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Who is the vulnerable child?

Who is the vulnerable child?. Priscilla Akwara, PhD UNICEF, New York Co-authors: B. Noubary, P. Lim Ah Ken, K. Johnson, R. Yates, W. Winfrey, U. Chandan, D. Mulenga, J. Kolker & C. Luo. Using survey data to identify children at risk in the era of HIV and AIDS. Outline.

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Who is the vulnerable child?

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  1. Who is the vulnerable child? Priscilla Akwara, PhD UNICEF, New York Co-authors: B. Noubary, P. Lim Ah Ken, K. Johnson, R. Yates, W. Winfrey, U. Chandan, D. Mulenga, J. Kolker & C. Luo Using survey data to identify children at risk in the era of HIV and AIDS

  2. Outline • Purpose of research • Data • Outcome measures, explanatory variables, and definition of vulnerability, • Results (3 questions) • Summary & conclusions

  3. Purpose of the research • To explore the utility of frequently used indicators of child vulnerability in the context of HIV and AIDS by investigating three questions: • Are orphans more likely to have worse outcomes than non-orphans? • Are children living in households with chronically ill or HIV positive adults more likely to have worse outcomes than those who don’t? • Aside from orphaning, chronic illness or HIV positive adults in household, what other factors are associated with poor child outcomes?

  4. Data: sources, sample sizes and regional coverage • Data sources: • Demographic & Health Surveys (DHS), • Multiple Indicator Cluster Surveys (MICS) • AIDS Indicator Surveys (AIS) • Sample sizes • Range: 4,368-40,511 households • Average: 9,638 households • Median: 8,380 households • Regional coverage • West and Central Africa: 16 countries • Eastern and Southern Africa: 15 countries • Latin American and Caribbean: 5 countries (4 are Caribbean) • Other: 2 countries (Ukraine and Thailand)

  5. Analytical methods • Descriptive & bivariate analysis comparing outcomes for children who are vulnerable to those who are not (according to vulnerability definitions examined in this study) • Multivariate analysis using logistic regressions to control for confounders and to identify additional factors associated with poor child outcomes • Statistical significant differences were examined at the p ≤0.05 level

  6. 3 child outcome measures • Wasting (measure of nutritional status) Among children aged 0-4 years old, whether they are considered wasted i.e., <2 standard deviations below the median of the new WHO Child Growth Standards. • School attendance. Among children aged 10-14 years old, whether they have attended school in the past year. • Early sexual debut. Among girls and boys age 15-17, whether their first sexual intercourse occurred before age 15.

  7. Explanatory variables • Vulnerability variables: orphanhood; chronically ill parent/adult in household; HIV positive adult in household • Socio-demographic & economic variables: • Household wealth status; education; household dependency ratio; urban-rural residence; Head of household; age, sex, living arrangements; average HIV prevalence; & average stigma levels in community

  8. Definition of vulnerability at the global level • Proxy definition of child vulnerability in the context of HIV & AIDS • Orphaning: child has lost one or both parents • Living arrangements: lives outside of family care • Chronic illness of parent or adult in household • HIV positive adult in household after chronic illness

  9. Question #1 • Are orphans more likely to have worse outcomes than non-orphans? Examines the distribution of orphans and non-orphans by 3 selected well-being outcome measures

  10. Question #1: bivariate results * Statistically significant at the p ≤ 0.05 level

  11. Key points:Are orphans worse-off than non-orphans? • Orphans are not significantly worse off than non-orphans on all the three outcomes of child well-being In most countries, orphans do not have significantly worse outcomes than non-orphans. In these countries, targeting resources to children based on orphan status may not be the best approach to using scarce resources. • National context matters • Countries with high levels of overall wasting did not have large differences between orphans and non-orphans. • At high levels of overall school attendance, both orphans and non-orphans attended school in large and nearly the same percentages. • Differences between orphans and non-orphans were more distinct where levels of the respective outcomes were lower.

  12. Question #2: Examines the distribution of children living in households with chronically ill or HIV positive adults by 3 selected well-being outcome measures Are children living in households with chronically ill or HIV positive adults more likely to have worse outcomes than those who don’t?

  13. Question #2: bivariate results * Statistically significant at the p ≤ 0.05 level

  14. Question #3 Aside from orphaning, chronic illness or HIV positive adult in the household, what other factors are associated with poor child outcomes? Multivariate analysis examining the contribution of orphaning, having a chronically ill or HIV positive adult in the household and other confounding factors such as household wealth status, education, residence, etc., on the 3 outcome measures.

  15. 9 countries included in multivariate analysis East and Southern AfricaWest & Central Africa • Malawi 2004 DHS Cameroon 2004 DHS • Rwanda 2005 (AIS) Côte d’Ivoire 2005 (AIS) • Tanzania 2003 (AIS) Mali 2006 DHS • Uganda 2004 (AIS) • Zimbabwe 2005-2006 DHS Caribbean • Haiti 2005 DHS

  16. Note: Most chronically ill adults are not HIV positive

  17. Odds of wasting (0-4 years): chronically ill or HIV positive adult in the household * P ≤ 0.05 * P ≤ 0.05

  18. Odds of school attendance (10-14 years): chronically ill or HIV positive adult in the household * P ≤ 0.05

  19. Odds of early sexual debut (males 15-17 years)

  20. Odds of early sexual debut (females 15-17 years)

  21. Summary of multivariate logistic regression results for odds of wasting among children aged 0-4 years ↓ = decreases the odds ↑= increases the odds NS=not significant

  22. Summary of multivariate logistic regression results for odds of attending school in the past year among children 10-14 years ↓ = decreases the odds ↑= increases the odds NS=not significant

  23. Summary of logistic regression results for odds of having sexual debut before age 15 years among boys and girls aged 15-17 years • In 2 out of 8 multivariate analyses (Haiti & Uganda), having an HIV positive adult in the household was significantly associated with early sexual debut for both boys and girls. • None of the variables were consistently associated with early sexual debut for either boys or girls across the eight countries.

  24. So, what are the other measures associated with vulnerability? • Orphaning and adult health status are not useful on their own in identifying vulnerable children, even in countries with high HIV prevalence • Other indicators of vulnerability more frequently associated with poor outcomes. • Household wealth status was associated, in many instances, with wasting and school attendance. • Education of adult household members was significantly associated with low school attendance.

  25. Other measures associated with selected child outcomes … cont’d • Household wealth and education of adults in the household • almost invariably were positively associated with good outcomes for two of the three indicators of vulnerability • lower odds of wasting • higher odds of school attendance • Other measures associated with selected outcomes: • better sanitation in the household for wasting • living arrangements – lack of familial guardianship (though not necessarily orphaning) for both wasting & school attendance • Only school attendance was in one way or another associated with adult chronic illness or HIV status of adults in the household

  26. Summary & conclusions • Standard OVC-related indicators of vulnerability by themselves are not informative enough for policy & programmatic purposes • Household wealth and parents/caretakers education are better correlates of child health and well-being outcomes • Small differences may nevertheless be meaningful • even if lacking in statistical significance, the consistency with which orphans are more likely to experience poorer outcomes on these selected indicators of vulnerability – as demonstrated in the bivariate analysis – suggests there is still value in continually assessing the status of orphans as it likely nevertheless contributes to a child’s vulnerability profile • The analysis results support a broader approach to defining vulnerability which: • Incorporates age- and sex-specific vulnerabilities • Tries to capture multiple aspects of vulnerability

  27. Implications for policy and programming Results helped inform discussions at the Global Partners Forum on children affected by AIDS held in Dublin in October of 2008 and to formulate the recommendations for the way forward (The Communique) The Communique agreed upon in the Global Partners Forum emphasises an inclusive approach to programming for children affected by AIDS: “Use the resources and programmes focused on children affected by HIV and AIDS to reach communities and families and build/strengthen systems for s trengthening overall child well-being. In areas of widespread poverty and high HIV prevalence, there is high convergence of these sources of vulnerability. In this regard, promote and advocate for AIDS-sensitive, rather than AIDS- exclusive programming” The results are also being used to inform a global process of redefining vulnerability and development of indicators on how to measure progress toward care and support services for children affected by HIV and AIDS. The will be published in AIDS Care journal

  28. ACKNOWLEDGEMENTS • Co-authors: BehzadNoubary, Patricia Lim Ah Ken, Kiersten Johnson, Rachel Yates, William Winfrey, UpjeetChandan, Doreen Mulenga, Jimmy Kolker and CheweLuo • MEASURE DHS and MICS Teams & Futures Institute • Members of the children affected by AIDS Interagency Task Team (CABA IATT) Monitoring & Evaluation Working Group • International AIDS Society/Coalition on CABA for the award of prize of excellence in research for this paper • All the countries and children who contributed to the data used in this study

  29. Thank you!

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