Is economic inequality in maternal and child healthcare decreasing in India? Trends between 2005–2006, 2015–2016 and 2019–2021

Abstract

Objective

This study examined economic inequality in coverage of selected maternal and child healthcare (MCH) indicators in India and its states over the last 15 years.

Design

The study analysed last three rounds of the National Family Health Survey data, conducted during 2005–2006, 2015–2016 and 2019–2021. Bivariate analyses, ratio of richest to poorest, slope index of inequality (SII) and multivariate binary logistic regression analyses were used to examine the coverage as well as inequalities in the outcome indicators for India and its states and at district level.

Primary outcomes

The outcome variables analysed in the study were full antenatal care, institutional delivery, postnatal care of mothers within 48 hours of delivery, and full immunisation among children.

Participants

Women aged 15–49 who had given a birth in the last 5 years before the surveys were unit of analysis for the maternal healthcare indicators, and children aged 12–23 months were unit of the analysis for childhood immunisation.

Results

Over the last 15 years, coverage of the MCH indicators has increased in India and across socioeconomic segment of the population, and the absolute increase was higher among the worse-off segments than the better-off. This led to decline in the inequality in coverage of all the MCH indicators. For instance, the value of SII for institutional births decreased from 0.76 in 2005–2006 to 0.45 in 2015–2016 and further to 0.37 in 2019–2021. Although inequality has decreased, geographic disparities persist across states and districts.

Conclusion

Though substantial improvement was observed, coverage of MCH indicators increased and the economic inequality declined; certain geographies are still characterised with the low coverage and persistent high inequality. This suggests that adding a spatial perspective to the inequality research and targeted strategies is essential for achieving universal access to reproductive healthcare services by 2030 in India.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This is the first study, which examined the trends in economic inequality in selected maternal and child healthcare indicator at the district level. Hence, this study provides evidence going beyond to the national level, which masks the gap at the subnational level.

• The analysis of large datasets at the district level inequality comparable to its state’s slope index of inequality. This result carves paths to have more in-depth analysis to target intrastate inequalities.

• The findings are based on the cross-sectional data, which may not allow to demonstrate a causal relation between impact of programme efforts on increase in service coverage among women.

• The analysis did not include union territories of the country owing to a very small sample to provide an estimate.

• District-level data are available to the latest two rounds of the National Family Health Survey only, restricting the estimation at the district level situation pre-National Health Mission period, aligned with the national and state-level estimates.

Introduction

The year 2023 marked the midpoint of the global ‘Sustainable Development Goals (SDGs)’ implementation and achievement deadline. Out of the 17 SDGs, one is to ‘ensure healthy lives and promote well-being for all at all ages’ with the target to reduce the global Maternal mortality ratio (MMR) which is estimated as, number of maternal deaths per 100,000 live births, to less than 70 per 100 000 live births by 2030. While the nations are in the run to achieve the targets, the gap in the risk of maternal deaths between developed and developing countries is considered the most significant health divide in the world.¹ According to the United Nations estimates, about 24 million children were born in 2017 in India, and about 35 000 mothers died during childbirth or shortly thereafter, giving an MMR of 145 per 100 000 live births. Many of these deaths are concentrated in rural areas, and most likely not being reported.^{2 3}

In the last two decades, wherein India enjoyed a fair share of economic growth, the human development indicators have taken a toll. In India, maternal and child health indicators remain high compared with South and East Asian countries, and inequities in health concur with the multiple axes of caste, socioeconomic status, gender and geographical differences.⁴ Moreover, recent studies in India suggest that despite substantial increase in utilisation of maternal healthcare services, especially in skilled attendants at birth, the gap across socioeconomic groups exists significantly.⁵ Another study showed that while coverage of antenatal care (ANC) services increased 12% between 1992 and 2006 in India, the increase among the poor was only 0.1%.⁶ Furthermore, in the use of skilled birth attendants, there was 13% increase overall, but this was only 2% among the women of the poorest wealth quintile.⁷ Another study, which calculated Coverage Gap Index (CGI) in reproductive, maternal, newborn and child health (RMNCH) service across Indian states and districts, showed that high variation in CGI exists between states; CGI was 10.5% for Kerala and 55% for Nagaland. Among the wealth quintiles, the poorest had 2.5 times more CGI compared with the richest.⁸

The country has been concerned to bring down its high level of maternal and child mortality rate and has been making investments since decades. In 2005, notably, India launched the National Rural Health Mission (NRHM) with the Janani Suraksha Yojana (JSY) programme in 18 states (including 8 Empowered Action Group states) to provide accessible, affordable and quality healthcare to the rural population, especially to the vulnerable groups with a focus on women and children. Subsequently, it was expanded in all states, and the National Urban Health Mission was launched in 2013. The programme focused on low performing states (such as Uttar Pradesh, Uttarakhand, Bihar, Jharkhand, Madhya Pradesh, Chhattisgarh, Assam, Rajasthan, Odisha and Jammu and Kashmir) with low institutional delivery rates and high performing states with high institutional delivery rates, differently.⁹ Both missions were unified under the National Health Mission (NHM). There was significant progress in maternal and child healthcare (MCH) indicators and reduction in Infant Mortality Rate and Under-five Mortality Rates, which suggest improvement in the post-NHM period. Despite these improvement, benefits of these health programme did not reach equally to all socioeconomic groups and across geography.¹⁰ In the last two decades, MMR has declined from 212 in 2007–2009 to 178 in 2010–2012 and further to 97 in 2018–2020.^{3 11} The decline has happened at different paces in different states. Even with the achieved number of MMR, the overall average masks the socioeconomic differences hidden at states and below state level.^{12 13}

Previous studies on coverage of MCH inequalities were mostly focused on the national level or in few priority states, by comparing states from north and south part of the country.^{5 7 8} Moreover, these studies mostly provide the situation before the launch of NRHM period, and what happened post the NRHM period is less explored. Though previous studies have paved the way to understand the pattern of change at a broader level, none of them have measured inequality at the district level in a comparable manner taking state as a reference for their respective districts. Hence, generating evidence that how the coverage of healthcare services and related economic inequality changed post-NRHM period will help to understand the effectiveness of the programme. The present study, therefore, examines the trends in coverage of critical MCH services in India and 28 states over the last 15 years. The study further examined socioeconomic inequality in coverage of these MCH services across the states and districts. To the best of our knowledge, this is the first study that examined the socioeconomic inequality in coverage of MCH services at the district level. The findings of this study will help programmes and policymakers to prioritise the geographies, going beyond national level, where coverage of the services is low, and inequality is high.

Methods

Data

The study used data from the last three rounds of the National Family Health Survey (NFHS) of India conducted during 2005–2006,¹⁴ 2015–2016¹⁵ and 2019–2021.¹⁶ The NFHS is a multirounds large-scale household survey conducted across the states and union territories of India which covers more than 99% of the population of the country in each round. International Institute for Population Sciences, Mumbai, remained a nodal agency to conduct multirounds of the NFHS with collaborative assistance from several national/international organisations and development partners. The surveys aimed to provide estimates on fertility and family planning, infant and childhood mortality, nutritional status of children and mother, and use of MCH services, at the national and state level. Additionally, the NFHS 2015–2016 and NFHS 2019–2021 also provide estimates of some of these indicators at the district level. The survey adopted multistage sampling designs across rural and urban areas. The NFHS data were collected using a household schedule and eligible women/individual schedules. Details of the sampling design, sample size estimation and response rates are given in reports of various rounds of the NFHS.¹⁷

Measures

Outcome variables

In the present study, we analysed four outcome variables of MCH services, which covers a wide range of healthcare needs and stages—prenatal, childbirth, postnatal and early childhood. The outcome variables are defined as follows:

Full antenatal care(full ANC): is defined as percentage of mothers who received four or more antenatal visits and received at least one tetanus toxoid injection and consumed iron folic acid for a minimum of 100 days during their last pregnancy in the last 5 years before the survey date.

Institutional births: is defined as the percentage of mothers who delivered their babies in any health institution. This indicator is estimated for all births that occurred in the last 5 years before the survey date.

Postnatal care (PNC): is defined as the percentage of mothers who received PNC from a doctor/nurse/female health visitors/auxiliary nurse midwives/other health personnel within 2 days of delivery. This indicator is estimated for the last birth delivered in the last 5 years before the survey date.

Childhood immunisation: is defined as children aged 12–23 months who have received one dose of Bacille Calmette–Guérin vaccine, three doses of polio vaccine, three doses of the combined diphtheria, TT and pertussis vaccine and one dose of measles vaccine recommended by the WHO. Those children who received all these recommended doses of vaccines were considered fully immunised. This indicator is estimated for all children born in the last 5 years before the survey date.

Predictor variable

Household wealth is the key predictor in the study and is used to measure socioeconomic inequality. The household wealth index was used as a proxy for a household’s economic status. The wealth index was computed from economic proxies such as housing quality, household amenities, consumer durables and size of land holding. In all the three rounds of the survey data, the NFHS computed a wealth index using principal component analysis, and the index was divided into five quintiles: poorest, poorer, middle, richer and richest. The wealth index is comparable over the survey years. The index was divided into five quintiles (20% each): poorest, poorer, middle, richer and richest. These wealth indices, calculated at the national level, were used to examine economic inequalities at states and within the states as well.

Statistical analysis

Univariate, bivariate, and multivariate analyses are applied in the study. Univariate analysis is used to examine trends in coverage of the selected outcome variables in India and states. Bivariate analysis is applied to examine the differences in coverage of selected MCH outcomes by household wealth quintiles. Average annual growth in percentage points is calculated to measure the progress in coverage of the MCH services across five wealth groups in India and states across the different NFHS rounds. To measure the socioeconomic inequality in the outcome variables, first, we calculated the ratio of richest to poorest wealth quintiles. However, given that the ratio includes only two extreme groups and ignores other groups, we estimated the slope index of inequality (SII), which is a measure of overall socioeconomic inequalities in health, and established a linear association between socioeconomic status and health in a way that enables valid comparison between-population.¹⁷ The value of SII ranges between –1 and +1, where a positive value indicates concentration of the indicator among the advantaged and negative value indicates the concentration among the disadvantaged population.¹⁸ When the SII takes a value of 0, it indicates perfect equality in coverage of the services. The advantages of using SII is that it considers the patterns of all economic groups and hence preferred over other measures of the inequality.19,21 Multivariate logistic regression analysis is conducted at the state level to measure association of outcome indicators with household wealth quintiles. All the analyses are conducted using STATA V.15. As the NFHS used a multistage sampling design, all the values reported in the study were estimated after applying appropriate weights. The result is organised in the following four sections: trends in coverage of MCH services in India and states, differences in coverage of MCH services across household wealth quintiles, trends in socioeconomic inequality in MCH services in India and states, and trends in the inequality in coverage of the services at the district-level.

Patient and public involvement

We declare no patient and public involvement.

Result

Trends in coverage of MCH services at the national and state level

Coverage of MCH services has increased consistently in India over last 15 years. For instance, coverage of full ANC has increased from 12% in 2005–2006 to 21% in 2015–2016 and further to 31% in 2019–2021 (table 1). Similarly, coverage of PNC has increased from 33% to 65% to 78% and full immunisation increased from 44% to 62% to 76% during 2005–2006 to 2015–2016 to 2019–2021, respectively. This increasing trend is observed across the states in India. For instance, in Bihar (state from north India), coverage of institutional delivery increased from 20% in 2005–2006 to 64% in 2015–2916 and 76% in 2019–2021 (table 2). In Karnataka (state from south India), coverage of PNC increased from 55% in 2005–2006 to 68% in 2015–2016 and further to 88% in 2019–2021 (table 3). In Meghalaya, a northeastern state, full immunisation has increased from 33% to 64%, while in Gujarat, state located in the western part of the country, coverage of full ANC increased from 21% to 49% from 2005–2006 and 2019–2021 respectively.

Table 1. Trends in coverage of maternal and child healthcare services in India, 2005–2021.

	Per cent coverage			Average annual growth
	2005–2006	2015–2016	2019–2021	2005–2016	2015–2021	2005–2021
Full ANC	11.6	21.0	31.2	0.94	2.04	1.31
Institutional births	38.7	78.9	88.6	4.02	1.94	3.33
Postnatal care	33.2	65.1	77.7	3.19	2.52	2.97
Full immunisation	43.6	62.0	76.4	1.84	2.88	2.19

ANCantenatal care

Table 2. Trends in coverage (%) of maternal and child healthcare services across states of India, 2005–2021.

State	Full ANC			Institutional births
	2005–2006	2015–2016	2019–2021	2005–2006	2015–2016	2019–2021
Andhra Pradesh	22.1	43.9	46.8	64.4	91.6	96.5
Arunachal Pradesh	4.9	3.5	14.4	28.5	52.3	79.2
Assam	6.7	18.1	26.9	22.4	70.6	84.1
Bihar	4.2	3.3	7.5	19.9	63.8	76.2
Chhattisgarh	5.6	21.7	29.6	14.3	70.2	85.7
Goa	57.6	63.4	81.2	92.3	96.9	99.7
Gujarat	20.7	30.7	48.7	52.7	88.7	94.3
Haryana	11.9	19.5	34.9	35.7	80.4	94.9
Himachal Pradesh	15.8	36.8	45.3	43.1	76.4	88.2
Jammu & Kashmir	12.7	26.8	23.3	50.2	85.7	92.4
Jharkhand	4.9	8.0	14.9	18.3	61.9	75.8
Karnataka	24.8	32.8	33.9	64.7	94.3	97.0
Kerala	67.3	61.2	68.2	99.3	99.9	99.8
Madhya Pradesh	4.7	11.4	32.9	26.2	80.8	90.7
Maharashtra	14.7	32.4	37.6	64.6	90.3	94.7
Manipur	5.8	33.9	46.0	45.9	69.1	79.9
Meghalaya	4.2	23.5	26.4	29.0	51.4	58.1
Mizoram	11.8	38.3	37.3	59.8	79.8	85.8
Nagaland	0.6	2.4	5.3	11.6	32.8	45.7
Delhi	24.3	39.0	55.6	59.0	84.5	91.8
Odisha	12.4	23.0	49.8	35.6	85.4	92.2
Punjab	11.8	30.7	34.1	51.3	90.5	94.3
Rajasthan	6.3	9.7	21.6	29.6	84.0	94.9
Sikkim	22.4	39.0	34.9	47.2	94.7	94.7
Tamil Nadu	27.5	45.0	70.8	87.8	99.0	99.6
Tripura	7.5	7.6	14.6	46.9	79.9	89.2
Uttar Pradesh	2.7	5.9	11.8	20.6	67.8	83.4
Uttarakhand	12.7	11.5	31.4	32.6	68.7	83.2
West Bengal	9.7	21.8	47.5	42.0	75.2	91.7

ANCantenatal care

Table 3. Trends in coverage (%) of maternal and child healthcare services across states of India, 2005–2021.

State	Postnatal care			Full immunisation
	2005–2006	2015–2016	2019–2021	2005–2006	2015–2016	2019–2021
Andhra Pradesh	58.2	83.0	91.8	46.0	65.3	73.0
Arunachal Pradesh	21.8	33.5	55.8	28.8	38.2	64.9
Assam	14.3	57.0	65.0	31.4	47.1	66.4
Bihar	13.1	44.3	55.7	32.9	61.7	71.0
Chhattisgarh	13.2	64.7	81.0	48.7	76.4	79.7
Goa	84.6	92.8	97.6	78.9	88.4	81.9
Gujarat	49.4	66.0	88.9	45.5	50.4	76.3
Haryana	35.0	69.0	91.1	65.3	62.2	76.9
Himachal Pradesh	35.0	71.9	84.6	74.7	69.5	89.3
Jammu & Kashmir	45.2	77.2	81.3	66.7	75.1	86.2
Jharkhand	14.1	46.0	63.8	34.5	61.9	73.9
Karnataka	54.5	67.7	87.7	55.1	62.6	84.1
Kerala	90.7	92.5	97.5	75.6	82.1	77.8
Madhya Pradesh	22.9	56.5	82.2	40.3	53.6	77.1
Maharashtra	55.2	80.1	84.1	58.8	56.2	73.5
Manipur	46.3	68.8	74.9	47.2	65.8	68.8
Meghalaya	28.2	51.0	53.4	32.9	61.4	63.8
Mizoram	50.1	68.9	78.1	46.5	50.7	72.5
Nagaland	9.6	24.2	39.8	21.0	35.4	57.9
Delhi	51.4	65.1	84.8	63.4	68.8	76.0
Odisha	27.9	75.5	88.8	51.9	78.6	90.5
Punjab	49.8	88.1	85.9	60.1	89.1	76.2
Rajasthan	24.5	64.7	84.3	26.5	54.8	80.4
Sikkim	47.9	77.9	74.8	69.6	83.0	80.6
Tamil Nadu	83.6	88.4	98.2	80.9	69.7	89.2
Tripura	31.2	65.7	73.7	49.7	54.5	69.5
Uttar Pradesh	12.6	55.6	71.5	23.2	51.1	69.6
Uttarakhand	28.2	56.9	77.2	60.0	57.6	80.8
West Bengal	34.2	62.2	69.2	64.3	84.4	87.8

Trends in MCH service coverage across household wealth quintiles

The coverage of MCH services increased across all five wealth quintiles. For instance, between 2005–2006 and 2019–2021, the coverage of institutional delivery increased from 13% to 76% among women of the poorest quintile, 24% to 87% among the poorer quintile, 39% to 92% among middle quintile, 58% to 95% among richer quintile and 84% to 97% among richest quintile (table 4). In the last 15 years, the maximum point increase has occurred among the poorest and poorer quintile in full ANC, PNC and full immunisation, compared with their better-off counterparts. Despite the increase in coverage of the healthcare services among poor women, there still exists differences across the five wealth quintiles. For instance, in 2019–2021, coverage of full ANC was 18% among the poorest women, 25% among poorer women, 33% among middle women, 38% among richer women and 44% among the richest women. The pattern of increase in the utilisation of MCH services across the wealth quintiles as well as the difference between the groups was similar across states of India (online supplemental appendices 1–4).

Table 4. Trends in coverage of maternal and child health indicators by household wealth quintiles in India, 2005–2021.

	Per cent coverage			Average annual growth
	2005–2006	2015–2016	2019–2021	2005–2016	2015–2021	2005–2021
Full ANC
Poorest	2.4	6.8	18.2	0.44	2.28	1.05
Poorer	4.6	14.3	25.1	0.97	2.16	1.37
Middle	9.3	22.6	32.7	1.33	2.02	1.56
Richer	16.2	29.2	38.4	1.30	1.84	1.48
Richest	32.1	38.1	44.1	0.60	1.20	0.80
Institutional births
Poorest	12.7	59.6	76.2	4.69	3.32	4.23
Poorer	23.5	75.2	87.2	5.17	2.40	4.25
Middle	39.3	85.0	92.3	4.57	1.46	3.53
Richer	57.9	90.6	95.4	3.27	0.96	2.50
Richest	83.7	95.3	97.4	1.16	0.42	0.91
Postnatal care
Poorest	8.6	44.7	61.0	3.61	3.26	3.49
Poorer	17.6	58.9	73.8	4.13	2.98	3.75
Middle	31.3	70.4	81.9	3.91	2.30	3.37
Richer	48.5	76.9	86.3	2.84	1.88	2.52
Richest	75.2	81.7	90.6	0.65	1.78	1.03
Full immunisation
Poorest	24.4	52.8	71.0	2.84	3.64	3.11
Poorer	33.3	60.6	75.4	2.73	2.96	2.81
Middle	46.9	64.2	79.6	1.73	3.08	2.18
Richer	55.4	66.9	79.3	1.15	2.48	1.59
Richest	71.0	70.0	78.7	−0.10	1.74	0.51

ANCantenatal care

Economic inequality in MCH services at the national and state levels

Economic inequality in coverage of MCH services has reduced over time in India. For instance, the ratio of richest to poorest in full ANC coverage declined from 13.6 in 2005–2006 to 5.6 in 2015–2016 to 2.4 in 2019–2021 (online supplemental table 1). Such decline was consistent for institutional births, PNC and full immunisation as well. This pattern remained similar when the overall inequality was measured through the SII. For instance, the value of SII for institutional births decreased from 0.76 in 2005–2006 to 0.45 in 2015–2016 and further to 0.37 in 2019–2021.

State-wise results also showed that socioeconomic inequality in coverage of MCH services has declined over time. For instance, the ratio of richest to poorest in coverage of institutional birth has decreased from 8.3 to 1.4 in Bihar, 7.4 to 1.2 in Uttar Pradesh, 4.5 to 1.2 in Maharashtra, 5.3 to 1.3 in Punjab and 22.4 to 1.3 in Assam during 2005–2006 to 2019–2021, respectively (online supplemental table 2). The results of the summary measure of inequality showed a similar trend. For instance, the value of SII for child immunisation decreased from 0.27 to 0.05 in West Bengal, 0.67 to 0.12 in Mizoram and 0.38 to 0.09 in Andhra Pradesh during 2005–2006 to 2019–2021, respectively (online supplemental table 3).

Trends in district-level inequality

The results showed decreasing inequality in MCH service coverage across states; however, such an average value can mask the within-state variation in the inequality. Therefore, we estimated the SII at the district level for each of the outcome variables. Based on the findings, the districts are categorised as follows: districts where SII is less than 0 (shown in blue colour), low category are those where districts with SII is between 0 and less than half of its respective state’s SII (shown in green colour), medium category refers to districts with SII more than half of its respective state’s SII to average state SII (shown in light brown colour), the district with SII more than the state average but below to two times of the state average are the high category districts (shown in medium brown colour) and lastly, districts with SII more than two times of the state value are the highest category (shown in dark red colour) (figure 1). The findings showed that in a state like Assam, Bihar, Chhattisgarh, Jharkhand and Uttar Pradesh, which has comparatively higher inequality, many districts have negative SII indicating the pro-poor situation in the use of MCH service coverage. A contrasting picture is observed in states like Andhra Pradesh, Kerala, Karnataka and Maharashtra—lower state-level inequality but very high inequality in many districts within these states.

Figure 1. Maps showing trends in district-level inequality for outcome indicators for the year 2015–2016 and 2019–2021. The district level inequality calculated using slope index of inequalities (SII) for each district. The colour coding is done in comparison to each district’s respective to the state SII index. The districts are categorised as follows: districts where SII is less than 0 (shown in blue colour), low category are those where districts with SII is between 0 to less than half of its respective state’s SII (shown in green colour), medium category refers to districts with SII more than half of its respective state’s SII to average state SII (shown in light brown colour), the districts with SII more than the state average but below to two times of the state average are the high category districts (shown in medium brown colour), and lastly, districts with SII more than two times of the state value are the highest category (shown in dark red colour). Figures in parenthesis are the number of districts of respective categories.

Multivariate analysis

The result from the multivariate logistic regression at the state level indicates that the odds ratio (OR) of availing full ANC has increased at the national level from the NFHS 2005 to 2019. However, the gap between the poorest and the richest quintile is still substantial. Richest wealth quintile is more likely to avail maternal and child health services than the poorest quintile. This trend is constant among all indicator across almost all states. For instance, the OR of full ANC was significantly higher among women of poorer to richest wealth quintile than the poorest quintile, quite noticeable in Bihar (1.3–5.5), Jharkhand (1.2–3.9) and in northeastern states such as Nagaland (1.3–9.1), Mizoram (2.5–7.2) and Manipur (2.0–6.2). States such as Kerala and Goa, however, have managed to keep the gap little with the richest having the probability of availing full ANC only 0.10 times more than the poorest (online supplemental appendices 5–8).

Discussion

Owing to the fact of high level of maternal and childhood mortality rates caused by low level of MCH utilisation, particularly due to high level of home delivery, the Government of India launched several initiatives under the flagship of the National Health Mission in 2005. Using the three latest rounds of the NFHS data conducted between 2005 and 2021, the current paper examined trends in coverage and economic inequality in important MCH services in India, and at the subnational level, during the post-NHM period.

Findings showed that the coverage of the MCH indicators has increased in India, states and across household wealth quintiles in the last 15 years. Furthermore, increase in the coverage was much higher among poor women than their better-off counterparts. These findings showed that the health programmes and initiatives after launch of the NHM helped in expanding the services with increasing awareness, demand, availability and accessibility. This further indicates that the NHM interventions has worked in a targeted manner and reached out to the vulnerable population in the country and its states. For instance, in 2005, the government of India launched JSY or safe motherhood schemes under the NHM to reduce maternal and child deaths by incentivising institutional births. The JSY initially focused on all pregnant women in low and performing states and pregnant women in high-performing states.

The findings further showed that the JSY has worked effectively; as in number of districts, share of deliveries in public hospitals has increased. The share has increased more in low-performing states than in high-performing states. Similarly, the impact of Mission Indradhanush on universal immunisation has increased the pace of improvement in immunisation coverage, but compared with the achievement of institutional births, the growth is tapered.²² The impact of the NHM on maternity services uptake is discussed in several pieces of literature and has documented positive impact of the world’s largest demand side financial incentive programme. The cash incentive did increase the utilisation of maternity services, especially for poor and uneducated women.²³ The finding can be corroborated with the analysis in the study in which the gap between the poorest and richest quintile in institutional delivery has reduced in the latest round. This reduction was achieved after highest increase of services among the poorest—an average annual growth of 4.23 percentage points in the last 15 years, while for the same period, the richest quintiles experienced growth rate of 0.91 percentage points (table 4). Other studies too support these findings,²⁴ which highlight the role of health infrastructure improvements in increasing service utilisation, particularly in rural areas. Previous research also emphasised on programmes like JSY and Mission Indradhanush to ensure long-term improvement as they have an important role to play by reducing the financial barriers in accessing maternal health services.²⁵ Comparative analyses, such as those by the WHO (Global Health Observatory) show that India’s progress in reducing MCH inequalities aligns with trends observed in other developing countries, although specific regional and socioeconomic contexts vary.

Though the inequality in services coverage has reduced over time, the poorest wealth quintile in all the outcome variables has still not reached the level the richest wealth quintiles were in 2005. This indicates that the health programmes should be continued targeting the marginalised group of women to bring them close to the universal level. Moreover, the extent of inequality varies among regions, with Kerala, Sikkim and Goa having the least inequality and Bihar, Uttar Pradesh, Nagaland and Manipur having the most. This indicates that while the focused programmes resulted in improvement of the services coverage, there is still a certain group of population in certain geographies who lag to tap the opportunity. While the literature suggests that there exist several factors behind utilisation of maternal health services, including education, place of residence, caste, religion, exposure to mass media etc.,²⁶ the study emphasis more on the need for some geography and population, which needs targeted intervention.

Findings of this paper need to be interpretated with certain limitations. First, the findings are based on cross-sectional data, which may not allow to demonstrate a causal relation between programme efforts and increase in service coverage among women. Second, the analysis did not include union territories of the country owing to the very small sample to provide an estimate. Third, district-level data are available to the latest two rounds of the NFHS only, which restricts to estimate the district level situation pre-National Health Mission period, aligned with the national and state-level estimates.

Conclusion

In conclusion, a consistent increase in coverage of MCH services in the country, states, districts and among women of low socioeconomic status is indeed good news and reflects the success of the long-standing MCH programmes in India, which are increasingly reaching to poor. This has resulted in closing the gap between the poor and the rich. Despite this success, the current level of MCH use among the poorest mothers is to the level that the richest women had 15 years back. This indicates that the ongoing MCH programmes in the country should continue to focus on targeting marginalised sections of the population, if India has to achieve universal access to reproductive health as committed to achieving the SDGs 3.7. The high inequality in selected geographies underscores the need to increase the accessibility of services in hard-to-reach areas with special targets among poor and marginalised populations. This will help in ensuring overall universal coverage of reproductive and child health services as well as achieving the SDG targets related to maternal and childhood mortality. The analysis across the districts indicates that the NHM has intervened in the correct directions but at some level still need to understand the unique challenges to aim for better implementation.

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.