AIDS and Behavior

, Volume 21, Issue 12, pp 3515–3526 | Cite as

The Impact of Food Assistance on Dietary Diversity and Food Consumption among People Living with HIV/AIDS

Original Paper

Abstract

Little is known about the outcomes of food assistance targeted to food insecure people living with HIV/AIDS. Using primary data from Zambia, we estimated the impact of food assistance on the dietary diversity and consumption expenditures of households with HIV infected members receiving antiretroviral therapy. Propensity score matching estimates show that food assistance increased dietary diversity by 9.8 points (23%) mainly through the consumption of food items provided in the ration. Food assistance recipients were 20% points more likely to have acceptable food consumption and 15% points less likely to have poor food consumption than non-recipients. Food assistance also increased food consumption expenditures but had no significant impact on food purchases and total consumption expenditures. Overall, our findings demonstrate that food assistance can be an effective instrument for improving diets and enhancing the food security of people living with HIV/AIDS.

Keywords

HIV/AIDS Food assistance Dietary diversity Food consumption Zambia 

Notes

Acknowledgements

This study was financed by UNAIDS, the World Health Organization, the Ford Foundation and the Poverty, Equity and Growth Network. The authors acknowledge the support received from the Zambian Ministry of Health, the World Food Program Regional and Zambia offices, the Central Statistical Office of the Republic of Zambia, the Program for Urban Self Help and the enumerators. The funding bodies were not involved in the study design, data collection, analysis, interpretation, or manuscript preparation.

Funding

This study was funded by the UNAIDS, the World Health Organization, the Ford Foundation and the Poverty, Equity and Growth Network.

Compliance with Ethical Standards

Conflict of interest

The Authors declares that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Weiser SD, Tsai AC, Gupta R, Frongillo EA, Kawuma A, Senkungu J, Hunt PW, Emenyonu NI, Mattson JE, Martin JN, Bangsberg DR. Food insecurity is associated with morbidity and patterns of healthcare utilization among HIV-infected individuals in a resource-poor setting. AIDS. 2012;26:67–75.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Weiser SD, Young SL, Cohen CR, Kushel MB, Tsai AC, Tien PC, Hatcher AM, Frongillo EA, Bangsberg DR. Conceptual framework for understanding the bidirectional links between food insecurity and HIV/AIDS. Am J Clin Nutr. 2011;94:1729S–39S.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wang EA, McGinnis KA, Fiellin DA, Goulet JL, Bryant K, Gibert CL, Leaf DA, Mattocks K, Sullivan LE, Vogenthaler N, Justice AC. Food insecurity is associated with poor virologic response among HIV-infected patients receiving antiretroviral medications. J Gen Intern Med. 2011;26:1012–8.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    de Pee S, Semba RD. Role of nutrition in HIV infection: review of evidence for more effective programming in resource-limited settings. Food Nutr Bull. 2010;31:S313–44.CrossRefGoogle Scholar
  5. 5.
    Johannessen A, Naman E, Ngowi BJ, Sandvik L, Matee MI, Aglen HE, Gundersen SG, Bruun JN. Predictors of mortality in HIV-infected patients starting antiretroviral therapy in a rural hospital in Tanzania. BMC Infect Dis. 2008;8(52)Google Scholar
  6. 6.
    Tirivayi N, Groot W. Health and welfare effects of integrating AIDS treatment with food assistance in resource constrained settings: a systematic review of theory and evidence. Soc Sci Med. 2011;73:685–92.CrossRefPubMedGoogle Scholar
  7. 7.
    Byron E, Gillespie S, Nangami M. Integrating nutrition security with treatment of people living with HIV: lessons from Kenya. Food Nutr Bull. 2008;29(2):87–97.CrossRefPubMedGoogle Scholar
  8. 8.
    Tirivayi N, Koethe JR, Groot W. Clinic-based food assistance is associated with increased medication adherence among HIV-infected adults on long-term antiretroviral therapy in Zambia. J AIDS Clin Res. 2012;3:171–8.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Cantrell RA, Sinkala M, Megazinni K, Lawson-Marriott S, Washington S, Chi B, Tambatamba-Chapula B, Levy J, Stringer E, Mulenga L, Stringer J. A Pilot Study of Food Supplementation to Improve Adherence to Antiretroviral Therapy among Food-Insecure Adults in Lusaka, Zambia. JAIDS J Acquir Immune Defic Syndr. 2008;49(2):190–5.CrossRefPubMedGoogle Scholar
  10. 10.
    Rawat R, Kadiyala S, McNamara PE. The impact of food assistance on weight gain and disease progression among HIV-infected individuals accessing AIDS care and treatment services in Uganda. BMC Public Health. 2010;10:316–23.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rawat R, Faust E, Maluccio JA, Kadiyala S. The impact of a food assistance program on nutritional status, disease progression, and food security among people living with HIV in Uganda. JAIDS J Acquir Immune Defic Syndr. 2014;66(1):e15–22.CrossRefPubMedGoogle Scholar
  12. 12.
    Jones AD, Ngure FM, Pelto G, Young SL. What are we assessing when we measure food security? A compendium and review of current metrics. Adv Nutr. 2013;4(5):481–505.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Kennedy GL, Pedro MR, Seghieri C, Nantel G, Brouwer I. Dietary diversity score is a useful indicator of micronutrient intake in non-breast-feeding Filipino children. J Nutr. 2007;137(2):472–7.PubMedGoogle Scholar
  14. 14.
    Steyn N, Nel J, Nantel G, Kennedy G, Labadarios D. Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy? Public Health Nutr. 2006;9:644–50.CrossRefPubMedGoogle Scholar
  15. 15.
    Arimond M, Ruel MT. Dietary diversity is associated with child nutritional status: evidence from 11 demographic and health surveys. J Nutr. 2004;134(10):2579–85.PubMedGoogle Scholar
  16. 16.
    Rawat R, McCoy SI, Kadiyala S. Poor diet quality is associated with low CD4 count and anemia and predicts mortality among antiretroviral therapy–naive HIV-positive adults in Uganda. JAIDS J Acquir Immune Defic Syndr. 2013;12:246–53.CrossRefGoogle Scholar
  17. 17.
    Rosenbaum PR. Observational studies. New York: Springer; 2002.CrossRefGoogle Scholar
  18. 18.
    Wiesmann D, Bassett L, Benson T, Hoddinott J. Validation of the World Food Program’s food consumption score and alternative in-dicators of household food security. 2009. IFPRI Discussion Paper 00870. Washington, DC: IFPRI.Google Scholar
  19. 19.
    World Food Program (WFP). Food consumption analysis: calculation and use of the food consumption score in food consumption and food security analysis. Technical Guidance Sheet. Rome: World Food Program; 2007a.Google Scholar
  20. 20.
    Gilligan DO, Margolies A, Quiñones E, Roy S. Impact evaluation of cash and food transfers at early childhood development centers in Karamoja, Uganda. Washington, DC: IFPRI; 2013.Google Scholar
  21. 21.
    Heckman JJ, Ichimura H, Todd P. Matching as an econometric evaluation estimator. Rev Econ Stud. 1998;65(2):261–94.CrossRefGoogle Scholar
  22. 22.
    Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–5.CrossRefGoogle Scholar
  23. 23.
    Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv. 2008;22(1):31–72.CrossRefGoogle Scholar
  24. 24.
    Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Version 3.1.5. 2003Google Scholar
  25. 25.
    Smith J, Todd P. Does matching overcome LaLonde’s critique of nonexperimental estimators? J Econom. 2005;125(1–2):305–53.CrossRefGoogle Scholar
  26. 26.
    Dehejia RH, Wahba S. Propensity score matching methods for nonexperimental causal studies. Rev Econ Stat. 2000;84(1):151–61.CrossRefGoogle Scholar
  27. 27.
    Sianesi B. An evaluation of the Swedish system of active labor market programs in the 1990s. Rev Econ Stat. 2004;86(1):133–55.CrossRefGoogle Scholar
  28. 28.
    Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39:33–8.Google Scholar
  29. 29.
    DiPrete TA, Gangl M. Assessing bias in the estimation of causal effects: rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociol Methodol. 2004;34(1):271–310.CrossRefGoogle Scholar
  30. 30.
    Becker SO, Caliendo M. Sensitivity analysis for average treatment effect. Stata J. 2007;7(1):71–83.Google Scholar
  31. 31.
    Caliendo M, Hujer R, Thomsen S. The employment effects of job creation schemes in Germany—a microeconometric evaluation. In: Millimet DL, Smith JA, Vytlacil E, editors. Modeling and evaluating treatment effects in econometrics, advances in econometrics, vol. 21. Amsterdam: Elsevier; 2008. p. 381–428.Google Scholar
  32. 32.
    Hoddinott J, Sandstrom S, Upton J. The impact of cash and food transfers: evidence from a randomized intervention in Niger. 2013. http://ssrn.com/abstract=2366796 or http://dx.doi.org/10.2139/ssrn.2366796.
  33. 33.
    Hidrobo MJ, Hoddinott J, Peterman A, Margolies A, Moreira V. Cash, food, or vouchers? evidence from a randomized experiment in northern Ecuador. J Dev Econ. 2014;107:144–56.CrossRefGoogle Scholar
  34. 34.
    Aberman NL, Rawat R, Drimie S, Claros JM, Kadiyala S. Food security and nutrition interventions in response to the aids epidemic: assessing global action and evidence. AIDS Behav. 2014;18(5):554–65.CrossRefGoogle Scholar
  35. 35.
    World Food Program (WFP). HIV and AIDS and OVC beneficiary profiles: vulnerability analysis from six countries in Southern Africa. Rome: World Food Program; 2007b.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.UNU-MERIT, United Nations UniversityMaastrichtThe Netherlands
  2. 2.Department of Health Services ResearchMaastricht UniversityMaastrichtThe Netherlands

Personalised recommendations