Factors Influencing Women’s Choice  for Institutional Deliveries in Rural and Urban India: A Multidimensional Analysis

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• July 29, 2025
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Navya Garg[1], R. Ahalya[2], Samriddhi Sethi³

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[1] Department of Economics, Daulat Ram College, University of Delhi

Email: navyagargonline@gmail.com  | LinkedIn: www.linkedin.com/in/navyagarg09

[2] Department of Economics, Daulat Ram College, University of Delhi

Email: rahalya@dr.du.ac.in | LinkedIn: https://www.linkedin.com/in/r-ahalya-73b28b25b/  

Google Scholar: https://scholar.google.com/citations?user=HveUDj0AAAAJ&hl=en&authuser=1

 

³ Department of Economics, Daulat Ram College, University of Delhi

Email: sethisamriddhi3108@gmail.com | LinkedIn: www.linkedin.com/in/samriddhi-sethi-ba6929288/

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Title:	Factors Influencing Women’s Choice  for Institutional Deliveries in Rural and Urban India: A Multidimensional Analysis
Author(s):	Navya Garg, R. Ahalya and Samriddhi Sethi
Keywords:	health care workers, Healthcare facilities, Institutional deliveries, Public health, Women’s health
Issue Date:	25 July 2025
Publisher:	IMPRI Impact and Policy Research Institute
Abstract:	Promoting institutional births is crucial for a developing country like India. In this paper, the factors affecting institutional deliveries in public health facilities are explored. To better understand the factors that encourage people to opt for institutional deliveries, the impact of variables such as out-of-pocket expenditure on delivery in a public health facility, health insurance coverage, female literacy, and the influence of health care workers on institutional deliveries is explored. Secondary data from NFHS-5 state and district-level fact sheets have been used for the study. To predict the direction and extent of causality of these variables on institutional deliveries, a GLM regression (with a logit link) is used, since the dependent variable is in the form of proportions (lying between 0 and 1). The preliminary analysis of the data reveals the complex landscape of institutional deliveries in India. Visualizations demonstrate significant variations across demographic, economic, and regional dimensions. Female literacy rates show a strong positive correlation with healthcare facility utilization, while wealth quintile data expose stark disparities in institutional delivery access. Religious and caste-based analyses uncover differential healthcare-seeking behaviours, with urban-rural comparisons highlighting structural inequities in maternal healthcare. These visual insights complement our quantitative regression analysis, providing a nuanced understanding of the factors influencing women’s healthcare choices in India.
Page(s):	78-95
URL:	https://iprr.impriindia.com/factors-influencing-womens-choice-for-institutional-deliveries-in-rural-and-urban-india/
ISSN:	2583-3464 (Online)
PDF Link:	https://iprr.impriindia.com/wp-content/uploads/2025/07/YV1-Factors-Influencing-Womens-Choice-for-Institutional-Deliveries-in-IPPR-V4I1-Jan-June-2025.pdf

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Introduction

Maternal and child health outcomes represent critical indicators of a nation’s healthcare system and overall development. In India, despite significant strides in healthcare provision over recent decades, maternal mortality remains a pressing concern, with approximately 45,000 women dying annually due to pregnancy and childbirth-related complications (World Health Organization, 2023). The promotion of institutional births—deliveries conducted in healthcare facilities under skilled supervision—has emerged as a cornerstone strategy in global and national efforts to reduce maternal and neonatal mortality. This approach is founded on substantial evidence linking institutional deliveries to improved outcomes through timely access to emergency obstetric care, proper hygiene practices, and professional medical interventions.

This study examines the multidimensional factors influencing women’s choices for institutional deliveries across rural and urban India, particularly concerning public health facility utilization. Our primary research question investigates: What demographic, socioeconomic, and healthcare system factors significantly impact women’s decisions to deliver in institutional settings, particularly in public health facilities? The study specifically explores how variables including out-of-pocket expenditure, health insurance coverage, female literacy rates, and community health worker engagement (through Accredited Social Health Activists or ASHAs) interact to shape institutional delivery patterns across diverse Indian contexts.

India has witnessed a remarkable transformation in its institutional birth landscape, with rates increasing from 38.7% in 2005-2006 to 78.9% in 2015-2016, and further to 88.6% in 2019-2021 (National Family Health Survey [NFHS]-5, 2021). Despite this impressive national progress, significant disparities persist across states, socioeconomic strata, and demographic groups. The reliance on public versus private healthcare facilities varies dramatically across states, influenced by complex socioeconomic dynamics.

The decision to opt for institutional delivery is influenced by a complex interplay of factors extending beyond mere healthcare accessibility. Socioeconomic factors, including education, income, and occupation, have been consistently identified as significant determinants in previous research (Bhattacharyya et al., 2016; Joe et al., 2018). Cultural norms, traditional practices, and deeply entrenched gender dynamics further complicate this decision-making process, particularly in rural and tribal communities where home births supervised by traditional birth attendants remain common practice (Gupta et al., 2019). Additionally, healthcare system factors such as facility quality, staff behaviour, perceived costs, and geographical accessibility play crucial roles in shaping women’s childbirth preferences (Vellakkal et al., 2017).

While the existing literature has examined various determinants of institutional births in India, most studies have focused on specific geographic regions or population subgroups, limiting their generalizability. Furthermore, the rapidly evolving healthcare landscape in India, characterized by ongoing policy initiatives and changing socioeconomic conditions, necessitates contemporary analysis. The most recent wave of the National Family Health Survey (NFHS-5) conducted during 2019-2021 provides an invaluable opportunity to examine current patterns and determinants of institutional births across the country.

By employing a Generalized Linear Model with a logit link function on data from the National Family Health Survey-5 (NFHS-5), this study aims to identify the direction and magnitude of key variables affecting institutional delivery choices. Understanding these determinants is crucial for several reasons. First, it enables policymakers to design targeted interventions that address specific barriers to institutional delivery among vulnerable populations. Second, it allows for more efficient allocation of resources within maternal health programs by identifying priority areas and groups. Third, it contributes to the broader discourse on health equity in India by highlighting disparities in access to essential maternal healthcare services.

This research stands at the intersection of public health, social policy, and development economics, offering both theoretical and practical contributions to these fields. By employing rigorous quantitative methods to analyze nationally representative data, we aim to produce findings that are both statistically robust and practically relevant for policy formulation. The findings will contribute to the growing body of literature on maternal healthcare-seeking behaviours in developing contexts and offer practical insights for healthcare planning and resource allocation in India’s diverse socio-cultural landscape. As India strives to achieve the Sustainable Development Goal target of reducing maternal mortality to less than 70 per 100,000 live births by 2030 (SDG Target 3.1 Maternal mortality), a comprehensive understanding of institutional birth determinants becomes increasingly important for strategic planning and implementation.

2. Literature Review

The landscape of institutional delivery in India reveals a complex interplay of socioeconomic, cultural, and individual factors that significantly influence women’s healthcare choices.

Socioeconomic status consistently emerges as a significant determinant of institutional births. Kumar and Mandava (2022) found that women from poorer households, backwards social groups, and rural areas have substantially lower odds of accessing institutional delivery services. Kesterton et al. (2010) revealed that only 10-15% of births in the poorest households occur in medical facilities, compared to significantly higher rates among wealthier households. Their study emphasized that economic status is a more significant determinant than geographical access.

Joe et al. (2018) demonstrated a significant reduction in socioeconomic inequality in institutional deliveries from 2004 to 2014, with 51% of this reduction attributed to improved access to public sector births among poorer households. However, individual factors such as maternal education and social class continued to influence the likelihood of using institutional delivery services, with educated women and those from wealthier households more likely to use private sector services.

Education plays a pivotal role in shaping delivery preferences. Ravi and Kulasekaran (2014) found that women with higher secondary education were more likely to choose health institutions for delivery, while a higher percentage of home deliveries occurred among illiterate women. Similarly, Bansal et al. (2019) confirmed that maternal education significantly predicts the likelihood of institutional delivery. Swain et al. (2020) emphasized that the educational attainment of both husband and wife plays a critical role, with lower education levels correlating with higher home delivery rates.

Access to healthcare facilities significantly influences institutional delivery rates. Vora et al. (2015) identified access to health facilities via all-weather roads as crucial for increasing institutional deliveries. Kusuma et al. (2023) found that the absence of all-weather roads was associated with lower rates of adequate antenatal care and institutional deliveries.

Kesterton et al. (2010) noted significant regional disparities, with institutional delivery rates varying widely across different states—59% of births in the South were institutional, compared to only 16% in the North. Mukherjee and Dular (2022) found that in rural West Bengal, 80% of women who opted for home deliveries lived more than 4 km from health facilities. The quality of healthcare services and provider behaviour significantly influence delivery decisions. Mukherjee and Dular (2022) revealed that motivation from healthcare workers was a universal factor (100%) among women who opted for institutional delivery in rural West Bengal. The same study found that the availability of healthcare staff, particularly nurses (67%), was a key factor in encouraging institutional deliveries.

Government financial incentives have demonstrated varied impacts. Vora et al. (2015) found that participation in Janani Suraksha Yojana (JSY) significantly predicted institutional delivery in Gujarat, with participants being 3.9 times more likely to deliver in a health facility. However, this effect was not observed in Tamil Nadu. Joe et al. (2018) documented the impact of the National Rural Health Mission (NRHM), showing that the percentage of institutional deliveries in India rose significantly from 43% in 2004 to 83% in 2014, with a notable increase in public sector usage (from 21% to 53%).

Cultural norms and family dynamics significantly affect childbirth decisions. Ravi and Kulasekaran (2014) found that family traditions and restrictions significantly influence women’s choices, with 55.2% of women citing family restrictions as a reason for home delivery. Mukherjee and Dular (2022) reported that decision-making was often influenced by in-laws (74%).

A qualitative study by Acharya et al. (2024) in rural Nepal revealed that decision-making is rarely an individual process, with husbands, mothers-in-law, and extended family members significantly influencing childbirth choices.

Various demographic factors correlate with institutional delivery preferences. Ravi and Kulasekaran (2014) observed that first-time mothers were less likely to deliver at home compared to those with higher birth orders. Swain et al. (2020) similarly found that women with more children and those who have their first child at a younger age are more likely to opt for home deliveries. Antenatal care emerges as a critical predictor of institutional delivery. Studies consistently show that women with multiple antenatal visits are significantly more likely to choose institutional deliveries.

The convergence of these research findings underscores the complexity of institutional delivery choices. Effective strategies must adopt a holistic approach, recognizing the intricate interactions between individual, familial, social, and economic factors that shape women’s healthcare decisions across different regions of India and similar contexts.

3. Data Sources

This analysis examines the patterns and determinants of institutional births across Indian states, focusing on socioeconomic, demographic, and healthcare system factors. The study utilizes data from the National Family Health Survey (NFHS-5) 2019–2021, which provides insights into key indicators related to maternal healthcare.

The following variables are derived from NFHS-5 (2019–2021):

• Institutional births (in the five years before the survey, %)
• Institutional births in public facilities (in the five years before the survey, %)
• Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the five years before the survey, Rs.)
• Households with any usual member covered under a health insurance/financing scheme (%)
• Female population aged six years and above who have  ever attended school (%)
• Women (age 15-49) with ten or more years of schooling (%)
• Health workers ever talked to female non-users about family planning (%)

Table 1: Summary Statistics of Variables Used

Summary Statistics	Average	MAX	MIN
Institutional births (in the 5 years before the survey) (%)	89.4	99.8	45.7
Institutional births in public facility (in the 5 years before the survey) (%)	65.6	94.7	34.1
Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)	4155	14518	677
Households with any usual member covered under a health insurance/financing scheme (%)	39.6	87.8	1.6
Female population age 6 years and above who ever attended school (%)	77.0	95.5	60.9
Women (age 15-49) with 10 or more years of schooling (%)	46.5	77.0	23.2
Health worker ever talked to female non-users about family planning (%)	21.6	35.8	6.0

4. Preliminary Analysis

The comprehensive data analysis reveals significant improvements in institutional delivery rates nationwide, though with notable disparities across regions and social groups. This preliminary analysis serves to identify key trends and areas requiring further investigation.

4.1 Temporal Trends and Current Status

The analysis reveals remarkable progress in institutional births across India over the past fifteen years.

Between 2005-2006 and 2019-2020, institutional births increased from 38.7% to 88.6%, with public facility utilization rising from 18% to 61.9% (Figure 1). The first decade (2005-2015) saw approximately a 40 percentage point increase, followed by a 10 percentage point rise in the next five years. The share of public facilities in institutional births increased from approximately 46% (2005-06) to 70% (2019-2020).

Figure 1: Trend of Institutional Births (%) in India

Source 1: Own Calculations using NFHS data

Educational attainment emerges as a crucial determinant of institutional births, showing strong positive correlation across states (Figure 2). Female education levels (6+ years of schooling) range from 61.1% in Bihar to 95.5% in Kerala. Regions with female school attendance exceeding 80% consistently demonstrate institutional delivery rates above 90%.

Figure 2: Institutional Deliveries by Female Literacy Rates (%) Across Indian States

Source 2: Own Calculations using NFHS data

Economic factors present a multilayered picture. Out-of-pocket expenditure (Figure 3) varies from ₹677 (Dadra and Nagar Haveli) to ₹14,518 (Manipur), showing a general negative correlation with institutional birth rates. Health insurance coverage (Figure 4) ranges from 1.6% (Andaman & Nicobar) to 87.8% (Rajasthan), with surprisingly weak correlation to institutional births.

Figure 3: Institutional Deliveries by Out-of-Pocket Expenditures (insert rupee sign)

Source 3: Own Calculations using NFHS data

Figure 4: : Institutional Deliveries by Health Insurance Coverage (%) Across States

Source 4: Own Calculations using NFHS data

The analysis of social categories reveals nuanced patterns. Other Backwards Classes (OBCs) show variable healthcare-seeking behavior but generally maintain high institutional birth rates. Scheduled Tribes (ST) demonstrate mixed performance with a stronger preference for public facilities. Scheduled Castes (SC) show the most consistent pattern with strong public facility utilization. Religious minority populations, ranging from 2.80% (Puducherry) to 95.00% (Nagaland), show varied patterns in institutional birth rates.

Public healthcare facilities play a crucial role, with utilization rates varying from 23.9% to 94.7% across states (Figure 5). In urban areas, there’s a marked preference for private facilities, evidenced by lower public facility utilization (52.6%) compared to rural areas (65.3%).

Figure 5: Public vs. Private Facility Utilization for Institutional Births in Rural and Urban Areas

Source 5: Own Calculations using NFHS data

Health worker engagement shows significant variation across states (6.6% to 35.8%) (Figure 6). States with higher health worker coverage demonstrate consistently better institutional birth rates, suggesting the importance of community-level healthcare outreach.

Figure 6: Institutional Deliveries by Health Worker Engagement (%) Across States

Source 6: Own Calculations using NFHS data

Wealth quintile analysis reveals substantial disparities (Figures 7 and 8). In Kerala, the lowest quintile comprises 0.80% while the highest is 40.10%, with minimal impact on institutional births (99.8%). In contrast, Bihar shows 42.80% in the lowest quintile versus 7.60% in the highest, with a significant impact on institutional births (76.2%).

Multidimensional poverty shows significant variation, from 0.55% (Kerala) to 33.76% (Bihar), with a strong negative correlation to institutional births (Figure 9). However, notable exceptions exist, such as Madhya Pradesh maintaining 90.7% institutional births despite 20.63% poverty levels.

Figure 7: Institutional Deliveries by State-wise Wealth Quintiles

Source 7: Own Calculations using NFHS data

Figure 8: Institutional Deliveries by State-wise Wealth Quintiles

Source 8: Own Calculations using NFHS data

Figure 9: Institutional Deliveries by Multidimensional Poverty

Source 9: Own Calculations using NFHS data

This preliminary analysis reveals significant progress in institutional delivery rates across India, while highlighting persistent disparities requiring targeted intervention. The complex interplay of socioeconomic, demographic, and healthcare system factors suggests the need for nuanced, region-specific approaches to further improve institutional birth rates. The success of somestates in maintaining high institutional delivery rates despite challenging socioeconomic conditions provides valuable lessons for policy replication.

5. Methodology

The dependent variable in our model is the proportion of institutional births, with values lying between 0 and 1. Therefore, we use a Generalized Linear Model (GLM) with a logit link.

The model is represented as follows:

where Θi is the probability of institutional births, and

is the log odds of occurrence of institutional births. The odds of institutional births represent the ratio of the probability of institutional births and the probability of non-institutional births.

 represents the term of the explanatory variable, is the coefficient term for the exaplanatory variable, and is the constant term.

Results

Table 2: Results of GLM

Variable	IB_Public_Proportion: Proportion of births in a public health facility	IB_Proportion Proportion of overall institutional births
Multi Dimensional Poverty Index	1.006294(.0058641)	0.924562(0.006233)***
Average Out of the pocket expenditure in a public health facility (in rupees)	1.000026(.0000116)**	.9999483(0.0000136)***
Talked to health worker regarding birth control measures (%)	1.004708(.0030414)	1.026935(0.0042626)***
Use of modern family planning (%)	1.012381(.0026931)***	1.006399(0.0030641)**
Third order or higher births in the past five years (%)	.8502327(.0179249)***	0.9700077(0.233024)
Women married before 18 years in age 20-24 years (%)	.9910266(.0030505)***	1.02459(0.0039744)***
Women with more than 10 years schooling (%)	.9811239(0039261)***	1.022643(0.0049085)***
Women who are literate (%)	.9971756(.0045796)	0.973281(0.0052344)***
Households with a member covered under health insurance (%)	.9991787(.0012145)	1.003084(0.0014133)**
Population living in household with health insurance	.9979125(.0060861)	0.9860195(0.0068364)**
Population of households with sanitation facilities (%)	.9968161(.031187)	1.005255(0.003735)
Sex ratio at birth for children born in last 5 years (per 1000 males)	1.000147(.0002127)	0.9998422(0.0002732)
Constant	4.723483(4.089406)*	71.23992(73.63481)***

(The Standard errors are robust standard errors)

The regression analysis provides crucial insights into the determinants of institutional births in India, allowing us to establish causal relationships beyond the initial trends observed in the data analysis. While our preliminary analysis using graphs highlighted disparities in institutional deliveries based on socioeconomic, demographic, and healthcare-related factors, the regression model quantifies these relationships and identifies the key drivers influencing institutional births.

The Multidimensional Poverty Index (MDPI) has a statistically significant negative effect on institutional births, as indicated by a coefficient of less than 1 in the regression model. This confirms that higher poverty rates reduce the odds of institutional births, reinforcing the idea that financial hardship remains a significant barrier to accessing maternal healthcare. This aligns with our initial analysis, which showed states with high poverty rates generally reporting lower institutional deliveries. However, the regression model clarifies that this relationship is not absolute—certain states overcome poverty-related challenges through strong public health initiatives.

Educational attainment emerges as a crucial factor, with the proportion of women with more than ten years of schooling showing a strong positive association with institutional births. The regression results show that states with higher female literacy and schooling have coefficients significantly greater than 1, confirming that education increases the likelihood of institutional births. This supports our earlier observation that higher female education rates correlate with better maternal healthcare-seeking behavior, reinforcing the role of education in improving institutional delivery rates.

Household economic conditions, as reflected by out-of-pocket healthcare expenditures, exhibit a negative but less pronounced effect on institutional births. The regression results show coefficients below 1, indicating that higher out-of-pocket costs reduce the likelihood of institutional deliveries, though the effect size is smaller than that of poverty. While our initial data suggested that higher costs may deter institutional deliveries, the model indicates that the impact is not uniform across states, possibly due to state-led financial assistance programs that offset costs in certain regions. Additionally, health insurance coverage has a positive but relatively weaker association with institutional births, with coefficients slightly above 1, suggesting that while insurance can play a role, it is not the primary determinant.

5.1 Demographic and Social Influences

The model confirms that early marriage significantly reduces the likelihood of institutional births, as evidenced by a coefficient below 1 for the variable representing women married before the age of 18. This finding underscores the need for policy interventions targeting child marriage prevention as a means of improving maternal health outcomes. The regression results also reveal that third or higher-order births are associated with lower institutional delivery rates, as indicated by a coefficient below 1. This suggests that first-time mothers are more likely to seek institutional care, while women with multiple previous births may rely more on home deliveries, particularly in areas where institutional healthcare is less accessible or culturally less favored.

A crucial finding from the regression model is the strong positive association between health worker engagement and institutional births, with a coefficient significantly greater than 1. States where a higher proportion of women receive antenatal visits from health workers report significantly higher institutional delivery rates. This finding establishes a clear causal link between community health outreach and improved maternal health outcomes, reinforcing the importance of expanding health worker programs.

Public healthcare facility utilization is another key factor identified in the model. States with higher reliance on public health infrastructure exhibit significantly higher institutional births, as indicated by coefficients above 1. This validates our earlier observation that public hospitals remain the backbone of maternal healthcare in many regions. This relationship is particularly strong in rural areas, where private sector penetration is limited.

6. Conclusion and Policy Recommendations

India has made remarkable progress in increasing institutional deliveries, with rates rising from 38.7% in 2005-2006 to 88.6% in 2019-2021, largely attributable to targeted policy interventions. However, persistent disparities across demographic, socioeconomic, and regional dimensions necessitate a refined policy approach. India’s maternal health policy landscape has evolved significantly over the past two decades, with flagship programs like Janani Suraksha Yojana (JSY) and the National Health Mission (NHM) substantially increasing institutional delivery rates.

The JSY conditional cash transfer scheme has demonstrated considerable success, with participation predicting a 3.9 times higher likelihood of institutional delivery in states like Gujarat1. Similarly, the National Rural Health Mission has contributed to increasing public sector deliveries from 21% to 53% between 2004 and 2014, with a notable shift toward pro-poor distribution.

Despite these achievements, implementation challenges persist across diverse regional contexts. The variable impact of JSY across states like Gujarat and Tamil Nadu highlights the need for context-specific approaches rather than uniform implementation. Furthermore, while national averages show improvement, significant inequities remain across socioeconomic strata, with only 10-15% of births in the poorest households occurring in medical facilities compared to substantially higher rates among wealthier households. Research indicates that 55.2% of women cite family restrictions as reasons for home delivery, while 74% report in-laws’ significant influence in decision-making. These findings underscore the necessity for policy interventions that extend beyond financial incentives to address socio-cultural determinants of healthcare choices.

6.1 Targeted Female Literacy Programs

Given the established correlation between maternal education and institutional delivery rates, we recommend developing specialized educational interventions for reproductive-age women in low-literacy districts. These programs should integrate practical health literacy components with formal education, focusing particularly on regions where female literacy and institutional delivery rates remain below national averages.

The National Education Policy should incorporate mandatory maternal health education modules in secondary school curricula, particularly emphasizing the benefits of skilled birth attendance and institutional deliveries. Additionally, community-based adult literacy programs should be expanded and linked with health awareness initiatives, creating synergistic effects between education and health-seeking behaviors.

6.2 Gender-Inclusive Health Communication Strategies

Current awareness campaigns often target women exclusively, overlooking the significant influence of male partners and extended family members in healthcare decisions. Policy interventions should adopt family-centred communication approaches that engage husbands, mothers-in-law, and community elders who substantially influence childbirth choices. Mass media campaigns should be supplemented with interpersonal communication strategies, leveraging the demonstrated effectiveness of healthcare worker motivation in encouraging institutional deliveries. The persistent urban-rural divide in institutional delivery rates necessitates targeted infrastructure development in underserved rural areas.

We recommend implementing a tiered maternal healthcare facility system with clearly defined referral pathways between primary, secondary, and tertiary care centers. This system should ensure that every village has access to at least basic delivery services within a 5km radius, addressing the finding that 80% of women who opted for home deliveries lived more than 4km from health facilities. Infrastructure development should prioritize all-weather road connectivity to health facilities, identified as crucial for increasing institutional deliveries in studies from Gujarat, Tamil Nadu, and tribal communities.

6.3 Graduated Incentive Structure

While JSY has successfully increased institutional delivery rates, its uniform incentive structure inadequately addresses varying barriers across socioeconomic groups. We recommend implementing a graduated incentive system that provides higher financial support to women from the lowest wealth quintiles and those facing additional vulnerabilities such as geographical isolation or disability. The financial support should extend beyond delivery to cover antenatal and postnatal care, creating a continuum of care incentive that encourages comprehensive maternal healthcare utilization.

6.4 Comprehensive Health Insurance Integration

Current health insurance schemes should be better integrated with maternal health services, expanding coverage beyond delivery to include pregnancy-related complications and newborn care. The Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) should specifically strengthen its maternal health component, ensuring that all pregnancy-related services are included in benefits packages. Insurance schemes should implement simplified enrollment procedures for pregnant women, with automatic coverage triggers during antenatal registration. Additionally, insurance literacy programs should be developed to increase awareness and utilization among vulnerable populations with historically low institutional delivery rates.

6.5 Community Engagement and Ownership

To address the significant influence of cultural norms and family dynamics, we recommend establishing community maternal health committees involving local leaders, traditional birth attendants, and influential family members. These committees should participate in planning and monitoring local maternal health services, fostering ownership and cultural acceptability.

In areas with strong traditional birthing practices, culturally adapted delivery rooms should be developed within health facilities, incorporating familiar elements while maintaining medical standards. This approach acknowledges the finding that psychosocial beliefs play a significant role in home childbirth preferences for 60% of women.

6.6 Digital Health Solutions

Leveraging India’s growing digital infrastructure, we recommend developing a National Maternal Health Digital Platform integrating telemedicine, electronic health records, and mobile health applications. This platform should enable remote consultations, automated appointment reminders, and digital birth preparedness planning, particularly benefiting women in remote areas with limited physical access to healthcare facilities. Additionally, geospatial mapping technologies should be employed to identify maternal healthcare deserts and optimize resource allocation for new facility development.

6.7 Predictive Analytics for High-Risk Identification

Advanced data analytics should be implemented to identify high-risk pregnancies and communities with low institutional delivery uptake.

6.8 Comprehensive Monitoring Framework

We recommend developing an enhanced monitoring system that tracks not only institutional delivery rates but also quality indicators, satisfaction measures, and equity dimensions. This system should incorporate both routine health information system data and periodic specialized surveys to capture both quantitative outcomes and qualitative experiences.

Specific attention should be given to monitoring differential impacts across socioeconomic strata, religious and caste groups, and geographical regions, addressing the documented disparities in institutional delivery utilization. Real-time monitoring dashboards should be implemented at district levels to enable responsive management and course correction.

6.9 Implementation Research Integration

Finally, we recommend establishing a dedicated funding stream for implementation research on maternal health interventions. This research should systematically evaluate both established programs and innovative approaches, generating context-specific evidence on effective strategies to increase equitable institutional delivery utilization.

Research priorities should include comparative effectiveness studies of different incentive structures, evaluation of quality improvement initiatives, and assessment of integrated models addressing multiple determinants simultaneously. Findings should be regularly incorporated into policy revisions through established feedback mechanisms.

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