Food Benefit-Risk Assessment with Bayesian Belief Networks and Multivariable Exposure-Response

Food Benefit-Risk Assessment with

Bayesian Belief Networks and

Multivariable Exposure-Response

Food Benefit-Risk Assessment with

Bayesian Belief Networks and

Multivariable Exposure-Response

Proefschrift

ter verkrijging van de graad van doctor

aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben,

voorzitter van het College voor Promoties,

in het openbaar te verdedigen

op woensdag 8 mei 2013 om 10.00 uur

door

Patrycja Lubomira GRADOWSKA

wiskundig ingenieur

Dit proefschrift is goedgekeurd door de promotor: Prof. dr. R.M. Cooke

Samenstelling promotiecommissie: Rector Magnificus voorzitter

Prof. dr. R.M. Cooke Technische Universiteit Delft, promotor

Prof. B.H. Lindqvist Norwegian University of Science and Technology Prof. dr. F.H.J. Redig Technische Universiteit Delft

Prof. dr. E.A. Cator Radboud Universiteit Nijmegen Dr. D. Kurowicka Technische Universiteit Delft

Dr. J.T. Tuomisto National Institute for Health and Welfare, Finland Dr. V. Flari Food and Environment Research Agency, UK Prof. dr. ir. A.W. Heemink Technische Universiteit Delft, reservelid

isbn 978-94-6203-338-2

Copyright c 2013 by P.L. Gradowska

All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.

Contents

Page

Glossary of selected terms and abbreviations

CHAPTER 1

Introduction

1.1 General overview of food and human health . . . 1

1.2 Benefit-risk assessment for foods . . . 2

1.3 Bayesian belief network approach to the benefit-risk assessment of fish consumption - introduction . . . 5

1.4 Multivariable exposure-response relationships . . . 10

CHAPTER 2

Bayesian networks in human health benefit-risk assessment of foods

2.1 Introduction . . . 13

2.2 Bayesian belief networks - an overview . . . 13

2.3 Non-parametric continuous-discrete BBNs . . . 17

2.4 Benefits and risks of fish consumption . . . 19

2.4.1 Scope and boundaries of the study . . . 19

2.4.2 Schematic representation of the BBN model . . . 23

2.5 Summary of the BBN approach to the benefit-risk assessment of foods 26

CHAPTER 3

Belief network quantification and use

3.1 Introduction . . . 29

3.2 Fish consumption . . . 30

3.3 Chemical concentration . . . 32

3.3.1 Methyl mercury . . . 32

3.3.2 Omega-3 fatty acids . . . 34

3.3.3 Doxins and PCBs . . . 34

ii Contents

3.4 Personal and demographic variables . . . 35

3.4.1 Body weight and height . . . 36

3.4.2 Body mass index and body fat percentage . . . 36

3.4.3 Age and gender . . . 38

3.5 Dietary intake of chemicals from fish . . . 38

3.6 Quantitative exposure-response assessments . . . 39

3.6.1 Methyl mercury and DHA versus cognitive development . . . . 39

3.6.2 Dioxins and PCBs versus developmental dental defects . . . 42

3.6.3 Methyl mercury and omega-3 fatty acids versus CHD mortality 44 3.6.4 Dioxins and PCBs versus cancer . . . 46

3.7 Modeling and assessment of health impacts related to fish consumption 47 3.7.1 Generalized approach for modeling multivariable exposure-response relationships . . . 47

3.7.2 Health effects due to fish consumption in Finland . . . 51

3.8 Examples of using the network . . . 53

3.9 Conclusions . . . 59

CHAPTER 4

On building multivariable exposure-response models from

epidemi-ologic data

4.1 Introduction . . . 61

4.2 Brief overview of three most important regression techniques in epi-demiology . . . 62

4.2.1 Linear regression . . . 63

4.2.2 Logistic regression . . . 64

4.2.3 Cox regression . . . 65

4.3 Problem formulation and solution approaches . . . 68

4.3.1 LS-based method . . . 69 4.3.2 ML-type method . . . 72 4.4 Simulation examples . . . 82 4.4.1 Simulation details . . . 82 4.4.2 Results . . . 84 4.5 Conclusions . . . 86

CHAPTER 5

Least squares type estimation for Cox regression model and

specifica-tion error

5.1 Introduction . . . 89

5.2 Notation and preliminaries . . . 91

5.3 Least squares type estimation for the Cox model . . . 92

5.3.1 Uncensored data . . . 94

5.3.2 Right censored data . . . 96

5.4 Model specification error . . . 97

Contents iii

5.5.1 Uncorrelated covariates . . . 100

5.5.2 Correlated covariates . . . 102

5.5.3 Correlated and uncorrelated covariates with censoring . . . 104

5.5.4 Error approximation . . . 106

5.6 Application . . . 107

5.7 Conclusions and future work . . . 109

CHAPTER 6

Conclusions

6.1 BBNs in food benefit-risk assessment . . . 111

6.2 Multivariable exposure-response functions . . . 113

APPENDIX A

Quantifying multivariable exposure-response models using expert

judgment

APPENDIX B

Heuristic argument for neglecting L

and L

in the error formula

(5.11) in Chapter 5

Bibliography

Summary

Samenvatting (Dutch summary)

Acknowledgements

Glossary of selected terms and abbreviations

µg microgram

AT _{transpose of matrix A}

BBN Bayesian belief network

BF% body fat percentage

BMI body mass index

BW body weight

cf. an abbreviation for the Latin word confer, meaning “compare” or “consult”

CHD coronary heart disease

CI confidence interval

CSF cancer slope factor

CVD cardiovascular disease

DHA docosahexaenoic acid

Dioxins polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs)

E-a exponential notation that represents times ten raised to power -a

EPA eicosapentaenoic acid

ERS exposure-response slope; slope of exposure-response relationship - a quantitative measure of the relation-ship between chemical exposure and the health effect

exp(x) the exponent of x; ex

g gram

Hg chemical symbol for mercury

ICD-10 the Tenth International Classification of Diseases

IQ intelligence quotient

kg kilogram

L liter

log(x) the natural logarithm of x

MeHg methyl mercury, the chemical form of mercury found in fish

vi Glossary of selected terms and abbreviations

mg milligram

Mortality rate a measure of the frequency of occurrence of death (in general, or due to a specific cause) in a defined popu-lation during a specified period of time. It is usually expressed as the number of deaths per 10n_individuals

per year, where n is a positive integer.

ng nanogram

Omega-3 fatty acids one of two major types of essential polyunsaturated fatty acids

PCB polychlorinated biphenyl

PCDD/F see Dioxins

pg picogram

RR of death relative risk of death (in general, or due to a specific cause); a ratio of the mortality rate in the exposed population to the mortality rate in the unexposed pop-ulation

SD standard deviation

sgn(x) the signum function of x

TCDD 2,3,7,8 - tetrachlorodibenzo-p-dioxin

TEQ toxic equivalent; 17 congeners of PCDD/Fs and 12

dioxin-like PCB congeners are assigned so-called TEFs (toxic equivalency factors) that reflect their toxic po-tency relative to that of the most toxic dioxin congener TCDD. The TEF for TCDD is set at 1 whereas TEF values of all other dioxin-like compounds are less than 1. The toxicity of a mixture is stated as TEQ calcu-lated asP

iT EFi·Ci, where Ciand T EFiare

respect-ively the concentration and TEF value of congener i. TEQ is then the concentration of TCDD that is pre-dicted to be of equal toxicity to the sum of the toxicity of all the different dioxin and/or PCB congeners under analysis.

THL National Institute for Health and Welfare, Finland total PCBs sum of 37 dioxin-like and non-dioxin-like PCBs U.S. EPA U.S. Environmental Protection Agency

WHO World Health Organization

WHO-TEQ TEQ calculated based on TEF data agreed at the WHO expert meeting in 1998

CHAPTER 1

Introduction

1.1

General overview of food and human health

Food is a complex mixture of many different ingredients which can be divided into two broad groups - nutrients and non-nutrients (Kotsonis and Burdock, 2008). The first group comprises micronutrients (vitamins, minerals) and macronutrients (water, proteins, fats, carbohydrates) necessary for sustaining life. The second group consists of ingredients which are not considered to have nutritive value but may nevertheless have an impact on people’s health. These include chemicals such as food additives (e.g. colors, preservatives, sweeteners), agricultural residues (e.g. pesticide residues, veterinary drug residues), environmental contaminants (heavy metals, dioxins, PCBs, etc.), substances from food packaging materials, contam-inants formed during food processing (e.g. acrylamide) and naturally occurring contaminants (e.g. mycotoxins, phycotoxins, allergens) as well as microorganisms (bacteria, fungi, viruses, algae, etc.). Many foods, but also some food components have the potential to cause both beneficial and adverse health effects in humans. Examples of such foods and food ingredients which have already received some re-search attention are fruits and vegetables, fish, breast milk and micronutrients (see e.g. FAO/WHO, 2004b; Mozaffarian and Rimm, 2006; Shenkin, 2006; Mead, 2008; Bushkin-Bedient and Carpenter, 2010). Factors that determine the occurrence, nature and the size of adverse/positive effects are the inherent characteristics of the food (compound), the amount of food (compound) consumed (i.e. magnitude of exposure or dietary intake), the frequency and duration of exposure, and the nature of the exposed population, among other factors.

Food related benefits and risks have usually been addressed, assessed and communicated to the public separately. Furthermore, information given to con-sumers regarding the nutritional value and health risks associated with food has often been conflicting. This has created an overall doubt and confusion as to what foods and food ingredients, and in what amounts should be con-sumed in order to maintain the optimal health of general public and/or

2 CHAPTER 1

ular population groups. Considering the fact that improper dietary advice and improper dietary choices may lead to unexpected and undesirable health con-sequences, many international and national organizations involved with food and health have recognized the importance and need to bring the assessment of both health benefits and health risks together in order to facilitate decision makers (risk managers, policy makers) identify measures that manage the risks while securing the benefits and ultimately aid consumers in making healthier eating choices. To address this need, a number of research activities aiming at de-veloping methodology and best practices for food benefit-risk assessment have been initiated over the past several years, including four European Union pro-jects: Beneris (Benefit-Risk Assessment for Food: a Value-of-Information Ap-proach; http://en.opasnet.org/w/Beneris), Qalibra (Quality of life - integrated benefit and risk analysis; http://www.qalibra.eu), BRAFO (Benefit-Risk Ana-lysis of Foods; http://www.ilsi.org/Europe/Pages/BRAFO.aspx) and Beprari-bean (Best Practices for Risk-Benefit Analysis: experience from out of food into food; http://en.opasnet.org/w/Bepraribean), and a handful of national research projects (e.g. Fransen et al., 2010). The issue has also been investigated by the European Food Safety Authority (EFSA) whose efforts resulted in the recent guid-ance document on human health benefit-risk assessment of foods (EFSA, 2010).

This dissertation discusses a number of mathematical problems emerging from the Beneris project in which the author was involved (for general information about Beneris see Section 1.3). Before introducing them, however, a general overview of human health benefit-risk assessment of foods is given.

1.2

Benefit-risk assessment for foods

In this section, a brief overview of human health benefit-risk assessment of foods is provided. The presentation is kept at a general level. More specific and technical details can be found in and through the references cited in the text.

Benefit-risk assessment is a relatively new and actively developing research field in the area of food and nutrition (Tijhuis et al., 2012). It can be defined as a process that estimates and weights health benefits and risks for humans following exposure to a particular food or food constituent as basis for benefit-risk man-agement decisions and communication to the public. Term “risk” is commonly understood as the probability of an adverse health effect occurring to humans in reaction to exposure to a food (ingredient) and “benefit” as the probability of a positive health effect or a reduction of risk resulting from exposure to a food (compound). Examples of situations in which benefit-risk assessment could be useful include cases where a single food compound is recognized to have a poten-tial to pose both health benefits and health risks to humans, where positive and negative health effects are linked to different components in the same food, and where a public health intervention is planned (e.g. fortification of food with mi-cronutrients) (EFSA, 2010). Nevertheless, as concluded by EFSA, the benefit-risk assessment should only be undertaken when a substantial public health impact is expected.

Introduction 3

While the field is still developing there is a general consensus that the human health benefit-risk assessment of foods should consist of three elements: risk as-sessment, benefit assessment and benefit-risk comparison. Risk assessment is a well-established and internationally recognized process consolidated through years that deals with the assessment of food related risks in order to assure a high level of human health protection (e.g. NRC, 1983; FAO/WHO, 1995; EC, 2000; IPCS, 2009). It entails four steps: (i) hazard identification (the recognition of adverse health effects associated with particular food (ingredient)), (ii) hazard charac-terization (also called dose-response assessment or exposure-response assessment; the determination of the relationship between the magnitude of exposure to a food (compound) and the magnitude and/or frequency of each adverse effect), (iii) exposure assessment (the evaluation of the likely intake of food (compound)) and (iv) risk characterization (the estimation of risk, including the attendant uncertainty, based on hazard identification, hazard characterization and expos-ure assessment). In contrast, the guidelines for performing benefit assessment of foods have not yet been well-defined, but it is recommended that it mirrors the steps of risk assessment (EC, 2000; EFSA, 2006, 2010). Thus, benefit assessment can be divided into the following steps: positive health effect/reduced adverse ef-fect identification, positive health efef-fect/reduced adverse efef-fect characterization, exposure assessment and benefit characterization (e.g. EFSA, 2010).

Risk assessment and benefit assessment are two separate processes (they util-ize different sources of information, types of data, procedures and may be relevant to different food components or even different population subgroups) and as such they should be conducted independently and in parallel in the benefit-risk as-sessment process. The results of both these asas-sessments serve as an input for the benefit-risk comparison stage in which benefits and risks are integrated to determ-ine the health benefit-risk balance (or net health impact) of food (ingredients). In practice, balancing benefits and risks is not a simple task, but can be facilit-ated by using a common scale of measurement (“composite/integrfacilit-ated metric”) for all risks and benefits (e.g. quality-adjusted life years or disability-adjusted life years). The most appropriate measure, however, has not yet been agreed upon. The outcomes of the benefit-risk assessment, including a detailed specification of assumptions and uncertainties involved, can help decision makers take better informed decisions and actions.

A number of stepwise (tiered) approaches for performing food benefit-risk as-sessment have been proposed (EFSA, 2006; Hoekstra et al., 2008; EFSA, 2010; Fransen et al., 2010; Hoekstra et al., 2010). The key advantages of these ap-proaches are that they are transparent, allow for communication with the decision maker throughout the whole assessment process and assist in making informed choices about when to terminate or to continue with the assessment. In all ap-proaches proposed, the actual assessment is preceded by a problem formulation stage in which the purpose and scope of the assessment are carefully and pre-cisely stated (subject of analysis - food or food constituent(s), target population, exposure, etc.). In principle, well-defined benefit-risk problem ensures that the assessment outcomes will be useful and relevant to the decision maker. After problem formulation, the benefit-risk assessment starts and follows a stepwise

4 CHAPTER 1

approach. In the first step, an initial assessment is commonly conducted to de-termine whether both benefits and risks are present. To establish this the benefit assessment and the risk assessment are undertaken. At this and each subsequent step (if entered), the benefits and risks are balanced against each other to check if the assessment can stop (the balance between risks and benefits is clear and hence the initial problem can already be solved) or should continue (the balance of benefits and risks is still not clear). Furthermore, before proceeding to next step, a consultation with the decision maker is greatly encouraged to ensure the relevance of the benefit-risk assessment problem that is being addressed. Gen-erally, the higher step in the assessment is attained the more sophisticated and accurate methods allowing to conclude the assessment are used. The summary of the various benefit-risk assessment approaches can be found in FAO/WHO (2010) and Tijhuis et al. (2012).

Different strategies to compare benefits and risks can be used in the benefit-risk assessment process. At initial steps a qualitative, or descriptive, comparison is typically made (comparing benefits and risks in their own currency or using standard screening methods, e.g. comparing estimated (extremes of) exposure with the relevant guidance levels such as tolerable daily intake (TDI), acceptable daily intake (ADI), reference dose (RfD), etc.). This strategy is both time- and cost-effective, but is sufficient only when either benefits or risks prevail. On the other hand, in cases where the balance between benefits and risks is uncertain, a quantitative comparison involving expressing benefits and risks in the same composite health metric is required to help reach the conclusion. This strategy, however, is data expensive and hence it may not be possible if no or limited data is available.

Inevitably, there are many different sources of uncertainty in the process of food benefit-risk assessment (Hoekstra et al., 2010; EFSA, 2010). They may con-tribute to the uncertainty in the assessment outcome and in the end affect the decision-making. Thus, it is important to identify and characterize, qualitatively and where possible quantitatively, these sources at each assessment step. Usu-ally, the qualitative uncertainty analysis takes place first to determine whether the uncertainties may alter the results and management decisions and if so, the quantitative uncertainty analysis is employed to estimate the uncertainty in the final result or some other intermediate quantity of interest. The quantitative analysis can be based on either deterministic or probabilistic approaches. In the deterministic approach, different combinations of (plausible) values are assigned to uncertain variables whereas probabilistic approaches use probability distribu-tions for some or all variables to reflect their uncertainty and then propagate this uncertainty through exposure and effect/risk models to the outputs. A variety of methods for uncertainty propagation exist in the literature, but the one that has received the most attention to date, mainly due to its extensive use in the risk assessment, is the Monte Carlo simulation (see e.g. Rubinstein, 1981; Vose, 1996; US EPA, 1997a; Frey and Cullen, 1999).

In this thesis, a Bayesian belief network (BBN) approach to human health benefit-risk assessment of foods is presented and used to evaluate benefits and risks of fish consumption. In short, BBNs are graphical models used to represent high

Introduction 5

dimensional probability distributions. The main feature of this approach is that it is fully quantitative and probabilistic, that is, it provides quantitative estimates of food related health impacts and expresses them in the form of probability distributions. In the following section, a brief introduction to BBNs and their application in the area of food and public health is given whereas the complete exposition is provided in Chapter 2 and Chapter 3. We emphasize at this point that benefit-risk assessment may deal with chemicals (including nutrients) and/or microorganisms in or on food (see Section 1.1). The focus of this thesis, however, is restricted to chemicals only.

1.3

Bayesian belief network approach to the benefit-risk

assessment of fish consumption - introduction

Beneris was a research project financed under the European Commission’s 6th Framework Programme on Food Quality and Safety over a three-year period 2006-2009. It brought together professionals from multiple disciplines including epidemiologists, toxicologists, nutrition scientists, exposure assessors, risk ana-lysts and authorities in order to forge major advancements in human health food benefit-risk assessment. The participating organizations were National Institute for Health and Welfare (THL, Finland), Delft University of Technology (TU Delft, the Netherlands), Oy Foodfiles Ltd (FFiles, Finland), Food Safety Authority of Ireland (FSAI, Ireland), Technical University of Denmark (DTU, Denmark), Food Safety Authority of Denmark (FVST, Denmark), Lednac Ltd (Ireland) and Fun-daci´on Privada para la Investigaci´on Nutricional (FIN, Spain).

The general objective of Beneris project was to develop methods and tools for assessing health benefits and risks associated with foods and apply them to two practical case studies - one focusing on fish and another on vegetable consumption. Specifically, the main aims of the project were: 1) to develop a causal network model to handle complex food benefit-risk situations and create a decision support system based on this model, 2) to develop an open web-workspace for performing assessments in collaborative way and 3) to develop a web-database collecting data needed in benefit-risk assessments. A complete list of project goals can be found on the project website (see Section 1.1). The major outcomes were the improved methodology for benefit-risk assessment called “open assessment” and the web-workspace Opasnet which contains a wiki-interface, modeling environment and a data repository (called Opasnet Base). The author’s main role in Beneris was to build a Bayesian belief network model for the benefit-risk assessment of fish consumption and to explore its potential uses. A substantial part of this thesis is therefore devoted to the description of the development and quantification process of this model.

BBNs are graphical models for representing dependencies among uncertain variables (Jensen, 1996, 2001). In the network, variables are represented by nodes and a direct influence between a pair of variables is represented by a directed edge called an arc. An arc points from the influencing variable to the influenced variable. In general, many arcs may go in and out of each node, but they may not

6 CHAPTER 1

form directed cycles. Hence, the BBN is formally a directed acyclic graph. In es-sence, the BBN specifies a joint probability distribution over all variables involved and can therefore be used to perform probabilistic inferences (conditionalization). This feature has made BBNs an attractive and powerful decision aid tool for com-plex problems with inherent uncertainty. Fundamental reasons for using BBNs in the food benefit-risk assessment, and hence in Beneris, are that they allow spe-cifying (more or less complex) benefit-risk problem to be addressed in a language that is easy to understand and communicate among benefit-risk assessors and decision makers having different roles and responsibilities in the assessment, and also provide a user interface for underlying inferential calculations.

In general, nodes in the BBN may represent discrete and/or continuous ran-dom variables. The most frequently encountered types of BBNs in practice are discrete BBNs whose nodes are associated with discrete random variables, normal BBNs with variables following a joint normal distribution and discrete-normal BBNs where discrete and normal variables coexist. In this thesis, however, a different BBN type from the class of mixed discrete-continuous BBNs finds its application, namely a non-parametric continuous-discrete BBN (Hanea et al., 2006; Hanea, 2008). In contrast to discrete-normal BBN, this type of BBN al-lows the continuous variables to have arbitrary invertible cumulative distribution functions. This is important for quantitative benefit-risk assessments since not all continuous variables involved in these assessments (e.g. chemical concentra-tion, fish consumption) are necessarily normal. More information about the above mentioned network types, including requirements regarding their quantification, is provided in Chapter 2. Naturally, the greatest emphasis is put on non-parametric continuous-discrete BBNs.

Briefly, the first practical case study in Beneris looked at positive and adverse health effects in the Finnish population following exposure to various substances present in fish. The fish constituents identified as beneficial and included in the assessment were omega-3 fatty acids, more specifically eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), whereas the potentially harmful chemic-als considered were environmental pollutants such as methyl mercury, dioxins and polychlorinated biphenyls (PCBs). Omega-3 fatty acids and methyl mercury have been recognized to have contrasting impacts on cardiovascular system and the de-velopment of the central nervous system while both dioxins and PCBs may affect tooth development and cause cancer. In consequence, the assessment focused on the intelligence and dental defects in children, and cancer and coronary heart disease in adults. Additionally, since different population subgroups consume dif-ferent types of fish in difdif-ferent amounts and also the concentrations of substances selected vary among different fish species several most commonly consumed spe-cies of fish in Finland were taken into account. More detailed assessment scope is given in Chapter 2.

In total, 1151 variables involved in the assessment were treated as uncertain and these correspond to the nodes in the BBN. Figure 1.1 shows the BBN model developed in Beneris and indicates the major variable groups: fish consumption variables, personal and demographic variables that characterize exposed popula-tion groups, environmental variables (concentrapopula-tions of chemicals in fish),

expos-Introduction 7

Fish consumption variables and demographic/personal variables Environmental variables

Exposure variables and demographic/personal variables Health effect variables and exposure-response variables

Figure 1.1: Bayesian belief network for the benefit-risk assessment of fish consumption developed in Beneris project.

ure variables, exposure-response variables (slopes of exposure-response relation-ships1) and health effect variables. The number of arcs in this model is 1767 and most of them represent functional relationships between connected nodes. The development and quantification of this BBN was a challenging task and a joint effort of several project partners, especially TU Delft and THL. The primary sources of data used to derive the marginal probability distributions for the BBN nodes were Finnish databases on contaminant levels in fish, fish consumption and food composition, Finnish demographic statistics, scientific literature and experts. The contents of the BBN built is fully discussed in Chapter 2 whereas the process of quantifying this model is the subject of Chapter 3.

A fully quantified BBN is a powerful tool for scenario analysis and prediction, and it therefore offers a valuable assistance to food decision makers. Due to its inherent inference capability, a BBN not only enables the assessment and com-parison of the effects of alternative food consumption (or exposure) scenarios, but it can also be used to identify food consumption patterns and level of chemical exposure associated with the greatest/smallest health benefits and risks or with a preselected level of health impact. Importantly, with the help of available BBN software (UniNet in case of non-parametric continuous-discrete BBNs) the in-ferential calculations can be performed rather quickly, allowing decision makers (and other users of the BBN) to compute answers to their own queries in real

1_{Throughout the thesis, exposure-response relationship (or exposure-response}

model/function) denotes a mathematical function describing the relationship between the level of chemical exposure and, possibly transformed, magnitude or frequency of associated health effect (health response). The slope of this function is called an exposure-response slope.

8 CHAPTER 1

IQ score (points)

Mean 5th 50th 95th

score percentile percentile percentile

Scenario 0 99.93 99.49 100.00 100.14

Scenario 1 100.06 99.99 100.06 100.13

Scenario 2a 99.61 99.07 99.63 100.10

Scenario 3 100.07 99.99 100.06 100.17

a_{Under this scenario, the assumed daily intakes of the various fish species in units of g/day are as}

follows: Baltic herring - 10.52, vendace - 3.60, whitefish - 13.71, pike - 7.42, perch - 2.28 and Atlantic salmon - 5.07.

Table 1.1: Estimated child’s IQ by scenario. Result as the mean value and 5, 50 and 95th percentiles.

time.

In the thesis, the use of BBNs as food benefit-risk modeling and decision sup-port tool is exemplified by a submodel of the BBN in Figure 1.1 which examines the benefits of DHA and the risks of methyl mercury from maternal fish consump-tion during pregnancy for the cognitive development of children, where cognitive development is measured by the intelligence quotient (IQ) score. The examples of use of this submodel are provided at the end of Chapter 3. For illustrative purposes, however, one example is also presented here.

According to the model Finnish pregnant women do not eat the recommended weekly minimum of 2 portions of fish set by the Finnish Food Safety Author-ity. Specifically, the estimated weekly average fish consumption frequency during pregnancy in Finland amounts to 1.01 portions (5th - 95th percentile range is 0.3 - 1.97 portions)2_{. This level of fish intake is associated with an average loss of}

0.13 IQ points per child relative to the IQ score corresponding to zero maternal prenatal fish consumption and an average loss of 0.07 points with respect to the average IQ score in the population of 100 points. Thus, a valid question arises as to whether pregnant women can improve their offspring’s IQ by changing their fish-eating habits.

To assess the potential impact of shifts in maternal prenatal fish consumption patterns on child IQ four fish consumption scenarios are compared in Table 1.1: Scenario 0 - the baseline scenario, which assumes that pregnant woman does not change her diet, Scenario 1 - non-fish consumer, which assumes a zero fish consumption, Scenario 2 - high-end fish consumer, in which pregnant woman is assumed to ingest large amounts of fish, and Scenario 3 - low-MeHg fish consumer, which assumes that only fish species with low levels of methyl mercury are consumed during pregnancy, i.e. Baltic herring and Atlantic salmon. In this example, the conditioning variables are those BBN variables which indicate consumer/non-consumer of selected fish species and variables describing species-specific daily fish consumption. Further, in Scenario 2, the 95th percentile of every fish intake distribution is chosen to represent the high-end of consumption. It can be seen from Table 1.1 that compared with the baseline scenario,

Introduction 9

Figure 1.2: Results for Scenario 0 -no change in diet.

Figure 1.3: Results for Scenario 1 -non-fish consumer.

Figure 1.4: Results for Scenario 2 -high-end fish consumer.

Figure 1.5: Results for Scenario 3 -low-MeHg fish consumer.

ing fish during pregnancy (Scenario 1) may have beneficial effects on child’s IQ (average gain of 0.13 IQ points) whereas the excessive fish consumption reaching an average of ≈ 3 portions/week (Scenario 2) may cause cognitive decline (av-erage loss of 0.32 IQ points). Nevertheless, the results suggest that the highest IQ improvement may be achieved not by eliminating all fish from the diet, but by avoiding only those fish species that are high in methyl mercury (Scenario 3). Hence, based on this analysis adopting Scenario 3 seems most favourable.

To further demonstrate how the model behaves under the different scenarios four figures, Figures 1.2 - 1.5, are presented. Each of these figures displays the conditional histogram (in black) as well as the conditional mean and conditional standard deviation (underneath the histogram as mean ± standard deviation) of fish-originating methyl mercury exposure (units in µg/kg bw/day) and DHA exposure (units in mg/kg bw/day), IQ score and exposure-response slopes for a selected scenario. The unconditional histogram of each variable is shown in gray in the background. All figures were generated in UniNet.

10 CHAPTER 1

1.4

Multivariable exposure-response relationships

As mentioned in the previous section, the case study on fish consumption in Ben-eris considered a number of nutrients and environmental pollutants in fish and linked them to one of four health points. More specifically, each health end-point was related to more than one chemical; cancer and developmental dental defects were associated with dioxins and PCBs, and cognitive development and coronary heart disease were related to methyl mercury and omega-3 fatty acids (either DHA only or both DHA and EPA)3_{. In general, no method for assessing}

the combined effect of all compounds sharing the same end-point exists and the standard practice is to first evaluate the impacts of the different compounds sep-arately and then combine them to determine the overall effect. For instance, the overall impact of prenatal methyl mercury and DHA exposure on child IQ is typic-ally estimated by summing up the individual effects of DHA and methyl mercury on IQ, each of which is calculated as a product of exposure to the chemical and the exposure-response slope for that chemical which expresses the change in IQ per unit increase in exposure (see e.g. FAO/WHO, 2010; Leino et al., 2011). In Beneris, however, a different approach to deal with this issue was taken in which the impact of multiple substances on a common end-point is computed based on a multivariable exposure-response relationship4. Since chemicals are typically studied independently of one another the multivariable exposure-response rela-tionships are unknown. This created the need for the development of procedures for deriving these functions based on available single chemical exposure-response information.

In response to this need, the generalized method for modeling multivariable exposure-response relationships was developed. Its basic idea is to approximate the unknown multivariable relationship with a truncated polynomial in several variables, each representing the exposure to a chemical associated with the health end-point of interest, and then estimate the coefficients of all terms appearing in this approximation. Three approaches to quantify the polynomial approximations are presented in this thesis. The first of these approaches, developed and used in Beneris, applies to the first-order polynomial approximation only. It estimates the zero-order (constant) term based mainly on statistics/published information regarding the health effect under study and replaces the coefficients of the first-order terms with exposure-response slopes for individual chemicals extracted from published reports and studies. The second approach, developed after Beneris has ended, quantifies the approximating polynomial by integrating findings of a

num-3_{It should be emphasized that term “dioxins” is the common name for a large group of}

chemicals (210 different dioxin and furan congeners, see Section 2.4 of Chapter 2) which for the purpose of facilitating the risk assessment are usually treated as one substance. That was also the case in Beneris. The traditional method for the risk assessment of dioxins is the toxic equivalency factors (TEF) approach (Van den Berg et al., 1998; Safe, 1998). Similarly, PCBs make up a group of 209 different PCB congeners some of which elicit dioxin-like toxicity. PCBs can be evaluated alone as one compound using the total PCB approach (US EPA, 1996, 1997c) or together with dioxins using the TEF approach. In Beneris both these cases were included.

4_{In the thesis, we use the term multivariable exposure-response relationship/model/function}

to describe a mathematical function that relates exposures to multiple chemicals to a single (transformed) health response.

Introduction 11

ber of epidemiological studies, each evaluating the effect of a different compound on the end-point of interest. Under certain circumstances, the polynomial to be quantified coincides with commonly used regression models in epidemiology (i.e. linear, logistic and Cox models) and hence the second approach basically comes down to building a multivariable regression model5_{from a number of single}

chem-ical epidemiologchem-ical studies. Finally, the last approach estimates the unknown polynomial coefficients with the help of experts. The general discussion on the generalized method and a brief overview of the approaches just mentioned are a part of Section 3.7 of Chapter 3. In the same chapter the application of the first approach in the case study on fish in Beneris is also described. Chapter 4 is entirely devoted to the second approach, while the third expert judgement based approach is outlined in Appendix A.

A primary tool for characterizing multivariable (or univariable) exposure-response relationships based on censored survival data (also called time-to-event data) collected in epidemiological studies is the Cox regression model (Cox, 1972). It specifies an expression for the conditional hazard function of the event time given a set of covariates (exposure variables and possibly other variables related to the heath end-point under study) as a product of an unknown baseline hazard function and an exponential function of covariates (see Section 4.2.3 of Chapter 4). In the Cox model, the covariate effects are estimated through the hazard ratio what makes it the most often reported model output.

It is well known that the omission of one or several pertinent covariates in the Cox model results in biased estimates of the regression coefficients for the remain-ing covariates and in the end leads to incorrect estimates of covariate effects (see e.g. Bretagnolle and Huber-Carol, 1988; Abrahamowicz et al., 2004). The bias oc-curs regardless of whether the omitted covariates are independent of or dependent on the included covariates. The omitted covariate problem in the Cox regression has been studied since the early 1980s. Yet the theoretical results concentring the bias are only available for the case where the included covariates are independent of each other and of omitted covariates. In case of dependence, the properties of the bias have been investigated solely through computer simulations. This dissertation attempts to contribute to filling this theoretical gap. Specifically, in Chapter 5, we develop a new estimation procedure for the Cox model which al-lows to derive an explicit expression for the error caused by neglecting covariates (i.e. the difference in the estimates of regression coefficients between two nested models) in terms of a sample covariance matrix of covariates. It is hoped that this result will help better understand the influence of dependences among covari-ates on the specification bias. The comparison of the new estimation procedure with the standard estimation approach for the Cox model, the maximum partial likelihood method, is also included in this chapter.

5_{In the literature, multivariable regression model is more commonly known as multiple}

CHAPTER 2

Bayesian networks in human health benefit-risk

assessment of foods

1

2.1

Introduction

This chapter focuses on Bayesian belief networks and their application to the human health benefit-risk assessment of foods. It is organized as follows. We start in Section 2.2 with a general overview of BBNs. Then, in Section 2.3 the non-parametric continuous-discrete BBNs which are of main interest in this thesis are discussed. The presentation is kept brief. For more detailed information we refer the reader to the literature quoted throughout the text. As mentioned in the introduction, in the thesis the use of BBNs to assess food related health impacts will be illustrated on one of the practical case studies of the Beneris project. The food commodity analyzed in this case study is fish. The scope and boundaries of this study as well as the general contents of the BBN model developed are presented in Section 2.4. The process of quantifying this model will be described in Chapter 3. Finally, Section 2.5 summarizes the main advantages of the BBN approach to the benefit-risk assessment of foods.

2.2

Bayesian belief networks - an overview

Bayesian belief networks (also known as belief networks, Bayesian networks, prob-abilistic networks or causal networks) are graphical models for representing high dimensional probability distributions (Pearl, 1988; Cowell et al., 1999; Jensen, 1996, 2001). Formally, a BBN is a directed acyclic graph2_{in which nodes}

repres-ent univariate (discrete or continuous) random variables and arcs represrepres-ent direct

1_{This chapter is to a large extent based on the report by Gradowska and Cooke (2009).} 2_{A directed acyclic graph is a set of nodes connected by directed edges (arcs) such that there}

are no directed cycles, where a directed cycle is a directed path that leads from a node back to itself.

14 CHAPTER 2

X1 X2

X3

X4

Figure 2.1: A simple BBN on 4 variables X1, X2, X3 and X4.

influences between the variables3_{. The direction of an arc indicates the direction}

of the influence. An arc connects a parent (influencing) node to a child (influ-enced) node. The influences can be probabilistic or deterministic. Nodes which are deterministic functions of other nodes are typically called deterministic or functional, whereas nodes which correspond to truly random variables are called probabilistic. The discussion in this and the next section refers to the probabilistic variables only, unless explicitly stated otherwise.

The absence of arcs in a BBN implies conditional independence relations among the variables. Specifically, the graph encodes the assertion that given its parents, each variable is conditionally independent of all its other predecessors in some strict, usually non-unique, topological ordering of the nodes4_{. Predecessors}

of a given node may involve its ancestors (i.e. parents, the parents’ parents, parents of parents’ parents, etc.) and other nodes as long as they are not its descendants (i.e. children, children’s children, etc.).

Figure 2.1 presents a simple example of a BBN on four variables X1, X2,

X3 and X4. The graph says that variables X1 and X2 are parents of X3 and

are independent of each other. It also shows that variable X4 is influenced by

variable X3 and is independent of X1 and X2given X3. The possible topological

orderings for this structure are: X1, X2, X3, X4 and X2, X1, X3, X4.

In the BBN, the strength of the relationships between the variables and their parents is expressed in terms of conditional probability distributions. The product of these conditional distributions defines the joint probability distribution over all BBN nodes. This can be seen by repeatedly applying the product rule of prob-ability to the joint probprob-ability distribution (the joint probprob-ability mass function or density function) over the variables in the network and exploiting conditional independence relations implied by the graph. Specifically, let XT_{= (X}

1, . . . , Xk)

be a 1 × k vector of BBN variables sorted in the topological order and let pai be

the set of parents of Xi (i = 1, . . . , k). Throughout the thesis, the superscript T

stands for the transpose. Then, the joint probability distribution for X, f (x), is

3_{For convenience we will refer to nodes and variables interchangeably.}

4_{Topological ordering of a directed acyclic graph is an order of its nodes where each node}

Bayesian networks in human health benefit-risk assessment of foods 15

factored as

f (x) = f (x1, . . . , xk)

= f (x1, x2, . . . , xk−1)f (xk|x1, x2, . . . , xk−1) (by the product rule)

= f (x1)f (x2|x1)f (x3|x1, x2) . . . f (xk|x1, x2, . . . , xk−1) = f (x1) k Y i=2 f (xi|x1, . . . , xi−1) = k Y i=1

f (xi|pai), (due to conditional independence)

where f (xi|pai) is the conditional probability distribution of Xi given its parents

such that f (xi|pai) = f (xi) if pai= ∅. For a BBN in Figure 2.1 the factorization

of the joint distribution (regardless of the topological ordering of the nodes) is f (x1, x2, x3, x4) = f (x1)f (x2)f (x3|x1, x2)f (x4|x3).

Thus, to completely represent a joint distribution over a set of variables through a BBN one has to specify a graph of dependence relationships among the variables and a conditional probability distribution of each node given its parents in the graph.

The graphical dependence structure gives the qualitative component of the BBN whereas the conditional probability distributions associated with its nodes constitute the quantitative component. Accordingly, the process of building a BBN proceeds in two successive stages: qualitative and quantitative. The first stage consists in identifying the graphical structure of the network. The second stage is the network quantification. Typically, both the graph and the condi-tional probability distributions are constructed manually. Another possibility is to learn either the graph structure or the probability distributions (known as the parameters) given a structure from large databases. For a review of the differ-ent methods for learning Bayesian networks from data and relevant literature see Buntine (1996) and Daly et al. (2011).

The main point of BBNs is to make probabilistic inferences concerning vari-ables involved, that is, to answer probabilistic queries about them in the light of observed events (evidence). This is equivalent to updating marginal distributions given observations or simply to calculating conditional probability distributions from the joint distribution over all model variables and can be achieved by apply-ing Bayes’ rule. Therefore, the inference in a BBN is also called conditionaliza-tion. The evidence can be propagated both forwards (i.e. following the directions of arcs) and backwards (i.e. in the opposite direction to arcs) throughout the network. Hence, the BBN can be used for both prognosis and diagnosis. This distinctive feature of BBNs made them a very attractive and powerful tool for reasoning and decision-making in uncertain situations.

Depending on the types of distributions assumed for the variables, different classes of BBNs can be distinguished:

16 CHAPTER 2

– discrete, where each node is associated with a discrete random variable having a finite number of mutually exclusive and collectively exhaustive states (or values),

– continuous, where all nodes correspond to continuous random variables, – hybrid, where the nodes represent both discrete and continuous random

vari-ables.

The most commonly used types of BBNs are discrete and normal BBNs. Nor-mal BBN is a continuous network in which nodes are assumed to follow a joint normal (Gaussian) distribution. A different type of a continuous BBN that is gain-ing popularity is a non-parametric continuous BBN. In this network, nodes may represent arbitrary continuous random variables with invertible cumulative dis-tribution functions, but no specific parametric form for their joint disdis-tribution is assumed. Both normal and non-parametric continuous BBNs can be extended to include discrete random variables, leading to discrete-normal and non-parametric continuous-discrete BBNs.

Each of the aforementioned network types has its own specific properties, advantages and disadvantages. Some of them are summarized below. A more complete list can be found in Cowell et al. (1999) and Hanea et al. (2006).

Each variable in a discrete BBN is assigned a conditional probability table that describes the conditional probability distribution of that variable for each possible configuration of states of its parents (if any). The main disadvantage of discrete BBNs is that they have a very high assessment burden. For instance, if all BBN nodes have m states and a given child node has n parents, then mn+1

conditional probabilities for that child must be assessed in a consistent manner. This number grows rapidly as the number of states and the number of parents increase. Moreover, as a parent is added or removed the entries in the conditional probability tables of all its children have to be requantified. Another complication arises when the marginal distributions of some (or all) child nodes are known. In a discrete BBN, only the source nodes (nodes with an empty set of parents) are assigned marginal distributions. The marginal distributions for other nodes are calculated from the conditional probability tables. If these marginal distributions were known a priori then constructing probability tables that comply with the marginal data becomes a very demanding task. For these reasons discrete BBNs are more suitable for modeling small-scale problems.

Many probabilistic models naturally contain both discrete and continuous nodes. The standard approach for handling continuous variables is to discret-ize them into a number of states (or values) and to approximate the model by the respective discrete BBN. However, for good accuracy a large number of states is required that, as mentioned earlier, makes the process of quantifying BBN challenging. A different way to cope with this issue is to use normal BBNs.

In the normal BBN, each node is associated with a conditional normal distri-bution given its parents which is specified by unconditional means of that node and its parents, constant conditional variance and partial regression coefficients in the regression of the child on all its parents (Shachter and Kenley, 1989). In gen-eral, the regression coefficient expresses the influence of parent on a child and is

Bayesian networks in human health benefit-risk assessment of foods 17

therefore often associated with the parent-child arc in the BBN. Discrete nodes, if included, can have continuous children but not continuous parents. Normal BBNs work well in practice if the distribution of the continuous variables is indeed nor-mal and the data on partial regression coefficients and conditional variances are available. In case when the normality assumption does not hold the quantification of a normal BBN becomes difficult.

Another way to incorporate continuous nodes is by using a non-parametric continuous-discrete BBN. Because this particular type of Bayesian network is the central focus in this thesis it is described separately and in greater detail in the next section.

2.3

Non-parametric continuous-discrete BBNs

In this section, we recapitulate the basic facts about non-parametric continuous-discrete BBNs (hereafter called “non-parametric BBNs”), but refer the reader to Kurowicka and Cooke (2006), Hanea et al. (2006) and Hanea (2008) for a complete exposition.

In the non-parametric BBNs, probabilistic nodes may represent arbitrary con-tinuous or discrete random variables and each probabilistic influence between the parent and its child is represented as a conditional rank correlation. The con-ditional rank correlation is the Spearman’s rank correlation (r ) in a bivariate conditional distribution and is, by assumption, constant5. The only requirements are that the continuous nodes have invertible cumulative distribution function and the discrete variables have ordinal values (states)6. Functional influences between the variables are also allowed with the restriction that functional nodes cannot have probabilistic nodes as children.

Spearman’s rank correlation measures the strength of monotonic relationship between a pair of variables and it indicates how well low/high values of variables occur together. Formally, it is a product moment correlation of two random variables after they have been transformed to uniform random variables on the unit interval [0,1]. This measure of dependence is of interest for the following reasons:

– as opposed to other dependence measures (e.g. the product moment correlation) it always exists,

– it is invariant under strictly increasing transformations of the underlying ran-dom variables,

– the values of the conditional rank correlations in a BBN are algebraically inde-pendent, meaning that any number between -1 to 1 can be assigned to the arcs of the BBN,

5_{The assumption of constant conditional rank correlations is unnecessary from the}

theoret-ical point of view, but it simplifies the computations.

6_{Ordinal values (states) are the values (states) that can be ordered in a meaningful sequence.}

Hence, the examples of discrete variables that have ordinal values are socioeconomic status (e.g. low, middle, high), age group (e.g. 0-24, 25-39, 40-54, 55+ years) or BMI category. Counterexamples include variables like political preference (e.g. left-wing, right-wing), ethnicity and region of residence.

18 CHAPTER 2

– successful protocols for eliciting conditional rank correlations from experts have been developed.

The conditional rank correlations are associated with the arcs of the BBN and assigned according to a protocol that depends on the ordering of parents (Kurowicka and Cooke, 2005). That is, for a child node with parents the in-fluence of the first parent (in an ordering) is described in terms of uncondi-tional rank correlation and the influence of next parents (if they exist) is rep-resented as conditional rank correlation between child and the current parent given previous parents. Since the order of parents is not generally unique dif-ferent assignments are possible. In the BBN in Figure 2.1 the following sets of rank correlations may be assigned: {r(X1, X3), r(X2, X3|X1), r(X3, X4)} or

{r(X2, X3), r(X1, X3|X2), r(X3, X4)}. Trivially, no correlations are attached to

arcs between functional and probabilistic nodes, and to arcs joining functional nodes.

The quantification of the non-parametric BBN requires specifying marginal probability distributions for all probabilistic nodes, a number of conditional rank correlations equal to the number of arcs between probabilistic nodes and also deterministic functions associated with functional nodes. This information can be retrieved from data, published literature or elicited from experts (Cooke, 1991; Morales et al., 2008; Morales N´apoles, 2010). A convenient property of non-parametric BBNs is that if after quantification new parent nodes are added or the marginal distributions of some of the variables are changed the previously specified correlations do not need to be requantified.

The conditional rank correlations associated with the arcs of the BBN are realized using conditional bivariate copulae. By definition, a conditional bivariate copula is a conditional distribution on the unit square whose marginal distribu-tions are uniform on the interval [0,1] (Joe, 1997; Nelsen, 2006). In principle, any one-parameter copula that can be indexed by the rank correlation and for which the zero conditional rank correlation entails conditional independence can be used. In practice, however, only the normal (or Gaussian) copula7_{offers rapid}

computations required for large applications8_{. The copula and the conditional}

in-dependence relations embedded in the structure of the BBN define the in-dependence structure among the probabilistic variables, which combined with the marginal distributions results in a joint probability distribution for the probabilistic nodes. Since there is no analytic expression available for this joint distribution, the only way to realize it is by sampling it. The details of the sampling procedures for non-parametric BBNs are explained in Kurowicka and Cooke (2005, 2006) and Hanea et al. (2006), among others. Once the distribution for the probabilistic nodes is obtained the joint probability distribution for the functional nodes can be derived.

The joint distribution over the BBN nodes can be used for inference. The

7_{The bivariate normal copula is the copula of the bivariate normal distribution. It is}

para-meterized by one parameter - the product moment correlation ρ, which depends on the rank correlation r in the following way ρ(X1, X2) = 2 sin(π₆ · r(X1, X2)) (Kruskal, 1958).

8_{Note that in case where the normal copula is used the conditional rank correlations specified}

Bayesian networks in human health benefit-risk assessment of foods 19

evidence entered may concern both probabilistic and functional nodes. If one chooses the normal copula to realize the conditional rank correlations, the condi-tionalization on probabilistic nodes can be done analytically and is very fast. On the other hand, irrespective of the copula selected, the conditionalization on func-tional nodes can only be performed sample-wise. That is, the condifunc-tionalization on functional nodes involves generating a large sample from the joint probability distribution over all BBN nodes and selecting a subset of samples that complies with the observed evidence. This task, however, becomes challenging when the event of interest is rare or represents a very narrow interval. In addition to updat-ing, the multivariate sample from the joint distribution represented by the BBN can be also analyzed using various post-processing tools, including graphical in-terpretation and sensitivity analysis tools.

The non-parametric BBNs with assumed normal copula have been implemen-ted in UniNet software developed at the Department of Applied Mathematics of Delft University of Technology9_{. UniNet assists both analytic and} sample-based inference. Analytic inference allows for conditioning on single values of probabilistic variables only, while a sample-based inference is applicable to both probabilistic and functional nodes and conditionalizes on intervals. The software is available at http://www.lighttwist.net/wp/uninet.

2.4

Benefits and risks of fish consumption

As mentioned in Section 1.3, there were two practical case studies in the Beneris project. One focused on the beneficial and harmful effects of nutrients and pol-lutants in fish and another looked at vegetables in the diet of a specific age group. Our focus is on the former study only which, for convenience, we will refer to in the rest of this thesis as the fish case study.

2.4.1

Scope and boundaries of the study

The goal of the fish case study was to evaluate health benefits and risks associated with the consumption of fish, its contaminants and nutrients. In this section the scope and boundaries of this case study are discussed.

Spatial boundaries

The primary aim of the study was to conduct the benefit-risk assessment of fish consumption for four European countries: Finland, Ireland, Spain and Denmark, and to compare the results obtained. However, since Finnish databases concern-ing the fish consumption and the concentration of nutrients and environmental pollutants in fish were most extensive and accessible at the moment of doing the case study, the assessment focused on the Finnish population only.

9

UniNet was originally developed to support Causal Model for Air Transport Safety com-missioned by the Dutch Ministry of Transport and Water Management.

20 CHAPTER 2

Target population subgroups

Based on age the following five subgroups of the general Finnish population were distinguished and included in the assessment: infants - children under the age of 2 years, children and adolescents individuals aged 2 to 18 years, adults -individuals between 18 and 55 years of age, elderly adults - -individuals who are 55 years old or older10 _{and pregnant women}11_.

The age ranges for adults and children were selected for consistency with the data on the fish consumption (see Section 3.2). Moreover, in addition to age also gender differences in the fish consumption patterns and in dietary exposure to selected fish compounds within both children’s age groups were addressed (see Sections 3.2 and 3.5).

Fish compounds

The analysis of benefits and risks of fish consumption was restricted to certain nutrients and environmental pollutants considered by the project team to be the most important, and for which data on concentration and health effects were also available. These compounds are: polychlorinated dibenzo-p-dioxins and diben-zofurans (PCDD/Fs; hereafter collectively called “dioxins”), polychlorinated bi-phenyls (PCBs), mercury (Hg) as methyl mercury (MeHg) and omega-3 fatty acids - eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Initially also other nutritional constituents such as iodine, vitamin D and selenium were taken into account. Selenium was excluded from the study because the evidence about its health effects is too weak or simply lacking. Similarly, vitamin D was excluded since its exposure-response functions have not been established yet. Fi-nally, iodine was omitted since iodine deficiency, a leading cause of intellectual impairments in children, is not an issue of concern in Finland. A short description of chemicals under study is provided below. To emphasize, the references men-tioned there are only exemplary and do not exhaust the vast literature published on each compound.

Dioxins and PCBs

Dioxins and PCBs are groups of compounds with similar chemical structures but varying toxicity. Each individual compound is called a congener. Dioxins consist of 75 polychlorinated dibenzo-p-dioxin (PCDD) congeners and 135 poly-chlorinated dibenzofuran (PCDF) congeners, of which only 17 are of toxicolo-gical concern. The most extensively studied and the most toxic of the dioxins is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). PCBs comprise 209 different con-geners which can be divided into two subgroups according to their toxicological properties: the dioxin-like PCBs, a group of 12 congeners which exhibit

toxico-10_{Age ranges correspond to left-closed, right-open intervals, except for infants. That is, the}

age groups considered were (0,2), [2,18), [18,55) and ≥ 55 years. For convenience, we will refer to these intervals as 0-1, 2-17, 18-54 and 55+ years.

Bayesian networks in human health benefit-risk assessment of foods 21

logical properties similar to dioxins, and the non-dioxin-like PCBs which have a different toxicological profile.

Dioxins are not produced commercially, but are unintentional by-products in many industrial processes such as waste incineration, chemical manufacturing, chlorine bleaching of paper pulp and metal smelting. They can also be generated from natural processes (e.g. forest fires and volcanic eruptions), but these sources are small compared to those resulting from human activity. The primary way dioxins enter the environment is by deposition from the air.

As opposed to dioxins, all PCBs are man-made. They were manufactured on a large scale throughout the world from around 1930 until the 1980s and their production was ultimately banned by the Stockholm Convention on Persist-ent Organic Pollutants in 2001. Because of their superior insulation and flame resistant properties, PCBs have been widely used as coolants and lubricants in transformers, capacitors and other electrical equipment. They have also been used in the manufacturing of many common products including plastics, paints, inks, adhesives and varnishes. PCBs may be released into the environment in a variety of ways such as via improper and illegal waste disposal and storage, accidental leakage from and fires of PCB-containing equipment and by burning waste containing PCBs at municipal and industrial sites.

Both dioxins and PCBs are classified as persistent organic pollutants. They are poorly soluble in water and highly soluble in fat. As a consequence, they concentrate in animal and human fatty tissues and bind to the organic matter in sediment, soil, water and air. Moreover, they are resistant to degrading and therefore persist in the environment and accumulate in the food chain.

Most human exposure to dioxins and PCBs is through the intake of PCB-and dioxin-contaminated food, especially fatty foods such as diary products (e.g. butter, cheese, fatty milk), meat, egg and fish (mainly fatty fish such as salmon, Baltic herring). The health effects linked to the dioxin and PCB exposure in-clude cancer, adverse effects on the immune system, disruption of reproductive functions, developmental deficits and skin problems, but some of them still lack human evidence and exposure-response functions (ATSDR, 2000; NRC, 2006).

Methyl mercury

Mercury is a highly toxic metal found throughout the environment. It is released into the air, water and soil from a range of natural sources such as volcanic activity, forest fires and the weathering of rocks, but it can also be emitted from anthropogenic sources like the burning of fossil fuels and other industrial activities such as metal smelting, cement production, and municipal and medical waste incineration.

Mercury exists in three different forms: elemental, inorganic and organic. The most common and the most toxic organic form is methyl mercury, which is formed from inorganic mercury by the action of microorganisms (bacteria and fungi) liv-ing in the water and sediments. MeHg is easily absorbed by tiny aquatic organisms and since not readily eliminated accumulates in the aquatic food chain. Thus, the concentrations of MeHg tend to be higher in older, larger, predatory fish and

mar-22 CHAPTER 2

ine mammals rather than in non-predatory fish or species at lower levels in the food chain. In addition, MeHg concentrates in fish’s muscle tissue (the flesh; the edible part of the fish) more than in fat or skin, indicating that lean predatory fish (e.g. pike, perch, pike-perch, burbot) contain the highest MeHg levels.

Consumption of contaminated fish is the major source of human exposure to MeHg whereas the central nervous system is the critical target for its damaging ef-fects (NRC, 2000; ATSDR, 2003; Poulin and Gibb, 2008; D´ıez, 2009). MeHg may affect both mature and developing central nervous system, but the nervous system of the foetus appears to be more susceptible to the compound than that of adults. Therefore, pregnancy is regarded as the most sensitive period of MeHg exposure. Prenatal MeHg exposure has been associated with a variety of neurodevelopmental effects in infancy and childhood such as delays in walking and talking, mental re-tardation, impaired intelligence, sensory impairment (blindness and deafness), learning disability, paralysis and disturbance in the physical growth, among oth-ers. In adults, MeHg has been linked to multiple central nervous system effects (e.g. ataxia and paresthesia) and to a potentially increased risk of cardiovascular outcomes including acute myocardial infarction and death from coronary heart disease (CHD) and cardiovascular disease (CVD).

Omega-3 fatty acids

Omega-3 fatty acids belong to a group of essential fatty acids. This means that they are necessary for good health, but the human body cannot produce them on its own. Therefore, they need to be supplied through the diet. Omega-3 fatty acids include α-linolenic acid (ALA), EPA and DHA. ALA is mostly found in plant-based foods such as canola oil, flaxseeds, soybeans, walnuts and their oils. The most significant source of DHA and EPA is fish oil which can be acquired by eating fish or taking daily supplements. Fish species rich in these acids are fatty fish like for example herring, salmon, tuna, trout, sardines and mackerel. In fact, EPA and DHA can also be produced by the human body from ALA, but only in very small amounts.

Omega-3 fatty acids have been related to a wide range of health benefits. Both ALA, EPA and DHA have been shown to reduce the risk of CHD, particularly CHD death, in men and in women (Kris-Etherton et al., 2003; Oh, 2005; K¨onig et al., 2005). The supporting evidence, however, comes mainly from studies eval-uating fish-derived fatty acids EPA and DHA. Omega-3 fatty acids, particularly DHA, are also recognized as essential for the development of brain, retina and the central nervous system of children, before and after they are born (Fleith and Clandinin, 2005; Swanson et al., 2012). This underlines the importance of an ad-equate maternal intake of DHA during pregnancy and lactation. Other benefits associated with omega-3 fatty acids include reduced risk of Alzheimer’s disease (Morris et al., 2003) and dementia (Lim et al., 2006), but these require further study.

Bayesian networks in human health benefit-risk assessment of foods 23

Health effects of eating fish

Health effects of fish consumption included in the study encompassed effects on the developing central nervous system, developmental defects, cardiovascular disease and cancer, for which there was adequate exposure-response data to conduct a quantitative analysis. In particular, the following health effects were considered: – the effect of prenatal exposure to MeHg and DHA on cognitive development in

children measured in terms of IQ score,

– developmental dental defects in children exposed to dioxins and PCBs; more specifically, the end-point of interest was developmental defects of enamel in all permanent teeth excluding wisdom,

– the effect of exposure to MeHg and omega-3 fatty acids (EPA and DHA) on mortality from coronary heart disease in adults,

– the effect of exposure to dioxins and PCBs on cancer morbidity in adults.

Fish species

Fish species covered in the assessment were the most commonly consumed species caught in Finland’s marine and freshwater areas for which both consumption and concentration data were available. These species are: Baltic herring (Clupea harengus membras), vendace (Coregonus albula), whitefish (Coregonus lavaretus), pike (Esox lucius), perch (Perca fluviatilis), Atlantic salmon (Salmo salar ) and pike-perch (Stizostedion lucioperca).

2.4.2

Schematic representation of the BBN model

The process of developing the non-parametric BBN model for the benefit-risk assessment of fish consumption in Beneris was broken down into two main phases. In the first phase three separate BBNs were created. The first of these models described the effect of prenatal exposure to MeHg and DHA on IQ in children, the second model examined the impact of dioxins and PCBs on developmental dental defects in children, and the third model evaluated the risk of cancer and CHD death for adults exposed to all fish compounds selected for the assessment. In the second phase, due to the presence of overlapping variables, all three models were integrated into one single BBN.

A schematic representation of a BBN for the fish case study is depicted in Figure 2.2. It shows six different groups of uncertain variables involved in the assessment (described below) and connections among them. As a matter of fact, the same scheme applies to each of three submodels of the general network.

The BBN variables were divided into six major groups:

1. Fish consumption variables describing the daily consumption of different fish species by various subgroups of the Finnish population. Special-interest subgroups were pregnant women, male and female children in age groups 0-1 and 2-17 years, and adults in groups 18-54 and 55+ years.