Related Experiment Video
Updated: Jul 10, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Revisiting the predisposing, enabling, and need factors of unsafe abortion in India using the Heckman Probit model
Margubur Rahaman1, Avijit Roy2, Pradip Chouhan3
1Department of Migration & Urban Studies, International Institute for Population Sciences (IIPS), Mumbai, India.
Abstract:
Unsafe abortion refers to induced abortions performed without trained medical assistance. While previous studies have investigated predictors of unsafe abortion in India, none have addressed these factors with accounting sample selection bias. This study aims to evaluate the contributors to unsafe abortion in India by using the latest National Family Health Survey data conducted during 2019-2021, incorporating the adjustment of sample selection bias. The study included women aged 15 to 49 who had terminated their most recent pregnancy within five years prior to the survey (total weighted sample (N) = 4,810). Descriptive and bivariate statistics and the Heckman Probit model were employed. The prevalence of unsafe abortion in India was 31%. Key predictors of unsafe abortion included women's age, the gender composition of their living children, gestation stage, family planning status, and geographical region. Unsafe abortions were typically performed in the early stages of gestation, often involving self-administered medication. The primary reasons cited were unintended pregnancies and health complications. This study underscores the urgent need for targeted interventions that take into account regional, demographic, and social dynamics influencing abortion practices in India.
More Related Videos
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Statistical Methods for Analyzing Epidemiological Data
Hazard Rate
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

