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Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to examine the spatial clustering of HIV sero-discordancy, and the impact of local variation in HIV prevalence on patterns of sero-discordancy in high HIV prevalence countries in SSA. Methods: We described the spatial patterns of sero-discordancy among stable couples by analyzing Demographic and Health Survey data from Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. We identified spatial clusters of SDPs in each country through a Kulldorff spatial scan statistics analysis. After a geographical cluster was identified, epidemiologic measures of sero-discordancy were calculated and analyzed. Results: Spatial clusters with significantly high numbers of SDPs were identified and characterized in Kenya, Malawi, and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. There was a positive correlation between HIV prevalence and the proportion of SDPs among all stable couples across within and outside clusters. Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and the proportion of SDPs among all stable couples with at least one HIV-infected individual in the partnership. Discussion: There does not appear to be distinct spatial patterns for HIV sero-discordancy that are independent of HIV prevalence patterns. The variation of the sero-discordancy measures with HIV prevalence across clusters and outside clusters demonstrated similar patterns to those observed at the national level. The spatial variable does not appear to be a fundamental nor independent determinant of the observed patterns of sero-discordancy in high HIV prevalence countries in SSA.

No MeSH data available.


Associations between measures of sero-discordancy and HIV prevalence. (A) Scatter plot of the proportion of stable discordant couples among all stable couples () versus HIV prevalence within and outside the high HIV prevalence clusters; (B) scatter plot of the proportion of individuals engaged in stable HIV discordant couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (C) scatter plot of the proportion of HIV discordant couples among all stable couples with at least one HIV-infected individual in the couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (D) scatter plot of the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships () versus HIV prevalence within and outside the high HIV prevalence clusters. Correlations were determined using Pearson correlation coefficient (PCC).
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ijerph-13-00865-f003: Associations between measures of sero-discordancy and HIV prevalence. (A) Scatter plot of the proportion of stable discordant couples among all stable couples () versus HIV prevalence within and outside the high HIV prevalence clusters; (B) scatter plot of the proportion of individuals engaged in stable HIV discordant couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (C) scatter plot of the proportion of HIV discordant couples among all stable couples with at least one HIV-infected individual in the couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (D) scatter plot of the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships () versus HIV prevalence within and outside the high HIV prevalence clusters. Correlations were determined using Pearson correlation coefficient (PCC).

Mentions: There was a positive correlation between HIV prevalence and (PCC = 0.89; 95% Confidence Interval (CI) 0.68–0.96), and HIV prevalence and (PCC = 0.60; 95% CI 0.1–0.86) across within and outside HIV prevalence clusters (Figure 3A,B). Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and (PCC = −0.38; 95% CI −0.75–0.18), and HIV prevalence and (PCC = −0.44; 95% CI −0.79–0.11) across within and outside HIV prevalence clusters (Figure 3C,D).


Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa
Associations between measures of sero-discordancy and HIV prevalence. (A) Scatter plot of the proportion of stable discordant couples among all stable couples () versus HIV prevalence within and outside the high HIV prevalence clusters; (B) scatter plot of the proportion of individuals engaged in stable HIV discordant couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (C) scatter plot of the proportion of HIV discordant couples among all stable couples with at least one HIV-infected individual in the couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (D) scatter plot of the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships () versus HIV prevalence within and outside the high HIV prevalence clusters. Correlations were determined using Pearson correlation coefficient (PCC).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036698&req=5

ijerph-13-00865-f003: Associations between measures of sero-discordancy and HIV prevalence. (A) Scatter plot of the proportion of stable discordant couples among all stable couples () versus HIV prevalence within and outside the high HIV prevalence clusters; (B) scatter plot of the proportion of individuals engaged in stable HIV discordant couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (C) scatter plot of the proportion of HIV discordant couples among all stable couples with at least one HIV-infected individual in the couple () versus HIV prevalence within and outside the high HIV prevalence clusters; (D) scatter plot of the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships () versus HIV prevalence within and outside the high HIV prevalence clusters. Correlations were determined using Pearson correlation coefficient (PCC).
Mentions: There was a positive correlation between HIV prevalence and (PCC = 0.89; 95% Confidence Interval (CI) 0.68–0.96), and HIV prevalence and (PCC = 0.60; 95% CI 0.1–0.86) across within and outside HIV prevalence clusters (Figure 3A,B). Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and (PCC = −0.38; 95% CI −0.75–0.18), and HIV prevalence and (PCC = −0.44; 95% CI −0.79–0.11) across within and outside HIV prevalence clusters (Figure 3C,D).

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to examine the spatial clustering of HIV sero-discordancy, and the impact of local variation in HIV prevalence on patterns of sero-discordancy in high HIV prevalence countries in SSA. Methods: We described the spatial patterns of sero-discordancy among stable couples by analyzing Demographic and Health Survey data from Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. We identified spatial clusters of SDPs in each country through a Kulldorff spatial scan statistics analysis. After a geographical cluster was identified, epidemiologic measures of sero-discordancy were calculated and analyzed. Results: Spatial clusters with significantly high numbers of SDPs were identified and characterized in Kenya, Malawi, and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. There was a positive correlation between HIV prevalence and the proportion of SDPs among all stable couples across within and outside clusters. Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and the proportion of SDPs among all stable couples with at least one HIV-infected individual in the partnership. Discussion: There does not appear to be distinct spatial patterns for HIV sero-discordancy that are independent of HIV prevalence patterns. The variation of the sero-discordancy measures with HIV prevalence across clusters and outside clusters demonstrated similar patterns to those observed at the national level. The spatial variable does not appear to be a fundamental nor independent determinant of the observed patterns of sero-discordancy in high HIV prevalence countries in SSA.

No MeSH data available.