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Construction of a socio-economic index to facilitate analysis of health data in developing countries

Author

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  • Cortinovis, I.
  • Vella, V.
  • Ndiku, J.

Abstract

In order to plan, implement and monitor health interventions for the most deprived sector of the population, it is necessary to identify socioeconomic groups at risk. Multiple Correspondence Analysis was used to construct a socio-economic index based on data collected from a sample of 2698 households in South-West district of the Ugandan Republic in 1988. This study is a part of the baseline survey done by the Government of Uganda in collaboration with UNICEF. Its aim was to reduce the incidence of death of children below 5 years from diahorrea. Two factorial axes, representing respectively the socio-cultural and the anthropological conditions, explained more than 80% of the total variability. Among the 11 variables employed the most useful in characterizing the socio-economic classification were: father's occupation, parent's literacy, father's professional position and ownership of a radio. A classification in 7 levels was obtained. The first two levels are characterized as professionals and civil servants. The bottom two levels include households where both parents are illiterate and where father's primary activity is agricultural at a subsistence level. The three middle levels represent a transitional situation. In order to classify the family into the different levels, the other related variables, such as father's professional position or ownership of radio or father's religion or presence of latrine proved to be very useful. A flow chart which identifies which level a household belongs to was constructed. A general and valid observation is that families classified into the last two levels (6 and 7) constituted the population at risk for health conditions.

Suggested Citation

  • Cortinovis, I. & Vella, V. & Ndiku, J., 1993. "Construction of a socio-economic index to facilitate analysis of health data in developing countries," Social Science & Medicine, Elsevier, vol. 36(8), pages 1087-1097, April.
    • Handle: RePEc:eee:socmed:v:36:y:1993:i:8:p:1087-1097
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    1. Diane Bélanger & Belkacem Abdous & Pierre Gosselin & Pierre Valois, 2015. "An adaptation index to high summer heat associated with adverse health impacts in deprived neighborhoods," Climatic Change, Springer, vol. 132(2), pages 279-293, September.
    2. Nosier, Shereen & Beram, Reham & Mahrous, Mohamed, 2021. "Household Poverty in Egypt: Poverty Profile, Econometric Modeling and Policy Simulations," SocArXiv d8spt, Center for Open Science.
    3. Muhammad Jami Husain, 2013. "Life Expectancy and Economic Well-being: A Within-country Regional-level Analysis Using the Micro-data of Bangladesh," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 7(4), pages 443-474, November.
    4. Diego Bernardo Avanzini, 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," WIDER Working Paper Series RP2009-41, World Institute for Development Economic Research (UNU-WIDER).
    5. Ricard Giné Garriga & Agustí Pérez Foguet, 2013. "Unravelling the Linkages Between Water, Sanitation, Hygiene and Rural Poverty: The WASH Poverty Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1501-1515, March.
    6. Susan Vos, 2005. "Indicating Socioeconomic Status among Elderly People in Developing Societies: An Example from Brazil," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 73(1), pages 87-108, August.
    7. Shivani A. Patel & Susan G. Sherman & Subarna K. Khatry & Steven C. LeClerq & Joanne Katz & James M. Tielsch & Parul Christian, 2016. "An Index of Community-Level Socioeconomic Composition for Global Health Research," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(2), pages 639-658, November.
    8. Kaguongo, Wachira & Ortmann, Gerald F. & Wale, Edilegnaw & Darroch, Mark A.G. & Low, Jan W., 2010. "Factors influencing adoption and intensity of adoption of orange flesh sweetpotato varieties: evidence from an extension intervention in Nyanza and Western province, Kenya," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 96805, African Association of Agricultural Economists (AAAE).
    9. Pavitra Paul, 2020. "The distributive fairness of out-of-pocket healthcare expenditure in the Russian Federation," International Journal of Health Economics and Management, Springer, vol. 20(1), pages 13-40, March.
    10. Rana Khan & Muhammad Raza, 2016. "Determinants of malnutrition in Indian children: new evidence from IDHS through CIAF," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 299-316, January.
    11. Kassie, Girma Tesfahun & Erenstein, Olaf & Mwangi, Wilfred & La Rovere, Roberto & Setimela, Peter S. & Langyintuo, Augustine S., 2012. "Characterization of Maize Production in Southern Africa: Synthesis of CIMMYT/DTMA Household Level Farming System Surveys in Angola, Malawi, Mozambique, Zambia and Zimbabwe," Socioeconomics Program Working Papers 147108, CIMMYT: International Maize and Wheat Improvement Center.
    12. Mduduzi Biyase & Talent Zwane, 2018. "An Empirical Analysis Of The Determinants Of Poverty And Household Welfare In South Africa," Journal of Developing Areas, Tennessee State University, College of Business, vol. 52(1), pages 115-130, January-M.
    13. Mensah, Omer A. & Kumaranayake, Lilani, 2004. "Malaria incidence in rural Benin: does economics matter in endemic area?," Health Policy, Elsevier, vol. 68(1), pages 93-102, April.

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