ABSTRACT
Objective:
To analyze the overall and cancer-specific five-year survival rates for female breast cancer in Greater Cuiabá, Mato Grosso, Brazil.
Methods:
A non-concurrent, population-based cohort study using the Population-Based Cancer Registry of Greater Cuiabá (Cuiabá and Varzea Grande), including women diagnosed with breast cancer from 2008 to 2013, followed through 2018 in the regional mortality database. The sample consisted of a total of 1,220 women. Five-year survival analysis was performed using Kaplan-Meier curves and the Cox proportional hazards regression model, computing hazard ratios for variable estimation. Survival curves were compared using the log-rank test (p<0.05). Probabilistic linkage technique by the RecLink III software and survival analysis were conducted using STATA software version 12.0.
Results:
There was no statistical difference between the overall (OS) and cancer-specific survival (SS) rates (OS 78.0%, 95%CI 75.6–80.2; SS 81.0%, 95%CI 78.7–83.2). Women with lower educational levels (OS=58.33%; SS=64.89%) and those without a partner (OS 64.81%; SS 70.41%) exhibited poorer survival.
Conclusion:
This study demonstrates that educational level and marital status significantly impact both overall and cancer-specific survival rates for female breast cancer. There is a need to propose policies that address the profile of women with lower survival rates.
Keywords: Women's health, Breast neoplasms, Survival analysis, Health information systems
INTRODUCTION
Breast cancer is the most prevalent cancer among women globally. In 2022, the estimated incidence rate was 46.8/100,000 women. By 2045, the number of new cases is projected to increase by 46.5%, with over 3.3 million women expected to be diagnosed with the disease. In South America, the projected increase in new cases for the same period is 47.8% 1 .
Globally, breast cancer is also the leading cause of cancer-related deaths in women, with a mortality rate of 12.7/100,000 women reported in 2022. By 2045, cancer-related deaths are projected to increase by 59.1% worldwide. In South American countries, the estimated mortality rate in 2022 was 13.8/100,000 women, with a projected 63.3% increase in deaths by 2045 1,2 .
In Brazil, breast cancer is the most prevalent cancer among women across all regions of the country. For the three-year period from 2023 to 2025, the age-adjusted incidence rate was estimated at 66.5/100,000 women, with a projected increase of 47.6% by 2045. In 2022, the age-adjusted mortality rate was estimated at 13.9/100,000 women, with breast cancer mortality in the country expected to rise by 61.9% by 2045 2,3 .
Cancer survival rates in the Americas exhibit significant heterogeneity. In North America and Costa Rica, survival rates are among the highest (≥85.0%). Within Latin America, disparities are evident. For example, Argentina, Peru, and Puerto Rico report survival rates ranging from 80.0 to 84.0%, while countries such as Brazil, Colombia, Cuba, and Ecuador have survival rates between 70.0 and 79.0% 4 .
In addition to early diagnosis, several factors contribute to improved survival rates in women with breast cancer. These include positive hormone receptor status, a diagnosis of postmenopausal breast cancer, the absence of the triple-negative subtype, white race/skin color, having a partner, higher socioeconomic status, and access to health insurance 5-7 .
Another contributing factor is that, despite being a priority in the state's Oncology Plan, the control of female breast cancer remains a challenge. This is primarily due to issues in improving network coordination and the need for increased investment to reduce delays between initial suspicion, diagnosis, and the initiation of treatment. Additionally, the study aimed to provide insights into the current situation to inform future actions for more effective disease control 8 .
The aim of the study was to analyze five-year overall survival (OS) and specific survival (SS) rates for female breast cancer in a cohort from 2008 to 2013, based on the population of Greater Cuiabá, Mato Grosso, Brazil.
METHODS
A non-concurrent, population-based cohort was established, comprising women diagnosed with breast cancer and recorded in the Population-Based Cancer Registry (Registro de Câncer de Base Populacional – RCBP/Cuiabá). This registry includes data from the cities of Cuiabá and Várzea Grande in Mato Grosso State, Brazil, covering the period from January 2008 to December 2013. Only incident cases (definitive indicator "true") were included, while cases of in situ breast cancer were excluded. Follow-up was conducted passively through the Mato Grosso Mortality Information System (Sistema de Informação sobre Mortalidade – SIM) for a period of five years.
The RCBP Cuiabá coverage includes the municipalities of Cuiabá and Várzea Grande, the two largest municipalities in the state, contiguous, with a population of almost one million inhabitants according to the 2022 census, with 650,877 inhabitants in Cuiabá 9 and 300,078 in Várzea Grande; the urban conglomerate is called Greater Cuiabá 10 .
The RCBP Cuiabá was established in 1999 by the State Department of Health of Mato Grosso. However, the data became outdated after 2007. In response, a partnership was initiated in 2016 with Universidade Federal de Mato Grosso to update the databases, a process that continued until March 2021. Currently, the RCBP Cuiabá relies on 38 health establishments as reporting sources, including hospitals and diagnostic support clinics 11 . Among these, three hospitals are registered with INCA as High Complexity Oncology Units (Unidade de Alta Complexidade em Oncologia – Unacon) 12 . However, the Hospital Cancer Registries that contribute to the RCBP Cuiabá still exhibit significant incompleteness in key variables, such as education, marital status, tumor-node-metastasis (TNM) classification, staging, and disease status at the conclusion of the first treatment 13 .
Regarding the preparation of the databases, the SIM database was cleaned prior to pairing, with cases where the deceased's name was listed as "blank," "unknown," "ignored," or "indigent" being excluded, resulting in 72 exclusions. In the RCBP database, duplicates were removed. Records with identical data for patient name, mother's name, gender, date of birth, disease code, and date of diagnosis were classified as duplicates, leading to the exclusion of 18 records (Figure 1).
Figure 1. Flowchart.
The probabilistic linkage between the RCBP and SIM databases was performed according to the three steps proposed by Coeli and Camargo Jr. 14 : standardization, which involved standardizing common fields for pairing; blocking, using the gender variable; and, finally, pairing by constructing concordance scores based on the variables of patient name (death), mother's name, and date of birth.
Based on the probabilistic linkage between the databases, 273 pairs were identified, all of which were confirmed as true. Of these, five records lacked information on "age" and "date of birth," and one record had an age of zero (with the date of birth matching the date of diagnosis). After attempting to recover this data from the Unified Health System User Registration System (Sistema de Cadastramento de Usuários do Sistema Único de Saúde – CADSUS), it was only possible to retrieve the record with an age of zero. The other records without age information were excluded from the database, resulting in a total of 1,220 records: 268 failures (women who experienced the outcome of death during the study period) and 952 censored cases (women who did not experience the outcome of death during the study period) 15 (Figure 1).
The dependent variable was the time from the women's entry into the study (date of breast cancer diagnosis) to death from any cause (OS) or from breast cancer specifically (SS). Such studies are referred to as survival analysis 15 . Survival time was measured in months, and women who did not experience the event of interest during the study period were considered censored.
The following independent variables were analyzed: age group (20–49 years, 50–69 years, ≥70 years); race/skin color (yellow, white, brown, black, and no information); marital status (with partner, without partner, and no information); education level (0–7 years of study, 8+ years of study, and no information); municipality of residence at the time of diagnosis (Cuiabá, Várzea Grande); diagnostic method (histology of the primary tumor, others); and morphology (infiltrating ductal carcinoma, others).
An initial assessment was performed using absolute and relative frequencies. The variables education, marital status and race/skin color presented 42.70, 26.48, and 9.75% of missing data, respectively. Given the impossibility of multiple imputation of missing data due to the reduced number of independent variables available and with complete data 16 , the multiple model was performed only with complete data (excluding those without information) (n=645).
Survival probabilities and their respective 95% confidence intervals (95%CI) were calculated for each independent variable. The Kaplan-Meier estimator was employed to estimate survival curves for the event of interest, and the curves were compared using the log-rank test, with a significance level of 5%.
The effect of independent variables on survival was estimated using the Cox proportional hazards regression model, with hazard ratios (HR) and their corresponding 95%CI computed. The variables morphology and diagnostic method did not satisfy the Cox proportional hazards assumption; therefore, the multiple model was stratified by these variables. The proportional hazards assumption was assessed using Schoenfeld residuals, ensuring no rejection of the null hypothesis (p > 0.05) 16 .
The probabilistic linkage was performed using Link Plus 2.0 software, while survival analysis was conducted using Stata 12.0.
The study adheres to the principles outlined in Resolution 466/2012 of the National Health Council. It is part of the matrix project entitled "Cancer and its associated factors: analysis of population and hospital records in Cuiabá-MT," which was approved by the Ethics Committee of the Júlio Muller University Hospital and the Ethics Committee of the School of Public Health of the State of Mato Grosso — State Secretariat of Health of Mato Grosso.
RESULTS
Among the 1,220 women included in the study, 80.08% resided in Cuiabá, 59.67% identified as non-white, 29.75% had more than eight years of schooling, and 36.80% were unpartnered. A total of 92.62% were diagnosed through histology of the primary tumor, and 78.85% had invasive ductal carcinoma morphology. The majority of women were aged 50 years old or older (60.33%), with a mean age of 54 years and a median age of 53 years (interquartile range: 45 to 63 years), with ages ranging from 21 to 99 years (Table 1).
Table 1. Characterization of women diagnosed with breast cancer. Cuiabá (MT), 2008–2013.
| Characteristic | Total n=1,220 (%) |
Death from any cause n=268 (%) |
Death from breast cancer n=228 (%) |
Censored n=952 (%) |
|
|---|---|---|---|---|---|
| Age range (years) | |||||
| 20–49 | 484 (39.67) | 99 (36.94) | 90 (39.47) | 385 (40.45) | |
| 50–69 | 572 (46.89) | 114 (42.54) | 99 (43.42) | 458 (48.11) | |
| 70 + | 164 (13.44) | 55 (20.52) | 39(17.11) | 109 (11.44) | |
| Race/color | |||||
| Yellow | 36 (2.95) | 4 (1.49) | 3 (1.32) | 32 (3.36) | |
| White | 373 (30.57) | 94 (35.07) | 75 (32.89) | 279 (33.41) | |
| Brown | 635 (52.05) | 150 (55.97) | 132 (57.89) | 485 (50.95) | |
| Black | 57 (4.67) | 18 (6.72) | 16 (7.02) | 39 (4.10) | |
| No information | 119 (9.75) | 2 (0.75) | 2 (0.87) | 117 (12.28) | |
| Education level (years) | |||||
| 0–7 | 336 (27.54) | 140 (52.24) | 114 (50.00) | 196 (20.59) | |
| 8+ | 363 (29.75) | 116 (43.28) | 103 (45.18) | 247 (25.95) | |
| No information | 521 (42.70) | 12 (4.48) | 11 (4.82) | 509 (53.47) | |
| Marital status | |||||
| With partner | 448 (36.72) | 106 (39.55) | 95 (41.67) | 342 (35.92) | |
| Without partner | 449 (36.80) | 158 (58.96) | 129 (56.58) | 291 (30.57) | |
| No information | 323 (26.48) | 4 (1.49) | 4 (1.75) | 319 (33.51) | |
| Municipality | |||||
| Cuiabá | 977 (80.08) | 216 (80.60) | 181 (79.39) | 761 (79.94) | |
| Várzea Grande | 243 (19.92) | 52 (19.40) | 47 (20.61) | 191 (20.06) | |
| Diagnosis method | |||||
| Histology of primary tumor | 1,130 (92.62) | 219 (81.72) | 179 (78.51) | 911 (95.69) | |
| Others | 88 (7.21) | 49 (18.28) | 49 (21.49) | 39 (4.10) | |
| No information | 2 (0.16) | 0 | 0 | 2 (0.21) | |
| Morphology | |||||
| Invasive ductal carcinoma | 962 (78.85) | 184 (68.66) | 155 (67.98) | 778 (81.72) | |
| Outros | 258 (21.15) | 84 (31.34) | 73 (32.02) | 174 (18.28) | |
Source: Research data; 2021.
By the end of the study, among the 1,220 women being followed, 268 had died. Of these, 228 (85.76%) deaths were due to breast cancer, 14 (5.23%) resulted from other types of cancer, and 26 (9.71%) were attributed to other identified causes.
The OS and SS rates did not show a statistically significant difference (OS: 78.03%, 95%CI: 75.60–80.25%; SS: 81.05%, 95%CI: 78.72–83.16%). In the OS analysis, women aged 70 years old or older and those with an education level of zero to seven years demonstrated lower survival rates. Additionally, women without a partner exhibited lower OS and SS rates (log-rank test, p<0.05) (Table 2 and Figure 2). Histology of the primary tumor showed the highest OS and SS rates, as did invasive ductal carcinoma regarding morphology (p<0.05) (Table 2).
Table 2. Overall survival functions and five-year specific survival of women diagnosed with breast cancer, according to the study variables. Cuiabá (MT), 2008–2013.
| Characteristic | Overall survival % (95%CI) |
Specific survival % (95%CI) |
|
|---|---|---|---|
| Age range (Years) | |||
| 20–49 | 79.55 (75.67–82.87) | 81.17 (77.37–84.40) | |
| 50–69 | 80.07 (76.56–83.12) | 82.51 (79.12–85.40) | |
| 70 + | 66.46 (58.68–73.12) | 75.58 (68.12–81.53) | |
| Race/color | |||
| Yellow | 88.89 (73.05–95.68) | 91.43 (75.73–97.15) | |
| White | 74.80 (70.07–78.90) | 79.47 (74.95–83.27) | |
| Brown | 76.38 (72.88–79.49) | 78.96 (75.56–81.95) | |
| Black | 68.42 (54.66–78.79) | 71.55 (57.82–81.50) | |
| No information | 98.32 (93.45–99.58) | 98.32 (93.45–99.58) | |
| Education level (years) | |||
| 0–7 | 58.33 (52.87–63.39) | 62.87 (54.92–69.82) | |
| 8+ | 68.04 (62.98–72.57) | 73.83 (66.29–79.94) | |
| No information | 97.66 (96.01–98.64) | 97.84 (96.23–98.77) | |
| Marital status | |||
| With partner | 85.73 (83.06–88.01) | 87.09 (84.51–89.27) | |
| Without partner | 64.81 (60.20–69.03) | 70.41 (65.87–74.47) | |
| No information | 98.76 (96.73–99.53) | 98.76 (96.73–99.53) | |
| Municipality | |||
| Cuiabá | 77.89 (75.16–80.37) | 81.21 (78.60–83.54) | |
| Várzea Grande | 78.60 (72.89–83.25) | 80.41 (74.80–84.90) | |
| Diagnosis method | |||
| Histology of primary tumor | 80.62 (78.19–82.81) | 83.87 (81.57–85.91) | |
| Others | 44.32 (33.79–54.32) | 44.32 (33.79–54.32) | |
| Morphology | |||
| Invasive ductal carcinoma | 80.97 (78.24–82.22) | 83.62 (81.10–85.89) | |
| Others | 67.44 (61.35–72.79) | 71.52 (65.56–76.63) | |
Source: Research data, 2021.
Figure 2. Overall Survival and Five-Year Specific Survival of Female Breast Cancer, Population-Based Cancer Registry (RCBP) — Cuiabá (MT), Brazil.
RCBP: Population-Based Cancer Registry (Registro de Câncer de Base Populacional); SIM: Mortality Information System (Sistema de Informações sobre Mortalidade).
In the adjusted Cox model, the age group variable demonstrated statistical significance only for OS. For the adjusted models, including the stratified Cox model, marital status was statistically significant for both survival rates (OS and SS). The remaining variables did not exhibit statistically significant differences (Table 3).
Table 3. Unadjusted and adjusted hazard ratios (HR) for overall survival and five-year specific survival of women diagnosed with breast cancer, according to variables selected for the multiple model. Cuiabá (MT), 2008–2013.
| Characteristics | Non-adjusted | Adjusted analysis | |||
|---|---|---|---|---|---|
| COX Model*,‡ | Stratified COX model†,‡ | ||||
| HR (95%CI) | HR (95%CI) | HR (95%CI) | |||
| Overall survival | |||||
| Age range (years) | |||||
| 20–49 | 1 | 1 | 1 | ||
| 50–69 | 0.98 (0.75–1.28) | 0.97 (0.73–1.28) | 0.94 (0.71–1.25) | ||
| 70 + | 1.78 (1.28–2.47) | 1.55 (1.08–2.25) | 1.29 (0.89–1.87) | ||
| Race/color§ | |||||
| Yellow | 1 | 1 | 1 | ||
| White | 0.95 (0.35–2.58) | 0.91 (0.33–2.49) | 0.76 (0.28–2.09) | ||
| Brown | 0.81 (0.30–2.20) | 0.79 (0.29–2.16) | 0.67 (0.24–1.82) | ||
| Black | 1.20 (0.41–3.55) | 1.09 (0.37–3.25) | 0.98 (0.33–2.91) | ||
| Education level// (years) | |||||
| 0–7 | 1.40 (1.09–1.79) | 1.24 (0.95–1.60) | 1.23 (0.94–1.60) | ||
| 8+ | 1 | 1 | 1 | ||
| Marital status¶ | |||||
| With partner | 1 | 1 | 1 | ||
| Without partner | 1.59 (1.24–2.03) | 1.44 (1.11–1.87) | 1.49 (1.15–1.94) | ||
| Municipality | |||||
| Cuiabá | 1.03 (0.76–1.40) | 1.02 (0.75–1.41) | 1.01 (0.73–1.38) | ||
| Várzea Grande | 1 | 1 | 1 | ||
| Specific survival | |||||
| Age range (years) | |||||
| 20–49 | 1 | 1 | 1 | ||
| 50–69 | 0.93 (0.70–1.24) | 0.93 (0.69–1.25) | 0.89 (0.66–1.21) | ||
| +70 | 1.38 (0.95–2.01) | 1.22 (0.80 –1.87) | 0.99 (0.64–1.51) | ||
| Race/color§ | |||||
| White | 1 | 1 | 1 | ||
| Yellow | 0.99 (0.31–3.17) | 0.97 (0.30–3.11) | 0.80 (0.25–2.58) | ||
| Brown | 0.95 (0.30–2.98) | 0.92 (0.29–2.91) | 0.76 (0.24–2.41) | ||
| Black | 1.42 (0.41–4.87) | 1.34 (0.39–4.62) | 1.23 (0.35–4.24) | ||
| Education level// (years) | |||||
| 0–7 | 1.32 (0.88–1.97) | 1.17 (0.88–1.55) | 1.16 (0.87–1.53) | ||
| 8+ | 1 | 1 | 1 | ||
| Marital status¶ | |||||
| With partner | 1 | 1 | 1 | ||
| Without partner | 1.45 (1.11–1.89) | 1.37 (1.03–1.81) | 1.44 (1.09–1.91) | ||
| Municipality | |||||
| Cuiabá | 0.96 (0.69–1.32) | 0.97 (0.69–1.36) | 0.95 (0.68–1.35) | ||
| Várzea Grande | 1 | 1 | 1 | ||
Source: Research data, 2021.
Global test value for the proportional hazards assumption: p=0.733 (OS); p=0.285 (SS).
Global test value for the proportional hazards assumption: p=0.853 (OS); p=0.754 (SS); Stratified by morphology and diagnostic method variables;
n=645;
n=1.010;
n=699;
n=897.
DISCUSSION
This study analyzed the OS and SS of women with breast cancer in Greater Cuiabá, Mato Grosso, Brazil. No statistical difference was observed between the two survival rates. In the multiple analysis, marital status demonstrated statistical significance for both survival rates (p<0.05).
The findings of this study align with the survival rates reported for Brazil (data from the RCBP of Aracaju, Cuiabá, Curitiba, Goiânia, Jaú, and São Paulo), analyzed by CONCORD-3 4 . This study compared the net survival of 18 cancers across 71 countries. In countries such as Denmark, France, Norway, Finland, Israel, Japan, Australia, New Zealand, Canada, the United States, and Costa Rica, survival rates for female breast cancer were higher (≥85.0%). Argentina, Peru, and Puerto Rico reported survival rates of 80.0–84.0%. Brazil was among the countries with survival rates of 70.0–79.0%, similar to Cuba, Ecuador, Bulgaria, and Poland. Another study conducted in Poland from 2000 to 2019 found a five-year survival rate of 77.3% 17 , a result comparable to the findings of this study.
A population-based study conducted in Goiânia 18 , which included women diagnosed with breast cancer between 1995 and 2003, reported a 5-year OS rate of 72.1%, lower than the rate observed in the present study. This difference may be attributed to the study period or potential changes in screening and treatment protocols for the disease over time.
Higher survival rates were observed in Spain 19 , where a cohort study conducted from 2000 to 2007 analyzed all primary tumors diagnosed in adults across nine population-based cancer registries. The study reported a five-year OS rate of 82.8% for female breast cancer.
In the OS, women aged 70 years or older exhibited worse survival compared to the other age groups. This finding is consistent with the literature on survival for female breast cancer 19,20 . The poorer survival may be attributed to advanced age, a higher proportion of late diagnoses, and the fact that standard treatment for breast cancer may not be administered in this age group 19 .
Several studies 19,21,22 have shown differences in survival between younger women and those over 50 years of age. A study conducted in Iran 20 found that women over 50 had worse survival outcomes compared to younger women. This study also indicated that the presence of comorbidities leads to secondary disabilities, complicates the treatment of breast cancer, and increases the overall complications associated with cancer in patients.
Education was a variable that showed an association with OS for female breast cancer in the bivariate analysis. Due to the strong correlation between education, unemployment, occupation, and income, this variable can serve as a proxy for a person's socioeconomic status 23,24 .
Several studies 19,21 from countries with varying human development indices (HDI) have demonstrated better breast cancer survival among women with higher levels of education. In Brazil 23 , the five-year disease-free survival for breast cancer was 85.0% for women with higher levels of education, compared to 70.9% for those with lower levels. In India 24 , the discrepancy was even more pronounced, with OS for women with higher education levels at 94.0%, compared to 61.5% for those with lower education levels.
According to a systematic review conducted by Coughlin 25 , education level and the municipality of residence are associated with breast cancer survival. The review also highlights that poverty, lower levels of education, racial segregation, racial discrimination, lack of racial support, and social isolation are critical factors influencing both the staging of the disease and survival outcomes.
Early diagnosis is a well-established factor related to breast cancer survival 21,22,24,26 , and adherence to screening is one way to achieve this. Several studies have shown an association between marital status 27,28 and higher education levels with increased adherence to breast cancer screening, including in Brazil 23,29 . In Brazil, sociodemographic variables associated with two years without screening were more prominent among women with lower education levels in the North Region and those living without a partner in the South Region 23 .
Marital status was another variable associated with OS and SS. Some studies have shown that married women have better survival outcomes compared to single/widowed/divorced women 5,27,28,30 . Although the association between marital status and survival varied according to factors such as race/skin color, tumor subtype, and neighborhood socioeconomic status, the strongest association with worse survival was observed among women without a partner living in low-income neighborhoods, even after stratification by tumor subtype 5 .
These findings reflect the association between social inequities and breast cancer, as demonstrated in other studies 25,31 , which show worse survival outcomes for women with lower socioeconomic status.
One limitation of the study is the incompleteness of the RCBP database. According to a previous study 13 , a high number of mandatory variables were missing in the Cancer Hospital Registry (Registro Hospitalar de Câncer – RHC) of Mato Grosso, one of the sources that directly feeds the RCBP/Cuiabá. As a result, variables such as staging, which had a high degree of incompleteness in the RHC of Cuiabá, exhibited the same issue in the RCBP database. This made it impossible to analyze the stage of diagnosis, as well as other variables like morphology and extent of the disease, and to examine the association of these variables with education and marital status. However, a major strength of the RCBP database is its availability of historical data on new cases up to 2016, which is more current than other databases in Brazil and Latin America 32 . Another limitation of the study relates to the passive monitoring of new breast cancer cases, which could result in missed deaths that occurred in other states 33 . Additionally, the use of the Kaplan-Meier curve to estimate SS may be less accurate, as it does not account for the conditional independence between the date of diagnosis and time until death for a known set of covariates 34 .
It can be concluded that age group, education level, and marital status influence the OS and SS of female breast cancer, with worse survival observed in women aged 70 years old or older, those with lower education levels, and those without a partner. This finding contributes to the existing evidence from other studies that indicate poorer survival for women with these characteristics.
This research highlights the usefulness of secondary data in profiling individuals treated for breast cancer, as well as in analyzing survival rates for different types of cancer. However, the findings should be considered in the context of the RCBP database's incompleteness, which underscores the need to develop strategies for improving the management of cancer registry data to achieve greater data completeness.
Furthermore, it is necessary to reassess the strategies for controlling female breast cancer in Greater Cuiabá, with the goal of proposing policies focused on early diagnosis, timely treatment, and improving the quality of life for women affected by this pathology.
ACKNOWLEDGMENTS:
To the Mato Grosso State Health Secretariat and the Public Labor Ministry of the 23rd Region for their support in conducting this study. To Universidade Federal de Mato Grosso for supporting the publication in qualified journals (internal research support call No. 03/PROPeq/2024).
Funding Statement
State Health Secretariat of Mato Grosso, for funding the extension project "Cancer Surveillance and Its Associated Factors: Updating of Population-based and Hospital-based Registry" (contract 088/2016); Public Ministry of Labor of the 23rd Region, for funding the research project "Cancer and Its Associated Factors: Analysis of Population-based and Hospital-based Registry" (Technical Cooperation Agreement 08/2019)
Footnotes
FUNDING: State Health Secretariat of Mato Grosso, for funding the extension project "Cancer Surveillance and Its Associated Factors: Updating of Population-based and Hospital-based Registry" (contract 088/2016); Public Ministry of Labor of the 23rd Region, for funding the research project "Cancer and Its Associated Factors: Analysis of Population-based and Hospital-based Registry" (Technical Cooperation Agreement 08/2019).
ETHICAL APPROVAL: This study was part of the research titled "Cancer and its associated factors: analysis of the population-based and hospital-based registry in Cuiabá-MT," approved by the Ethics Committee of the Júlio Muller University Hospital (opinion No. 4.001.695) and the Ethics Committee of the School of Public Health of the State of Mato Grosso – Mato Grosso State Health Secretariat (opinion No. 3.263.744).
REFERENCES
- 1.International Agency for Research on Cancer . Globocan, cancer tomorrow. Predictions of the future cancer incidence and mortality burden worldwide up until 2050 [Internet] World Health Organization; 2024. [[cited on Jan 10, 2025]]. Available at: https://gco.iarc.fr/tomorrow/en . [Google Scholar]
- 2.International Agency for Research on Cancer . Globocan, cancer today. Data visualization tools for exploring the global cancer burden in 2022 [Internet] World Health Organization; 2024. [[cited on Jan 10, 2025]]. Available at: https://gco.iarc.fr/today/home . [Google Scholar]
- 3.Instituto Nacional de Câncer José Alencar Gomes da Silva . Estimativa 2023: incidência de câncer no Brasil [Internet] 2022. [[cited on Jan 10, 2025]]. Available at: https://www.inca.gov.br/sites/ufu.sti.inca.local/files/media/document/estimativa-2023.pdf . [Google Scholar]
- 4.Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391(10125):1023–1075. doi: 10.1016/S0140-6736(17)33326-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Martínez ME, Unkart JT, Tao L, Kroenke CH, Schwab R, Komenaka I, et al. Prognostic significance of marital status in breast cancer survival: a population-based study. PLoS One. 2017;12(5):1–14. doi: 10.1371/journal.pone.0175515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Li X, Zhang X, Liu J, Shen Y. Prognostic factors and survival according to tumour subtype in women presenting with breast cancer bone metastases at initial diagnosis: a SEER-based study. BMC Cancer. 2020;20(1):1102–1102. doi: 10.1186/s12885-020-07593-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hossain F, Danos D, Prakash O, Gilliland A, Ferguson TF, Simonsen N, et al. Neighborhood social determinants of triple negative breast cancer. Front Public Health. 2019;7:18–18. doi: 10.3389/fpubh.2019.00018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Mato Grosso . Plano estadual de educação permanente do Estado de Mato Grosso [Internet] Mato Grosso: Secretaria de Estado de Saúde. Escola de Saúde Pública; 2017. [[cited on Jan 10, 2025]]. Available at: https://www.saude.mt.gov.br/storage/old/files/ad-referendum-n008-anexo-%5B16479-270319-SES-MT%5D.pdf . [Google Scholar]
- 9.Instituto Brasileiro de Geografia e Estatística . Cidades e Estados. Cuiabá: Panorama [Internet]; 2022. [[cited on Aug 18, 2024]]. Available at: https://cidades.ibge.gov.br/brasil/mt/cuiaba/panorama . [Google Scholar]
- 10.Instituto Brasileiro de Geografia e Estatística . Cidades e Estados. Várzea Grande: Panorama [Internet]; 2022. [[cited on Aug 18, 2024]]. Available at: https://cidades.ibge.gov.br/brasil/mt/varzea-grande/panorama . [Google Scholar]
- 11.Galvão ND, Souza RAG, Souza BSN, Melanda FN, Andrade ACS, Sousa NFS, et al. Cancer surveillance in Mato Grosso, Brazil: methodological and operational aspects of a university extension/research project. Rev Bras Epidemiol. 2022;25(Supl 1):e220002. doi: 10.1590/1980-549720220002.supl.1.1. [DOI] [PubMed] [Google Scholar]
- 12.Instituto Nacional de Câncer . Onde tratar pelo SUS. Centro-Oeste; Mato Grosso [Internet]: 2022. [[cited on Jan 10, 2025]]. Available at: https://www.gov.br/inca/pt-br/assuntos/cancer/tratamento/onde-tratar-pelo-sus . [Google Scholar]
- 13.Oliveira JCS, Azevedo EFS, Caló RS, Atanaka M, Galvão ND, Silva AMC. Registros hospitalares de câncer de Mato Grosso: análise da completitude e da consistência. Cad Saúde Colet. 2021;29(3):330–343. doi: 10.1590/1414-462X202129030230. [DOI] [Google Scholar]
- 14.Coeli CM, Camargo KR de., Jr. Avaliação de diferentes estratégias de blocagem no relacionamento probabilístico de registros. Rev Bras Epidemiol. 2002;5(2):185–196. doi: 10.1590/S1415-790X2002000200006. [DOI] [Google Scholar]
- 15.Carvalho MS, Andreozzi VL, Codeço CT, Campos DP, Barbosa MTS, Shimakura SE. Análise de sobrevivência: teoria e aplicações em saúde. Rio de Janeiro: Editora Fiocruz; 2011. [Google Scholar]
- 16.Nunes LN, Klück MM, Fachel JMG. Multiple imputations for missing data: a simulation with epidemiological data. Cad Saude Publica. 2009;25(2):268–278. doi: 10.1590/s0102-311x2009000200005. [DOI] [PubMed] [Google Scholar]
- 17.Santos FLC, Michalek IM, Wojciechowska U, Didkowska J. Changes in the survival of patients with breast cancer: Poland, 2000-2019. Breast Cancer Res Treat. 2023;197(3):623–631. doi: 10.1007/s10549-022-06828-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Freitas R, Júnior, Nunes RD, Martins E, Curado MP, Freitas NMA, Soares LR, et al. Fatores prognósticos do câncer de mama e sobrevida global em cinco e dez anos na cidade de Goiânia, Brasil: estudo de base populacional. Rev Col Bras Cir. 2017;44(5):435–443. doi: 10.1590/0100-69912017005003. [DOI] [PubMed] [Google Scholar]
- 19.Baeyens-Fernández JA, Molina-Portillo E, Pollán M, Rodríguez-Barranco M, Del Moral R, Arribas-Mir L, et al. Trends in incidence, mortality and survival in women with breast cancer from 1985 to 2012 in Granada, Spain: a population-based study. BMC Cancer. 2018;18(1):781–781. doi: 10.1186/s12885-018-4682-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Fallahzadeh H, Momayyezi M, Akhundzardeini R, Zarezardeini S. Five year survival of women with breast cancer in Yazd. Asian Pac J Cancer Prev. 2014;15(16):6597–6601. doi: 10.7314/APJCP.2014.15.16.6597. [DOI] [PubMed] [Google Scholar]
- 21.DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Sauer AG, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 2019;69(6):438–451. doi: 10.3322/caac.21583. [DOI] [PubMed] [Google Scholar]
- 22.Dialla PO, Quipourt V, Gentil J, Marilier S, Poillot ML, Roignot P, et al. In breast cancer, are treatments and survival the same whatever a patient's age? A population-based study over the period 1998-2009. Geriatr Gerontol Int. 2015;15(5):617–626. doi: 10.1111/ggi.12327. [DOI] [PubMed] [Google Scholar]
- 23.Barbosa YC, Oliveira AGC, Rabêlo PPC, Silva FS, Santos AM. Factors associated with lack of mammography: National Health Survey, 2013. Rev Bras Epidemiol. 2019;22:e190069. doi: 10.1590/1980-549720190069. [DOI] [PubMed] [Google Scholar]
- 24.Gadgil A, Roy N, Sankaranarayanan R, Muwonge R, Sauvaget C. Effect of comprehensive breast care on breast cancer outcomes: a community hospital based study from Mumbai, India. Asian Pac J Cancer Prev. 2012;13(4):1105–1109. doi: 10.7314/APJCP.2012.13.4.1105. [DOI] [PubMed] [Google Scholar]
- 25.Coughlin SS. Social determinants of breast cancer risk, stage, and survival. Breast Cancer Res Treat. 2019;177(3):537–548. doi: 10.1007/s10549-019-05340-7. [DOI] [PubMed] [Google Scholar]
- 26.World Health Organization . World cancer report 2014 [Internet] Geneva: World Health Organization; 2014. [[cited on Jan 10, 2025]]. Available at: http://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/World-Cancer-Report-2014 . [Google Scholar]
- 27.Diniz RW, Guerra MR, Cintra JRD, Fayer VA, Teixeira MTB. Disease-free survival in patients with non-metastatic breast cancer. Rev Assoc Med Bras (1992) 2016;62(5):407–413. doi: 10.1590/1806-9282.62.05.407. [DOI] [PubMed] [Google Scholar]
- 28.Gadeyne S, Menvielle G, Kulhanova I, Bopp M, Deboosere P, Eikemo TA, et al. The turn of the gradient? Educational differences in breast cancer mortality in 18 European populations during the 2000s. Int J Cancer. 2017;141(1):33–44. doi: 10.1002/ijc.30685. [DOI] [PubMed] [Google Scholar]
- 29.Sreedevi A, Quereshi MA, Kurian B, Kamalamma L. Screening for breast cancer in a low middle income country: Predictors in a rural area of Kerala, India. Asian Pac J Cancer Prev. 2014;15(5):1919–1924. doi: 10.7314/APJCP.2014.15.5.1919. [DOI] [PubMed] [Google Scholar]
- 30.Ding W, Ruan G, Lin Y, Zhu J, Tu C, Li Z. Dinamic changes in marital status and survival in women with breast cancer: population-based study. Sci Rep. 2021;11(1):5421–5421. doi: 10.1038/s41598-021-84996-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Aizer AA, Chen MH, McCarthy EP, Mendu ML, Koo S, Wilhite TJ, et al. Marital status and survival in patients with cancer. J Clin Oncol. 2013;31(31):3869–3876. doi: 10.1200/JCO.2013.49.6489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Santos MAP, Fernandes FCGM, Santos EGO, Souza DLB, Barbosa IR. Trends in incidence and mortality by ovarian cancer in Latin American coutries. Rev Bras Cancerol. 2020;66(4):e-06813. doi: 10.32635/2176-9745.RBC.2020v66n4.813. [DOI] [Google Scholar]
- 33.Perme MP, Stare J, Estève J. On estimation in relative survival. Biometrics. 2012;68(1):113–120. doi: 10.1111/j.1541-0420.2011.01640.x. [DOI] [PubMed] [Google Scholar]
- 34.Seppä K, Hakulinen T, Pokhrel A. Choosing the net survival method for cancer survival estimation. Eur J Cancer. 2015;51(9):1123–1129. doi: 10.1016/j.ejca.2013.09.019. [DOI] [PubMed] [Google Scholar]
