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.

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

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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

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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

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