Razaq L, Dhali A, Maity R, Faisal AR, Hafeez AS, Zaman A, Humayun MA, Faizan M, Shahid M, Majeed M, Singh P. Demographic trends in mortality due to ovarian cancer in the United States, 1999-2020. World J Clin Oncol 2025; 16(6): 108393 [PMID: 40585843 DOI: 10.5306/wjco.v16.i6.108393]
Corresponding Author of This Article
Arkadeep Dhali, Academic Unit of Gastroenterology, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Herries Road, Sheffield S5 7AU, United Kingdom. arkadipdhali@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Laiba Razaq, Mavra Shahid, Mamoona Majeed, Department of Internal Medicine, Akhtar Saeed Medical and Dental College, Lahore 54000, Pakistan
Arkadeep Dhali, Academic Unit of Gastroenterology, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Sheffield S5 7AU, United Kingdom
Rick Maity, Department of General Medicine, Institute of Post Graduate Medical Education and Research, Kolkata 700020, India
Abdul Rafae Faisal, Ali Shan Hafeez, Asad Zaman, Department of Internal Medicine, CMH Multan Institute of Medical Sciences, Multan 59070, Pakistan
Mohammad Abdullah Humayun, Department of Internal Medicine, Shalamar Institute of Health Sciences, Lahore 54840, Pakistan
Muhammad Faizan, Department of Internal Medicine, Hamad Medical Corporation, Doha 3050, Qatar
Pramod Singh, Department of Internal Medicine, Barhabise Primary Health Care Centre, Barhabise 45302, Nepal
Author contributions: Razaq L and Dhali A conceptualized the article and contributed equally as co-first authors. Razaq L, Dhali A, and Maity R conducted literature review; Faisal AR, Hafeez AS, Zaman A, Humayun MA, Faizan M, Shahid M, and Majeed M collected and curated data; Singh P supervised the work and wrote the revised manuscript; Razaq L, Dhali A, Maity R, Faisal AR, Hafeez AS, Zaman A, Humayun MA, Faizan M, Shahid M, and Majeed M wrote the primary manuscript. All authors have read and approved the final manuscript.
Institutional review board statement: This is a study from a publicly available dataset (https://wonder.cdc.gov/). Therefore, institutional review board approval is not applicable.
Informed consent statement: This is a study from a publicly available dataset (https://wonder.cdc.gov/). Therefore, informed consent is not applicable.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The raw data required to reproduce the above findings are available to download from https://wonder.cdc.gov/. The processed data required to reproduce the above findings are available to download from https://zenodo.org/records/14562503.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arkadeep Dhali, Academic Unit of Gastroenterology, Sheffield Teaching Hospitals NHS Foundation Trust, Northern General Hospital, Herries Road, Sheffield S5 7AU, United Kingdom. arkadipdhali@gmail.com
Received: April 14, 2025 Revised: April 25, 2025 Accepted: May 23, 2025 Published online: June 24, 2025 Processing time: 68 Days and 19.4 Hours
Abstract
BACKGROUND
Ovarian carcinoma has the highest mortality rate among all gynecological cancers. Several reproductive and hormonal risk factors, including early menarche, late menopause, limited use of oral contraceptives, and a low pregnancy rate, have been identified as contributors to the increased susceptibility to ovarian cancer. Advancements in cancer therapy over the past century, including the emergence of precision oncology, underscore the importance of early detection and tailored interventions, factors particularly critical in ovarian cancer, where late-stage diagnosis remains a persistent barrier to survival. This challenge is compounded by the lack of a universally endorsed screening program, resulting in late-stage identification and widespread metastasis.
AIM
To evaluate demographic differences in ovarian cancer-related mortality from 1999 to 2020 among adult females aged ≥ 25 years within the United States.
METHODS
Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was used to collect de-identified death certificate data for malignant neoplasm of the ovaries related deaths in female adults aged 25 years and older from the year 1999 to 2020. Crude mortality rates and age-adjusted mortality rates (AAMRs) per 100000 people were calculated. Join point regression program was used to assess annual percent changes in mortality trends, with statistical significance set at P value < 0.05.
RESULTS
Between 1999 and 2020, 337619 deaths due to ovarian cancer occurred among United States females aged 25 to > 85. The AAMR decreased from 14.62 in 1999 to 10.15 in 2020, with significant declines across various demographics. The AAMRs were highest among non-Hispanic White women, i.e., 13.53. Based on region, they were the highest in the Northeast (13.06) and Midwest (12.94). The steepest decline was observed in metropolitan areas as compared to nonmetropolitan ones. The study highlights significant progress in reducing ovarian cancer mortality across age, race/ethnicity, and geographic regions during this period.
CONCLUSION
The mortality trends for ovarian carcinoma patients showed an overall decrease, with the highest mortality rates observed among older individuals (65 to > 85 years) and non-Hispanic Whites. These disparities underscore the need for equitable healthcare access and targeted policy interventions.
Core Tip: This epidemiological study analyzed ovarian cancer-related mortality trends in the United States from 1999 to 2020 using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database. Findings revealed a significant decline in age-adjusted mortality rates, dropping from 14.62 per 100000 in 1999 to 10.15 per 100000 in 2020. The greatest improvements occurred among non-Hispanic White women, in metropolitan areas, and within the Northeast and Midwest regions. Despite overall progress, geographic and demographic disparities persist, underscoring the need for targeted preventive strategies and equitable healthcare interventions to further decrease ovarian cancer mortality.
Citation: Razaq L, Dhali A, Maity R, Faisal AR, Hafeez AS, Zaman A, Humayun MA, Faizan M, Shahid M, Majeed M, Singh P. Demographic trends in mortality due to ovarian cancer in the United States, 1999-2020. World J Clin Oncol 2025; 16(6): 108393
Among gynecological cancers, ovarian cancer ranks third in terms of mortality rates[1]. Data indicates an 84.2% rise in the global ovarian cancer mortality rates from 1990 to 2017. In 2019, there were a reported 294422 new cases of ovarian cancer, resulting in 198412 deaths[1]. In 2020, epithelial ovarian carcinoma emerged as the seventh most frequently diagnosed cancer among women worldwide, bearing the highest mortality rate among all gynecological cancers[2]. Advancements in cancer therapy over the past century, spanning surgery, chemotherapy, radiotherapy, and targeted treatments, have significantly improved outcomes across various malignancies[3]. The emergence of precision oncology, including targeted inhibitors, immunotherapy, antibody drug conjugates, and biomarker-driven treatments, underscores the importance of early detection and tailored interventions, factors particularly critical in ovarian cancer, where late-stage diagnosis remains a persistent barrier to survival[3,4]. Several reproductive and hormonal risk factors, including early menarche, late menopause, limited use of oral contraceptives, and low pregnancy rates, contribute to increased susceptibility to ovarian cancer[2]. The survival rates in ovarian cancer are linked to age, a pivotal demographic factor influencing outcomes in this disease[5]. Discovering ovarian cancer in approximately 60% of women at an advanced stage emphasizes the difficulty in treatment, with an overall 5-year survival rate of 49%[6]. This challenge is compounded by the lack of a screening program, resulting in late-stage identification and widespread metastasis, underscoring the critical necessity for enhanced early detection strategies[7,8]. However, efforts to develop robust screening tools are complicated by inherent biases in bulk data analyses, such as survivor bias, tumor heterogenity, and bias in result interpretation, which may distort risk associations and limit generalizability[9]. Further, translational discordance between biomarkers (e.g., mRNA-protein expression discrepancies) introduces additional complexity in identifying reliable screening targets, as highlighted in multi-omics research[10]. A striking observation is the significant mortality associated with ovarian cancer in African populations, possibly linked to social determinants of health. This disparity underscores the need for a comprehensive understanding of the factors influencing ovarian cancer outcomes and targeted interventions to address the specific challenges faced by diverse populations[6].
Despite advancements in treatment approaches such as extensive surgical cytoreduction and newer adjuvant therapies, the overall survival rates for ovarian cancer remain dishearteningly low at 40% for stage III and 20% for stage IV[11]. Furthermore, the prognosis for recurrent epithelial ovarian carcinoma is bleak, underscoring the limited efficacy of current treatment options in improving outcomes for patients facing disease recurrence[11]. Therefore, this study aimed to evaluate demographic and regional differences in ovarian cancer-related mortality in a retrospective observational approach from 1999-2020 among adult females of ≥ 25 years of age within the United States. We seek to examine changes in ovarian cancer-related morbidity rates over time and variations across different regions. Additionally, it is to identify demographic factors, such as age, gender, and race, associated with an increased risk of ovarian cancer-related morbidity and explore significant shifts in ovarian cancer epidemiology.
MATERIALS AND METHODS
Study setting and population
This retrospective cohort study utilized death certificate data extracted from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database and analyzed from January 1, 1999, through December 31, 2020, for longitudinal trends in ovarian cancer-related mortality in adult females aged 25 years and above using the International Statistical Classification of Diseases and Related Health Problems-10th Revision (ICD-10) code for ovarian cancer, i.e., C56. The same ICD code has been previously used to identify ovarian cancer in administrative databases[12,13].
This dataset, which comprises information regarding cause of death from death certificates from all the 50 states and the District of Columbia, has been previously utilized in a number of studies to identify trends in mortality of genitourinary or gynecological diseases and neoplasms. The Multiple Cause of Death Public Use record death certificates were examined to select ovarian cancer-related deaths, which were identified as those with this cancer reported on the death certification as a contributing cause of death. Focusing on adult females aged 25 years or older at the time of death, this study categorizes them into ten-year age groups. A comparable age cutoff has been used by previous studies to define adult females[14,15]. This study did not require approval from the local institutional review board as it made use of a publicly available government-issued dataset. Additionally, it adhered to the STROBE guidelines for reporting. The prerequisite for informed consent was waived.
Data extraction
From the CDC WONDER database, the following data were extracted: Female population size, year, location of death, demographics, urban-rural classification, region, and states. Demographic data comprised age, race/ethnicity, and location of death including medical facilities (outpatient, inpatient, emergency room, death on arrival, or status unknown), home, hospice, and nursing home/long-term care facility. Race/ethnicity consisted of the following categories: Non-Hispanic (NH) White, NH Black or African American, Hispanic or Latino, NH American Indian or Alaska Native, and NH Asian or Pacific Islander. These data are based upon information provided on death certificates and have been used in previous analyses of the WONDER database[16]. As per the National Center for Health Statistics Urban-Rural Classification Scheme, the female population was dichotomized into two categories based on the 2013 United States census classification: Urban [large metropolitan area (population ≥ 1 million), medium/small metropolitan area (population 50000-999999)] and rural (population < 50000)[17]. Regions were categorized as Northeast, Midwest, South, and West based on the United States Census Bureau’s definitions[17].
Statistical analysis
To analyze national mortality trends in ovarian cancer, crude and age-adjusted mortality rates (AAMRs) per 100000 female population were computed with 95% confidence intervals (CIs) from 1999 to 2020 and stratified by year, race/ethnicity, census region, state, and 2013 urbanization status. Crude mortality rates were derived by dividing the number of fatalities from ovarian cancer by the matching United States population for the year. AAMRs were obtained by homogenizing ovarian cancer-related fatalities to the United States population in 2000[17]. The Joinpoint Regression Program (Joinpoint version 4.9.0.0, National Cancer Institute) was used to measure national annual trends in ovarian cancer-related mortality by computing the annual percent change (APC) with 95%CI in AAMR[18]. Using suitable log-linear regression models, this program identifies significant temporal variations in AAMR. APCs were regarded as either increasing or decreasing depending on whether the gradient characterizing the change in mortality was significantly different from zero using 2-tailed t-testing. A P value < 0.05 was considered statistically significant.
RESULTS
The total number of deaths in females (aged 25 and above) due to malignant neoplasm of ovary from the year 1999 to 2020 was 337619 (Table 1). Based on location, 29% of these deaths occurred in medical facility, 13% occurred in long term care/nursing home facility, 8% in hospice while 41% of the deaths were reported to have been occurred at home (Table 2).
Table 1 Ovarian carcinoma-related deaths, stratified by race, in females in the United States, 1999 to 2020.
The AAMR of malignant ovarian neoplasm at the start of study period was 14.62 which remained stable till 2003 and reached 14.7 (APC: 0.41, 95%CI: -0.48 to 1.79). From 2003 onwards there was significant drop in AAMR such that it reached 10.15 by the end of study period in 2020 (APC: -2.28, 95%CI: -2.39 to -2.16) (Figure 1, Tables 3 and 4).
Malignant ovarian neoplasm-related mortality stratified by age groups
The old aged (65 to > 85 years) females consistently showed the highest AAMR (total AAMR: 42.41, 95%CI: 42.23-42.59) followed by middle aged (45-64 years) (total AAMR: 11.09, 95%CI: 11.02-11.16) and young aged (25-44 years) females (total AAMR: 1.16, 95%CI: 1.14-1.19). Both the young aged (25-44 years) and middle aged (45-64 years) females exhibited similar significant downward trend continuously in AAMR from 1999 to 2020. The old aged (65 to > 85 years) group differed from other such that the AAMR showed an increase from 1999 to 2001 initially followed by a downward trend till 2007. From 2007 to 2020 there was a significant dramatic decrease in AAMR, and it reached 33.01 by 2020 (APC: -2.88, 95%CI: -3.12 to -2.67) (Figure 2, Tables 3 and 5).
Figure 2 Ovarian carcinoma-related age-adjusted mortality rates per 1000000, stratified by age groups in the United States, 1999 to 2020.aP value < 0.05, statistically significant annual percent change values. APC: Annual percent change; CI: Confidence interval.
Table 5 Ovarian carcinoma-related age-adjusted mortality rates per 100000, stratified by age groups in females in the United States, 1999 to 2020.
Malignant ovarian neoplasm-related mortality stratified by race
NH White women showed the highest AAMR, followed by NH Black women, NH American Indian or Alaska Native women, Hispanic or Latina women, and NH Asian or Pacific Islander women. Both NH American Indian or Alaska Native and NH Asian or Pacific Islanders showed a downward trend in AAMR beginning from 1999 and ending in 2020. The NH Black or African Americans and NH White both showed an initial rise in AAMR from 1999 till 2020. Following 2002, the trend in AAMR differed for the two groups such that for NH Black or African Americans the AAMR kept on significantly declining till 2020 (APC: -1.71, 95%CI: -3.26 to -1.46), while NH Whites differed such that they showed significant decrease in AAMR during two periods 2002 to 2006 and then 2006 to 2020 respectively. The Hispanics or Latino were different from all other groups such that there AAMR remained relatively stable from 1999 to 2005 (APC: 0.30, 95%CI: -1.10 to 5.47) followed by a dramatic decline till 2020 (APC: -1.72, 95%CI: -2.77 to -1.41) (Figure 3, Tables 3 and 6).
Figure 3 Ovarian carcinoma-related age-adjusted mortality rates per 100000, stratified by race in the United States, 1999 to 2020.aP value < 0.05, statistically significant annual percent change values. NH: Non-Hispanic; APC: Annual percent change; CI: Confidence interval.
Table 6 Ovarian carcinoma-related age-adjusted mortality rates per 100000 (95% confidence interval), stratified by race in females in the United States, 1999 to 2020.
Year
AAMR (95%CI) in NH White
AAMR (95%CI) in NH Black or African American
AAMR (95%CI) in NH American Indian or Alaska Native
Malignant ovarian neoplasm-related mortality stratified by urbanization group
Although there was no significant difference between the total AAMR of both groups, however non-metropolitan areas had a slightly higher AAMR (12.84, 95%CI: 12.74-12.95) than the metropolitan areas (12.61, 95%CI: 12.58-12.66). The AAMR for metropolitan areas remained stable from 1999 to 2003 (APC: 0.27, 95%CI: -0.69 to 1.79), while the AAMR for non-metropolitan areas remained stable from 1999 till 2004 (APC: 0.50, 95%CI: -0.56 to 2.55). The AAMR of both areas then showed a significant downward trend till the end of the study period 2020 (APC of metropolitan areas 2003-2020: -2.31, 95%CI: -2.45 to -2.19, APC of non-metropolitan areas 2004-2020: -2.20, 95%CI: -2.47 to -1.98) (Figure 4, Tables 3 and 7).
Figure 4 Ovarian carcinoma-related age-adjusted mortality rates per 1000000, stratified by urbanization group in the United States, 1999 to 2020.aP value < 0.05, statistically significant annual percent change values. APC: Annual percent change; CI: Confidence interval.
Table 7 Ovarian carcinoma-related age-adjusted mortality rates per 100000, stratified by urban-rural classification in females in the United States, 1999 to 2020.
Malignant ovarian neoplasm-related mortality stratified by geographical regions
Northeast region had the highest AAMR (13.06, 95%CI: 12.96-13.16) followed by Midwest (12.94, 95%CI: 12.85-13.03), West (12.86, 95%CI: 12.76-12.95) and South region (12.16, 95%CI: 12.09-12.23). The AAMR of both Northeast and Midwest region showed an initial rise in AAMR till 2002 and 2003 respectively. This was followed by a significant downward trend till 2020 exhibited by both regions. The South region was different such that it showed two declining trends from 1999 to 2005 and then 2005 till 2020 with the latter being significant. West region was unique in the regard that its AAMR remained stable from 1999 to 2004, however, following 2004 the AAMR started decreasing and showed decline in three significant periods 2004 to 2008, 2008 to 2016 and finally from 2016 to 2020 (Figure 5, Tables 3 and 8). In terms of States Oregon, West Virginia and Washington were in the 90th percentile while Texas, Florida, and North Dakota were in the 10th percentile in terms of mortality due to malignant ovarian neoplasm (Figure 6, Table 9).
Figure 5 Ovarian carcinoma-related age-adjusted mortality rates per 100000, stratified by census regions, in the United States, 1999 to 2020.aP value < 0.05, statistically significant annual percent change values. APC: Annual percent change; CI: Confidence interval.
The present study comprehensively examined trends in ovarian cancer-related mortality across various demographic, geographic, and healthcare settings in the United States from 1999 to 2020. The findings demonstrate a substantial reduction in ovarian cancer AAMRs, reflecting progress in early detection, advanced therapeutic interventions, and improvements in healthcare delivery. However, persistent disparities across racial/ethnic groups, age categories, geographic regions, and levels of urbanization underscore the need for equitable healthcare access and targeted policy interventions.
Decline in ovarian cancer mortality
The reduction in ovarian cancer AAMRs from 14.62 per 100000 in 1999 to 10.15 per 100000 in 2020 underscores significant progress in managing this malignancy. The decline was most pronounced between 2003 and 2020, with an APC of -2.28 (95%CI: -2.39 to -2.16). Contributing factors include advancements in cytoreductive surgeries, improved chemotherapeutic regimens, and the introduction of molecularly targeted therapies such as poly (ADP-ribose) polymerase inhibitors[19-21].
In addition, the growing application of genomic profiling has enabled personalized treatment strategies tailored to tumor biology, particularly for BRCA-mutated and homologous recombination deficiency-positive ovarian cancers[22]. Advances in molecular diagnostics, including next-generation sequencing, have further enhanced therapeutic decision-making. Although no universally endorsed screening program exists, increased public awareness and risk-reducing strategies, such as prophylactic surgery for high-risk populations, may also have contributed to the observed decline[7,23].
Racial and ethnic disparities
Significant racial and ethnic disparities in ovarian cancer mortality persist, reflecting underlying socio-economic inequities, biological variations, and healthcare system barriers. NH White women exhibited the highest AAMRs at 13.53 (95%CI: 13.48-13.58), while NH Asian or Pacific Islander women recorded the lowest at 7.56 (95%CI: 7.4-7.73). Population-specific genetic variations, such as differences in BRCA1/BRCA2 mutation frequencies, may partially explain the disparities. NH Black women have lower BRCA testing rates despite comparable mutation frequencies, potentially due to underrepresentation in genetic research[23,24]. Further, emerging data suggest variations in single-nucleotide polymorphisms related to ovarian cancer risk that differ by ancestry[25]. Socio-economic factors, including income level, education, and insurance coverage, heavily influence access to high-quality care. Studies indicate that NH Black and Hispanic women experience delays in diagnosis and limited access to cancer specialists, often receiving care at lower-resourced hospitals[26-28]. These barriers result in later-stage diagnoses and suboptimal treatment, contributing to poorer survival outcomes[29,30].
Age-related mortality patterns
Ovarian cancer mortality rates were highest among women aged 65 and older, reflecting the disease’s well-established link with advancing age[31]. Although mortality rates showed an increase in this age group until 2001, a slight decline was seen till 2007 (APC: -0.90, 95%CI: -2.80 to -0.45) followed by a notable decline (APC: -2.88, 95%CI: -3.12 to -2.67), likely due to the delayed adoption of novel therapies driven by concerns about treatment toxicity and comorbidities[32]. Younger women (under 50) demonstrated more consistent reductions in AAMRs, possibly due to better treatment tolerance and earlier intervention with aggressive multimodal therapies[33-36]. Expanding access to fertility-preserving treatment options could further improve outcomes for younger patients[30].
Geographic and regional variations
Geographic disparities in ovarian cancer mortality ranged from 9.29 per 100000 in Hawaii to 14.66 per 100000 in Oregon, with the Northeastern region of the United States reporting the highest rates. States with elevated mortality, such as Oregon and Pennsylvania, may face healthcare infrastructure limitations and uneven distribution of oncologic care providers[37,38]. Conversely, states like Hawaii and Alaska reported lower mortality rates, potentially benefiting from state-funded cancer prevention initiatives and smaller, more centralized healthcare systems[39]. Regional cancer control programs, such as the CDC’s national comprehensive cancer control program, have played a critical role in mitigating these disparities[40,41]. Healthcare access disparities are exacerbated by implicit bias, mistrust in the healthcare system, and limited representation in clinical trials[41]. Enhancing access to culturally sensitive care and expanding Medicaid coverage could mitigate these gaps. Policy interventions such as the Affordable Care Act’s Medicaid expansion have shown promise in reducing treatment delays[42].
Urban-rural disparities
Although AAMRs were similar between metropolitan and nonmetropolitan areas, rural regions reported slightly higher mortality rates (AAMR: 12.84; 95%CI: 12.74-12.95), likely due to delayed diagnoses and limited oncology care access[43,44]. Telehealth expansion and regional cancer networks have facilitated improved care delivery in remote areas, helping bridge these gaps[45].
Healthcare settings and place of death
Most ovarian cancer-related deaths occurred at home (41.8%), followed by medical facilities (29.6%) and long-term care homes (13.7%). This pattern highlights the increased integration of hospice and palliative care services[46]. Home-based end-of-life care reflects patient preferences supported by multidisciplinary palliative care teams and enhanced family caregiver training[47].
Study limitations
Despite comprehensive national data, several limitations exist. The absence of individual-level data, such as lifestyle factors, tumor histology, and specific treatment protocols, limits causal inference. Additionally, racial/ethnic misclassification and incomplete place-of-death documentation could introduce biases[48-50].
Future directions
Continued progress in ovarian cancer management requires multifaceted strategies integrating clinical, public health, and policy-level interventions. Expanding access to personalized therapies such as poly (ADP-ribose) polymerase inhibitors and immunotherapy agents through precision medicine initiatives could significantly improve treatment outcomes[51]. Public health programs like the CDC’s Inside Knowledge Campaign, which raises awareness of gynecologic cancers, should be broadened to enhance community-level engagement advancing early detection methods through liquid biopsy technologies and biomarker-driven screening trials holds promise for facilitating earlier and more accurate diagnoses[52,53]. Policy reforms focused on strengthening insurance coverage, expanding Medicaid, and investing in safety-net hospitals could improve access to equitable care for underserved populations[54]. Additionally, expanding mobile screening units and organizing cancer education workshops targeting socio-economically disadvantaged communities could help reduce barriers to timely diagnosis and treatment[55]. Emerging evidence also highlights the role of complementary medicine, including herbal remedies, acupuncture, massage, and psychological therapies, in improving quality of life and symptom management for cancer patients, though rigorous evaluation of their efficacy and safety is needed[56,57]. Targeted public health initiatives addressing systemic healthcare inequities, coupled with ongoing research into ovarian cancer pathobiology, are critical for sustaining mortality reductions and achieving equitable cancer care.
CONCLUSION
The mortality trends for ovarian carcinoma patients showed an overall decrease. However, these trends varied across all demographic factors. The highest mortality rates were reported among older individuals (65 to > 85 years), and NH Whites. Additionally, geographic trends for urbanization and census regions showed highest mortalities among nonmetropolitan areas and Northeast region respectively. These disparities underscore the need for equitable healthcare access and targeted policy interventions.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: United Kingdom
Peer-review report’s classification
Scientific Quality: Grade A, Grade B
Novelty: Grade B, Grade B
Creativity or Innovation: Grade A, Grade B
Scientific Significance: Grade A, Grade A
P-Reviewer: Weng JL S-Editor: Wu S L-Editor: A P-Editor: Zheng XM
Ng D, Ross W, Traverso-Ortiz M, Rim SH, Wike JM, Moore AR; Ovarian Cancer Treatment Study Group. Rationale and Methodologic Approach for Assessing Ovarian Cancer Treatment and Gynecologic Oncologist Involvement in the Midwest Region of the United States.J Registry Manag. 2023;50:85-91.
[PubMed] [DOI]
Centers for Disease Control and Prevention.
About Multiple Cause of Death, 1999-2020. [cited 14 December 2024]. Available from: http://wonder.cdc.gov/mcd-icd10.html.
[PubMed] [DOI]
Armstrong DK, Alvarez RD, Bakkum-Gamez JN, Barroilhet L, Behbakht K, Berchuck A, Chen LM, Cristea M, DeRosa M, Eisenhauer EL, Gershenson DM, Gray HJ, Grisham R, Hakam A, Jain A, Karam A, Konecny GE, Leath CA, Liu J, Mahdi H, Martin L, Matei D, McHale M, McLean K, Miller DS, O'Malley DM, Percac-Lima S, Ratner E, Remmenga SW, Vargas R, Werner TL, Zsiros E, Burns JL, Engh AM. Ovarian Cancer, Version 2.2020, NCCN Clinical Practice Guidelines in Oncology.J Natl Compr Canc Netw. 2021;19:191-226.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 123][Cited by in RCA: 434][Article Influence: 108.5][Reference Citation Analysis (0)]
Moore K, Colombo N, Scambia G, Kim BG, Oaknin A, Friedlander M, Lisyanskaya A, Floquet A, Leary A, Sonke GS, Gourley C, Banerjee S, Oza A, González-Martín A, Aghajanian C, Bradley W, Mathews C, Liu J, Lowe ES, Bloomfield R, DiSilvestro P. Maintenance Olaparib in Patients with Newly Diagnosed Advanced Ovarian Cancer.N Engl J Med. 2018;379:2495-2505.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1408][Cited by in RCA: 1962][Article Influence: 280.3][Reference Citation Analysis (0)]
Jacobs IJ, Menon U, Ryan A, Gentry-Maharaj A, Burnell M, Kalsi JK, Amso NN, Apostolidou S, Benjamin E, Cruickshank D, Crump DN, Davies SK, Dawnay A, Dobbs S, Fletcher G, Ford J, Godfrey K, Gunu R, Habib M, Hallett R, Herod J, Jenkins H, Karpinskyj C, Leeson S, Lewis SJ, Liston WR, Lopes A, Mould T, Murdoch J, Oram D, Rabideau DJ, Reynolds K, Scott I, Seif MW, Sharma A, Singh N, Taylor J, Warburton F, Widschwendter M, Williamson K, Woolas R, Fallowfield L, McGuire AJ, Campbell S, Parmar M, Skates SJ. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial.Lancet. 2016;387:945-956.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 698][Cited by in RCA: 721][Article Influence: 80.1][Reference Citation Analysis (0)]
Peres LC, Risch H, Terry KL, Webb PM, Goodman MT, Wu AH, Alberg AJ, Bandera EV, Barnholtz-Sloan J, Bondy ML, Cote ML, Funkhouser E, Moorman PG, Peters ES, Schwartz AG, Terry PD, Manichaikul A, Abbott SE, Camacho F, Jordan SJ, Nagle CM; Australian Ovarian Cancer Study Group, Rossing MA, Doherty JA, Modugno F, Moysich K, Ness R, Berchuck A, Cook L, Le N, Brooks-Wilson A, Sieh W, Whittemore A, McGuire V, Rothstein J, Anton-Culver H, Ziogas A, Pearce CL, Tseng C, Pike M, Schildkraut JM; African American Cancer Epidemiology Study and the Ovarian Cancer Association Consortium. Racial/ethnic differences in the epidemiology of ovarian cancer: a pooled analysis of 12 case-control studies.Int J Epidemiol. 2018;47:460-472.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 28][Cited by in RCA: 33][Article Influence: 4.7][Reference Citation Analysis (0)]
Ferrell BR, Temel JS, Temin S, Alesi ER, Balboni TA, Basch EM, Firn JI, Paice JA, Peppercorn JM, Phillips T, Stovall EL, Zimmermann C, Smith TJ. Integration of Palliative Care Into Standard Oncology Care: American Society of Clinical Oncology Clinical Practice Guideline Update.J Clin Oncol. 2017;35:96-112.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 988][Cited by in RCA: 1401][Article Influence: 155.7][Reference Citation Analysis (0)]
Kalsi JK, Ryan A, Gentry-Maharaj A, Margolin-Crump D, Singh N, Burnell M, Benjamin E, Apostolidou S, Habib M, Massingham S, Karpinskyj C, Woolas R, Widschwendter M, Fallowfield L, Campbell S, Skates S, McGuire A, Parmar M, Jacobs I, Menon U. Completeness and accuracy of national cancer and death registration for outcome ascertainment in trials-an ovarian cancer exemplar.Trials. 2021;22:88.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 6][Cited by in RCA: 9][Article Influence: 2.3][Reference Citation Analysis (0)]
