SS_Part.B_Work Organizaiton Predictors of Depression in Women_0630

B. Collections of Information Employing Statistical Methods

1. Respondent Universe and Sampling Methods

As discussed in Section A2, the proposed survey will allow a comprehensive assessment of workplace stressors that may be linked with depression in women. To date, studies which have examined the relationship between workplace factors and depression in women have focused on only a few of the potential risk (e.g., hours of work, discrimination). Consequently, the amount of variance in depression in working women accounted for by these studies, although statistically significant, is often small. Most of these studies have also been cross-sectional in nature, not allowing researchers to better understand the antecedents and causal pathways between risk factors and depression. Nor have these studies examined the potential moderating effects of preventive factors in the workplace (i.e., workplace policies, practices and programs) on levels of depression. Studies of workplace policies, practices and programs have generally focused on outcomes such as productivity, commitment and turnover rather than on worker health. (An exception is Thomas and Ganster [1995] who found family supportive policies to be linked with reduced levels of stress and depression.) Consequently, although such policies, programs and practices show potential for reducing workplace hazards associated with depression in women, and thus positively influencing the health of women workers, there have been no comprehensive, longitudinal studies examining this relationship.

The data collected in this study will allow the determination of what job characteristics are associated with the development, worsening, or prevention/alleviation of depressive symptoms. Additionally, these data will allow a determination of whether workplace policies, procedures and practices modify the relationship between job characteristics and depressive symptoms, and which policies, procedures and practices are most protective. These data will be used to make recommendations to employers on how to identify and reduce or eliminate problematic job characteristics, and about workplace policies, procedures, and practices that are effective in reducing the risk for depression.

The respondent universe for this study is support occupations common across industries. We employed this strategy because comparability of occupations is needed across industry categories. Therefore, we recruited subjects who work in administrative services and support jobs within each participating company (e.g., clerical staff and administrative assistants, accounting, billing, word/data processing). As policies and procedures are likely to be implemented or enforced to different degrees in different departments or work units within a company, recruitment efforts focused on obtaining subjects from a number of different departments and work units within each participating company. Employees from both managerial and non-managerial positions within the targeted jobs were recruited to allow us to obtain input from workers at different career levels. As depression is most common in young women (and to reduce the likelihood of a healthy worker effect), the age range of recruits was limited to the ages of 18-45. These ages also encompass prime marriage and child-rearing years, so the likelihood of these women (and men) experiencing work-family conflict and wanting and using family-friendly programs is increased. A representative from the HR department in each company who oversees, or has extensive knowledge of, the company policies, practices and programs, was recruited for the study as well.

Participation in this study is entirely voluntary. We have recruited 314 participants from 14 companies. The Center for Epidemiological Studies – Depression (or CES-D) scale was used to measure depressive symptoms in these participants. Information from published studies gave an a priori mean CES-D value of 10 and a standard deviation of 9.1 (drawn from Sayetta and Johnson, 1980), which is matched quite closely by our actual baseline CES-D values (mean = 10.2; standard deviation = 8.58). Based on the conventional statistical practice of not using data from a study to calculate power for that study, we used available data from a prior study with a similar sample size and study variables (Burnfield et al., 2006). The study did not include the CES-D, but it did include Profile of Mood States (POMS) variables (anxiety, tension, fatigue) that are very similar to depressive symptoms measured by the CES-D. It also included the same job stress measures used in the present study. These measures (POMS and job stress) were used to estimate model parameters for study power calculations. We assumed the use of multivariate regression at a significance level of 0.05 (two-tailed). Partial correlations among the study variables were conservatively estimated based on prior analyses on the relationship between job stressors and mood state. We calculated power for analyses containing 5, 10, or 15 predictor variables, as these are typical numbers of predictor variables we might use in regression models using these study data. Based on these estimates, we project that our study sample will have, at minimum, 93% power (see Figure 1).

Figure 1. Power estimates for multivariate analyses of the relationship between job stressors and depression for the present study.

Although the study sample is not representative of the working population as a whole, it is representative of white collar occupations whose members are likely to score above the mean on depressive symptoms, such as clerks and typists (Grosch and Murphy, 1998). In this study, we are attempting to maximize our ability to assess the impact of job demands on depression. Thus, we are sampling from a population with an intersection of risk factors. These include female gender, youth, jobs/occupations with higher levels of exposure to job stressors and occupational demographic factors that increase the risk of exposure to nontraditional stressors. The sample for this study is highly representative of this population.

2. Procedures for the Collection of Information

The methodology for sample selection has been described in the previous section (B.1).

RTI, the contract research firm, recruited 14 companies using sources such as Dun and Bradstreet, combined with data from the Bureau of Labor Statistics on gender distributions by industry and occupation, to select companies who fit the profile required for the study. A Human Resources point of contact (POC) was identified within each company, and assisted RTI with recruitment of eligible participants. Each POC identified employees that fit the study criteria. Employees were recruited in two ways: 1) the POC supplied the contractor with the names and mailing addresses of eligible employees and RTI mailed the employees packets containing the study consent form, a form requesting contact information and preferred contact times, and business return envelopes addressed to NIOSH; or, 2) the POC requested the consent form packets from RTI and distributed them to the eligible employees. The employees sent the completed consent forms to NIOSH, who compiled a list of participants which was then sent to RTI. RTI is contacting these workers during their preferred contact times and conducting the telephone interview with them (see Appendix C for the study questionnaires and Appendix C1 for the interview transcript). Questions posed by telephone interviewers require only an oral “yes”, “no”, “agree”, “disagree”, etc. or numeric answer from respondents, and thus do not compromise respondent privacy and confidentiality if they choose to be interviewed while at the workplace. For sensitive questions, such as questions about depression symptoms, substance use, or medications, interviewers transfer respondents into a “Telephone Computer-Assisted Self Interviewing” system (T-ACASI) in which a respondent follows recorded instructions and answers questions by pressing keys on their telephone. Given that T-ACASI is a more expensive interviewing system, only the most sensitive questions are being administered by this system. After each completed wave of data collection, RTI is supplying NIOSH with a dataset which is ready to be analyzed.

At yearly intervals, during the next eighteen months, RTI will repeat the telephone surveys with the study participants. All participants are being given an incentive of $25.00 for each survey (wave) that they complete. The incentive is in the form of a check, received in the mail within three to four weeks after completion of a survey.

It is expected that there will be some degree of subject attrition during the study. An attempt will be made to identify these individuals and conduct exit interviews with them to determine their reasons for leaving (e.g., better job opportunity elsewhere). After the study is completed and the results are analyzed, a summary of the evaluation will be provided by NIOSH to study participants.

3. Methods to Maximize Response Rates and Deal with Nonresponse

RTI and/or NIOSH investigators contacted the point of contact (POC) at each participating company to explain the purpose and goals of the study, and the potential benefit to the company of participating (i.e., that the company will learn what working conditions are risk factors for depression, and what working conditions, policies, and practices may be protective against depression). The POCs determined who the eligible employees were and distributed an invitation to participate in the study. Study packets, including consent forms and business reply envelopes (with the address of the NIOSH PI), were either distributed to potential participants by RTI or by the POC.

Participants have provided information about best place, number and time of day for RTI to phone them. RTI is making up to eight contacts to reach a participant. Callbacks have been scheduled as needed to accommodate the participant’s schedule. Since this is a strictly voluntary study and participants have been informed that they may leave the study at any time for any reason, we have instructed RTI NOT to attempt to convert definite refusals. Using these methods, a response rate of 82% was obtained for the baseline data collection.

With regard to maximizing response rates for the follow-up surveys, RTI is sending letters to participants one to two weeks prior to the 18-month follow up surveys to inform them that they will be contacted for a re-interview. (See the 18month follow-up letters in Appendix F.) A “non-response letter” has been developed for those participants RTI is unable to reach for a follow-up interview which encourages the participant to contact RTI and arrange for the follow-up interview (see Appendix G for the non-response letter).

4. Tests of Procedures or Methods to be Undertaken

The study contractor, RTI, conducted two pilot tests of the survey and CATI/T-ACASI administration (9 subjects each) in February and March 2003. Based on these pilot tests, the survey was modified and shortened significantly. Additionally, NIOSH project staff were able to make recommendations regarding telephone interviewer training based on observation of the administration of the pilot tests.

5. Individuals Consulted on Statistical Aspects and Individuals Collecting and/or

Analyzing Data

1. The person who designed the data collection:

Naomi G. Swanson, Ph.D.

Chief, Work Organization and Stress Research Section

NIOSH, Taft Laboratories MSC24

4676 Columbia Parkway

Cincinnati, OH 45226

2. The contractor who will collect the data:

Research Triangle Institute International (RTI)

3040 Cornwallis Road

PO Box 12194

Research Triangle Park, NC 27709

The person responsible for receiving and approving contract deliverables:

Vallerie Redd

Contract Specialist, CDC/PGO

2920 Brandywine Road

Atlanta, GE 30341-5539

3. The person who will analyze the data:

Edward Krieg, Jr., Ph.D.

Health Statistician

NIOSH, Taft Laboratories, MSC22

4676 Columbia Parkway

Cincinnati, OH 45226

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