Skip to main content
Top
Published in: Archives of Public Health 1/2020

01-12-2020 | Human Immunodeficiency Virus | Research

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models

Authors: Di Fang, Anqi Lang, Jeffrey R. Wilson

Published in: Archives of Public Health | Issue 1/2020

Login to get access

Abstract

Background

The analysis of correlated responses obtained one at a time in survey data is not as informative or as useful as modeling them simultaneously. Simultaneous modeling allows for the opportunity to evaluate the system in a more pragmatic form rather than to allow for responses that assumedly originated in isolation.

Methods

This research uses the Mozambique National Survey data to demonstrate the benefits of simultaneous modeling on blood test results, knowledge of HIV/AIDS, and awareness of an HIV/AIDS campaign. This simultaneous modeling also addresses the correlation inherent due to the hierarchical structure in the data collection.

Results

Employment and self-perceived risk of HIV/AIDS have different impact on blood test, awareness of an HIV/AIDS campaign, and knowledge of HIV/AIDS when examined simultaneously as opposed to separate modeling.

Conclusion

Simultaneous modeling of correlated responses improves the reliability of the estimates. More importantly, it provides an opportunity to engage in cost-saving decisions when designing future surveys and make better health policies.
Appendix
Available only for authorised users
Literature
1.
go back to reference Lawn SD, Kranzer K, Wood R. Antiretroviral therapy for control of the HIV-associated tuberculosis epidemic in resource limited settings. Clin Chest Med. 2009;30(4):685–99.CrossRef Lawn SD, Kranzer K, Wood R. Antiretroviral therapy for control of the HIV-associated tuberculosis epidemic in resource limited settings. Clin Chest Med. 2009;30(4):685–99.CrossRef
2.
go back to reference Mbachu C, Okoli C, Onwujekwe O, et al. Willingness to pay for antiretroviral drugs among HIV and AIDS clients in south-East Nigeria. Health Expect. 2017;21(1):270–8.CrossRef Mbachu C, Okoli C, Onwujekwe O, et al. Willingness to pay for antiretroviral drugs among HIV and AIDS clients in south-East Nigeria. Health Expect. 2017;21(1):270–8.CrossRef
3.
go back to reference Collett D. Modelling Binary Data. New York: Chapman and Hall; 1991. Collett D. Modelling Binary Data. New York: Chapman and Hall; 1991.
4.
go back to reference Irimata KM, Wilson JR. Identifying intraclass correlations necessitating hierarchical modeling. J Appl Stat. 2017;16:1–16. Irimata KM, Wilson JR. Identifying intraclass correlations necessitating hierarchical modeling. J Appl Stat. 2017;16:1–16.
5.
go back to reference Mayer KH, Beyrer C. HIV epidemiology update and transmission factors: risks and risk contexts-16th Int. AIDS conference epidemiology plenary. Clin Infect Dis. 2007;44(7):981–7.CrossRef Mayer KH, Beyrer C. HIV epidemiology update and transmission factors: risks and risk contexts-16th Int. AIDS conference epidemiology plenary. Clin Infect Dis. 2007;44(7):981–7.CrossRef
6.
go back to reference Pradhan, N. S., Su, Y., Fu, Y., et al (2011). Analyzing the effectiveness of policy implementation at the local level: a case study of management of the 2009–2010. Pradhan, N. S., Su, Y., Fu, Y., et al (2011). Analyzing the effectiveness of policy implementation at the local level: a case study of management of the 2009–2010.
7.
go back to reference Snijders T, Bosker R. Multilevel analysis: an introduction to basic and advanced multilevel modeling. London/Thousand Oaks/New Delhi: SAGE Publications; 1999. Snijders T, Bosker R. Multilevel analysis: an introduction to basic and advanced multilevel modeling. London/Thousand Oaks/New Delhi: SAGE Publications; 1999.
8.
go back to reference Ene, M., Leighton, E. A., Blue, G. L., et al. (2015). Multilevel models for categorical data using SAS® PROC GLIMMIX: the basics. SAS Global Forum 2015 Proceedings.. Ene, M., Leighton, E. A., Blue, G. L., et al. (2015). Multilevel models for categorical data using SAS® PROC GLIMMIX: the basics. SAS Global Forum 2015 Proceedings..
10.
go back to reference Iddi S, Molenberghs G. A joint marginalized multilevel model for longitudinal outcomes. J Appl Stat. 2002;39(11):241–2430. Iddi S, Molenberghs G. A joint marginalized multilevel model for longitudinal outcomes. J Appl Stat. 2002;39(11):241–2430.
11.
go back to reference Fang D, Sun R, Wilson JR. Joint modeling of correlated binary outcomes: the case of contraceptive use and HIV knowledge in Bangladesh. PLoS One. 2018;13(1):e0190917.CrossRef Fang D, Sun R, Wilson JR. Joint modeling of correlated binary outcomes: the case of contraceptive use and HIV knowledge in Bangladesh. PLoS One. 2018;13(1):e0190917.CrossRef
12.
go back to reference Gueorguieva RV, Agresti A. A correlated probit model for joint modeling of clustered binary and continuous responses. J Am Stat Assoc. 2001;96:1102–12.CrossRef Gueorguieva RV, Agresti A. A correlated probit model for joint modeling of clustered binary and continuous responses. J Am Stat Assoc. 2001;96:1102–12.CrossRef
13.
go back to reference Meng XL, Rubin DB. Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika. 1993;80(2):267–78.CrossRef Meng XL, Rubin DB. Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika. 1993;80(2):267–78.CrossRef
Metadata
Title
HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models
Authors
Di Fang
Anqi Lang
Jeffrey R. Wilson
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Archives of Public Health / Issue 1/2020
Electronic ISSN: 2049-3258
DOI
https://doi.org/10.1186/s13690-020-00453-8

Other articles of this Issue 1/2020

Archives of Public Health 1/2020 Go to the issue