The Impact of Social Inequalities on the Survival of Patients With Pancreatic Cancer: Analysis of a Cohort of 1,426 Cases in Brazil

The Impact of Social Inequalities on the Survival of Patients With Pancreatic Cancer: Analysis of a Cohort of 1,426 Cases in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Social Inequalities on the Survival of Patients With Pancreatic Cancer: Analysis of a Cohort of 1,426 Cases in Brazil Camila Drumond Muzi, Raphael Mendonça Guimarães, Clara Soares Rosas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7552704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Pancreatic cancer remains one of the most lethal malignancies worldwide, with a five-year survival rate below 10%. In Brazil, significant regional and social inequalities shape access to diagnostic and therapeutic services, leading to disparities in survival outcomes. This study aimed to evaluate healthcare-related factors associated with survival among Brazilian patients with pancreatic cancer, focusing on institutional barriers, race, and health system entry point. Methods We conducted a retrospective cohort study of 1,426 patients diagnosed with pancreatic adenocarcinoma at the National Cancer Institute (INCA 1, Brazil) between 2000 and 2023. Data were obtained from the Hospital Cancer Registry. Survival analyses were structured into three temporal models: (1) consultation–death, (2) diagnosis–death, and (3) treatment–death. Kaplan–Meier curves and log-rank tests were used for bivariate analysis, and Cox proportional hazards models were applied to estimate hazard ratios (HR) with 95% confidence intervals (CI). Results Median survival was 81 days from diagnosis to death and 75 days from first consultation to death. After treatment initiation, median survival increased to 170 days. In pre-treatment models, survival was significantly influenced by race, referral source, and prior diagnosis/treatment. Black patients (median 94.3 days) had shorter survival than white patients (131.5 days; p < 0.001). Patients entering care via the public health system (SUS) survived less (101.4 days) than those treated outside SUS (123.2 days; p = 0.05). Institutional follow-up was the strongest protective factor, associated with survival differences exceeding 260 days. In the treatment–death model, most inequalities attenuated, but institutional follow-up remained highly predictive (451.3 vs. 121.3 days; HR = 2.28; 95% CI: 1.34–3.89). Conclusions Social and institutional inequalities significantly affect survival in pancreatic cancer, particularly before treatment initiation. Race, referral source, and prior diagnosis/treatment strongly influenced prognosis in early stages, but disparities decreased after specialized therapy began. Institutional follow-up emerged as the main determinant of improved survival, highlighting the importance of timely diagnosis, equitable referral pathways, and patient-centered continuity of care to reduce health inequities in highly lethal cancers. Pancreatic Cancer Neoplasms Survival Analysis Cancer Registries INTRODUCTION Pancreatic cancer represents one of the most significant challenges in contemporary oncology due to its high lethality, frequent late diagnosis, and poor therapeutic response. Globally, it is the seventh leading cause of cancer-related death, and projections indicate a worsening of this scenario in the coming decades. According to the Global Cancer Observatory of the International Agency for Research on Cancer (IARC), in 2022, there were approximately 510,992 new cases of pancreatic cancer and 467,409 deaths from the disease, resulting in a mortality rate almost equivalent to its annual incidence, with an age-standardized rate of 5.9 per 100,000 inhabitants worldwide [ 1 ]. Although pancreatic cancer is relatively less frequent than other malignant neoplasms, its five-year survival rate remains extremely low, ranging from 5% to 10% in high-income countries [ 2 ]. It is estimated that by 2040, it will become the second leading cause of cancer-related death in the United States, surpassing colorectal cancer, primarily due to population aging and the absence of effective screening strategies [ 3 ]. In Brazil, updated data from the National Cancer Institute José Alencar Gomes da Silva (INCA), published in Estimativa 2023: Incidência de Câncer no Brasil , indicate that pancreatic cancer accounts for 10,980 new cases per year during the 2023–2025 triennium, with 5,690 in men and 5,290 in women [ 4 ]. Although it does not rank among the ten most frequent cancers, its lethality is striking: in 2021, 11,971 deaths were reported from this cause, 5,949 in men and 6,022 in women, according to the Mortality Information System (SIM) of the Ministry of Health [ 4 ]. Between 1990 and 2021, the global burden of pancreatic cancer, measured in DALYs, increased from 5.21 million to 11.32 million. Mortality rates showed a similar upward trend. The Global Burden of Disease study also recognizes notable increases in age-standardized incidence and mortality rates in low-middle and low sociodemographic index regions [ 5 ]. In fact, Chaves et al. [ 6 ] estimated age-standardized incidence and mortality rates of pancreatic cancer in Brazil and found a significant increase. While incidence rose from 5.33 (95% CI: 5.06–5.51) to 6.16 (95% CI: 5.68–6.53) per 100,000 inhabitants, states with lower SDI presented lower absolute rates but the highest annual increases. This panorama highlights not only the magnitude of pancreatic cancer mortality but also significant regional and socioeconomic inequalities in Brazil. Previous studies have identified sharp increases in mortality rates in the North and Northeast regions between 2000 and 2014, while reductions in the South and Southeast may be associated with greater access to specialized services and earlier diagnosis [ 7 ]. Nonetheless, even in more developed regions, pancreatic cancer is characterized by silent progression, aggressive clinical course, and limited curative options. The primary established risk factors for pancreatic cancer include smoking, responsible for approximately 20% of cases, obesity, type 2 diabetes mellitus, excessive alcohol consumption, chronic pancreatitis, family history of the disease, and genetic factors such as mutations in BRCA1/2 and CDKN2A [ 8 , 9 ]. Moreover, epidemiological studies indicate that diets rich in saturated fats and processed meats, as well as occupational exposure to chemical solvents, may contribute to its development [ 10 ]. The lack of specific symptoms in early stages is one of the main reasons why most cases are diagnosed late, often in stage III or IV, when surgical resection—the only potentially curative option—is no longer feasible. Combined with the aggressive biological phenotype of the tumor, this results in one of the worst prognoses among all solid cancers [ 2 ]. A Brazilian study by Jesus et al. [ 11 ], which evaluated a cohort of 6,855 patients between 2000 and 2014, found that socioeconomic variables, particularly health system funding, were the main determinants of survival. In this study, the median overall survival was 4.9 months (95% CI: 4.7–5.2). Factors such as older age, male sex, lower educational level, treatment in public facilities, absence of treatment, and advanced stage were associated with lower survival. Current evidence indicates that more than 75% of patients with pancreatic cancer are diagnosed at advanced stages, with vascular and lymph node involvement, which precludes curative treatment in most cases. Even with palliative chemotherapy, median survival rarely exceeds 6 to 9 months [ 12 ]. Early detection of pancreatic cancer is challenging due to the lack of specific symptoms in its initial phases. Consequently, many patients are diagnosed at advanced stages, limiting therapeutic options and contributing to poor survival rates. Furthermore, socioeconomic barriers and inequalities in access to healthcare services may delay diagnosis and treatment, exacerbating disparities in clinical outcomes. In this context, it is crucial to understand the factors influencing survival in Brazilian patients with pancreatic cancer in order to guide public health policies and intervention strategies. Therefore, the primary objective of this article was to investigate healthcare-related factors associated with survival by mapping the therapeutic pathway, with special attention to inequalities and barriers to timely access to diagnosis and treatment. METHODS Study design It is an observational, analytical, retrospective cohort study aiming to evaluate the survival of patients with a confirmed diagnosis of pancreatic cancer. Data were obtained from the Cancer Hospital Registry (Registro Hospitalar de Câncer, RHC) of the National Cancer Institute José Alencar Gomes da Silva (INCA), Unit INCA 1, covering the period from January 2000 to December 2023. Population and eligibility criteria The study analyzed secondary data from the Hospital Cancer Registry of Hospital do Câncer 1, National Cancer Institute of Brazil. This database includes patients diagnosed with pancreatic cancer. Eligible participants were those with a primary diagnosis of pancreatic cancer (ICD-10: C25) registered in the RHC, with a diagnosis date between 01/01/2000 and 12/31/2023. Inclusion criteria were: histopathological confirmation of pancreatic adenocarcinoma; complete records of diagnosis and last follow-up dates (death or censoring); and age ≥ 18 years at diagnosis. Exclusion criteria were: secondary pancreatic cancer (metastases from other sites); inconsistent data or missing essential information (e.g., absence of diagnosis or outcome dates); and cases in which the death date preceded the diagnosis date. After applying the eligibility criteria, the final cohort comprised 1,426 patients. All eligible individuals admitted to the hospital during the study period were followed until death, loss to follow-up, or the end of the study period, as per institutional protocol. Study variables Independent variables included sex (male/female), age categorized (< 60 years and ≥ 60 years), education level (illiterate/elementary; secondary; higher), race (whites and blacks only, with blacks considered as a proxy for socioeconomic status; Asians and Indigenous patients were excluded due to the need for specific analysis), prior diagnosis (No diagnosis or treatment; diagnosis without treatment; diagnosis with treatment – ordinal variable), marital status (married vs. single, used as a proxy for social support), and case source (referred through the Unified Health System – SUS, or through private/insurance-based care). The censoring variable was defined as death from pancreatic cancer, with patients alive at the end of follow-up or lost to follow-up censored. Dependent variables corresponded to three-time intervals, which allowed reconstruction and analysis of patients’ trajectories within the healthcare system. Data analysis Data were first checked for inconsistencies and impossible values in survival times. Outliers were excluded using the boxplot criterion (values above Q3 + 1.5 × IQR), aiming to minimize distortions and ensure analytical robustness. The analysis followed three steps: (i) exploratory analysis, (ii) bivariate analysis, and (iii) Cox regression modeling. i. Descriptive analysis Population characteristics were described using absolute and relative frequencies for categorical variables, and mean ± standard deviation or median (with interquartile range) for continuous variables, depending on the distribution (assessed by the Shapiro-Wilk test). Statistics were presented separately for each time interval to visualize distributions and proportions of patients with delays beyond clinically critical thresholds (e.g., diagnosis-to-treatment latency > 60 days). ii. Bivariate analysis Survival outcomes were assessed using survival analysis methods. For each patient, time to death or censoring was calculated from different starting points. Process intervals were compared across independent variable groups using nonparametric tests: Mann-Whitney for dichotomous and Kruskal-Wallis for polytomous variables. Survival curves were estimated using the Kaplan-Meier method and compared across subgroups (e.g., sex, age, race), along with descriptive statistics and 95% confidence intervals. Differences were tested with the log-rank (Mantel-Cox) test, with statistical significance set at p < 0.05. Null hypothesis (H₀): no statistically significant differences between survival curves of compared groups. (I) Alternative hypothesis (H₁): statistically significant differences exist between groups. (II) iii. Cox regression Variables with p < 0.20 in univariate analysis were included in multivariate modeling. The proportional hazards Cox model was applied to identify independent factors associated with mortality. The survival function S(t) was defined as the probability of a patient surviving beyond time t, empirically estimated. We expressed the Cox model as: h(t|X) = h₀(t) × exp(β₁X₁ + β₂X₂ + … + βpXp) where h(t|X) is the hazard rate at time t given covariates X, h₀(t) is the baseline hazard, and βi are the coefficients of independent variables. Modeling steps included: a) Variables with p < 0.20 in univariate analysis were included in the multivariate model; b) The final model was obtained through backward stepwise selection; c) The proportional hazards assumption was tested by visual inspection of Schoenfeld residuals and global proportionality test (p > 0.05 indicating adequacy). Results were expressed as hazard ratios (HR) with 95% confidence intervals. Findings were interpreted considering the pancreatic cancer therapeutic pathway, identifying bottlenecks, inequalities, and determinants associated with survival. Significant subgroup differences were discussed considering social determinants of health, institutional barriers, and features of the Brazilian healthcare system. Model evaluation Model fit and discrimination were assessed using the following: log-likelihood statistics (nested model comparisons), Akaike and Bayesian Information Criteria (AIC, BIC: lower values indicating better fit), Harrell’s concordance index (c-index: values close to 1 indicating high predictive accuracy), and residual analysis (deviance and Martingale) for influential observations and collinearity. All analyses were performed using R software (version 4.2.0), with packages survival , survminer , car , and rms . RESULTS We analyzed a cohort of 1,426 patients, predominantly women (52.9%), while men accounted for 47.1%. When we examined education, we identified incomplete/complete elementary school as the most frequent level (47.6%), followed by incomplete/complete high school (36.3%), while 6.5% of the records contained missing data. Regarding race/skin color, we observed that most patients self-identified as white (62.1%), while black and brown patients represented 35.6%, and missing values accounted for 2.3%. When we evaluated referral source, we found that access through the Brazilian Unified Health System (SUS) predominated (82.6%), compared to private or insurance-based services (17.4%), with missing data in only 0.1%. We identified adenocarcinoma as the most frequent histological type (88.9%), followed by neuroendocrine tumors (8.2%) and other types (2.9%), with no relevant missing values (Table 1 ). Table 1 Sociodemographic, clinical, and healthcare access characteristics of patients with pancreatic cancer treated at INCA (2000–2023). Variable Category n % Sex Male 663 47.1 Female 745 52.9 Age Group < 60 Yrs 584 41.5 60 + 824 58.5 Race White 668 47.4 Black 711 50.5 Missing (System) 29 2.1 Familiar History Yes 704 50.0 No 506 35.9 Missing (System) 198 14.1 Marital Status Single 630 44.7 Married 767 54.5 Missing (System) 11 0.8 Educational Level Low 780 55.4 High 601 42.7 Missing (System) 27 1.9 Smoking No 643 45.7 Current or Former Consumer 641 45.5 Missing (System) 124 8.8 Alcohol Use No 665 47.2 Current or Former Consumer 609 43.3 Missing (System) 134 9.5 ICD-O Adenocarcinoma 704 50.0 Neuroendocrine 124 8.8 Indeterminate 580 41.2 Missing (System) 580 41.2 Origin Public Health System (SUS) 629 44.7 Private Practice|Health Insurance Plan 295 21.0 Missing (System) 484 34.4 Diagnosis vs. First Consultation Gap Diagnosis After First Consultation 483 34.3 Pre-First Consultation Diagnosis 780 55.4 Missing (System) 145 10.3 Previous Diagnosis or Treatment No diagnosis or treatment 555 39.4 Diagnosis without treatment 739 52.5 Diagnosis with treatment 71 5.0 Missing (System) 43 3.1 Reason for Non-Treatment Refusal 5 0.4 Outside Treatment 22 1.6 Advanced Disease 569 40.4 Abandonment 6 0.4 Complications of Treatment 2 0.1 Death 242 17.2 Other 33 2.3 Not Applicable 456 32.4 Missing (System) 73 5.2 Disease Status After First Treatment Complete Remission 40 2.8 Partial Remission 14 1.0 Stable Disease 20 1.4 Progressive Disease 85 6.0 Therapeutic Support 94 6.7 Death 101 7.2 Not Applicable 926 65.8 Missing (System) 128 9.1 First Treatment Received None 921 65.4 Surgery 88 6.3 RXT 14 1.0 QT 284 20.2 Other 55 3.9 Surgery + QT 32 2.3 RXT + QT 14 1.0 Death (Cancer) Yes 1113 80.1 No 150 10.7 Missing (System) 145 19.9 Legend : Data are presented as absolute numbers (n) and relative frequencies (%). Percentages may not add up to 100% due to rounding. Missing values are indicated separately. Abbreviations: SUS – Unified Health System (Brazil); ICD-O – International Classification of Diseases for Oncology; RXT – radiotherapy; QT – chemotherapy. All dates measured in days. When we described continuous intervals, we revealed a clear picture of the cancer care trajectory. For the time from first diagnosis to death, we observed a mean of 122.5 days (standard deviation: 111.4) and a median of 81 days (25th percentile: 41; 75th percentile: 176). We included 1,135 valid cases and identified 273 missing records. For the interval from first hospital consultation to death, we found a mean of 115.6 days (standard deviation: 112.6) and a median of 75 days (25th percentile: 34; 75th percentile: 164). In this variable, we analyzed 1,155 valid cases and recorded 253 missing. For the time from first treatment to death, we calculated a mean of 224.8 days (standard deviation: 196.8) and a median of 170 days (25th percentile: 69.5; 75th percentile: 327). We included 361 valid cases and identified 1,047 missing cases, which indicated that many patients did not initiate formal treatment or lacked registered data. Overall, we found wide variability in survival times and treatment access, along with a substantial volume of missing post-treatment data. We observed that the relatively short times from diagnosis to death and consultation to death (medians under six months) highlighted the severity and poor prognosis of this cohort. In contrast, survival after treatment initiation tended to be longer, although available only for a smaller subset of patients. In the context of cancer care, we structured survival analysis into three successive temporal models that represent distinct stages of the patient care pathway: (1) from first consultation to death, (2) from diagnosis to death, and (3) from first treatment to death. We used these three intervals in the bivariate analysis and incorporated them into multivariate models. Each milestone reflected a critical stage of the patient journey, shaped by different barriers and opportunities along the continuum of care (Table 2 ). Table 2 Kaplan-Meyer analysis according to sociodemographic, clinical, and healthcare access variables: diagnosis–death, consultation–death, and treatment–death models. Variable Category Mean in days (95% CI) p-value (Log-Rank) Model #1: Diagnosis to death Prior diagnosis/ treatment No diagnosis or treatment 108.95 (96.72–121.18) < 0.001 Diagnosis without treatment 123.83 (114.46–133.19) Diagnosis with treatment 241.55 (189.33–293.77) Global 121.62 (114.11–129.13) Case origin Public health system (SUS) 113.36 (103.66–123.07) 0.033 Private Practice|Health Insurance Plan 134.99 (116.50–153.48) Global 119.28 (110.57–127.98) Race White 133.02 (121.22–144.81) 0.004 Black 111.28 (101.95–120.60) Global 120.72 (113.34–128.11) ICD-O Adenocarcinoma 138.65 (127.85–149.45) 0.055 Neuroendocrine 193.39 (131.42–255.36) Global 141.35 (130.60–152.10) Diagnosis and consultation After the first consultation 101.02 (89.49–112.55) 0.005 Before the first consultation 120.16 (111.09–129.23) Global 113.35 (106.19–120.51) Model #2: First consultation to death Prior diagnosis/ treatment No diagnosis or treatment 139.97 (127.70–152.24) < 0.001 Diagnosis without treatment 91.86 (82.59–101.13) Diagnosis with treatment 139.93 (99.21–180.65) Global 111.43 (103.99–118.87) Case origin Public health system (SUS) 101.45 (91.53–111.36) 0.050 Private Practice|Health Insurance Plan 123.21 (105.77–140.64) Global 107.52 (98.84–116.20) Race White 131.54 (120.04–143.04) < 0.001 Black 94.32 (85.10–103.53) Global 107.78 (100.49–115.08) ICD-O Adenocarcinoma 141.61 (130.99–152.24) 0.643 Neuroendocrine 154.70 (103.99–205.42) Global 142.35 (131.93–152.76) Diagnosis and consultation After the first consultation 131.54 (120.04–143.04) < 0.001 Before the first consultation 94.32 (85.10–103.53) Global 107.78 (100.49–115.08) Model #3: First treatment to death Prior diagnosis/ treatment No diagnosis or treatment 221.89 (190.56–253.21) 0.691 Diagnosis without treatment 218.75 (181.49–256.01) Diagnosis with treatment 270.57 (149.44–391.71) Global 223.09 (199.54–246.64) Case origin Public health system (SUS) 210.91 (176.40–245.43) 0.205 Private Practice|Health Insurance Plan 254.48 (199.36–309.60) Global 225.74 (196.17–255.31) Race White 222.87 (189.66–256.07) 0.669 Black 229.63 (195.48–263.78) Global 226.06 (202.26–249.86) ICD-O Adenocarcinoma 218.04 (192.88–243.20) 0.054 Neuroendocrine 303.60 (177.10–430.10) Global 225.29 (199.83–250.74) Diagnosis and consultation After the first consultation 205.35 (173.11–237.58) 0.275 Before the first consultation 235.29 (198.70–271.89) Global 219.86 (195.52–244.20) Legend: Survival estimates are expressed as mean survival time in days, with standard error (SE) and 95% confidence intervals (95% CI). Comparisons between groups were performed using the log-rank (Mantel–Cox) test. P-values < 0.05 were considered statistically significant. Global values correspond to overall estimates for each category. In the consultation–death model, we captured the full hospital course from entry, including diagnostic confirmation, treatment initiation, and follow-up until outcome. In the diagnosis–death model, we focused on the period following diagnostic confirmation, excluded diagnostic delays, and investigated differences after cancer identification. In the treatment–death model, we restricted the analysis to the period after therapeutic intervention began. When we compared the three models, we found that sociodemographic, clinical, and access-related variables had variable effects depending on the stage of care. In the first two models, we identified substantial and statistically significant associations for institutional follow-up, race, referral source, and prior diagnosis/treatment. In the third model, after treatment initiation, almost all variables lost significance except institutional follow-up, which remained dominant (Table 3 ). Table 3 Factors associated with pancreatic cancer mortality: multivariate analysis using the Cox regression model Variable Category HR (95% CI) crude p value HR (95% CI) adjusted p value Model #1: Diagnosis to death Prior diagnosis/ treatment Diagnosis with treatment 1 < 0.001 1 0.005 Diagnosis without treatment 2.01 (1.32–3.06) 1.95 (1.16–3.29) No diagnosis or treatment 2.34 (1.53–3.58) 2.28 (1.34–3.89) Race White 1 0.005 1 0.004 Black 1.32 (1.06–1.39) 1.29 (1.08–1.54) Diagnosis and consultation Before the first consultation 1 0.006 1 0.009 After the first consultation 1.23 (1.06–1.42) 1.16 (1.02–1.38) Case origin Private Practice|Health Insurance Plan 1 1 Public health system (SUS) 1.22 (1.08–1.46) 0.003 1.17 (1.01–1.43) 0.043 Model #2: First consultation to death Prior diagnosis/ treatment Diagnosis with treatment 1 < 0.001 1 < 0.001 Diagnosis without treatment 1.28 (1.14–1.42) 1.15 (1.02–1.29) No diagnosis or treatment 1.69 (1.46–1.92) 1.58 (1.23–1.92) Race White 1 < 0.002 1 0.006 Black 1.29 (1.08–1.54) 1.23 (1.08–1.41) Diagnosis and consultation Before the first consultation 1 < 0.001 1 0.014 After the first consultation 1.32 (1.18–1.29) 1.16 (1.08–1.27) Case origin Private Practice|Health Insurance Plan 1 1 Public health system (SUS) 1.19 (1.03–1.31) 0.004 1.15 (1.01–1.30) 0.009 Legend : Results are presented as crude and adjusted hazard ratios (HR) with 95% confidence intervals (95% CI). Variables with p < 0.20 in the univariate analysis were included in the multivariate Cox regression model. Backward stepwise selection was used to determine the final model. Proportional hazards assumption was tested using Schoenfeld residuals. Abbreviations: HR – hazard ratio; CI – confidence interval; SUS – Unified Health System (Brazil). Model #3 (First treatment to death) showed no significant variable from Kaplan Meyer analysis. In the consultation–death model, we observed the most significant influence of access inequalities and the patient's previous trajectory. Institutional follow-up showed a survival difference of 268 days between followed patients (368.8 days; 95% CI: 341.2–396.4) and non-followed patients (100.5 days; 95% CI: 94.0–107.1). White patients lived longer on average (131.5 days; 95% CI: 120.0–143.0) than black patients (94.3 days; 95% CI: 85.1–103.5). Patients outside SUS survived 123.2 days (95% CI: 105.8–140.6) compared with 101.4 days (95% CI: 91.5–111.4) for SUS patients. A previous trajectory also stood out: patients without a prior diagnosis/treatment lived 140.0 days (95% CI: 127.7–152.2), compared with only 91.9 days (95% CI: 82.6–101.1) for those with a diagnosis but no treatment. In the diagnosis–death model, we found similar patterns with different magnitudes. Institutional follow-up maintained a substantial impact, with a 263.5-day difference between followed patients (372.7 days; 95% CI: 344.8–400.7) and non-followed patients (109.2 days; 95% CI: 102.6–115.7). Prior diagnosis/treatment also had a strong influence: patients with both reached 241.6 days (95% CI: 189.3–293.8), nearly double that of patients without diagnosis/treatment (109.0 days; 95% CI: 96.7–121.2). Differences by race (white: 133.0 days; 95% CI: 121.2–144.8 vs. blacks: 111.3 days; 95% CI: 102.0–120.6) and referral source (non-SUS: 135.0 days; 95% CI: 116.5–153.5 vs. SUS: 113.4 days; 95% CI: 103.7–123.1) persisted, but the relative gaps decreased slightly, suggesting that part of the inequalities occur before diagnosis. Finally,in the treatment–death model, we observed apparent attenuation of group differences. Institutional follow-up still showed a strong effect (around 330 days: followed patients 451.3 days; 95% CI: 412.4–490.3 vs. non-followed patients 121.3 days; 95% CI: 108.7–133.9), becoming nearly the only significant variable. Race, referral source, and prior diagnosis/treatment lost significance, and group survival means converged. These findings suggest that hospital treatment initiation acted as a “leveler” of outcomes for patients who managed to overcome the initial barriers to specialized care. DISCUSSION We recognize that the survival rate in pancreatic cancer is notoriously low. This outcome reflects not only the aggressive biology of the disease but also the multiple structural and institutional barriers that limit timely access to diagnosis and treatment. We found recent estimates indicating that five-year survival rarely exceeds 10% in developing countries, with even lower rates among racialized and socioeconomically vulnerable populations [ 13 , 14 ]. These findings motivated us to conduct a detailed analysis of the therapeutic pathway, from the initial interaction with the healthcare system to the outcome. We observed that contemporary literature confirms this hypothesis, showing that barriers to access, social vulnerability, and structural racism interfere mainly in the early stages of care. At the same time, adherence to specialized institutional protocols after treatment initiation tends to mitigate these adverse effects. We structured our empirical survival analysis into three temporal models (consultation–death, diagnosis–death, and treatment–death), which allowed us to demonstrate the impact of social and institutional inequalities along the care pathway. In the consultation–death model, we found that structural barriers such as race, referral source (public vs. private), and absence of prior diagnosis/treatment strongly influenced survival. Patients under institutional follow-up survived an average of 368.8 days, while those without follow-up survived only 100.5 days — a difference of 268 days. This gap reflects differences not only in access but also in quality of care, effectiveness of referrals, and coordination. We confirmed this in the literature, which shows that institutional follow-up increases the likelihood of appropriate diagnostic testing, early treatment initiation, and continuous support [ 15 ]. We also found that race was a key marker of inequality. White patients survived a mean of 131.5 days, while black patients survived only 94.3 days. Previous studies show that black patients often face diagnostic delays, fewer surgical indications, and reduced access to chemotherapy, even after controlling for clinical factors [ 16 , 17 ]. These disparities stem from spatial segregation of services, institutional racism, and historical distrust in healthcare systems [ 18 ]. Furthermore, we observed that non-public patients (non-SUS) survived longer than those referred through SUS (123.2 vs. 101.4 days). This result suggests that the entry point into the health system conditions diagnostic and therapeutic opportunities. Literature supports this, showing that patients with private insurance or access to philanthropic hospitals tend to receive earlier diagnoses and more aggressive treatment than those relying solely on the public system [ 19 ]. We also identified an effect of prior diagnosis/treatment. Patients previously diagnosed and treated had shorter survival, likely reflecting advanced or recurrent disease. At the same time, those without such a history lived longer, suggesting the benefits of early diagnosis and immediate intervention. In the diagnosis–death model, inequalities persisted, albeit with a reduced magnitude. Patients under institutional follow-up lived an average of 372.7 days compared to 109.2 days among those without. We believe that continuity of care facilitates quicker access to therapies, multidisciplinary follow-up, and adherence to evidence-based protocols, improving survival [ 20 ]. Racial and public/private differences remained but decreased in magnitude. White patients lived an average of 21.7 days longer than black patients, and non-SUS patients continued to outlive SUS patients. This pattern suggests that much of the inequality occurs before diagnosis, particularly in access to specialized services, imaging, and biopsy. We confirmed this in studies showing longer delays from symptom onset to diagnosis among black and low-income patients due to underuse of imaging, transportation barriers, and long waiting times [ 21 , 22 ]. We also found that patients with prior diagnosis/treatment survived an average of 241.6 days, more than twice as long as those without (109 days). It shows that prior engagement with health services and treatment confers a survival advantage, reinforcing the role of continuity of care [ 23 ]. In the treatment–death model, inequalities diminished substantially. Race, referral source, and prior diagnosis/treatment lost statistical significance, showing that once hospital treatment began, survival became more homogeneous. However, institutional follow-up remained highly predictive, with a survival gap of 330 days between patients who were followed and those who were not. This finding showed us that inequalities concentrate in access and diagnostic stages, but engagement and retention in specialized services continue to improve outcomes after treatment initiation. Institutional follow-up ensures clinical monitoring, management of adverse events, control imaging, and access to additional therapies or palliative care [ 24 ]. We confirmed that race operates as a structural determinant of health inequities. It should not be treated as a neutral demographic variable but as a historical marker of exclusion that directly affects early diagnosis, timely treatment, and continuity of care. International literature supports this view. Studies in the U.S. demonstrate that black patients are less likely to receive curative surgery or complete chemotherapy [ 16 , 17 , 25 , 26 ], face longer delays [ 17 ], more often decline resection [ 27 ], and live in socially vulnerable areas associated with worse outcomes [ 28 – 33 ]. We also found that the type of hospital matters, since minority-serving hospitals show lower resection rates and higher mortality even after adjustment [ 34 , 35 ]. These converging findings led us to the central conclusion of our study: access inequalities, particularly in early stages of care, condition survival in pancreatic cancer, and these disparities derive not only from biology but from complex social, racial, territorial, and institutional determinants. Once patients overcome entry barriers and access qualified treatment, inequalities diminish substantially, highlighting points of strategic policy intervention. From a policy perspective, we identified two priorities: (1) strengthening primary care, expanding imaging and biopsy services, and regulating referral for high-risk patients; (2) investing in specialized reference centers with multidisciplinary teams and standardized protocols. We also emphasize the urgency of addressing structural racial inequalities within healthcare. This requires culturally competent training for professionals and affirmative policies in the public system to guarantee equitable access to diagnostics, treatments, and technologies. Ultimately, we acknowledge institutional follow-up as a crucial protective factor throughout the therapeutic process. More than organizational efficiency, it reflects patient-centered continuity and coordination of care. Investing in institutional follow-up may represent one of the most cost-effective strategies to improve survival in high-risk cancers. In summary, our three temporal models clearly demonstrated that social and institutional inequalities profoundly affect the patient trajectory, particularly in early stages. Still, that initiation of qualified treatment has the potential to act as an equalizer of outcomes. These finding challenges health systems to guarantee equitable access to diagnosis, treatment, and continuity of care for all patients, regardless of race, origin, or social condition. CONCLUSIONS Through the three models, we demonstrated a temporal trajectory in which access barriers, racial and historical inequalities, and referral context strongly shape the pre-treatment phase, exerting a significant impact on survival. However, once treatment begins, we identified that the primary determinant of prognosis becomes the guarantee of qualified institutional follow-up. This transition showed us the importance of designing policies and implementing actions that promote early access, ensure diagnostic equity, and retain patients within specialized services, so that all individuals may fully benefit from oncologic treatment and continuous care. We recognize that the unequal distribution of health resources, the scarcity of specialized reference centers for pancreatic oncology, the underfunding of cancer care within the SUS, and the low rate of genetic testing further aggravate clinical outcomes in Brazil. We argue that policymakers must address these aspects by integrating intersectoral actions, including primary prevention, timely diagnosis, and adequate treatment, based on updated clinical guidelines and risk stratification. Based on this evidence, we recommend strengthening patient navigation programs, prioritizing referrals of vulnerable populations to high-complexity centers, allocating structural resources to hospitals that serve racialized populations, and adopting composite indicators — such as the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI) — to guide regulatory and budgetary decisions in SUS. We understand that pancreatic cancer represents both a mirror of inequality in access to specialized care and an opportunity to build more equitable and efficient pathways to confront highly lethal malignancies. Declarations Ethics approval and consent to participate This study was conducted using secondary data from the Hospital Cancer Registry (RHC) of the Brazilian National Cancer Institute (INCA). The project was approved by the Ethics Committee of the National Cancer Institute (Protocol Number: #4.538.738/2020). As the study was based on anonymized secondary data, the requirement for informed consent was waived in accordance with national regulations. Consent for publication Not applicable. No individual or identifiable personal data are included in this article. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution •Camila Drumond Muzi, conception, design of the study, interpretation of results, supervision, and final approval. •Raphael Mendonça Guimarães, data curation, statistical analysis, and drafting of the manuscript. •Rafael Tavares Jomar, interpretation of results, literature review, and manuscript writing. •Clara Soares Rosas, critical revision of the manuscript for important intellectual content. •Lara Barbosa de Souza Moura Canas Lara, critical revision of the manuscript for important intellectual content. Acknowledgements None Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions of the INCA Hospital Cancer Registry but may be available from the corresponding author upon reasonable request and with permission of the National Cancer Institute. References International Agency for Research on Cancer. Global Cancer Observatory – Cancer Today. Lyon: IARC. Disponível em; 2023. https://gco.iarc.fr/today . McGuigan A, Kelly P, Turkington RC, Jones C, Coleman HG, McCain RS. Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes. World J Gastroenterol. 2018;24(43):4846–61. Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–21. (2022). Estimativa 2023: Incidência de câncer no Brasil . Rio de Janeiro: INCA. Instituto Nacional de Câncer José Alencar Gomes da Silva, Disponível. em https://www.inca.gov.br/publicacoes/livros/estimativa-2023-incidencia-de-cancer-no-brasil Li T, Lin C, Wang W. Global, regional, and national burden of pancreatic cancer from 1990 to 2021, its attributable risk factors, and projections to 2050: A systematic analysis of the Global Burden of Disease Study 2021. BMC Cancer. 2025;25:189. Chaves DO, Bastos AC, Almeida AM, Guerra MR, Teixeira MTB, Melo APS, Passos VMA. (2022). The increasing burden of pancreatic cancer in Brazil from 2000 to 2019: Estimates from the Global Burden of Disease Study 2019. Rev Soc Bras Med Trop, 55(Suppl 1), e0271. Barbosa IR, Santos CA dos, de Souza DLB. (2018). Pancreatic Cancer in Brazil: Mortality Trends and Projections until 2029. Arquivos de Gastroenterologia, 55 (3), 230–236. Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic cancer: Global trends, etiology and risk factors. World J Oncol. 2019;10(1):10–27. Karamitopoulou E. Emerging Prognostic and Predictive Factors in Pancreatic Cancer. Mod Pathol. 2023;36(11):100328. Li Q, Feng Z, Miao R, Liu X, Liu C, Liu Z. Prognosis and Survival Analysis of Patients with Pancreatic Cancer: A Retrospective Experience from a Single Institution. World J Surg Oncol. 2022;20(1):11. de Jesus VHF, da Costa WL, Claro LCL, de Souza RB, et al. Disparities in access to health care system as determinant of survival for patients with pancreatic cancer in the State of São Paulo, Brazil. Sci Rep. 2021;11:6346. Ioannou, L. J., Maharaj, A. D., Zalcberg, J. R., Loughnan, J. T., Croagh, D. G., Pilgrim,C. H., … Evans, S. M. (2022). Prognostic models to predict survival in patients with pancreatic cancer: A systematic review. HPB, 24(8), 1201–1216.. McGuigan A, Kelly P, Turkington RC, Jones C, Coleman HG, McCain RS. Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes. World J Gastroenterol. 2018;24(43):4846–61. Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J Oncol. 2019;10(1):10–27. Zafar SY, Abernethy AP. Financial toxicity, Part I: a new name for a growing problem. Oncol (Williston Park). 2013;27(2):80–1. Noel M, Fiscella K. Disparities in pancreatic cancer treatment and outcomes. Health Equity. 2019;3(1):532–40. Tavakkoli A, Singal AG, Waljee AK, Elmunzer BJ, Pruitt SL, McKey T, Rubenstein JH, Scheiman JM, Murphy CC. Racial Disparities and Trends in Pancreatic Cancer Incidence and Mortality in the United States. Clin Gastroenterol Hepatol. 2020;18(1):171–e17810. Armstrong K, Ravenell KL, McMurphy S, Putt M. Racial/Ethnic Differences in Physician Distrust in the United States. Am J Public Health. 2007;97(7):1283–9. Alves MNT, Monteiro MFV, Alves FT, Dos Santos Figueiredo FW. Determinants of Lack of Access to Treatment for Women Diagnosed with Breast Cancer in Brazil. Int J Environ Res Public Health. 2022;19(13):7635. Chan RJ, Milch VE, Crawford-Williams F, Agbejule OA, Joseph R, Johal J, Dick N, Wallen MP, Ratcliffe J, Agarwal A, Nekhlyudov L, Tieu M, Al-Momani M, Turnbull S, Sathiaraj R, Keefe D, Hart NH. Patient navigation across the cancer care continuum: An overview of systematic reviews and emerging literature. CA Cancer J Clin. 2023 Nov-Dec;73(6):565–89. Freeman HP, Rodriguez RL. History and principles of patient navigation. Cancer. 2011;117(15 Suppl):3537–40. Montgomery KB, Ross E, Amu-Nnadi C, Bhatia S, Broman KK. Patterns of Referral for Common Cancer Surgery in the United States. Ann Surg Oncol. 2025;32(5):3429–40. Shavers VL, Brown ML. Racial and ethnic disparities in the receipt of cancer treatment. J Natl Cancer Inst. 2002;94(5):334–57. Niranjan SJ, Wenzel JA, Martin MY, Fouad MN, Vickers SM, Konety BR, Durant RW. Perceived Institutional Barriers Among Clinical and Research Professionals: Minority Participation in Oncology Clinical Trials. JCO Oncol Pract. 2021;17(5):e666–75. Natale-Pereira A, Enard KR, Nevarez L, Jones LA. The role of patient navigators in eliminating health disparities. Cancer. 2011;117(15 Suppl):3543–52. Cuevas AG, O’Brien K, Saha S. African American experiences in healthcare: I always feel like I’m getting skipped over. Health Psychol. 2016;35(9):987–95. Moaven O, Richman JS, Reddy S, Wang T, Heslin MJ, Contreras CM. Healthcare disparities in outcomes of patients with resectable pancreatic cancer. Am J Surg. 2019;217(4):725–31. 10.1016/j.amjsurg.2018.12.007 . Eskander MF, Hamad A, Li Y, et al. From street address to survival: Neighborhood socioeconomic status and pancreatic cancer outcomes. Surgery. 2022;171(3):770–6. 10.1016/j.surg.2021.10.027 . Lee HS, et al. Rising incidence and racial disparities of early-onset pancreatic cancer in the United States, 1995–2018. Gastroenterology. 2022;163(2):310–2. 10.1053/j.gastro.2022.03.011 . Holland MM, Khan H, Amin K, et al. Disparities in access to surgical resection in patients with pancreatic cancer: A systematic review. J Gastrointest Surg. 2025;29:102037. 10.1016/j.gassur.2025.102037 . Morenz AM, Liao JM, Au DH, Hayes SA. Area-level socioeconomic disadvantage and health care spending: A systematic review. JAMA Netw Open. 2024;7(2):e2356121. 10.1001/jamanetworkopen.2023.56121 . Shaw V, Zhang B, Tang M, et al. Racial and socioeconomic disparities in survival improvement of eight cancers. BJC Rep. 2024. 10.1038/s44276-024-00044-y . Azap RA, Diaz A, Hyer JM, et al. Impact of race/ethnicity and county-level vulnerability on receipt of surgery among older Medicare beneficiaries with early pancreatic cancer. Ann Surg Oncol. 2021;28(10):6309–16. 10.1245/s10434-021-09911-1 . Olecki EJ, Holguin RAP, Mayhew MM, et al. Disparities in surgical treatment of resectable pancreatic adenocarcinoma at minority serving hospitals. J Surg Res. 2024;294:160–8. 10.1016/j.jss.2023.09.066 . Williamson CG, Ebrahimian S, Sakowitz S, et al. Race, insurance, and sex-based disparities in access to high-volume centers for pancreatectomy. Ann Surg Oncol. 2023;30(5):3002–10. 10.1245/s10434-022-13032-8 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 15 Sep, 2025 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 06 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:43:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1338330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7552704/v1/79e69793-3980-45fe-89f1-ab54091c58ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Impact of Social Inequalities on the Survival of Patients With Pancreatic Cancer: Analysis of a Cohort of 1,426 Cases in Brazil\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePancreatic cancer represents one of the most significant challenges in contemporary oncology due to its high lethality, frequent late diagnosis, and poor therapeutic response. Globally, it is the seventh leading cause of cancer-related death, and projections indicate a worsening of this scenario in the coming decades. According to the Global Cancer Observatory of the International Agency for Research on Cancer (IARC), in 2022, there were approximately 510,992 new cases of pancreatic cancer and 467,409 deaths from the disease, resulting in a mortality rate almost equivalent to its annual incidence, with an age-standardized rate of 5.9 per 100,000 inhabitants worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough pancreatic cancer is relatively less frequent than other malignant neoplasms, its five-year survival rate remains extremely low, ranging from 5% to 10% in high-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is estimated that by 2040, it will become the second leading cause of cancer-related death in the United States, surpassing colorectal cancer, primarily due to population aging and the absence of effective screening strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Brazil, updated data from the National Cancer Institute Jos\u0026eacute; Alencar Gomes da Silva (INCA), published in \u003cem\u003eEstimativa 2023: Incid\u0026ecirc;ncia de C\u0026acirc;ncer no Brasil\u003c/em\u003e, indicate that pancreatic cancer accounts for 10,980 new cases per year during the 2023\u0026ndash;2025 triennium, with 5,690 in men and 5,290 in women [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although it does not rank among the ten most frequent cancers, its lethality is striking: in 2021, 11,971 deaths were reported from this cause, 5,949 in men and 6,022 in women, according to the Mortality Information System (SIM) of the Ministry of Health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBetween 1990 and 2021, the global burden of pancreatic cancer, measured in DALYs, increased from 5.21\u0026nbsp;million to 11.32\u0026nbsp;million. Mortality rates showed a similar upward trend. The Global Burden of Disease study also recognizes notable increases in age-standardized incidence and mortality rates in low-middle and low sociodemographic index regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In fact, Chaves et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] estimated age-standardized incidence and mortality rates of pancreatic cancer in Brazil and found a significant increase. While incidence rose from 5.33 (95% CI: 5.06\u0026ndash;5.51) to 6.16 (95% CI: 5.68\u0026ndash;6.53) per 100,000 inhabitants, states with lower SDI presented lower absolute rates but the highest annual increases.\u003c/p\u003e\u003cp\u003eThis panorama highlights not only the magnitude of pancreatic cancer mortality but also significant regional and socioeconomic inequalities in Brazil. Previous studies have identified sharp increases in mortality rates in the North and Northeast regions between 2000 and 2014, while reductions in the South and Southeast may be associated with greater access to specialized services and earlier diagnosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nonetheless, even in more developed regions, pancreatic cancer is characterized by silent progression, aggressive clinical course, and limited curative options.\u003c/p\u003e\u003cp\u003eThe primary established risk factors for pancreatic cancer include smoking, responsible for approximately 20% of cases, obesity, type 2 diabetes mellitus, excessive alcohol consumption, chronic pancreatitis, family history of the disease, and genetic factors such as mutations in \u003cem\u003eBRCA1/2\u003c/em\u003e and \u003cem\u003eCDKN2A\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, epidemiological studies indicate that diets rich in saturated fats and processed meats, as well as occupational exposure to chemical solvents, may contribute to its development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe lack of specific symptoms in early stages is one of the main reasons why most cases are diagnosed late, often in stage III or IV, when surgical resection\u0026mdash;the only potentially curative option\u0026mdash;is no longer feasible. Combined with the aggressive biological phenotype of the tumor, this results in one of the worst prognoses among all solid cancers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A Brazilian study by Jesus et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which evaluated a cohort of 6,855 patients between 2000 and 2014, found that socioeconomic variables, particularly health system funding, were the main determinants of survival. In this study, the median overall survival was 4.9 months (95% CI: 4.7\u0026ndash;5.2). Factors such as older age, male sex, lower educational level, treatment in public facilities, absence of treatment, and advanced stage were associated with lower survival. Current evidence indicates that more than 75% of patients with pancreatic cancer are diagnosed at advanced stages, with vascular and lymph node involvement, which precludes curative treatment in most cases. Even with palliative chemotherapy, median survival rarely exceeds 6 to 9 months [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEarly detection of pancreatic cancer is challenging due to the lack of specific symptoms in its initial phases. Consequently, many patients are diagnosed at advanced stages, limiting therapeutic options and contributing to poor survival rates. Furthermore, socioeconomic barriers and inequalities in access to healthcare services may delay diagnosis and treatment, exacerbating disparities in clinical outcomes. In this context, it is crucial to understand the factors influencing survival in Brazilian patients with pancreatic cancer in order to guide public health policies and intervention strategies. Therefore, the primary objective of this article was to investigate healthcare-related factors associated with survival by mapping the therapeutic pathway, with special attention to inequalities and barriers to timely access to diagnosis and treatment.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eIt is an observational, analytical, retrospective cohort study aiming to evaluate the survival of patients with a confirmed diagnosis of pancreatic cancer. Data were obtained from the Cancer Hospital Registry (Registro Hospitalar de C\u0026acirc;ncer, RHC) of the National Cancer Institute Jos\u0026eacute; Alencar Gomes da Silva (INCA), Unit INCA 1, covering the period from January 2000 to December 2023.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eThe study analyzed secondary data from the Hospital Cancer Registry of Hospital do C\u0026acirc;ncer 1, National Cancer Institute of Brazil. This database includes patients diagnosed with pancreatic cancer. Eligible participants were those with a primary diagnosis of pancreatic cancer (ICD-10: C25) registered in the RHC, with a diagnosis date between 01/01/2000 and 12/31/2023. Inclusion criteria were: histopathological confirmation of pancreatic adenocarcinoma; complete records of diagnosis and last follow-up dates (death or censoring); and age\u0026thinsp;\u0026ge;\u0026thinsp;18 years at diagnosis. Exclusion criteria were: secondary pancreatic cancer (metastases from other sites); inconsistent data or missing essential information (e.g., absence of diagnosis or outcome dates); and cases in which the death date preceded the diagnosis date.\u003c/p\u003e\u003cp\u003eAfter applying the eligibility criteria, the final cohort comprised 1,426 patients. All eligible individuals admitted to the hospital during the study period were followed until death, loss to follow-up, or the end of the study period, as per institutional protocol.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eIndependent variables included sex (male/female), age categorized (\u0026lt;\u0026thinsp;60 years and \u0026ge;\u0026thinsp;60 years), education level (illiterate/elementary; secondary; higher), race (whites and blacks only, with blacks considered as a proxy for socioeconomic status; Asians and Indigenous patients were excluded due to the need for specific analysis), prior diagnosis (No diagnosis or treatment; diagnosis without treatment; diagnosis with treatment \u0026ndash; ordinal variable), marital status (married vs. single, used as a proxy for social support), and case source (referred through the Unified Health System \u0026ndash; SUS, or through private/insurance-based care).\u003c/p\u003e\u003cp\u003eThe censoring variable was defined as death from pancreatic cancer, with patients alive at the end of follow-up or lost to follow-up censored. Dependent variables corresponded to three-time intervals, which allowed reconstruction and analysis of patients\u0026rsquo; trajectories within the healthcare system.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData were first checked for inconsistencies and impossible values in survival times. Outliers were excluded using the boxplot criterion (values above Q3\u0026thinsp;+\u0026thinsp;1.5 \u0026times; IQR), aiming to minimize distortions and ensure analytical robustness. The analysis followed three steps: (i) exploratory analysis, (ii) bivariate analysis, and (iii) Cox regression modeling.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ei. Descriptive analysis\u003c/h3\u003e\n\u003cp\u003ePopulation characteristics were described using absolute and relative frequencies for categorical variables, and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (with interquartile range) for continuous variables, depending on the distribution (assessed by the Shapiro-Wilk test). Statistics were presented separately for each time interval to visualize distributions and proportions of patients with delays beyond clinically critical thresholds (e.g., diagnosis-to-treatment latency\u0026thinsp;\u0026gt;\u0026thinsp;60 days).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eii. Bivariate analysis\u003c/h2\u003e\u003cp\u003eSurvival outcomes were assessed using survival analysis methods. For each patient, time to death or censoring was calculated from different starting points. Process intervals were compared across independent variable groups using nonparametric tests: Mann-Whitney for dichotomous and Kruskal-Wallis for polytomous variables. Survival curves were estimated using the Kaplan-Meier method and compared across subgroups (e.g., sex, age, race), along with descriptive statistics and 95% confidence intervals. Differences were tested with the log-rank (Mantel-Cox) test, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eNull hypothesis (H₀): no statistically significant differences between survival curves of compared groups. (I)\u003c/p\u003e\u003cp\u003eAlternative hypothesis (H₁): statistically significant differences exist between groups. (II)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eiii. Cox regression\u003c/h3\u003e\n\u003cp\u003eVariables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariate analysis were included in multivariate modeling. The proportional hazards Cox model was applied to identify independent factors associated with mortality. The survival function S(t) was defined as the probability of a patient surviving beyond time t, empirically estimated. We expressed the Cox model as:\u003c/p\u003e\u003cp\u003eh(t|X)\u0026thinsp;=\u0026thinsp;h₀(t) \u0026times; exp(β₁X₁ + β₂X₂ + \u0026hellip; + βpXp)\u003c/p\u003e\u003cp\u003ewhere h(t|X) is the hazard rate at time t given covariates X, h₀(t) is the baseline hazard, and βi are the coefficients of independent variables.\u003c/p\u003e\u003cp\u003eModeling steps included: a) Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariate analysis were included in the multivariate model; b) The final model was obtained through backward stepwise selection; c) The proportional hazards assumption was tested by visual inspection of Schoenfeld residuals and global proportionality test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating adequacy). Results were expressed as hazard ratios (HR) with 95% confidence intervals. Findings were interpreted considering the pancreatic cancer therapeutic pathway, identifying bottlenecks, inequalities, and determinants associated with survival. Significant subgroup differences were discussed considering social determinants of health, institutional barriers, and features of the Brazilian healthcare system.\u003c/p\u003e\n\u003ch3\u003eModel evaluation\u003c/h3\u003e\n\u003cp\u003eModel fit and discrimination were assessed using the following: log-likelihood statistics (nested model comparisons), Akaike and Bayesian Information Criteria (AIC, BIC: lower values indicating better fit), Harrell\u0026rsquo;s concordance index (c-index: values close to 1 indicating high predictive accuracy), and residual analysis (deviance and Martingale) for influential observations and collinearity. All analyses were performed using R software (version 4.2.0), with packages \u003cem\u003esurvival\u003c/em\u003e, \u003cem\u003esurvminer\u003c/em\u003e, \u003cem\u003ecar\u003c/em\u003e, and \u003cem\u003erms\u003c/em\u003e.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe analyzed a cohort of 1,426 patients, predominantly women (52.9%), while men accounted for 47.1%. When we examined education, we identified incomplete/complete elementary school as the most frequent level (47.6%), followed by incomplete/complete high school (36.3%), while 6.5% of the records contained missing data. Regarding race/skin color, we observed that most patients self-identified as white (62.1%), while black and brown patients represented 35.6%, and missing values accounted for 2.3%. When we evaluated referral source, we found that access through the Brazilian Unified Health System (SUS) predominated (82.6%), compared to private or insurance-based services (17.4%), with missing data in only 0.1%. We identified adenocarcinoma as the most frequent histological type (88.9%), followed by neuroendocrine tumors (8.2%) and other types (2.9%), with no relevant missing values (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic, clinical, and healthcare access characteristics of patients with pancreatic cancer treated at INCA (2000\u0026ndash;2023).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;60 Yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamiliar History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent or Former Consumer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent or Former Consumer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-O\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndeterminate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Health System (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis vs. First Consultation Gap\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis After First Consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-First Consultation Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious Diagnosis or Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eReason for Non-Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefusal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutside Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvanced Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplications of Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease Status After First Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplete Remission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial Remission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgressive Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTherapeutic Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Treatment Received\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRXT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;QT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRXT\u0026thinsp;+\u0026thinsp;QT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath (Cancer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing (System)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: Data are presented as absolute numbers (n) and relative frequencies (%). Percentages may not add up to 100% due to rounding. Missing values are indicated separately. Abbreviations: SUS \u0026ndash; Unified Health System (Brazil); ICD-O \u0026ndash; International Classification of Diseases for Oncology; RXT \u0026ndash; radiotherapy; QT \u0026ndash; chemotherapy. All dates measured in days.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhen we described continuous intervals, we revealed a clear picture of the cancer care trajectory. For the time from first diagnosis to death, we observed a mean of 122.5 days (standard deviation: 111.4) and a median of 81 days (25th percentile: 41; 75th percentile: 176). We included 1,135 valid cases and identified 273 missing records. For the interval from first hospital consultation to death, we found a mean of 115.6 days (standard deviation: 112.6) and a median of 75 days (25th percentile: 34; 75th percentile: 164). In this variable, we analyzed 1,155 valid cases and recorded 253 missing. For the time from first treatment to death, we calculated a mean of 224.8 days (standard deviation: 196.8) and a median of 170 days (25th percentile: 69.5; 75th percentile: 327). We included 361 valid cases and identified 1,047 missing cases, which indicated that many patients did not initiate formal treatment or lacked registered data.\u003c/p\u003e\n\u003cp\u003eOverall, we found wide variability in survival times and treatment access, along with a substantial volume of missing post-treatment data. We observed that the relatively short times from diagnosis to death and consultation to death (medians under six months) highlighted the severity and poor prognosis of this cohort. In contrast, survival after treatment initiation tended to be longer, although available only for a smaller subset of patients.\u003c/p\u003e\n\u003cp\u003eIn the context of cancer care, we structured survival analysis into three successive temporal models that represent distinct stages of the patient care pathway: (1) from first consultation to death, (2) from diagnosis to death, and (3) from first treatment to death. We used these three intervals in the bivariate analysis and incorporated them into multivariate models. Each milestone reflected a critical stage of the patient journey, shaped by different barriers and opportunities along the continuum of care (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKaplan-Meyer analysis according to sociodemographic, clinical, and healthcare access variables: diagnosis\u0026ndash;death, consultation\u0026ndash;death, and treatment\u0026ndash;death models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean in days (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value (Log-Rank)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel #1: Diagnosis to death\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003ePrior diagnosis/ treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.95 (96.72\u0026ndash;121.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.83 (114.46\u0026ndash;133.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241.55 (189.33\u0026ndash;293.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.62 (114.11\u0026ndash;129.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health system (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.36 (103.66\u0026ndash;123.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.99 (116.50\u0026ndash;153.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.28 (110.57\u0026ndash;127.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.02 (121.22\u0026ndash;144.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.28 (101.95\u0026ndash;120.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.72 (113.34\u0026ndash;128.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICD-O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.65 (127.85\u0026ndash;149.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.39 (131.42\u0026ndash;255.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.35 (130.60\u0026ndash;152.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiagnosis and consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.02 (89.49\u0026ndash;112.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.16 (111.09\u0026ndash;129.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.35 (106.19\u0026ndash;120.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel #2: First consultation to death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePrior diagnosis/ treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.97 (127.70\u0026ndash;152.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.86 (82.59\u0026ndash;101.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.93 (99.21\u0026ndash;180.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.43 (103.99\u0026ndash;118.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health system (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.45 (91.53\u0026ndash;111.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.21 (105.77\u0026ndash;140.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.52 (98.84\u0026ndash;116.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.54 (120.04\u0026ndash;143.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.32 (85.10\u0026ndash;103.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.78 (100.49\u0026ndash;115.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICD-O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.61 (130.99\u0026ndash;152.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.70 (103.99\u0026ndash;205.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.35 (131.93\u0026ndash;152.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiagnosis and consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.54 (120.04\u0026ndash;143.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.32 (85.10\u0026ndash;103.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.78 (100.49\u0026ndash;115.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel #3: First treatment to death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePrior diagnosis/ treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.89 (190.56\u0026ndash;253.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.75 (181.49\u0026ndash;256.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270.57 (149.44\u0026ndash;391.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.09 (199.54\u0026ndash;246.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCase origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health system (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.91 (176.40\u0026ndash;245.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254.48 (199.36\u0026ndash;309.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225.74 (196.17\u0026ndash;255.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222.87 (189.66\u0026ndash;256.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.63 (195.48\u0026ndash;263.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.06 (202.26\u0026ndash;249.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICD-O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.04 (192.88\u0026ndash;243.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroendocrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303.60 (177.10\u0026ndash;430.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225.29 (199.83\u0026ndash;250.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiagnosis and consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.35 (173.11\u0026ndash;237.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.29 (198.70\u0026ndash;271.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219.86 (195.52\u0026ndash;244.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u0026nbsp;\u003c/strong\u003eSurvival estimates are expressed as mean survival time in days, with standard error (SE) and 95% confidence intervals (95% CI). Comparisons between groups were performed using the log-rank (Mantel\u0026ndash;Cox) test. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Global values correspond to overall estimates for each category.\u003c/p\u003e\n\u003cp\u003eIn the consultation\u0026ndash;death model, we captured the full hospital course from entry, including diagnostic confirmation, treatment initiation, and follow-up until outcome. In the diagnosis\u0026ndash;death model, we focused on the period following diagnostic confirmation, excluded diagnostic delays, and investigated differences after cancer identification. In the treatment\u0026ndash;death model, we restricted the analysis to the period after therapeutic intervention began. When we compared the three models, we found that sociodemographic, clinical, and access-related variables had variable effects depending on the stage of care. In the first two models, we identified substantial and statistically significant associations for institutional follow-up, race, referral source, and prior diagnosis/treatment. In the third model, after treatment initiation, almost all variables lost significance except institutional follow-up, which remained dominant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors associated with pancreatic cancer mortality: multivariate analysis using the Cox regression model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003ecrude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003cp\u003eadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eModel #1: Diagnosis to death\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior diagnosis/ treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01 (1.32\u0026ndash;3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95 (1.16\u0026ndash;3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34 (1.53\u0026ndash;3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28 (1.34\u0026ndash;3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.06\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (1.08\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis and consultation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.06\u0026ndash;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (1.02\u0026ndash;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health system (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.08\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (1.01\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel #2: First consultation to death\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior diagnosis/ treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis with treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis without treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (1.14\u0026ndash;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.02\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo diagnosis or treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69 (1.46\u0026ndash;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 (1.23\u0026ndash;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (1.08\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.08\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis and consultation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter the first consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.18\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (1.08\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Practice|Health Insurance Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health system (SUS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (1.03\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.01\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: Results are presented as crude and adjusted hazard ratios (HR) with 95% confidence intervals (95% CI). Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in the univariate analysis were included in the multivariate Cox regression model. Backward stepwise selection was used to determine the final model. Proportional hazards assumption was tested using Schoenfeld residuals. Abbreviations: HR \u0026ndash; hazard ratio; CI \u0026ndash; confidence interval; SUS \u0026ndash; Unified Health System (Brazil). Model #3 (First treatment to death) showed no significant variable from Kaplan Meyer analysis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the consultation\u0026ndash;death model, we observed the most significant influence of access inequalities and the patient\u0026apos;s previous trajectory. Institutional follow-up showed a survival difference of 268 days between followed patients (368.8 days; 95% CI: 341.2\u0026ndash;396.4) and non-followed patients (100.5 days; 95% CI: 94.0\u0026ndash;107.1). White patients lived longer on average (131.5 days; 95% CI: 120.0\u0026ndash;143.0) than black patients (94.3 days; 95% CI: 85.1\u0026ndash;103.5). Patients outside SUS survived 123.2 days (95% CI: 105.8\u0026ndash;140.6) compared with 101.4 days (95% CI: 91.5\u0026ndash;111.4) for SUS patients. A previous trajectory also stood out: patients without a prior diagnosis/treatment lived 140.0 days (95% CI: 127.7\u0026ndash;152.2), compared with only 91.9 days (95% CI: 82.6\u0026ndash;101.1) for those with a diagnosis but no treatment.\u003c/p\u003e\n\u003cp\u003eIn the diagnosis\u0026ndash;death model, we found similar patterns with different magnitudes. Institutional follow-up maintained a substantial impact, with a 263.5-day difference between followed patients (372.7 days; 95% CI: 344.8\u0026ndash;400.7) and non-followed patients (109.2 days; 95% CI: 102.6\u0026ndash;115.7). Prior diagnosis/treatment also had a strong influence: patients with both reached 241.6 days (95% CI: 189.3\u0026ndash;293.8), nearly double that of patients without diagnosis/treatment (109.0 days; 95% CI: 96.7\u0026ndash;121.2). Differences by race (white: 133.0 days; 95% CI: 121.2\u0026ndash;144.8 vs. blacks: 111.3 days; 95% CI: 102.0\u0026ndash;120.6) and referral source (non-SUS: 135.0 days; 95% CI: 116.5\u0026ndash;153.5 vs. SUS: 113.4 days; 95% CI: 103.7\u0026ndash;123.1) persisted, but the relative gaps decreased slightly, suggesting that part of the inequalities occur before diagnosis.\u003c/p\u003e\n\u003cp\u003eFinally,in the treatment\u0026ndash;death model, we observed apparent attenuation of group differences. Institutional follow-up still showed a strong effect (around 330 days: followed patients 451.3 days; 95% CI: 412.4\u0026ndash;490.3 vs. non-followed patients 121.3 days; 95% CI: 108.7\u0026ndash;133.9), becoming nearly the only significant variable. Race, referral source, and prior diagnosis/treatment lost significance, and group survival means converged. These findings suggest that hospital treatment initiation acted as a \u0026ldquo;leveler\u0026rdquo; of outcomes for patients who managed to overcome the initial barriers to specialized care.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe recognize that the survival rate in pancreatic cancer is notoriously low. This outcome reflects not only the aggressive biology of the disease but also the multiple structural and institutional barriers that limit timely access to diagnosis and treatment. We found recent estimates indicating that five-year survival rarely exceeds 10% in developing countries, with even lower rates among racialized and socioeconomically vulnerable populations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings motivated us to conduct a detailed analysis of the therapeutic pathway, from the initial interaction with the healthcare system to the outcome. We observed that contemporary literature confirms this hypothesis, showing that barriers to access, social vulnerability, and structural racism interfere mainly in the early stages of care. At the same time, adherence to specialized institutional protocols after treatment initiation tends to mitigate these adverse effects.\u003c/p\u003e\u003cp\u003eWe structured our empirical survival analysis into three temporal models (consultation\u0026ndash;death, diagnosis\u0026ndash;death, and treatment\u0026ndash;death), which allowed us to demonstrate the impact of social and institutional inequalities along the care pathway.\u003c/p\u003e\u003cp\u003eIn the consultation\u0026ndash;death model, we found that structural barriers such as race, referral source (public vs. private), and absence of prior diagnosis/treatment strongly influenced survival. Patients under institutional follow-up survived an average of 368.8 days, while those without follow-up survived only 100.5 days \u0026mdash; a difference of 268 days. This gap reflects differences not only in access but also in quality of care, effectiveness of referrals, and coordination. We confirmed this in the literature, which shows that institutional follow-up increases the likelihood of appropriate diagnostic testing, early treatment initiation, and continuous support [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe also found that race was a key marker of inequality. White patients survived a mean of 131.5 days, while black patients survived only 94.3 days. Previous studies show that black patients often face diagnostic delays, fewer surgical indications, and reduced access to chemotherapy, even after controlling for clinical factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These disparities stem from spatial segregation of services, institutional racism, and historical distrust in healthcare systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, we observed that non-public patients (non-SUS) survived longer than those referred through SUS (123.2 vs. 101.4 days). This result suggests that the entry point into the health system conditions diagnostic and therapeutic opportunities. Literature supports this, showing that patients with private insurance or access to philanthropic hospitals tend to receive earlier diagnoses and more aggressive treatment than those relying solely on the public system [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We also identified an effect of prior diagnosis/treatment. Patients previously diagnosed and treated had shorter survival, likely reflecting advanced or recurrent disease. At the same time, those without such a history lived longer, suggesting the benefits of early diagnosis and immediate intervention.\u003c/p\u003e\u003cp\u003eIn the diagnosis\u0026ndash;death model, inequalities persisted, albeit with a reduced magnitude. Patients under institutional follow-up lived an average of 372.7 days compared to 109.2 days among those without. We believe that continuity of care facilitates quicker access to therapies, multidisciplinary follow-up, and adherence to evidence-based protocols, improving survival [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRacial and public/private differences remained but decreased in magnitude. White patients lived an average of 21.7 days longer than black patients, and non-SUS patients continued to outlive SUS patients. This pattern suggests that much of the inequality occurs before diagnosis, particularly in access to specialized services, imaging, and biopsy. We confirmed this in studies showing longer delays from symptom onset to diagnosis among black and low-income patients due to underuse of imaging, transportation barriers, and long waiting times [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe also found that patients with prior diagnosis/treatment survived an average of 241.6 days, more than twice as long as those without (109 days). It shows that prior engagement with health services and treatment confers a survival advantage, reinforcing the role of continuity of care [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the treatment\u0026ndash;death model, inequalities diminished substantially. Race, referral source, and prior diagnosis/treatment lost statistical significance, showing that once hospital treatment began, survival became more homogeneous. However, institutional follow-up remained highly predictive, with a survival gap of 330 days between patients who were followed and those who were not.\u003c/p\u003e\u003cp\u003eThis finding showed us that inequalities concentrate in access and diagnostic stages, but engagement and retention in specialized services continue to improve outcomes after treatment initiation. Institutional follow-up ensures clinical monitoring, management of adverse events, control imaging, and access to additional therapies or palliative care [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe confirmed that race operates as a structural determinant of health inequities. It should not be treated as a neutral demographic variable but as a historical marker of exclusion that directly affects early diagnosis, timely treatment, and continuity of care. International literature supports this view. Studies in the U.S. demonstrate that black patients are less likely to receive curative surgery or complete chemotherapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], face longer delays [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], more often decline resection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and live in socially vulnerable areas associated with worse outcomes [\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We also found that the type of hospital matters, since minority-serving hospitals show lower resection rates and higher mortality even after adjustment [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e These converging findings led us to the central conclusion of our study: access inequalities, particularly in early stages of care, condition survival in pancreatic cancer, and these disparities derive not only from biology but from complex social, racial, territorial, and institutional determinants. Once patients overcome entry barriers and access qualified treatment, inequalities diminish substantially, highlighting points of strategic policy intervention.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, we identified two priorities: (1) strengthening primary care, expanding imaging and biopsy services, and regulating referral for high-risk patients; (2) investing in specialized reference centers with multidisciplinary teams and standardized protocols.\u003c/p\u003e\u003cp\u003eWe also emphasize the urgency of addressing structural racial inequalities within healthcare. This requires culturally competent training for professionals and affirmative policies in the public system to guarantee equitable access to diagnostics, treatments, and technologies.\u003c/p\u003e\u003cp\u003eUltimately, we acknowledge institutional follow-up as a crucial protective factor throughout the therapeutic process. More than organizational efficiency, it reflects patient-centered continuity and coordination of care. Investing in institutional follow-up may represent one of the most cost-effective strategies to improve survival in high-risk cancers.\u003c/p\u003e\u003cp\u003eIn summary, our three temporal models clearly demonstrated that social and institutional inequalities profoundly affect the patient trajectory, particularly in early stages. Still, that initiation of qualified treatment has the potential to act as an equalizer of outcomes. These finding challenges health systems to guarantee equitable access to diagnosis, treatment, and continuity of care for all patients, regardless of race, origin, or social condition.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThrough the three models, we demonstrated a temporal trajectory in which access barriers, racial and historical inequalities, and referral context strongly shape the pre-treatment phase, exerting a significant impact on survival. However, once treatment begins, we identified that the primary determinant of prognosis becomes the guarantee of qualified institutional follow-up. This transition showed us the importance of designing policies and implementing actions that promote early access, ensure diagnostic equity, and retain patients within specialized services, so that all individuals may fully benefit from oncologic treatment and continuous care.\u003c/p\u003e\u003cp\u003e We recognize that the unequal distribution of health resources, the scarcity of specialized reference centers for pancreatic oncology, the underfunding of cancer care within the SUS, and the low rate of genetic testing further aggravate clinical outcomes in Brazil. We argue that policymakers must address these aspects by integrating intersectoral actions, including primary prevention, timely diagnosis, and adequate treatment, based on updated clinical guidelines and risk stratification.\u003c/p\u003e\u003cp\u003eBased on this evidence, we recommend strengthening patient navigation programs, prioritizing referrals of vulnerable populations to high-complexity centers, allocating structural resources to hospitals that serve racialized populations, and adopting composite indicators \u0026mdash; such as the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI) \u0026mdash; to guide regulatory and budgetary decisions in SUS. We understand that pancreatic cancer represents both a mirror of inequality in access to specialized care and an opportunity to build more equitable and efficient pathways to confront highly lethal malignancies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted using secondary data from the Hospital Cancer Registry (RHC) of the Brazilian National Cancer Institute (INCA). The project was approved by the Ethics Committee of the National Cancer Institute (Protocol Number: #4.538.738/2020). As the study was based on anonymized secondary data, the requirement for informed consent was waived in accordance with national regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual or identifiable personal data are included in this article.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e\u0026bull;Camila Drumond Muzi, conception, design of the study, interpretation of results, supervision, and final approval.\u003cbr\u003e\u0026bull;Raphael Mendon\u0026ccedil;a Guimar\u0026atilde;es, data curation, statistical analysis, and drafting of the manuscript.\u003cbr\u003e\u0026bull;Rafael Tavares Jomar, interpretation of results, literature review, and manuscript writing.\u003cbr\u003e\u0026bull;Clara Soares Rosas, critical revision of the manuscript for important intellectual content.\u003cbr\u003e\u0026bull;Lara Barbosa de Souza Moura Canas Lara, critical revision of the manuscript for important intellectual content.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional restrictions of the INCA Hospital Cancer Registry but may be available from the corresponding author upon reasonable request and with permission of the National Cancer Institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Agency for Research on Cancer. 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Ann Surg Oncol. 2023;30(5):3002\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1245/s10434-022-13032-8\u003c/span\u003e\u003cspan address=\"10.1245/s10434-022-13032-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic Cancer, Neoplasms, Survival Analysis, Cancer Registries","lastPublishedDoi":"10.21203/rs.3.rs-7552704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7552704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePancreatic cancer remains one of the most lethal malignancies worldwide, with a five-year survival rate below 10%. In Brazil, significant regional and social inequalities shape access to diagnostic and therapeutic services, leading to disparities in survival outcomes. This study aimed to evaluate healthcare-related factors associated with survival among Brazilian patients with pancreatic cancer, focusing on institutional barriers, race, and health system entry point.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study of 1,426 patients diagnosed with pancreatic adenocarcinoma at the National Cancer Institute (INCA 1, Brazil) between 2000 and 2023. Data were obtained from the Hospital Cancer Registry. Survival analyses were structured into three temporal models: (1) consultation\u0026ndash;death, (2) diagnosis\u0026ndash;death, and (3) treatment\u0026ndash;death. Kaplan\u0026ndash;Meier curves and log-rank tests were used for bivariate analysis, and Cox proportional hazards models were applied to estimate hazard ratios (HR) with 95% confidence intervals (CI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMedian survival was 81 days from diagnosis to death and 75 days from first consultation to death. After treatment initiation, median survival increased to 170 days. In pre-treatment models, survival was significantly influenced by race, referral source, and prior diagnosis/treatment. Black patients (median 94.3 days) had shorter survival than white patients (131.5 days; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients entering care via the public health system (SUS) survived less (101.4 days) than those treated outside SUS (123.2 days; p\u0026thinsp;=\u0026thinsp;0.05). Institutional follow-up was the strongest protective factor, associated with survival differences exceeding 260 days. In the treatment\u0026ndash;death model, most inequalities attenuated, but institutional follow-up remained highly predictive (451.3 vs. 121.3 days; HR\u0026thinsp;=\u0026thinsp;2.28; 95% CI: 1.34\u0026ndash;3.89).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSocial and institutional inequalities significantly affect survival in pancreatic cancer, particularly before treatment initiation. Race, referral source, and prior diagnosis/treatment strongly influenced prognosis in early stages, but disparities decreased after specialized therapy began. Institutional follow-up emerged as the main determinant of improved survival, highlighting the importance of timely diagnosis, equitable referral pathways, and patient-centered continuity of care to reduce health inequities in highly lethal cancers.\u003c/p\u003e","manuscriptTitle":"The Impact of Social Inequalities on the Survival of Patients With Pancreatic Cancer: Analysis of a Cohort of 1,426 Cases in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 07:35:17","doi":"10.21203/rs.3.rs-7552704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-22T21:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108927184904746987541695778937887828523","date":"2025-10-12T08:46:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T17:50:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T13:20:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T10:16:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T10:14:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-09-06T19:05:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1011d1f5-12b6-4af8-91bd-29d07e4b35f5","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T07:35:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 07:35:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7552704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7552704","identity":"rs-7552704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}