Magnitude of catastrophic health expenditure and its determinants among cancer patients in low and middle-income countries: a systematic review and meta-analysis

Abstract

Background

The World Health Organization promotes Universal Health Coverage, which emphasizes providing healthcare services without a financial burden. However, the majority of cancer patients in resource-constrained countries were exposed to catastrophic healthcare Expenditure (CHE) due to costly cancer care. Even though there are many primary studies in low and middle-income countries (LMICs), there is a need for aggregated information on the magnitude of CHE among cancer patients. Therefore, this study aimed to estimate the pooled magnitude of CHE and its determinants among cancer patients in low and middle-income countries.

Methods

A comprehensive search was conducted on electronic databases such as PubMed/Medline, Web of Science, Scopus, Science Direct, African Journals Online, and Google Scholar for articles published from 2010 to June 30, 2025. Data were extracted using Microsoft Excel and exported to STATA version 17 software for analysis. The study quality appraisal was performed using the Joanna Briggs Institute critical appraisal tool. The potential heterogeneity of included studies was checked using Cochrane’s Q test and I-squared statistic. The publication bias of the study was checked using visual inspection of the funnel plot. In addition, subgroup and sensitivity analyses were also conducted. To determine the pooled magnitude of CHE and its determinants among cancer patients in LMICs, a random-effect model was used.

Result

In this review, a total of 38 articles, with 50,968 participants, were included. The pooled magnitude of CHE among cancer patients in LMICs was 58.42% (95%CI: 52.29%, 64.55%). Older age (AOR = 1.44, [95% CI: 1.17, 1.76]), larger family size (AOR = 2.43, [95% CI: 1.01, 5.91]), low educational level (AOR = 4.18, [95% CI: 2.10, 8.30]), female (AOR = 2.64, [95% CI: 1.28, 5.45]), rural residence (AOR:3.56, [95% CI:2.87, 4.40]), unemployed cancer patients (AOR = 2.19, [95% CI: 1.26, 3.82]), poor wealth index (AOR = 5.18, [95%CI: 3.61, 7.44]), visiting private health facilities (AOR = 8.97, 95% CI: 1.65, 48.85]), distance to health facilities (AOR = 2.56, [95% CI: 1.07, 6.11]), advanced cancer stage (AOR = 3.10, [95% CI: 1.31, 7.30]), longer disease duration (AOR = 2.93, [95% CI: 1.55, 5.56]), having multiple cancer treatments (AOR = 4.25, [95% CI: 2.46, 7.33]), multiple cycles of chemotherapy (AOR = 3.88, [95% CI:2.00, 7.54]), and uninsured patients (AOR = 2.54, 95% CI: 1.71, 3.79]) were significantly associated with CHE among cancer patients in LMICs.

Conclusion

The review indicated that nearly three-fifths of cancer patients in LMICs faced catastrophic health expenditure during service delivery. The government should decentralize cancer care to improve accessibility and affordable of care, which has the potential to reduce financial burden. In addition, healthcare providers should provide awareness of the importance of early screening and insurance enrollment to reduce CHE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-025-13723-4.

Background

The World Health Organization (WHO) promotes Universal Health Coverage (UHC), which emphasizes providing healthcare services without a financial burden [1]. Achieving the goal of universal health coverage requires overcoming significant financial barriers due to costly health services [2]. One of the factors that hinders financial risk protection is out-of-pocket (OOP) payments at the service delivery point, which leads households, caregivers, and patients to financial hardship or impoverishment. In the healthcare system, households suffer from Catastrophic Health Expenditure (CHE) when the household health expenditure exceeds 10% of the total household expenditure or 40% of household’s capacity to pay or non-food household expenditure [3, 4]. Despite the WHO’s emphasis on addressing UHC and the need to protect households against financial burden, around 2 billion people incurred catastrophic out-of-pocket health spending, with 344 million people living in extreme poverty in 2019 [5]. The high level of OOP health spending is mainly concentrated in low-income countries (LICs) and low-middle income countries (LMICs) that have higher poverty rates. In low-income countries, 30% of healthcare financing comes from out-of-pocket expenses. In addition, the average household’s OOP medical expenses per capita increased by 66% in these nations between 2000 and 2017 [6]. Globally, the incidence of cancer cases is estimated to be 21.3 million annually, with approximately 70% will be in LMICs [7]. The healthcare programs of these countries need a huge budget to effectively manage their cancer burden, which has a substantial impact on economic growth and pushes patients, households/caregivers to financial burden [8, 9].

In developing countries, the level of financial burden among cancer patients varies from continent to continent and WHO region to region, depending on various factors. For instance, the magnitude of CHE among upper-middle-income countries ranges from 28.7% to 96% in China [10–13], 7.5% to 72.7% in Iran, 62.5% to 85.7% in Vietnam, and 16.1% to 86.5% in Malaysia [14–16]. In addition, the magnitude of financial burden among cancer patients in low-middle-income countries such as India ranges from 34% to 90% [17–20], while it ranges from 64% to 79.4% in low-income countries such as Sudan, Malawi, and Ethiopia [21–24]. In developing countries, the high economic burden of cancer was due to costly cancer care, which related to prolonged hospital admission, frequent outpatient visits, expensive laboratory tests, medicines, and treatments [25, 26]. In addition, the other factor that pushes cancer patients to CHE is inaccessible and centralized cancer care, which forces the patients to opt for OOP payment due to the absence of well-organized prepayment (i.e., health insurance) [27, 28]. Moreover, previous studies also indicated that a lack of enrollment in a prepayment scheme, such as health insurance, increases the potential to get financial burden in developing countries [24, 29]. Furthermore, the other determinants that influence the magnitude of financial burden were sociodemographic factors such as age, residence, education, wealth status/income, and family size [16, 22–24, 30, 31]. Health and health-related factors such as distance to facilities, type of facilities visited, cycle of chemotherapy, type of treatment used, use of multiple cancer treatments, and course of disease were also found to determine the level of financial catastrophe [16, 22–24, 32]. In low and middle-income countries, there is a lack of aggregate studies that estimate the pooled level of catastrophic healthcare expenditure and its determinants among cancer patients. This limits health policymakers from designing preventive policies to reduce levels of CHE in resource-constrained countries. Thus, it is important to estimate the CHE in healthcare systems and to identify its determinants that push households/caregivers and patients at risk of incurring CHE. Therefore, this systematic review and meta-analysis aimed to determine the pooled magnitude of catastrophic health expenditure and its determinants among cancer patients in low and middle-income countries.

Method and material

The systematic review and meta-analysis protocol was registered in the PROSPERO database (ID = CRD42022304861). Preferred Reporting Items for systematic review and meta-analysis (PRISMA) 2020 guidelines were used for the write-up of the review.

Search strategy

We searched electronic databases such as PubMed/Medline, Web of Science, Scopus, African Journals Online, Science Direct, and Google Scholar for articles published from 2010 to June 30, 2025. In addition, the search was conducted on the websites, organizations, and Universities’ repositories manually. Moreover, the reference lists of included articles were screened, and the title of articles was used to retrieve additional records regardless of their publication status. The articles searched from electronic databases used keywords and terms such as catastrophe, catastrophic, financial, Health expenditure, out of pocket payment, cancer, neoplasm, and low and middle-income countries, combined with Boolean operators “AND/OR “to retrieve relevant records (S1).

Eligibility criteria

Inclusion and exclusion criteria

The review used population, exposure of interest, context, and Outcome (PICO) as a framework to design the exclusion/inclusion criteria. All observational studies, regardless of their publication status, containing information related to adult cancer patients (P), determinants of catastrophic health expenditure (I), and Catastrophic health expenditure (O), and studies conducted in countries categorized as LMICs by the World Bank (C), were included in the review. Any source of evidence produced until June 30, 2025, and written in the English language was included. Articles or documents that did not report the magnitude of catastrophic health expenditure among cancer patients, and had poor qualities, editorial, qualitative studies, conference reports, systematic reviews, letters, and studies done outside of LMICs were excluded from the review.

Outcome of the study measurement

This systematic review and meta-analysis have two main outcomes. The primary outcome was catastrophic Healthcare expenditure among cancer patients in LMICs, which is measured using either household capacity to pay (non-food household expenditure/total household income) or its share of total household consumption (food household expenditure). Household is exposed to catastrophic health expenditure when the household’s financial expenditure on health care exceeds 10% of total household expenditure or 40% of capacity to pay (i.e., non-food household expenditure) [33, 34]. The secondary outcome was determinants of financial burden including socioeconomic factors (age, gender, residence, wealth index, educational status, occupation, and family size); health and health-related factors (distance to health facility, type of health facilities, course of disease, stage of cancer, treatment methods, a cycle of chemotherapy, length of admission), and health insurance scheme related factors.

Selection and screening of the study

Two reviewers (AWK and GA) screened and selected the eligible articles through the inclusion and exclusion criteria. The reviewers conducted two-stage screening processes to select the study articles. The articles retrieved from different sources were screened using the title and abstract. After that, the remained articles were screened for full text. During the review and screening process, the duplicated articles were removed using Zotero software. Any disagreement between the two reviewers were solved by discussion with third reviewer (AHR).

Data extraction

Three reviewers (AWK, TK, and GA) collected data using Microsoft Excel from the included records. The data collection form developed in Excel contains authors’ names, year of publication, country name, World Bank country classification, study design, cancer type, sample size, determinant factors of CHE, and coping strategies. In case the study determined factors associated with financial burden using more than one CHE definition, data were extracted for both CHE measurements. If the authors extracted pertinent data differently, the fourth reviewer (AHR) participated in solving discrepancies through discussion.

Quality of studies assessment

The Joanna Briggs Institute (JBI) checklist was used to assess the quality of eligible articles. Three (AWK, GA, and AHR) authors participated in the quality appraisal of the included studies using the JBI checklist. The fourth author (GH) was involved in solving the discrepancy among reviewers during quality appraisal through discussion. The authors involved in quality assessment provide a value of “1” if the records met the required criteria and “0” if the study didn’t meet the criteria. Then, the overall score was computed and changed to a percentage to determine the status of the risk of bias. Those who scored (≤ 49%), (50–69%), and (≥ 70%) were classified as high risk, moderate risk, and low risk of bias based on the computed [35, 36].

Data analysis

Data were collected by Excel and exported to STATA software version 17 for analysis. The characteristics of eligible studies and patients’ coping strategies with financial burden were described narratively. Cochran’s Q-statistic and I² tests were used to determine heterogeneity among the included studies [30, 37]. We used a random effect model to estimate catastrophic health expenditure among cancer patients, and the pooled odds ratio with 95% CI was used to report factors associated with CHE. The subgroup analysis was conducted by the CHE outcome definition used and cancer type. The publication bias was also assessed using visual inspection of the asymmetry of the funnel plot and Egger’s and Begg’s tests. Sensitivity analysis was conducted to check the change in the pooled CHE estimate after omitting a single study at a time.

Results

Study selection

We identified 865 articles, of which 838 were from electronic databases, 11 from registers, and 16 from other sources. Out of 849 articles identified from electronic databases and registers, 510 articles were removed as duplicate records, and nine articles were marked as ineligible by automation tools. Then, 346 articles were screened for title and abstract, from which 272 articles were excluded. Of the 74 articles sought for retrieval, 5 were not retrieved. Among 69 articles assessed for eligibility, 36 articles were omitted for not reporting the outcome of interest, having poor quality, incorrect population, conference proceeding papers, and systematic reviews. We also identified 16 articles from websites, organizations, and searching citations, of which eight were not retrieved. Out of the remaining eight articles, three articles were excluded for not reporting the outcome of interest, inappropriate population, and poor quality. Finally, a total of 38 articles that met the inclusion criteria were included in the review (Fig. 1).

Fig. 1.

Flowchart for selection of studies per preferred reporting items for systematic review and meta-analysis (PRISMA) 2020 guidelines

Characteristics of the included study

The review included 38 observational studies for quantitative analysis. The review included twenty-six studies from upper-middle-income countries [10–16, 31, 38–57], six studies from low-middle-income countries [17–20, 58, 59], two studies from low/upper-middle-income countries [38, 48], and four studies from low-income countries [21, 22, 24, 60]. In this study, 25 (65.8%) and 9 (26.7%) of the included studies utilized 40% (i.e., capacity to and a 10% cut-off point definition (i.e., total health expenditure) for measuring CHE. The majority of the included studies were cross-sectional study designs (38 studies), followed by cohort studies (10 studies). Concerning the type of cancer, 24 studies conducted on all types of cancers [10–13, 15, 16, 20, 22, 24, 38–40, 44, 47, 49, 50, 53, 55, 59, 60], four studies enrolled on breast cancer patients [21, 31, 46, 58], four studies included gastrointestinal patients [17, 19, 42, 61] and five studies [18, 41, 43, 51, 57] performed among oral, prostate, head and neck, Urologic, and gynecological cancer patients. The sample sizes of included studies range from 52 to 12,148, with a total sample size of 50,968 (Table 1).

Table 1.

Descriptive summary of 38 studies included to estimate the magnitude of catastrophic health expenditure among cancer patients in LMICs, 2025

Authors	Country	World Bank Classification	Type of cancer	Sample size	CHE definition cut off point	CHE	Study design	Risk of bias
Kasahun et al., 2020	Ethiopia	Low income	Cancer	352	10%	74.4	CS	Low
Matebie et al., 2024	Ethiopia	Low income	Cancer	305	40%	77.7	CS	Low
Fu W et al., 2024	China	Upper Middle income	Cancer	2534	40%	72	CS	low
Zhao Y et al., 2020	China	Upper Middle income	Cancer	381	40%	26.77	CS	Low
Kavosi et al., 2014	Iran	Upper middle income	Cancer	245	40%	67.9	CS	Low
Puteh et al., 2024	Malaysia	Upper middle income	Cancer	206	10%	26.2	CS	Low
Chauhan et al., 2019	India	Middle Income	Head & neck	288	40%	34	CoS	low
Maurya et al., 2021	India	Middle Income	Cancer	474	40%	61.6	CS	Low
Raman et al., 2022	Malaysia	Upper middle income	Oral cancer	52	10%	86.5	CS	low
Puteh, 2023	Malaysia	Upper middle Income	Cancer	630	10%	54.4	CS	low
Azzani et al., 2017	Malaysia	Upper middle Income	Colorectal	138	40%	47.8	CS	Low
Basavaiah et al., 2018	India	Middle Income	Pancreatic	98	10%	76.5	CS	low
Bhoo-Pathy et al., 2019	Malaysia	Upper middle Income	Cancer	1294	30%	51	CoS	low
Hoang et al., 2017	Vietnam	Upper middle Income	Cancer	10,000	40%	64.7	CoS	low
Zheng et al., 2018	China	Upper middle Income	Cancer	1344	40%	42.78	CS	Low
Zaman et al., 2025	Malaysia	Upper middle income	Cancer	209	10%	15.3	CS	Low
Elimam et al., 2024	Sudan	Low income	Breast cancer	170	40%	79.4	CS	Low
Sun et al., 2021	China	Upper middle income	Breast cancer	639	40%	87.95	CS	Low
Deng et al., 2022	China	Upper middle Income	Cancer	388	40%	50.1	CoS	Low
Prinja et al., 2023	India	Middle Income	Cancer	12,148	40%	70.6	CS	Low
Liew et al., 2022	Malaysia	Upper middle Income	Gynecological	120	10%	64	CS	Low
Rezapour et al., 2022	Iran	Upper middle income	cancer	220	40%	70	CS	Low
Leng et al., 2019	China	Upper middle Income	cancer	792	40%	95.7	CS	Low
Bates et al., 2021	Malawi	Low income	Cancer	89	20%	64	CoS	Moderate
CROCODILE study group, 2022	India	Middle Income	Colorectal	226	25%	90	CoS	Low
Piroozi et al., 2019	Iran	Upper middle income	gastrointestinal	189	40%	72.7	CS	Low
Sun et al., 2021	China	Upper middle income	Lung cancer	470	40%	78.1	CoS	Low
Ahmadi et al., 2021	Iran	Upper middle income	Breast Cancer	138	40%	13.77	CS	Low
					10%	40.58
ACTION Study Group, 2015	South east Asia	Low /upper middle Income	Cancer	6787	30%	48	CoS	low
Mohanty et al., 2024	India	Middle Income	Breast cancer	500	40%	84.2	CoS	low
Alinezhad et al., 2023	Iran	Upper middle Income	Prostate cancer	297	40%	31	CS
Ngoc et al., 2022	Vietnam	Upper middle Income	cancer	102	40%	62.7	CS	low
Xuan et al., 2024	Vietnam	Upper middle Income	Cancer	300	25	85.7	CS	Low
ACTION Study Group, 2015	South East Asia	Low /Upper middle income	Cancer	4584	30%	31	Cos	low
Chen et al., 2017	China	Upper middle Income	Cancer	227	40%	72.7	CS	low
Ting et al., 2019	Malaysia	Upper middle Income	Urological cancer	429	40%	16.1	CS	low
Aminuddin et al., 2025	Malaysia	Upper middle Income	Cancer	430	10%	67.2	CS	Low
					25%	48.8
					40%	32.8
Mao et al., 2017	China	Upper middle Income	Cancer	2091	40%	51.55	CS	Low

Magnitude of catastrophic health expenditure among cancer patients in low and middle-income countries

We used the I² to assess the Heterogeneity, and the I² test result indicates high (99.55%, P-value < 0.01) heterogeneity for the study. Thus, a random effect model was used to estimate the pooled catastrophic health expenditure among cancer patients in LMIC countries. In this review, the pooled magnitude of catastrophic health expenditures among cancer patients in LMICs was 58.42% (95%CI: 52.29, 64.55) [10–22, 24, 31, 38–60] (Fig. 2).

Fig. 2.

Pooled magnitude of catastrophic health expenditure among cancer patients in LMICs, 2025

Publication bias

The Publication bias of included studies was evaluated using Begg’s and Egger’s test and funnel plot. The visual inspection of the funnel plot showed the absence of publication bias, which is also statistically supported by Egger’s test (P = 0.979) and Begg’s test (P = 0.590) (Fig. 3).

Fig. 3.

Funnel plot for publication bias of studies reporting catastrophic health expenditure among cancer patients in LMICs, 2025

Subgroup analysis

The subgroup analysis was performed by CHE outcome measurement cut-off poin, and cancer type, to compare the level of catastrophic health expenditure. The pooled magnitude of catastrophic health expenditures among cancer patients in LMICs at 10% and 40% threshold were 56.89% (95%CI: 41.54%, 72.24%) [16, 17, 24, 31, 41, 43, 47, 50] and 58.66% (95%CI: 51.64%, 65.68%) [10–14, 16, 18, 20–22, 31, 39, 40, 42, 44–46, 49, 51, 52, 54, 55, 57–59] respectively. Regarding the level of catastrophic health expenditure by cancer type, the magnitude of CHE was 71.96% for gastrointestinal cancer [14, 17, 19, 42], 72.2% for lung cancer [46, 52, 54], 62.99% for breast cancer [21, 31, 46], and 56.22% for all cancer patients [10–13, 15, 16, 20, 22, 24, 38–40, 44, 47, 49, 50, 53, 55, 59, 60] in LMICs (Table 2).

Table 2.

Sub group analysis of CHE among cancer patients in LMICs by CHE cut off point definitions and type of cancer, 2025

Variable	# of studies included	Prevalence (95%CI)	I2 (%)	Q-value	P-value
CHE cut off point definition
> 10% of annual income	9	56.89 (41.54,72.24)	98.45	516.27	0.001
> 20% of annual income	1	64 (54.03,73.97)	-	1391.07	0.001
> 25% of annual income	3	74.87 (50.89,98.85	98.99	197.37	0.001
> 30% of annual income	3	43.31 (30.48,56.13)	99.50	397.89	0.001
> 40% of capacity to pay	25	58.66 (51.64, 65.68)	99.50	4761.38	0.001
Type of cancer
All cancer	24	56.27 (48.70,63.84)	99.67	6922.92	0.001
Gastrointestinal cancer	4	71.96 (54.67,89.26)	99.60	88.15	0.001
Breast cancer	5	62.99 (39.40, 86.59)	99.34	603.3	0.001
Other	5	45.99 (25.18,66.80)	98.49	265.51	0.001

Note: others, urologic cancer, gynecological cancer, head and neck cancer, prostate cancer, and oral cancer

Sensitivity analysis

A leave-one-out sensitivity analysis was conducted to identify the potential source of heterogeneity in the analysis. We found that no single study affected the overall catastrophic health expenditure among cancer patients in LMICs (Fig. 4).

Fig. 4.

Sensitivity analysis of included studies for the influence of one study on the overall estimate

Determinants of catastrophic health expenditure among cancer patients in low and middle-income countries

This review found socioeconomic factors (age, gender, wealth index, educational status, occupation, and family size), health and health-related factors (distance to health facility, type of health facilities, course of disease, stage of cancer, treatment methods, and cycle of chemotherapy), and having an insurance scheme as determinants of catastrophic health expenditure among cancer patients.

Sociodemographic factors associated with catastrophic health expenditure among cancer patients

Nine studies [12, 16, 23, 31, 38, 45, 48, 53, 57] were used to determine the association between age and catastrophic healthcare expenditure among cancer patients. The odds of having CHE among old-aged (≥ 40 years) cancer patients were 1.44 times higher than among cancer patients aged less than 40 years (AOR: 1.44, [95% CI: 1.17, 1.76], I² = 98.72%), p < 0.001. Four studies [14, 19, 38, 53]revealed that he odds of having CHE among female cancer patients were 2.64 times higher compared to male patients (AOR: 2.64, [95% CI: 1.28, 5.45], I² = 72.78%), p < 0.001. The pooled report of two studies [22, 59] found that cancer patients who lived in rural areas were 3.56 times more likely to have catastrophic health expenditure compared to urban residents (AOR 3.56, [95% CI: 2.87, 4.40], I² = 1.99%), p < 0.04. Four studies [10, 11, 16, 50] were used to determine the association between family size and catastrophic healthcare expenditure among cancer patients in LMICs. The likelihood of having catastrophic health expenditure among cancer patients was 2.43 times higher among large family members compared to their counterparts (AOR 2.43, [95% CI: 1.01, 5.91], I² = 86.32%), p < 0.001 (Fig. 5).

Fig. 5.

The association between age, gender, family size, residence, and catastrophic health expenditure among cancer patients in LMICS, 2025

The pooled analysis of six studies [10, 19, 31, 38, 46, 56] indicated that cancer patients who had low educational level were almost four times more likely to have catastrophic health expenditure compared to those who had a college degree and above (AOR:4.18, [95% CI: 2.11, 8.28], I² = 93.22%), p < 0.001. Four studies [11, 12, 49, 53, 56] reported that the odds of having CHE among unemployed cancer patients were almost two times higher compared to employed patients (AOR: 2.19, [95% CI: 1.26, 3.82], I² = 84.82%), p < 0.001. Sixteen studies [10, 11, 14, 15, 18, 21, 22, 38, 41, 42, 45–48, 50, 59] were included to investigate the relationship between wealth/income status and catastrophic health expenditure among cancer patients. Cancer patients who had poor wealth status had almost five times higher odds of having catastrophic health expenditure than their counterparts (AOR = 5.18, [95%CI: 3.61, 7.44], I² = 89.46%, p < 0.001 (Fig. 6).

Fig. 6.

The association between occupation, education, wealth index, and catastrophic health expenditure among cancer patients in LMICS, 2025

Health and health-related factors associated with catastrophic health expenditure among cancer patients

The pooled analysis of four studies [16, 22, 47, 50] found that the odds of experiencing catastrophic health expenditure among cancer patients who live at a far distance from the study setting were almost three times more likely to have catastrophic health expenditure than their counterparts (AOR: 2.56, 95% CI: 1.07, 6.11], I2 = 89.33%). We included four studies [10, 15, 19, 51] to estimate the association between the type of health facilities and catastrophic health expenditure among cancer patients in LMICs. Cancer patients who visited private health facilities for cancer care had almost nine times higher odds of facing catastrophic health expenditure than those who visited government health institutions only (AOR = 8.97, 95% CI: 1.65, 48.85], I2 = 92.35. The pooled effect of three studies [10, 45, 46] revealed that cancer patients with a greater than 1-year course of disease had 2.93 times higher odds of facing catastrophic health expenditure compared to their counterparts (AOR = 2.93, [95% CI: 1.55, 5.56], I² = 85.37%). The pooled analysis of five studies [16, 19, 38, 53, 59] also indicated that the cancer stage was positively associated with catastrophic health expenditure. Cancer patients who were diagnosed at a late stage (III and IV) were almost three times more likely to have catastrophic health expenditure compared to early stage (I and II) (AOR = 3.10, [95% CI: 1.31, 7.30], I2 = 84.04%) (Fig. 7).

Fig. 7.

The association between type and distance to health facility, course diseases, cancer stage, and catastrophic health expenditure among cancer patients in LMICS, 2025

Four studies [46, 47, 51, 62] were used to determine the association between having multiple cancer treatments and catastrophic health expenditure in LMICs. Cancer patients who had multiple cancer treatments had 2.68 times higher odds of having catastrophic health expenditure compared to their counterparts (AOR = 4.25, [95% CI: 2.46, 7.33], I² = 0.00%). The studies [22, 24] also indicated the likelihood of having catastrophic health expenditure was almost four times higher among cancer patients who received more than five cycles of chemotherapy compared to their counterparts (AOR = 3.88, [95% CI:2.00, 7.54], I² = 0.00%). Furthermore, three studies [14, 19, 62] and two studies [15, 41] were included to investigate the association between surgery resuscitation and chemotherapy treatment, and catastrophic health expenditure, respectively. The odds of cancer patients who had surgery or chemotherapy treatment were almost four and three times higher compared to their counterparts (AOR = 3.97, 95% CI: 2.39, 6.60], I² = 0.00) and (AOR = 3.84, 95% CI: 1.16, 12.65], I2 = 67.79) respectively (Fig. 8).

Fig. 8.

The association between type and number of treatment methods and catastrophic health expenditure among cancer patients in LMICS, 2025

Community-based health insurance-related factors

Ten studies [10, 15, 19, 21, 22, 31, 42, 47, 56, 59]were used to determine the association between having health insurance and catastrophic health expenditure among cancer patients in LMICs. Cancer patients who were not insured had 2.54 times higher odds of facing catastrophic health expenditure compared to patients who were insured (AOR = 2.54, [95% CI: 1.56, 4.14], I² = 92.62%) (Fig. 9).

Fig. 9.

The association between health insurance and catastrophic health expenditure among cancer patients in LMICS, 2025

Discussion

Cancer was a public health threat and caused huge morbidity and mortality in low and middle-income countries. The high burden of cancer exposed a significant number of households in developing countries to financial hardship while accessing healthcare [63]. The study aimed to determine the pooled magnitude of catastrophic health expenditure among cancer patients and its determinants in low and middle-income countries. The pooled magnitude of catastrophic health expenditures among cancer patients in LMICs was 58.42% (95%CI: 52.29%, 64.55%). This finding is concurrent with the previous meta-analysis, 56.96% [64], 47% [61], and 56% [65], and higher than the previous reviews among cancer patients, 43.3% [66], and the study conducted in China, 23.3% [67]. The main reason for the variation might be due to differences in the study period, study population, and socioeconomic status of the population.

In this review and meta-analysis, older cancer patients were at risk of CHE compared to younger cancer patients. This finding is supported by studies [66, 68, 69] which reported a high level of CHE among older cancer patients. This is explained by the fact that older patients are vulnerable to different illnesses and have no capacity to engage in economic activities to get money for care when they get unpredictable illnesses, which exposes them to catastrophic health expenses. The study revealed that residences of cancer patients were significantly associated with catastrophic health expenditure in Ethiopia. Female cancer patients were at risk of CHE compared to male cancer patients. This finding is in line with previous studies [66]. This may be driven by the fact that females are more affected by cancers such as breast and cervical cancer, which may need prolonged and expensive treatment regimens. In addition, females may have less financial independence or income, which makes it difficult to get treatment costs without experiencing financial burden. Cancer patients who were from rural areas had higher odds of facing catastrophic health expenditures than those from urban areas. This finding is similar to previous studies [68–70]. This is probably explained by the fact that patients from rural areas travel far distances to seek medical care, and they incur high direct non-medical costs. Unemployed cancer patients were at risk of having CHE compared to employed cancer patients. In addition, the review revealed education status of respondents was significantly associated with catastrophic health expenditure. The odds of facing CHE were almost four times higher among respondents who had no formal education compared to respondents who attained a college degree or above. This finding is supported by previous studies [65, 66] that found the level of catastrophic health expenditure among cancer patients decreases with increasing educational level. This is explained by the fact that patients with higher educational levels have higher incomes, which enables them to cover the medical costs of cancer without financial difficulty. The meta-analysis also revealed that income/wealth status determines the level of CHE among cancer patients. Cancer patients who had low income/wealth status had higher odds of having catastrophic health expenditure than their counterparts. This finding is similar to previous studies [66, 68, 69, 71, 72], which reported a higher odds of CHE among low-income/poor wealth index cancer patients. This is because low-income/poor wealth index patients/households cannot bear the costly cancer care costs, which exposes them to catastrophic healthcare expenditure.

The meta-analysis revealed that distance to health facilities was significantly associated with catastrophic health expenditure among cancer patients. Cancer patients who live at a far distance from treatment health facilities had higher odds of facing CHE than those who live near the health facilities. This finding is supported by previous meta-analyses and studies [66]. This is explained by the fact that patients who live far from treatment health facilities might incur catastrophic healthcare expenditures due to high direct non-medical costs such as transportation and caregiver costs. Types of health facilities visited for cancer care are other factors that significantly determine the level of catastrophic health expenditure. The likelihood of having CHE among cancer patients who visited private health facilities was almost five times higher than those who visited public health facilities only. This finding is consistent with previous meta-analyses and studies [63, 68, 69, 73]. This is due to the fact that patients who attend private healthcare facilities may be subject to catastrophic out-of-pocket medical expenses because private institutions charge higher medical prices.

The systematic review and meta-analysis found course of the disease was associated with catastrophic health expenditure among cancer patients. The odds of respondents who had more than a year of a courses of diseases were almost three times higher compared to their counterparts. This finding is similar to the findings of previous studies [65] that reported an association between the course of disease and CHE among cancer patients. Moreover, the cycle of chemotherapy was positively associated with catastrophic health expenditure among cancer patients. The review found that the odds of respondents who had many cycles of chemotherapy treatment were almost four times higher compared to their counterparts. This finding is similar to the findings of previous studies [65, 69, 74]. This is explained by the fact that respondents who had many cycles of chemotherapy treatment might have high out-of-pocket expenses, especially for indirect costs that expose them to financial hardship if they have no means of covering their medical costs. In addition, the meta-analysis revealed that respondents who had treatment such as chemotherapy, surgery, and multiple treatments had higher odds of having catastrophic healthcare expenditure compared to their counterparts. This finding is supported by previous studies [65, 74], which reported high CHE with an increased number of cancer treatments. This is because the costs of cancer treatment were high, and individuals who get treated by multiple treatments might be unable to bear the high costs of treatment, which exposes them to CHE. The meta-analysis found that insurance had the potential to reduce catastrophic health expenditure among cancer patients during service delivery. This finding is in line with the previous Meta-analysis [61, 65, 66], which reported a low level of CHE among insured cancer patients/caregivers. This might be because uninsured households must pay for their medical care out-of-pocket expenses, which is disastrous, whereas insured families seek medical care without financial difficulty using their membership card. There are some limitations to our review. The majority of the studies were from middle-income and upper-middle-income countries, which limits the representativeness of the studies, as only a few studies were available from low-income countries. In addition, insufficient information was available for a few articles to conduct subgroup analysis by the World Bank categorization of countries by income, that have a significant importance for policymakers for designing effective interventions in each country to reduce CHE. Moreover, although sub-group analysis was performed by the cut-off point used for measuring CHE, high heterogeneity might influence the result to be interpreted with care. Furthermore, the difference in the category of determinant variables made it challenging to assess the determinant of CHE among cancer patients.

Conclusion

The result of the review indicated that around three-fifths of cancer patients in LMICs faced catastrophic health expenditure during service delivery. Besides, several factors were associated with financial burden in this review. In the review, patients with older age, large families, unemployment, low educational status, and poor wealth index are at high risk of incurring financial hardship, indicating the need for designing a community-based charity assistance to ease the financial burden of households/patients. The lack of health insurance was also significantly associated with a higher risk of facing financial burden, emphasizing the need for expanding insurance coverage. In addition, living at a far distance from the cancer treatment raises the risk of suffering from CHE, suggesting a need for decentralizing cancer care to remote health facilities to improve accessibility and affordability of care. Moreover, patients treated at private health facilities faced increased risk of financial burden, which signifies a need for developing a system for public-private partnership in managing advanced care. Furthermore, patients who take prolonged cancer care, number of cycle of chemotherapy, and undergo multiple cancer treatments suffer from financial hardship, suggesting a need for improving the coverage of prepayment schemes (i.e., health insurance) that have the potential to reduce the households, caregivers, or patients from impact of catastrophic health expenditure.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CHE

Catastrophic Health Expenditure, CI: Confidence Interval

JBI

Joanna Briggs Institute

LIC

Low Income Countries, LMIC: Low and Medium-Income Countries

MIC

Middle Income Countries

OR

Odds Ratio

UHC

Universal Health Coverage

OOP

Out-of-Pocket Payment

PRISMA

Preferred Reporting Items for Systematic Review and Meta-Analysis

WHO

World Health Organization

Author contributions

Abdene Weya Kaso prepared the protocol for registration in PROSPERO, conceived the idea, and designed the study. Abdene Weya Kaso, Taha Kaso, Hiluf Kalayou, Gebi Agero, Ashenafi Habtamu Regesu, and Gebi Husein participated in database searching, study selection, data abstraction, and statistical analysis. Abdene Weya Kaso, Gebi Agero, Hiluf Kalayou, Taha Kaso, Gebi Husein, Ashenafi Habtamu Regesu, Alemayehu Hailu, Regien Biesma, and Jelle Stekelenburg performed report writing and manuscript drafting. All the authors read and approved the final version of the manuscript before it was considered for publication.

Funding

No specific funding was received for this study.

Data availability

Data will be made available by request from principal investigators.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.