Abstract
Objective: This study examines the association of socioeconomic status (SES) with delays in care and treatment timeline for endometrial cancer (EC) patients receiving primary surgery.
Methods: This was a retrospective cohort study of patients who underwent surgical management of EC between 2018 and 2024. SES was determined by Area Deprivation Index (ADI) national percentile rank. Demographics, dates of symptom onset, provider visits, tissue diagnosis, and surgery were abstracted from the electronic medical record.
Results: Of the 2191 patients who had surgical management for EC, we analyzed 185 (ADI1, most advantaged) and 257 (ADI4, least advantaged). Patients in ADI1 had a lower mean BMI (31.1 kg/m2 vs 35.6 kg/m2, p < 0.001). Although the majority of the cohort was White (91.0%), ADI4 included more Black patients (6.6% vs 1.1%, p = 0.003).
Time from symptom onset to surgery was 30 days longer in ADI4 compared to ADI1 (126.0 days vs 96.0 days, p = 0.003). Specifically, time from symptom onset to first visit with a healthcare provider differed significantly between ADI1 and ADI4, with a median of 20.5 days versus 50.0 days (p < 0.001). There were no statistically significant differences in intervals between other care timepoints.
Conclusions: The discrepancy in time to surgical management of EC was driven by delays in first provider visit, indicating that disparities are related to symptom recognition and access to initial evaluation rather than delays within specialty care. These findings can inform interventions for reducing socioeconomic and geographic disparities in endometrial cancer care.
Keywords: Endometrial cancer, Area deprivation index, Disparities, Socioeconomic status, Social determinants of health
Graphical abstract
Highlights
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Socioeconomic disadvantage was associated with delays in endometrial cancer surgery.
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This was driven by interval from symptom onset to seeing any healthcare provider.
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Primary care access and delayed symptom recognition contributes to this disparity.
1. Introduction
Endometrial cancer (EC) is the most common gynecologic malignancy in the United States, with rising incidence over the past three decades likely related to increasing rates of obesity, metabolic disease, and other environmental factors (Henley et al., 2020). This rising incidence has disproportionately affected certain racial groups, with more aggressive histologic subtypes and increased mortality amongst non-Hispanic Black patients (Cote et al., 2015; Somasegar et al., 2023). EC patients of lower socioeconomic status (SES) are also less likely to receive guideline-adherent care and have poorer overall survival when compared to higher SES groups (Rodriguez et al., 2021).
Timeliness of diagnosis and treatment for EC significantly impacts outcomes, as there is a relatively good prognosis for low-grade, uterine-limited disease, whereas advanced stage disease is associated with increased mortality (Najor et al., 2023). Numerous studies have demonstrated the impact of social determinants of health on timeliness of surgical management for EC (Rodriguez et al., 2021; Najor et al., 2023; Fife et al., 2024; Maroko et al., 2016). EC patients with lower income levels or without insurance coverage have significantly longer intervals between diagnosis and treatment, and are more likely to present with advanced stage disease at time of diagnosis (Najor et al., 2023). Patients without an established primary care physician or gynecologist are more likely to experience delays in care and to have extrauterine disease at diagnosis (Fife et al., 2024). Black and Hispanic patients also have an increased likelihood of experiencing treatment delays for EC compared to White patients (Strohl et al., 2016). These disparities represent a confluence of vulnerabilities including patient lack of awareness of concerning symptoms, limited access to evaluation and treatment, differences in insurance coverage, and systemic racial and ethnic inequities.
The Area Deprivation Index (ADI) is a nationally validated tool for quantifying neighborhood-level disadvantage and contextualizing those values on a state and national scale. ADI is calculated using United States census block data including income, education, employment, and housing quality, and is inversely associated with SES: a higher ADI rank is indicative of increased geographic deprivation and lower SES. High ADI neighborhoods have been linked to poorer outcomes and increased mortality rates in a variety of cancers, including lung, cervical, breast and colorectal cancer (Maroko et al., 2016; Hufnagel et al., 2021; Snider et al., 2023; Fairfield et al., 2020). Gamble et al. identified that higher ADI was associated with a 24% increase in all-cause mortality in EC patients compared to lowest ADI group. Despite these associations, previous studies have not evaluated how the timeline of care for EC can differ amongst patients with varying ADI.
This study aimed to evaluate the association between patient socioeconomic and geographic status, as represented by ADI, and timeliness of EC workup and management. This study also aimed to identify where in the care timeline delays occur. We hypothesized that patients in ADI quartile 4 (ADI4, high deprivation) would have a longer interval from symptom onset to surgical management of EC when compared to ADI quartile 1 (ADI1, low deprivation). We also hypothesized that patients who were first evaluated by a provider outside our healthcare institution would have a longer interval from symptom onset to surgical management of EC. Understanding these relationships can help optimize the EC care pathway for all patients, improving timely diagnosis and surgical management.
2. Methods
This is a retrospective cohort study of patients who underwent curative-intent surgery for EC at any of the three Mayo Clinic hospital sites (Minnesota, Arizona, Florida) between January 2018 and March 2024. Patients undergoing surgery for recurrent disease or who had multiple invasive cancers were excluded, as were patients without a valid United States street address as ADI was not able to be calculated. Patients were also excluded if they had non-surgical management of EC.
Sociodemographic and geographic data for the study cohort, including ADI and Rural-Urban Commuting Area (RUCA) Codes, were obtained using the Mayo Clinic Housing-Based Socioeconomic Status (HOUSES) program. RUCA codes are assigned by the United States federal government to define U.S. census tracts by degree of urbanization as well as commuting patterns. Primary RUCA codes were divided into Urban (RUCA 1–3) and Rural (RUCA 4–10), consistent with the definition by the U.S. Department of Health and Human Services Federal Office of Rural Health Policy (Rural-Urban Commuting Area Codes - Users' Guide, 2026). Patients were divided into quartiles based on ADI national rank. Only patients in ADI1 and ADI4 were analyzed to represent the highest and lowest SES, respectively. Demographic and clinical characteristics, including dates of symptom onset, provider visits, imaging, tissue diagnosis, and surgical management of EC were abstracted from electronic medical records. Additional data was obtained from an institutional database of all patients treated for EC, which includes information on patient co-morbidities as well as surgical and oncologic outcomes.
Date of symptom onset was defined as the date the patient first reported experiencing a symptom of EC as recorded by healthcare providers in the electronic medical record. This was most commonly post-menopausal bleeding but also included abdominal or pelvic pain and abdominal bloating or fullness. When only a month was given, the first day of that month was used. Patients for whom date of symptom onset was not documented were excluded from analysis of this time point. Date of first visit was defined as the first visit with a medical provider during which EC symptoms were discussed and evaluated. This was typically an office visit with a primary care provider or gynecologist but also included emergency department evaluations. For patients whose cancer was identified incidentally on imaging, the first visit to discuss these imaging findings was used and date of symptom onset was excluded from analysis. Date of biopsy was the date of successful endometrial sampling – either endometrial biopsy or hysteroscopy with dilation and curettage – that led to surgery. Date of imaging was the first pelvic imaging (ultrasound (US), computerized tomography (CT) scan, or magnetic resonance imaging (MRI)) after symptom onset. Date of gynecologic oncology consult was the first visit with a gynecologic oncologist to discuss diagnosis and management recommendations. Any unclear data points were discussed amongst at least two authors with appropriate clinical background (E.I., E.D., S.M.), to achieve consensus.
Patient demographics and clinical characteristics are presented as either mean (standard deviation [SD]) or median (quartile 1 [Q1], quartile 3 [Q3]) for continuous variables and as sample size (percent) for categorical variables. In comparisons of demographic, clinical characteristics, and timeline of care by ADI1 versus ADI4, p-values are based on t-tests or Mood's median test for continuous variables and Chi-squared tests or analysis of variance (ANOVA) tests for categorical variables. Statistical significance was based on an alpha level of 0.05. Statistical analyses were performed using R version 4.4.1.
3. Results
Of the 2191 patients who underwent surgical management of EC within our institution during this timeframe, 1687 patients met inclusion criteria. Of 504 patients excluded, 37% did not have ADI available for the address given, 25% did not give authorization for research, 16% had surgery for recurrence or with non-curative intent, 14% had an invasive synchronous cancer, 7% were medically managed prior to surgery, and 2% otherwise did not meet inclusion criteria (data not shown). The median age at surgery was 66.2 years (IQR 59.4–72.7). The mean body mass index (BMI) was 34.5 kg/m2 (SD = 9.1). Sixty-one percent of patients resided in urban areas, as defined by RUCA codes 1–3, with 38.8% living in rural areas (RUCA codes 4–10). The majority of cases were FIGO 2009 stage I (80.3%) and grade 1 (52.1%). The majority of the cohort identified as White/non-Hispanic (95.3%) (Table 1).
Table 1.
Demographic and Tumor Characteristics of Study Population.
| Variable | Study Population (n = 1687) |
|---|---|
| BMI (kg/m2), mean (SD) | 34.5 (9.1) |
| Age at Surgery (years), median (IQR) | 66.2 (59.4, 72.7) |
| Race, n (%) | |
| White | 1572 (93.2%) |
| Black or African American | 56 (3.3%) |
| Asian | 27 (1.6%) |
| Other | 14 (0.8%) |
| Unknown/Not Reported | 18 (1.1%) |
| Ethnicity, n (%) | |
| Non-Hispanic/Latina | 1607 (95.3%) |
| Hispanic/Latina | 53 (3.1%) |
| Unknown/Not Reported | 27 (1.6%) |
| Primary RUCA Code | |
| Urban (1–3) | 1029 (61.0%) |
| Rural (4–10) | 655 (38.8%) |
| Unknown/Not Reported | 3 (0.2%) |
| FIGO 2009 Stage, n (%) | |
| Stage I | 1355 (80.3%) |
| Stage II | 69 (4.1%) |
| Stage III | 194 (11.5%) |
| Stage IV | 68 (4.0%) |
| Unknown/Not Reported | 1 (0.1%) |
| Histologic Grade, n (%) | |
| 1 | 879 (52.1%) |
| 2 | 339 (20.1%) |
| 3 | 459 (27.2%) |
| Unknown/Not Reported | 10 (0.6%) |
Abbreviations: BMI, Body Mass Index; FIGO, International Federation of Gynecology and Obstetrics; IQR, Interquartile Range; RUCA, Rural-Urban Commuting Area; SD, Standard Deviation.
Within the cohort, 185 patients were identified in ADI1 and 257 in ADI4 as shown in Table 2. The median age at surgery did not differ significantly between ADI1 and ADI4 (66.2 years vs 66.8 years respectively, p = 0.5). Patients in ADI1 had significantly lower mean BMI when compared to ADI4 (31.1 kg/m2 vs 35.6 kg/m2, p < 0.001). ADI1 differed from ADI4 with regard to racial identity (p = 0.003). Although the majority of the cohort identified as White (91.0%), ADI4 included a higher proportion of Black patients (6.6% vs 1.1%) and ADI1 included a higher proportion of Asian patients (2.7% vs 0.4%). Based on RUCA codes, patients in ADI1 were significantly more likely to reside in urban areas compared to ADI4 (91.9% vs 32.9%, p < 0.001). There were no statistically significant differences in disease stage or histologic grade between ADI1 and ADI4 (Table 2).
Table 2.
Demographic, Tumor, and Clinical Characteristics of Patients in ADI Quartile 1 (ADI1) vs Patients in ADI Quartile 4 (ADI4).
| Variable | ADI1 (n = 185) | ADI4 (n = 257) | p-value |
|---|---|---|---|
| BMI (kg/m2), mean (SD) | 31.1 (8.5) | 35.6 (8.8) | <0.001 |
| Age at surgery (years), median (Q1, Q3) | 66.2 (60.0, 73.6) | 66.8 (60.5, 74.0) | 0.500 |
| Race, n (%) | 0.003 | ||
| White | 171 (92.4%) | 231 (89.9%) | |
| Black or African American | 2 (1.1%) | 17 (6.6%) | |
| Asian | 5 (2.7%) | 1 (0.4%) | |
| American Indian/Alaska Native | 1 (0.5%) | 0 (0.0%) | |
| More Than One Race | 0 (0.0%) | 4 (1.6%) | |
| Unknown/Not Reported | 6 (3.2%) | 6 (2.3%) | |
| Ethnicity, n (%) | 0.686 | ||
| Non-Hispanic/Latina | 178 (96.2%) | 250 (97.2%) | |
| Hispanic/Latina | 7 (3.8%) | 7 (2.7%) | |
| Unknown/Not Reported | 6 (3.2%) | 6 (2.3%) | |
| Primary RUCA Code | < 0.001 | ||
| Urban (1–3) | 170 (91.9%) | 84 (32.9%) | |
| Rural (4–10) | 15 (8.1%) | 171 (67.1%) | |
| Unknown/Not Reported | 0 (0.0%) | 2 (0.8%) | |
| FIGO 2009 Stage, n (%) | 0.217 | ||
| Stage I | 143 (78.1%) | 204 (79.9%) | |
| Stage II | 14 (7.7%) | 9 (3.5%) | |
| Stage III | 20 (10.9%) | 36 (14.1%) | |
| Stage IV | 6 (3.3%) | 7 (2.7%) | |
| Unknown/Not Reported | 2 (1.1%) | 1 (0.4%) | |
| Histology, n (%) | 0.997 | ||
| Endometrioid | 144 (77.8%) | 200 (77.8%) | |
| Non-Endometrioid | 41 (22.2%) | 57 (22.2%) | |
| Histologic Grade, n (%) | 0.384 | ||
| Grade 1 | 86 (46.5%) | 128 (49.8%) | |
| Grade 2 | 48 (25.9%) | 50 (19.5%) | |
| Grade 3 | 51 (27.6%) | 78 (30.4%) | |
| Location of First Visit, n (%) | <0.001 | ||
| Mayo Primary Hospital Campus | 57 (30.8%) | 26 (10.1%) | |
| MCHS | 3 (1.6%) | 65 (25.3%) | |
| Outside Facility | 122 (65.9%) | 161 (62.6%) |
Abbreviations: BMI, Body Mass Index; FIGO, International Federation of Gynecology and Obstetrics; IQR, Interquartile Range; MCHS, Mayo Clinic Health System; Q1, Quartile 1; Q3, Quartile 3; RUCA, Rural-Urban Commuting Area; SD, Standard Deviation.
Data availability for the time intervals described above was similar between ADI1 and ADI4. Time from symptom onset to surgery differed significantly between ADI1 and ADI4 as shown in Table 3 and Fig. 1, with a median of 96.0 days versus 126.0 days, respectively (p = 0.003). Importantly, these significant differences persist when eliminating outliers of patients with >1000 days from symptom onset to provider visit (results not shown). Time from symptom onset to first visit with a healthcare provider also differed significantly, with a median of 20.5 days in ADI1 versus 50.0 days in ADI4 (p < 0.001). There were no statistically significant differences in time from first visit to biopsy (18.0 days vs 17.0 days, p = 0.725), time from biopsy to gynecologic oncology consult (16.0 days vs 18.0 days, p = 0.068), or consult to surgery (13.0 vs 15.0 days, p = 0.189) (Table 3).
Table 3.
Median Intervals in Days in ADI1 and ADI4 Between Points in Timeline of Endometrial Cancer Care.
| Interval, Days – Median (Q1, Q3) | ADI1 (n = 185) | ADI4 (n = 257) | p-value |
|---|---|---|---|
| Total (Symptom onset to Surgery) | 96.0 (61.2, 205.5) | 126.0 (74.5, 246.5) | 0.003 |
| Unknown/Not Reported, n (%) | 45 (24.3%) | 50 (19.5%) | |
| Symptoms to First Visit | 20.5 (5.0, 70.0) | 50.0 (12.0, 127.0) | <0.001 |
| Unknown/Not Reported, n (%) | 53 (28.6%) | 58 (22.6%) | |
| First Visit to Biopsy | 18.0 (1.0, 50.0) | 17.0 (1.0, 42.0) | 0.725 |
| Unknown/Not Reported, n (%) | 18 (9.7%) | 20 (7.8%) | |
| Biopsy to Gynecologic Oncology consult | 16.0 (9.0, 23.5) | 18.0 (12.0, 26.0) | 0.068 |
| Unknown/Not Reported, n (%) | 2 (1.3%) | 2 (0.8%) | |
| Gynecologic Oncology Consult to Surgery | 13.0 (6.0, 24.0) | 15.0 (5.0, 27.0) | 0.189 |
| Unknown/Not Reported, n (%) | 0 (0.0%) | 0 (0.0%) |
Abbreviations: Q1, Quartile 1; Q3, Quartile 3.
Fig. 1.
Distribution of Days Between Points in Timeline of Endometrial Cancer Care from Date of Symptom Onset to Surgery Date in ADI1 versus ADI4.
When subdividing patients by rural versus urban areas, there was an overall difference across ADI1 Rural, ADI1 Urban, ADI4 Rural, and ADI4 Urban in both time from symptom onset to surgery (p = 0.006) and from symptom onset to first visit (p = 0.037), with longer intervals amongst patients in ADI4 and urban areas (Table 4). Similar to the overall analysis, there was a difference in time from gynceologic oncology consult to surgery (p = 0.002), with patients in ADI4 Urban experiencing the longest times to surgery (29.6 days) compared to less than 20 days for each of the remaining groups. There were no significant differences in time from first visit to biopsy (p = 0.906) or from biopsy to gynecologic oncology consult (p = 0.976) when stratifying by both ADI and rural versus urban areas.
Table 4.
Mean Intervals in Days in ADI1 versus ADI4 and Rural versus Urban Status Between Points in Timeline of Endometrial Cancer Care.
| Interval, Days – Mean (SD) | ADI1 Rural (n = 15) | ADI1 Urban (n = 170) | ADI4 Rural (n = 171) | ADI4 Urban (n = 84) |
p-value |
|---|---|---|---|---|---|
| Total (Symptom onset to Surgery) | 83 (71.1) |
143.9 (128.6) | 193.0 (326.9) | 271.7 (290.1) | 0.006 |
| Unknown/Not Reported, n (%) | 7 (46.7%) |
38 (22.4%) |
34 (19.9%) | 16 (19.0%) | |
| Symptoms to First Visit | 31.4 (55.0) | 63.1 (97.9) |
109.6 (310.4) | 164.2 (262.7) | 0.037 |
| Unknown/Not Reported, n (%) | 7 (46.7%) |
46 (27.1%) |
38 (22.2%) | 20 (23.8%) | |
| First Visit to Biopsy | 59,4 (141.6) | 43.3 (73.3) |
46.9 (118.4) | 51.6 (105.1) | 0.906 |
| Unknown/Not Reported, n (%) | 1 (6.7%) |
17 (10.0%) |
11 (6.4%) | 9 (10.7%) | |
| Biopsy to Gynecologic Oncology consult | 22.0 (13.1) | 22.2 (31.5) |
22.1 (32.5) | 23.8 (19.0) | 0.976 |
| Unknown/Not Reported, n (%) | 0 (0.0%) |
2 (1.2%) |
1 (0.6%) |
1 (1.2%) |
|
| Gynecologic Oncology Consult to Surgery | 18.6 (23.3) | 19.5 (23.2) |
17.6 (22.4) | 29.6 (29.0) | 0.002 |
| Unknown/Not Reported, n (%) | 0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
Abbreviations: SD, standard deviation.
Across our cohort, 18.8% of patients had their first provider visit at one of three Mayo Clinic hospital campuses, 15.4% at a regionally affiliated facility within the Mayo Clinic Health System, and 64.0% with a provider unaffiliated with Mayo Clinic. Patients in ADI4 were significantly more likely to have been seen within the regional Mayo Clinic Health System (25.3% in ADI4 versus 1.6% in ADI1) while patients in ADI1 were more commonly seen at the primary Mayo Clinic campus (30.8% in ADI1 vs. 10.1% in ADI4) (p < 0.001) (Table 2).
4. Discussion
Our study aimed to describe the EC care timeline from symptom onset to surgical management, stratifying by ADI quartile to evaluate the role of socioeconomic status and geography in timeliness of EC care. Previous research has shown increased mortality for patients with an interval between diagnosis and surgical management of EC of greater than eight weeks (56 days) (Shalowitz et al., 2017). Within our cohort, median time from biopsy to surgery was 29 days for ADI1 and 33 days for ADI4, well within this benchmark. However, this study highlights disparities related to symptom recognition and access to initial symptom evaluation, with a 29.5 day difference between ADI1 and ADI4 in median time from symptom onset to first visit with a healthcare provider. Further analysis by ADI and RUCA show that residing in urban versus rural areas may also impact timeline of care.
These findings can be understood within the Pathway to Treatment framework, which distinguishes between symptom appraisal, help-seeking, and treatment intervals (Najor et al., 2023; Scott et al., 2013). Our results suggest that disparities in endometrial cancer care are concentrated in the appraisal and help-seeking phases rather than within-system treatment processes. This distinction is important, as it shifts the focus of intervention from improving care delivery timelines to addressing barriers that influence when and how patients enter the healthcare system. These results suggest that while patients within our cohort may experience delays in initial evaluation, once patients are seen by a medical provider, patients progress to diagnosis and surgical management in a timely manner within the health care system, without significant disparities related to socioeconomic status or geography.
Prior studies have identified socioeconomic disparities in timeliness of EC care; however, this work adds important detail as to when these delays in EC care occur (Rodriguez et al., 2021; Najor et al., 2023). Doll et al. (2018) demonstrated disparities in the receipt of guideline-concordant workup of postmenopausal bleeding for Black patients (Doll et al., 2018). Fife et al. (2024) similarly describe a “presentation delay” from symptom onset to endometrial sampling, and noted that this was more likely to occur in patients without an established primary care provider or outpatient obstetrician-gynecologist (Fife et al., 2024). Our study identified a similar, but more specific, interval as the key source of delay in the most socioeconomically disadvantaged section of our cohort.
Initial presentation to a provider is likely impacted by an individual's access to gynecologic care. The most advantaged group in our study was three times as likely to have an initial evluation at the same tertiary center in which they underwent surgery, which suggests increased access to specialist care in this cohort. Provider training/background has been associated with receipt of guideline-concordant practices in EC evaluation (Sage et al., 2023). Patients may also be deterred from seeking gynecologic care due to concerns about being dismissed or not taken seriously by providers, as demonstrated in qualitative research (Bellon et al., 2026; Doll et al., 2022; Doll et al., 2020). Together, these factors point to a combination of structural access barriers, care navigation challenges, and differences in symptom awareness that collectively shape delays in initial presentation. Strengthening relationships between patients and providers and alleviating insurance and logistical barriers to primary gynecologic care may improve delays in EC symptom evaluation. While there are currently no approved options for non-invasive home testing for EC, in the future, such tools may
expedite diagnosis, especially in patients with limited access to primary gynecologic care (Bakkum-Gamez et al., 2026).
Our findings also suggest a role for increasing patient education on abnormal symptoms that warrant provider evaluation. One survey conducted in the midwestern United States found that less than half of post-menopausal women had been counseled by a healthcare provider that post-menopausal bleeding could represent a symptom of EC, implying a major gap in patient education (Wise et al., 2024). Another study identified that a majority of women were unaware of major risk factors for EC (Washington et al., 2020). Public health measures directed at improving counselling and patient awareness of symptoms and risk factors may therefore help reduce delays in care. Such interventions could address the delays related to symptom appraisal and help seeking, as described in the Pathway to Treatment framework (Najor et al., 2023).
This study is limited by our retrospective approach and that we only evaluated patients who underwent surgery at a referral academic center. Our cohort may therefore fail to represent EC patients who are not able to access a well-resourced tertiary care center due to higher levels of disadvantage. By focusing on patients who underwent curative-intent surgery for EC, we are also not able to represent patients with advanced-stage disease who were determined to not be surgical candidates. Our study population was also limited in diversity of racial identity which limits our understanding of the relationship between the EC care timeline ADI quartile in systemically marginalized groups. Our use of ADI to represent patient SES further limits our generalizability as this excludes patients with PO boxes, those who live outside the United States, and those who live in very low population areas. The results may have also been impacted by missing data, specifically date of symptom onset. Patients missing this date may represent a population with increased barriers to care and subsequent delays in care.
Overall, this work highlights differences in EC care timeline by SES with significantly increased interval to surgical management of EC for patients in ADI4 compared to ADI1. This delay was driven by delays from symptom onset to first provider evaluation. Future research regarding disparities in endometrial cancer care may focus on association of delays in access to care by insurance status, as it is unclear what role financial constraints may play in timely access to care for EC (Doll et al., 2016). The role of preoperative ultrasound has recently been shown to significantly delay EC diagnosis in Black patients (Gross et al., 2025), but has not yet been studied in the context of geographic disparities. This study is particularly important as current proposed Medicaid/Medicare cuts may lead to even further delays due to lack of access to both primary and gynecologic care. These findings have important implications for intervention design. Efforts to reduce disparities in endometrial cancer outcomes may be more effective if they target earlier stages of the care pathway, including symptom recognition, access to initial evaluation, and continuity with primary gynecologic care. Ideally, future research will also include potential interventions targeting public and provider knowledge of EC symptoms, as well as access to primary gynecologic care.
CRediT authorship contribution statement
Elizabeth Ingersent: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. Emma Donigan: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. Amanda Tapia: Formal analysis, Writing – review & editing. Jessica Gorzelitz: Conceptualization, Writing – review & editing. Manisha Salinas: Conceptualization, Writing – review & editing. Shariska Harrington: Writing – review & editing, Conceptualization. Evelyn A. Reynolds: Conceptualization, Writing – review & editing. Amanika Kumar: Conceptualization, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. Shilpa Mokshagundam: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing.
Funding
Mayo Clinic Women's Health Research Center and Mayo Clinic Women's Health, as well as the Munn Career Development Award.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This study was made possible using the resources of the HOUSES Program, part of the Mayo Clinic Precision Population Science Lab. The content of this article is solely the responsibility of the author(s) and does not represent the official views of the HOUSES Program. Funding was provided through a grant from the Mayo Clinic Women's Health Research Center and Mayo Clinic Women's Health, as well as the Munn Career Development Award.
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