Journal of Religion and Health

, Volume 51, Issue 1, pp 20–31

Psychological and Social Characteristics Associated with Religiosity in Women’s Health Initiative Participants

Authors

    • Yeshiva CollegeYeshiva University
  • Solomon Kalkstein
    • Philadelphia VA Medical CenterUniversity of Pennsylvania
  • George Fitchett
    • Rush University Medical Center
  • Elena Salmoirago-Blotcher
    • University of Massachusetts Medical School
  • Judith Ockene
    • University of Massachusetts Medical School
  • Hilary Aurora Tindle
    • University of Pittsburgh
  • Asha Thomas
    • Sinai Hospital of Baltimore
  • Julie R. Hunt
    • Fred Hutchinson Cancer Research Center
  • Sylvia Wassertheil-Smoller
    • Albert Einstein College of Medicine
Original Paper

DOI: 10.1007/s10943-011-9549-6

Cite this article as:
Schnall, E., Kalkstein, S., Fitchett, G. et al. J Relig Health (2012) 51: 20. doi:10.1007/s10943-011-9549-6

Abstract

Measures of religiosity are linked to health outcomes, possibly indicating mediating effects of associated psychological and social factors. We examined cross-sectional data from 92,539 postmenopausal participants of the Women’s Health Initiative Observational Study who responded to questions on religious service attendance, psychological characteristics, and social support domains. We present odds ratios from multiple logistic regressions controlling for covariates. Women attending services weekly during the past month, compared with those not attending at all in the past month, were less likely to be depressed [OR = 0.78; CI = 0.74–0.83] or characterized by cynical hostility [OR = 0.94; CI = 0.90–0.98], and more likely to be optimistic [OR = 1.22; CI = 1.17–1.26]. They were also more likely to report overall positive social support [OR = 1.28; CI = 1.24–1.33], as well as social support of four subtypes (emotional/informational support, affection support, tangible support, and positive social interaction), and were less likely to report social strain [OR = 0.91; CI = 0.88–0.94]. However, those attending more or less than weekly were not less likely to be characterized by cynical hostility, nor were they less likely to report social strain, compared to those not attending during the past month.

Keywords

Religion and healthReligion and psychologyReligious behavior and healthReligious attendance and healthReligious behavior and social supportReligious behavior and social strainReligious behavior and psychological characteristics

Introduction

Numerous recent academic (e.g., Gillum et al. 2008; Joshi et al. 2008; Lazar and Bjorck 2008; Schnall et al. 2010) and popular press articles (Time Magazine, special issue, February 23, 2009) underscore the continued interest in the relationship between religious involvement and health outcomes. Such relationships are found in studies that span various cultures and faiths, and different gender and age groups. One systematic review of the literature found that on average those attending religious services experience a 25% reduction in mortality even after adjusting for confounders (Powell et al. 2003). Other investigators highlight the mental health benefits associated with religiosity, such as reduced depression and anxiety (Braam et al. 2001; Hughes et al. 2004). A recently published Women’s Health Initiative (WHI) paper (Schnall et al. 2010) concluded that religious affiliation and service attendance may reduce mortality risk.

In explanation of the positive relationship between religiosity and health, many cite the beneficial health behaviors and psychosocial factors that may be associated with religion and its practice (Gillum et al. 2008; Powell et al. 2003; Schnall et al. 2010). Religious people may be more likely to avoid smoking or drinking alcohol in excess, to attend physician visits, and to engage in other healthy lifestyle behaviors (Salmoirago-Blotcher et al. 2011). Importantly, religious practice also may encourage a positive worldview, include calming rituals, and have other psychological and social benefits. As such, a first aim of the current paper is to test the association between religious involvement and psychological characteristics, specifically in terms of optimism, depression, and cynical hostility, all known to be associated with health outcomes (Berkman et al. 1986; Rasmussen et al. 2009; Tindle et al. 2009).

Among the relevant psychosocial factors, social support is of particular interest; almost all studies that have examined religion and social support found a positive correlation between them (Koenig et al. 2001). Furthermore, it has long been observed that varieties of social support are linked to improved physical and mental health (Casell 1976; Cobb 1976; House et al. 1988). Related constructs such as social integration (Moen et al. 1989; Seeman 1996) and emotional support (Krumholz et al. 1998) similarly seem beneficial. In line with recent research (Fiala et al. 2002; Lazar and Bjorck 2008) another aim of the current paper is to identify further the precise forms of social support that may be associated with religiosity.

Social strain (also referred to as negative social support; see Ray 1992), by contrast, has been examined in the context of the negative impact it may have on health (DeLongis et al. 2004; Rook 1990). Despite the many beneficial effects of religiosity cited above, it has been suggested that certain characteristics of religion might actually encourage interpersonal strain (Exline 2002; Exline and Rose 2005; Krause 2008). For example, differing with one’s clergyman or with important members of one’s social network on religious issues may lead to strife or ill will. In fact, Lehrer and Chiswick (1993) found that interfaith marriages are more likely than others to end in divorce. In sum, inquiry into the question of whether religious involvement may be associated with social strain, the third aim of the current paper, is also of particular interest, especially given that researchers only recently have focused on the possible negative social consequences of religious involvement.

The large sample size of, and extensive data collected through, the Women’s Health Initiative (WHI) Observational Study (OS) provides a unique opportunity to examine the possible association between religious service attendance and both psychological and social factors. WHI’s diverse study population is particularly appropriate for the aforementioned analyses given that it was culled from various regions across the United States and represents numerous religious and ethnic groups and those of varying socioeconomic status. The fact that WHI participants are postmenopausal women further adds to the appeal of conducting the proposed study with this group. Firstly, it has been suggested that religious involvement may be particularly important in enhancing social interaction in older Americans (Koenig 2005). Secondly, the link between religious activity and health may be most evident in women (House et al. 1982; Koenig et al. 1999), perhaps specifically older women (Oman et al. 2002). Finally, the comprehensive data collected from participants allow controlling for possible confounding factors.

Method

Participants

Funded by the National Institutes of Health, WHI involved 40 clinical centers. Mass mailings (based on driver’s license, voter registration, and other lists) were used to recruit women aged 50–79 for the research. Some participants were enrolled in a placebo-controlled, double-blind, randomized clinical trial (CT) of hormone replacement therapy and/or dietary modification. Women ineligible for, or unwilling to take part in, the CT were assigned to the OS, along with participants recruited specifically for the latter arm of the investigation. The OS was intended to assess health risks in light of the potential influence of biological and lifestyle factors. Of the 93,676 total participants in the OS, the current analyses involve 92,539 who completed the relevant self-report religion related items upon enrollment. [Similar exclusion criteria were used by Schnall et al. (2010) when studying this population. See there for comparison of participants included to those excluded.]

All research was conducted with approval of the relevant institutional review board.

Measures

Religiosity (Independent Variable)

Information about religious service attendance was collected as part of self-administered questionnaires. The question read: “How often have you gone to a religious service or to a church during the past month?” Consistent with previous literature and a recent paper examining religiosity and mortality in this population (Schnall et al. 2010), responses were collapsed into: not at all in past month (reference); less than once per week; once per week; and more than once per week.

Psychological Factors (Outcome Variables)

Three psychological factors were among the study outcomes: depression, cynical hostility, and optimism. Assessment of depression was based on eight items appearing on the baseline questionnaire. The first six of these items were drawn from the Center for Epidemiological Studies—Depression Scale, a validated measure originally developed by Radloff (1977). The items selected by WHI relate to affective, behavioral, and cognitive depressive symptoms, and subjects are asked to rate the frequency of these symptoms over the previous week. The remaining two depression-related items were drawn from the Diagnostic Interview Schedule (Robins et al. 1981), a validated instrument intended to be administered by lay interviewers and aid in diagnosis of psychiatric disorders. Responses to the eight total items were analyzed using an algorithm developed by Burnam et al. (1988), which predicts the probability of having a depressive disorder. Cynical hostility was measured using the appropriate subscale of the Cook-Medley Questionnaire (Cook and Medley 1954), which includes 13 true/false items. High scores reflect a generally negative view of others, describing them as unworthy, deceitful, and selfish. The revised version of the Life Orientation Test (Scheier et al. 1994) was used to assess optimism. It contains six items that measure notions such as perceived control, positive expectations, and hopefulness. Higher scores on this measure indicate greater optimism.

Social Factors (Outcome Variables)

Six social factors were among the study’s outcomes: overall positive social support; four subcategories, including emotional/informational support, affection support, tangible support, and positive social interaction; and negative social support/social strain. Positive support and its components were assessed using nine items drawn from the original Medical Outcomes Study Social Support Survey (Sherbourne and Stewart 1991). Higher scores indicate greater support. Negative support/social strain is a construct that measures the negative aspects of social relations. It has been identified in the literature as an independent component of social resources that may impede social support and negatively impact health. This measure contains four items selected from the original 7-item scale devised by Antonucci et al. (1989). Higher scores on this measure indicate greater social strain.

Covariates

The following covariates were selected based on previous literature (see Salmoirago-Blotcher et al. 2011; Schnall et al. 2010): age (50–59, 60–69 and 70–79); race/ethnicity [Black or African-American, Hispanic/Latino, White (not of Hispanic origin), other]; marital status (never married, married, in marriage-like relationship, widowed, divorced/separated); education (less than high school, high school or vocational degree, college degree, graduate degree); income (<20,000; 20,000–49,999; 50,000–74,999; 75,000–99,999, ≥100,000); self-reported general health (excellent or very good, good, and fair or poor). Covariate data were collected at baseline via self-administered questionnaires.

Analysis

Several analyses were conducted to assess respondents included in the study. Percentages of respondents endorsing each level of the demographic and religious service attendance variables were calculated. Psychological factors measured in the study (depression, cynical hostility and optimism), as well as positive and negative social support scores, were summarized at baseline using descriptive statistics that included means, medians, and standard deviations (Table 1).
Table 1

Demographic, psychosocial, and religious characteristics of WHI Observational Study sample (N = 92,539)

Demographic variables

N (%)

#Missing

Age

 50–59

29359 (31.73%)

0

 60–69

40737 (44.02%)

 70–79

22443 (24.25%)

Ethnicity/race

 Black or African-American

7485 (8.11%)

258

 Hispanic/Latino

3479 (3.77%)

 White (not of Hispanic origin)

77232 (83.69%)

 Other

4085 (4.43%)

Marital status

 Never married

4341 (4.71%)

436

 Divorced or separated

14513 (15.76%)

 Widowed

16051 (17.43%)

 Presently married

55707 (60.48%)

 Marriage-like relationship

1491 (1.62%)

Income

 <20000

13783 (16.06%)

6737

 20000–49999

37274 (43.44%)

 50000–74999

17316 (20.18%)

 75000–99999

8095 (9.43%)

 100000+

9334 (10.88%)

Education

 Less than high school

4737 (5.16%)

733

 High school or vocational degree

48488 (52.82%)

 College degree

21450 (23.36%)

 Graduate degree

17131 (18.66%)

General health

 Excellent or very good

53789 (58.38%)

404

 Good

29378 (31.89%)

 Fair or poor

8968 (9.73%)

Psychosocial and religion variables

#Missing

Mean (SD)

Median

Psychological factors

 Depression algorithm

2245

0.04 (0.13)

0.00

 Cynical Hostility

3558

3.70 (2.84)

3

 Optimism

2263

23.26 (3.48)

23

Social Factors

 Overall positive social support

2250

35.93 (7.85)

37

 Emotional/Informational support

139

15.67 (3.90)

16

 Affection support

558

4.13 (1.13)

5

 Tangible support

207

7.94 (2.22)

9

 Positive social interaction

219

8.02 (1.92)

8

 Negative social support/social strain

1919

6.50 (2.53)

6

Religion variable N (%)

 Religious services attendance

   

 None in the last month

31564 (34.11%)

  

 Less than once/wk

19826 (21.42%)

  

 Once/wk

27949 (30.20%)

  

 More than once/wk

13200 (14.26%)

  
Medians for each of the outcome variables were then calculated at each level of service attendance and reported with the interquartile range (IQR). The IQR demonstrates the statistical dispersion equal to the range between the 1st and 3rd quartiles. As depression was a categorical variable, the percentage of depressed respondents by service attendance level was provided instead (Table 2).
Table 2

Outcome variables according to service attendance, Median (IQR)

Variables

None

<Once/wk

Once/wk

>Once/wk

Depression algorithma

12.43%

12.58%

9.91%

9.94%

Cynical hostility

3 (4)

3 (4)

3 (4)

4 (4)

Optimism

23 (5)

23 (5)

23 (5)

24 (4)

Overall positive social support

37 (12)

37 (11)

38 (11)

38 (10)

Emotional/Informational support

16 (6)

16 (6)

16 (5)

17 (6)

Affection support

4 (2)

4 (1)

5 (1)

5 (1)

Tangible support

8 (4)

8 (3)

9 (3)

9 (3)

Positive social interaction

8 (4)

8 (3)

8 (3)

8 (3)

Negative social support/social strain

6 (4)

6 (4)

6 (4)

6 (4)

aA percentage of respondents is provided for this categorical variable

In further analyses, psychological and social outcome variables of interest were treated as dichotomous, using the median score as a cutpoint. They were modeled as a function of religious service attendance, first using univariate logistic regression models. Multivariate models then adjusted for potential confounding variables (Tables 3, 4). Each multivariate model was adjusted for the demographic variables listed above so that odds ratios (OR) reflected unique associations between the independent variable (religious service attendance) and the psychological and social outcomes. No attendance during the past month was used as the reference group for analyses. Results are presented as ORs for each level of services attendance, with 95% confidence intervals. All statistical analyses were performed using SAS statistical software, version 9.1 (SAS Institute Inc., Cary, NC).
Table 3

Odds ratios of psychological factors by religious service attendance level

Psychological factor

None last month (reference)

Less than once/week

Once/week

More than once/week

Depression algorithm

1

1.01

(0.96, 1.07)

0.78

(0.74, 0.82)

0.78

(0.73, 0.83)

Cynical hostility

1

1.04

(0.99, 1.08)

1.01

(0.97, 1.05)

1.17

(1.11, 1.23)

Optimism

1

1.05

(1.01, 1.09)

1.12

(1.09, 1.16)

1.33

(1.27, 1.39)

Depression algorithma

1

0.98

(0.92, 1.04)

0.78

(0.74, 0.83)

0.73

(0.68, 0.79)

Cynical hostilitya

1

0.99

(0.94, 1.03)

0.94

(0.90, 0.98)

1.02

(0.97, 1.08)

Optimisma

1

1.09

(1.04, 1.13)

1.22

(1.17, 1.26)

1.56

(1.49, 1.64)

Psychosocial outcome variables were analyzed as two levels, with median as the cutpoint

aAdjusted for age, ethnicity/race, marital status, income, education, general health

The bold values represent those findings that were significant (P < 0.05)

Table 4

Odds ratios of social factors by religious service attendance level

Social factor

None last month (reference)

Less than once/week

Once/week

More than once/week

Overall positive social support

1

1.07

(1.03, 1.11)

1.27

(1.23, 1.31)

1.40

(1.35, 1.46)

Emotional/informational support

1

1.08

(1.05, 1.12)

1.22

(1.18, 1.26)

1.44

(1.38, 1.51)

Affection support

1

1.04

(1.00, 1.08)

1.27

(1.23, 1.32)

1.33

(1.28, 1.39)

Tangible support

1

0.98

(0.95, 1.02)

1.22

(1.18, 1.26)

1.25

(1.20, 1.30)

Positive social interaction

1

1.12

(1.08, 1.17)

1.26

(1.21, 1.30)

1.26

(1.20, 1.32)

Negative social support/social strain

1

1.09

(1.05, 1.13)

0.91

(0.88, 0.94)

1.00

(0.96, 1.05)

Overall positive social supporta

1

1.09

(1.05, 1.13)

1.28

(1.24, 1.33)

1.54

(1.46, 1.61)

Emotional/Informational supporta

1

1.10

(1.06, 1.15)

1.28

(1.23, 1.33)

1.62

(1.54, 1.69)

Affection supporta

1

1.05

(1.01, 1.10)

1.26

(1.21, 1.30)

1.43

(1.37, 1.50)

Tangible supporta

1

0.98

(0.94, 1.02)

1.17

(1.13, 1.22)

1.29

(1.23, 1.35)

Positive social interactiona

1

1.19

(1.14, 1.24)

1.32

(1.27, 1.37)

1.48

(1.41, 1.56)

Negative social support/social straina

1

1.06

(1.02, 1.11)

0.91

(0.88, 0.94)

0.96

(0.92, 1.01)

Psychosocial outcome variables were analyzed as two levels, with median as the cutpoint

aAdjusted for age, ethnicity/race, marital status, income, education, general health

The bold values represent those findings that were significant (P < 0.05)

Results

Baseline Characteristics

The sample consisted of 92,539 WHI women. Missing data for each of the outcome variables are listed in Table 1. Of the three age bands, the largest group of women were between 60 and 69 (44%), and most were presently married (60%). The overwhelming majority was White (84%), fewer than half had a college degree, and the majority (60%) reported a personal income of less than $50,000. In most cases, general health was listed as “excellent or very good” (58%). Approximately one-third of respondents reported no religious service attendance during the past month. However, 21% of the overall sample stated that they attend less than once per week, 30% reported attending once per week and 14% reported attending more than once per week.

Univariate and Multivariate Models

Medians for each of the outcome variables (and percentages for the depression variable) were calculated at each level of service attendance (Table 2). Results of univariate and multivariate regression models are presented in Tables 3 and 4, including ORs. An OR > 1 in which 1 does not appear within the 95% confidence interval indicates statistically significant (P < 0.05) increased probability of the outcome occurring, while an OR < 1 indicates decreased probability.

Results of both univariate and multivariate models showed a likelihood of greater optimism among women in all religious service attendance levels compared to nonattenders. In both the unadjusted and adjusted models, women who attended religious services at least once per week were also less likely to be depressed compared to the reference group. Weekly attenders were also less likely, in the adjusted model, to exhibit cynical hostility.

Regarding the social outcome factors, both models demonstrated that women who reported attendance in any of the three categories were more likely to report greater overall positive social support, as well as greater emotional/informational support, affection support, and more positive social interaction. Weekly attenders were also more likely to report greater tangible social support, compared to nonattenders.

Negative social support/social strain also was assessed according to service attendance level. The adjusted model indicated that women who attended less than once per week were more likely to report higher levels of negative social support. By contrast, the opposite was true for women who attended religious services at a frequency of once per week.

Discussion

Numerous studies (e.g., Gillum et al. 2008; Schnall et al. 2010) suggest that religious behavior is linked to improved health. However, these relationships are not fully understood. In partial explanation, many (e.g., Powell et al. 2003; Schnall et al. 2010) cite the beneficial psychological factors that may be associated with religion and its practice. Indeed, increased optimism, and reduced depression and hostility, are known to be associated with good health (Berkman et al. 1986; Rasmussen et al. 2009; Tindle et al. 2009). Our results may offer support to this line of interpretation. We found that those who reported any religious service attendance at all in the past month, even if less than once per week, were significantly more optimistic, compared with the reference group of nonattenders (Table 3). In fact, after adjusting for potential confounders, results were striking, in that those who reported the most frequent attendance were 56% more likely to be above the median, in terms of optimism level (OR = 1.56; CI = 1.49–1.64). Similarly, we found that weekly attenders were less likely to be depressed, compared with the reference group. After adjusting for confounders, those attending the most often were 27% less likely to be depressed (OR = 0.73; CI = 0.68–0.79). It should be noted, however, that results were inconclusive regarding cynical hostility. Although weekly attenders were less likely to be high in this trait (after adjusting for confounders), compared with the reference group (OR = 0.94; CI = 0.90–0.98), results were not significant for those attending either more or less than once per week.

In further explanation of the relationship between religious behavior and health, many (see Koenig et al. 2001) have pointed to the social benefits of religious affiliation and attendance, often focusing on the social support a religious community may offer. However, most researchers have considered social support as a single overarching construct, rather than individually examining each of the manifold forms it may take. Firstly, our research buttresses the assertion that service attenders may experience greater social support than others. All who reported attendance, regardless of frequency, were significantly more likely to have higher than the median level of overall social support. In fact, after adjusting for confounders, those who attended more than once per week were 54% more likely to be high in social support (OR = 1.54; CI = 1.46–1.61). Secondly, we examined four subcomponents of social support and found that those attending at least once per week were at greater likelihood of above median levels of all four forms of support, compared with the reference group.

Nonetheless, religious attendance does not seem equally associated with each of the aforementioned forms of social support. Emotional/informational support showed the greatest increase in likelihood (OR = 1.62; CI = 1.54–1.69, for those attending more that once per week, multivariate model). Tangible support, by contrast, showed the smallest increase in likelihood (OR = 1.29; CI = 1.23–1.35, for those attending more that once per week, multivariate model). In fact, tangible support was the only form of support where results were not significant when comparing those who attended only less than once per week to the reference group. The WHI questionnaire’s tangible support items, asking whether the respondent has “someone to take you to the doctor if you need it” or “to help you with daily chores if you are sick,” may reflect forms of support not primarily associated with religious services. However, it is noteworthy that there was still a significantly increased likelihood of even this form of support, at least in those who attended once per week or more. Clergy and/or the faith-based community involved with worship services may provide attenders with significant tangible assistance, in conjunction with other types of social support.

In addition to the many forms of positive social support associated with religious behavior, some have hypothesized that religious involvement also may bring with it negative social support, or social strain (Exline 2002; Exline and Rose 2005; Krause 2008). For instance, religious involvement could become fodder for marital discord or lead to strife with clergymen or friends and family members whose beliefs differ from one’s own. However, our research does not readily support this contention. Although those who attended the least frequently (less than once per week) were at a small increased risk of reporting above the median levels of social strain (OR = 1.06; CI = 1.02–1.11, multivariate model), this finding was absent in those attending more frequently. In fact, those attending once per week were actually at significantly reduced risk of reporting above the median levels of social strain, compared with the reference group (OR = 0.91; CI = 0.88–0.94, both univariate and multivariate models). Of course, given the nature of cross-sectional data, it is possible that our results reflect the fact that those who develop conflicts with members of their congregation or other related forms of social strain subsequently choose to attend services less frequently.

Regarding limitations of our study, although our analyses, based on a very large sample of WHI participants (N = 92,539), included adjustments for various potential confounders, there may be other relevant variables for which we did not account. Also, our data, cross-sectional in nature, do not permit inference of causation. For example, religious involvement may lead to optimism, or optimistic persons may be drawn to religious activities. Additionally, WHI asked participants to quantify their religious attendance in the previous month, whereas other researchers asked for attendance over the past year, a method of measurement perhaps less likely affected by temporary disruptions such as from illness or other events (Salmoirago-Blotcher et al. 2011). Finally, our investigation focused exclusively on WHI’s pool of postmenopausal women. Future researchers might examine whether our findings hold true in younger and male populations.

In conclusion, our research suggests that those reporting regular religious service attendance may be most likely to be characterized by many of the psychological and social characteristics associated with health and longevity. Our findings are particularly important in that they highlight the many specific subforms of social support that service attenders may be most likely to report, areas that have not received adequate attention in investigations of positive social support in general. Furthermore, our research contributes to the relatively new area of inquiry surrounding whether religious behavior also may be associated with negative social support (social strain).

Finally, there may be public health implications to our findings. For example, for those as yet uninvolved with religious groups, participation might be encouraged, if consistent with the individual’s beliefs. Alternately, such individuals may be encouraged to seek the benefits associated with service attendance by participating in other activities that may provide similar benefits to psychosocial health.

Acknowledgments

Dr. Wassertheil-Smoller had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Grateful acknowledgment is extended to Linzhi Xu and Victor Kamensky for statistical programming. Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller. Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles Kooperberg; (Medical Research Labs, Highland Heights, KY) Evan Stein; (University of California at San Francisco, San Francisco, CA) Steven Cummings. Clinical Centers: (Albert Einstein College of Medicine, Bronx, NY) Sylvia Wassertheil-Smoller; (Baylor College of Medicine, Houston, TX) Haleh Sangi-Haghpeykar; (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (Brown University, Providence, RI) Charles B. Eaton; (Emory University, Atlanta, GA) Lawrence S. Phillips; (Fred Hutchinson Cancer Research Center, Seattle, WA) Shirley Beresford; (George Washington University Medical Center, Washington, DC) Lisa Martin; (Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA) Rowan Chlebowski; (Kaiser Permanente Center for Health Research, Portland, OR) Erin LeBlanc; (Kaiser Permanente Division of Research, Oakland, CA) Bette Caan; (Medical College of Wisconsin, Milwaukee, WI) Jane Morley Kotchen; (MedStar Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Northwestern University, Chicago/Evanston, IL) Linda Van Horn; (Rush Medical Center, Chicago, IL) Henry Black; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (State University of New York at Stony Brook, Stony Brook, NY) Dorothy Lane; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Alabama at Birmingham, Birmingham, AL) Cora E. Lewis; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of California at Davis, Sacramento, CA) John Robbins; (University of California at Irvine, CA) F. Allan Hubbell; (University of California at Los Angeles, Los Angeles, CA) Lauren Nathan; (University of California at San Diego, LaJolla/Chula Vista, CA) Robert D. Langer; (University of Cincinnati, Cincinnati, OH) Margery Gass; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Hawaii, Honolulu, HI) J. David Curb; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Massachusetts/Fallon Clinic, Worcester, MA) Judith Ockene; (University of Medicine and Dentistry of New Jersey, Newark, NJ) Norman Lasser; (University of Miami, Miami, FL) Mary Jo O’Sullivan; (University of Minnesota, Minneapolis, MN) Karen Margolis; (University of Nevada, Reno, NV) Robert Brunner; (University of North Carolina, Chapel Hill, NC) Gerardo Heiss; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (University of Tennessee Health Science Center, Memphis, TN) Karen C. Johnson; (University of Texas Health Science Center, San Antonio, TX) Robert Brzyski; (University of Wisconsin, Madison, WI) Gloria E. Sarto; (Wake Forest University School of Medicine, Winston-Salem, NC) Mara Vitolins; (Wayne State University School of Medicine/Hutzel Hospital, Detroit, MI) Michael S. Simon. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221.

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