Prostate Cancer Survival and Mortality according to a 13-year retrospective cohort study in Brazil: Competing-Risk Analysis

Sobrevida e mortalidade por câncer da próstata no Brasil por 13 anos em um estudo de coorte retrospectivo: Análise de riscos competitivos

Sonia Faria Mendes Braga Rumenick Pereira da Silva Augusto Afonso Guerra Junior Mariangela Leal Cherchiglia About the authors

ABSTRACT:

Objective:

To analyze cancer-specific mortality (CSM) and other-cause mortality (OCM) among patients with prostate cancer that initiated treatment in the Brazilian Unified Health System (SUS), between 2002 and 2010, in Brazil.

Methods:

Retrospective observational study that used the National Oncological Database, which was developed by record-linkage techniques used to integrate data from SUS Information Systems, namely: Outpatient (SIA-SUS), Hospital (SIH-SUS), and Mortality (SIM-SUS). Cancer-specific and other-cause survival probabilities were estimated by the time elapsed between the date of the first treatment until the patients’ deaths or the end of the study, from 2002 until 2015. The Fine-Gray model for competing risk was used to estimate factors associated with patients’ risk of death.

Results:

Of the 112,856 studied patients, the average age was 70.5 years, 21% died due to prostate cancer, and 25% due to other causes. Specific survival in 160 months was 75%, and other-cause survival was 67%. For CSM, the main factors associated with patients’ risk of death were: stage IV (AHR = 2.91; 95%CI 2.73 - 3.11), systemic treatment (AHR = 2.10; 95%CI 2.00 - 2.22), and combined surgery (AHR = 2.30, 95%CI 2.18 - 2.42). As for OCM, the main factors associated with patients’ risk of death were age and comorbidities.

Conclusion:

The analyzed patients with prostate cancer were older and died mainly from other causes, probably due to the presence of comorbidities associated with the tumor.

Keywords:
Prostatic neoplasms; Survival; Mortality; Aged; Comorbidity; Unified Health System

RESUMO:

Objetivo:

Analisar a mortalidade câncer-específica (MCE) e a mortalidade por outras causas (MOC) em pacientes diagnosticados com câncer da próstata que iniciaram tratamento no Sistema Único de Saúde (SUS) entre 2002 e 2010, no Brasil.

Métodos:

Estudo observacional retrospectivo utilizando a “Base Nacional em Oncologia”, desenvolvida por meio de pareamento determinístico-probabilístico dos sistemas de informação do SUS: Ambulatorial (SIA), Hospitalar (SIH) e de Mortalidade (SIM). Probabilidades de sobrevivência específicas do câncer e por outras causas foram estimadas pelo tempo decorrido entre a data do primeiro tratamento até a morte do paciente ou o final do estudo, de 2002 a 2015. O modelo de riscos competitivos de Fine & Gray foi utilizado para estimar os fatores associados ao risco de morte do paciente.

Resultados:

Dos 112.856 pacientes estudados, a idade média foi de 70,5 anos, 21% foi a óbito devido ao câncer de próstata e 25% por outras causas. A probabilidade de sobrevida específica em160 meses foi de 75% e a por outras causas de 67%. Na CSM, os principais fatores associados ao risco de óbito dos pacientes foram: estágio IV (AHR = 2,91; IC95% 2,73 - 311), tratamento sistêmico (AHR = 2,10; IC95% 2,00 - 2,22) e cirurgia combinada (AHR = 2,30; IC95% 2,18 - 2,42). Na MOC, os principais fatores associados ao risco de óbito do paciente foram idade e comorbidades.

Conclusão:

Os pacientes com câncer da próstata analisados mostraram-se mais velhos e faleceram principalmente por outras causas, provavelmente devido às comorbidades associadas ao tumor.

Palavras-chave:
Neoplasias da próstata; Sobrevida; Mortalidade; Idoso; Comorbidade; Sistema Único de Saúde

INTRODUCTION

Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death in men worldwide. In 2018, the incidence and mortality estimates registered about 1.3 million new cases and 358,989 deaths in the world11. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136(5): E359-86. https://doi.org/10.1002/ijc.29210
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In the medical literature, well-established risk factors for prostate cancer are advanced age, ethnicity, genetics, and family history44. Rawla P. Epidemiology of Prostate Cancer. World J Oncol 2019; 10(2): 63-89. https://dx.doi.org/10.14740%2Fwjon1191
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. Consequently, the risk of dying from the prostatic neoplasms may be difficult to be observed due to other causes88. Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, et al. Comorbidity and Competing Risks for Mortality in Men with Prostate Cancer. Cancer 2011; 117(20): 4642-50. https://doi.org/10.1002/cncr.26104
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. The event that hinders or modifies the possibility of observing the main event is named competitive event88. Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, et al. Comorbidity and Competing Risks for Mortality in Men with Prostate Cancer. Cancer 2011; 117(20): 4642-50. https://doi.org/10.1002/cncr.26104
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. By interpreting the results of survival studies, in which death is the event of interest, competitive risks are an important issue to be assessed. Therefore, methods specifically designed for such analyses should be employed, such as Fine and Gray’s method for competitive risks1313. Nanda A, Chen M-H, Moran BJ, Braccioforte MH, Dosoretz D, Salenius S, et al. Predictors of prostate cancer-specific mortality in elderly men with intermediate-risk prostate cancer treated with brachytherapy with or without external beam radiation therapy. Int J Radiat Oncol Biol Phys 2010; 77(1): 147-52. https://doi.org/10.1016/j.ijrobp.2009.04.085
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, whereas many studies use traditional methods such as the Kaplan-Meier estimator and Cox’s proportional hazards model1818. Ferraz RO, Moreira-Filho DC. Survival analysis of women with breast cancer: competing risk models. Ciênc Saúde Coletiva 2017; 22(11): 3743-54. https://doi.org/10.1590/1413-812320172211.05092016
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.

Analytical studies are needed to assess survival in different countries. Such studies can use information produced by their health systems such as cancer registries or administrative databases1919. Angelis R, Sant M, Coleman MP, Francisci S, Baili P, Pierannunzio D, et al. Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE-5 - a population-based study. Lancet Oncol 2014; 15(1): 23-34. https://doi.org/10.1016/s1470-2045(13)70546-1
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,2020. White MC, Babcock F, Hayes NS, Mariotto AB, Wong FL, Kohler BA, et al. The History and Use of Cancer Registry Data by Public Health Cancer Control Programs in the United States. Cancer 2017; 123(Suppl. 24): 4969-76. https://doi.org/10.1002/cncr.30905
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. There is a growing trend toward the use of large administrative databases (big data) to investigate health outcomes. These datasets allow to identify many patients across a broad spectrum of comorbidities, providing information regarding disparities in care and outcomes, such as mortality and survival, at local, state, and national levels in the countries2020. White MC, Babcock F, Hayes NS, Mariotto AB, Wong FL, Kohler BA, et al. The History and Use of Cancer Registry Data by Public Health Cancer Control Programs in the United States. Cancer 2017; 123(Suppl. 24): 4969-76. https://doi.org/10.1002/cncr.30905
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,2121. Willems SM, Abeln S, Feenstra KA, Bree R, van der Poel EF, Jong RJBJ, et al. The potential use of big data in oncology. Oral Oncol 2019; 98: 8-12. https://doi.org/10.1016/j.oraloncology.2019.09.003
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. Cancer survival analysis based on big data supports the public health system for preventing new cases, extending survival after cancer diagnosis, and reducing inequalities in access to cancer treatment1717. Mariotto AB, Noone A-M, Howlader N, Cho H, Keel GE, Garshell J, et al. Cancer Survival: An Overview of Measures, Uses, and Interpretation. J Natl Cancer Inst Monogr 2014; 2014(49): 145-86. https://doi.org/10.1093/jncimonographs/lgu024
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,1818. Ferraz RO, Moreira-Filho DC. Survival analysis of women with breast cancer: competing risk models. Ciênc Saúde Coletiva 2017; 22(11): 3743-54. https://doi.org/10.1590/1413-812320172211.05092016
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,1919. Angelis R, Sant M, Coleman MP, Francisci S, Baili P, Pierannunzio D, et al. Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE-5 - a population-based study. Lancet Oncol 2014; 15(1): 23-34. https://doi.org/10.1016/s1470-2045(13)70546-1
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,2020. White MC, Babcock F, Hayes NS, Mariotto AB, Wong FL, Kohler BA, et al. The History and Use of Cancer Registry Data by Public Health Cancer Control Programs in the United States. Cancer 2017; 123(Suppl. 24): 4969-76. https://doi.org/10.1002/cncr.30905
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.

To date, to the best of the authors’ knowledge, there are no population-based studies that investigate the factors associated with the survival probability of patients diagnosed with prostate cancer in Brazil. In addition, a survival model best suited to the case of prostate cancer was used, the competitive risk model. The advantage of using this model is that the other causes of death are considered in the estimates of the model parameters, in such a way the risks are more accurately estimated. A better understanding about the survival probability of patients diagnosed with prostate cancer and the associated factors may enable us to develop actions aiming to improve the health care, besides contributing to the current scientific knowledge. Thus, the aims of this study were to analyze the survival probability of patients diagnosed with prostate cancer in the Brazilian Unified Health System (SUS), who have initiated oncologic treatment from 2002 to 2010, and factors associated with risk of death by prostate cancer and other causes according to SUS information systems in Brazil.

METHODS

DATA SOURCE

This is an observational retrospective cohort study evaluating time elapsed between the onset of oncologic treatment at SUS and the death of the prostate cancer patient. The data source was the National Oncological Database, a national population-based cohort that comprises all records of patients under oncological treatment in the SUS, between 2001 and 2015. This database is a subset from the National Database of Health and was developed by record-linkage techniques used to integrate data from the major SUS Information Systems: the Outpatient Information System (from Portuguese, Sistema de Informações Ambulatoriais - SIA-SUS), the Hospital Information System (Sistema de Informações Hospitalares - SIH-SUS), and the Mortality Information System (Sistema de Informações sobre Mortalidade - SIM-SUS), from 2000 to 2015, in order to enable the cohort follow-up. The SIA-SUS contains data on the national provision of outpatient care, such as chemotherapy, radiotherapy, exceptional drugs, and renal replacement therapy in SUS. The SIH-SUS deals with information about hospital admissions in the SUS, with data from the national provision of all care in the hospital. The SIM-SUS deals with population-based information on mortality in Brazil2222. Guerra Jr. AA, Pereira RG, Andrade EIG, Cherchiglia M, Dias LV, Ávila JD, et al. Building the National Database of Health centred on the individual: Administrative and epidemiological record linkage - Brazil, 2000-2015. Int J Popul Data Sci 2018; 3(3): 20. https://doi.org/10.23889/ijpds.v3i1.446
https://doi.org/https://doi.org/10.23889...
.

STUDY POPULATION

According to the National Oncological Database, 317,484 men with prostate cancer were identified, who received outpatient oncological treatment at SUS between 2001 and 2015. Following the criteria adopted in this study, patients who initiated outpatient cancer treatment at SUS between January 1st, 2002 and July 31, 2015; with ages from 19 to 100 years were included.

In data analysis, patients without information on staging (n = 91,711), with stage 0 (n = 13,291), follow-up time, in days, less than one (n = 99), and with first treatment date prior to January 1st, 2002 and after December 31, 2010 (n = 99,527) were excluded. In the end, 112,856 patients were studied.

DEFINITION OF VARIABLES

The response variable consisted in the time elapsed from the date of the first oncological treatment to the date of death by prostate cancer or other causes or the final date of the studied follow-up (July 31, 2015).

The variables analyzed were: age at the beginning of follow-up, age group (19-59, 60-69, 70-79, or ≥80 years old), geographic region of the patient’s residence (Southeast, Northeast, South, Midwest, and North) in the first register, cancer stage at the moment of diagnosis (I, II, III, or IV), first treatment received by patients (radiotherapy, systemic treatment, radiotherapy and systemic treatment, combined surgery with systemic treatment or radiotherapy), number of the Elixhauser Comorbidity2323. Elixhauser A, Steiner C, Harris RD, Coffey RM. Comorbidity Measures for Use with Administrative Data. Med Care [Internet] 1998 [cited on Jun 9, 2020]; 36(1): 8-27. Available from: Available from: https://pdfs.semanticscholar.org/330f/dfb4bef1682723fdfc266cacc01d06d2da82.pdf https://doi.org/10.1097/00005650-199801000-00004
https://pdfs.semanticscholar.org/330f/df...
(0, 1-3, or ≥4), and number of hospital admissions (0, 1, 2, 3, 4, or ≥5). The patients’ length of stay ranged from 1 to 2,920 days during the entire period of the cohort follow-up. Cancer stage was measured at the start of treatment and classified according to the TNM classification of malignant tumors by the Union for International Cancer Control (UICC)2424. Union for International Cancer Control. TNM classification of malignant tumors. UICC [Internet]. [cited on Jun 9, 2020]. Available from: Available from: https://www.uicc.org/news/8th-edition-uicc-tnm-classification-malignant-tumors-published
https://www.uicc.org/news/8th-edition-ui...
. To calculate the number of comorbidities as proposed by Elixhauser2323. Elixhauser A, Steiner C, Harris RD, Coffey RM. Comorbidity Measures for Use with Administrative Data. Med Care [Internet] 1998 [cited on Jun 9, 2020]; 36(1): 8-27. Available from: Available from: https://pdfs.semanticscholar.org/330f/dfb4bef1682723fdfc266cacc01d06d2da82.pdf https://doi.org/10.1097/00005650-199801000-00004
https://pdfs.semanticscholar.org/330f/df...
, all codes of the International Classification of Diseases (ICD-10) registered in the National Database of Health were retrospectively investigated. The look-back period was extended until the oldest date of the database records (01/01/2000). Therefore, all patients had at least one complete year as look-back period to register comorbidities. Deaths were computed using the ICD-10 code C61.

STATISTICAL ANALYSIS

Demographic and clinical characteristics of the patients included in the study were described with proportions, measures of position, and variability. In the analysis of cancer-specific mortality (CSM), death by prostate cancer (C61) should be present in the primary cause of the death certificate, but also in one of the underlying causes. On the other hand, other ICDs not related to malignant prostate neoplasms were considered as competitive events. Concerning the analysis of other-cause mortality (OCM), death from other causes was considered the event of interest, whereas death from prostate cancer (C61) was deemed the competitive event. In both analyses, patients who did not experience the event of interest or the competitive event, or who were not found on the SIM-SUS database until July 31, 2015, were excluded.

In order to estimate cancer-specific and other-cause survival probabilities at each time period, the Aalen-Johansen nonparametric estimator2525. Aalen OO, Johansen S. An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations. Scand J Stat 1978; 5(3): 141-50., which considers the presence of competitive events, was used. Probabilities of death from prostate cancer or other causes at a specific time period of 163 months were obtained through Cumulative Incidence Function (CIF)2626. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. Nova York: John Wiley & Sons; 1980. https://doi.org/10.1002/9781118032985
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, which considers the presence of competitive risks, thus being equivalent to the Aalen-Johansen estimator. The test of Gray2727. Gray RJ. A class of K sample tests for comparing the cumulative incidence of a competing risk. Ann Stat 1988; 16(3): 1141-54. was used to verify accumulated equality incidences among categories of evaluated covariates on the presence of competitive risks. All covariates with p-value on Gray’s test associated with a significance level lower than 0.10 were included in the Fine-Gray multiple regression model 2828. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94(446): 496-509. https://doi.org/10.1080/01621459.1999.10474144
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, which allows to model risk subdistribution through covariates in order to estimate the factors associated with patients’ mortality risk.

The proposition of proportionality among failure rates over time according to the Fine-Gray2828. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94(446): 496-509. https://doi.org/10.1080/01621459.1999.10474144
https://doi.org/https://doi.org/10.1080/...
model was verified using the proportionality test2929. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994; 81(3): 515-26. https://doi.org/10.2307/2337123
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. This test evaluates whether there are evidences in Pearson correlation between times and standardized Schoenfeld residuals for each covariate different from zero, as correlations close to zero indicate there is no evidence for rejecting the hypothesis of proportional failure rates. Furthermore, graphical analyses of standardized Schoenfeld residuals against time were conducted for each covariate from the final model. Residuals with lack of patterns over time reinforce the validity of failure rate proportionality.

The statistical procedures were executed in the R software, version 3.5.1. The Survival, Chron, Cmprsk, and RiskRegression libraries were used.

RESULTS

The population of this study consists in 112,856 patients who initiated oncological treatment at SUS in Brazil between 2002 and 2010 (Supplementary Material Table 1). From these patients, 23,167 (20.5%) died from prostate cancer and 27,382 (24.3%) from other causes, and 62,307 (55.2%) were censored. The total time of follow-up was 163 months, an average time of 70.7 months (SD ± 40. 3), and a median of 70 months.

According to the demographic characteristics, the average age of the patients was 70.5 (SD ± 9.0) years and most patients aged between 60 and 80 years or over (88.7%), and over half patients (53.2%) resided in the Southeast region. Regarding clinical and treatment characteristics, 56.4% of patients were diagnosed in advanced stages (III and IV). Most treatment modalities were systemic treatment (32.6%), most patients presented one to four comorbidities (87.8%), and 63.4% required one or more hospital admissions (Supplementary Material Table 1).

Supplementary Material Table 2 presents the cancer-specific and other-cause survival probabilities and survival time. The general specific survival probability in 160 months was 75% (0.75), and from other causes, 67% (0.67). Specific and other-cause survival decreased as patients’ age advanced. Among those aged from 70 to 79 years, and 80 years or over, specific survival probability was 75% (0.75) and 69% (0.69) respectively; as for other-cause survival probability, 61% (0.61) and 55% (0.55) respectively. The South region presented the lowest survival probabilities, 69% (0.69) for cancer-specific and 64% (0.64) for other-cause. The probability of cancer-specific survival decreased with advancing stages of the disease, presenting 60% (0.60) in stage-IV patients. Patients who underwent systemic treatment or combined surgery had lower probabilities compared with other treatment modalities. Concerning survival from other causes, tumor stage and treatment modalities did not present clear tendencies in the estimations.

Figures 1 and 2 present the curves from the Cumulative Incidence Function (CIF), which calculated failure probability from an event of interest, considering the presence of competitive risks. In Figure 1, in curves up to 50 months of follow-up, the death probability from prostate cancer is similar to death from other causes; however, risk of death from other causes is higher until the end of follow-up.

Figure 1.
Cumulative Incidence Function (CIF) for data of patients diagnosed with prostate cancer and treated between 2002 and 2010 in the Brazilian Public Health System (SUS), Brazil.

Figure 2.
Cumulative Incidence Function (CIF) concerning cancer-specific mortality (CSM) and other-cause mortality (OCM) according to the categories of the exposure variables of patients diagnosed with prostate cancer and treated between 2002 and 2010 in the Brazilian Public Health System (SUS): (a) age range; (b) region of residence; (c) cancer stages; (d) first treatment; (e) number of Elixhauser comorbidity; and (f) number of hospital admissions.

Figure 2 presents the CIF according to the categories of the exposure variables considered in the study. For every category, both regarding death from prostate cancer and death from other causes, comparison among every curve showed statistically significant differences in the test of Gray (p<0.05).

Table 1 presents the models of Fine and Gray for Cancer-Specific Mortality (CSM) and Other-Cause Mortality (OCM) in prostate cancer patients. The risk of death from prostate cancer increased 2% as the patients’ age advanced, and 3% in in relation to death from other causes. Patients who lived in the South region showed risk increased by 13% (Adjusted Hazard Ratio [AHR] = 1.13; 95%CI 1.10 - 1.17) in prostate cancer death and 7% (AHR = 1.07; 95%CI 1.03 - 1.10) in other causes compared with patients living in the Southeastern region. In the remaining regions, the patient’s risk of death from cancer and other causes is smaller than in Southeastern region. In terms of tumor staging, there was increased risk of death from prostate cancer as tumor stage increased, almost tripling in stage IV (AHR = 2.91; 95%CI 2.73 - 3.11) compared with stage I. The risk increased from stage II to III, but decreased in stage IV, showing an inconclusive pattern of tumor staging regarding death from other causes. Among treatment modalities the patients underwent, systemic treatment or combined surgery showed more expressive risks of death due to prostate cancer when compared with radiotherapy, with combined surgery having the worst prognosis (AHR = 2.30; 95%CI 2.18 - 2.42). Concerning death from other causes, treatment modalities demonstrated better prognosis, that is, decreased risk of death when compared with radiotherapy at the end of follow-up. The number of Elixhauser comorbidity showed a 1% increase (AHR = 1.01; 95%CI 1.01 - 1.02) in risk of death from prostate cancer for each additional comorbidity affecting the patients, and a 15% increase (AHR =1.15; 95%CI 1.14 - 1.15) in risk of death from other causes. Regarding number of admissions, risk of death from prostate cancer increased 6% (AHR = 1.06; 95%CI 1.05 - 1.06) after each admission, although there is a reduction in mortality due to other causes.

The Pearson correlation between time and standardized Schoenfeld residuals were all close to zero, indicating proportionality among subdistribution failure rates of death from prostate cancer and other causes (Supplementary Material Table 3). The graphical analysis of Schoenfeld residuals reinforced the proportionality hypothesis.

Table 1.
Estimates obtained for the Fine-Gray model adjusted to the data of patients diagnosed with prostate cancer and treated between 2002 and 2010 in the Brazilian Public Health System (SUS), Brazil.

DISCUSSION

KEY RESULTS

This study analyzed the survival probability of 112,856 patients diagnosed with prostate cancer, who started oncologic treatment in SUS, accounting for more than 13 years of follow-up. On average, a patient diagnosed with prostate cancer survived about 8.5 years after receiving the first treatment. The probability of cancer-specific survival at 160 months was 75%, and that of other causes, 67%. The risk of specific death from prostate cancer increased with advancing age; residing in the South region; being classified with a higher tumor stage (almost tripling in stage IV); having undergone systemic treatment or surgery combined with other treatments as the initial modality of treatment; having some comorbidity; and increased number of hospital admissions. The risk of death from other causes increased with patients’ advancing age and having some comorbidity.

INTERPRETATIONS

Regarding the event of interest, death due to prostate cancer, the proportion of patients dying from other causes (24.3%) was higher than death from prostate cancer (20.5%). Furthermore, a proportion of 89% was verified for patients aged over 60 years (average age of 70.5 years), demonstrating a profile of older patients, with most of them (88%) presenting more than one comorbidity. Similarly, many studies have shown that prostate cancer affects older men, who have other diseases in addition to the tumor, thus affecting survival and risk of death in these individuals66. Chowdhury S, Robinson D, Cahill D, Rodriguez-Vida A, Holmberg L, Moller H. Causes of death in men with prostate cancer: an analysis of 50 000 men from the Thames Cancer Registry. BJU International 2013; 112(2): 182-9. https://doi.org/10.1111/bju.12212
https://doi.org/https://doi.org/10.1111/...
,77. Hoffman KE, Cheng MH, Moran BJ, Braccioforte MH, Dosoretz D, Salenius S, et al. Prostate cancer-specific mortality and the extent to therapy in healthy elderly men with high-risk prostate cancer. Cancer 2010; 116(11): 2590-5. https://doi.org/10.1002/cncr.24974
https://doi.org/https://doi.org/10.1002/...
,88. Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, et al. Comorbidity and Competing Risks for Mortality in Men with Prostate Cancer. Cancer 2011; 117(20): 4642-50. https://doi.org/10.1002/cncr.26104
https://doi.org/https://doi.org/10.1002/...
,99. Abdollah F, Sun M, Thuret R, Jeldres C, Tian Z, Briganti A, et al. A Competing-Risks Analysis of Survival After Alternative Treatment Modalities for Prostate Cancer Patients: 1988-2006. Eur Urol 2011; 59(1): 88-95. https://doi.org/10.1016/j.eururo.2010.10.003
https://doi.org/https://doi.org/10.1016/...
,1010. Abdollah F, Sun M, Schmitges J, Tian Z, Jeldres C, Briganti A, et al. Cancer-Specific and Other-Cause Mortality After Radical Prostatectomy Versus Observation in Patients with Prostate Cancer: Competing-Risks Analysis of a Large North American Population-Based Cohort. Eur Urol 2011; 60(5): 920-30. https://doi.org/10.1016/j.eururo.2011.06.039
https://doi.org/https://doi.org/10.1016/...
,1111. Braga SFM, Souza MC, Oliveira RR, Andrade EIG, Acurcio FA, Cherchiglia ML, et al. Patient survival and risk of death after prostate cancer treatment in the Brazilian Unified Health System. Rev Saúde Pública 2017; 51: 46. https://doi.org/10.1590/s1518-8787.2017051006766
https://doi.org/https://doi.org/10.1590/...
,1212. Nguyen-Nielsen M, Møller H, Tjønneland A, Borre M. Causes of death in men with prostate cancer: Results from the Danish Prostate Cancer Registry (DAPROCAdata). Cancer Epidemiol 2019; 59: 249-57. https://doi.org/10.1016/j.canep.2019.02.017
https://doi.org/https://doi.org/10.1016/...
,1313. Nanda A, Chen M-H, Moran BJ, Braccioforte MH, Dosoretz D, Salenius S, et al. Predictors of prostate cancer-specific mortality in elderly men with intermediate-risk prostate cancer treated with brachytherapy with or without external beam radiation therapy. Int J Radiat Oncol Biol Phys 2010; 77(1): 147-52. https://doi.org/10.1016/j.ijrobp.2009.04.085
https://doi.org/https://doi.org/10.1016/...
.

Moreover, many studies have shown that due to the long natural history of prostate cancer, a many patients might succumb to other causes, as verified in studies conducted by Daskivich et al.88. Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, et al. Comorbidity and Competing Risks for Mortality in Men with Prostate Cancer. Cancer 2011; 117(20): 4642-50. https://doi.org/10.1002/cncr.26104
https://doi.org/https://doi.org/10.1002/...
and Abdollah et al.99. Abdollah F, Sun M, Thuret R, Jeldres C, Tian Z, Briganti A, et al. A Competing-Risks Analysis of Survival After Alternative Treatment Modalities for Prostate Cancer Patients: 1988-2006. Eur Urol 2011; 59(1): 88-95. https://doi.org/10.1016/j.eururo.2010.10.003
https://doi.org/https://doi.org/10.1016/...
,1010. Abdollah F, Sun M, Schmitges J, Tian Z, Jeldres C, Briganti A, et al. Cancer-Specific and Other-Cause Mortality After Radical Prostatectomy Versus Observation in Patients with Prostate Cancer: Competing-Risks Analysis of a Large North American Population-Based Cohort. Eur Urol 2011; 60(5): 920-30. https://doi.org/10.1016/j.eururo.2011.06.039
https://doi.org/https://doi.org/10.1016/...
analyzing mortality from prostate cancer and other causes in the United Stated of America, with data from the SEER-Medicare linked database (Surveillance, Epidemiology and End Results - SEER); Briganti et al.3030. Briganti A, Spahn M, Joniau S, Gontero P, Bianchi M, Kneitz B, et al. Impact of Age and Comorbidities on Long-term Survival of Patients with High-risk Prostate Cancer Treated with Radical Prostatectomy: A Multi-institutional Competing-risks Analysis. Eur Urol 2013; 63(4): 693-701. https://doi.org/10.1016/j.eururo.2012.08.054
https://doi.org/https://doi.org/10.1016/...
,3131. Briganti A, Karnes RJ, Gandaglia G, Spahn M, Gontero P, Tosco L, et al. European Multicenter Prostate Cancer Clinical and Translational Research Group (EMPaCT). Natural history of surgically treated high-risk prostate cancer. Urol Oncol 2015; 33(4): 163.e7-163.e13. http://doi.org/10.1016/j.urolonc.2014.11.018
https://doi.org/http://doi.org/10.1016/j...
, according to the European Multicenter Prostate Cancer Clinical and Translational Research Group (EMPaCT), and Boehm et al.3232. Boehm K, Dell’Oglio P, Tian Z, Capitanio U, Chun FKH, Tilki D, et al. Comorbidity and age cannot explain variation in life expectancy associated with treatment of non-metastatic prostate cancer. World J Urol 2017; 35(7): 1031-6. https://doi.org/10.1007/s00345-016-1963-7
https://doi.org/https://doi.org/10.1007/...
, using SEER data, analyzed the OCM in individuals treated with radical prostatectomy, brachytherapy, external beam radiation therapy, and androgen deprivation therapy. The authors stated that most patients, analyzed for 10 years of follow-up, have died from other causes rather than from prostate cancer. Likewise, these authors have used competitive risk of death in their methodology as well.

As for the cancer-specific and other-cause survival probability estimated at 13.5 years, patients presented survival rates of 75% and 67%, respectively. These results are similar to the study of Abdollah et al.99. Abdollah F, Sun M, Thuret R, Jeldres C, Tian Z, Briganti A, et al. A Competing-Risks Analysis of Survival After Alternative Treatment Modalities for Prostate Cancer Patients: 1988-2006. Eur Urol 2011; 59(1): 88-95. https://doi.org/10.1016/j.eururo.2010.10.003
https://doi.org/https://doi.org/10.1016/...
,1010. Abdollah F, Sun M, Schmitges J, Tian Z, Jeldres C, Briganti A, et al. Cancer-Specific and Other-Cause Mortality After Radical Prostatectomy Versus Observation in Patients with Prostate Cancer: Competing-Risks Analysis of a Large North American Population-Based Cohort. Eur Urol 2011; 60(5): 920-30. https://doi.org/10.1016/j.eururo.2011.06.039
https://doi.org/https://doi.org/10.1016/...
, conducted in the USA, with specific and other-cause survival rates being 73% and 69%, respectively, in 10 years. The authors used a database with health registers from diverse localities throughout the USA, similar to the database used in the present study. Hospital-based studies or treatment centers may differ in relation to the survival probability. The study conducted by Stone and Stock3333. Stone NN, Stock RG. 15-Year Cause Specific and All-Cause Survival Following Brachytherapy for Prostate Cancer: Negative Impact of Long-Term Hormonal Therapy. J Urol 2014; 192(3): 754-9. http://doi.org/10.1016/j.juro.2014.03.094
https://doi.org/http://doi.org/10.1016/j...
analyzed the survival of 1,669 patients in the USA by estimating specific and other-cause survival probabilities over 10 years of follow-up, accounting for 98.1% and 86.8% respectively. Prognosis was much better than those presented by the patients in this study. Such differences can be attributed to the origin of the data used in such studies - hospital-based data versus population-based cancer registries and the methodology used in data analysis. Hospital-based records refer to cases treated in an institution. They ensure the monitoring of these patients and also contribute to the patients’ individual care1111. Braga SFM, Souza MC, Oliveira RR, Andrade EIG, Acurcio FA, Cherchiglia ML, et al. Patient survival and risk of death after prostate cancer treatment in the Brazilian Unified Health System. Rev Saúde Pública 2017; 51: 46. https://doi.org/10.1590/s1518-8787.2017051006766
https://doi.org/https://doi.org/10.1590/...
. Huang et al.3434. Huang B, Guo J, Charnigo R. Statistical Methods for Population-Based Cancer Survival in Registry Data. J Biomet Biostat 2014; 5(3): 1-3. http://doi.org/10.472/2155-6180.1000e129
https://doi.org/http://doi.org/10.472/21...
state that population-based cancer registries play an important role in improving care programs aimed at cancer patients, assessing care patterns, estimating survival, and providing evidence-based results for physicians, researchers, and public health policymakers.

Considering both estimated events, on average, patients have survived for 8.5 years from total follow-up time. This aspect reinforces the long natural history of this cancer compared with other types of cancer, considering that patients might live with the disease for a long time and, depending on the age and clinical conditions, they may be followed up in active surveillance in many cases3131. Briganti A, Karnes RJ, Gandaglia G, Spahn M, Gontero P, Tosco L, et al. European Multicenter Prostate Cancer Clinical and Translational Research Group (EMPaCT). Natural history of surgically treated high-risk prostate cancer. Urol Oncol 2015; 33(4): 163.e7-163.e13. http://doi.org/10.1016/j.urolonc.2014.11.018
https://doi.org/http://doi.org/10.1016/j...
,3535. Butler SS, Mahal BA, Lamba N, Mossanen M, Martin NE, Mouw KW, et al. Use and Early Mortality Outcomes of Active Surveillance in Patients with Intermediate-Risk Prostate Cancer. Cancer 2019; 125(18): 3164-71. https://doi.org/10.1002/cncr.32202
https://doi.org/https://doi.org/10.1002/...
,3636. Saman DM, Lemieux AM, Lutfiyya MN, Lipsky MS. A review of the current epidemiology and treatment options for prostate cancer. Disease-a-Month 2014; 60(4): 150-4. http://doi.org/10.1016/j.disamonth.2014.02.003
https://doi.org/http://doi.org/10.1016/j...
.

The competitive risk model was used to estimate patients’ mortality. In both models, CSM and OCM, the risk of death increased 2% and 3%, respectively, as the patients’ age advanced. Abdollah et al.99. Abdollah F, Sun M, Thuret R, Jeldres C, Tian Z, Briganti A, et al. A Competing-Risks Analysis of Survival After Alternative Treatment Modalities for Prostate Cancer Patients: 1988-2006. Eur Urol 2011; 59(1): 88-95. https://doi.org/10.1016/j.eururo.2010.10.003
https://doi.org/https://doi.org/10.1016/...
,1010. Abdollah F, Sun M, Schmitges J, Tian Z, Jeldres C, Briganti A, et al. Cancer-Specific and Other-Cause Mortality After Radical Prostatectomy Versus Observation in Patients with Prostate Cancer: Competing-Risks Analysis of a Large North American Population-Based Cohort. Eur Urol 2011; 60(5): 920-30. https://doi.org/10.1016/j.eururo.2011.06.039
https://doi.org/https://doi.org/10.1016/...
found an increase in risk of death of 4% and 10%, respectively. Hoffman77. Hoffman KE, Cheng MH, Moran BJ, Braccioforte MH, Dosoretz D, Salenius S, et al. Prostate cancer-specific mortality and the extent to therapy in healthy elderly men with high-risk prostate cancer. Cancer 2010; 116(11): 2590-5. https://doi.org/10.1002/cncr.24974
https://doi.org/https://doi.org/10.1002/...
has found an increase of 4% in risk of death and Boehm et al.3232. Boehm K, Dell’Oglio P, Tian Z, Capitanio U, Chun FKH, Tilki D, et al. Comorbidity and age cannot explain variation in life expectancy associated with treatment of non-metastatic prostate cancer. World J Urol 2017; 35(7): 1031-6. https://doi.org/10.1007/s00345-016-1963-7
https://doi.org/https://doi.org/10.1007/...
, 0an increase of 7% regarding OCM as the patients’ age advanced.

The South region had a higher risk of death in CSM and OCM compared with other regions. The incidence and mortality rates are the highest in the country33. Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA). Estimativa 2018: incidência e mortalidade por câncer no Brasil [Internet]. Rio de Janeiro: INCA; 2018 [cited on May 6, 2019]. Available from: Available from: http://www.epi.uff.br/wp-content/uploads/2013/08/estimativa-incidencia-de-cancer-no-brasil-2018.pdf
http://www.epi.uff.br/wp-content/uploads...
. This could overestimate survival and risk of death among patients from this region in the present study.

Risk of death in CSM increased as tumor stage increased, tripling in stage IV, although this pattern was not observed for OCM; in this case, the risk decreased for stage-IV patients, which indicates staging is more important for death due to prostate cancer. Hsiao et al.3737. Hsiao W, Moses KA, Goodman M, Jani AB, Rossi PJ, Master VA. Master Stage IV Prostate Cancer: Survival Differences in Clinical T4, Nodal and Metastatic Disease. J Urol 2010 184(2): 512-8. https://doi.org/10.1016/j.juro.2010.04.010
https://doi.org/https://doi.org/10.1016/...
developed a study in the USA, in which they found stage-IV patients with a 60% specific cancer survival in a 10-year period, most of whom received systemic treatment, and differences in survival mainly depended on the patients’ age. Similarly, patients in stage IV of this study showed 60% of specific survival in a 10-year period and most of them were treated with systemic treatment. However, in OCM, patients’ risk of death in other treatment modalities was smaller compared with those undergoing radiotherapy. Finally, patients with many comorbidities had higher risk of death, mainly in OCM. Briganti et al.3030. Briganti A, Spahn M, Joniau S, Gontero P, Bianchi M, Kneitz B, et al. Impact of Age and Comorbidities on Long-term Survival of Patients with High-risk Prostate Cancer Treated with Radical Prostatectomy: A Multi-institutional Competing-risks Analysis. Eur Urol 2013; 63(4): 693-701. https://doi.org/10.1016/j.eururo.2012.08.054
https://doi.org/https://doi.org/10.1016/...
,3131. Briganti A, Karnes RJ, Gandaglia G, Spahn M, Gontero P, Tosco L, et al. European Multicenter Prostate Cancer Clinical and Translational Research Group (EMPaCT). Natural history of surgically treated high-risk prostate cancer. Urol Oncol 2015; 33(4): 163.e7-163.e13. http://doi.org/10.1016/j.urolonc.2014.11.018
https://doi.org/http://doi.org/10.1016/j...
, when investigating CSM and OCM risks in patients with prostate cancer, reported that age and comorbidities were the main determinants of OCM, whereas their impact on CSM was negligible. Regarding CSM, hospitalized patients had increased risk of death, whereas for OCM there was a smaller risk, which could indicate patients would be more closely monitored for presenting other diseases.

Limitations in the use of an administrative database must be mentioned, such as failures in filling out clinical information, difficulty in coding procedures, absence of socioeconomic and demographic variables, and also in the use of the death certificate as a source of cause of death. Attention to underreporting and inadequate data filling must be paid, considering that the percentage of ill-defined causes may imply an overestimation of the survival probability. In this study, it was not possible to include patients who underwent exclusive surgery due to their lack of information of cancer stage at the onset of treatment. The lack of data from these patients must be mentioned, taking into account that surgery is commonly the treatment modality recommended for patients on early stages of prostate cancer. However, these limitations are overcome by the benefits of using a large database that includes the entire population of patients treated for cancer in SUS.

Furthermore, deaths of patients from other causes were higher than deaths from prostate cancer, which was related to the higher proportion of older patients and the greater number of comorbidities in this population. The risk factors associated with patients’ deaths were age, comorbidities, and tumor staging, considering that most patients were diagnosed in stages III and IV and were mainly treated with systemic therapy. These results highlight the need for diagnosing prostate cancer patients at earlier stages, so that they receive curative and non-palliative treatments at the appropriate time in the Brazilian Unified Health System.

ACKNOWLEDGEMENT

The authors would like to thank Daniel Nogueira Mendes Braga for translating and revising the article in the English language.

REFERENCES

  • Financial support: Coordination for the Improvement of Higher Education Personnel (CAPES), National Postdoctoral Program (PNPD) (grant number: 88882.316037/2019-01 and 306030/2018-7), and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), grant number: CDS - PPM-00716-17.

Publication Dates

  • Publication in this collection
    06 Jan 2021
  • Date of issue
    2021

History

  • Received
    13 Aug 2020
  • Reviewed
    24 Aug 2020
  • Accepted
    25 Aug 2020
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