HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women

Gibbs, Andrew; Reddy, Tarylee; Dunkle, Kristin; Jewkes, Rachel

doi:10.1371/journal.pone.0230105

Cited by 40 publications

(33 citation statements)

References 20 publications

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“…The national HIV prevalence for Zambia has generally been highest in urban areas (13,35,39) and this is similar to other countries within the region such as Malawi, Kenya, South Africa and Zimbabwe (40)(41)(42)(43). However, district level estimates from our study have revealed that HIV prevalence in some rural districts is comparable and sometimes even higher than the prevalence in urban districts.…”

Section: Discussionsupporting

confidence: 82%

Estimating District HIV Prevalence in Zambia Using Small Area Estimation Methods (SAE)

Mweemba

Halmai

Fwemba

et al. 2021

Preprint

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BackgroundThe HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need for studies to estimate HIV prevalence at appropriate levels to improve HIV related planning and resource allocation. MethodsWe estimated the district level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE Models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay-Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.ResultsThe results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 35% of the districts (n=26) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates. ConclusionsHIV prevalence in Zambian districts is driven by population mobility Districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence, tend to have high HIV prevalence. The variations in the burden of HIV across districts in Zambia points to the need for a differentiated approaches in HIV programming in Zambia. HIV resources need to be prioritized towards districts with high population mobility.

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Section: Discussionsupporting

confidence: 82%

Estimating District HIV Prevalence in Zambia Using Small Area Estimation Methods (SAE)

Mweemba

Halmai

Fwemba

et al. 2021

Preprint

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“…It is necessary to investigate what commonalities and differences in the health status of slum dwellers exist [ 20 ]. This refers both to the differences within a city between groups living in formal and informal settlements [ 24 ], which was done for HIV in South Africa [ 25 ], and to the differences across slums in different global regions. The aim here is to become aware of cultural, economic, geographical, infrastructural, religious, or other circumstances, their influences on the health of occupants, and the associated allocation to risk groups.…”

Section: What We Should Knowmentioning

confidence: 99%

COVID-19 and Slums: A Pandemic Highlights Gaps in Knowledge About Urban Poverty

Friesen¹,

Pelz²

2020

JMIR Public Health Surveill

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According to the United Nations, about 1 billion persons live in so-called slums. Numerous studies have shown that this population is particularly vulnerable to infectious diseases. The current COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, emphatically underlines this problem. The often high-density living quarters coupled with a large number of persons per dwelling and the lack of adequate sanitation are reasons why measures to contain the pandemic only work to a limited extent in slums. Furthermore, assignment to risk groups for severe courses of COVID-19 caused by noncommunicable diseases (eg, cardiovascular diseases) is not possible due to inadequate data availability. Information on people living in slums and their health status is either unavailable or only exists for specific regions (eg, Nairobi). We argue that one of the greatest problems with regard to the COVID-19 pandemic in the context of slums in the Global South is the lack of data on the number of people, their living conditions, and their health status.

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“…Human Immunode ciency Virus has been described as a disease of poverty [11][12][13][14][15]28] , a variable that is used to allocate CHWs. If CHWs are under allocated in relation to PLWH, this may mean that the CHWs are not reaching the targeted poor individuals in the province.…”

Section: Discussionmentioning

confidence: 99%

“…Human Immunode ciency Virus is one of the infectious diseases of poverty [11] . Poverty has a complex association with HIV as shown in South African and other international studies [12][13][14][15] . KwaZulu-Natal is the third poorest province in South Africa, with communities in the rural areas having particularly high levels of poverty [7,16] which have been associated with high HIV prevalence [17][18][19][20] .…”

Section: Introductionmentioning

confidence: 89%

Geo-Analysis: The Distribution of Community Health Workers in Relation to the HIV Prevalence in Kwazulu-Natal Province, South Africa

Khumalo

Ntuli

Lutge

et al. 2021

Preprint

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Background: The Ward Based Primary Health Care Outreach Team (WBPHCOT) policy framework states that community health workers (CHW)s should be distributed mainly according to poverty lines for each catchment population. We aimed to describe the spatial distribution of CHWs in relation to the HIV prevalence which has been associated with poverty in previous studies.Methods: We utilized geographic information system (GIS) method to visualize the distribution of CHWs in relation to HIV prevalence in KwaZulu-Natal (KZN) districts. Dot density mapping, was used to visualize the random distribution of CHWs in relation to HIV prevalence in the districts. The districts’ HIV prevalence, number of PLWH, CHW: PLWH ratio and poverty scores were mapped using choropleth mapping. MapInfo 17, a GIS software was used to map geospatial presentation of the data.Results: Ten out of 11 KZN districts had more than a required CHW:PLWH ratio of 1:160-267, which indicates a significant shortage of CHWs regardless of the HIV prevalence or poverty scores. Furthermore, our findings showed extensive geospatial heterogeneity with no clear pattern in the distribution CHWs in relation to the HIV prevalence and poverty scores in the districts Conclusion: This finding represents inequalities in the provision of HIV services by the CHWs. It is critical to strengthen response to the HIV epidemic through the well informed and justified distribution of CHWs especially to the poor districts.

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HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women

Cited by 40 publications

References 20 publications

Estimating District HIV Prevalence in Zambia Using Small Area Estimation Methods (SAE)

Estimating District HIV Prevalence in Zambia Using Small Area Estimation Methods (SAE)

COVID-19 and Slums: A Pandemic Highlights Gaps in Knowledge About Urban Poverty

Geo-Analysis: The Distribution of Community Health Workers in Relation to the HIV Prevalence in Kwazulu-Natal Province, South Africa

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