Search tips
Search criteria 


Logo of demKargerHomeAlertsResources
Dement Geriatr Cogn Disord. 2010 August; 30(1): 12–19.
Published online 2010 July 5. doi:  10.1159/000315498
PMCID: PMC2945272

Occupational Exposure to Solvents and Cognitive Performance in the GAZEL Cohort: Preliminary Results



The impact of occupational exposure to solvents on cognitive ageing remains unclear. We examined whether long-term occupational exposure is associated with poor cognitive performance in late midlife.


Participants in the GAZEL cohort, set up in 1989, are employees of the French national electricity and gas company. Data on the working environment were used to create measures of cumulative exposures to solvents using a job-exposure matrix. In 2002–2004, cognitive performance was assessed using the Digit Symbol Substitution Test (DSST) and the Mini Mental State Examination in 5,242 participants (aged 55–65 years).


In cross-sectional analysis using multiple logistic regression, there was a greater risk of poor cognitive performance (DSST score <25th percentile) among those with high exposure to benzene (OR = 1.58; 95% CI 1.31–1.90) and the grouped categories of chlorinated (OR = 1.39; 95% CI 1.3–2.3), aromatic (OR = 1.76; 95% CI 1.08–2.87), and petroleum solvents (OR = 1.50; 95% CI 1.23–1.81).


These results suggest that occupational exposures to solvents may be associated later in life with cognitive impairment, even after taking into account the effects of education, employment grade, and numerous health factors.

Key Words: Cognition, Chemical exposure, Solvent, Midlife, Cohort, Occupation


It remains unclear whether occupational exposure during working life affects cognitive functioning later in life [1]. Some [2] but not all studies [3] suggest a greater risk of dementia among manual workers. This inconsistency may be due to the multifaceted nature of occupational positions (namely as an indicator of environmental exposures), material deprivation, access to medical care and attitudes to health, or a surrogate marker of premorbid intelligence or cognitive abilities. Our focus here is on the impact of chemical exposures at work on cognitive ageing, an area that has not yet been sufficiently investigated.

Chronic exposure to organic solvents induces central nervous system (CNS) damage, usually called chronic solvent-induced encephalopathy [4]. It typically results in CNS depression and psychomotor or attentional deficits. The acute effects often resolve after a cessation or decrease in exposure, except for extremely high exposure [5]. Some findings also suggest residual CNS dysfunction, persisting years after the end of exposure, particularly with long-term exposure to organic solvents [6]. Neuropsychological changes associated with acute and chronic exposure to organic solvents have been well documented in cross-sectional and longitudinal studies in those still at work or less than 60 years old [4, 7, 8]. Neuroimaging results also appear to support these findings [9]. Results obtained in studies using the case-control design for dementia [10,11,12] are limited due to the retrospective determination of exposures. A recent review highlighted the need for further studies with rigorous exposure descriptions, adjustment for important confounders, and cognitive tests sensitive in the detection of poor performance [13]. The GAZEL cohort allowed us to examine whether long-term occupational exposure to solvents is associated with poor cognitive performance on two tests in late midlife.

Population and Methods


The GAZEL cohort was initiated in 1989 among the employees of the French national electricity and gas company, Electricité de France – Gaz de France (EDF-GDF), the only utility company in France at that time. In January 1989, after an information campaign in the company and union newsletters, an invitation was sent to all male employees then aged 40–50 years and all female employees then aged 35–50 years [14]. At baseline, 20,625 individuals agreed to participate, and these have been followed using an annual self-report questionnaire. In 2002–2004, the GAZEL study undertook a medical examination by inviting participants to 1 of the 54 health screening centers (Centres d'examens de santé) of the French social security system located all over France. However, the cognitive measures were added to the study after the start of the medical examination campaign. Thus, only a subsample of the cohort (n = 14,751) was eligible for cognitive testing. A decision was made to invite only participants aged ≥55 years (n = 10,537) to the cognitive testing. The present study is based on subjects who participated in these cognitive tests (n = 5,242, 49.7% of the target population).

Occupational Exposure

From recruitment into the company until 1998, data on the workforce's working environment were collected systematically [15]. Assessment of various physical and chemical exposures (n = 29) is based on a job-exposure matrix specific to the company (Matex) developed from expert judgment using a standardized procedure in order to study cumulative exposure to occupational chemicals [16, 17]. In the present analysis, we focused on the most frequent solvent exposure with 8 specific solvent species (toluene diisocyanate, TDI; hydrazine; tetrachloromethane, trichloroethylene, perchloroethylene, Dichloromethane, trichloroethane, benzene). Besides, benzene, these can be regrouped into 3 categories of solvents: chlorinated, aromatic and petroleum solvents [18].

Solvents were reported in the matrix as semiquantitative exposure indices with time-weighted average exposures. Cumulative doses were calculated taking into account the level of exposure in each episode together with the probability of exposure. Finally, for each solvent, the subjects were classified into three exposure categories: unexposed / moderate (exposed with level lower than the median of exposition) / high exposure (exposed with level equal or above the median). For exposure to tetrachloromethane, present only in 0.6% of subjects, we considered two classes, unexposed and moderate exposure.

Cognitive Function

Cognitive function was assessed in 2002–2004 using two tests, the French version of the 30-point Mini Mental State Examination (MMSE) [19] and the Digit Symbol Substitution Test (DSST) [20]. DSST is generally thought to be more sensitive in non-demented elderly populations than the widely used MMSE. It requires response speed, sustained attention, visual spatial skills, associative learning, and memory. For these reasons it was chosen in the NHANES study in 2005 ( The DSST is a subtest of the Wechsler Adult Intelligence Scale-Revised, a timed paper and pencil task requiring translation of numbers to symbols using a key provided at the top of the test form. The score is the number of correct symbols drawn within 90 s for a maximum score of 93. For both tests, poor cognitive performance was defined by a score below or equal to the 25th percentile.


Covariates included in the analysis were sociodemographic factors: sex, age (in years), education (finished secondary school, baccalaureate, yes/no), and employment grade at age 35 (unskilled/skilled/manager); lifestyle factors: smoking (current smoker yes/no) and alcohol consumption (no alcohol consumption; moderate defined as 1–20 drinks/week for women and 1–27 for men; heavy drinker defined for more than 20 drinks/week for women and 27 for men); and health factors, via medical interview for hypertension, asthma, and other respiratory symptoms. Depression symptoms were assessed by the Center for Epidemiological Studies-Depression (CESD) scale using a score of 17 for men and 23 for women to define depressive symptomatology [21]. We also adjusted for the geographical location of the screening center (‘Paris and suburbs’ or ‘other’). For descriptive purposes, we examined the association with retirement status (yes/no) at time of medical screening.

Statistical Analysis

Analysis was performed on the 5,242 subjects with data on occupational exposure and the DSST. For MMSE, the analyses were restricted to a subset of 4,904 participants with MMSE data.

In the main analysis on the DSST, missing data for education (n = 89) and grade at age 35 years (n = 107) were replaced by the modal value. For 3 factors with a greater proportion of missing data, a missing data category was used in the analysis; this was the case for alcohol (n = 437), tobacco (n = 562), and the CESD (n = 1,084). Finally, as 718 subjects had no data on their medical history, a dummy variable ‘missing data on health’ was used in the analysis.

We first examined the bivariate associations between all the covariates and poor cognitive performance using logistic regression. Multivariable logistic regression analysis was performed for all solvents that were found to have a robust association (p < 0.05) in the bivariate analysis. These analyses were adjusted for covariates in 2 steps. Model 1 included sociodemographic factors (sex, age, education, and grade at age 35 years); model 2 additionally included screening center and lifestyle and health factors selected according to their association with major occupational exposures. The Wald test of significance was examined for each exposure. All analyses were performed using the SAS software, version 9.1.


DSST was completed by 5,242 participants, 90.7% of which were retired at the time of cognitive examination. Characteristics of the study population are shown in table table1.1. Eighty-four percent of subjects were men, and the mean age was 59 years (range 55–65 years). Less than a quarter of the participants had completed secondary school and, at age 35 years, 62.7% were skilled workers and 16.7% managers. Depressive symptomatology was observed in 13.2% of the participants. Among those with data on health measures, the prevalence was: hypertension 28.1%, history of vascular disease 6.1%, asthma 5.8%, and other respiratory diseases 15.7%.

Table 1
Characteristics of participants

Mean DSST score was 48.4 with an interquartile range from 42 to 55. Mean MMSE score was 28.7 with an interquartile range from 28 to 30. Poor cognitive performance was defined by a score below or equal to the 25th percentile, corresponding to a value of 41 for DSST and 27 for the MMSE. Due to a skewed distribution, poor performance on the MMSE corresponds to 18.8% (n = 920) of the 4,904 subjects with MMSE scores.

In this population, 69.0% of subjects were unexposed to the 8 specific solvent types examined, 14.7% were exposed to 1 type, 8.7% to 2 types, and 7.6% to ≥3 solvent types. Exposure to TDI, hydrazine and tetrachloromethane was present in <10% of the sample, while exposure to trichloroethylene, perchloroethylene and benzene was recorded for more than 20%. Exposures to dichloromethane and trichloroethane were observed in 16.0 and 11.4% of the population, respectively. Exposures to the 3 overall categories of solvents (chlorinated, aromatic and petroleum, excluding benzene) were respectively 31.8, 2.8 and 24.1%.

Table Table22 shows the bivariate association between the covariates and poor cognitive performance. When using the DSST score, we observed expected associations with age, education, and employment grade at age 35 years. Women performed better than men, and so did those who were seen at the screening center located in Paris or its suburbs. Smokers, participants with cardiovascular disease or respiratory symptoms other than asthma, and those with depressive symptoms had poorer cognitive performance. With the MMSE, these associations were not observed for age, sex, smoking habits, and depressive symptomatology. The bivariate analysis showed poorer cognitive performance, both for the DSST and the MMSE, in workers with the highest estimated cumulative exposure to TDI, trichloroethylene, perchloroethylene, dichloromethane, trichloroethane and benzene. There was no association with exposure to hydrazine or tetrachloromethane.

Table 2
Bivariate association between poor cognitive performance (score below the 25th percentile on DSST or MMSE) and sociode-mographic factors, lifestyle, health factors, and exposure to solvents

Table Table33 presents results on the multiple regression analysis using the DSST. Model 1 was adjusted for sociodemographic characteristics (sex, age, education, and employment grade at age 35 years). Significant trends were observed for all exposures except TDI, hydrazine and tetrachloromethane. Additional adjustment on screening center, lifestyle, and health factors (model 2) did not substantially change the associations. As compared to unexposed individuals, high-exposure groups (above median of exposure in subjects exposed) had a greater risk of poor cognitive performance: OR of 1.33 (95% CI 1.10–1.60) for trichloroethylene, 1.36 (1.11–1.68) for perchlorethylene, 1.54 (1.22–1.96) for dichloromethane, 1.52 (1.18–1.96) for trichloroethane, 1.58 (1.31–1.90) for benzene. The moderate exposure category suggested no robust effects.

Table 3
Multiple regression associations between poor DSST performance (<25th percentile of distribution) and exposure to solvents

Table Table44 shows similar results using the MMSE as the outcome. As compared to unexposed individuals, high-exposure groups had a greater risk of poor cognitive performance: OR 1.32 (95% CI 1.07–1.63) for trichloroethylene, 1.41 (1.12–1.78) for perchloroethylene, 1.36 (1.04–1.77) for dichloromethane, 1.53 (1.15–2.02) for trichloroethane, and 1.28 (1.03–1.59) for benzene. Furthermore, moderate exposure to perchloroethylene (OR = 1.40, 95% CI 1.10–1.77) and to benzene (OR = 1.39, 95% CI 1.12–1.74) were also associated with poor MMSE performance.

Table 4
Multiple regression associations between poor MMSE performance (<25th percentile of distribution) and exposure to solvents


This paper is one of the first to document the relationship between chronic exposure to solvents and cognitive performance in a large sample of 55- to 65-year-olds, most of whom were retired. Participants whose cumulative exposure to solvents was above the median exposure had an elevated risk of cognitive impairment compared to the non-exposed individuals. The risk was greater in workers with the highest estimated cumulative exposure to chlorinated solvents and to the 3 types of chlorates studied, to petroleum solvents, but also to aromatic solvents and benzene.

The data used in the present analysis were collected for a pilot medical screening in this cohort. Thus, only a short cross-sectional cognitive evaluation was proposed, and we have no previous evaluation of pre-morbid IQ. The MMSE is often used as a screening tool for dementia in the elderly, but is less appropriate for exploring cognitive performance in younger age-groups (such as our population aged 55–65 years). The DSST has a large inter-individual range in this age group, and is relatively unaffected by intellectual ability, memory, or learning. It is also a more sensitive test at higher levels of cognitive function than the MMSE [22]. It requires response speed, sustained attention, visual spatial skills, associative learning, and memory. The DSST score has been linked to dementia [23] and to mortality [24]. Overall it showed good metrological properties in this population. This test has also been shown to be sensitive to cognitive change linked with chronic exposure to both lead and solvents [25, 26].

The GAZEL cohort, like all other longitudinal studies relies on the willingness of the participants to continue to take part in the study and as such is subject to potential selection biases. Lower occupational position is associated with lower response rates over the follow-up [27]. If exposure to solvents is greater in the lower employment grades, then the current analysis is likely to exclude those most exposed to solvents. Nevertheless, the measure of cumulative exposure for most of the solvents in this cohort was such that dose-effect associations could be examined. Our measure of cumulative exposure integrates full employment history because, in most cases, participants started working for the electricity and gas companies on which GAZEL is based in their twenties and continued till retirement. Furthermore, the GAZEL cohort has a full spectrum of participants, across all occupational categories from manual laborers to executives.

Most studies on aging are based on elderly subjects and did not have the opportunity to document detailed occupational exposure over the participants’ working lives. Furthermore, exposure assessments are most often restricted to job titles. One of the unique features of this study is the detailed history of exposures alongside data on numerous potential confounding factors. Literature on aging increasingly points to the effects of long-term exposure [28], which is difficult to document retrospectively.

Since the 1970s, beginning with several reports from Scandinavia, various studies have suggested that chronic low-level occupational exposure to organic solvents may have a negative impact on cognitive and psychological functioning [4, 29]. Indeed, a cluster of clinical symptoms, alternatively named ‘chronic painter's syndrome’, ‘solvent syndrome’, or ‘chronic toxic encephalopathy’ have been reported among exposed workers. This cluster included headache, fatigue, difficulties with memory and concentration, irritability, depression, and personality changes.

However, most studies were performed during active life using a cross-sectional design. They were often based on small selected samples and comparisons have often lacked suitable control groups. Exposure assessment was retrospective and potential confounders were not fully taken into account. Furthermore, as neuropsychological tests vary between studies, comparisons of results may be limited [6]. Residual effects on cognitive functioning years after the cessation of neurotoxin exposure have been the target of very few previous studies. In 89 retired male workers with previous long-term exposure to solvents assessed retrospectively [30], lower mean scores on test measures of motor, memory and reasoning ability were described. The study with the longest follow-up was performed in Sweden [8, 31]; Nilson et al. followed 41 floor layers exposed to organic solvents (solvent-based glues) and 40 carpenters using 10 neuropsychological tests at baseline and then again at a 18-year follow-up. This study assessed exposures extensively and found that among the oldest subjects higher cumulative exposure was associated with decrements in visual episodic memory, perceptual speed and attention and visuospatial skill with significant dose-effect associations. There was also evidence of an effect on neuropsychiatric functioning indicating that general well-being later in life is affected in floor layers with past heavy solvent exposure, strengthening the evidence that long-term heavy occupational solvent exposure may negatively impact the normal ageing process [32]. In the 1947 Scottish Mental Survey [26] on 336 subjects aged 67 years with low lifetime exposures, there was no clear evidence of an association between organic solvent exposure and cognitive function.

These results suggest that occupational exposure to solvents may interact with the normal aging process, primarily in the most heavily exposed individuals. The effects are quite similar for all solvents and evident in this study on mostly retired workers, suggesting a potential long-term residual effect of solvents. It could be hypothesized that chronic exposure to various organic solvents or other exposures would lead to different patterns of cognitive disturbance. This hypothesis could not be tested in the present study but we hope, in the near future, to add measures of memory, language, attention and concentration capacities to this cohort.

Several hundred million tons of organic solvents are still used worldwide per year, although regulatory pressure and concerns for the environment are gradually leading to a reduction in use [33]. Occupational exposures are clearly modifiable factors. The solvents examined in our study have been extensively linked to cancer, with fraction of all cancers attributable to occupational exposure being at least 5% [34, 35]. Their importance to cognitive aging and risk of dementia needs to be more completely evaluated in future studies.


This work is part of a project funded by the Agence nationale de la recherche (ANR, French National Research Agency), and Agence française de sécurité sanitaire de l'environnement et du travail (AFSSET, French Agency for Sanitary Security of Environment and Work). A.S.M. is supported by the National Institute on Aging, NIH (R01AG013196; R01AG034454).

We would like to thank Ellen Imbernon (INVS, Saint Maurice, France) for her assistance in the construction of the job-exposure matrix and for some fruitful discussions.


1. Smyth KA, Fritsch T, Cook TB, McClendon MJ, Santillan CE, Friedland RP. Worker functions and traits associated with occupations and the development of AD. Neurology. 2004;63:498–503. [PubMed]
2. Qiu C, Karp A, von Strauss E, Winblad B, Fratiglioni L, Bellander T. Lifetime principal occupation and risk of Alzheimer's disease in the Kungsholmen project. Am J Ind Med. 2003;43:204–211. [PubMed]
3. Helmer C, Letenneur L, Rouch I, RichardHarston S, Barbergergateau P, Fabrigoule C, Orgogozo JM, Dartigues JF. Occupation during life and risk of dementia in French elderly community residents. J Neurol Neurosurg Psychiatry. 2001;71:303–309. [PMC free article] [PubMed]
4. White RF, Proctor SP. Solvents and neurotoxicity. Lancet. 1997;349:1239–1243. [PubMed]
5. Urban P, Pelclova D, Lukas E, Kupka K, Preiss J, Fenclova Z, Smerhovsky Z. Neurological and neurophysiological examinations on workers with chronic poisoning by 2,3,7,8-TCDD: follow-up 35 years after exposure. Eur J Neurol. 2007;14:213–218. [PubMed]
6. Gamble JF. Low-level hydrocarbon solvent exposure and neurobehavioural effects. Occup Med (Lond) 2000;50:81–102. [PubMed]
7. Dick F, Semple S, Osborne A, Soutar A, Seaton A, Cherrie JW, Walker LG, Haites N. Organic solvent exposure, genes, and risk of neuropsychological impairment. QJM. 2002;95:379–387. [PubMed]
8. Nilson LN, Backman L, Sallsten G, Hagberg S, Barregard L. Dose-related cognitive deficits among floor layers with previous heavy exposure to solvents. Arch Environ Health. 2003;58:208–217. [PubMed]
9. Visser I, Lavini C, Booij J, Reneman L, Majoie C, de Boer AG, Wekking EM, de Joode EA, van der Laan G, van Dijk FJ, Schene AH, Den Heeten GJ. Cerebral impairment in chronic solvent-induced encephalopathy. Ann Neurol. 2008;63:572–580. [PubMed]
10. Kukull WA, Larson EB, Bowen JD, McCormick WC, Teri L, Pfanschmidt ML, Thompson JD, O'Meara ES, Brenner DE, van Belle G. Solvent exposure as a risk factor for Alzheimer's disease: a case-control study. Am J Epidemiol. 1995;141:1059–1071. discussion 1072–1079. [PubMed]
11. Gun RT, Korten AE, Jorm AF, Henderson AS, Broe GA, Creasey H, Mccusker E, Mylvaganam A. Occupational risk factors for Alzheimer disease: a case-control study. Alz Dis Assoc Disorder. 1997;11:21–27. [PubMed]
12. Palmer K, Inskip H, Martyn C, Coggon D. Dementia and occupational exposure to organic solvents. Occup Environ Medicine. 1998;55:712–715. [PMC free article] [PubMed]
13. Meyer-Baron M, Blaszkewicz M, Henke H, Knapp G, Muttray A, Schaper M, van Thriel C. The impact of solvent mixtures on neurobehavioral performance: conclusions from epidemiological data. Neurotoxicology. 2008;29:349–360. [PubMed]
14. Goldberg M, Leclerc A, Bonenfant S, Chastang JF, Schmaus A, Kaniewski N, Zins M. Cohort profile: The GAZEL cohort study. Int J Epidemiol. 2007;36:32–39. [PMC free article] [PubMed]
15. Goldberg M, Chevalier A, Imbernon E, Coing F, Pons H. The epidemiological information system of the French national electricity and gas company: the SI-EPI project. Med Lav. 1996;87:16–28. [PubMed]
16. Imbernon E. Matex: Une matrice emplois-expositions destinés à la surveillance épidémiologique des travailleurs d'une grande entreprise (EDF-GDF) Arch Mal Prof. 1991;52:559–566.
17. Guenel P, Nicolau J, Imbernon E, Warret G, Goldberg M. Design of a job exposure matrix on electric and magnetic fields: selection of an efficient job classification for workers in thermoelectric power production plants. Int J Epidemiol. 1993;22(suppl 2):S16–S21. [PubMed]
18. Martin JC, Imbernon E, Goldberg M, Chevalier A, Bonenfant S. Occupational risk factors for lung cancer in the French electricity and gas industry: a case-control survey nested in a cohort of active employees. Am J Epidemiol. 2000;151:902–912. [PubMed]
19. Folstein MF, Folstein SE, McHugh PR. ‘Mini-mental state’: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. [PubMed]
20. Lezak M. Neuropsychological Assessment. New York: Oxford University Press; 1995.
21. Fuhrer R, Antonucci TC, Gagnon M, Dartigues JF, Barbergergateau P, Alperovitch A. Depressive symptomatology and cognitive functioning – an epidemiological survey in an elderly community sample in France. Psychol Med. 1992;22:159–172. [PubMed]
22. Proust-Lima C, Amieva H, Dartigues JF, Jacqmin-Gadda H. Sensitivity of four psychometric tests to measure cognitive changes in brain aging-population-based studies. Am J Epidemiol. 2007;165:344–350. [PMC free article] [PubMed]
23. Shimomura T, Mori E, Yamashita H, Imamura T, Hirono N, Hashimoto M, Tanimukai S, Kazui H, Hanihara T. Cognitive loss in dementia with Lewy bodies and Alzheimer disease. Arch Neurol. 1998;55:1547–1552. [PubMed]
24. Pavlik VN, de Moraes SA, Szklo M, Knopman DS, Mosley TH, Jr, Hyman DJ. Relation between cognitive function and mortality in middle-aged adults: The Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2003;157:327–334. [PubMed]
25. Fiedler N, Weisel C, Lynch R, Kelly-McNeil K, Wedeen R, Jones K, Udasin I, Ohman-Strickland P, Gochfeld M. Cognitive effects of chronic exposure to lead and solvents. Am J Ind Med. 2003;44:413–423. [PubMed]
26. Dick FD, Bourne VJ, Semple S, Fox HC, Miller BG, Deary IJ, Whalley LJ. Solvent exposure and cognitive ability at age 67: a follow-up study of the 1947 Scottish Mental Survey. Occup Environ Med. 2010;67:401–407. [PubMed]
27. Goldberg M, Chastang JF, Zins M, Niedhammer I, Leclerc A. Health problems were the strongest predictors of attrition during follow-up of the GAZEL cohort. J Clin Epidemiol. 2006;59:1213–1221. [PubMed]
28. Richards M, Deary IJ. A life course approach to cognitive reserve: a model for cognitive aging and development? Ann Neurol. 2005;58:617–622. [PubMed]
29. Mikkelsen S. Epidemiological update on solvent neurotoxicity. Environ Res. 1997;73:101–112. [PubMed]
30. Daniell WE, Claypoole KH, Checkoway H, Smith-Weller T, Dager SR, Townes BD, Rosenstock L. Neuropsychological function in retired workers with previous long-term occupational exposure to solvents. Occup Environ Med. 1999;56:93–105. [PMC free article] [PubMed]
31. Nilson LN, Sallsten G, Hagberg S, Backman L, Barregard L. Influence of solvent exposure and aging on cognitive functioning: an 18 year follow up of formerly exposed floor layers and their controls. Occup Environ Medicine. 2002;59:49–57. [PMC free article] [PubMed]
32. Nordling Nilson L, Barregard L, Sallsten G, Hagberg S. Self-reported symptoms and their effects on cognitive functioning in workers with past exposure to solvent-based glues: an 18-year follow-up. Int Arch Occupat Environ Health. 2007;81:69–79. [PubMed]
33. Caldwell DJ, Armstrong TW, Barone NJ, Suder JA, Evans MJ. Hydrocarbon solvent exposure data: compilation and analysis of the literature. Aihaj. 2000;61:881–894. [PubMed]
34. Boffetta P, Jourenkova N, Gustavsson P. Cancer risk from occupational and environmental exposure to polycyclic aromatic hydrocarbons. Cancer Causes Control. 1997;8:444–472. [PubMed]
35. Harvard Report on Cancer Prevention. Volume 1: Causes of human cancer. Cancer Causes Control. 1996;7(suppl 1):S3–S59. [PubMed]

Articles from Dementia and Geriatric Cognitive Disorders are provided here courtesy of Karger Publishers