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Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse

Listed author(s):
  • Riccardo Borgoni

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Francesco C. Billari

    (Max Planck Institute for Demographic Research, Rostock, Germany)

In this paper we analyze the spatial patterns of the risk of unprotected sexual intercourse for Italian women during their initial experience with sexual intercourse. We rely on geo-referenced survey data from the Italian Fertility and Family Survey, and we use a Bayesian approach relying on weakly informative prior distributions. Our analyses are based on a logistic regression model with a multilevel structure. The spatial pattern uses an intrinsic Gaussian conditional autoregressive (CAR) error component. The complexity of such a model is best handled within a Bayesian framework, and statistical inference is carried out using Markov Chain Monte Carlo simulation. In contrast with previous analyses based on multilevel model, our approach avoids the restrictive assumption of independence between area effects. This model allows us to borrow strength from neighbors in order to obtain estimates for areas that may, on their own, have inadequate sample sizes. We show that substantial geographical variation exists within Italy (Southern Italy has higher risks of unprotected first-time sexual intercourse), and that the spatial pattern is stable across birth cohorts. The findings are robust with respect to the specification of the prior distribution. We argue that spatial analysis can give useful insights on unmet reproductive health needs. (KEYWORDS: spatial statistical demography, contraceptive use, hierarchical Bayesian modeling, Monte Carlo Markov Chain, multilevel statistical models, Italy, FFS)

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File URL: http://www.demogr.mpg.de/papers/working/wp-2002-048.pdf
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Paper provided by Max Planck Institute for Demographic Research, Rostock, Germany in its series MPIDR Working Papers with number WP-2002-048.

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Length: 44 pages
Date of creation: Oct 2002
Handle: RePEc:dem:wpaper:wp-2002-048
Contact details of provider: Web page: http://www.demogr.mpg.de/

References listed on IDEAS
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  1. Paul Elliott & Jon Wakefield, 2001. "Disease clusters: should they be investigated, and, if so, when and how?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 3-12.
  2. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
  3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
  4. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
  5. Francesco C. Billari & Riccardo Borgoni, 2001. "Spatial profiles in the analysis of event histories: an application to first sexual intercourse in Italy," MPIDR Working Papers WP-2001-025, Max Planck Institute for Demographic Research, Rostock, Germany.
  6. repec:aph:ajpbhl:2000:90:9:1403-1408_7 is not listed on IDEAS
  7. HÃ¥vard Rue, 2001. "Fast sampling of Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 325-338.
  8. repec:cai:popine:popu_p1997_52n6_1400 is not listed on IDEAS
  9. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 11-30, March.
  10. Leonhard Knorr-Held, 1999. "Conditional Prior Proposals in Dynamic Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 129-144.
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