Cost of Intensive Care Treatment for Liver Disorders at Tertiary Care Level in India

Cost of Intensive Care Treatment for Liver Disorders at Tertiary Care Level in India PharmacoEconomics Open (2018) 2:179–190 https://doi.org/10.1007/s41669-017-0041-4 ORIGINAL RESEARCH ARTICLE Cost of Intensive Care Treatment for Liver Disorders at Tertiary Care Level in India 1 1 2 1 • • • • Shankar Prinja Pankaj Bahuguna Ajay Duseja Manmeet Kaur Yogesh Kumar Chawla Published online: 14 July 2017 The Author(s) 2017. This article is an open access publication Abstract Results In 2013–2014, health system costs per patient Background Liver diseases contribute significantly to the treated in the ICU and HDU were US$2728 [Indian health and economic burden globally. We undertook this National Rupee (INR) 1,63,664] and US$1966 (INR study to assess the health system costs, out-of-pocket 1,17,985), respectively. The mean OOP expenditures for (OOP) expenditure and extent of financial risk protection treatment in the ICU and HDU were US$2372 (INR associated with treatment of liver disorders in a tertiary 1,42,297) and US$1752 (INR 1,05,093), respectively. care public sector hospital in India. Indirect costs of hospitalization in ICU and HDU patients Methodology The present study was undertaken in an were US$166 (INR 9952) and US$182 (INR 10,903), intensive care unit (ICU) of a tertiary care hospital in North respectively. India. It comprised an ICU and an HDU (high dependency Conclusion Treatment of chronic liver disorders poses an unit). Bottom-up micro-costing was undertaken to assess economic challenge for both the health system and the health system costs. Data on OOP expenditure and patients. There is a need to focus on prevention of liver indirect costs were collected for 150 liver disorder patients disorders, and finding ways to treat patients without admitted to the ICU or HDU from December 2013 to exposing their households to the catastrophic effect of OOP October 2014. Per-patient and per-bed-day costs of treat- expenditure. ment were estimated from both health system and patient perspectives. Financial risk protection was assessed by computing prevalence of catastrophic health expenditure as Key Points for Decision Makers a result of OOP expenditure. Significant evidence of the health burden and consequences of liver disorders exists in India. There has been no evidence published of the significant economic implications which emerge alongside the rising burden of risk factors for liver disorders. Electronic supplementary material The online version of this Our study reports the direct medical costs from both article (doi:10.1007/s41669-017-0041-4) contains supplementary material, which is available to authorized users. the health system and patient perspectives, as well as the indirect costs on account of lost productivity. & Shankar Prinja shankarprinja@gmail.com Our findings could also be used for setting reimbursement decisions for treatment of liver School of Public Health, Post Graduate Institute of Medical disorders in various publicly financed insurance Education and Research, Chandigarh 160012, India schemes as well as assessing the cost effectiveness of Department of Hepatology, Post Graduate Institute of related interventions. Medical Education and Research, Chandigarh, India 180 S. Prinja et al. 1 Introduction intensive care treatment of liver diseases in a tertiary care hospital setting. Secondly, from patient perspective, we determine the OOP expenditure on treatment, the extent of Liver diseases contribute significantly to the global burden financial risk protection in terms of catastrophic health of mortality and morbidity [1, 2]. Globally, liver cirrhosis expenditure (CHE), and mechanisms to cope with the OOP alone accounts for more than a million deaths, which is 2% expenditure. of overall deaths, and 31 million disability-adjusted life- years (DALYs), which is 1.2% of total DALYs. Along similar lines, liver disorders are widely prevalent 2 Methodology in India. These represent a wide spectrum ranging from those with chronic infections, to those affected by alcohol 2.1 Study Setting consumption, and finally comprising the non-alcoholic fatty liver disorders. With over 40 million hepatitis B virus We conducted this study in the Post Graduate Institute of (HBV) carriers in India, the country falls into the inter- Medical Education and Research (PGIMER), a tertiary care mediate level of HBV endemicity [3]. The population hospital, situated in the North Indian city of Chandigarh. prevalence of HBV and hepatitis C virus (HCV) infection With a total of 356 consultants and 2000 resident doctors, in India is 3.7 and 1%, respectively. In the developed the hospital caters to an annual inpatient and outpatient countries, dominant risk factors for chronic liver disease attendance of 78,568 and 2,061,911, respectively [13]. (CLD) include alcohol and HCV. On the other hand, HBV The present cost analysis was undertaken in a special- and HCV are responsible for the majority of CLDs in India ized intensive care unit (ICU) for the treatment of liver [4]. Since 2007, alcohol has fast emerged as an important disorders, under the Department of Hepatology. The ICU is risk factor, and it constituted the leading cause of CLD- broadly classified into two parts: the intensive care unit related morbidity and mortality in India during 2010–2011 (ICU) and the high dependency unit (HDU). The criterion [4]. for this classification is based on the severity of patients Liver disorders pose a significant economic challenge in with liver disorders, with more severe patients being terms of management of these chronic infections. In 2007, admitted to the ICU. In addition to the HDU facilities, the in the US, cirrhosis was graded as one of the leading causes ICU has ventilator and dialysis support, and endoscopic of death. The estimated economic burden due to liver cir- interventions for critical care required for more severe rhosis was significant, with the main cost of treatment patients. Both the units have five beds each. A common ranging from US$14 million to US$2 billion, depending on pool of human resources was involved in provision of disease etiology [5]. Treatment costs of morbidities related services in both ICU and HDU, supported by a laboratory to HCV in ten European Union countries were estimated to and ultrasound and fibro-scan facility. These diagnostic be €50 million, and hence a significant burden on society. facilities are utilized for the ICU (and HDU) patients along Similarly, €3 billion was reported to have been lost in with the other liver disorder patients who consult the out- Spain as a result of HCV over a 20-year time period [6]. patient department and those admitted to general wards in Despite such a high economic burden, as evident from the hospital. Besides this, the general diagnostic (patho- Western countries, there are no Indian estimates for the logical and radiological) facilities of the hospital are also cost of treating liver disorders. In fact, there is very little used for the liver ICU patients. A total of 171 and 142 new evidence of cost of curative care in the public sector from a admissions were treated in the ICU and HDU, respectively, health-system perspective [7–11], and whatever is avail- during the year 2014. able is mostly for primary and secondary care [8–11]. The economic data becomes even scarcer for the tertiary care 2.2 Data Collection sector [7] and chronic diseases. Apart from significant economic implications for the health system, treatment of 2.2.1 Health System Costs liver disorders leads to high out-of-pocket (OOP) expen- diture for patients. This OOP expenditure in turn manifests We adopted a bottom-up micro methodology to assess the as catastrophic spending by households which pushes them health system costs. We collected data on health system below the poverty line [6, 12]. Treatment for liver disease resources used to provide patient care during a 1-year is also likely to impose high OOP expenditure on account period from April 2013 to March 2014. The methodology of its intensive resource requirement and chronic nature. for data collection comprised record reviews, physical Hence, we undertook the present study to bridge this gap inspection of facility, and staff interviews. The collected in the evidence base. Firstly, from a health system per- data included number of human resources (i.e. medical, spective, we estimated per-bed-day admission costs of Cost of Liver Disorders 181 paramedical staff, administrative, support staff, etc.), space expenditure (food and non-food). Patients were also inter- in the building, numbers and types of equipment, other viewed to elicit mechanisms to cope with the OOP non-consumable items, diagnostic tests (laboratory and expenditure for treatment. Lastly, in order to assess indirect radiological), medicines, consumables and other overhead costs owing to lost productivity, both for patients and costs. Data was collected on the quantity of different caregivers, we also collected data on time spent by patient resources being used exclusively for ICU or HDU patients, on routine activities (i.e. professional work, household or in shared manner for both [Supplementary Appendix, activities, childcare, voluntary and social activities, physi- Tables S1–S7, see electronic supplementary material cal or leisure activities, etc.) in the days of good health (ESM)]. before his/her admission to ICU or HDU. All resources were classified as recurrent or non-recur- rent/capital resources. Recurrent costs included staff sal- 2.2.3 Follow-Up Patient Interviews aries, medicine and consumables, diagnostic tests and overheads costs (i.e. electricity, water consumption, laun- We also followed up the patients telephonically at Month dry, dietetics, etc.). Non-recurrent or capital resources 1, 3 and 6 from the date of discharge to record OOP mainly comprised building or space, equipment (medical expenditure for further treatment (if any) and the survival and non-medical) and furniture with a lifespan of[1 year. status of the patient. Any OOP expenditure for out-patient Price data was collected from the procurement department or in-patient treatment after discharge from any health of the Institute [14]. For prices that were not available from facility was assessed. Data on OOP expenditure collected the procurement department, we used the average market at each follow-up was mutually exclusive in nature (i.e. price from among the leading three manufacturers/suppli- specifically for that period). For example, at Month 1 fol- ers, which was then adjusted based on a factor between low-up, data on OOP expenditure was collected from dis- market price and government procurement price. However, charge date to completion of Month 1. The same occurred since the market prices are higher than the government for Month 1 to Month 3 (2-month period) at Month 3 procurement price, we adjusted the same using a scaling follow-up, and for Month 3 to Month 6 (3-month period) at factor. This scaling factor was the average ratio of health the Month 6 follow-up. system price and market price for other drugs and con- sumables, where prices were available from both the 2.3 Data Analysis sources. All prices were adjusted to current values using gross domestic Product (GDP) deflators. Besides the data 2.3.1 Health System Costs on quantity of resources utilized and their prices, we also Data was analysed using Statistical Package for Social collected data on number of patients treated during the same reference period, separately for the ICU and HDU. Sciences (SPSS) version 21 and MS Excel. The cost of Face-to-face interviews of staff members (faculty, resident space for the hepatology ICU was estimated by applying doctors, nursing staff, support and administrative staff) rental price for the area. Costs of various equipment (and were conducted to elicit time spent on different activities furniture) available in the ICU and HDU were annualized during a 1-week period. based on their average useful life and discounting to arrive at an equivalent uniform annual cost. An average discount 2.2.2 Out-of-Pocket Expenditure rate of 3% was used to compute the annualization factor [15]. Replacement costs of equipment were preferred over For OOP expenditure, all new patients admitted to the liver original costs. These replacement costs were computed by ICU during the period from December 2013 to October 2014 adjusting original costs using the consumer price index. All were recruited. Written informed consent of the patient or the costs were converted to US dollars (US$) for compa- accompanying caregiver (if patient was not conscious) was rability at a wider level at the rate of US$1 equal to 60 obtained. Data was collected at the time of recruitment, Indian National Rupees (INR) [16]. Overall cost of service followed by a daily interview to elicit OOP expenditure provision was estimated. Finally, all the cost estimates incurred for treatment over the last 24 h. This was continued were converted to 2014 prices to adjust for inflation, on a daily basis till the discharge or final outcome of the applying a discounting factor of 3% per year. patient. OOP expenditure was elicited for hospital charges, medicines, laboratory tests, procedure or surgery, trans- 2.3.1.1 Apportioning Statistics Appropriate apportioning portation, boarding/lodging and meals of attendants with statistics were used to allocate shared or joint resources to patient, and lastly informal payments (if any). the ICU and HDU. Firstly, the shared cost of human Secondly, we collected data on socio-demographic resources was apportioned to ICU care, non-ICU inpatient characteristics including household consumption care, outpatient care and other general teaching, research 182 S. Prinja et al. and administrative work. Interviews with various staff and impoverishment due to OOP expenditure [19–21]. We working with the ICU unit were done to collect the data on computed CHE to measure financial risk protection, which time allocation patterns. We interviewed different cate- is defined as OOP spending for healthcare exceeding a gories of personnel involved in ICU services, which mainly given threshold of households’ paying capacity [19–21]. included consultants, resident doctors, nurses and techni- More specifically, it implies any OOP expenditure on cians to capture data on work flow patterns, and time spent health which exceeds 40% of household non-food con- per activity during the previous week. Proportion of time sumption expenditure. spent on each activity was used as the basis for appor- There are two thresholds available in the literature to tioning the shared human resource costs to various cost estimate the prevalence of CHE based on households’ centres and functional activities. Data obtained through paying capacity. The first approach considers any health these interviews was used for apportioning the share cost of expenditure exceeding 10% of a household’s total con- human resource. sumption expenditure as catastrophic, while the second Secondly, costs of shared building/space (i.e. laboratory, approach considers 40% of non-subsistence expenditure (or waiting area, discussion room, doctor’s room, etc.), non-food related) as the threshold. The second approach is equipment and overheads were apportioned among the ICU considered more appropriate from an equity perspective, and HDU on the basis of proportion of bed-days of ICU and hence we adopted the latter [19–22]. To compute and HDU patients in a year. Costs of medicines and con- prevalence of CHE, OOP expenditure that was in excess of sumables could not simply be apportioned based on inpa- 40% of household non-food consumption expenditure was tient bed-days of admission in the ICU and HDU, as these considered as catastrophic. In addition, we undertook a are dependent on the patients’ severity of illness. Hence, sensitivity analysis by computing prevalence of CHE based we apportioned the cost of medicines and consumables on the 10% of total expenditure cut-off. Lastly, we also being used jointly for HDU and ICU patients, based on a analysed the coping mechanisms for OOP expenditure by ratio of average OOP cost of medicines for ICU and HDU calculating the percentage of OOP expenditure which was patients. This was considered appropriate as there were no met through salaries/savings, borrowing without interest, prioritization criteria used for issue of medicines and borrowing with interest, selling of assets, or any form of consumables between the ICU and HDU. health insurance. 2.3.1.2 Unit Costs Per-patient and per-bed-day costs of 2.3.4 Indirect Costs treatment were estimated for both ICU and HDU patients. Data collected on indirect costs (i.e. productivity loss of patients/caregivers due to hospitalization) was analysed 2.3.2 Out-of Pocket Expenditure using a human capital (HC) approach. There are two broad We estimated the mean and standard error of OOP approaches for valuing productivity loss due to illness, HC expenditure at the overall level and by socio-demographic and friction cost (FC) [23, 24]. In the HC approach, income characteristics of individuals, and also by diagnostic cate- and fringe benefits of an employee (or market wage) are gory. Patients with liver disorders were classified into five considered as a proxy of his productivity loss due to illness, categories: acute viral hepatitis/acute liver failure, cirrhosis while the FC approach considers replacement cost of an (includes alcohol-related cirrhosis, HBV, HCV, autoim- employee to carry out his work. Although the FC method mune hepatitis (AIH), non-alcoholic steatohepatitis and measures the productivity loss in a more realistic way, it is others), acute-on-chronic liver failure (ACLF), hepatocel- data intensive which introduces significant uncertainties. lular carcinoma (HCC) and extrahepatic biliary tract On the other hand, HC demands less data and is amenable obstruction (EHBO). for easy communication [23, 24]. Hence, we used the HC approach for analysis of indirect cost data in our study. 2.3.3 Financial Risk Protection 2.3.5 Estimates Financial risk protection is one of the components of uni- versal health coverage (UHC) [17]. It ensures that the All the estimates for health system costs, OOP expenditure population of a state/country can access quality healthcare and indirect costs are reported in both INR and US$. Also, services at the time of need without any financial hardship 95% confidence intervals (CI) are reported for OOP [17–19]. In general, there are two methods to measure expenditure and indirect costs along with their base financial risk protection, which include prevalence of CHE estimates. Cost of Liver Disorders 183 Using a 40% threshold for CHE, we found that 87% of 3 Results patients admitted to the ICU incurred CHE, while its 3.1 Sample Characteristics prevalence was 71% for HDU patients (Table 3). Preva- lence of CHE in our study did not vary much when we used A total of 150 patients were recruited for estimation of 10% of total consumption expenditure (i.e. 98 and 84% for the ICU and HDU, respectively). This signifies that the OOP expenditure, of which 85 and 65 were from HDU and ICU, respectively. Of all patients, males represented nearly conclusion is robust regarding choice of thresholds used to define catastrophic expenditure. The mean indirect cost 75% in the ICU and nearly 62% in the HDU. More than 80% of patients both in the ICU and HDU were aged estimation for ICU and HDU patients was US$166 (95% CI 117–215) and US$182 (95% CI 139–224), respectively [30 years and almost 45% were aged [50 years. Mean (Table 4). length of stay for ICU and HDU patients was 13 and 11 days, respectively. Around 72 and 87% of patients were 3.3.1 Follow-Up discharged alive from the ICU and HDU, respectively (Table 1). Almost 68% of the patients admitted to the Out of patients eligible for Month 1, 3 and 6 follow-ups, hepatology ICU had some form of cirrhosis, followed by 13.5% with EHBO and ACLF, 10.8% with hepatocellular approximately 95% were followed up to record the data on post-hospitalization OOP expenditure (Supplementary carcinoma and 7.4% with acute viral hepatitis/acute liver failure. Appendix, Fig. S1, see ESM). Mean OOP expenditures for patients 1 month after discharge from the ICU and HDU were US$366 (95% CI 178–554) and US$977 (95% CI 3.2 Health System Costs 0–2153), respectively. Mean OOP expenditures at Month 3 follow-up, which reflected a 2-month period, were US$894 The annual cost incurred by the health system for ICU (95% CI 59–1729) and US$635 (95% CI 286–985) for ICU and HDU care in the year 2014 was US$386,199 and HDU patients, respectively, and at Month 6 follow-up, (INR231, INR71,939) and US$336,651 (INR201, INR99,069), respectively (Table 2). For the ICU and for a period of 3 months, they were US$498 (95% CI 143–778) and US$568 (95% CI 166–893) for ICU and HDU, the share of personnel costs was highest (37% ICU and 43% HDU), followed by physical infrastructure HDU patients, respectively. (27% ICU and 31% HDU) and diagnostics (20% ICU and 12% HDU). Per-patient treated and per-bed-day admission cost for treatment in the ICU were US$2728 4 Discussion (INR163,664) and US$212 (INR12,697), respectively. 4.1 Summary of Study Findings Similarly, the cost of treatment was US$1966 (INR117,985) per patient and US$185 (INR11,068) per bed-day in the HDU (Table 2). We undertook this study to assess the health system costs and OOP expenditure on account of tertiary care intensive 3.3 Out-of-Pocket Expenditures treatment for liver disorders in India. Overall, we found that the health system cost per patient treated and per bed- The mean OOP expenditures for treatment in the ICU and day admission to the ICU were US$2728 (95% CI HDU were US$2372 (95% CI 1881–2862) and US$1752 2580–3125) and US$212 (95% CI 200–242), respectively. (95% CI 1329–2174), respectively (Table 3). Medicines Similarly, the cost of treatment was US$1966 (95% CI accounted for a major share of OOP expenditure—85 and 1860–2253) per patient and US$185 (95% CI 174–211) per 79% among ICU and HDU patients, respectively (Fig. 1). bed-day in the HDU. From the patients’ perspective, the Mean OOP expenditures per patient bed-day in the ICU treatment of liver disorders incurred an OOP expenditure and HDU were US$220 (INR13,194) and US$151 of US$2372 (95% CI 1881–2862) and US$1752 (95% CI (INR9088), respectively. Salary or savings was the pre- 1329–2174) in the ICU and HDU, respectively. All patients dominant source of finance to meet the OOP expenditure admitted to the ICU, and 88% of those admitted to the among 51 and 57% patients treated in the ICU and HDU, HDU, experienced catastrophic expenditures. Of the total respectively (Fig. 2). Mean OOP expenditure was rela- hospitalized patients, 29% had to borrow money to pay for tively higher in patients diagnosed with ACLF [US$3170 treatment costs. (INR190,202)], followed by cirrhosis and acute viral hep- We found that the OOP expenditure during the period atitis/acute liver failure [US$2131 (INR127,899) and immediately after discharge was higher than later months. US$1928 (INR115,668), respectively]. This is reflected in a higher OOP expenditure from 1 to 184 S. Prinja et al. Table 1 Characteristics of liver disorder patients admitted to the 3 months following discharge, as compared with intensive care unit of a tertiary care hospital in India 4–6 months after discharge. This could be related to a relative improvement in patients’ condition in subsequent Characteristics ICU HDU Total months and hence a lesser need for more intense N % N % N % medication. Gender We acknowledge that there are significant variations in Male 49 75.4 53 62.4 102 68.0 the healthcare infrastructure across the different states of Female 16 24.6 32 37.6 48 32.0 India, as well as costs of resources which are used for Total 65 100 85 100 150 100 delivery of services. Further, we estimated the cost of Age group delivering treatment in one large tertiary care hospital. We \30 years 10 15.4 14 16.5 24 16 chose this hospital as the treatment for most chronic liver 31–50 years 25 38.5 31 36.5 56 37.3 disorders is usually not provided in secondary level hos- [50 years 30 46.2 40 47.1 70 46.7 pitals and is available in similar tertiary care teaching Total 65 100 85 100 150 100 hospitals only. However, all these factors may limit the Locality extent of the generalizability of our cost estimates to the Urban 31 47.7 53 62.4 84 56 whole of India. We recommend undertaking a study incorporating a variety of geographic settings and levels of Slum 2 3.1 3 3.5 5 3.3 Rural 32 49.2 29 34.1 61 40.7 care in India to improve the generalizability of results. Total 65 100 85 100 150 100 4.2 Financial Risk Protection: Current Status Education Illiterate 5 7.7 4 4.7 9 6.0 In general, In India there is a high prevalence of CHE. \8th standard 17 26.2 13 15.3 30 20.0 Some community-based research studies reported the 8th–12th standard 25 38.5 30 35.3 55 36.7 prevalence of CHE for any kind of illness in the range of Graduate and above 18 28 38 44.7 56 37 30–56% [19, 22, 25, 26]. On the other hand, prevalence of Total 65 100 85 100 150 100 CHE is extremely high in diseases requiring intensive care Occupation like chronic liver disorders, cancers, acute coronary syn- Labourer 8 12.3 7 8.2 15 10.0 drome, etc. One North Indian study reported the prevalence Self-employed 9 13.8 21 24.7 30 20.0 of CHE among households with a family member suffering Unemployed 21 32.3 33 38.8 54 36.0 from breast cancer to be 84% [27]. Similarly, a study done Salaried 27 41.5 24 28.2 51 34.0 for the Asian region depicted that prevalence of CHE Total 65 100 85 100 150 100 among households with an acute coronary syndrome event Marital in India was more (i.e. 60% among the uninsured popula- Unmarried 8 12.3 8 9.4 16 10.7 tion) compared with other Asian countries like Malaysia, Married 57 87.7 77 90.6 134 89.3 Thailand, Singapore, Vietnam, etc. [28]. Moreover, Total 65 100 85 100 150 100 specifically in the Indian context where the income dis- Wealth status parities are high, even a small amount of OOP expenditure Poorest 12 18.5 18 21.2 30 20 becomes catastrophic for low-income segments of society Poor 12 18.5 17 20 29 19.3 [19, 26, 29]. Hence, our study findings are in concordance Middle 17 26.2 13 15.3 30 20 with the literature when specifically compared to other Rich 14 21.5 17 20 31 20.7 severe diseases requiring intensive care. Richest 10 15.4 20 23.5 30 20 Total 65 100 85 100 150 100 4.3 Policy Implication: Prevention of Liver Duration of stay Disorders \3 days 11 16.9 21 24.7 32 21.3 3–10 days 20 30.8 30 35.3 50 33.3 Our results have significant policy implications. Policy [10 days 34 52.3 34 40.0 68 45.3 discourse in India is gradually building towards universal Total 65 100 85 100 150 100 provision of healthcare services [30]. How the care provi- Outcome at discharge sion will be organized is debatable; however, most policy Dead 18 27.7 11 12.9 29 19.3 documents recommend greater reliance on a tax-funded Alive 47 72.3 74 87.1 121 80.7 system of financing. In terms of provisioning of healthcare Total 65 100 85 100 150 100 services, both models of publicly delivered healthcare HDU high dependency unit, ICU intensive care unit services and purchasing of healthcare services through Cost of Liver Disorders 185 Table 2 Health system costs of Health system costs ICU US$ (INR) HDU US$ (INR) treatment of liver disorder patients in the intensive care Annual costs unit of a tertiary care hospital in Personnel 143,402 (8,604,123) 143,402 (8,604,123) India Equipment 30,526 (1,831,542) 20,764 (1,245,861) Laboratory tests 76,514 (4,590,849) 38,910 (2,334,601) Medicines and consumables 6945 (416,719) 4763 (285,778) Stationary 2973 (178,371) 2973 (178,371) Physical infrastructure 104,760 (6,285,600) 104,760 (6,285,600) Utilities/overheads 21,079 (1,264,735) 21,079 (1,264,735) Total 386,199 (23,171,939) 336,651 (20,199,069) Unit costs Cost per patient 2728 (163,664) 1966 (117,985) Cost per bed-day 212 (12,697) 185 (11,068) Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, US$ United States dollar publicly financed health insurance schemes are evident of safe nosocomial practices reduces HCV transmission, [31]. The increasing incidence of liver disease and its risk but usually comes at a high cost of implementation that factors [32, 33] highlights that prevention of liver disorders exceeds the fiscal ability of low-income countries [39, 40]. and their risk factors is likely to be a more cost-effective However, there have been recent instances of HCV out- action and has to remain the mainstay of the policy. In breaks in India which have resulted from unsafe injection terms of preventive actions, HBV vaccination remains a practices by unqualified practitioners and quack doctors cornerstone. [41]. Use of auto-disable syringes is one of the strategies to In general, the most cost-effective vaccination strategy prevent re-use and its associated infections. An HCV sero- is usually determined by the endemicity of disease, the ease prevalence of as high as 71% has been reported among of implementing a vaccination programme with high cov- injection drug users (IDUs) in North-East India [42]. In erage, the efficacy of vaccination, and the infectiousness of India, an Opioid Substitution Therapy (OST) strategy for the causative agent [34]. From an Indian viewpoint, a IDUs was introduced by the Government of India under the pentavalent vaccine that includes HBV has been reported National AIDS Control Program (NACP). Under OST, to be very cost effective with an incremental cost of opiate-dependant persons are made to shift to orally US$277 per disability-adjusted life-year (DALY) averted administered opiates such as buprenorphine and metha- [35]. As per the Government of India’s latest policy, HBV done, replacing illicit drug use [43]. Accounting for the is given as part of a pentavalent vaccine that comprises social costs, OST therapy using buprenorphine has been diphtheria, pertussis and tetanus (DPT), haemophilus shown to have higher benefits at lower costs than no influenza type ‘b’ (Hib) and HBV. The National Technical treatment [44]. Advisory Group on Immunization (NTAGI) in India rec- The third set of preventive interventions, besides those ommended the introduction of the pentavalent vaccine in against HBV and HCV, are those directed against alcohol the Universal Immunization Program (UIP) in 2008, which consumption and its health effects. It is evident that alcohol was subsequently launched in 2011 in two South Indian consumption leads to liver cirrhosis and related mortality states [36, 37]. This has now been extended to children in and hence, policies and procedures intended to restrict several other states also. However, the coverage of this alcohol consumption are likely to benefit [44–47]. These vaccine remains very low as per recent estimates [38], and policies include those which are targeted to a specific hence needs strengthening for effective protection. Further, population group such as school-based campaigns. Alter- the Government of India also recommends a birth dose of natively, these could be general public education cam- HBV for all institutional births, which needs to be paigns on harmful effects of alcohol. Similarly, those at universalized. risk for alcoholism could also be targeted for counselling. The major preventive strategy for HCV remains con- Stringent alcohol purchase laws, government monopolies trolling nosocomial exposure (i.e. blood screening, safe on alcohol, restrictions on alcohol marketing campaigns, injection, and infection control) and reducing high-risk and increased taxes on alcohol have all been shown to have behaviours (i.e. intravenous drug use) [39]. Implementation positive effects on reduction in alcohol consumption [48]. 186 S. Prinja et al. Table 3 Direct costs (INR) as out-of-pocket expenditure and financial risk protection for treatment of liver disorder patients in the intensive care unit of a tertiary care hospital in India Characteristic ICU US$ (INR) HDU US$ (INR) Catastrophic health expenditure (%) ICU HDU Total Mean SE Mean SE N % N % N % Age group \30 years 3179 (190,723) 1006 (60,336) 1319 (79,158) 430 (25,798) 9 90 10 77 19 83 31–50 years 1947 (116,834) 329 (19,765) 2188 (131,260) 407 (24,395) 19 76 20 71 39 74 [50 years 2456 (147,374) 328 (19,655) 1565 (93,891) 327 (19,635) 25 96 27 69 52 80 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Gender Male 2390 (143,424) 318 (19,082) 2120 (127,221) 289 (17,315) 38 83 40 80 78 81 Female 2314 (138,844) 308 (18,473) 1141 (68,444) 290 (17,395) 15 100 17 57 32 71 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Locality Urban 2331 (139,830) 376 (22,579) 1710 (102,588) 273 (16,353) 24 86 34 69 58 75 Slum 2844 (170,668) 2364 (141,868) 1133 (67,986) 274 (16,425) 2 100 3 100 5 100 Rural 2382 (142,914) 344 (20,634) 1892 (113,509) 392 (23,549) 27 87 20 71 47 80 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Education Illiterate 3267 (196,037) 616 (36,956) 1123 (67,400) 638 (38,298) 5 100 2 50 7 78 \8th standard 2489 (149,318) 573 (34,376) 2905 (174,283) 532 (31,943) 14 88 13 100 27 93 8th–12th standard 2595 (155,691) 405 (24,297) 1576 (94,550) 349 (20,963) 21 95 23 79 44 86 Graduate and above 1702 (102,136) 412 (24,709) 1562 (93,714) 333 (19,960) 13 72 19 56 32 62 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Occupation Labourer 1811 (108,666) 694 (41,666) 3330 (199,786) 778 (46,683) 6 75 6 100 12 86 Self-employed 1685 (101,082) 353 (21,201) 2302 (138,113) 485 (29,099) 6 86 17 89 23 88 Unemployed 3338 (200,258) 512 (30,698) 1219 (73,147) 285 (17,113) 20 100 21 66 41 79 Salaried 2015 (120,919) 348 (20,877) 1542 (92,507) 399 (23,920) 21 81 13 57 34 69 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Marital Unmarried 3504 (210,261) 1088 (65,265) 1267 (76,011) 471 (28,271) 8 100 5 63 13 81 Married 2213 (132,758) 239 (14,357) 1802 (108,114) 233 (13,977) 45 85 52 72 97 78 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Wealth status Poorest 2739 (164,368) 585 (35,075) 1289 (77,341) 403 (24,151) 8 100 9 69 17 81 Poor 1818 (109,103) 499 (29,946) 2030 (121,781) 551 (33,078) 11 92 15 88 26 90 Middle 2767 (166,005) 680 (40,795) 3016 (180,956) 732 (43,924) 16 94 12 92 28 93 Rich 2459 (147,568) 415 (24,878) 1351 (81,058) 289 (17,321) 12 86 11 65 23 74 Richest 1799 (107,960) 399 (23,966) 1450 (87,004) 407 (24,439) 6 60 10 50 16 53 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Duration of stay \3 days 692 (41,495) 65 (3923) 235 (14,100) 47 (2797) 9 82 10 53 19 95 3–10 days 1284 (77,025) 173 (10,409) 998 (59,909) 120 (7225) 16 80 21 70 37 74 [10 days 3555 (213,304) 361 (21,679) 3353 (201,163) 385 (23,082) 28 93 26 84 54 89 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Outcome at discharge Dead 2128 (127,651) 272 (16,308) 3553 (213,176) 761 (45,636) 13 87 10 100 23 92 Alive 2465 (147,906) 331 (19,850) 1484 (89,027) 205 (12,311) 40 87 47 67 87 75 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, SE standard error of the mean, US$ United States dollar Cost of Liver Disorders 187 Fig. 1 Determinants (%) of out-of-pocket expenditure for treatment of liver disorders in an intensive care unit in India. HDU high dependency unit, ICU intensive care unit Fig. 2 Coping mechanisms (%) for out-of-pocket expenditure for treatment of liver disorders in an intensive care unit in India. HDU high dependency unit, ICU intensive care unit Table 4 Indirect costs for Admission status N Mean US$ (INR) Standard error US$ (INR) treatment of liver disorder patients in the intensive care ICU 65 166 (9952) 25.1 (1507) unit of a tertiary care hospital in HDU 85 182 (10,903) 21.7 (1304) India Total 150 175 (10,491) 16.4 (984) Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, US$ United States dollar 4.4 Policy Implications: Treatment and Care A study done in 2009 to assess the cost effectiveness of various policy options available for restricting alcohol The high cost of treating chronic liver disorders has twin consumption reported that educational and counselling programmes for alcohol users are not cost effective, challenges: firstly from patient perspective, wherein there whereas enforcing increased taxes on alcohol purchase and are a large proportion of households who face CHE as a restrictions on alcohol sales are generally cost effective result of treatment. Any attempt at achieving universal [48]. health care will need to bring treatment of these chronic 188 S. Prinja et al. liver disorders into the benefit package. This brings us to hospitals or purchasing treatment through publicly financed the second fiscal challenge, which is for the payer or the healthcare insurance schemes should be considered. health system to sustain the high cost of care. In view of the Strategies to reduce cost of care through application of high cost of management of chronic liver disease, there is a cost-effective methods of treatment should be considered. need to identify more cost-effective approaches to man- Acknowledgements We gratefully acknowledge the funding support agement. For example, there are a range of drugs and received as part of Intramural Research Grant of the Post Graduate newer molecules that are being used for treatment of HCV- Institute of Medical Education and Research, Chandigarh, India. related cirrhosis. Whether or not these are cost effective Data Availability Statement Data on health system resources has should be assessed before any decision on commissioning been enclosed as a supplementary appendix. Patient level data anal- these drugs for clinical use is taken. ysed during the current study are available from the corresponding Our study reveals that around 80% of the OOP expen- author on reasonable request. The corresponding author can be con- diture for treatment of liver disorders in an ICU setting was tacted via email: shankarprinja@gmail.com spent on purchasing medicines. This depicts the low availability of medicines in public sector hospitals [49, 50], Author Contributions Conceived the study: SP and YKC. Study which inflicts a high burden of OOP expenditure for buying design: SP, PB and MK. Study tools: SP and PB. Data collection: PB, medicines, specifically among those who utilize the public SP and AD. Data analysis: PB and SP. Validation of estimates: SP, sector hospitals, and exposes them to CHE [19, 22, 25]. AD, YKC and MK. First draft: SP and PB. All authors reviewed and Mere allocation of funds for free availability of medicines approved the final study draft. in public sector hospitals may not be enough; in fact, Compliance with Ethical Standards procurement systems should be evaluated frequently for better efficiency and making necessary reforms for better Funding The study was funded by the intramural Research Grant of availability of medicines [50]. Post Graduate Institute of Medical Education and Research, Chandigarh, India (http://pgimer.edu.in/PGIMER_PORTAL/ PGIMERPORTAL/home.jsp). 4.5 Limitations Ethical approval The study was approved by the Institute Ethics First, we did not collect data on severity of disease or risk Committee of the Post Graduate Institute of Medical Education and Research, Chandigarh, India. Informed consent was taken from con- factors associated with disease at baseline, that is, at the cerned persons at the time of data collection. time of admission. We identify this as a limitation in our study as this has some bearing on OOP expenditure for Conflict of interest Shankar Prinja, Pankaj Bahuguna, Ajay Duseja, treatment. Second, estimates from our study may have Manmeet Kaur and Yogesh K. Chawla declare no conflict of interest. limited generalizability due to several factors. Our study Open Access This article is distributed under the terms of the estimates are based on data from a single tertiary care Creative Commons Attribution-NonCommercial 4.0 International public sector hospital. In India, to date, there is no provi- License (http://creativecommons.org/licenses/by-nc/4.0/), which per- sion for intensive care treatment at secondary level in the mits any noncommercial use, distribution, and reproduction in any public sector, although these services are available with medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons private tertiary care hospitals. Patients accessing these license, and indicate if changes were made. services from the deregulated private sector are susceptible to incurring high OOP expenditure. Third, while we fol- lowed up the patients until 6 months from discharge to References collect data on post-hospitalization OOP expenditure, we could not collect health system cost data for patients uti- 1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans lizing the public sector for post-hospitalization care. V, et al. 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Cost of Intensive Care Treatment for Liver Disorders at Tertiary Care Level in India

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Abstract

PharmacoEconomics Open (2018) 2:179–190 https://doi.org/10.1007/s41669-017-0041-4 ORIGINAL RESEARCH ARTICLE Cost of Intensive Care Treatment for Liver Disorders at Tertiary Care Level in India 1 1 2 1 • • • • Shankar Prinja Pankaj Bahuguna Ajay Duseja Manmeet Kaur Yogesh Kumar Chawla Published online: 14 July 2017 The Author(s) 2017. This article is an open access publication Abstract Results In 2013–2014, health system costs per patient Background Liver diseases contribute significantly to the treated in the ICU and HDU were US$2728 [Indian health and economic burden globally. We undertook this National Rupee (INR) 1,63,664] and US$1966 (INR study to assess the health system costs, out-of-pocket 1,17,985), respectively. The mean OOP expenditures for (OOP) expenditure and extent of financial risk protection treatment in the ICU and HDU were US$2372 (INR associated with treatment of liver disorders in a tertiary 1,42,297) and US$1752 (INR 1,05,093), respectively. care public sector hospital in India. Indirect costs of hospitalization in ICU and HDU patients Methodology The present study was undertaken in an were US$166 (INR 9952) and US$182 (INR 10,903), intensive care unit (ICU) of a tertiary care hospital in North respectively. India. It comprised an ICU and an HDU (high dependency Conclusion Treatment of chronic liver disorders poses an unit). Bottom-up micro-costing was undertaken to assess economic challenge for both the health system and the health system costs. Data on OOP expenditure and patients. There is a need to focus on prevention of liver indirect costs were collected for 150 liver disorder patients disorders, and finding ways to treat patients without admitted to the ICU or HDU from December 2013 to exposing their households to the catastrophic effect of OOP October 2014. Per-patient and per-bed-day costs of treat- expenditure. ment were estimated from both health system and patient perspectives. Financial risk protection was assessed by computing prevalence of catastrophic health expenditure as Key Points for Decision Makers a result of OOP expenditure. Significant evidence of the health burden and consequences of liver disorders exists in India. There has been no evidence published of the significant economic implications which emerge alongside the rising burden of risk factors for liver disorders. Electronic supplementary material The online version of this Our study reports the direct medical costs from both article (doi:10.1007/s41669-017-0041-4) contains supplementary material, which is available to authorized users. the health system and patient perspectives, as well as the indirect costs on account of lost productivity. & Shankar Prinja shankarprinja@gmail.com Our findings could also be used for setting reimbursement decisions for treatment of liver School of Public Health, Post Graduate Institute of Medical disorders in various publicly financed insurance Education and Research, Chandigarh 160012, India schemes as well as assessing the cost effectiveness of Department of Hepatology, Post Graduate Institute of related interventions. Medical Education and Research, Chandigarh, India 180 S. Prinja et al. 1 Introduction intensive care treatment of liver diseases in a tertiary care hospital setting. Secondly, from patient perspective, we determine the OOP expenditure on treatment, the extent of Liver diseases contribute significantly to the global burden financial risk protection in terms of catastrophic health of mortality and morbidity [1, 2]. Globally, liver cirrhosis expenditure (CHE), and mechanisms to cope with the OOP alone accounts for more than a million deaths, which is 2% expenditure. of overall deaths, and 31 million disability-adjusted life- years (DALYs), which is 1.2% of total DALYs. Along similar lines, liver disorders are widely prevalent 2 Methodology in India. These represent a wide spectrum ranging from those with chronic infections, to those affected by alcohol 2.1 Study Setting consumption, and finally comprising the non-alcoholic fatty liver disorders. With over 40 million hepatitis B virus We conducted this study in the Post Graduate Institute of (HBV) carriers in India, the country falls into the inter- Medical Education and Research (PGIMER), a tertiary care mediate level of HBV endemicity [3]. The population hospital, situated in the North Indian city of Chandigarh. prevalence of HBV and hepatitis C virus (HCV) infection With a total of 356 consultants and 2000 resident doctors, in India is 3.7 and 1%, respectively. In the developed the hospital caters to an annual inpatient and outpatient countries, dominant risk factors for chronic liver disease attendance of 78,568 and 2,061,911, respectively [13]. (CLD) include alcohol and HCV. On the other hand, HBV The present cost analysis was undertaken in a special- and HCV are responsible for the majority of CLDs in India ized intensive care unit (ICU) for the treatment of liver [4]. Since 2007, alcohol has fast emerged as an important disorders, under the Department of Hepatology. The ICU is risk factor, and it constituted the leading cause of CLD- broadly classified into two parts: the intensive care unit related morbidity and mortality in India during 2010–2011 (ICU) and the high dependency unit (HDU). The criterion [4]. for this classification is based on the severity of patients Liver disorders pose a significant economic challenge in with liver disorders, with more severe patients being terms of management of these chronic infections. In 2007, admitted to the ICU. In addition to the HDU facilities, the in the US, cirrhosis was graded as one of the leading causes ICU has ventilator and dialysis support, and endoscopic of death. The estimated economic burden due to liver cir- interventions for critical care required for more severe rhosis was significant, with the main cost of treatment patients. Both the units have five beds each. A common ranging from US$14 million to US$2 billion, depending on pool of human resources was involved in provision of disease etiology [5]. Treatment costs of morbidities related services in both ICU and HDU, supported by a laboratory to HCV in ten European Union countries were estimated to and ultrasound and fibro-scan facility. These diagnostic be €50 million, and hence a significant burden on society. facilities are utilized for the ICU (and HDU) patients along Similarly, €3 billion was reported to have been lost in with the other liver disorder patients who consult the out- Spain as a result of HCV over a 20-year time period [6]. patient department and those admitted to general wards in Despite such a high economic burden, as evident from the hospital. Besides this, the general diagnostic (patho- Western countries, there are no Indian estimates for the logical and radiological) facilities of the hospital are also cost of treating liver disorders. In fact, there is very little used for the liver ICU patients. A total of 171 and 142 new evidence of cost of curative care in the public sector from a admissions were treated in the ICU and HDU, respectively, health-system perspective [7–11], and whatever is avail- during the year 2014. able is mostly for primary and secondary care [8–11]. The economic data becomes even scarcer for the tertiary care 2.2 Data Collection sector [7] and chronic diseases. Apart from significant economic implications for the health system, treatment of 2.2.1 Health System Costs liver disorders leads to high out-of-pocket (OOP) expen- diture for patients. This OOP expenditure in turn manifests We adopted a bottom-up micro methodology to assess the as catastrophic spending by households which pushes them health system costs. We collected data on health system below the poverty line [6, 12]. Treatment for liver disease resources used to provide patient care during a 1-year is also likely to impose high OOP expenditure on account period from April 2013 to March 2014. The methodology of its intensive resource requirement and chronic nature. for data collection comprised record reviews, physical Hence, we undertook the present study to bridge this gap inspection of facility, and staff interviews. The collected in the evidence base. Firstly, from a health system per- data included number of human resources (i.e. medical, spective, we estimated per-bed-day admission costs of Cost of Liver Disorders 181 paramedical staff, administrative, support staff, etc.), space expenditure (food and non-food). Patients were also inter- in the building, numbers and types of equipment, other viewed to elicit mechanisms to cope with the OOP non-consumable items, diagnostic tests (laboratory and expenditure for treatment. Lastly, in order to assess indirect radiological), medicines, consumables and other overhead costs owing to lost productivity, both for patients and costs. Data was collected on the quantity of different caregivers, we also collected data on time spent by patient resources being used exclusively for ICU or HDU patients, on routine activities (i.e. professional work, household or in shared manner for both [Supplementary Appendix, activities, childcare, voluntary and social activities, physi- Tables S1–S7, see electronic supplementary material cal or leisure activities, etc.) in the days of good health (ESM)]. before his/her admission to ICU or HDU. All resources were classified as recurrent or non-recur- rent/capital resources. Recurrent costs included staff sal- 2.2.3 Follow-Up Patient Interviews aries, medicine and consumables, diagnostic tests and overheads costs (i.e. electricity, water consumption, laun- We also followed up the patients telephonically at Month dry, dietetics, etc.). Non-recurrent or capital resources 1, 3 and 6 from the date of discharge to record OOP mainly comprised building or space, equipment (medical expenditure for further treatment (if any) and the survival and non-medical) and furniture with a lifespan of[1 year. status of the patient. Any OOP expenditure for out-patient Price data was collected from the procurement department or in-patient treatment after discharge from any health of the Institute [14]. For prices that were not available from facility was assessed. Data on OOP expenditure collected the procurement department, we used the average market at each follow-up was mutually exclusive in nature (i.e. price from among the leading three manufacturers/suppli- specifically for that period). For example, at Month 1 fol- ers, which was then adjusted based on a factor between low-up, data on OOP expenditure was collected from dis- market price and government procurement price. However, charge date to completion of Month 1. The same occurred since the market prices are higher than the government for Month 1 to Month 3 (2-month period) at Month 3 procurement price, we adjusted the same using a scaling follow-up, and for Month 3 to Month 6 (3-month period) at factor. This scaling factor was the average ratio of health the Month 6 follow-up. system price and market price for other drugs and con- sumables, where prices were available from both the 2.3 Data Analysis sources. All prices were adjusted to current values using gross domestic Product (GDP) deflators. Besides the data 2.3.1 Health System Costs on quantity of resources utilized and their prices, we also Data was analysed using Statistical Package for Social collected data on number of patients treated during the same reference period, separately for the ICU and HDU. Sciences (SPSS) version 21 and MS Excel. The cost of Face-to-face interviews of staff members (faculty, resident space for the hepatology ICU was estimated by applying doctors, nursing staff, support and administrative staff) rental price for the area. Costs of various equipment (and were conducted to elicit time spent on different activities furniture) available in the ICU and HDU were annualized during a 1-week period. based on their average useful life and discounting to arrive at an equivalent uniform annual cost. An average discount 2.2.2 Out-of-Pocket Expenditure rate of 3% was used to compute the annualization factor [15]. Replacement costs of equipment were preferred over For OOP expenditure, all new patients admitted to the liver original costs. These replacement costs were computed by ICU during the period from December 2013 to October 2014 adjusting original costs using the consumer price index. All were recruited. Written informed consent of the patient or the costs were converted to US dollars (US$) for compa- accompanying caregiver (if patient was not conscious) was rability at a wider level at the rate of US$1 equal to 60 obtained. Data was collected at the time of recruitment, Indian National Rupees (INR) [16]. Overall cost of service followed by a daily interview to elicit OOP expenditure provision was estimated. Finally, all the cost estimates incurred for treatment over the last 24 h. This was continued were converted to 2014 prices to adjust for inflation, on a daily basis till the discharge or final outcome of the applying a discounting factor of 3% per year. patient. OOP expenditure was elicited for hospital charges, medicines, laboratory tests, procedure or surgery, trans- 2.3.1.1 Apportioning Statistics Appropriate apportioning portation, boarding/lodging and meals of attendants with statistics were used to allocate shared or joint resources to patient, and lastly informal payments (if any). the ICU and HDU. Firstly, the shared cost of human Secondly, we collected data on socio-demographic resources was apportioned to ICU care, non-ICU inpatient characteristics including household consumption care, outpatient care and other general teaching, research 182 S. Prinja et al. and administrative work. Interviews with various staff and impoverishment due to OOP expenditure [19–21]. We working with the ICU unit were done to collect the data on computed CHE to measure financial risk protection, which time allocation patterns. We interviewed different cate- is defined as OOP spending for healthcare exceeding a gories of personnel involved in ICU services, which mainly given threshold of households’ paying capacity [19–21]. included consultants, resident doctors, nurses and techni- More specifically, it implies any OOP expenditure on cians to capture data on work flow patterns, and time spent health which exceeds 40% of household non-food con- per activity during the previous week. Proportion of time sumption expenditure. spent on each activity was used as the basis for appor- There are two thresholds available in the literature to tioning the shared human resource costs to various cost estimate the prevalence of CHE based on households’ centres and functional activities. Data obtained through paying capacity. The first approach considers any health these interviews was used for apportioning the share cost of expenditure exceeding 10% of a household’s total con- human resource. sumption expenditure as catastrophic, while the second Secondly, costs of shared building/space (i.e. laboratory, approach considers 40% of non-subsistence expenditure (or waiting area, discussion room, doctor’s room, etc.), non-food related) as the threshold. The second approach is equipment and overheads were apportioned among the ICU considered more appropriate from an equity perspective, and HDU on the basis of proportion of bed-days of ICU and hence we adopted the latter [19–22]. To compute and HDU patients in a year. Costs of medicines and con- prevalence of CHE, OOP expenditure that was in excess of sumables could not simply be apportioned based on inpa- 40% of household non-food consumption expenditure was tient bed-days of admission in the ICU and HDU, as these considered as catastrophic. In addition, we undertook a are dependent on the patients’ severity of illness. Hence, sensitivity analysis by computing prevalence of CHE based we apportioned the cost of medicines and consumables on the 10% of total expenditure cut-off. Lastly, we also being used jointly for HDU and ICU patients, based on a analysed the coping mechanisms for OOP expenditure by ratio of average OOP cost of medicines for ICU and HDU calculating the percentage of OOP expenditure which was patients. This was considered appropriate as there were no met through salaries/savings, borrowing without interest, prioritization criteria used for issue of medicines and borrowing with interest, selling of assets, or any form of consumables between the ICU and HDU. health insurance. 2.3.1.2 Unit Costs Per-patient and per-bed-day costs of 2.3.4 Indirect Costs treatment were estimated for both ICU and HDU patients. Data collected on indirect costs (i.e. productivity loss of patients/caregivers due to hospitalization) was analysed 2.3.2 Out-of Pocket Expenditure using a human capital (HC) approach. There are two broad We estimated the mean and standard error of OOP approaches for valuing productivity loss due to illness, HC expenditure at the overall level and by socio-demographic and friction cost (FC) [23, 24]. In the HC approach, income characteristics of individuals, and also by diagnostic cate- and fringe benefits of an employee (or market wage) are gory. Patients with liver disorders were classified into five considered as a proxy of his productivity loss due to illness, categories: acute viral hepatitis/acute liver failure, cirrhosis while the FC approach considers replacement cost of an (includes alcohol-related cirrhosis, HBV, HCV, autoim- employee to carry out his work. Although the FC method mune hepatitis (AIH), non-alcoholic steatohepatitis and measures the productivity loss in a more realistic way, it is others), acute-on-chronic liver failure (ACLF), hepatocel- data intensive which introduces significant uncertainties. lular carcinoma (HCC) and extrahepatic biliary tract On the other hand, HC demands less data and is amenable obstruction (EHBO). for easy communication [23, 24]. Hence, we used the HC approach for analysis of indirect cost data in our study. 2.3.3 Financial Risk Protection 2.3.5 Estimates Financial risk protection is one of the components of uni- versal health coverage (UHC) [17]. It ensures that the All the estimates for health system costs, OOP expenditure population of a state/country can access quality healthcare and indirect costs are reported in both INR and US$. Also, services at the time of need without any financial hardship 95% confidence intervals (CI) are reported for OOP [17–19]. In general, there are two methods to measure expenditure and indirect costs along with their base financial risk protection, which include prevalence of CHE estimates. Cost of Liver Disorders 183 Using a 40% threshold for CHE, we found that 87% of 3 Results patients admitted to the ICU incurred CHE, while its 3.1 Sample Characteristics prevalence was 71% for HDU patients (Table 3). Preva- lence of CHE in our study did not vary much when we used A total of 150 patients were recruited for estimation of 10% of total consumption expenditure (i.e. 98 and 84% for the ICU and HDU, respectively). This signifies that the OOP expenditure, of which 85 and 65 were from HDU and ICU, respectively. Of all patients, males represented nearly conclusion is robust regarding choice of thresholds used to define catastrophic expenditure. The mean indirect cost 75% in the ICU and nearly 62% in the HDU. More than 80% of patients both in the ICU and HDU were aged estimation for ICU and HDU patients was US$166 (95% CI 117–215) and US$182 (95% CI 139–224), respectively [30 years and almost 45% were aged [50 years. Mean (Table 4). length of stay for ICU and HDU patients was 13 and 11 days, respectively. Around 72 and 87% of patients were 3.3.1 Follow-Up discharged alive from the ICU and HDU, respectively (Table 1). Almost 68% of the patients admitted to the Out of patients eligible for Month 1, 3 and 6 follow-ups, hepatology ICU had some form of cirrhosis, followed by 13.5% with EHBO and ACLF, 10.8% with hepatocellular approximately 95% were followed up to record the data on post-hospitalization OOP expenditure (Supplementary carcinoma and 7.4% with acute viral hepatitis/acute liver failure. Appendix, Fig. S1, see ESM). Mean OOP expenditures for patients 1 month after discharge from the ICU and HDU were US$366 (95% CI 178–554) and US$977 (95% CI 3.2 Health System Costs 0–2153), respectively. Mean OOP expenditures at Month 3 follow-up, which reflected a 2-month period, were US$894 The annual cost incurred by the health system for ICU (95% CI 59–1729) and US$635 (95% CI 286–985) for ICU and HDU care in the year 2014 was US$386,199 and HDU patients, respectively, and at Month 6 follow-up, (INR231, INR71,939) and US$336,651 (INR201, INR99,069), respectively (Table 2). For the ICU and for a period of 3 months, they were US$498 (95% CI 143–778) and US$568 (95% CI 166–893) for ICU and HDU, the share of personnel costs was highest (37% ICU and 43% HDU), followed by physical infrastructure HDU patients, respectively. (27% ICU and 31% HDU) and diagnostics (20% ICU and 12% HDU). Per-patient treated and per-bed-day admission cost for treatment in the ICU were US$2728 4 Discussion (INR163,664) and US$212 (INR12,697), respectively. 4.1 Summary of Study Findings Similarly, the cost of treatment was US$1966 (INR117,985) per patient and US$185 (INR11,068) per bed-day in the HDU (Table 2). We undertook this study to assess the health system costs and OOP expenditure on account of tertiary care intensive 3.3 Out-of-Pocket Expenditures treatment for liver disorders in India. Overall, we found that the health system cost per patient treated and per bed- The mean OOP expenditures for treatment in the ICU and day admission to the ICU were US$2728 (95% CI HDU were US$2372 (95% CI 1881–2862) and US$1752 2580–3125) and US$212 (95% CI 200–242), respectively. (95% CI 1329–2174), respectively (Table 3). Medicines Similarly, the cost of treatment was US$1966 (95% CI accounted for a major share of OOP expenditure—85 and 1860–2253) per patient and US$185 (95% CI 174–211) per 79% among ICU and HDU patients, respectively (Fig. 1). bed-day in the HDU. From the patients’ perspective, the Mean OOP expenditures per patient bed-day in the ICU treatment of liver disorders incurred an OOP expenditure and HDU were US$220 (INR13,194) and US$151 of US$2372 (95% CI 1881–2862) and US$1752 (95% CI (INR9088), respectively. Salary or savings was the pre- 1329–2174) in the ICU and HDU, respectively. All patients dominant source of finance to meet the OOP expenditure admitted to the ICU, and 88% of those admitted to the among 51 and 57% patients treated in the ICU and HDU, HDU, experienced catastrophic expenditures. Of the total respectively (Fig. 2). Mean OOP expenditure was rela- hospitalized patients, 29% had to borrow money to pay for tively higher in patients diagnosed with ACLF [US$3170 treatment costs. (INR190,202)], followed by cirrhosis and acute viral hep- We found that the OOP expenditure during the period atitis/acute liver failure [US$2131 (INR127,899) and immediately after discharge was higher than later months. US$1928 (INR115,668), respectively]. This is reflected in a higher OOP expenditure from 1 to 184 S. Prinja et al. Table 1 Characteristics of liver disorder patients admitted to the 3 months following discharge, as compared with intensive care unit of a tertiary care hospital in India 4–6 months after discharge. This could be related to a relative improvement in patients’ condition in subsequent Characteristics ICU HDU Total months and hence a lesser need for more intense N % N % N % medication. Gender We acknowledge that there are significant variations in Male 49 75.4 53 62.4 102 68.0 the healthcare infrastructure across the different states of Female 16 24.6 32 37.6 48 32.0 India, as well as costs of resources which are used for Total 65 100 85 100 150 100 delivery of services. Further, we estimated the cost of Age group delivering treatment in one large tertiary care hospital. We \30 years 10 15.4 14 16.5 24 16 chose this hospital as the treatment for most chronic liver 31–50 years 25 38.5 31 36.5 56 37.3 disorders is usually not provided in secondary level hos- [50 years 30 46.2 40 47.1 70 46.7 pitals and is available in similar tertiary care teaching Total 65 100 85 100 150 100 hospitals only. However, all these factors may limit the Locality extent of the generalizability of our cost estimates to the Urban 31 47.7 53 62.4 84 56 whole of India. We recommend undertaking a study incorporating a variety of geographic settings and levels of Slum 2 3.1 3 3.5 5 3.3 Rural 32 49.2 29 34.1 61 40.7 care in India to improve the generalizability of results. Total 65 100 85 100 150 100 4.2 Financial Risk Protection: Current Status Education Illiterate 5 7.7 4 4.7 9 6.0 In general, In India there is a high prevalence of CHE. \8th standard 17 26.2 13 15.3 30 20.0 Some community-based research studies reported the 8th–12th standard 25 38.5 30 35.3 55 36.7 prevalence of CHE for any kind of illness in the range of Graduate and above 18 28 38 44.7 56 37 30–56% [19, 22, 25, 26]. On the other hand, prevalence of Total 65 100 85 100 150 100 CHE is extremely high in diseases requiring intensive care Occupation like chronic liver disorders, cancers, acute coronary syn- Labourer 8 12.3 7 8.2 15 10.0 drome, etc. One North Indian study reported the prevalence Self-employed 9 13.8 21 24.7 30 20.0 of CHE among households with a family member suffering Unemployed 21 32.3 33 38.8 54 36.0 from breast cancer to be 84% [27]. Similarly, a study done Salaried 27 41.5 24 28.2 51 34.0 for the Asian region depicted that prevalence of CHE Total 65 100 85 100 150 100 among households with an acute coronary syndrome event Marital in India was more (i.e. 60% among the uninsured popula- Unmarried 8 12.3 8 9.4 16 10.7 tion) compared with other Asian countries like Malaysia, Married 57 87.7 77 90.6 134 89.3 Thailand, Singapore, Vietnam, etc. [28]. Moreover, Total 65 100 85 100 150 100 specifically in the Indian context where the income dis- Wealth status parities are high, even a small amount of OOP expenditure Poorest 12 18.5 18 21.2 30 20 becomes catastrophic for low-income segments of society Poor 12 18.5 17 20 29 19.3 [19, 26, 29]. Hence, our study findings are in concordance Middle 17 26.2 13 15.3 30 20 with the literature when specifically compared to other Rich 14 21.5 17 20 31 20.7 severe diseases requiring intensive care. Richest 10 15.4 20 23.5 30 20 Total 65 100 85 100 150 100 4.3 Policy Implication: Prevention of Liver Duration of stay Disorders \3 days 11 16.9 21 24.7 32 21.3 3–10 days 20 30.8 30 35.3 50 33.3 Our results have significant policy implications. Policy [10 days 34 52.3 34 40.0 68 45.3 discourse in India is gradually building towards universal Total 65 100 85 100 150 100 provision of healthcare services [30]. How the care provi- Outcome at discharge sion will be organized is debatable; however, most policy Dead 18 27.7 11 12.9 29 19.3 documents recommend greater reliance on a tax-funded Alive 47 72.3 74 87.1 121 80.7 system of financing. In terms of provisioning of healthcare Total 65 100 85 100 150 100 services, both models of publicly delivered healthcare HDU high dependency unit, ICU intensive care unit services and purchasing of healthcare services through Cost of Liver Disorders 185 Table 2 Health system costs of Health system costs ICU US$ (INR) HDU US$ (INR) treatment of liver disorder patients in the intensive care Annual costs unit of a tertiary care hospital in Personnel 143,402 (8,604,123) 143,402 (8,604,123) India Equipment 30,526 (1,831,542) 20,764 (1,245,861) Laboratory tests 76,514 (4,590,849) 38,910 (2,334,601) Medicines and consumables 6945 (416,719) 4763 (285,778) Stationary 2973 (178,371) 2973 (178,371) Physical infrastructure 104,760 (6,285,600) 104,760 (6,285,600) Utilities/overheads 21,079 (1,264,735) 21,079 (1,264,735) Total 386,199 (23,171,939) 336,651 (20,199,069) Unit costs Cost per patient 2728 (163,664) 1966 (117,985) Cost per bed-day 212 (12,697) 185 (11,068) Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, US$ United States dollar publicly financed health insurance schemes are evident of safe nosocomial practices reduces HCV transmission, [31]. The increasing incidence of liver disease and its risk but usually comes at a high cost of implementation that factors [32, 33] highlights that prevention of liver disorders exceeds the fiscal ability of low-income countries [39, 40]. and their risk factors is likely to be a more cost-effective However, there have been recent instances of HCV out- action and has to remain the mainstay of the policy. In breaks in India which have resulted from unsafe injection terms of preventive actions, HBV vaccination remains a practices by unqualified practitioners and quack doctors cornerstone. [41]. Use of auto-disable syringes is one of the strategies to In general, the most cost-effective vaccination strategy prevent re-use and its associated infections. An HCV sero- is usually determined by the endemicity of disease, the ease prevalence of as high as 71% has been reported among of implementing a vaccination programme with high cov- injection drug users (IDUs) in North-East India [42]. In erage, the efficacy of vaccination, and the infectiousness of India, an Opioid Substitution Therapy (OST) strategy for the causative agent [34]. From an Indian viewpoint, a IDUs was introduced by the Government of India under the pentavalent vaccine that includes HBV has been reported National AIDS Control Program (NACP). Under OST, to be very cost effective with an incremental cost of opiate-dependant persons are made to shift to orally US$277 per disability-adjusted life-year (DALY) averted administered opiates such as buprenorphine and metha- [35]. As per the Government of India’s latest policy, HBV done, replacing illicit drug use [43]. Accounting for the is given as part of a pentavalent vaccine that comprises social costs, OST therapy using buprenorphine has been diphtheria, pertussis and tetanus (DPT), haemophilus shown to have higher benefits at lower costs than no influenza type ‘b’ (Hib) and HBV. The National Technical treatment [44]. Advisory Group on Immunization (NTAGI) in India rec- The third set of preventive interventions, besides those ommended the introduction of the pentavalent vaccine in against HBV and HCV, are those directed against alcohol the Universal Immunization Program (UIP) in 2008, which consumption and its health effects. It is evident that alcohol was subsequently launched in 2011 in two South Indian consumption leads to liver cirrhosis and related mortality states [36, 37]. This has now been extended to children in and hence, policies and procedures intended to restrict several other states also. However, the coverage of this alcohol consumption are likely to benefit [44–47]. These vaccine remains very low as per recent estimates [38], and policies include those which are targeted to a specific hence needs strengthening for effective protection. Further, population group such as school-based campaigns. Alter- the Government of India also recommends a birth dose of natively, these could be general public education cam- HBV for all institutional births, which needs to be paigns on harmful effects of alcohol. Similarly, those at universalized. risk for alcoholism could also be targeted for counselling. The major preventive strategy for HCV remains con- Stringent alcohol purchase laws, government monopolies trolling nosocomial exposure (i.e. blood screening, safe on alcohol, restrictions on alcohol marketing campaigns, injection, and infection control) and reducing high-risk and increased taxes on alcohol have all been shown to have behaviours (i.e. intravenous drug use) [39]. Implementation positive effects on reduction in alcohol consumption [48]. 186 S. Prinja et al. Table 3 Direct costs (INR) as out-of-pocket expenditure and financial risk protection for treatment of liver disorder patients in the intensive care unit of a tertiary care hospital in India Characteristic ICU US$ (INR) HDU US$ (INR) Catastrophic health expenditure (%) ICU HDU Total Mean SE Mean SE N % N % N % Age group \30 years 3179 (190,723) 1006 (60,336) 1319 (79,158) 430 (25,798) 9 90 10 77 19 83 31–50 years 1947 (116,834) 329 (19,765) 2188 (131,260) 407 (24,395) 19 76 20 71 39 74 [50 years 2456 (147,374) 328 (19,655) 1565 (93,891) 327 (19,635) 25 96 27 69 52 80 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Gender Male 2390 (143,424) 318 (19,082) 2120 (127,221) 289 (17,315) 38 83 40 80 78 81 Female 2314 (138,844) 308 (18,473) 1141 (68,444) 290 (17,395) 15 100 17 57 32 71 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Locality Urban 2331 (139,830) 376 (22,579) 1710 (102,588) 273 (16,353) 24 86 34 69 58 75 Slum 2844 (170,668) 2364 (141,868) 1133 (67,986) 274 (16,425) 2 100 3 100 5 100 Rural 2382 (142,914) 344 (20,634) 1892 (113,509) 392 (23,549) 27 87 20 71 47 80 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Education Illiterate 3267 (196,037) 616 (36,956) 1123 (67,400) 638 (38,298) 5 100 2 50 7 78 \8th standard 2489 (149,318) 573 (34,376) 2905 (174,283) 532 (31,943) 14 88 13 100 27 93 8th–12th standard 2595 (155,691) 405 (24,297) 1576 (94,550) 349 (20,963) 21 95 23 79 44 86 Graduate and above 1702 (102,136) 412 (24,709) 1562 (93,714) 333 (19,960) 13 72 19 56 32 62 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Occupation Labourer 1811 (108,666) 694 (41,666) 3330 (199,786) 778 (46,683) 6 75 6 100 12 86 Self-employed 1685 (101,082) 353 (21,201) 2302 (138,113) 485 (29,099) 6 86 17 89 23 88 Unemployed 3338 (200,258) 512 (30,698) 1219 (73,147) 285 (17,113) 20 100 21 66 41 79 Salaried 2015 (120,919) 348 (20,877) 1542 (92,507) 399 (23,920) 21 81 13 57 34 69 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Marital Unmarried 3504 (210,261) 1088 (65,265) 1267 (76,011) 471 (28,271) 8 100 5 63 13 81 Married 2213 (132,758) 239 (14,357) 1802 (108,114) 233 (13,977) 45 85 52 72 97 78 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Wealth status Poorest 2739 (164,368) 585 (35,075) 1289 (77,341) 403 (24,151) 8 100 9 69 17 81 Poor 1818 (109,103) 499 (29,946) 2030 (121,781) 551 (33,078) 11 92 15 88 26 90 Middle 2767 (166,005) 680 (40,795) 3016 (180,956) 732 (43,924) 16 94 12 92 28 93 Rich 2459 (147,568) 415 (24,878) 1351 (81,058) 289 (17,321) 12 86 11 65 23 74 Richest 1799 (107,960) 399 (23,966) 1450 (87,004) 407 (24,439) 6 60 10 50 16 53 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Duration of stay \3 days 692 (41,495) 65 (3923) 235 (14,100) 47 (2797) 9 82 10 53 19 95 3–10 days 1284 (77,025) 173 (10,409) 998 (59,909) 120 (7225) 16 80 21 70 37 74 [10 days 3555 (213,304) 361 (21,679) 3353 (201,163) 385 (23,082) 28 93 26 84 54 89 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Outcome at discharge Dead 2128 (127,651) 272 (16,308) 3553 (213,176) 761 (45,636) 13 87 10 100 23 92 Alive 2465 (147,906) 331 (19,850) 1484 (89,027) 205 (12,311) 40 87 47 67 87 75 Total 2372 (142,297) 250 (15,021) 1752 (105,093) 216 (12,939) 53 87 57 71 110 78 Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, SE standard error of the mean, US$ United States dollar Cost of Liver Disorders 187 Fig. 1 Determinants (%) of out-of-pocket expenditure for treatment of liver disorders in an intensive care unit in India. HDU high dependency unit, ICU intensive care unit Fig. 2 Coping mechanisms (%) for out-of-pocket expenditure for treatment of liver disorders in an intensive care unit in India. HDU high dependency unit, ICU intensive care unit Table 4 Indirect costs for Admission status N Mean US$ (INR) Standard error US$ (INR) treatment of liver disorder patients in the intensive care ICU 65 166 (9952) 25.1 (1507) unit of a tertiary care hospital in HDU 85 182 (10,903) 21.7 (1304) India Total 150 175 (10,491) 16.4 (984) Conversion rate: $US1 = INR60 HDU high dependency unit, ICU intensive care unit, INR Indian National Rupee, US$ United States dollar 4.4 Policy Implications: Treatment and Care A study done in 2009 to assess the cost effectiveness of various policy options available for restricting alcohol The high cost of treating chronic liver disorders has twin consumption reported that educational and counselling programmes for alcohol users are not cost effective, challenges: firstly from patient perspective, wherein there whereas enforcing increased taxes on alcohol purchase and are a large proportion of households who face CHE as a restrictions on alcohol sales are generally cost effective result of treatment. Any attempt at achieving universal [48]. health care will need to bring treatment of these chronic 188 S. Prinja et al. liver disorders into the benefit package. This brings us to hospitals or purchasing treatment through publicly financed the second fiscal challenge, which is for the payer or the healthcare insurance schemes should be considered. health system to sustain the high cost of care. In view of the Strategies to reduce cost of care through application of high cost of management of chronic liver disease, there is a cost-effective methods of treatment should be considered. need to identify more cost-effective approaches to man- Acknowledgements We gratefully acknowledge the funding support agement. For example, there are a range of drugs and received as part of Intramural Research Grant of the Post Graduate newer molecules that are being used for treatment of HCV- Institute of Medical Education and Research, Chandigarh, India. related cirrhosis. Whether or not these are cost effective Data Availability Statement Data on health system resources has should be assessed before any decision on commissioning been enclosed as a supplementary appendix. Patient level data anal- these drugs for clinical use is taken. ysed during the current study are available from the corresponding Our study reveals that around 80% of the OOP expen- author on reasonable request. The corresponding author can be con- diture for treatment of liver disorders in an ICU setting was tacted via email: shankarprinja@gmail.com spent on purchasing medicines. This depicts the low availability of medicines in public sector hospitals [49, 50], Author Contributions Conceived the study: SP and YKC. Study which inflicts a high burden of OOP expenditure for buying design: SP, PB and MK. Study tools: SP and PB. Data collection: PB, medicines, specifically among those who utilize the public SP and AD. Data analysis: PB and SP. Validation of estimates: SP, sector hospitals, and exposes them to CHE [19, 22, 25]. AD, YKC and MK. First draft: SP and PB. All authors reviewed and Mere allocation of funds for free availability of medicines approved the final study draft. in public sector hospitals may not be enough; in fact, Compliance with Ethical Standards procurement systems should be evaluated frequently for better efficiency and making necessary reforms for better Funding The study was funded by the intramural Research Grant of availability of medicines [50]. Post Graduate Institute of Medical Education and Research, Chandigarh, India (http://pgimer.edu.in/PGIMER_PORTAL/ PGIMERPORTAL/home.jsp). 4.5 Limitations Ethical approval The study was approved by the Institute Ethics First, we did not collect data on severity of disease or risk Committee of the Post Graduate Institute of Medical Education and Research, Chandigarh, India. Informed consent was taken from con- factors associated with disease at baseline, that is, at the cerned persons at the time of data collection. time of admission. We identify this as a limitation in our study as this has some bearing on OOP expenditure for Conflict of interest Shankar Prinja, Pankaj Bahuguna, Ajay Duseja, treatment. Second, estimates from our study may have Manmeet Kaur and Yogesh K. Chawla declare no conflict of interest. limited generalizability due to several factors. Our study Open Access This article is distributed under the terms of the estimates are based on data from a single tertiary care Creative Commons Attribution-NonCommercial 4.0 International public sector hospital. In India, to date, there is no provi- License (http://creativecommons.org/licenses/by-nc/4.0/), which per- sion for intensive care treatment at secondary level in the mits any noncommercial use, distribution, and reproduction in any public sector, although these services are available with medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons private tertiary care hospitals. Patients accessing these license, and indicate if changes were made. services from the deregulated private sector are susceptible to incurring high OOP expenditure. Third, while we fol- lowed up the patients until 6 months from discharge to References collect data on post-hospitalization OOP expenditure, we could not collect health system cost data for patients uti- 1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans lizing the public sector for post-hospitalization care. V, et al. 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