|Year : 2021 | Volume
| Issue : 1 | Page : 29-36
Economic and non-economic burden of cancer: A propensity score matched analysis using household health survey data of India
Department of Economics, Indraprastha College for Women, University of Delhi, Delhi, India
|Date of Submission||11-Jan-2021|
|Date of Decision||21-Feb-2021|
|Date of Acceptance||10-Mar-2021|
|Date of Web Publication||26-Mar-2021|
Department of Economics, Indraprastha College for Women, University of Delhi, 31, Sham Nath Marg, Delhi
Source of Support: None, Conflict of Interest: None
Background: Cancer affects the well-being not only of the patients but also of the other members of the household.
Objectives: In this study, we assessed the nature and magnitude of the economic and non-economic burden on patients with cancer and their families due to the inpatient and outpatient cancer care.
Materials and Methods: This study was conducted using the secondary data from the 75th round of the National Sample Survey Organization survey on health and morbidity, titled “Social Consumption: Health,” for the year 2017–2018. The burden of cancer on individuals was assessed in terms of the health-care expenditure and utilization of inpatient and outpatient cancer treatment. At the household level, cancer burden was assessed in terms of per person health-care expenditure, impact on the standard of living, strategies adopted for financing the health-care expenditure, and utilization of and expenditure on health-care by other members of the family. Propensity score matching was used to generate matched data separately for inpatient and outpatient cases and at individual and household levels to control for confounders. The difference in the burden between the matched cancer-affected and unaffected individuals/households was estimated using the average treatment effect.
Results: For the year 2017–2018, data were available for a total of 113,823 households with 555,352 individuals across India. The mean out-of-pocket expenditure (OOPE) for a patient with cancer exceeded that of patients with other chronic diseases by ₹2895 for each outpatient visit and ₹52393 for each inpatient admission. The mean length of the hospital stay due to cancer was found to be 7 days longer than that due to any other chronic disease. The per person inpatient health-care expenditure for the other members of a cancer-affected household was ₹11,000 less than that of other members of the unaffected households. More than 50% of households with cancer patients had to borrow money to pay for inpatient care compared to control households. The share of OOPE for outpatient care in the monthly consumption expenditure of a cancer-affected household was twice as high as that of an unaffected household. Moreover, the number of outpatient visits for other ailing persons in a cancer-affected household was one-fourth that of an unaffected household.
Conclusion: Cancer imposes an immense economic and non-economic burden on affected individuals and households. Therefore, there is a need to design appropriate health-care strategies for providing optimal financial support to patients with cancer.
Keywords: Burden of cancer, oncology, out-of-pocket expenditure, propensity score matching
|How to cite this article:|
Goyanka R. Economic and non-economic burden of cancer: A propensity score matched analysis using household health survey data of India. Cancer Res Stat Treat 2021;4:29-36
|How to cite this URL:|
Goyanka R. Economic and non-economic burden of cancer: A propensity score matched analysis using household health survey data of India. Cancer Res Stat Treat [serial online] 2021 [cited 2021 Oct 24];4:29-36. Available from: https://www.crstonline.com/text.asp?2021/4/1/29/312119
| Introduction|| |
Cancer is one of the leading causes of premature death among adults in most countries of the world. With an aging population and improvements in the early detection and treatment, the number of cancer survivors is increasing. Due to the ongoing nature of cancer care and lasting treatment effects, cancer survivors bear greater costs of treatment for a prolonged period of time.,
In India, the share of cancer in the total disability adjusted life years doubled from 2.3% to 5% from 1990 to 2016, and the out-of-pocket expenditure (OOPE) for cancer treatment was found to be the highest among other ailments in 2014.
Multimodality treatment protocols incorporating surgery, chemotherapy and radiotherapy, combined with the long duration of treatment, high cost of drugs, sophisticated diagnostic techniques and procedures, postacute care, readmissions, and long-term follow-up of patients lead to considerable financial burden of medical care on patients with cancer.,
Various studies have reported different cancer cost estimates depending on the cancer site, types of data, study population, study period, and study methodology. In the United States of America, the mean expenditure per person for patients with cancer is four times ($16346) as high as that for those without cancer. In Sri Lanka, the total OOPE per person per month for cancer-affected households was Rs. 1343 (Sri Lankan Rupees) more than that for other households. In Bangladesh, the OOPE outlays on cancer push about 3.4% of the households into poverty, annually.
In India, the cost of cancer care gets escalated due to the expenditure on food, transportation, and lodging by the patients (since cancer treatment facilities have a poor geographical dispersion) and their care-givers and the indirect burden due to the loss of productivity.,
In the year 2006-2007, the average cost of treatment for all common cancer types at the All India Institute of Medical Science was ₹36,812. In the year 2014-2015, the average OOPE for a patient with head-and-neck cancer in a tertiary care public hospital in India was ₹ 37,485. Using the data from the National Sample Survey Organization's (NSSO) household health survey for the year 2014, Tripathy et al. estimated that cancer led to an OOPE of US $ 357 (approximately ₹21,800)) per episode of hospitalization, while Rajpal et al. found that the average total expenditure for cancer care was ₹29,066. However, these studies did not control for confounding factors and comorbidities, which could have led to biased estimates. Mahal et al., using the data from NSSO's household health survey of 2004, while controlling for confounders, estimated that cancer-affected households incurred a per member additional OOPE on inpatient care between ₹3576 and ₹4438 per annum. The findings from this study have however become outdated. Moreover, the study controlled for confounding factors only at the household level, but not at the individual level. Therefore, updated estimates of the economic and non-economic burden of the disease, while controlling for confounders at the individual level, such as age and gender, are required.
Cancer has adverse effects not only on the well-being of the patients, but also on that of the other members of the household. In this study, we aimed to estimate the economic and non-economic burden of cancer on the affected individuals and households in India, so as to draw the attention of the policy makers and health-care practitioners and help them design suitable strategies to reduce this burden.
| Materials and Methods|| |
General study details
This study was conducted using the secondary data from the 75th Round of the NSSO survey on health. No funding was obtained for this study. As data for analysis were available in the public domain, approval from the Institutional Ethics Committee was not required for this study. None of the patients were directly contacted as a part of the study, and no patient identifiers were included. Therefore, informed consent from the patients was not required. The study was conducted according to the ethical guidelines established by the Declaration of Helsinki and the Indian Council of Medical Research.
The study used data from the 75th round of the nationally representative government household survey of NSSO on health and morbidity, titled “Social Consumption: Health,” for the year 2017–2018. This survey collects information related to the type of ailments, health-care utilization and expenditure, as well as the demographic and socio-economic characteristics of the individuals and their households. For the year 2017–2018, data were available for a total of 113,823 households with 555,352 individual members across India. Of these, 326,033 individuals resided in the rural areas, while 229,319 were urban residents. The reference period for health-care expenditure and utilization incurred on account of hospitalized care was the last 365 days and for the non-hospitalized care was the past 15 days. Information related to health-care expenditure and the type of ailment was available in the form of a self-reported record provided by the respondents. A proxy respondent from the household was used when someone was unable to respond to the survey. This survey covered 61 different well-defined ailments, including normal and cesarean childbirth and two more categories to cover any other ailments or the inability to define the ailment clearly. The survey also collected information about any deceased household members in the preceding 1 year. For the current survey, households were sampled evenly in quarterly subrounds from July 1, 2017, to June 30, 2018 with an equal number of households being allotted to each quarterly subround, to account for the seasonality in some types of ailments. The survey also provided information on sample weights that could be used to obtain nationally representative estimates.
In this study, separate outcome indicators were estimated at the individual and household levels.
Individual level outcome variables
The burden of cancer on individuals was assessed in terms of the health-care expenditure and utilization for inpatient and outpatient treatment of cancer.
The following variables were used to estimate expenditure: total inpatient OOPE; total outpatient OOPE; inpatient and outpatient OOPE at public and private health-care facilities; and components of inpatient and outpatient OOPE, such as expenditure on drugs, diagnostics and any non-medical expenditure incurred on transportation, food and lodging of patients during the period of utilization of health-care.
Health-care utilization for inpatient care was assessed in terms of the number of days of hospital stay for each episode of inpatient admission within a reference period of the preceding 365 days. Health-care utilization for outpatient care was assessed in terms of the number of per person visits to a health-care facility within a reference period of the preceding 15 days.
Household level outcome variables
At the household level, the burden of cancer was assessed in terms of the health-care expenditure, impact on the standard of living, strategies adopted for financing the health-care expenditure, and utilization and expenditure of health-care by other members of the family. All these indicators were analyzed separately for inpatient and outpatient treatments. Accordingly, the variables used were OOPE per person, the share of OOPE in total household expenditure, loss in per person household income, proportion of households that had to borrow money or sell their assets such as land and livestock to finance the expenditure on health-care, per person OOPE on other chronic ailments and the number of per person outpatient visits.
OOPE was defined as the total money spent by an individual at the time of using health-care services without any prepayment support like an insurance or free health-care services.
Propensity score was defined as the predicted likelihood of belonging to the treatment group (persons reporting as suffering from cancer) or the control group (persons similar in observed characteristics to the treatment group but not reporting cancer), based on observed characteristics.
The differential burden of cancer was estimated by calculating the differences in the mean values of the outcome indicators between persons/households reporting cancer and those reporting other ailments in a matched group. The propensity score matching (PSM) method was used to create matched groups, separately for inpatient and outpatient cases at both the individual and household levels.
PSM can control for selection bias in estimating the impact of any event (in our case, the incidence of cancer) while using observational data (when data are not drawn from randomized control trials [RCTs]).
Matched groups of individuals and households were created by comparing the units reporting chronic ailments. Robustness check was done by creating a matched sample of persons/households reporting only those chronic ailments that involved high OOPE, such as hypertension, heart disease, diabetes, thyroid or other endocrine diseases, nutritional diseases such as obesity and undernutrition, joint or bone disease, and body aches.
Matched samples were created using the nearest neighbor method, with replacement within a caliper of 0.001 on common support. To estimate the propensity scores for matching, a separate logit regression was performed for all four of the following cases - (i) ailing individuals with outpatient care, (ii) ailing individuals with inpatient care, (iii) households with at least one member who sought outpatient care, and (iv) households with at least one member who sought inpatient care. The dependent variable for each of these regressions was whether an individual or a household had at least one episode of cancer. All the individuals who were currently living or deceased in the last 1 year were included in the analysis. The matching variables used were: gender, age and age-squared, dummy for place of residence (rural/urban), quintiles of living standard measure (i.e., total household consumption expenditure which is a proxy for household income as per the survey data), state of residence (grouped into five geographical regions), caste (three groups) and religion (three groups) of respondents, logarithmic values of household size, access of households to safe drinking water, whether the households had a toilet at home, arrangement for garbage disposal by the municipal corporation, and use of safe cooking energy. Summary statistics of these indicators are given in [Supplementary Table S1].
A formal sample size calculation was not required to be performed this study, as it used data from a nationally representative household survey. Estimates were obtained for all the individuals with chronic ailments who sought outpatient or inpatient care. The list of health conditions and per episode OOPE for each of the health conditions included in the two comparison groups is presented in [Supplementary Table S2]. The number of individuals and households before and after PSM are shown in [Table 1]. The individuals and households that did not match were excluded from the analysis. The statistical analyses were performed in StataCorp. 2015 (Stata Statistical Software: Release 14. College Station, Texas, USA).
|Table 1: Percentage of population reporting utilization of health-care for cancer and non-cancer ailments by gender and age groups|
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| Results|| |
Unmatched cohort results
Information about the individuals who reported the utilization and expenditure on inpatient and outpatient care for cancer and other diseases in the unmatched groups is presented in this section.
The health-care utilization rate per 100 persons for cancer and non-cancer cases disaggregated by gender and age groups (children, youth, and elderly) is presented in [Table 1]. For non-cancer ailments, the inpatient utilization rates (percentage of the population getting inpatient care in a reference period of 365 days) were estimated to be 2.8% for women and 2.7% for men. For cancer, the inpatient utilization rates were 0.07% for both men and women. Both cancer- and non-cancer-related hospital admission rates increased with an increase in the age of the individuals.
The OOPE burden for cancer, high-expenditure chronic ailments, other chronic ailments, and all chronic ailments (including cancer and high-expenditure chronic ailments) is shown in [Table 2]. While the proportion of individuals with cancer was much lower than the proportion of individuals with any other ailment, the OOPE burden on individuals with cancer for each episode of outpatient and inpatient treatment was much greater than that on individuals with other ailments.
|Table 2: Per episode mean out-of-pocket expenditure in Indian rupees (INR) and its components for cancer and non-cancer ailments|
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Results of propensity score matching
A logit regression analysis was carried out to generate propensity scores for matching [Supplementary Table S3]a and [Supplementary Table S3]b. Results of the logit regression analysis at the level of individuals showed that the odds ratio for outpatient cancer care increased with age and was found to be 1.08 (95% confidence interval [CI], 1.05–1.1). The odds ratio for outpatient cancer care in males was 1.15 (95% CI, 0.9–1.4), and for persons living in the rural areas, belonging to the higher income quintile and living in the North-Eastern states, the odds were greater. The odds ratio for outpatient cancer care was lower for individuals who had access to safe drinking water and toilets at home. The odds ratio for inpatient cancer care was higher for the older individuals (odds ratio, 1.07; 95% CI, 1.05–1.08), those having a larger household size (odds ratio, 1.01; 95% CI, 0.09–1.12), those living in rural areas (odds ratio, 1.18; 95% CI, 1–1.3), and those belonging to the higher income quintile (odds ratio, 0.84; 95% CI, 0.73–0.98). However, the odds ratio for inpatient cancer care was lower for males (odds ratio, 0.84; 95% CI, 0.76-0.93) and those who had access to safe drinking water (odds ratio, 0.83; 95% CI, 0.73–0.93).
For individual analysis, the matched sample of all chronic cases for inpatient observations reduced from 21,268 to 2696 (1606 cancer and 1090 control cases), and that for outpatient care reduced from 26,599 to 713 (359 cancer and 354 control cases). Similarly, for household-level analysis of inpatient data, the sample reduced after matching from 18,056 to 2071 households and for outpatient from 18,731 to 675 [Table 3].
Balancing property with the number of observations and reduction in bias after matching is presented in [Supplementary Table S4]a and [Supplementary Table S4]b, respectively. [Supplementary Figure S1] and [Supplementary Figure S2] give a graphical representation of the balancing test in matched individual samples for inpatient and outpatient care, respectively.
Average treatment effect after matching on individual-level outcome variables
The difference in the per episode OOPE for inpatient and outpatient care between individuals with cancer and two matched control groups (any chronic condition and high OOPE chronic ailments) is shown in [Table 4]. For inpatient treatment, the per episode OOPE for cancer was ₹52,393 more than that for any other matched chronic ailment and ₹48,293 more than that for matched high-expenditure chronic ailments. The per episode OOPE was higher for cancer treatment both in the public and private sector hospitals. The per episode expenditure on inpatient treatment for cancer in a private facility was ₹115,000. The per episode expenditure on drugs and diagnostics for patients receiving outpatient cancer care was about five times greater than that for patients receiving outpatient care for any other chronic ailments and high-OOPE chronic ailments. Patients with cancer also spent about ₹4500 more on non-medical items including transportation, food, lodging, etc., for each inpatient episode of cancer when compared to those with any other ailments. Similar trends were observed for outpatient treatment. The OOPE for cancer treatment was significantly greater than that for the treatment of other ailments for all the indicators of expenses.
|Table 4: Out-of-pocket expenditure and utilization of health-care in the matched sample of individuals|
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As far as health-care utilization is concerned, patients with cancer were likely to have double the number of days of hospital stay as compared to the matched patients with other chronic ailments and more than double as compared to the matched patients with high-OOPE chronic ailments.
Average treatment effect after matching on household level outcome variables
The burden of cancer on the households is described using various indicators in [Table 5]. Treatment of cancer led to a considerably higher per person burden of health-care expenditure on the households. The per person OOPE for households with at least one cancer patient exceeded that of the other households by ₹19,371 for inpatient care and ₹866 for outpatient care. Households with ailing members faced a loss in income due to the inability of the ailing persons and their caregivers to go to work. The per person loss in income for a cancer-affected household was 2.5 times that of a non-cancer-affected household for inpatient care and 4 times that of a non-cancer-affected household for outpatient care. The number of households per 100 that borrowed money to pay for inpatient health-care was 15 more for cancer than that for non-cancer ailments; for outpatient health-care this difference was 4.5. The share of monthly income spent on inpatient or outpatient OOPE by a cancer-affected household was double of that spent by a non-cancer-affected household. Health-care utilization for other chronic ailments by the rest of the family members in a cancer-affected household was lower than that for a non-cancer-affected household by 26–28 visits per 100 persons.
| Discussion|| |
Our results showed that in addition to a significant disruption in the overall well-being of the individuals and caregivers, cancer imposed an enormous economic burden on the affected households. The results of this study are robust because they demonstrate a greater burden due to cancer not only in comparison to all chronic conditions but also in comparison to those chronic conditions that have a high OOPE. Like several other studies, from various countries including India, this study reflects a greater financial burden in terms of OOPE and income loss due to cancer.,,,
Our results show that in the absence of expenditure support, a large proportion of households resort to selling their assets or borrowing money to undertake OOPE when there is a cancer-affected individual in the family. In addition, we have reported reduced health-care utilization and expenditure for other members of the cancer-affected households.
Over a period of time, the magnitude of differential OOPE (in current prices) has increased tremendously. Our results show that the per person OOPE for inpatient care in a cancer-affected household is ₹31,401 per annum which is much greater than ₹3576-4438 as was reported by a previous study. This indicates that various government policies and public insurance schemes over the years have not been able to take care of the high economic burden of cancer treatment. The magnitude of OOPE burden estimated in our study can be used for designing a prepayment expenditure support policy and to determine the quantum of public expenditure needed to provide affordable cancer care.
In the context of the acute distress experienced by patients with cancer and their families, our study draws attention to the need for designing preventive health-care policies such as regular screening and training of health-care workers at the primary care facilities for the early detection and prevention of some of the common cancers. The integration of preventive care at the primary level has become more pertinent as the National Health Policy 2017 has brought a paradigm shift in the primary care from selective to comprehensive care.
The strength of our study is the use of the PSM method for the analysis of the data from the most recent nationally representative household survey of individuals on health for determining the economic and non-economic burden of cancer. This method helps to control for confounders and mitigate the bias in estimating the differential burden of cancer. Our study is quite robust as we used a check of robustness by matching the test group with two alternative sets of control groups: those with all chronic ailments and those with a set of ailments that have high OOPE. Matching was also done separately at the individual and household levels. The results hold up on all these different empirical estimates.
However, our study has some limitations. The information on health-care utilization and expenditure was self-reported by the survey participants, and hence, may have involved an element of under measurement. In comparison to hospital-based studies, survey-based estimates of the OOPE incurred on cancer care tend to be smaller. This could be because of recall bias, or inability to capture some expenditure on diagnosis and long-term follow-up care, that are incurred outside the reference period before and after the main treatment. However, studies based on survey data, such as this one, are more representative of the population estimates. The data also do not capture information about the different types of cancers, and all kinds of cancer were treated as a single disease, despite the fact that different types of cancers may have different prognoses. This may have led to some loss of information in determining the severity of the illness and the quantum of expenditure. Another limitation of the study is that the information on lifestyle and occupational-risk factors such as tobacco and alcohol consumption and pursuing occupations that involve carcinogen exposure were not captured in the survey. This led to the non-inclusion of the “at-risk” variables which could be an important source of bias in our estimates.
Most studies on the financial burden of cancer focus on the treatment of the disease, but very often cancer care also entails the provision of palliative and emotional care. Public policies and clinical care also need to strengthen the weak link in cancer care and focus on providing holistic care for cancer, including early screening and palliative, emotional, and mental care. Health-care surveys do not cover such expenditures because of the infrequent provision of such care.
| Conclusion|| |
Given that it is not possible to carry out RCTs for a study on events like the treatment of cancer, the use of the PSM method to generate quasi-experimental data and estimate the true burden of cancer from survey data is an appropriate methodology. Using this methodology, we found that persons and households with cancer face greater economic and non-economic burden due to the illness in several dimensions of health-care expenditure, utilization, and financing of care. In addition, the burden of OOPE is greater if the treatment is taken at a private hospital. The burden of health-care utilization and expenditure on drugs and diagnostics for patients receiving cancer care is much greater than that for patients receiving care for other ailments. The per person expenditure and utilization of health-care for other members in a cancer-affected household is much lower than that for a non-cancer affected household. A greater proportion of cancer-affected households face financial stress on account of the need to borrow money or sell their assets to pay for health-care.
The author is thankful to Dr. Anup Karan, IIPH, Delhi, for the rich discussions during the writing of this paper.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]