Page 243 - Special Topic Session (STS) - Volume 2

STS489 Chibuzor C. N. et al.
taking place in Kenya, Nigeria and Senegal. Second, the characteristics of the
identified hotspots may be exploited by policymakers and programme
implementers in the design and evaluation of bespoke programmatic
interventions.

Keyword
Bayesian Geo-additive models; Spatial modelling; Space-time interactions;
Female circumcision; Social norms

1. Introduction
World Health Organisation defines Female Genital Mutilation/cutting
(FGM/C) as all forms of injury caused to the external female genitalia for non-
medical reasons [1]. FGM/C is a public health and human right issue, which is
deeply rooted in customs and traditions. The practice has both short-term and
longterm consequences with immediate consequences including
haemorrhage and shock. while long term consequences include increased risk
of complications during child birth [2]. It is estimated that over 200 million
women and girls alive today globally, have undergone FGM/C at some point
in their lives. FGM/C is a common practice in most African countries with some
3 million girls being at risk of cutting each year [4].
Recent studies showed that FGM/C prevalence among women aged 15-49
in Kenya was estimated at 27.1% in 2008-9. On the other hand, in 2017, FGM/C
prevalence among girls aged 0-14 years was estimated at 14.0% and 25.3% in
Senegal and Nigeria, respectively [3]. There are several programmatic
interventions in the affected countries geared towards eliminating the practice.
Consequently, decline in prevalence has been reported albeit sluggishly.
This study aims to
1) Identify and map FGM/C hotspots in Nigeria, Kenya and Senegal.
2) Identify the key individual-level and community-level factors and see
how these compare across the three countries.

2. Methodology
Data Sources
Data on FGM/C prevalence in Nigeria were drawn from six nationally-
representative surveys from Nigeria Demographic and Health Surveys (DHS)
and Nigeria Multiple Indicators Cluster Surveys (MICS) comprising of
2003DHS, 2007MICS, 2008DHS, 2011MICS, 2013DHS, and 2016-17MICS. Data
from FGM/C prevalence 0-14 years old in Kenya were drawn KDHS 1998. KDHS
2003, KDHS 2008, KDHS 2014. Finally, data on prevalence among Senegalese
girls were drawn from 2005 SDHS, 2010-11SDHS, 2015 SDHS, and 2017SDHS

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