Sharma, Preeti (2025) Socio-Economic and Demographic determinants of Unmet Need for Family Planning Among Indian Women of Reproductive Age. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (1MB) | Preview |
Abstract
This study investigates the socio-economic and demographic determinants of unmet need for family planning among married women aged 15–49 using the NFHS-5 dataset (2019- 21). The dataset contains a total of 724,115 individual records before filtering and approximately 1,400 core variables. A weighted logistic regression model, supported by Elastic Net regularisation and two machine-learning classifiers (Random Forest and XGBoost), was used to analyse associations and validate predictive patterns. Results show that unmet need (prevalence: 10.9%) is strongly shaped by fertility preferences, parity, and information access. Women wanting to delay or avoid further births exhibited the highest risk (OR ≈ 4.09 for “later/unsure”; OR ≈ 3.24 for “no more”). Each additional living child increased unmet need by 24%, while knowledge of modern methods reduced it by 82% (OR ≈ 0.17). Machine-learning models achieved higher discrimination (XGBoost ROC-AUC = 0.760), confirming non-linear interaction effects. Overall, unmet need is concentrated among poorer, higher-parity women with limited information exposure, indicating the need for targeted counselling and communication-focused interventions.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Rifai, Hicham UNSPECIFIED |
| Uncontrolled Keywords: | Unmet need; family planning; National Family Health Survey; ChiSquare; logistic regression; machine learning |
| Subjects: | H Social Sciences > HQ The family. Marriage. Woman R Medicine > RG Gynecology and obstetrics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HQ The family. Marriage. Woman > Sexual life |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 09 Sep 2026 08:52 |
| Last Modified: | 09 Sep 2026 08:52 |
| URI: | https://norma.ncirl.ie/id/eprint/9908 |
Actions (login required)
![]() |
View Item |
Tools
Tools