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. 2017 Aug 1:2:22.
doi: 10.1186/s41256-017-0041-z. eCollection 2017.

Joint spatial modelling of disease risk using multiple sources: an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

D Ntirampeba  1 , I Neema  2 , L N Kazembe  3
Affiliations

Joint spatial modelling of disease risk using multiple sources: an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

D Ntirampeba et al. Glob Health Res Policy. .

Abstract

Background: In disease mapping field, researchers often encounter data from multiple sources. Such data are fraught with challenges such as lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This paper presents a joint model in the effort to deal with the sampling bias and misalignment.

Methods: A joint (bivariate) spatial model was applied to estimate HIV prevalence using two sources: 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15-49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS).

Results: Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection. Also, the study showed that place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia.

Conclusion: This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping. Moreover, a joint modelling approach was used in order to deal with spatially misaligned data. Finally, it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.

Keywords: HIV prevalence; Joint (bivariate) analysis; Namibia; Spatial analysis.

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Figures

Fig. 1
Fig. 1
Convex hull meshes: Constrained refined Delaunay triangulation
Fig. 2
Fig. 2
Observed HIV prevalence (in %): (a) constituencies’ HIV prevalence (2013 NDHS data); (b) health districts’ HIV prevalence (2014 NHSS data)
Fig. 3
Fig. 3
Estimated HIV prevalence using separate models: (a) HIV prevalence estimates from 2014 NHSS data; (b) HIV prevalence estimates from 2013 NDHS data
Fig. 4
Fig. 4
Estimated HIV prevalence using bivariate model: (a) HIV prevalence estimates from 2014 NHSS data; (b) HIV prevalence estimates from 2013 NDHS data
Fig. 5
Fig. 5
Estimated nonlinear effects of age on HIV infection and corresponding 95% credible intervals: (a) NHSS data; (b) NDHS data
Fig. 6
Fig. 6
Posterior means of random effects from univariate models: (a) Spatial random effects (NHSS data); (b) spatial random effects (NDHS data)
Fig. 7
Fig. 7
Posterior means of random effects from bivariate model: (a) Spatial random effects (NHSS data); (b) spatial random effects (NDHS data)
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