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Patterns of malnutrition by HIV status & covariates of malnutrition in 1-4 year olds in rural South Africa Kimani-Murage, Norris SA, Pettifor JM, Tollman.

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Presentation on theme: "Patterns of malnutrition by HIV status & covariates of malnutrition in 1-4 year olds in rural South Africa Kimani-Murage, Norris SA, Pettifor JM, Tollman."— Presentation transcript:

1 Patterns of malnutrition by HIV status & covariates of malnutrition in 1-4 year olds in rural South Africa Kimani-Murage, Norris SA, Pettifor JM, Tollman SM, Klipstein-Grobusch K, Gómez- Olivé FX, Dunger DB, Kahn K

2 Why malnutrition? Access to adequate nutrition is a basic human right Malnutrition, ‘the forgotten MDG’ has received little attention and investment … hence major public health concern especially in Sub-Saharan Africa (SSA) “Sub-Saharan Africa is not on track to achieve a single MDG. … it is off track on the hunger goal—and is the only region where child malnutrition is not declining….” —World Bank, 2005

3 HIV and Malnutrition South Africa undergoing one of the worst HIV epidemics; 1/3 of pregnant women infected HIV/AIDS heightens nutritional deficiencies in infected children HIV/AIDS impacts on food security of affected households, hence malnutrition Malnutrition influences disease progression, increases morbidity and lowers survival of HIV infected children

4 Objectives Describe HIV prevalence in1-4 year olds living in Agincourt in 2007 Describe patterns of malnutrition by HIV status Determine covariates of malnutrition

5 Methods

6 Data Data Collection Study Area: Agincourt Time Frame: April to July 2007 Sample: 3511 children 1-20y; 671 children 1-4 years Anthropometric Measurements Weight, Height HIV Testing Children 1-5 years Two concurrent rapid tests; Unigold & Determine Disclosure of results, counselling & referral for further counselling & standard health care

7 Data Analysis Height & weight for age & weight for height z-scores –Using WHO growth standards (2006) –Stunting, underweight & wasting: <-2 z-scores Statistical Analysis Bivariate analysis: difference by HIV status Multiple regression analysis to determine covariates

8 Maternal Factors Age Nationality Education Union status Co-residence with child Place of delivery Child Factors Age Sex Birth weight HIV Status Household Factors Age of HHH Sex of HHH Education of HHH HHH Relationship to child Food (in) security Socio-economic status Community Factors Village of residence Outcome Measures HAZ, WAZ, WHZ, Stunting, Underweight, Wasting

9 Results

10 HIV test success rate: 95% (640/671) HIV prevalence: 4.4%

11 Chart 1: HAZ, WAZ & WHZ by HIV status

12 Chart 2: Stunting, underweight and wasting by HIV status 17 29 0 10 20 30 % Stunted 10 16 0 10 20 30 % Underweight 7 13 0 10 20 30 % Wasted HIV NegativeHIV Positive

13 Multivariate results HIV Status was a key determinant of nutritional status (HAZ & WAZ) Other significant predictors of nutritional status include: –Child’s age –birth weight –maternal age –age of household head and; – area of residence

14 HIV+ children are at increased risk of malnutrition –Important as malnourished HIV+ children are at increased risk of death High success rate for HIV test indicates potential for HIV testing in communities Prompt paediatric HIV testing may ensure early appropriate interventions for HIV+ children such as nutritional supplementation and ART and ease adverse health & nutritional outcomes for these children Key Conclusions

15 Study Limitations Infants not included while they may be more vulnerable to malnutrition; could also have reduced observed prevalence of HIV Rapid tests used for screening while confirmation with test to detect antigens are recommended for children aged less than 18 months…but only 3 children who were found HIV+ were below 18 months Mother was not tested

16 Acknowledgements Funding: Flora and William Hewlett Foundation, USA, National Research Foundation (NRF), Medical Research Council (MRC) Logistical support from the Agincourt research unit including from the LinC Office, data collection team & data management team particularly Ben Clarke. Logistical support from the Agincourt research unit including from the LinC Office, data collection team & data management team particularly Ben Clarke. Technical support from Mark Collinson Study participants


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