Can Unintended Pregnancies Be Predicted to Better Prevent Them?

In India, nearly half of all pregnancies are unplanned. This phenomenon poses a major challenge for public health and influences the country’s demographic growth. A recent analysis of national data has led to the development of a tool capable of identifying at-risk situations with remarkable accuracy.

Researchers used advanced data analysis techniques to study the factors associated with unintended pregnancies. Among the most determining elements are limited access to contraception, lack of awareness about available methods, and the optimal timing for conception. Other aspects such as the age at first motherhood, women’s education level, their economic status, and the duration of marriage also play a key role. This information helps better understand why some women find themselves facing an unplanned pregnancy.

The developed tool is based on computer models capable of processing large amounts of data. It has been successfully tested on information collected between 2015 and 2021, proving its effectiveness in identifying the most exposed areas and populations. For example, women with low education or living in disadvantaged regions appear particularly vulnerable. Similarly, those who already have several children or are unaware of how their menstrual cycle works are more often affected.

The originality of this approach lies in its ability to adapt to local specificities. The results show that information campaigns and prevention efforts could be much more targeted. By identifying the specific needs of each region, it becomes possible to act before an unintended pregnancy occurs. This could reduce the need for abortion and its sometimes dramatic consequences for women’s health.

The tool has already demonstrated its usefulness by analyzing recent data. It offers policymakers a concrete opportunity to improve access to contraception and strengthen education on reproductive health. The stakes are high, as better anticipating these pregnancies also helps reduce risks for both mother and child, while easing the pressure on health services. Such an advance could be a game-changer in a country where inequalities in access to healthcare remain significant.

Bibliographie

Source de l’étude

DOI : https://doi.org/10.1007/s44199-026-00168-9

Titre : A Machine Learning Modeling Approach to Predict Unwanted Pregnancy in India

Revue : Journal of Statistical Theory and Applications

Éditeur : Springer Science and Business Media LLC

Auteurs : Prashant Verma; Mukti Khetan; Kaushalendra Kumar Singh; Ujjaval Srivastava