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Bayesian Spatial Modeling of HIV Using Conditional Autoregressive Model

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Modern Biostatistical Methods for Evidence-Based Global Health Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

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Abstract

Background: In the spatial analysis, the conventional method for disease modeling and mapping is based on a log-linear relationship between relative risk and local variation, while the covariates are ignored. On the other hand, the general assumption in spatial modeling is the stationarity of the mean, which implies the associations between the relative risk and some set of covariates, which is constant over regions. In reality, the comparative risk modeling usually infringes on this stationarity assumption because of spatial dependencies. Thus, non-stationarity of the mean can be employed using the Spatially Varying Coefficients (SVCs) model. Method: In this study, we propose a generalized linear model (GLM) with Bayesian inference to build the SVC model and compared it with the stationary model. The SVC model is used to relax the stationarity assumption in which nonlinear effects of age are captured through the random walk of order two and by allowing the covariates to vary spatially using a conditional autoregressive model. This study aimed to profile people living with HIV in Nigeria. In this chapter, identical spatial regression models are fitted for Bayesian approach, using General Household Survey (GHS) data for the year 2015. Result and Conclusion: The finding of this study highlights a nonlinear relationship between the incidence of HIV and age. Among others, this study highlights areas where women are at higher risk of HIV infection across the six regions of Nigeria. The modeling of the socio-demographic predictors of HIV infection and spatial maps provided in this study could aid in developing a framework to alleviate HIV and identify its hotspots for urgent intervention in the endemic regions.

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References

  • AIDSinfo, U. (2016). Epidemiological status.

    Google Scholar 

  • Aitkin, M. (2010). Statistical inference: an integrated Bayesian/likelihood approach. Chapman and Hall/CRC.

    Book  MATH  Google Scholar 

  • Alexander, N. (2011). Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(2), 512–513.

    Article  Google Scholar 

  • Amornkul, P. N., Vandenhoudt, H., Nasokho, P., Odhiambo, F., Mwaengo, D., Hightower, A., Buvé, A., Misore, A., Vulule, J., Vitek, C., et al. (2009). HIV prevalence and associated risk factors among individuals aged 13–34 years in rural western Kenya. PloS One, 4(7), e6470.

    Article  Google Scholar 

  • Aniekwu, N. I. (2002). Gender and human rights dimensions of HIV/aids in Nigeria. African Journal of Reproductive Health, 30–37.

    Google Scholar 

  • Armstrong, A., Nagata, J. M., Vicari, M., Irvine, C., Cluver, L., Sohn, A. H., Ferguson, J., Caswell, G., Njenga, L. W., Oliveras, C., et al. (2018). A global research agenda for adolescents living with HIV. Journal of Acquired Immune Deficiency Syndromes (1999), 78(1), S16.

    Article  Google Scholar 

  • Awofala, A. A., & Ogundele, O. E. (2018). HIV epidemiology in Nigeria. Saudi Journal of Biological Sciences, 25(4), 697–703.

    Article  Google Scholar 

  • Baral, S., Beyrer, C., Muessig, K., Poteat, T., Wirtz, A. L., Decker, M. R., Sherman, S. G., & Kerrigan, D. (2012). Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. The Lancet infectious diseases, 12(7), 538–549.

    Article  Google Scholar 

  • Bekker, L.-G., Alleyne, G., Baral, S., Cepeda, J., Daskalakis, D., Dowdy, D., Dybul, M., Eholie, S., Esom, K., Garnett, G., et al. (2018). Advancing global health and strengthening the HIV response in the era of the sustainable development goals: the international aids society—lancet commission. The Lancet, 392(10144), 312–358.

    Article  Google Scholar 

  • Bernardinelli, L., & Montomoli, C. (1992). Empirical bayes versus fully bayesian analysis of geographical variation in disease risk. Statistics in Medicine, 11(8), 983–1007.

    Article  Google Scholar 

  • Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological), 192–236.

    Google Scholar 

  • Beyrer, C., Baral, S. D., Collins, C., Richardson, E. T., Sullivan, P. S., Sanchez, J., Trapence, G., Katabira, E., Kazatchkine, M., Ryan, O., et al. (2016). The global response to hiv in men who have sex with men. The Lancet, 388(10040), 198–206.

    Article  Google Scholar 

  • Carlin, B. P., Gelfand, A. E., & Banerjee, S. (2014). Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC.

    MATH  Google Scholar 

  • Chimoyi, L. A., & Musenge, E. (2014). Spatial analysis of factors associated with hiv infection among young people in Uganda, 2011. BMC Public Health, 14(1), 555.

    Article  Google Scholar 

  • Cogneau, D., & Grimm, M. (2006). Socioeconomic status, sexual behavior, and differential aids mortality: evidence from cote D’ivoire. Population Research and Policy Review, 25(4), 393–407.

    Article  Google Scholar 

  • Cohen, M. S. (1998). Sexually transmitted diseases enhance hiv transmission: no longer a hypothesis. The Lancet, 351, S5–S7.

    Article  Google Scholar 

  • Cohen, M. S., Chen, Y. Q., McCauley, M., Gamble, T., Hosseinipour, M. C., Kumarasamy, N., Hakim, J. G., Kumwenda, J., Grinsztejn, B., Pilotto, J. H., et al. (2011). Prevention of hiv-1 infection with early antiretroviral therapy. New England Journal of Medicine, 365(6), 493–505.

    Article  Google Scholar 

  • Cressie, N., & Wikle, C. K. (2015). Statistics for spatio-temporal data. John Wiley & Sons.

    MATH  Google Scholar 

  • Currie, I., & Durban, M. (2002). Flexible smoothing with p-splines: a unified approach. Statistical Modelling, 2(4), 333–349.

    Article  MATH  Google Scholar 

  • Djukpen, R. O. (2012). Mapping the hiv/aids epidemic in Nigeria using exploratory spatial data analysis. GeoJournal, 77(4), 555–569.

    Article  Google Scholar 

  • Eilers, P. H., & Marx, B. D. (1996). Flexible smoothing with b-splines and penalties. Statistical Science, 89–102.

    Google Scholar 

  • EO, O., Folayan, M., & Adedigba, M. (2005). Oral health-care workers and hiv infection control practices in Nigeria. Tropical Doctor, 35(3), 147–150.

    Google Scholar 

  • Fahrmeir, L., & Tutz, G. (2013). Multivariate statistical modelling based on generalized linear models. Springer Science & Business Media.

    Google Scholar 

  • Gelfand, A. E., Kim, H.-J., Sirmans, C., & Banerjee, S. (2003). Spatial modeling with spatially varying coefficient processes. Journal of the American Statistical Association, 98(462), 387–396.

    Article  MATH  Google Scholar 

  • Gilks, W. R., Richardson, S., & Spiegelhalter, D. (1995). Markov chain Monte Carlo in practice. Chapman and Hall/CRC.

    Book  MATH  Google Scholar 

  • Group, G. H. P. W. (2003). Access to hiv prevention: closing the gap. Foreign Affairs, 1–42.

    Google Scholar 

  • Hankins, C. A., Friedman, S. R., Zafar, T., & Strathdee, S. A. (2002). Transmission and prevention of hiv and sexually transmitted infections in war settings: implications for current and future armed conflicts. Aids, 16(17), 2245–2252.

    Article  Google Scholar 

  • Johnson, K., & Way, A. (2006). Risk factors for hiv infection in a national adult population: evidence from the 2003 Kenya demographic and health survey. JAIDS Journal of Acquired Immune Deficiency Syndromes, 42(5), 627–636.

    Article  Google Scholar 

  • Lamont, J., Bajzak, K., Bouchard, C., Burnett, M., Byers, S., Cohen, T., Fisher, W., Holzapfel, S., & Senikas, V. (2012). Female sexual health consensus clinical guidelines. Journal of Obstetrics and Gynaecology Canada, 34(8), 769–775.

    Article  Google Scholar 

  • Lawson, A. B. (2013). Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Chapman and Hall/CRC.

    Book  MATH  Google Scholar 

  • Lawson, A., Biggeri, A., Böhning, D., Lesaffre, E., Viel, J.-F., Bertollini, R., et al. (1999). Disease mapping and risk assessment for public health. New York: Wiley.

    MATH  Google Scholar 

  • Macro, I., Commission, N. P., et al. (2014). Nigeria demographic and health survey 2013.

    Google Scholar 

  • Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: new features. Computational Statistics & Data Analysis, 67, 68–83.

    Article  MATH  Google Scholar 

  • Michelo, C., Sandøy, I. F., Dzekedzeke, K., Siziya, S., & Fylkesnes, K. (2006). Steep hiv prevalence declines among young people in selected Zambian communities: population-based observations (1995–2003). BMC Public Health, 6(1), 279.

    Article  Google Scholar 

  • Ngesa, O., Achia, T., & Mwambi, H. (2013). Spatial joint disease modeling and mapping with application to hiv and hsv-2. In Annual Proceedings of the South African Statistical Association Conference, vol. 2013 (pp. 61–68). South African Statistical Association (SASA).

    Google Scholar 

  • Ngesa, O., Mwambi, H., & Achia, T. (2014). Bayesian spatial semi-parametric modeling of HIV variation in Kenya. PloS One, 9(7), e103299.

    Article  MATH  Google Scholar 

  • Niragire, F., Achia, T. N., Lyambabaje, A., & Ntaganira, J. (2015). Bayesian mapping of HIV infection among women of reproductive age in Rwanda. PloS One, 10(3), e0119944.

    Article  Google Scholar 

  • Pearce, M. S. (1999). Geographically weighted regression: A method for exploring spatial nonstationarity. Stata Technical Bulletin, 8(46).

    Google Scholar 

  • Røttingen, J.-A., Cameron, D. W., & Garnett, G. P. (2001). A systematic review of the epidemiologic interactions between classic sexually transmitted diseases and HIV: how much really is known? Sexually Transmitted Diseases, 28(10), 579–597.

    Article  Google Scholar 

  • Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 319–392.

    Article  MATH  Google Scholar 

  • Salaam-Blyther, T., & Kendall, A. (2012). The global fund to fight aids, tuberculosis, and malaria: Issues for congress and us contributions from fy2001 to fy2013. In CRS Report for Congress: Prepared for Members and Committees of Congress.

    Google Scholar 

  • Sherman, M. (2011). Spatial statistics and spatio-temporal data: covariance functions and directional properties. John Wiley & Sons.

    MATH  Google Scholar 

  • Speckman, P. L., & Sun, D. (2003). Fully Bayesian spline smoothing and intrinsic autoregressive priors. Biometrika, 90(2), 289–302.

    Article  MATH  Google Scholar 

  • Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583–639.

    Article  MATH  Google Scholar 

  • Team, R. C. (2016). R: a language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing; 2015. R foundation for statistical computing.

    Google Scholar 

  • Tierney, L., & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81(393), 82–86.

    Article  MATH  Google Scholar 

  • Waller, L. A., Carlin, B. P., Xia, H., & Gelfand, A. E. (1997). Hierarchical spatio-temporal mapping of disease rates. Journal of the American Statistical association, 92(438), 607–617.

    Article  MATH  Google Scholar 

  • WHO (2015). Consolidated guidelines on HIV testing services.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Pretoria, Pretoria, South Africa

    Ropo Ebenezer Ogunsakin & Ding-Geng (Din) Chen

Authors
  1. Ropo Ebenezer Ogunsakin
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  2. Ding-Geng (Din) Chen
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Editor information

Editors and Affiliations

  1. College of Health Solutions, Arizona State University, Phoenix, AZ, USA

    Ding-Geng (Din) Chen

  2. Department of Statistics, University of Pretoria, Pretoria, South Africa

    Samuel O. M. Manda

  3. School of Public Health, Division of Epidemiology and Biostatistics, University of the Witwatersrand, Johannesburg, South Africa

    Tobias F. Chirwa

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Ogunsakin, R.E., Chen, DG.(. (2022). Bayesian Spatial Modeling of HIV Using Conditional Autoregressive Model. In: Chen, DG.(., Manda, S.O.M., Chirwa, T.F. (eds) Modern Biostatistical Methods for Evidence-Based Global Health Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-031-11012-2_13

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