Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data
In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, f...
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description | In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe.
We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.
We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of |
doi_str_mv | 10.1371/journal.pmed.1003042 |
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We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.
We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,-such as sex workers and migrant populations-could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA.</description><identifier>ISSN: 1549-1676</identifier><identifier>ISSN: 1549-1277</identifier><identifier>EISSN: 1549-1676</identifier><identifier>DOI: 10.1371/journal.pmed.1003042</identifier><identifier>PMID: 32142509</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acquired immune deficiency syndrome ; Age ; AIDS ; Biology and Life Sciences ; Disease transmission ; Economic activity ; Environmental factors ; Epidemics ; HIV ; Human immunodeficiency virus ; Human influences ; Mapping ; Medicine and health sciences ; Pandemics ; People and Places ; Population density ; Public health ; Questionnaires ; Regression analysis ; Roads & highways ; Sexual behavior ; Social Sciences ; Supervision ; Vegetation ; Women ; Young adults</subject><ispartof>PLoS medicine, 2020-03, Vol.17 (3), p.e1003042</ispartof><rights>2020 Bulstra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Bulstra et al 2020 Bulstra et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-6f35b08ffe8c627b4da5d3d890738e002f410ecc6c77294a12ef9af18fa2d1f33</citedby><cites>FETCH-LOGICAL-c526t-6f35b08ffe8c627b4da5d3d890738e002f410ecc6c77294a12ef9af18fa2d1f33</cites><orcidid>0000-0002-3710-0940 ; 0000-0002-5505-7923 ; 0000-0002-1830-5668 ; 0000-0002-4182-4212 ; 0000-0002-3397-2944 ; 0000-0002-9323-7861 ; 0000-0002-0798-022X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059914/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059914/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32142509$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bulstra, Caroline A</creatorcontrib><creatorcontrib>Hontelez, Jan A C</creatorcontrib><creatorcontrib>Giardina, Federica</creatorcontrib><creatorcontrib>Steen, Richard</creatorcontrib><creatorcontrib>Nagelkerke, Nico J D</creatorcontrib><creatorcontrib>Bärnighausen, Till</creatorcontrib><creatorcontrib>de Vlas, Sake J</creatorcontrib><title>Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data</title><title>PLoS medicine</title><addtitle>PLoS Med</addtitle><description>In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe.
We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.
We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,-such as sex workers and migrant populations-could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA.</description><subject>Acquired immune deficiency syndrome</subject><subject>Age</subject><subject>AIDS</subject><subject>Biology and Life Sciences</subject><subject>Disease transmission</subject><subject>Economic activity</subject><subject>Environmental factors</subject><subject>Epidemics</subject><subject>HIV</subject><subject>Human immunodeficiency virus</subject><subject>Human influences</subject><subject>Mapping</subject><subject>Medicine and health sciences</subject><subject>Pandemics</subject><subject>People and Places</subject><subject>Population density</subject><subject>Public health</subject><subject>Questionnaires</subject><subject>Regression analysis</subject><subject>Roads & highways</subject><subject>Sexual behavior</subject><subject>Social Sciences</subject><subject>Supervision</subject><subject>Vegetation</subject><subject>Women</subject><subject>Young adults</subject><issn>1549-1676</issn><issn>1549-1277</issn><issn>1549-1676</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNp1ks1u1DAQxyMEoqXwBggscc7ir8QxB6RVRelKRRz4uFoTx068ysbBzi7aZ-Al-iw8Gd7dtGoPnGyP5_-b8fifZa8JXhAmyPu134YB-sW4Mc2CYMwwp0-yc1JwmZNSlE8f7M-yFzGuMaYSS_w8O2OUcFpgeZ79-QLj6IYWwdAg3UEAPZng4jEUDET0200d6lzbod7sTB-Rt-h69RNNAYa4cTE6PyA3oLit829wIAxoaYPT8AEt_962xscRJgd9KgH9ProjYUghn85JFnZmjxqY4GX2zEIfzat5vch-XH36fnmd33z9vLpc3uS6oOWUl5YVNa6sNZUuqah5A0XDmkpiwSqTHmk5wUbrUgtBJQdCjZVgSWWBNsQydpG9PXHH3kc1zzEqykssJJeMpozVKaPxsFZjcBsIe-XBqWPAh1ZBmJzujZJCN1xzCZxpXpq60oW2tbCYa1lBdWB9nKtt6_RV2gxpcv0j6OObwXWq9TslcCEl4QnwbgYE_2tr4vSflvkpSwcfYzD2vgLB6mCYO5U6GEbNhkmyNw-7uxfdOYT9A6olwqo</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Bulstra, Caroline A</creator><creator>Hontelez, Jan A C</creator><creator>Giardina, Federica</creator><creator>Steen, Richard</creator><creator>Nagelkerke, Nico J D</creator><creator>Bärnighausen, Till</creator><creator>de Vlas, Sake J</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><scope>DOA</scope><scope>CZK</scope><orcidid>https://orcid.org/0000-0002-3710-0940</orcidid><orcidid>https://orcid.org/0000-0002-5505-7923</orcidid><orcidid>https://orcid.org/0000-0002-1830-5668</orcidid><orcidid>https://orcid.org/0000-0002-4182-4212</orcidid><orcidid>https://orcid.org/0000-0002-3397-2944</orcidid><orcidid>https://orcid.org/0000-0002-9323-7861</orcidid><orcidid>https://orcid.org/0000-0002-0798-022X</orcidid></search><sort><creationdate>20200301</creationdate><title>Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data</title><author>Bulstra, Caroline A ; 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We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe.
We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys.
We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,-such as sex workers and migrant populations-could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32142509</pmid><doi>10.1371/journal.pmed.1003042</doi><orcidid>https://orcid.org/0000-0002-3710-0940</orcidid><orcidid>https://orcid.org/0000-0002-5505-7923</orcidid><orcidid>https://orcid.org/0000-0002-1830-5668</orcidid><orcidid>https://orcid.org/0000-0002-4182-4212</orcidid><orcidid>https://orcid.org/0000-0002-3397-2944</orcidid><orcidid>https://orcid.org/0000-0002-9323-7861</orcidid><orcidid>https://orcid.org/0000-0002-0798-022X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acquired immune deficiency syndrome Age AIDS Biology and Life Sciences Disease transmission Economic activity Environmental factors Epidemics HIV Human immunodeficiency virus Human influences Mapping Medicine and health sciences Pandemics People and Places Population density Public health Questionnaires Regression analysis Roads & highways Sexual behavior Social Sciences Supervision Vegetation Women Young adults |
title | Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data |
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