Socioeconomic, Demographic, Occupational, and Health Factors Associated with Participation in a Long-term Epidemiologic Survey: A Prospective Study of the French GAZEL Cohort and Its Target Population

Marcel Goldberg1, Jean François Chastang1, Annette Leclerc1, Marie Zins1, Sébastien Bonenfant1, Isabelle Bugel1, Nadine Kaniewski1, Annie Schmaus1, Isabelle Niedhammer1, Michèle Piciotti1, Anne Chevalier1, Catherine Godard1 and Ellen Imbernon1

1 INSERM Unité 88, Hôpital National de Saint-Maurice, Saint-Maurice, France.
2 Électricité de France-Gaz de France, Service Général de Médecine de Contrôle, Paris, France.
3 Électricité de France-Gaz de France, Service Général de Médecine du Travail, Paris, France.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The purpose of this paper is to examine personal and health factors, both at the beginning of the study and thereafter, associated with participation in the GAZEL cohort, set up in 1989 in a large French company. The authors used logistic regression to analyze the associations between participation and data available for both participants (n = 20,093) and nonparticipants (n = 24,829). Higher participation was associated with male sex, marriage, children, managerial status, and residence in particular regions. Among men, lower participation was associated with sick leave in the year before recruitment and afterwards. During follow-up, participation was negatively associated with several groups of diseases, especially those associated with alcohol consumption. The risk of upper respiratory and digestive tract and lung cancer during follow-up was higher among nonparticipants. The same phenomenon occurred among women, but less markedly, for cancers of the breast and genital organs. During follow-up, mortality among men was higher among nonparticipants, especially for alcohol-related diseases. The association among women was less strong. Among men, but not among women, diseases caused by alcohol, smoking, or dangerous behavior were the primary reason for the health differences observed between participants and nonparticipants. Overall, the most important determinants of participation were cultural factors and lifestyle behaviors.

absenteeism; cohort studies; mortality; selection bias; socioeconomic factors

Abbreviations: CI, confidence interval; EDF-GDF, Électricité de France-Gaz de France; SES, socioeconomic status


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Epidemiologic studies that require the collection of data directly from subjects rely on the subjects' readiness to volunteer. Various investigators have shown that the voluntary nature of participation can bias the estimations of the frequency of disease and risk factors and the measurement of their associations (1GoGoGoGoGo–6Go). For practical reasons, epidemiologic studies often use mail surveys, even though they yield lower response rates than do the methods involving direct contact with potential subjects (7Go). The mail survey method can also induce effects associated with sociocultural factors because subjects in lower socioeconomic categories may be less at ease with expressing themselves in writing (8Go).

The studies that have been able to compare the characteristics of participants and nonparticipants reveal a certain variability in the effect of the socioeconomic, demographic, and lifestyle factors on participation (3Go, 5Go, 7GoGoGoGoGoGo–13Go). Among lifestyle-related factors, the most attention has been paid to tobacco and alcohol (7Go, 11GoGoGoGoGoGoGo–18Go).

Neither the existence nor the nature and scope of a selection effect linked to health status has been clearly established. General health indicators, such as mortality (10Go, 19Go, 20Go) or sick leaves (12Go, 14Go), have been studied, as well as specific health problems: laboratory test anomalies, self-reported symptoms, and disorders (3Go, 7Go, 11Go, 13Go, 15Go, 16Go, 18Go, 20GoGoGo–23Go). Numerous other factors can influence the participation rate, including the methods used to contact subjects, the effort required of the participants, and the subjects' opinion of the utility of the study (24Go).

Our goal was to study the social, demographic, occupational, and health factors associated with the participation rate in a cohort set up in 1989 in a large French company. The voluntary participation of the cohort members is required for completion of annual mail surveys. An important aspect of this study is that much of the data is collected longitudinally for the entire target population. Both groups (volunteers and nonparticipants) were followed, and it was thus possible to analyze how participation at baseline was associated with these factors at the time the study began and to examine the changes in both groups over several years.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
GAZEL cohort
The GAZEL cohort was established in 1989 among the employees of the national electricity and gas company, Électricité de France-Gaz de France (EDF-GDF). This firm employs approximately 150,000 people of diversified trades and socioeconomic statuses (SES) throughout France. Epidemiologic observations of this firm benefit from the population's stability; the employees have a status comparable with that of civil servants and can be followed up after retirement because their pensions are paid by EDF-GDF. The company has an occupational medicine department and its own medical insurance system, both of which systematically monitor employees' health. It has constructed an epidemiologic database that allows exhaustive recording of the most important disorders (25Go, 26Go) and occupational exposures (27Go, 28Go). The personnel department maintains a file that includes the employees' socioeconomic and occupational characteristics. The epidemiologic profile of this population is very close to that of the French general population: The proportions of disorders and causes of death and the socioeconomic, occupational, and geographic disparities are almost identical for both (29GoGo–31Go).

The methods used to recruit, follow up, and collect data from the GAZEL cohort have been described previously (32Go, 33Go) and are summarized only briefly here. In January 1989, after an information campaign in the company and union newsletters, an invitation to participate in the cohort, accompanied by a questionnaire, was sent to all male employees then aged 40–50 years and all women employees then between ages 35 and 50 years. A reminder letter was sent to the entire target population 2 weeks after the first mailing. The letter did not mention diseases or specific risk factors, but simply proposed participation in a long-term health study to help medical research. The objective was to build an "epidemiology laboratory" made up of volunteers who agreed to respond regularly to questionnaires and to undergo medical monitoring. Follow-up methods include the collection of data from different sources about health status, lifestyle, and socioeconomic and occupational factors. A mail questionnaire is sent to the volunteers each year, and data are extracted regularly from the files of the personnel and medical departments at EDF-GDF and from other sources outside the firm. This multipurpose cohort is open to the epidemiologic community, whose members can propose research projects concerning all or some of the participants. Currently, roughly 20 projects involve the GAZEL cohort (34GoGoGoGoGoGoGoGoGoGoGo–45Go).

Population and data
We have analyzed factors associated with participation for the entire target population invited to participate in the GAZEL cohort, that is, the population of EDF-GDF employees aged 40–50 years for the men and 35–50 years for the women. On January 1, 1989, this population comprised 44,922 persons. Demographic, socioeconomic, and occupational data as of that date available for each subject in the target population were age, gender, marital status, number of children, educational level, job grade, type of housing, seniority in the company, and region of residence (103 subjects were excluded because of missing data). The department of occupational medicine developed a job-exposure matrix that was used to reconstruct cumulative occupational exposures to 30 chemical agents in groups I (known carcinogen in humans), IIa (probable carcinogen), and IIb (possible carcinogen) of the International Agency for Research on Cancer (27Go). These exposures covered the period from the start of employment at EDF-GDF. From the EDF-GDF health insurance department, we were able to obtain the medical absenteeism data for the year preceding the invitation to participate (1988) and for the years 1989 through 1994, with diagnoses coded according to the International Classification of Diseases, Ninth Revision (46Go). The same source provided the causes of the death that occurred through 1996 for the employed subjects (that is, those who had not yet retired), coded according to the International Classification of Diseases, Ninth Revision (46Go). The company's cancer registry (31Go) provided data about the incident cancers between 1978 and 1995 in the members of the target population during their employment.

The follow-up of the cohort was almost complete. By the end of 1998, only 123 subjects were lost to follow-up (57 decided to quit the cohort, and 66 had left the company); hence, the percentage of participants at baseline who were lost to follow-up during this 10-year period was 0.6 percent.

Statistical analysis
This study compared the characteristics of participants and nonparticipants at the time the recruitment of the GAZEL cohort took place. Then health events occurring in both groups during the follow-up period (1989 through 1994, 1995, or 1996, depending of the specific events) were compared prospectively, allowing for a long-term assessment of the health problems associated with the initial participation.

Social, demographic, and occupational variables. The differences in the participation rates at baseline in 1989 were compared by the chi-square test according to the social, demographic, and occupational variables. Crude odds ratios were computed, comparing the characteristics of participants and nonparticipants. All of these variables were then included in a multivariable logistic model separately for men and women to estimate their effects on the participation rates, and odds ratios adjusted on all these variables and their 95 percent confidence intervals were calculated. The role of working conditions was assessed by considering the cumulative, career-long exposures of employees to chemical agents. Because exposure to these chemicals was strongly associated with grade (unskilled workers, skilled workers, managers), the proportion of subjects exposed to each factor at least once in their careers was compared between participants and nonparticipants by chi-square tests within each grade, and the effect of each factor was studied by introducing it and its interaction with the grade into a logistic model that included the social, demographic, and occupational variables.

Health status. Three types of variables—absenteeism, cancer incidence, and mortality—were used to analyze the role of health status on participation.

The absenteeisms of the participants and nonparticipants were compared for the year that preceded inclusion and for each of the 6 years that followed (1989–1994). During this period, 58 participants were lost to follow-up (19 decided to quit the cohort, and 39 had left the company). Because we do not know the number of nonparticipants who had left the company from 1989 to 1994, we assumed that the size of both groups was constant during the follow-up period and that all of the lost participants had no sick leave. In view of the very small number of lost subjects, the results of the analyses should not be significantly modified (the same hypothesis was assumed for cancer incidence and mortality analyses). The role of overall absenteeism in 1988 was studied separately among men and women by introducing a frequency variable (0, 1, 2, and >=3 sick leaves during the year) into a logistic model that included the social, demographic, and occupational variables. The same analyses were repeated with the cumulative sick days (0, 1–7, 8–14, 15–30, and >=31 sick days). Because the results were almost identical, only those with the frequency variable are presented here. Global tests based on the logarithm of the likelihood were also used to assess the significance of the variables, taking into account all of the modalities. Analyses were also carried out according to the disorders occasioning the sick leave, grouped into 16 categories, and a separate class of the diseases strongly associated with excessive alcohol consumption (upper respiratory and digestive tract and esophageal cancer, psychiatric and neurologic alcohol-related disorders, and cirrhosis). For each category of diseases, subjects who had no sick leaves for this cause were used as the reference group. A variable indicating at least one sick leave for each disease group was introduced into a logistic model that included the social, demographic, and occupational variables as well as the overall frequency of absenteeism in 1988. Odds ratios, adjusted for the social, demographic, and occupational variables, were calculated separately for men and women for each year from 1989 through 1994 for each disease group, also using the subjects without any sick leaves for the same disease group as the reference. Odds ratios were adjusted in the same way as those for 1988; despite the fact that the results were almost not modified, the frequency of absenteeism in 1988 was also introduced in the model to take into account the effect potentially associated with diseases occurring during this particular year on subsequent differences between participants and nonparticipants. A trend test for the odds ratios was performed for each group of diseases to investigate whether any associations observed were stable over time.

For cancer, the analysis was conducted separately among men and women. We used chi-square or exact tests, depending on the number of subjects, to compare the frequency of subjects with at least one incident cancer in each group during the periods from 1978 through 1988 and after inclusion in the cohort through 1995, and we calculated the odds ratios adjusted for the social, demographic, and occupational variables. For the period 1989 through 1995, odds ratios adjusted for the same variables were also calculated according to the cancer site. For this analysis, because of their low numbers, we consolidated the cancers in men into seven categories: respiratory and digestive tract, colon, lung, breast, genitourinary, central nervous system, and endocrine. Because of smaller numbers, only breast and genitourinary cancers were considered among the women.

Mortality data for participants and nonparticipants during their employment were analyzed separately for men and women: The frequency of death was compared with the chi-square test, and odds ratios were calculated, adjusted for the social, demographic, and occupational variables. Adjusted odds ratios were also calculated by cause of death, consolidated into five categories: cancer, circulatory system disorders, accident, alcoholism, and suicide. This analysis considered only men because of the small numbers of deaths among women (n = 132, of whom 40 were participants).

SAS software was used to perform the analyses (47Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Demographic, socioeconomic, and occupational factors in 1989
Of the 44,922 subjects (31,411 men and 13,511 women) asked to participate in the GAZEL cohort, 20,093 (44.7 percent) accepted: 14,773 men (47 percent) and 5,320 (39.4 percent) women.

Table 1 presents the participation rates and corresponding odds ratios according to the demographic, socioeconomic, and occupational variables. Chi-square tests showed that among men, all of the demographic, socioeconomic, and occupational variables considered were significantly associated with participation; among women, only the number of children, educational level, job grade, and region were significantly associated (details not shown). Odds ratios for each variable changed only slightly when adjusted for the other variables and showed that married men responded more often than those who were not married. For men and women, the response rate increased with the number of children. It varied greatly by job grade and educational level, with a manager more than three times as likely to participate as an unskilled worker. Employees living in housing provided through EDF-GDF had a higher rate of participation. Seniority in the company was positively associated with the participation rate among men, but not among women.


View this table:
[in this window]
[in a new window]
 
TABLE 1. Participation rate in the GAZEL cohort, crude and adjusted odds ratios, and confidence intervals, according to the principal demographic, socioeconomic, and occupational characteristics in 1989

 
France is divided into 23 regions, and there was a very clear geographic effect. The participation rates were highly scattered, without any clear geographic tendency. The ratio between the regions with the best and the worst participation rates was 2.9 among men and 2.5 among women. The odds ratios, taking into account all of the variables and calculated by reference to a mean odds ratio (geometric mean of all the odds ratios) varied from 0.31 (95 percent confidence interval (CI): 0.25, 0.38) to 1.44 (95 percent CI: 1.15, 1.8) among men and from 0.44 (95 percent CI: 0.31, 0.64) to 1.63 (95 percent CI: 1.27, 2.1) among women (details not shown).

After region, grade presented the largest differences between categories. Because the effect of grade was important, we analyzed the interactions between this variable and the others for men and women separately. Only the variables that interacted with grade (p < 0.1) for both genders were kept in the models. Table 2 shows the results of the logistic regressions within each category of job grade. Among men, age mattered only among unskilled workers. Educational level played a significant role only among unskilled and skilled workers. In both of these categories, the greater the seniority of the men, the less they participated.


View this table:
[in this window]
[in a new window]
 
TABLE 2. Factors associated with the probability of participation in the GAZEL cohort: demographic, socioeconomic, and occupational factors in 1989*

 
Among women, age was associated with participation among the managers. Managers with the most seniority volunteered most often. Educational level was significant among skilled workers and managers.

The study of occupational exposures was limited to men because only 147 women had been exposed to at least one chemical agent during their career. There were basically few differences between the participants and the nonparticipants within each grade: The frequencies of exposure were very similar in both categories, and all odds ratios adjusted for social and demographic variables were quite close to one (data not shown).

Health status
Absenteeism. Absenteeism in 1988 and during the 6 years after recruitment (only diseases with more than 150 cases each year) are shown in tables 3 and 4. Table 3, which concerns only men, shows that sick leaves were globally associated with participation in 1988 and in each of the subsequent years. Subjects with at least one sick leave in the year before the study started participated less, especially among those with three or more sick leaves. During the following years, the difference between the men who volunteered and those who did not decreased for a single absence episode, but clearly persisted for two episodes and, especially, for three or more. There was no significant association between a particular cause and participation in 1988, except for accidents, because participants were less likely to have taken a sick leave for this cause. The same was not true for subsequent years, since participation at baseline was negatively and significantly associated with several groups of diseases for at least 3 years of the follow-up. The association was especially strong for alcohol-related diseases and was also observed for psychiatric and respiratory diseases. Less clearly diminished odds ratios for participation were found for other diseases. We did not observe any time trend: The odds ratios remained relatively stable for the 6 years of follow-up, and the trend test was significant only for the digestive system diseases (slight trend of a mean linear increase of 0.04 per year).


View this table:
[in this window]
[in a new window]
 
TABLE 3. Odds ratios for participation in the GAZEL cohort with comparing "number of sick leaves" with "no sick leave" (model type 1) or comparing "any sick leave due to a specific cause" with "no sick leave due to this specific cause" (model type 2), by year* of leave from 1988 through 1994, adjusting by logistic regression for the demographic, socioeconomic, and occupational factors in 1989 and sick leave in 1988 for men only (n = 31,325)

 

View this table:
[in this window]
[in a new window]
 
TABLE 4. Odds ratios for participation in the GAZEL cohort with comparing "number of sick leaves" with "no sick leave" (model type 1) or comparing "any sick leave due to a specific cause" with "no sick leave due to this specific cause" (model type 2), by year* of leave from 1988 through 1994, adjusting by logistic regression for the demographic, socioeconomic, and occupational factors in 1989 and sick leave in 1988 for women only (n = 13,494)

 
Among women (table 4), overall absenteeism was less clearly associated with participation. Only for 1988 and 1992 were sick leaves associated with participation; the odds ratios varied little (in directions that changed from year to year) and were not significantly different from one except in 1991 and 1992. The analysis by medical causes showed that only the occurrence of sick leaves for psychiatric disorders was negatively associated for at least 3 years with participation at baseline; we observed the same phenomenon for cancers, but this was significant only in 1993 (not shown). Inversely, musculoskeletal disorders were positively and significantly associated for 4 years with participation. As with men, no time trend was apparent.

Cancer incidence. Because of the age of the target population, participants and nonparticipants alike had few incident cancers between 1978 and 1988 (table 5), and we observed no association between past cancer and cohort participation. The odds ratio was nonetheless elevated and was nearly significant among women, indicating the possibility of greater participation among women who had had cancer. Among men, not participating at baseline was strongly associated with cancer incidence in the years after the cohort was founded. This negative association is entirely due to cancers of the upper respiratory and digestive tract and the lungs. Among women, those who participated showed a lower cancer risk during the follow-up of the cohort (not significant).


View this table:
[in this window]
[in a new window]
 
TABLE 5. Proportion of cancer cases from 1988 through 1995 among participants compared with the target population and odds ratios for participation in the GAZEL cohort comparing cancer of all sites or of a specific site with no cancer of all sites or of this specific site, adjusting by logistic regression for the demographic, socioeconomic, and occupational factors in 1989 (men: n = 31,325; women: n = 13,494)

 
Mortality from 1989 through 1996. Among men, all-cause mortality during the 8 years of follow-up was lower among participants, whose chances of dying were approximately 40 percent lower than those of the nonparticipants (table 6). Roughly the same difference was found for each year of the follow-up (data not shown) and for each group of causes. It is particularly clear for cancers and especially for the alcohol-related diseases, for which the risk of death was four times higher among the nonparticipants. Among women, the association between all-cause mortality and participation was less strong (odds ratio adjusted for the socio-economic and occupational variables = 0.77, 95 percent CI: 0.53, 1.12), but there were few deaths among them (40 deaths among the women participants during the 8 years of follow-up) (data not shown).


View this table:
[in this window]
[in a new window]
 
TABLE 6. Proportion of deaths from 1988 through 1996 among participants compared with the target population and odds ratios for participation in the GAZEL cohort comparing death from all causes or due to a specific cause with no death from all causes or due to this specific cause, adjusting by logistic regression for the demographic, socioeconomic, and occupational factors in 1989 for men only (n = 31,325)

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We studied the effect of diverse social, demographic, occupational, and health factors on the participation rate in a cohort study. We also monitored changes in health status for several years after inclusion among participants and nonparticipants alike. We were therefore able to compare numerous characteristics of the participants and the nonparticipants. Some variables were contemporaneous with the moment of recruitment, while others involved the year before or the years that followed. All of the data came from company records, were gathered independent of the subjects, and were identical for participants and nonparticipants.

This population comprises persons in a specific age bracket. This limits the conclusions of this study, which did not include very young or very old people or various occupational categories not represented. Nevertheless, the interest in these results comes from the size of the population, the variety of the data collected independent of the subjects, and the longitudinal follow-up over several years of participants and nonparticipants.

Social, demographic, and occupational variables
The analysis of the social, demographic, and occupational variables shows that the situation in France is fairly similar to that observed in most studies performed in other countries. SES is the most important individual variable: Job grade shows that among both men and women the highest category had a participation rate that was double that of the lowest category and the probability of participation among male managers was 3.15 times higher than that of unskilled male workers when all of the other variables were taken into account. SES cannot be compared easily between countries, both because different classifications are used and because this status may be assessed by membership in a social category, income level, or educational level. Nevertheless, whatever the variable used, the higher categories are nearly always overrepresented among volunteers. This has been found repeatedly in Great Britain (3Go, 10Go, 48Go) and in the United States (18Go, 21Go, 49Go). In Sweden, one study, which assessed SES by income, also found a similar effect (12Go), while another did not find social class to have an effect (13Go).

Marital status affects participation: Married people participate more than do those who are unmarried (10Go, 12Go, 13Go), in proportions similar to those observed in the GAZEL cohort. We did not note any overall effect of age on the participation rate, although most studies in other countries do. Nevertheless, age seems to have a varied effect: Some studies have observed that participation increases with age (3Go, 9GoGo–11Go) and others, on the contrary, that it is higher among the young (18Go, 21Go, 50Go). Moreover, the age range in the GAZEL cohort is narrow, which limits the interpretation of our results. Women participated at a lower rate than did men in the GAZEL cohort, although the inverse is most often observed (9Go, 11Go, 22Go, 50Go). This may be explained by the overwhelmingly male character of the firm (about 80 percent men). The GAZEL cohort was probably perceived as a company project, and acceptance was all the better among subjects who felt that they were part of the company. The fact that residence in company housing and seniority among men (but not women) played a positive role in participation reinforces this hypothesis.

The strong role played by geographic region needs to be stressed. This factor accounts for the greatest variation in the participation rate: The ratio between the regions with the best and the worst participation was 4.6 among men and 3.7 among women. Studies conducted in several European countries have also found participation rates that differ very clearly by country (51Go, 52Go), with lower response rates in the "Latin" countries (Italy, France, and Spain). Of all of the numerous individual characteristics analyzed, the roles of grade, which is a proxy for SES, and of geographic region were far and away the most important. There are therefore cultural factors that particularly affect interest in participating in a health study. The effect of such cultural factors has been observed in studies that found higher participation rates among Whites than among those in other ethnic groups (8Go, 49Go, 53Go). Nevertheless, the absence of these interethnic differences in a study in Montreal (7Go) indicates that cultural factors must be analyzed in their specific context and that the SES variables can have a different significance from one community or country to another.

Occupational exposure to chemical factors is closely linked to specific trades and to working conditions among the men at the company (27Go). The absence of notable differences between participants and nonparticipants seems to indicate that working conditions do not influence the participation rate.

Health status
The analysis of sick leaves shows that taking more than one sick leave a year was associated with a lower participation rate at baseline than taking no sick leave. This association was observed for the year that preceded inclusion in the cohort and for the 6 years that followed only for more than one leave per year and was stronger for more than two; both of these findings show that these leaves reflect damaged health. A previous study in this company showed that numerous and long sick leaves were due to important health problems, while few and brief leaves were due more to factors involving working conditions (29Go). Few analyses have examined the association of sick leave with participation in epidemiologic studies. One Swedish study found an excess of sick leaves among nonparticipants (12Go), while another reported that this factor did not distinguish participants and nonparticipants (14Go).

Among the men, the health problems that explained the excess of sick leave among the nonparticipants were particularly linked to alcohol, directly or indirectly through accidents, which are strongly associated with excessive alcohol consumption. These associations are clear throughout the 6 years of follow-up. It is probable that smoking is also an explanatory factor because the other health problems associated with participation were respiratory diseases and, to a lesser extent, cardiovascular disease and cancer, especially alcohol and smoking-related cancers. Besides diseases directly related to these two factors, diagnosed psychiatric disorders were negatively associated with participation.

Overall absenteeism in women did not seem to be associated with participation, but we did observe that participation at baseline was negatively associated with the occurrence of psychiatric diseases during follow-up, as well as of cancers not caused by alcohol or smoking. Inversely, there was a clear positive association between musculoskeletal disorders and participation. The absence of an association with overall absenteeism is probably not explained by a lack of power because the women had a higher rate of sick leaves than did men. The results observed among women most likely reflect the fact that they use alcohol and tobacco much less than do men as well as that they behave differently than men in relation to participation in a health study.

The study of incident cancers during follow-up showed that the strong negative association with participation at baseline observed among men is entirely explained by a clear excess incidence for two sites, the upper respiratory and digestive tract and the lungs, and that the principal risk factors for cancers of these sites are smoking or the combination of alcohol and smoking. Our results probably underestimated these associations because only cancers first diagnosed during the period when the subjects were employed at the company are recorded (31Go). Bad health status, however, is a factor associated with early departure from work (23Go), and it is thus probable that nonparticipants left the company more frequently throughout the follow-up period because of health problems due to alcohol or smoking. Among women, the negative association between participation and incidence of breast and genital cancers, which account for the vast majority of incident cancers, was not significant.

The analysis of mortality also confirms the importance of alcohol and tobacco consumption on men's participation. We consistently observed for each of the 8 years of follow-up a strong excess of mortality among the nonparticipants because of disorders associated with diseases directly related to excess drinking, cancers, circulatory disorders, accidents, and suicides. For the same reasons as for the incidence of cancers (early departure from the company because of poor health status), the associations observed are most likely underestimated. The small number of deaths observed among women prevents the confirmation of any association with participation.

Globally, health status does not seem to play a notable role per se for men, since the greatest part of the health differences observed between participants and nonparticipants was due to disorders caused by alcohol, smoking, or other dangerous behaviors. Only psychiatric disorders were associated with participation; this accords with the results of an English study showing that psychological factors affected participation (54Go). Among women, the role of health status seems notably different because musculoskeletal disorders and some cancers not associated with either smoking or alcohol seemed to play a role. No clear explanation is evident, since the effects observed did not all go in the same direction. In addition, the small number of cases among women prevented a detailed analysis.

Several investigators have sought to examine various specific health problems. In some cases, no selection effect associated with health problems has been observed, and subjects with health problems or symptoms cannot be distinguished from the others by their participation rate (7Go, 10Go, 11Go, 15Go, 16Go, 20Go). On the contrary, other researchers have noted participation rates that varied according to health problems or symptoms; in some studies, those with a problem or a symptom participate less (3Go, 13Go, 18Go), while the inverse has sometimes been observed (21Go, 22Go). It should nevertheless be stressed that comparing results between studies is difficult because neither the same disorders nor the same symptoms are taken into account, and the age groups are often different.

Our results concerning mortality are notably different from those observed in other countries because most often the authors of those studies have not found the mortality rate to differ between the two groups (10Go, 19Go, 20Go). Nevertheless, these studies were carried out in Scandinavian countries or in the United States, where alcohol consumption is lower than in France. This difference might, at least in part, explain the differences observed.

The most striking results in our study involve the role of smoking and alcohol in the health problems associated with participation. In fact, we did not have direct data about the consumption of alcohol and tobacco, but the analysis of the causes of sick leaves, cancer, and death among men clearly indicates the effect of these factors. This is consistent with the results of most studies that have analyzed this phenomenon. It appears that the people who drink too much are less likely to participate in health studies (12GoGo–14Go). The results concerning smoking are more varied: Smokers have lower participation rates in some studies (11Go, 15GoGoGo–18Go), although other investigators have observed no difference between smokers and nonsmokers (7Go, 13Go).

Overall, this study indicates that the most important determinants of participation in a health study arise from two domains: cultural (gender, social category, geographic region, and marital status) and at-risk health behaviors (smoking, drinking, and accidents). In this working population, we did not observe any effect associated with the occupational environment or with working conditions when grade was taken into account.

The voluntary nature of participation in an epidemiologic inquiry is likely to bias the estimations of frequency of disease and risk factors and the measurement of their associations (1GoGoGoGoGo–6Go). This study allowed us to identify various factors associated with participation in the GAZEL cohort and to estimate the extent of the differences between participants and nonparticipants in their social, demographic, and occupational characteristics and in the frequency of many health problems. It has also allowed us to show that the selection effect can vary greatly according to the factors and the population. This study has thus provided information that will help investigators improve the planning of studies in the GAZEL cohort and better forecast and take into account different types of potential bias.


    ACKNOWLEDGMENTS
 
The authors express their thanks to EDF-GDF, especially to the Service des Etudes Médicales.


    NOTES
 
Reprint requests to Dr. Marcel Goldberg, INSERM Unité 88, Hôpital National de Saint-Maurice, 14, rue du Val d'Osne, 94415 Saint-Maurice Cedex, France.


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Criqui MH. Response bias and risk ratios in epidemiologic studies. Am J Epidemiol 1979;109:394–9.[ISI][Medline]
  2. Greenland S. Response and follow-up bias in cohort studies. Am J Epidemiol 1977;106:184–7.[ISI][Medline]
  3. Sheikh K, Mattingly S. Investigating non-response bias in mail surveys. J Epidemiol Community Health 1981;35:293–6.[Abstract]
  4. Austin MA, Criqui MH, Barrett-Connor E, et al. The effect of response bias on the odds ratio. Am J Epidemiol 1981;114:137–43.[Abstract]
  5. Szklo M. Population-based cohort studies. Epidemiol Rev 1998;20:81–90.[ISI][Medline]
  6. Vestbo J, Rasmunssen FV. Baseline characteristics are not sufficient indicators of nonresponse bias in follow-up studies. J Epidemiol Community Health 1992;46:617–19.[Abstract]
  7. Siemiatycki J, Campbell S. Nonresponse bias and early versus all responders in mail and telephone surveys. Am J Epidemiol 1984;120:291–301.[Abstract]
  8. Vernon SW, Roberts RE, Lee ES. Ethnic status and participating in longitudinal health surveys. Am J Epidemiol 1984;119:99–113.[Abstract]
  9. Bakke P, Gulsvik A, Lilleng P, et al. Postal survey on airborne occupational exposure and respiratory disorders in Norway: causes and consequences of non-response. J Epidemiol Community Health 1990;44:316–20.[Abstract]
  10. Walker M, Shaper AG, Cook DG. Non-participation and mortality in a prospective study of cardiovascular disease. J Epidemiol Community Health 1987;41:295–9.[Abstract]
  11. Jacobsen BK, Thelle DS. The Tromso Heart Study: responders and non-responders to a health questionnaire, do they differ? Scand J Soc Med 1988;16:101–4.[ISI][Medline]
  12. Bergstrand R, Vedin A, Wilhelmsson C, et al. Bias due to non-participation and heterogeneous sub-groups in population surveys. J Chronic Dis 1983;36:725–8.[ISI][Medline]
  13. Janzon L, Hanson BS, Isacsson SO, et al. Factors influencing participation in health surveys. Results from the prospective population study 'Men born in 1914' in Malmo, Sweden. J Epidemiol Community Health 1986;40:174–7.[Abstract]
  14. Ohlson CG, Ydreborg B. Participants and nonparticipants of different categories in a health survey. A cross-sectional register study. Scand J Soc Med 1985;13:67–74.[ISI][Medline]
  15. Criqui MH, Barrett-Connor E, Austin M. Differences between respondents and non-respondents in a population-based cardiovascular disease study. Am J Epidemiol 1978;108:367–72.[Abstract]
  16. Macera CA, Jackson KL, Davis DR, et al. Patterns of non-response to a mail survey. J Clin Epidemiol 1990;43:1427–30.[ISI][Medline]
  17. Klesges RC, Williamson JE, Somes GW, et al. A population comparison of participants and nonparticipants in a health survey. Am J Public Health 1999;89:1228–31.[Abstract]
  18. Tell GS, Fried LP, Hermanson B, et al. Recruitment of adults 65 years and older as participants in the Cardiovascular Health Study. Ann Epidemiol 1993;3:358–66.[Medline]
  19. Ostfeld AM, Lebovits BZ, Shekelle RB, et al. A prospective study of the relationship between personality and coronary heart disease. J Chronic Dis 1964;17:265–76.[ISI][Medline]
  20. Selikoff IJ, Seidman H. Evaluation of selection bias in a cross-sectional survey. Am J Ind Med 1991;20:15–627.
  21. Cohen BB, Barbano HE, Cox CS, et al. Plan and operation of the NHANES I Epidemiologic Followup Study: 1982–84. Vital Health Stat 1 1987;22:1–142.[Medline]
  22. de Marco R, Verlato G, Zanolin E, et al. Nonresponse bias in EC Respiratory Health Survey in Italy. Eur Respir J 1994;7:2139–45.[Abstract/Free Full Text]
  23. Checkoway H, Pearce N, Crawford-Brown DJ. Research methods in occupational epidemiology. New York, NY: Oxford University Press, 1989:77–91.
  24. Brogan DR. Non-response in sample surveys. The problem and some solutions. Phys Ther 1980;60:1026–32.[ISI][Medline]
  25. Goldberg M, Blanc M, Chastang JF, et al. The health data base of a nationwide company. Its use in epidemiological studies. J Occup Med 1982;24:47–52.[ISI][Medline]
  26. Goldberg M, Chevalier A, Imbernon E, et al. The epidemiological information system of the French national electricity and gas company: the SI-EPI project. Med Lav 1996;87:16–28.[Medline]
  27. Imbernon E, Goldberg M, Guenel P, et al. MATEX: une matrice emplois-expositions destinée à la surveillance épidémiologique des travailleurs d'une grande entreprise (EDF-GDF) (In French). Arch Mal Prof 1991;52:559–66.
  28. Imbernon E, Goldberg M, Guénel P, et al. Validation of asbestos exposure assessment in a job-exposure matrix in the electricity and gas industry in France: the MATEX project. Occup Hyg 1996;3:193–8
  29. Chevalier A, Luce D, Goldberg M. Sickness absence at the French national electric and gas company. Br J Ind Med 1987;44:101–10.[ISI][Medline]
  30. Chevalier A, Leclerc A, Goldberg M. Disparités sociales et professionnelles de la mortalité des travailleurs d'Electricité de France-Gaz de France. (In French). Population 1987;6:863–80.
  31. Chevalier A, Goldberg M, Godard C, et al. Incidence des cancers dans la population masculine des salariés en activité à électricité de France et Gaz de France. (In French). Rev Epidemiol Sante Publique 1996;44:25–36.[ISI][Medline]
  32. Goldberg M, Leclerc A, Chastang JF, et al. Mise en place d'une cohorte épidémiologique à Electricité de France-Gaz de France. Recrutement des volontaires. Principales caractéristiques de l'échantillon. (In French). Rev Epidemiol Sante Publique 1990;38:265–8, 378–80.
  33. Goldberg M, Leclerc A, Bugel I, et al. La cohorte GAZEL, laboratoire épidémiologique. Bilan des cinq premières années (1989–1993) de suivi des 20 000 volontaires d'électricité de France-Gaz de France. (In French). Paris, France: INSERM—Collection Grandes Enquê tes, 1994.
  34. Lagorio S, Guenel P, Luce D, et al. Estimated confounding from smoking in a cohort of 20,000 French electrical workers. Epidemiol Prev 1992;50:43–51.
  35. Leclerc A, Zins M, Bugel I, et al. Consommation de boissons alcoolisées et situation professionnelle dans la cohorte GAZEL (EDF-GDF). (In French). Arch Mal Prof 1994;55:509–17.
  36. Annesi I, Frette C. Geoepidemiology of asthma in France: data from a large French working population. (Abstract). Eur Respir J 1990;3(Suppl)10:2765.
  37. Dang Tran P, Leclerc A, Chastang JF, et al. Prévalence des problèmes de santé dans la cohorte GAZEL (EDF-GDF): répartition et disparités géographiques. (In French). Rev Epidemiol Sante Publique 1994;42:285–300.[ISI][Medline]
  38. Ringa V, Ledesert B, Breart G. Determinants of hormone replacement therapy among postmenopausal women enrolled in the French GAZEL cohort. Osteoporos Int 1994;4:16–20.[ISI][Medline]
  39. Ledesert B, Ringa V, Breart G. Menopause and perceived health status among the women of the French GAZEL cohort. Maturitas 1995;20:113–20.[ISI]
  40. Consoli SM, Cordier S, Ducimetière P. Validation d'un questionnaire de personnalité destiné à repérer des sous-groupes à risque de cardiopathie ischémique ou de cancer dans la cohorte GAZEL. (In French). Rev Epidemiol Sante Publique 1993;41:315–26.[ISI][Medline]
  41. Boumendil E. Descriptive study of lipid modulating drug use in a French professional population. J Clin Epidemiol 1994;47:1163–71.[ISI][Medline]
  42. Tubert P, Boumendil E. Depression-induced absenteeism in relation with antihyperlipidemic treatment: a study using GAZEL cohort data. Epidemiology 1995;6:322–5.[ISI][Medline]
  43. Michel P, Lindoulsi AC, Dartigues JF, et al. The cohort hemicrania: methodology and prevalence. (Abstract). Cephalalgia 1993;13(Suppl 13):70.
  44. Moneta GB, Leclerc A, Chastang JF, et al. Time-trend of sleep disorder in relation to night work: a study of sequential one-year prevalences. J Clin Epidemiol 1996;49:1133–41.[ISI][Medline]
  45. Niedhammer I, Goldberg M, Leclerc A, et al. Psychosocial factors at work and subsequent depressive symptoms in the GAZEL cohort. Scand J Work Environ Health 1998;24:197–205.[ISI][Medline]
  46. World Health Organization. International classification of diseases. Manual of the international statistical classification of diseases, injuries, and causes of death. Ninth Revision. Geneva, Switzerland: World Health Organization, 1977.
  47. SAS Institute, Inc. SAS user's guide, Cary, NC: SAS Institute, Inc, 1985.
  48. Marmot M, Finney A, Shipley M, et al. Sickness absence as a measure of health status and functioning: from the UK Whitehall II study. J Epidemiol Community Health 1995;49:124–30.[Abstract]
  49. Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 1988;41:1105–16.[ISI][Medline]
  50. Eaker S, Bergstrom R, Bergstrom A, et al. Response rate to mailed epidemiologic questionnaires: a population-based randomized trial of variations in design and mailing routines. Am J Epidemiol 1998;147:74–82.[Abstract]
  51. O'Neill TW, Marsden D, Matthis C, et al. Survey response rate: national and regional differences in a European multicentre study of vertebral osteoporosis. J Epidemiol Community Health 1995;49:87–93.[Abstract]
  52. European Community Respiratory Health Survey. Variations in the prevalence of respiratory symptoms, self-reported asthma attacks, and use of asthma medication in the European Community Respiratory Health Survey (ECRHS). Eur Respir J 1996;9:687–95.[Abstract/Free Full Text]
  53. Jackson R, Chambless LE, Yang K, et al. Differences between respondents and nonrespondent in a multicenter community-based study vary by gender and ethnicity. J Clin Epidemiol 1996;49:1441–6.[ISI][Medline]
  54. Cox A, Rutter M, Yule B, et al. Bias resulting from missing information: some epidemiological findings. Br J Prev Soc Med 1977;31:131–6.[ISI][Medline]
Received for publication February 25, 2000. Accepted for publication January 17, 2001.