Back

Awareness, Knowledge, and Attitude toward Vasectomy among Ugandan Men: A Cross-sectional Study

Oumo, D.; Chebet, F.; Eketu, Y.; Wabwire, K.; Ekalu, M.

2026-05-15 sexual and reproductive health
10.64898/2026.05.11.26352868 medRxiv
Show abstract

Background: Vasectomy remains one of the most underutilized contraceptive methods in Uganda, with a prevalence of only 0.2% despite its safety, effectiveness, and potential contribution to fertility reduction. Understanding the factors influencing awareness, knowledge, and attitudes toward vasectomy acceptance is crucial for developing effective promotion strategies in the Ugandan context. Methods: A cross-sectional study was conducted among 617 men aged 20-60 years, selected through simple random sampling of participants attending Kapchorwa General Hospital. Data were collected using a structured questionnaire. Results: Knowledge scores showed a negative association with age ({beta} = -0.044, p < 0.001) and varied significantly by marital status, with married participants demonstrating higher knowledge than single ({beta} = -0.624, p < 0.001) and widowed ({beta} = -0.950, p < 0.001) individuals. Counterintuitively, higher knowledge was associated with more negative attitudes ({beta} = -1.729, p < 0.001). Age demonstrated the strongest negative effect on attitudes ({beta} = -0.249, p < 0.001), and 99.9% of participants believed contraception is primarily women's responsibility. Behavioral data revealed that 75.0% desired more children, with 51.2% preferring a family size of 3-4 as the ideal. Conclusion: The study shows a disconnect between knowledge, attitudes, and behaviors regarding vasectomy. While general awareness is high, deep-seated misconceptions, cultural norms around masculinity and contraceptive responsibility, and fertility preferences present significant barriers to acceptance.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.