Spatial Clustering of All-Cause and HIV-Related Mortality in a Rural South African Population (2000–2006)
Identifieur interne : 002215 ( Ncbi/Merge ); précédent : 002214; suivant : 002216Spatial Clustering of All-Cause and HIV-Related Mortality in a Rural South African Population (2000–2006)
Auteurs : Elias Namosha [Afrique du Sud] ; Benn Sartorius [Afrique du Sud] ; Frank Tanser [Afrique du Sud]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.
Kulldorff’s spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (
HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest.
Url:
DOI: 10.1371/journal.pone.0069279
PubMed: 23935972
PubMed Central: 3720283
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<affiliation><nlm:aff id="aff2"><addr-line>Papua New Guinea Institute of Medical Research, Papua, New Guinea</addr-line>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Spatial Clustering of All-Cause and HIV-Related Mortality in a Rural South African Population (2000–2006)</title>
<author><name sortKey="Namosha, Elias" sort="Namosha, Elias" uniqKey="Namosha E" first="Elias" last="Namosha">Elias Namosha</name>
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<region type="region" nuts="2">Gauteng</region>
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<affiliation><nlm:aff id="aff2"><addr-line>Papua New Guinea Institute of Medical Research, Papua, New Guinea</addr-line>
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<wicri:noCountry code="subfield">New Guinea</wicri:noCountry>
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<author><name sortKey="Sartorius, Benn" sort="Sartorius, Benn" uniqKey="Sartorius B" first="Benn" last="Sartorius">Benn Sartorius</name>
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<author><name sortKey="Tanser, Frank" sort="Tanser, Frank" uniqKey="Tanser F" first="Frank" last="Tanser">Frank Tanser</name>
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<country xml:lang="fr">Afrique du Sud</country>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.</p>
</sec>
<sec><title>Methodology and Results</title>
<p>Kulldorff’s spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (<italic>p</italic>
<0.05) in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000–2006). The analysis was split pre (2000–2003) and post (2004–2006) rollout of antiretroviral therapy, respectively. Between 2000–2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related) over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000). During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001) and high all-cause mortality (RR = 1.35/1.38, <italic>p</italic>
= 0.001) was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, <italic>p</italic>
= 0.003/0.005) and in the first time period (2000–2003) a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.</p>
</sec>
<sec><title>Conclusions</title>
<p>HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest.</p>
</sec>
</div>
</front>
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<article-id pub-id-type="doi">10.1371/journal.pone.0069279</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
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<subj-group subj-group-type="Discipline-v2"><subject>Biology</subject>
<subj-group><subject>Population Biology</subject>
<subj-group><subject>Epidemiology</subject>
<subj-group><subject>Infectious Disease Epidemiology</subject>
<subject>Spatial Epidemiology</subject>
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<subj-group subj-group-type="Discipline-v2"><subject>Earth Sciences</subject>
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<subj-group><subject>Cartography</subject>
<subj-group><subject>Gis</subject>
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<subj-group subj-group-type="Discipline-v2"><subject>Mathematics</subject>
<subj-group><subject>Statistics</subject>
<subj-group><subject>Biostatistics</subject>
<subject>Statistical Methods</subject>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Medicine</subject>
<subj-group><subject>Epidemiology</subject>
<subj-group><subject>Epidemiological Methods</subject>
<subject>Disease Mapping</subject>
<subject>Infectious Disease Epidemiology</subject>
<subject>Spatial Epidemiology</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Spatial Clustering of All-Cause and HIV-Related Mortality in a Rural South African Population (2000–2006)</article-title>
<alt-title alt-title-type="running-head">Spatial Clustering of Mortality</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Namosha</surname>
<given-names>Elias</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sartorius</surname>
<given-names>Benn</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tanser</surname>
<given-names>Frank</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label>
<addr-line>School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>Papua New Guinea Institute of Medical Research, Papua, New Guinea</addr-line>
</aff>
<aff id="aff3"><label>3</label>
<addr-line>The Africa Centre for Health and Population Studies, Mtubatuba, South Africa</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Vermund</surname>
<given-names>Sten H.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>Vanderbilt University, United States of America</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>tanserf@africacentre.ac.za</email>
</corresp>
<fn fn-type="conflict"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed the experiments: EN BS FT. Performed the experiments: EN FT. Analyzed the data: EN FT. Contributed reagents/materials/analysis tools: EN BS FT. Wrote the paper: EN FT.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>23</day>
<month>7</month>
<year>2013</year>
</pub-date>
<volume>8</volume>
<issue>7</issue>
<elocation-id>e69279</elocation-id>
<history><date date-type="received"><day>13</day>
<month>3</month>
<year>2013</year>
</date>
<date date-type="accepted"><day>10</day>
<month>6</month>
<year>2013</year>
</date>
</history>
<permissions><copyright-year>2013</copyright-year>
<copyright-holder>Namosha et al</copyright-holder>
<license><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<abstract><sec><title>Background</title>
<p>Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.</p>
</sec>
<sec><title>Methodology and Results</title>
<p>Kulldorff’s spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (<italic>p</italic>
<0.05) in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000–2006). The analysis was split pre (2000–2003) and post (2004–2006) rollout of antiretroviral therapy, respectively. Between 2000–2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related) over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000). During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001) and high all-cause mortality (RR = 1.35/1.38, <italic>p</italic>
= 0.001) was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, <italic>p</italic>
= 0.003/0.005) and in the first time period (2000–2003) a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.</p>
</sec>
<sec><title>Conclusions</title>
<p>HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest.</p>
</sec>
</abstract>
<funding-group><funding-statement>Elias Namosha was supported by the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Disease (TDR). Frank Tanser was supported by grant 1R01-HD058482-01 from the National Institute of Child Health and Human Development (NICHD). Funding for the Africa Centre's Demographic Surveillance Information System and Population-based HIV Survey was received from the Wellcome Trust, UK (grant # 082384/Z/07/Z)]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><page-count count="8"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Afrique du Sud</li>
</country>
<region><li>Gauteng</li>
</region>
<settlement><li>Johannesbourg</li>
</settlement>
<orgName><li>Université du Witwatersrand</li>
</orgName>
</list>
<tree><country name="Afrique du Sud"><region name="Gauteng"><name sortKey="Namosha, Elias" sort="Namosha, Elias" uniqKey="Namosha E" first="Elias" last="Namosha">Elias Namosha</name>
</region>
<name sortKey="Sartorius, Benn" sort="Sartorius, Benn" uniqKey="Sartorius B" first="Benn" last="Sartorius">Benn Sartorius</name>
<name sortKey="Tanser, Frank" sort="Tanser, Frank" uniqKey="Tanser F" first="Frank" last="Tanser">Frank Tanser</name>
</country>
</tree>
</affiliations>
</record>
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