J Korean Med Sci.  2015 Nov;30(Suppl 2):S167-S177. 10.3346/jkms.2015.30.S2.S167.

Factor Configurations with Governance as Conditions for Low HIV/AIDS Prevalence in HIV/AIDS Recipient Countries: Fuzzy-set Analysis

• Lee HY
• Yang BM
• Kang M

• KMID: 2351155
• DOI: http://doi.org/10.3346/jkms.2015.30.S2.S167

Abstract

This paper aims to investigate whether good governance of a recipient country is a necessary condition and what combinations of factors including governance factor are sufficient for low prevalence of HIV/AIDS in HIV/AIDS aid recipient countries during the period of 2002-2010. For this, Fuzzy-set Qualitative Comparative Analysis (QCA) was used. Nine potential attributes for a causal configuration for low HIV/AIDS prevalence were identified through a review of previous studies. For each factor, full membership, full non-membership, and crossover point were specified using both author's knowledge and statistical information of the variables. Calibration and conversion to a fuzzy-set score were conducted using Fs/QCA 2.0 and probabilistic tests for necessary and sufficiency were performed by STATA 11. The result suggested that governance is the necessary condition for low prevalence of HIV/AIDS in a recipient country. From sufficiency test, two pathways were resulted. The low level of governance can lead to low level of HIV/AIDS prevalence when it is combined with other favorable factors, especially, low economic inequality, high economic development and high health expenditure. However, strengthening governance is a more practical measure to keep low prevalence of HIV/AIDS because it is hard to achieve both economic development and economic quality. This study highlights that a comprehensive policy measure is the key for achieving low prevalence of HIV/AIDS in recipient country.

Keyword

Fuzzy-set Qualitative Comparative Analysis; HIV/AIDS; Effectiveness of Official Development Assistance; Corruption; Democratic Accountability

MeSH Terms

Acquired Immunodeficiency Syndrome/*epidemiology/prevention & control
Computer Simulation
Developing Countries/*economics/statistics & numerical data
Economic Development/statistics & numerical data
Fraud/economics/*statistics & numerical data
Fuzzy Logic
HIV Infections/*epidemiology/prevention & control
Humans
Models, Statistical
Prevalence
Risk Factors
Socioeconomic Factors

Figure

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  Fig. 1 Framework of analysis.

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  Fig. 2 The scatter plots of fuzzy-set score.

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