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**0**

**3**

*Brazil*

**Assessing the Outline Uncertainty**

1*Departament of Mathematics, Universidade Federal de Ouro Preto*

3*Departament of Statistics, Universidade Federal de Minas Gerais*

The spatial analysis of disease incidence is a fundamental tool in public health monitoring (Lawson et al., 1999). Suppose that a geographic study area is divided into administrative areas, with known populations at risk and observed cases of disease within a certain period of time. An interesting question is the possible existence of spatial anomalies in the study area: are there localized regions within the map for which the relative concentration of cases among the population at risk is significantly higher than would be expected if the cases were distributed at random? Such anomalies, known as *spatial clusters*, are inherently difficult to delineate, for several reasons (Cancado et al., 2010; Lawson, 2009). Due to the stochastic nature of the number of observed cases of disease, the uncertainty may be elevated in the disease rate estimation for aggregated area maps, especially for small population areas. Thus the most likely disease cluster produced by any given method for the detection and inference of spatial clusters (like SaTScan (Kulldorff, 1999) or any other irregularly shaped scan) is subject to a lot of variation. If it is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we have enough information to exclude them from a health

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**1. Introduction**

program of prevention?

**of Spatial Disease Clusters**

Fernando L. P. Oliveira1, André L. F. Cançado2, Luiz H. Duczmal3 and Anderson R. Duarte1

2*Departament of Statistics, Universidade de Brasília*

<http://www.healthscotland.com/uploads/documents/10772-

Views%20of%20C&YP%20on%20what%20impacts%20on%20their%20mental%20h ealth%20-%20Final%20report.pdf>

### **Assessing the Outline Uncertainty of Spatial Disease Clusters**

Fernando L. P. Oliveira1, André L. F. Cançado2,

Luiz H. Duczmal3 and Anderson R. Duarte1 1*Departament of Mathematics, Universidade Federal de Ouro Preto* 2*Departament of Statistics, Universidade de Brasília* 3*Departament of Statistics, Universidade Federal de Minas Gerais Brazil*

#### **1. Introduction**

50 Public Health – Methodology, Environmental and Systems Issues

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The spatial analysis of disease incidence is a fundamental tool in public health monitoring (Lawson et al., 1999). Suppose that a geographic study area is divided into administrative areas, with known populations at risk and observed cases of disease within a certain period of time. An interesting question is the possible existence of spatial anomalies in the study area: are there localized regions within the map for which the relative concentration of cases among the population at risk is significantly higher than would be expected if the cases were distributed at random? Such anomalies, known as *spatial clusters*, are inherently difficult to delineate, for several reasons (Cancado et al., 2010; Lawson, 2009). Due to the stochastic nature of the number of observed cases of disease, the uncertainty may be elevated in the disease rate estimation for aggregated area maps, especially for small population areas. Thus the most likely disease cluster produced by any given method for the detection and inference of spatial clusters (like SaTScan (Kulldorff, 1999) or any other irregularly shaped scan) is subject to a lot of variation. If it is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we have enough information to exclude them from a health program of prevention?

A criterion was proposed (Goovaerts, 2006) to measure the uncertainty of each area being part of a possible localized anomaly in the map, finding error bounds for the delineation of spatial clusters in maps of areas with known populations and observed number of cases. A given map with the vector of real data (the number of observed cases for each area) was considered as just one of the possible realizations of the random variable vector with an unknown expected number of cases. In this methodology, *m* Monte Carlo replications were performed, considering that the simulated number of cases for each area is the realization of a random variable with average equal to the observed number of cases of the original map. Then the most likely cluster for each replicated map was detected. Finally, to each area *ai* it was assigned the number of simulations that *ai* was included in a most likely cluster. If an area belonged to the most likely cluster on all the *m* replications, it was colored as black;

**2. The intensity function**

*f*(*j*) = *LLR*(*j*), *j* = 1, . . . , *m*.

For each area *ai*, let:

for further details.

**3.1 Introduction**

formalize this procedure in the following.

Monte Carlo replication distributing randomly the *C* = ∑*K*

*q*(*ai*) = 1

**3. Genetic algorithm for spatial cluster finding**

the number of simulated cases in the area *ai*, *i* = 1, . . . , *K*, where ∑*K*

In this section we define a criterion to measure the plausibility of each area being part of a possible localized anomaly in the map. Following Oliveira et al. (2011), instead of finding the most likely cluster in the original map with the observed number of cases for each area, we consider maps where the number of cases are replications of a vector of random variables, whose averages are defined based on the observed number of cases of the original map. We

Assessing the Outline Uncertainty of Spatial Disease Clusters 53

The original map has *ci* observed cases in the area *ai*, *i* = 1, . . . , *K*. Now we construct a

*a*1,..., *aK* according to a multinomial distribution where the probability associated to the area *ai* is *ci*/*C*. Let *V* = (*s*1,...,*sK*) the realization of the multinomial random vector where *si* is

finder algorithm (in our setting we use the circular scan or we use the elliptic scan) now finds the most likely cluster *MLC*1 with likelihood ratio value *LLR*1. The Monte Carlo procedure above is repeated *m* times, generating a set of *m* likelihood ratio values {*LLR*1,..., *LLRm*} corresponding to the most likely clusters {*MLC*1,..., *MLCm*}. The likelihood ratio values are sorted in increasing order as {*LLR*(1),..., *LLR*(*m*)} for the corresponding most likely clusters found {*MLC*(1),..., *MLC*(*m*)}. We now define the *intensity f unction f* : {1, . . . , *m*} −→ **R** by

*m* arg max 1≤*j*≤*m*,*ai*∈*MLC*(*j*)

If the area *ai* does not belong to any of the sets *MLC*(1),..., *MLC*(*m*) then we set *q*(*ai*) = 0. The value *q*(*ai*) represents the quantile of the highest likelihood ratio among the ranked values of the likelihood ratios of the most likely clusters found in the *m* Monte Carlo replications, which take into account the variability of the number of cases in each area. In this sense, the value *q*(*ai*) may be interpreted as the relative importance of the area *ai* as part of the anomaly of the map, where the value *f*(*ai*) represents the maximum likelihood ratio found for the most likely clusters which contain the area *ai*. This concept gives more information about the anomaly than the clear-cut division between cluster and non-cluster areas, as given by the usual process of finding the most likely cluster in the original map. See Oliveira et al. (2011)

Genetic algorithms (GA's) constitute an important class of optimization methods. Its importance comes from the fact that the GA's are robust algorithms, in the sense that they are able to treat a wide variety of problems. While some optimization methods require certain assumptions about the problem to be solved, without which these methods fail, the GA's do not require any assumption of continuity, convexity, differentiability and unimodality. In fact,

*f*(*j*), *i* = 1, . . . , *K*

*i*=1 *ci* cases among the *K* areas

*i*=1 *si* = *C*. The cluster

otherwise, if it never was part of a most likely cluster, then it was colored as white, with intermediate shades of gray in-between. A Bayesian variant along these lines, to detect and represent spatial clusters, was also proposed recently Neill (2011).

Another approach to represent the uncertainty in the delineation of spatial clusters appeared recently (Oliveira et al., 2011), employing a ranking based scheme known as *intensity function*. That procedure uses the circular spatial scan statistic (Kulldorff, 1999) to find the circularly shaped most likely cluster for each replicated map. The corresponding *m* likelihood values (obtained by means of the *m* Monte Carlo replications) are ranked. For each area *ai* , the maximum likelihood value, obtained among the most likely clusters containing the area *ai*, is determined. Finally, the intensity function associated to each areaŠs ranking of its respective likelihood value among the *m* obtained values is constructed. The latest procedure generally produce less biased results when compared with the two previous schemes.

However, the circular spatial scan has several limitations, which were discussed in the literature (Duczmal et al., 2006; Kulldorff et al., 2006). Particularly, the circular window is not suitable to make the correct delineation of irregularly shaped clusters because it either chooses a proper subset of the true cluster (underestimation) or chooses a large circle containing the cluster as a proper subset (overestimation). One important consequence is the reduction of the power of detection (Duczmal et al., 2006). In order to overcome this limitation, many algorithms were recently proposed to detect irregularly shaped clusters, replacing the circularly shaped window scheme for any strategy of finding irregularly shaped solutions. Usually, the only limitation in shape for those clusters is a connectivity requirement. In this work, we will analyze the utilization of irregularly shaped algorithms for the application of the intensity function (Oliveira et al., 2011), compared to the use of the simple circular scan, which was employed as the standard method. Due to the regular shape of the most likely cluster found, a question was left, at least in part unanswered: do all the areas inside the cluster have the same importance from a practitioner perspective? In this work is proposed an application of the intensity function for irregularly shaped algorithms, thus avoiding a potential problem inherent in the use of the circular spatial scan, which may described as the lack of resolution inside the circular cluster. As a consequence, it may be difficult or impossible to distinguish the relative importance of the areas inside the detected circular cluster. As we shall see, this problem does not occur when using irregularly shaped scans. Besides, the maximum allowed size for the most likely cluster has a large influence in the result of the cluster search (Chen J, 2008).

In this work novel results are presented, applying the multi-objective genetic algorithm scan (Duarte et al., 2010; Duczmal et al., 2008; 2007), adapted for the weighted non-connectivity penalty function (Cancado et al., 2010). Also, by allowing several different maximum sizes for the most likely cluster, the possible anomaly could be identified with greater precision. As will be demonstrated in the following sections, much better delineated cluster maps of the intensity function will be generated, as compared with the previous version using the simpler circular scan. As a consequence, the relative importance of individual regions composing the spatial anomalies may be assessed, and several interesting phenomena related to the geographical distribution of chronic and acute diseases may be visualized.

#### **2. The intensity function**

2 Will-be-set-by-IN-TECH

otherwise, if it never was part of a most likely cluster, then it was colored as white, with intermediate shades of gray in-between. A Bayesian variant along these lines, to detect and

Another approach to represent the uncertainty in the delineation of spatial clusters appeared recently (Oliveira et al., 2011), employing a ranking based scheme known as *intensity function*. That procedure uses the circular spatial scan statistic (Kulldorff, 1999) to find the circularly shaped most likely cluster for each replicated map. The corresponding *m* likelihood values (obtained by means of the *m* Monte Carlo replications) are ranked. For each area *ai* , the maximum likelihood value, obtained among the most likely clusters containing the area *ai*, is determined. Finally, the intensity function associated to each areaŠs ranking of its respective likelihood value among the *m* obtained values is constructed. The latest procedure generally

However, the circular spatial scan has several limitations, which were discussed in the literature (Duczmal et al., 2006; Kulldorff et al., 2006). Particularly, the circular window is not suitable to make the correct delineation of irregularly shaped clusters because it either chooses a proper subset of the true cluster (underestimation) or chooses a large circle containing the cluster as a proper subset (overestimation). One important consequence is the reduction of the power of detection (Duczmal et al., 2006). In order to overcome this limitation, many algorithms were recently proposed to detect irregularly shaped clusters, replacing the circularly shaped window scheme for any strategy of finding irregularly shaped solutions. Usually, the only limitation in shape for those clusters is a connectivity requirement. In this work, we will analyze the utilization of irregularly shaped algorithms for the application of the intensity function (Oliveira et al., 2011), compared to the use of the simple circular scan, which was employed as the standard method. Due to the regular shape of the most likely cluster found, a question was left, at least in part unanswered: do all the areas inside the cluster have the same importance from a practitioner perspective? In this work is proposed an application of the intensity function for irregularly shaped algorithms, thus avoiding a potential problem inherent in the use of the circular spatial scan, which may described as the lack of resolution inside the circular cluster. As a consequence, it may be difficult or impossible to distinguish the relative importance of the areas inside the detected circular cluster. As we shall see, this problem does not occur when using irregularly shaped scans. Besides, the maximum allowed size for the most likely cluster has a large influence in the result of the

In this work novel results are presented, applying the multi-objective genetic algorithm scan (Duarte et al., 2010; Duczmal et al., 2008; 2007), adapted for the weighted non-connectivity penalty function (Cancado et al., 2010). Also, by allowing several different maximum sizes for the most likely cluster, the possible anomaly could be identified with greater precision. As will be demonstrated in the following sections, much better delineated cluster maps of the intensity function will be generated, as compared with the previous version using the simpler circular scan. As a consequence, the relative importance of individual regions composing the spatial anomalies may be assessed, and several interesting phenomena related to the

geographical distribution of chronic and acute diseases may be visualized.

represent spatial clusters, was also proposed recently Neill (2011).

produce less biased results when compared with the two previous schemes.

cluster search (Chen J, 2008).

In this section we define a criterion to measure the plausibility of each area being part of a possible localized anomaly in the map. Following Oliveira et al. (2011), instead of finding the most likely cluster in the original map with the observed number of cases for each area, we consider maps where the number of cases are replications of a vector of random variables, whose averages are defined based on the observed number of cases of the original map. We formalize this procedure in the following.

The original map has *ci* observed cases in the area *ai*, *i* = 1, . . . , *K*. Now we construct a Monte Carlo replication distributing randomly the *C* = ∑*K i*=1 *ci* cases among the *K* areas *a*1,..., *aK* according to a multinomial distribution where the probability associated to the area *ai* is *ci*/*C*. Let *V* = (*s*1,...,*sK*) the realization of the multinomial random vector where *si* is the number of simulated cases in the area *ai*, *i* = 1, . . . , *K*, where ∑*K i*=1 *si* = *C*. The cluster finder algorithm (in our setting we use the circular scan or we use the elliptic scan) now finds the most likely cluster *MLC*1 with likelihood ratio value *LLR*1. The Monte Carlo procedure above is repeated *m* times, generating a set of *m* likelihood ratio values {*LLR*1,..., *LLRm*} corresponding to the most likely clusters {*MLC*1,..., *MLCm*}. The likelihood ratio values are sorted in increasing order as {*LLR*(1),..., *LLR*(*m*)} for the corresponding most likely clusters found {*MLC*(1),..., *MLC*(*m*)}. We now define the *intensity f unction f* : {1, . . . , *m*} −→ **R** by *f*(*j*) = *LLR*(*j*), *j* = 1, . . . , *m*.

For each area *ai*, let:

$$q(a\_i) = \frac{1}{m} \quad \text{arg} \quad \max\_{1 \le j \le m, a\_i \in MLC\_{(j)}} f(j), i = 1, \dots, K$$

If the area *ai* does not belong to any of the sets *MLC*(1),..., *MLC*(*m*) then we set *q*(*ai*) = 0. The value *q*(*ai*) represents the quantile of the highest likelihood ratio among the ranked values of the likelihood ratios of the most likely clusters found in the *m* Monte Carlo replications, which take into account the variability of the number of cases in each area. In this sense, the value *q*(*ai*) may be interpreted as the relative importance of the area *ai* as part of the anomaly of the map, where the value *f*(*ai*) represents the maximum likelihood ratio found for the most likely clusters which contain the area *ai*. This concept gives more information about the anomaly than the clear-cut division between cluster and non-cluster areas, as given by the usual process of finding the most likely cluster in the original map. See Oliveira et al. (2011) for further details.

#### **3. Genetic algorithm for spatial cluster finding**

#### **3.1 Introduction**

Genetic algorithms (GA's) constitute an important class of optimization methods. Its importance comes from the fact that the GA's are robust algorithms, in the sense that they are able to treat a wide variety of problems. While some optimization methods require certain assumptions about the problem to be solved, without which these methods fail, the GA's do not require any assumption of continuity, convexity, differentiability and unimodality. In fact,

**3.2.1 Generating the initial population**

**3.2.2 The selection operator**

**3.2.3 The crossover operator**

gray).

**3.2.4 The mutation operator**

region, provided the cluster's connectivity.

grows until it reaches a maximum size set by the user.

is repeated *n* times, thus producing a set of *n* selected individuals.

operator that ensures that every generated solution is feasible.

The initial population is generated by a greedy procedure. Given a map with *n* regions we generate a population of *n* individuals, each of which is generated from one region of the map. So, starting with a region, the solution incorporates more regions, choosing at each step to aggregate, among all the regions that are neighbors of some region in the actual solution, the one that makes the *LLR* value to increase the most when added to the solution. The individual

Assessing the Outline Uncertainty of Spatial Disease Clusters 55

Each solution is evaluated by means of its *LLR* value and this is the adaptation indicator: higher *LLR*-valued individuals are more adapted. The selection operator will then give more chances to the more adapted individuals to generate offspring. This is done through a mechanism called binary tournment. For each tournment two individuals are chosen from the current population, each individual having the same probability of being chosed. Then we compare the two solutions and the one with higher *LLR* value is selected. This procedure

Now, selected individuals have the chance to trasmit their genetic information to new individuals by generating offspring. Crossover is applied to pairs of parents randomly chosen from the list of selected individuals. The offspring is generated in a way that the children inherit characteristis from both parents. In addition, it is well known that GA's particularly designed for a specific problem perform much better than multiple-purpose generic GA's. Thus, it is highly recommended that operators are designed so that they can take advantage of the intrinsic structure of the problem. For example, in our case we would discard any disconnected cluster candidate because it is an infeasible solution. While a generic crossover operator could, most of the time, generate infeasible clusters, we chose to use a crossover

The crossover operator described by Duczmal et al. (2007) presents all these features, being capable of efficiently generating feasible offspring having characteristics of both parents. The operator is implemented in sucha a way that it is only possible to perform a crossover between two parents if they share a nonempty intersection. Once this condition is verified, the offspring is generated. Figure 1 shows an example with two parents (*A* and *B*) and the generated offspring 1-5. Note that the offspring constitutes a "path" from one parent to another, with child 1 being more like parent *A*, while child 5 is almost like parent *B*. In the middle of the figure we can see parents inside the map with the two intersection regions (in

Each individual generated by the crossover process has a probability of suffering a mutation. Mutation consistis in introducing a random perturbation in the genetic code of the individual. In our case, the mutation consists of adding to or removing from the cluster a randomly chosen

the only assumption a GA requires is that the function to be optimized presents a "global trend" that can be captured or learned by the algorithm. Of course, not making any kind of assumption and, consequently, not using these characteristics in favor, GA'a tend to be computationally intensive, so its usage is justified for difficult problems.

When looking for a most likely cluster, one faces a challenging otimization problem: given a set *R* of *n* regions in a map, some of which are neighbors, find the connected subset *S* of *R* that assumes the highest *LLR* value. By "connected" we mean that, starting from any region in *S* there's always a path to any other region of *S* formed by a chain of neighbors, all of them inside *S*.

Solving this problem exactly means that we would have to look at all of the 2*n* subsets of *R*, test which ones are connected, evaluate their *LLR* values and pick up the most likely one. For maps with just a few dozens of regions this problem is already intractable. So we need another strategy to find such optimal solution. GA's showed to be a good alternative for the spatial cluster finding problem (Duczmal et al., 2008; 2007).

#### **3.2 The genetic algorithm**

The natural evolution of living beings can be compared to an optimization process. In fact, if individuals who are best adapted survive - in the sense of transmitting their genetic information - while less adapted individuals tend to disappear, it is expected that after a number of generations the population is composed of individuals who are generally better adapted than those of earlier generations. This is also the idea behind a genetic algorithm. It tries to simulate the mechanisms of random variation and selection of adaptive evolution. The mechanisms (or genetic operators) that form the basis of a genetic algorithm are:

In this context, an individual is a candidate-solution to the optimization problem and a population is a set of individuals. For the spatial cluster detection problem a solution - or individual - is a set of connected regions of the map (the candidate cluster). So, the population is a set of lists, each list being a set of regions that form the solution.

Starting with an initial population the GA forms a sequence of generations. At each iteration it applies the selection, crossover and mutation operators to the current population, generating a new population. The GA used in this work was primarily described in Duczmal et al. (2007) and its biobjective versions were used by Duczmal et al. (2008), Cancado et al. (2010) and Duarte et al. (2010).

#### **3.2.1 Generating the initial population**

4 Will-be-set-by-IN-TECH

the only assumption a GA requires is that the function to be optimized presents a "global trend" that can be captured or learned by the algorithm. Of course, not making any kind of assumption and, consequently, not using these characteristics in favor, GA'a tend to be

When looking for a most likely cluster, one faces a challenging otimization problem: given a set *R* of *n* regions in a map, some of which are neighbors, find the connected subset *S* of *R* that assumes the highest *LLR* value. By "connected" we mean that, starting from any region in *S* there's always a path to any other region of *S* formed by a chain of neighbors, all of them

Solving this problem exactly means that we would have to look at all of the 2*n* subsets of *R*, test which ones are connected, evaluate their *LLR* values and pick up the most likely one. For maps with just a few dozens of regions this problem is already intractable. So we need another strategy to find such optimal solution. GA's showed to be a good alternative for the

The natural evolution of living beings can be compared to an optimization process. In fact, if individuals who are best adapted survive - in the sense of transmitting their genetic information - while less adapted individuals tend to disappear, it is expected that after a number of generations the population is composed of individuals who are generally better adapted than those of earlier generations. This is also the idea behind a genetic algorithm. It tries to simulate the mechanisms of random variation and selection of adaptive evolution.

• crossover operator, which combines the information of two or more individuals - called

• mutation operator, which applies a random perturbation to the information of an

• selection operator, which defines the probability of an individual to transmit its genetic

In this context, an individual is a candidate-solution to the optimization problem and a population is a set of individuals. For the spatial cluster detection problem a solution - or individual - is a set of connected regions of the map (the candidate cluster). So, the population

Starting with an initial population the GA forms a sequence of generations. At each iteration it applies the selection, crossover and mutation operators to the current population, generating a new population. The GA used in this work was primarily described in Duczmal et al. (2007) and its biobjective versions were used by Duczmal et al. (2008), Cancado et al. (2010) and

The mechanisms (or genetic operators) that form the basis of a genetic algorithm are:

computationally intensive, so its usage is justified for difficult problems.

spatial cluster finding problem (Duczmal et al., 2008; 2007).

parents - generating new individuals - called children;

information (generate children) based on its adaptation level.

is a set of lists, each list being a set of regions that form the solution.

individual, generating a new one;

Duarte et al. (2010).

inside *S*.

**3.2 The genetic algorithm**

The initial population is generated by a greedy procedure. Given a map with *n* regions we generate a population of *n* individuals, each of which is generated from one region of the map. So, starting with a region, the solution incorporates more regions, choosing at each step to aggregate, among all the regions that are neighbors of some region in the actual solution, the one that makes the *LLR* value to increase the most when added to the solution. The individual grows until it reaches a maximum size set by the user.

#### **3.2.2 The selection operator**

Each solution is evaluated by means of its *LLR* value and this is the adaptation indicator: higher *LLR*-valued individuals are more adapted. The selection operator will then give more chances to the more adapted individuals to generate offspring. This is done through a mechanism called binary tournment. For each tournment two individuals are chosen from the current population, each individual having the same probability of being chosed. Then we compare the two solutions and the one with higher *LLR* value is selected. This procedure is repeated *n* times, thus producing a set of *n* selected individuals.

#### **3.2.3 The crossover operator**

Now, selected individuals have the chance to trasmit their genetic information to new individuals by generating offspring. Crossover is applied to pairs of parents randomly chosen from the list of selected individuals. The offspring is generated in a way that the children inherit characteristis from both parents. In addition, it is well known that GA's particularly designed for a specific problem perform much better than multiple-purpose generic GA's. Thus, it is highly recommended that operators are designed so that they can take advantage of the intrinsic structure of the problem. For example, in our case we would discard any disconnected cluster candidate because it is an infeasible solution. While a generic crossover operator could, most of the time, generate infeasible clusters, we chose to use a crossover operator that ensures that every generated solution is feasible.

The crossover operator described by Duczmal et al. (2007) presents all these features, being capable of efficiently generating feasible offspring having characteristics of both parents. The operator is implemented in sucha a way that it is only possible to perform a crossover between two parents if they share a nonempty intersection. Once this condition is verified, the offspring is generated. Figure 1 shows an example with two parents (*A* and *B*) and the generated offspring 1-5. Note that the offspring constitutes a "path" from one parent to another, with child 1 being more like parent *A*, while child 5 is almost like parent *B*. In the middle of the figure we can see parents inside the map with the two intersection regions (in gray).

#### **3.2.4 The mutation operator**

Each individual generated by the crossover process has a probability of suffering a mutation. Mutation consistis in introducing a random perturbation in the genetic code of the individual. In our case, the mutation consists of adding to or removing from the cluster a randomly chosen region, provided the cluster's connectivity.

f 1

Assessing the Outline Uncertainty of Spatial Disease Clusters 57

Fig. 2. A set of solutions in the objectives space: dominated solutions (×) and Pareto-set (•).

Once the most likely cluster is identified, we want to check its significace. This will allow the practitioner to verify if the cluster can be considered a disease outbreak or if the disease cases are randomly spreaded over the map. Since the distribution of LLR under *H*0 is not known we must perform a Monte Carlo simulation. For the mono-objective case, the LLR value is calculated for the most likely cluster in each Monte Carlo replication under *H*0 and the *p*-value is computed comparing the value of LLR for the observed data and the empirical

For the biobjective case, we consider the attainment function (da Fonseca et al., 2001; Fonseca et al., 2005), as also used by Cancado et al. (2010). A single run of the biobjective GA would produce a Pareto-set, defining two distinct regions in the objectives space: points that are dominated by the Pareto-set and points that are not dominated by it. Then, for inference purposes we can consider, for each point of the Pareto-set obtained for the observed data, the proportion times that the point is dominated by the Pareto-sets under *H*0. This is exactly the

One of the possible penalties that takes in account the cluster geometric shape is the called compactness geometric penalty function. This penalty function introduced in Duczmal et al.

f

*p*-value for that point.

**3.5 The geometric penalty function**

2

**3.4 Inference and the attainment function**

distribution obtained through the Monte Carlo procedure.

Fig. 1. A splitted vision of parents *A* and *B* (left), parents *A* and *B* inside the map (middle) and offspring (right).

#### **3.3 The biobjective genetic algorithm**

Many times one wants to find a solution that simultaneously optimizes two or more functionals. For example, a costumer may want to buy a car which is powerful and cheap. Of course, it is very unlikely that, say, the most powerful is also the cheaper car, because these two criteria are conflicting. Based on these two criteria, a whole set of cars can be of interest for this costumer: powerful (but expensive) cars and cheap (but underpowered) cars. Of course, a costumer (again, based on just these criteria) will reject cars that cost too much and are low powered.

Following the same reasoning, a biobjective GA was proposed (Cancado et al., 2010; Duarte et al., 2010; Duczmal et al., 2008) to deal with the problem of spatial cluster detection. Using the LLR as the only objective to be maximized would lead to geographically meaningless tree-shaped solutions and it is necessary to consider some shape regularity measure, such as geometric compactness (Duczmal et al., 2008) or topological corrections (Cancado et al., 2010; Yiannakoulias et al., 2007). This regularity measure works as a second objective to be maximized. As in the power/price car example, LLR and regularity are conflicting objectives, because high values of LLR are associated to very irregular clusters, while regular solutions tend to

Instead of an optimal solution, a biobjective maximization problem will lead, in general, to a set of optimal solutions, called the Pareto-set. This set is composed by all solutions that are not worse than any other solution in both objectives simulteanously. Such solution is called nondominated solution. Because GA's work with a population of candidate-solutions they can find the Pareto-set in one execution with almost the same effort spent by its mono-objective version. Figure 2 illustrates a set of solutions in the objectives space. Nondominated solutions are indicated by black dots.

6 Will-be-set-by-IN-TECH

Fig. 1. A splitted vision of parents *A* and *B* (left), parents *A* and *B* inside the map (middle)

Many times one wants to find a solution that simultaneously optimizes two or more functionals. For example, a costumer may want to buy a car which is powerful and cheap. Of course, it is very unlikely that, say, the most powerful is also the cheaper car, because these two criteria are conflicting. Based on these two criteria, a whole set of cars can be of interest for this costumer: powerful (but expensive) cars and cheap (but underpowered) cars. Of course, a costumer (again, based on just these criteria) will reject cars that cost too much and are low

Following the same reasoning, a biobjective GA was proposed (Cancado et al., 2010; Duarte et al., 2010; Duczmal et al., 2008) to deal with the problem of spatial cluster detection. Using the LLR as the only objective to be maximized would lead to geographically meaningless tree-shaped solutions and it is necessary to consider some shape regularity measure, such as geometric compactness (Duczmal et al., 2008) or topological corrections (Cancado et al., 2010; Yiannakoulias et al., 2007). This regularity measure works as a second objective to be maximized. As in the power/price car example, LLR and regularity are conflicting objectives, because high values of LLR are associated to very irregular clusters, while regular solutions

Instead of an optimal solution, a biobjective maximization problem will lead, in general, to a set of optimal solutions, called the Pareto-set. This set is composed by all solutions that are not worse than any other solution in both objectives simulteanously. Such solution is called nondominated solution. Because GA's work with a population of candidate-solutions they can find the Pareto-set in one execution with almost the same effort spent by its mono-objective version. Figure 2 illustrates a set of solutions in the objectives space.

and offspring (right).

powered.

tend to

**3.3 The biobjective genetic algorithm**

Nondominated solutions are indicated by black dots.

Fig. 2. A set of solutions in the objectives space: dominated solutions (×) and Pareto-set (•).

#### **3.4 Inference and the attainment function**

Once the most likely cluster is identified, we want to check its significace. This will allow the practitioner to verify if the cluster can be considered a disease outbreak or if the disease cases are randomly spreaded over the map. Since the distribution of LLR under *H*0 is not known we must perform a Monte Carlo simulation. For the mono-objective case, the LLR value is calculated for the most likely cluster in each Monte Carlo replication under *H*0 and the *p*-value is computed comparing the value of LLR for the observed data and the empirical distribution obtained through the Monte Carlo procedure.

For the biobjective case, we consider the attainment function (da Fonseca et al., 2001; Fonseca et al., 2005), as also used by Cancado et al. (2010). A single run of the biobjective GA would produce a Pareto-set, defining two distinct regions in the objectives space: points that are dominated by the Pareto-set and points that are not dominated by it. Then, for inference purposes we can consider, for each point of the Pareto-set obtained for the observed data, the proportion times that the point is dominated by the Pareto-sets under *H*0. This is exactly the *p*-value for that point.

#### **3.5 The geometric penalty function**

One of the possible penalties that takes in account the cluster geometric shape is the called compactness geometric penalty function. This penalty function introduced in Duczmal et al.

**4. Results and discussion**

treatment.

**4.1 Real data case studies**

Epidemiological surveillance is essential to monitoring possible changes in the geographical distribution pattern of both acute and chronic diseases. To illustrate the techniques presented in this chapter, four diseases (dengue fever, tuberculosis, diabetes and hypertension) are analyzed. Those four diseases are currently among the most serious health threats to the Brazilian population. Our studies were concentrated in the Minas Gerais state in southeast Brazil, with 853 municipalities and total population of 19,597,330 (2010 census). For each disease, only the specific population at risk at each municipality was considered. Population data was available at Instituto Brasileiro de Geografia e Estatística (www.ibge.gov.br), and disease data was obtained through DATASUS, the Brazilian Ministry of Health's central data system (www.datasus.gov.br). Dengue fever data was collected by SINAN/MS system from the Brazilian Ministry of Health (www.sinam.org.br). During the period 2007-2010, 349.005 cases were registered, and the population at risk was assumed to be the total population of the 2010 census. Tuberculosis disease cases, using SINAN/MS data, were considered for the 2001-2010 period for the following age groups (years): 15-19, 20-39 and 40-59, making a total of 41,824 cases for a population at risk of 12,892,744. Hypertension data was obtained through the Hiperdia program of Brazilian Ministry of Health from January 2002 to January 2011. Data was available to the following age groups (years): 50-59, 60-69, 70-79 and 80+, with a total population at risk of 4,365,352 individuals and 941,710 cases. Diabetes types 1 and 2 data were also obtained through the Hiperdia program from January 2002 to May 2011. The age groups were: 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79 and 80+ years, with 28.039 cases. Diabetes mellitus and hypertension are considered chronic diseases and their control and treatment depend on the individuals behavior in relation to their lifestyle: healthy eating, physical activity, and weight control. These diseases are responsible for high rates of hospital expenses, so the investment in shares of health promotion and prevention is potentially very cost effective. The importance of dengue in our study lies in the fact that it is an infectious disease and even in regions with previous low incidence rates are subject to outbreaks. This disease is subject to major public health campaigns in Brazil. The report on the epidemiology of dengue published by the Secretariat of Health Surveillance in 2010 indicates Minas Gerais state as one of the critical states in need of stricter monitoring. Hypertension and diabetes are very common chronic diseases, and hypertension is a major public health problem in Brazil. Tuberculosis has become relevant to this study due to its high incidence, and its early diagnosis and effective treatment are of great importance to public health. The biggest challenge for public health professionals has been to promote action to encourage compliance and continuity of care, since many individuals do not join or do not follow the prescribed

Assessing the Outline Uncertainty of Spatial Disease Clusters 59

In what follows, we present the obtained sets of intensity function maps for dengue fever, tuberculosis, diabetes and hypertension in Minas Gerais state (Figures 3, 4, 5 and 6, respectively). North is up for all the maps. For each disease set we present six maps: (a) the quantiles of population at risk, (b) the quantiles of disease rate and the intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30

(2006) aims to penalize zones in the map that have very irregular shape. The compactness geometric function *k*(*z*) of a zone *z* is given by the area of *z* divided by the area of a circle with the same perimeter as the convex hull of *z*. The compactness geometric function takes values between zero and one, the circle has the most compact shape (*k*(*z*) = 1). Compactness depends on the shape of the zone, but not on its size. The expression for *k*(*z*) is given by:

$$k(z) = \frac{4\pi A(z)}{H(z)^2} \tag{1}$$

where *A*(*z*) is the area of the zone *z* and *H*(*z*) the perimeter of the convex hull of *z*. The compactness penalyzed scan statistic is defined as *maxz*∈*Zk*(*z*).*LLR*(*z*).

#### **3.6 The non-connectivity penalty function**

Yiannakoulias et al. (2007) proposed a greedy algorithm to scan the set *Z* of all possible zones *z*. A new penalty function called non-connectivity was proposed. It was based on the ratio of the number of nodes *v*(*z*) to the number of edges *e*(*z*) of the subgraph associated with the zone *z*. The non-connectivity penalty was used as a multiplier for the *LLR*(*z*). The non-connectivity penalty function of a zone *z* is defined by:

$$mc(z) = \frac{e(z)}{\left[\Im\left(v(z) - 2\right)\right]}\tag{2}$$

the expression in the denominator represents the maximum number of edges of a planar graph given its number of vertices. The most penalized zones are the ones whith tree-like associated graphs, meaning that they have a small number of nodes compared with the number of edges. Although there is some similarity between the non-connectivity penalty to the geometric compactness penalty, there is an important difference: the non-connectivity penalty does not rely on the geometric shape of the candidate cluster, which could be an interesting feature when searching for real clusters which are highly irregularly shaped, but present good connectivity properties.

#### **3.7 Evaluation of the candidate solutions**

Differently from the previous procedure employing the circular scan, each run of the multiobjective genetic scan produces a set of several non-dominated solutions.

In the circular scan, the scan statistic value for the most likely cluster was assigned to each area of the solution cluster, and later the maximum value of this quantity was obtained for all the executions. However, in the multiobjective procedure, the scan statistic value will be assigned for each component area of each solution cluster of the non-dominated solution set. In the event that a given area belongs to more than one solution cluster, the largest scan statistic value is assigned to the area. The remaining of the process is identical to the usual procedure using the circular scan, obtaining the maximum value of this quantity for all the executions, and building the intensity function as usual.

#### **4. Results and discussion**

8 Will-be-set-by-IN-TECH

(2006) aims to penalize zones in the map that have very irregular shape. The compactness geometric function *k*(*z*) of a zone *z* is given by the area of *z* divided by the area of a circle with the same perimeter as the convex hull of *z*. The compactness geometric function takes values between zero and one, the circle has the most compact shape (*k*(*z*) = 1). Compactness depends on the shape of the zone, but not on its size. The expression for *k*(*z*) is given by:

*k*(*z*) = 4*πA*(*z*)

where *A*(*z*) is the area of the zone *z* and *H*(*z*) the perimeter of the convex hull of *z*. The

Yiannakoulias et al. (2007) proposed a greedy algorithm to scan the set *Z* of all possible zones *z*. A new penalty function called non-connectivity was proposed. It was based on the ratio of the number of nodes *v*(*z*) to the number of edges *e*(*z*) of the subgraph associated with the zone *z*. The non-connectivity penalty was used as a multiplier for the *LLR*(*z*). The non-connectivity

*nc*(*z*) = *e*(*z*)

the expression in the denominator represents the maximum number of edges of a planar graph given its number of vertices. The most penalized zones are the ones whith tree-like associated graphs, meaning that they have a small number of nodes compared with the number of edges. Although there is some similarity between the non-connectivity penalty to the geometric compactness penalty, there is an important difference: the non-connectivity penalty does not rely on the geometric shape of the candidate cluster, which could be an interesting feature when searching for real clusters which are highly irregularly shaped, but

Differently from the previous procedure employing the circular scan, each run of the

In the circular scan, the scan statistic value for the most likely cluster was assigned to each area of the solution cluster, and later the maximum value of this quantity was obtained for all the executions. However, in the multiobjective procedure, the scan statistic value will be assigned for each component area of each solution cluster of the non-dominated solution set. In the event that a given area belongs to more than one solution cluster, the largest scan statistic value is assigned to the area. The remaining of the process is identical to the usual procedure using the circular scan, obtaining the maximum value of this quantity for all the executions,

multiobjective genetic scan produces a set of several non-dominated solutions.

compactness penalyzed scan statistic is defined as *maxz*∈*Zk*(*z*).*LLR*(*z*).

**3.6 The non-connectivity penalty function**

penalty function of a zone *z* is defined by:

present good connectivity properties.

**3.7 Evaluation of the candidate solutions**

and building the intensity function as usual.

*H*(*z*)2 (1)

[3 (*v*(*z*) − 2)] (2)

Epidemiological surveillance is essential to monitoring possible changes in the geographical distribution pattern of both acute and chronic diseases. To illustrate the techniques presented in this chapter, four diseases (dengue fever, tuberculosis, diabetes and hypertension) are analyzed. Those four diseases are currently among the most serious health threats to the Brazilian population. Our studies were concentrated in the Minas Gerais state in southeast Brazil, with 853 municipalities and total population of 19,597,330 (2010 census). For each disease, only the specific population at risk at each municipality was considered. Population data was available at Instituto Brasileiro de Geografia e Estatística (www.ibge.gov.br), and disease data was obtained through DATASUS, the Brazilian Ministry of Health's central data system (www.datasus.gov.br). Dengue fever data was collected by SINAN/MS system from the Brazilian Ministry of Health (www.sinam.org.br). During the period 2007-2010, 349.005 cases were registered, and the population at risk was assumed to be the total population of the 2010 census. Tuberculosis disease cases, using SINAN/MS data, were considered for the 2001-2010 period for the following age groups (years): 15-19, 20-39 and 40-59, making a total of 41,824 cases for a population at risk of 12,892,744. Hypertension data was obtained through the Hiperdia program of Brazilian Ministry of Health from January 2002 to January 2011. Data was available to the following age groups (years): 50-59, 60-69, 70-79 and 80+, with a total population at risk of 4,365,352 individuals and 941,710 cases. Diabetes types 1 and 2 data were also obtained through the Hiperdia program from January 2002 to May 2011. The age groups were: 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79 and 80+ years, with 28.039 cases.

Diabetes mellitus and hypertension are considered chronic diseases and their control and treatment depend on the individuals behavior in relation to their lifestyle: healthy eating, physical activity, and weight control. These diseases are responsible for high rates of hospital expenses, so the investment in shares of health promotion and prevention is potentially very cost effective. The importance of dengue in our study lies in the fact that it is an infectious disease and even in regions with previous low incidence rates are subject to outbreaks. This disease is subject to major public health campaigns in Brazil. The report on the epidemiology of dengue published by the Secretariat of Health Surveillance in 2010 indicates Minas Gerais state as one of the critical states in need of stricter monitoring. Hypertension and diabetes are very common chronic diseases, and hypertension is a major public health problem in Brazil. Tuberculosis has become relevant to this study due to its high incidence, and its early diagnosis and effective treatment are of great importance to public health. The biggest challenge for public health professionals has been to promote action to encourage compliance and continuity of care, since many individuals do not join or do not follow the prescribed treatment.

#### **4.1 Real data case studies**

In what follows, we present the obtained sets of intensity function maps for dengue fever, tuberculosis, diabetes and hypertension in Minas Gerais state (Figures 3, 4, 5 and 6, respectively). North is up for all the maps. For each disease set we present six maps: (a) the quantiles of population at risk, (b) the quantiles of disease rate and the intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

=0 0 to 0.92 0.92 to 0.94 >0.94

=0 0 to 0.81 0.81 to 0.83 >0.83

40 (c, d, e and f respectively)

Intensity function quantile

Intensity function quantile

(a)

(c)

(e)

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

Assessing the Outline Uncertainty of Spatial Disease Clusters 61

=0 0 to 0.85 0.85 to 0.88 >0.88

=0 0 to 0.69 0.69 to 0.76 >0.76

Fig. 3. Population at risk quantiles (a), dengue fever rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and

Intensity function quantile

Intensity function quantile

(b)

(d)

(f)

and 40 (c, d, e and f respectively). The population at risk was different for each disease in our study.

As can be noted on all four disease sets, the probability that each area belongs to the "'true"' cluster decreases as the maximum cluster size increases from 10 to 40. For instance, in the dengue fever set, the dark brown areas have probability of belonging to the "'true cluster"' greater than 94%, 88%, 83% and 76%, as the maximum cluster size increases from 10, 20, 30 and 40, respectively. It means that the intensity function maps produced with the smaller maximum cluster sizes (10 and 20)indicate inner "'core"' regions within the "'true cluster"'. On the other hand, the intensity function maps produced with the larger maximum cluster sizes (30 and 40)indicate "'borderline"' regions with respect of the "'true cluster"'.

Another important feature is the complexity of the shapes displayed in the sequence of intensity function maps as the maximum cluster size increases.

#### **4.1.1 Dengue fever**

In Figure 3c, the maximum size 10 inner core region of dengue fever includes the municipalities arround the state capital Belo Horizonte urban area (population 4 million) in the central part of the state. The maximum sizes 20 and 30 intensity function maps (Figures 3d and 3e respectively) show the anomaly spreading northward following the São Francisco river basin, a region with elevated humidity and high mosquito incidence. Finally, the larger maximum size 40 anomaly (Figure 3f) spreads along the highway joining the cities of Ipatinga, Valadares and Teofilo Otoni in the eastern part of the state.

#### **4.1.2 Tuberculosis**

In Figure 4c, the maximum size 10 inner core region of tuberculosis includes the predominantly urban area of Belo Horizonte (in the central part of the state) and two weaker urban regions: (i) the highway joining the cities of Ipatinga, Valadares and Teofilo Otoni in the eastern part of the state, and (ii) the areas surrounding the city of Juiz de Fora, the second largest city of the state in the south. As the maximum cluster size increases (Figures 4d, 4e and 4f), the tuberculosis anomaly is reinforced to include the surrounding municipalities, and also the neighbors of the populous Montes Claros city in the northern part of the state.

#### **4.1.3 Diabetes**

In Figure 5c, the maximum size 10 inner core region of diabetes includes the southwest part of the state and the weaker urban region of Valadares city in the east. As the maximum cluster size increases (Figures 5d, 5e and 5f), the diabetes anomaly is reinforced to include the surrounding municipalities.

#### **4.1.4 Hypertension**

In Figure 6c, the maximum size 10 inner core region of hypertension includes several scattered regions in the center and mid southeast parts of the state. As the maximum cluster size increases (Figures 6d, 6e and 6f), the hypertension anomaly is reinforced to include the surrounding municipalities.

10 Will-be-set-by-IN-TECH

and 40 (c, d, e and f respectively). The population at risk was different for each disease in our

As can be noted on all four disease sets, the probability that each area belongs to the "'true"' cluster decreases as the maximum cluster size increases from 10 to 40. For instance, in the dengue fever set, the dark brown areas have probability of belonging to the "'true cluster"' greater than 94%, 88%, 83% and 76%, as the maximum cluster size increases from 10, 20, 30 and 40, respectively. It means that the intensity function maps produced with the smaller maximum cluster sizes (10 and 20)indicate inner "'core"' regions within the "'true cluster"'. On the other hand, the intensity function maps produced with the larger maximum cluster

Another important feature is the complexity of the shapes displayed in the sequence of

In Figure 3c, the maximum size 10 inner core region of dengue fever includes the municipalities arround the state capital Belo Horizonte urban area (population 4 million) in the central part of the state. The maximum sizes 20 and 30 intensity function maps (Figures 3d and 3e respectively) show the anomaly spreading northward following the São Francisco river basin, a region with elevated humidity and high mosquito incidence. Finally, the larger maximum size 40 anomaly (Figure 3f) spreads along the highway joining the cities of Ipatinga,

In Figure 4c, the maximum size 10 inner core region of tuberculosis includes the predominantly urban area of Belo Horizonte (in the central part of the state) and two weaker urban regions: (i) the highway joining the cities of Ipatinga, Valadares and Teofilo Otoni in the eastern part of the state, and (ii) the areas surrounding the city of Juiz de Fora, the second largest city of the state in the south. As the maximum cluster size increases (Figures 4d, 4e and 4f), the tuberculosis anomaly is reinforced to include the surrounding municipalities, and also the neighbors of the populous Montes Claros city in the northern part of the state.

In Figure 5c, the maximum size 10 inner core region of diabetes includes the southwest part of the state and the weaker urban region of Valadares city in the east. As the maximum cluster size increases (Figures 5d, 5e and 5f), the diabetes anomaly is reinforced to include

In Figure 6c, the maximum size 10 inner core region of hypertension includes several scattered regions in the center and mid southeast parts of the state. As the maximum cluster size increases (Figures 6d, 6e and 6f), the hypertension anomaly is reinforced to include the

sizes (30 and 40)indicate "'borderline"' regions with respect of the "'true cluster"'.

intensity function maps as the maximum cluster size increases.

Valadares and Teofilo Otoni in the eastern part of the state.

study.

**4.1.1 Dengue fever**

**4.1.2 Tuberculosis**

**4.1.3 Diabetes**

**4.1.4 Hypertension**

the surrounding municipalities.

surrounding municipalities.

(b)

(d)

Fig. 3. Population at risk quantiles (a), dengue fever rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and 40 (c, d, e and f respectively)

(b)

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

=0 0 to 0.86 0.86 to 0.88 >0.88

=0 0 to 0.74 0.74 to 0.83 >0.83

e and f respectively)

Intensity function quantile

Intensity function quantile

(a)

(c)

(e)

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

Assessing the Outline Uncertainty of Spatial Disease Clusters 63

=0 0 to 0.78 0.78 to 0.86 >0.86

=0 0 to 0.77 0.77 to 0.79 >0.79

Fig. 5. Population at risk quantiles (a), diabetes rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and 40 (c, d,

Intensity function quantile

Intensity function quantile

(b)

(d)

(f)

(c)

(d)

Fig. 4. Population at risk quantiles (a), tuberculosis rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and 40 (c, d, e and f respectively)

12 Will-be-set-by-IN-TECH

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

> =0 0 to 0.77 0.77 to 0.83 >0.83

=0 0 to 0.61 0.61 to 0.62 >0.62

Fig. 4. Population at risk quantiles (a), tuberculosis rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and

Intensity function quantile

Intensity function quantile

(b)

(d)

(f)

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

=0 0 to 0.89 0.89 to 0.93 >0.93

> =0 0 to 0.69 0.69 to 0.72 >0.72

40 (c, d, e and f respectively)

Intensity function quantile

Intensity function quantile

(a)

(c)

(e)

Fig. 5. Population at risk quantiles (a), diabetes rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and 40 (c, d, e and f respectively)

**5. Conclusion**

Our methodology takes into account the variability in the observed number of disease cases on area aggregated maps to nonparametrically infer the uncertainty in the delineation of spatial clusters. A given real data map is regarded as just one possible realization of an unknown random variable vector with expected number of cases. The real data vector of the number of observed cases in each area is used to construct a new vector of expected values of random variables, considering the count of cases as the average of the random variables. This vector is now an estimate of the unknown random variable vector with expected number of cases. Our methodology performs *m* Monte Carlo replications based on this estimated vector of averages. The most likely cluster of each replicated map is detected and the *m* corresponding likelihood values obtained in the replications are ranked. For each area we determine the maximum likelihood value among the most likely clusters containing that area. Thus, we obtain the intensity function associated to each area's ranking of their respective likelihood value among the *m* values. The intensity of each area can be interpreted as the importance of that area in the delineation of the possibly existing anomaly on the map, considering only the initially given information of the observed number of cases. This procedure, based on the empirical distribution, takes into account the intrinsic variability of the observed number of cases, which generally is not considered directly in the existing algorithms used to detect spatial clusters. In our case studies we could see different situations with respect to the intrinsic variability of the existing spatial anomaly. When the most likely cluster is quite prominent, as in the diabetes case study, the intensity function is such that almost all areas associated with the most likely clusters found in the *m* replications coincides with those areas composing the most likely cluster detected for the original observed cases. In this situation the geographic anomaly is highly focused. However, in a different scenario, a disease map may present an intrinsically wide variability of data. Many areas near or adjacent to the most likely cluster have values of the intensity function close to the values corresponding to areas of the most likely cluster. In the case study of hypertension, this intrinsic variability produces a map with clearly unrelated areas, but with rather close probability ranking, indicating a situation of

Assessing the Outline Uncertainty of Spatial Disease Clusters 65

multiplicity of clusters, i. e., the most likely cluster is clearly poorly delineated.

"'borderline"' regions, with different levels of uncertainty.

provided by ordinary methods of cluster finding.

In this work we included two new features that extended the original ideas of the previous paper Oliveira et al. (2011). First, instead of the circular scan, we have used an irregularly shaped cluster finder based on a multiobjective genetic algorithm. It allowed a much better delineation of the complex shapes found in the real data clusters. As a consequence, several new phenomena could be distinguished in the spatial distribution of disease, which could not be observed with the simples spatial scan. The second modification was the sequential execution of runs with different sizes for the maximum allowed cluster to composing the intensity function maps. With this modified procedure, instead of only one map, it was obtained a sequence of intensity function maps: as the maximum cluster size increased, larger anomalies of lesser intensity were displayed. This allowed the identification of "'core"' and

The visualization tool developed in this work may serve as a support for the decision making process to prioritize areas of public health intervention, in a more precise manner than

(c)

Fig. 6. Population at risk quantiles (a), hypertension rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and 40 (c, d, e and f respectively)

#### **5. Conclusion**

14 Will-be-set-by-IN-TECH

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

> =0 0 to 0.76 0.76 to 0.79 >0.79

> > =0 0 to 0.60 0.60 to 0.66 >0.66

Fig. 6. Population at risk quantiles (a), hypertension rates (b), and intensity function maps based on the genetic multi-objective algorithm for maximum clusters of sizes 10, 20, 30 and

Intensity function quantile

Intensity function quantile

(b)

(d)

(f)

<=C10 C10 − C30 C30 − C70 C70 − C90 >= C90

=0 0 to 0.87 0.87 to 0.93 >0.93

> =0 0 to 0.7 0.7 to 0.73 >0.73

40 (c, d, e and f respectively)

Intensity function quantile

Intensity function quantile

(a)

(c)

(e)

Our methodology takes into account the variability in the observed number of disease cases on area aggregated maps to nonparametrically infer the uncertainty in the delineation of spatial clusters. A given real data map is regarded as just one possible realization of an unknown random variable vector with expected number of cases. The real data vector of the number of observed cases in each area is used to construct a new vector of expected values of random variables, considering the count of cases as the average of the random variables. This vector is now an estimate of the unknown random variable vector with expected number of cases. Our methodology performs *m* Monte Carlo replications based on this estimated vector of averages. The most likely cluster of each replicated map is detected and the *m* corresponding likelihood values obtained in the replications are ranked. For each area we determine the maximum likelihood value among the most likely clusters containing that area. Thus, we obtain the intensity function associated to each area's ranking of their respective likelihood value among the *m* values. The intensity of each area can be interpreted as the importance of that area in the delineation of the possibly existing anomaly on the map, considering only the initially given information of the observed number of cases. This procedure, based on the empirical distribution, takes into account the intrinsic variability of the observed number of cases, which generally is not considered directly in the existing algorithms used to detect spatial clusters.

In our case studies we could see different situations with respect to the intrinsic variability of the existing spatial anomaly. When the most likely cluster is quite prominent, as in the diabetes case study, the intensity function is such that almost all areas associated with the most likely clusters found in the *m* replications coincides with those areas composing the most likely cluster detected for the original observed cases. In this situation the geographic anomaly is highly focused. However, in a different scenario, a disease map may present an intrinsically wide variability of data. Many areas near or adjacent to the most likely cluster have values of the intensity function close to the values corresponding to areas of the most likely cluster. In the case study of hypertension, this intrinsic variability produces a map with clearly unrelated areas, but with rather close probability ranking, indicating a situation of multiplicity of clusters, i. e., the most likely cluster is clearly poorly delineated.

In this work we included two new features that extended the original ideas of the previous paper Oliveira et al. (2011). First, instead of the circular scan, we have used an irregularly shaped cluster finder based on a multiobjective genetic algorithm. It allowed a much better delineation of the complex shapes found in the real data clusters. As a consequence, several new phenomena could be distinguished in the spatial distribution of disease, which could not be observed with the simples spatial scan. The second modification was the sequential execution of runs with different sizes for the maximum allowed cluster to composing the intensity function maps. With this modified procedure, instead of only one map, it was obtained a sequence of intensity function maps: as the maximum cluster size increased, larger anomalies of lesser intensity were displayed. This allowed the identification of "'core"' and "'borderline"' regions, with different levels of uncertainty.

The visualization tool developed in this work may serve as a support for the decision making process to prioritize areas of public health intervention, in a more precise manner than provided by ordinary methods of cluster finding.

**4**

*Iran*

**Review of Ames Assay Studies**

**of the Urine of Clinical Pathology and**

**Other Occupations, such as Oncology**

Majid Rezaei Basiri1,5, Mahmoud Ghazi-khansari2, Hasan Rezazadeh1,

Mohammad Ali Eghbal1,4\*, Iraj Aswadi-kermani3, H. Hamzeiy1, Hossein Babaei1, Ali Reza Mohajjel Naebi1 and Alireza Partoazar2 *1Department of Pharmacology and Toxicology in School of Medicine of Tabriz,*

*5Students Researches Committee of Tabriz, Iran Medical Sciences University*

This chapter we refer to mutagenicity activity in the urine samples of persons who are exposed to carcinogenic materials in their occupations, and so some studies evaluated mutagenicity determination in individuals who worked with and are exposed to active potential mutagenic materials. There are some mutagenic compounds present in workplaces such as among nursing personnel in oncology hospitals, farmers' fields, clinical pathology laboratories, clinical forensic laboratories and pharmacology investigation laboratories etc. Also, clinical forensic laboratory personnel use some dangerous solvents such as chloroform which is mixed with other solvents in solutions of preparations of tank thin layer chromatography. They are also exposed to some mutagenic compounds such as formaldehyde, benzene and some solvents and colour regents. We refer to some of the below mentioned compounds, such as benzene, formaldehyde, paraffin, colour regents, organochlorin, smear fixators and so on. The colour regent might be contained in heavy metal carcinogenic substances which were used for smear colouring in clinical pathology laboratories by technicians and clinical forensic laboratories. According to review of some studies, after the filling out of questionnaire forms by these individuals, urine samples were

**1. Introduction**

 \*

Corresponding Author

**Forensic Laboratory Personnel and**

**Hospitals and Nursing Personnel**

*2Department of Pharmacology in School of Medicine of Tehran,*

*3Shahid Ghazi Oncology Research Centre Departments in Tabriz,*

 *Iran Medical Sciences University*

*Iran Medical Sciences University*

*Iran Medical Sciences University*

*4Drug Applied Research Centre of Tabriz*

#### **6. References**

### **Review of Ames Assay Studies of the Urine of Clinical Pathology and Forensic Laboratory Personnel and Other Occupations, such as Oncology Hospitals and Nursing Personnel**

Majid Rezaei Basiri1,5, Mahmoud Ghazi-khansari2, Hasan Rezazadeh1, Mohammad Ali Eghbal1,4\*, Iraj Aswadi-kermani3, H. Hamzeiy1, Hossein Babaei1, Ali Reza Mohajjel Naebi1 and Alireza Partoazar2 *1Department of Pharmacology and Toxicology in School of Medicine of Tabriz, Iran Medical Sciences University 2Department of Pharmacology in School of Medicine of Tehran, Iran Medical Sciences University 3Shahid Ghazi Oncology Research Centre Departments in Tabriz, Iran Medical Sciences University 4Drug Applied Research Centre of Tabriz 5Students Researches Committee of Tabriz, Iran Medical Sciences University Iran*

#### **1. Introduction**

16 Will-be-set-by-IN-TECH

66 Public Health – Methodology, Environmental and Systems Issues

Cancado, A. L. F., Duarte, A. R., Duczmal, L., Ferreira, S. J., Fonseca, C. M. & Gontijo, E. C.

da Fonseca, V. G., Fonseca, C. M. & Hall, A. O. (2001). Inferential performance assessment of

Duarte, A. R., Duczmal, L. H., Ferreira, S. J. & Cancado, A. L. F. (2010). Internal cohesion and

Duczmal, L., Cancado, A. L. F. & Takahashi, R. H. C. (2008). Delineation of irregularly shaped

Duczmal, L., Cancado, A. L. F., Takahashi, R. H. C. & Bessegato, L. F. (2007). A genetic

Duczmal, L., Kulldorff, M. & Huang, L. (2006). Evaluation of spatial scan statistics

Fonseca, C. M., da Fonseca, V. G. & Paquete, L. (2005). Exploring the performance of stochastic

*Notes In Computer Science*, Vol. 3410, Springer-Verlag, Berlin, pp. 250–264. Goovaerts, P. (2006). Geostatistical analysis of disease data: visualization and propagation

Kulldorff, M. (1999). Spatial scan statistics: Models, calculations and applications, *in* J. Glaz

Kulldorff, M., Huang, L., Pickle, L. & Duczmal, L. (2006). An elliptic spatial scan statistic,

Lawson, A., Biggeri, A. & Bohning, D. (1999). *Disease mapping and risk assessment for public*

Neill, D. B. (2011). Fast bayesian scan statistics for multivariate event detection and

Oliveira, F. L. P., Duczmal, L., Cancado, A. L. F. & Tavares, R. (2011). Nonparametric

Yiannakoulias, N., Rosychuk, R. J. & Hodgson, J. (2007). Adaptations for finding irregularly shaped disease clusters, *International Journal of Health Geographics* 6(28).

intensity bounds for the delineation of spatial clusters, *International Journal of Health*

*Science*, Vol. 1993, Springer-Verlag, Berlin, pp. 213–225.

*Data Analysis* 52: 43–52. DOI:10.1016/j.csda.2007.01.016.

simulation, *International Journal of Health Geographics* 5(7).

D. M. (2010). Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters, *International Journal of Health Geographics* 55(9). Chen J, Roth RE, N. A. L. E. M. A. (2008). Geovisual analytics to enhance spatial scan statistic

interpretation: an analysis of u.s. cervical cancer mortality, *International Journal of*

stochastic optimisers and the attainment function, *Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes In Computer*

geometric shape of spatial clusters, *Environmental and Ecological Statistics* 17: 203–229.

disease clusters through multiobjective optimization, *Journal of Computational and*

algorithm for irregularly shaped spatial scan statistics, *Computational Statistics and*

for irregularly shaped clusters, *Journal of Computational and Graphical Statistics*

multiobjective optimisers with the second-order attainment function, *Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, Lecture*

of spatial uncertainty in cancer mortality risk using poisson kriging and p-field

& N. Balakrishnan (eds), *Scan Statistics and Applications*, Springer Netherlands,

**6. References**

*Health Geographics* 7(57).

*Graphical Statistics* 17(2): 243–262.

*Statistics in Medicine* 25: 3929–3943. Lawson, A. (2009). *Bayesian Disease mapping*, CRC Press.

*health*, John Wiley and Sons, New York.

visualization, *Statistics in Medicine* 30(28): 455–469.

15(2): 428–442.

pp. 303–322.

*Geographics* 1(10).

This chapter we refer to mutagenicity activity in the urine samples of persons who are exposed to carcinogenic materials in their occupations, and so some studies evaluated mutagenicity determination in individuals who worked with and are exposed to active potential mutagenic materials. There are some mutagenic compounds present in workplaces such as among nursing personnel in oncology hospitals, farmers' fields, clinical pathology laboratories, clinical forensic laboratories and pharmacology investigation laboratories etc. Also, clinical forensic laboratory personnel use some dangerous solvents such as chloroform which is mixed with other solvents in solutions of preparations of tank thin layer chromatography. They are also exposed to some mutagenic compounds such as formaldehyde, benzene and some solvents and colour regents. We refer to some of the below mentioned compounds, such as benzene, formaldehyde, paraffin, colour regents, organochlorin, smear fixators and so on. The colour regent might be contained in heavy metal carcinogenic substances which were used for smear colouring in clinical pathology laboratories by technicians and clinical forensic laboratories. According to review of some studies, after the filling out of questionnaire forms by these individuals, urine samples were

\* Corresponding Author

Review of Ames Assay Studies of the Urine of Clinical Pathology and Forensic

**4. Sampling of volunteers and urine extraction**

[3,4,6,12,17]

**5. Ames assay**

**6. Ames assay protocol**

Laboratory Personnel and Other Occupations, such as Oncology Hospitals and Nursing Personnel 69

technique called Ames assay. The first experiments by professor Ames and his co-workers were conducted in the 1960s and 1970s and they were accepted by international agencies, including the American FDA. The test has its own specific conditions because it shows that mutagenicity activities with cost less and the samples of tested results would be expressed in a few days. As well as this, it can simultaneously test up some carcinogenic material over the same time in less than three days. It is held that it also tested for ease of mutagenicity activities which are available for review, of which there may be more in the future. However, in many work environments there are promutagenic and carcinogenic compounds that are present to which workers are exposed. The review of studies of more jobs and environments globally show that using this test for biological fluids – including the urine of personnel – proves potential mutagenic activities. It is also held that the assessment of them in all jobs as well as review by the study process including the preparation of urine samples, extraction of urine mutagenic compounds and the Ames test performed on the samples and biological extracted compounds have similarities processes. [1,2,3,6,12,13,17]

All of the urine samples were collected at the end of the week from the Iranian forensic organisation's laboratories and other relationship laboratories personnel and stored in a refrigerator, although some appropriate methods are accessible for the urine extracts' preparation, but in this review we almost see solid phase extraction methods. Extraction columns were filled with 1 g from one kind of C18 or XAD-2 resins between two layers of cotton, depending on studies' strategies. First, the resins were activated by Milli-Q water and methanol then the volunteer's clear urine was passed through a column by vacuum and the mutagenic substances were concentrated with V/V of methanol/Acetonitrile in tubes.

In these studies, mutagenicity was followed by the plates' incorporated procedures, as described by Mortelmans and Zeiger (2000), in overnight cultures of TA98 and TA100 salmonella typhymorium tester strains with and without the S9 mix fraction in the forensic organisation's personnel and just TA100 in the clinical laboratory technicians in Iranian laboratories. For this strain, the spontaneous background number of the revertant was approximately 20-50 for TA98 and 75-200 for TA100 salmonella typhymorium tester strains. In this study, Sodium azide was used as a positive control without using the S-9 mix buffer and the 2-amino anthracene as a positive control with the S-9 mix buffer. DMSO and distilled water were considered as negative controls and were used in this study. [1,2,3,6,12,13,17]

The first dose/response was determined for all urine extract samples. In our studies, the appropriate dose of urine extracts was 1/100 for exposed bacterial strains and 0.05ml of overnight bacterial culture (TA98 or TA100) was added in 2ml of melting top agar containing trace amounts of biotin and histidine then, and 0.05ml 1/100 of diluted urine extract with DMSO solvent was added in the melting top agar tube as well; after shaking the tube's contents were transferred in Glucose minimum agar plates which were incubated at 37C for 48 hours. The above mentioned protocols were repeated with the addition of 0.5ml

collected from candidates in all occupations at the end of the week and they were then held in a refrigerator for the urine extraction process. Special instruments – such as vacuum – and special materials such as MilliQ water and some solvents such as methanol and acetonitrile were used for urine solid phase extraction. Some relationship studies showed that urine extracts were prepared by resins of C18 and amberlite XAD-2 because they are particularly absorbent of the carcinogenic compounds of urine; the importance of the above mentioned resins and the sterilisation of urine extracts will be described in this chapter as well. The personnel who work for a long time in these fields are exposed to these potentially carcinogenic materials. According to the review studies, we will show the mutagenic activity evaluation of the urine of some professional personnel by Ames monitoring. After the preparation of the sterile urine extracts of these personnel, they were exposed to salmonella strains of bacteria for the determination of mutagenic activity. The function of salmonella typhimorium strains such as: TA98,TA100, and the overnight culture of these bacteria show the importance of suspension preparation in the Ames test ratio and all kinds of bacterial environment cultures and the holding of the above mentioned bacteria will be considered in this chapter too. All methods of Ames assay application in these work places will be explained in detail in this chapter. And finally, a risk-benefit assessment and the conditions of healthy work places and individuals' protection will be described as well at the end of this chapter.

#### **2. Mutagenic or carcinogenic material**

Some compounds are present in the occupations of clinical pathology and forensic laboratory personnel and other occupations, such as oncology hospitals. Others in this review are: hamatoxilene, eosin and some solvents and colour reagent. Benzene, formaldehyde, paraffin, colour reagents, organochlorin, smear fixators, antineoplastic drugs etc. So the personal of these places might absorb these materials in their work environment through the lungs, the skin, orally etc. These compounds are metabolised in their liver by the P450 enzyme to activate potential mutagenic compounds. Figure.1 shows some these compounds. [5,6,7,8,9,10,12,13,14,15,17,18,19,20,21]

Fig. 1. Mutagenic or Carcinogenic material in laboratory, oxide metabolites of these compounds cause genotoxic effects.

#### **3. Samples and Ames test history**

For the mutagenicity activity evaluation of biological fluids in different job environments in previous decades, numerous studies of the founder of mutagenicity have a review up technique called Ames assay. The first experiments by professor Ames and his co-workers were conducted in the 1960s and 1970s and they were accepted by international agencies, including the American FDA. The test has its own specific conditions because it shows that mutagenicity activities with cost less and the samples of tested results would be expressed in a few days. As well as this, it can simultaneously test up some carcinogenic material over the same time in less than three days. It is held that it also tested for ease of mutagenicity activities which are available for review, of which there may be more in the future. However, in many work environments there are promutagenic and carcinogenic compounds that are present to which workers are exposed. The review of studies of more jobs and environments globally show that using this test for biological fluids – including the urine of personnel – proves potential mutagenic activities. It is also held that the assessment of them in all jobs as well as review by the study process including the preparation of urine samples, extraction of urine mutagenic compounds and the Ames test performed on the samples and biological extracted compounds have similarities processes. [1,2,3,6,12,13,17]

### **4. Sampling of volunteers and urine extraction**

All of the urine samples were collected at the end of the week from the Iranian forensic organisation's laboratories and other relationship laboratories personnel and stored in a refrigerator, although some appropriate methods are accessible for the urine extracts' preparation, but in this review we almost see solid phase extraction methods. Extraction columns were filled with 1 g from one kind of C18 or XAD-2 resins between two layers of cotton, depending on studies' strategies. First, the resins were activated by Milli-Q water and methanol then the volunteer's clear urine was passed through a column by vacuum and the mutagenic substances were concentrated with V/V of methanol/Acetonitrile in tubes. [3,4,6,12,17]

#### **5. Ames assay**

68 Public Health – Methodology, Environmental and Systems Issues

collected from candidates in all occupations at the end of the week and they were then held in a refrigerator for the urine extraction process. Special instruments – such as vacuum – and special materials such as MilliQ water and some solvents such as methanol and acetonitrile were used for urine solid phase extraction. Some relationship studies showed that urine extracts were prepared by resins of C18 and amberlite XAD-2 because they are particularly absorbent of the carcinogenic compounds of urine; the importance of the above mentioned resins and the sterilisation of urine extracts will be described in this chapter as well. The personnel who work for a long time in these fields are exposed to these potentially carcinogenic materials. According to the review studies, we will show the mutagenic activity evaluation of the urine of some professional personnel by Ames monitoring. After the preparation of the sterile urine extracts of these personnel, they were exposed to salmonella strains of bacteria for the determination of mutagenic activity. The function of salmonella typhimorium strains such as: TA98,TA100, and the overnight culture of these bacteria show the importance of suspension preparation in the Ames test ratio and all kinds of bacterial environment cultures and the holding of the above mentioned bacteria will be considered in this chapter too. All methods of Ames assay application in these work places will be explained in detail in this chapter. And finally, a risk-benefit assessment and the conditions of healthy work places and individuals' protection

Some compounds are present in the occupations of clinical pathology and forensic laboratory personnel and other occupations, such as oncology hospitals. Others in this review are: hamatoxilene, eosin and some solvents and colour reagent. Benzene, formaldehyde, paraffin, colour reagents, organochlorin, smear fixators, antineoplastic drugs etc. So the personal of these places might absorb these materials in their work environment through the lungs, the skin, orally etc. These compounds are metabolised in their liver by the P450 enzyme to activate potential mutagenic compounds. Figure.1 shows some these

Fig. 1. Mutagenic or Carcinogenic material in laboratory, oxide metabolites of these

For the mutagenicity activity evaluation of biological fluids in different job environments in previous decades, numerous studies of the founder of mutagenicity have a review up

will be described as well at the end of this chapter.

compounds. [5,6,7,8,9,10,12,13,14,15,17,18,19,20,21]

compounds cause genotoxic effects.

**3. Samples and Ames test history**

**2. Mutagenic or carcinogenic material**

In these studies, mutagenicity was followed by the plates' incorporated procedures, as described by Mortelmans and Zeiger (2000), in overnight cultures of TA98 and TA100 salmonella typhymorium tester strains with and without the S9 mix fraction in the forensic organisation's personnel and just TA100 in the clinical laboratory technicians in Iranian laboratories. For this strain, the spontaneous background number of the revertant was approximately 20-50 for TA98 and 75-200 for TA100 salmonella typhymorium tester strains. In this study, Sodium azide was used as a positive control without using the S-9 mix buffer and the 2-amino anthracene as a positive control with the S-9 mix buffer. DMSO and distilled water were considered as negative controls and were used in this study. [1,2,3,6,12,13,17]

#### **6. Ames assay protocol**

The first dose/response was determined for all urine extract samples. In our studies, the appropriate dose of urine extracts was 1/100 for exposed bacterial strains and 0.05ml of overnight bacterial culture (TA98 or TA100) was added in 2ml of melting top agar containing trace amounts of biotin and histidine then, and 0.05ml 1/100 of diluted urine extract with DMSO solvent was added in the melting top agar tube as well; after shaking the tube's contents were transferred in Glucose minimum agar plates which were incubated at 37C for 48 hours. The above mentioned protocols were repeated with the addition of 0.5ml

Review of Ames Assay Studies of the Urine of Clinical Pathology and Forensic

positive control plates with the S-9mix buffer.

S-9 mix buffer. Rezai-basiri et al., 2008.

Laboratory Personnel and Other Occupations, such as Oncology Hospitals and Nursing Personnel 71

persons' urine sample colony counts, so the ratio of all the samples was more than 2 and significant for the description of mutagenic compounds in all occupations. According to figure.3, in these studies we used a 0.05ml DMSO solvent and diluted water for negative control plates with and without using the S-9mix buffer, and we also used sodium azide for positive control plates without using the S-9mix buffer and 2-amino anthracene for

Fig. 3a. Diluted water (D.W) for the negative control colony count plate without using the

Fig. 3b. Sodium azide (5µg/plate) for the positive control colony count plate (and TA100 Salmonella typhymorium tester strain) without using the S-9 mix buffer. Rezai-basiri et al., 2008.

of the S-9mix buffer with the contents of the rat liver p450 enzyme. After the incubation of all plates with and without S-9mix buffer, the colony counts of all the plates were reported for data collection. [1,2,3,4,6,12,13,17]

#### **7. Ames assay on urine samples of hospitals nursings**

Most of the anti-neoplastic drugs are cytotoxic as well as nephrotoxic and cause DNA damage. For example, some of them are: doxorubicin, bleomycin, vinblastine, dacarbazine, methotrexate, fluorouracil , prednisone , epirubicin , irinotecan , leucoverin, prednisone, 6-mercaptopurine, procarbazine, lomustine, cisplatin (platinum), etoposide, 6-thioguanine, dexamethasone. According to figure.2, these drugs (such as cycloposphamide, mechlortamine, melphalan, chlorambucil) are nitrogen mustard group and so they have genotoxic effects because they will link to DNA, and they have carcinogenic effects on individuals who are exposed to them in their occupations. They involve a rapid proliferation of normal tissues (bone marrow, hair follicles and the intestinal epithelium). Therefore, most hospital nursing personnel are chronically exposed to anti-neoplastic drugs, especially during the course of giving therapeutic doses to cancer patients. Some studies have shown mutagenic activity in the urine of these nursing staff through an Ames assay. [3,4,6,7,8,9,11,12,13,17]

Fig. 2. Some antineoplastic drugs with nitrogen mustard groups.

#### **8. Studies' results**

The data was collected from all the occupational personnel and their negative group control urine extracts were exposed to salmonella bacteria tester strains, such as TA98 and TA100 in these review studies; then, according to the below mentioned results and data from the Iranian clinical pathology and medicinal forensic laboratories, they show the colony counts of all the tester strains of salmonella typhymorium bacteria (such as TA 98 and TA100) as being more than 400 for the positive samples of personnel. On the other hand, the colony count of these individuals' urine samples in the bacterial culture shows that these personnel excrete mutagenic compounds in their urine. Each ratio is determined from individuals' urine samples colony counts per negative group of control

of the S-9mix buffer with the contents of the rat liver p450 enzyme. After the incubation of all plates with and without S-9mix buffer, the colony counts of all the plates were reported

Most of the anti-neoplastic drugs are cytotoxic as well as nephrotoxic and cause DNA damage. For example, some of them are: doxorubicin, bleomycin, vinblastine, dacarbazine, methotrexate, fluorouracil , prednisone , epirubicin , irinotecan , leucoverin, prednisone, 6-mercaptopurine, procarbazine, lomustine, cisplatin (platinum), etoposide, 6-thioguanine, dexamethasone. According to figure.2, these drugs (such as cycloposphamide, mechlortamine, melphalan, chlorambucil) are nitrogen mustard group and so they have genotoxic effects because they will link to DNA, and they have carcinogenic effects on individuals who are exposed to them in their occupations. They involve a rapid proliferation of normal tissues (bone marrow, hair follicles and the intestinal epithelium). Therefore, most hospital nursing personnel are chronically exposed to anti-neoplastic drugs, especially during the course of giving therapeutic doses to cancer patients. Some studies have shown mutagenic activity in the urine of these nursing staff through an Ames assay.

for data collection. [1,2,3,4,6,12,13,17]

[3,4,6,7,8,9,11,12,13,17]

**8. Studies' results**

**7. Ames assay on urine samples of hospitals nursings**

Fig. 2. Some antineoplastic drugs with nitrogen mustard groups.

The data was collected from all the occupational personnel and their negative group control urine extracts were exposed to salmonella bacteria tester strains, such as TA98 and TA100 in these review studies; then, according to the below mentioned results and data from the Iranian clinical pathology and medicinal forensic laboratories, they show the colony counts of all the tester strains of salmonella typhymorium bacteria (such as TA 98 and TA100) as being more than 400 for the positive samples of personnel. On the other hand, the colony count of these individuals' urine samples in the bacterial culture shows that these personnel excrete mutagenic compounds in their urine. Each ratio is determined from individuals' urine samples colony counts per negative group of control persons' urine sample colony counts, so the ratio of all the samples was more than 2 and significant for the description of mutagenic compounds in all occupations. According to figure.3, in these studies we used a 0.05ml DMSO solvent and diluted water for negative control plates with and without using the S-9mix buffer, and we also used sodium azide for positive control plates without using the S-9mix buffer and 2-amino anthracene for positive control plates with the S-9mix buffer.

Fig. 3a. Diluted water (D.W) for the negative control colony count plate without using the S-9 mix buffer. Rezai-basiri et al., 2008.

Fig. 3b. Sodium azide (5µg/plate) for the positive control colony count plate (and TA100 Salmonella typhymorium tester strain) without using the S-9 mix buffer. Rezai-basiri et al., 2008.

Review of Ames Assay Studies of the Urine of Clinical Pathology and Forensic

bacteria strains both with and without the S-9mix (cell of rat liver).

\*\*=Positive cases in the personnel of the medicinal forensic organisation.

bacteria strains both with and without the S-9mix (cell of rat liver).

**laboratory technicians**

technicians. Rezai-basiri et al., 2008.

Laboratory Personnel and Other Occupations, such as Oncology Hospitals and Nursing Personnel 73

\*\*=The samples belonged to the anatomy personnel of the medicinal forensic organisation. Partoazr, et al., 2009. Table 2. Positive and doubtful cases. The results of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA100 salmonella typhymorium

 with+S-9mix without-S-9mix without-S-9mix with+S-9mix 1\*\* 550 55 20 2.2 2\* 340 30 15 1.2 3\* 41 31 1.86 1.24

Samples Colony count TA100 Ratio of TA100 with+S-9mix without-S-9mix without-S-9mix with+S-9mix 1\* 95 95 1 1.26 2\* 95 78 1 1.04 3\*\* 85 74 0.89 0.98

\*=The samples belonged to the pathology personnel of the medicinal forensic organisation.

Samples Colony count TA98 Ratio of TA98

\*=Doubtful cases in the personnel of the medicinal forensic organisation. Partoazr, et al., 2009. Table 3. Positive and doubtful cases. The results of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA98 salmonella typhymorium

**11. Evaluation of mutagenic compound in urine of clinical pathology**

At least four clinical pathology laboratories witnessed the evaluation of the mutagenicity of the urine of technicians by the Ames assay. That the ratio would be more than two was significant for the potential of the mutagenic activity of the urine of technician.

Fig. 4. The plates of positive cases of the TA100 salmonella tester strain cultures at 37C for 48hours for the Ames assay on the urine extracts of the clinical pathology laboratory

Fig. 3c. DMSO for the positive control colony count plate (and TA100 Salmonella typhymorium tester strain) with using the S-9 mix buffer. Rezai-basiri et al., 2008.

#### **9. Statistical analysis methods**

In the Ames assay on the Iranian forensic organisation's laboratory personnel's urines with two bacterial tester strains of TA98, TA100 for colony counts in exposure to urine extract samples, and positive or negative samples the Anova two-way statistical method was used.

#### **10. Evaluation of mutagenic compounds the in urine of medicinal forensic laboratory personnel**

Partoazr, et al., 2009.

Table 1. Results of the control group of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA98 and TA100 bacteria strains both with and without the S-9mix.

\*=The samples belonged to the pathology personnel of the medicinal forensic organisation. \*\*=The samples belonged to the anatomy personnel of the medicinal forensic organisation. Partoazr, et al., 2009.

Table 2. Positive and doubtful cases. The results of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA100 salmonella typhymorium bacteria strains both with and without the S-9mix (cell of rat liver).

\*\*=Positive cases in the personnel of the medicinal forensic organisation.

72 Public Health – Methodology, Environmental and Systems Issues

Fig. 3c. DMSO for the positive control colony count plate (and TA100 Salmonella typhymorium tester strain) with using the S-9 mix buffer. Rezai-basiri et al., 2008.

In the Ames assay on the Iranian forensic organisation's laboratory personnel's urines with two bacterial tester strains of TA98, TA100 for colony counts in exposure to urine extract samples, and positive or negative samples the Anova two-way statistical method was used.

**10. Evaluation of mutagenic compounds the in urine of medicinal forensic**

Control group Colony count TA100 Colony countTA98 without-S-9mix with+S-9mix without-S-9mix with+S-9mix Male 86 65 26 25 Male 106 72 23 27 Male 100 77 27 21 Male 85 75 19 24 Male 88 68 22 25 Male 98 65 21 14 Male 95 85 23 29 Female 91 85 20 20 Female 102 86 24 28 Female 98 76 18 24 Average 94.9 75.4 22.2 24.7

Table 1. Results of the control group of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA98 and TA100 bacteria strains both with and

**9. Statistical analysis methods**

**laboratory personnel**

Partoazr, et al., 2009.

without the S-9mix.

\*=Doubtful cases in the personnel of the medicinal forensic organisation. Partoazr, et al., 2009.

Table 3. Positive and doubtful cases. The results of the mutagenicity assay in the urine of medicinal forensic laboratory personnel with the use of TA98 salmonella typhymorium bacteria strains both with and without the S-9mix (cell of rat liver).

#### **11. Evaluation of mutagenic compound in urine of clinical pathology laboratory technicians**

At least four clinical pathology laboratories witnessed the evaluation of the mutagenicity of the urine of technicians by the Ames assay. That the ratio would be more than two was significant for the potential of the mutagenic activity of the urine of technician.

Fig. 4. The plates of positive cases of the TA100 salmonella tester strain cultures at 37C for 48hours for the Ames assay on the urine extracts of the clinical pathology laboratory technicians. Rezai-basiri et al., 2008.

Review of Ames Assay Studies of the Urine of Clinical Pathology and Forensic

database, CRS Press, Boca Raton, FL, 1997, 687-729.

method, Mutat Res, 2002; 520(1-2): 199-205.

9(6): 269-72.

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June 2009, 67(3): 184-189.

Biochem Suppl, 1996, 25: 92-8.

Pharm, 1981, 38(11): 1686-93.

691-9.

Laboratory Personnel and Other Occupations, such as Oncology Hospitals and Nursing Personnel 75

[2] Zeiger E, Genotoxicity Database, Hand book of carcinogenic potency and genotoxicity

[3] Andre V, Deslandes D, Henry-Amar M, Gauduchon P, Biomonitoring of urine

[4] Hyde PM, Evaluation of drug extraction procedures from urine, J Anal Toxicol, 1985,

[5] Aeschbacher HU, Finot PA, Wolleb U, Interaction of histidine- containing test substances

[6] Andre V, Lebailly P, Pottier D, Deslandes E, De Meo M, Henry-Amar M, Gauduchon P,

[8] Harrison BR, Developing guidelines for working with antineoplastic drugs, Am J Hosp

[9] Zimmerman PF, Larsen RK, Barkley EW, Gallelli JF, Recommendations for the safe

[10] Anderson RW, Puckett WH Jr, Dana WJ, Nguyen TV, Theiss JC, Matney, TS, Risk of handling injectable antineoplastic agents, Am J Hosp Pharm,1982;39(11):1881-7. [11] Rezaei-Basiri M, Ghazi-khansari M, Faghih A, Sadeghi M, Lotfalizadeh N, Eghbal M,

[12] Rezaei-Basiri M, Samini M, Ghazi- khansari M, Rezayat M, Sahebgharani M and

Technicians, Journal of Pharmacology and Toxicology, 2008, 3 (3): 230-235. [13] Partoazar A, Ghazi -Khansari M, Abedi MH, Kaviani M, Norashrafeddin SM, Rezaei-

[14] Shelef LA and Chin B, Effect of Phenolic Antioxidants on the Mutagenicity of Aflatoxin B1, Applied and Environmental Microbiology, Dec. 1980: 1039-1043. [15] Cerná M, Pastorková A, Bacterial urinary mutagenicity test for monitoring of exposure

[16] De Flora S, Camoirano A, Bagnasco M, Bennicelli C, van Zandwijk N, Wigbout G, Qian

[17] Ames B, Methods for detecting Carcinogens and mutagens with the salmonella/ mammalian micro some mutagenicity test, Mutation Res, 1975, 31: 347-364.

to genotoxic compounds: a review, Sep. 2002, 10(3): 124-9.

mutagenicity with the Ames test: improvement of the extraction/concentration

and extraction methods with the Ames mutagenicity test, Mutation Res, 1983;

Urine mutagenicity of farmers occupationally exposed during a 1-day use of chlorothalonil and insecticides, Int Arch Occup Environ Health, 2003, 76(1): 55-62. [7] Ahlborg G Jr, Bergstrom B, Hogstedt C, Einisto P, Sorsa M, Urinary screening for

potentially genotoxic exposures in a chemical industry, Br J Ind Med, 1985, 42(10):

handling of injectable antineoplastic drug products, Am J Hosp Pharm, 1981,

Mohajell-Nayebi A, Rezazadeh H, Arshad Zadeh M, Screening of Morphine & Codeine in Urine of Opioid Abusers by Rapid and TLC Analysis, Eur J Gen Med,

Partoazar A, Monitoring Ames Assay on Urine of Clinical Pathology Laboratories

Basiri M,Talebi M, Determining urine sample mutagenicity ratio using Ames test: Tehran forensic medicine laboratory personnel, Tehran University Medical Journal,

GS, Zhu YR, Kensler TW, Smokers and urinary genotoxins: implications for selection of cohorts and modulation of endpoints in chemoprevention trials, J Cell

The number of personnel who had greater than two ratios was five people in this study. According to the below mentioned, we see some significant results showing mutagenic excretions in the urine of clinical pathology laboratory technicians:

The ratios without the S-9mix for two of the technicians were 2.01 and 2.0, and the ratio with the S-9mix for these individuals was 2.05 and 2.01. [3,6,12,13,17]

#### **12. Conclusion and recommendation**

In this review study, we observed mutagenic activity in the urine extracts of some of the technicians of clinical pathology laboratories and the forensic organisation laboratories. According to these studies, in order to reduce mutagenicity in technicians it was suggested to all of them to use masks, gloves and work under laminar flu and avoid drinking in the laboratories. Considering the contamination of all personnel with mutagenic substances such as colour reagents contained in carcinogenic heavy metals, formaldehyde, benzene, hamatoxilen-Eosin and so on in these laboratories, the observation of the principles of health conditions in clinical pathology laboratories and forensic organisation laboratories is recommended. According to this review study, it is held that the long duration of working and exposure to mutagenic and carcinogenic substances in high risk conditions leads to the excreting of mutagenic compounds in the urine of these individuals and so they should decrease their time spent working over the week. The some studies showed which personnel has used antioxidant compounds, such as thiol group drugs; they had decreased the mutagenic activity in their urine. Also these results were shown to smokers who have used thiol group drugs such as acetyl cystein, had low mutagenic activity in urine samoles. As such, it considers the consumption of vitamins with antioxidant effects which are useful to individuals who are exposed to mutagenic compounds in their occupations. [3,6,12,13,16].

#### **13. Acknowledgments**

We are grateful to the below mentioned departments and the research centres of universities and organisation laboratories as well as to the volunteer personnel for their participation in these studies and their professors for the writing of this article:

Department of Pharmacology in the School of Medicine of Tehran/Iran Medical Sciences University.

Department of Pharmacology and Toxicology in the School of Medicine of Tabriz/Iran Medical Sciences University.

Laboratories of Forensic Organization of Tehran/Iran.

Shahid Ghazi Oncology Research Centre Department in Tabriz/Iran Medical Sciences University.

Drug Applied Research Centre of Tabriz/Iran.

Students Researches Committee of Tabriz/Iran Medical Sciences University.

#### **14. References**

[1] Mortelmans K, Zeiger E, the Ames Salmonella/Microsome Mutagenicity Assay; Mutat Research, 2000, 29-60.

The number of personnel who had greater than two ratios was five people in this study. According to the below mentioned, we see some significant results showing mutagenic

The ratios without the S-9mix for two of the technicians were 2.01 and 2.0, and the ratio with

In this review study, we observed mutagenic activity in the urine extracts of some of the technicians of clinical pathology laboratories and the forensic organisation laboratories. According to these studies, in order to reduce mutagenicity in technicians it was suggested to all of them to use masks, gloves and work under laminar flu and avoid drinking in the laboratories. Considering the contamination of all personnel with mutagenic substances such as colour reagents contained in carcinogenic heavy metals, formaldehyde, benzene, hamatoxilen-Eosin and so on in these laboratories, the observation of the principles of health conditions in clinical pathology laboratories and forensic organisation laboratories is recommended. According to this review study, it is held that the long duration of working and exposure to mutagenic and carcinogenic substances in high risk conditions leads to the excreting of mutagenic compounds in the urine of these individuals and so they should decrease their time spent working over the week. The some studies showed which personnel has used antioxidant compounds, such as thiol group drugs; they had decreased the mutagenic activity in their urine. Also these results were shown to smokers who have used thiol group drugs such as acetyl cystein, had low mutagenic activity in urine samoles. As such, it considers the consumption of vitamins with antioxidant effects which are useful to individuals who are exposed to mutagenic compounds in their occupations. [3,6,12,13,16].

We are grateful to the below mentioned departments and the research centres of universities and organisation laboratories as well as to the volunteer personnel for their participation in

Department of Pharmacology in the School of Medicine of Tehran/Iran Medical Sciences

Department of Pharmacology and Toxicology in the School of Medicine of Tabriz/Iran

Shahid Ghazi Oncology Research Centre Department in Tabriz/Iran Medical Sciences

[1] Mortelmans K, Zeiger E, the Ames Salmonella/Microsome Mutagenicity Assay; Mutat

Students Researches Committee of Tabriz/Iran Medical Sciences University.

excretions in the urine of clinical pathology laboratory technicians:

the S-9mix for these individuals was 2.05 and 2.01. [3,6,12,13,17]

these studies and their professors for the writing of this article:

Laboratories of Forensic Organization of Tehran/Iran.

Drug Applied Research Centre of Tabriz/Iran.

Research, 2000, 29-60.

**12. Conclusion and recommendation**

**13. Acknowledgments**

Medical Sciences University.

University.

University.

**14. References**

**5**

*UK*

**Old Obstacles on New Horizons:**

**Gene X Environment Discoveries in**

Conrad Iyegbe, Gemma Modinos and Margarita Rivera Sanchez

Genetics and Social Sciences are divergent disciplines for whom it is customary to compete to explain the greater part of Schizophrenia risk 1. These days, a convincing case can be made for the prospective public health value of either discipline 2,3. However the practical implementation of such knowledge continues to prove challenging for either field alone: From a genetic perspective, progress was traditionally hindered by the inconsistent nature of discoveries made in the pre-GWAS (Genome-wide Association Study) era. It is now held back by the fact that the heritability attributed to this disorder remains largely impermeable

Socio-environmental research, on the other hand, has not progressed to the point of being able to pinpoint the precise origins of the high attributable risk fractions repeatedly

However ongoing progress on two fronts is fuelling hopes that a successful marriage of the two fields will benefit both the rate and the integrity of new discoveries, so that clinical interventions can eventually be targeted to patient sub-groups on the basis of their

i. Firstly, the credibility of Schizophrenia genetics is benefiting from a recent upswing in the generation of verifiable new findings. This has led to a palpable mood change

ii. Secondly, it is anticipated that social science research will benefit from an unprecedented program of investment that will stimulate the emergence of newer methodologies designed to improve the resolution with which social risk factors are

There are high hopes that the formal integration of these two fields will help to invigorate the search for tailored clinical interventions, whether they be therapeutic or prophylactic in nature. Thus it seems an opportune time to consider the potential obstacles that lie ahead for Schizophrenia research in the newly revitalised era of translational research. We do this by

**1. Introduction**

encountered 2.

measured 5,6.

to GWAS and other genomic approaches.

combined genetic and environmental risk profile:

within the psychiatric genetics community 4.

**The Challenge of Implementing**

**Schizophrenia Research**

*Institute of Psychiatry, Kings College London,*

### **Old Obstacles on New Horizons: The Challenge of Implementing Gene X Environment Discoveries in Schizophrenia Research**

Conrad Iyegbe, Gemma Modinos and Margarita Rivera Sanchez *Institute of Psychiatry, Kings College London, UK*

#### **1. Introduction**

76 Public Health – Methodology, Environmental and Systems Issues

[18] Bartczak AW, Sangaiah R, Ball LM, Warren SH1 and Gold A, Synthesis and bacterial

[19] Varella SD, Rampazo RA, Varanda EA. Urinary mutagenicity in chemical laboratory

[20] Siwińska E, Mielzyńska D, Kapka L. Association between urinary 1-hydroxypyrene and genotoxic effects in coke oven workers. Occup Environ Med, 2004, 61: e10. [21] Chamberlain PL, Brynes SD. The regulatory status of xylazine for use in food-producing

[22] Goodson-Gregg N and De Stasio EA Reinventing the Ames Test as a Quantitative Lab that Connects Classical and Molecular Genetics. Genetics , 2009,181: 21-30. [23] Ming-Fang Wu, Fu-Chuo Peng, Yung-Liang Chen et al, Evaluation of Genotoxicity of

[24] Meltem Boyacoğlu, Özlem Çakal Arslan, Hatice Parlak, Muhammet Ali Karaaslan,

[25] Alva Biran , Rami Pedahzur, Sebastian Buchinger, Georg Reifferscheid ,and Shimshon

[26] Jadwiga Marczewska, Ewa Karwicka et al, Assessment of Cytotoxic and Genotoxic

Antrodia cinnamomea in the Ames Test and the In Vitro Chromosomal Aberration

Mutagenicity of Nonylphenol and Octylphenol Using *Salmonella* Mutation Assay,

Belkin, Genetically Engineered Bacteria for Genotoxicity Assessment, Hdb Env

Activity of Alcohol Extract of Polyscias Filicifolia Shoot, Leaf, Cell Biomass of Suspension Culture and Saponin Fraction, Acta Poloniae Pharmaceutica,2011, 703-