Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

Sasmita Behera; Jalandhar Pradhan

doi:10.1371/journal.pone.0260628

. 2021 Dec 10;16(12):e0260628. doi: 10.1371/journal.pone.0260628

Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

Sasmita Behera ^1,^*, Jalandhar Pradhan ¹

Editor: Petri Böckerman²

PMCID: PMC8664228 PMID: 34890400

Abstract

Background

Non-communicable diseases (NCDs) are the leading global cause of death and disproportionately concentrate among those living in low-income and middle-income countries. However, its economic impact on households remains less well known in the Indian context. This study aims to assess the economic impact of NCDs in terms of out-of-pocket expenditure (OOPE) and its catastrophic impact on NCDs affected households in India.

Materials and methods

Data were collected from the 75^th round of the National Sample Survey Office, Government of India, conducted in the year 2017–18. This is the latest round of data available on health, which constitutes a sample of 113,823 households. The collection of data is based on a stratified multi-stage sampling method. Generalised Linear Regression model was employed to identify the socio-economic covariates associated with the catastrophic health expenditure (CHE) on hospitalisation.

Results

The result shows a higher burden of OOPE on NCDs affected households. The mean expenditure by NCDs households in public hospitals is INR 13,170 which is more than twice as compared to the non-NCDs households INR 6,245. Particularly, the proportion of total medical expenditure incurred on medicines (0.39) and diagnostics (0.15) is troublesome for households with NCDs, treated in public hospitals. Moreover, results from the generalised linear regression model confirm the significant relationship between CHE with residence, caste, religion, household size, and economic status of households. The intensity of CHE is more for the households who are poor, drinking unsafe water, using firewood as cooking fuel, and household size of 1–5 members.

Conclusion

Therefore, an urgent need for a prevention strategy should be made by the government to protect households from the economic burden of NCDs. Specifically, to reduce the burden of CHE associated with NCDs, a customised disease-specific health insurance package should be introduced by the government of India in both public and private facilities.

Introduction

The escalating burden of non-communicable diseases (NCDs) is presently being experienced by all the countries across the globe [1, 2]. However, the disproportionate concentration of this burden is well documented in the case of low-income and middle-income countries where NCDs kill 15 million people every year, out of which 85% are premature deaths [3]. NCDs also account for 58% of Disability Adjusted Life Years (DALYs) [4]. The adverse impact of NCDs is a growing concern for developing countries where public spending on NCDs is relatively scant, and people have limited resources to accommodate their healthcare needs [5, 6]. In the absence of an adequate financial mechanism, out-of-pocket expenditure (OOPE) for the treatment and care of NCDs often traps the households in the cycle of catastrophic expenditure that forces the households to various financial shocks such as borrowing money or (and) selling assets [7, 8]. NCDs curtail household income and a family’s ability to spend on its basic necessities like food, clothing, and education expenditure [9–11]. The economic cost of NCDs has also a significant macroeconomic effect on the Indian economy. NCDs reduce the productivity of the workforce, resulting in the reduction of overall economic output [12, 13]. It is estimated that every 10% increase in NCDs mortality results in a 0.5% reduction in annual economic growth. [14].

The increasing burden of OOPE confronted two important aspects of Universal Health Coverage (UHC): first, every population, irrespective of rich and poor should get needed health care services, not only those who can afford it (equity prospective) [15, 16]; second, the cost of health care should not put people at the risk of any financial hardship (financial risk protection) [17]. The burden of NCDs further puts a grave threat to Sustainable Development Goals (SDGs-3.4) i.e. to reduce one-third premature mortality from NCDs. The health financing system should be focused on the aspect of equity as well as provide financial protection to the poor. Budgetary allocation for healthcare spending is also an important aspect to look forward to lessen the devastating burden of NCDs on households.

India is experiencing the rising burden of NCDs, with limited access to health care and social security [18, 19]. The World Health Organisation (WHO) estimates that NCDs account for 63% of all deaths, out of which 27% of the deaths are from cardiovascular disease, 9% from cancer, 3% from diabetes, and 11% from chronic respiratory disease in India [20]. Apart from mortality, NCDs burden can also be well captured in terms of DALYs where it captures not only mortality but also years of productive life lost due to premature mortality and years with disability. As per the study by the Indian Council of Medical Research, in India over the years 1990 to 2016, the proportion of total DALY attributable to NCDs increased from 30.5% to 55.4% [21].

National Health Account estimates that OOPE constitutes 58.7% of total health expenditure in India [22]. However, India is spending only 1.2% of its Gross Domestic Product (GDP) on health expenditure for the year 2016–17 [22]. Due to the lack of a better financial mechanism in India, excessive dependence of households on OOPE occurs for treatment of NCDs (including medication, diagnostic test, and drug therapy) that forces 8.50% of people to below the poverty line in the year 2014, which again increase to 12.43% in 2017–18 [23]. An epidemiological assessment of OOPE associated with NCDs households and their distribution across various socio-economic groups in India would provide policymakers with additional information about the households’ spending patterns, which could help them in formulating effective and multisectoral interventions to reduce household financial burden.

Based on the available literature, it is clear that although there is a plethora of literature available that gives us the evidence of poverty impact of OOPE in the context of health care financing [24–28], the impact of NCDs on OOPE experienced by households is poorly researched, particularly in the Indian context [29, 30]. Further, the existing studies are limited to some particular types of NCDs like diabetes, hypertension, stroke, and cancer. The present study attempts to fill these gaps by investigating the NCDs attributable OOPE and its catastrophic impact on Indian households by focusing on a group of NCDs. Further, it will give a comparative analysis of OOPE experienced by both NCDs and non-NCDs prevalence households. The study also provides an idea about the type of healthcare facility (public/private) people are choosing for treatment of NCDs as well as associate OOPE from each type of healthcare facility in India.

Materials and methods

Data

The present study is based on household social consumption on health data, collected by National Sample Survey Office (NSSO), Government of India, in the year 2017–18 [31]. This is the latest round of data available on health, which constitutes a sample of 113,823 households. The collection of data is based on a stratified multi-stage sampling method. In this survey, an investigation was made to know the nature of ailment for those people who are hospitalised, the extent to which people are using public and private hospitals, and the amount of expenditure incurred for the treatment received from both government and private healthcare facilities. The recall period in the survey was 365 days for inpatient care and 15 days for outpatient care. However, the present study is based on inpatient care that takes into account hospitalisation cases only. Hospitalisation is defined as an overnight stay in the hospital any time in 365 days prior to the survey date. Expenditure on hospitalization has been recorded separately under three broad categories: medical, non-medical, and transport expenditure. The medical expenditure includes doctor’s fee, purchase of medicine and drugs, clinical test (x-ray, ECG, scan), bed charges, and other medical expenses like physiotherapy, blood, oxygen, etc. Similarly, the non-medical expenses include expenditure on food, escort, lodging charges, etc. Adding together medical, non-medical, and transport expenditures provide total expenditure on hospitalisation.

Study design

The study is based on household level analysis where the aim is to compare the economic burden of OOPE among households associated with the prevalence of NCDs and non-NCDs. A household can be considered as an NCDs household if at least one of its members reported having one or more NCDs within the last 365 days. The NSSO (75^th round) data provides a list of 60 diseases under 15 broad categories of infection, cancer, psychiatric disorder, respiratory, gastrointestinal, blood disease, metabolic and nutritional, obstetric, cardiovascular, skin, injury, genito-urinary, eyes, ear, and musculoskeletal. For our analysis, we have grouped 60 diseases into two broad categories, based on the International classification of diseases (ICD-10), where the first category includes only NCDs and the rest of all infectious/ communicable/ nutritional diseases/ injuries are grouped into the second category. Accordingly, we have categorized households with NCDs (the first group) and households with non-NCDs (the second group). Out of the 56,731 households with hospitalisation cases, the number of NCDs households is 21,776 and non-NCDs households are 34,955.

Variables

The outcome variables of the study are OOPE and catastrophic health expenditure (CHE) for hospitalization by NCDs and non-NCDs households. The OOPE is further classified as medical, non-medical, transport, and other expenses by types of services (public/private). We calculate each component of OOPE separately, for both public and private facilities. The selection of independent variables is based on the past literature that shows socio-economic and demographic variables like caste, religion, economic capability, age, etc. have a significant impact on the pattern of healthcare spending, especially in the Indian context [32, 33]. Therefore, we investigated the hypothesis of whether these variables have an impact on OOPE. The independent variables included in the analysis are, the number of elderly (60+) in the household (no elderly, only one elderly, two or more than two elderly), the place of residence (rural and urban), religion (Hindu, Muslim, and others), caste (SC, ST, and Others), household size (1–5, 6–7, and 8+members), cooking fuel (firewood, LPG & other gases, and others), drinking water (safe and unsafe), type of latrine [(flush/pit), (open space /others)], wealth index (Poorest, Poorer, Middle, Richer and Richest), and region (North, East, Northeast, Central, West, and South).

Method

The comparison between NCDs households and non-NCDs households has been made on the basis of the following grounds: (a) socio-economic factors determining the prevalence of diseases experienced by NCDs and non-NCDs households, (b) mean OOPE by type of health care facility (public/private), (c) proportion of households experiencing CHE, and (d) estimation of Generalised Linear Regression Model (GLM) to calculate the effect of various independent variables on the level of CHE. To measure OOPE we have followed the approach recommended by World Bank [34]. Here, OOPE is the share of health spending by the patient themselves at the time of receiving care. It can be of different forms, such as user fees which are directly paid to the healthcare provider at public facilities; co-payments which are paid by the insured person to the insurer; and payments made by individuals to private health care providers, not covered by any form of health insurance [35, 36]. Similarly, when the level of health expenditure exceeds some fraction of the household’s total income/expenditure is known as CHE [35–37]. In the present study, households spending more than 10 percent of their total consumption expenditure on health are considered to be catastrophic. This is because the 10% threshold is the most widely used threshold level [35, 38–40].

Results

Socio-economic profile of sample households

Table 1 provides a descriptive analysis of households that shows out of 56,731 hospitalisation cases, 21,776 households were hospitalised due to NCDs, and 34,955 households hospitalised for disability, communicable, and infectious diseases. Households having more than two elderly reported more hospitalisation cases due to NCDs and fewer cases reported for non-NCDs. About 62% of households hospitalised due to NCDs are from rural areas and 37% are from urban areas. Similarly, 67% of non-NCDs households are from rural areas and 33% are from urban areas. Nearly 80% of households, both NCDs and non-NCDs, belong to the Hindu religion, 14% belong to Muslims, and the rest of the 6% belongs to other categories. The distribution of households by social classes shows that almost 43% of NCDs households are from other backward classes, 23% are from scheduled caste/tribe, and 34% are from other categories. Similarly, for non-NCDs households, it is 44%, 29%, and 27% for OBC, other categories, and SC/ST, respectively. Of those with hospitalisation cases, 96% NCDs households are drinking safe water. However, the figure is also same for non-NCDs households. NCDs households with 1–5 members have a higher rate of hospitalisation (66%) than those with 6–7 (23%) and 8+ (11%) members. Similarly, the hospitalisation rate is 71% for non-NCDs households having 1–5 members, 20% for 6–7 members, and 9% for 8+ members. The analysis of hospitalisation cases by wealth quintiles indicates that households with better economic status are being hospitalised more as compared to the households with lower economic status. At the regional level, more households are found to be hospitalised in the case of the southern region as compared to any other region.

Table 1. Socio-economic characteristics of households, NSSO survey, 2017–18.

Household’s Characteristic	NCDs	NON-NCDs	TOTAL
Elderly members
No elderly	58.70	70.26	79.01
One elderly	25.54	19.62	17.70
2+ elderly	15.76	10.12	3.29
Residence
Rural	62.49	66.68	65.01
Urban	37.51	33.32	34.99
Religion
Hindu	79.54	80.11	79.89
Muslim	14.07	14.01	14.03
Others	6.39	5.88	6.08
Caste
SC/ST	23.40	26.73	25.41
OBC	42.76	43.86	43.42
Others	33.84	29.41	31.17
Household size
1–5	66.43	70.97	69.16
6–7	22.51	20.21	21.12
8+	11.06	8.82	9.71
Type of Cooking fuel
Firewood	34.38	36.01	35.36
LPG/Other gas	60.80	59.01	59.64
Other	4.82	4.98	5.00
Drinking water
Safe	96.48	96.53	96.51
Unsafe	3.52	3.47	3.49
Type of latrine
Flush/pit	82.02	78.88	80.13
Open space/others	17.98	21.12	19.87
Wealth quintile
Poorest	14.89	14.86	14.87
Poorer	17.87	16.63	17.12
Middle	18.02	19.35	18.82
Richer	20.56	22.13	21.50
Richest	28.67	27.03	27.68
Region
North	13.87	13.70	13.76
Central	19.72	19.79	19.76
East	20.35	20.29	20.31
Northeast	2.05	2.68	2.43
West	15.79	14.06	14.75
South	28.22	29.49	28.99
Total (N)	21,776	34,955	56,731

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Source: Author’s estimation based on NSSO survey, 75^th round, 2017–18.

Out-of-pocket expenditure on hospitalisation by NCDs and Non-NCDs households

Fig 1 shows mean OOPE by type of disease (NCDs and non-NCDs) by type of healthcare facilities. The total expenditure by NCDs households is INR 35, 512, whereas it is INR 21, 214 for non-NCDs households. However, there is a huge difference in mean OOPE found between NCDs and non-NCDs households in public facilities, where it is INR 13,170 for NCDs households which is more than twice as compared to the non-NCDs households (INR 6,245).

The details of mean OOPE on hospitalisation by the public facility have been reported in Table 2. In the case of NCDs households, medical expenditure as a proportion of total expenditure is highest (0.80), followed by non-medical expenditure (0.13), and transport (0.06). Similarly, for non-NCDs households, it is 0.74, 0.17, and 0.08 for medical, non-medical, and transport expenditure respectively. Total expenditure on hospitalisation in the case of NCDs is higher (INR 22,808) for those households having more than two elder members as compared to the non-NCDs households (INR 10,795) having the same number of elderly. The rural-urban difference in total expenditure is more in the case of non-NCDs households as compared to the NCDs households. In comparison to the Muslim religion, households from the Hindu religious category are spending more on hospitalisation for the treatment of both NCDs (INR 13,417) and non-NCDs (INR 6,295). Households from SC/ST categories are spending INR 11,828 due to hospitalisation associated with NCDs, whereas it is INR 4,879 for non-NCDs hospitalisation. The mean OOPE is more for households having more than 8 members as compared with the households having 1–7 members, both in the case of NCDs and non-NCDs. People from NCDs households who are drinking unsafe water have higher medical expenses (INR 17,456) when compared to non-NCD households (INR 4,905). The economic status of households significantly influenced spending on hospitalisation. It clearly shows that the richest households are spending more as compared to all other income groups. However, the spending is more in the case of NCDs households (INR 20,541) than non-NCDs households (INR 6,399).

Table 2. Mean out-of-pocket expenditure by NCDs and non-NCDs households in public health care facility, NSSO survey 2017–18.

	Out-of-pocket expenditure in INR (US$)
Household’s Characteristic	NCDs households				Non-NCDs households
Household’s Characteristic	Medical	Transport	Other non-medical	Total	Medical	Transport	Other non-medical	Total
Elderly member
No elderly	8630 (133)	770 (12)	1614 (25)	11014 (170)	4066 (63)	495 (8)	1004 (15)	5564 (86)
One elderly	10186 (157)	957 (15)	1870 (29)	13013 (201)	5059 (78)	628 (10)	1194 (18)	6881 (106)
2+ elderly	19539 (301)	1178 (18)	2092 (32)	22808 (352)	8638 (133)	669 (10)	1488 (23)	10795 (166)
Residence
Rural	10372 (160)	936 (14)	1772 (27)	13080 (202)	4270 (66)	548 (8)	1087 (17)	5905 (91)
Urban	10911 (168)	751 (12)	1694 (26)	13355 (206)	5630 (87)	495 (8)	1059 (16)	7184 (111)
Religion
Hindu	10790 (166)	858 (13)	1769 (27)	13417 (207)	4657 (72)	536 (8)	1102 (17)	6295 (97)
Muslim	8244 (127)	895 (14)	1532 (24)	10671 (165)	4231 (65)	507 (8)	936 (14)	5674 (87)
Others	14072 (217)	1120 (17)	2082 (32)	17274 (266)	5595 (86)	595 (9)	1190 (18)	7380 (114)
Caste
SC/ST	9201 (142)	903 (14)	1723 (27)	11828 (182)	3340 (51)	484 (7)	1055 (16)	4879 (75)
OBC	9468 (146)	849 (13)	1915 (30)	12232 (189)	4482 (69)	519 (8)	1117 (17)	6118 (94)
Others	13304 (205)	881 (14)	1552 (24)	15738 (243)	6660 (103)	627 (10)	1050 (16)	8338 (129)
Household size
1–5	10653 (164)	869 (13)	1753 (27)	13275 (205)	4107 (63)	504 (8)	1021 (16)	5632 (87)
6–7	9858 (152)	935 (14)	1686 (26)	12480 (192)	5381 (83)	625 (10)	1185 (18)	7191 (111)
8+	11437 (176)	777 (12)	1845 (28)	14059 (217)	8418 (130)	612 (9)	1438 (22)	10468 (161)
Cooking fuel
Firewood	9123 (141)	1008 (16)	1807 (28)	11939 (184)	3982 (61)	566 (9)	1033 (16)	5581 (86)
LPG/Other gas	11963 (184)	753 (12)	1692 (26)	14408 (222)	4972 (77)	503 (8)	1114 (17)	6589 (102)
Other	7991 (123)	1002 (15)	1791 (28)	10783 (166)	7406 (114)	527 (8)	1183 (18)	9116 (141)
Drinking water
Safe	10234 (158)	836 (13)	1715 (26)	12786 (197)	4622 (71)	530 (8)	1067 (16)	6220 (96)
Unsafe	17456 (269)	1739 (27)	2431 (37)	21626 (333)	4905 (76)	632 (10)	1421 (22)	6958 (107)
Type of latrine
Flush/pit	11385 (176)	876 (14)	1690 (26)	13950 (215)	4706 (73)	539 (8)	1043 (16)	6288 (97)
Open space/others	7387 (114)	874 (13)	1961 (30)	10222 (158)	4415 (68)	520 (8)	1186 (18)	6121 (94)
Wealth quintile
Poorest	7628 (118)	681 (11)	1413 (22)	9722 (150)	4266 (66)	507 (8)	1001 (15)	5774 (89)
Poorer	7003 (108)	773 (12)	1428 (22)	9204 (142)	5275 (81)	474 (7)	1022 (16)	6770 (104)
Middle	9075 (140)	786 (12)	1694 (26)	11554 (178)	4520 (70)	519 (8)	1062 (16)	6101 (94)
Richer	10563 (163)	984 (15)	2029 (31)	13577 (209)	4510 (70)	582 (9)	1096 (17)	6188 (95)
Richest	17339 (267)	1106 (17)	2096 (32)	20541 (317)	4627 (71)	575 (9)	1197 (18)	6399 (99)
Region
North	15496 (239)	1164 (18)	1892 (29)	18552 (286)	5970 (92)	657 (10)	1174 (18)	7801 (120)
Central	9622 (148)	656 (10)	1465 (23)	11743 (181)	6139 (95)	405 (6)	987 (15)	7531 (116)
East	11900 (183)	931 (14)	1477 (23)	14308 (221)	4340 (67)	557 (9)	928 (14)	5826 (90)
Northeast	7239 (112)	750 (12)	1558 (24)	9546 (147)	4554 (70)	610 (9)	1018 (16)	6182 (95)
West	9055 (140)	594 (9)	1235 (19)	10884 (168)	5003 (77)	475 (7)	745 (11)	6224 (96)
South	7331 (113)	904 (14)	2419 (37)	10653 (164)	3111 (48)	515 (8)	1388 (21)	5014 (77)
Total (N)	10549 (163)	875 (13)	1747 (27)	13170 (203)	4632 (71)	534 (8)	1079 (17)	6245 (96)

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INR: Indian rupees and values in bracket are in terms of US dollar (US$) as per average exchange rate in 2017–18 (64.86; www.rbi.org.in).

Note:- Results in bold are significant at 5% level as tested by ANOVA.

Table 3 presents the mean OOPE by private facilities. It shows that the total expenditure on hospitalisation in the case of NCDs households is INR 51,243, whereas it is INR 32,641 for non-NCDs households. Medical expenditure constitutes a larger share of total expenditure experienced by both NCDs and non-NCDs households. Particularly, the proportion of total medical expenditure incurred on medicines (0.40) and diagnostics (0.15) is troublesome for households with NCDs, treated in public hospitals. Similarly, the proportion of total medical expenditure incurred on diagnostic tests is higher (0.10) for NCDs households availing private facilities as compared to the non-NCDs households (0.09) (S1 Appendix).

Table 3. Mean out-of-pocket expenditure by NCDs and non-NCDs households in private health care facilities, NSSO survey 2017–18.

	Out-of-pocket expenditure in INR (US$)
Household’s Characteristic	NCDs households				Non-NCDs households
Household’s Characteristic	Medical	Transport	Other non-medical	Total	Medical	Transport	Other non-medical	Total
Elderly member
No elderly	40041 (617)	1115 (17)	2300 (35)	43456 (670)	26472 (408)	813 (13)	1790 (28)	29074 (448)
One elderly	50805 (783)	1365 (21)	2816 (43)	54986 (848)	35659 (550)	946 (15)	2569 (40)	39174 (604)
2+ elderly	66587 (1027)	1460 (23)	2950 (45)	70997 (1095)	38693 (597)	1028 (16)	2374 (37)	42095 (649)
Residence
Rural	38798 (598)	1313 (20)	2612 (40)	42723 (659)	26644 (411)	954 (15)	2193 (34)	29791 (459)
Urban	59762 (921)	1134 (17)	2454 (38)	63350 (977)	34741 (536)	722 (11)	1735 (27)	37199 (574)
Religion
Hindu	47532 (733)	1263 (19)	2553 (39)	51348 (792)	30202 (466)	876 (14)	2088 (32)	33167 (511)
Muslim	41131 (634)	1179 (18)	2408 (37)	44718 (689)	26701 (412)	839 (13)	1662 (26)	29202 (450)
Others	57263 (883)	1106 (17)	2712 (42)	61081 (942)	29940 (462)	772 (12)	1796 (28)	32508 (501)
Caste
SC/ST	38999 (601)	1114 (17)	2388 (37)	42501 (655)	29260 (451)	831 (13)	2613 (40)	32704 (504)
OBC	39931 (616)	1199 (18)	2401 (37)	43531 (671)	26030 (401)	865 (13)	1802 (28)	28697 (442)
Others	60994 (940)	1352 (21)	2806 (43)	65152 (1004)	35303 (544)	885 (14)	1942 (30)	38130 (588)
Household size
1–5	47672 (735)	1206 (19)	2502 (39)	51379 (792)	29931 (461)	830 (13)	2066 (32)	32827 (506)
6–7	41629 (642)	1228 (19)	2415 (37)	45272 (698)	27899 (430)	932 (14)	1930 (30)	30761 (474)
8+	57831 (892)	1463 (23)	3076 (47)	62371 (962)	32523 (501)	960 (15)	1864 (29)	35347 (545)
Cooking fuel
Firewood	37311 (575)	1367 (21)	2686 (41)	41364 (638)	25935 (400)	972 (15)	1817 (28)	28725 (443)
LPG/Other gas	52189 (805)	1196 (18)	2482 (38)	55867 (861)	31713 (489)	828 (13)	2145 (33)	34686 (535)
Other	39581 (610)	1127 (17)	2641 (41)	43349 (668)	23856 (368)	774 (12)	1336 (21)	25967 (400)
Drinking water
Safe	47453 (732)	1227 (19)	2547 (39)	51228 (790)	29834 (460)	868 (13)	2026 (31)	32728 (505)
Unsafe	47589 (734)	1641 (25)	2522 (39)	51752 (798)	27668 (427)	771 (12)	1769 (27)	30208 (466)
Type of latrine
Flush/pit	50688 (781)	1261 (19)	2586 (40)	54534 (841)	31386 (484)	851 (13)	2073 (32)	34310 (529)
Open space/others	30209 (466)	1125 (17)	2337 (36)	33671 (519)	22195 (342)	929 (14)	1756 (27)	24880 (384)
Wealth quintile
Poorest	28067 (433)	1000 (15)	1826 (28)	30894 (476)	24202 (373)	854 (13)	1529 (24)	26585 (410)
Poorer	39741 (613)	1159 (18)	2567 (40)	43467 (670)	28261 (436)	814 (13)	2495 (38)	31570 (487)
Middle	44260 (682)	1261 (19)	2387 (37)	47909 (739)	29211 (450)	878 (14)	1795 (28)	31884 (492)
Richer	43423 (669)	1247 (19)	2334 (36)	47004 (725)	27073 (417)	831 (13)	1732 (27)	29636 (457)
Richest	62718 (967)	1355 (21)	3024 (47)	67097 (1034)	34800 (537)	909 (14)	2304 (36)	38013 (586)
Region
North	55543 (856)	1420 (22)	2715 (42)	59678 (920)	32252 (497)	977 (15)	1839 (28)	35068 (541)
Central	46149 (712)	1240 (19)	2613 (40)	50002 (771)	33232 (512)	1052 (16)	1927 (30)	36211 (558)
East	44880 (692)	1508 (23)	2821 (43)	49210 (759)	32402 (500)	1075 (17)	2539 (39)	36016 (555)
Northeast	55171 (851)	3325 (51)	4093 (63)	62589 (965)	31933 (492)	1680 (26)	3404 (52)	37016 (571)
West	47239 (728)	1050 (16)	1866 (29)	50155 (773)	27784 (428)	618 (10)	1306 (20)	29708 (458)
South	46363 (715)	1115 (17)	2705 (42)	50183 (774)	26458 (408)	731 (11)	2312 (36)	29501 (455)
Total (N)	47457 (732)	1239 (19)	2547 (39)	51243 (790)	29759 (459)	865 (13)	2017 (31)	32641 (503)

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INR: Indian rupees and values in bracket are in terms of US dollar (US$) as per average exchange rate in 2017–18 (64.86; www.rbi.org.in).

Note:- Results in bold are significant at 5% level as tested by ANOVA.

Catastrophic health expenditure on hospitalisation by NCDs and non-NCDs households

CHE on hospitalisation by household’s socio-economic characteristics has been presented in Table 4. At the public facility, 27.68% of NCDs households are exposed to CHE due to hospitalisation and 14.59% of non-NCDs households are experiencing the same. The incidence of CHE is even more in the case of private facilities, where it is 72.09% (3 times more than public hospitals) for NCDs households and 55.85% for non-NCDs households. Households where the number of elderly is higher incurred a greater burden of CHE (74.75%) due to hospitalisation associated with NCDs at private hospitals. The level of CHE is higher for rural NCDs households as compared to the rural non-NCDs households in both public and private facilities; however, the difference is more in public facilities. In private facilities, CHE for NCDs households is highest (73.05%) for those who belong to the Hindu religion as compared to Muslims and others. Similarly, NCDs households from SC/ST category encounter more CHE (73.58%) than non-NCDs households (58.56%) in private facilities.

Table 4. Percentage of households experiencing CHE due to hospitalisation by type of facility, NSSO survey 2017–18.

Household’s Characteristic	Public		Private
	NCDs	Non-NCDs	NCDs	Non-NCDs
Elderly member
No elderly	25.70	13.14	70.75	54.06
One elderly	30.24	18.03	73.10	59.88
2+ elderly	31.67	20.90	74.75	60.10
Residence
Rural	30.24	16.66	76.23	61.46
Urban	23.85	10.66	68.27	50.27
Religion
Hindu	28.08	15.24	73.05	56.69
Muslim	26.25	12.29	68.54	53.17
Others	27.16	13.76	68.06	51.53
Caste
SC/ST	28.24	14.54	73.58	58.56
OBC	28.19	15.08	71.97	56.00
Others	26.45	13.99	71.47	53.98
Household size
1–5	29.75	15.19	75.24	58.54
6–7	24.38	12.92	68.29	51.38
8+	20.75	13.25	60.64	46.14
Cooking fuel
Firewood	32.56	17.32	75.55	62.06
LPG/Other gas	24.54	12.04	70.99	53.82
Other	26.84	19.59	72.98	57.81
Drinking water
Safe	27.28	14.37	72.11	55.66
Unsafe	34.77	18.67	71.66	61.80
Type of latrine
Flush/pit	26.78	13.17	71.20	54.41
Open space/others	32.57	20.85	78.47	64.48
Wealth quintile
Poorest	35.86	22.76	82.58	67.01
Poorer	29.60	15.06	75.20	60.73
Middle	26.27	14.02	73.56	57.19
Richer	24.51	11.95	67.95	53.54
Richest	23.90	10.33	68.83	49.83
Region
North	28.89	15.58	67.91	50.69
Central	29.52	16.90	74.93	63.27
East	32.50	18.77	74.09	59.61
Northeast	28.57	13.42	71.06	58.45
West	18.04	10.03	67.64	48.27
South	22.57	10.15	74.66	55.90
Total (%)	27.68	14.59	72.09	55.85

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Source: Author’s estimation based on NSSO survey, 75^th round, 2017–18.

With the increase in household size from 1–5 members to 6–7 and 8+ members, the level of CHE decreases. In the case of non-NCDs households, CHE is more for those households who are using firewood (62.06%), compared with those who are using LPG and other gas (53.82%) in private facilities. The extent of CHE is more for those households who are using unsafe drinking water and open space for defecation. In private facilities, the incidence of CHE is 67.01% for the poorest wealth quintile and 49.83% for the richest quintile in the case of non-NCDs households, while in the case of NCDs households it is 82.58% and 68.83% for the poorest and richest category respectively. CHE in the northern region is 28.57% for NCDs households, which is 2 times more than the CHE experienced by the non-NCDs households, in public facilities.

Results from generalised linear model

Results from Table 5 indicate the socio-economic determinants of CHE among the households in India. It shows that households with more elderly members are incurring more CHE as compared to the households with no elderly in both private and public facilities. At the public facility, CHE is less in the case of NCDs households living in the urban area (β = -0.402*) in comparison to rural areas. Analysis by religious category shows that NCDs households from other religious categories are experiencing more catastrophic spending (β = 0.511*) than households with Hindu religion at the public facility. However, NCDs households availing private facility shows that other religious categories (Christianity, Jainism, Sikhism, Buddhism) are experiencing less CHE (β = -0.100*) as compared to the Hindu religious category.

Table 5. Socio-economic factors associated with CHE due to hospitalisation by type of facility, NSSO survey 2017–18.

Household’s characteristics	Public		Private
Household’s characteristics	NCDs	Non-NCDs	NCDs	Non-NCDs
No elderly (Ref.)
One elderly	0.290^*	0.530^*	0.040^*	0.127^*
2+ elderly	0.426^*	0.422^*	0.130^*	0.217^*
Rural (Ref.)
Urban	-0.402^*	-0.458^*	-0.143^*	-0.250^*
Hindu (Ref.)
Muslim	0.065	-0.291^*	-0.081^*	-0.167^*
Others	0.511^*	0.344^*	-0.100^*	-0.161^*
SC/ST (Ref.)
OBC	0.005	0.415^*	-0.058^*	0.009
Others	-0.061	0.465^*	0.029	0.060^*
1–5 (Ref.)
6–7	-0.609^*	-0.402^*	-0.175^*	-0.306^*
8+	-0.518^*	-0.396^*	-0.303^*	-0.477^*
Firewood (Ref.)
LPG/Other gas	-0.059	-0.278^*	0.005	0.012
Other	-0.140	0.208^**	-0.023	-0.015
Safe (Ref.)
Unsafe	0.351^*	0.089	-0.094^*	-0.008
Flush/pit (Ref.)
Open space/others	-0.073	0.097^***	0.014	-0.021
Poorest (Ref.)
Poorer	-0.335^*	-0.369^*	-0.127^*	-0.127^*
Middle	-0.565^*	-0.667^*	-0.200^*	-0.257^*
Richer	-0.593^*	-0.848^*	-0.302^*	-0.366^*
Richest	-0.656^*	-1.059^*	-0.314^*	-0.429^*
North (Ref.)
Central	-0.018	-0.007	0.034	0.188^*
East	-0.015	0.008	0.013	0.129^*
Northeast	-0.272^**	-0.132	-0.030	0.089
West	-0.425^*	-0.208^**	-0.055^**	-0.127^*
South	-0.312^*	-0.212^*	0.006	0.000

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Source: Author’s estimation based on NSSO survey, 75^th round, 2017–18.

*significant at 1% level

**significant at 5% level.

Similarly, the incidence of CHE in the case of non-NCDs households is significantly higher for the OBC category than SC/ST in public facilities. Size of households is another important determinant of CHE that indicates the incidence of CHE is significantly lower for households having 6–7 and 8+ members as compared to the reference category. Similarly, sources of cooking indicate that in public facilities non-NCDs related CHE is less (β = -0.278*) for those households who are using LPG and other gas as compared with those who are using firewood. In the public facilities, NCDs attributable CHE is more (β = 0.351*) for those households who are using unsafe water as compared to the households using safe water. The economic status of the household is significantly associated with CHE, which shows that CHE is lower for the households with better economic status as compared to the households with poor economic status. Similarly, the incidence of CHE is significantly lesser for the NCDs households from the Northeast (-0.272**), West (-0.425*), and Southern region (-0.312*) when compared to the reference category in the public facilities.

Discussion

The primary goal of the 2011 United Nations high-level meeting on NCDs is to protect people from premature death caused by NCDs like stroke, diabetes, cancer, and respiratory diseases. To achieve this target, the WHO Global Action Plan on the prevention and treatment of NCDs puts greater emphasis on ensuring affordable access to early diagnosis and treatment for those with NCDs. Our result shows that we are far from achieving this goal because the households that have a member hospitalised due to NCDs are more vulnerable to catastrophic spending as compared to someone hospitalised due to other communicable or infectious diseases.

Socio-economic characteristics of sample households demonstrate that out of 56,731 households with hospitalisation cases, 21,776 have been hospitalised due to NCDs, and 34,955 households have been hospitalised due to some injuries/communicable/ infectious diseases, other than NCDs. Majority of household (both NCDs and non-NCDs) belongs to other backward class, Hindu religion, and residing in the rural area. There are fewer households from poor economic status have been hospitalised as compared to the richer households. Here the possible reason might be that poor people are not going for seeking care due to cost constraints [41, 42]. Notable regional variation in hospitalisation cases is found in the present study with the highest number of hospitalisation cases reported from the southern region and lowest from the northeast. Literature that supports this finding is that southern states are more affluent in terms of higher per capita income, which may cause them to seek more healthcare, as compared to other states [43].

NCDs induced households are experiencing more OOPE than households with non-NCDs. As per our findings, the economic burden of NCDs (measured in terms of OOPE) in public hospitals is more than twice for NCDs affected households as compared to non-NCDs households. In line with our findings, previous studies also found that NCDs affected households are spending comparatively higher OOPE than households with non-NCDs [44–46]. The share of medical expenditure is highest in the total expenditure followed by other non-medical and transport expenditure. A substantial proportion of medical expenditure is for medication, diagnosis, and other medical expenditures like physiotherapy, blood, oxygen, etc. Consistent with other studies, OOPE is much more in private facilities, relative to public facilities [47–48].

The study further reveals that the burden of OOPE is disproportionately distributed among the different subgroups of the population. The burden is more among the households having more than two elderly members in both public and private facilities. This may be because the elderly are more prone to multiple health conditions than the younger adults. Similar findings are found in many other studies [49–51]. In public health centers, the rural-urban difference in mean OOPE is found to be very less among NCDs households, but the difference is more while considering non-NCDs households. However, in both cases, the brunt of OOPE is more among urban people as compared to rural counterparts, likely due to the higher cost of treatment in urban areas than rural. Analysis by religious affiliation shows that households that belong to the Hindu religion are spending more on hospitalisation as compared to Muslim households. As evidenced by the previous literature, Muslims have higher poverty rates and lower education levels than Hindus, which may explain why they spend less money on hospitalisation [33, 52]. Hospitalisation is found to be varied by different social groups, indicating that mean OOPE (in both private and public facilities) is lower for SC/ST and OBC households than the households of other castes. This finding demonstrates that even in the twenty-first century, despite all medical advances and institutional changes, social institutions continue to have a considerable impact on household healthcare seeking behaviour and expenditure patterns [53].

In public facilities, households using unsafe drinking water are reporting a higher burden of OOPE expenditure compared to the households using safe drinking water. This is because the chances of infection are more in the case of the former than in the latter. As documented by previous studies [54, 55], OOPE on hospitalisation is found to be directly related to the economic status of the households which indicates that households with lower economic status are spending less compared to households with higher economic status, reflecting the ability to pay principle of paying for healthcare.

Regarding the economic burden of NCDs in terms of CHE, our result shows that overall, 27.68% of NCDs affected households and 14.59% of non-NCDs households are experiencing CHE in public facilities, whereas, in the private facilities, it is 72.09% and 55.85% for NCDs and non-NCDs households respectively. A higher incidence of CHE is found among the households from the lower-income category compared to the households from the higher-income group which indicates that NCDs disproportionately affect poor households, thus increasing inequalities. The burden of CHE is hefty for rural households as compared to their urban counterparts. This may be because in rural areas a larger proportion of households are already concentrated around the poverty line, as a result of which a smaller proportion of OOPE leads to catastrophic spending.

The result from generalised linear regression model shows a significant relationship between CHE with residence, caste, religion, household size, economic status of households, and households having more elderly members. The intensity of CHE is more for the households who are poor, drinking unsafe water, using firewood as cooking fuel, and household size of 1–5 members.

Conclusion

Our study provides evidence of the economic burden of NCDs faced by households in India and associated socio-economic factors with it. Compared to the non-NCDs households, the healthcare burden in terms of OOPE is higher for the NCDs affected households, particularly for those who are seeking care in private facilities than in public healthcare facilities. Therefore, our analysis shows that despite the effort made by the government of India in introducing various social insurance schemes, a notable proportion of Indian households are still facing higher CHE due to NCDs. Based on our findings, it can be said that India is far from achieving financial risk protection for the people with NCDs, in the context of SDGs. There is an urgent need for government to make affordable health insurance policies available to the economically weaker sections to protect them from the catastrophic cost of treating NCDs. Particularly, a customised disease-specific health insurance package should be introduced by the government of India in both the public and private facilities, as well as raise public awareness about the availability of the same. As NCDs have a disproportionate economic impact on poor households, redistributive measures such as income taxation, subsidies for healthy substitutes, and intervention targeting vulnerable populations may need to be considered for population-based strategies to combat NCDs diseases.

Limitations of the study

The current research has some limitations. First, the cross-sectional nature of data enables us to investigate only the short-term impact of NCDs on household OOPE payments. Secondly, since NSSO data are self-reported, there could be a chance of over- or under-estimation of results. Third, it deals with the consumption expenditure of households and ignores the income aspect associated with healthcare spending. Fourth, this study was unable to account for any price differentials or the cost impact of healthcare, particularly for those who had not sought any medical attention.

Supporting information

S1 Appendix. Out-of-pocket expenditure as a proportion of total medical expenditure, NSSO survey 2017–18.

(DOCX)

Click here for additional data file.^{(34.5KB, docx)}

Acknowledgments

The authors are grateful to the Department of Humanities and Social Sciences, National Institute of Technology (NIT), Rourkela for their support and encouragement, which has helped in improving this paper.

Data Availability

Data are available in the website of Ministry of Statistics and Programme Implementation (MOSPI), government of India. It can be retrieved from http://mospi.nic.in/unit-level-data-report-nss-75th-round-july-2017-june-2018-schedule-250social-consumption-health.

Funding Statement

The authors received no specific funding for this work.

References

1.Girum T., Mesfin D., Bedewi J., & Shewangizaw M. (2020). The burden of non-communicable diseases in Ethiopia, 2000–2016: analysis of evidence from global burden of disease study 2016 and global health estimates 2016. International journal of chronic diseases, 2020. doi: 10.1155/2020/3679528 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Lozano R., Fullman N., Abate D., Abay S. M., Abbafati C., Abbasi N., et al. (2018). Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 2091–2138. doi: 10.1016/S0140-6736(18)32281-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.World Health Organisation (2018). Non-communicable diseases [fact sheet]. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. [Google Scholar]
4.Hay S. I., Abajobir A. A., Abate K. H., Abbafati C., Abbas K. M., Abd-Allah F., et al. (2017). Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390(10100), 1260–1344. 10.1016/S0140-6736(17)32130-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Bollyky T. J., Templin T., Cohen M., & Dieleman J. L. (2017). Lower-income countries that face the most rapid shift in noncommunicable disease burden are also the least prepared. Health Affairs, 36(11), 1866–1875. doi: 10.1377/hlthaff.2017.0708 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Kankeu H. T., Saksena P., Xu K., & Evans D. B. (2013). The financial burden from non-communicable diseases in low-and middle-income countries: a literature review. Health Research Policy and Systems, 11(1), 1–12. http://www.health-policy-systems.com/content/11/1/31. doi: 10.1186/1478-4505-11-31 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Kastor A., & Mohanty S. K. (2018). Disease-specific out-of-pocket and catastrophic health expenditure on hospitalization in India: do Indian households face distress health financing? PloS one, 13(5), e0196106. doi: 10.1371/journal.pone.0196106 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Jan S., Laba T. L., Essue B. M., Gheorghe A., Muhunthan J., Engelgau M., et al. (2018). Action to address the household economic burden of non-communicable diseases. The Lancet, 391(10134), 2047–2058. doi: 10.1016/S0140-6736(18)30323-4 [DOI] [PubMed] [Google Scholar]
9.Datta B. K., Husain M. J., Fatehin S., & Kostova D. (2018). Consumption displacement in households with noncommunicable diseases in Bangladesh. PloS one, 13(12), e0208504. doi: 10.1371/journal.pone.0208504 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Kumara A. S., & Samaratunge R. (2017). Impact of ill-health on household consumption in Sri Lanka: Evidence from household survey data. Social Science & Medicine, 195, 68–76. doi: 10.1016/j.socscimed.2017.11.015 [DOI] [PubMed] [Google Scholar]
11.Engelgau M., Rosenhouse S., El-Saharty S., & Mahal A. (2011). The economic effect of non-communicable diseases on households and nations: a review of existing evidence. Journal of health communication, 16(sup2), 75–81. doi: 10.1080/10810730.2011.601394 [DOI] [PubMed] [Google Scholar]
12.Kundu M. K., Hazra S., Pal D., & Bhattacharya M. (2018). A review on Noncommunicable Diseases (NCDs) burden, its socio-economic impact and the strategies for prevention and control of NCDs in India. Indian journal of public health, 62(4), 302. doi: 10.4103/ijph.IJPH_324_16 [DOI] [PubMed] [Google Scholar]
13.Chen S., & Bloom D. E. (2019). The macroeconomic burden of noncommunicable diseases associated with air pollution in China. PloS one, 14(4), e0215663. doi: 10.1371/journal.pone.0215663 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Stuckler D., Basu S., McKee M. (2010). Drivers of inequalities in Millennium Development Goal progress: A statistical analysis. PLoS Med; 7: e1000241. doi: 10.1371/journal.pmed.1000241 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ensuring Healthy Life for All- Non-communicable disease and universal health coverage, NCD Alliance, December 2018.
16.Wagstaff A., Flores G., Hsu J., Smitz M. F., Chepynoga K., Buisman L. R., et al. (2018). Progress on catastrophic health spending in 133 countries: a retrospective observational study. The Lancet Global Health, 6(2), e169–e179. doi: 10.1016/S2214-109X(17)30429-1 [DOI] [PubMed] [Google Scholar]
17.Boerma T., Eozenou P., Evans D., Evans T., Kieny M. P., & Wagstaff A. (2014). Monitoring progress towards universal health coverage at country and global levels. PLoS Med, 11(9), e1001731. doi: 10.1371/journal.pmed.1001731 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Mohanty S. K., Rodgers J., Singh R. R., Mishra R. S., Kim R., Khan J., et al. (2021). Morbidity compression or expansion? A temporal analysis of the age at onset of non-communicable diseases in India. GeroScience, 1–14. doi: 10.1007/s11357-020-00297-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Arokiasamy P. (2018). India’s escalating burden of non-communicable diseases. The lancet global health, 6(12), e1262–e1263. doi: 10.1016/S2214-109X(18)30448-0 [DOI] [PubMed] [Google Scholar]
20.WHO report on Non-Communicable Diseases Country Profiles 2018, Geneva, World Health Organization, 2018. [Google Scholar]
21.Indian Council of Medical Research, Public Health Foundation of India, and Institute for Health Metrics and Evaluation. India: Health of the Nation’s States- The India State Level Disease Burden Initiative. New Delhi, India: ICMR, PHFI, and IHME; 2017. [Google Scholar]
22.National Health Systems Resource Centre (2019). National Health Accounts Estimates for India (2016–17). New Delhi: Ministry of Health and Family Welfare, Government of India. [Google Scholar]
23.Verma V. R., Kumar P., & Dash U. (2021). Assessing the household economic burden of non-communicable diseases in India: evidence from repeated cross-sectional surveys. BMC public health, 21(1), 1–22. doi: 10.1186/s12889-020-10013-y [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Kumara A. S., & Samaratunge R. (2016). Patterns and determinants of out-of-pocket health care expenditure in Sri Lanka: evidence from household surveys. Health policy and planning, 31(8), 970–983. doi: 10.1093/heapol/czw021 [DOI] [PubMed] [Google Scholar]
25.Dwivedi R., & Pradhan J. (2020). Does affordability matter? Examining the trends and patterns in health care expenditure in India. Health Services Management Research, 33(4), 207–218. doi: 10.1177/0951484820923921 [DOI] [PubMed] [Google Scholar]
26.Ngcamphalala C., & Ataguba J. E. (2018). An assessment of financial catastrophe and impoverishment from out-of-pocket health care payments in Swaziland. Global health action, 11(1), 1428473. doi: 10.1080/16549716.2018.1428473 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Kiros M., Dessie E., Jbaily A., Tolla M. T., Johansson K. A., Norheim O. F., et al. (2020). The burden of household out-of-pocket health expenditures in Ethiopia: estimates from a nationally representative survey (2015–16). Health policy and planning, 35(8), 1003–1010. doi: 10.1093/heapol/czaa044 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Ataguba J. E. (2021). Assessing financial protection in health: Does the choice of poverty line matter? Health economics, 30(1), 186–193. doi: 10.1002/hec.4172 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Engelgau M. M., Karan A., & Mahal A. (2012). The economic impact of non-communicable diseases on households in India. Globalization and health, 8(1), 1–10. doi: 10.1186/1744-8603-8-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Yadav J., Menon G. R., & John D. (2021). Disease-Specific Out-of-Pocket Payments, Catastrophic Health Expenditure and Impoverishment Effects in India: An Analysis of National Health Survey Data. Applied Health Economics and Health Policy, 1–14. doi: 10.1007/s40258-021-00641-9 [DOI] [PubMed] [Google Scholar]
31.NSSO (2018) Key Indicators of Social Consumption in India: Health (NSSO 75th Round, July–June 2018). National Sample Survey Office Ministry of Statistics and Programme Implementation (MOSPI), Government of India. [Google Scholar]
32.Nayar K. R. (2007). Social exclusion, caste & health: a review based on the social determinants framework. Indian Journal of Medical Research, 126(4), 355. [PubMed] [Google Scholar]
33.Olasehinde N., & Olaniyan O. (2017). Determinants of household health expenditure in Nigeria. International Journal of Social Economics. [Google Scholar]
34.Wagstaff A., Eozenou P., & Smitz M. (2020). Out-of-Pocket Expenditures on Health: A Global Stocktake. The World Bank Research Observer, 35(2), 123–157. doi: 10.1093/wbro/lkz009 [DOI] [Google Scholar]
35.WHO (2005). Distribution of Health Payments and Catastrophic Expenditures Methodology. No. EIP/FER/DP. 05.2. World Health Organization, Geneva. [Google Scholar]
36.Xu K., Evans DB., Kawabata K., Zeramdini R., Klavus J., & Murray C. J. (2003). Household catastrophic health expenditure: a multicountry analysis. The Lancet 362(9378), 111–117. doi: 10.1016/S0140-6736(03)13861-5 [DOI] [PubMed] [Google Scholar]
37.Wagstaff A., & Doorslaer E. V. (2003). Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993–1998. Health economics, 12(11), 921–933. doi: 10.1002/hec.776 [DOI] [PubMed] [Google Scholar]
38.Buigut S., Ettarh R., & Amendah D. D. (2015). Catastrophic health expenditure and its determinants in Kenya slum communities. International journal for equity in health, 14(1), 1–12. doi: 10.1186/s12939-015-0168-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Dwivedi R., Pradhan J., & Athe R. (2021). Measuring catastrophe in paying for healthcare: A comparative methodological approach by using National Sample Survey, India. The International Journal of Health Planning and Management, 36(5), 1887–1915. doi: 10.1002/hpm.3272 [DOI] [PubMed] [Google Scholar]
40.Zhao Y., Tang S., Mao W., & Akinyemiju T. F. (2021). Socio-economic and rural-urban differences in healthcare and catastrophic health expenditures among cancer patients in China: analysis of the China Health and Retirement Longitudinal Study. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Prinja S., Bahuguna P., Pinto A. D., Sharma A., Bharaj G., Kumar V., et al. (2012). The cost of universal health care in India: a model based estimate. PLoS One, 7(1), e30362. doi: 10.1371/journal.pone.0030362 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Prinja S., Kanavos P., & Kumar R. (2012). Health care inequities in north India: role of public sector in universalizing health care. The Indian journal of medical research, 136(3), 421. [PMC free article] [PubMed] [Google Scholar]
43.Shanmugam K. R. (2012). Efficiency of Raising Health Outcomes in the Indian States. Madras School of Economics, Chennai, India. [Google Scholar]
44.Wang Q., Brenner S., Leppert G., Banda T. H., Kalmus O., & De Allegri M. (2015). Health seeking behaviour and the related household out-of-pocket expenditure for chronic non-communicable diseases in rural Malawi. Health policy and planning, 30(2), 242–252. doi: 10.1093/heapol/czu004 [DOI] [PubMed] [Google Scholar]
45.Rasul F. B., Kalmus O., Sarker M., Adib H. I., Hossain M. S., Hasan M. Z., et al. (2019). Determinants of health seeking behavior for chronic non-communicable diseases and related out-of-pocket expenditure: results from a cross-sectional survey in northern Bangladesh. Journal of Health, Population and Nutrition, 38(1), 1–14. doi: 10.1186/s41043-019-0195-z [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Rijal A., Adhikari T. B., Khan J. A, & Berg-Beckhoff G. (2018). The economic impact of non-communicable diseases among households in South Asia and their coping strategy: a systematic review. PloS one, 13(11), e0205745. doi: 10.1371/journal.pone.0205745 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Pallegedara A. (2018). Impacts of chronic non-communicable diseases on households’ out-of-pocket healthcare expenditures in Sri Lanka. International journal of health economics and management, 18(3), 301–319. doi: 10.1007/s10754-018-9235-2 [DOI] [PubMed] [Google Scholar]
48.Pradhan J., & Behera S. (2020). Does choice of health care facility matter? Assessing out-of-pocket expenditure and catastrophic spending on emergency obstetric care in India. Journal of Biosocial Science, 1–16. doi: 10.1017/S0021932020000310 [DOI] [PubMed] [Google Scholar]
49.Lin H., Li Q., Hu Y., Zhu C., Ma H., Gao J., et al. (2017). The prevalence of multiple non-communicable diseases among middle-aged and elderly people: the Shanghai Changfeng Study. European journal of epidemiology, 32(2), 159–163. doi: 10.1007/s10654-016-0219-6 [DOI] [PubMed] [Google Scholar]
50.Verma M., Grover S., Tripathy J. P., Singh T., Nagaraja S. B., Kathirvel S., et al. (2019). Co-existing non-communicable diseases and mental illnesses amongst the elderly in Punjab, India. European endocrinology, 15(2), 106. doi: 10.17925/EE.2019.15.2.106 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Niessen L. W., Mohan D., Akuoku J. K., Mirelman A. J., Ahmed S., Koehlmoos T. P., et al. (2018). Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. The Lancet, 391(10134), 2036–2046. doi: 10.1016/S0140-6736(18)30482-3 [DOI] [PubMed] [Google Scholar]
52.Basant R. (2007). Social, economic and educational conditions of Indian Muslims. Economic and Political Weekly, 828–832. [Google Scholar]
53.Karan A., Selvaraj S., & Mahal A. (2014). Moving to universal coverage? Trends in the burden of out-of-pocket payments for health care across social groups in India, 1999–2000 to 2011–12. PloS one, 9(8), e105162. doi: 10.1371/journal.pone.0105162 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Mohanty S. K., Ladusingh L., Kastor A., Chauhan R. K., & Bloom D. E. (2016). Pattern, growth and determinant of household health spending in India, 1993–2012. Journal of Public Health, 24(3), 215–229. doi: 10.1007/s10389-016-0712-0 [DOI] [Google Scholar]
55.Baird K. E. (2016). The financial burden of out-of-pocket expenses in the United States and Canada: How different is the United States? SAGE open medicine, 4, 2050312115623792. doi: 10.1177/2050312115623792 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0260628.r001

Decision Letter 0

Petri Böckerman

6 Sep 2021

PONE-D-21-18147Uneven economic burden of non-communicable diseases among Indian households: A comparative analysisPLOS ONE

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Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: N/A

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Reviewer #1: Thanks for providing me the opportunity to review this manuscript. The authors analyze the Indian national survey NSSO 75th round. The analysis has value in terms understanding the economic burden of NCDs in India. This research could provide reflections on how the recent healthcare reforms in lines with India’s UHC aspirations has succeeded/failed to produce coveted effects. Presently the analysis and overall manuscript stand week. There are several lacks related to methodological description, presentation of descriptive results, theoretical basis of choice of variables in regression, focused discussion, policy implications etc. This manuscript can benefit from major revision. The specific comments for each section are given below.

Abstract

1. The term ‘lower and middle income countries’ is incorrect, it is not clear whether the authors are referring to ‘low and middle income countries’ or lower-middle income countries’ please revise accordingly.

2. Please provide the acronym-NCDs at its first use.

3. Please elaborate the methods used.

4. Results: please mention if you are reporting mean expenditures

5. Results: please replace ‘2 times more’ with ‘more than twice’

6. Results: The text ‘Particularly, expenditures on medication and diagnosis are troublesome for households with NCDs.’ seems generic. It would be good if authors can provide proportion of total OOP expenditure incurred on medicines and diagnostics.

7. For results of generalised linear regression model please provide odds ratio and p-values.

8. Presently, the conclusion statement is crude. It needs to be specific with expansion.

Introduction

1. General comments: There are multiple grammatical errors in the text. I have attempted to point out many of these mistakes, but a thorough copy-edit is required to catch all of these errors.

Additionally, the introduction is unnecessarily lengthy owing to repetition of concepts. The structure of the introduction needs to be reworked to ensure that distinct ideas are grouped together into paragraphs and that concepts flow to build a context across paragraphs.

Specific comments:

1. Page 2 line 3, as explained above please replace the term ‘lower and middle income countries’

2. Page 2 line 3 please replace the word ‘kills’ with ‘kill’

3. Page 2 line 4 please pluralize the word ‘death’

4. Please be consistent in the use of the acronym for non-communicable diseases. There is an inconsistency in the use of the full form and the acronym. Additionally at some places ‘NCD’ is used and at others it is ‘NCDs’.

5. Page 2 line 9 please pluralize the verb ‘trap’

6. Page 2 line 10 please replace the word like with ‘such as’

7. Page 2 line 11 again the authors have used the full form instead of the abbreviation for non-communicable diseases. It is advised to give the full form at first-in-text reference along with the acronym in parenthesis and then use the acronym in the subsequent references.

8. Page 2 line 13 please revise the sentence ‘the economic cost of NCDs..’ for it to be grammatically correct. Additionally please specify ‘…macroeconomic effect’ for whom?

9. Page 2 line 14 please replace the word ‘force’ with ‘workforce’

10. Page 3 lines 1-2, there is incorrect use of prepositions, please revise accordingly.

11. Page 3 lines 4-5, please do not capitalize the words succeeding colon and semi colon.

12. Page 3 line 15 please pluralize the word ‘death’

13. Page 3 line 15 please add ‘the’ before the word ‘..death are’

14. Page 3 Please do not provide the full form for DALYs again. Also look out for similar errors for other acronyms (UHC, SDG) used in the manuscript.

15. Page 3, the authors provide DALYs from 1990-2016, it is advised to provide more recent figures using GBD study 2019.

16. Page 3 please provide a reference for the sentence ‘OOPE constitutes 58.7% of total health expenditure in India’

17. Page 3 the figures cited by the authors ‘23 to 32 million people below the poverty line’ does not refer to impoverishment due to NCDs. Furthermore, the reference cited uses data from 1999-2000 which is more than two decades old. The authors are advised to accordingly revise these facts.

18. Page 3 please pluralize the word ‘policymaker’

19. Page 3 it is not clear how understanding of OOP expenditure would provide information on prevention and control of NCDs. Additionally, how would this reduce financial hardship. These lines need additional clarifications.

20. Page 4 contrary to the authors’ claim there are multiple studies assessing the OOP expenditure for NCDs apart from the two cited. Please provide a strong justification of the novelty of your study and how would it contribute to existing literature.

METHODOLOGY

General comments: The authors have analysed nationally representative NSSO data and provide useful descriptive results. The findings are important but empirically obvious. Furthermore, the depth of analysis is thin, the authors do not justify the methodology for computation of catastrophic health expenditures and the theoretical basis of selection of independent variables for their regression model.

Specific comments:

1. The authors do not provide a clear justification on how have they grouped the 15 broad disease categories into NCDs and non-NCDs. For instance respiratory ailments can be either NCDs or infectious diseases.

Similarly, when you have already included wealth quintile as an independent variable why do you include type of ‘latrine’ which is a component of estimating proxy of economic status of household.

3. Page 6, the authors state that they use world bank criteria to measure OOPE. It is not clear what is meant by this since OOPE is already reported in NSSO data.

4. The authors do not provide a justification of calculating CHE based on 10% of total income. Previous literature reports that using consumption expenditure is a better indicator for calculating CHE in comparison to income. Further a range of thresholds is used 10%, 25%, 30% and 40%, please justify the selection of 10% cut-off.

5. Page 6 para 2 there is repetition of what is given in the preceding text.

6. Page 6 para 3, the authors provide theoretical description of a generalized linear model which may not be needed. Instead as mentioned previously please consider adding details on the reason for selection of variables in your model. Further, there is no mention about how multicollinearity issue was assessed and addressed?

Results

1. For results ‘Households hospitalised due to NCDs have more elderly members as compared to the non-NCD’ please mention corresponding figures-what proportion

2. ‘Nearly 80% of households, both NCDs and non-NCDs’ this is obvious depending on overall NSSO sample and India’ demographic statistics where majority of the population is Hindu.

3. Page 7, lines 4-5 please the authors report results for social class for NCDs households, please report corresponding results for Non-NCDs households as well.

4. Page 7 lines 6 the authors report combined results for NCD and non-NCDs households, please report the findings separately.

5. Also reporting results for all categories is redundant for example the authors state 96% drink safe water so it is not necessary to state that 4% are drinking unsafe water. It is suggested to report major findings only.

6. Please provide corresponding numbers ‘More number of hospitalisation cases

are found in the case of households having 1-5 members as compared to 6-7 and 8+ members’ and also how does it differ in NCDs versus non-NCDs households.

7. Page 7 in the sentence ‘As expected, analysis of hospitalisation.’, the authors should refrain from making speculations.

8. Overall, please use a standardized pattern for reporting descriptive results. For some variables the authors provide compare results for both NCDs and non-NCDs households and for others overall results for the complete sample are provided.

9. Page 9 lines 1-5, please provide results as proportion of total expenditure rather than absolute numbers.

10. The authors report Hindus are spending more than Muslims for NCDs is this difference statistically significant?

11. There is an inconsistency in reporting results for OOP expenditure, for some variables the authors make an intra-variable comparison (Hindus versus Muslims) for others the authors compare differences for NCDs vs non-NCDs households.

12. Table 2 and 3 please reconsider the title of the table ‘Difference in mean out-of-pocket expenditure’, the authors do not report the difference in these tables.

13. The authors report CHE is less for others categories for public hospitals and more for private hospitals with Hindus as reference category. Have the authors controlled for confounders for this analysis. Also in your discussion section please provide plausible reasons why this difference is observed.

14. Why is the role of gender not explored for OOPE and CHE. Previous literature has identified gender as one of the major independent variables affecting OOP and CHE

Discussion

General comments: The discussion section largely repeats the results of analysis and the authors do not comprehensively discuss the reasons for observing specific findings.

Specific comments

1. Page 18 line 1, the findings differ from what has been reported in results section and Table 1

2. Page 18 sentence ‘poor people are not going for seeking care due to cost constraints’, please reference this appropriately.

3. Page 18 sentence ‘highest number of hospitalisation cases reported from the southern region and lowest from the northeast’. Please discuss the basis of this finding.

4. ‘In public health centres, the rural-urban difference in mean OOPE is found to be very less among NCD households, however, this difference is more while considering non-NCD households. Why? Please discuss.

5. ‘households that belong to the Hindu religion are spending more on hospitalisation as compared to Muslim households’ Is this difference significant? Explore possible reasons for the difference. Does this finding has any policy implications in Indian context.

6. The authors state that ‘In public facilities, households using unsafe drinking water are reporting a higher burden of OOPE expenditure compared to the households using safe drinking water. This is because the chances of infection are more in the case of former than in the latter.’ NCDs do not occur as a result of infection. Please clarify the statement how it relates in the context of current research.

7. Please include a section on limitations of your study

Conclusion: Please discuss appropriate policy implications of your findings

Reviewer #2: 1. Introduction:

• There are some spelling mistakes.

• Some abbreviations are written without the whole word at first such as GDP in page 3

• On the other hand, some phrases are repeated and abbreviations should be used such as DALY use the abbreviation not the Disability Adjusted Life Years and United Nations Sustainable Development Goals in page 3.

• The estimated figure in page 3.. what figure?

2. Study design:

• An NCD should be a NCD.

• Use the abbreviation for non-communicable disease

3. Results:

• Table 1: I think a column for the total % should be added

• INR: I understood it is for Indian Rupee but the whole word was not written anywhere in the manuscript

• Table 3: I think you can test the significant difference between different variables and put the p value

• Comment for table 3 should be before the table.

• Table 4: how is the percentage calculated? It is not equal 100%

• Table 5: why there is mention for 10% significant? Mostly we use 1% or 5% only. 10% consider not-significant

4. Discussion:

• Why there is a table 6 in the discussion? It should be in the result section and it is not present in the manuscript at all.

5. Conclusion:

• Use abbreviations for non-communicable disease, out-of-pocket expenditure and catastrophic health expenditure

6. Ethical consideration:

• I don't understand why there is no ethical approval or statement?

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Dec 10;16(12):e0260628. doi: 10.1371/journal.pone.0260628.r002

Author response to Decision Letter 0

27 Oct 2021

Response to reviewers

Title: “Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis"

Ref: PONE-D-21-18147

At the outset, the authors express their deep sense of gratitude to the Editor-in-Chief of Plos One and anonymous reviewers for their valuable suggestions that helped to improve the literal and technical content of this manuscript. Response to the reviewers comments are marked in blue colour.

Reviewer: 1

Comments to the Corresponding Author

Thanks for providing me the opportunity to review this manuscript. The authors analyze the Indian national survey NSSO 75th round. The analysis has value in terms understanding the economic burden of NCDs in India. This research could provide reflections on how the recent healthcare reforms in lines with India’s UHC aspirations has succeeded/failed to produce coveted effects. Presently the analysis and overall manuscript stand week. There are several lacks related to methodological description, presentation of descriptive results, theoretical basis of choice of variables in regression, focused discussion, policy implications etc. This manuscript can benefit from major revision. The specific comments for each section are given below.

Response: Authors are thankful to the anonymous reviewers for appreciation. The concerns of the reviewer have been addressed in a most effective way as listed out point-wise below:

Abstract

Response: Revised as suggested.

2. Please provide the acronym-NCDs at its first use.

Response: Revised as suggested.

3. Please elaborate the methods used.

Response: Thank you so much for the suggestion. We have elaborated the method now.

4. Results: please mention if you are reporting mean expenditures.

Response: As suggested, we have mentioned mean expenditure in the text.

5. Results: please replace ‘2 times more’ with ‘more than twice’.

Response: As suggested, ‘2 times more’ has been replaced with ‘more than twice’

Response: Thank you so much for the kind observation. As suggested, we have provided proportion of total OOP expenditure incurred on medicines and diagnostics.

7. For results of generalised linear regression model please provide odds ratio and p-values.

Response: We have provided odds ratio and p-values in table-5. If we will provide the same in abstract it will become lengthier as we have to provide for both NCDs and non-NCDs in private and public facilities.

8. Presently, the conclusion statement is crude. It needs to be specific with expansion.

Response: Revised as per suggestion.

Introduction

1. General comments: There are multiple grammatical errors in the text. I have attempted to point out many of these mistakes, but a thorough copy-edit is required to catch all of these errors. Additionally, the introduction is unnecessarily lengthy owing to repetition of concepts. The structure of the introduction needs to be reworked to ensure that distinct ideas are grouped together into paragraphs and that concepts flow to build a context across paragraphs.

Specific comments:

1. Page 2 line 3, as explained above please replace the term ‘lower and middle income countries’

Response: Revised as suggested.

2. Page 2 line 3 please replace the word ‘kills’ with ‘kill’.

Response: As suggested, we have replaced the word.

3. Page 2 line 4 please pluralize the word ‘death’.

Response: As suggested, we have pluralized the word.

4. Please be consistent in the use of the acronym for non-communicable diseases. There is an inconsistency in the use of the full form and the acronym. Additionally, at some places ‘NCD’ is used and at others it is ‘NCDs’.

Response: Thank you so much for your suggestion. We have revised this now.

5. Page 2 line 9 please pluralize the verb ‘trap’

Response: As suggested, we have pluralized the word.

6. Page 2 line 10 please replace the word like with ‘such as’

Response: As suggested, we have replaced the word.

Response: Revised as suggested.

8. Page 2 line 13 please revise the sentence ‘the economic cost of NCDs.’ for it to be grammatically correct. Additionally, please specify ‘…macroeconomic effect’ for whom?

Response: Revised as suggested.

9. Page 2 line 14 please replace the word ‘force’ with ‘workforce’

Response: Replaced the word as suggested.

10. Page 3 lines 1-2, there is incorrect use of prepositions, please revise accordingly.

Response: Revised as per suggestion.

11. Page 3 lines 4-5, please do not capitalize the words succeeding colon and semi colon.

Response: Revised as suggested.

12. Page 3 line 15 please pluralize the word ‘death’

Response: As suggested, we have pluralized the word.

13. Page 3 line 15 please add ‘the’ before the word ‘...death are’

Response: Added ‘the’ as suggested.

14. Page 3 Please do not provide the full form for DALYs again. Also look out for similar errors for other acronyms (UHC, SDG) used in the manuscript.

Response: Made correction as suggested.

15. Page 3, the authors provide DALYs from 1990-2016, it is advised to provide more recent figures using GBD study 2019.

Response: Thank you for your observation. But we are unable to provide more recent figure because in GBD study 2019, DALY has given for particular NCD, not for NCDs as a whole that we have given in our manuscript.

16. Page 3 please provide a reference for the sentence ‘OOPE constitutes 58.7% of total health expenditure in India’

Response: References have been provided in the text.

Response: Revised as per suggestion.

18. Page 3 please pluralize the word ‘policymaker’

Response: As suggested, we have pluralized the word.

Response: We have revised these lines.

Response: Incorporated as suggested.

METHODOLOGY

Specific comments:

1. The authors do not provide a clear justification on how have they grouped the 15 broad disease categories into NCDs and non-NCDs. For instance, respiratory ailments can be either NCDs or infectious diseases.

Response: We have grouped the disease into two categories, based on the International classification of diseases (ICD-10), where the first category includes only NCDs and the rest of all infectious/communicable/ injuries/nutritional related diseases are grouped into the second category i.e. non-NCDs. And regarding respiratory ailments, some of them are NCDs for.eg like asthma and some of other respiratory ailments like acute upper respiratory infections (cold, runny nose, sore throat with cough, allergic colds) have been classified as non-NCDs.

2. Page 5: under variables section it is not clear why the authors choose these particular variables in this analysis. For instance, why do the authors think religion is an important variable impacting your outcome variables-OOP expenditure due to NCDs. Similarly, when you have already included wealth quintile as an independent variable why do you include type of ‘latrine’ which is a component of estimating proxy of economic status of household.

Response: As reported in the past studies, socio-economic variables have a significant impact on pattern of healthcare spending, especially in Indian Context. Therefore, this paper tries to understand the socioeconomic differentials in mean OOPE on NCDs and non-NCDs households in public and private health facilities. So, accordingly, we have considered caste, religion along with all other socio-economic and demographic variables that are available in the data set. Regarding the use of both wealth quintile and type of latrine used, it has clearly mentioned in NSSO report that the calculation of wealth quintile is based on household’s usual monthly consumer expenditure and type of latrine is not a component of it.

3. Page 6, the authors state that they use world bank criteria to measure OOPE. It is not clear what is meant by this since OOPE is already reported in NSSO data.

Response: The methods of calculating OOPE is initially suggested by world bank and it is a globally adopted method. NSSO has also adopted the same method what we are using.

Response: Thank you for your suggestion. We have mistakenly written income instead of consumption expenditure. Because in NSSO monthly consumption expenditure has been reported (see Q-16, D-3, scheduled 25.0 in NSSO report). Now we have corrected it. And regarding cut off point for CHE, though many previous studies have used 10%, 25%, 30% and 40% cut-off, the present study uses 10% threshold for calculating CHE because it is the most widely used threshold level, particularly, the United Nation SDG 3.8.2 has used 10% as its cut-off level (reference no. 33, 46, 55, 56).

5. Page 6 para 2 there is repetition of what is given in the preceding text.

Response: We have deleted the repetition part.

Response: We have deleted the theoretical description of a generalized linear model and incorporated other necessary changes as suggested.

Results

1. For results ‘Households hospitalised due to NCDs have more elderly members as compared to the non-NCD’ please mention corresponding figures-what proportion

Response: Incorporated as suggested.

2. ‘Nearly 80% of households, both NCDs and non-NCDs’ this is obvious depending on overall NSSO sample and India’ demographic statistics where majority of the population is Hindu.

Response: Yes, it is obvious. But, we are just trying to give descriptive statistics of our study.

3. Page 7, lines 4-5 please the authors report results for social class for NCDs households, please report corresponding results for Non-NCDs households as well.

Response: Incorporated as suggested.

4. Page 7 lines 6 the authors report combined results for NCD and non-NCDs households, please report the findings separately.

Response: Now we have reported the findings separately for NCD and non-NCDs household.

Response: Text is revised as per suggestion.

6. Please provide corresponding numbers ‘More number of hospitalisation cases are found in the case of households having 1-5 members as compared to 6-7 and 8+ members’ and also how does it differ in NCDs versus non-NCDs households.

Response: Incorporated as suggested.

7. Page 7 in the sentence ‘As expected, analysis of hospitalisation.’, the authors should refrain from making speculations.

Response: Revised the sentence as per suggestion.

Response: Incorporated as suggested.

9. Page 9 lines 1-5, please provide results as proportion of total expenditure rather than absolute numbers.

Response: Incorporated as suggested.

10. The authors report Hindus are spending more than Muslims for NCDs is this difference statistically significant?

Response: Results of OOPE are not statistically significant, we have provided significance level for CHE only.

11. There is an inconsistency in reporting results for OOP expenditure, for some variables the authors make an intravariable comparison (Hindus versus Muslims) for others the authors compare differences for NCDs vs non-NCDs households.

Response: We have revised it now.

12. Table 2 and 3 please reconsider the title of the table ‘Difference in mean out-of-pocket expenditure’, the authors do not report the difference in these tables.

Response: Title of the table 2 and 3 has been revised.

Response: Yes, we have controlled for confounders for this analysis and also provided plausible reason for why this difference is observed.

14. Why is the role of gender not explored for OOPE and CHE. Previous literature has identified gender as one of the major independent variables affecting OOP and CHE.

Response: Our study is based on household level analysis, so we cannot consider gender as an independent variable because it represents an individual characteristic. So, we have taken into consideration of only household level variables.

Discussion

General comments: The discussion section largely repeats the results of analysis and the authors do not comprehensively discuss the reasons for observing specific findings.

Specific-comments

1. Page 18 line 1, the findings differ from what has been reported in results section and Table 1

Response: We have deleted it as it is a repetition in results section in Table 1 and we have revised the sentence as well.

2. Page 18 sentence ‘poor people are not going for seeking care due to cost constraints’, please reference this appropriately.

Response: Appropriate references are incorporated.

3. Page 18 sentence ‘highest number of hospitalisation cases reported from the southern region and lowest from the northeast’. Please discuss the basis of this finding.

Response: Now we have discussed this finding.

5. ‘households that belong to the Hindu religion are spending more on hospitalisation as compared to Muslim Households’ Is this difference significant? Explore possible reasons for the difference. Does this finding has any policy implications in Indian context.

Response: Possible reason has been provided for this finding. Yes, it has a policy implication in Indian context. As Muslims have lower education level (as per evidence by previous literature), they may not able to grab the available insurance facilities provided by Indian government. So, government must increase public awareness regarding the availability of the insurance facility. Along with this the government should also take necessary steps to improve the education of the Muslim community.

Response: Though the main focus of this study is to calculate the burden of NCDs, we are also comparing the burden of NCDs with non-NCDs households and as per the previous literature drinking water is an important factor affecting communicable/infectious disease. So, we are using drinking water just as a socio-economic variable in our model with the hypothesis that unsafe drinking water will affect more to non-NCDs households than NCDs households.

7. Please include a section on limitations of your study

Response: Limitations have been added in the study.

Conclusion: Please discuss appropriate policy implications of your findings

Response: Incorporated as suggested.

Reviewer: 2

Comments to the Corresponding Author

1. Introduction:

• There are some spelling mistakes.

Response: Now we have made the correction.

• Some abbreviations are written without the whole word at first such as GDP in page 3

Response: Full form of GDP has been given.

Response: Revised as suggested.

• The estimated figure in page 3. what figure?

Response: Revised it now.

2. Study design:

• An NCD should be a NCD.

Response: Revised as suggested.

• Use the abbreviation for non-communicable disease

Response: We have used as suggested.

3. Results:

• Table 1: I think a column for the total % should be added.

Response: Thank you so much for the kind observation. A column for the total % have been added in the text.

• INR: I understood it is for Indian Rupee but the whole word was not written anywhere in the manuscript

Response: Thank you for your suggestion. Now we have written it right below the table.

• Table 3: I think you can test the significant difference between different variables and put the p value

Response: Since you have asked for significant difference between different variables, now we have run ANOVA for that and accordingly given p value. Results in bold letter are significant at 5% in table 2 and 3.

• Comment for table 3 should be before the table.

Response: Revised as suggested.

• Table 4: how is the percentage calculated? It is not equal 100%

We have calculated percentage separately for each group. For e.g. we want to see how many people from rural area are facing CHE due to hospitalization, and the same for urban area also. As per our result (Table 4), 30.24% rural people are exposed to catastrophic health payment and the rest 69.76% are not experiencing the same. Similarly, in urban area 23.85% are experiencing CHE due to hospitalization.

• Table 5: why there is mention for 10% significant? Mostly we use 1% or 5% only. 10% consider not-significant

Response: Thank you so much for the suggestion. Now we have removed that.

4. Discussion:

• Why there is a table 6 in the discussion? It should be in the result section and it is not present in the manuscript at all.

Response: Revised as suggested.

5. Conclusion:

• Use abbreviations for non-communicable disease, out-of-pocket expenditure and catastrophic health expenditure

Response: Revised as suggested.

6. Ethical consideration:

• I don’t understand why there is no ethical approval or statement?

Ethical approval is not applicable because the study has used publicly available unit-level data from a secondary source.

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Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

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Petri Böckerman

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PONE-D-21-18147R1

Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

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Supplementary Materials

S1 Appendix. Out-of-pocket expenditure as a proportion of total medical expenditure, NSSO survey 2017–18.

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Data Availability Statement

[pone.0260628.ref001] 1.Girum T., Mesfin D., Bedewi J., & Shewangizaw M. (2020). The burden of non-communicable diseases in Ethiopia, 2000–2016: analysis of evidence from global burden of disease study 2016 and global health estimates 2016. International journal of chronic diseases, 2020. doi: 10.1155/2020/3679528 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref002] 2.Lozano R., Fullman N., Abate D., Abay S. M., Abbafati C., Abbasi N., et al. (2018). Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 2091–2138. doi: 10.1016/S0140-6736(18)32281-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref003] 3.World Health Organisation (2018). Non-communicable diseases [fact sheet]. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. [Google Scholar]

[pone.0260628.ref004] 4.Hay S. I., Abajobir A. A., Abate K. H., Abbafati C., Abbas K. M., Abd-Allah F., et al. (2017). Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390(10100), 1260–1344. 10.1016/S0140-6736(17)32130-X. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref005] 5.Bollyky T. J., Templin T., Cohen M., & Dieleman J. L. (2017). Lower-income countries that face the most rapid shift in noncommunicable disease burden are also the least prepared. Health Affairs, 36(11), 1866–1875. doi: 10.1377/hlthaff.2017.0708 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref006] 6.Kankeu H. T., Saksena P., Xu K., & Evans D. B. (2013). The financial burden from non-communicable diseases in low-and middle-income countries: a literature review. Health Research Policy and Systems, 11(1), 1–12. http://www.health-policy-systems.com/content/11/1/31. doi: 10.1186/1478-4505-11-31 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref007] 7.Kastor A., & Mohanty S. K. (2018). Disease-specific out-of-pocket and catastrophic health expenditure on hospitalization in India: do Indian households face distress health financing? PloS one, 13(5), e0196106. doi: 10.1371/journal.pone.0196106 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref008] 8.Jan S., Laba T. L., Essue B. M., Gheorghe A., Muhunthan J., Engelgau M., et al. (2018). Action to address the household economic burden of non-communicable diseases. The Lancet, 391(10134), 2047–2058. doi: 10.1016/S0140-6736(18)30323-4 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref009] 9.Datta B. K., Husain M. J., Fatehin S., & Kostova D. (2018). Consumption displacement in households with noncommunicable diseases in Bangladesh. PloS one, 13(12), e0208504. doi: 10.1371/journal.pone.0208504 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref010] 10.Kumara A. S., & Samaratunge R. (2017). Impact of ill-health on household consumption in Sri Lanka: Evidence from household survey data. Social Science & Medicine, 195, 68–76. doi: 10.1016/j.socscimed.2017.11.015 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref011] 11.Engelgau M., Rosenhouse S., El-Saharty S., & Mahal A. (2011). The economic effect of non-communicable diseases on households and nations: a review of existing evidence. Journal of health communication, 16(sup2), 75–81. doi: 10.1080/10810730.2011.601394 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref012] 12.Kundu M. K., Hazra S., Pal D., & Bhattacharya M. (2018). A review on Noncommunicable Diseases (NCDs) burden, its socio-economic impact and the strategies for prevention and control of NCDs in India. Indian journal of public health, 62(4), 302. doi: 10.4103/ijph.IJPH_324_16 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref013] 13.Chen S., & Bloom D. E. (2019). The macroeconomic burden of noncommunicable diseases associated with air pollution in China. PloS one, 14(4), e0215663. doi: 10.1371/journal.pone.0215663 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref014] 14.Stuckler D., Basu S., McKee M. (2010). Drivers of inequalities in Millennium Development Goal progress: A statistical analysis. PLoS Med; 7: e1000241. doi: 10.1371/journal.pmed.1000241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref015] 15.Ensuring Healthy Life for All- Non-communicable disease and universal health coverage, NCD Alliance, December 2018.

[pone.0260628.ref016] 16.Wagstaff A., Flores G., Hsu J., Smitz M. F., Chepynoga K., Buisman L. R., et al. (2018). Progress on catastrophic health spending in 133 countries: a retrospective observational study. The Lancet Global Health, 6(2), e169–e179. doi: 10.1016/S2214-109X(17)30429-1 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref017] 17.Boerma T., Eozenou P., Evans D., Evans T., Kieny M. P., & Wagstaff A. (2014). Monitoring progress towards universal health coverage at country and global levels. PLoS Med, 11(9), e1001731. doi: 10.1371/journal.pmed.1001731 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref018] 18.Mohanty S. K., Rodgers J., Singh R. R., Mishra R. S., Kim R., Khan J., et al. (2021). Morbidity compression or expansion? A temporal analysis of the age at onset of non-communicable diseases in India. GeroScience, 1–14. doi: 10.1007/s11357-020-00297-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref019] 19.Arokiasamy P. (2018). India’s escalating burden of non-communicable diseases. The lancet global health, 6(12), e1262–e1263. doi: 10.1016/S2214-109X(18)30448-0 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref020] 20.WHO report on Non-Communicable Diseases Country Profiles 2018, Geneva, World Health Organization, 2018. [Google Scholar]

[pone.0260628.ref021] 21.Indian Council of Medical Research, Public Health Foundation of India, and Institute for Health Metrics and Evaluation. India: Health of the Nation’s States- The India State Level Disease Burden Initiative. New Delhi, India: ICMR, PHFI, and IHME; 2017. [Google Scholar]

[pone.0260628.ref022] 22.National Health Systems Resource Centre (2019). National Health Accounts Estimates for India (2016–17). New Delhi: Ministry of Health and Family Welfare, Government of India. [Google Scholar]

[pone.0260628.ref023] 23.Verma V. R., Kumar P., & Dash U. (2021). Assessing the household economic burden of non-communicable diseases in India: evidence from repeated cross-sectional surveys. BMC public health, 21(1), 1–22. doi: 10.1186/s12889-020-10013-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref024] 24.Kumara A. S., & Samaratunge R. (2016). Patterns and determinants of out-of-pocket health care expenditure in Sri Lanka: evidence from household surveys. Health policy and planning, 31(8), 970–983. doi: 10.1093/heapol/czw021 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref025] 25.Dwivedi R., & Pradhan J. (2020). Does affordability matter? Examining the trends and patterns in health care expenditure in India. Health Services Management Research, 33(4), 207–218. doi: 10.1177/0951484820923921 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref026] 26.Ngcamphalala C., & Ataguba J. E. (2018). An assessment of financial catastrophe and impoverishment from out-of-pocket health care payments in Swaziland. Global health action, 11(1), 1428473. doi: 10.1080/16549716.2018.1428473 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref027] 27.Kiros M., Dessie E., Jbaily A., Tolla M. T., Johansson K. A., Norheim O. F., et al. (2020). The burden of household out-of-pocket health expenditures in Ethiopia: estimates from a nationally representative survey (2015–16). Health policy and planning, 35(8), 1003–1010. doi: 10.1093/heapol/czaa044 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref028] 28.Ataguba J. E. (2021). Assessing financial protection in health: Does the choice of poverty line matter? Health economics, 30(1), 186–193. doi: 10.1002/hec.4172 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref029] 29.Engelgau M. M., Karan A., & Mahal A. (2012). The economic impact of non-communicable diseases on households in India. Globalization and health, 8(1), 1–10. doi: 10.1186/1744-8603-8-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref030] 30.Yadav J., Menon G. R., & John D. (2021). Disease-Specific Out-of-Pocket Payments, Catastrophic Health Expenditure and Impoverishment Effects in India: An Analysis of National Health Survey Data. Applied Health Economics and Health Policy, 1–14. doi: 10.1007/s40258-021-00641-9 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref031] 31.NSSO (2018) Key Indicators of Social Consumption in India: Health (NSSO 75th Round, July–June 2018). National Sample Survey Office Ministry of Statistics and Programme Implementation (MOSPI), Government of India. [Google Scholar]

[pone.0260628.ref032] 32.Nayar K. R. (2007). Social exclusion, caste & health: a review based on the social determinants framework. Indian Journal of Medical Research, 126(4), 355. [PubMed] [Google Scholar]

[pone.0260628.ref033] 33.Olasehinde N., & Olaniyan O. (2017). Determinants of household health expenditure in Nigeria. International Journal of Social Economics. [Google Scholar]

[pone.0260628.ref034] 34.Wagstaff A., Eozenou P., & Smitz M. (2020). Out-of-Pocket Expenditures on Health: A Global Stocktake. The World Bank Research Observer, 35(2), 123–157. doi: 10.1093/wbro/lkz009 [DOI] [Google Scholar]

[pone.0260628.ref035] 35.WHO (2005). Distribution of Health Payments and Catastrophic Expenditures Methodology. No. EIP/FER/DP. 05.2. World Health Organization, Geneva. [Google Scholar]

[pone.0260628.ref036] 36.Xu K., Evans DB., Kawabata K., Zeramdini R., Klavus J., & Murray C. J. (2003). Household catastrophic health expenditure: a multicountry analysis. The Lancet 362(9378), 111–117. doi: 10.1016/S0140-6736(03)13861-5 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref037] 37.Wagstaff A., & Doorslaer E. V. (2003). Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993–1998. Health economics, 12(11), 921–933. doi: 10.1002/hec.776 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref038] 38.Buigut S., Ettarh R., & Amendah D. D. (2015). Catastrophic health expenditure and its determinants in Kenya slum communities. International journal for equity in health, 14(1), 1–12. doi: 10.1186/s12939-015-0168-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref039] 39.Dwivedi R., Pradhan J., & Athe R. (2021). Measuring catastrophe in paying for healthcare: A comparative methodological approach by using National Sample Survey, India. The International Journal of Health Planning and Management, 36(5), 1887–1915. doi: 10.1002/hpm.3272 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref040] 40.Zhao Y., Tang S., Mao W., & Akinyemiju T. F. (2021). Socio-economic and rural-urban differences in healthcare and catastrophic health expenditures among cancer patients in China: analysis of the China Health and Retirement Longitudinal Study. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref041] 41.Prinja S., Bahuguna P., Pinto A. D., Sharma A., Bharaj G., Kumar V., et al. (2012). The cost of universal health care in India: a model based estimate. PLoS One, 7(1), e30362. doi: 10.1371/journal.pone.0030362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref042] 42.Prinja S., Kanavos P., & Kumar R. (2012). Health care inequities in north India: role of public sector in universalizing health care. The Indian journal of medical research, 136(3), 421. [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref043] 43.Shanmugam K. R. (2012). Efficiency of Raising Health Outcomes in the Indian States. Madras School of Economics, Chennai, India. [Google Scholar]

[pone.0260628.ref044] 44.Wang Q., Brenner S., Leppert G., Banda T. H., Kalmus O., & De Allegri M. (2015). Health seeking behaviour and the related household out-of-pocket expenditure for chronic non-communicable diseases in rural Malawi. Health policy and planning, 30(2), 242–252. doi: 10.1093/heapol/czu004 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref045] 45.Rasul F. B., Kalmus O., Sarker M., Adib H. I., Hossain M. S., Hasan M. Z., et al. (2019). Determinants of health seeking behavior for chronic non-communicable diseases and related out-of-pocket expenditure: results from a cross-sectional survey in northern Bangladesh. Journal of Health, Population and Nutrition, 38(1), 1–14. doi: 10.1186/s41043-019-0195-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref046] 46.Rijal A., Adhikari T. B., Khan J. A, & Berg-Beckhoff G. (2018). The economic impact of non-communicable diseases among households in South Asia and their coping strategy: a systematic review. PloS one, 13(11), e0205745. doi: 10.1371/journal.pone.0205745 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref047] 47.Pallegedara A. (2018). Impacts of chronic non-communicable diseases on households’ out-of-pocket healthcare expenditures in Sri Lanka. International journal of health economics and management, 18(3), 301–319. doi: 10.1007/s10754-018-9235-2 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref048] 48.Pradhan J., & Behera S. (2020). Does choice of health care facility matter? Assessing out-of-pocket expenditure and catastrophic spending on emergency obstetric care in India. Journal of Biosocial Science, 1–16. doi: 10.1017/S0021932020000310 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref049] 49.Lin H., Li Q., Hu Y., Zhu C., Ma H., Gao J., et al. (2017). The prevalence of multiple non-communicable diseases among middle-aged and elderly people: the Shanghai Changfeng Study. European journal of epidemiology, 32(2), 159–163. doi: 10.1007/s10654-016-0219-6 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref050] 50.Verma M., Grover S., Tripathy J. P., Singh T., Nagaraja S. B., Kathirvel S., et al. (2019). Co-existing non-communicable diseases and mental illnesses amongst the elderly in Punjab, India. European endocrinology, 15(2), 106. doi: 10.17925/EE.2019.15.2.106 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref051] 51.Niessen L. W., Mohan D., Akuoku J. K., Mirelman A. J., Ahmed S., Koehlmoos T. P., et al. (2018). Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. The Lancet, 391(10134), 2036–2046. doi: 10.1016/S0140-6736(18)30482-3 [DOI] [PubMed] [Google Scholar]

[pone.0260628.ref052] 52.Basant R. (2007). Social, economic and educational conditions of Indian Muslims. Economic and Political Weekly, 828–832. [Google Scholar]

[pone.0260628.ref053] 53.Karan A., Selvaraj S., & Mahal A. (2014). Moving to universal coverage? Trends in the burden of out-of-pocket payments for health care across social groups in India, 1999–2000 to 2011–12. PloS one, 9(8), e105162. doi: 10.1371/journal.pone.0105162 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0260628.ref054] 54.Mohanty S. K., Ladusingh L., Kastor A., Chauhan R. K., & Bloom D. E. (2016). Pattern, growth and determinant of household health spending in India, 1993–2012. Journal of Public Health, 24(3), 215–229. doi: 10.1007/s10389-016-0712-0 [DOI] [Google Scholar]

[pone.0260628.ref055] 55.Baird K. E. (2016). The financial burden of out-of-pocket expenses in the United States and Canada: How different is the United States? SAGE open medicine, 4, 2050312115623792. doi: 10.1177/2050312115623792 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Uneven economic burden of non-communicable diseases among Indian households: A comparative analysis

Sasmita Behera

Jalandhar Pradhan

Roles

Abstract

Background

Materials and methods

Results

Conclusion

Introduction

Materials and methods

Data

Study design

Variables

Method

Results

Socio-economic profile of sample households

Table 1. Socio-economic characteristics of households, NSSO survey, 2017–18.

Out-of-pocket expenditure on hospitalisation by NCDs and Non-NCDs households

Fig 1. Mean OOPE by type of disease and healthcare facility, 2017–18.

Table 2. Mean out-of-pocket expenditure by NCDs and non-NCDs households in public health care facility, NSSO survey 2017–18.

Table 3. Mean out-of-pocket expenditure by NCDs and non-NCDs households in private health care facilities, NSSO survey 2017–18.

Catastrophic health expenditure on hospitalisation by NCDs and non-NCDs households

Table 4. Percentage of households experiencing CHE due to hospitalisation by type of facility, NSSO survey 2017–18.

Results from generalised linear model

Table 5. Socio-economic factors associated with CHE due to hospitalisation by type of facility, NSSO survey 2017–18.

Discussion

Conclusion

Limitations of the study

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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Petri Böckerman

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Associated Data

Supplementary Materials

Data Availability Statement

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