Dataset Information

Socio-economic inequality and HIV in South Africa.

ABSTRACT:

Background

The linkage between the socio-economic inequality and HIV outcomes was analysed using data from a population-based household survey that employed multistage-stratified sampling. The goal is to help refocus attention on how HIV is linked to inequalities.

Methods

A socio-economic index (SEI) score, derived using Multiple Correspondence Analysis of measures of ownership of durable assets, was used to generate three SEI groups: Low (poorest), Middle, and Upper (no so poor). Distribution of HIV outcomes (i.e. HIV prevalence, access to HIV/AIDS information, level of stigma towards HIV/AIDS, perceived HIV risk and sexual behaviour) across the SEI groups, and other background characteristics was assessed using weighted data. Univariate and multivariate logistic regression was used to assess the covariates of the HIV outcomes across the socio-economic groups. The study sample include 14,384 adults 15 years and older.

Results

More women (57.5%) than men (42.3%) were found in the poor SEI [P<0.001]. HIV prevalence was highest among the poor (20.8%) followed by those in the middle (15.9%) and those in the upper SEI (4.6%) [P<0.001]. It was also highest among women compared to men (19.7% versus 11.4% respectively) and among black Africans (20.2%) compared to other races [P<0.001]. Individuals in the upper SEI reported higher frequency of HIV testing (59.3%) compared to the low SEI (47.7%) [P< 0.001]. Only 20.5% of those in poor SEI had "good access to HIV/AIDS information" compared to 79.5% in the upper SEI (P<0.001). A higher percentage of the poor had a stigmatizing attitude towards HIV/AIDS (45.6%) compared to those in the upper SEI (34.8%) [P< 0.001]. There was a high personal HIV risk perception among the poor (40.0%) and it declined significantly to 10.9% in the upper SEI.

Conclusions

Our findings underline the disproportionate burden of HIV disease and HIV fear among the poor and vulnerable in South Africa. The poor are further disadvantaged by lack of access to HIV information and HIV/AIDS services such as testing for HIV infection. There is a compelling urgency for the national HIV/AIDS response to maximizing program focus for the poor particularly women.

SUBMITTER: Wabiri N

PROVIDER: S-EPMC4228412 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Publications

Socio-economic inequality and HIV in South Africa.

Wabiri Njeri N Taffa Negussie N

BMC public health 20131104

<h4>Background</h4>The linkage between the socio-economic inequality and HIV outcomes was analysed using data from a population-based household survey that employed multistage-stratified sampling. The goal is to help refocus attention on how HIV is linked to inequalities.<h4>Methods</h4>A socio-economic index (SEI) score, derived using Multiple Correspondence Analysis of measures of ownership of durable assets, was used to generate three SEI groups: Low (poorest), Middle, and Upper (no so poor). ...[more]

PMID: 24180366

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Project description:IntroductionEthiopia has achieved the fourth Millennium Development Goal by reducing under 5 mortality. Nevertheless, there are challenges in reducing maternal and neonatal mortality. The aim of this study was to estimate maternal and neonatal mortality and the socio-economic inequalities of these mortalities in rural south-west Ethiopia.MethodsWe visited and enumerated all households but collected data from those that reported pregnancy and birth outcomes in the last five years in 15 of the 30 rural kebeles in Bonke woreda, Gamo Gofa, south-west Ethiopia. The primary outcomes were maternal and neonatal mortality and a secondary outcome was the rate of institutional delivery.ResultsWe found 11,762 births in 6572 households; 11,536 live and 226 stillbirths. There were 49 maternal deaths; yielding a maternal mortality ratio of 425 per 100,000 live births (95% CI:318-556). The poorest households had greater MMR compared to richest (550 vs 239 per 100,000 live births). However, the socio-economic factors examined did not have statistically significant association with maternal mortality. There were 308 neonatal deaths; resulting in a neonatal mortality ratio of 27 per 1000 live births (95% CI: 24-30). Neonatal mortality was greater in households in the poorest quartile compared to the richest; adjusted OR (AOR): 2.62 (95% CI: 1.65-4.15), headed by illiterates compared to better educated; AOR: 3.54 (95% CI: 1.11-11.30), far from road (?6 km) compared to within 5 km; AOR: 2.40 (95% CI: 1.56-3.69), that had three or more births in five years compared to two or less; AOR: 3.22 (95% CI: 2.45-4.22). Households with maternal mortality had an increased risk of stillbirths; OR: 11.6 (95% CI: 6.00-22.7), and neonatal deaths; OR: 7.2 (95% CI: 3.6-14.3). Institutional delivery was only 3.7%.ConclusionHigh mortality with socio-economic inequality and low institutional delivery highlight the importance of strengthening obstetric interventions in rural south-west Ethiopia.

Project description:BackgroundThe incidence of preterm birth and subsequent low birth weight (LBW) are vital global public health issues. It contributes to high infant and child mortality in the early stages of life and later on in adult life; it increases the risk for non-communicable diseases. The study aims to understand the socio-economic status-related inequality for LBW among children in India. It hypothesises that there is no association between the socio-economic status of the household and the newborn's LBW in India.MethodsThe study utilised data from the fourth round of the National Family Health Survey, a national representative cross-sectional survey conducted in 2015-16 (N = 127,141). The concentration index (CCI) and the concentration curve (CC) measured socio-economic inequality in low birth status among newborns. Wagstaff decomposition further analysed key contributors in CCI by segregating significant covariates.ResultsAbout 18.2% of children had low birth weight status. The value of concentration was - 0.05 representing that low birth weight status is concentrated among children from lower socio-economic status. Further, the wealth quintile explained 76.6% of the SES related inequality followed by regions of India (- 44%) and the educational status of mothers (43.4%) for LBW among children in India. Additionally, the body mass index of the women (28.4%), ante-natal care (20.8%) and residential status (- 15.7%) explained SES related inequality for LBW among children in India.ConclusionAdequate attention should be given to the mother's nutritional status. Awareness of education and usage of health services during pregnancy should be promoted. Further, there is a need to improve the coverage and awareness of the ante-natal care (ANC) program. In such cases, the role of the health workers is of utmost importance. Programs on maternal health services can be merged with maternal nutrition to bring about an overall decline in the LBW of children in India.

Dataset Information

Socio-economic inequality and HIV in South Africa.

Background

Methods

Results

Conclusions

Publications

Socio-economic inequality and HIV in South Africa.

Similar Datasets

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