SOCIOECONOMIC AND REGIONAL DETERMINANTS OF FEMALE GENITAL MUTILATION PREVALENCE IN NIGERIA: A STATISTICAL ANALYSIS USING MULTINOMIAL LOGISTIC REGRESSION

  • Lawal Olumuyiwa Mashood Nigerian Air Force Base, Mando, Kaduna State, Nigeria,
  • Jessica Ibrahim Musa
  • Oluwafemi Samson Balogun
  • Joshua Sholademi Ojebisi
Keywords: Circumcision Status, FGM, Health Survey, Prevalence, Multinomial regression, Nigeria

Abstract

Female Genital Mutilation (FGM) is a critical public health concern that perpetuates gender inequality
and poses significant health risks for women and girls. This study aims to investigate the
socioeconomic and regional factors associated with the circumcision status of Nigerian women, thereby
contributing to the understanding of the persistence of FGM in Nigeria. A cross-sectional design was
utilized, employing data from the 2018 Nigeria Demographic and Health Survey (NDHS). A
multinomial logistic regression model was applied to identify potential predictors of circumcision
status, with adjusted odds ratios (AORs) and 95% confidence intervals (CIs) calculated to quantify the
associations. The findings reveal that older age, urban residency, higher educational attainment, and
increased socioeconomic status correlate with a decreased likelihood of circumcision. Additionally,
significant regional variations were observed, with women from the South-East and South-West
regions displaying markedly higher odds of being circumcised compared to their counterparts in other
regions. These results highlight the imperative for targeted public health interventions in Nigeria,
particularly focusing on the regions and demographics most affected by FGM. Strategies should centre
on education and economic empowerment to mitigate the prevalence of this practice and advance
women's rights.

Author Biographies

Lawal Olumuyiwa Mashood, Nigerian Air Force Base, Mando, Kaduna State, Nigeria,

Department of Statistics, Faculty of Science, Air Force Institute of Technology, Nigerian Air Force
Base, Mando, Kaduna State, Nigeria,

Jessica Ibrahim Musa

Department of Statistics, Faculty of Science, Air Force Institute of Technology, Nigerian Air Force
Base, Mando, Kaduna State, Nigeria,

Oluwafemi Samson Balogun

School of Computing, University of Eastern Finland, FI-70211, Kuopio, Finland.

Joshua Sholademi Ojebisi

Department of Statistics, Faculty of Science, Air Force Institute of Technology, Nigerian Air Force
Base, Mando, Kaduna State, Nigeria, 

Published
2024-11-29
Section
Articles