Survival and Predictors of Mortality among Breast Cancer Patients in Northwest Ethiopia: A Retrospective Cohort Study

Survival Patterns Among Patients With Breast Cancer in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

Importance

Breast cancer is the most prevalent cancer globally with tremendous disparities both within specific regions and across different contexts. The survival pattern of patients with breast cancer remains poorly understood in sub-Saharan African (SSA) countries.

Objective

To investigate the survival patterns of patients with breast cancer in SSA countries and compare the variation across countries and over time.

Data Sources

Embase, PubMed, Web of Science, Scopus, and ProQuest were searched from inception to December 31, 2022, with a manual search of the references.

Study Selection

Cohort studies of human participants that reported 1-, 2-, 3-, 4-, 5-, and 10-year survival from diagnosis among men, women, or both with breast cancer in SSA were included.

Data Extraction and Synthesis

Independent extraction of study characteristics by multiple observers was performed using open-source software, then exported to a standard spreadsheet. A random-effects model using the generalized linear mixed-effects model was used to pool data. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guideline for reporting was followed.

Main Outcome and Measures

Survival time from diagnosis.

Results

Forty-nine studies were included in the review with a sample size ranging from 21 to 2311 (total, 14 459; 196 [1.35%] men, 13 556 [93.75%] women, and 707 [4.90%] unspecified; mean age range, 38 to 71 years), of which 40 were summarized using meta-analysis. The pooled 1-year survival rate of patients with breast cancer in SSA was 0.79 (95% CI, 0.67-0.88); 2-year survival rate, 0.70 (95% CI, 0.57-0.80); 3-year survival rate, 0.56 (95% CI, 0.45-0.67); 4-year survival rate, 0.54 (95% CI, 0.43-0.65); and 5-year survival rate, 0.40 (95% CI, 0.32-0.49). The subgroup analysis showed that the 5-year survival rate ranged from 0.26 (95% CI, 0.06-0.65) for studies conducted earlier than 2010 to 0.47 (95% CI, 0.32-0.64) for studies conducted later than 2020. Additionally, the 5-year survival rate was lower in countries with a low human development index (HDI) (0.36 [95% CI, 0.25-0.49) compared with a middle HDI (0.46 [95% CI, 0.33-0.60]) and a high HDI (0.54 [95% CI, 0.04-0.97]).

Conclusions and Relevance

In this systematic review and meta-analysis, the survival rates for patients with breast cancer in SSA were higher in countries with a high HDI compared with a low HDI. Enhancing patient survival necessitates a comprehensive approach that involves collaboration from all relevant stakeholders.

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

BACKGROUND

Despite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study.

METHODS

A retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made.

RESULTS

The study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model.

CONCLUSIONS

Compared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.

Mortality rate and predictors among patients with breast cancer at a referral hospital in northwest Ethiopia: A retrospective follow-up study

BACKGROUND

Breast cancer is one of the common global health concerns that affects2.1 million women each year and causes the highest number of cancer-related morbidity and mortality among women. The objective of this study was to determine the mortality rate and its predictors among breast cancer patients at the referral hospitals, in northwest Ethiopia.

METHODS

A retrospective follow-up study was conducted on breast cancer patients registered between February 01, 2015 and February 28, 2018. They were selected by simple random sampling using computer-generated method and followed until February 29, 2020, in Amhara region referral hospital. A pre-tested data extraction checklist was used to collect data from the registration book and patient medical records. The collected data were entered into Epi-Data version 3.1 and exported to STATA version 14 for analysis. The mortality rate by person-year observation was computed. The Kaplan-Meier survival curve with the log-rank test was used to estimate the survival probabilities of the patients. Bivariate and multivariate Cox regression model was used to identify predictors of mortality.

RESULTS

The overall mortality rate of breast cancer was 16.9 per 100 person-years observation. The median survival time was 38.3 (IQR: 26.23, 49.4) months. Independent predictors of breast cancer mortality was; Clinical stage IV and stage III (aHR:10.44,95% CI: 8.02,11.93 and aHR: 9.43, 95% CI: 6.29,11.03respectively), number of positive lymph node in the category of 10 and more and number of positive lymph node within the category of 4-9 (aHR:12.58, 95%CI: 5.2, 30.46 and aHR: 4.78, 95% CI: 2.19, 10.43respectively), co-morbidities (aHR:1.5, 95%CI: 1.01,2.21), Postmenopausal (aHR:2.03,95% CI: 1.37, 3), histologic grade III (aHR:2.12, 95% CI: 1.26,3.55) and not received hormonal therapy (aHR: 2.19, 95%CI: 1.52,3.15) were independent predictors of mortality.

CONCLUSION

The overall mortality rate was 16.9 per 100 person-years. The finding was higher compared to high-income countries. Advanced clinical stage, co-morbidities, menopausal status, and hormonal therapy are the significant predictors of mortality. Early detection and treatment of breast cancer is needed to reduce the mortality rate.

Survival and predictors of breast cancer mortality in South Ethiopia: A retrospective cohort study

BACKGROUND

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in over 100 countries. In March 2021, the World Health Organization called on the global community to decrease mortality by 2.5% per year. Despite the high burden of the disease, the survival status and the predictors for mortality are not yet fully determined in many countries in Sub-Saharan Africa, including Ethiopia. Here, we report the survival status and predictors of mortality among breast cancer patients in South Ethiopia as crucial baseline data to be used for the design and monitoring of interventions to improve early detection, diagnosis, and treatment capacity.

METHODS

A hospital-based retrospective cohort study was conducted among 302 female breast cancer patients diagnosed from 2013 to 2018 by reviewing their medical records and telephone interviews. The median survival time was estimated using the Kaplan-Meier survival analysis method. A log-rank test was used to compare the observed differences in survival time among different groups. The Cox proportional hazards regression model was used to identify predictors of mortality. Results are presented using the crude and adjusted as hazard ratios along with their corresponding 95% confidence intervals. Sensitivity analysis was performed with the assumption that loss to follow-up patients might die 3 months after the last hospital visit.

RESULTS

The study participants were followed for a total of 4,685.62 person-months. The median survival time was 50.81 months, which declined to 30.57 months in the worst-case analysis. About 83.4% of patients had advanced-stage disease at presentation. The overall survival probability of patients at two and three years was 73.2% and 63.0% respectively. Independent predictors of mortality were: patients residing in rural areas (adjusted hazard ratio = 2.71, 95% CI: 1.44, 5.09), travel time to a health facility ≥7 hours (adjusted hazard ratio = 3.42, 95% CI: 1.05, 11.10), those who presented within 7-23 months after the onset of symptoms (adjusted hazard ratio = 2.63, 95% CI: 1.22, 5.64), those who presented more than 23 months after the onset of symptoms (adjusted hazard ratio = 2.37, 95% CI: 1.00, 5.59), advanced stage at presentation (adjusted hazard ratio = 3.01, 95% CI: 1.05, 8.59), and patients who never received chemotherapy (adjusted hazard ratio = 6.69, 95% CI: 2.20, 20.30).

CONCLUSION

Beyond three years after diagnosis, patients from southern Ethiopia had a survival rate of less than 60% despite treatment at a tertiary health facility. It is imperative to improve the early detection, diagnosis, and treatment capacities for breast cancer patients to prevent premature death in these women.

Treatment Outcomes and Its Associated Factors Among Adult Patients with Selected Solid Malignancies at Kenyatta National Hospital: A Hospital-Based Prospective Cohort Study

Introduction

The treatment outcome of cancer is poor in the African setting due to inadequate treatment and diagnostic facilities. However, there is a paucity of data on solid cancers in Kenya. Hence, this study aimed to investigate the treatment outcomes and its determinant factors among adult patients diagnosed with selected solid malignancies at Kenyatta National Hospital (KNH).

Materials and Methods

A prospective cohort study was employed at the Oncology Department of KNH from 1^st July 2020 to 31^st December 2021. All new patients with a confirmed diagnosis of lymphoma, prostate cancer and breast cancer were studied. A consecutive sample of 99 breast cancer, 50 lymphomas, and 82 prostate cancer patients was included in the study. Semi-structured questionnaires consisting of socio-demographics, clinical characteristics, and quality of life were employed to collect the data. All enrolled patients were followed prospectively for 12 months. Treatment outcomes were reported as mortality, cancer-specific survival and health-related quality of life. The data were entered and analyzed using the SPSS 20.0 statistical software. Survival outcomes and its predictors were evaluated using the Kaplan–Meier analysis and Cox regression analyses, respectively.

Results

The study showed that the mortality rate among breast and prostate cancer patients was 3% and 4.9%, respectively. In contrast, the mortality rate was 10% among lymphoma patients. Most of the patients had partial remission and a good overall global health-related quality of life. Older age above 60 years, co-morbidity, distant metastasis and advanced stages of disease were significant predictors of mortality.

Conclusion

Although the mortality was not high at 12 months, only a few patients had complete remission. For many patients, the disease was progressing, despite 12-month mortality was not high. Therefore, longer follow-up will be required to report cancer mortality accurately. In addition, most of the patients had a good overall global health-related quality of life.