Introduction: Nearly 220000 patients are diagnosed with end-stage renal disease (ESRD) every year, which calls for an additional demand of 34 million dialysis sessions in India. The government of India has announced a National Dialysis Programme to provide for free dialysis in public hospitals. In this article we estimate the overall cost of performing hemodialysis (HD) in a tertiary care hospital. Second, we assess the catastrophic impact of out-of-pocket expenditures (OOPEs) for HD on households and its determinants. Methods: The economic health system cost of HD was estimated using bottom-up costing methods. All resources, capital and recurrent, utilized for service delivery from April 2015 to March 2016 were identiﬁed, measured and valued. Capital costs were annualized after accounting for their useful life and discounting at 3% for future years. Sensitivity analyses were undertaken to determine the effect of variation in the input prices and other assumptions on the annual health system cost. OOPEs were assessed by interviewing 108 patients undergoing HD in the study hospital to account for costs from the patient’s perspective. The prevalence of catastrophic health expenditures (CHEs) was computed per threshold of 40% of non-food expenditures. Results: The overall average cost incurred by the health system per HD session was INR 4148 (US$64). Adjusting for capacity utilization, the health system incurred INR 3025 (US$47) per HD at 100% bed occupancy. The mean OOPE per patient per session was INR 2838 (US$44; 95% conﬁdence interval US$34–55). The major components of this OOPE were medicines and consumables (64.1%). The prevalence of a CHE per HD session was 11.1%. Conclusion: Our study ﬁndings would be useful in the context of planning for dialysis services, setting provider payment rates for dialysis under various publicly sponsored health insurance schemes and undertaking future cost-effectiveness analysis to guide resource allocation decisions. Key words: catastrophic expenditure, cost analysis, dialysis, economic evaluation, hemodialysis, out-of-pocket expenditure Received: August 26, 2017. Editorial decision: November 28, 2017 V C The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com 726 Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Cost of hemodialysis in India | 727 delivery models. A recent systematic review of economic evalu- Introduction ation points to a gross lack of data on the cost of health services The burden of end-stage renal disease (ESRD) is rising dramati- in India . In order to bridge this gap in evidence, we under- cally in India, with the proportion of deaths due to kidney fail- took this study to assess the cost of HD in a tertiary care public ure increasing from 2.1% in 2001–3 to 2.9% in 2010–13 . The sector hospital. We report our estimates from both a health sys- age-adjusted incidence of ESRD in India is 226 per million popu- tem and societal perspective. Second, we assess the economic lation . It is estimated that 220 000 new ESRD patients are impact of OOPEs for HD on households and its determinants. added to the pool every year. Dialysis is a life-sustaining treat- ment modality for these patients. The shortage of nephrolo- Materials and methods gists, late referral of patients, inadequate health awareness about preventive measures and a lack of more cost-effective Study setting alternatives like renal transplantation or peritoneal dialysis (PD) The study hospital is a large tertiary care multispecialty public are important issues in the provision of care to ESRD patients [3, 4]. Unequal distribution of nephrologists, with a concentration sector hospital in North India with 1950 beds. It has a large in large cities and in the private sector are major barriers to referral base and receives patients from adjoining states such as Haryana, Punjab, Himachal Pradesh, Uttarakhand, Jammu equitable provision of dialysis to all sections of the society [5, 6]. Inadequate insurance coverage further aggravates the situation and Kashmir as well as Uttar Pradesh, Bihar and Jharkhand. The [6, 7]. Furthermore, 70% of those who start dialysis in India study hospital has an exclusive 24-station HD unit that operates eventually give up dialysis due to financial constraints or death three shifts, with each HD session lasting 4 h. More details on [8, 9]. Thus only 10–20% of dialysis patients in India continue infrastructure are provided in the supplementary material long-term treatment. This high need for care is particularly rele- (Table S1). An independent water treatment plant provides vant given the way health care is financed in India. Most of the high-quality water for this HD unit. The institute also has facili- health expenditures in India are borne by households as direct ties for emergency dialysis and continuous ambulatory PD as out-of-pocket expenditures (OOPEs). This poses significant bar- well as kidney transplantation. riers to accessing services. About 60 million households are pushed below the poverty line every year in India as a result of Costing methodology and data collection OOPEs . In this study, the economic health system cost of HD was esti- Although access to dialysis, particularly hemodialysis (HD), mated using bottom-up costing methods [16, 17]. All resources, has increased in recent years, only a minority of patients are capital and recurrent, utilized for service delivery from April able to continue long-term HD, mostly because of the high 2015 to March 2016 were identified, measured and valued from OOPEs. Making dialysis available to all who can benefit from the health system and societal perspectives. First, all cost centers therapy will create an additional demand for 34 million dialysis were identified, along with the nature of the service being pro- sessions in India . Taking into account the financial pres- vided. Further, all the resources used in providing services were sures on the affected households, the government of India identified and measured. The total cost was estimated by add- recently announced a National Dialysis Service Programme ing the individual costs of each cost center. Unit cost value was (now referred as the Pradhan Mantri National Dialysis arrived at by dividing this total cost by the number of units of Programme) to provide free dialysis services to the poor in pub- services rendered. In addition, capacity utilization of the unit lic sector hospitals in its Union Budget 2016–17 . was estimated from a provider perspective. OOPEs incurred on Implementation of this ambitious programme will involve HD were assessed by interviewing 108 HD patients in the study major augmentation of existing service delivery infrastructure. hospital to account for the cost from the patient’s perspective. Alternatively, the government may consider purchasing dialysis A primary costing survey was undertaken in the study hos- services from the private sector. Presently the National Dialysis pital, as per standard norms of costing [16, 17], and with the tool Programme is in its nascent stages in India. The proposed pro- used elsewhere in costing studies to capture the health system gramme aims to deliver dialysis services to the poor through a costs [18–20]. As mentioned previously, the cost centers public–private partnership mode. In this programme the private involved in the provision of HD were identified. The cost center partner provides for medical human resources, dialysis that was directly rendering the service for treating the patient, machines, water treatment infrastructure, dialyzer and con- that is, the dialysis unit, was deemed as primary. Cost centers sumables. The state government provides space, power and not directly involved in treating the patient, like laundry and water within district hospitals so as to provide dialysis care . administration, were considered as secondary cost centers. It is important to note that dialysis is not the final curative After identifying service centers, the resources used during the treatment for those with ESRD. The management of ESRD needs reference period (April 2015–March 2016) were assessed from to be considered on a holistic basis, which implies adequate the hospital records, stock registers, etc. (Table 1) and were cate- attention on prevention of ESRD through better primary and gorized into capital and recurrent based on their time use. secondary prevention strategies. There is also a need to develop Buildings, medical and non-medical equipment and any other capacity and infrastructure for provision of renal transplanta- item lasting for >1 year were considered as capital resources. In tion. PD, found to be cost-containing in the long term, should be strongly considered in the low- and middle-income country contrast, resources such as salaries of staff, consumables and context . While the more expensive HD is the dominant dialy- drugs were considered as recurrent. For capital resources such as buildings, the utility of each sis modality in the health benefit plans in Malaysia, Taiwan and the UK, PD is preferred in Thailand and Hong Kong . room, whether related to HD or not, was assessed. The data on the area of rooms, including the waiting rooms and any other Whatever the service provisioning model, it first calls for estimating the economic implications of such a programme. space used for HD patients, were elicited by direct observation Second, given that it will entail a significant cost , it becomes and records obtained from the hospital engineering depart- imperative to assess the cost-effectiveness of various service ment. All medical and non-medical equipment and furniture Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 728 | G. Kaur et al. Table 1. Overview of study methodology for HD health system costing Type of resource Source of data Data collection method Apportioning statistic Capital Building/space used Observation of unit, records (maps) Estimated ﬂoor (in square feet) and Utility of each room ascertained by monthly rental price direct observation and hospital records Equipment (medical and Stock register, direct observation Purchase price with annual main- Those involved in provision of HD non-medical) tenance costs taken and annual- included ized. Average life taken by expert opinion and records Recurrent Drugs and consumables Stock registers, vouchers, indent Amount consumed annually; price Amount indented to HD cost records data also taken center Human resource Interview, direct observation, Gross salary multiplied by propor- Apportioning done based on the record review, salary slips tion of time spent annually in time spent allocated to HD cost HD unit center. Most of human resource stationed was dedicated fully to HD unit Other consumables Record review, stock register, Annual amount consumed, price Amount taken for HD cost center (like stationary) indent slips from hospital procurement department Overheads such as Record review of monthly bills Annual consumption in HD unit Apportioning done based on ﬂoor electricity, water area utilized for the HD unit were determined from stock registers, cost of building/space being used for provision of HD, the esti- supplemented by direct observation. Assumptions regarding mated floor area was multiplied by the rental price value of a the life of the equipment were made after discussion with similar space. As for the cost of recurrent resources such as experts. Prices for equipment, consumables and medicines drugs, the quantities of resources were multiplied by their unit were obtained from department records or hospital procure- price to arrive at their overall cost. All costs were discounted at ment section. In case of non-availability of a price for any item, 3% . market prices were used. Joint or shared costs, be it for capital or recurrent resources, Information on recurrent resources like salaries of the medi- were apportioned by suitable statistics. For example, if the cal and non-medical staff involved with HD was drawn from space was being jointly used for more than one activity, then it their salary slips. In case a staff member was involved in more was apportioned by the proportion of person-time hours for than one activity, their time spent on different activities was which the space was used for dialysis services. Similarly, for estimated using the standard interview schedules . This was recurrent costs of human resources, cost apportioning was further validated by direct observation during the period of data done based on the proportion of time spent in HD care. collection. The capacity utilization rate of the HD unit was Overhead costs such as sanitation, electricity, etc. were appor- assessed based on the bed occupancy rate in the unit. The tioned as per proportional floor area . These costs were esti- clean-up time for preparing an HD bed for the next patient as mated in Indian National Rupees (INR) and converted into US well as the annual time spent on infection control measures dollars. As per the average conversion rate in the year 2015–16 was also taken into account. , US$1 was equivalent to INR 65. Since the majority of A total of 108 patients who underwent HD at the study hos- patients using HD did so during daytime shifts, the health sys- pital during the data collection period were recruited consecu- tem cost was calculated specifically during daytime shifts. We tively and interviewed to elicit data on OOPEs on various assessed the standardized unit cost for an HD session at 90% components such as medicines, diagnostics, travel, food, etc. and 100% bed occupancy. In order to do so, we assumed human These patients or their accompanying caregivers (when the lat- resources, equipment and capital as fixed while consumables ter knew more about expenses) were interviewed with their and overhead costs were taken as variable. prior written informed consent. Besides OOPEs, patients were interviewed to record their demographic details, including age, Sensitivity analysis gender, education, area of residence and socio-economic profile in terms of occupation, income and monthly consumption A univariate sensitivity analysis was performed to determine expenditure. the effect of any variation in the inputs on the annual costs. In order to do so, the base values of capital resources such as building, medical and non-medical equipment and salaries of Data analysis the staff were varied by 25% on both the sides of the base value. Health system costs Since the study hospital was a government-funded institution, a wide variation in the prices of consumables and drugs outside All capital costs were annualized after accounting for their use- ful life and discounting for future years. The original price of the hospital was assumed. Thus the prices of the latter were equipment was adjusted with the consumer price index to varied by 50% on either side of the base value in the sensitivity arrive at replacement costs. In order to estimate the opportunity analysis. The assumptions on variations in prices are consistent Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Cost of hemodialysis in India | 729 with what has been reported in other recent costing studies OOPEs from India . The mean OOPE per patient per HD session was INR 2838 (US$44; 95% CI US$34–55). The majority of the OOPE was on Financial risk protection medicines and consumables (64.1%), followed by travel (18.4%), boarding/food (7.9%) and diagnostics (5.2%) (Figure 2). Males The mean OOPE with 95% confidence interval (CI) was com- reported a higher mean OOPE [INR 3029 (US$47)] than females puted. Wealth quintiles were estimated based on the annual [INR 2592 (US$40)] (Table 3). Similarly, those patients residing in household consumption expenditure per capita. The prevalence rural areas reported higher OOPEs [INR 3128 (US$49)] than those of catastrophic health expenditures (CHEs) was computed as from urban areas [INR 2049 (US$32)]. Patients in the poorest per the threshold of 40% of capacity to pay (CTP) or non-food expenditure . As per the methodology for CHE by Xu , the CHE variable was calculated with the help of a dummy variable with a value of 1 indicating the presence of CHE and 0 for Table 2. Annual cost of dialysis care in a tertiary care hospital of absence of CHE: India CHE is present (equivalent to 1) if OOPE/non-food consumption Cost type Annual health system cost, INR (US$) expenditure 0.4 Human resource 23 904 865 (367 767) CHE is absent (equivalent to 0) if OOPE/non-food consumption Building/space 15 277 896 (235 045) expenditure <0.4  Medical equipment 16 541 265 (254 481) Non-medical equipment 1 73 540 (2770) Logistic regression was used to examine the association of Drugs and consumables 14 09 839 (21 690) CHE with independent covariates including gender, age, area of Overhead 1 34 600 (2070) living (rural/urban), education, occupation and wealth quintiles. Total cost 55 826 805 (858 873) Before using multivariate analysis, a bivariate correlation matrix was ascertained to determine if there was any signifi- US$1 is equivalent to INR 65. cant interaction (>0.4) between the independent factors and to check for multi-collinearity. Based on the initial assessment, education was excluded from the final regression model. In this logistic regression technique, the effect of each independent variable was assessed by keeping the first or the last category as a reference and all the independent variables were entered in the model. The odds ratio (OR) along with its 95% CI and corre- sponding P-value was reported. Ethical considerations Ethical approval was obtained from the Institute Ethics Committee of the Postgraduate Institute of Medical Education and Research, Chandigarh. Written informed consent was sought from all study participants. Results Sample characteristics Fig. 1. Distribution of health system costs for HD in a tertiary care hospital of India. Of the 108 patients interviewed concerning OOPEs, 56.5% were males, predominantly married, literate (80.6%) and employed (51%). Close to two-thirds of the respondents lived in rural areas and 50% had visited a private doctor/hospital for HD before coming to the study hospital. Health system costs Overall the health system incurred a cost of INR 4148 (US$64) per HD session. The details of annual health system costs are shown in Table 2. Among the various cost heads identified, time cost of human resources was the major component (45%), followed by building (29%) and medical equipment (27%) (Figure 1). The cost of dialysis during the daytime shifts was INR 3621 (US$56), which was at a capacity utilization rate of 83%. As the capacity utilization increases to 90–100%, the cost per HD session decreased to INR 3348 (US$52) and INR 3025 (US$47), respectively. Uncertainty in the cost of human resources had the major effect on the cost of HD (supplementary material Fig. 2. Distribution of components (%) of OOPEs for HD in a tertiary care hospital Figure S1) of India. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 730 | G. Kaur et al. Table 3. OOPEs for HD in a tertiary care hospital in India sessions per week were 21 times and 45 times higher, respec- tively, in the poorest versus the richest quintile (P< 0.001) Mean OOPE, SE, INR 95% CI (Table 4). Characteristic INR (US$) (US$) (in INR) Gender Discussion Male (n ¼ 61) 3029 (47) 433 (6.7) 2211–3875 Overall, we found the health system incurred INR 4148 (US$64) Female (n ¼ 47) 2592 (40) 548 (8.4) 1609–3816 per HD session, while the patients spent INR 2838 (US$44) per Total 2838 (44) 339 (5.2) 2203–3546 Age HD session. Assuming two dialysis sessions in a week, the <47 years (n ¼ 55) 2845 (44) 491 (7.6) 2002–3773 patient experiences 38.1% catastrophic spending, which >47 years (n ¼ 53) 2832 (44) 477 (7.3) 1937–3797 increases to 52% with thrice-weekly HD. Our findings showed Total 2838 (44) 339 (5.2) 2203–3546 that the odds of CHE at two and three HD sessions per week Religion were 21 times and 45 times higher, respectively, in the poorest Hindu (n ¼ 82) 2621 (40) 373 (5.7) 1976–3395 than the richest quintiles. Muslim (n ¼ 5) 6004 (92) 2453 (37.7) 1156–11 710 Sikh (n ¼ 21) 2932 (45) 749 (11.5) 1550–4543 Comparison of findings Total 2838 (44) 339 (5.2) 2203–3546 Caste Our study is the most comprehensive economic analysis of HD SC (n ¼ 23) 2067 (32) 759 (11.7) 906–3899 cost in India to date. Previous studies from India have analyzed ST (n ¼ 2) 1025 (16) 245 (3.8) 700–1350 OOPEs on dialysis from patient perspective. In a public sector OBC (n ¼ 21) 3092 (48) 703 (10.8) 1806–4596 tertiary hospital , the mean OOPE on dialysis was estimated Gen (n ¼ 62) 3098 (47) 462 (7.1) 2220–4077 to be INR 2230 (US$34). Another study reported median direct Total 2838 (44) 339 (5.2) 2203–3546 costs for dialysis [INR 2628 (US$43)] . In a private tertiary Area care hospital in South India, the cost per HD session borne by Rural (n ¼ 79) 3128 (48) 432 (6.6) 2304–4063 the patient was found to be INR 4428 (US$68) . Most studies Urban (n ¼ 29) 2049 (32) 466 (7.2) 1233–3072 involving HD documented the expenditures on medicines and Total 2838 (44) 339 (5.2) 2203–3546 consumables to be responsible for a substantial proportion of Marital status Unmarried (n ¼ 84) 1843 (28) 482 (7.4) 1051–2984 OOPEs, as noted in our study as well [26–28]. Married (n ¼ 18) 3083 (47) 414 (6.4) 2308–3928 The calculation of health system costs and OOPEs in our Widow/widower (n ¼ 6) 2405 (37) 800 (12.3) 1010–4188 analysis is entirely mutually exclusive. Typically, in public Total 2838 (44) 339 (5.2) 2203–3546 health care facilities in India, resources are spent by the Education government on the provision of health services, such as person- Illiterate (n ¼ 21) 2357 (36) 659 (10) 1156–3742 nel salaries, capital infrastructure (building and equipment) and Literate (n ¼ 87) 2955 (45) 396 (6.1) 2250–3799 basic medical supplies. However, the supply of medicines and Total 2838 (44) 339 (5.2) 2203–3546 consumables generally does not cover all the required items for Occupation treatment, including dialyzer and tubing. Moreover, there are Unemployed (n ¼ 48) 2564 (39) 479 (7.4) 1695–3622 frequent stock-outs of medicines. A survey of health facilities in Employed (n ¼ 60) 3059 (47) 467 (7.2) 2187–4045 North Indian states showed that only about half of the essential Total 2838 (44) 339 (5.2) 2203–3546 drugs are available . Similar findings reflecting a lack of Wealth quintiles availability of all essential medicines in public facilities are Poorest (n ¼ 21) 3967 (61) 943 (14.5) 2129–5788 reported in the recent National Sample Survey 71st Round Poor (n ¼ 22) 2369 (37) 789 (12.2) 1074–4164 Report [30, 31]. Therefore most medicines and consumables Moderate rich (n ¼ 22) 2605 (40) 671 (10.3) 1447–4085 need to be purchased by patients, thereby incurring OOPEs. Richer (n ¼ 22) 3321 (51) 786 (12.1) 1866–5041 Expenditures on medicines constitute the largest share of Richest (n ¼ 21) 1943 (30) 522 (8) 1026–3026 OOPEs on health care in public facilities in India [30–34]. The Total 2838 (44) 339 (5.2) 2203–3546 cost of dialyzer reuse and erythropoiesis-stimulating agents US$1 is equivalent to INR 65. Gen, general; OBC, other backward class; SC, were borne by the patient and hence were elicited under the Scheduled Caste; ST, Scheduled Tribe. OOPE in our study. Second, we would like to clarify that differences in absolute OOPEs among the population subgroups based on wealth status quintile experienced almost double the OOPEs of those belong- was not statistically significant. However, it is important to note ing to the richest quintile. that while there is no difference in the mean OOPE, CHEs were was significantly greater among the poorer quintiles as com- Financial risk protection pared to the richest. This finding is similar to other studies, The prevalence of CHE per HD session was 11.1%. With an which showed that the CHEs were regressively skewed at a assumption of twice-weekly HD sessions, the prevalence of CHE higher rate among the poorer sections [31–34]. None of the among those undergoing HD was 38.1%. With thrice-weekly HD patients in our study sample had any insurance. However, the sessions, it rose to 51.9%. Of all the patients interviewed about findings from a recent systematic review of impact evaluations their sources of finance for these OOPEs on HD, 59.3% confirmed for publicly financed insurance schemes in India showed the borrowing from friends/relatives, 35.2% confirmed using their absence of any reduction in OOPEs or CHEs . In view of this, salary/savings and 5.6% sold assets. it is important to strengthen the public sector delivery of serv- Multiple logistic regression analysis showed that wealth sta- ices through adequate availability of medicines and supplies, tus was the only significant predictor of incurring CHE as a such that OOPEs are minimized, which is likely to result in result of OOPEs on HD. The odds of CHE at two and three HD adequate financial risk protection. Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 Cost of hemodialysis in India | 731 Table 4. Determinants of CHEs in HD in the study hospital CHE (38%) CHE (51.9%) Characteristic OR P-value 95% CI OR P-value 95% CI Age <47 years 0.8 0.63 0.31–2.05 0.53 0.2 0.2–1.40 >47 years Ref. Gender Male Ref. Female 0.4 0.13 0.11–1.34 0.44 0.2 0.12–1.6 Area Urban 0.61 0.35 0.21–1.72 0.8 0.7 0.3–2.23 Rural Ref. Occupation Unemployed 1.5 0.50 0.5–5.20 0.9 0.8 0.23–3.13 Employed Ref. Wealth quintiles Poorest 21.89 <0.001 4.25–112.85 45.4 <0.001 7.43–277.07 Poor 2.9 0.2 0.60–13.83 14.64 0.001 2.94–72.80 Moderate 3.3 0.14 0.68–16.08 5.73 0.02 1.2–27.53 Richer 3.4 0.12 0.72–15.8 5.8 0.02 1.23–28.12 Richest Ref. Adjusted R 0.257 0.341 Unadjusted R 0.187 0.255 Findings in terms of the distribution of health system costs health care setting also explains the difference . Many (with the time cost of human resources as the predominant options such as frequent dialyzer reuse, philanthropist contri- component) are worth highlighting. Appropriate dialysis deliv- butions and limiting the visits of nephrologists are some of the ery requires contributions from doctors, nurses, nutritionists, mechanisms of cutting costs in the non-government sector social workers, technicians and other staff . Our findings [5, 43]. Procedures like vascular access creation for HD are gen- also suggest that HD costs vary at different points of capacity erally charged for in the private sector and are directly borne by utilization. There is a relative reduction in the health system the patient, compared with free or highly subsidized in the gov- unit cost at 100% bed occupancy. This suggests there is room for ernment or charitable sector . A free-standing (minimal making service delivery in the public sector more efficient. One care) HD unit  or a charitable HD unit often has a free choice way to address this could be by utilizing the late shifts or night- in the selection of stable patients eligible for day care dialysis time to cater to patients, especially those who are willing to be . On the contrary, acutely ill patients or those in advanced treated at that time—be it an inpatient case or outpatient. The stages of kidney disease get referred to public sector hospitals marginal cost of such an increase in service delivery is likely to by the private sector [5, 25, 46]. In our study hospital, the major- be less than the average cost and increase overall efficiency. ity of patients had reported in the late stages of kidney disease [5, 25]. The recent National Sample Survey on health found that only Existing insurance packages covering dialysis 13% of the rural population and 12% of the urban population are It is important to compare our findings with the dialysis service covered under any insurance . Without financial risk protec- packages being offered by existing health insurance schemes in tion, nearly one-fourth of the households resort to either borrow- India [37–40]. The Chief Minister’s Comprehensive Health ing or selling of assets to finance their health expenditures . A Insurance Scheme in Tamil Nadu offers INR 8000 (US$123) per recent systematic review  also documented an increase in month for maintenance HD . The Aarogyasri scheme in service utilization but noted a lack of evidence that shows any Telangana  and Mahatma Jyotiba Phule Jan Arogya Yojana in reduction in OOPEs due to these publicly financed health insur- Maharashtra (earlier known as Rajiv Gandhi Jeevandayee ance schemes. These prohibitive costs compel patients to forgo Arogya Yojana)  pay INR 10 000 (US$154) every month for care or end up with suboptimal dialysis [5, 47]. dialysis (eight sessions) and supportive care, and the Rajiv Our study once again highlights the high OOPEs incurred by Aarogyasri Community Health Insurance Scheme in Andhra patients on HD and extends this by highlighting the inequities. Pradesh pays INR 12 500 (US$192) (for a minimum of 10 dialysis The economically worse off are much more likely to suffer cata- sessions) . Further, in the central government health strophic OOPEs, which will deepen the rich–poor gap. Similar to scheme, the entitlement for an HD patient is around INR 2900– our findings, another study reported that 63% of those who 3335 (US$45–51) per dialysis session . underwent HD had borrowed money from an employer/friends, In a nephrologist-owned unit, the average cost of an HD ses- 20% opted for loans and 30% resorted to selling assets to finance sion was reported as INR 700 (US$11) , which is significantly for dialysis treatment . Previous evidence also suggests that lower than the cost calculated in this study. In that study, the dwindling finances act as a barrier in accessing dialysis treat- authors included only recurrent operational costs and did not ment for the majority of patients [5, 49]. consider capital costs. Variation in HD practice based on the Our study has several strengths. This is the first study to trained capacity, capital infrastructure as well as the level of undertake a comprehensive economic analysis of dialysis care Downloaded from https://academic.oup.com/ckj/article-abstract/11/5/726/4825049 by Ed 'DeepDyve' Gillespie user on 17 October 2018 732 | G. Kaur et al. and incorporated assessment of health system costs as well as References patient OOPEs. Standard methods were used for data collection. 1. Dare AJ, Fu SH, Patra J et al. Renal failure deaths and their Generally, there is the possibility of recall bias when OOPEs are risk factors in India 2001-13: nationally representative esti- recalled by the patient over long reference periods. We inter- mates from the Million Death Study. Lancet Glob Health 2017; viewed patients for OOPEs incurred for the HD session on the 5: e89–e95 day of the survey, which was verified by written bills. Hence the 2. Modi GK, Jha V. The incidence of end-stage renal disease in possibility of any recall bias is weak. 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Clinical Kidney Journal – Oxford University Press
Published: Oct 1, 2018
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