Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns
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BioMed Central
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Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns Elizabeth A Madigan*1, Olivier Louis Curet2 and Miklos Zrinyi3 Address: 1Frances Payne Bolton School of Nursing, Case Western Reserve, 10900, Euclid Ave., Cleveland OH 44106-4904, USA, 2Frances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Ave., Cleveland OH 44106-4904, USA and 3World Health Organization, Geneva, Switzerland Email: Elizabeth A Madigan* - [email protected]; Olivier Louis Curet - [email protected]; Miklos Zrinyi - [email protected] * Corresponding author
Published: 31 January 2008 Human Resources for Health 2008, 6:2
doi:10.1186/1478-4491-6-2
Received: 4 December 2006 Accepted: 31 January 2008
This article is available from: http://www.human-resources-health.com/content/6/1/2 © 2008 Madigan 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: The achievement of the Millennium Development Goals (MDGs) depends on sufficient supply of health workforce in each country. Although country-level data support this contention, it has been difficult to evaluate health workforce supply and MDG outcomes at the country level. The purpose of the study was to examine the association between the health workforce, particularly the nursing workforce, and the achievement of the MDGs, taking into account other factors known to influence health status, such as socioeconomic indicators. Methods: A merged data set that includes country-level MDG outcomes, workforce statistics, and general socioeconomic indicators was utilized for the present study. Data were obtained from the Global Human Resources for Health Atlas 2004, the WHO Statistical Information System (WHOSIS) 2000, UN Fund for Development and Population Assistance (UNFDPA) 2000, the International Council of Nurses "Nursing in the World", and the WHO/UNAIDS database. Results: The main factors in understanding HIV/AIDS prevalence rates are physician density followed by female literacy rates and nursing density in the country. Using general linear model approaches, increased physician and nurse density (number of physicians or nurses per population) was associated with lower adult HIV/AIDS prevalence rate, even when controlling for socioeconomic indicators. Conclusion: Increased nurse and physician density are associated with improved health outcomes, suggesting that countries aiming to attain the MDGs related to HIV/AIDS would do well to invest in their health workforce. Implications for international and country level policy are discussed.
Background The socio-political impact of HIV/AIDS is increasingly being identified as a global crisis, rather than only a health crisis. There are estimates that generatio
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