Mapping the Hidden Geography of High-Risk Fertility in India: What District-Level Data Reveals

India has reached below-replacement fertility, yet risky childbearing practices persist in specific districts. Using national survey data and spatial analysis, our study uncovers hidden fertility hotspots and explains why child marriage and education matter most.

Published in Social Sciences

Mapping the Hidden Geography of High-Risk Fertility in India: What District-Level Data Reveals
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Explore the Research

SpringerLink
SpringerLink SpringerLink

Decomposing spatial signatures of high-risk fertility behaviors in India - Discover Public Health

Maternal and child health vulnerability resulting from high-risk fertility behavior (HRFB) has been a significant concern in India and other developing countries. Although India has achieved below-replacement fertility, substantial district-level variation in fertility behavior persists. This study, therefore, seeks to examine the spatial heterogeneity of HRFB and identify the key factors driving these spatial disparities. The fifth round of India’s National Family Health Survey (NFHS) 2019-21, a cross-sectional survey data is used which comprises 1,268,079 birth records of women aged 15–49 years. Univariate LISA delineated hotspots, coldspots, and spatial outliers of HRFB, while the bivariate LISA identified spatial autocorrelation between child marriage and HRFB. Multivariable logistic regression model examined the predictors of HRFB. Fairlie decomposition model presented the factors responsible for spatial heterogeneity in HRFB. Almost one in three women experienced HRFB, with a significant geographical variation. The hotspots of HRFB were predominantly located in Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand. Women’s age, education, mass media exposure, and engagement in family planning programs were significantly associated with the spatial heterogeneity of HRFB in India. To address HRFB vulnerability in India, there is need to prioritize small-area and community-level approaches that consider spatial socio-economic, demographic, and fertility variations.

Why We Looked Beyond National Averages

India’s fertility decline is often cited as a demographic success. However, national and state averages can conceal deep local inequalities. We were concerned that high-risk fertility behaviours—such as early or late childbearing, short birth intervals, and high birth order—may still be concentrated in specific pockets of the country, continuing to expose women and children to avoidable health risks.

Seeing Fertility Through a Spatial Lens

Using over 1.2 million birth records from the National Family Health Survey (2019–21), we applied district-level spatial analysis to identify clusters of high-risk fertility behaviour (HRFB). Instead of viewing districts in isolation, spatial methods allowed us to see how neighbouring districts shared similar fertility risks, revealing clear hotspots and coldspots across India.

What the Maps Revealed

Nearly one-fourth of India’s districts emerged as HRFB hotspots, forming large contiguous belts across parts of Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, Telangana, and West Bengal. In contrast, southern and hill states showed consistent coldspots. These patterns would remain invisible without district-level spatial analysis.

Caption

The Social Roots of Spatial Inequality

Hotspot districts were marked by significantly higher levels of child marriage, low female education, poverty, limited mass media exposure, and weaker engagement with family planning information. These disadvantages overlap spatially, reinforcing cycles of high-risk fertility across generations.

Why Child Marriage Matters Most

Decomposition analysis showed that child marriage alone explained nearly 70% of the gap in high-risk fertility between hotspot and coldspot districts. Early marriage extends women’s reproductive span while limiting autonomy over birth spacing and family size, making it a central driver of spatial inequality in fertility risk.

Implications for Policy and Practice

Many HRFB hotspots overlap with districts targeted under Mission Parivar Vikas, but our findings suggest the need to expand both coverage and scope. Beyond limiting births, district-specific strategies must address birth spacing, delayed childbearing, prevention of child marriage, and female education—especially in spatially clustered high-risk areas.

Reflections from the Research Journey

Working with spatial tools transformed how we interpreted familiar data. Seeing risk mapped across districts reinforced a key lesson: progress at the national level does not guarantee equity at the local level. Geography matters—and ignoring it risks leaving the most vulnerable behind.

Looking Ahead

Future research should combine spatial analysis with qualitative insights to understand how social norms and local contexts shape fertility behaviour. For policymakers, district-level evidence offers a powerful pathway to design targeted, equitable reproductive health interventions.

Further reading our paper: https://link.springer.com/article/10.1186/s12982-025-01263-5

Follow the Topic

Applied Demography
Humanities and Social Sciences > Society > Population and Demography > Applied Demography
Spatial Demography
Humanities and Social Sciences > Society > Population and Demography > Spatial Demography
Gender Geography
Humanities and Social Sciences > Society > Population and Demography > Human Geography > Social and Cultural Geography > Gender Geography
Fertility
Humanities and Social Sciences > Society > Population and Demography > Fertility

Related Collections

With Collections, you can get published faster and increase your visibility.

The Economics of Longevity: Health Expenditure and Societal Implications for Public Health Systems

The relationship between health investment and life expectancy has long been a focal point in public health research and policy. As nations grapple with rising healthcare costs and varying health outcomes, understanding how financial allocations influence population health becomes increasingly critical. Numerous studies have illustrated that increased spending on health services often correlates with improvements in life expectancy; however, the complexities of this relationship are not fully understood. Factors such as socioeconomic status, healthcare accessibility, and the efficiency of health systems play significant roles in determining how investments translate into health outcomes.

At the national level, health investments primarily encompass government expenditure on healthcare systems, public health initiatives, and health infrastructure. Nations that allocate substantial resources toward healthcare often experience better health outcomes and increased life expectancy among their populations. For instance, countries with robust healthcare funding tend to provide more comprehensive services, including preventive care, chronic disease management, and health promotion programs, all of which contribute to enhanced population health. However, it is crucial to consider how these investments are distributed across different segments of the population. Inequities in health spending can exacerbate existing disparities, leading to significant differences in life expectancy among various demographic groups. Effective health investment strategies must address these disparities to ensure that improvements in life expectancy are equitable and inclusive.

The motivation for this Collection arises from the urgent need to bridge the gap between health expenditures and tangible improvements in life expectancy. Recent global health crises and ongoing disparities in health outcomes across different populations have underscored the necessity for a deeper exploration of how resource allocation affects health dynamics. This Collection seeks to provide a platform for researchers and practitioners to discuss empirical evidence, theoretical frameworks, and innovative approaches to understanding these critical interactions. By fostering dialogue around effective health investment strategies, we aim to contribute to the development of more equitable and efficient health systems worldwide.

The purpose of this Collection is to gather comprehensive research that elucidates the dynamics between health investment and life expectancy across diverse contexts. We welcome contributions that analyze historical trends, comparative studies, and policy evaluations, as well as those that explore the implications of financial investments on health equity. By highlighting interdisciplinary perspectives and fostering collaboration, this Collection aspires to advance the discourse on optimizing health investments for improved population health outcomes.

Topics of interest include, but are not limited to:

- Economic evaluations of health interventions

- Policy impacts on health spending and life expectancy

- Health equity and investment disparities

- Longitudinal studies on health expenditures and population health

This Collection supports and amplifies research related to SDG 3.

Keywords: health expenditure; healthcare economics; life expectancy; economic evaluation; population health; health investment; health policy

Publishing Model: Open Access

Deadline: Dec 31, 2026

Digital Frontiers in Public Health: Data-driven Solutions and Technologies for Efficiency, Effectiveness, and Equity

In recent years, the landscape of public health has been transformed by unprecedented access to high-quality data, advances in data storage and processing, and a growing willingness to share information across jurisdictions. The global response to the COVID-19 pandemic exemplified how coordinated data use and technological innovation can accelerate decision-making, enhance surveillance, and save lives. These developments mark a new era in which the application of modern data analytics in evidence-driven public health practices is not only possible but essential.

Building on this momentum, contemporary innovations are contributing to significant improvements in the three Es of public health: efficiency, effectiveness, and equity. Improved efficiency enables resources to be allocated and managed in a manner that generates the maximum attainable outputs; enhanced effectiveness ensures that public health policies, interventions, and practices achieve the expected health outcomes; and strengthened equity promotes fair access to healthcare and public health services across all populations. Together, these dimensions reflect the evolving priorities and capabilities of modern public health systems.

This Collection, “Digital Frontiers in Public Health: Data-driven Solutions and Technologies for Efficiency, Effectiveness, and Equity,” invites contributions that explore these themes through empirical studies, methodological advancements, case studies, and policy analyses. We seek manuscripts that highlight innovative data applications, cross-sector collaborations, digital health initiatives, and strategies that enhance equity through technology and data integration, as well as collaborative public health practices.

Topics of interest include, but are not limited to:

- Applications of novel data linkage and analytics methods in public health

- The role of participation and co-creation in addressing public health issues

- Digital and technological innovations in public health

- Complete or partial assessments of triple Es in public health

- Economic evaluations of public health interventions

By showcasing diverse approaches from around the world, this Collection aims to provide a comprehensive overview of how data-driven innovations are reshaping public health practice today, and in the future. Researchers, practitioners, and policymakers are encouraged to share their insights and experiences to contribute to this critical dialogue on the future of public health.

This Collection supports and amplifies research related to SDG 3.

Keywords: digital applications; data sharing; data infrastructure; economic evaluations; health equity; public health; policy analysis; health informatics

Publishing Model: Open Access

Deadline: Sep 30, 2026