Background:
Maternal Neonatal and Child Health (MNCH) is among the most prioritized areas for health across the countries. MNCH indicators such as Maternal Mortality Rate and Infant Mortality rate not just reflect the health status of the population in a nation but also reflect its level of development. MNCH indicators are among the several indicators which enabled creation of a new category within the Indian states under the terminology of Empowered Action Group (EAG) states. EAG states, consisting of Bihar, Uttar Pradesh, Jharkhand, Chhattisgarh, Uttarakhand, Odisha, Rajasthan and Madhya Pradesh are provided additional health system investments and support so as to enable them perform in par with the other developed states of the country.
The Indian health eco-system in the last three decades witnessed massive investments and health system restructuring greatly influencing its MNCH indicators on a positive note. However, within the Indian scenario, there is a stark difference in the performance of the states in the dimensions of MNCH. The southern states with comparatively better health systems fall into the positive side with Empowered Action Group states often faring at the lower level. Moreover, MNCH aliments are among the leading causes of mortality and morbidity among the EAG states(1), reflecting the disease burden needing addressal.
One of the most important indicators targeted with respect to improving MNCH is Maternal mortality rate. Adequate Nutrition, Complete Antenatal Care, Institutional Delivery and Postnatal care are the chief components reported as means to reduce MMR. Institutional Delivery in specific is essential to ensure that the delivery is conducted by the trained health worker, greatly reducing the odds of maternal and infant mortality(2).
The out of pocket expenditure, defined as the cost incurred by the patients/households of patients for accessing health care is an important dimension in public health planning and evaluation(3,4). It reflects on the affordability component of the particular health service delivery. Earlier literature on MNCH from India chiefly focussed on other components of the health service delivery, with a little attention paid to health care financing, in particular OOPE. However, given the increasing prominence to improve affordability of health care, and even shift towards providing cashless health services through programmes like Ayushmaan Bharat Yojana, it becomes important to quantify the out of pocket expenditure (OOPE). In this regard 4 EAG states (i.e., Bihar, Uttar Pradesh, Rajasthan and Chhattisgarh) were chosen to analyse the OOPE for institutional deliveries. These states were chosen as top two (Chhattisgarh and Rajasthan) and bottom two (Uttar Pradesh and Bihar) states among EAG states (excluding Uttarakhand since it was an outlier with annual per capita income of over 1.3 lakh) depending on their per-capita income in 2014-2015.
In this regard, the current study primarily is based on two objectives
- To estimate out of pocket expenditure for institutional delivery in EAG states of Uttar Pradesh, Bihar, Chhattisgarh and Rajasthan.
- To assess the variation in the out of pocket expenditure with respect to Individual and Socio-economic characteristics.
Methodology:
To address the objectives, the study relied on the secondary data analysis of existing National Family Health Survey 4 data. The data collection for the survey occurred in the year 2015-16.
Data cleaning:
The micro-data was obtained from the data repository of IIPS, available at https://www.dhsprogram.com/data/dataset_admin. The data was downloaded following the process outlined by the data repository managing authority. The data was downloaded in the format which could be analysed by International Business Machine’s Statistical Package for Social Sciences, Version 20. The same software was used for further data cleaning and data analysis processes.
The researcher’s followed the standard operating procedures for data assessment for secondary data analysis as laid out by Johnston (2017)(5). Prior to the data analysis the state specific microdata was extracted and cleaned. The state specific data was extracted in to separate SPSS data sheets using the state specific codes provided in the variable state (V024). Value 5 was used to extract Bihar, 7 for Chhattisgarh, 33 for Uttar Pradesh and 29 for Rajasthan. The extracted data sheets were cleaned separately. No missing variables (identified by running frequencies for variables under study) were found in the individual state specific data sheets. However, within the data sheet the chief outcome variable out of pocket expenditure for delivery also included “don’t know” which was coded as 99998. Given that the variable was continuous in nature, and considering the impact of this value on the overall outcome of the study, it was decided to exclude/eliminate the individuals who reported don’t know, since it was identified that it will not yield in any meaningful conclusions. Subsequently, 326; 255; 756; 823 cases were deleted from Bihar, Chhattisgarh, Uttar Pradesh and Rajasthan respectively, leaving the final sample of 1254; 2410; 6337; and 5493 cases to be analysed per state.
Data Analysis Plan:
The chief outcome variable for the study was the Out of pocket expenditure incurred by the households towards the delivery. With respect to the objectives of the study, the data analysis was planned to be conducted using univariate statistics of Mean, Median and Standard deviation. Additionally, the confidence intervals for 95% confidence level, and percentiles were calculated. Dummy tables were constructed reflecting the variation of OOPE related parameters across the four states. Additionally, in order to address the second objective, the univariate parameters of OOPE were computed and compared against various socio-economic factors.
Results:
The number of cases included for the analysis were highest in Uttar Pradesh (6337) followed by Rajasthan (5493), Chhattisgarh (2410) and Bihar (1254) respectively. With respect to the mean out of pocket expenditure per institutional delivery Uttar Pradesh Ranked highest with INR 7616.77/- and Chhattisgarh ranking the lowest with INR 3917.80/-. The detailed insights with respect to the out of pocket expenditure across the states is given in Table 1.
| Table 1: Table providing an overview of the OOPE for institutional delivery in each of the states. |
| State |
N |
Mean |
Median |
Standard Deviation |
95% Confidence Level |
Percentile |
Lower |
Upper |
5 |
95 |
Bihar |
1254 |
5942 |
2000 |
10782 |
5384 |
6576.7 |
0.00 |
30000 |
Chattisgarh |
2410 |
3917.80 |
0.00 |
10026.81 |
3513.4 |
4294.25 |
0.00 |
30000 |
Uttar Pradesh |
6337 |
7616.77 |
2000 |
12446.77 |
7310.26 |
7923.28 |
0.00 |
35000 |
Rajasthan |
5493 |
3996.25 |
600 |
8762.73 |
3764.51 |
4227.99 |
0 |
20000 |
The state specific variations in the OOPE with respect to socio-demographic variables was assessed. The variables with respect to which the state specific variations were assessed were place of residence, religion, education level of the women, wealth index and insurance coverage.
 |
 |
Figure 1: Rural-Urban differences in out of pocket expenditure across the states |
Figure 2: Differences in OOPE with respect to educational status. |
 |
 |
Figure 3: OOPE differentials with respect to religion across the states. |
Figure 4: OOPE differentials with respect to wealth index across the states. |
 |
< Figure 5: OOPE variations with respect to Insurance coverage across states |
The out of pocket expenditure is consistently higher in the state of Uttar Pradesh for majority of the variables except that of wealth index where the state of Bihar reported higher category specific OOPE. The state of Chhattisgarh reported the lowest category specific out of pocket expenditures across all the five variables of place of residence, educational level, religion, wealth index and Insurance coverage. The specific variances in the measures of OOPE across the states are provided in Annexure 1 (given as the supplementary file).
Discussion:
The empowered action group (EAG) states are the sates identified by the Government of India considering their socio-economic backwardness. The eight EAG states account for over 46% percentage of India’s total population and contribute to over 60% of the population living in poverty i.e., living below the poverty line(6). Within EAG states Bihar, Uttar Pradesh, Rajasthan and Chhattisgarh account for majority of the population distribution. Analysing the out of pocket expenditure incurred for deliveries in these four states was the chief objective of the study.
The study identified that the out of pocket expenditure for institutional delivery is highest in the state of Uttar Pradesh, followed by Bihar, Chhattisgarh and Rajasthan. This number is in line with the findings of Kumar and Singh (2016), where they identified that access to public health facilities is poorest in Uttar Pradesh and Bihar standing at an average of 15% and 17% respectively. The same number was 38% and 42.5% for Chhattisgarh and Rajasthan respectively (7). Provision of health services through public health facilities is the key to reduce OOPE and improve affordability of health care(8). The lack of availability of public health facilities increase the patient’s dependency on private health care providers, which will further increase the out of pocket expenditures. The dependency on private health care providers was observed at 83.5% in UP, 83% in Bihar and 60% each in Rajasthan and Chhattisgarh(7). This explains the trends shown by the states with respect to the distribution of out of pocket expenditure.
With respect to the factor specific variances, education and wealth index were the most significant factors resulting in OOPE fluctuations. While the highest extremes were noted in religion, the number of cases (as informed in Table 4 provided in annexure 1) report that these numbers could be because of the outliers.
The Individuals having highest education and those belonging to richest economic strata were paying the higher OOPE twice to that of the state specific mean OOPE. Moreover, the profound difference in the OOPE paid among the extremes (i.e., no-education and higher education; poorest wealth category and richest wealth category), reflect that those in the higher extremes are more likely to access high-cost health care. Studies across India, support the argument these observations, since individuals with higher education and higher wealth tend to use more private health care, incurring higher OOPE(9, 10). However, the higher OOPE payments by the individuals belonging to poorest in the wealth index poses a significant challenge to the public health system. It was observed that among all the states, the state of Chhattisgarh and Rajasthan recorded lower OOPE across wealth and education strata. Specifically, the lowest was observed in the state of Chhattisgarh with individuals belonging to poorer wealth index spending an average of INR 863.58/- whereas in the state of Uttar Pradesh the same was INR 2895.50/-, approximately 2000 rupees higher. The lesser costs in the states like Chhattisgarh could be attributed to the availability of the public health facilities, state specific priorities assigned and also the usage of innovative practices(7).
Overall, it could be said that within the EAG states the variations in the Out of Pocket expenditures for institutional delivery is visible. Uttar Pradesh and Bihar, the EAG states with lesser per capita income than their counter parts (standing at INR 43861/- and INR 31380/- as per 2014-15 estimates) pay the highest among the four. This reflects the need for the solutions for reducing out of pocket expenditure which might push the families in to financial catastrophe. The programmes such as Government of India’s Ayushman Bharat Yojana, promise cashless health care with a potential to zero-down the OOPE. However, a close look at figure 5, reflects that even with insurance coverage, the OOPE might not be reduced (particularly considering that OOPE among Insured in UP is higher than average OOPE in other states) unless the health services are made universally available and accessible. Studies on various insurance schemes across the country reflect that while health insurance could potentially reduce OOPE, its utility is minimal when the availability of health services is compromised (11, 12).
Conclusion:
Out of pocket health care expenditure for institutional delivery was high in the states with lesser availability and accessibility of health services. This reflects the importance of health system strengthening in improving public health care access and thereby reducing OOPE. EAG states face the dual challenge with respect to the physical availability of the public health services and utilization of the services which in-turn require them to be accessible, and of acceptable quality. These challenges when addressed along with specific interventions are key for reducing OOPE.
Conflict of Interest: None
References:
- Dandona L, Dandona R, Kumar GA, Shukla D, Paul VK, Balakrishnan K, et al. Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study. The Lancet. 2017;390(10111):2437-60.
- Donnay F. Maternal survival in developing countries: what has been done, what can be achieved in the next decade. International Journal of Gynecology & Obstetrics. 2000;70(1):89-97.
- Falkingham J. Poverty, out-of-pocket payments and access to health care: evidence from Tajikistan. Social Science & Medicine. 2004;58(2):247-58.
- Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. The Lancet. 2003;362(9378):111-7.
- Johnston MP. Secondary data analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries. 2017;3(3):619-26.
- Census India. Census of India 2011: provisional population totals-India data sheet. Office of the Registrar General Census Commissioner, India Indian Census Bureau. 2011.
- Kumar V, Singh P. Access to healthcare among the Empowered Action Group (EAG) states of India: Current status and impeding factors. The National Medical Journal of India. 2016;29(5):267-73.
- Balarajan Y, Selvaraj S, Subramanian S. Health care and equity in India. The Lancet. 2011;377(9764):505-15.
- Skordis-Worrall J, Pace N, Bapat U, Das S, More NS, Joshi W, et al. Maternal and neonatal health expenditure in Mumbai slums (India): a cross sectional study. BMC Public Health. 2011;11(1):150.
- Mohanty SK, Srivastava A. Out-of-pocket expenditure on institutional delivery in India. Health Policy and Planning. 2012;28(3):247-62.
- Jütting JP. Do community-based health insurance schemes improve poor people’s access to health care? Evidence from rural Senegal. World Development. 2004;32(2):273-88.
- Moreno-Serra R, Smith PC. Does progress towards universal health coverage improve population health? The Lancet. 2012;380(9845):917-23.
Annexure 1
| Table 2: Variances in OOPE in Bihar with respect to selected variables |
| Variable |
N (%) |
Mean |
Median |
Standard Deviation |
95% Confidence Level |
Percentile |
Lower |
Upper |
5 |
95 |
Place of Residence |
Urban |
143 (11.4%) |
8484.8 |
3000 |
11570.70 |
6572 |
10397 |
0 |
34400 |
Rural |
1103 (88.6%) |
5575.2 |
1500 |
10649.5 |
4946 |
6204.4 |
0 |
25000 |
Highest Education level |
No education |
555 (44.25%) |
3782.1 |
1000 |
8296.32 |
3090.41 |
4473.87 |
0 |
19200 |
Primary |
150 (11.96%) |
3767.33 |
1450 |
7270.30 |
2594.34 |
4940.33 |
0 |
19500 |
Secondary |
450 (35.88%) |
8323.54 |
3000 |
13111.27 |
7108.86 |
9538.21 |
0 |
32000 |
Higher |
91 (7.25%) |
10472.53 |
5000 |
12540 |
7860.94 |
13084.11 |
0 |
37000 |
Religion |
Hindu |
1072 (85.48%) |
5963.56 |
1600 |
10882.43 |
5311.37 |
6615.74 |
0 |
30000 |
Muslim |
174 (13.71%) |
5573.85 |
2000 |
10257.96 |
4038.94 |
7108.76 |
0 |
25000 |
Wealth index |
Poorest |
582 (46.41%) |
3938.38 |
1000 |
9073.34 |
3199.2 |
4677.56 |
0 |
20000 |
Poorer |
310 (24.72%) |
4773.61 |
1500 |
8313.91 |
3844.48 |
5702.74 |
0 |
25000 |
Middle |
182 (14.51%) |
8072.70 |
3000 |
12666.74 |
6220 |
9925.34 |
0 |
31700 |
Richer |
117 (9.33%) |
11279 |
5000 |
15324.64 |
8473.32 |
14085.48 |
0 |
50000 |
Richest |
55 (4.38%) |
14580 |
10000 |
12550.22 |
11187.20 |
17972.80 |
0 |
40000 |
Insurance Scheme |
No |
1133 (90.35%) |
5856 |
2000 |
10553.10 |
5240.91 |
6471.20 |
0 |
27300 |
Yes |
113 (9.65%) |
6441.29 |
1400 |
13010.51 |
4016.24 |
8866.34 |
0 |
31500 |
| Table 3: Variances in OOPE in Chhattisgarh with respect to selected variables |
| Variable |
N (%) |
Mean |
Median |
Standard Deviation |
95% Confidence Level |
Percentile |
Lower |
Upper |
5 |
95 |
Place of Residence |
Urban |
616 (25.56%) |
7613.93 |
950 |
13368.71 |
6556.13 |
8671.73 |
0.00 |
35000 |
Rural |
1794 (74.44%) |
2648.66 |
.00 |
8214.02 |
2268.31 |
3029.02 |
0.00 |
20000 |
Highest Education level |
No education |
528 (21.90%) |
1094.69 |
0.00 |
5082.23 |
660.19 |
1529.18 |
0 |
3550 |
Primary |
452 (18.75%) |
2474.67 |
0.00 |
7654.94 |
1767.07 |
3182.27 |
0 |
20000 |
Secondary |
1197 (49.66%) |
4222.21 |
0.00 |
10594 |
3621.45 |
4822.97 |
0 |
28000 |
Higher |
233 (9.66%) |
11550.86 |
6000 |
14546 |
9673.34 |
13428.38 |
0 |
40000 |
Religion |
Hindu |
2300 (95.43%) |
3805.7 |
0.00 |
9978.542 |
3397.67 |
4213.71 |
0 |
25000 |
Muslim |
56 (2.32%) |
8810.71 |
1000 |
12454.29 |
5475.43 |
12146 |
0 |
35750 |
Christian |
45 (1.86%) |
1342.22 |
0.00 |
3339.19 |
339.02 |
2345.43 |
0 |
7000 |
Jain |
6 (0.24%) |
15500 |
13000 |
7791.02 |
7323.83 |
23676.17 |
7000 |
NA |
Wealth index |
Poorest |
795 (32.98%) |
863.58 |
0.00 |
3303.04 |
633.63 |
1093.53 |
0 |
4000 |
Poorer |
562 (23.31%) |
2323.50 |
0.00 |
8121.55 |
1650.6 |
2929.4 |
0 |
15850 |
Middle |
402 (16.68%) |
4305.55 |
0.00 |
10975.06 |
3229.44 |
5381.65 |
0 |
30000 |
Richer |
310 (12.86%) |
5353.55 |
0.00 |
11534.09 |
4064.54 |
6642.55 |
0 |
30000 |
Richest |
341 (14.14%) |
11903.52 |
6000 |
14934.00 |
10312.79 |
13494.25 |
0 |
40000 |
Insurance Scheme |
No |
1541 (63.94%) |
3442.37 |
0.00 |
9599.12 |
2962.72 |
3922.01 |
0 |
25000 |
Yes |
869 (36.06%) |
4760.87 |
200 |
10697.29 |
4048.64 |
5473.10 |
0 |
30000 |
| Table 4: Variances in OOPE in Uttar Pradesh with respect to selected variables |
| Variable |
N (%) |
Mean |
Median |
Standard Deviation |
95% Confidence Level |
Percentile |
Lower |
Upper |
5 |
95 |
Place of Residence |
Urban |
1872 (29.54%) |
11205.43 |
6000 |
14163.02 |
10563.43 |
11847.42 |
0 |
40000 |
Rural |
4455 (70.46%) |
6108.19 |
1200 |
11321.61 |
5775.65 |
6440.74 |
0 |
30000 |
Highest Education level |
No education |
2074 (32.72%) |
5117.71 |
1000 |
10011.39 |
4746.60 |
5608.82 |
0 |
25000 |
Primary |
826 (13.03%) |
5573.84 |
1500 |
9636.22 |
4195.73 |
6231.96 |
0 |
29650 |
Secondary |
2460 (37.96%) |
8254.42 |
2150 |
12979.00 |
7741.28 |
8767.56 |
0 |
35000 |
Higher |
967 (15.25%) |
12968.80 |
8000 |
15675.83 |
11978.80 |
13957.31 |
0 |
45000 |
Religion |
Hindu |
4923 (77.68%) |
7319.74 |
1600 |
12608.94 |
6967.44 |
7672.05 |
0 |
32800 |
Muslim |
1362 (21.49%) |
8428.35 |
4000 |
11697.79 |
7806.55 |
9050.15 |
0 |
35000 |
Christian |
4 (0.06%) |
14000 |
15500 |
10984.84 |
-3479.33 |
31479.33 |
0 |
NA |
Sikh |
30 (0.47%) |
16699.93 |
15000 |
11623.14 |
12359.78 |
21040.09 |
0 |
37250 |
Jain |
6 (0.09%) |
19508.33 |
16000 |
22295.973 |
-3889.86 |
42906.52 |
0 |
NA |
Wealth index |
Poorest |
1368 (21.58%) |
2895.50 |
900 |
6872.92 |
2530.97 |
3260.03 |
0 |
15000 |
Poorer |
1287 (20.30%) |
4673.03 |
1000 |
9566.04 |
4149.91 |
5196.15 |
0 |
25000 |
Middle |
1223 (19.29%) |
6834.12 |
2000 |
11664.47 |
6179.74 |
7488.50 |
0 |
30000 |
Richer |
1105 (17.43%) |
9746.61 |
5000 |
13551.65 |
8946.71 |
10546.51 |
0 |
40000 |
Richest |
1344 (21.20%) |
14200.28 |
10000 |
15521.15 |
13369.74 |
15030.83 |
0 |
45750 |
Insurance Scheme |
No |
6187 (97.63%) |
11596.54 |
5000 |
16540.31 |
8832.62 |
14360.45 |
0 |
32000 |
Yes |
140 (2.37%) |
7526.27 |
2000 |
12328.14 |
7219.02 |
7833.52 |
0 |
50000 |
| Table 5: Variances in OOPE in Rajasthan with respect to selected variables |
| Variable |
N (%) |
Mean |
Median |
Standard Deviation |
95% Confidence Level |
Percentile |
Lower |
Upper |
5 |
95 |
Place of Residence |
Urban |
1300 (23.66%) |
5980.9 |
1000 |
11052.19 |
5379.44 |
6582.15 |
0.00 |
30000 |
Rural |
4193 (76.34%) |
3380.48 |
500 |
7821.74 |
3143.66 |
3617.30 |
0.00 |
18300 |
Highest Education level |
No education |
2065 (37.59%) |
2523.25 |
500 |
6539.55 |
2241.03 |
2805.47 |
0.00 |
11000 |
Primary |
999 (18.18%) |
3307.06 |
500 |
7231.19 |
2858.10 |
3756.01 |
0.00 |
20000 |
Secondary |
1859 (33.84%) |
4595.57 |
1000 |
9371.92 |
4169.26 |
5021.87 |
0.00 |
25000 |
Higher |
570 (10.37%) |
8582.41 |
2000 |
13296.00 |
7488.56 |
9676.26 |
0.00 |
40000 |
Religion |
Hindu |
4914 (89.45%) |
3984.48 |
500 |
8827.28 |
3737.60 |
4231.35 |
0 |
20000 |
Muslim |
486 (8.84%) |
3401.10 |
800 |
7020.48 |
2777.31 |
4024.90 |
0 |
15000 |
Sikh |
69 (1.25%) |
6673.91 |
1000 |
11127.58 |
4000.80 |
9347.05 |
0 |
35000 |
Jain |
18 (0.32%) |
11444.44 |
7000 |
14463.84 |
4251.75 |
18637.14 |
0 |
NA |
Wealth index |
Poorest |
1051 (19.13%) |
1673.10 |
400 |
4304.69 |
1412.55 |
1933.65 |
0 |
8000 |
Poorer |
1242 (22.61%) |
2738.76 |
500 |
6957.46 |
2351.45 |
3126.07 |
0 |
12850 |
Middle |
1147 (20.88%) |
3395.41 |
500 |
7719.75 |
2948.18 |
3842.64 |
0 |
15000 |
Richer |
1026 (18.67%) |
4438.45 |
1000 |
8993.06 |
3887.52 |
4989.38 |
0 |
25000 |
Richest |
1027 (18.69%) |
8121.75 |
2500 |
12696.70 |
7344.31 |
8899.20 |
0 |
38400 |
Insurance Scheme |
Yes |
609 (11.09%) |
3964.84 |
500 |
9350.99 |
3220.70 |
4709.00 |
0 |
20000 |
No |
4884 (88.91%) |
3999.75 |
600 |
8689.28 |
3756 |
4243.51 |
0 |
20000 |
|
