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Epidemiological methods
  1. D. K. Kriebel1,
  2. T. J. Smith2
  1. 1Department of Work Environment, University of Massachusetts Lowell
  2. 2Department of Environmental Health, Harvard School of Public Health

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    We hypothesise that better quantification of environmental risks is possible through better linkage of epidemiology and exposure assessment. This synthesis can be based on explicit biologic models of the dynamic processes leading from exposures through pathogenic processes to disease.


    We propose that two elements of any disease process are critical when designing exposure-response studies: (1) whether the process appears reversible or irreversible; and (2) whether the response is proportional to the tissue concentration or alternatively occurs in a discrete step – from absent to present. Combining these two characteristics, there are four distinct models which can be used as the starting point for studying most environmental diseases. These model types define profoundly different time courses and exposure–response relationships, but their basic features can often be identified using readily observable information about the adverse effects. While a very large number of pathologic processes occur, their temporal behaviour is typically limited by one or a few slow “rate limiting” steps. Thus often the observed behaviours of many different processes follow similar patterns. Starting from a description of the temporal features of the outcome and a hypothesis about the environmental agent, one can propose an explicit disease process model. The approach is illustrated for all four disease processes: type I – reversible proportional; type II – irreversible proportional; type III – reversible discrete; and type IV – irreversible discrete.


    The use of explicit temporal models resolves many ambiguities of exposure assessment – choosing what agent to study, how to measure it, what time scale for averaging, and what final dose metric to calculate. The model is an explicit hypothesis about the relationship between exposure and outcome. If the model does not fit the data, it can be rejected or revised.


    Explicit but simple biological process models can be used to integrate exposure assessment …

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