**3.2 Parametric reliability models**

To obtain the reliability functions the normal distribution model was used and for their contrast a U of Mann-Whitney and t-test was used. For this each one of the binary variables was transformed into another equivalent referring to duration of exposure.

The parametric model was only used as a descriptor of the variables and for testing certain controls, hypotheses and predictions, starting with the probability distributions: (1) Tests to establish controls (regarding the population percentage, with reference to one or more alterations, which must not be exceeded). The tests concern establishing a common "cut off point" for the modalities of the variable "degree of hearing alteration"; (2) Hypothesis tests (regarding the development of hearing alteration). These involve the analysis of the differences in probabilities based on a real value and a theoretical value. An individual with a particular duration of exposure experiences a degree of hearing alteration (real value). In turn, this individual, with that duration of exposure, could experience other degrees of

Exploration Databases on Occupational Hearing Loss 199

As can be seen in figure 2, the survival functions obtained through the Kaplan-Meyer method define how hearing alteration appears in individuals by event and for each work

Thus, in event 1 the "noise with metalworking fluids" atmosphere causes a delay in hearing alteration which is significant (p<0.05), whereas the "noise only" atmosphere and the "noise with welding fumes" atmosphere develop in unison, showing no differences between them

For events two and three, the curves that characterise each atmosphere are separated from one another significantly (p<0.05), indicating the time differences that exposure to each of them represents and for the same period (see variation of the medians and contrasts, Table 4 and 5). It was demonstrated, furthermore, that the 0 to 15 years period of exposure to noise was low risk, in general presenting hearing alteration of less than 10% in the individuals

The percentage of individuals affected, over this period gradually decreased as the event continued. Thus, event 1 principally characterises the variations in the hearing threshold of the recoverable type (initial trauma), event 2 non-recoverable but without alteration in conversational speech (advanced trauma) and in event 3 non recoverable variations with losses in conversational speech (hypoacusis). The situation described gives a dynamic to the process of hearing alteration which is characterised by the migration of the set of survival functions to the right. This explains the existence of a lower risk in the initial periods over

Obtaining univariate, bivariate and trivariate models (Table 6), based on the Cox regression,

In considering the "smoking habit" variable it was found that its effect was antagonistic to atmospheres with metalworking fluids, although the hazard are more or less balanced, depending on the event. This indicates uniform action over time, which is different from metalworking fluids atmosphere, which tend to intensify the effects of smoking (Figure 3.2.).

MF 1 146 91 55 38 0.94 32 1.03 (30,34) 25 1.29 N 1 177 124 53 33 0.70 28 0.75 (27,29) 21 1.65 WF 1 235 185 50 32 0.73 27 0.61 (26,28) 22 0.77 MF 2 146 35 111 44 2.43 40 1.69 (37,43) 35 1.10 N 2 177 51 126 41 2.05 34 0.88 (32,36) 29 1.37 WF 2 235 118 117 36 0.90 31 0.75 (30,32) 26 0.65 MF 3 146 12 134 45 \* 44 3.60 (37,51) 40 1.15 N 3 177 25 152 45 \* 41 1.72 (38,44) 32 1.62 WF 3 235 62 173 40 0.44 36 1.49 (33,39) 30 0.76

N E C Q25 SE Q50 SE CI 95% Q75 SE

explains the effect of the various variables in the study, based on the hazard ratio.

Atmosphere Event Sample Percentiles

atmosphere. They show clear differences between event 1 and the others.

time, demonstrating the suitability of the model (Figure 2).

N=cases; E=events; C=censored; SE=standard error

Table 4. Characteristics of no parametric survival functions

(p>0.05).

exposed (Figure 2).

hearing alteration (theoretical values); (3) Tests of predictions. This involves predicting the development of certain exposed populations, based on the previous controls and hypotheses, making it possible to improve preventative management systems.

#### **3.3 Comparison of survival models**

Survival functions obtained for the data from the sample, using a parametric and nonparametric model, they were represented together in a graph to assess their equivalence. The interesting aspect of this equivalence is the complementarity of the results, allowing them to be used together i.e. where one model is not suitable, the other is. For example, for the initial data, regression is possible in a non-parametric approach but not in a parametric approach.

Factorial methods were also used with the aim of exploring the relationships between variables. The most suitable factorial method was correspondence analysis carried out using a Burt table (Benzecri, 1992). The heterogeneity of frequency distributions between the variables implies a low degree of dependency between them, above all when considering the "habits" variables with respect to the "exposure" variables. This situation makes the final solution (analogous with the regression results) more contrived than deductive, an aspect which limits the formal application of the factorial model. The problem can be solved using differential topological models (Cova, Márquez & Tovar, 2001), based on Thom's morphogenetic theory (1971), which is a future direction for this research.

#### **4. Results**

The characteristics of the sample are shown in Tables 1 and 2, which summarise its structure with respect to the various variables considered.

It is interesting to examine the categories within the "habits" variables, where the degree of personal protection, i.e. use of hearing protection, non-exposure to non-occupational noise and not smoking, is related to the atmosphere at work. It can be seen that as the noise level becomes more harmful the individuals tend to protect themselves more (Figure 1). This fact is very interesting when interpreting the effect on hearing of the noise and chemicals combination.

Yes HP No NOW No SH

Fig. 1. Distribution of personal habits according the exposure atmosphere

hearing alteration (theoretical values); (3) Tests of predictions. This involves predicting the development of certain exposed populations, based on the previous controls and

Survival functions obtained for the data from the sample, using a parametric and nonparametric model, they were represented together in a graph to assess their equivalence. The interesting aspect of this equivalence is the complementarity of the results, allowing them to be used together i.e. where one model is not suitable, the other is. For example, for the initial data, regression is possible in a non-parametric approach but not in a parametric

Factorial methods were also used with the aim of exploring the relationships between variables. The most suitable factorial method was correspondence analysis carried out using a Burt table (Benzecri, 1992). The heterogeneity of frequency distributions between the variables implies a low degree of dependency between them, above all when considering the "habits" variables with respect to the "exposure" variables. This situation makes the final solution (analogous with the regression results) more contrived than deductive, an aspect which limits the formal application of the factorial model. The problem can be solved using differential topological models (Cova, Márquez & Tovar, 2001), based on Thom's

The characteristics of the sample are shown in Tables 1 and 2, which summarise its structure

It is interesting to examine the categories within the "habits" variables, where the degree of personal protection, i.e. use of hearing protection, non-exposure to non-occupational noise and not smoking, is related to the atmosphere at work. It can be seen that as the noise level becomes more harmful the individuals tend to protect themselves more (Figure 1). This fact is very interesting when interpreting the effect on hearing of the noise and chemicals

29.5

3 1.8 27.9 27.9

Yes HP No NOW No SH

MF N WF

56.8

40 .6

44.2

hypotheses, making it possible to improve preventative management systems.

morphogenetic theory (1971), which is a future direction for this research.

Fig. 1. Distribution of personal habits according the exposure atmosphere

13.7

Individuals (%)

2 7.6

with respect to the various variables considered.

**3.3 Comparison of survival models** 

approach.

**4. Results** 

combination.

As can be seen in figure 2, the survival functions obtained through the Kaplan-Meyer method define how hearing alteration appears in individuals by event and for each work atmosphere. They show clear differences between event 1 and the others.

Thus, in event 1 the "noise with metalworking fluids" atmosphere causes a delay in hearing alteration which is significant (p<0.05), whereas the "noise only" atmosphere and the "noise with welding fumes" atmosphere develop in unison, showing no differences between them (p>0.05).

For events two and three, the curves that characterise each atmosphere are separated from one another significantly (p<0.05), indicating the time differences that exposure to each of them represents and for the same period (see variation of the medians and contrasts, Table 4 and 5). It was demonstrated, furthermore, that the 0 to 15 years period of exposure to noise was low risk, in general presenting hearing alteration of less than 10% in the individuals exposed (Figure 2).

The percentage of individuals affected, over this period gradually decreased as the event continued. Thus, event 1 principally characterises the variations in the hearing threshold of the recoverable type (initial trauma), event 2 non-recoverable but without alteration in conversational speech (advanced trauma) and in event 3 non recoverable variations with losses in conversational speech (hypoacusis). The situation described gives a dynamic to the process of hearing alteration which is characterised by the migration of the set of survival functions to the right. This explains the existence of a lower risk in the initial periods over time, demonstrating the suitability of the model (Figure 2).

Obtaining univariate, bivariate and trivariate models (Table 6), based on the Cox regression, explains the effect of the various variables in the study, based on the hazard ratio.

In considering the "smoking habit" variable it was found that its effect was antagonistic to atmospheres with metalworking fluids, although the hazard are more or less balanced, depending on the event. This indicates uniform action over time, which is different from metalworking fluids atmosphere, which tend to intensify the effects of smoking (Figure 3.2.).


N=cases; E=events; C=censored; SE=standard error

Table 4. Characteristics of no parametric survival functions

Exploration Databases on Occupational Hearing Loss 201

Atmospheres Event Log Rank df Sig MF / N 1 15.64 1 0.0001 WF / N 1 0.02 1 0.8904 MF / N 2 9.22 1 0.0024 WF / N 2 7.92 1 0.0049 MF / N 3 9.79 1 0.0018 WF / N 3 4.84 1 0.0279

Table 5. Contrast of no parametric survival function

Table 6. Cox regression models

Models Vi Event-1 Event-2 Event-3 Hazard Density CI 95% Wald

Hazard Density

Wald

Hazard Density

Wald

CI 95%

χ2

CI 95%

χ2

χ2

MF MF -0.524 0.467-0.748 0.000 -0.926 0.274-0.570 0.000 -1.368 0.139-0.466 0.000

WF WF 0.389 1.208-1.801 0.001 0.816 1.686-3.034 0.000 1.159 2.031-4.998 0.000

SH SH 0.492 1.203-2.227 0.001 0.391 0.974-2.245 0.065 -0.263 0.347-1.702 0.516

NOW NOW 0.826 1.540-3.391 0.000 0.812 1.207-4.201 0.010 0.789 0.915-5.298 0.078

HP HP -0.338 0.521-0.974 0.033 -0.647 0.323-0.847 0.008 -1.058 0.172-0.701 0.003

MF / SH MF -0.492 0.429-0.869 0.006 -0.904 0.239-0.685 0.001 -1.582 0.072-0.584 0.003 SH 0.473 1.179-2.185 0.002 0.347 0.930-2.151 0.105 -0.376 0.304-1.548 0.364

MF / NOW MF -0.269 0.542-1.075 0.123 -0.867 0.232-0.762 0.004 -1.514 0.079-0.617 0.004 NOW 0.780 1.466-3.249 0.000 0.665 1.040-3.636 0.037 0.574 0.739-4.271 0.199

MF / HP MF -0.383 0.469-0.988 0.043 -0.864 0.219-0.811 0.010 -1.507 0.067-0.731 0.013 HP -0.271 0.554-1.048 0.095 -0.526 0.362-0.963 0.035 -0.881 0.204-0.843 0.015

WF / SH WF 0.322 1.020-1.866 0.036 0.726 1.354-3.154 0.001 1.196 1.550-7.049 0.002 SH 0.477 1.184-2.194 0.002 0.349 0.933-2.155 0.102 -0.371 0.308-1.548 0.369

WF / NOW WF 0.346 1.045-1.912 0.024 1.062 1.746-4.795 0.000 1.450 1.976-9.192 0.002 NOW 0.781 1.470-3.243 0.000 0.652 1.030-3.576 0.040 0.575 0.741-4.267 0.198

WF / HP WF 0.411 1.076-2.117 0.017 1.134 1.735-5.571 0.000 1.432 1.686-10.39 0.002 HP -0.219 0.579-1.112 0.187 -0.363 0.423-1.145 0.154 -0.717 0.237-1.007 0.052

NOW / HP NOW 0.939 1.654-3.956 0.000 0.989 1.289-5.601 0.008 1.100 1.111-8.117 0.030 HP -0.375 0.502-0.939 0.018 -0.692 0.308-0.813 0.005 -1.104 0.164-0.671 0.002

MF -0.317 0.501-1.058 0.096 -0.805 0.232-0.863 0.016 -1.446 0.071-0.779 0.018

NOW 0.891 1.573-3.780 0.000 0.867 1.140-4.972 0.021 0.943 0.951-6.940 0.063

HP -0.321 0.526-0.997 0.048 -0.583 0.341-0.913 0.020 -0.946 0.190-0.794 0.010

WF 0.381 1.041-2.057 0.028 1.104 1.680-5.413 0.000 1.396 1.622-10.06 0.003

NOW 0.906 1.601-3.826 0.000 0.875 1.156-4.980 0.019 0.962 0.974-7.035 0.056

HP -0.259 0.555-1.071 0.121 -0.405 0.404-1.101 0.113 -0.760 0.226-0.969 0.041

MF / NOW / HP

WF / NOW / HP

Fig. 2. No parametric survival functions, Kaplan-Meyer

MF N WF

0 5 10 15 20 25 30 35 40 45 50 Time (years)

MF N WF

0 5 10 15 20 25 30 35 40 45 50 Time (years)

MF N WF

0 5 10 15 20 25 30 35 40 45 50 Time (years)

Fig. 2. No parametric survival functions, Kaplan-Meyer

0

1

0

1

0

Event 3

Probability

Event 2

Probability

Event 1

Probability

1


Table 5. Contrast of no parametric survival function


Table 6. Cox regression models

Exploration Databases on Occupational Hearing Loss 203

have an antagonistic effect; tobacco loses its effect in relation to welding fumes in the

The effect of non-occupational noise is antagonistic to that of metalworking fluids (Figure 3.4.), accelerating hearing alteration uniformly depending on the event, although it is in event 2 where it is most apparent, decreasing in the following event (p>0.05). By contrast, the effect of MF atmospheres strengthens over time, or to put it another way, the delay in

> Sig 2 tailed

Fig. 4.1. Normal

0 10 20 30 40 50 Time (years)

Fig. 4.2. Cumulated Normal Fig. 4.3. Log Normal Fig. 4. Parametric survival functions obtained for all atmospheres (MF+N+WF, N=558)

0

Probability

1

H 13.13 11.39 85.63% 2.076 0.0004 2.05 1.16 0.852 0.708 IAT 24.73 8.72 35.26% 1.001 0.2636 3.11 0.54 0.738 0.925 AAT 27.38 8.07 29.47% 0.959 0.3163 3.24 0.42 0.702 0.938 MH 29.31 7.92 27.02% 0.658 0.7788 3.34 0.31 0.679 0.930 AH 32.62 7.30 22.38% 0.926 0.3571 3.45 0.32 0.631 0.942

μ ln(Xi)

H IAT AAT MH AH

σ ln(Xi) R2 Normal

0 10 20 30 40 50 Time (years)

H IAT AAT

MH AH

R2

Log Normal

medium term (event 2) and long term (event 3), both with p>0.05.

Normality Linearity

(Z)

VC: Variation coefficient; K-S: Kolmogorov-Smirnov test; R2: Determination coefficient

(Xi) VC K-S

hearing alteration increases with time p<0.05).

σ

Table 7. Normality and linearity conditions

0

5

Frequencies

10

Hearing alteration

μ (Xi)

Fig. 3. Risk factor comparison with the personal habits through the hazard and according the event

Smoking in the WF atmosphere produces a synergistic effect, the action of which is minimised over time (Figure 3.1.). It is curious that for event 3 welding fumes and tobacco

Fig. 3.1. Synergy (Competence) Fig. 3.2. Antagonism

Fig. 3.3. Synergy Fig. 3.4. Antagonism

Fig. 3.5. Antagonism Fig. 3.6. Synergy Fig. 3. Risk factor comparison with the personal habits through the hazard and according


1 2 3

H M

1 2 3

NOW MF

123

SH M


0



0

1

2





0

1

Smoking in the WF atmosphere produces a synergistic effect, the action of which is minimised over time (Figure 3.1.). It is curious that for event 3 welding fumes and tobacco

the event


1 2 3

H W

1 2 3

NOW WF

1 2 3 Even

SH WF


0

1

2

0

1

2


0

Hazard Ratio

1

have an antagonistic effect; tobacco loses its effect in relation to welding fumes in the medium term (event 2) and long term (event 3), both with p>0.05.

The effect of non-occupational noise is antagonistic to that of metalworking fluids (Figure 3.4.), accelerating hearing alteration uniformly depending on the event, although it is in event 2 where it is most apparent, decreasing in the following event (p>0.05). By contrast, the effect of MF atmospheres strengthens over time, or to put it another way, the delay in hearing alteration increases with time p<0.05).


VC: Variation coefficient; K-S: Kolmogorov-Smirnov test; R2: Determination coefficient

Table 7. Normality and linearity conditions

Fig. 4. Parametric survival functions obtained for all atmospheres (MF+N+WF, N=558)

Exploration Databases on Occupational Hearing Loss 205

by the low frequency of individuals that are affected as the degree of hearing alteration increases. The spread of hearing alteration over time can be seen. Thus, once the level of advanced acoustic trauma is reached, the individual undergoes a more rapid process of hearing alteration. This can be substantiated, because the curves tend to unite more than in

Fig. 5.1. Healthy

0 10 20 30 40 50 60 Time (years)

Fig. 5.2. IAT Fig. 5.3. AAT

0

1

0

Probability

Probability

0 20 40 60 Time (years)

0 20 40 60 Time (years)

1

Fig. 5.4. MH Fig. 5.5. AH

The qualitative methodology proposed for the study of the combined influence of noise and chemical pollutants on hearing loss (Conte et al., 2009) differs from that used in traditional

Fig. 5. Survival function equivalence obtained by parametric and no parametric models

the IAT /AAT transition.

0

1

0

1

0

Probability

Probability

0 20 40 60 Time (years)

0 20 40 60 Time (years)

1

Probability

**5. Discussion** 


(1, 2) 2 tailed; CV-t: critical value for t

Table 8. Contrast of parametric survival functions (U of Mann-Whitney and t-test)

In WF atmospheres non-occupational noise produces a uniform effect depending on the event. It plays a more active role in event 1 in hearing alteration in relation to smoke (Figure 3.3.).

The use of individual protection equipment produces an effect similar to that of metalworking fluids, although their effectiveness increases over time (Figure 3.6.). This is characteristic when the protection equipment is not used continuously. It also explains the major delay produced in MF atmospheres.

In WF atmospheres the use of individual protection equipment is clearly antagonistic (Figure 3.5.), increasing in effectiveness over time, although the action of the WF atmosphere is much more powerful than the protection equipment.

Subsequently the effect on hearing of exposure to all atmospheres in the metal industry was analysed. To do this the initial sample was subdivided into the 3 atmospheres studied and in turn each one of these was divided into the five phases of hearing alteration. In doing this the frequencies were considerably reduced and as a consequence the analysis was not very consistent. Using the combined analysis of atmospheres to study the various phases of hearing alteration was the most useful option. For this analysis a parametric model (lognormal) was used to obtain the survival functions (Figure 4.3.). This has the advantage over those non-parametric models of the probability distribution of the event using continuous functions. This gives more precision to the distribution of each degree of hearing alteration and as a consequence to the identification of the time of the event (Figure 4, Table 8).

In this case, the survival curves must be understood as the combination of individuals who present a specific hearing alteration, independently of the atmosphere to which they are exposed and their personal habits. Each function associated with a degree of hearing alteration characterises an average value i.e. a theoretical value consisting of the combination of the three atmospheres to which must be added the combination of "habits" of the individuals in the sample (Figure 4.1.).

The conditions of normality and linearity of each degree of hearing alteration, obtained according to time, were assessed (Table 7).

The similarity of the survival functions for the different degrees of hearing alteration was also assessed using parametric and non-parametric methods, with the objective of making both the results and their interpretation homogeneous (Figure 5). It should be noted that except for the group of healthy people who do not follow a normal distribution, the remaining degrees of hearing alteration do follow a normal distribution. It can also be seen that in accordance with the degree hearing alteration the mean value of the distributions are displaced to the right. This confirms the suitability of the sample, which is also corroborated

H-IAT 6811 -9.066 0.0000 3.7024 1.9839 0.0003 IAT-AAT 8349 -2.698 0.0070 0.5595 1.9804 0.5768 AAT-MH 3283 -1.193 0.2325 0.4361 1.9804 0.6635 MH-AH 715 -2.309 0.0209 0.7795 1.9802 0.4372

In WF atmospheres non-occupational noise produces a uniform effect depending on the event. It plays a more active role in event 1 in hearing alteration in relation to smoke (Figure 3.3.).

The use of individual protection equipment produces an effect similar to that of metalworking fluids, although their effectiveness increases over time (Figure 3.6.). This is characteristic when the protection equipment is not used continuously. It also explains the

In WF atmospheres the use of individual protection equipment is clearly antagonistic (Figure 3.5.), increasing in effectiveness over time, although the action of the WF atmosphere

Subsequently the effect on hearing of exposure to all atmospheres in the metal industry was analysed. To do this the initial sample was subdivided into the 3 atmospheres studied and in turn each one of these was divided into the five phases of hearing alteration. In doing this the frequencies were considerably reduced and as a consequence the analysis was not very consistent. Using the combined analysis of atmospheres to study the various phases of hearing alteration was the most useful option. For this analysis a parametric model (lognormal) was used to obtain the survival functions (Figure 4.3.). This has the advantage over those non-parametric models of the probability distribution of the event using continuous functions. This gives more precision to the distribution of each degree of hearing alteration

and as a consequence to the identification of the time of the event (Figure 4, Table 8).

In this case, the survival curves must be understood as the combination of individuals who present a specific hearing alteration, independently of the atmosphere to which they are exposed and their personal habits. Each function associated with a degree of hearing alteration characterises an average value i.e. a theoretical value consisting of the combination of the three atmospheres to which must be added the combination of "habits"

The conditions of normality and linearity of each degree of hearing alteration, obtained

The similarity of the survival functions for the different degrees of hearing alteration was also assessed using parametric and non-parametric methods, with the objective of making both the results and their interpretation homogeneous (Figure 5). It should be noted that except for the group of healthy people who do not follow a normal distribution, the remaining degrees of hearing alteration do follow a normal distribution. It can also be seen that in accordance with the degree hearing alteration the mean value of the distributions are displaced to the right. This confirms the suitability of the sample, which is also corroborated

U Z Sig 1 t CV-t 2 Sig

Categories Normal Log Normal

Table 8. Contrast of parametric survival functions (U of Mann-Whitney and t-test)

(1, 2) 2 tailed; CV-t: critical value for t

major delay produced in MF atmospheres.

of the individuals in the sample (Figure 4.1.).

according to time, were assessed (Table 7).

is much more powerful than the protection equipment.

by the low frequency of individuals that are affected as the degree of hearing alteration increases. The spread of hearing alteration over time can be seen. Thus, once the level of advanced acoustic trauma is reached, the individual undergoes a more rapid process of hearing alteration. This can be substantiated, because the curves tend to unite more than in the IAT /AAT transition.

Fig. 5. Survival function equivalence obtained by parametric and no parametric models

#### **5. Discussion**

The qualitative methodology proposed for the study of the combined influence of noise and chemical pollutants on hearing loss (Conte et al., 2009) differs from that used in traditional

Exploration Databases on Occupational Hearing Loss 207

For Event2 the ideal models are MF with NOW and with HP, as well as WF with NOW.

For Event3 only the MF-HP model is considered suitable, with the other two habits losing

This indicates the influence obtained for each habit variable: SH influences IAT; NOW influences the development of AAT; HP is influential as protection at all stages, even if it is

The influence of smoking habits (SH) on the initial auditory alteration recognised in this study coincides with the results obtained by other authors (Pouryaghoub et al., 2007; Ferrite et al., 2005; Mizoue et al., 2003), but indicates the need for further research in order to

A methodological framework was presented which made it possible to use employment related health databases with limited information. The limitations of the data, resulting from possible changes in the way the data was obtained and recorded during the period under study, led to the use of qualitative, binary response variables and only one quantitative

With this situation as the starting point, it was established that that survival analysis is one of the best ways of analysing this type of data, both in relation to defining probability-time functions and their contrasts, and for modelling using Cox regression, in relation to both the application possibilities and the results reached (descriptive-explanatory in character).

This research was aimed at the analysis of the interaction between noise and chemicals and its influence on occupational hearing loss. It was found that in the Aragonese metal sector, which was the focus of this study, there were three main atmospheres: noise with

The analysis made it possible to establish that hearing alteration in individuals was related to the exposure atmosphere. Thus, workers exposed to noise and metalworking fluids, who protected themselves less, experienced slower hearing alteration compared to those who were exposed to only noise, and workers exposed to welding fumes, who protected themselves

Agrawal S, Platz EA, Niparko JK. (2009). Risk Factors for Hearing Loss in EU Adults: Data

Conte JC, Dominguez AI, Garcia AI, Rubio E, Perez A. (2010). Cox Regression Model of

*Neurotology*, Vol. 30, Nº2, (February 2009), pp. 139-145, ISSN 1531-7129 Benzecri J. (1992). *Correspondence Analysis Handbook*. Marcel Decker, ISBN 0824784375, New

from the National Health and Nutrition Examination Survey. *Otology &* 

Hearing Loss in Workers Exposed to Noise and Metalworking Fluids or Welding Fumes. *Anales del Sistema Sanitario de Navarra*, Vol. 33, Nº 1, (April 2010), pp. 11-21,

more, suffered hearing alterations sooner than those who were only exposed to noise.

There is a decline in accuracy with respect to the previous event.

significance.

ineffective against fumes.

properly assess this influence.

variable, namely the time of exposure to noise.

metalworking fluids, noise only and noise with welding fumes.

**6. Conclusions** 

**7. References** 

York, EEUU

ISSN 1137-6627

studies on the same topic. These perform a quantitative analysis of decreases in the hearing threshold, an aspect which was replaced by an audiogram classification based on a diagnostic reference. The duration of each individual's exposure to noise was also used, instead of their age, thereby improving the linear behaviour of the temporal variable. Finally, each chemical contaminant was characterised by a binary variable, thus avoiding the use of an environmental measurement value, which provides more general and less restrictive identification than quantitative environmental measurements.

This study shows the influence of noise on hearing alteration, whether temporary (IAT) or permanent (AAT, MH, AH). This situation is consistent with studies conducted on the influence of this physical agent on hearing.

Moreover, chemical agents taken as interacting with noise (MF and WF) have been considered by various researchers as pulmonary toxins (Godderis et al., 2008; Schaller et al., 2007), due to the principal way they enter the body: by inhalation. It is nonetheless true that the influence of these agents on hearing loss, a toxic effect that can be considered indirect, has not been given due attention.

This study confirms the existence of an interaction between physical and chemical factors in the metal industry which influence the alteration of auditory function, and which can be characterised by three different exposure environments, WF with noise, MF with noise, and noise only.

The interaction of the pollutants with the individual determines whether the auditory effects caused by the main risk factor (noise) develop more quickly or slowly in the worker. Thus, it can be identified that metalworking fluids delay the development and worsening of the various stages of auditory alteration, whereas welding fumes speed up the development of same. In this respect, the behaviour of one contaminant with another is antagonistic.

The study also indicates that, in the case of welding fumes, the chemical agent is shown to be more detrimental to hearing. One of the main problems regarding welding fumes in the presence of noise is that, in general, the protection used is effective in muffling noise intensity but not in reducing the effect of the chemical agent. In this situation, cellulose masks or those of similar compounds have little effect, as their capacity to filter particles (such as charcoal) is not effective for gaseous molecules such as carbon monoxide, which is highly ototoxic (Gwin et al., 2005; Morley et al., 1999).

As regards personal habits, there is a growing tendency to use hearing protection as the harmfulness of the environment increases. The interpretation of this fact is due to an increased personal willingness to use protective equipment when the individual feels some discomfort, which may be intuitively associated with the work environment. This study verifies that the increase in using protection is not sufficiently capable of improving auditory health conditions, supporting the negative effects of welding fumes on workers.

With regard to the regression models, it has been demonstrated that the univariate models (MF and WF) are those which best, and more accurately, define each model according to the event.

Despite a loss of accuracy, the bivariate models may be more interesting as regards application. For Event1, the variable SH is shown to be the most influential and best represented of the models. For this event, NOW is also considered an acceptable model, along with WF.

For Event2 the ideal models are MF with NOW and with HP, as well as WF with NOW. There is a decline in accuracy with respect to the previous event.

For Event3 only the MF-HP model is considered suitable, with the other two habits losing significance.

This indicates the influence obtained for each habit variable: SH influences IAT; NOW influences the development of AAT; HP is influential as protection at all stages, even if it is ineffective against fumes.

The influence of smoking habits (SH) on the initial auditory alteration recognised in this study coincides with the results obtained by other authors (Pouryaghoub et al., 2007; Ferrite et al., 2005; Mizoue et al., 2003), but indicates the need for further research in order to properly assess this influence.
