Abstract
Introduction
Several European studies have shown health‐related social inequalities in pelvic gynecological cancers, with a social gradient in incidence and mortality, partly explained by more advanced stages at diagnosis in low socioeconomic populations. Disparities in treatment and quality of care in these patients could be another cause of inequality in care.
Objectives
This study evaluates the impact of socioeconomic factors on quality of care for pelvic gynecological cancers (adnexal, uterine corpus, and cervix).
Methods
This retrospective multicentric cohort study included women diagnosed with pelvic gynecological invasive cancer, between January 1 and December 31, 2022, in six university hospitals in North Paris. Two socioeconomic indicators, the FDep index and the Evaluation of Deprivation and Inequalities in Health Examination Centers (EPICES) score, were collected. The quality of care was assessed using a binary quality indicator based on selected European Society of Gynecological Oncology (ESGO) quality indicators for each cancer. We compared the “Non‐adherence to Quality Indicator” (NAQI) group, where one or more of the ESGO quality indicators were not met, to the “Adherence to Quality Indicator” (AQI) group using univariate analysis. An analysis of the time to first treatment, according to FDep and EPICES groups, using a Kaplan–Meier, estimation was completed.
Results
A total of 189 patients were included: 50 with ovarian cancer, 76 with endometrial cancer, and 63 with cervical cancer. Ninety‐nine patients (52.4%) belonged to the NAQI group and 90 patients (46.7%) to the AQI group. Patients were significantly in poorer general condition and presented more advanced Federation International of Gynecology Obstetrics (FIGO) stages in the NAQI than in the AQI group (ASA score 3–4: 20/84 [23.8%] vs. 5/76 (6.6%), P < 0.01 and FIGO III–IV stage: 55/99 [55.6%] vs. 35/90 [38.9%], P = 0.03). There was no significant difference for socioeconomic indicators between the NAQI and the AQI groups (FDep quintiles 3–4‐5 = 41/99 (41.4%) vs. FDep quintiles 1–2 = 39/90 (43.3%), P = 0.91; EPICES score ≤ 30 = 31/63 (49.2%) vs. EPICES score > 30 = 21/51 (41.2%), P = 0.50). According to FDep and EPICES groups, we found no difference in the time to first treatment.
Conclusion
We found no socioeconomic impact on hospital quality of care in pelvic gynecological cancers.
Keywords: deprived population, pelvic gynecological cancer, quality of care, social deprivation, socioeconomic indicator
1. INTRODUCTION
Social deprivation, defined by the French High Council for Public Health as “the result of a chain of events and experiences leading to situations of economic, social and family fragility,” affects from 20% to 25% of the French population. 1 According to the World Health Organization 2 and Santé Publique France, the deprived population is exposed to social inequalities in health, which are “systematic, avoidable and lead to significant differences in health”. 3 This is particularly true in the specific case of cancer, with lower survival for people belonging to disadvantaged groups, whether measured by education level or socio‐professional category. 4 , 5
Concerning gynecological cancers, that social gradient also exists in terms of incidence and mortality. 5 , 6 , 7 As an example, the EUROCARE‐5 study evaluated the 5‐year gynecological cancer relative survival, for women diagnosed from 2000 to 2007, by including cases archived in over 80 population‐based cancer registries, in 29 European countries. It demonstrated that 5‐year‐survival was lower for patients resident in Eastern Europe (34% ovary and 74% breast) compared with patients resident in Northern Europe (41% ovary and 85% breast) likely due to suboptimal access to adequate care in those countries. 6 Similarly, the study of Jensen et al. included 3007 patients with cervical cancer, 3826 with endometrial cancer, and 3855 with ovarian cancer in a cohort of 3.22 million persons born between 1925 and 1973 in Denmark. 7 Their findings showed that cervical cancer incidence was higher among women with a low level of education, while cervical cancer relative survival was greatest among those with a high socioeconomic status. Additionally, they observed that increased mortality rates for endometrial and ovarian cancer were linked to a low educational level.
A better understanding of social health inequalities related to cancer survival could help guide priorities for pelvic gynecological cancer treatment policies. First, cancer stage at diagnosis has been described as one major prognosis factor. 8 An analysis of UK patients diagnosed with any of 10 common cancers, between 2006 and 2010, found that deprived population was more likely to be diagnosed in advanced stages for endometrial cancer. 8 It was also observed for cervical cancer in studies by Lyratzpoulos et al. 8 and Bradley et al., 9 with more advanced stages at diagnosis, mostly due to lack of screening in deprived populations. Indeed, several French and American studies have highlighted disparities in both medical and surgical treatments based on socioeconomic status. 10 , 11 These studies also report that patients with lower socioeconomic status are less likely to receive care consistent with international guidelines, even though adherence to these guidelines has been shown to improve survival rates. 12 , 13 , 14
The objective of the present study was to evaluate the impact of socioeconomic factors on quality of care in pelvic gynecological cancers.
2. METHODS
2.1. Population, study design and data
This retrospective multicenter cohort study included patients with gynecological cancer managed through the multidisciplinary tumor boards of six university hospitals in North Paris (Bichat, Lariboisière, Saint‐Louis, Louis‐Mourier, Robert‐Debré, and Beaujon), between January 1 and December 31, 2022. Patients included were over 18 years of age and newly diagnosed with pelvic gynecological invasive cancer (ovary, endometrium, or cervix). We excluded patients not treated in our six hospitals (expert opinions only) and those with recurrent cancers, vulvar cancers, and noninvasive pelvic gynecological cancer, those who did not have gynecological cancer, and patients with deprivation indicators missing (FDep).
The study was approved by the CEROG Committee (Obstetrics and Gynecology Research Ethics Committee) under the number CEROG 2023‐GYN‐0103. Moreover, informed consent procedures for retrospective data collection were followed. In our six university hospitals in the Northern Paris University Hospital Group (Bichat, Lariboisière, Saint‐Louis, Louis‐Mourier, Robert Debré, and Beaujon), every patient was informed that their anonymized data may be used for research purposes.
2.2. Socioeconomic indicators
The following socioeconomic indicators were collected or derived from patients' medical files:
Statutory health insurance affiliation
Voluntary health insurance status
Place of birth and length of time spent in France for patients born elsewhere
French language spoken
Level of education (from no diploma to college degree)
The social deprivation index of where patients live, assessed by the French DEPrivation index (FDep) (It is a multidimensional, area‐based index that measures socioeconomic level differences using four variables with two positive and two negative dimensions: the median income per household, the high school graduation rate in the population aged 15 years or older, the unemployment rate in the labor force and the rate of blue‐collar workers in the labor force. It was developed specifically for France, and the higher the index, the greater the social disadvantage [minimum = −6.1; maximum = 10.3]. 15 It was measured at the IRIS [“Ilots Regroupés pour l'Information Statistique,” or aggregated units for statistical information] level; i.e., the smallest geographical area for which statistical data is available in France, which was obtained from patients' addresses. The FDep was divided into quintiles following the national distribution of the variable and then grouped into two study groups: quintiles 1 and 2 [least disadvantaged] and quintiles 3, 4, and 5 [most disadvantaged].)
The Evaluation of Deprivation and Inequalities in Health Examination Centers (EPICES) score, obtained from a validated questionnaire (It includes 11 yes/no questions covering multiple dimensions of social and material deprivation shown to be strongly correlated with several health indicators [Data S1] 16 ).
Time to first treatment (time between the initial consultation and the first treatment).
2.3. Quality of care indicator
The European Society of Gynecological Oncology (ESGO) has published guidelines concerning surgical and oncological quality indicators for each pelvic gynecological cancer (ovary, endometrium, and cervix). 17 , 18 , 19 , 20 , 21
To assess quality of care, we first selected 30 of the ESGO quality indicators specific to our study population, following on a reviewer consensus: 9 for ovarian cancer, 11 for endometrial cancer and 10 for cervical cancer (Figure 1). Second, we created for each patient a binary quality indicator depending on whether they met all the ESGO indicators or not; patients were either in the “Adherence to Quality Indicators” (AQI) group if all of the ESGO indicators were met or in the “Non‐Adherence to Quality Indicators” (NAQI) group if one or more of the ESGO indicators were not met. All selected ESGO quality indicators contributed equally to this group assignment.
FIGURE 1.
The 30 selected European Society of Gynecological Oncology (ESGO) quality‐of‐care indicators for the three pelvic gynecological cancers.
2.4. Statistical analysis
First, we compared the NAQI group and the AQI group for socioeconomic factors. We used a univariate analysis; the two groups were compared using the χ2 or Fisher's test for qualitative variables and Student's test, analysis of variance, or the Kruskal–Wallis test for quantitative variables. Odds ratios (ORs) with their 95% confidence intervals (CI) were also used for qualitative variables. Then, an analysis of the time to first treatment was carried out using Kaplan–Meier curves according to the FDep groups (FDep quintiles 1–2 vs. FDep quintiles 3–4–5) and the EPICES groups (EPICES score ≤ 30 vs. EPICES score > 30), using a Kaplan–Meier estimation. Hazard ratios (HR) with 95% confidence intervals and P‐values were calculated to assess the significance of the differences between groups.
All analyses were two‐tailed, and a threshold of P < 0.05 was considered for significance. Statistical analyses were performed using RStudio, Version 2023.03.0 + 386, with the following packages: ggplot2, gtsummary, survival, finalfit, and survminer.
3. RESULTS
3.1. Study population
Figure 2 presents the flow chart of patients included in the study. A total of 625 patients were included in the oncology multidisciplinary tumor board cohort of the North AP‐HP University Hospitals between January 1, 2022, and December 31, 2022. After applying our inclusion and exclusion criteria, 189 patients were included in our study: 50 with ovarian cancer, 76 with endometrial cancer, and 63 with cervical cancer. We excluded 436 patients, including two patients with missing deprivation indicators. These two patients were specifically excluded due to the absence of the FDep index, which was essential to our primary objective: assessing the impact of deprivation on quality of care. Given the very small number of exclusions for this reason (only two patients), we did not perform a statistical comparison of their clinical characteristics with those of the included population. We believe that such a minimal exclusion is unlikely to have introduced any significant bias, especially considering the overall robustness of our final study population (n = 189).
FIGURE 2.
Flowchart.
Characteristics of the study population are presented in Table 1. There were 99 patients in the NAQI group (52.4%) and 90 patients in the AQI group (47.6%). Patients were significantly older (63.0 ± 14.2 years vs. 55.0 ± 14.0 years, P < 0.01) and in poorer general condition (ASA score 3–4: 20/84, 23.8% vs. 5/76, 6.6% P < 0.01) in the NAQI group than in the AQI group. The distribution of pelvic gynecological cancers between the two groups was significantly different (P < 0.01), with a higher proportion of ovarian cancers in the NAQI group (37.4% vs. 14.4%) and a higher proportion of cervical cancer in the AQI group (45.6% vs. 22.2%). Significantly more advanced Federation International of Gynecology Obstetrics (FIGO) stages were observed in the NAQI than in the AQI group (FIGO III–IV: 55.6% vs. 38.9%, P = 0.03). No differences were found between the two groups for the other characteristics.
TABLE 1.
Characteristics of the study population.
| Variables | Non‐adherence to quality indicator N = 99 | Adherence to quality indicator N = 90 | All population N = 189 | OR CI 95% | P |
|---|---|---|---|---|---|
| Age at diagnosis (years) Mean ± SD | 63.0 ± 14.2 | 55.0 ± 14.0 | 59 ± 15 | – | <0.01 a |
| ASA score n (%) | |||||
| ASA 1–2 | 64/84 (76.2%) | 71/76 (93.4%) | 135/160 (84.4%) | 1 | 0.01 b |
| ASA 3–4 | 20/84 (23.8%) | 5/76 (6.6%) | 25/160 (15.6%) | 0.23 [0.08–0.64] | |
| BMI (m2/kg) n (%) | |||||
| BMI < 30 | 63/97 (64.9%) | 58/85 (68.2%) | 121/182 (66.5%) | 1 | 0.76 b |
| BMI > 30 | 34/97 (35.1%) | 27/85 (31.8%) | 61/182 (35.5%) | 0.86 [0.46–1.60] | |
| History of chronic illness n (%) | |||||
| Yes | 58/99 (58.6%) | 41/89 (46.1%) | 99/188 (52.7%) | 1 | 0.12 b |
| No | 41/99 (41.4%) | 48/89 (53.9%) | 89/188 (47.3%) | 1.66 [0.93–2.95] | |
| History of gynecological cancer n (%) | |||||
| Yes | 10/99 (10.1%) | 4/89 (4.5%) | 14/188 (7.4%) | 1 | 0.24 b |
| No | 89/99 (89.9%) | 85/89 (95.5%) | 174/188 (92.6%) | 2.39 [0.72–7.90] | |
| History of abdominal surgery n (%) | |||||
| Yes | 53/99 (53.5%) | 42/89 (47.2%) | 95/188 (50.5%) | 1 | 0.47 b |
| No | 46/99 (46.5%) | 47/89 (52.8%) | 93/188 (49.5%) | 1.29 [0.73–2.29] | |
| Tabaco use n (%) | |||||
| Yes | 14/78 (17.9%) | 15/67 (22.4%) | 29/145 (20%) | 1 | 0.14 b |
| No | 55/78 (70.5%) | 50/67 (74.6%) | 105/145 (72.4%) | 0.85 [0.37–1.94] | |
| Weaned | 9/78 (11.5%) | 2/67 (3.0%) | 11/145 (7.6%) | 0.21 [0.04–1.15] | |
| Menopause n (%) | |||||
| Yes | 77/96 (80.2%) | 49/87 (56.3%) | 126/183 (68.9%) | 1 | <0.01 b |
| No | 19/96 (19.8%) | 38/87 (43.7%) | 57/183 (31.1%) | 3.14 [1.63–6.06] | |
| Type of pelvic gynecological cancer n (%) | |||||
| Ovarian | 37/99 (37.4%) | 13/90 (14.4%) | 50/189 (26.5%) | 1 | <0.01 b |
| Endometrial | 40/99 (40.4%) | 36/90 (40.0%) | 76/189 (40.2%) | 2.56 [1.18–5.56] | |
| Cervix | 22/99 (22.2%) | 41/90 (45.6%) | 63/189 (33.3%) | 5.3 [2.34–12] | |
| FIGO stage n (%) | |||||
| FIGO I–II | 44/99 (44.4%) | 55/99 (61.1%) | 99/189 (52.4%) | 1 | 0.03 b |
| FIGO III–IV | 55/99 (55.6%) | 35/99 (38.9%) | 90/189 (47.6%) | 0.51 [0.28–0.91] | |
Note: Statistical tests were carried out on the values available for each variable. Results for qualitative variables are given as a number (%), with the denominator corresponding to the total of data available for each variable.
Abbreviations: 95% CI, 95% confidence interval; ASA, American Society of Anesthesiologists; BMI, body mass index; FIGO, Federation International of Gynecology Obstetrics; OR, odds ratio; SD, standard derivation.
χ2‐test or Fisher's test.
Student test, Anova's or Kruskal–Wallis' test.
3.2. Quality of care
Socioeconomic indicators of the study population are presented in Table 2. No significant differences were found between the NAQI group and the AQI group for any of the socioeconomic factors: social security affiliation and supplementary pension scheme (P = 0.26), place of birth (P = 0.05), French spoken (P = 0.095), level of education (P = 0.095), nor length of time living in France (P = 0.08). Overall, 42.3% of the population was socially deprived according to the FDep index and 45.6% according to the EPICES score. No significant difference was found either for the FDep groups: FDep quintiles 3–4–5 = 41/99 (41.4%) vs. FDep quintiles 1–2 = 39/90 (43.3%) nor for the EPICES groups: EPICES score ≤ 30 = 31/63 (49.2%) vs. EPICES score > 30 = 21/51 (41.2%).
TABLE 2.
Quality of care depending on socioeconomic factors.
| Variables | Non‐adherence to quality indicator N = 99 | Adherence to quality indicator N = 90 | All population N = 189 | OR IC95% | P |
|---|---|---|---|---|---|
| Type of health insurance n (%) | |||||
| SHI + VHI | 30/99 (30.3%) | 25/90 (27.8%) | 55/189 (29.1%) | 1 | 0.26 a |
| SHI + no VHI | 5/99 (5.1%) | 2/90 (2.2%) | 7/189 (3.7%) | 0.67 [0.24–1.88] | |
| SHI + unknown VHI | 11/90 (11.1%) | 18/90 (20%) | 29/189 (15.3%) | 0.22 [0.09–0.51] | |
| AME | 6/99 (6.1%) | 4/90 (4.4%) | 10/189 (5.3%) | 0.09 [0.02–0.34] | |
| CMU | 44/99 (44.4%) | 41/90 (45.6%) | 85/189 (45%) | 0.12 [0.07–0.21] | |
| No SHI | 3 (3%) | 0 (0%) | 3/189 (1.6%) | 0.02 [0–0.41] | |
| Place of birth n (%) | |||||
| France | 51/97 (52.6%) | 40/89 (44.9%) | 91/186 (48.9%) | 1 | 0.05 a |
| Europe | 7/97 (7.2%) | 11/89 (12.4%) | 18/186 (9.7%) | 2.0 [0.71–5.63] | |
| Northern Africa | 14/97 (14.4%) | 12/89 (13.5%) | 26/186 (14%) | 1.09 [0.45–2.62] | |
| Sub‐Saharan Africa | 17/97 (17.5%) | 10/89 (11.2%) | 27/186 (14.5%) | 0.75 [0.31–1.82] | |
| Asia | 8/97 (8.2%) | 9/89 (10.1%) | 17/186 (9.1%) | 1.43 [0.51–4.04] | |
| Other | 0/97 (0.0%) | 7/89 (7.9%) | 7/186 (3.9%) | 17.85 [0.98–324] | |
| French spoken n (%) | |||||
| Yes | 79/88 (89.8%) | 76/78 (97.4%) | 155/166 (93.4%) | 1 | 0.095 a |
| No | 9/88 (10.2%) | 2/78 (2.6%) | 11/186 (6.6%) | 0.23 [0.05–1.10] | |
| Level of education n (%) | |||||
| No Diploma | 20/59 (33.9%) | 8/57 (14.0%) | 28/116 (24.1%) | 1 | 0.095 a |
| Middle school ‐ BEP – CAP | 8/59 (13.6%) | 9/57 (15.8%) | 17/116 (14.7%) | 2.81 [0.80–9.87] | |
| Hight School Level (Baccalaureat) | 11/59 (18.6%) | 13/57 (22.8%) | 24/116 (20.7%) | 2.95 [0.94–9.29] | |
| Higher Education – College | 20/59 (33.9%) | 27/57 (47.4%) | 47/116 (40.5%) | 3.38 [1.24–9.22] | |
| Length of time living in France n (%) | |||||
| Less than a year | 3/72 (4.2%) | 1/69 (1.4%) | 4/141 (2.8%) | 1 | 0.08 a |
| From 1 to 5 years | 0/72 (0.0%) | 4/69 (5.8%) | 4/141 (2.8%) | 24 [0.59–980] | |
| More than 5 years | 69/72 (95.8%) | 64/69 (92.8%) | 133/141 (94.3%) | 2.78 [0.28–27.41] | |
| FDep groups n (%) | |||||
| Quintiles 1–2 (least deprived) | 58/99 (58.6%) | 51/90 (56.7%) | 109/189 (57.7%) | 1 | 0.91 a |
| Quintiles 3–4–5 (most deprived) | 41/99 (41.4%) | 39/90 (43.3%) | 80/189 (42.3%) | 1.08 [0.61–1.92] | |
| EPICES groups n (%) | |||||
| Score ≤ 30 (not deprived) | 32/63 (50.8%) | 30/51 (58.8%) | 62/114 (54.4%) | 1 | 0.50 a |
| Score > 30 (deprived) | 31/63 (49.2%) | 21/51 (41.2%) | 52/114 (45.6%) | 0.72 [0.34–1.52] | |
Note: Statistical tests were carried out on the values available for each variable. Results for qualitative variables are given as a number (%), with the denominator corresponding to the total of data available for each variable.
Abbreviations: 95% CI, 95% confidence interval; AME, State Medical Aid; CMU, Universal Medical Coverage; EPICES, Evaluation of Deprivation and Inequalities in Health Examination Centers; FDep, French DEPrivation index; OR, odds ratio; SS, social security.
Chi2's or Fisher's test.
Time to first treatment Kaplan–Meier curves are presented in Figure 3. There was no significant difference in time to first treatment between the FDep groups (HR = 0.92, 95% CI [0.69–1.24], P = 0.60), nor between the EPICES groups (HR = 0.88, 95% CI [0.60–1.29], P = 0.52).
FIGURE 3.
Time to first treatment Kaplan–Meier curves according to the French DEPrivation index (FDep) groups (FDep quintiles 1–2 vs. FDep quintiles 3–4–5) and the Evaluation of Deprivation and Inequalities in Health Examination Centers (EPICES) groups (EPICES score ≤ 30 vs. EPICES score > 30). EPICES, Evaluation of Deprivation and Inequalities in Health Examination Centers; FDep, French DEPrivation index; HR, Hazard Ratio.
4. DISCUSSION
Our study did not find any significant relationship between socioeconomic factors and quality of care in pelvic gynecological cancers, neither for our binary quality indicator, based on ESGO criteria, nor for the time to first treatment.
Our findings diverge from previous research, where a significant difference in quality of care according to socioeconomic status is often reported. For example, the study of Gardy et al. showed a difference in cytoreduction surgery rates for ovarian cancer according to FDep quintiles. 14 Further, the study of Abdel‐Rahman et al. showed that the difference in survival according to socioeconomic status for ovarian cancer disappeared if equivalent treatments were received, thus assuming that initial treatments received were different. 22 Moreover, some US studies also showed differences in management and adherence to national guidelines according to socioeconomic status for pelvic gynecological cancers, although the specificities of the healthcare system in the USA make it difficult to compare those findings to ours. 10 , 12 , 23 This literature discordance might be explained by our selected cohort population. Indeed, the university hospitals of North AP‐HP are mostly located in deprived areas with significant deprived population, as shown by the fact that the proportion of deprived patients in our study population was higher than in the French national population. 1 Those centers are therefore certainly more experienced with the complex management of social deprivation and so could limit socioeconomic inequalities in health. Indeed, these AP‐HP hospitals provide comprehensive and specialized medical services that are accessible to the entire population, regardless of socioeconomic status. They implement measures specifically aimed at promoting healthcare equity with programs targeting high‐risk and disadvantaged populations. A few non‐exhaustive examples include the “PASS” program (Permanent Access to Healthcare), which offers free, universal, and continuous access to health care for individuals in precarious situations, 24 multilingual patient support services designed to overcome language barriers for non‐French‐speaking patients, and embedded social worker assistance to support vulnerable patients in navigating administrative and financial challenges.
Our study has some particular strengths. First, we used multiple socioeconomic indicators, as recommended when studying social inequalities in health. On one hand, the FDep index is a national proxy for economic and financial disadvantages. It could be less accurate for the Ile‐de‐France region because of its high population density and its significant social heterogeneity in very close geographical areas. For this reason, in our study, the patient's distribution across FDep quintiles was not uniform (41.8% in Quintile 1, 15.9% in Quintile 2, 11.1% in Quintile 3, 10% in Quintile 4, and 21.2% in Quintile 5). On the other hand, the EPICES score is a robust proxy for social disadvantage. Because it was constructed on a selected population of patients consulting in health examination centers, it could generate a selection bias and potentially a less accurate socioeconomic indicator in the general population. However, the FDep index and the EPICES score remain two recognized socioeconomic indicators used in several other studies, 14 making our results robust and comparable. Second, we selected 30 quality indicators from the ESGO Guidelines concerning surgical and oncological quality indicators for pelvic gynecological cancers (ovary, endometrium, and cervix). Those 30 selected ESGO quality indicators concerned both individual patient management (from diagnosis to surgical/medical treatment) and global center performance (e.g., number of surgeries performed in the center per year per surgeon). 17 , 18 , 19 , 20 , 21
Some limitations of this study should be highlighted. First, the retrospective design of the cohort used in this study with the proportion of missing data could lead to some bias. Notably, there was a rate of 40.2% missing data for the EPICES score, which could have reduced the power to detect a potential association. As our study was based on univariate analyses rather than multivariate modelstgaa, we did not perform a multiple imputation for missing data or a sensitivity analysis approach. Indeed, each variable was analyzed using available case data, which is an accepted method in univariate designs. No patients were excluded from the study due to missing EPICES data; however, analyses involving the EPICES score were conducted only on patients for whom the score was available. In future studies involving multivariate analyses, a missing data handling approach would be warranted. Second, the limited sample size of our current study, which might reduce the statistical power to detect small or moderate associations between socioeconomic factors and quality of care, even in the presence of true underlying disparities. However, our study included all eligible patients with pelvic gynecological invasive cancers treated in 2022 across six university hospitals of the North AP‐HP group, thus representing a comprehensive and real‐life cohort of a population particularly exposed to precariousness. A larger follow‐up study including a broader sample across multiple regions will be planned to confirm and extend these findings. Additionally, we managed to include all three types of pelvic gynecological cancers and we could have carried out a subgroup analysis by cancer, but the numbers of patients would not have allowed us to conclude that there was a difference. Third, we chose to classify patients as NAQI if they failed to meet even one of the selected ESGO quality indicators but to adopt a conservative and stringent approach to defining non‐adherence. This binary classification allowed us to clearly distinguish between full adherence and any deviation from guideline‐recommended care, which we considered clinically relevant in a quality‐of‐care evaluation. However, a threshold or score‐based approach, accounting for the number or relative weight of unmet indicators, or a sensitivity analysis could have offered a more nuanced and graduated assessment of adherence. We will explore that perspective in future studies with larger sample sizes and sufficient statistical power to support such stratified analyses.
5. CONCLUSION
Our study did not find any significant difference in terms of quality of care in invasive pelvic gynecological cancers due to socioeconomic factors. The results suggest the strength of the North AP‐HP hospital healthcare system in managing patients with severe social deprivation. Further studies should be undertaken, covering a wider geographical area outside the Ile de France region.
AUTHOR CONTRIBUTIONS
Lea Mauny: conceptualization, data collection, data analysis, drafting of the manuscript. Camille Mimoun: data analysis, critical revision of the manuscript. Morgane Michel: methodology, statistical analysis, critical revision of the manuscript. Bérengère Tate: data collection, interpretation of results, critical revision of the manuscript. Martin Koskas: supervision, clinical expertise, critical revision of the manuscript. Blandine Colmont: patient coordination, clinical data collection. Abida Haneefa: patient coordination, clinical data collection. Corine Alberti: methodology, validation, supervision. Karine Chevreul: conceptualization, supervision, critical revision of the manuscript. Cyrille Huchon: project administration, supervision, critical revision of the manuscript.
FUNDING INFORMATION
No funding source was involved for this study.
CONFLICT OF INTEREST STATEMENT
The authors have no conflicts of interest to declare.
Supporting information
Data S1.
DATA AVAILABILITY STATEMENT
Research data are not shared.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1.
Data Availability Statement
Research data are not shared.