An average/deprivation/inequality (ADI) analysis of chronic disease outcomes and risk factors in Argentina
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BioMed Central
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An average/deprivation/inequality (ADI) analysis of chronic disease outcomes and risk factors in Argentina Fernando G De Maio*1, Bruno Linetzky2 and Mario Virgolini2 Address: 1Department of Sociology & Anthropology, Simon Fraser University, Burnaby, British Columbia, Canada and 2Ministerio de Salud de la Nación, Buenos Aires, Argentina Email: Fernando G De Maio* - [email protected]; Bruno Linetzky - [email protected]; Mario Virgolini - [email protected] * Corresponding author
Published: 8 June 2009 Population Health Metrics 2009, 7:8
doi:10.1186/1478-7954-7-8
Received: 15 September 2008 Accepted: 8 June 2009
This article is available from: http://www.pophealthmetrics.com/content/7/1/8 © 2009 De Maio et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background: Recognition of the global economic and epidemiological burden of chronic noncommunicable diseases has increased in recent years. However, much of the research on this issue remains focused on individual-level risk factors and neglects the underlying social patterning of risk factors and disease outcomes. Methods: Secondary analysis of Argentina's 2005 Encuesta Nacional de Factores de Riesgo (National Risk Factor Survey, N = 41,392) using a novel analytical strategy first proposed by the United Nations Development Programme (UNDP), which we here refer to as the Average/Deprivation/ Inequality (ADI) framework. The analysis focuses on two risk factors (unhealthy diet and obesity) and one related disease outcome (diabetes), a notable health concern in Latin America. Logistic regression is used to examine the interplay between socioeconomic and demographic factors. The ADI analysis then uses the results from the logistic regression to identify the most deprived, the best-off, and the difference between the two ideal types. Results: Overall, 19.9% of the sample reported being in poor/fair health, 35.3% reported not eating any fruits or vegetables in five days of the week preceding the interview, 14.7% had a BMI of 30 or greater, and 8.5% indicated that a health professional had told them that they have diabetes or high blood pressure. However, significant variation is hidden by these summary measures. Educational attainment displayed the strongest explanatory power throughout the models, followed by household income, with both factors highlighting the social patterning of risk factors and disease outcomes. As educational attainment and household income increase, the probability of poor health, unhealthy diet, obesity, and diabetes decrease. The analyses also point toward important provincial effects and reinforce the notion that both compositional factors (i.e., characteristics of individuals) and contextual factors (i.e., characteristics of places) are
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