Determinants of out-of-pocket expenditures amongst cancer patients undergoing modern radiotherapy modalities in public healthcare facilities of Tamil Nadu, South India

Abstract

Background

The advancement of medical technology has improved cancer treatment, yet financial disparities remain, particularly in low-income regions. Modern radiotherapy modalities, including LINAC, Cobalt Therapy, and Brachytherapy, impose significant Out-Of-Pocket Expenditure (OOPE) on patients despite public healthcare provisions. This study aims to assess OOPE and its determinants among patients undergoing advanced radiotherapy in public healthcare facilities in Tamil Nadu.

Methods

A cross-sectional study was conducted in secondary and tertiary hospitals across Tamil Nadu, enrolling 500 patients undergoing radiotherapy. Patient-level costing included direct medical costs (procedure, material), direct non-medical costs (travel, accommodation, food), and indirect costs (income loss for patients and caregivers). Log-linear regression models in STATA 17 identified key OOPE determinants, including socioeconomic status, insurance coverage, travel distance, and waiting time.

Results

While procedural costs were covered under insurance, non-medical and indirect costs significantly contributed to OOPE. The median total per-patient cost per radiotherapy procedure was ₹900 for LINAC, ₹840 for cobalt therapy, and ₹1,550 for brachytherapy. Higher socioeconomic status and greater travel distances increased expenditures. Patients who were insured but did not utilize insurance faced catastrophic expenses (₹55,900 median cost), highlighting gaps in awareness and accessibility.

Conclusion

Despite insurance coverage, financial burdens persist, necessitating targeted interventions. Strengthening insurance utilization, decentralizing radiotherapy services, and integrating patient support systems can reduce OOPE. Future research should explore long-term financial interventions to improve equitable cancer care.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12913-026-15096-8.

Introduction

The rapid development of medical technology has substantially influenced healthcare delivery over recent decades [1, 2]. Modern therapeutic technologies have improved disease management and expanded treatment options; however, access to such technologies remains uneven across health systems, particularly in low- and middle-income settings [3]. Modern radiotherapy modalities such as linear accelerator (LINAC), cobalt therapy, and brachytherapy are important components of cancer treatment and contribute to improved therapeutic outcomes [4]. However, these modalities require substantial public investment in equipment, infrastructure, trained personnel, maintenance, and service delivery. Therefore, their availability in public facilities should be evaluated not only in terms of clinical access, but also in terms of affordability and financial protection for patients [5].

In India, out-of-pocket expenditure (OOPE) remains a major concern despite expansion of health insurance coverage and public healthcare infrastructure [6]. For cancer patients, OOPE may arise not only from direct medical payments, but also from repeated travel, food, accommodation, caregiver expenses, and income loss. These expenses can contribute to catastrophic health expenditure and may deter patients from seeking or completing necessary care [7]. Tamil Nadu has been at the forefront of progressive health policies and has consistently performed well on health indicators [8]. The state has also expanded access to modern medical technologies, including radiotherapy, through public healthcare facilities [9]. However, the extent to which these investments reduce patient-level financial burden remains uncertain.

Public healthcare facilities are particularly important for patients from lower socioeconomic backgrounds, for whom private-sector cancer treatment may be unaffordable [10]. Nevertheless, the availability of radiotherapy equipment in public hospitals does not automatically ensure financial protection [11]. Patients’ ability to benefit from these services may still be affected by geographic accessibility, awareness and utilization of insurance coverage, direct non-medical expenditure, and indirect costs [12]. Understanding OOPE for modern radiotherapy modalities is therefore important because it captures the actual financial burden experienced by patients and families, including costs beyond procedure charges such as transport, lodging, food, and missed income [13]. It also helps identify barriers to access and informs policy decisions aimed at improving equity in cancer care [14].

Analysis of OOPE determinants can reveal differences in financial protection across socioeconomic groups, residence, insurance utilization, and travel burden [15]. Such evidence is essential for designing targeted interventions to reduce financial barriers and improve the efficiency of public healthcare delivery [16]. Tamil Nadu provides a relevant setting for this assessment because of its strong public health system and health financing initiatives [17]. At the same time, the state faces increasing costs related to non-communicable diseases [18], including cancers requiring modern radiotherapy modalities [19]. Existing evidence on procedure-specific OOPE for LINAC, cobalt therapy, and brachytherapy in public healthcare facilities remains limited. Hence, this study was conducted to assess OOPE and its determinants among patients undergoing LINAC, cobalt therapy, and brachytherapy in public healthcare facilities of Tamil Nadu, South India [19].

Methodology

Study design, setting, sampling and sample size

This cross-sectional survey was conducted between January and December 2023 as part of a larger operational research project evaluating utilization and economic costs of advanced diagnostic and therapeutic equipment in public healthcare facilities of Tamil Nadu. The present analysis was restricted to cancer patients undergoing treatment through modern radiotherapy modalities, namely linear accelerator (LINAC), cobalt therapy, and brachytherapy, in public secondary and tertiary healthcare facilities.

A multistage sampling strategy was used for selection of study districts and facilities. In the first stage, all districts of Tamil Nadu were stratified into low, medium, and high categories based on Human Development Index (HDI). Four districts were selected from each HDI stratum, while ensuring geographical representation and availability of modern radiotherapy modalities. In the second stage, public secondary and tertiary healthcare facilities within the selected districts were included based on the availability and functionality of radiotherapy equipment. Overall, the parent study covered 23 public healthcare facilities across 12 districts, including 14 medical colleges and 9 government hospitals. For the present radiotherapy costing analysis, all seven brachytherapy units available in the state were included, along with 7 of 11 LINAC units and 8 of 15 cobalt therapy units.

The patient-level costing sample was determined using an equipment-based operational sampling plan within the parent study rather than a hypothesis-testing sample size calculation. This approach was used because the primary objective was to estimate patient-level out-of-pocket expenditure and its determinants across specific radiotherapy modalities. Within each selected facility, patients undergoing LINAC, cobalt therapy, or brachytherapy during the data collection period were approached consecutively. Patients were included if they received one of the selected radiotherapy procedures, were able to provide cost-related information, and gave informed consent. The final radiotherapy costing sample included 500 patients: 319 undergoing LINAC, 146 undergoing cobalt therapy, and 35 undergoing brachytherapy.

Study procedure

An investigator team was recruited for data collectors of this study. Three Research Assistants (RA) were recruited and trained to conduct patient costing survey from the selected healthcare facilities. Each RA was responsible for four districts covering at least 8 facilities. The primary data source for costing was patient interviews using a validated tool; billing and claim documents were consulted when available to triangulate self-reported data. A structured, pre-tested survey tool was developed specifically for this study and used for data collection (see Supplementary File 1).

Patient level costing analysis

Our patient-level costing analysis aimed to understand the total cost per patient per procedure for three different types of modern radiotherapy modalities: LINAC, Cobalt therapy unit, and Brachytherapy. The total cost per patient per procedure was calculated by incorporating all unit costs. This included direct medical costs, direct non-medical costs, and indirect costs. Direct medical costs included consumables not covered by insurance, medications, procedure-associated charges not reimbursed. Direct non-medical costs included self-reported travel fare, food, snacks, accommodation expenses. Indirect costs were calculated from self-reported income loss of the patient and accompanying caregiver for the current radiotherapy visit, including loss of paid work, unpaid work, and time-based opportunity cost.

The comprehensive cost per procedure provided a detailed understanding of the financial burden borne by patients for each type of equipment. The cost data were primarily collected through structured patient interviews using a pre-tested tool. Where feasible, cost estimations were cross-verified with hospital billing records and Chief Minister’s Comprehensive Health Insurance Scheme (CMCHIS) claim summaries to improve accuracy. For each radiotherapy modality, total per-patient OOPE was calculated by summing direct medical, direct non-medical, and indirect costs for each patient; the median of these summed per-patient costs was then reported separately for LINAC, cobalt therapy, and brachytherapy. Catastrophic health expenditure (CHE) was defined, consistent with the WHO SDG 3.8.2 financial protection indicator, as total per-patient OOPE for the radiotherapy procedure exceeding 10% of annual household income; annual household income was calculated by multiplying self-reported monthly household income obtained during patient interviews by 12. For each equipment type, we used the median of the summed costs across patients.

Statistical analysis

All the analysis was performed using STATA 17 software. Descriptive statistics were reported in terms of mean and standard deviation (SD) or median and interquartile range (IQR) depending on the normality of data.

Log-linear regression model

Determinants of OOPE was obtained using log-linear regression. This approach is particularly suitable for modelling costs, as it can handle skewed cost data typically observed in health economics.

The general form of the log-linear regression model is: [20]

where ln(Yi) is the natural logarithm of the total cost for the i^th patient,

X1i, X2i,…,Xki are the independent variables (such as patient demographics, clinical characteristics, and other relevant factors),

β0,β1,…,βk are the coefficients to be estimated, and ϵiɛ i is the error term.

In our analysis, the dependent variable was the logarithm of the total cost per patient per radiotherapy equipment. The independent variables included a range of characteristics such as residence (urban/rural), socioeconomic status (classified based on the latest modified BG Prasad classification 2023 for self-reported income) [21], insurance coverage (stratified into insured and utilized, insured and not-utilized and uninsured), distance travelled from home to hospital (in kilometers [kms]), and waiting time after reaching hospital till the patient undergoes the procedure, depending on the equipment type. The coefficients obtained from the regression provide estimates of the percentage change in the total cost associated with a one-unit change in the independent variable, holding all other variables constant.

Model fitting and interpretation

The three radiotherapy equipments (Cobalt therapy, LINAC, Brachytherapy) were combined into a single model. The exponentiated coefficients from the log-linear models were interpreted as multiplicative effects on the total cost. A coefficient greater than one indicates an increase in the total cost associated with the independent variable, while a coefficient less than one indicates a decrease. Factors with p-value less than 0.20 in univariable analysis were included into multivariable analysis (adjusted model).

Statistical significance was determined based on P-values, with values less than 0.05 considered statistically significant. The models also included an assessment of potential multicollinearity among the independent variables.

Results

Characteristics of the study participants

Of the 500 patients included in the radiotherapy costing analysis, 319 underwent LINAC, 146 underwent cobalt therapy, and 35 underwent brachytherapy. The majority of participants (36.8%) were aged 41–60, followed by 27.2% in the 19–40 age category. The gender distribution revealed an almost equal distribution, with 51.6% male and 48.0% female. About 178 (35.6%) participants were from urban areas and 322 (64.4%) were from rural areas. The socioeconomic distribution showed that 27.00% of participants fell into Class III of the Modified BG Prasad Scale, while 32% were in Class IV (Table 1).

Table 1.

Details of the study participants who utilized radiotherapy procedures (N = 500)

S.No	Characteristics	Frequency (%)
1	Age Category (Years)
	≤ 18	82 (16.40)
	19–40	136 (27.20)
	41–60	184 (36.80)
	≥ 61	98 (19.60)
2	Gender
	Male	258 (51.60)
	Female	240 (48.00)
	Transgender	2 (0.40)
3	Distance travelled (in kms)
	First quintile (0–10)	62 (12.40)
	Second quintile (11–20)	92 (18.40)
	Third quintile (21–39)	64 (12.80)
	Fourth quintile (40–85)	121 (24.20)
	Fifth quintile (≥86)	163 (32.60)
4	Insurance coverage and utilization
	Insured and utilized	491 (98.20)
	Insured and not utilized	5 (1.00)
	Uninsured	4 (0.80)
5	Residence
	Rural	178 (35.60)
	Urban	322 (64.40)
6	Waiting time (in minutes)
	< 120	360 (72.00)
	≥120	140 (28.00)
7	Per Capita Income (Modified BG Prasad Scale)
	Class I	24 (4.80)
	Class II	87 (17.40)
	Class III	135 (27.00)
	Class IV	160 (32.00)
	Class V	94 (18.80)

OOPE incurred by the patients undergoing radiotherapy procedures in Tamil Nadu

Table 2 provides a detailed analysis of the financial burden borne by patients undergoing various medical procedures among 500 study participants. For patients who utilized public insurance coverage, procedure-related radiotherapy charges were generally covered under CMCHIS/public financing; therefore, the remaining OOPE primarily reflected direct non-medical costs, indirect costs, and any non-reimbursed patient-borne expenses. Therefore, for treatments like LINAC, and cobalt therapy, the procedure costs were negligible or zero, yet other costs like travel, food, and loss of pay for patients and their attenders constituted a considerable portion of the expenses. The table highlights the diverse nature of out-of-pocket expenditures, underscoring the need to consider various cost types, not just the direct cost of the procedure, when assessing the financial impact on patients.

Table 2.

Per-patient out-of-pocket expenditure by radiotherapy modality and cost category among patients undergoing LINAC, cobalt therapy, and brachytherapy in public healthcare facilities of Tamil Nadu (N = 500)

Cost Type	LINAC Median (IQR)	Cobalt Therapy Median (IQR)	Brachytherapy Median (IQR)
Procedure Cost	0 (0-65000)*	0 (0–500)	0 (0–0)*
Material Cost	0 (0-2700)*	0 (0–300)*	0 (0–250)*
Travel Cost	200 (80–300)	122.5 (60–300)	400 (100–600)
Accommodation	0 (0–10000)*	0 (0–0)*	0 (0–400)*
Food Cost	150 (50–200)	150 (100–200)	250 (200–300)
Snacks Cost	50 (0 -100)	50 (20–100)	100 (40–100)
Patient Loss-of- pay	0 (0–300)*	150 (0–400)	0 (0–550)*
Patient Attender Loss of Pay	200 (0-500)	160 (0–500)	500 (300–700)
Other Cost	0 (0–500)*	0 (0-1500)*	0 (0-3000)*

*For cost with median value equal to zero, the range is provided in place of the interquartile range

Approximate US dollar equivalents were calculated using an average exchange rate of ₹83 per US$ during the study period (2023). The median total per-patient OOPE was approximately US$11 for LINAC, US$10 for cobalt therapy, and US$19 for brachytherapy

Note: All costs are reported as per-patient costs in Indian Rupees (INR). Total OOPE was calculated separately for each patient by summing direct medical, direct non-medical, and indirect costs

Table 3 provides an insightful overview of the median costs incurred per patient for different modern radiotherapy modalities, with a focus on direct medical, direct non-medical, indirect costs, and the total cost, all measured in Indian Rupees (INR). Radiotherapy procedures like LINAC, cobalt therapy, and brachytherapy had minimal or no direct medical costs; however, their total costs per patient were significantly impacted by direct non-medical and indirect expenses, such as travel and loss of pay.

Table 3.

Median direct (medical & non-medical), indirect and total per-patient costs for utilizing modern radiotherapy modalities in Tamil Nadu (N = 500)

Equipment	Direct medical cost Median (IQR)	Direct non-medical cost Median (IQR)	Indirect cost Median (IQR)	Total cost per-patient Median (IQR)
LINAC	0 (0–65000)*	365 (220–600)	400 (200–700)	900 (500–1250)
Cobalt	0 (0–500)*	350 (240–520)	400 (200–700)	840 (530–1200)
Brachytherapy	0 (0–250)*	750 (400–1020)	600 (400–950)	1550 (1100–1800)

*For cost with median value equal to zero, the range is provided in place of the interquartile range

Determinants of out-of-pocket expenditure amongst patients utilizing radiotherapy services (LINAC, Cobalt therapy and brachytherapy) in Tamil Nadu

Table 4 investigates the factors affecting out-of-pocket expenditure for patients undergoing various radiotherapy procedures (Cobalt therapy, LINAC, Brachytherapy) in Tamil Nadu, with a total sample of 500 patients. The table includes both unadjusted and adjusted exponentiated coefficients from log-linear regression models, which help in understanding the relative impact of different factors on healthcare costs.

Table 4.

Determinants of out-of-pocket expenditure amongst patients undergoing radiotherapy procedure (Cobalt therapy, LINAC, Brachytherapy) in secondary and tertiary care facilities of Tamil Nadu (N = 500)

S.N.	Characteristic	N	Total cost per patient Median (IQR)	Unadjusted Exponentiated Coefficient (95% CI)	Adjusted Exponentiated Coefficient (95% CI)	P value
1	Residence
	Urban	178	1020 (600–1300)	1.02 (0.86–1.20)	[Not included in the model]
	Rural	322	835 (520–1300)	Ref
2	Socioeconomic status
	Class I	24	1320 (753-11150)	3.70 (2.47–5.54)	1.69 (1.16–2.47)	0.007
	Class II	87	910 (540–1300)	1.27 (0.98–1.66)	1.29 (1.03–1.62)	0.02
	Class III	135	900 (600–1210)	1.22 (0.96–1.55)	1.30 (1.06–1.59)	0.01
	Class IV	160	950 (570–1400)	1.25 (0.99–1.57)	1.26 (1.04–1.54)	0.02
	Class V	94	810 (370–1270)	Ref	Ref	-
3	Insurance coverage and utilization
	Insured and utilized	491	900 (530–1300)	Ref	Ref	-
	Insured and not utilized	5	55,900 (55350–56800)	35.62 (17.66–71.86)	35.48 (17.73–71.01)	< 0.001
	Uninsured	4	37,050 (18325–60615)	41.19 (18.81–90.20)	35.44 (15.44–81.33)	< 0.001
4	Distance travelled (in kms)
	First quintile (0–10)	62	680 (320–1150)	Ref	Ref	-
	Second quintile (11–20)	92	925 (600–1220)	1.18 (0.88–1.59)	1.28 (0.99–1.65)	0.06
	Third quintile (21–39)	64	890 (495–1375)	1.11 (0.80–1.53)	1.39 (1.06–1.82)	0.02
	Fourth quintile (40–85)	121	850 (500–1250)	1.15 (0.86–1.52)	1.32 (1.04–1.68)	0.02
	Fifth quintile (≥86)	163	1000 (730–1650)	1.55 (1.18–2.03)	1.90 (1.51–2.38)	< 0.001
5	Waiting time (in minutes)
	< 120	360	900 (525–1300)	Ref	[Not included in the model]
	≥120	140	970 (567.5–1400)	0.99 (0.82–1.19)

Residence

The median cost per patient per radiotherapy procedure for urban residents was INR 1,020, compared to INR 835 for rural residents. The unadjusted exponentiated coefficient for urban residents was 1.02 (95% CI: 0.86–1.20), indicating a negligible increase in costs compared to rural residents. The residence variable was not included in the adjusted model.

Socioeconomic status

The cost varied across socioeconomic classes. For instance, Class I had a median cost of INR 1,320 and an adjusted exponentiated coefficient of 1.69 (95% CI: 1.16–2.47), suggesting a 69% increase in costs per patient per radiotherapy procedure compared to the reference group (Class V, p = 0.007). Classes II, III, and IV also showed significant increases in costs per patient with adjusted exponentiated coefficients of 1.29 (p = 0.02), 1.30 (p = 0.01), and 1.26 (p = 0.02) respectively, indicating that higher socioeconomic status was associated with higher out-of-pocket expenses for radiotherapy procedures.

Insurance coverage and utilization

The majority of patients were insured and utilized their insurance, with a median cost per patient per radiotherapy procedure of INR 900. However, the few who were insured but did not utilize their insurance (5 patients) faced dramatically higher costs, with a median of INR 55,900 and an adjusted exponentiated coefficient of 35.48 (95% CI: 17.73–71.01), indicating a 3448% increase in costs (p < 0.001). Uninsured patients (4 patients) also experienced high costs, with a median of INR 37,050 and an adjusted exponentiated coefficient of 35.44 (95% CI: 15.44–81.33), suggesting a similar scale of increase in costs (p < 0.001).

Distance travelled

The median cost per patient increased with the distance travelled. Patients in the fifth quintile (≥ 86 km) incurred a median cost of INR 1,000, with an adjusted exponentiated coefficient of 1.90 (95% CI: 1.51–2.38), indicating a 90% increase in costs compared to the first quintile (p < 0.001). The third and fourth quintiles also showed significant increases in costs with adjusted coefficients of 1.39 (p = 0.02) and 1.32 (p = 0.02) respectively.

Waiting time

The median cost per patient for those waiting less than 120 min was INR 900. Waiting time was not included in the adjusted model, suggesting it might not have a statistically significant impact on out-of-pocket expenditure due to utilization of radiotherapy procedure.

Discussion

This study evaluated OOPE among cancer patients undergoing modern radiotherapy modalities in public healthcare facilities of Tamil Nadu. The key finding was that procedure-related costs were largely minimized through public insurance coverage, but patients continued to incur OOPE through direct non-medical and indirect costs. Median total per-patient OOPE was ₹900 for LINAC, ₹840 for cobalt therapy, and ₹1,550 for brachytherapy, with travel, food, accommodation, and income loss contributing to the financial burden. The higher median OOPE observed for brachytherapy should be interpreted cautiously. The estimate represents per-patient OOPE captured for the brachytherapy procedure and does not represent the full cost of the entire cancer treatment pathway, including prior external-beam radiotherapy, chemotherapy, or repeated visits. Because cancer site, treatment intent, and fractionation details were not adjusted for, differences across LINAC, cobalt therapy, and brachytherapy should be interpreted as descriptive modality-level cost patterns rather than evidence that one modality intrinsically causes higher OOPE.

These findings align with existing literature from low- and middle-income countries, where non-medical and indirect costs may persist despite subsidized treatment [22, 23]. In the present study, the low procedural cost reflects the protective role of public financing and insurance coverage, while the remaining OOPE highlights the need to address patient-incurred costs beyond the procedure itself.

Socioeconomic differences in OOPE were observed, with patients in higher income classes incurring significantly higher expenditures [24]. Patients in Class I spent 69% more than those in Class V. This may reflect differences in travel choices, food and accommodation preferences, opportunity costs, or ability to spend on additional supportive needs rather than differences in the radiotherapy procedure itself. Treatment quality was not assessed in this study; therefore, higher expenditure among higher socioeconomic groups should not be interpreted as evidence of better radiotherapy quality. Previous studies have similarly reported higher absolute healthcare spending among wealthier groups because of differences in care-seeking preferences and capacity to pay [25]. Therefore, this finding should not be interpreted as poorer financial protection among higher-income patients, but as evidence that patient-level expenditure patterns vary across socioeconomic groups.

Insurance utilization was strongly associated with financial protection. Patients who were insured and utilized coverage had low median OOPE, whereas patients who were insured but did not utilize coverage had markedly higher costs, with a median OOPE of ₹55,900. This indicates that insurance enrollment alone is insufficient unless patients are aware of benefits and able to complete claim processes. The specific reasons for non-utilization among these insured patients were not collected at the individual level; therefore, this finding was interpreted cautiously as reflecting possible gaps in awareness, administrative access, documentation, or claim utilization rather than confirmed causal reasons. Similar gaps between insurance coverage and effective utilization have been reported in other settings [26, 27]. For contextual interpretation, the reported monthly household income estimates in Tamil Nadu are approximately ₹12,000–15,000; therefore, even modest radiotherapy-related OOPE may be meaningful for lower-income households, while costs among insured-but-not-utilizing patients may exceed the predefined catastrophic health expenditure threshold [28].

Travel distance was an important determinant of OOPE. Patients travelling ≥ 86 km incurred 90% higher costs than those in the nearest distance category. This finding reflects the repeated-visit nature of radiotherapy and shows that geographic distance contributes to patient-incurred costs even when treatment is provided in public facilities. This finding reflects the repeated-visit nature of radiotherapy and shows that geographic distance contributes to patient-incurred costs even when treatment is provided in public facilities. Existing studies have also consistently highlighted that geographic inaccessibility is a significant barrier to equitable healthcare in India [29, 30].

The determinants identified in this analysis should be interpreted as determinants of financial burden rather than determinants of clinical outcome or treatment quality. Socioeconomic status, insurance utilization, and travel distance influence OOPE through patient-incurred expenses and opportunity costs. These findings are clinically relevant because financial burden may affect treatment adherence, completion of repeated visits, and patient distress, although these downstream outcomes were not directly measured in the present study.

Uninsured patients also experienced substantial financial burden, with a median OOPE of ₹37,050. Although this subgroup was small, the magnitude of expenditure suggests that lack of effective insurance coverage can expose patients to catastrophic health expenditure. This is consistent with previous evidence from India showing that uninsured patients are vulnerable to high healthcare expenditure and economic hardship [31].

This study has a few limitations to consider. First, the cross-sectional design captures OOPE at a single point in time and cannot establish causal relationships between determinants and financial burden. Second, the study was conducted only in public healthcare facilities in Tamil Nadu, facility selection was partly constrained by the sampling strategy of the parent study and by the availability and functionality of radiotherapy equipment; therefore, this may introduce selection bias and limit generalizability to all public radiotherapy facilities in Tamil Nadu. Third, indirect costs such as income loss and travel expenses were self-reported and may be affected by recall bias or underreporting. Fourth, the number of uninsured and insured-but-not-utilizing patients was small, so estimates for these subgroups should be interpreted cautiously. Fifth, the analysis did not adjust for cancer site, stage, treatment intent, fractionation schedule, or total duration of radiotherapy, all of which may influence repeated visits, caregiver time, and indirect costs. Lastly, patient satisfaction, quality of care, and clinical outcomes were not assessed. Future research should include multicentric and longitudinal designs with clinical variables, treatment intent, fractionation details, and patient-reported outcomes.

The study findings suggest that policy interventions should focus on reducing non-medical and indirect costs and improving effective insurance utilization. Practical strategies may include patient counselling on insurance eligibility, simplified claim processes, help desks for CMCHIS-related documentation, and targeted support for travel and accommodation among patients travelling longer distances. Broader interventions such as satellite radiotherapy services, transport support models, or community-based navigation may be considered as hypotheses for future implementation research rather than direct recommendations from the present cross-sectional study.

Conclusion

The study underscores the need for holistic policies addressing the multifaceted financial burden on patients undergoing advanced radiotherapy in Tamil Nadu. While insurance schemes have alleviated procedural costs, indirect and non-medical expenses remain significant barriers. By addressing geographic, socioeconomic, and systemic challenges, Tamil Nadu’s public healthcare system can serve as a model for reducing OOPE and enhancing equitable access to advanced medical technologies. Future research should focus on longitudinal assessments of healthcare costs and the impact of policy interventions in mitigating financial inequities.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

OOPE

Out-of-Pocket Expenditure

LINAC

Linear Accelerator

CMCHIS

Chief Minister’s Comprehensive Health Insurance Scheme

BG

B.G. Prasad Socioeconomic Scale

IQR

Interquartile Range

Author contributions

AB conceptualized the study and led the overall design, data collection, and manuscript drafting. YK contributed to study design, data analysis, and critical revision of the manuscript. DG supported statistical analysis and interpretation of results. SK coordinated field implementation and supervised data acquisition. DK assisted with data management and quality checks. VE contributed to literature review and formatting of the manuscript. MP supported data collection and initial data cleaning. AJP contributed to the development of data collection tools and training of data collectors. MGM provided overall supervision, contributed to interpretation of findings, and critically reviewed the final draft. All authors reviewed and approved the final manuscript.

Funding

This study was conducted as part of an Operations Research project with a grant from the Tamil Nadu Health Systems Reforms Program to study the utilization and economic evaluation of advanced medical equipment in public health facilities of Tamil Nadu. The grant was awarded to Dr. Yuvaraj Krishnamoorthy.

Data availability

Due to institutional regulations and patient confidentiality concerns, the data cannot be made publicly available. However, the datasets used and analyzed during the current study are available from the corresponding author upon reasonable request and with permission from the relevant institutional ethics committee.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Ethics Committee of ESIC Medical College and Hospital, Chennai, India. All participants provided written informed consent prior to their inclusion in the study. The study was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.