Age of onset in health impact assessment of chemical substances

Int. J. Environment and Health, Vol. 6, No. 1, 2012 37

Copyright © 2012 Inderscience Enterprises Ltd.

Age of onset in health impact assessment of chemical substances

Bas G.H. Bokkers*, A. Gerlienke Schuur and Marco J. Zeilmaker Centre for Substances and Integrated Risk Assessment, National Institute for Public Health and the Environment (RIVM), P.O. Box 1,3720 BA Bilthoven, The Netherlands Email: [email protected] Email: [email protected] Email: [email protected] *Corresponding author

Abstract: The age at which a disability starts is important information when performing a quantitative health impact assessment of any particular chemical substance. From the fields of epidemiology and toxicology it is known that this age of onset may be influenced by chemical exposure and that this influence is dose (or exposure) dependent, i.e. effects will occur at younger ages when dose is increased. Approaches are suggested to obtain or estimate a decreased in age of onset related to chemical exposure. Dose-dependent age of onset information may be obtained from epidemiological and toxicological studies provided that these studies report time- and dose-response data in sufficient detail. In situations where such data are not available, assessing worst and best case situations could indicate the range of the health impact. It is suggested that the dose dependency of age of onset is accounted for in future health impact assessments of chemical substances.

Keywords: health impact assessment; risk-benefit assessment; dose-response; time-response.

Reference to this paper should be made as follows: Bokkers, B.G.H., Schuur, A.G. and Zeilmaker, M.J. (2012) ‘Age of onset in health impact assessment of chemical substances’, Int. J. Environment and Health, Vol. 6, No. 1, pp.37–47.

Biographical notes: Bas G.H. Bokkers is a Modeller and European Certified Toxicologist at the Centre for Substances and Integrated Risk Assessment at the Dutch National Institute for Public Health and the Environment (RIVM). He received his PhD in probabilistic risk assessment of chemicals from the Institute for Risk Assessment Sciences, Utrecht University. His current work is primarily associated with benchmark dose modelling and probabilistic exposure assessment, hazard characterisation and risk assessment of chemicals.

A. Gerlienke Schuur is a Toxicologist and Risk Assessor at the Centre for Substances and Integrated Risk Assessment at the Dutch National Institute for Public Health and the Environment (RIVM). She received her PhD in toxicology on the interactions of organohalogens and the thyroid hormone system at Wageningen University. Her current work concerns risk assessment, mostly in the framework of REACH, with a focus on (aggregate) consumer exposure assessment and health impact assessment.

38 B.G.H. Bokkers, A.G. Schuur and M.J. Zeilmaker

Marco J. Zeilmaker is a Senior Scientist at the Centre for Substances and Integrated Risk Assessment of the Dutch National Institute for Public Health and the Environment (RIVM). He received his PhD in genetic toxicology from the Department of Radiation Genetics and Chemical Mutagenesis, Leiden University. He has extensive experience in the field of mathematical modelling in the risk assessment of environmental pollutants. His current work focuses on the risk assessment of dietary exposure to dioxins, polybrominated diphenyl ethers and perfluorinated compounds, as well as the modelling of transfer of contaminants in feed to animal food products.

1 Introduction

Policy measures may influence the exposure of the general public or specific subpopulations to particular chemical substances. The health impact of an increase or decrease in exposure to chemical substances can be quantified in several ways. Traditionally mortality has been an important indicator of health impact. With the increasing life expectancy and related occurrence of chronic disease, public health attention shifted towards morbidity and health-related quality of life, in addition to mortality. This has led to the development of various indicators called composite health measures. Often used composite health measures in a Health Impact Assessment (HIA) are Disability Adjusted Life Years (DALYs) or Quality Adjusted Life Years (QALYs) (Murray and Lopez, 1996; Melse et al., 2000; Schuur et al., 2008).

In general the composite health measures are informed by several parameters including the age at which the disability starts, here referred to as Age of Onset (AoO). It is recognised that the AoO may be defined in various ways, e.g. as the start of disability, disease, discomfort or the moment the diagnosis is made. In the context of this paper any of the definitions can be applied.

As an example of a composite health measure the DALY is defined as the sum of years of life lost (compared to life expectancy at background exposure, LE) and years lived with Disability Weighted (DW) for severity purposes (de Hollander et al., 1999; Havelaar et al., 2000). This can be denoted as

( )1

,n

i i i ii

DALY LE AoD DW AoD AoO=

= − + −∑

where n is the number of affected individuals i and AoD is the Age of Death. In Figure 1 an example is given on the simplified course of quality of life of an individual who is exposed to a particular substance causing a disability at AoO (dashed line). Subsequently, he lives with the disability until death (AoD). In this example, exposure to the chemical leads to an untimely death. If it would somehow be possible to avoid the compound, and thus the disability and untimely death, he would have lived until (his) life expectancy (dotted line). The sum of the areas between the dashed and dotted lines for each affected individual is the DALY.

In this paper, we will discuss the relevance of age of onset within a quantitative HIA for chemical substances. It is illustrated that AoO is an important part of a quantitative HIA. Approaches are suggested to obtain or estimate changes in AoO related to the chemical exposure, in particular when informative epidemiological studies are not

Age of onset in health impact assessment of chemical substances 39

available. Other parts of a quantitative HIA, such as a detailed, population wide exposure assessment and establishing DW are outside the scope of this paper. For more information on these topics the reader is referred to Price and Chaisson (2005), Slob (2006), James and Foster (1999) and Schwarzinger et al. (2003).

Figure 1 Schematic representation of the DALY concept indicating AoO and AoD (of one individual)

2 Current handling of AoO information

As stated above the AoO is the age at which the disability starts. AoO should not be confused with the duration of the disability or the time lived with disability, which is the time span between AoO and the Age of Death (AoD). In individuals, AoO is a single (age) point in life. The issues raised in the following text consider irreversible effects occurring after repeated (i.e. chronic) exposure. For reversible effects the duration of the disability is of interest and by definition the AoO of acute effects is directly after exposure occurs.

Often, the AoO is linked to a particular disability and obtained from actual, reported incidences (Crettaz et al., 2002; van Kreijl et al., 2006; Schuur et al., 2008). For example, based on data as presented in Figure 2, the average age of individuals getting renal failure may be derived. This kind of time-response information is sufficient to assess the actual health status of a population. However, to be able to assess the health impact of exposure to a particular chemical substance more information is required. It is questionable whether a decrease in the average age of individuals getting renal failure can truly and solely be attributed to exposure to the compound of interest. It may just as well be that the effect is not caused by the compound of interest or that only a minor part of the cases are due to the compound of interest. Knowledge about the causality is a necessity. Furthermore, this data do not provide information about the change in AoO between unexposed and exposed individuals. In conclusion, the information about exposure and age should be separated to enable HIA.


Figure 2 Incidence of chronic renal failure per age group and sex

Source: Data are from Jungers et al. (1996)

3 Dose-dependency

It is important to realise that a decrease in AoO is usually not a fixed number but is influenced by exposure to a chemical substance in a dose-dependent way. At higher (chronic) exposure the AoO will be lower. Or, in other words, when the exposure to a substance increases the incidence (or prevalence)-against-age curve shifts to the left.

The dose-dependency of changes in AoO is illustrated by controlled animal studies in which rats of the same age are exposed to different doses of a particular substance. For example, in Figure 3 the mortality is given of rats exposed to seven dose levels of 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin (HpCDD). It can be seen that at higher doses the animals die earlier due to substance-related effects (wasting and haemorrhage).

Figure 3 Mortality incidences against time of rats exposed to various doses (♦ 2.8; ○ 3.1; ■ 3.4; ▲ 3.8; ● 4.1; ∇ 5.0; □ 10.0 mg/kg) of HpCDD

Source: Data are from Rozman (1999)


In toxicology the relationship between time, dose (or exposure) and response is known as Haber’s rule. Haber stated that the product of the dose of a compound and the dosing time produce a fixed response. While Haber concluded this for the acute lethality of war gases, it seems this rule is also applicable to other effects (e.g. time-to-tumour), classes of compounds and durations and routes of exposure (Druckrey, 1967; Brown and Hoel, 1983; Kalbfleisch et al., 1983; Peto et al., 1991; Rozman, 1998; Rozman, 1999; Weller et al., 1999; Gaylor, 2000; Miller et al., 2000; Rozman, 2000; Rozman and Doull, 2000; Bunce and Remillard, 2003; Saghir et al., 2005). However, the potency of some chemical substances may be too small to detect the time-dose-response relationship (i.e. shifting of AoO) using the current experimental protocols.

In epidemiology, the relationship between time, dose and response is also recognised. Miller and Hurley (2003) illustrated the subject using life tables, which allow for the changes in future population shape that are induced by changes in (dose, and subsequently in) risks. For a typical impact assessment, for example, of a change in air pollution concentration, it is first needed to predict how a change in concentrations will affect future hazards (see Figure 4), then to quantify the ensuing change in predicted baseline mortality, using measures such as life years.

Figure 4 Cumulative survival for men, based on England and Wales, 1995 (solid line). Broken lines show survival curves for hazards doubled (dashed line) or halved (dotted line) throughout

Source: Data are from Miller and Hurley (2003)

4 Possible approaches

Reduced AoO could be obtained from epidemiological studies. However, in general epidemiology aims to provide an association between exposure and effect. Even when such studies are performed according to the best practices (including avoidance of bias and random error, taking sufficiently large sample sizes and accounting for variation in exposure), this does not always require that the AoO is documented. Studies providing only the incidences of an effect present at the end of observation are not informative for the AoO. Besides incidences also the age at which the exposed population gets the


disability needs to be documented. Furthermore, the exposure should be reported in sufficient detail, also taking into account possible fluctuations during life. To provide an association between exposure and effect, subjects are often divided into only a few dose groups (e.g. unexposed, low, medium and high exposed) to increase study power. For the analysis of AoO it would be even more informative when the exposure is presented in a more realistic way on a continuous scale. However, even the categorical representation of the exposure can already give practical information.

For many chemical substances epidemiological data are not available. When this is the case, changes in the AoO may be obtained from (toxicological) dose-response studies with animals. In contrast with most epidemiological studies, the exposure (or dose) in toxicological studies is generally controlled, and reported in sufficient detail. Changes in AoO could be obtained from a toxicological study when measurements are taken during the course of the study. It depends on the endpoints of interest whether interim measurements are obtained on a regular basis. It is relatively easy to obtain information using non-invasive methods (e.g. on body weight, food intake, markers in urine and survival) during a study (Figure 5). Therefore, information about this type of endpoints is reported regularly. To go to the other extreme, a toxicological study needs significant extension to obtain data of effects which require destructive measuring methods, for example, effects on a histopathological level (Figure 6). Such experiments are less common. However, HIA would benefit from a slight modification of the experimental protocol by incorporating interim measurements.

Figure 5 An illustration of AoD data from a two-year (male) mouse study with o-nitrotoluene (■ 0; ○ 1250; ∆ 2500; □ 5000 ppm). Probability of survival is plotted against time for each dose group

Source: Data are from NTP (2002)

For many chemical substances and effects, changes in AoO from epidemiology or (animal) toxicology studies are probably limited or not available at all. In those cases assessing extreme situations is suggested to require the range of the health impact. Here, dose-response data from less than chronic studies are not informative because it is unknown what the incidence would have been when exposure was prolonged. When


dose-response data are available at the end of a long-term study, some assumptions may be made regarding changes in the AoO. In a worst case situation an effect has an AoO immediately after the first exposure (dashed line in Figure 7). In a best-case situation the effect has an AoO just before the end of the study (solid line in Figure 7). These two situations describe both extremes in the range of all possibilities. Applying them may provide information on the sensitivity of the DALY analysis to changes in AoO.

Figure 6 An illustration of AoO data from a two-year mouse study with 1,3-butadiene (Dose: □ 0; ∇ 6.25; ● 20; ▲ 62.5; ■ 200; ○ 625 ppm). Fraction of animals with ovarian atrophy against the time on study for each applied dose group


Figure 7 Best (solid line) and worst (dashed line) case assumptions regarding AoO in a very homogenous population. Note that the best and worst case assumptions both result in the same (known) fraction at the end of the long-term study


5 Discussion and conclusive remarks

A health impact assessment of any particular chemical substance should take dose-dependent changes in AoO into account. From the fields of epidemiology and toxicology it is known that changes in the AoO are exposure (or dose) dependent. Such exposure dependent AoO information may be obtained from epidemiological and toxicological studies provided that these studies are reported in sufficient detail. Reports should contain time- and dose-response data, which allow for fitting of time-response curves as described below. In situations where such data are not available, assessing worst and best case situations could indicate the range of the health impact.

When study reports do contain suitable time- and dose-response data (e.g. such as presented in Figures 3–6), then time-response curves (see e.g. Kalbfleisch et al., 1983) can be derived for each dose group to obtain the AoO corresponding to a particular response percentage. As an example the ovarian atrophy data from Figure 6 are analysed (see Figure 8). From such analysis it can be concluded that 10% of the considered population animals has ovarian atrophy in 19th month when exposed to 6.25 ppm. The AoO for this percentage of the population decreases with dose to approximately two months when exposed to 625 ppm. AoOs between dose groups can be derived by interpolation. Of course other percentages of the population can be analysed when considered more relevant. For a human health impact assessment the AoO needs to be scaled (linearly, allometrically or based on physiology) from animal to human (Schmidt-Nielsen, 1984; Quinn, 2005).

Figure 8 An illustration of deriving the AoO from a two-year mouse study with 1,3-butadiene (Dose: □ 0; ∇ 6.25; ● 20; ▲ 62.5; ■ 200; ○ 625 ppm). Fraction of animals with ovarian atrophy against the time on study for each applied dose group (data are from NTP, 1993). The horizontal dashed line shows the 10% effect level and the vertical dashed lines shows the time to reach the effect level after exposure to each dose



Caution is in order when deriving changes in AoO from actual, reported incidences (see e.g. Figure 2). In general causality between the chemical of interest and the effect is not established, and even when it is, detailed exposure characterisation is often lacking.

The potency of some chemical substances to affect an endpoint may be too small to detect a decreasing AoO with increasing exposure/dose. It should be noted that the ability to detect decreasing AoOs depends not only on the potency of the chemical, but also on the experimental set-up as well. For example, when sample sizes are too small relevant decreases in AoO cannot be detected.

If changes in AoO are very small, one may argue that these changes can be neglected in the health impact assessment. However, a priori it is not straightforward to decide to neglect changes in AoO (Brunekreef and Hoek, 2000; Brunekreef et al., 2007). Obviously this also depends on the number of individuals affected. When AoO decreases with only one hour in 1000 individuals then there are 0.11 years lived with disability. However, when ten million individuals are affected then already 1100 years are lived with disability.

It is advised that the dose dependency of changes in AoO is accounted for in future health impact assessments of chemical substances. Or, if this is not possible, it should be mentioned why it is not taken into account and what information is lacking.

Acknowledgements

This investigation has been performed by order and for the account of the Ministry of Health, Welfare and Sport (VWS), within the framework of project ‘prioritisation and exposure data of chemicals in consumer products’ (V/320015/10). Wouter ter Burg, Susan Dekkers and Marcel van Raaij are acknowledged for their critical reading of the manuscript and the helpful suggestions for improvement.

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Age of onset in health impact assessment of chemical substances