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Patterns of malnutrition by HIV Patterns of malnutrition by HIV status & covariates of malnutrition in status & covariates of malnutrition in
1-4 year olds in rural South Africa1-4 year olds in rural South Africa
Kimani-MurageKimani-Murage,, Norris SA, Pettifor JM, Norris SA, Pettifor JM, Tollman SM, Klipstein-Grobusch K, Gómez-Tollman SM, Klipstein-Grobusch K, Gómez-Olivé FX, Dunger DB, Kahn KOlivé FX, Dunger DB, Kahn K
Why malnutrition?Why malnutrition?Access to adequate nutrition is a basic human Access to adequate nutrition is a basic human rightright
Malnutrition, ‘the forgotten MDG’ has received little Malnutrition, ‘the forgotten MDG’ has received little attention and investment … hence major public attention and investment … hence major public health concern especially in Sub-Saharan Africa health concern especially in Sub-Saharan Africa (SSA)(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
HIV and MalnutritionHIV and Malnutrition
South Africa undergoing one of the worst HIV South Africa undergoing one of the worst HIV epidemics; 1/3 of pregnant women infectedepidemics; 1/3 of pregnant women infected
HIV/AIDS heightens nutritional deficiencies in HIV/AIDS heightens nutritional deficiencies in infected children infected children
HIV/AIDS impacts on food security of affected HIV/AIDS impacts on food security of affected householdshouseholds, hence malnutrition, hence malnutrition
Malnutrition influences disease progression, Malnutrition influences disease progression, increases morbidity and lowers survival of HIV increases morbidity and lowers survival of HIV infected children infected children
ObjectivesObjectives
Describe HIV prevalence in1-4 year olds living Describe HIV prevalence in1-4 year olds living in Agincourt in 2007in Agincourt in 2007
Describe patterns of malnutrition by HIV status Describe patterns of malnutrition by HIV status
Determine covariates of malnutritionDetermine covariates of malnutrition
DataDataData CollectionData CollectionStudy Area: AgincourtStudy Area: AgincourtTime Frame: April to July 2007 Time Frame: April to July 2007 Sample: 3511 children 1-20y; 671 children 1-4 years Sample: 3511 children 1-20y; 671 children 1-4 years
Anthropometric MeasurementsAnthropometric MeasurementsWeight, HeightWeight, Height
HIV TestingHIV Testing Children 1-5 yearsChildren 1-5 yearsTwo concurrent rapid tests; Unigold & Determine Two concurrent rapid tests; Unigold & Determine Disclosure of results, counselling & referral for Disclosure of results, counselling & referral for further counselling & standard health carefurther counselling & standard health care
Data AnalysisData AnalysisHeight & weight for age & weight for height z-scoresHeight & weight for age & weight for height z-scores– Using WHO growth standards (2006)Using WHO growth standards (2006)– Stunting, underweight & wasting: <-2 z-scoresStunting, underweight & wasting: <-2 z-scores
Statistical AnalysisStatistical Analysis Bivariate analysis: difference by HIV statusBivariate analysis: difference by HIV statusMultiple regression analysis to determine covariatesMultiple regression analysis to determine covariates
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 MeasuresHAZ, WAZ, WHZ, Stunting, Underweight, Wasting
HIV test success rate: 95% (640/671)HIV test success rate: 95% (640/671)
HIV prevalence: 4.4% HIV prevalence: 4.4%
Chart 2: Stunting, underweight and Chart 2: Stunting, underweight and wasting by HIV statuswasting by HIV status
17
29
010
2030
% S
tunt
ed
1016
010
2030
% U
nder
wei
ght
713
010
2030
% W
aste
d
HIV Negative HIV Positive
Multivariate resultsMultivariate results
HIV Status was a key determinant of HIV Status was a key determinant of nutritional status (HAZ & WAZ) nutritional status (HAZ & WAZ) Other significant predictors of nutritional Other significant predictors of nutritional status include: status include: – Child’s ageChild’s age– birth weight birth weight – maternal age maternal age – age of household head and;age of household head and;– area of residence area of residence
HIV+ children are at increased risk of HIV+ children are at increased risk of malnutritionmalnutrition– Important as malnourished HIV+ children are at Important as malnourished HIV+ children are at
increased risk of deathincreased risk of death
High success rate for HIV test indicates High success rate for HIV test indicates potential for HIV testing in communitiespotential for HIV testing in communitiesPrompt paediatric HIV testing may ensure Prompt paediatric HIV testing may ensure early appropriate interventions for HIV+ early appropriate interventions for HIV+ children such as nutritional supplementation children such as nutritional supplementation and ART and ease adverse health & and ART and ease adverse health & nutritional outcomes for these childrennutritional outcomes for these children
Key ConclusionsKey Conclusions
Study LimitationsStudy Limitations
Infants not included while they may be Infants not included while they may be more vulnerable to malnutrition; could also more vulnerable to malnutrition; could also have reduced observed prevalence of HIVhave reduced observed prevalence of HIVRapid tests used for screening while Rapid tests used for screening while confirmation with test to detect antigens confirmation with test to detect antigens are recommended for children aged less are recommended for children aged less than 18 months…but only 3 children who than 18 months…but only 3 children who were found HIV+ were below 18 monthswere found HIV+ were below 18 monthsMother was not testedMother was not tested
Acknowledgements Acknowledgements
Funding: Flora and William Hewlett Foundation, USA, Funding: Flora and William Hewlett Foundation, USA, National Research Foundation (NRF), Medical National Research Foundation (NRF), Medical Research Council (MRC) Research Council (MRC)
Logistical support from the Agincourt research unit Logistical support from the Agincourt research unit including from the LinC Office, data collection team & including from the LinC Office, data collection team & data management team particularly Ben Clarke. data management team particularly Ben Clarke.
Technical support from Mark CollinsonTechnical support from Mark CollinsonStudy participantsStudy participants