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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.

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Geographical clustering of the number of HIV infections and the number of HIV sero-discordant partnerships in Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. Black circles delineate spatial locations of high HIV prevalence clusters and red circles delineates high HIV SDP clusters in Cameroon (A,B), Kenya (C,D), Lesotho (E,F), Tanzania (G,H), Malawi (I,J), Zambia (K,L), and Zimbabwe (M,N). Continuous surfaces of HIV prevalence (A,C,E,G,I,K,M) and sero-discordant partnership prevalence (B,D,F,H,J,L,N) within a country were generated using the inverse distance weighted mapping algorithm [23].
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ijerph-13-00865-f001: Geographical clustering of the number of HIV infections and the number of HIV sero-discordant partnerships in Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. Black circles delineate spatial locations of high HIV prevalence clusters and red circles delineates high HIV SDP clusters in Cameroon (A,B), Kenya (C,D), Lesotho (E,F), Tanzania (G,H), Malawi (I,J), Zambia (K,L), and Zimbabwe (M,N). Continuous surfaces of HIV prevalence (A,C,E,G,I,K,M) and sero-discordant partnership prevalence (B,D,F,H,J,L,N) within a country were generated using the inverse distance weighted mapping algorithm [23].

Mentions: From the 17,863 couples sampled in the seven countries, 16,140 (90.04%) had HIV biomarker collection for both individuals, and were included in our analyses. Figure 1 illustrates the location of the clusters with high numbers of HIV SDPs, and clusters with high HIV prevalence in each of the countries included in our study. Spatial clusters with significantly high numbers of SDPs were identified in Kenya, Malawi and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. Even in countries or regions where no statistically significant SDP clusters could be identified, there was tendency for SDP clustering that strongly overlapped with HIV clustering (Figure 1). Therefore, the remaining analyses and cluster characterization were conducted using the clusters with high HIV prevalence. The locations and epidemiological characteristics of these spatial clusters with high HIV prevalence have been described elsewhere [14]. Briefly, SatScan identified five clusters with high HIV prevalence in Tanzania, two in Cameroon, one in Malawi, two in Kenya, three in Zambia, one in Zimbabwe, and three in Lesotho. HIV prevalence within these clusters ranged from 9% in Tanzania to 26% in Lesotho.


Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa
Geographical clustering of the number of HIV infections and the number of HIV sero-discordant partnerships in Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. Black circles delineate spatial locations of high HIV prevalence clusters and red circles delineates high HIV SDP clusters in Cameroon (A,B), Kenya (C,D), Lesotho (E,F), Tanzania (G,H), Malawi (I,J), Zambia (K,L), and Zimbabwe (M,N). Continuous surfaces of HIV prevalence (A,C,E,G,I,K,M) and sero-discordant partnership prevalence (B,D,F,H,J,L,N) within a country were generated using the inverse distance weighted mapping algorithm [23].
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00865-f001: Geographical clustering of the number of HIV infections and the number of HIV sero-discordant partnerships in Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. Black circles delineate spatial locations of high HIV prevalence clusters and red circles delineates high HIV SDP clusters in Cameroon (A,B), Kenya (C,D), Lesotho (E,F), Tanzania (G,H), Malawi (I,J), Zambia (K,L), and Zimbabwe (M,N). Continuous surfaces of HIV prevalence (A,C,E,G,I,K,M) and sero-discordant partnership prevalence (B,D,F,H,J,L,N) within a country were generated using the inverse distance weighted mapping algorithm [23].
Mentions: From the 17,863 couples sampled in the seven countries, 16,140 (90.04%) had HIV biomarker collection for both individuals, and were included in our analyses. Figure 1 illustrates the location of the clusters with high numbers of HIV SDPs, and clusters with high HIV prevalence in each of the countries included in our study. Spatial clusters with significantly high numbers of SDPs were identified in Kenya, Malawi and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. Even in countries or regions where no statistically significant SDP clusters could be identified, there was tendency for SDP clustering that strongly overlapped with HIV clustering (Figure 1). Therefore, the remaining analyses and cluster characterization were conducted using the clusters with high HIV prevalence. The locations and epidemiological characteristics of these spatial clusters with high HIV prevalence have been described elsewhere [14]. Briefly, SatScan identified five clusters with high HIV prevalence in Tanzania, two in Cameroon, one in Malawi, two in Kenya, three in Zambia, one in Zimbabwe, and three in Lesotho. HIV prevalence within these clusters ranged from 9% in Tanzania to 26% in Lesotho.

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.


Related in: MedlinePlus