Catastrophic Healthcare Expenditure and Coping Strategies among Patients Attending Cancer Treatment Services in Addis Ababa, Ethiopia

doi:10.21203/rs.2.15467/v3

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Catastrophic Healthcare Expenditure and Coping Strategies among Patients Attending Cancer Treatment Services in Addis Ababa, Ethiopia

https://doi.org/10.21203/rs.2.15467/v3

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published 22 Jun, 2020

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Background: With the rapid increase in magnitude and mortality of cancer, which is costly disease to manage, several patients particularly in developing countries are facing a huge financial burden. The aim of the study was to examine the incidence of catastrophic health expenditure (CHE), identify associated factors and coping strategies among patients attending cancer treatment services in Addis Ababa, Ethiopia.

Methods: A hospital based cross-sectional survey of patients with cancer was conducted in public and private hospitals between January and March 2018. Data was collected using a structured questionnaire. All direct medical and nonmedical expenditures were measured and reported as expenditure (US$) per patient (1US$ equivalent to 23.41 Ethiopian Birr). The CHE was estimated using a threshold of 10% of annual household income.

Results: A total of 352 (response rate of 87.1%) participants were interviewed. Majority (73.3%) of the respondents were females; most (94%) from public hospitals and their mean (±SD) age was 48±13.2 years. Breast (36.9%) and Cervical (16.5%) cancers accounted the largest proportion. Vast majority (74.4%) of patients experienced CHE with mean overall expenditure of $2366 per patient (median: $1708). Medical expenditure sharedthe highest overall expenditure (83.6%) with mean medical and nonmedical costs of $1978 (median: $1394) and $388 (median: $222), respectively. Patients who took greater than six cycles of chemotherapy (AOR: 3.64; 95% CI: 1.11-11.92), and age (AOR: 1.03; 95% CI: 1.01-1.06) were significantly associated with CHE. Household saving (85.5%) followed by financial support (43.0%) were the main coping strategies.

Conclusion: A substantial number of patients with cancer are exposed to CHE with considerable medical expenditure. Hence, while efficient mobilization of the over introduced health insurance scheme other better prepayment or insurance mechanisms should also be considered to ensure financial risk protection and realize universal health coverage for patients with cancer.

Health Economics & Outcomes Research

Health Policy

Cancer Biology

Catastrophic health expenditure

coping strategy

out-of-pocket expenditure

cancer

Out of pocket (OOP) healthcare financing leaves households exposed to the risk of unanticipated catastrophic financial expenditures that absorb a large share of the household budget [1]. Catastrophic health expenditure (CHE) is healthcare spending that exceeds some specified critical level of tolerance or threshold from the household total income in a given specified period [2-5]. According to Wagstaff and Van Doorslaer [4], the incidence of CHE is estimated from the fraction of OOP payments, which exceeds a certain threshold usually 10% of total household annual income, usually for one-year interval. It occurs when the available health service is mainly dependent on OOP payment, households have low capacity to pay, and if there is no prepayment [6]. This is highly pronounced when the diseased household member is at productive age and when the individual is the main source of household income [1].

Cancer is one of the chronic non-communicable diseases with high likelihood of imposing CHE [7]. It is a collection of diseases in which cells change, multiply and metastasize out of control in the body. Lung, cervical, prostate, stomach, colorectal, liver and breast are among the most prevalent cancer types in the world [8]. According to world health organization (WHO) global cancer report, about 14.1 million new cancer cases and 8.2 million deaths occurred in 2012 globally. The global burden of cancer is projected to continue, particularly in developing countries. It is projected to increase to 19.3 million new cancer cases per year by 2025 worldwide [9]. It imposes the most devastating economic impact where in 2010 alone about 808 million people had incurred catastrophic health spending worldwide [10]. Moreover, due to premature death and disability, about $895 billion of economic impact was debited in 2008 globally without including the direct medical cost which could further increase the economic lost [11].

Similar to any other countries especially low-middle-income countries [12], there are rapid increase in magnitude and mortality of cancer in Ethiopia. Besides, it is believed that cancer is costly to manage as a result several patients are facing a huge financial burden. With the intention of sharing financial risks and improving access to essential services, the government of Ethiopia had developed a strategic plan for establishment and expansion of mandatory health insurance [13]. Cancer is among the services covered by the insurance scheme but its coverage remains very low and in pilot phase [14]. As a result, majority of the population are still dependent on OOP payment for healthcare service. On the other hand, there is limited evidence about the level of CHE and OOP healthcare expenditure for the diagnosis and treatment of cancer in Ethiopia [15, 16]. Therefore, the aim of the study was to examine the incidence of CHE, identify associated factors and coping strategies among patients attending cancer treatment services in Addis Ababa, Ethiopia.

Study area and study design

A hospital based cross-sectional study design was employed. The study was conducted in Addis Ababa, the capital city of Ethiopia (the second most populous country in Africa) and headquarters of African Union. Addis Ababa has 13 public hospitals, 32 private hospitals and 93 health centers. About 150-200 new cases of cancer are registered monthly which is compiled from ten cancer diagnostic and treatment service providing hospitals. About 1200 cancer patients visit these health facilities either for follow up or treatment monthly. From the public hospitals, Tikur Anbessa Specialized Hospital (TASH) is the largest teaching and referral public hospital with the highest number of cases. The study was conducted in TASH and three private hospitals (Hallelujah, Bete-Zata and Leghar General Hospitals) between January and March 2018.

Study population and sampling procedure

The source population was all cancer patients attending treatment service in Addis Ababa healthcare facilities. Patients with cancer confirmed on histological and/or pathological exam, patients who took at least one of the oncologic treatment options (either chemotherapy, radiotherapy or hormonal/others), had regular follow up for the last one year, and who are volunteer to participate and have no co-morbid conditions were the eligible study participants.

Sample size was determined using single proportion population formula [17] and a total of 404 participants were recruited from one government hospital (TASH) and three private hospitals. TASH was included as it is the sole oncology referral and radiotherapy center in the entire country and it has also the highest number of cancer patients. Private hospitals were also selected based on their voluntariness to be enrolled and patient load. Participants were recruited using a convenience sampling technique and the number of participants to be drawn from each hospital was decided based on proportion to patient load.

Data collection and management

A structured questionnaire (S1 Table 1 Data collection instrument) was developed from the WHO SAGE (Study of Global Ageing and Adult Health) study [18] and other relevant literatures [15, 19, 20]. This English version of the questionnaire was translated into Amharic (national language) and back translated to English to ensure its consistency.The questionnaire was pretested before the commencement of the data collection process. Appropriate modification and validation process were made according the pretest result of the questionnaire.

The data collection tool was consisted of six parts: (1) socio-demographic characteristics (age. gender, occupation, marital status, education level and household size); (2) medical information (type of cancer, time of first diagnosis, treatment initiated, type of treatment taken and private health facility visit history); (3 and 4) Outpatient department (OPD) and inpatient department (IPD) service expenditures including (consultation, investigation, medicine and other relevant costs); (5) household essential consumption and income (weekly food and others spending, monthly house rent, cloths, transport and other cost, annual education payment, durable materials e.g., television, phone, furniture, vehicles, ceremonies and others spending, overall household annual expenditure and income and patient income if available); and (6) households financial situation outlook (rate of financial burden, coping mechanisms taken and its amount). In addition, a total of 352 patient medical charts were reviewed to collect clinical information of patients, investigations sought and treatments taken which also helped in estimating patient treatment/diagnostic expenditure.

Data was collected in a face-to-face exit interview and daily supervision was made by the principal investigator to ensure its completeness and consistency.

Measurements

Magnitude of CHE was estimated using Wagstaff and Van Doorslaer [4] approach and we considered catastrophic when previous one year patient households OOP expenditure for cancer care exceeded 10% of total annual household income.The overall, outpatient and inpatient cancer diagnostic and treatment service expenditures of the last twelve months were estimated and presented as average expenditure per patient. Therefore, these households incurred a catastrophic expenditure assumed as catastrophic payment head counts (H_cat).

Healthcare service OOP expenditure is direct payment made to healthcare providers on receiving a service excluding prepayment, reimbursement, and other sources of payment mechanisms [4, 5, 21]. However, households can use different mechanisms of coping strategies to solve their financial burden. Coping mechanisms were assessed by asking study participants to explain any strategy used to cover their treatment/diagnostic cost during their treatment course. This includes any means of income obtained from household member and other savings including Eqqub and Iddir; any financial support from relatives, religious organization and other sources which are nonrefundable; any income obtained from selling household assets like land, property, livestock, jewellery and other household items; and any types of borrowing from financial institutions taken as a debt including from individuals. This was later rearranged as; savings, financial support, selling assets and borrowings [7, 20]. Households had used different coping strategies and classified into two as expenditure covered by themselves and by other mechanisms to figure out the overall expense covered by the household themselves only and to estimate the CHE level of cancer care.

To see households’ subjective rate of self-reported financial burden, respondents were asked to rate their current household economic situation outlook due to cancer care imposed financial burden up on the household compared to the past; as very good, good, medium/similar, bad and very bad. Later, it was reclassified as “manageable” for very good, good and medium/similar and “unmanageable” for bad and very bad.

Expenditure was estimated as all expenditures spent for cancer care prior to the interview time in the last year of medical service upon possible probing approaches to minimize recall bias. This expenditure includes both medical and nonmedical expenditures. Medical expenditures were all expenditures related to consultation, investigation, medicine, bed and traditional medicine expenditure. On the other hand, nonmedical expenditure includes transportation, food and other accommodation expenditures associated with the disease care. Expenditures were also categorized as outpatient expenditures (consultation, investigation, medicine, and other spending associated with outpatient care), and inpatient expenditures (consultation, investigation, medicine, bed and other expenditure associated with inpatient care). There are controversies on estimating cost data especially when the data have a skewed nature. Some studies reported mean is a reasonable choice although it could be affected by skewed distribution of cost [22, 23]. Others studies, however, preferred to report using median since it is not affected by skewness although it only shows the position of the distribution [24]. Hence, we used both mean and median measurements to provide a full picture of the estimation which might help in the ease health policy decision making and resource allocation.

Household income and expenditure were measured based on respondents’ self-reported daily or monthly income and expenditure, respectively. This was then converted to annual household income and expenditure. Participants with in-kind incomes were approached for their type of income and amount. This was changed to monetary value based on the time of its exchange when it is supplied to the local market. Participants were also informed to include any type of income. All expenditures were measured and values are reported as expenditure (US$) per patient (1US$ equivalent to 23.41 Ethiopian Birr).

Covariates

Various covariates were included in this analysis. Socio-demographic, clinical and economic variables including: type of health facility (private and public); type of cancer (breast cancer, colorectal cancer, cervical cancer, Nasopharyngeal cancer (NPC) and other); cycles of chemotherapy taken (less than or equal to three cycles, four to six cycles, greater than six cycles and on other treatment options); private health facility history (had history of visit and had not history of visit); gender (male and female); marital status (single, divorced, married and widowed); residence (out of Addis Ababa and in Addis Ababa); level of education (have not attended formal education, grade 1-8, grade 9-12, and college and above); occupation (employed at governmental or private institution, own private business, housewife/husband, retired and other); income quintile (lowest, second, middle, fourth and highest); and expenditure quintile (lowest, second, middle, fourth and highest) were the variables considered in the analysis.

Statistical analysis

Data was coded, entered in to and analyzed using STATA version 14 (https://www.stata.com/stata14/). Descriptive statistics such as mean, median, standard deviation (SD) and Inter-Quartile Range (IQR) were used to compute socio-demographic, clinical and economic characteristics. Hosmer-Lemeshow test was employed for the goodness of fit test of the logistic regression model. Multivariable logistic regression model was used to assess the relationship of CHE and potential explanatory variables. The significance level was set at 95% confidence interval (CI) of crude odds ratio (COR) and adjusted odds ratio (AOR).

Socio-demographic and clinical information

A total of 404 participants were approached and 352 study participants were interviewed, with a response rate of 87.1%. Majority (73.3%) of the patients were females and 285 (81.0%) of them were married. Most (94%) of the respondents were from public hospitals (Table 1). More than three-fourth (85.2%) of the study participants were in the productive age category and their mean (±SD) age was 48 (±13.2) years (ranged 19 to 87 years).

More than one-third (36.9%) of the participants were breast cancer patients followed by cervical cancer 58 (16.5%) and colorectal cancer 46 (13.1%). Majority (89.0%) of patients have taken chemotherapy on their treatment course and 155 (44.0%) of them were on 4-6 cycles of chemotherapy. All patients have used supportive treatments. Majority (63.4%) of respondents had history of visit at private health facilities during the course of the disease (Table 2).

Overall, outpatient and inpatient services expenditure

The average overall expenditure per patient in the last one year was estimated to be $2366 (SD: $4262), median: $1709 (IQR: $1153-2424). The inpatient services accounted two-third of the total expenditure with mean cost of $1584 (SD: $4002), median: $1067 (IQR: $641-1580) per patient. The remaining expenditure was spent for outpatient service with mean of $782 (SD: $1468), median: $557 (IQR: $256-940).

The overall mean expenditure of patients with colorectal cancer who attended at private hospitals, who had history of visit at private health facilities, and who were living out of Addis Ababa was higher compared to their counterparts (Table 3).

Medical and non-medical expenditure

The direct mean and median medical expenditures per patient in the last year was estimated to be $1978 (SD: $3554) and $1394 (IQR: $917-1982), respectively. On the other hand, the patients had a mean and median non-medical expenditure of $388 (SD: $993) and $222 (IQR: $122-461), respectively. The medical expenditure constituted the highest expenditure (83.6%), taking the large share of the overall expenditure. The mean expenditure on traditional medicines was also estimated to be $7 (SD: $65).

Magnitude of catastrophic healthcare expenditure

The average headcount adult equivalent income and expenditure was found to be $1821 (median: $1220) and $1111 (median: $949) per year, respectively. Moreover, the mean annual unadjusted household income and expenditure was estimated to be $4997 (median: $3076) and $3006 (median: $2563) per year, respectively. The incidence of incurring CHE was found to be 74.4% (95%, CI: 69.6-78.7). Patients in the fourth income quintile had faced higher CHE. Regarding the expenditure quintile, patients in the middle quintile had encountered higher catastrophic cancer care expenditure (Table 4).

Based on participants self-report, it was estimated that 69% (95% CI: 64.0-73.7) of patients’ household were faced with unmanageable financial burden which could lead them to a financial crisis. In counterpart, 31% (95% CI: 26.3-36.0) of patients’ household faced with a manageable financial burden.

Factors associated with catastrophic health expenditure

The multivariable logistic regression analysis revealed that taking greater than six cycles of chemotherapy (AOR: 3.64; 95% CI: 1.11-11.92), and age (AOR: 1.03; 95% CI: 1.01-1.06) were factors statistically associated with CHE of cancer diagnosis and treatment. The tendency of encountering CHE of cancer care was associated with older age and increased cycles of chemotherapy (Table 4).

Financial burden coping strategies of cancer diagnosis and treatment

Majority of households had used their own household savings (85.5%) for healthcare payment. However, a considerable percentage (43%) of households have received a substantial financial support from their relatives, religious and other non-governmental organizations to cover the financial burden imposed to the household. The remaining expenditure was covered by selling assets and borrowings as illustrated in Table 5.

This study is the first to assess the incidence of CHE, associated factors and its coping strategies for cancer care in Ethiopia. The average overall cancer care expenditure was half of the average unadjusted household annual income and nearly three-fourth of the average unadjusted household annual expenditure which is higher as compared to Australian patients [25]. This was however; lower than the findings of other studies in which patients spent 59.9% of household annual income [19]. The overall expenditure was very high as compared to the per capital income of the population of Ethiopia [26] and this could be an indicative of the fact that uncountable number of patients are at home and are dying without getting any treatment and diagnostic services because of the catastrophic expenditure.

Similar to a study reported by Pourreza et al [27], the present study have showed the payment for colorectal cancer was higher compared to other cancer types. This might be due to the extended duration of treatment and multiple combination regimens used which could result in higher OOP expenditure [28].

As our study finding, the level of CHE was found to be very high as compared to the study reported from Korean patients with cancer (39.8%) [29]. In addition, although direct comparison seems infeasible because of the different threshold level of CHE used, our finding was higher as compared to other studies reported from Asian countries (47.8%-67.9%) [30-32]. But, it was lower compared to Indian breast cancer patients (84%) [33]. This high level of CHE might be due to the limited number of treatment/diagnostic centers available which can increase its side expenditures and frequent stock out of prescribed medicines. A higher level of CHE of cancer care was also found when compared to cardiovascular patients in Ethiopia (27.0%) [20]. Similarly, this was higher compared to a study conducted in households of persons with depression and high disability (24.0%) [34], and severe mental disorder (20.3%) [35] in rural Ethiopia with the threshold set at 10% household consumption expenditure. Limited geographic access to cancer care centers, and higher cost of medicines might the reasons for such difference. The government is providing all oncology medicines in a fifty percent subsidized price but still there is an imbalance in the demand and supply of medicines [36]. As a result, patients have no choice but to buy from the private retail outlets which might result patients’ household to face to other intolerable financial burden.

The level of CHE also showed variation among income quintiles although it was not statistically significant. Other studies revealed that the lowest income category was highly associated with catastrophic expenditure [32, 37, 38]. However, in the present study the fourth income strata were highly faced with CHE than the others. This could be due to patients with lower income status might not visit and purchase for expensive services not available with affordable price although the disease condition gets complicated but these fourth income quintiles might pay while facing catastrophic expenditure.

The level of CHE and self-reported financial pressure was simultaneously high and approximately parallel. This self-reported unmanageable rate of financial burden was lower compared to what was documented in China (75%) [19]. But, this was higher as compared to the finding of United States which showed that an overall financial distress level of 47% [39]. The presence of financial sharing policy and difference in income status of the population would influence the level of financial distress across countries, indicating the need to institute risk pooling and sharing mechanisms.

Similar to other studies, patient’ households use more than one coping strategies to overcome their financial distress with the most prevalent being borrowing, selling assets, spending savings and financial aid [43]. Among those, household saving was the main coping strategy used in the study which was similar to other studies [7, 20, 44]. However, some other studies indicated that borrowing and selling assets were the major coping strategies [27, 44-46]. This could be due to the varied economic income status and saving culture of households which can affect the coping mechanism of patients’ family [47, 48].

Although the study comes with those results it was not without limitations. First, due to the nature of data collection technique, recall bias could be a problem. Obtaining reliable information on the household annual income and expenditure was also another challenge. Since the study design was hospital based in which significant number of populations could not access because of affordability and limited availability; most patients’ households were more likely with relatively higher income status.

Based on a 10% threshold of annual household income, a substantial number of patients with cancer are exposed to a catastrophic level of healthcare expenditure with considerable medical cost. Predominantly, patients with increased cycles of chemotherapy and older age were incurring a catastrophic level of healthcare expenditure. Household saving was the main coping strategy however; significant number of patients’ household had enforced to look for other coping mechanisms. Hence, efficient mobilization of the health insurance scheme is urgently needed to ensure financial risk protection and realize universal health coverage for patients with cancer. Increased tax funding and/or other better prepayment mechanisms should also be considered while mobilizing the over introduced health insurance scheme.

Ethical approval and consent to participate

The study was approved by the Institutional Ethics Review Committee of School of Pharmacy, Addis Ababa University (Ref: ERB/SOP/01/10/2018) and permission was sought from all study hospitals. Written consent form was provided before the beginning of the interview and their consent was assured before proceeding to the interview. Questionnaires were coded to prevent any identification so as to ensure participants anonymity and questionnaires were stored in lockable cabinet except for the study team to ensure participants confidentiality.

Consent to publish

Not applicable.

Availability of data and materials

The dataset should be accessible from the corresponding author on reasonable request.

Competinginterest

The authors declare that they have no competing interest.

Funding

The study was funded by Addis Ababa University Graduate program. The funder had no role in thestudy design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ contribution

GGK, GBG and TGF conceptualized and designed the study. GGK made substantial contribution in the data collection, data analysis, data interpretation and writing of the manuscript. GBG, TGF, YHG and AAW contributed in the data analysis, data interpretation and evaluation of the manuscript. All authors read and approve the final version of the manuscript.

Acknowledgements

We would like to thank the data collectors (Nurses), Pharmacists and Physicians for their humbled support and involvement during the data collection process. In addition, we would like to extend our special gratitude to the study participants for their volunteered participation that supported for the easily realization of the work.

Authors’ information

¹Department of Pharmacy, College of Health Science, Aksum University, Aksum, Tigray, Ethiopia. ²Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Science, Addis Ababa University, Ethiopia. ³School of Public Health, College of Health Science, Addis Ababa University, Ethiopia.⁴School of Medicine, College of Health Science, Addis Ababa University, Ethiopia.

AOR: Adjusted Odds Ratio; CHE: Catastrophic Health Expenditure; COR: Crude Odds Ratio; Inter-Quartile Range: IQR; OOP: Out of Pocket; SD: Standard Deviation; TASH: Tikur Anbessa Specialized Hospital; WHO: World Health Organization

Buigut S, Ettarh R, Amendah DD. Catastrophic health expenditure and its determinants in Kenya slum communities. International journal for equity in health. 2015;14:1-12.
Abul Naga R, Lamiraud K. Catastrophic health expenditure and household well-being. 2011;1-20.
Puteh S, Almualm Y. Catastrophic health expenditure among developing countries. Health Syst Policy Res. 2017;4:1-5.
Wagstaffa A, van Doorslaerc E. Catastrophe and Impoverishment in Paying for Health Care: With Applications to Vietnam 1993-98. Health Econ. 2003;12:921-34.
Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. The Lancet. 2003;362:111-7.
O’Donnell O, van Doorslaer E, Rannan-Eliya RP, Somanathan A, Garg CC, Hanvoravongchai P, et al. Explaining the incidence of catastrophic expenditures on health care: Comparative evidence from Asia. Equitap. 2005;1-28.
Engelgau MM, Karan A, Mahal A. The economic impact of non-communicable diseases on households in India. Globalization and health. 2012;8:1-10.
DeSantis C, Naishadham D, Jemal A. Cancer statistics for African Americans, 2013. CA: a cancer journal for clinicians. 2013;63:151-66.
The World Health Report: Research for Universal Health Coverage. International Agency for Research on Cancer. World Health Organization. 2013;1-146.
Wagstaff A, Flores G, Hsu J, Smitz MF, Chepynoga K, Buisman LR, et al. Progress on catastrophic health spending in 133 countries: a retrospective observational study. The Lancet Global Health. 2018;6:1-11.
John R, Ross H. The global economic cost of cancer. Atlanta, GA: American Cancer Society and Livestrong. 2010;1-10.
Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trendsan update. Cancer Epidemiology and Prevention Biomarkers. 2016;25:16-27.
Health insurance strategy, planning and programming department. Addis Ababa, Ethiopia. 2008;1-17.
Ethiopia’s Community-based Health Insurance: A Step on the Road to Universal Health Coverage. 2015;2-11.
Hailu A, Mariam DH. Patient side cost and its predictors for cervical cancer in Ethiopia: a cross sectional hospital based study. BMC cancer. 2013;13:1-8.
Mamo G, Worku A, Lemma S, Demas T. Cost of Illness of Breast Cancer Patients on Chemotherapy in Addis Ababa Public Hospitals, the Case of Tikur Anbessa Specialized Teaching Hospital-Cross-Sectional Types of Study. Health Economics and Outcome Research Open Access. 2017;3:1-5.
Daniel WW. Biostatistics: a foundation for analysis in the health sciences. John Wiley and Sons. Inc, New York, NY. 1995. p. 192-3.
World Health Organization. SAGE Survey Manual: The WHO Study on Global AGEing
and Adult Health (SAGE). Geneva, Switzerland: World Health Organization. 2006. p. 1-226.
Huang HY, Shi JF, Guo LW, Bai YN, Liao XZ, Liu GX, et al. Expenditure and financial burden for the diagnosis and treatment of colorectal cancer in China: a hospital-based, multicenter, cross-sectional survey. Chinese journal of cancer. 2017;36:1-15.
Tolla MT, Norheim OF, Verguet S, Bekele A, Amenu K, Abdisa SG, et al. Out-of-pocket expenditures for prevention and treatment of cardiovascular disease in general and specialised cardiac hospitals in Addis Ababa, Ethiopia: a cross-sectional cohort study. BMJ global health. 2017;2:1-10.
Xu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health affairs. 2007;26:972-83.
Barber JA, Thompson SG. Analysis of cost data in randomized trials: an application of the non‐parametric bootstrap. Statistics in medicine. 2000;19:3219-36.
White IR, Thompson SG. Choice of test for comparing two groups, with particular application to skewed outcomes. Statistics in Medicine. 2003;22:1205-15.
Manikandan S. Measures of central tendency: Median and mode. Journal of pharmacology and pharmacotherapeutics. 2011;2:214-5.
Newton JC, Johnson CE, Hohnen H, Bulsara M, Ives A, McKiernan S, et al. Out-of-pocket expenses experienced by rural Western Australians diagnosed with cancer. Supportive Care in Cancer. 2018;26:3543-52.
World Bank. Ethiopia Public Expenditure Review 2015. Washington, DC: World Bank Group. 2016.
Pourreza A, Harirchi I, Bazyar M. Differentiation of out-of-pocket expenditures in cancer patients; a case study in the cancer institute of Iran. Evidence Based Health Policy, Management and Economics. 2017;1:65-73.
Stintzing S. Management of colorectal cancer. F1000prime reports. 2014;6:1-12.
Choi JW, Cho KH, Choi Y, Han KT, Kwon JA, Park EC. Changes in economic status of households associated with catastrophic health expenditures for cancer in South Korea. Asian Pac J Cancer Prev, 2014;15:2713-7.
Azzani M, Yahya A, Roslani AC, Su TT. Catastrophic Health Expenditure Among Colorectal Cancer Patients and Families: A Case of Malaysia. Asia Pacific Journal of Public Health. 2017;29:485-94.
Delavari H, Keshtkaran A, Setoudehzadeh F. Catastrophic health expenditures and coping strategies in households with cancer patients in Shiraz Namazi hospital. Middle East Journal of Cancer. 2014;5:13-22.
Group AS. Catastrophic health expenditure and 12-month mortality associated with cancer in Southeast Asia: results from a longitudinal study in eight countries. BMC medicine. 2015;13:1-13.
Jain M, Mukherjee K. Economic burden of breast cancer to the households in Punjab, India. International Journal of Medicine and Public Health. 2016;6:13-8.
Hailemichael Y, Hanlon C, Tirfessa K, Docrat S, Alem A, Medhin G, et al.Catastrophic health expenditure and impoverishment in households of persons with depression: a cross-sectional, comparative study in rural Ethiopia. BMC public health. 2019;19:1-13.
Hailemichael Y, Hailemariam D, Tirfessa K, Docrat S, Alem A, Medhin G,et al. Catastrophic out-of-pocket payments for households of people with severe mental disorder: a comparative study in rural Ethiopia. International journal of mental health systems. 2019;13:1-13.
Tikur Anbessa Specialized Hospital. Oncology Pharmacy. Addis Ababa, Ethiopia. (Accessed on May 12, 2018).
Bernard DS, Farr SL, Fang Z. National estimates of out-of-pocket health care expenditure burdens among nonelderly adults with cancer: 2001 to 2008. Journal of Clinical Oncology. 2011;29:2821-6.
Tripathy J, Prasad B, Shewade H, Kumar A, Zachariah R, Chadha S, et al. Cost of hospitalisation for non‐communicable diseases in India: are we pro‐poor? Tropical medicine &international health. 2016;21:1019-28.
Meisenberg BR, Varner A, Ellis E, Ebner S, Moxley J, Siegrist E, et al. Patient attitudes regarding the cost of illness in cancer care. The oncologist. 2015;20:1199-204.
Blumen H, Fitch K, Polkus V. Comparison of treatment costs for breast cancer, by tumor stage and type of service. American health & drug benefits. 2016;9:23-32.
Kakushadze Z, Raghubanshi R, Yu W. Estimating cost savings from early cancer diagnosis. Data. 2017;2:1-16.
Kutikova L, Bowman L, Chang S, Long SR, Obasaju C, Crown WH. The economic burden of lung cancer and the associated costs of treatment failure in the United States. Lung Cancer. 2005;50:143-54.
Chakrabarty J, Pai MS, Ranjith V, Fernandes D. Economic burden of cancer in India. Indian Journal of Public Health. 2017;137-41.
Bogale T, Mariam DH, Ali A. Costs of illness and coping strategies in a coffee-growing rural district of Ethiopia. Journal of Health, Population and Nutrition. 2005:192-9.
Kruk ME, Goldmann E, Galea S. Borrowing and selling to pay for health care in low-and middle-income countries. Health Affairs. 2009;28:1056-66.
Leive A, Xu K. Coping with out-of-pocket health payments: empirical evidence from 15 African countries. Bulletin of the World Health Organization. 2008;86:849-56C.
Mirach TH, Hailu YM. Determinants of Household Saving In Ethiopia: A Case of North Gondar Zone Amhara Regional State. International Journal of Development and Economic Sustainability. 2014;2:37-49.
Zwane T, Greyling L, Maleka M. The determinants of household saving in South Africa: a panel data approach. Int Bus Econ Res J. 2016;15:209-18.

Table 1 Socio-demographic and economic characteristics among patients attending cancer treatment services in Addis Ababa, Ethiopia 2018.

Characteristics	Frequency (N)	Percentage (%)
Type of health facility
Public Private	331 21	94.0 6.0
Gender
Female Male	258 94	73.3 26.7
Age (mean, ±SD)	48ⁱ	13.2ⁱⁱ
Marital status
Married Single Divorced Widowed	285 34 19 14	81.0 9.6 5.4 4.0
Residence
Out of Addis Ababa Addis Ababa	209 143	59.4 40.6
Education
No formal education^a College/certificate and above Grade 9-12 Grade 1-8	127 122 69 34	36.1 34.6 19.6 9.7
Occupation
Housewife/Husband Employed (private/government) Own private business Retired Others^b	139 102 49 36 26	39.5 29.0 13.9 10.2 7.4
Household economic income quintile
Lowest	74	21.0
Second	74	21.0
Middle	64	18.2
Fourth	71	20.2
Highest	69	19.6
Household economic expenditure quintile
Lowest	71	20.2
Second	70	19.9
Middle	72	20.5
Fourth	85	24.1
Highest	54	15.3

ⁱ: age (Mean),

ⁱⁱ: age (SD=Standard deviation),

^a: include illiterates and individuals able to read and write due to informal education,

^b: include individuals with no work/stopped to work.

Table 2 Clinical information of patients attending cancer treatment services in Addis Ababa, Ethiopia 2018.

Clinical variables	N	% (95% CI)
Type of cancer
Breast cancer Cervical cancer Colorectal cancer NPC^a Others^b	130 58 46 13 105	36.9 (32.0-42.1) 16.5 (13.0-20.7) 13.1 (9.9-17.0) 3.7 (2.2-6.3) 29.8 (25.3-35.0)
Type of treatments taken^c
Supportive treatment Chemotherapy Surgery Radiotherapy Hormonal	352 313 201 176 80	100.0 89.0 (85.2-91.8) 57.1 (51.8-62.2) 50.0 (44.8-55.2) 22.7 (18.6-27.4)
Cycle of treatment taken
4-6 cycles 1-3 cycles >6 cycles On other treatment options	155 97 61 39	44.0 (38.9-49.3) 27.6 (23.1-32.5) 17.3 (13.7-21.7) 11.1 (8.2-14.8)
Private health facility visit history
Yes No	223 129	63.4 (58.2-68.2) 36.6 (31.8-41.8)

^a: NPC = Nasopharyngeal cancer,

^b: Other cancer types including (skin, oral, lung, liver, esophageal, bone, Hodgkin lymphoma, non-Hodgkin lymphoma, thyroid and other unspecified but pathologically and clinically confirmed tumors).

^C: Frequencies and percentage will not be added up because multiple responses were possible.

Table 3 Mean overall expenditure per patient by different subgroups among patients attending cancer treatment services in Addis Ababa, Ethiopia 2018.

Subgroup variables

Mean overall expenditure per patient (SD) ($)

By type of cancer

Breast cancer

Cervical cancer

Colorectal cancer

NPC

Others

2325 (2776)

1308 (722)

2931 (3287)

1652 (486)

2842 (6751)

By type of health facility

Private

Government

5341 (10814)

2177 (3410)

By history of visit at private health facility

Visited

Not visited

2870 (5256)

1494 (822)

Have no history of visit at private health facility by type of cancer

Breast cancer

Cervical cancer

Colorectal cancer

NPC

Others

1658 (923)

1074 (581)

2144 (806)

---

1446 (653)

Have a history of visit at private health facility by type of cancer

Breast cancer

Cervical cancer

Colorectal cancer

NPC

Others

2799 (3479)

1616 (783)

3150 (3673)

1652 (486)

3454 (8035)

By their catastrophic level

Not catastrophic

Catastrophic

1990 (2324)

2495 (4745)

By gender

Male

Female

2704 (5293)

2243 (3822)

By marital status

Single

Divorced

Married

Widowed

3751 (8255)

2598 (1873)

2244 (3736)

1174 (761)

By residence

Addis Ababa

Out of Addis Ababa

2223 (4310)

2464 (4236)

By educational level

No formal education

Grade 1-8

Grade 9-12

College/certificate and above

1674 (913)

1685 (771)

2306 (2986)

3310 (6716)

By occupational status

Retired

Employed (private/government)

Own private business

Housewife/Husband

Others

3229 (8288)

2455 (3024)

2396 (2038)

2164 (4488)

1842 (1022)

---: There are no cases of NPC with history of visit at private health facility; SD: Standard deviation; 1 US$ =23.41 Ethiopian Birr.

Table 4 Factors associated with CHE and proportion of CHE among subgroups of patients attending cancer treatment services in Addis Ababa, Ethiopia 2018.

Variables

Proportion of CHE

N, % (95% CI)

COR (95% CI)

AOR (95% CI)

Type of Health Facility

Private

Public^*

16, 76.2 (53.3-89.9)

246, 74.3 (69.3-78.7)

1.11 (0.39-3.11)

1.00

1.29 (0.38-4.40)

1.00

Type of cancer

Breast cancer

Colorectal cancer

NPC

Others

Cervical cancer^*

94, 72.3 (63.9-79.4)

32, 69.6 (54.7-81.2)

11, 84.6 (53.3-96.3)

84, 80.0 (71.2-86.6)

41, 70.7 (57.6-81.0)

1.10 (0.55-2.14)

0.95 (0.41-2.21)

2.28 (0.46-11.40)

1.66 (0.79-3.48)

1.00

0.72 (0.31-1.66)

0.68 (0.22-2.10)

2.92 (0.41-20.7)

1.33 (0.52-3.43)

1.00

Cycles of chemotherapy taken

≤ 3 cycles

4-6 cycles

> 6 cycles

On other treatment options^*

71, 73.2 (63.4-81.1)

113, 72.9 (65.3-79.3)

51, 83.6 (72.0-91.0)

27, 69.2 (53.0-81.8)

1.21 (0.54-2.74)

1.20 (0.55-2.57)

2.27 (0.87-5.92)

1.00

1.25 (0.47-3.35)

1.31 (0.52-3.43)

3.64 (1.11-11.92)

1.00

Private health facility visit

Yes^*

97, 75.2 (67.0-82.0)

165, 74.0 (67.8-79.3)

1.06 (0.65-1.75)

1.00

1.19 (0.66-2.13)

1.00

Age

---

1.01 (0.99-1.03)

1.03 (1.01-1.06)

Household size

---

0.95 (0.87-1.04)

0.95 (0.85-1.07)

Gender

Male

Female^*

72, 76.6 (66.9-84.1)

190, 73.6 (67.9-78.7)

1.17 (0.67-2.03)

1.00

1.01 (0.41-2.46)

1.00

Marital status

Single

Divorced

Widowed

Married^*

26, 76.5 (59.2-87.9)

14, 73.7 (49.4-88.9)

11, 78.6 (49.2-93.2)

211, 74.0 (68.6-78.8)

1.14 (0.50-2.63)

0.98 (0.34-2.82)

1.29 (0.35-4.74)

1.00

1.91 (0.60-6.13)

1.20 (0.34-4.20)

1.48 (0.31-6.96)

1.00

Residence

Out of Addis Ababa

In Addis Ababa^*

158, 75.6 (69.3-81.0)

104, 72.7 (64.8-79.4)

1.16 (0.72-1.89)

1.00

1.07 (0.61-1.86)

1.00

Level of Education

No formal education

Grade 1-8

Grade 9-12

College and above^*

93, 73.2 (64.8-80.2)

28, 82.4 (65.5-92.0)

50, 72.5 (60.7-81.8)

91, 74.6 (66.1-81.6)

0.93 (0.53-1.64)

1.59 (0.60-4.20)

0.89 (0.46-1.75)

1.00

0.47 (0.18-1.26)

0.99 (0.30-3.26)

0.59 (0.25-1.42)

1.00

Occupation

Own private business

Housewife/Husband

Retired

Others

Employed private/government^*

39, 79.6 (65.8-88.7)

107, 77.0 (69.2-83.3)

22, 61.1 (44.5-75.6)

19, 73.1 (52.8-86.8)

75, 73.5 (64.1-81.2)

1.40 (0.62-3.20)

1.21 (0.67-2.17)

0.56 (0.25-1.26)

0.98 (0.37-2.58)

1.00

2.21 (0.79-6.17)

2.30 (0.87-6.01)

0.48 (0.17-1.33)

1.17 (0.30-4.55)

1.00

Income quintile

Lowest

Second

Middle

Fourth

Highest^*

49, 66.2 (54.6-76.1)

58, 78.4 (67.4-86.4)

50, 78.1 (66.2-86.7)

60, 84.5 (74.0-91.3)

45, 65.2 (53.2-75.6)

1.04 (0.52-2.09)

1.93 (0.92-4.06)

1.91 (0.88-4.12)

2.91 (1.29-6.55)

1.00

0.85 (0.18-3.86)

1.30 (0.38-4.38)

1.21 (0.41-3.57)

2.66 (0.97-7.29)

1.00

Expenditure quintile

Lowest

Second

Middle

Fourth

Highest^*

48, 67.6 (55.8-77.5)

51, 72.8 (61.2-82.1)

62, 86.1 (75.9-92.4)

67, 78.8 (68.7-86.3)

34, 63.0 (49.3-74.8)

1.27 (0.58-2.58)

1.58 (0.74-3.38)

3.65 (1.53-8.68)

2.19 (1.02-4.67)

1.00

1.49 (0.30-7.34)

1.52 (0.42-5.50)

3.04 (0.91-10.17)

1.49 (0.52-4.33)

1.00

^*: reference group; Bold: significant association at COR and AOR 95% CI; AOR: Adjusted odds ratio; COR: Crude odds ratio

Table 5 Coping strategies used by patients’ household for the financial constraints of cancer care in Addis Ababa, Ethiopia 2018.

Coping strategies	N	% (95 % CI)
Savings^*	301	85.5 (81.4-88.8)
Financial support^**	151	43.0 (38.0-48.1)
Selling assets ^***	42	12.0 (8.9-15.8)
Borrowings^****	30	8.5 (6.0-12.0)

^*: It includes any method of household savings and sources from the household member (including Eqqub and Iddir).

^**: Financial source from relatives, NGOs, religious organization and others source of payments which are nonrefundable.

^***: Any means of payment made by selling any household assets like land, property, livestock, jewellery and other household items

^****: Any types of borrowing from financial institutions taken as a debt including from individuals

NB: Frequencies and percentage would not be added up because multiple responses were possible.

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published 22 Jun, 2020

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Editorial decision: Major revision
03 Jun, 2020
Review #1 received at journal
02 Jun, 2020
Reviewers invited by journal
20 May, 2020
Reviewer #1 agreed at journal
20 May, 2020
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18 May, 2020
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You are reading this latest preprint version

Catastrophic Healthcare Expenditure and Coping Strategies among Patients Attending Cancer Treatment Services in Addis Ababa, Ethiopia

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Abstract

Background

Methods

Measurements

Results

Socio-demographic and clinical information

Medical and non-medical expenditure

Magnitude of catastrophic healthcare expenditure

Discussion

Conclusion

Declarations

Availability of data and materials

Abbreviations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 3