Perceived Discrimination Is Associated with Reduced Breast and Cervical Cancer Screening: The Study of Women's Health Across the Nation (SWAN)

Abstract

Background:

Racial disparities in breast and cervical cancer screening have been documented in African American, Hispanic, and Asian populations. Perceived discrimination may contribute to this disparity. The aim of this study was to understand the relationship between perceived everyday racial/ethnic and other discrimination and receipt of breast and cervical cancer screening in a multiethnic population of women.

Methods:

We analyzed data from 3,258 women participating in the Study of Women's Health Across the Nation (SWAN), a multiethnic/racial, longitudinal cohort study of the natural history of the menopausal transition conducted at seven U.S. sites. Participants completed a validated measure of perceived discrimination and reasons for believing that they were treated differently, along with Pap smears, clinical breast exams (CBE), and mammography at each follow-up period. We used multiple logistic regression for the binary outcomes of having a Pap smear, CBE, or mammogram in each of the two follow-up years, using self-reported “race discrimination” and “other discrimination” at baseline as the main predictors.

Results:

African American women reported the highest percentage of racial discrimination (35%), followed by Chinese (20%), Hispanic (12%), Japanese (11%), and non-Hispanic white women (3%). Non-Hispanic white women reported the highest percentage of “other” discrimination (40%), followed by Chinese (33%), African American (24%), Japanese (23%), and Hispanic women (16%). Perceived racial discrimination was not associated with reduced receipt of preventive screening, except in one fully adjusted model. Reported discrimination owing to “other” reasons, such as age or gender, was associated with reduced receipt of Pap smear (odds ratio [OR] 0.85; 95% confidence interval [CI] 0.74–0.99), CBE (OR 0.78; 95% CI 0.67–0.91), and mammography (OR 0.80; 95% CI 0.69–0.92) regardless of patient race.

Conclusions

: Perceived discrimination is an important issue across racial/ethnic groups and is negatively associated with receipt of breast and cervical cancer screening. This is an important issue that needs to be further explored and addressed in efforts to improve the delivery of healthcare to all groups.

Introduction

Discrimination, whether perceived to be occurring specifically in the healthcare setting or in the world at large, has been associated with lower receipt of healthcare.^1

–8 The majority of the research investigating the relationship between perceived discrimination and receipt of healthcare has focused on perceived racial/ethnic discrimination in the world at large rather than specifically in the healthcare setting and in receipt of preventive health services, including various types of cancer screening.

This focus has likely been because racial disparities in breast and cervical cancer screening have been documented in African American, Hispanic, and Asian populations.^{9

–18} Although disparities for African American women have been greatly reduced and/or eliminated in some recent studies, they persist in many instances.¹⁹ Research exploring the reasons for these disparities has found that socioeconomic status, access factors, health beliefs, and knowledge only partially explain these disparities.^{11,12,16,18,20,21} The literature on the relationship between discrimination and use of preventive health services suggests that perceived discrimination may contribute to the remaining unexplained disparity. Although results vary across studies and between different screening types, many studies have found perceived racial/ethnic discrimination to be associated with decreased utilization of services, including screening for colorectal,^1,2,22,23 breast,²³ prostate,²⁴ and cervical cancer.^2,22,25 Others have found no association.^24

–27 We found very few studies that investigated the relationship between types of discrimination other than racial/ethnic discrimination and receipt of cancer screening.^8,28

The aim of this study was to understand the relationship between perceived everyday racial/ethnic and other discrimination and receipt of breast and cervical cancer screening in a multiethnic population of women. We chose to use a more general measure of discrimination rather than one specific to healthcare, because this type of “regular,” everyday discrimination has been shown to negatively impact health in other ways.^29

–35 Our hypotheses were that perceived discrimination for any reason would be higher among African American, Hispanic, and Asian women compared to Caucasian participants and that perceived discrimination would be associated with reduced breast and cervical cancer screening after accounting for other factors that are commonly recognized to influence receipt of preventive services.

Materials and Methods

Sample

The 3,258 women in this study are participants in the Study of Women's Health Across the Nation (SWAN). A detailed description of the SWAN methods and study design has been previously published.³⁶ Briefly, SWAN is organized as a prospective, multiethnic, multidisciplinary study of the natural history of the menopausal transition. The study is conducted at sites in Boston, Chicago, Oakland, Detroit, Los Angeles, Newark, and Pittsburgh. SWAN was designed to create a sample in which half the women were Caucasian and the other half were from one of four other racial/ethnic groups: African American, Chinese American, Japanese American, or Hispanic (Puerto Ricans, Mexicans, Dominicans, Central Americans, Cubans, and South Americans).³⁶ Each site recruited Caucasian women and women from one of these other racial/ethnic groups. Sampling strategies and frames varied from site to site and included random-digit dialing, voter registration lists, health maintenance organization subscription lists, census track/block enumeration, and “snowball” sampling techniques. At least 450 women were recruited at each site. Eligibility criteria required that participants be between 42 and 52 years old, not have taken hormone replacement therapy in the past 3 months, have an intact uterus and at least one ovary, have had a menstrual period in the 3 months prior to entry into the study, and not be pregnant or breastfeeding. In addition, we limited the present analyses to women who had not had breast, cervical, or uterine cancer. At study entry (1996–1997) and approximately annually thereafter, women at all sites completed a standard assessment that included interviewer-administered and self-administered questionnaires assessing various social, economic, behavioral, psychological, and lifestyle characteristics, along with collection of fasting blood and urine specimens. The SWAN research protocol was approved by the institutional review board at each site, and all women consented to participate in the study.

Study variables

Data for the present study were taken from the baseline, year 1, and year 2 follow-up questionnaires. Data on discrimination and all covariates were collected at baseline; breast and cervical cancer screening data were collected in all 3 years.

The 10-item Everyday Discrimination Scale used in SWAN was developed by David Williams et al. to measure relatively common experiences of discrimination, such as being treated with less courtesy or respect than others and receiving poorer service than others in such places as restaurants or stores.³⁷ The SWAN scale has been shown to be reliable and valid for use across racial/ethnic groups^38,39 and in the SWAN population⁴⁰ and has been associated with negative health outcomes in SWAN and other populations.^29

–35 The stem question is “In your day-to-day life, how often do any of the following things happen to you?” A representative item is “You are treated with less courtesy than other people are.” The frequency of each experience of discrimination is rated on a 4-point scale (1=often, 2=sometimes, 3=rarely, 4=never). If a respondent reports discrimination (e.g., answers 1 or 2 on one or more items), she is then asked what she thinks is “the main reason for these experiences” and to circle only one of nine possible reasons: race, ethnicity, gender, age, income level, language, physical appearance, sexual orientation, or other.³⁷ Women who answered “rarely” or “never” to all 10 discrimination items were assigned to the “no discrimination” category. Women who responded that they experienced 1 or more of the 10 items “often” or “sometimes” were classified as experiencing discrimination. Those who attributed their experiences to race or ethnicity were assigned to the “race discrimination” category. Those who attributed their experiences to one of the other seven possibilities were grouped into the “other discrimination” category.

Covariate data include sociodemographic variables and two measures of healthcare utilization (Table 1). Age is measured in years. Race reflects respondents' self-identification; women were asked to categorize themselves as belonging to one of five categories: African American, Chinese, Japanese, Hispanic, or non-Hispanic white. Insurance includes four categories: private insurance, federal insurance (Medicare or Medicaid), other insurance (including veterans' care), and no insurance. Respondents reported one of five educational levels from less than high school to postbaccalaureate schooling. Women reported their family's annual income within the range of less than $10,000 to more than $150,000. In addition to reporting their income, women were asked whether they had had money problems in the past year and if they did, how stressful those problems were (not upsetting, somewhat upsetting, or very upsetting). Marital status had five categories for respondents to choose from (married/living as if married, divorced, separated, widowed, or single). Healthcare utilization was measured using women's self-report of the number of times they had been hospitalized and had talked to a doctor in the past year.

Table 1.

Descriptive Statistics of SWAN Study Sample at Baseline

		n (3,258)	%
Race	African American	918	28
	Chinese	240	7
	Japanese	271	8
	Hispanic	219	7
	Caucasian	1,610	50
Discrimination	Due to race/ethnicity	465	14
	Due to “other” reasons	1,040	32
	None	1,749	54
Education	Less than high school	187	6
	Completed high school	510	16
	Some college	1,018	33
	Complete college	661	21
	Postcollege	765	24
Income	Less than $10,000	171	5
	$10,000–$19,000	241	8
	$20,000–$34,999	472	15
	$35,000–$49,000	548	17
	$50,000–$74,999	773	24
	$75,000–$99,999	448	14
	$100,000–$149,999	366	12
	Greater $150,000	144	5
Insurance	Private insurance	2,706	83
	Medicare or Medicaid	130	4
	Other	196	6
	No insurance	211	7
Marital status	Married	2,135	68
	Divorced	408	13
	Separated	135	4
	Widowed	61	2
	Single	413	13
Money problems	No problems	2,176	67
	Problems, not upsetting	150	5
	Problems, somewhat upsetting	591	18
	Problems, very upsetting	323	10
		Mean (SD)
Age	Years	45.8 (2.7)
Hospitalizations	Number in past year	0.08 (0.4)
Spoken to doctor	Number in past year	3.5 (4.5)

SD, standard deviation; SWAN, Study of Women's Health Across the Nation.

Information on utilization of breast and cervical cancer screening was collected via self-report. At each visit, women were asked whether they had had a Pap smear, clinical breast exam (CBE), and mammogram in the past year. Because the interviews did not necessarily coincide neatly with a woman's screening schedule, we chose a 2-year period for each of these screenings to be completed for a participant to be considered as “screened.” For example, if a woman had a mammogram scheduled for 1 week after her interview in her first-year follow-up, she would appear “unscreened” when in fact she was up-to-date. She could have been screened in one or both years to be considered “screened.” Given that the data from this study are from 1996 to 2000, this is consistent with the guidelines at the time.

Analysis

The basic analytic framework for this study was logistic regression for the binary outcomes of having a Pap smear, CBE, or mammogram in each of the two follow-up years. The main predictors of interest were “race discrimination” and “other discrimination” at baseline. We then adjusted our analyses in a stepwise manner for potential confounders of the relationship between discrimination and cancer screening: baseline age (Model 1), socioeconomic factors (Model 2), and exposure to healthcare (Model 3). For each outcome, three models were fitted. In Model 1, we adjusted for baseline age. In Model 2, we further adjusted for age, race, education (coded 1–5), income (coded 1–8), insurance, marital status, and report of money problems (coded 1–4). In the last model (Model 3), we adjusted for annual exposure to healthcare in addition to all other covariates. Models 2 and 3 were fitted to the data to investigate the direct effect of the covariates on use of screening services and to examine whether adjustment for these variables changed the association between perceived discrimination and use of screening services. Separate sets of logistic models were conducted for each outcome and are reported later in this article. We also included an interaction term in a final model to test for effect modification of “discrimination” by racial/ethnic group.

Two features of the SWAN data required special statistical treatment. First, the data were collected longitudinally, yielding up to three measures per woman in the study. To account for correlation among the repeated measures, we fitted our logistic regression models, using generalized estimating equations with an exchangeable correlation structure and robust estimates of standard error.⁴¹

The second challenging feature is the considerable variability of the SWAN populations across sites. A first-line approach for multisite studies would be to develop a single model that includes both adjustors and a site-specific random or fixed effect. However, in SWAN, each site includes Caucasian women and women from one other race/ethnic group, with four sites including African American women and one site each including Hispanic, Japanese, or Chinese women. Samples also had different levels and types of insurance coverage. As a result, adjustment models with random site effects would have to be specified differentially from site to site to account for between-site differences, leading to awkward and difficult-to-reproduce adjustment models involving many interaction terms. Furthermore, owing to the cross-site heterogeneity, the levels and effects of perceived discrimination were also variable across sites.

To address these challenges, we took a meta-analytic approach to our data analyses.⁴² Specifically, we fitted logistic models separately across the seven sites and then pooled the resulting regression coefficients, obtaining overall estimates of perceived discrimination and other predictors, with corresponding pooled standard errors for testing and constructing confidence intervals on this overall association. Working site by site allowed us to flexibly handle between-site differences in covariate structure, examine results site by site, and check for any model misspecifications at each site, before pooling results on discrimination effects. In pooling, we weighted the coefficients by the inverse of the site-specific estimate of variance (square of standard error). Note that with this method, a site that has, for example, no participants of Chinese origin will automatically not contribute to estimation of the association between Chinese ethnicity and screening behavior.

Results

Table 1 shows the racial and sociodemographic characteristics of the sample. Although the sample includes substantial numbers of participants identifying as African American, Chinese, Japanese, and Hispanic, participants identifying as Caucasian made up 50% of the study population. Most participants were highly educated, middle class, insured, and married. Most women had had exposure to healthcare in the prior year at baseline, and less than 30% had experienced stressful money problems. Overall, more than half the sample reported no discrimination, less than 15% reported racial discrimination, and 32% reported discrimination owing to other reasons. After race, which was the most frequently reported reason for discrimination (28%), the next most commonly reported reasons were gender (14%), physical appearance (12%), and “other” (27%; results not shown). Report of racial discrimination varied by ethnic group (see Table 2), with African Americans reporting the highest percentage of racial discrimination (35%), followed by Chinese (20%), Hispanic (12%), Japanese (11%), and non-Hispanic white women (3%; p<0.01, chi-squared test of independence adjusting for age, income, and educational level). Non-Hispanic white women reported the highest percentage of “other” discrimination (40%), followed by Chinese (33%), African Americans (24%), Japanese (23%), and Hispanic women (16%). The majority of women did not have a Pap smear, CBE, or mammogram between the baseline visit and year 2 follow-up (Table 3).

Table 2.

Report of Discrimination by Race at Baseline

	African American		Chinese		Japanese		Hispanic		Caucasian		Total
Type of discrimination	n	%	n	%	n	%	n	%	n	%	n	%
Race	319	35	48	20	30	11	26	12	42	3	465	14
Other^a	216	24	80	33	62	23	35	16	647	40	1040	32
None	381	42	112	47	179	66	158	72	919	57	1749	54

p<0.001 for distribution of report of discrimination within each race category compared to Caucasians, adjusting for age, educational level, and income.

Due to gender, age, income level, physical appearance, language, sexual orientation, other.

Table 3.

Report of Pap Smear, Clinical Breast Exam, and Mammography in Years 1 and 2

	Year 1		Year 2
	n	%	n	%
Pap smear	1,081	35	907	31
CBE	955	31	819	28
Mammogram	1,400	46	1,181	41

CBE, clinical breast exam.

The results of the three models exploring the relationship between discrimination and receipt of each of the three measures of breast and cervical cancer screening are shown in Table 4. Racial discrimination was not significantly related to receipt of Pap smears or mammography in any of the three models and was significantly related to receipt of CBE only in the fully adjusted Model 3. Women who reported experiencing discrimination attributed to nonracial factors were approximately 15%–22% less likely to receive Pap smears, CBE, and mammograms across all three models (each p<0.01).

Table 4.

Odds Ratios for Receiving Breast and Cervical Cancer Screening by Report of Discrimination and Race

	Pap smears OR (95% CI)
	Model 1^a	Model 2^b	Model 3^c
Race discrimination	1.00 (0.31–2.45)	0.96 (0.77–1.19)	0.88 (0.79–1.10)
Other discrimination	0.84 (0.73–0.96)	0.88 (0.76–1.02)	0.85 (0.74–0.99)
No discrimination	1.00	1.00	1.00
African American		1.28 (1.04–1.58)	1.28 (1.04–1.59)
Chinese		0.61 (0.44–0.86)	0.61 (0.43–0.86)
Japanese		0.60 (0.42–0.86)	0.65 (0.45–0.94)
Hispanic		0.96 (0.54–1.71)	0.95 (0.53–1.72)
Caucasian		1.00	1.00

	Clinical breast exam OR (95% CI)
	Model 1^a	Model 2^b	Model 3^c
Race discrimination	0.88 (0.72–1.06)	0.82 (0.66–1.03)	0.77 (0.61–0.97)
Other discrimination	0.78 (0.68–0.90)	0.81 (0.69–0.94)	0.78 (0.67–0.91)
No discrimination	1.00	1.00	1.00
African American		1.50 (1.21–1.87)	1.50 (1.20–1.88)
Chinese		0.64 (0.44–0.92)	0.63 (0.44–0.91)
Japanese		0.58 (0.40–0.83)	0.64 (0.45–0.92)
Hispanic		1.13 (0.63–2.04)	1.12 (0.62–2.02)
Caucasian		1.00	1.00

	Mammography OR (95% CI)
	Model 1 ^a	Model 2 ^b	Model 3 ^c
Race discrimination	0.92 (0.77–1.10)	0.89 (0.73–1.08)	0.82 (0.67–1.01)
Other discrimination	0.80 (0.70–0.91)	0.83 (0.72–0.94)	0.80 (0.69–0.92)
No discrimination		1.00	1.00
African American		1.28 (1.06–1.55)	1.29 (0.95–1.58)
Chinese		0.62 (0.44–0.87)	0.61 (0.43–0.86)
Japanese		0.73 (0.52–1.01)	0.79 (0.57–1.09)
Hispanic		1.21 (0.70–2.09)	1.20 (0.69–2.09)
Caucasian		1.00	1.00

Adjusted for age.

Adjusted for age, race, education, income, insurance status, marital status, and report of stressful money problems.

Adjusted for variables in Model 2 plus number of hospitalizations and times spoken to a physician in the past year.

CI, confidence interval; OR, odds ratio.

Table 4 also reports the odds ratios for receipt of each of the cancer screenings for each of the race categories included in Models 2 and 3. In Model 2, the odds of having had a Pap smear, CBE, or mammogram in the 2 years of the study were significantly greater for African American women and significantly less for Chinese women when compared with Caucasian women. The odds ratios for receipt of preventive services for Hispanic women were similar to those for Caucasian women. The odds of receipt of cancer screening compared to Caucasian women did not change significantly for these three racial ethnic groups in Model 3, except that the greater odds of receipt of mammography for African American women was no longer significant (1.29; CI, 0.91–1.58). Compared to Caucasian women. Japanese women had significantly reduced odds of receipt of Pap smears and CBE but not mammography in both Models 1 and 2.

Additional covariates significantly negatively associated with receipt of Pap smear, CBE, and mammography in Models 2 and 3 were younger age, lower income, lack of insurance, and fewer reported episodes of talking to a physician in the past year (results not shown). Having less education was associated with lower odds of receipt of Pap smear and CBE but not mammography, and being widowed was associated with lower Pap-smear odds in both models as well (results not shown). There were no significant relationships between report of stressful money problems (Models 2 and 3) and number of hospitalizations (Model 3) and breast and cervical cancer screening. There were no significant interactions between race/ethnicity and reporting discrimination (final model not shown).

Discussion

We found that that perceived racial discrimination was higher among African American, Hispanic, and Asian women compared to Caucasian participants in our study, but reports of racial discrimination were generally not associated with reduced breast and cervical cancer screening. We also found that reports of discrimination owing to other factors was high across all the groups in our study—highest, in fact, among the Caucasians in our study—and that reports of discrimination owing to reasons other than race were strongly related to reduced self-reported receipt of CBE, Pap smears, and mammography.

Our findings are consistent with other studies that have shown higher rates of perceived racial discrimination among non-Caucasian racial/ethnic groups and mixed results regarding the relationship between this type of discrimination and receipt of preventive cancer screening, including breast and cervical cancer screening.^{23

–26,43,44} This is surprising given that the negative association between reports of racial/ethnic discrimination and other health outcomes is usually clear and consistent.^5,44 This inconsistency may be due to several factors. First, there are documented psychometric differences between the measures of perceived racial discrimination used across these studies⁴⁵ and the types of discrimination measured—both in the healthcare setting and the experience of discrimination more broadly. In a brief review of the studies we cite in this article, we noted that those that found an association between discrimination and health outcomes were more likely to use measures of perceived discrimination specific to healthcare.^{7,23,26,46

–49} Second, it may be that other determinants of receipt of preventive cancer screening are more salient than perceived racial/ethnic discrimination. For example, in our study, we were unable to measure and control for additional factors that are known to influence cancer screening, such as differences across racial/ethnic groups in beliefs about susceptibility to cancer, fatalism, whether a doctor recommended screening, previous screening behavior and so on.^50
–52

In contrast, report of perceived discrimination owing to other factors, such as physical appearance or gender, was clearly negatively associated with receipt of all three screening measures, indicating that discrimination has an impact on receipt of healthcare services across all groups. This finding is consistent with a few previous studies reporting an association between perceived discrimination related to gender, obesity, and sexual orientation and nonadherence to breast and cervical cancer screening guidelines.^3,8,23 We were unable to disentangle how each of the other categories of discrimination contributed to this relationship, owing to the small number of women attributing their experiences of discrimination to each “other” category. This finding suggests that something about perceived discrimination, no matter what factor it is attributed to, interferes with a woman's ability to get breast and cervical cancer screening. It may be that women experience this same discrimination in the healthcare setting and therefore do not get the services they should or that women reporting discrimination are likely to expect discrimination in the healthcare setting and therefore avoid getting healthcare. More research is needed to elucidate how perceived discrimination is related to reduced utilization of healthcare and whether the reason attributed for the mistreatment changes this relationship.

This study was not without limitations. The fact that the sampling strategies and racial groups differed by site may have limited our ability to understand the relationship between racial discrimination and receipt of breast and cervical cancer screening. In addition, unlike most comparative studies, the African American women in this study were significantly more likely than Caucasian women to have had Pap smears, CBEs, and mammograms. Given that African American women reported the most perceived racial discrimination of any group in this study, it is likely that in a different population, the relationship between racial discrimination and breast and cervical cancer screening would have been stronger than that found in our study. In addition, perceived discrimination could potentially vary across regions of the country, and our results might have been different if we had included additional regions of the United States. The women in our sample, with frequent exposure to healthcare, may be different in respect to the relationship between perceived discrimination and preventive screening than women without less frequent exposure to healthcare. That said, we still found a significant relationship between screening and perceived discrimination, and this relationship was consistent across the models, whether or not we controlled for exposure to healthcare. Our measure of perceived discrimination did not provide us with information about how discrimination, for whatever reason, reduces breast and cervical cancer screening. This underscores the points made in recent reviews that the relationship between discrimination and receipt of healthcare needs to be explored in further depth.^5,44

Despite these limitations, this study adds to the growing body of literature documenting the relationship between discrimination and health and healthcare. This study indicates that perceived discrimination due to “other” factors, regardless of race, also likely plays an important role in whether people get the healthcare they need. Clearly, delivering healthcare involves more than providing insurance, access, and the services that patients need. The racial and social contexts in which patients and providers operate also influence their receipt of healthcare. If we are serious about providing optimal care to all the women we serve, we must begin to address these issues as well.

Footnotes

Acknowledgments

The Study of Women's Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), Department of Health and Human Services (DHHS), through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), and the NIH Office of Research on Women's Health (ORWH) (Grants U01NR004061, U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.

We also acknowledge the study staff and Principal Investigators at the following clinical centers: University of Michigan, Ann Arbor (Siobán Harlow, 2011–present; MaryFran Sowers,1994–2011); Massachusetts General Hospital, Boston, MA (Joel Finkelstein, 1999–present; Robert Neer, 1994–1999); Rush University Medical Center, Chicago, IL (Howard Kravitz, 2009–present; Lynda Powell, 1994–2009); University of California, Davis/Kaiser (Ellen Gold); University of California, Los Angeles (Gail Greendale); Albert Einstein College of Medicine, Bronx, NY (Carol Derby, 2011–present, Rachel Wildman, 2010–2011; Nanette Santoro, 2004–2010); University of Medicine and Dentistry, New Jersey Medical School, Newark, NJ (Gerson Weiss, 1994–2004); and the University of Pittsburgh, Pittsburgh, PA (Karen Matthews).

Additionally, we acknowledge the following: NIH Program Office, National Institute on Aging, Bethesda, MD (Winifred Rossi, 2012–present; Sherry Sherman, 1994–2012; Marcia Ory, 1994–2001); Program Officers at the National Institute of Nursing Research, Bethesda, MD; the Central Laboratory at the University of Michigan, Ann Arbor (Daniel McConnell); Central Ligand Assay Satellite Services; the Coordinating Center at the University of Pittsburgh, Pittsburgh, PA (Principal Investigators Maria Mori Brooks, 2012–present; Kim Sutton-Tyrrell, 2001–2012); New England Research Institutes, Watertown, MA (Principal Investigator Sonja McKinlay, 1995–2001); as well as the current and former chairs of the Steering Committee: Susan Johnson and Chris Gallagher, respectively.

Author Disclosure Statement

No competing financial interests exist for any of the authors.

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