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

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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.


Epidemiological measures of sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the study. (A) The proportion of stable discordant partnerships among all stable partnerships (); (B) the proportion of HIV discordant partnerships among all stable partnerships with at least one HIV-infected individual in the partnership (); (C) the proportion of individuals engaged in stable HIV discordant partnerships (); (D) the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships (). Countries are shown in order of increasing national HIV prevalence.
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ijerph-13-00865-f002: Epidemiological measures of sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the study. (A) The proportion of stable discordant partnerships among all stable partnerships (); (B) the proportion of HIV discordant partnerships among all stable partnerships with at least one HIV-infected individual in the partnership (); (C) the proportion of individuals engaged in stable HIV discordant partnerships (); (D) the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships (). Countries are shown in order of increasing national HIV prevalence.

Mentions: Table 2 and Figure 2 summarizes the epidemiological measures for HIV sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the analyses. was highest within clusters with high HIV prevalence in Kenya (0.25) and lowest outside clusters with high HIV prevalence in Tanzania (0.03). Likewise, was highest within clusters of high HIV prevalence in Kenya (0.86) and lowest outside clusters of high HIV prevalence in Zimbabwe (0.52). and were also highest within clusters with high HIV prevalence in Kenya (0.12 and 0.27, respectively), whereas and were lowest outside of clusters with high HIV prevalence in Tanzania (0.011) and Lesotho (0.11), respectively.


Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa
Epidemiological measures of sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the study. (A) The proportion of stable discordant partnerships among all stable partnerships (); (B) the proportion of HIV discordant partnerships among all stable partnerships with at least one HIV-infected individual in the partnership (); (C) the proportion of individuals engaged in stable HIV discordant partnerships (); (D) the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships (). Countries are shown in order of increasing national HIV prevalence.
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00865-f002: Epidemiological measures of sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the study. (A) The proportion of stable discordant partnerships among all stable partnerships (); (B) the proportion of HIV discordant partnerships among all stable partnerships with at least one HIV-infected individual in the partnership (); (C) the proportion of individuals engaged in stable HIV discordant partnerships (); (D) the proportion of all HIV-infected individuals engaged in stable HIV discordant partnerships (). Countries are shown in order of increasing national HIV prevalence.
Mentions: Table 2 and Figure 2 summarizes the epidemiological measures for HIV sero-discordancy within and outside of clusters with high HIV prevalence in each of the countries included in the analyses. was highest within clusters with high HIV prevalence in Kenya (0.25) and lowest outside clusters with high HIV prevalence in Tanzania (0.03). Likewise, was highest within clusters of high HIV prevalence in Kenya (0.86) and lowest outside clusters of high HIV prevalence in Zimbabwe (0.52). and were also highest within clusters with high HIV prevalence in Kenya (0.12 and 0.27, respectively), whereas and were lowest outside of clusters with high HIV prevalence in Tanzania (0.011) and Lesotho (0.11), respectively.

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.