Financial Catastrophe of Breast Cancer Treatment: Evidences from a Longitudinal Cohort study in India

Full text

1
Financial Catastrophe of Breast Cancer
Treatment: Evidences from a
Longitudinal Cohort study in India
Sanjay K Mohanty, Tabassum Wadasadawala,
Soumendu Sen, Suraj Maiti, Jishna E
INTERNATIONAL INSTITUTE FOR POPULATION SCIENCES
Mumbai, India
Website:www.iipsindia.ac.in
March, 2023
27
IIPS Working Paper Series

2
From the Editors’ Desk…..
Greetings!!!
The International Institute for Population Sciences has taken a new initiative
to bring out a series of working papers based on the projects / studies
undertaken by the Institute. The main objective of the working paper series is
to disseminate new research ideas, theoretical developments and
methodological insights to the national and international research community
as quickly as possible. The papers published under this series are peer
reviewed by experts in the subject. We hope you will find the working papers
interesting and useful.
Editorial Team, Working Paper Series
Editorial Team:
Prof. K.S. James
Prof. T.V. Sekher
Dr. Dipti Govil
Ms. Irin Anna Solomon
Design and Layout:
Publications Unit, IIPS
Suggested Citation: Mohanty S. K, Wadasadawala T., Sen S., Maiti S.,
Jishna E (2023). “Financial Catastrophe of Breast Cancer Treatment:
Evidences from a Longitudinal Cohort study in India”, Working Paper
No. 27, International Institute for Population Sciences, Mumbai.

3
IIPS Working Paper
Financial Catastrophe of Breast Cancer Treatment: Evidences
from a Longitudinal Cohort study in India
Author(s): Sanjay K Mohanty1, Tabassum Wadasadawala2, Soumendu Sen3,
Suraj Maiti4, Jishna E5
1. Professor and Head, Department of Population and Development, IIPS
Mumbai
2. Professor, Department of Radiation Oncology, Advanced Center for
Treatment Research and Evaluation in Cancer (ACTREC), Tata Memorial
Center, Navi Mumbai
3. Senior Research Scholar, IIPS Mumbai
4. Project Officer, IIPS Mumbai
5. Research Officer, IIPS Mumbai
March, 2023
INTERNATIONAL INSTITUTE FOR POPULATION SCIENCES
Govandi Station, Deonar, Mumbai - 400 088, India

4
Abstract
Background
Breast cancer accounts for one-seventh of the two million cancer cases in India.
It exerts a high economic, social and health burden on patients and households
during and after treatment. The aim of this study is to examine the financial
catastrophe of breast cancer treatment in India.
Data and Method
The study used data of 500 new invasive breast cancer patients seeking
treatment at one of the oldest and largest cancer treatment centres in the country,
the Tata Memorial Centre (TMC), Mumbai. Data was collected from June, 2019
to March, 2022 using a longitudinal study design. Financial catastrophe was
measured using household income, consumption and loan before and during
cancer treatment, cost of treatment, out-of-pocket (OOP) payment, catastrophic
health expenditure (CHE) and distress financing. Descriptive statistics,
bivariate analysis and logistic regression was used in the analysis.
Results
Half of the breast cancer patients diagnosed were under the age of 47 years. The
average income of the households reduced by 14% during cancer treatment.
The average expenditure on travel and accommodation increased by four and
five times, respectively, during cancer treatment. The average cost of breast
cancer treatment was ₹ 219,621 for non-chargeable or general category patients,
and ₹ 416,198 for private patients. Less than 10% of the breast cancer patients
had any form of health insurance at the time of registration although 73.2% had
some form of financial assistance. The mean out-of-pocket expenditure was
₹149,315 for general and non-chargeable patients and ₹414,910 for private
patients. The average loan of a breast cancer patient was ₹ 108,179. Overall,
84.6% of the households incurred CHE and 55% of households were facing
impoverishment. The significant predictors of distress financing for cancer
treatment are high OOP payment, poorer households and those who came from
outside the state of Maharashtra for treatment.

5
Conclusion
Breast cancer in India primarily affects women in the prime working and
reproductive age group. We found high OOP payment, CHE and indebtedness
while treating breast cancer. It is recommended to increase awareness, early
diagnosis, multi-disciplinary treatment, health insurance coverage and
subsidise breast cancer treatment to reduce the financial distress of breast cancer
patients in India.
Keywords: Breast cancer, loans, catastrophic health expenditure, distress
financing, India.
Highlights
1. From a cohort of 500 breast cancer patients registered for treatment at Tata
memorial Center (TMC), 86% (429) patients successfully completed treatment
and were interviewed at the end of treatment completion. Among those
completed treatment, about 48% (206) of them received follow up treatment at
TMC and were interviewed again in 6 months. The study was conducted over
a period of 34 months; from June, 2019 to March, 2022. Data using structure
schedule was collected at baseline, endline and follow up visit and cost
expenditure on cancer treatment was collected at each visit of treatment to
TMC.
2. The median age of breast cancer patients at diagnosis was 47 years, suggesting
that the majority of patients were young and in the reproductive age group
3. Over two-third of the breast cancer patients were diagnosed at an advanced
stage of cancer
4. Late diagnosis and longer duration of treatment was higher among less educated
and poorer women
5. The mean duration of the treatment for breast cancer patients was 276 days and
on average a breast cancer patient made 49 visits to TMC, Mumbai to complete
their treatment

6
6. Among all the patients, 5.2% discontinued their treatment due to death and
4.8% due to financial hardships
7. Less than 10% of the breast cancer patients had insurance coverage
8. At the time of registration for treatment, one-fourth of the breast cancer patients
had any co-morbidity and at the time of completion of treatment it has increased
to 32%
9. More than half of the patients were from outside of Maharashtra and on an
average they travelled 1813 km from their native place to get treatment from
TMC, Mumbai
10. The average cost of treatment of breast cancer treatment was ₹ 258,095 ; ₹
219,621 for general or non changeable patients and ₹ 416,198 for private
patients. These estimates were higher than estimated cost of treatment from
previous studies
11. About 73.2% patients had any form of reimbursement for cancer treatment. The
mean out-of-pocket expenditure on breast cancer treatment was ₹ 149,315 for
general or non-chargeable patients and ₹ 414,910 for private patients
12. Average monthly household income of breast cancer patients was ₹ 17,802
before diagnosis of cancer treatment which decreased to ₹ 15,376 soon after
cancer diagnosis
13. At the time of beginning cancer treatment, 38% had loans for treatment and it
has increased to 65% during treatment at TMC
14. About two-fifth of the breast cancer patients reported poor self-rated health at
baseline and end-line which decreased to 18% during follow-up
15. The chance of incurring loans, selling assets and loans and borrowings was
higher among patients who incurred higher out-of-pocket (more than
₹150,000), who were poor and who came from outside of Maharashtra to seek
treatment

7
1. Introduction
Globally, cancer accounts for 9.6 million deaths annually and an estimated 234
million disability adjusted life years (DALYs) in 2018 (Bray et al., 2018;
Fitzmaurice et al., 2019). Majority of the cancer deaths (70%) occurred in low-
and-middle income countries (Forouzanfar et al., 2016). Of all the cancer
deaths, 7% are due to breast cancer, the second leading cause of all oncological
deaths (Bray et al., 2018). Unlike other cancers, breast cancer largely affects
women in the prime working age (Ginsburg et al., 2017). About two million
new cases of breast cancer are diagnosed annually, which is nearly 25% of all
oncological diagnoses among women (Ginsburg et al., 2017; Ferlay et al.,
2015). The incidence of breast cancer increased by 30% in both developed and
developing countries in the last three decades (Herback & Grant,2017). In the
recent decades, while there has been a decline in stomach, cervical and penile
cancer, the incidence of colorectal, prostate and breast cancer, has been
increasing (Smith et al., 2019). The major risk factors of breast cancer are
changing fertility pattern (early age at menarche, later menopause,
childlessness, late childbearing, reduced breast feeding), changing life style
(drinking alcohol, smoking), increasing obesity, physical inactivity and family
history (Economic Intelligence Unit, 2016; Ginsburg et al., 2017; Youn and
Han 2020).
India accounts for 6.4% of the global cancer patients and cancer is the fifth
leading cause of death. Of all the cancer cases, 21.8% were diagnosed with
breast cancer in India where the mortality of breast cancer patient is higher than
the global average (Kulothungan et al., 2022). Presently, breast cancer is the
most common cancer among women globally, and has also become the most
common cancer in India. Late diagnosis, non-availability of cancer treatment
facilities in rural areas, familial negligence, social stigma, low standard of living
and lack of social safety nets are some of the probable causes of high cancer
mortality.
Compared to any other disease, cancer has adverse short and long-term
consequences on the health of survivors and socio-economic condition of
households. Cancer introduces sudden shock and fear into Indian households
mainly because of poor survival and the high economic burden of treatment
(Brown et al., 2001). Treatment of cancer is of long duration and most

8
expensive among all diseases. The cost of treatment of cancer is rising at an
unparalleled rate which varies considerably within different treatment settings
(Natarajan et al., 2020). Breast cancer treatment cost adversely affects the
economic well-being of households, directly and indirectly. Households resort
to borrowing and selling assets and absenteeism from work (direct) . The
indirect cost includes the loss of wages and salaries of patients and
accompanying persons, along with loss of productivity and time (Zheng et al.,
2016; Ekwueme et al., 2014). In 2018, the mean out-of-pocket (OOP) payment
for any cancer treatment on hospitalisation in India was estimated at ₹85,595;
₹38,859 at public and ₹115,771 at private hospitals (Goyanka et al., 2021).
Medicine and hospitalization accounted for 60% of the total cost of breast
cancer treatment (Jain & Mukherjee, 2016). Most of the OOP payment was
spent for medication, transportation, and physician visits (Arozullah et al 2004).
The direct medical cost of breast cancer patients treated in a private hospital
was almost three times higher than that at a public hospital (Afkar et al., 2021).
Studies have found that cancer is the leading cause of high catastrophic health
spending and distress financing in India (Rajpal et al., 2018; Kastor & Mohanty,
2018). A study in the state of Punjab showed that 84% of the households with
breast cancer patients experienced catastrophic health expenditure (CHE) and
51% of those faced distress financing (Jain & Mukherjee, 2016). Distance, type
of work and insurance coverage are the major factors that increase CHE (Bose
et al., 2022). Besides, cancer treatment increases hospital service utilization and
patients who had received surgery, radiation therapy and chemotherapy had
higher CHE (Zhao et al., 2020; Zhao et al., 2022). The probability of incurring
CHE is high for those who undergo surgery, female-headed households, longer
duration of stay, type of health insurance, poor households, and household size
(Azzani et al., 2017; Zheng et al., 2018; Sun et al., 2021; Kim & Kwon, 2015).
Incidence of CHE on cancer is higher among the poor and those who seek
treatment in private hospitals (Rajpal et al., 2018; Singh et al., 2020).
Though cancer statistics are increasingly available in recent years, there are
limited studies on economic adversity of breast cancer treatment in India. The
economic burden of cancer on households and individuals is enormous.
Existing studies estimated the overall cost of cancer treatment based on cross-
sectional household data which is likely to underestimate the true cost of
treatment (Mahal et al., 2013; Rajpal et al., 2018; Goyanka 2021). As cancer

9
treatment is prolonged, lasting over a year, reliable treatment cost is difficult to
estimate at a point of time. Besides, there is no study that estimated debt due to
cancer treatment. Against this background, this paper provides a comprehensive
estimate of the financial catastrophe of breast cancer treatment India.
2. Data & Methods
2.1. Study Design
We used a longitudinal study design and collected data from a tertiary public
sector cancer center in India. Data was collected under a project entitled
“Health Expenditure on Breast Cancer Treatment in Women: A Study from
Public Sector Tertiary Cancer Centre” (EXPERT), conducted by the Tata
Memorial Centre (TMC), Mumbai and the International Institute for Population
Sciences (IIPS), Mumbai, Maharashtra, India. The study had obtained prior
approval from the institutional ethics committee of the TMC and is registered
on the Clinical Trial Registry of India (CTRI/2019/07/020142).
2.2. Data collection and follow-up surveys
All the participants of the study were female breast cancer patients who sought
treatment at TMC between June 2019 and March 2022. The current study was
restricted to new invasive breast cancer patients treated with curative intent
undergoing a multi-modality therapy consisting of surgery, chemotherapy, and
radiotherapy. Considering the non-response rate, permissible error, and
sufficient power, a maximum of 500 non-metastatic female breast cancer cases
was considered for inclusion in the study. The inclusion criteria were:
i. Pathologically confirmed new invasive breast cancer case
ii. Non-metastatic invasive breast cancer (AJCC 8th edition)
iii. Intention to receive the entire treatment at TMC
iv. Multi-modal treatment comprising surgery, chemotherapy, and
radiotherapy with or without hormone therapy or targeted therapy
v. Age > 18 years
vi. Willingness to provide all estimates of expenditure before and after
coming to the tertiary hospital
vii. Willingness to share relevant socio-demographic information
viii. Willingness to fill out or respond to QoL instruments

10
The exclusion criteria were:
i. Inability or unwillingness to give written informed consent
ii. Inability to follow up after treatment completion
iii. Unwillingness to follow up for two years
iv. Recurrent or progressive disease
After carefully considering the inclusion criteria, each participant was assigned
a unique identification number (record id) that was used as key identifier.
Written informed consent was taken from the participants and their
accompanying person before conducting the interviews.
2.3. Stage of Data Collection
The study collected comprehensive socio-economic and health data at three
time points viz., baseline, endline and follow up and expenditure on treatment
during each visit to TMC during a period of 34 months from 26th June, 2019 till
31st March, 2022. The base line survey began on 26th June, 2019 and continued
till 1st July, 2021. On an average, patients made 49 visits for treatment at TMC
and expenditure on each episode of visit was collected.
The baseline survey is the first contact of the patient with the survey team at the
time of registration. During the baseline survey, data was collected for the
socio-demographic, health and medical history of the patients and economic
condition of the households. Of the 500 patients registered at baseline, 71
patients discontinued treatment therefore, 429 patients could be interviewed
successfully at endline. Endline survey began as soon as a patient completed
her treatment. The endline survey began on 7th February, 2020 and continued
till 31st March, 2022. Six months after completion of treatment, the patient
visited for follow-up after a follow up schedule was canvassed. The follow up
survey began on 18th January 2021 and was completed on 17th March, 2022.
Data on household expenditure and quality of life was collected till the first
follow-up visit, i.e., six months after concluding the treatment. In the follow up,
a total of 206 patients were interviewed successfully. A large number of patients
did not come for follow up services within the stipulated time. It was difficult
to get follow up patients as many of them did not visit even after one year of
completion of treatment due to COVID restrictions.

11
The data collection took 34 months against an estimated time of 24 months. The
delay in survey was primarily due to the COVID-19 pandemic and need to
minimize the risk of COVID exposure in cancer patients by minimizing the time
spent in the hospital. Fewer patients visited during lockdown due to travel
restrictions and this increased the time span for data collection. Some patients
had stopped treatment due to financial crisis during the lockdown period and
hence, their treatment got delayed. Many patients had missed follow-up after
completing the treatment due to travel restrictions. Sometimes patients did not
allow the medical social workers to conduct the interview out of fear of
contracting COVID-19. Project staff and principal investigators also suffered
from Covid-19. During the treatment period, social workers interviewed each
patient for each of their visits to the hospital and only expenditure related
information was collected.
2.4. Study questionnaire
The household questionnaire covered demographic and socio-economic
characteristics of a participant’s household including income, consumption
pattern, health expenditure in the last one year, health-seeking behaviour, and
loans and debts of the household at the time of registration at TMC. The
individual questionnaire collected information on treatment history about
current breast cancer diagnosis, treatment history at TMC, detailed record of
the direct and in-direct health expenditure per hospital visit during the entire
course of treatment, commodities and self-rated health status of patients. Both
the household and individual questionnaires were canvassed at baseline. A total
of four instruments pertaining to the quality of life were canvassed to the
patients at the baseline, endline and follow-up period. These are the quality of
life developed by European Organization for Research and Treatment of Cancer
(EORTC): QLQ-C30 and BR23, World Health Organization Disability
Assessment schedule (WHODAS) developed by WHO and EQ-5D-5L
developed by EuroQol group. A shorter version of the questionnaire (baseline)
was canvassed at the endline and follow up. Follow-up and end-line surveys
collected information on self-reported health, comorbidities, health financing,
insurance and reimbursement, loans and debts due to cancer treatment. All
these questionnaires were canvassed in English/ Hindi / Marathi, based on the

12
preference of respondents. The medical terminologies were explained to the
participants at the time of the interview, and no difficulties in understanding
was reported. The questionnaires were validated by a panel of experts
comprised of oncologists, health economists, demographers, and university
professors.
The data collection process was executed by three trained medical social
workers who captured every single visit made by the patient or attendant during
the entire period of treatment that generally lasted for 6-12 months depending
upon the modalities of treatment appropriate as per the stage of cancer. Various
data quality measures like regular monitoring of data collection and re-
validation were undertaken. Inconsistency and irrelevant data were identified
and corrected regularly by principal investigators and researchers of the project.
2.5. Socio-demographic, economic and health variables
A set of socio-economic and health variables were used in the analyses. These
were broadly categorised as patient related characteristics, health characteristics
and household characteristics. The patient related characteristics included age
(<30, 31-40, 41-50, 51-60, 60+ years), education (never attended /up to
secondary /higher secondary and above), marital status (currently
married/other), health insurance (yes/no), type of patient (non-chargeable or
general/private). The health-related characteristics included treatment taken
outside before coming to TMC (yes/no), co- morbidity (no co-morbidity/one or
more co-morbidity) and stage of cancer diagnosis (stage I-II/stage III/ stage IV).
The household related characteristics include residence at the time of treatment
(hotel or rental room /own house/relatives and friends house/ ashram and other),
religion (Hindu/ Muslim/ Other), social group (general /OBC/ SC,ST or other),
residence (urban/rural), state (Maharashtra/Outside Maharashtra), major source
of income (agriculture/labour/self-employed/service), distance from native
place to Mumbai(<500kms, 500- 2000kms, >2000kms), duration of the
treatment (<9 Months, 9-12 Months , >12Months), place of treatment (TMC /
at least one outside TMC), income tertile (poor/middle/rich).
At TMC, the patients are classified as a) general b) non-chargeable and c)
private. During registration, a patient is registered either as a general or as a
private patient depending on their ability to pay for the treatment. Private

13
patients paid for the treatment as per the market rates while general patients
were charged at subsidized rates. The cost of treatment for private patients’
category was higher while the waiting time for availing treatment is relatively
lower than for general or non-chargeable category patients. After careful
scrutiny of the general patients by social workers, treatment is made available
for extremely marginalized patients at very low cost or almost negligible cost
which have been categorized as non-chargeable.
2.6. Outcome variable
A set of outcome variables were used in the analyses. These include, monthly
per capita expenditure (MPCE), average monthly income of household, total
cost and OOP payment incurred for treatment, source of reimbursement
received by patients. The MPCE is defined as the total consumption expenditure
divided by household size. The total consumption expenditure did not include
health expenditure. The OOP is defined as total expenditure of the household
excluding the reimbursement. The catastrophic health spending was estimated
using capacity to pay approach (Xu et al.,2003). Many of these outcome
variables were assessed at baseline, endline and follow up. A variable on
distress financing, defined as those patients who either sold their jewellery or
assets or took loans or borrowed money from any other sources to finance their
cost of treatment, was computed and used in analyses.
2.7. Statistical analysis
Descriptive statistics, and multivariate logistic regression model was used in the
analyses. The statistical analysis was carried out using STATA 17.
3. Results
Socio-demographic profile
3.1. Socio-demographic characteristics of breast cancer patients
Table 1 presents the profile of 500 breast cancer patients registered for
treatment at TMC, Mumbai. Of all the patients registered for treatment 429
completed their treatment while 71 had discontinued. Of the 429 patients who
sought treatment and had been successfully interviewed at the time of

14
conclusion (henceforth termed as endline), 206 patients were interviewed
during their follow up visit after six months.
Table 1: Duration of survey and completion rate of baseline, endline and
follow up visit
No
Subjects
Starting date
of interview
Ending date
of interview
Duration of
survey in
months
Target
Achieved
Completion
rate (in %)
1
Number of
patients
accrual
(Baseline)
26-06-2019
01-07-2021
25
500
500
100
2
Number of
concluded
patients
(Endline)
07-02-2020
31-03-2022
25
500
429
85.8
3
Number of
follow-up
patients
18-01-2021
17-03-2022
14
429
206
48.0
4
Number of
non-
cancerous
women
22-09-2021
19-11-2021
2
200
200
100
Figure 1: Distribution of age at diagnosis of breast cancer patients

15
The age distribution of the patients at the time of cancer diagnosis is shown in
figure 1. The mean/median age of breast cancer diagnosis was 47 years. The
youngest age at breast cancer diagnosis was 21 years and the oldest age at
diagnosis was 84 years. Most of the cancer patients diagnosed were between 40
to 60 years.
Table 2: Socio-demographic profile of breast cancer patients and economic
profile of households at baseline, end line and follow-up
Baseline
Endline
Follow up
Patient’s
characteristics
%
N
%
N
%
N
Age (years)
< 30
5.6
28
5.6
24
4.4
9
31- 40
24.8
124
25.9
111
28.6
59
41- 50
32.6
163
33.8
145
34.0
70
51- 60
26.8
134
25.2
108
24.8
51
> 60
10.2
51
9.6
41
8.3
17
Years of
schooling
Never attended
26.6
133
23.1
99
21.8
45
Up to secondary
45.8
229
47.3
203
47.6
98
Higher secondary
and above
27.6
138
29.6
127
30.6
63
Marital status
Currently married
84.4
422
85.3
366
88.4
182
Other
15.6
78
14.7
63
11.7
24
Health insurance
Yes
9.0
45
8.9
38
12.6
26
No
91.0
455
91.1
391
87.4
180
Health
characteristics
Patient category
Non-chargeable
1.2
6
5.4
23
9.2
19
General
85.8
429
80.7
346
77.2
159
Private
13.0
65
13.9
60
13.6
28
Co-morbidity
No co-morbidity
75.6
378
69.0
296
74.3
153
One or more co-
morbidity
24.4
122
32.0
133
25.7
53
Household
characteristics
Residence during
treatment

16
Hotel or rental
room
37.0
185
38.2
164
34.5
71
Own house
28.6
143
28.2
121
27.2
56
Relatives’ and
friends’ house
23.0
115
22.6
97
26.7
55
Ashram and
others
11.4
57
11.0
47
11.7
24
Religion
Hindu
78.8
394
77.4
332
80.1
165
Muslim
17.2
86
18.7
80
14.6
30
Other
4.0
20
4.0
17
5.3
11
Social group
General
51.8
259
52.7
226
43.2
89
OBC
33.8
169
33.8
145
40.8
84
SC/ ST / Others
14.4
72
13.5
58
16.0
33
Residence
Urban
46.4
232
45.7
196
49.0
101
Rural
53.6
268
54.3
233
51.0
105
State
Maharashtra
45.4
227
55.2
237
46.6
96
Outside of
Maharashtra
54.6
273
44.8
192
53.4
110
Distance from native place
< 500 kms
43.4
217
43.1
185
44.2
91
501 - 2000 kms
37.2
186
36.4
156
39.8
82
> 2000 kms
19.4
97
20.5
88
16.0
33
Major source of
income
Agriculture
12.8
64
12.6
54
12.6
26
Labour
25.8
129
24.0
103
30.1
62
Self-employed
15.8
79
15.4
66
13.1
27
Service
45.6
228
48.0
206
44.2
91
Income tertile
Poor
35.6
178
33.3
143
35.4
73
Middle
31.8
159
32.6
140
33.5
69
Rich
32.6
163
34.0
146
31.1
64
Total
100
500
100
429
100
206
Table 2 shows the sample characteristics of breast cancer patients undergoing
treatment at TMC at various stages of data collection. The basic demographic
and social characteristics did not change during baseline and endline. At the
time of baseline, 5.6% patients were under 30 years, 57.4% were between 31-
50 years, and 37% were 50 years or older. The distribution remains similar at
the endline. Almost half of the patients in the baseline/endline had completed

17
only secondary schooling (46%), and the mean years of schooling was 7 years.
More than four-fifths of the patients were married at the time of the baseline
and endline survey. Only 9% of the patients were covered by any health
insurance scheme in the baseline and endline each and this was 13% at follow-
up. During baseline, about 86% of the patients were registered for treatment at
TMC under the general category and this was 81% at the time of endline. The
proportion of non-chargeable patients increased from 1.2% at baseline to 5.4%
at the time of endline. Only 13% of the patients were registered under the
private category, and their numbers remained similar at the baseline and
endline. A majority of the patients belonged to the Hindu religion (78%). More
than half of the patients were from outside the state of Maharashtra. About two-
third of the breast cancer patients were diagnosed at an advanced stage (stage
III& IV), only 33% of the breast cancer patients were diagnosed at stage II while
very few patients were diagnosed at stage I.
Table 3: Average number of visits and duration of treatment of breast cancer in
TMC, Mumbai, 2019-22
Patients’ characteristics
Average number of visits
Mean duration of
treatment (Days)
Mean
SD
Mean
SD
Age (years)
< 40
51
15
281
84
41-59
50
15
277
82
> 60
42
20
257
92
Years of schooling
Never attended
52
17
293
91
Up to secondary
49
15
274
87
Higher secondary and above
50
16
269
71
Marital status
Currently married
50
16
278
84
Other
48
16
269
82
Health insurance
Yes
50
16
287
112
No
45
13
275
80
Health characteristics
Treatment taken outside before
coming to TMC
Yes
52
16
269
64
No
49
16
278
86
Patient category

18
Non-chargeable
57
15
321
99
General
49
16
274
83
Private
47
14
275
83
Co-morbidity
No co-morbidity
50
16
277
85
One or more co-morbidity
48
16
274
81
Stage of cancer diagnosis
I-II
45
15
258
86
III
52
16
287
76
IV
49
13
289
154
Household characteristics
Residence during treatment
Hotel or rental room
51
15
281
72
Own house
46
16
253
75
Relatives’ and friends’ house
50
16
303
100
Ashram and others
54
18
273
92
Religion
Hindu
49
17
275
84
Muslim
50
12
292
86
Other
46
11
232
69
Social group
General
49
16
268
63
OBC
52
17
290
95
SC/ ST / Others
47
15
280
118
Residence
Urban
49
16
268
71
Rural
50
16
284
93
State
Maharashtra
47
16
262
79
Outside of Maharashtra
51
15
288
85
Distance from native place
< 500 kms
47
16
262
79
501 -1500 kms
50
16
311
112
>1500 kms
51
15
280
74
Major source of income
Agriculture
51
18
276
82
Labour
51
15
284
89
Self-employed
48
15
290
95
Service
49
16
268
78
Income tertile
Poor
50
17
285
92
Middle
50
14
272
85
Rich
49
16
273
74
Total
49
16
276
84

19
Table 3 shows the average duration of treatment and the average number of
visits for breast cancer patients at TMC. On an average, a patient made 49 visits
to TMC and received treatment for an average of 276 days. Both, the number
of visits and duration of treatment, varied by patient characteristics. The
duration of treatment was higher among patients with no education, low income
and those from rural areas. Similarly, those who had health insurance had longer
duration of treatment (287 days) compared to uninsured patients (275 days).
The mean duration of treatment was higher among non-chargeable patients (321
days) and patients staying in a relative’s or friend’s house (303 days) during
treatment. Patients who came from outside Maharashtra had longer duration of
treatment (288 days) compared to those from Maharashtra (262days).
Economic profile
3.2. Consumption and income details of breast cancer patients
Table 4 presents the monthly per capita expenditure (MPCE) of breast cancer
patients before and after their breast cancer diagnosis. The components of
MPCE are expenditure related to food, non-food items and other expenditure.
The average MPCE on food was ₹ 1,345 before cancer diagnosis compared to
₹1,788 after cancer diagnosis, an increase of 33 %. The MPCE of non-food
expenditure increased from ₹ 1,555 before cancer diagnosis to ₹3,133 after
cancer diagnosis. Utility and entertainment related expenditure declined
following cancer diagnosis. The mean travel expenditure increased almost five
folds following cancer diagnosis while the rent increased six times. The overall
MPCE increased by 69.7%, from ₹ 2,900 to ₹ 4,921.
Table 4: Monthly per capita expenditure of breast cancer patients (in ₹) before
and after diagnosis of breast cancer
Variable
Before cancer
diagnosis
After cancer diagnosis
Percentage
change
MPCE
(in ₹)
SD
MPCE (in
₹)
SD
Food
1345
1407
1788
1143
33.0
Utility
393
298
387
334
-1.4
Travel
201
313
1024
1420
408.7
Entertainment
62
131
60
143
-4.0

20
Maid, cook, laundry
etc
17
104
17
110
0
Rent
156
633
919
1780
489.5
Non-food
1555
3683
3133
5002
101.5
MPCE
2900
4094
4921
5553
69.7
Figure 2: Percentage share of rent, travel, utility, and food expenditure to
MPCE before and after breast cancer diagnosis
Figure 2 represents the percentage share of specific consumption to the MPCE
of breast cancer patients before and after cancer diagnosis. Following cancer
diagnosis, the relative share of expenditure on rent to MPCE increased by about
3 times while that of travel increased by 2 times. The relative share of food,
utility and other expenditure to MPCE declined during cancer treatment.
Table 5: Variation in monthly average household income (in ₹) before and after
cancer diagnosis
Patient’s
characteristics
Baseline
Percentage
difference
Before cancer
diagnosis
After cancer
diagnosis
Median
IQR
Median
IQR
Age (years)
< 40
9333
5916.-20000
6352
500-15000
-31.9
41 - 59
10000
6250-20000
9416
4000-20000
-5.8
> 60
15000
6200-20000
12000
6000-20000
-20.0
57
14
25
46
19 21
8
15
36
0
10
20
30
40
50
Rent Travel Utility Other
expenditure Food
Before cancer diagnosis After cancer diagnosis

21
Years of schooling
Never attended
10000
5000-16666.
7000
1340-14500
-30.0
Up to secondary
10000
6000-18000
7500
3000-16500
-25.0
Higher secondary and
above
16000
8000-35000
14250
6000-30000
-10.9
Marital status
Currently married
10000
6000-20000
8002
3600-18000
-20.0
Other
10000
6000-20000
10000
3833-20000
0.0
Health insurance
Yes
34000
6000-18000
30000
3272-16000
-11.8
No
10000
6000-18000
8000
3272-16000
-20.0
Health characteristics
Patient category
Non-chargeable
6000
5000-9000
0
0-2000
-100.0
General
10000
6000-18000
8000
4000-16000
-20.0
Private
30000
19000-
65000
30000
17000-
65000
0.0
Stage of cancer
diagnosis
I-II
14000
7000-25750
10000
5000-25000
-28.6
III
10000
6000-18000
8000
3000-17000
-20.0
IV
10000
7750-17000
6668
7750-17000
-33.3
Household
characteristics
Residence during
treatment
Hotel or rental room
10000
6000-20000
9000
3000-20000
-10.0
Own house
15000
8000-23000
12000
6404-22000
-20.0
Relatives and friends
house
9000
6000-15000
5350
500-13500
-40.6
Ashram and others
8000
5000-18000
6000
0-12500
-25.0
Religion
Hindu
10250
6000-20000
9208
4000-20000
-10.2
Muslim
9000
6000-16000
6000
0-12000
-33.3
Other
16000
7500-26250
16000
9000-30000
0.0
Residence
Urban
13250
7500-22000
12000
5833-20400
-9.4
Rural
9000
6000-18000
7000
1683-15000
-22.2
State

22
Maharashtra
13000
6000-20000
11400
1421-16872
-12.3
Outside of Maharashtra
9000
7500-20000
7000
5833-20000
-22.2
Distance from native
place
< 500 kms
13500
8000-20000
12000
6000-20000
-11.1
501 - 1500 kms
10000
6000-28000
8550
3800-25500
-14.5
> 1500 kms
9000
6000-16000
6301
0-15000
-30.0
Major Source of
income
Agriculture
6000
4583.-8500
5816
3466.-8000
-3.1
Labour
8333
6000-12000
3000
0-8668
-64.0
Self-employed
10000
6000-20000
8000
2000-20000
-20.0
Service
17000
9458.-
33166.
16936
8000-30000
-0.4
Total
10000
6000-20000
8834
3716-20000
-11.7
Table 5 presents the variation of monthly median household income before and
after diagnosis of cancer, collected during baseline survey. The monthly median
household income decreased from ₹ 10,000 to ₹ 8,834 soon after cancer
diagnosis. Patients who were younger had higher decrease in household income
following cancer diagnosis compared to the older patients. For instance, among
the patients aged below 40 years, 41 to 59 years, and 60 years and above the
monthly household income declined by 31.9%, 5.8% and, 20.0%, respectively.
Patients who never attended school recorded higher decrease in income. The
median monthly income before cancer diagnosis of rural patients was ₹9,000
and it decreased to ₹7,000 after diagnosis of cancer. Households that earned
their income through labour showed a drastic reduction in income, by 64%, post
cancer diagnosis.
3.3 Cost of breast cancer treatment
In Figure 3, the average cost of treatment, reimbursement and OOP payment of
breast cancer patients’ at TMC is shown. The average cost of treatment was
₹258, 095 and the mean OOP payment for the patients was ₹186, 461.

23
Figure 3: Average cost of treatment, mean reimbursement and OOP payment for
treatment of breast cancer
Figure 4: Percent distribution of treatment cost of breast cancer by component at TMC,
Mumbai
Registration
Cost
Consultation Cost
1%
Admission Cost
2%
Investigation Cost
8%
Medicine Cost
3%
Surgery Cost
10%
Chemotherapy
Cost 20%
Radiotherapy
Cost
13%
Food Cost
18%
Travel Cost
7%
Accomodation
Cost
18%
Non-Medical
Cost
Medical Cost

24
Of the total cost of treatment at TMC, direct medical cost accounted for 56%
while 44% was the non-medical cost (Figure 4). The distribution of total cost
further suggests that chemotherapy accounted for the largest share (20%)
followed by food, accommodation (18% each) and radiotherapy (13%). The
largest share of medical cost of treatment was due to chemotherapy (35%),
followed by radiotherapy (23%) and surgery (17%).
Table 6: Socio-economic differentials in the total cost and OOP payment (in ₹) for
breast cancer treatment, and share of OOP payment to total cost at TMC,
Mumbai
Cost of treatment (in ₹)
OOP payment (in ₹)
OOP payment
as a share of
total cost
SES Variables
N
Mean
SD
Median
Mean
SD
Median
Mean
Median
Age of Patients
Up to 45 Years
202
266258
206515
203078
188367
190410
122746
70.7
60.4
Over 45 Years
227
250831
211496
196028
184765
205041
129396
73.7
66
Marital Status
Others
63
192676
143139
155099
124540
112976
97261
64.6
62.7
Currently
Married
366
269355
216577
207976
197120
207502
133406
73.2
64.1
Location of
Residence
Urban
196
206389
168610
146781
131193
144752
79508
63.6
54.2
Rural
233
301590
229302
239031
232953
223628
168688
77.2
70.6
Education Level
Never Attended
99
236252
174948
199308
166617
152383
128938
70.5
64.7
Primary
36
235438
212503
146450
176455
205583
115697
74.9
79
Secondary
167
209950
143408
181628
141081
133004
105610
67.2
58.1
Higher
Secondary
50
275740
216266
210003
214277
203545
146627
77.7
69.8
Above HS
77
389730
295105
322061
297013
296970
212713
76.2
66
Religion
Hindu
332
263135
218016
203078
189326
204650
126859
72
62.5
Muslim
80
252210
180129
198839
193549
182085
156025
76.7
78.5
Others
17
187350
140208
144211
97149
101822
70597
51.9
49
Caste
General
226
287088
234406
232646
214131
222445
149845
74.6
64.4
OBC
145
239628
184472
195045
168999
172390
119180
70.5
61.1
SC/ST/Other
58
191289
128787
150464
122299
127145
95986
63.9
63.8
Distance to
Mumbai
<500 kms
185
164606
136894
126897
95887
107201
58948
58.3
46.5
501-1500 kms
60
348865
217688
279415
290706
234587
196948
83.3
70.5

25
>1500 kms
184
322493
228902
257530
243534
217536
181237
75.5
70.4
Income Source
Agriculture
54
280074
167454
277682
214717
157732
186843
76.7
67.3
Labour
103
216336
151523
182797
150144
135471
120902
69.4
66.1
Self-Employed
66
300722
277027
250029
230801
254569
152633
76.7
61
Service
206
259556
216000
188951
183007
210415
116679
70.5
61.8
MPCE quintile
Poorest
83
147955
99480
133938
90430
80432
81763
61.1
61
Poorer
78
175336
117582
138642
115596
113540
91213
65.9
65.8
Middle
89
218674
126750
199635
152673
126933
124478
69.8
62.4
Richer
89
293421
231277
232682
215062
228390
170478
73.3
73.3
Richest
90
435442
261561
389533
341569
254451
292253
78.4
75
Type of Patient
General/ Non-
chargeable
369
210246
145917
179275
149315
138086
112644
71
62.8
Private
60
552368
286145
512822
414910
322408
448882
75.1
87.5
Stage of Cancer
I/II
155
231335
196810
166697
164721
175238
106154
71.2
63.7
III
259
271367
212571
216391
195395
206657
136931
72
63.3
IV
15
305444
252624
248612
256848
249931
183321
84.1
73.7
Comorbidities
No Comorbidity
296
251805
187493
199697
182909
181845
129167
72.6
64.7
At least 1
comorbidity
133
272093
250738
211089
194367
230667
115494
71.4
54.7
Place of
treatment
TMC
243
217448
193408
163732
148239
178280
99481
68.2
60.1
At least one
Outside TMC
186
311198
217211
250617
236397
211556
184705
76
73.7
Duration of
Treatment
< 9M
214
232674
186360
174066
156228
162259
105490
67.1
60.6
9 M-12 M
174
262883
202038
211647
196180
199095
139478
74.6
65.9
12M
41
370456
299491
280350
303018
298157
187141
81.8
66.8
Total
429
258095
209064
200819
186461
198065
126988
72.2
63.2
Table 6 shows the socio-economic differentials in total treatment cost at TMC,
total OOP payment and share of OOP payment to the total cost. The total cost
of treatment/OOP payment at TMC was higher for patients who were younger,
belonged to rural areas, had comorbidity, were diagnosed at later stage and
sought at least one treatment outside TMC. The mean OOP payment was ₹
186,461, accounting for 72% of the total cost.

On an average, the mean OOP payment for the richest quintile was three times higher
than that of the poorest quintile. Further, the share of OOP payment to the total cost
varied from 61% in the poorest quintile to 78% in the richest quintile. Similarly, the
OOP payment for patients in stage I/II was ₹ 164,721, accounting for 64% of the total
cost compared to ₹ 256,848 for stage IV patients, accounting for 74% of the total cost
of treatment. The OOP payment also increased with the duration of treatment.
Patients with less than 9 months of treatment incurred about half the OOP payment
compared to patients treated for more than one year (₹ 156,628 vs ₹ 303,018).
Figure 5: Percent distribution of source of reimbursement received by patients at TMC.
Figure 5 presents the percent distribution of source of reimbursement received by the
breast cancer patients. The highest reimbursement was received from Tata trust
(30%) followed by Mahatma Phule health insurance schemes (17%). Almost, one-
fourth of the patients did not receive any reimbursement.
3.4. Loan and Debt of Breast cancer patients
Figure 6 presents the percentage of patients who had taken loan for treatment. At the
baseline, only 38% of the patients had taken a loan, which increased to 65% at the
endline and 69% at the follow-up period.

27
Figure 6: Percentage of patients taking loan for treatment at baseline, endline and follow up
Table 8: Incidence and intensity of CHE and impoverishment by socio-demographic
and economic characteristics among breast cancer patients
Variables
Incidence of CHE
Intensity of CHE
Impoverishment
Age
n
%
95% CI
Mean
95% CI
%
95% CI
Up to 45 years
202
85.2
[79.5, 89.8]
1.27
[1.0, 1.6]
53.5
[46.3, 60.5]
Over 45 years
227
84.1
[78.7. 88.6]
2.56
[-0.1, 5.2]
56.4
[49.7, 62.9]
Marital Status
Other
63
84.1
[72.7, 92.1]
0.95
[-0.2, 2.1]
41.3
[29.0, 54.4]
Currently Married
366
84.7
[80.6, 88.2]
2.1
[4.9, 3.7]
57.4
[52.1, 62.5]
MPCE quintile
Poorest
83
84.3
[74.7, 91.4]
5.4
[-1.9, 12.7]
63.9
[52.6, 74.1]
Poorer
78
83.3
[73.2, 90.8]
1.59
[1.1, 2.1]
56.4
[44.7, 67.6]
Middle
89
84.3
[75.1, 91.1]
1.26
[0.9, 1.6]
55.1
[44.1, 65.6]
Richer
89
85.4
[76.3, 92.0]
0.93
[0.7, 1.1]
48.3
[37.6, 59.2]
Richest
90
85.6
[76.6, 92.1]
0.79
[0.7, 0.9]
52.2
[41.4, 62.9]
Place of residence
Urban
196
78.1
[71.6, 83.6]
2.04
[-0.2, 4.3]
43.9
[36.8, 51.1]
Rural
233
90.1
[75.6, 93.6]
1.89
[0.8, 3.7]
64.4
[57.9, 70.5]
Level of Education
Never Attended
99
89.9
[82.2, 95.0]
0
[-2.2, 2.2]
60.6
[50.3, 70.3]
Primary
36
83.3
[67.2, 93.7]
6.92
[-4.1, 18.0]
55.6
[38.1, 72.1]
Secondary
167
82
[75.4, 87.5]
2.6
[0.8, 5.1]
47.9
[40.1, 55.8]
Higher Secondary
50
90
[78.2, 96.7]
1.23
[3.9, 8.5]
62
[47.2, 75.3]
Above HS
77
80.5
[69.9,88.7]
1.49
[1.1, 1.8]
58.4
[46.7, 69.6]
Religion
37.8
64.6 68.9
0
10
20
30
40
50
60
70
80
Baseline Endline 6 Months Follow Up

28
Hindu
332
85.2
[81.0, 88.9]
2.1
[0.3, 3.9]]
55.7
[50.2, 61.1]
Muslim
80
82.5
[72.4, 90.1]
1.42
[0.9, 1.9]
56.3
[44.7, 67.3]
Other
17
82.4
[56.6, 96.2]
1.7
[-0.2, 3.6]
35.3
[14.2, 61.7]
Caste
General
226
84.1
[78.6, 88.6]
2.11
[0.42, 3.80]
55.8
[49.0, 62.3]
OBC
145
86.9
[80.3, 91.9]
1.87
[-1.2, 5.0]
53.1
[44.7, 61.4]
SC/ST/Other
58
81
[68.6, 90.1]
1.5
[0.8, 2.2]
56.9
[43.2, 69.8]
Occupation
Agriculture
54
98.1
[90.1, 99.9]
4.04
[-2.2, 10.3]
66.7
[52.5, 78.9]
Labour
103
86.4
[78.2, 92.4]
0.4
[-1.7, 2.5]
56.3
[46.2, 66.1]
Self-employed
66
80.3
[68.7, 89.1]
0.98
[-0.1, 2.1]
57.6
[44.8, 69.7]
Service
206
81.6
[75.6, 86.6]
2.41
[0.4, 4.5]
50.5
[43.5, 57.5]
Type of patient
General
369
85.1
[81.0, 88.6]
2.05
[0.44,3.7]
54
[48.7, 59.1]
Private
60
81.7
[69.6, 90.5]
1.26
[0.99,1.5]
61.7
[48.2, 73.9]
Stage of Cancer
Early Stage
155
81.3
[74.2, 87.1]
1.3
[0.78, 1.82]
52.3
[44.1, 60.3]
Advanced Stage
274
86.5
[81.9, 90.3]
2.42
[0.71, 4.13]
56.6
[50.5, 62.5]
Duration of Treatment
<9 M
214
81.3
[75.4, 86.3]
2.04
[0.41, 3.68]
53.3
[46.4, 60.1]
9 M-12M
174
87.4
[81.5, 91.9]
2.06
[0.16, 3.96]
54
[46.3, 61.6]
>12 M
41
90.2
[76.9, 97.3]
1.77
[1.11, 2.43]
68.3
[51.9, 81.9]
State
Maharashtra
145
75.5
[68.8, 81.4]
2.3
[-0.1, 4.8]
41.1
[34.1, 48.5]
West Bengal
74
89.2
[80.4, 94.9]
1.6
[1.2, 1.9]
71.1
[60.1, 80.5]
Bihar
48
92.3
[81.5, 97.9]
4.7
[-2.1, 11.5]
59.6
[45.1, 73.0]
Uttar Pradesh
39
97.5
[86.9, 99.9]
-1.1
[-5.8, 3.6]
67.5
[50.8, 81.4]
Other
57
91.9
[82.2, 97.3]
1.2
[0.96, 1.5]
64.5
[51.3, 76.3]
Total
429
84.6
[80.8, 87.9]
1.95
[0.55, 3.3]
55
[51.3, 76.3]
Table 8 presents the estimates of incidence and intensity of CHE and impoverishment
by socio-economic and demographic characteristics of the breast cancer patients. The
socio-economic gradient of CHE and impoverishment is strong. Overall, 84.6% of
the households incurred CHE and 55.0% of the households were facing
impoverishment. About 84.3% of the households in the poorest MPCE quintile
incurred CHE. The difference in CHE between the poorest and richest MPCE quintile
was small (1.3%). The intensity of CHE and impoverishment declined across each
MPCE quintile. Both CHE and impoverishment was higher in rural areas compared
to urban areas. CHE and impoverishment by type of income source showed a lower
prevalence in self-employed and service households but high prevalence in
households with labour and agriculture as the source of income. Households without
any education had higher prevalence of CHE and impoverishment than households

29
with some level of educational attainment. Households with general or non-
chargeable patients had higher CHE compared to private patients but lower
prevalence of impoverishment compared to private patients. Breast cancer patients
who belonged to other states like Bihar and Uttar Pradesh had higher CHE and
impoverishment compared to patients from Maharashtra. On an average, households
incurring CHE incurred 195% more than their capacity to pay.
3.7 Distress financing
Treatment cost of breast cancer was financed through various sources as shown in
Table 9. Only 5.7% of the patients resorted to income for financing, 48.56% resorted
to savings only, 66.59% had loans & borrowings and 72.36% had either sold assets
or borrowed to finance the cost of treatment. The share of total cost of treatment was
mainly covered by either selling assets or borrowing (78.9%) followed by
contribution from friends (63.4%) and insurance (52.6%).
Table 9: Source of treatment financing and share to total cost of treatment.
Source of financing
%
N
Mean amount
spent from
source
Average
treatment
cost
Source of
financing as a
share to total
cost of treatment
Income
5.77
24
59917
173244
34.6
Savings
48.56
202
14097
280830
5.0
Selling assets, jewellery, property
11.78
49
251939
357209
70.5
Loans & borrowings
66.59
277
108179
238314
45.4
Either selling assets or borrowing
72.36
301
195195
247384
78.9
Contribution from friends
44.95
187
157101
247659
63.4
Insurance
39.66
165
106536
202673
52.6
Table 10 presents the results of logistic regression analyses with odds ratio and 95%
CI of breast cancer patients incurring distress financing. Patients who had OOP
payment of more than ₹150,000 for cancer treatment were twice more likely to incur
distress financing than patients with OOP payment less than ₹60,000. The odds of
incurring distress financing were significantly higher among patients who belonged
to poor (OR:3.25; 95% CI: 1.79, 5.90) or middle (OR:2.86; 95% CI: 1.60, 5.09)

30
income tertile, patients who were from outside Maharashtra (OR: 2.25; 95% CI: 1.26,
4.02) and lived in urban areas (OR: 1.82; 95% CI:1.05, 3.16).
Table 10: Odds ratio and 95% CI of distress financing of cancer treatment
Distress Financing
Odds ratio
95% CI
OOP payment (₹)
<60,000
1
60,000-150,000
1.26
[0.70, 2.25]
>150,000
2.71**
[1.45, 5.08]
Age in Years
<40
1
41-50
1.37
[0.79, 2.38]
51-60
1.00
[0.55, 1.83]
60 and above
0.56
[0.23, 1.39]
Marital status
Other
1
Currently married
1.79
[0.93, 3.42]
Residence
Rural
1
Urban
1.82*
[1.05, 3.16]
Income tertile
Poor
3.25***
[1.79, 5.90]
Middle
2.86***
[1.60, 5.09]
Rich
1
Years of schooling
Never attended
0.76
[0.42, 1.37]
Up to secondary
0.73
[0.37, 1.47]
Higher secondary and above
1
Religion
Hindu
1
Muslim
1.06
[0.60, 1.86]
Other
1.83
[0.60, 5.54]
Social group
General
1
OBC
1.30
[0.79, 2.15]
SC/ ST / Others
0.91
[0.46, 1.77]
Stage of Cancer
I/II
1
III/IV
0.75
[0.47, 1.18]
State
Maharashtra
1
Outside of Maharashtra
2.25**
[1.26, 4.02]
Patient category
General
1.66
[0.78, 3.52]
Private
1
Health insurance
Yes
1.45
[0.63, 3.38]

31
No
1
Duration of Treatment
<9 Month
1
9Month -1Year
1.01
[0.64, 1.61]
> 1Year
2.33
[0.92, 5.89]
(R): reference category; *, **, *** refers to <0.05, <0.01 and <0.001 level of significance respectively.
4. Discussion
Cancer has been increasing in India and is the fifth leading cause of death (ICMR-
NCDIR-NCRP; 2020). Among all cancer types, breast cancer had the highest share,
accounting for 21.8% of all cancer cases among women in the country (Kulothungan
et al., 2022). While cancer registry provides macro estimates on the volume of cancer
and death, there is limited information on individual and household characteristics of
cancer patients in India. This is a comprehensive longitudinal study from a sample of
500 breast cancer patients who were registered for treatment at TMC. We present the
financial catastrophe of breast cancer patients using OOP, CHE, distress financing,
loans, loss of income and expenditure pattern. Following are the salient findings of
the study.
Firstly, it was observed that the per capita household income of breast cancer
households declined during the treatment period. The reduction in income was higher
among households having labourers. Reduction in income was possibly due to
absence from work by the bread winner of the family as well as of the cancer patient.
Since a majority of the households were nuclear households comprising labourers, it
is likely that they lost jobs due to having to accompany the patients for treatment.
Secondly, the average food expenditure increased from ₹ 1345 before cancer
diagnosis to ₹ 1788 after cancer diagnosis but the food expenditure as a share of
MPCE declined from 46% to 36% during this period. Thirdly, the duration of
treatment was higher among patients who were less educated, poor and patients who
came from outside the state of Maharashtra. Higher treatment duration among less
educated and non-chargeable patients could be owing to their lack of knowledge and
understanding about the treatment procedure. Concurrently, patients coming from
outside the state of Maharashtra had to stay longer for treatment. Fourthly, it was
estimated the average cost of treatment was ₹ 258,095 and the OOP payment was ₹
186,461 during cancer treatment. The average treatment cost for general or non-
chargeable patients was ₹219,621 while it was ₹416,198 for private patients. Fifthly,

32
the high OOP payment was supported by the fact that 34% of the patients had taken
a loan at the time of registration, 65% had loan at the time of completion of treatment
and 68.9% had a loan at the time of concluding treatment. Loan as a share of
household income was higher among the poor, less educated and rural residents.
Seventhly, it was estimated that 85% of the patients had incurred CHE and that
reimbursement from multiple sources reduced the CHE by only 13%. The odds of
incurring distress financing were higher among patients incurring higher OOP
payment, belonging to poor or middle-income group, coming from outside
Maharashtra and living in urban areas.
We provide some plausible explanation of our results. The increase in food
expenditure during treatment may be due to the cost of living in Mumbai since, before
diagnosis of cancer food expenditure was at the native place, but during treatment it
was at TMC. In addition, certain foods might have been prescribed as supplements,
thereby, increasing food expenditure. However, food expenditure as a share of
consumption expenditure declined after cancer diagnosis as the major contributor to
increased expenditure became travel and accommodation. The effect was more
prominent for patients coming from rural areas. Cancer treatment facilities in India
are limited in number and mostly metro-centric. The socially and economically
disadvantaged population from rural areas face numerous challenges in accessing
cancer treatment. Patients from remote and rural areas travel long distances for
treatment, which has a significant effect on their economic and health status. Our
estimates of OOP payment for treatment were much higher than previous estimates
(Mahal et al., 2013; Rajpal et al., 2018; Goyanka,2021). One of the probable causes
of high OOP payments is low insurance coverage among cancer patients. According
to a recent study, there are 1,575 hospitals in India where cancer treatment costs can
be reimbursed through this scheme; however, only 438 hospitals, including TMC,
have multimodality treatment facilities. These public schemes included 86.2% of the
patients in the present cohort and covered approximately 31% of their treatment
expenses.
One of our recent papers estimated that the median age of breast cancer patients was
47 years and found that 15% of the patients discontinued treatment (Mohanty et al.
2023). This average age is higher than the average age of Indian women above 20
years estimated to be 33 years by the recently conducted NFHS 5. The average
duration of schooling was similar as observed from our survey and the national

33
population aged above 20 years. The present study indicated that 46% of the urban
patients were registered for treatment compared to 34% urban population estimated
from NFHS-5. This suggests that while the breast cancer patients were a little older
than the overall population, their socio-economic conditions were similar. However,
the low median age of breast cancer patients in India compared to that in developed
countries could be due to genetic, behavioural and life style factors.
In 2018, the Government of India launched a comprehensive cashless health
insurance scheme, Ayushman Bharat, for the bottom 40% of the population,
providing ₹500,000 per family per year for health care expenditure. This scheme has
the potential to deliver quality health care for cancer by linking reimbursement
directly to the evidence-based management guidelines recommended by India’s
National Cancer Grid, which is important for cancer treatment where affordability of
treatment is a big issue (Caduff et al., 2019; Pramesh et al., 2019).
Although this study is one of its kind, being a large study highlighting important
factors about the economic, social and health aspects of breast cancer patients, it is
not without limitations. The study used 500 breast cancer patients as samples from a
single centre, hence the results cannot be generalized. Secondly, estimates of
expenditure or cost of treatment prior to TMC might be prone to recall bias and
dependent on the recall of the respondents and their families. Finally, the study period
also coincided with the Covid-19 pandemic which delayed the study, impacted the
treatment period and might have increased the cost.
5.Conclusions
Majority of the women with breast cancer are in the working and reproductive age
group. We found early age at onset of breast cancer, late diagnosis and high
indebtedness in treating breast cancer. It is recommended to increase awareness, early
diagnosis, multi-disciplinary treatment and increase coverage of health insurance for
breast cancer patients. Though the National Programme on Cancer Screening
recommended screening for all women above 30 years, less than 1% of the eligible
women in the 30-49 years age group are ever screened (Sen et al., 2022). It is
suggested to effectively implement the recommendation of the National Programme
for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke
(NPCDCS) that would diagnose patients at the right time and save lives. Long travel
distance to avail treatment, low insurance coverage and lack of sufficient treatment

34
facilities are the major contributing factors to the economic and health burden of
cancer patients. It is also recommended to build an affordable and accessible medical
infrastructure in remote areas.
Acknowledgments: We thank two anonymous reviewers for providing valuable
comments and suggestion in our earlier version of the paper. We are thankful to Prof
K.S. James, Director & Sr. Professor, IIPS & Prof. Sudeep Gupta, Director,
Advanced Centre for Treatment, Research and Education in Cancer (ACTREC),
Homi Bhabha National Institute, for constant support in executing the project and
bringing out research publications. The Publication Cell at IIPS facilitated the peer
review, editing and timely publication of our working paper and we are grateful for
their sincere efforts. This working paper is based on data collected from a research
project entitled “Health Expenditure on Breast Cancer Treatment in Women: A
Study from Public Sector Tertiary Cancer Centre (EXPERT)”. We are thankful
to Women’s Cancer Initiative, the Nag Foundation, the International Institute for
Population Sciences, and the Tata Memorial Centre for providing financial support
in executing the project. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
References
Afkar, A., Jalilian, H., Pourreza, A., Mir, H., Sigaroudi, A. E., & Heydari, S.
(2021). Cost analysis of breast cancer: a comparison between private and public
hospitals in Iran. BMC Health Services Research, 21(1), 1-11.
Arozullah, A. M., Calhoun, E. A., Wolf, M., Finley, D. K., Fitzner, K. A.,
Heckinger, E. A., et al., (2004). The financial burden of cancer: estimates from
a study of insured women with breast cancer. J Support Oncol, 2(3), 271-278.
Azzani, M., Yahya, A., Roslani, A. C., & Su, T. T. (2017). Catastrophic health
expenditure among colorectal cancer patients and families: a case of
Malaysia. Asia Pacific Journal of Public Health, 29(6), 485-494.
Bose, B., Clarke, J., Glasbey, J. C., Haque, P. D., Jolly, K., Kingsley, P. A., et al.,
(2022). Catastrophic expenditure and treatment attrition in patients seeking

35
comprehensive colorectal cancer treatment in India: A prospective multicentre
study. The Lancet Regional Health-Southeast Asia, 6, 100058.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A.
(2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and
mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for
Clinicians, 68(6), 394–424
Brinton, L. A., Figueroa, J. D., Awuah, B., Yarney, J., Wiafe, S., Wood, S. N., et
al., (2014). Breast cancer in Sub-Saharan Africa: Opportunities for prevention.
Breast Cancer Research and Treatment, 144(3), 467–478.
Brown, M. L., Lipscomb, J., & Snyder, C. (2001). The burden of illness of cancer:
economic cost and quality of life. Annual Review of Public Health, 22(1), 91–
113.
Caduff, C. A., Sullivan, R., Pramesh, C. S., & Booth, C. (2019). India's new health
scheme: what does it mean for cancer care? The Lancet Oncology.
Economic Intelligence Unit, 2016. Breast cancer in India: The Challenges and
Responses https://worldcancerinitiative.economist.com/pdf/Pfizer-Breast-
cancer-in-Asia/EIU%20Breast%20Cancer%20in%20Asia_Final.pdf
Ekwueme, D. U., Yabroff, K. R., Guy Jr, G. P., Banegas, M. P., de Moor, J. S., Li,
C., et al., (2014). Medical costs and productivity losses of cancer survivors—
United States, 2008–2011. Morbidity and Mortality Weekly Report, 63(23),
505.
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., et al.,
(2015). Cancer incidence and mortality worldwide: sources, methods and major
patterns in GLOBOCAN 2012. International Journal of Cancer, 136(5), E359–
E386.
Fitzmaurice, C., Abate, D., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdel-
Rahman, O., et al., (2019). Global, regional, and national cancer incidence,

36
mortality, years of life lost, years lived with disability, and disability-adjusted
life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the
global burden of disease study. JAMA Oncology, 5(12), 1749–1768.
Forouzanfar, M. H., Afshin, A., Alexander, L. T., Anderson, H. R., Bhutta, Z. A.,
Biryukov, S., et al., (2016). Global, regional, and national comparative risk
assessment of 79 behavioural, environmental and occupational, and metabolic
risks or clusters of risks, 1990–2015: a systematic analysis for the Global
Burden of Disease Study 2015. The Lancet, 388(10053), 1659–1724.
Ginsburg, O., Bray, F., Coleman, M. P., Vanderpuye, V., Eniu, A., Kotha, S. R.,
et al., (2017). The global burden of women’s cancers: a grand challenge in
global health. The Lancet, 389(10071), 847–860.
Goyanka, R. (2021). Economic and non-economic burden of cancer: A propensity
score matched analysis using household health survey data of India. Cancer
Research, Statistics, and Treatment, 4(1), 29.
Harbeck, N., & Gnant, M. (2017). Breast cancer. Lancet (London,
England), 389(10074), 1134–1150. https://doi.org/10.1016/S0140-
6736(16)31891-8
ICMR-NCDIR-NCRP (2020). Cancer statistics, 2020: report from national cancer
registry programme, 2012-16, India
Jain, M., & Mukherjee, K. (2016). Economic burden of breast cancer to the
households in Punjab, India. International Journal of Medicine and Public
Health, 6(1).
Kastor, A., & Mohanty, S. K. (2018). Disease-specific out-of-pocket and
catastrophic health expenditure on hospitalization in India: Do Indian
households face distress health financing? PLOS ONE, 13(5), e0196106.

37
Kim, S., & Kwon, S. (2015). Impact of the policy of expanding benefit coverage
for cancer patients on catastrophic health expenditure across different income
groups in South Korea. Social Science & Medicine, 138, 241-247.
Kulothungan, V., Sathishkumar, K., Leburu, S., Ramamoorthy, T., Stephen, S.,
Basavarajappa, D., et al., (2022). Burden of cancers in India - estimates of
cancer crude incidence, YLLs, YLDs and DALYs for 2021 and 2025 based on
National Cancer Registry Program. BMC Cancer, 22(1), 1–12.
Mahal, A., Karan, A., Fan, V. Y., & Engelgau, M. (2013). The economic burden
of cancers on Indian households. PloS one, 8(8), e71853.
Mohanty, S.K., Wadasadawala, T., Sen, S.Khan,pk., (2023) Socio-economic
variations of breast cancer treatment and discontinuation: a study from a public
tertiary cancer hospital in Mumbai, India. BMC Women's Health 23, 113.
https://doi.org/10.1186/s12905-023-02275-6
Natarajan, A., Mehra, N., & Rajkumar, T. (2020). Economic perspective of cancer
treatment in India. Medical Oncology, 37(11), 1–7.
Pramesh, C. S., Chaturvedi, H., Reddy, V. A., Saikia, T., Ghoshal, S., Pandit, M.,
et al., (2019). Choosing Wisely India: ten low-value or harmful practices that
should be avoided in cancer care. The Lancet Oncology, 20(4), e218–e223.
Rajpal, S., Kumar, A., & Joe, W. (2018). Economic burden of cancer in India:
Evidence from cross-sectional nationally representative household survey,
2014. PloS One, 13(2), e0193320.
Sen, S., Khan, P. K., Wadasadawala, T., & Mohanty, S. K. (2022). Socio-
economic and regional variation in breast and cervical cancer screening among
Indian women of reproductive age: a study from National Family Health
Survey, 2019-21. BMC Cancer 22(1), 1–13. https://doi.org/10.1186/S12885-
022-10387-9/FIGURES/1

38
Singh, M. P., Chauhan, A. S., Rai, B., Ghoshal, S., & Prinja, S. (2020). Cost of
treatment for cervical cancer in India. Asian Pacific Journal of Cancer
Prevention: APJCP, 21(9), 2639.
Smith, R. D., & Mallath, M. K. (2019). History of the growing burden of cancer
in India: from antiquity to the 21st century. Journal of Global Oncology, 5, 1–
15.
Sun, C. Y., Shi, J. F., Fu, W. Q., Zhang, X., Liu, G. X., Chen, W. Q., et al., (2021).
Catastrophic health expenditure and its determinants in households with lung
cancer patients in China: a retrospective cohort study. BMC cancer, 21(1),
1323.
Xu, K., Evans, D. B., Kawabata, K., Zeramdini, R., Klavus, J., & Murray, C. J.
(2003). Household catastrophic health expenditure: a multicountry
analysis. The lancet, 362(9378), 111-117.
Youn, H. J., & Han, W. (2020). A Review of the Epidemiology of Breast Cancer
in Asia: Focus on Risk Factors. Asian Pacific Journal of Cancer Prevention:
APJCP, 21(4), 867.
Zhao, Y., Tang, S., Mao, W., & Akinyemiju, T. (2022). Socio-economic and rural-
urban differences in healthcare and catastrophic health expenditure among
cancer patients in China: analysis of the China health and retirement
longitudinal study. Frontiers in Public Health, 9, 2283.
Zhao, Y., Zhang, L., Fu, Y., Wang, M., & Zhang, L. (2020). Socioeconomic
disparities in cancer treatment, service utilization and catastrophic health
expenditure in China: a cross-sectional analysis. International journal of
environmental research and public health, 17(4), 1327.
Zheng, A., Duan, W., Zhang, L., Bao, X., Mao, X., Luo, Z., et al., (2018). How
great is current curative expenditure and catastrophic health expenditure among
patients with cancer in China? A research based on “System of Health Account
2011”. Cancer medicine, 7(8), 4036-4043.

39
Appendix 1: Brief overview of baseline, end line and follow up schedules
Baseline
Endline
Follow up
Household Schedule
Endline extended
schedule
Endline
extended
schedule
Socio- demographic profile of
the household
Health and Comorbidity
Health and
Comorbidity
Consumption expenditure of the
household
Insurance and
Reimbursement
Insurance and
Reimbursement
Income details of the household
and patients
Health financing
Health financing
Health seeking behaviour of the
household
Loans and debts
Loans and debts
Individual Schedule
Quality of life
Income work and
employment
Demographic
C30
Quality of life
Medical history of the patients
BR23
C30
Treatment history at TMC
WHODAS
BR23
Socio- economic and work
EQ-5D-5L
WHODAS
Salary and wage
EQ-5D-5L
Health and comorbidities
Insurance
Cost of hospitalization
Quality of life
C30
BR23
WHODAS
EQ-5D-5L

40
International Institute for Population Sciences
The International Institute for Population Sciences (IIPS),
formerly known as the Demographic Training and Research
Centre (DTRC), was established in July 1956 under the joint
sponsorship of Sir Dorabji Tata Trust, the Government of India,
and the United Nations. The Institute is under the administrative
control of the Ministry of Health and Family Welfare,
Government of India.
The Institute served as a regional centre for Training and
Research in Population Studies for the ESCAP region. The
Institute was re- designated to its present title in 1985 to facilitate
the expansion of its academic activities and was declared as a
‘Deemed University’ in August 19, 1985 under Section 3 of the
UGC Act, 1956 by the Ministry of Human Resource
Development, Government of India. This recognition has
facilitated the award of degrees by the Institute itself and paved
the way for further expansion as an academic institution. The
faculty members and the supporting staff belong to diverse
interdisciplinary background with specialization in some core
areas of population sciences, trained in India and abroad.
Institute is the hub of population and health related teaching and
research in India, playing a vital role for planning and
development of the country. During the past years, students from
different countries of Asia and the Pacific region, Africa and
North America have been trained at the Institute. The alumni are
occupying prestigious positions at national and international
organisations, universities and non-governmental organisations.
The Institute offers Post-Graduate, Doctoral, and Post-Doctoral
courses. After completing the course, students are well prepared
for: (i) admission to higher degree programmes in the best
universities of the world; (ii) a good career in teaching &
research; (iii) for a multi-disciplinary professional career; (iv) as
independent consultant.

41
Vision
“To position IIPS as a premier teaching and research Institution in
population sciences responsive to emerging national and global needs
based on values of inclusion, sensitivity and rights protection.”
Mission
“The Institute will strive to be a centre of excellence on population,
health and development issues through high quality education,
teaching and research, This will be achieved by (a) creating competent
professionals, (b) generating and disseminating scientific knowledge
and evidence, (c) collaboration and exchange of knowledge and (d)
advocacy and awareness.”
About the Authors
Dr. Sanjay K Mohanty is Professor and Head, Department of Population and
Development, IIPS, Mumbai
Dr. Tabassum Wadasadawala is Professor, Department of Radiation Oncology,
Advanced Center for Treatment Research and Evaluation in Cancer (ACTREC),
Tata Memorial Center, Navi Mumbai
Soumendu Sen is Senior Research Scholar, IIPS, Mumbai
Suraj Maiti is Project Officer, IIPS, Mumbai.
Jishna E is Research Officer, IIPS, Mumbai.