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Evaluating changes in the prevalence of female genital mutilation/cutting among 0-14 years old girls in Nigeria using data from multiple surveys: A novel Bayesian hierarchical spatio-temporal model

Author

Listed:
  • Chibuzor Christopher Nnanatu
  • Glory Atilola
  • Paul Komba
  • Lubanzadio Mavatikua
  • Zhuzhi Moore
  • Dennis Matanda
  • Otibho Obianwu
  • Ngianga-Bakwin Kandala

Abstract

Female genital mutilation/cutting (FGM/C) is considered a public health and human rights concern, mainly concentrated in Africa, and has been targeted for elimination under the sustainable development goals. Interventions aimed at ending the practice often rely on data from household surveys which employ complex designs leading to outcomes that are not totally independent, thus requiring advanced statistical techniques. Combining data from multiple surveys within robust statistical framework holds promise to provide more precise estimates due to increased sample size, and accurately identify ‘hotspots’ and allow for assessment of changes over time. In this study, rich datasets from six (6) successive waves of the Nigeria Demographic and Health Surveys and Multiple Indicator Cluster Surveys undertaken between 2003 and 2016/17, were combined and analyzed in order to better assess changes in the likelihood and prevalence of FGM/C among 0-14-year old girls in Nigeria. We used Bayesian hierarchical regression models which explicitly accounted for the inherent spatial and temporal autocorrelations within the data while simultaneously adjusting for variations due to different survey methods and the effects of linear and non-linear covariates. Parameters were estimated using Markov chain Mote Carlo techniques and model fit assessments were based on Deviance Information Criterion. Results show that prevalence of FGM/C among 0–14 years old girls in Nigeria varied over time and across geographical locations and peaked in 2008 with a shift from South to North. A girl was more likely to be cut if her mother was cut, supported FGM/C continuation, or had no higher education. The effects of mother’s age, wealth and type of residence (urban-rural) were no longer significant in 2016. These results reflect the gains of interventions over the years, but also echo the belief that FGM/C is a social norm thus requiring tailored all-inclusive interventions for the total abandonment of FGM/C in Nigeria.

Suggested Citation

  • Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Dennis Matanda & Otibho Obianwu & Ngianga-Bakwin Kandala, 2021. "Evaluating changes in the prevalence of female genital mutilation/cutting among 0-14 years old girls in Nigeria using data from multiple surveys: A novel Bayesian hierarchical spatio-temporal model," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-31, February.
    • Handle: RePEc:plo:pone00:0246661
    DOI: 10.1371/journal.pone.0246661
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    References listed on IDEAS

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    1. Anselin, Luc & Getis, Arthur, 1992. "Spatial Statistical Analysis and Geographic Information Systems," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 26(1), pages 19-33, April.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).
    4. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
    5. Shell-Duncan, Bettina & Wander, Katherine & Hernlund, Ylva & Moreau, Amadou, 2011. "Dynamics of change in the practice of female genital cutting in Senegambia: Testing predictions of social convention theory," Social Science & Medicine, Elsevier, vol. 73(8), pages 1275-1283.
    6. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    7. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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