Skip to main content
NCBI home page
International Journal of Epidemiology logoLink to International Journal of Epidemiology
. 2017 Dec 2;47(2):460–472. doi: 10.1093/ije/dyx252

Racial/ethnic differences in the epidemiology of ovarian cancer: a pooled analysis of 12 case-control studies

Lauren C Peres 1,✉, Harvey Risch 2, Kathryn L Terry 3,4, Penelope M Webb 5, Marc T Goodman 6,7, Anna H Wu 8, Anthony J Alberg 9, Elisa V Bandera 10, Jill Barnholtz-Sloan 11, Melissa L Bondy 12, Michele L Cote 13, Ellen Funkhouser 14, Patricia G Moorman 15, Edward S Peters 16, Ann G Schwartz 13, Paul D Terry 17, Ani Manichaikul 1,18, Sarah E Abbott 1, Fabian Camacho 1, Susan J Jordan 5, Christina M Nagle 5; Australian Ovarian Cancer Study Group5, Mary Anne Rossing 19,20, Jennifer A Doherty 21, Francesmary Modugno 22,23,24, Kirsten Moysich 25, Roberta Ness 26, Andrew Berchuck 27, Linda Cook 28, Nhu Le 29, Angela Brooks-Wilson 30,31, Weiva Sieh 32, Alice Whittemore 33, Valerie McGuire 33, Joseph Rothstein 32, Hoda Anton-Culver 34,35, Argyrios Ziogas 34, Celeste L Pearce 8,36, Chiuchen Tseng 8, Malcom Pike 8,37, Joellen M Schildkraut 1; the African American Cancer Epidemiology Study and the Ovarian Cancer Association Consortium
PMCID: PMC5913601  PMID: 29211900

Abstract

Background

Ovarian cancer incidence differs substantially by race/ethnicity, but the reasons for this are not well understood. Data were pooled from the African American Cancer Epidemiology Study (AACES) and 11 case-control studies in the Ovarian Cancer Association Consortium (OCAC) to examine racial/ethnic differences in epidemiological characteristics with suspected involvement in epithelial ovarian cancer (EOC) aetiology.

Methods

We used multivariable logistic regression to estimate associations for 17 reproductive, hormonal and lifestyle characteristics and EOC risk by race/ethnicity among 10 924 women with invasive EOC (8918 Non-Hispanic Whites, 433 Hispanics, 911 Blacks, 662 Asian/Pacific Islanders) and 16 150 controls (13 619 Non-Hispanic Whites, 533 Hispanics, 1233 Blacks, 765 Asian/Pacific Islanders). Likelihood ratio tests were used to evaluate heterogeneity in the risk factor associations by race/ethnicity.

Results

We observed statistically significant racial/ethnic heterogeneity for hysterectomy and EOC risk (P = 0.008), where the largest odds ratio (OR) was observed in Black women [OR = 1.64, 95% confidence interval (CI) = 1.34–2.02] compared with other racial/ethnic groups. Although not statistically significant, the associations for parity, first-degree family history of ovarian or breast cancer, and endometriosis varied by race/ethnicity. Asian/Pacific Islanders had the greatest magnitude of association for parity (≥3 births: OR = 0.38, 95% CI = 0.28–0.54), and Black women had the largest ORs for family history (OR = 1.77, 95% CI = 1.42–2.21) and endometriosis (OR = 2.42, 95% CI = 1.65–3.55).

Conclusions

Although racial/ethnic heterogeneity was observed for hysterectomy, our findings support the validity of EOC risk factors across all racial/ethnic groups, and further suggest that any racial/ethnic population with a higher prevalence of a modifiable risk factor should be targeted to disseminate information about prevention.

Key Messages

  • Considerable racial/ethnic differences exist in ovarian cancer incidence, yet the cause of these differences remains largely unknown.

  • The objective of this study was to examine the association between 17 reproductive, hormonal and lifestyle factors and ovarian cancer risk by race/ethnicity (Non-Hispanic Whites, Hispanics, Blacks, Asian/Pacific Islanders) using data from the African American Cancer Epidemiology Study and the Ovarian Cancer Association Consortium.

  • We observed heterogeneity by race/ethnicity in the association between hysterectomy and ovarian cancer risk (P = 0.008), where the greatest magnitude of the association was observed in Black women (OR = 1.64, 95% CI = 1.34–2.02) compared with other racial/ethnic groups.

  • Our findings support the validity of ovarian cancer risk factors among all racial/ethnic groups, and highlight the need for a greater representation of minority racial/ethnic groups in epidemiological studies of ovarian cancer to elucidate the causes of racial/ethnic differences in ovarian cancer incidence.

Introduction

Ovarian cancer incidence differs appreciably by race/ethnicity.1 Data from the Surveillance, Epidemiology, and End Results programme for 2010–14 indicate that in the USA, the age-adjusted ovarian cancer incidence rate is highest in White women (12.2 per 100 000) followed by Hispanics (10.6 per 100 000), Asian/Pacific Islanders (9.5 per 100 000), and Blacks (9.4 per 100 000).2 The causes of the observed differences in incidence are likely multifactorial, yet remain relatively unknown because of the under-representation of non-White women in epidemiological studies of ovarian cancer.

There are considerable differences in the prevalence of risk factors for ovarian cancer by race/ethnicity, which may contribute to the inter-group variation in ovarian cancer incidence rates. For example, the National Center for Health Statistics reports that Hispanic and Black women have a greater number of pregnancies,3 and National Health and Nutrition Examination Survey data suggest that the prevalence of obesity among adult women is higher for Black and Hispanic women at 58.6% and 40.7%, respectively.4 To date, only four studies5–8 have compared race- or ethnicity-specific associations in ovarian cancer. These studies mainly focused on White and Black women, yet each study had fewer than 150 Black women with ovarian cancer. Only one study reported risk factor associations among Hispanic women5 and Asian/Pacific Islanders were not included in any of these studies. To address this knowledge gap, we capitalized on existing data from the Ovarian Cancer Association Consortium (OCAC)9 and the largest case-control study of African American women with ovarian cancer, the African American Cancer Epidemiology Study (AACES),10 to examine race/ethnicity-specific associations of various characteristics known or suspected to play a role in the aetiology of epithelial ovarian cancer (EOC).

Methods

Participating studies

We included AACES and any OCAC study that collected epidemiological risk factor data and had at least 10 cases that self-reported a racial/ethnic group other than Non-Hispanic White. Table 1 provides the characteristics of the 12 population-based case-control studies contributing to this analysis.

Table 1.

Characteristics of 12 case-control studies included in the analyses, n = 27074

Race/ethnicity
Non-Hispanic White (n = 22537)
 Hispanic (n = 966)
 Black (n = 2144)
 Asian/Pacific Islander (n = 1427)
Case-control studies Acronym Location Dates of interview Cases Controls Cases Controls Cases Controls Cases Controls
African American Cancer Epidemiology Study10 AACES USA 2011–16 0 0 0 0 595 743 0 0
Australian Ovarian Cancer and Australian Cancer Study41 AUS Australia 2002–05 1268 1401 0 0 0 0 37 27
Connecticut Ovarian Cancer Study42 CON USA 1999–2003 368 493 6 17 8 34 3 6
Diseases of the Ovary and their Evaluation Study43,44 DOV USA 2002–09 1012 1679 33 40 11 29 55 47
Hawaii Ovarian Cancer Case-Control Study45,46 HAW USA 1993–008 268 383 0 0 0 0 222 355
Hormones and Ovarian Cancer Prediction Study47 HOP USA 2003–09 700 1752 2 6 24 29 4 1
North Carolina Ovarian Cancer Study48,49 NCO USA 1999–2008 777 832 6 11 112 180 7 5
New England Case-Control Study of Ovarian Cancer50,51 NEC USA 1992–2008 1427 2030 6 10 20 23 24 10
Ovarian Cancer in Alberta and British Columbia Study OVA Canada 2002–12 1087 2271 0 0 1 3 55 80
Genetic Epidemiology of Ovarian Cancer52 STA USA 1997–2002 325 349 49 62 16 66 73 73
University of California, Irvine Ovarian Cancer Study53 UCI USA 1995–2005 339 495 32 21 0 0 21 8
Los Angeles County Case-Control Studies of Ovarian Cancer5,54,55 USC USA 1993–2010 1347 1934 299 366 124 126 161 153
Totals 8918 13619 433 533 911 1233 662 765
Open in a new tab

Epidemiologic variables

Individual-level epidemiological data from each study were pooled and harmonized for the following established or suggested EOC risk factors: parity (0, 1, 2, ≥3 live births); duration of oral contraceptive use (never use, <5 years, ≥5 years); first-degree family history of ovarian or breast cancer (yes, no); recent body mass index (BMI) (normal weight: <25 kg/m2, overweight: 25–29.9 kg/m2, obese: ≥30 kg/m2); hysterectomy at least 1 year before the reference date (interview date for controls or diagnosis date for cases) (yes, no); tubal ligation at least 1 year before the reference date (yes, no); age at menarche (<12, 12–13, ≥14 years); history of endometriosis (yes, no); education (<high school, high school graduate/higher education); body powder exposure (never use, any regular genital use, only non-genital use); breastfeeding (yes, no); regular use of aspirin (yes, no), acetaminophen (yes, no), non-steroidal anti-inflammatory drugs (NSAIDs) (yes, no); hormone therapy (yes, no); estrogen-only hormone therapy (yes, no). The following variables were not available or set to missing for certain sites (acronyms: see Table 1): body powder exposure (CON, OVA, STA, UCI); endometriosis (OVA, STA); analgesic medications (OVA, STA); BMI (OVA, STA); tubal ligation (UCI); breastfeeding; (UCI) and estrogen-only hormone therapy use (AUS, CON, STA). For parity, data on the number of live births was unavailable in CON so the number of full-term births was used as a proxy. As in previous OCAC manuscripts, we defined recent BMI as BMI one year before the reference date except for CON, DOV and HAW, where 5 years before the reference date was used.11 Analgesic medication use was defined as medication use at least once per week.12 Three sites had missing data on analgesic medications for specific ascertainment periods: HAW did not collect data on analgesic medications between 1993 and 1999, NCO did not collect data on aspirin use for the first 2 years of the study and USC only provided data on analgesic medications collected during 2000–05.

Statistical analysis

All analyses were performed using SAS 9.4 (Cary, NC). Using multivariable logistic regression, we estimated odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between each characteristic and EOC risk separately for each self-reported racial/ethnic group: Non-Hispanic White, White Hispanic (herein, referred to as Hispanic), Black and Asian/Pacific Islander. Models were adjusted for age, study site and well-established risk factors with complete data across all studies: parity, duration of oral contraceptive use and family history of ovarian/breast cancer. To estimate the association between breastfeeding and EOC risk, data were restricted to parous women, and for the association between hormone therapy use (overall and estrogen only) and EOC risk, data were restricted to postmenopausal women. Within the study population, we assessed racial/ethnic heterogeneity in each risk factor association using a likelihood ratio test that compared a model where the OR for the risk factor of interest varied by race/ethnicity (Non-Hispanic Whites as the referent group) versus a model where the OR for the risk factor of interest did not vary by race/ethnicity. We used the false discovery rate13 to correct for multiple comparisons.

To assess between-study heterogeneity, we used the metaanal SAS macro.14 Within each racial/ethnic group, study-specific ORs were combined into a pooled estimate for each risk factor association, which was weighted by the reciprocal of the combined study-specific variance plus the across-studies variance under a random effects model.15,16 Due to sparse data for Hispanics, Blacks and Asian/Pacific Islanders in some studies, we could only reliably estimate study-specific associations and assess study heterogeneity for Non-Hispanic Whites.

Given the heterogeneity of ovarian cancer, we repeated the analyses restricted only to the most common histotype, high-grade serous ovarian cancer (HGSOC).17,18 We defined HGSOC as any patient diagnosed with serous histology and tumour grade ≥2 (n = 5049). As the majority of serous EOC is high-grade, serous cases with missing grade were classified as HGSOC (n = 1162). We re-classified cases with endometrioid histology and grade ≥2 as HGSOC (n = 979) because the majority are actually HGSOC.19,20 Also, undifferentiated/poorly differentiated EOC with unspecified histology and grade ≥2 was considered HGSOC (n = 183). Therefore, 7,373 HGSOC cases were analysed.

In an effort to summarize the impact of differences in the distribution of risk factors for EOC by race/ethnicity on EOC incidence, we used a method described in Risch et al.21 to calculate the average OR among the controls within each racial/ethnic group. We assumed that the controls comprised a representative sample of subjects within each racial/ethnic group, and the average OR within each racial/ethnic group was estimated according to the race/ethnicity-specific covariates of a model of established risk factors for EOC (parity, oral contraceptive use, family history of ovarian or breast cancer, endometriosis and tubal ligation) with additional adjustment for age and study site (See Supplementary Methods, available as Supplementary data at IJE online). The average OR represents a mean OR across the control distribution of the modelled covariates of interest, with respect to a fully unexposed individual, and as adjusted for other covariates as needed. In this case, a fully unexposed individual is a woman that was ever pregnant, used oral contraceptives, does not have a family history of ovarian or breast cancer, does not have a history of endometriosis, and had a tubal ligation.

Because self-reported race/ethnicity and genetic ancestry may disagree somewhat,22 we determined the concordance between self-identified race/ethnicity and genetically inferred ancestry among women who had available genetic data (5866 cases, 8754 controls). As described in Amos, et al.,23 the FastPop R package24 was used to estimate the proportion of intercontinental ancestry using 2318 ancestry informative markers with minor allele frequencies ≥0.05. The proportion of European, African and Asian ancestry was estimated for each individual, summing to 100%. Women with a proportion of >80% European ancestry were considered European and women with >50% African and >50% Asian ancestry were considered African and Asian, respectively. The concordance for Hispanics was not evaluated because the term ‘Hispanic’ is more indicative of ethnicity and Hispanics are typically an admixture of European, Native American and African ancestry.25,26

Results

We identified 10 924 women with invasive EOC and 16 150 controls with available data on race/ethnicity and adjusted covariates (age, study site, parity, duration of oral contraceptive use and family history of ovarian or breast cancer) (Table 1). The prevalence of most characteristics differed considerably by race/ethnicity (Table 2). Hispanic and Black women were more likely to have three or more births, whereas Asian/Pacific Islanders less frequently reported oral contraceptive use. A striking difference in BMI was observed; among controls, 51% of Black women were obese compared with 25% of Hispanics, 21% of Non-Hispanic Whites and only 5% of Asian/Pacific Islanders. Black women were more likely to report use of body powders and to have had a hysterectomy and a tubal ligation. Hispanic women reported lower levels of educational attainment, with 26% of Hispanic controls having less than a high school education. Breastfeeding was more common among Asian/Pacific Islanders and was least prevalent among Black women, and Non-Hispanic White women were more likely than other racial/ethnic groups to report use of hormone therapy. The most common histotype and tumour stage was HGSOC and distant stage, respectively. The distribution of histotype and stage was similar among racial/ethnic groups except for Asian/Pacific Islanders who were more frequently diagnosed with clear cell EOC and less frequently diagnosed with HGSOC and distant stage disease.

Table 2.

Frequency distribution of participant characteristics by racial/ethnic group

Participant characteristics Race/ethnicity
Non-Hispanic White
 Hispanic
 Black
 Asian/Pacific Islander
Cases (n = 8918)
 Controls (n = 13619)
 Cases (n = 433)
 Controls (n = 533)
 Cases (n = 911)
 Controls (n = 1233)
 Cases (n = 662)
 Controls (n = 765)
n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%)
Age
 18–50 years 2242 (25) 4355 (32) 155 (36) 286 (54) 245 (27) 439 (36) 310 (47) 344 (45)
 51–60 years 2923 (33) 4385 (32) 142 (33) 147 (27) 294 (32) 430 (35) 162 (25) 197 (26)
 ≥61 years 3753 (42) 4879 (36) 136 (31) 100 (19) 372 (41) 364 (29) 190 (29) 224 (30)
Parity
 0 live births 2230 (25) 2240 (16) 84 (19) 73 (14) 164 (18) 166 (13) 220 (33) 151 (20)
 1 live birth 1250 (14) 1797 (13) 65 (15) 73 (14) 163 (18) 237 (19) 118 (18) 128 (17)
 2 live births 2689 (30) 4802 (35) 93 (21) 139 (26) 209 (23) 339 (28) 167 (25) 246 (32)
 ≥3 live births 2749 (31) 4790 (35) 191 (44) 248 (46) 375 (41) 491 (40) 157 (24) 240 (31)
Duration of oral contraceptive use
 Never used 3521 (40) 3623 (27) 224 (52) 202 (38) 311 (34) 306 (25) 437 (66) 385 (50)
 <5 years 3132 (35) 4819 (35) 139 (32) 198 (37) 363 (40) 526 (43) 165 (25) 232 (30)
 ≥5 years 2265 (25) 5177 (38) 70 (16) 133 (25) 237 (26) 401 (32) 60 (9) 148 (19)
Family history of ovarian/breast cancera
 No 7065 (79) 11409 (84) 363 (84) 476 (89) 677 (74) 1041 (84) 589 (89) 680 (89)
 Yes 1853 (21) 2210 (16) 70 (16) 57 (11) 234 (26) 192 (16) 73 (11) 85 (11)
Recent BMIb
 Normal (<25 kg/m2) 3518 (48) 5458 (50) 140 (37) 201 (43) 161 (18) 238 (21) 382 (73) 445 (74)
 Overweight (25–29.9 kg/m2) 2117 (29) 3159 (29) 125 (33) 151 (32) 243 (27) 325 (28) 108 (20) 126 (21)
 Obese (≥30 kg/m2) 1739 (23) 2328 (21) 113 (30) 116 (25) 485 (55) 594 (51) 35 (7) 33 (5)
 Missing 132 54 6 3 5 7 9 8
Hysterectomyc
 No 7176 (81) 11304 (83) 362 (84) 478 (90) 599 (66) 957 (78) 595 (90) 707 (93)
 Yes 1705 (19) 2273 (17) 70 (16) 54 (10) 310 (34) 273 (22) 66 (10) 57 (7)
 Missing 37 42 1 1 2 3 1 1
Tubal ligationd
 No 6983 (82) 9732 (75) 332 (83) 383 (75) 614 (68) 747 (61) 565 (88) 620 (82)
 Yes 1536 (18) 3310 (25) 69 (17) 127 (25) 294 (32) 483 (39) 76 (12) 136 (18)
 Missing 60 82 0 2 3 3 0 1
Age at menarche
 <12 years 1824 (21) 2779 (21) 104 (24) 143 (27) 223 (25) 329 (27) 124 (19) 163 (21)
 12–13 years 4881 (55) 7477 (55) 220 (51) 245 (46) 457 (50) 593 (48) 315 (48) 365 (48)
 ≥14 years 2134 (24) 3265 (24) 108 (25) 144 (27) 230 (25) 311 (25) 214 (33) 234 (31)
 Missing 79 98 1 1 1 0 9 3
History of endometriosis
 No 6675 (89) 10115 (92) 359 (94) 447 (95) 809 (91) 1113 (96) 461 (87) 566 (93)
 Yes 790 (11) 845 (8) 21 (6) 22 (5) 81 (9) 49 (4) 67 (13) 44 (7)
 Missing 41 39 4 2 4 2 6 2
Education
 Less than high school 839 (10) 790 (6) 115 (35) 103 (26) 129 (15) 147 (12) 70 (11) 67 (9)
 ≥High school graduate 7473 (90) 11884 (94) 210 (65) 290 (74) 734 (85) 1048 (88) 573 (89) 675 (91)
 Missing 606 945 108 140 48 38 19 23
Body powder use
 Never use 3273 (53) 5447 (59) 220 (64) 311 (73) 354 (42) 537 (50) 313 (75) 366 (74)
 Any genital use 1876 (30) 2227 (24) 60 (18) 65 (15) 344 (40) 335 (31) 36 (9) 38 (8)
 Body/non-genital use 1029 (17) 1500 (16) 61 (18) 50 (12) 150 (18) 203 (19) 70 (17) 90 (18)
 Missing 621 837 5 7 38 55 91 104
Breastfeedinge
 No 2831 (44) 3817 (35) 170 (52) 219 (50) 500 (67) 650 (61) 123 (29) 143 (24)
 Yes 3601 (56) 7140 (65) 154 (48) 220 (50) 247 (33) 417 (39) 306 (71) 465 (76)
Aspirin usef
 No 4173 (80) 6132 (79) 145 (82) 172 (88) 604 (85) 878 (85) 229 (87) 231 (81)
 Yes 1051 (20) 1625 (21) 32 (18) 23 (12) 110 (15) 155 (15) 35 (13) 53 (19)
 Missing 2282 3242 207 276 180 131 270 328
Acetaminophen usef
 No 4316 (80) 6358 (80) 155 (87) 169 (87) 621 (84) 909 (86) 234 (88) 245 (86)
 Yes 1062 (20) 1554 (20) 23 (13) 25 (13) 116 (16) 148 (14) 32 (12) 39 (14)
 Missing 2128 3087 206 277 157 107 268 328
NSAID usef
 No 3881 (73) 5655 (72) 125 (73) 148 (79) 546 (74) 777 (74) 221 (85) 246 (88)
 Yes 1428 (27) 2164 (28) 47 (27) 39 (21) 191 (26) 280 (26) 39 (15) 34 (12)
 Missing 2197 3180 212 284 157 107 274 332
Hormone therapy useg
 No 3163 (49) 4122 (47) 171 (61) 160 (61) 474 (73) 574 (74) 211 (58) 211 (54)
 Yes 3266 (51) 4703 (53) 110 (39) 103 (39) 171 (27) 202 (26) 153 (42) 180 (46)
 Missing 37 48 2 1 5 2 0 0
Any estrogen-only therapy useg
 No 2480 (65) 3523 (65) 158 (72) 143 (77) 468 (79) 554 (83) 187 (76) 186 (71)
 Yes 1311 (35) 1865 (35) 61 (28) 43 (23) 121 (21) 114 (17) 59 (24) 76 (29)
 Missing 1212 2109 37 43 49 63 71 97
Histology
 Serous
 High-grade seroush 6060 (68) 303 (70) 669 (76) 341 (52)
 Low-grade serous 288 (3) 23 (5) 27 (3) 4 (1)
 Mucinous 455 (5) 38 (9) 52 (6) 77 (12)
 Endometrioid (low-grade) 532 (6) 18 (4) 26 (3) 45 (7)
 Clear cell 609 (7) 17 (4) 28 (3) 120 (18)
 Mixed 296 (3) 3 (1) 11 (1) 13 (2)
 Other or unspecified epithelial 660 (7) 31 (7) 69 (8) 61 (9)
 Missing 164 0 29 1
Stage
 Localized 1364 (18) 81 (19) 147 (18) 195 (33)
 Regional 1307 (17) 67 (16) 130 (15) 126 (21)
 Distant 5053 (65) 279 (65) 558 (67) 275 (46)
 Missing 107 6 75 11
Open in a new tab

Number of participants with missing data was determined from only those sites that provided data for that covariate. The following variables were not available or considered missing for certain sites: body powder exposure (CON, OVA, STA, UCI); endometriosis (OVA, STA); analgesic medications (OVA, STA); BMI (OVA, STA); tubal ligation (UCI); breastfeeding (UCI); estrogen-only hormone therapy use (AUS, CON, STA); stage (OVA).

aFamily history of ovarian/breast cancer in a first-degree relative.

bRecent BMI is defined as BMI 1 year before reference date (interview date for controls and diagnosis date for cases) for AAS, AUS, HOP, NCO, NEC, UCI and USC or 5 years before reference date for CON, DOV and HAW.

cHysterectomy that occurred at least 1 year before the reference date.

dTubal ligation that occurred at least 1 year before the reference date.

eBreastfeeding was assessed among women who had one or more live births.

fAnalgesic medication use was defined as use at least once a week. Three sites had missing data on analgesic medications for specific ascertainment periods: HAW did not collect data on analgesic medications between 1993 and 1999, NCO did not collect data on aspirin use for the first 2 years of the study and USC only provided data on analgesic medications collected during 2000–05.

gHormone therapy use was assessed among postmenopausal women.

hHGSOC was defined as any patient diagnosed with serous histology and tumour grade ≥2 or missing, endometrioid histology and grade ≥2, and undifferentiated/poorly differentiated EOC with unspecified histology and grade ≥2.

Estimated ORs and 95% CIs for the association between each characteristic and EOC risk are shown in Table 3, stratified by racial/ethnic group. Risk factor associations were similar across race/ethnicity for most exposures. However, we observed statistically significant heterogeneity by race/ethnicity in the OR for hysterectomy [false discovery rate (FDR) corrected P = 0.008], where the association was strongest among Black women (OR = 1.64, 95% CI = 1.34–2.02) and appreciably different from that among Non-Hispanic White women (OR = 1.13, 95% CI = 1.05–1.22). Although not statistically significant after FDR correction, associations for parity, family history of ovarian or breast cancer, and endometriosis also varied by race/ethnicity. An inverse association with parity was observed for each racial/ethnic group, but the magnitude of the association was strongest among Asian/Pacific Islanders (≥3 live births: OR = 0.38, 95% CI = 0.28–0.54). The association with family history of ovarian or breast cancer was more pronounced among Black and Hispanic women (OR = 1.77, 95% CI = 1.42–2.21 and OR = 1.63, 95% CI = 1.10–2.43, respectively) compared with Non-Hispanic White women (OR = 1.35, 95% CI = 1.25–1.45), whereas no association was observed in Asian/Pacific Islanders. History of endometriosis was positively associated with EOC risk in all racial/ethnic groups, with the largest OR observed in Black women (OR = 2.42, 95% CI = 1.65–3.55).

Table 3.

Estimated ORs and 95% CIs for the association between participant characteristics and invasive ovarian cancer overall and stratified by racial/ethnic group

Race/ethnicity
Non-Hispanic White (n = 22537)
 Hispanic (n = 966)
 Black (n = 2144)
 Asian/Pacific Islander (n = 1427)
Participant characteristics OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Exact Pa FDR Pa
Parity 0.04 0.16
 0 live births 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 1 live birth 0.69 (0.63–0.76) 0.83 (0.51–1.34) 0.67 (0.49–0.90) 0.65 (0.46–0.91)
 2 live births 0.53 (0.49–0.58) 0.53 (0.34–0.83) 0.57 (0.43–0.76) 0.48 (0.36–0.65)
 ≥3 live births 0.45 (0.41–0.49) 0.50 (0.33–0.74) 0.59 (0.45–0.78) 0.38 (0.28–0.54)
Duration of oral contraceptive use 0.46 0.68
 Never used 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 <5 years 0.77 (0.72–0.83) 0.77 (0.57–1.05) 0.78 (0.63–0.97) 0.64 (0.49–0.84)
 ≥5 years 0.48 (0.45–0.52) 0.56 (0.38–0.81) 0.63 (0.49–0.79) 0.38 (0.26–0.53)
Family history of ovarian/breast cancerb 0.009 0.07
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 1.35 (1.25–1.45) 1.63 (1.10–2.43) 1.77 (1.42–2.21) 1.08 (0.75–1.53)
Recent BMIc 0.95 0.95
 Normal (<25 kg/m2) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Overweight (25–29.9 kg/m2) 1.02 (0.95–1.10) 1.05 (0.75–1.48) 1.10 (0.84–1.45) 1.08 (0.80–1.47)
 Obese (≥30 kg/m2) 1.19 (1.10–1.28) 1.16 (0.81–1.66) 1.19 (0.93–1.52) 1.59 (0.94–2.68)
Hysterectomyd 0.0005 0.008
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 1.13 (1.05–1.22) 1.41 (0.94–2.12) 1.64 (1.34–2.02) 1.42 (0.95–2.12)
Tubal ligatione 0.84 0.90
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 0.80 (0.74–0.86) 0.73 (0.51–1.05) 0.81 (0.66–1.00) 0.90 (0.65–1.25)
Age at menarche 0.68 0.80
 <12 years 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 12–13 years 0.98 (0.91–1.05) 1.19 (0.86–1.65) 1.11 (0.89–1.38) 1.15 (0.86–1.55)
 ≥14 years 0.95 (0.87–1.03) 0.98 (0.68–1.43) 1.02 (0.79–1.31) 1.13 (0.81–1.56)
History of endometriosis 0.04 0.16
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 1.43 (1.29–1.59) 1.20 (0.62–2.32) 2.42 (1.65–3.55) 1.87 (1.22–2.87)
Education 0.14 0.31
 Less than high school 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 ≥High school graduate 0.70 (0.62–0.78) 0.59 (0.41–0.85) 0.94 (0.71–1.24) 0.85 (0.57–1.26)
Body powder use 0.12 0.31
 Never use 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Any genital use 1.30 (1.20–1.41) 1.41 (0.93–2.13) 1.62 (1.32–2.00) 1.02 (0.61–1.70)
 Only non–genital use 1.00 (0.91–1.11) 1.55 (1.00–2.39) 1.13 (0.87–1.46) 0.82 (0.56–1.19)
Breastfeedingf 0.23 0.46
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 0.75 (0.70–0.80) 1.21 (0.88–1.68) 0.77 (0.63–0.95) 0.76 (0.57–1.03)
Aspirin useg 0.32 0.56
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 0.93 (0.85–1.02) 1.23 (0.66–2.31) 0.91 (0.68–1.20) 0.88 (0.52–1.48)
Acetaminophen useg 0.70 0.80
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 1.02 (0.93–1.12) 0.85 (0.44–1.65) 1.21 (0.92–1.59) 1.08 (0.63–1.86)
NSAID useg 0.47 0.68
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 0.95 (0.87–1.03) 1.48 (0.85–2.57) 0.99 (0.79–1.24) 1.47 (0.85–2.54)
Hormone therapy useh 0.58 0.77
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 0.95 (0.88–1.01) 1.06 (0.73–1.55) 1.07 (0.83–1.38) 0.94 (0.69–1.28)
Any estrogen-only therapy useh 0.09 0.28
 No 1.00 (Referent) 1.00 (Referent) 1.00 (Referent) 1.00 (Referent)
 Yes 1.03 (0.94–1.13) 1.23 (0.76–1.98) 1.17 (0.87–1.58) 0.87 (0.56–1.33)
Open in a new tab

All models adjusted for age (age at diagnosis for cases or age at interview for controls), study site, parity, duration of oral contraceptive use and family history of ovarian or breast cancer in a first-degree relative.

aInteraction was assessed by including cross-product interaction terms for each risk factor and race/ethnicity (Non-Hispanic White was the referent group) in a model of all racial/ethnic groups.

bFamily history of ovarian/breast cancer in a first-degree relative.

cRecent BMI is defined as BMI 1 year before reference date (interview date for controls and diagnosis date for cases) for AAS, AUS, HOP, NCO, NEC, UCI and USC or 5 years before reference date for CON, DOV and HAW.

dHysterectomy that occurred at least 1 year before the reference date.

eTubal ligation that occurred at least 1 year before the reference date.

fBreastfeeding was assessed among women who had one or more live births.

gAnalgesic medication use was defined as use at least once a week. Three sites had missing data on analgesic medications for specific ascertainment periods: HAW did not collect data on analgesic medications between 1993 and 1999, NCO did not collect data on aspirin use for the first 2 years of the study and USC only provided data on analgesic medications collected during 2000–05.

hHormone therapy use was assessed among postmenopausal women.

Supplementary Table 1 (available as Supplementary data at IJE online) provides the estimated ORs and 95% CIs for the fixed and random effects models among Non-Hispanic Whites. Study heterogeneity was present for several characteristics (Q statistic P-value < 0.05 for duration of oral contraceptive use, recent BMI, hysterectomy, age at menarche, education, body powder exposure and NSAID use); however, the risk factor associations were similar for the fixed and random effects models and the conclusions remained the same.

The results of the analyses restricted to women diagnosed with HGSOC are shown in Supplementary Table 2 (available as Supplementary data at IJE online). In comparison with the overall results, the associations in HGSOC were weaker in magnitude for parity and endometriosis, yet stronger in magnitude for family history of ovarian or breast cancer and for body powder exposure. The magnitude and direction of the associations for all other examined risk factors were similar for EOC overall and HGSOC. Yet, racial/ethnic heterogeneity was not observed for any characteristic after correction for multiple comparisons.

For a model of established EOC risk factors (parity, oral contraceptive use, family history of ovarian or breast cancer, tubal ligation and endometriosis), the average ORs among the controls were estimated by race/ethnicity. Non-Hispanic Whites and Hispanics had the largest average ORs (OR = 1.90, 95% CI = 1.70–2.10 and OR = 1.90, 95% CI = 1.21–2.59, respectively) followed by Asian/Pacific Islanders (OR = 1.41, 95% CI = 0.84–1.97) and Blacks (OR = 1.18, 95% CI = 0.83–1.53) (data not shown).

Genetically inferred ancestry and self-reported race/ethnicity were in high concordance; 99.2% of the women who self-identified as Non-Hispanic White were of European ancestry, 96.2% of the women who identified as Black were of African ancestry and 93.2% of the women who identified as Asian/Pacific Islander were of Asian ancestry (data not shown). Defining race/ethnicity by genetic ancestry rather than self-reports had minimal to no effect on our results.

Discussion

Our pooled analysis provides the largest investigation, to date, of EOC risk factors by race/ethnicity. We evaluated 17 epidemiological risk factors, many of which have never been examined in specific racial/ethnic groups, particularly Hispanics and Asian/Pacific Islanders (e.g. analgesic medication use, education, hysterectomy). We observed appreciable differences in the prevalence of several characteristics by race/ethnicity, most notably for parity, recent BMI and education. Most of the associations were similar across race/ethnicity, but the strength of the association with hysterectomy differed by race/ethnicity although all ORs were in the same direction.

In general, our findings comparing risk factor associations by race/ethnicity are consistent with the limited number of published studies in this area.5–8 Two of these reports, Wu et al.5 and Moorman et al.6 provide results from OCAC studies included in the present manuscript, USC and NCO respectively, and are not independent from the current study. The only notable difference between Blacks and Whites was reported by Ness et al.7 for the association between breastfeeding duration and EOC risk; however, this study was small, including only 84 Black women with ovarian cancer.

We observed racial/ethnic heterogeneity for the association between hysterectomy and EOC risk, with a more pronounced association among Black women in comparison with other racial/ethnic groups. It is possible that the prevalence of benign gynaecological conditions that are indications for hysterectomy may confound this association. The incidence of uterine fibroids, a common indication for hysterectomy, is higher among Black women in comparison with Whites and contributes to a higher rate of hysterectomy in this population.27,28 However, the association between hysterectomy and risk of EOC was not in the expected direction. Epidemiological studies before 2000 suggest that women who have undergone a hysterectomy have a lower risk of EOC;29 however, along with several recent studies,6,30–32 we observed a positive association between hysterectomy and EOC risk. A meta-analysis by Jordan et al.33 speculates that a temporal shift may have occurred in this association, possibly related to changes in hormone therapy recommendations and patterns of hormone therapy use over time. Peres et al.34 evaluated this hypothesis in AACES, which was included in the present analysis, and observed an inverse association for premenopausal hysterectomy and EOC risk only among women using estrogen therapy. However, Peres et al.34 also observed an inverse association for premenopausal hysterectomy after adjustment for indications of surgery (e.g. uterine fibroids, ovarian cysts) irrespective of hormone therapy use. A further investigation of this association, with more attention to secular trends, indication of surgery and hormone therapy use, is warranted.

Some of the racial/ethnic differences in risk factor associations may be due to racial/ethnic differences in the prevalence of histotypes, which have unique risk factor patterns.35 For example, Asian/Pacific Islanders are more commonly diagnosed with clear cell EOC,36 and although reproductive risk factors are associated with EOC overall, they are more strongly associated with clear cell EOC compared with the other histotypes.35,37,38 In this study, Asian/Pacific Islanders had a higher prevalence of clear cell EOC and had a stronger association with parity in comparison with other racial/ethnic groups. However, our ability to examine race/ethnicity specific differences in the less common histotypes was hindered by insufficient power.

In the exploratory analysis of the average ORs among the controls by race/ethnicity, the CIs for the average ORs of Blacks and Non-Hispanic Whites did not overlap, indicating that there was appreciable heterogeneity between these two racial/ethnic groups. These results suggest that the distribution of established risk factors account for more of the incidence of EOC in Non-Hispanic Whites than Blacks. The average ORs also track reasonably well with EOC incidence rates by race/ethnicity, where the highest average OR and highest EOC incidence rate are in Non-Hispanic Whites.2 This analysis is dependent on the assumption that the controls from each study are representative of the underlying population within each racial/ethnic group, which may be appropriate since the controls in each of these studies were population-based controls.

Although consortial data increase the potential to examine EOC risk factor associations by race/ethnicity, such data present several challenges. This analysis included only case-control studies where the exposure information was based on self-report. A concern with self-reported data is recall bias, especially for characteristics that are difficult to report with accuracy, require subjective summarization or can be influenced by the investigator, media or similar factors. Such problematic characteristics may include body powder exposure, analgesic medication use, breastfeeding and possibly family history. Additionally, several studies did not collect information on certain covariates and data were missing at the respondent-level for some women. Missing data limited our ability to evaluate certain characteristics in further detail, such as analgesic medication use where dose and duration may impact on the association with EOC risk.12,39 Nevertheless, even with missing data, we had improved power over previously published race-specific analyses, because of the large sample size afforded by pooling AACES and OCAC studies. Some of the race-specific analyses in the non-White racial/ethnic groups were still limited by sample size, especially with respect to evaluating study heterogeneity. Another limitation stems from the OCAC data grouping all Hispanic and all Asian/Pacific Islander women into single categories, although cultural and genetic diversity exists within these groups.26,40 Such grouping may have masked potential differences in risk factor prevalence and the corresponding associations across Hispanic and Asian/Pacific Islander subgroups.

By combining AACES and OCAC studies, the current analysis provides one of the largest and most comprehensive assessments of a variety of epidemiological characteristics in EOC by race/ethnicity. Although we observed racial heterogeneity for hysterectomy, our findings support the validity of EOC risk factors across all racial/ethnic groups, and further suggest that any racial/ethnic population with a higher prevalence of a modifiable risk factor should be targeted to disseminate information about prevention. A better understanding of the contributing causes to racial/ethnic differences in EOC incidence will be achieved with the inclusion of a greater proportion of non-White races/ethnicities in future epidemiological studies of EOC and by assessing additional risk factors beyond those included in this study, such as genetic susceptibility loci, area-level measures, migration and acculturation.

Supplementary Data

Supplementary data are available at IJE online.

Funding

This work was supported by the National Institutes of Health (R01 CA142081 to AACES; R01 CA074850 and CA080742 to CON; R01 CA112523 and CA087538 to DOV; R01 CA58598, N01 CN55424 and N01 PC67001 to HAW; K07 CA080668, R01 CA95023 and P50 CA159981 to HOP; R01 CA076016 to NCO; R01 CA054419 and P50 CA105009 to NEC; R01 CA160669 to OVA; U01 CA71966, R01 CA16056 and K07 CA143047 to STA; U01 CA69417 for recruitment of controls by the Cancer Prevention Institute of California; R01 CA058860 to UCI; P01 CA17054, P30 CA14089, R01 CA61132, N01 PC67010, R03 CA113148, R03 CA115195 and N01 CN025403 to USC; and R01 CA207260 to AACES, NCO and USC); the Department of Defense (DAMD17–02–1–0669 to HOP; DAMD17–02–1–0666 to NCO; W81XWH-10–1–02802 to NEC); the Canadian Institutes of Health (MOP-86727 to OVA); the Lon V. Smith Foundation (LVS-39420 to UCI); the California Cancer Research Program (00–01389V-20170 and 2II0200 to USC); the U.S. Army Medical Research and Material Command (DAMD17–01–1–0729 to AUS); the National Health & Medical Research Council of Australia (199600 and 400281 to AUS); and the Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania, Cancer Foundation of Western Australia (Multi-State Application Numbers 191, 211 and 182 to AUS).

Supplementary Material

Supplementary Data
Click here for additional data file. (30.1KB, docx)

Acknowledgements

The authors thank all the individuals who participated in these studies as well as the researchers, clinicians and support staff who contributed to this work. The Australian Ovarian Cancer Study Management Group thanks all the clinical and scientific collaborators and the women for their contribution (AUS). The Connecticut Ovary Study group gratefully acknowledges the cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access (CON). Certain data used in the CON study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The CON study assumes full responsibility for analyses and interpretation of these data.

Conflict of interest: Patricia Moorman has received compensation for work related to litigation in regard to talc and ovarian cancer. The other authors declare no conflicts.

References

  • 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin 2016;66:7–30. [DOI] [PubMed] [Google Scholar]
  • 2. Howlader N, Noone A, Krapcho M, et al. SEER Cancer Statistics Review, 1975–2014. Bethesda, MD: National Cancer Institute, 2017. [Google Scholar]
  • 3. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Mathews TJ. National Vital Statistics Reports Births: Final Data for 2015. Hyattsville, MD: Natl. Vital Stat. Reports, 2017. [PubMed] [Google Scholar]
  • 4. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 2012;307:491–97. [DOI] [PubMed] [Google Scholar]
  • 5. Wu AH, Pearce CL, Tseng CC, Pike MC. African Americans and hispanics remain at lower risk of ovarian cancer than non-hispanic whites after considering nongenetic risk factors and oophorectomy rates. Cancer Epidemiol Biomarkers Prev 2015;24:1094–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Moorman PG, Palmieri RT, Akushevich L, Berchuck A, Schildkraut JM. Ovarian cancer risk factors in African-American and white women. Am J Epidemiol 2009;170:598–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Ness RB, Grisso JA, Klapper J, Vergona R. Racial differences in ovarian cancer risk. J Natl Med Assoc 2000;92:176–82. [PMC free article] [PubMed] [Google Scholar]
  • 8. John EM, Whittemore AS, Harris R, Itnyre J. Characteristics relating to ovarian cancer risk: collaborative analysis of seven U.S. case-control studies. Epithelial ovarian cancer in black women. Collaborative Ovarian Cancer Group. J Natl Cancer Inst 1993;85:142–47. [DOI] [PubMed] [Google Scholar]
  • 9. Berchuck A, Schildraut JM, Pearce CL, Chenevix-Trench G, Pharoah PD. Role of genetic polymorphisms in ovarian cancer susceptibility: development of an international ovarian cancer association consortium. Adv Exp Med Biol 2008;622:53–67. [DOI] [PubMed] [Google Scholar]
  • 10. Schildkraut JM, Alberg AJ, Bandera EV, et al. A multi-center population-based case-control study of ovarian cancer in African-American women: the African American Cancer Epidemiology Study (AACES). BMC Cancer 2014;14:688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Olsen CM, Nagle CM, Whiteman DC, et al. Obesity and risk of ovarian cancer subtypes: Evidence from the Ovarian Cancer Association Consortium. Endocr Relat Cancer 2013;20:251–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Trabert B, Ness RB, Lo-Ciganic W-H, et al. Aspirin, nonaspirin nonsteroidal anti-inflammatory drug, and acetaminophen use and risk of invasive epithelial ovarian cancer: a pooled analysis in the Ovarian Cancer Association Consortium. J Natl Cancer Inst 2014;106:djt431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 1995;57:289–300. [Google Scholar]
  • 14. Takkouche B, Khudyakov P, Costa-Bouzas J, Spiegelman D. Confidence intervals for heterogeneity measures in meta-analysis. Am J Epidemiol 2013;178:993–1004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Smith-Warner SA, Spiegelman D, Ritz J, et al. Methods for pooling results of epidemiologic studies: The pooling project of prospective studies of diet and cancer. Am J Epidemiol 2006;163:1053–64. [DOI] [PubMed] [Google Scholar]
  • 16. Takkouche B, Cadarso-Suárez C, Spiegelman D. Evaluation of old and new tests of heterogeneity in epidemiologic meta-analysis. Am J Epidemiol. 1999;150:206–15. [DOI] [PubMed] [Google Scholar]
  • 17. McCluggage WG. Morphological subtypes of ovarian carcinoma: a review with emphasis on new developments and pathogenesis. Pathology 2011;43:420–32. [DOI] [PubMed] [Google Scholar]
  • 18. Soslow RA. Histologic subtypes of ovarian carcinoma. Int J Gynecol Pathol 2008;27:161–74. [DOI] [PubMed] [Google Scholar]
  • 19. Gilks CB, Ionescu DN, Kalloger SE, et al. Tumor cell type can be reproducibly diagnosed and is of independent prognostic significance in patients with maximally debulked ovarian carcinoma. Hum Pathol 2008;39:1239–51. [DOI] [PubMed] [Google Scholar]
  • 20. Clarke BA, Gilks B. Ovarian carcinoma: recent developments in classification of tumour histological subtype. Can J Pathol 2011;3:33–42. [Google Scholar]
  • 21. Risch HA, Yu H, Lu L, Kidd MS. Detectable symptomatology preceding the diagnosis of pancreatic cancer and absolute risk of pancreatic cancer diagnosis. Am J Epidemiol 2015;182:26–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Mersha TB, Abebe T. Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities. Hum Genomics 2015;9:1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Amos CI, Dennis J, Wang Z, et al. The OncoArray Consortium: a Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomarkers Prev 2017;26:125–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Li Y, Byun J, Cai G, et al. FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data. BMC Bioinformatics 2016;17:122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Risch N, Burchard E, Ziv E, Tang H. Categorization of humans in biomedical research: genes, race and disease. Genome Biol 2002;3:comment2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. González Burchard E, Borrell LN, Choudhry S, et al. Latino populations: A unique opportunity for the study of race, genetics, and social environment in epidemiological research. Am J Public Healt. 2005;95:2161–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Okolo S. Incidence, aetiology and epidemiology of uterine fibroids. Best Pract Res Clin Obstet Gynaecol 2008;22:571–88. [DOI] [PubMed] [Google Scholar]
  • 28. Jacoby VL, Fujimoto VY, Giudice LC, Kuppermann M, Washington AE. Racial and ethnic disparities in benign gynecologic conditions and associated surgeries. Am J Obstet Gynecol 2010;202:514–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Rice MS, Murphy MA, Tworoger SS. Tubal ligation, hysterectomy and ovarian cancer: A meta-analysis. J Ovarian Res 2012;5:13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Ness RB, Dodge RC, Edwards RP, Baker JA, Moysich KB. Contraception methods, beyond oral contraceptives and tubal ligation, and risk of ovarian cancer. Ann Epidemiol 2011;21:188–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Jordan SJ, Green AC, Whiteman DC, et al. Serous ovarian, fallopian tube and primary peritoneal cancers: a comparative epidemiological analysis. Int J Cancer 2008;122:1598–603. [DOI] [PubMed] [Google Scholar]
  • 32. Mills PK, Riordan DG, Cress RD. Epithelial ovarian cancer risk by invasiveness and cell type in the Central Valley of California. Gynecol Oncol 2004;95:215–25. [DOI] [PubMed] [Google Scholar]
  • 33. Jordan SJ, Nagle CM, Coory MD, et al. Has the association between hysterectomy and ovarian cancer changed over time? A systematic review and meta-analysis. Eur J Cancer 2013;49:3638–47. [DOI] [PubMed] [Google Scholar]
  • 34. Peres LC, Alberg AJ, Bandera EV, et al. Premenopausal hysterectomy and risk of ovarian cancer in African American women. Am J Epidemiol. 2017;186:46–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Wentzensen N, Poole EM, Trabert B, et al. Ovarian cancer risk factors by histologic subtype: An analysis from the Ovarian Cancer Cohort Consortium. J Clin Oncol 2016;34:2888–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Fuh KC, Shin JY, Kapp DS, et al. Survival differences of Asian and Caucasian epithelial ovarian cancer patients in the United States. Gynecol Oncol 2015;136:491–97. [DOI] [PubMed] [Google Scholar]
  • 37. Tung K-H, Goodman MT, Wu AH, et al. Reproductive factors and epithelial ovarian cancer risk by histologic type: a multiethnic case-control study. Am J Epidemiol 2003;158:629–38. [DOI] [PubMed] [Google Scholar]
  • 38. Fortner RT, Ose J, Merritt MA, et al. Reproductive and hormone-related risk factors for epithelial ovarian cancer by histologic pathways, invasiveness and histologic subtypes: Results from the EPIC cohort. Int J Cancer 2015;137:1196–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Peres LC, Camacho F, Abbott SE, et al. Analgesic medication use and risk of epithelial ovarian cancer in African American women. Br J Cancer 2016;114:819–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Srinivasan S, Guillermo T. Toward improved health: Disaggregating Asian American and Native Hawaiian/Pacific Islander data. Am J Public Health 2000;90:1731–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Merritt MA, Green AC, Nagle CM, et al. Talcum powder, chronic pelvic inflammation and NSAIDs in relation to risk of epithelial ovarian cancer. Int J Cancer 2008;122:170–76. [DOI] [PubMed] [Google Scholar]
  • 42. Risch HA, Bale AE, Beck PA, Zheng W. PGR +331 A/G and increased risk of epithelial ovarian cancer. Cancer Epidemiol Biomarkers Prev 2006;15:1738–41. [DOI] [PubMed] [Google Scholar]
  • 43. Rossing MA, Cushing-Haugen KL, Wicklund KG, Doherty JA, Weiss NS. Menopausal hormone therapy and risk of epithelial ovarian cancer. Cancer Epidemiol Biomarkers Prev 2007;16:2548–56. [DOI] [PubMed] [Google Scholar]
  • 44. Bodelon C, Cushing-Haugen KL, Wicklund KG, Doherty JA, Rossing MA. Sun exposure and risk of epithelial ovarian cancer. Cancer Causes Control 2012;23:1985–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Goodman MT, Lurie G, Thompson PJ, McDuffie KE, Carney ME. Association of two common single-nucleotide polymorphisms in the CYP19A1 locus and ovarian cancer risk. Endocr Relat Cancer 2008;15:1055–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Lurie G, Wilkens LR, Thompson PJ, et al. Combined oral contraceptive use and epithelial ovarian cancer risk. Epidemiology. 2008;19:237–43. [DOI] [PubMed] [Google Scholar]
  • 47. Lo-Ciganic W-H, Zgibor JC, Bunker CH, Moysich KB, Edwards RP, Ness RB. Aspirin, nonaspirin nonsteroidal anti-inflammatory drugs, or acetaminophen and risk of ovarian cancer. Epidemiology 2012;23:311–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Schildkraut JM, Iversen ES, Wilson MA, et al. Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer. PLoS One 2010;5:4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Schildkraut JM, Moorman PG, Bland AE, et al. Cyclin E overexpression in epithelial ovarian cancer characterizes an etiologic subgroup. Cancer Epidemiol Biomarkers Prev 2008;17:585–93. [DOI] [PubMed] [Google Scholar]
  • 50. Terry KL, Vivo I, De, Titus-Ernstoff L, Shih MC, Cramer DW. Androgen receptor cytosine, adenine, guanine repeats, and haplotypes in relation to ovarian cancer risk. Cancer Res 2005;65:5974–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Merritt MA, Pari M, De, Vitonis AF, Titus LJ, Cramer DW, Terry KL. Reproductive characteristics in relation to ovarian cancer risk by histologic pathways. Hum Reprod 2013;28:1406–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. McGuire V, Felberg A, Mills M, et al. Relation of contraceptive and reproductive history to ovarian cancer risk in carriers and noncarriers of BRCA1 gene mutations. Am J Epidemiol 2004;160:613–18. [DOI] [PubMed] [Google Scholar]
  • 53. Ziogas A, Gildea M, Cohen P, et al. Cancer risk estimates for family members of a population-based family registry for breast and ovarian cancer. Cancer Epidemiol Biomarkers Prev 2000;9:103–11. [PubMed] [Google Scholar]
  • 54. Pike MC, Pearce CL, Peters R, Cozen W, Wan P, Wu AH. Hormonal factors and the risk of invasive ovarian cancer: A population-based case-control study. Fertil Steril 2004;82:186–95. [DOI] [PubMed] [Google Scholar]
  • 55. Wu AH, Pearce CL, Tseng C-C, Templeman C, Pike MC. Markers of inflammation and risk of ovarian cancer in Los Angeles county. Int J Cancer 2009;124:1409–15. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data
Click here for additional data file. (30.1KB, docx)

Articles from International Journal of Epidemiology are provided here courtesy of Oxford University Press

RESOURCES