ozonoff Al & White Laura Forsberg & Pagano Marcello, 2013. "Distance-Based Mapping of Disease Risk," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 265-290, May.
    3. Portnov, Boris A. & Dubnov, Jonathan & Barchana, Micha, 2009. "Studying the association between air pollution and lung cancer incidence in a large metropolitan area using a kernel density function," Socio-Economic Planning Sciences, Elsevier, vol. 43(3), pages 141-150, September.
    4. Exeter, Daniel John & Rodgers, Sarah & Sabel, Clive Eric, 2014. "“Whose data is it anyway?” The implications of putting small area-level health and social data online," Health Policy, Elsevier, vol. 114(1), pages 88-96.

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