Estimating exposure-response functions
- The text on this page is taken from an equivalent page of the IEHIAS-project.
Data on exposure-response functions (ERFs) are essential in order to estimate heralth effects of exposures: they describe quantitatively how much a specified health effect changes when exposure to the specified agent changes by a given amount.
ERFs used in an assessment need to be defined and selected with care, for uncertainties at this stage inevitably carry forward into the estimates of health impact. Two principles, in particular, need to be followed:
- The exposure metric and health endpoints of the ERF should match exactly the exposures and health outcomes of interest in the assessment; i.e. they should -
- relate to the same phenomena (exposure agents and settings; diseases/attributes of well-being);
- relate to the same target population group (e.g. in terms of age, gender);
- relate to the same averaging times (e.g. exposure window, outcome period);
- be measured in the same ways (e.g. exposure categories/meaurement units; health metrics).
- The ERF should be, as far as possible, an unbiased estimate of the relationship between exposure and health outcome; i.e. it should -
- be based on a balanced evaluation of all the relevant evidence (including published and unpublished sources);
- properly represent the shape of the exposure-response relationship (e.g. reflect any non-linearity within that part of the relationship of relevance to the assessment);
- recognise (and as far as possible describe) uncertainties in the ERF.
Information on ERFs can be obtained in a number of ways. In general order of preference, these include:
- Using results from a previously published and reviewed systematic review or meta-analysis by an authoritative organisation, such as the World Health Organization.
- Conducting a purpose-designed systematic review (including if appropriate a meta-analysis) to derive an ERF for the key impact pathways;
- Estimating an ERF through a formal expert panel.
- Using an ERF from previous (published and peer-reviewed) studies, such as:
- health impact assessments;
- a good-quality meta-analysis;
- a key multi-centre study.
The methods outlined above all rely primarily on epidemiological evidence (though the formal expert panel may draw on other sources of information). ERFs can also be derived, however, from clinical trials and toxicological studies, both on humans and animals. Using these poses a number of problems, which need to be carefully addressed. First and foremost is the problem of how to translate results from animal studies to humans, and whether this is legitimate for the exposures and health outcomes of interest. Second is the difficulty of extrapolating from the relatively small numbers of individuals typically used in both animal and human studies to a large and potentially diverse human population group. A third difficulty is that toxicological studies do not always report results in terms of a (continuous) exposure-response function, but often define instead a safe level or exposure threshold - for example, in terms of a no-observed adverse effect level (NOAEL) or acceptable daily intake (ADI). For the purpose of health impact assessment, these usually need to be converted to a function indicating the absolute or relative change in risk per unit change in exposure. Doing so may involve making important assumptions and generate considerable uncertainty. For most purposes, therefore, ERFs based on epidemiological data are to be preferred.
For further information on sources of, and methods for estimating, ERFs see the links under See also and the References, below.
- Exposure-response functions
- Exposure-response function dataset
- Protocol for estimating ERFs
- Dealing with complex exposures
- Grellier, J., Bennett, J., Patelarou, E., Smith, R.B., Toledano, M.B., Rushton, L., Briggs, D.J. and Nieuwenhuijsen, M.J. 2010 Exposure to disinfection by-products, fetal growth, and prematurity: a systematic review and meta-analysis. Epidemiology 21, 300-313.
- Nieuwenhuijsen. M., Martinez. D., Grellier J., Bennett, J., Best, N., Iszatt, N., Vrijheid, M. and Toledano, M.B. 2009 Chlorination disinfection by-products in drinking water and congenital anomalies: review and meta-analyses. Environmental Health Perspectives. 117, 1486-1493.
Ultrafine air pollution
- Knol, A.B., de Hartog, J.J., Boogaard, H., Slottje, P., van der Sluijs, J.P., Lebret, E., Cassee, F.R., Wardekker, J.A., Ayres, J.G., Borm, P.J., Brunekreef, B., Donaldson, K., Forastiere, F., Holgate, S.T., Kreyling, W.G., Nemery, B., Pekkanen, J., Stone, V., Wichmann, H.E. and Hoek, G. 2009 Expert elicitation on ultrafine particles: likelihood of health effects and causal pathways. Particle and Fibre Toxicology 6, 19.
- Hoek, G., Boogaard, H., Knol, A., de Hartog, J., Slottje, P., Ayres, J.G., Borm, P., Brunekreef, B., Donaldson, K., Forastiere, F., Holgate, S., Kreyling, W.G., Nemery, B., Pekkanen, J., Stone, V., Wichmann, H.E. and van der Sluijs, J. 2010 Concentration response functions for ultrafine particles and all-cause mortality and hospital admissions: results of a European expert panel elicitation. Environmental Science and Technology 44(1), 476-482.
- Porta, D., Milani, S., Lazzarino, A.I., Perucci, C.A. and Forastiere, F. 2009 Systematic review of epidemiological studies on health effects associated with management of solid waste. Environmental Health 8, 60.