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Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria

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  • Samson B. Adebayo
  • Ludwig Fahrmeir
  • Christian Seiler
  • Christian Heumann

Abstract

The 2005 National HIV/AIDS and Reproductive Health Survey in Nigeria provides evidence that multiple sexual partnering increases the risk of contracting HIV and other sexually transmitted diseases. Therefore, partner reduction is one of the prevention strategies to accomplish the Millenium development goal of halting and reversing the spread of HIV/AIDS. In order to explore possible association between sexual partnering and some risk factors, this paper utilizes a novel Bayesian geoadditive latent variable model for count outcomes. This allows us to simultaneously analyze linear and nonlinear effects of covariates as well as spatial variations of one or more latent variables, such as attitude towards multiple partnering, which in turn directly influences the multivariate observable outcomes or indicators. Influence of demographic factors such as age, gender, locality, state of residence, educational attainment, etc., and knowledge about HIV/AIDS on attitude towards multiple partnering is also investigated. Results can provide insights to policy makers with the aim of reducing the spread of HIV and AIDS among the Nigerian populace through partner reduction.
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Suggested Citation

  • Samson B. Adebayo & Ludwig Fahrmeir & Christian Seiler & Christian Heumann, 2011. "Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria," Biometrics, The International Biometric Society, vol. 67(2), pages 620-628, June.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:2:p:620-628
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01492.x
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    References listed on IDEAS

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    1. Ludwig Fahrmeir & Alexander Raach, 2007. "A Bayesian Semiparametric Latent Variable Model for Mixed Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 327-346, September.
    2. Sylvia FrüHwirth-Schnatter & Helga Wagner, 2006. "Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling," Biometrika, Biometrika Trust, vol. 93(4), pages 827-841, December.
    3. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    4. 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.
    5. Quinn, Kevin M., 2004. "Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses," Political Analysis, Cambridge University Press, vol. 12(4), pages 338-353.
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    Cited by:

    1. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    2. Ezra Gayawan & Samson B. Adebayo, 2013. "A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(45), pages 1339-1372.
    3. Bayerstadler, Andreas & van Dijk, Linda & Winter, Fabian, 2016. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 244-252.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I1 - Health, Education, and Welfare - - Health

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