Skip to main content
Download PDF

Quality of life and its influencing factors among breast cancer patients at Tikur Anbessa specialised hospital, Addis Ababa, Ethiopia

BMC Cancer volume 22, Article number: 897 (2022) Cite this article

Abstract

Background

Quality of life (QoL) has become an important measure for evaluating cancer patients’ treatment and prognosis. Breast cancer patients are at an increased risk of experiencing poor QoL during active treatment of cancer. This study aimed to assess QoL and it’s influencing factors among breast cancer patients using the newly updated breast cancer specific tool of the European Organisation for Research and Treatment of Cancer EORTC Breast Cancer Specific Quality of Life Questionnaire QLQ-BR45.

Methods

An institutional based crossectional study was conducted with 248 breast cancer patients at Tikur Anbessa Specialized Hospital (TASH). Descriptive statistics, one-way analysis of variance (ANOVA), and linear regression were used to describe and analyze the data.

Results

The participant’s Global health status/QoL mean score was 65.6. Among the functional scales, future perspective scored the lowest (57.1, SD ± 37.3). The highest mean score on the symptom scales/items were financial difficulties (50, SD ± 38.6), followed by appetite loss (37.4, SD ± 36.4) and fatigue (34.3, SD ± 27.1) while the lowest symptom score was diarrhoea (6.4 ± 18.4). EORTC QLQ-BR45, future perspective (mean = 57.1, SD ± 37.3) and upset by hair loss (41.8, SD ± 34.6) were the most affected functioning and symptoms scales respectively. An increased stage of tumor was associated with more pain (P = 0.041), appetite loss (P = 0.042), and arm symptoms (P = 0.003). Patients who had no comorbidity had better physical (P < 0.001), cognitive (P = 0.013), and social (P = 0.009) function.

Conclusion

These specific functional scales and symptoms should be assessed individually to address unmet needs. Clinicians could design psychosocial interventions to improve these function and to reduce symptoms.

Peer Review reports

Introduction

Worldwide, an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths occurred in 2020. The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020 [1]. Breast cancer was the most commonly diagnosed cancer and the leading cause of cancer death among women worldwide in 2020. The age-standardized incidence and mortality rate of female breast cancer in east Africa was 33 and 17.9/100,000 person-years respectively in 2020 [2]. Breast cancer is the leading type of cancer in Ethiopia with an estimated 16,133 new cases annually [1].

Quality of life (QoL) is an individual’s perception of their life in relation to their goals, expectations, standards and concerns. It is a complex concept that includes an individual’s physical health, psychological health, personal beliefs, social interaction and its relationship to their environment [3]. In the field of oncology, patients’ QoL has become a major objective of cancer care. It has been shown that QoL assessment is helpful to predict treatment response and prognosis [4,5,6]. Several studies have shown that a better QoL measure is associated with longer survival of patients’ with various types of cancer [7,8,9]. Therefore, it is necessary to assess QoL and its predictors continuously and periodically from diagnosis to survivorship to get insight into patient management and care [10]. Various studies have shown that women are at increased risk of experiencing poor QoL during the active treatment and survivorship phases of cancer care [8]. Breast cancer patients undergoing chemotherapy have poor QoL [7, 11]. Although, breast cancer is the leading cause of morbidity and mortality in Ethiopia, to our knowledge there was no adequate study done to assess QoL and its influencing factors among breast cancer patients. Only two studies were conducted with some limitations. One was completed among breast cancer patients undergoing chemotherapy [12] and the other was done among breast cancer patients during and after systemic therapy [13]. This study used the global health status scale as a dependent variable, despite the presence of other functional and symptoms scales. In the present study, QoL was measured using European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core30 (EORTC QLQ-C30) [14] and the updated breast cancer specific tool (EORTC QLQ-BR45) [15]. The EORTC QLQ-C30 is a core questionnaire that assesses the QoL of cancer patients [14]. EORTC QLQ-BR45 is a breast cancer specific module that is used in combination with the EORTC QLQ-C30 core questionnaire [15]. One previous study were conducted using the former EORTC QLQ-BR23 breast cancer specific tool [16].

Major advances have been made in breast cancer diagnosis and treatment since the tool was developed; thus, the EORTC QLQ-BR23 needed to be updated.

This study aims to assess for the first time, quality of life and its influencing factors among breast cancer patients in Addis Ababa using the newly updated breast cancer specific (EORTC QLQ-BR45) and general (EORTC QLQ-C30) instruments.

Materials and methods

Study design and setting

A cross-sectional study was conducted from January 1 to March 30, 2021 at Tikur Anbessa Specialised Hospital (TASH) oncology centre in Addis Ababa, Ethiopia. The hospital is a tertiary level teaching hospital and the only radiotherapy centre in the country [17].

Participants

All female breast cancer patients in any stage of the disease and who had no recurrence were included. All patients were aged 18 years and above who visited TASH during the study period. The participants were receiving or had previously received curative treatment. Patients with psychiatric problems or cognitive impairment who were unable to understand or complete the interview questionnaires were excluded. In addition, patients with other severe illnesses, coexisting malignancies were excluded from the study.

Measurement

Socio-demographic characteristics

Age, educational status, religion, residence, marital status, occupation, and monthly income.

Clinical characteristics

Stage at diagnosis, comorbidity, admission status, and previous treatment taken.

Quality of life

It was measured by the Amharic version of EORTC QLQ-C30 and EORTC QLQ-BR45 Amharic version.

EORTC QLQ-C30 is a core questionnaire that used to assess QoL of cancer patients [14]. It consists of five functional scales (physical, role, cognitive, emotional, and social), three symptom scales (fatigue, pain, nausea and vomiting), and a Global Health Status (GHS)/ QoL scale. The other single items record symptoms include (dyspnoea, appetite loss, sleep disturbance, constipation, and diarrhoea), as well as financial difficulties [14]. The questionnaire was valid and reliable to assess QoL in cancer patients in Ethiopia [18].

EORTC QLQ-BR23 is breast cancer specific module developed in 1996 to assess breast cancer related specific symptoms [19]. It consists of 23 items which incorporates four functional scales/items (body image, sexual functioning, sexual enjoyment, and future perspective) and four symptom scales (systemic therapy side effects, breast symptoms, arm symptoms, and upset by hair loss) [19]. However, since 1996 the advancement in the diagnostics and therapeutics of breast cancer has brought a change that requires EORTC QLQ-BR23 update. Because, the original EORTC QLQ-BR23 cannot be able to cover many important QoL issues and potential side effects new treatment. Thus, the previous breast cancer specific module was updated to EORTC QLQ-BR45 [15]. EORTC QLQ-BR45 is a newly updated breast cancer specific tool developed by EORTC Quality of Life Group (QLG) which is used in combination with the EORTC QLQ-C30 [15]. The newly updated version has incorporated additional 22 items: target symptom scale (20 items) and breast satisfaction scale (2 items). The target symptom scale can be divided into three subscales: endocrine therapy, endocrine sexual and skin/mucosa scale. It is used to assess breast cancer specific symptoms caused by the disease and its newer therapeutic options. The breast satisfaction scale used to assess patient’s satisfaction to the appearance of skin of affected breast and the cosmetic effect of surgery [15]. The validity and reliability of the tool were assessed in our previous study, and its Amharic version was found to be valid and reliable to measure QoL in breast cancer patients. The Cronbach’s α coefficient was 0.80 and the test-retest reliability coefficient was 0.77 for all domains. The item content validity index (I-CVI) ranged from 0.83 to 1.

Scoring procedures of QoL

The item scoring procedure for the EORTC QLQ-C30 and the EORTC QLQ-BR45 was managed according to the EORTC QLQ-C30 scoring manual [20].

The EORTC QLQ-C30 and EORTC QLQ-BR45 are rated on a four-level Likert scales response system, from 1 “not at all” to 4 “very much.” Except for the EORTC QLQ-C30 GHS items, Q29 and Q30, a seven-level Likert scale is used, from 1 “very poor” to 7 “excellent” [20]. In this study, the raw scores for both EORTC QLQ-C30 and EORTC QLQ-BR45 scales and single item measures were transformed into scores ranging from 0 to 100. A high scale score represents a higher response level. Thus, a high score for a functional scale represents a high/ healthy level of functioning; a high score for the GHS/QoL represents a high QoL, but a high score for a symptom scale/item represents a high level of symptomatology/ problems [20].

For all scales, the raw score (RS) is the mean of the components items:

$$\text{Raw score}=\mathrm{RS}=\frac{\boldsymbol{I1}+\boldsymbol{I2}+\dots +\boldsymbol{In}}{\boldsymbol{n}}$$
(1)

Apply the linear transformation to 0–100 to obtain the score S,

$$\text{Functional scale}:\mathrm{S}=\left\{\mathbf{1}-\frac{\left(\boldsymbol{RS}-\mathbf{1}\right)}{\boldsymbol{range}}\right\}\ast \mathbf{100}$$
(2)
$$\text{Symptom scale and Global health status}/\mathrm{QoL}:\mathrm{S}=\left(\frac{\boldsymbol{RS}-\mathbf{1}}{\boldsymbol{range}}\right)\ast \mathbf{100}$$
(3)

Sample size

Two hundred forty breast cancer patients who visited the hospital during the data collection period, and who were willing to participate in the study, were included.

Data collection procedures

Data was collected by using self-administered questionnaire filled by participants. The socio-demographic characteristics were self-reported by the participants whereas; the clinical characteristics were extracted from the patient’s medical record by trained clinical nurses. Written informed consent was obtained from study participants after providing information about the study. To ensure good quality of data collection, an investigator was reviewing the filled questionnaires throughout the data collection period. If missing values were found, the questionnaire was returned to the patients for completion.

Statistical analysis

Data was entered, cleaned, and coded into Epidata 4.6 software and exported to Statistical Package for the Social Science (SPSS version 25) for analysis.

Descriptive statistics (frequencies, mean, and standard deviation) were used to describe socio-demographic data such as; age, marital status, educational status, place of residence, and clinical data such as; stage of tumor, comorbidity, and type of treatment. Independent t-test and one-way ANOVA were employed to determine whether differences in the mean score of QoL as measured by the functional and symptom scales of EORTC QLQ-C30 and EORTC QLQ-BR45 across socio-demographic and clinical variables of the participants were significant.

Variables with a p value ≤0.2 on correlation analysis were taken in to account in the multivariable linear regression model [21] to assess the predictors of EORTC QLQ-C30 and EORTC QLQ-BR45 scales. Multivariable linear regression analysis permits the study of multiple independent variables at the same time, with adjustment of their regression coefficients for possible confounding effects between variables [22]. Results of the multivariable linear regression analyses were expressed by B (beta) and the total amount of variance explained by the models in R squared. The P-value less than or equal to 0.05 were considered significant.

Results

Socio-demographic and clinical characteristics

Among a total number of 248 breasts cancer patients, 240 patients agreed to participate in this study with a total response rate 96.8%. The average age of the participants was 44.7 years (SD ± 11.2 years). A majority of the participants (57.9%) were married, Orthodox Christian (59%) and resided in urban area (76.6%).

Most of the study participants (40.8%) had a stage III tumours. Of the participants, 201 (83.8%) had no other illnesses or comorbidities, and 96.7% of participants were under active treatment follow-uThe remaining 3.3% of the particpants had surgery and waiting for the other treatment plan (Table 1).

Table 1 Socio-demographic and clinical characteristics of breast cancer patients at TASH, Addis Ababa, Ethiopia, 2021 (N = 240)
Full size table

Descriptive statistics of quality of life

EORTC QLQ-C30

The mean score of GHS/QoL of study participants was 65.6 (SD ± 18.64).

The mean score for EORTC QLQ-C30 functioning scales was reported as physical (69.9, SD ± 19.1), role (79.7, SD ± 26.9), emotional (73.3, SD ± 25.6), cognitive (77.4, SD ± 23.6) and social functioning (68.9, SD ± 28.4).

The highest mean score on the symptom scales/items were financial difficulties (50, SD ± 38.6), appetite loss (37.4, SD ± 36.4) and fatigue (34.3, SD ± 27.1), while the lowest symptom scores was diarrhoea (6.4 ± 18.4). Physical function, cognitive function, social function, fatigue, nausea vomiting, pain, insomnia, appetite loss, constipation, diarrhoea, and financial difficulties indicated lower QoL mean score than the EORTC QLQ-C30 reference values. The reference mean score values for each scales include: physical function (78.4), cognitive function (81.5), social function (77.0), fatigue (33.3), nausea vomiting (7.7), pain (28.7), insomnia (29.8), appetite loss (18.5), constipation (17.4), diarrhoea (5.9), and financial difficulties (18.3). Reference values provide data about the distribution of QoL scores for given cancer populations to compare a group of patients with similar characteristics, and to explain differences in clinical outcomes [23].

EORTC QLQ-BR45

The EORTC QLQ-BR45 results showed that, the mean score for functional scales ranged from future perspective (57.1, SD ± 37.3) to sexual enjoyment score (85.5, SD ± 26.3). The most affected functional scale was future perspective (57.1 ± 37.3). Upset by hair loss was the most affected symptom scale with mean score of (41.8, SD ± 34.6), majority of the participants (65.4%) were upset by hair loss. The endocrine sexual scale was the least affected symptom scale (mean = 7.2, SD ± 14.7), as only 26.2% of the participants had an affected endocrine sexual scale (Table 2).

Table 2 Mean and standard deviation of EORTC QLQ-C30 & BR45 components for breast cancer patients at TASH, Addis Ababa, Ethiopia, 2021
Full size table

An increased age was significantly associated with higher body image (P = 0.003), sexual functioning (P < 0.001), sexual enjoyment (P = 0.001) and future perspective (P = 0.039). An increased age was associated with lower cognitive function (P < 0.001). Tumor stage had no significant association with functional scores of EORTC QLQ-C30 (Table 3 and Table S1).

Table 3 EORTC QLQ-C30 functional scores by socio-demographic and clinical characteristics among breast cancer patients, at TASH, Addis Ababa, Ethiopia, 2021
Full size table

Those patients who had above secondary educational level had significantly a higher score for a physical function (P = 0.002). Those who lived in rural area had higher GHS/QoL scale than those who lived in urban area (P = 0.013). Farmers had experienced more nausea and vomiting (P = 0.044) as compared to others.

Married study participants had better cognitive function than participants with single, divorced and widowed marital status (P = 0.033). Absence of comorbidity was associated with better physical function (P < 0.001), cognitive function (P = 0.013), and social function (P = 0.009) (Table 3).

Age had no association with EORTC QLQ-C30 symptom scales. Whereas, patients who had comorbidity had more insomnia (P = 0.029), appetite loss (P = 0.014) and diarrhoea (P = 0.007). Presence of stage IV tumor was associated with more pain (P = 0.041) and appetite loss (P = 0.042). Educational status had a significant association with financial difficulties (P = 0.005) (Table 4).

Table 4 EORTC QLQ-C30 symptom scores by socio-demographic and clinical characteristics among breast cancer patients in TASH, Addis Ababa, Ethiopia, 2021
Full size table

There were no significant association between comorbidity and breast cancer specific functional and symptom scales. Stage IV breast cancer patients had more arm symptoms (P = 0.003) compared to other stages. An increased age was associated with lower upset by hair loss (P = 0.001) (Table S1).

EORTC-QLQ C30

The linear regression analysis showed the predictors association with the functional and symptom scales of EORTC QLQ-C30.

The predictors explained variations in physical functioning (R2 = 0.135), role functioning (R2 = 0.005), emotional functioning (R2 = 0.015), and social functioning (R2 = 0.027) (Table S2).

Patients with comorbidity, had lower physical functioning (B = − 0.278, p = 0.001), emotional functioning (B = − 0.138, p = 0.038), and social functioining (B = − 0.157, p = 0.018) than those who had no comorbidity.

Patients with secondary education and above had more role functioning (B = 0.198, p = 0.015) than those who had no education. Patients with stage IV breast cancer had lower physical functioning (B = − 0.174, p = 0.050) than those who with stage I breast cancer (Table S2).

Regarding EORTC QLQ-C30 symptom scales, stage IV breast cancer patients had more appetite loss (B = 0.245, p = 0.009) and constipation (B = 0.191, p = 0.044) than stage I breast cancer patients. In addition, patients with comorbidity had more appetite loss (B = 0.169, p = 0.011) than those who had no comorbidity (Table S3).

EORTC-QLQ BR45

The predictors explained variations from (R2 = 0.026) to (R2 = 0.213) in breast satisfaction and sexual functioning scales. In multivariable linear regression model, stage IV breast cancer patients had lower sexual functioning (B = − 0.184, p = 0.030) and sexual enjoyment (B = − 0.184, p = 0.041) than stage I breast cancer patients.

Those participants aged above 50 had lower body image (B = − 0.238, p = 0.008), sexual functioning (B = − 0.249, p = 0.002), sexual enjoyment (B = − 0.216, p = 0.012), and future presepctive (B = − 0.212, p = 0.018) than participants aged ≤36. Married participants had better sexual functioning (B = 0.267, p = 0.012), and sexual enjoyment (B = 0.285, p = 0.011) than participants with single marital status (Table 5).

Table 5 Linear regression model with parameter estimates for the EORTC QLQ-BR45 functioning scales among breast cancer patients in TASH, Addis Ababa, Ethiopia, 2021
Full size table

The predictors explained variations in breast symptom (R2 = 0.075), and upset by hair loss (R2 = 0.133). Married and divorced participants were less upset by hair loss (B = − 0.443, p = 0.015), (B = − 0. 302, p = 0.047) respectively as compared to participants with single marital status. Participants aged above 50 years had higher score in breast symptom scale (B = 0.298, p = 0.001), and lower score in upset by hair loss scale (B = − 0.344, p = 0.002) (Table 6).

Table 6 Linear regression model with parameter estimates for the EORTC QLQ-BR45 symptom scales among breast cancer patients in TASH, Addis Ababa, Ethiopia, 2021
Full size table

Discussion

The aim of this study was to assess quality of life and factors affecting it among breast cancer patients in Addis Ababa using the newly updated breast cancer-specific (EORTC QLQ-BR45) and general (EORTC QLQ-C30) instruments. The main findings in this study were: (1) EORTC QLQ-C30 and BR45 functioning and symptoms scales showed financial difficulties, fatigue, loss of appetite, social functioning, future perspective and upset by hair loss were the most affected EORTC QLQ scales among breast cancer patients (2) Factors associated to QoL of breast cancer patients were age, stage of tumor, educational status, comorbidity, and place of residence.

EORTC QLQ-C30 functioning and symptom scales

The mean GHS/QoL score of our study was 65.6. This value is similar to studies from Malaysia and Morocco conducted on quality of life in breast cancer patients [24, 25]. In these studies the value was 69.12 [24] and 68.5 [25]. These values can be explained by similarity of participant’s stage of disease among these studies. However, the GHS/QoL mean score in this study was higher than the EORTC QLQ-C30 reference value (GHS/QoL mean = 61.8, SD = 24.6) [23]. A study conducted in China [9] among breast cancer patients in which a larger proportion of patients who had received chemotherapy. This disparity can be explained by the fact that patients receiving chemotherapy may have multiple side effects that have a negative impact on GHS/QoL. Social function was the most affected scale, similar to previous studies conducted in Ethiopia [13] and China [9]. The reduction of social functioning in this study might reflect insufficient social support for the patients. In addition, most of the participants in this study were housewives who spend most of their time in the house that could affect their social interaction. Regarding symptom scales, severe impairment was observed in terms of financial difficulties (50, SD ± 38.6), appetite loss (37.4, SD ± 36.4), and fatigue (34.3, SD ± 27.1). This finding was consistent with other previous studies conducted in Ethiopia [13], China [9], Morocco [25], Egypt [26], Nepal [27], Malaysia [24], and Brazil [28]. As we see this can be explained by treatment side effects, which causes various symptoms like fatigue and appetite loss. The majority of the participants were in advanced stages, where it is common for such symptoms to occur. In addition, poor economic status, inability to work, and medical expenses can result in financial difficulties [11, 28]. Similarly, fatigue was the highest symptom scale among Brazilian breast cancer survivors [29] and Saudi Arabian breast cancer patients [30]. Moreover, financial difficulties were the least disturbing symptom among Saudi Arabian [30], Sweden [31], and Brazilian [28] breast cancer patients. The difference can be explained by difference in the economic status of the countries and Ethiopian population is categorized as low-income country. The majority of the Ethiopian participants were jobless. Thus, in addition to the usual household expenses, the medical expenses could have increased financial difficulties.

In our study, the EORTC QLQ-BR45 functional scale future perspective (mean = 57.1, SD ± 37.3) was the most affected scale. This result is in line with studies from Morocco [25] and China [9].

However, this finding contrasts to a study done in Kuwait, where about two-thirds of the participants were optimistic about their future health [32]. This difference in future perspective could be attributed to a lack of awareness about the disease and its treatment, associated stigma and sense of hopelessness, and the lengthy referral system to the country’s only oncology centre [33]. Regarding symptom scales, being upset by hair loss (41.8, SD ± 34.6) was the most affected scale, similar with other studies [9, 29]. This may be due to the young average age of the participants in this study; additionally, most of the participants had received chemotherapy, which often resulted in hair loss. Hair loss is one of the most common side effects of chemotherapy.

In contrast, the study from China showed that breast symptoms was the most affected scale [9]. This disparity could be due to the varying types of cancer treatment. In this study, the different treatment types for cancer included chemotherapy, which alone contributed to hair loss, whereas in the latter study, surgery was the only type of treatment among participants. Consistent with a previous study conducted in Morocco [25], participants in the younger age group had significantly lower body image (P = 0.040), sexual functioning (P = 0.001), sexual pleasure (P = 0.007), and future prospects (P = 0.039). However, one explanation could be that physical appearance, sexual functioning, and sexual pleasure are more important at younger ages and that women whose body image has changed because of hair loss and surgical procedures may feel emotionally depressed, which may prevent them from participating in sexual and social activities.

Factors associated to QoL of breast cancer patients

As we know from a study in Turkey [34], tumor stage was significantly associated with pain (p = 0.041) and arm symptoms (p = 0.042). Stage IV breast cancer patients had lower physical functioning, sexual functioning, sexual enjoyment and more arm symptom as compared to stage I breast cancer patients. In line with this study, another study reported that patients with advanced stage of cancer had unfavorable QoL scales [35] and patients with early stage of cancer showed higher functional score and lower symptom score [36]. Age had no significant association with EORTC QLQ-C30 symptom scales. On contrary to our study, age had a significant association with Functional Assessment of Cancer Therapy-Breast questionnaire (FACT B-4) breast cancer QoL assessment tool [37]. This discrepancy between studies might be due to the variation in socioeconomic status, and different QoL assessment tools. In the present study, patients who had comorbidity had also more insomnia (p = 0.029), appetite loss (p = 0.014), and diarrhoea (p = 0.007). A study conducted in Cairo had showed patients who had no comorbidities had significantly higher social wellbeing (p = 0.014) and FWB (p = 0.004) scores than those who had comorbidities [37]. Similarly in our study, patients who had no comorbidity had better physical function (p < 0.001), cognitive function (p = 0.013), and social function (p = 0.009). A previous study conducted in America on the impact of comorbidity on QoL of cancer patients concluded that comorbidity exerts negative impacts on QoL of breast cancer patients [37,38,39]. This indicates that integrating information about comorbidity status to breast cancer care would improve the overall QoL outcomes of the patients by providing additional support and care for comorbidity. In this study from Ethiopia, educational status had no significant association with the GHS/QoL which is incongruent with a study conducted in Turkey [34]. However, advancement in the educational level of the participants showed a higher in physical function (p = 0.002) and less financial difficulties (p = 0.005). This might be explained by the fact that having better educational status could improve the health seeking behaviour of the patients (i.e. physical activity and nutritional modification) and it provides job opportunities that will be a source of income respectively.

All EORTC QLQ-C30 symptom scales except dyspnea indicated lower QoL of breast cancer patients as compared to EORTC QLQ-C30 reference values. This lower QoL scores we found among Ethiopian breast cancer patients could influence treatment adherence and patient’s survival. Therefore, it requires attention and immediate action to improve their QoL.

Clinical implications

This study shows the need to improve QoL especially focusing on patients fatigue and financial difficulties. Clinicians need to focus on designing psychosocial interventions to reduce symptoms such as fatigue and support visions of future perspective to improve physical functioning, and QoL throughout their illness and treatment period. Planned education programs addressing patients’ needs, patient support and encouragement, financial aid, and incorporating group or individual psychosocial intervention into routine patient care are important interventions to consider for breast cancer patients.

In addition, our finding provided baseline information for future research on the QoL of Ethiopian breast cancer patients using a newly updated tool.

Strengths and limitations of the study

This study is to our knowledge the first study conducted using a newly updated breast cancer specific QoL assessment tool (EORTC QLQ-BR45) in Ethiopia. The other strength of this study was a high response rate.

The limitation of the study included: a crosssectional design was utilized, which does not allow the detection change in QoL throughout the different phases of the disease. The average time since diagnosis which might have affected QoL was not included as an independent variable. The Amharic versions of EORTC QLQ-C30 and EORTC QLQ-BR45 were not tested for their responsiveness to change analysis during validity and reliability tests.

Conclusion

Since our study showed considerable financial difficulties, social functioning from EORTC QLQ-C30 as well as impaired vision of future perspective among breast cancer patients in Ethiopia, efforts should be made to increase availability of social services at the tertiary referral center. Age, stage of tumor, educational status, comorbidity, and place of residence were important sociodemographic and clinical related factors associated with differences in QoL. Therefore, these specific groups of patients need additional attention about their ability to cope with the burden of the disease.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article and its additional file(s).

References

  1. Sung H, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

    Article  PubMed  Google Scholar 

  2. International Agency for Research on Cancer: World Health Organization, “The Global Cancer Observatory:Ethiopia,” 2021. https://gco.iarc.fr/today/data/factsheets/populations/231-ethiopia-fact-sheets.

  3. World Health Organization, “WHO | WHOQOL: Measuring Quality of Life,” 2020. https://www.who.int/healthinfo/survey/whoqol-qualityoflife/en/ (accessed Apr 22, 2020).

  4. Montazeri A, Gillis CR, McEwen J. Measuring quality of life in oncology: is it worthwhile? II. Experiences from the treatment of cancer. Eur J Cancer Care (Engl). 1996;5(3):168–75. https://doi.org/10.1111/j.1365-2354.1996.tb00229.x.

    Article  CAS  Google Scholar 

  5. Montazeri A, Milroy R, Hole D, McEwen J, Gillis CR. How quality of life data contribute to our understanding of cancer patients’ experiences? A study of patients with lung cancer. Qual Life Res. 2003;12(2):157–66. https://doi.org/10.1023/A:1022232624891.

    Article  PubMed  Google Scholar 

  6. Montazeri A. Health-related quality of life in breast cancer patients: a bibliographic review of the literature from 1974 to 2007. J Exp Clin Cancer Res. 2008;27(1):32. https://doi.org/10.1186/1756-9966-27-32.

    Article  PubMed  PubMed Central  Google Scholar 

  7. B. Yan et al., “Determinants of quality of life for breast Cancer patients in Shanghai, China,” PLoS One, 11, 4, 1–14, Apr. 2016, https://doi.org/10.1371/journal.pone.0153714.

  8. Pinheiro LC, et al. Examining health-related quality of life patterns in women with breast Cancer. Qual Life Res. 2017;26(7):1733–43. https://doi.org/10.1007/s11136-017-1533-5.Examining.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen Q, Li S, Wang M, Liu L, Chen G. Health-related quality of life among women breast Cancer patients in eastern China. Biomed Res Int. 2020;2018:1–12. https://doi.org/10.1155/2018/1452635.

    Article  Google Scholar 

  10. Park JH, Jung YS, Kim JY, Jo Y, Bae SH. Trajectories of health-related quality of life in breast cancer patients. Support Care Cancer. 2020;28(7):3381–9. https://doi.org/10.1007/s00520-019-05184-3.

    Article  PubMed  Google Scholar 

  11. Wondie Y, Hinz A. Quality of life among Ethiopian cancer patients. Support Care Cancer. 2020;28(11):5469–78. https://doi.org/10.1007/s00520-020-05398-w.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sibhat SG, Fenta TG, Sander B, Gebretekle GB. Health-related quality of life and its predictors among patients with breast cancer at Tikur Anbessa specialized hospital. Health Qual Life Outcomes. 2019;17(165):1–10.

    Google Scholar 

  13. Mohammed A, Taye G, Gizaw M, Hussien M. Quality of life and associated factors among patients with breast cancer under chemotherapy at Tikur Anbessa specialized hospital , Addis Ababa , Ethiopia. PLoS One. 2019;14(9):1–13. https://doi.org/10.1371/journal.pone.0222629.

    Article  CAS  Google Scholar 

  14. Aaronson NK, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85(5):365–76.

    Article  CAS  Google Scholar 

  15. Bjelic-Radisic V, et al. An international update of the EORTC questionnaire for assessing quality of life in breast cancer patients: EORTC QLQ-BR45. Ann Oncol. 2020;31(2):283–8. https://doi.org/10.1016/j.annonc.2019.10.027.

    Article  CAS  PubMed  Google Scholar 

  16. Gadisa DA, Gebremariam ET, Ali GY. Reliability and validity of Amharic version of EORTC QLQ-C30 and QLQ-BR23 modules for assessing health-related quality of life among breast cancer patients in Ethiopia. Health Qual Life Outcomes. 2019;17(1):182. https://doi.org/10.1186/s12955-019-1257-z.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Addis Ababa university, “Background of Tikur Anbessa Hospital | College of Health Sciences,” 2022. http://www.aau.edu.et/chs/tikur-anbessa-specialized-hospital/background-of-tikur-anbessa-hospital/ (accessed May 19, 2022).

  18. Ayana BA, Negash S, Yusuf L, Tigeneh W. Reliability and validity of Amharic version of EORTC QLQ-C 30 questionnaire among gynecological Cancer patients in Ethiopia. PLoS One. 2016;11(6):1–10. https://doi.org/10.1371/journal.pone.0157359.

    Article  CAS  Google Scholar 

  19. Sprangers MA, et al. The European Organization for Research and Treatment of Cancer breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study. J Clin Oncol. 1996;14(10):2756–68. https://doi.org/10.1200/JCO.1996.14.10.2756.

    Article  CAS  PubMed  Google Scholar 

  20. Fayers P. M. et al, EORTC QLQ-C30 Scoring Manual, 30. 2001.

  21. Tabachnick BG, Fidell LS. Using multivariate statistics, vol. 28, 8. 6th editio ed: Pearson; 1983.

    Google Scholar 

  22. Astrid Schneider MB, Hommel G. Linear regression analysis. Medicine (Baltimore). 2010;107(44):776–82. https://doi.org/10.3238/arztebl.2010.0776.

    Article  Google Scholar 

  23. Scott NW, et al. EORTC. QLQ-C30 Reference Values. 2008.

  24. Shin KNL, Mun CY, Shariff ZM. Nutrition indicators, physical function, and health-related quality of life in breast cancer patients. Asian Pacific J Cancer Prev. 2020;21(7):1939–50. https://doi.org/10.31557/APJCP.2020.21.7.1939.

    Article  CAS  Google Scholar 

  25. El Fakir S, et al. Health-related quality of life among breast Cancer patients and influencing factors in Morocco. Asian Pacific J. Cancer Prev. 2016;17:5063–9. https://doi.org/10.22034/APJCP.2016.17.12.5063.

    Article  Google Scholar 

  26. Enien M, Ibrahim N, Makar W, Darwish D, Gaber M. Health-related quality of life: impact of surgery and treatment modality in breast cancer. J Cancer Res Ther. 2018;14(5):957–63. https://doi.org/10.4103/0973-1482.183214.

    Article  PubMed  Google Scholar 

  27. Bhandari S, Sriyuktasuth A, Pongthavornkamol K. Treatment-related quality of life in Nepalese women with breast cancer. Asian Pacific J. Cancer Prev. 2017;18(12):3365–71. https://doi.org/10.22034/APJCP.2017.18.12.3365.

    Article  Google Scholar 

  28. Lôbo SA, Carvalho Fernandes AF, De Almeida C, De Lima Carvalho CM, Sawada NO. Quality of life in women with breast cancer undergoing chemotherapy. ACTA Paul Enferm. 2014;27(6):554–9. https://doi.org/10.1590/1982-0194201400090.

    Article  Google Scholar 

  29. Costa WA, Eleutério J, Giraldo C, Gonçalves AK. Quality of life in breast cancer survivors. Rev Assoc Med Bras. 2017;63(7):583–9. https://doi.org/10.1590/1806-9282.63.07.583.

    Article  PubMed  Google Scholar 

  30. Id MI, Al-wassia R, Alkhayyat SS, Baig M, Al-saati BA. Assessment of quality of life ( QoL) in breast cancer patients by using EORTC QLQ-C30 and BR-23 questionnaires : a tertiary care center survey in the western region of Saudi Arabia. PLoS One. 2019;14(7):1–13. https://doi.org/10.1371/journal.pone.0219093.

    Article  CAS  Google Scholar 

  31. Høyer M, et al. Health-related quality of life among women with breast cancer - a population-based study. Acta Oncol. (Madr). 2011;50(7):1015–26. https://doi.org/10.3109/0284186X.2011.577446.

    Article  Google Scholar 

  32. SA A, JU O. Health–related quality of life of Kuwaiti women with breast cancer: a comparative study using the EORTC quality of life questionnaire. BMC Cancer. 2009;9:222.

    Article  Google Scholar 

  33. Woldeamanuel YW, Girma B, Teklu AM. Cancer in Ethiopia. Lancet Oncol. 2013;14(4):289–90. https://doi.org/10.1016/S1470-2045(12)70399-6.

    Article  PubMed  Google Scholar 

  34. Gokgoz S, et al. Health related quality of life among breast Cancer patients : a study from Turkey. Glob J Health Sci. 2011;3(2):140–52. https://doi.org/10.5539/gjhs.v3n2p140.

    Article  Google Scholar 

  35. A. Techata, T. Muangmool, N. Wongpakaran, and K. Charoenkwan, “Effect of cancer stage on health-related quality of life of patients with epithelial ovarian cancer.,” J Obstet Gynaecol J Inst Obstet Gynaecol, 42, 1, 139–145, Jan. 2022, https://doi.org/10.1080/01443615.2021.1877647.

  36. B.-R. E. & V.-C. D, Cerezo O, Onate-Ocana LF, Arrieta-Joffe P, Gonzalez-Lara F, Garacia-Pasquel MJ. Validation of the Mexican-Spanish version of the EORTC QLQ-C30 and BR23 questionnaires to assess health-related quality of life in Mexican women with breast cancer. Eur. J. Cancer Care (Engl). 2012. https://doi.org/10.1111/j.1365-2354.2012.01336.x.

  37. Khater AI, Noaman MK, Hafiz MNA, Moneer MM, Elattar IA. Health-related quality of life among Egyptian female breast cancer patients at the National Cancer Institute, Cairo University. Asian Pacific J. Cancer Prev. 2019;20(10):3113–9. https://doi.org/10.31557/APJCP.2019.20.10.3113.

    Article  Google Scholar 

  38. Shiuann WH, Davis JE, Chen L. Impact of comorbidty on symptoms and quality of life among patients being treated for breast cancer. Cancer Nurs. 2019;42(5):381–7.

    Article  Google Scholar 

  39. Schoormans D, Czene K, Hall, Brandberg Y. The impact of co-morbidity on health-related quality of life in breast cancer survivors and controls. Acta Oncol (Madr). 2015;54(5):727–34. https://doi.org/10.3109/0284186X.2014.998277.

    Article  Google Scholar 

Download references

Funding

This work was supported by Else-Kroener-Foundation through Martin-Luther-University, Halle-Wittenberg, Germany, grant No. 2018_HA31SP.

Author information

Authors and Affiliations

  1. The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Mikiyas Amare Getu & Changying Chen

  2. School of Nursing and Health, Zhengzhou University, Zhengzhou, China

    Mikiyas Amare Getu & Panpan Wang

  3. Global Health Group, Martin-Luther-University, Halle (Saale), Germany

    Mikiyas Amare Getu

  4. Institute of Medical Epidemiology, Biostatistics and Informatics, Martin-Luther-University, Halle (Saale), Germany

    Eva Johanna Kantelhardt & Adamu Addissie

  5. Department of Gynecology, Martin-Luther-University, Halle (Saale), Germany

    Eva Johanna Kantelhardt

  6. School of Public Health, Department of Preventive Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

    Adamu Addissie

Authors
  1. Mikiyas Amare Getu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panpan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eva Johanna Kantelhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adamu Addissie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MAG contributed to conceptualization, methodology, and analysis of the study. MAG, CC, and WP contributed to the original draft preparation, EJK and AA participated in manuscript review and editing, CC and WP supervise the work, EJK and AA contributed in in acquisition of fund. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Changying Chen or Panpan Wang.

Ethics declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of Zhengzhou University (number: ZZURIB 2020–10; Date: 18/06/2020) and Addis Ababa University (number: 101/20/Onco; Date: 28/10/2020).

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Getu, M.A., Chen, C., Wang, P. et al. Quality of life and its influencing factors among breast cancer patients at Tikur Anbessa specialised hospital, Addis Ababa, Ethiopia. BMC Cancer 22, 897 (2022). https://doi.org/10.1186/s12885-022-09921-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-022-09921-6

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us