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Uncertainty in epidemiology and health risk and impact assessment

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Abstract

Environmental epidemiology and health risk and impact assessment have long grappled with problems of uncertainty in data and their relationships. These uncertainties have become more challenging because of the complex, systemic nature of many of the risks. A clear framework defining and quantifying uncertainty is needed. Three dimensions characterise uncertainty: its nature, its location and its level. In terms of its nature, uncertainty can be both intrinsic and extrinsic. The former reflects the effects of complexity, sparseness and nonlinearity; the latter arises through inadequacies in available observational data, measurement methods, sampling regimes and models. Uncertainty occurs in three locations: conceptualising the problem, analysis and communicating the results. Most attention has been devoted to characterising and quantifying the analysis—a wide range of statistical methods has been developed to estimate analytical uncertainties and model their propagation through the analysis. In complex systemic risks, larger uncertainties may be associated with conceptualisation of the problem and communication of the analytical results, both of which depend on the perspective and viewpoint of the observer. These imply using more participatory approaches to investigation, and more qualitative measures of uncertainty, not only to define uncertainty more inclusively and completely, but also to help those involved better understand the nature of the uncertainties and their practical implications.

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References

  • Anand, S., & Hanson, K. (1997). Disability-adjusted life years: A critical review. Journal of Health Economics, 16, 685–702.

    Article  CAS  Google Scholar 

  • Anderson, R. M., & Garnett, G. P. (2000). Mathematical models of the transmission and control of sexually transmitted diseases. Sexually Transmitted Diseases, 27(10), 617–626.

    Article  Google Scholar 

  • Atkinson, G. M., & Somerville, P. G. (1994). Calibration of time history simulation methods. Bulletin of the Seismological Society of America, 84(2), 400–414.

    Google Scholar 

  • Barker, C., & Green, A. (1996). Opening the debate on DALYs. Health Policy and Planning, 11, 179–183.

    Article  CAS  Google Scholar 

  • Baron, J. A., & Weiderpass, E. (2000). An introduction to epidemiological research with medical databases. Annals of Epidemiology, 10, 200–204.

    Article  CAS  Google Scholar 

  • Barros-Dios, J. M., Barreiro, M. A., Ruano-Ravina, A., & Figueiras, A. (2002). Exposure to residential radon and lung cancer in Spain: A population-based case–control study. American Journal of Epidemiology, 156, 548–555.

    Article  Google Scholar 

  • Beven, K. (2002). Towards a coherent philosophy for modelling the environment. Proceedings of the Royal Society of London Series A, 458, 2465–2484.

    Article  Google Scholar 

  • Beven, K. (2006). A manifesto for the equifinality hypothesis. Journal of Hydrology, 320, 18–36.

    Article  Google Scholar 

  • Beven, K., & Freer, J. (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249, 11–29.

    Article  Google Scholar 

  • Blair, A., Stewart, P., Lubin, J. H., & Forastiere, F. (2007). Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures. American Journal of Industrial Medicine, 50, 199–207.

    Article  Google Scholar 

  • Bridges, J. (2003). Human health and environmental risk assessment: The need for a more harmonised and integrated approach. Chemosphere, 52, 1347–1351.

    Article  CAS  Google Scholar 

  • Briggs, D. J. (2005). The role of GIS: Coping with space (and time) in air pollution exposure assessment. Journal of Toxicology and Environmental Health A, 68(13–14), 1243–1261.

    Article  CAS  Google Scholar 

  • Briggs, D. J. (2007). The role of GIS to evaluate traffic-related pollution. Editorial. Occupational and Environmental Medicine, 64, 1–2.

    Article  CAS  Google Scholar 

  • Briggs, D. J., Collins, S., Elliott, P., Kingham, S., Fisher, P., Lebret, W., et al. (1997). Mapping urban air pollution using GIS: A regression-based approach. International Journal of Geographical Information Science, 11(7), 699–718.

    Article  Google Scholar 

  • Briggs, D. J., de Hoogh, C., Gulliver, J., Wills, J., Elliott, P., Kingham, S., et al. (2000). A regression-based method for mapping traffic-related air pollution: Application and testing in four contrasting urban environments. Science of the Total Environment, 253, 151–167.

    Article  CAS  Google Scholar 

  • Briggs, D. J., de Hoogh, C., & Gulliver, J. (in press). Matching the metric to need: modelling exposures to traffic-related air pollution for policy support. Journal of Toxicology and Environmental Health.

  • Briggs, D. J., & Stern, R. (2007). Effective management of risk response—towards an ecological approach. Journal of Risk Research, 10(5–6), 593–622.

    Article  Google Scholar 

  • Brown, J. D. (2004). Knowledge, uncertainty and physical geography: Towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers, 29(3), 367–381.

    Article  Google Scholar 

  • Carey, S., & Sparks, R. S. J. (1986). Quantitative models of the fallout and dispersal of tephra from volcanic eruption columns. Bulletin of Volcanology, 48, 109–125.

    Article  Google Scholar 

  • Cohen, J. E. (1986). Population forecasts and confidence intervals for Sweden: A comparison of model-based and empirical approaches. Demography, 23, 105–126.

    Google Scholar 

  • Colvile, R., & Briggs, D. J. (2000). Dispersion modelling. In P. Elliott, N. Best, J. Wakefield & D. J. Briggs (Eds.), Spatial epidemiology: Methods and applications (pp. 375–392) Oxford: Oxford University Press.

  • Covello, V. T., & Merkhofer, M. W. (1993). Risk assessment methods. Approaches for assessing health and environmental risks (p. 318). New York: Plenum.

    Google Scholar 

  • Edejer, T. T.-T., Baltussen, R., Adam, T., Hutubessy, R., Acharya, A., Evans, D. B., et al. (2003). Making choices in health. WHO guide to cost-effectiveness analysis. Geneva: World Health Organisation.

    Google Scholar 

  • European Environment Agency. (2005). The European Environment—State and outlook. Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  • Gold, M. R., Stevenson, D., & Fryback, D. G. (2002). HALYS and QALYS and DALYS, oh my: similarities and differences in summary measures of population health. Annual Review of Public Health, 23, 115–134.

    Article  Google Scholar 

  • Greenland, S., & Morgenstern, H. (1989). Ecological bias, confounding, and effect modification. International Journal of Epidemiology, 18, 269–274.

    Article  CAS  Google Scholar 

  • Halfon, N., & Hochstein, M. (2002). Life course health development: an integrated framework for developing health, policy and research. The Millbank Quarterly, 80, 433–479.

    Article  Google Scholar 

  • Heimann, D. (2007). Three-dimensional linearised Euler model simulations of sound propagation in idealised urban situations with wind effects. Applied Acoustics, 68, 217–237.

    Article  Google Scholar 

  • Henshaw, D. (2002). Does our electricity distribution system pose a serious risk to public health? Medical Hypotheses, 59, 39–51.

    Article  CAS  Google Scholar 

  • Heuvelink, G. B. M. (1998). Uncertainty analysis in environmental modelling under a change of spatial scale. Nutrient Cycling in Agroecosystems, 50, 255–264.

    Article  Google Scholar 

  • Horritt, M. S., & Bates, P. D. (2002). Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology, 268, 87–99.

    Article  Google Scholar 

  • Huang, Y.-L., & Batterman, S. (2000). Residence location as a measure of environmental exposure: a review of air pollution epidemiology studies. Journal of Exposure Analysis and Environmental Epidemiology, 10, 66–85.

    Article  CAS  Google Scholar 

  • Hurst, A. W., & Turner, R. (1999). Performance of the program ASHFALL for forecasting ashfall during the 1995 and 1996 eruptions of Ruapehu volcano. New Zealand Journal of Geology and Geophysics, 42, 615–622.

    Google Scholar 

  • Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., et al. (2005). A review and evaluation of intraurban air pollution exposure models. Journal of Exposure Analysis and Environmental Epidemiology, 15, 185–204.

    Article  CAS  Google Scholar 

  • Joffe, M., & Mindell, J. (2002). A framework for the evidence base to support health impact assessment. Journal of Epidemiology and Community Health, 56, 132–138.

    Article  CAS  Google Scholar 

  • Kemm, J. (2005). The future challenges for HIA. Environmental Impact Assessment Review, 25, 799–807.

    Article  Google Scholar 

  • Kürner, T. (2003). Propagation models for macro-cells. In E. Damosso & L. M. Correia (Eds.), Digital mobile radio: towards future generation systems. COST 231 Final Report (pp. 134–148). Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  • Lamberti, P., & Pilati, S. (1996). Flood propagation models for real-time forecasting. Journal of Hydrology, 175, 239–265.

    Article  Google Scholar 

  • Law, C. M., & Shiell, A. W. (1996). Is blood pressure inversely related to birth weight? The strength of evidence from a systematic review of the literature. Journal of Hypertension, 14, 935–941.

    Article  CAS  Google Scholar 

  • Lee, R. D., & Tuljapurkar, S. (1994). Stochastic population forecasts for the United States: Beyond high, medium, and low. Journal of the American Statistical Association, 80, 1174–1189.

    Google Scholar 

  • Lynch, J., & Smith, G. D. (2005). A life course approach to chronic disease epidemiology. Annual Review of Public Health, 26, 1–35.

    Article  Google Scholar 

  • Murray, C. J. L., Ezzati, M., Lopez, A. D., Rodgers, A., & Vander Hoorn, S. 2005. Comparative quantification of health risks: Conceptual framework and methodological issues. Population Health Metrics 1. Available at: http://www.pophealthmetrics.com/content/1/1/1.

  • Nieuwenhuijsen, M. (Ed.). (2003). Exposure assessment in occupational and environmental epidemiology. Oxford: Oxford University Press.

    Google Scholar 

  • Nieuwenhuijsen, M., Paustenbach, D., & Duarte-Davidson, R. (2006). New developments in exposure assessment: The impact on the practice of health risk assessment and epidemiological studies. Environment International, 32, 996–1009.

    Article  CAS  Google Scholar 

  • Newell, C. (1988). Methods and models in demography (p. 217). New York: Guilford.

    Google Scholar 

  • Openshaw, S. (1984). The modifiable areal unit problem. CATMOG No 38. Norwich: Geo Books.

    Google Scholar 

  • Pebesma, E. J., de Jong, K., & Briggs, D. (2007). Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: An air quality example. International Journal of Geographical Information Science, 21(5), 515–527.

    Article  Google Scholar 

  • Phillips, D. I. W., Walker, B. R., Reynolds, R. M., Flanagan, D. E. H., Wood, P. J., Osmond, C., et al. (2000). Low birth weight predicts elevated plasma cortisol concentrations in adults from 3 populations. Hypertension, 35, 1301–1306.

    CAS  Google Scholar 

  • Ploysing, B. 2000. Nord2000: Comprehensive outdoor sound propagation model. Part 2. Propagation in an atmosphere with refraction. Report to Nordic Noise Group, AV1851/00. Lyngby: DELTA.

  • Ramachandran, G. (1999). Retrospective exposure assessment using Bayesian methods. Annals of Occupational Hygiene, 45r, 651–667.

    Google Scholar 

  • Rauch, W., Henze, M., Koncsos, L., Reichert, P., Shanahan, P., Somlyódy, L., et al. (1998). River water quality modelling: I State of the art. Water Science and Technology, 38(11), 237–244.

    Article  CAS  Google Scholar 

  • Refsgaard, J. C., Thorsen, M., Jensen, J. B., Kleeschulte, S., & Hansen, S. (1999). Large scale modelling of groundwater contamination from nitrate leaching. Journal of Hydrology, 221, 117–140.

    Article  CAS  Google Scholar 

  • Regan, H. M., Colyvan, M., & Burgman, M. A. (2002). A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications, 12, 618–628.

    Article  Google Scholar 

  • Renn, O. (1998). Three decades of risk research: accomplishments and new challenges. Journal of Risk Research, 1, 49–71.

    Article  Google Scholar 

  • Renn, O., & Klinke, A. (2006). Systemic risks as challenge for policy making in risk governance. Forum: Qualitative Social Research, 7, 33 pp.

  • Rogers, D. J., Onstad, D. W., & Killick-Kendrick, R. (1988). The dynamics of vector-transmitted diseases in human communities [and Discussion]. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , 321(1207), 513–539.

    Article  CAS  Google Scholar 

  • Romanowicz, R., & Young, P. (2003). Data assimilation and uncertainty analysis of environmental assessment problems—An application of Stochastic Transfer Function and Generalised Likelihood Uncertainty Estimation techniques. Reliability Engineering and Safety System, 79, 161–174.

    Article  Google Scholar 

  • Rotmans, J., & van Asselt, M. B. A. (1996). Integrated assessment: A growing child on its way to maturity: an editorial essay. Climatic Change, 34, 327–336.

    Article  Google Scholar 

  • Rotmans, J., & van Asselt, M. B. A. (2001a). Uncertainty in integrated assessment modeling: A lanarythic path. Integrated Assessment, 2, 43–55.

    Article  Google Scholar 

  • Rotmans, J., & van Asselt, M. B. A. (2001b). Uncertainty management in integrated assessment modeling: Towards a pluralistic approach. Environmental Monitoring and Assessment, 69, 101–130.

  • Sabel, C. E., Wilson, J. G., Kingham, S., Tisch, C., & Epton, M. (2007). Spatial implications of covariate adjustment on patterns of risk: Respiratory hospital admissions in Christchurch, New Zealand. Social Science & Medicine, 65, 43–59.

    Article  Google Scholar 

  • Slater, D., & Jones, H. (1999). Environmental risk assessment and the environment agency. Journal of Hazardous Materials, 65, 77–91.

    Article  CAS  Google Scholar 

  • Suter, G. W. I. I., Barnthouse, L. W., & O’Neill, R. V. (1987). Treatment of risk in environmental impact assessment. Environmental Management, 11, 295–303.

    Article  Google Scholar 

  • Suter, G. W. I. I., Vermeire, T., Munns, W. R., & Sekizawa, J. (2005). An integrated framework for health and ecological risk assessment. Toxicology and Applied Pharmacology, 207, S611–S616.

    Article  CAS  Google Scholar 

  • van Imhoff, E., & Post, W. (1998). Microsimulation methods for population projection. Population: An English Selection. Special Edition: New Methodological Approaches in the Social Sciences, 10, 97–138.

    Google Scholar 

  • van Maercke, D., & Defrance, J. (2007). Development of an analytical model for outdoor sound propagation within the Harmonoise Project. Acta Acustica united with Acustica, 93, 201–212.

    Google Scholar 

  • Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., et al. (2003). Defining uncertainty. A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4, 5–17.

    Article  Google Scholar 

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Acknowledgements

This paper arose out of multidisciplinary discussions held at the MULTITUDE/SEGH workshop, held in June 2007 in Liverpool, UK. Participants with a wide range of expertise were brought together with the author(s) and this interpretation owes a great deal to those resultant discussions. The participants in this particular theme of the workshop included Louise Ander, Katy Boon, Paul Cleary, Elisa Giubilato, James Grellier, Gibby Koshy, Maria Lathouri, Paolo Luria, George Onuoha, Lesley Rushton, Tom Shepherd and Chaosheng Zhang. This research and the workshop was supported by The Joint Environment & Human Health Programme, supported by the Natural Environment Research Council (NERC), Department for Environment, Food & Rural Affairs (Defra), Environment Agency (EA), Ministry of Defence (MOD), Economic & Social Research Council (ESRC), Medical Research Council (MRC), Biotechnology & Biological Sciences Research Council (BBSRC), Engineering & Physical Sciences Research Council (EPSRC), Health Protection Agency (HPA), and administered via NERC grant NE/E009484/1. The authors also gratefully acknowledge funding from the European Union, through the 6th Framework Programme INTARESE and HEIMTSA studies, for the work underlying the views presented here.

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  1. Department of Epidemiology and Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK

    David J. Briggs, Clive E. Sabel & Kayoung Lee

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  1. David J. Briggs
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  2. Clive E. Sabel
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Correspondence to David J. Briggs.

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Briggs, D.J., Sabel, C.E. & Lee, K. Uncertainty in epidemiology and health risk and impact assessment. Environ Geochem Health 31, 189–203 (2009). https://doi.org/10.1007/s10653-008-9214-5

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