Fig 1.
Map of Nigeria showing the 36 states and the FCT and the six (6) geopolitical zones.
Shapefile republished from DIVA-GIS database (https://www.diva-gis.org/) under a CC BY license, with permission from Global Administrative Areas (GADM), original copyright 2018.
Fig 2.
(A) Sample size distribution of women (aged 15–49 years) and girls (aged 0–14 year) across the Nigeria DHS and MICS surveys from 2003 to 2016. (B) Proportion of cut women and girls across the datasets.
Table 1.
Variable names, levels and descriptions.
Table 2.
Specifications of the model fitted to the datasets.
Table 3.
Characteristics surrounding female genital mutilation/cutting in Nigerian girls aged 0–14 from 2003–2017.
Fig 3.
Crude estimates of the spatial distribution of FGM/C prevalence among 0-14-year old girls across the Nigerian 36 states and the FCT.
Dark blue to dark red correspond to lowest to highest prevalent states. Shapefile republished from DIVA-GIS database (https://www.diva-gis.org/) under a CC BY license, with permission from Global Administrative Areas (GADM), original copyright 2018.
Table 4.
Posterior odds ratio estimates from the Bayesian geo-additive regression model (adjusted) fitted to the 2016 MICS data.
Fig 4.
Posterior estimates of the effects of a girl’s likelihood of undergoing FGM/C in Nigeria based on the 2016 MICS dataset.
Mean (top panel). Significance maps of the posterior estimates based on 95% credible interval (bottom panel). Dark blue to dark red represent lowest to highest FGM/C risk states. Black, white and grey represent significantly high, low, and nonsignificant FGM/C risk states, respectively. Shapefile republished from DIVA-GIS database (https://www.diva-gis.org/) under a CC BY license, with permission from Global Administrative Areas (GADM), original copyright 2018.
Fig 5.
Boxplot of the Deviance Information Criterion (DIC) for the six (6) models fitted to the pooled 2003 to 2016 dataset.
(A) Unadjusted model. (B) Adjusted model.
Fig 6.
Temporal trends in FGM/C among 0-14-year old girls in Nigeria (m3) based on the pooled 2003 to 2016 dataset.
The blue lines are the posterior means; the yellow lines are the 95% credible intervals of the posterior estimates; and the blue band is the width of the 95% credible interval.
Table 5.
Posterior odds ratio estimates from the Bayesian geo-additive regression model (adjusted) fitted to the pooled 2003 to 2016 dataset.
Fig 7.
Posterior estimates of the effects of a girl’s likelihood of undergoing FGM/C in Nigeria based on the pooled 2003 to 2016 dataset.
Mean (top panel). Significance maps of the posterior estimates based on 95% credible interval (bottom panel). Dark blue to dark red represent lowest to highest FGM/C risk states. Black, white and grey represent significantly high, low, and nonsignificant FGM/C risk states, respectively. Shapefile republished from DIVA-GIS database (https://www.diva-gis.org/) under a CC BY license, with permission from Global Administrative Areas (GADM), original copyright 2018.
Fig 8.
(A) Predicted prevalence of FGM/C among 0-14-year old girls in Nigeria from 2003 to 2016 based on m6. (B) Corresponding deviance maps for the estimates. Dark blue to dark red implies lowest to highest prevalence. Shapefile republished from DIVA-GIS database (https://www.diva-gis.org/) under a CC BY license, with permission from Global Administrative Areas (GADM), original copyright 2018.
Fig 9.
Non-linear effects (log-odds) of mother’s age on her daughter’s likelihood of being cut based on (A) m6 of the pooled 2003 to 2016 data and (B) Model B of the 2016 MICS data.
The blue lines are the posterior means; the yellow lines are the 95% credible intervals of the posterior estimates; and the blue band is the width of the 95% credible interval.