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PLOS One logoLink to PLOS One
. 2024 Mar 28;19(3):e0300631. doi: 10.1371/journal.pone.0300631

Willingness and ability to pay for breast cancer treatment among patients from Addis Ababa, Ethiopia: A cross-sectional study

Tamiru Demeke 1,2,3, Damen Hailemariam 2, Pablo Santos 1,3, Edom Seife 4, Adamu Addissie 2, Eric Sven Kroeber 1,3,5, Rafael Mikolajczyk 1, Birgit Silbersack 5, Eva Johanna Kantelhardt 1,3,6,‡, Susanne Unverzagt 3,5,‡,*
Editor: Ahmed Mancy Mosa7
PMCID: PMC10977721  PMID: 38547108

Abstract

Introduction

Breast cancer (BC) is the most common malignant neoplasm among women in Addis Ababa, Ethiopia. The willingness and ability to pay (WATP) for treatment is a contributing factor in the utilization of health care services. The World Health Organization Breast Cancer Initiative calls for 80% of patients to complete multimodality treatment and indicates payment as central factor to improve BC outcome. The objectives of this study are to identify how much female BC patients paid in Addis Ababa for BC treatment, their WATP for BC treatment, and the factors that affect WATP.

Methods

The researchers collected data from 204 randomly selected BC patients who were treated in one of four different health facilities (one public and three private) between September 2018 and May 2019. A structured questionnaire was used to assess their WATP for BC treatment and multivariable regression to investigate factors associated with patients’ WATP.

Results

Of interviewed patients, 146 (72%) were at reproductive age. Patients’ median expenditure for all BC treatment services was 336 US dollars (USD) in a public cancer center and 926 USD in privately owned health facilities. These amounts are in contrast with a reported WATP of 50 USD and 149 USD. WATP increased with increasing expenditure (OR 1.43; 95% CI 1.09 to 1.89 per 100 US), educational level (OR 1.37; 95% CI 1.02 to 1.85) and service quality (OR 1.34; 95% CI 1.04 to 1.72). In contrast, a monthly income increase by 100 USD corresponds to a 17% decrease of WATP (OR 0.83; 95% CI 0.70 to 0.99).

Conclusions

We demonstrated that BC treatment was very expensive for patients, and the cost was much higher than their WATP. Thus, we suggest that BC should be included in both social and community-based health insurance plans and treatment fees should consider patients’ WATP.

Introduction

Breast cancer (BC) is a growing public health challenge in Ethiopia [1,2]. According to data from the Addis Ababa Population Based Cancer Registry, the cumulative BC incidence in the capital city alone reached 4,500 cases as of 2019 [3]. The GLOBOCAN 2020 report indicated that in Ethiopia there were 16133 new BC cases and 9061 deaths due to BC. These figures put the country in the third rank among African countries [4].

Many breast cancer patients seek treatment at the Tikur Anbessa Specialized Hospital-Radiation Centre (hereafter called Public Cancer Centre [PCC]) to get treatment as, until 2022, it was the first and only government owned hospital that provided cancer treatment including radiotherapy. In addition, there are three privately owned health facilities in Addis Ababa that provide oncology service but are limited to chemotherapy only [5]. Among patients who presented in PCC for treatment, almost 71% were diagnosed at advanced stages due to lengthy intervals between the time of recognition of symptoms and getting treatment [6]. Apart from various medical and non-medical factors, many BC patients presented in both PCC and private health facilities (PHFs) at advanced stages due to low income and the high costs of BC treatment. Among these patients, some discontinued their treatment, frequently due to shortage of money [7–9].

Ethiopia’s national health accounts reports indicated that out-of-pocket (OOP) expenditure accounts for about 30% of the total health expenditure [10]. This is also applicable to BC patients. The heavy reliance on OOP expenditure triggered policy makers to pay attention to the cost of health care services because a large OOP expenditure can make the health expenditure catastrophic [11]. An OOP expenditure is said to be catastrophic if it is above the estimated threshold share of household expenditure, which can result in patients being forced to sacrifice other basic needs, sell assets, incur debt, or be impoverished [12,13] or if the OOP expenditure is equal to or greater than 40% of non-food expenditure or capacity to pay (CTP). CTP is the difference between subsistence expenditure and monthly household expenditure (i.e., consumption [14]. This in turn will compel patients and their families to reduce consumption of basic necessities and this further will push them into poverty. Thus, the Federal Democratic Republic of Ethiopia (FDRE) Ministry of Health (MOH) prepared and implemented a five-year national cancer control program to reduce the financial burden [15].

Affordability of BC treatment is an important factor for optimal treatment compliance. The WHO Global Breast Cancer Initiative has recommended that 80% of patients should complete multimodal treatment with an affordable charge as an essential component for success [16]. To determine the affordability of treatment, it is paramount to know what patients are willing and able to pay (WATP) for treatment. Willingness to pay (WTP) refers to a person’s willingness to pay for desired goods or services, while ability to pay (ATP) refers to their financial capacity to do so. [17]. Therefore, it is recommended to combine these two concepts and use WATP to estimate the real demand for goods and services including health care services [18–22]. Accordingly, many studies assessed the demand for BC treatment using either the contingent valuation (CV, see Methods) technique for stated preferences or the conjoint analysis (CA) technique for revealed preference. The CV technique is generally recommended for health economics research [19,23–28]. The method has been validated and employed in several similar studies [20,29,30].

It is a survey approach designed to fill the gap in the market for public goods by asking how much money people are willing to pay (WTP) for specified goods or services including health care services [31].

Analyzing WATP is crucial for understanding the demand for health care services and provides evidence to both policy makers and health professionals regarding the determination of health care service fees [32], to guiding financial strategies to ensure access to recommended BC treatments for all patients, and providing treatment based on patients’ economic [33–35].

The aims of this study were to (i) identify how much female patients were paying for their treatment both in public and private health facilities, (ii) investigate the WATP of BC patients living in Addis Ababa, and (iii) identify the factors that influenced their WATP.

Materials and methods

Study population

We conducted a quantitative cross-sectional study for which data was collected from BC patients who presented in one of four selected health facilities (one public and three privately owned) in Addis Ababa, Ethiopia. Data collection was done between September 2018 and May 2019. Data analysis was performed between April 2020 and January 2021. For this study, For this study, we included all BC patients permanently residing in Addis Ababa and receiving treatment at PCC and the three privately owned health facilities at the time of data collection. The sample design was simple random sampling that is BC patients who came to the aforementioned health facilities were randomly selected among those BC patients who came to the health facilities at the time of data collection.

Sample size calculation was done based on accuracy (two-sided width of the 95% confidence interval CI) of the estimate and an assumed standard deviation of direct medical costs of 355 USD per BC treatment, requiring a distance from the mean to both directions of 50 (width of the CI of 100) [36], resulting in a sample size of n = 194. In addition, 15% was added for non-response rate and the sample size became 223 patients. This sample size was distributed among the four health facilities in proportion to the number of BC patients they treated before data collection time using simple proportional allocation method. Accordingly, 133 patients from PCC and 90 patients from the three private health facilities were contacted.

Data collection

The data collection tool was a structured questionnaire consisting of the following sections (WHO): socio-economic, (1) history of BC and current health status (2), expenditures related to BC treatment, and (AAPCR) patients’ WATP for BC treatment. To measure WATP, patients were asked how much they would be willing and able to pay for each health care service they receive based on their present experience, without considering the frequency of the service or the number of chemotherapy cycles. The questionnaire was prepared based on the CV technique that consists of open-ended and close-ended questions to ask patients the maximum amount they were WATP for their treatment.

Statistics

The data were processed with SPSS version 26, STATA version 15 and the Statsmodels library version 0.14.0 in Python [37]. Results of descriptive statistics were presented as absolute and relative frequencies, with the corresponding medians. We assessed the effects of sociodemographic and medical factors on patients’ WATP while adjusting for confounders by using a multivariate regression model. Since WATP values did not follow a normal distribution, we avoided heteroscedasticity by fitting data as a generalized linear model (Gamma family with log link [38]). The exponentiated coefficients resulting from the multivariate regression were interpreted as Odds Ratios (ORs) [39,40]. The number of samples considered for the regression was the sample subset (n = 121) for which valid values were available for all assessed variables. Due to the lack of collinearity among the explanatory variables (Pearson’s ρ < 0.5 for all variable pairs), imputations were not performed. The number of missing data points that led to the final sample subset of 121 patients assessed in the regression are given for each variable in Table 1.

Table 1. Demographic characteristics of patients (n = 204).

Characteristic n (%) *
Age (years)
    23–49 146 (71)
    50–75 58 (28)
Employment status
    Unemployed or housewives 101 (50)
    Employed 73 (36)
    Self-employed 26 (13)
    Other 4 (2)
Educational level
    Without elementary level 54 (26)
    Elementary or secondary school level 70 (34)
    Higher education 78 (38)
    Unknown 2 (1)
Marital status
    Single 23 (11)
    Married 138 (68)
    Ex-married (widowed, separated, or divorced) 43 (21)
Family size
    ≤ 3 82 (40)
    4 to 7 100 (49)
    ≥ 8 19 (9)
    Unknown 3 (1)
Number of years with BC
    < 1 30 (15)
    1 67 (33)
    2 49 (24)
    3 46 (23)
    No response 12 (6)
Reasons for diagnosis
    A physician advised me to consult Oncologist about my BC when I went for other treatment 15 (7)
    Breast swelling 118 (58)
    Breast pain 59 (29)
    No response 12 (6)
Average monthly income (USD)
    ≤ 38 34 (17)
    > 38 to ≤ 75 29 (14)
    > 75 to ≤ 150 41 (20)
    ≥150
    No response
42 (21)
58 (28)

BC = Breast cancer; USD = United States Dollar

* Sums may not add up to 100% due to rounding.

Ethics approval

The Institutional Review Board (IRB) of the College of Health Sciences at Addis Ababa University in Ethiopia provided ethical approval prior to the collection of study data under Protocol Number 051/18/SPH. Informed consent was obtained orally from each patient at the beginning of the interview and documented on the questionnaire by the data collectors.

Results

Descriptive statistics

Of a total of 223 patients who were asked for interview, 204 (91%) agreed to participate in the study. Of the 204 interviewed patients, 151 (74%) were treated at PCC and 53 (26%) at the three PHFs. Patients were between 23 and 75 years old and 72% of patients were within reproductive age. Half of the patients were unemployed or housewives, 11% of patients were unable to read and write, and 69% were married. The median family size excluding the interviewed patient was four (range 0–12). 72% of patients were treated for up to two years. The most common symptoms leading to a diagnosis with BC were breast swelling (61%) followed by breast pain (28%) (Table 1).

Of the interviewed patients, 72% were willing to disclose their average monthly families’ income. Of these, 31% had a monthly income below 75 USD. There was a broad range of monthly income between 5 and 1,011 USD (Table 1). Participants in the study were grouped according to their monthly income based on the World Bank’s Poverty Assessment of Ethiopia and Ethiopia’s low income tax rate. Accordingly, an income of less than 1.25 USD per day was considered the poverty line. [41]. Monthly income greater than 38 USD but less than or equal to 75 USD is considered lower-middle income, monthly income greater than 75 USD but less than or equal to 150 USD is considered middle income, and monthly income greater than 150 USD is considered upper income.

Patients’ income sources were, their own (26%), their husband’s salary (23%), a combination of their own income and their husband’s salary (21%), pension (16%) or support from their children or other relatives (14%). Regarding average monthly household expenses, excluding expenditure for BC treatment, 56% spent more than 75 USD, with a median expenditure of 98 USD.

Expenditure and satisfaction with treatment

Most patients (82%) were satisfied with the treatment they received, while 57% of patients said the quality of the services was good or very good. However, 55% of patients deemed the treatments expensive.

Patients treated at PCC paid a median of 336 USD (interquartile range [IQR] 97 to 711), whereas patients treated in PHFs paid a median of 926 USD (IQR 206 to 1,581) for all services. Among the services provided at PCC, patients paid the highest median amount of 152 USD (IQR 76 to 253) for chemotherapy, followed by 126 USD (IQR 48 to 253) for surgery, and 106 USD (IQR 29 to 253) for medication. Patients treated at PHFs paid the highest median amount of 758 USD (IQR 758 to 910) for surgery; 278 USD (IQR 32–1,200) for inpatient service including all medical treatment, a bed, and meals; and 202 USD (IQR 126 to 455) for medication (Table 2).

Table 2. Comparison between PCC and PHFs concerning the amounts actually paid for each component of the BC treatment.

Service Median (1) IQR (USD)
PCC PHF PCC PHF
Consultation 0.1 (112) 10.1 (52) 0.1–0.1 8.8–10.1
Laboratory 12.6 (90) 35.4 (42) 5.1–38.9 21.5–97.9
Medication 106.1 (103) 202.2 (37) 29.1–252.7 126.4–454.9
Imaging 13.9 (96) 15.2 (12) 5.1–39.2 7.0–25.3
Radiotherapy 25.3 (21) N/A 7.6–176.9 N/A
Surgery 126.4 (76) 758.1 (25) 48.0–252.7 758.1–909.7
Pathology 7.6(87) 11.4 (35) 4.3–12.6 11.4–45.
Chemotherapy 151.6 (92) 88.4 (51) 75.8–252.7 29.9–202.2
Inpatient Service 75.8(76) 278.0 (10) 25.3–167.41 31.6–1,200.3
All Services 336.3 (145) 926.1 (5) 97.4–710.6 205.9–1,580.6

IQR: Interquartile range; USD: United States dollars; PCC: Tikur Anbessa Specialized Hospital-Radiation Centre; PHF: Private health care facilities; N/A: Not applicable.

About 36% of patients obtained financial support from relatives/friends and 20% used combinations of financial support, savings, and loans for their treatment. However, 79% of patients reported having difficulty obtaining money.

Willingness and ability to pay

151 patients treated at PCC had WATP median of 50 USD (IQR 19 to 134) and 53 patients treated at PHFs had WATP median 149 USD (IQR 66 to 383) for all health care services. Patients treated at PCC had WATP median 13 USD (IQR 2.5 to 37.27) for surgery, 13 USD (IQR 0.0 to 25.27) for chemotherapy and 13 USD (IQR 1.3 to 25.27) for inpatient service. The 13 USD was equivalent to 500 Birr and was higher than the median amounts of other health care services that were provided in PCC. Patients treated at PHFs were willing and able to pay a higher median amount of 253 USD (IQR 253 to 379) for surgery and 35 USD (IQR 23 to 76) for drugs (Table 3).

Table 3. Comparison between PCC and PHFs concerning WATP for each component of the BC treatment (n = 204).

Service Median (1) IQR (USD)
PCC PHF PCC PHF
Consultation 0.1 (151) 5.1 (51) 0.1–0.38 3.8–5.05
Laboratory 1.3 (133) 12.6 (38) 0.5–2.53 8.2–25.27
Drugs 7.6 (122) 35.4 (31) 2.5–25.27 22.7–75.81
Imaging 1.3 (123) 9.1 (12) 0.5–5.05 7.0–15.09
Radiotherapy 2.5 (94) N/A 1.1–12.63 N/A
Surgery 12.6 (118) 252.7 (22) 2.5–37.27 252.7–379.05
Pathology 1.3 (125) 12.6 (19) 0.8–2.53 11.4–21.33
Chemotherapy 12.6 (131) 20.2 (33) 0.0–25.27 12.6–50.54
Inpatient service 12.6 (97) 25.3 (11) 1.3–25.27 16.4–138.98
All services 49.8 (151) 149.1 (53) 19.1–134.0 65.7–382.8

IQR: Interquartile range; USD: United States dollars; PCC: Tikur Anbessa Specialized Hospital-Radiation Centre; PHF: Private health care facilities; Diff: difference between PHF and PCC medians; N/A: Not applicable.

The costs incurred for treatment were analyzed by years since diagnosis. We found that the patients who were treated for up to two years paid the median amount of 505 USD, while patients who were treated for more than two years paid the median amount of 454 USD for the whole treatment period. The patients differed in their willingness and ability to pay depending on the duration of treatment (medians of 71 USD and 27 USD, respectively).

Factors affecting WATP for BC treatment

The results of the multivariable regression are listed in Table 4 WATP increased with the total expenditure of patients, educational level and service quality. An increase of 100 USD in total expenditure corresponded to an increase by 43% in WATP (OR 1.43, 95% CI 1.09 to 1.89). Likewise, each increased educational or service quality level corresponded to an increase by 37% and 34% in WATP respectively (OR 1.37; 95% CI 1.02 to 1.85 and OR 1.34; 95% CI 1.04 to 1.72). An income increase of 100 USD corresponded to a 17% decrease of WATP (OR 0.83; 95% CI 0.70 to 0.99).

Table 4. Multivariable regression summary (dependent variable is WATP, in USD.

Variables OR 95% CI (lower) 95% CI (upper) p-value
Marital Status (Ref: Married, n = 86)
    Ex-married (n = 24) 0.20 0.11 0.34 < 0.001
    Single (n = 11) 0.91 0.44 1.86 0.787
Occupation (Ref: Housewife, n = 46)
    Employed (n = 75) 0.71 0.46 1.12 0.142
Treatment duration (Ref: >2 years, n = 35)
    ≤ 2 years (n = 86) 0.63 0.39 1.02 0.062
Higher monthly income (per 100 USD, n = 121) 0.83 0.70 0.99 0.043
Higher amount expended for BC treatment (per 100 USD, n = 121) 1.00 0.99 1.00 0.792
Higher Total Expenditure (per 100 USD, n = 121) 1.43 1.09 1.89 0.010
Higher Family Size (n = 121) 0.90 0.81 1.01 0.070
Increasing Educational Level (3 levels, n = 121) 1.37 1.02 1.85 0.036
Increasing Service Quality (3 levels, n = 121) 1.34 1.04 1.72 0.026
Higher Age (in years, n = 121) 1.00 0.97 1.02 0.708

Discussion

The main aims of this study were to assess how much BC patients spent on treatment and patients’ WATP for BC treatment in Addis Ababa, Ethiopia. The main finding of this study was that costs were considerably high at PHF and three times higher at PCC. The median amounts that patients eventually paid for all services both at PCC and PHFs were six times higher than the median amounts they stated being willing and able to pay both in PCC and PHF. This indicates that these patients were forced to pay a higher amount for treatment of a life-threatening disease regardless of their WATP. Our findings show that the actual cost is more than what patients are willing and able to pay and more than their demand for health care services. This apparent contradiction is likely due to poorer report concerning what patients remember having spent in the past.

High absolute costs of treatment

Total costs for treatment were far higher than WATP. Even if patients were economically poor and dependent on others, they had to pay high amounts for their BC treatment. Due to the high cost of treatment, 56% of interviewed patients as well as their families were exposed to catastrophic health expenditure and financial distress. Studies conducted in the Mandura District, Western Ethiopia and in Dessie Referral Hospital, Northeast Ethiopia indicated that 22.5% and 64.2% patients were exposed to catastrophic health expenditure due to direct medical costs respectively, though the participants in those studies were not cancer patients [42,43]. A study conducted in Nigeria also reported that the mean OOP expenditure for BC diagnosis and management was 2,049 USD [44]. According to a study that assessed the prices of health care in 15 African countries, including Ethiopia, patients were compelled to sell their fixed and precious assets and to borrow to cover their health care expenditures. Moreover, a high proportion of patients were not able to get health care services, because they could not afford the costs [45,46]. Other patients paid 2,325 USD in Addis Ababa for cancer treatment [47], but 600 USD in Uganda and 2100 USD in Nigeria [48]. Our finding is also compatible with the theoretical definition of catastrophic health care because the following factors are met: the cost of health care service is covered OOP, (2) the household CTP is low, and (3) there is no prepayment system for risk accumulation [11]. WHO also declared that a household is said to be exposed to catastrophic health expenditure if the OOP expenditure on health is greater than or equal to 40% of the household’s CTP [49]. In other words, the health expenditure is considered catastrophic if it is above the financial means of the household and this will lead the household to poverty or prevent the household from getting out of poverty. As indicated in the present study, most of the patients were exposed to catastrophic health expenditure because they incurred costs beyond their CTP. This shows that even though Ethiopian patients are in a lower economic category than patients in other countries, they spent similar amounts of money for their BC treatment. This underlines the high economic burden of BC on Ethiopian patients.

Patients estimated their personal WATP but eventually retrieved funding from a much larger extended family. Most of the patients were housewives who were financially dependent on income from their husbands, children, and relatives. These payments for the patients’ BC treatment created financial distress not just for the patient but for the extended family as well. A study conducted in Nigeria indicated that about 78% of study participants with BC were not able to get treatment because of financial barriers they faced [50] and research conducted in Iran indicated that due to the cost of cancer treatment, patients’ and their families’ living conditions were worsened [11].

Comparison of private vs. public health care facilities

We observed that patients paid more at PHFs compared with PCC for similar treatment because PHFs are profit making institutions and provide treatment more quickly than PCC. For instance, a study conducted at PCC, Addis Ababa, Ethiopia and Yaoundé General Hospital, Cameroon revealed that patients with BC had long waiting times between their first consultation and surgery or other treatments [51,52]. In order to avoid treatment delays and to reduce the chance of the cancer progressing during the wait, patients preferred to pay more.

Various studies found that WATP can be influenced by factors such as age, education, income, dependency ratio/household size, attitude towards treatment, quality of health care services, locality rural/urban, and ability to pay [53]. In our study, we stated an association between average monthly income, education, service quality, and total expenditure and WATP of BC patients.

Increasing income was associated with decreased WATP. This finding was contrary to other studies that dealt with the WTP for BC treatment and other types of cancers because these studies focused on WTP only while our study focused on WATP [26,54,55]. Thus, we found that with increasing income, patients were more able but less willing to contribute to a higher amount of treatment costs. This result is consistent with studies conducted in Bangladesh, Sweden, and Ethiopia had found that the impact of income on WTP for medical care was negligible [56–59].

Educational level is a predictor of WATP. Studies showed that patients with higher educational level have better health literacy, resulting in them being better informed about the treatment and disease and more likely to be willing and able to pay for breast cancer treatment [29,60,61].

Perceived service quality was also associated with WATP. As patients perceived that the service quality is good, they were more willing and able to pay for their BC treatment. Especially economically better-off patients were willing and able to pay more for services that they believed were good. This is consistent with other studies that reported, patients who obtained good quality health care service were willing to pay more [53].

Total expenditure was also associated with WATP. This is because expenditure was directly related to income. This finding is consistent with the economic theory that a person’s decision is a sequential process where the decision of whether to consume a particular commodity is followed by the choice of how much to consume. As health is a commodity and private good, this economic theory is also applicable to health care service, as we did in our study [62,63].

Potential limitations of the study

Patients were frequently unaware of the stage of their disease and staging might have changed during treatment. Thus, we did not consider the exact stages of BC of the interviewed patients and only differentiated between patients with up to 2 years and more than 2 years of total duration of treatment. Responses about actual household income had a high degree of uncertainty as income may vary greatly over time and estimating an average was difficult for respondents. Additionally, the definition of the household by participant varied and the respondents included closer or more distant relatives. Since the IQR of WATP only varied five-fold, we assume that the group of relatives with financial contributions to WATP were probably perceived as relatively similar among respondents.

Conclusion

The cost of BC treatment in Addis Ababa was beyond the patients’ WATP and they were compelled to pay the required amount by asking for financial support from their relatives, including borrowing. Patients from public and private centres had similar disparities between WATP and actual costs indicating that eventually most patients and their families were exposed to catastrophic health expenditure. This indicated that the amount BC patients paid for their treatment was beyond their WATP, but they paid the amount indicated in this study because BC is life threating disease and patients and their families wanted to increase survival time. Thus, the contribution of this research is that decision makers in the health sector at all levels including at private health facilities should consider the WATP of BC patients when deciding on the fee for BC treatment.

Recommendations

Therefore, seeing these catastrophic expenditures, costs need to be limited and communicated early to the patients. Policy makers in the health sector should include BC treatment in both social and community-based health insurances. Also, health facilities should ensure that their health care services fees are reasonable. Creative ways to co-finance from additional sources are needed to meet the objective of the WHO Global Breast Cancer Initiative assuring that 80% of the patients complete multimodal treatment.

Acknowledgments

We thank all collaborators including the employees of the public and private health facilities where data were collected and BC patients who were willing to be interviewed. Special thanks go to the School of Public Health, College of Health Science Addis Ababa University and Martin Luther University Halle-Wittenberg, Germany, for their major contribution to this study by providing the necessary technical and logistic support during the study as required. We acknowledge the proofreading assistance of Dawn M. Bielawski, PhD.

Data Availability

All relevant data are within the paper and its Supporting Information files. The minimal dataset dataset data required to replicate all study findings reported in the article can be found under doi: 10.6084/m9.figshare.25370860.v2 The python script showing all steps to reproduce data cleaning, model building, regression coefficients with 95%CI and and p-values can be found under doi: 10.6084/m9.figshare.25370950.v1.

Funding Statement

This work was supported by DAAD PAGEL project No. 57513614. It was also supported by Else-Kroener-Foundation through Martin-Luther-University, Halle-Wittenberg, Germany, Grant No. 2018_HA31SP. The work was also supported through the German Ministry of Research and Education, Grant 01KA2220B. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Ahmed Mancy Mosa

12 Feb 2024

PONE-D-23-33162Willingness and ability to pay for breast cancer treatment among patients from Addis Ababa, Ethiopia: a cross-sectional studyPLOS ONE

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[We thank all collaborators including the employees of the public and private health facilities where data were collected, BC patients who were willing to be interviewed and DAAD PAGEL project No. 57513614, Else-Kroener-Foundation, German Ministry of Research and Education that provided support in various ways for successful accomplishment of this study. Special thanks go to the School of Public Health, College of Health Science Addis Ababa University and Martin-Luther-University, Halle-Wittenberg, Germany for their major contribution to this study by providing the necessary technical and logistic support during the study as required. We acknowledge the proofreading assistance of Dawn M. Bielawski, PhD.]

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 [This work was supported by DAAD PAGEL project No. 57513614. It was also supported by Else-Kroener-Foundation through Martin-Luther-University, Halle-Wittenberg, Germany, Grant No. 2018_HA31SP. The work was also supported through the German Ministry of Research and Education, Grant 01KA2220B. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript]

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

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Reviewer #1: Manuscript “Willingness and ability to pay for breast cancer treatment among patients from Addis Ababa, Ethiopia: a cross-sectional study” has described the reality of Breast Cancer treatment in Ethiopia. The manuscript is well written and data provided support the author’s conclusion.

Reviewer #2: Comments to the Author

Congratulations on the submitted manuscript. The topic is timely and will be of interest to the readers of the journal. However, a few changes are suggested to improve the clarity of this manuscript. I have several recommendations and questions about the manuscript.

1.Abstract

Methods: We collected data from 204 randomly selected BC patients who received

treatment at one of four different health facilities (one public and three private) between

April 2020 and January 2021.

Recommend replacing the word “we” with “researchers”

What are the inclusion and exclusion criteria?

2. Introduction

Very good explanation

3. Methodology

Please state the sample design used in this study.

4. Recommendation

Recommend adding the subheading for recommendations for this study.

5.Conclusion

How do the findings add to the body of scientific knowledge on the particular issue?

Should highlight the implication of the results to health policy.

6. References

Most of the citations are less than 5 years. 39/61

Recommendation: 5 years above is more updated.

Please add doi in the article’s references.

Reviewer #3: Overall, the article is well-structured with distinct sections for abstract, introduction, methods, results, and conclusions. This makes it easy to follow.

1. Line 59; consider rephrasing "According to the Addis Ababa Population Based Cancer Registry database" to "According to data from the Addis Ababa Population Based Cancer Registry."

2. Line 63, instead of "Many BC patients come to Tikur Anbessa Specialized Hospital-Radiation Centre," you could write "Many breast cancer (BC) patients seek treatment at the Tikur Anbessa Specialized Hospital-Radiation Centre."

3. Line 74; consider rephrasing "This holds true for BC patients" to "This is also applicable to BC patients."

4. Line 89, it would be clearer to replace "To determine whether the treatment is affordable or not" with "To determine the affordability of treatment."

5. Line 90, "Willingness to pay (WTP) is defined as a person's willingness to pay for the good or services he/she wants to buy while ability to pay (ATP) is defined as the person's ability to pay for the chosen good or services" could be simplified to "Willingness to pay (WTP) refers to a person's willingness to pay for desired goods or services, while ability to pay (ATP) refers to their financial capacity to do so.

6. Line 102; consider rephrasing "Examining WATP is central to a meaningful analysis of demand for health care services" to "Analyzing WATP is crucial for understanding the demand for health care services."

7. While you've mentioned that the sample size was distributed among the four health facilities based on the number of BC patients they treated before data collection, it would be beneficial to provide more details on how this distribution was carried out. Was it a simple proportional allocation, or were there other considerations involved?

8. It's good that you accounted for a non-response rate of 15%. However, providing a brief justification or citing literature for choosing this specific rate would enhance the transparency of your methodology.

9. The assumed standard deviation of direct medical costs ($355 USD per BC treatment) seems crucial for sample size calculation. Providing a justification or reference for this assumption would strengthen the validity of your calculations.

10. It's mentioned that imputations were not performed for missing data. While this is acceptable, it would be useful to mention the extent of missing data and any sensitivity analyses conducted to assess the impact of missing data on the results.

11. Line 114 "For this study, we included all BC patients who were permanently residing in Addis Ababa and who were receiving BC treatment in PCC and the three privately owned health facilities at the time of data collection." - To improve clarity, consider rephrasing: "For this study, we included all breast cancer (BC) patients permanently residing in Addis Ababa and receiving treatment at PCC and the three privately owned health facilities at the time of data collection."

12. Line 149 "Informed patient consent was obtained orally at the beginning of each interview and documented on the questionnaire by the data collectors." - Consider revising for clarity: "Informed consent was obtained orally from each patient at the beginning of the interview and documented on the questionnaire by the data collectors."

13. In Table 1 Explanation of Income Categories: Provide a brief explanation of the income categories (e.g., low-income, middle-income, high-income) to give context to readers who may not be familiar with the currency or income distribution in the study setting

14. In Table 4, the sample sizes for some categories (e.g., unemployed) are very small, which could affect the reliability of estimates.

15. It might be beneficial to compare the costs reported in this study with national or regional averages for breast cancer treatment. This comparison could help contextualize whether the observed costs are consistent with broader trends in healthcare pricing.

16. Line 271; there is number 3 between ( ); it stands for what?

17. Line 245; the numbers order is not corrected, Please fix it?

**********

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Reviewer #1: No

Reviewer #2: Yes: DR RUSNANI AB LATIF

Reviewer #3: No

**********

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Attachment

Submitted filename: Reviewer MA comments.docx

pone.0300631.s001.docx (14.4KB, docx)
PLoS One. 2024 Mar 28;19(3):e0300631. doi: 10.1371/journal.pone.0300631.r002

Author response to Decision Letter 0


22 Feb 2024

Reviewer #2: Comments to the Author

1.Abstract

Methods: We collected data from 204 randomly selected BC patients who received

treatment at one of four different health facilities (one public and three private) between

April 2020 and January 2021.

Comment #1

Recommend replacing the word “we” with “researchers”

Answer of the authors: Action taken as recommended.

Comment #2

What are the inclusion and exclusion criteria?

Answer of the authors:

We tried to include the following statement in the abstract, but it will make the abstract to have words that exceed the limit i.e. the number of words of the abstract will be 308 while the word limit was 300 only. For this reason, we cannot add it but this statement is explained in the main body of the manuscript in method section.

The inclusion criteria were that being BC patient, residing in Addis Ababa and receiving treatment at the time of data collection as described in the objectives and the first sentence of the methods.

2. Introduction

Comment #3

Very good explanation

Answer of the authors: Thank you

3. Methodology

Comment #4

Please state the sample design used in this study.

Answer of the authors:

We added the following statement as suggested in the materials and method section and write now: “The sample design was simple random sampling that is BC patients who came to the aforementioned health facilities will be randomly selected among those BC patients who came to the health facilities at the time of data collection.”

4. Recommendation

Comment #5

Recommend adding the subheading for recommendations for this study.

Answer of the authors:

The word “Recommendations” is added as subheading

5.Conclusion

Comment #6

How do the findings add to the body of scientific knowledge on the particular issue?

Should highlight the implication of the results to health policy.

Answer of the authors:

We added the following paragraph in the conclusion:

“Thus, the contribution of this research is that decision makers in the health sector at all levels including at private health facilities should consider the WATP of BC patients when deciding on the fee for BC treatment.”

6. References

Comment #7

Most of the citations are less than 5 years. 39/61

Recommendation: 5 years above is more updated.

Answer of the authors:

Yes, we agree with your comment. However, since the research topic i.e. WATP for breast cancer treatment is a rare topic on which very few researches have been conducted especially in the recent times. For this reason, we tried to find papers on this and related topics. Unfortunately, we were able to get only 27 (43.5%) papers that were published during the last five years. For clarity purpose, we tried to categorized the papers by five years gap and found that 27 (43.5%) of the manuscripts were published between 2018 and 2023 while 8 (12.9%) and 10 (16.1%) manuscripts were published between 2014 and 2017, 2009 and 2013 respectively. The remaining papers i.e. 17 (27.4%) were published between 1986 and 2008. .

Comment #7

Please add doi in the article’s references.

Answer of the authors:

We added the ‘DOI’ of almost all manuscripts except very few manuscripts that do not have DOI.

Reviewer #3

Comments:

Overall, the abstract is well-structured with distinct sections for abstract, introduction, methods, results, and conclusions. This makes it easy to follow.

1. Line 59; consider rephrasing "According to the Addis Ababa Population Based Cancer Registry database" to "According to data from the Addis Ababa Population Based Cancer Registry."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

2. Line 63, instead of "Many BC patients come to Tikur Anbessa Specialized Hospital-Radiation Centre," you could write "Many breast cancer (BC) patients seek treatment at the Tikur Anbessa Specialized Hospital-Radiation Centre."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

3. Line 74; consider rephrasing "This holds true for BC patients" to "This is also applicable to BC patients."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

4. Line 89, it would be clearer to replace "To determine whether the treatment is affordable or not" with "To determine the affordability of treatment."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

5. Line 90, "Willingness to pay (WTP) is defined as a person's willingness to pay for the good or services he/she wants to buy while ability to pay (ATP) is defined as the person's ability to pay for the chosen good or services" could be simplified to "Willingness to pay (WTP) refers to a person's willingness to pay for desired goods or services, while ability to pay (ATP) refers to their financial capacity to do so.

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

6. Line 102; consider rephrasing "Examining WATP is central to a meaningful analysis of demand for health care services" to "Analyzing WATP is crucial for understanding the demand for health care services."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

7. While you've mentioned that the sample size was distributed among the four health facilities based on the number of BC patients they treated before data collection, it would be beneficial to provide more details on how this distribution was carried out. Was it a simple proportional allocation, or were there other considerations involved?

Answer of the authors:

The recommended statement is added as shown below: “This sample size was distributed among the four health facilities in proportion to the number of BC patients they treated before data collection time using simple proportional allocation method.”

8. It's good that you accounted for a non-response rate of 15%. However, providing a brief justification or citing literature for choosing this specific rate would enhance the transparency of your methodology.

Answer of the authors:

Non-response rate of 15% was chosen based on the experiences that we had from our previous researches.

9. The assumed standard deviation of direct medical costs ($355 USD per BC treatment) seems crucial for sample size calculation. Providing a justification or reference for this assumption would strengthen the validity of your calculations.

Answer of the authors:

The amount USD 355 per BC treatment was obtained by converting ETB 5140.10 using the exchange rate USD 1= ETB 14.50. ETB 5140 was a standard deviation of direct medical cost in Tikur Anbessa Specialized Hospital. This figure was taken from a research (HailuMariam 2013. Patient side cost and its predictors for cervical cancer in Ethiopa: a cross sectional hospital based study) which was conducted in the aforementioned hospital. Due to the lack of regional studies to estimate the costs of breast cancer therapy, a study on the costs of cervical cancer therapy was used and the costs of both therapies were assumed to be comparable.

10. It's mentioned that imputations were not performed for missing data. While this is acceptable, it would be useful to mention the extent of missing data and any sensitivity analyses conducted to assess the impact of missing data on the results.

Answer of the authors:

We did consider imputation as a method to increase sample size. We decided against it because a correlation analysis (Pearson linear correlation) yielded only low levels of correlation (all rho 0.5), which is insufficient for imputation with high confidence. We understand this to be less “harmful” to the regression analysis (basically leading to larger confidence intervals around the estimates, leaning towards a type-II error) than the imputation of, in the more extreme of cases, 58 data points concerning “Average Monthly Income”. The latter could potentially lead to type-I errors. We have now added one sentence to the end of the Statistics section to clarify this, as well as the amount of data missing in each variable.

11. Line 114 "For this study, we included all BC patients who were permanently residing in Addis Ababa and who were receiving BC treatment in PCC and the three privately owned health facilities at the time of data collection." - To improve clarity, consider rephrasing: "For this study, we included all breast cancer (BC) patients permanently residing in Addis Ababa and receiving treatment at PCC and the three privately owned health facilities at the time of data collection."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

12. Line 149 "Informed patient consent was obtained orally at the beginning of each interview and documented on the questionnaire by the data collectors." - Consider revising for clarity: "Informed consent was obtained orally from each patient at the beginning of the interview and documented on the questionnaire by the data collectors."

Answer of the authors:

Thanks for this advice. The recommended statement replaced the previous statement.

13. In Table 1 Explanation of Income Categories: Provide a brief explanation of the income categories (e.g., low-income, middle-income, high-income) to give context to readers who may not be familiar with the currency or income distribution in the study setting

Answer of the authors:

Explanation was added as per the reviewer’s comment as shown below:

“Participants in the study were grouped according to their monthly income based on the World Bank's Poverty Assessment of Ethiopia and Ethiopia's low income tax rate. Accordingly, an income of less than USD 1.25 per day was considered the poverty line (41). Monthly income greater than USD 38 but less than or equal to USD 75 is considered lower-middle income, monthly income greater than USD 75 but less than or equal to USD 150 is considered middle income, and monthly income greater than USD 150 is considered upper income.”

14. In Table 4, the sample sizes for some categories (e.g., unemployed) are very small, which could affect the reliability of estimates.

Answer of the authors:

We agree that considering a category such as “unemployed” with 2 data points has no positive contribution to the analysis. We have now grouped “unemployed” with “housewifes”. In this sense, the variable “occupation” has now a binary character of a woman being employed or not. We have re-run the multivariable regression and updated all results in table 4 and text. The regression with the new “occupation” variable leads to minimal changes in nearly all other estimates, p-values and confidence intervals, but does not have any influence on the interpretation of the results.

15. It might be beneficial to compare the costs reported in this study with national or regional averages for breast cancer treatment. This comparison could help contextualize whether the observed costs are consistent with broader trends in healthcare pricing.

Answer of the authors:

There are only few national or regional manuscripts on costs, but we cited them, e.g. in the subchapter “High absolute costs of treatment”:

“Studies conducted in the Mandura District, Western Ethiopia and in Dessie Referral Hospital, Northeast Ethiopia indicated that 22.5% and 64.2% patients were exposed to catastrophic health expenditure due to direct medical costs respectively, though the participants in those studies were not cancer patients (42, 43). A study conducted in Nigeria also reported that the mean OOP expenditure for BC diagnosis and management was 2,049 USD (44). According to a study that assessed the prices of health care in 15 African countries, including Ethiopia, patients were compelled to sell their fixed and precious assets and to borrow to cover their health care expenditures. Moreover, a high proportion of patients were not able to get health care services, because they could not afford the costs (45, 46). Other patients paid 2,325 USD in Addis Ababa for cancer treatment (47), but 600 USD in Uganda and 2100 USD in Nigeria (48).”

And

“A study conducted in Nigeria indicated that about 78% of study participants with BC were not able to get treatment because of financial barriers they faced (50) and research conducted in Iran indicated that due to the cost of cancer treatment, patients’ and their families’ living conditions were worsened (11).”

As well as under subchapter "Comparison of private vs public health care facilities"

“For instance, a study conducted at PCC, Addis Ababa, Ethiopia and Yaoundé General Hospital, Cameroon revealed that patients with BC had long waiting times between their first consultation and surgery or other treatments (51, 52).”

“Thus, we found that with increasing income, patients were more able but less willing to contribute to a higher amount of treatment costs. This result is consistent with studies conducted in Bangladesh, Sweden, and Ethiopia had found that the impact of income on WTP for medical care was negligible (56-59).”

16. Line 271; there is number 3 between ( ); it stands for what?

Answer of the authors:

Thank you, this was wrong. Even we do not know where it comes from. Thus, we deleted.

17. Line 245; the numbers order is not corrected, Please fix it?

Answer of the authors:

Correction is made as per the instruction.

Thank you, the end

Attachment

Submitted filename: Responses to Reviewer.docx

pone.0300631.s002.docx (22.4KB, docx)

Decision Letter 1

Ahmed Mancy Mosa

4 Mar 2024

Willingness and ability to pay for breast cancer treatment among patients from Addis Ababa, Ethiopia: a cross-sectional study

PONE-D-23-33162R1

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Acceptance letter

Ahmed Mancy Mosa

18 Mar 2024

PONE-D-23-33162R1

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Reviewer MA comments.docx

    pone.0300631.s001.docx (14.4KB, docx)
    Attachment

    Submitted filename: Responses to Reviewer.docx

    pone.0300631.s002.docx (22.4KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files. The minimal dataset dataset data required to replicate all study findings reported in the article can be found under doi: 10.6084/m9.figshare.25370860.v2 The python script showing all steps to reproduce data cleaning, model building, regression coefficients with 95%CI and and p-values can be found under doi: 10.6084/m9.figshare.25370950.v1.


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