Factors associated with adolescent pregnancy in Maharashtra, India: a mixed-methods study
Bibliographic record
Abstract
Reducing the adolescent birth rate is paramount in achieving the health-related Sustainable Development Goals, given that pregnancy and childbirth are the leading cause of mortality among young women aged 15–19. This study aimed to explore predictors of adolescent pregnancy among girls aged 13–18 years in Maharashtra, India, during the COVID-19 pandemic. Using a mixed-methods approach, primary data were gathered from two regions in Maharashtra between February and April 2022. Quantitative data from face-to-face interviews with 3049 adolescent girls assessed various household, social, and behavioural factors, as well as the socioeconomic and health impacts of COVID-19. Qualitative data from seven in-depth interviews were analysed thematically. The findings reveal that girls from low socioeconomic backgrounds face a higher likelihood of adolescent pregnancy. Multivariable analysis identified several factors associated with increased risk, including older age, being married, having more sexual partners, and experiencing COVID-19-related economic vulnerability. On the other hand, rural residence, secondary and higher secondary education of the participants, and higher maternal education were associated with a decreased likelihood of adolescent pregnancy. In the sub-sample of 565 partnered girls, partner's emotional abuse also correlated with higher rates of adolescent pregnancy. Thematic analysis of qualitative data identified four potential pathways leading to adolescent pregnancy: economic hardships and early marriage; personal safety, social norms, and early marriage; social expectations; and lack of knowledge on contraceptives. The findings underscore the significance of social position and behavioural factors and the impact of external shocks like the COVID-19 pandemic in predicting adolescent pregnancy in Maharashtra, India.Plain Language Summary: Adolescent pregnancy is an important health issue for young girls. In South Asia, one out of every five adolescent girls becomes a mother before turning 18, and in India, around 9% of girls aged 15–19 get pregnant yearly. This study focused on understanding the factors associated with adolescent pregnancy in Maharashtra, India, especially after the COVID-19 pandemic. We collected information from both urban and rural areas in Maharashtra. A total of 3049 adolescent girls participated in a survey, and seven girls participated in detailed interviews. Our analysis showed that factors like older age, being married, having multiple sexual partners, and experiencing economic difficulties due to COVID-19 increased the chances of adolescent pregnancy. On the other hand, living in rural areas, higher education for both the girls and their mothers reduced the likelihood of adolescent pregnancy. Qualitative analysis revealed that economic challenges, concerns about safety and societal norms, early marriage, societal expectations, and lack of knowledge about contraceptives could contribute to adolescent pregnancy in Maharashtra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".