Browsing by Author "Maiti, Suraj"

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    Catastrophic health expenditure and distress financing of breast cancer treatment in India: evidence from a longitudinal cohort study
    Mohanty, Sanjay K.; Wadasadawala, Tabassum; Sen, Soumendu; Maiti, Suraj; E, Jishna (Springer, 2024-07-23)
    Objective: To estimate the catastrophic health expenditure and distress financing of breast cancer treatment in India. Methods: The unit data from a longitudinal survey that followed 500 breast cancer patients treated at Tata Memorial Centre (TMC), Mumbai from June 2019 to March 2022 were used. The catastrophic health expenditure (CHE) was estimated using households' capacity to pay and distress financing as selling assets or borrowing loans to meet cost of treatment. Bivariate and logistic regression models were used for analysis. Findings: The CHE of breast cancer was estimated at 84.2% (95% CI: 80.8,87.9%) and distress financing at 72.4% (95% CI: 67.8,76.6%). Higher prevalence of CHE and distress financing was found among rural, poor, agriculture dependent households and among patients from outside of Maharashtra. About 75% of breast cancer patients had some form of reimbursement but it reduced the incidence of catastrophic health expenditure by only 14%. Nearly 80% of the patients utilised multiple financing sources to meet the cost of treatment. The significant predictors of distress financing were catastrophic health expenditure, type of patient, educational attainment, main income source, health insurance, and state of residence. Conclusion: In India, the CHE and distress financing of breast cancer treatment is very high. Most of the patients who had CHE were more likely to incur distress financing. Inclusion of direct non-medical cost such as accommodation, food and travel of patients and accompanying person in the ambit of reimbursement of breast cancer treatment can reduce the CHE. We suggest that city specific cancer care centre need to be strengthened under the aegis of PM-JAY to cater quality cancer care in their own states of residence.
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    Healthcare inequity arising from unequal response to need in the older (45+ years) population of India: Analysis of nationally representative data
    Mohanty, Sanjay K.; Khan, Junaid; Maiti, Suraj; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (Elsevier, 2024-11-20)
    Given the large and growing number of older (45+ years) people in India, inequitable access to healthcare in this population would slow global progress toward universal health coverage. We used a 2017-18 nationally representative sample of this population (n = 53,687) to estimate healthcare inequality and inequity by economic status. We used an extensive battery of indicators in nine health domains, plus age and sex, to adjust for need. We measured economic status by monthly per capita consumption expenditure and used a concentration index to measure inequalities in probabilities of any doctor visit and any hospitalisation within 12 months. We decomposed inequality with a regression method that allowed for economic and urban/rural heterogeneity in partial associations between healthcare and both need and non-need covariates. We used the associations achieved by the richest fifth of urban dwellers to predict a need-justified distribution of healthcare and compared the actual distribution with that benchmark to identify inequity. We found pro-rich inequalities in doctor visits and hospitalisations, which were driven by use of private healthcare. Adjustment for the greater need of poorer individuals revealed pro-rich inequity that exceeded inequality by about 65% and 39% for doctor visits and hospitalisations, respectively. These adjustments would have been substantially smaller, and inequity underestimated, without allowing for use-need heterogeneity, which accounted for 11% of the inequity in doctor visits and was 373% of inequity in hospitalisations. Targeting service coverage on poorer and rural groups, and increasing their access to private providers, would both reduce inequity and raise average coverage in the older population of India.
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    Out-of-pocket payment and financial risk protection for breast cancer treatment: a prospective study from India
    Wadasadawala, Tabassum; Mohanty, Sanjay K.; Sen, Soumendu; Kanala, Tejaswi S.; Maiti, Suraj; Puchali, Namita; Gupta, Sudeep; Sarin, Rajiv; Parmar, Vani (Elsevier, 2024-01-16)
    Background: Available data on cost of cancer treatment, out-of-pocket payment and reimbursement are limited in India. We estimated the treatment costs, out-of-pocket payment, and reimbursement in a cohort of breast cancer patients who sought treatment at a publicly funded tertiary cancer care hospital in India. Methods: A prospective longitudinal study was conducted from June 2019 to March 2022 at Tata Memorial Centre (TMC), Mumbai. Data on expenditure during each visit of treatment was collected by a team of trained medical social workers. The primary outcome variables were total cost (TC) of treatment, out-of-pocket payment (OOP), and reimbursement. TC included cost incurred by breast cancer patients during treatment at TMC. OOP was defined as the total cost incurred at TMC less of reimbursement. Reimbursement was any form of financial assistance (cashless or repayment), including social health insurance, private health insurance, employee health schemes, and assistance from charitable trusts, received by the patients for breast cancer treatment. Findings: Of the 500 patients included in the study, 45 discontinued treatment (due to financial or other reasons) and 26 died during treatment. The mean TC of breast cancer treatment was ₹258,095/US$3531 (95% CI: 238,225, 277,934). Direct medical cost (MC) accounted for 56.3% of the TC. Systemic therapy costs (₹50,869/US$696) were higher than radiotherapy (₹33,483/US$458) and surgery costs (₹25,075/US$343). About 74.4% patients availed some form of financial assistance at TMC; 8% patients received full reimbursement. The mean OOP for breast cancer treatment was ₹186,461/US$2551 (95% CI: 167,666, 205,257), accounting for 72.2% of the TC. Social health insurance (SHI) had a reasonable coverage (33.1%), followed by charitable trusts (29.6%), employee health insurance (5.1%), private health insurance (4.4%) and 25.6% had no reimbursement. But SHI covered only 40.1% of the TC of treatment compared to private health insurance that covered as much as 57.1% of it. Both TC and OOP were higher for patients who were younger, belonged to rural areas, had a comorbidity, were diagnosed at an advanced stage, and were from outside Maharashtra. Interpretation: In India, the cost and OOP for breast cancer treatment are high and reimbursement for the treatment flows from multiple sources. Though many of the patients receive some form of reimbursement, it is insufficient to prevent high OOP. Hence both wider insurance coverage as well as higher cap of the insurance packages in the health insurance schemes is suggested. Allowing for the automatic inclusion of cancer treatment in SHI can mitigate the financial burden of cancer patients in India. Funding: This work was funded by an extramural grant from the Women's Cancer Initiative and the Nag Foundation and an intramural grant from the International Institute of Population Sciences, Mumbai.
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    Public health insurance coverage in India before and after PM-JAY: Repeated cross-sectional analysis of nationally representative survey data
    Mohanty, Sanjay K.; Upadhyay, Ashish Kumar; Maiti, Suraj; Mishra, Radhe Shyam; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (BMJ, 2023-08-28)
    Introduction The provision of non-contributory public health insurance (NPHI) to marginalised populations is a critical step along the path to universal health coverage. We aimed to assess the extent to which Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (PM-JAY) - potentially, the world's largest NPHI programme - has succeeded in raising health insurance coverage of the poorest two-fifths of the population of India. Methods We used nationally representative data from the National Family Health Survey on 633 699 and 601 509 households in 2015-2016 (pre-PM-JAY) and 2019-2021 (mostly, post PM-JAY), respectively. We stratified by urban/rural and estimated NPHI coverage nationally, and by state, district and socioeconomic categories. We decomposed coverage variance between states, districts, and households and measured socioeconomic inequality in coverage. For Uttar Pradesh, we tested whether coverage increased most in districts where PM-JAY had been implemented before the second survey and whether coverage increased most for targeted poorer households in these districts. Results We estimated that NPHI coverage increased by 11.7 percentage points (pp) (95% CI 11.0% to 12.4%) and 8.0 pp (95% CI 7.3% to 8.7%) in rural and urban India, respectively. In rural areas, coverage increased most for targeted households and pro-rich inequality decreased. Geographical inequalities in coverage narrowed. Coverage did not increase more in states that implemented PM-JAY. In Uttar Pradesh, the coverage increase was larger by 3.4 pp (95% CI 0.9% to 6.0%) and 4.2 pp (95% CI 1.2% to 7.1%) in rural and urban areas, respectively, in districts exposed to PM-JAY and the increase was 3.5 pp (95% CI 0.9% to 6.1%) larger for targeted households in these districts. Conclusion The introduction of PM-JAY coincided with increased public health insurance coverage and decreased inequality in coverage. But the gains cannot all be plausibly attributed to PM-JAY, and they are insufficient to reach the goal of universal coverage of the poor.