A Spatial Analysis of the Prevalence of Female Genital Mutilation/Cutting among 0–14-Year-Old Girls in Kenya

Ngianga-Bakwin Kandala, Chibuzor Christopher Nnanatu, Glory Atilola, Paul Komba, Lubanzadio Mavatikua, Zhuzhi Moore, Gerry Mackie and Bettina Shell-Duncan
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Ngianga-Bakwin Kandala: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
Chibuzor Christopher Nnanatu: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
Glory Atilola: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
Paul Komba: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
Lubanzadio Mavatikua: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
Zhuzhi Moore: Independent Consultant, Vienna, Virginia, VA 22182 USA
Gerry Mackie: Department of Political Science, University of California, San Diego, CA 92093-0521, USA
Bettina Shell-Duncan: Department of Anthropology, University of Washington, Seattle, WA 98195-3100, USA

IJERPH, 2019, vol. 16, issue 21, 1-28

Abstract: Female genital mutilation/cutting (FGM/C), also known as female circumcision, is a global public health and human rights problem affecting women and girls. Several concerted efforts to eliminate the practice are underway in several sub-Saharan African countries where the practice is most prevalent. Studies have reported variations in the practice with some countries experiencing relatively slow decline in prevalence. This study investigates the roles of normative influences and related risk factors (e.g., geographic location) on the persistence of FGM/C among 0–14 years old girls in Kenya. The key objective is to identify and map hotspots (high risk regions). We fitted spatial and spatio-temporal models in a Bayesian hierarchical regression framework on two datasets extracted from successive Kenya Demographic and Health Surveys (KDHS) from 1998 to 2014. The models were implemented in R statistical software using Markov Chain Monte Carlo (MCMC) techniques for parameters estimation, while model fit and assessment employed deviance information criterion (DIC) and effective sample size (ESS). Results showed that daughters of cut women were highly likely to be cut. Also, the likelihood of a girl being cut increased with the proportion of women in the community (1) who were cut (2) who supported FGM/C continuation, and (3) who believed FGM/C was a religious obligation. Other key risk factors included living in the northeastern region; belonging to the Kisii or Somali ethnic groups and being of Muslim background. These findings offered a clearer picture of the dynamics of FGM/C in Kenya and will aid targeted interventions through bespoke policymaking and implementations.

Keywords: FGM/C; spatial modelling and mapping; social norms; space-time interactions (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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