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Treatment delay and treatment pattern modifications among epithelial ovarian cancer patients during the COVID‐19 pandemic: A retrospective cohort study

Abstract Background and Objectives The coronavirus disease 2019 (COVID‐19) pandemic disrupted healthcare access and medical treatment, including oncological care. Treatment delay in ovarian cancer could impact survival. We aimed to assess if there were delays and treatment changes in a cohort of epithelial ovarian cancer patients. Methods A retrospective cohort of epithelial ovarian cancer patients included cases diagnosed during the first 22 months of the COVID‐19 pandemic in the state of Sao Paulo and those diagnosed in the 22 months preceding the outbreak. Time‐to‐treat was measured in days. In each group, surgery and chemotherapy proportions were assessed according to healthcare insurance status. Results A 56.2% reduction in epithelial ovarian cancer diagnosis was identified during the pandemic group compared to the prepandemic group; fewer patients were diagnosed in stage I (p < 0.01). Time‐to‐treat increased from 18.9 to 23 days (p < 0.01). Surgery in the public sector fell from 74.6% to 65.3% during the pandemic, compared to 87.1% to 68.8% in the private sector. Conclusion There were fewer overall diagnoses, reduced stage I diagnosis, increased time‐to‐treat, and a reduction in the proportion of patients submitted to surgery. Brazil's public healthcare system demonstrated a higher resiliency to treatment change than the private sector.

started treatment during this 22-month period. We imposed this restriction to avoid bias since patients in the control group could have differences between diagnosis and treatment that the first group could not match. Furthermore, patients diagnosed previous to the pandemic but not yet treated when it began could be affected by it.
We also aimed to analyze if other variables differed between these two groups, such as: age, staging; if the patient was submitted to chemotherapy; if surgery was performed; the distance between the patient's city of residence and the city where the hospital was located. We also tracked whether the patient's treatment was in the public healthcare system or private treatment.
Last, we aimed to assess if there were changes in treatment patterns between the prepandemic and pandemic groups. In this step, groups were not restricted to patients who already had begun treatment; our goal in this analysis was not to assess the time up to treatment but rather the existence of treatment or not. We evaluated each group individually to see if there was a difference in the performance of chemotherapy and surgery according to the treatment provider.
A data file containing patients in the Hospital Cancer Registry (HCR), provided publicly by FOSP, was obtained. We proceeded to include all patients with the International Disease Code of C56, which corresponds to ovarian cancer. Patients were required to be residents of the State of Sao Paulo and have no previous cancer diagnosis or treatment. Patients were excluded if they had missing data regarding age, distance traveled, staging, or presented tumors not included in the epithelial histology.
We included in the control group patients diagnosed from May 2018 up to February 2020, and in the analysis group, patients diagnosed from March 2020 up to December 2021. For the analysis regarding time from diagnosis to treatment, we excluded from both groups patients who had not received treatment during the timeframe of their groups. Conversely, we excluded patients with unknown provider status for the analysis regarding changes in treatment patterns and possible association with an insurance provider.
Our file contained the following variables considered during the analysis. Age was available, expressed in years. The interval between diagnosis and treatment was available in the data file, described in days. Surgery and chemotherapy status were available separately as binary variables, thus present or absent. The staging was grouped as I, II, III, or IV. Treatment provider was classified as public healthcare or private treatment. Distance is expressed in kilometers and was calculated from available data: using the city of residence and the city of treatment, we applied the Haversine formula using publicly available latitude and longitude coordinates for each city. Thus, patients treated in the same city they resided in had a distance variable of zero.
We compared both groups using the Wilcoxon-Mann-Whitney test to analyze the interval between diagnosis and treatment. We used this same test to verify if there was a difference between the distance traveled between the two groups and their ages. We compared both groups staging variables using the Chi-square test.
We also performed the Chi-square test to compare both groups' surgery status and chemotherapy status rates.
As for the analysis of possible treatment pattern modifications, we analyzed each group individually. We used the Chi-square test for each of them to determine whether there was a difference in surgery and chemotherapy rates according to the healthcare provider status.
Additionally, we performed an exploratory analysis using the Chisquare test in each of the groups of the treatment pattern analysis to verify if there was a correlation between staging and healthcare provider status, which, if present, could work as a confusion variable.

| RESULTS
Using the method above, we included 650 patients in the time-totreat analysis; 198 patients were treated during the COVID-19 pandemic, and 452 patients were treated in the control group. This represents a 56.2% reduction from the prepandemic period to the pandemic period. Table 1 summarizes the characteristics of both groups.
It is worth noting the mean time between diagnosis and treatment was 23 days in the pandemic group and 18.9 days in the prepandemic group, and the Wilcoxon-Mann-Whitney for this association has a p < 0.01. Additionally, the pandemic group has a lower percentage of patients diagnosed and treated in stage I. The Chi-square test has a p < 0.01. Among patients diagnosed and treated before the pandemic began, 82.1% received surgery. This value is 65.7% in the pandemic group, and the Chi-square test has a p < 0.01. As for chemotherapy rates, an increase is identified, from 76.3% in the prepandemic group to 84.8% in the pandemic group. The Chi-square test has a p value of 0.019 for this analysis. Values for age, distance traveled, and healthcare provider status was similar between both groups, and statistical tests did not show a difference regarding these variables.
For the treatment pattern analysis, a total of 293 patients were included in the pandemic-affected group, and 663 patients were included in the control group, amounting to 958 patients. This displays a 55.8% reduction in the pandemic period compared to the prepandemic interval. Statistical analysis was performed separately in each group regarding healthcare provider status and treatmentrelated variables.
The pandemic group included 213 patients treated in the public healthcare system and 80 patients treated privately. Table 2 describes the age, surgery, and chemotherapy status of each of these groups. Statistical tests did not show a difference between public and private-treated patients regarding all variables. The exploratory analysis did not demonstrate clinical staging differences between healthcare provider status groups.
The control group included 516 patients treated in the public healthcare system and 147 patients treated privately. Results for each variable according to the healthcare provider status group are shown in Table 3. The surgery rate in the public healthcare group was 74.6%, while this value was 87.1% in the private treatment group.
Chi-square test results in a p < 0.01. Age and chemotherapy status  did not present statistically significant differences between groups.
The exploratory analysis did not find differences in staging regarding healthcare provider status.
The comparison between both groups' surgery rates, grouped by healthcare provider status, is illustrated in Figure 1. A multicenter prospective cohort study in France has also demonstrated the impact of the COVID-19 pandemic on ovarian cancer care. 13 In that study, 34.6% of ovarian cancer patients had their surgeries postponed or canceled. While quantitative comparisons are not possible due to significant methodological differences, these results converge to those found in this current analysis.

| DISCUSSION
A case series of Japanese and Korean patients has reported evidence of chemotherapy delay and interruption due to COVID-19 suspicion among ovarian cancer patients. 14 A different case series of Japanese patients with suspected or confirmed gynecological malignancies also demonstrated treatment delay due to concerns related to COVID-19. 15 These results are convergent to our research and also reinforce the perception of the widespread impact of COVID-19 among ovarian cancer patients from different countries.

A survey among gynecologic oncology specialists and trainees in
Turkey identified a change in ovarian cancer treatment patterns. 16 This study reported that 50% of physicians administered more neoadjuvant chemotherapy cycles to patients who had already completed their chemotherapy. Furthermore, 12.9% of physicians delayed advanced ovarian cancer surgery or referred these patients.
Similar to our research, these results demonstrate surgery reduction and treatment delay among ovarian cancer patients.
Regarding the impact of the pandemic on cancer care and its correlation with healthcare provider status, a retrospective cohort study has compared a Canadian publicly funded hospital results with an American privately funded cancer care center results. 17  Further studies are necessary to better comprehend the reasons behind this resiliency of the public healthcare system upon sudden and significant pressures, and also the reasons behind the previous gap between public and insurance-based treated groups.
Interpreting the changes in chemotherapy rates is challenging since this data was available as a binary variable. Studies considering the issue of chemotherapy have focused on changes in the neoadjuvant modality 12,13 Due to our data's nature, we cannot analyze similar hypotheses. It is possible the changes identified in this cohort are associated with the reduction of stage I patients in the pandemic group.
Before the pandemic, studies have considered ovarian cancer and its relationship with geospatial variables. 19 Geographical location has been found to be an independent predictor of advanced-stage ovarian cancer mortality; however, this significance was largely reduced after considering other variables associated with appropriate treatment. In the previously mentioned Indian cohort, the authors hypothesized travel restrictions played a role in treatment delay. 12 It has been shown cancer surgery can be safely performed during the COVID-19 outbreak, given a COVID-19-free path is obtained; whether that requires changing previously established referral paths depends on local epidemiological concerns. 20 Our study found no differences in the distance traveled between both treatment groups.

CONFLICT OF INTEREST
The authors declare no conflict of interest.