Our results section is divided into four sub-sections. First, estimate households OOPS in both households with and without elderly. Second, impoverishment effects of OOPS in the Indian sample. Third, estimate the state-wise prevalence of impoverishment effects. Fourth, estimate the region-wise prevalence of impoverishment. Finally, examine the determinants of impoverishment effects of OOPS using socio-demographic characteristics of sample households.
Estimation of Households OOPS in India
Appendix Table A2 presents cross-tabulation results between monthly per capita consumption expenditure (MPCE) patterns with patterns of households that include households with elderly (HWE) and households without elderly (HWOE). Further, MPCE patterns include household’s total consumption expenditure (HCE), household’s health spending (HHS), household’s out-of-pocket health spending (HOOPS). Concerning household usual expenditure, it shows that average MPCE on HE is INR 2467, (INR) has been spent from a HE, while MPCE on HWE is INR 2237. MPCE (INR) from an HWE. The second row shows that the average MPCE on HHE health is INR 578 (INR) spent from a HE, while the average MPCE on HWE is INR 334 (INR) from an HWE. Similarly, in the last row, Table A2 shows that the average MPCE on HOOPS spent as out-of-pocket spending on health is INR 537 (INR) from a HE, while the average MPCE on HWE is INR 315 (INR) from an HWE. Significantly, in all three cases, the expenditure patterns have been observed higher in the households with elderly than households without elderly; even it is higher than the national average.
Impoverishment effects of OOPS in India
Figure 1 presents the prevalence of impoverishment due to out-of-pocket health spending among the households with elderly and households without elderly and the National level. Our results show that the prevalence of impoverishment among HWE is 10.5 percent, while it is 9.8 percent among HWOE and 10 percent at the National level. It is seen that the maximum percentage of households that fall into the Below Poverty Line (BPL) has been found higher among the HWE compared to HWOE and the National level. It is also observed that the percentage of households affected by impoverishment due to OOPHE has been increasing with the number of elderly persons increasing.
[Please Insert Fig. 1 here]
Impoverishment effects of OOPS across Indian states
Table 1 presents the prevalence of households affected by impoverishment at the state level. The result shows that a higher percentage of households with elderly in most Indian states are impoverished due to higher OOPE than the households without elderly. There is a significant gap observed among the states regarding the pattern of impoverishment. For instance, states namely Chhattisgarh, Madhya Pradesh, Nagaland, Uttarakhand, Gujarat, Andhra Pradesh, Andaman & Nicobar Island, Dadra & Nagar Haveli, Daman & Diu, Punjab, and Kerala are found that households with elderly were having more impoverished. On the contrary, other states are showing the opposite result that the impoverishment effect is higher among the HWE than the HWOE. But at the national level, the impoverishment effects of OOPS is higher among household with the elderly than a household without the elderly.
Table 1
Impoverishment effects of OOPS across Indian states & UTs based on ETL (In %)
States
|
Percentage of households
|
With elderly
|
Without elderly
|
Total
|
Low ETL group
|
10.8
|
10.6
|
10.7
|
Bihar
|
10.5
|
12.6
|
12.3
|
Jharkhand
|
9.4
|
9.7
|
9.6
|
Uttar Pradesh
|
11.6
|
11.7
|
11.7
|
Rajasthan
|
6.5
|
6.5
|
6.5
|
Meghalaya
|
1.7
|
0.6
|
0.7
|
Assam
|
9.4
|
7.5
|
7.9
|
Chhattisgarh
|
16.3
|
11.6
|
12.5
|
Madhya Pradesh
|
11.0
|
8.2
|
8.9
|
Orissa
|
13.2
|
15.1
|
14.5
|
Lower-middle ETL group
|
6.9
|
4.3
|
5.0
|
Arunachal Pradesh
|
4.4
|
4.2
|
4.2
|
Mizoram
|
2.4
|
1.5
|
1.7
|
Nagaland
|
5.8
|
2.8
|
3.2
|
Uttarakhand
|
9.3
|
7.4
|
8.0
|
Gujarat
|
6.9
|
3.4
|
4.3
|
Tripura
|
4.6
|
6.9
|
6.3
|
Sikkim
|
4.0
|
5.3
|
5.0
|
Manipur
|
8.7
|
8.8
|
8.7
|
Higher-middle ETL group
|
10.7
|
10.0
|
10.2
|
Haryana
|
4.7
|
4.2
|
4.3
|
Delhi
|
3.4
|
4.2
|
3.9
|
Telangana
|
8.1
|
12.1
|
11.4
|
Andhra Pradesh
|
15.3
|
12.6
|
13.4
|
Jammu & Kashmir
|
4.2
|
5.5
|
5.1
|
Karnataka
|
9.9
|
8.7
|
9.1
|
West Bengal
|
10.1
|
10.0
|
10.0
|
Maharashtra
|
12.9
|
11.6
|
12.1
|
Lakshadweep
|
5.8
|
5.1
|
5.4
|
Pondicherry
|
7.1
|
9.5
|
8.7
|
Andaman & Nicobar Isl
|
9.0
|
6.0
|
6.8
|
Dadra & Nagar Haveli
|
6.6
|
2.6
|
3.1
|
Daman & Diu
|
9.0
|
4.3
|
4.9
|
Chandigarh
|
5.2
|
6.4
|
6.0
|
High ETL group
|
10.7
|
9.2
|
9.8
|
Himachal Pradesh
|
9.5
|
9.9
|
9.7
|
Punjab
|
11.5
|
7.2
|
8.7
|
Tamil Nadu
|
9.5
|
9.5
|
9.5
|
Goa
|
4.0
|
13.4
|
8.6
|
Kerala
|
11.5
|
9.5
|
10.5
|
India
|
10.5
|
9.8
|
10.0
|
Source: Computed from NSS 75th round data file |
Our results have also estimated the impoverishment effects of OOPS of Individual states and India. States that belong to a higher percentage of impoverished households with elderly than the national figure (10.5%) are Andhra Pradesh (15.3%), Orissa (13.2%), Maharashtra (12.9%), Uttar Pradesh (11.6%), Kerala (11.5%), Punjab (11.5%), Madhya Pradesh (11.0%) and Bihar (10.5%). Similarly, in the household without elderly, the impoverishment effect is higher of those states than the national level (9.8%) is Goa (13.4%), Bihar and Andhra Pradesh (12.6%), Telangana (12.1%), Rajasthan, Maharashtra, and Chhattisgarh (11.6%), and West Bengal (10.0%). But our results have found fewer variations in the rate of impoverishment among the ETL groups in India.
Impoverishment effects of OOPS across geographical regions in India
Regional variation has been observed between the mean out-of-pocket health expenditure and the impoverishment effect in the geographical regions of India (See Fig. 2–4). Figure 2–4 shows a significant variation in expenditure on health as a ratio of OOPS across regions in India. We have found a gender gap in health spending among households with the elderly. For instance, any regions spent regions namely Coastal Northern Tamil Nadu, Northern Bihar, Mahanadi Chhattisgarh, Central Himachal Pradesh, Coastal north Andhra Pradesh, Northern Madhya Pradesh, Lakshadweep, Coastal Tamil Nadu, Coastal Tamil Nadu, and Ranchi Plateau are shown a higher OOPE on male elderly than the female elderly.
Finally, Fig. 2–4 shows the regional variation of impoverishment effect due to OOPE. In the previous analysis, we have figured out that the impoverishment effect is more among the households with elderly than households without elderly (See Table 2). But here, we have found significant regional variations in impoverishment rate due to OOPS (See Figure A3a)., For instance, regions namely Ladakh of J&K (34.8%), South-eastern Rajasthan (21.8%), Northern Chhattisgarh (21.0%), Inland Eastern of Maharashtra (19.5%), Inland Northern of Maharashtra (18.8), Malwa of Madhya Pradesh (18.6%), Coastal Odisha (17.7%), south and inland south of Andhra Pradesh (16.7%), Eastern Maharashtra (16.3%), Mahanadi of Chhattisgarh (15.9%) are showing a significant percentage of households impoverished due to OOPHE. On the contrary, regional variations are also observed in the households without the elderly (See Table 2 and Fig. 3. b). The list of regions without elderly households which shows a higher rate of impoverishment due to OOPHE includes Inland North-Eastern Telangana (19.8%), Inland Eastern of Maharashtra (19.4%), Coastal Odisha (18.5), Himalayan West Bengal (18.0%), Malwa of Madhya Pradesh (16.4%), Northern Chhattisgarh (15.8%), Northern and southern Bihar (14.6%), Coastal Tamil Nadu (14.1%), Northern upper Ganga Plains of UP (13.6%).
Table 2
Impoverishment effects of OOPS in Top ten geographical regions of India (In %)
Rank
|
Regions
|
Impoverishment effect
|
Rank
|
Regions
|
Impoverishment effect
|
Households with elderly
|
Households without elderly
|
1
|
Ladakh of J&K
|
34.8
|
1
|
Inland North Eastern Telangana
|
19.8
|
2
|
South-eastern Rajasthan
|
21.8
|
2
|
Inland Eastern of Maharashtra
|
19.4
|
3
|
Northern Chhattisgarh
|
21.0
|
3
|
Coastal Odisha
|
18.5
|
4
|
Inland Eastern of Maharashtra
|
19.5
|
4
|
Himalayan West Bengal
|
18.0
|
5
|
Inland Northern of Maharashtra
|
18.8
|
5
|
Malwa of Madhya Pradesh
|
16.4
|
6
|
Malwa of Madhya Pradesh
|
18.6
|
6
|
Northern Chhattisgarh
|
15.8
|
7
|
Coastal Odisha
|
17.7
|
7
|
Bihar
|
14.6
|
8
|
south and inland south of Andhra Pradesh
|
16.7
|
8
|
Coastal Tamil Nadu
|
14.2
|
9
|
Eastern Maharashtra
|
16.3
|
9
|
Coastal north Andhra Pradesh
|
14.1
|
10
|
Mahanadi of Chhattisgarh
|
15.9
|
10
|
Northern upper Ganga Plains of UP
|
13.6
|
[Please Insert Fig. 2–4 here]
Impoverishment effects of OOPS across household characteristics (socio-economic-demographic)
The prevalence of impoverishment effect at the socio-economic and demographic characteristics has been presented in Table 3. The impoverishment effect is found to be higher among HWE as compared to HWOE across all demographic and socio-economic characteristics except the households with regular wages, from Schedule Caste (SC) category, sale physical assets to meet health care, use public hospital and Charitable/Trust/NGO. With the consideration of HE, impoverishment effect has been found higher among the households belonging to a small size (16.6%), another type (14.0%), rural area (11.0%), Christian religion (11.8%), other social categories (11.0%), lowest wealth quintile (14.6%), practising selling physical assets (43.3%), preferring private hospital (16.1%), no insurance coverage (10.7%), not having at any under-five children (13.2%) and having at least one non-communicable disease suffered member (16.6%) and Central geographic region (12.2%). Additionally, we have found a similar pattern of impoverishment in the context of HWOE, except households belonging to the Hindu religion (10.5%), lower wealth quintile (12.5%), and east geographic region (11.7%).
Table 3
Impoverishment effects of OOPS across household characteristics (In %)
Household Characteristics
|
Description
|
Percentage of Households
|
With Elderly
|
Without Elderly
|
Total
|
Household Size
|
1–3
|
16.6
|
13.8
|
14.5
|
4–6
|
11.6
|
9.8
|
10.3
|
7–9
|
6.4
|
5.8
|
6
|
10 and above
|
3.8
|
2.4
|
3.2
|
Household Type
|
Self-Employed
|
10.4
|
9.8
|
10
|
Regular Wage/Salary
|
7.9
|
8.8
|
8.5
|
Labour
|
11.3
|
10.5
|
10.6
|
Other
|
14
|
11
|
12.9
|
Household Head
|
Male
|
10.8
|
9.9
|
10.1
|
Female
|
8.4
|
9.3
|
9.1
|
Place of Residence
|
Rural
|
11
|
10.9
|
10.9
|
Urban
|
9.6
|
7.4
|
8.1
|
Religion
|
Hinduism
|
10.9
|
10.5
|
10.6
|
Islam
|
7.7
|
7.3
|
7.4
|
Christianity
|
11.8
|
7.6
|
9.3
|
Other
|
9.9
|
7.1
|
8.1
|
Social Category
|
ST
|
8.8
|
8
|
8.1
|
SC
|
9.8
|
10
|
9.9
|
OBC
|
10.6
|
10
|
10.2
|
Others
|
11
|
10.1
|
10.4
|
Wealth Quintile
|
Lowest
|
14.6
|
11.3
|
12.2
|
Lower
|
12.2
|
12.5
|
12.4
|
Middle
|
8.4
|
9.6
|
9.3
|
Higher
|
8.7
|
10
|
9.6
|
Highest
|
9.6
|
6.4
|
7.4
|
Major Source of Finance
|
Savings
|
8.9
|
8.5
|
8.7
|
Borrowing
|
23.7
|
21.2
|
22
|
Sale Physical Assets
|
43.3
|
48.1
|
46.2
|
Contribution from Other
|
12.5
|
11
|
11.5
|
Place of Hospitalized
|
Public
|
4
|
4.7
|
4.5
|
Charitable/Trust/NGO
|
8.8
|
12.5
|
11
|
Private
|
16.1
|
16.4
|
16.3
|
Health Insurance Coverage
|
Household with Elderly Coverage
|
9.9
|
-
|
9.9
|
Household without Elderly Coverage
|
-
|
9.3
|
9.3
|
Not Coverage
|
10.7
|
10
|
10.2
|
Household with at least 1 Under 5 Children
|
No
|
13.2
|
13.4
|
13.3
|
Yes
|
7.2
|
7.2
|
7.2
|
Household with at least 1 NCD member
|
No
|
7.1
|
7.6
|
7.5
|
Yes
|
16.6
|
15.7
|
16.1
|
Region of India
|
North
|
9
|
9.1
|
9.1
|
East
|
10.8
|
11.7
|
11.5
|
North-East
|
7.5
|
6.4
|
6.6
|
Central
|
12.2
|
9.1
|
9.8
|
West
|
11.3
|
8.8
|
9.7
|
South
|
11.2
|
10.3
|
10.6
|
India
|
10.5
|
9.8
|
10
|
Source: Computed from NSS 75th round data file |
Determinants of Impoverishment among housholds with elderly and without elderly
Table 4 presents the possible factors for the impoverishment of households due to high OOPS using Indian samples. We have estimated two models. First, estimation done with unadjusted effect, while in the second, we have used with the adjusted impact of impoverishment on elderly composition in households. The first model shows that the probability of the impoverishment effect has increased with the increasing number of older people. For instance, households with two elderly people are 11% more likely to get affected by impoverishment than the HWOE. Our result is statistically significant, but in this model, we have not controlled other socio-economic and demographic parameters.
Table 4
Logistic Regressions: Odds of Impoverishment effect of OOPS
Variables
|
AOR
|
95% CI
|
Lower Bound
|
Upper Bound
|
Number of Elderly within Households (0)
|
|
1
|
0.99***
|
0.99
|
0.99
|
2
|
1.06***
|
1.06
|
1.07
|
3 and above
|
2.05***
|
2.02
|
2.08
|
Head of the Household (female)
|
Male
|
1.27***
|
1.27
|
1.28
|
Household Size (10 and above) ®
|
1–3
|
8.39***
|
8.32
|
8.46
|
4–6
|
4.68***
|
4.64
|
4.72
|
7–9
|
2.21***
|
2.19
|
2.23
|
Household Type (Self-Employed) ®
|
Regular Wage/Salary
|
1.00
|
1.00
|
1.00
|
Labour
|
1.05*
|
1.04
|
1.05
|
Other
|
1.02*
|
1.01
|
1.02
|
Place of Residence (Urban) ®
|
Rural
|
2.13***
|
2.12
|
2.13
|
Wealth Quintile (Highest) ®
|
Lowest
|
4.37***
|
4.36
|
4.39
|
Lower
|
3.84***
|
3.82
|
3.85
|
Middle
|
2.17***
|
2.16
|
2.18
|
Higher
|
1.82***
|
1.82
|
1.83
|
Religion (Hindu) ®
|
Islam
|
0.72***
|
0.72
|
0.72
|
Christianity
|
0.84*
|
0.84
|
0.85
|
Other
|
0.77*
|
0.76
|
0.77
|
Social Category (ST) ®
|
SC
|
0.95***
|
0.94
|
0.95
|
OBC
|
0.99***
|
0.98
|
0.99
|
others
|
1.15***
|
1.15
|
1.16
|
Major Source of Finance (Savings) ®
|
Borrowing
|
2.05***
|
2.05
|
2.06
|
Sale Physical Assets
|
5.65*
|
5.55
|
5.74
|
Contribution from other
|
1.36
|
1.35
|
1.36
|
Place of Hospitalization (Public) ®
|
Charitable/Trust/NGO
|
3.15**
|
3.13
|
3.17
|
Private
|
5.19**
|
5.17
|
5.2
|
Health Insurance Coverage (Govt Sponsored)
|
Other Than Govt
|
0.89***
|
0.88
|
0.89
|
Not Covered
|
1.11**
|
1.1
|
1.11
|
Household with at least 1 U5Children (No)®
|
Yes
|
0.85*
|
0.84
|
0.86
|
Household with at least 1 NCD member (No)
|
Yes
|
1.94***
|
1.93
|
1.94
|
Sate based on ETL (Lower ETL) ®
|
LMETL
|
0.63***
|
0.62
|
0.63
|
HMETL
|
0.99***
|
0.98
|
0.99
|
HETL
|
1.16***
|
1.16
|
1.17
|
Region (North) ®
|
|
|
|
East
|
1.26***
|
1.25
|
1.26
|
North-East
|
1.39***
|
1.38
|
1.4
|
Central
|
1.02***
|
1.01
|
1.02
|
West
|
0.98***
|
0.98
|
0.99
|
South
|
0.92***
|
0.92
|
0.93
|
Note: ®: reference category; ***p < 0.001, **p < 0.01, *p < 0.05; Sample Size (N) = 80105; CI: Confidence Interval, AOR: Adjusted odds ratio. |
However, in the second model, we have found that the households with two elderly people are 6% more likely to get affected by impoverishment than the HWOE by controlling other factors. Table 4 also shows another significant result that the households which have male elderly members are more (OR: 1.17; P > z = 0.00) likely to be affected by impoverishment than the female elderly households. Additionally, we have found a better result in the case of place of resident, wealth quintile, social groups, assets, and geographic regions. It shows that households from rural (OR: 2.13; P > z = 0.00), lowest wealth quintile households (OR: 4.37; P > z = 0.00), other social categories (OR: 1.15; P > z = 0.00), selling physical assets (OR: 5.65; P > z = 0.00), preferring private hospital (OR: 5.19; P > z = 0.00), not insurance coverage (OR: 1.11; P > z = 0.00), having NCD suffered member (OR: 1.94; P > z = 0.00), high ETL group (OR: 1.16; P > z = 0.00), and North-East (OR: 1.39; P > z = 0.00), East (OR: 1.26; P > z = 0.00) and Central regions (OR: 1.02; P > z = 0.00) are more likely to be affected by impoverishment than the reference categories.