Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
International Journal for Equity in Health
Published in: International Journal for Equity in Health 1/2004

Open Access 01-12-2004 | Research

Mothers' education but not fathers' education, household assets or land ownership is the best predictor of child health inequalities in rural Uganda

Authors: Henry Wamani, Thorkild Tylleskär, Anne Nordrehaug Åstrøm, James K Tumwine, Stefan Peterson

Published in: International Journal for Equity in Health | Issue 1/2004

Login to get access

Abstract

Background

Health and nutrition inequality is a result of a complex web of factors that include socio-economic inequalities. Various socio-economic indicators exist however some do not accurately predict inequalities in children. Others are not intervention feasible.

Objective

To examine the association of four socio-economic indicators namely: mothers' education, fathers' education, household asset index, and land ownership with growth stunting, which is used as a proxy for health and nutrition inequalities among infants and young children.

Methods

This was a cross-sectional survey conducted in the rural district of Hoima, Uganda. Two-stage cluster sampling design was used to obtain 720 child/mother pairs. Information on indicators of household socio-economic status and child anthropometry was gathered by administering a structured questionnaire to mothers in their home settings. Regression modelling was used to determine the association of socio-economic indicators with stunting.

Results

One hundred seventy two (25%) of the studied children were stunted, of which 105 (61%) were boys (p < 0.001). Bivariate analysis indicated a higher prevalence of stunting among children of: non-educated mothers compared to mothers educated above primary school (odds ratio (OR) 2.5, 95% confidence interval (CI) 1.4–4.4); non-educated fathers compared to fathers educated above secondary school (OR 1.7, 95% CI 0.8–3.5); households belonging in the "poorest" quintile for the asset index compared to the "least poor" quintile (OR 2.1, 95% CI 1.2–3.7); Land ownership exhibited no differentials with stunting. Simultaneously adjusting all socio-economic indicators in conditional regression analysis left mothers' education as the only independent predictor of stunting with children of non-educated mothers significantly more likely to be stunted compared to those of mothers educated above primary school (OR 2.1, 95% CI 1.1–3.9). More boys than girls were significantly stunted in poorer than wealthier socio-economic strata.

Conclusions

Of four socio-economic indicators, mothers' education is the best predictor for health and nutrition inequalities among infants and young children in rural Uganda. This suggests a need for appropriate formal education of the girl child aimed at promoting child health and nutrition. The finding that boys are adversely affected by poverty more than their female counterparts corroborates evidence from previous studies.
Appendix
Available only for authorised users
Literature
1.
Claeson Mariam, Waldman Ronald J: The evolution of child health programmes in developing countries: from targeting diseases to targeting people. Bull World Health Organ. 2000, 78: 1234-1245.PubMedPubMedCentral
2.
Victora Cesar G, Wagstaff Adam, Schellenberg Joanna Armstrong, Gwatkin Davidson, Cleason Mariam, Habicht Jean-Pierre: Applying an equity lens to child health and mortality: more of the same is not enough. The Lancet. 2003, 362: 233-241. 10.1016/S0140-6736(03)13917-7.CrossRef
3.
Ehiri John E, Prowse Julie M: Child health promotion in developing countries: the case for integration of environmental and social interventions. Health Pol Plann. 1999, 14: 1-10. 10.1093/heapol/14.1.1.CrossRef
4.
Braveman P: Monitoring equity in health: a policy-oriented approach in low- and middle-income countries. WHO/CHS/HSS/981, Equity Initiative Paper No 3. 1998
5.
WHO Working Group: Use and interpretation of anthropometric indicators on nutritional status. Bull World Health Organ. 1986, 64: 929-941.PubMedCentral
6.
Beaton G, Kelly A, Kevany R, Mason J: Appropriate uses of child anthropometric indices in children-biological basis for interpretation. 1990, Geneva, ACC/SCN (Nutrition Policy Discussion Paper No. 7)
7.
Martorell R, Ramakrishnan U, Schroeder DG, Melgar P, Neufeld L: Intrauterine growth retardation, body size, body composition and physical performance in adolescence. Eur J Clin Nutr. 1998, 52: S43-53.PubMed
8.
Espo M, Kulmala T, Maleta K, Cullinan T, Salin ML, Ashorn P: Determinants of linear growth and predictors of severe stunting during infancy in rural Malawi. Acta Paediatr. 2002, 91: 1364-1370.CrossRefPubMed
9.
Adair Linda S., Guilkey David K.: Age-Specific Determinants of Stunting in Filipino Children. J Nutr. 1997, 127: 314-320.PubMed
10.
Begin France, Frongillo Edward A., Jr., Delisle Helene: Caregiver behaviors and resources influence child height-for-age in rural Chad. J Nutr. 1999, 129: 680-686.PubMed
11.
Cole TJ, Parkin JM: Infection and its effect on growth of young children: a comparison of the Gambia and Uganda. Trans R Soc Trop Med Hyg. 1977, 71: 196-198. 10.1016/0035-9203(77)90005-0.CrossRefPubMed
12.
Pradhan Menno, Sahn David E, Younger Stephen: Decomposing world health inequality. Journal of Health Economics. 2003, 22: 271-293. 10.1016/S0167-6296(02)00123-6.CrossRefPubMed
13.
Wagstaff A, Paci P, van Doorslaer E: On the measurement of inequalities in health. Social Science and Medicine. 1991, 33: 545-557. 10.1016/0277-9536(91)90212-U.CrossRefPubMed
14.
Kikafunda J, Walker A, Collett D, Tumwine J: Risk factors for early childhood malnutriton in Uganda. Pediatrics. 1998, 102: e45-10.1542/peds.102.4.e45.CrossRefPubMed
15.
Tumwine J, Barugahare W: Nutrition status of children in Kasese district. East Afr Med J. 2002, 79: 427-434.CrossRefPubMed
16.
Ukwuani Festus A., Suchindran Chirayath M.: Implications of women's work for child nutritional status in sub-Saharan Africa: a case study of Nigeria. Social Science & Medicine. 2003, 56: 2109-2121. 10.1016/S0277-9536(02)00205-8.CrossRef
17.
Vella V, Tomkins A, Borghesi A, Migliori GB, Oryem VY: Determinants of stunting and recovery from stunting in northwestern Uganda. Int J Epidemiol. 1994, 23: 782-786.CrossRefPubMed
18.
Zere Eyob, Mclnyre Diane: Inequities in under-five child malnutrition in South Africa. International Journal for Equity in Health. 2003, 2: 7-10.1186/1475-9276-2-7.CrossRefPubMedPubMedCentral
19.
Rasmus Heltberg: Does economic growth reduce child malnutrition?. 2002, Washington, D.C., The World Bank (Working Paper)
20.
Bollen A Kenneth, Glanville Jennifer L, Stecklov Guy: Economic status proxies in studies of fertility in developing countries: does the measure matter?. 2001, Chapel Hill, N.C., Working Paper No 38, MEASURE
21.
Uganda National Bureau of Statistics: Population and housing census, district population series, Hoima. 1992
22.
Bennett S, Woods T, Liyanage WM, Smith DL: A simplified general method for cluster-sample surveys of health in developing countries. World Health Stat Q. 1991, 44: 98-106.PubMed
23.
Ministry of Finance and Economic Planning Uganda: Demographic and health survey. 2000, Kampala, Uganda, 153 -1157.
24.
Cochran WG: Sampling Techniques. 1977, John Wiley & Sons, New York
25.
Haraldsdottir J: Minimizing error in the field: quality control in dietary surveys. Eur J Clin Nutr. 1993, 47: s19-s24.PubMed
26.
Gibson R: Principles of nutrition assessment. 1990, Oxford University Press
27.
MEASURE DHS: . wwwmeasuredhscom/methodology/startcfm.
28.
Filmer D, Pritchett L: Estimating wealth effects without expenditure --or tears: an application to educational enrollments in states of India. Demography. 2001, 38: 115-132.PubMed
29.
The Republic of Uganda: Uganda participatory poverty assessment report. 2000, Kampala, Ministry of Finance, Planning and Economic Development
30.
Lauritsen JM, Bruus M, Myatt MA: An extended tool for validated dataentry and documentation of data. 2001, Odense Denmark, The Epidata Association, v2.x
31.
Saleemi MA, Ashraf RN, Mellander L, Zaman SS: Determinants of stunting at 6, 12, 24 and 60 months and postnatal linear growth in Parkistani children. Acta Paediatr. 2001, 90: 1304-1308. 10.1080/080352501317130371.CrossRefPubMed
32.
Turrell Gavin, Hewitt Bellinda, Patterson Carla, Oldenburg Brian: Measuring socio-economic position in dietary research: is choice of socio-economic indicator important?. Public Health Nutrition. 2003, 6: 191-200. 10.1079/PHN2002416.PubMed
33.
UNICEF: The stats of the world's children 2004. 2004, New York
34.
Schellenberg Joanna Armstrong, Victora CG, Mushi Adiel, de Savigny Don, Schellenberg David, Mshinda Hassan, Bryce Jennifer: Inequities among the very poor: health care for children in rural southern Tanzania. Lancet. 2003, 361: 561-566. 10.1016/S0140-6736(03)12515-9.CrossRefPubMed
35.
Jones Gareth, Steketee Richard W, Black Robert E, Bhutta Zulfiqar A, Morris SS, and the Bellagio Child Survival Study Group: How many child deaths can we prevent this year?. Lancet. 2003, 362: 65-71. 10.1016/S0140-6736(03)13811-1.CrossRefPubMed
36.
Shrimpton R, Victora CG, de Onis M, Lima RC, Blossner M, Clugston G: Worldwide timing of growth faltering: implications for nutritional interventions. Pediatrics. 2001, 107: e75-10.1542/peds.107.5.e75.CrossRefPubMed
37.
Ngare DK, Muttunga JN: Prevalance of malnutrition in Kenya. East Afr Med J. 1999, 76: 376-380.PubMed
38.
Wells Janathan C K: Natural selection and sex differences in morbidity and mortality in early life. J theor Biol. 2000, 202: 65-76. 10.1006/jtbi.1999.1044.CrossRefPubMed
Metadata
Title
Mothers' education but not fathers' education, household assets or land ownership is the best predictor of child health inequalities in rural Uganda
Authors
Henry Wamani
Thorkild Tylleskär
Anne Nordrehaug Åstrøm
James K Tumwine
Stefan Peterson
Publication date
01-12-2004
Publisher
BioMed Central
Published in
International Journal for Equity in Health / Issue 1/2004
Electronic ISSN: 1475-9276
DOI
https://doi.org/10.1186/1475-9276-3-9

Other articles of this Issue 1/2004

International Journal for Equity in Health 1/2004 Go to the issue

Research

The effect of ethnicity on outcomes in a practice-based trial to improve cardiovascular disease prevention

Research

Primary source of income is associated with differences in HIV risk behaviors in street-recruited samples

Research

Socio-economic differences and health seeking behaviour for the diagnosis and treatment of malaria: a case study of four local government areas operating the Bamako initiative programme in south-east Nigeria

Research

The development of a strategy for tackling health inequalities in the Netherlands

Research

Musculo-skeletal pain among 40- and 45-year olds in Oslo: differences between two socioeconomically contrasting areas, and their possible explanations

Commentary

Self-help: What future role in health care for low and middle-income countries?