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Effect of modeling a multilevel structure on the Indian population to identify the factors influencing HIV infection

Effect of modeling a multilevel structure on the Indian population to identify the factors... Many studies have addressed the factors associated with HIV in the Indian population. Some of these studies have used sampling weights for the risk estimation of factors associated with HIV, but few studies have adjusted for the multilevel structure of survey data. The National Family Health Survey 3 collected data across India between 2005 and 2006. 38,715 females and 66,212 males with complete information were analyzed. To account for the correlations within clusters, a three-level model was employed. Bivariate and multivariable mixed effect logistic regression analysis were performed to identify factors associated with HIV. Intracluster correlation coefficients were used to assess the relatedness of each pair of variables within clusters. Variables pertaining to no knowledge of contraceptive methods, age at first marriage, wealth index and noncoverage of PSUs by Anganwadis were significant risk factors for HIV when the multileveled model was used for analysis. This study has identified the risk profile for HIV infection using an appropriate modeling strategy and has highlighted the consequences of ignoring the structure of the data. It offers a methodological guide towards an applied approach to the identification of future risk and the need to customize intervention to address HIV infection in the Indian population. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Effect of modeling a multilevel structure on the Indian population to identify the factors influencing HIV infection

Effect of modeling a multilevel structure on the Indian population to identify the factors influencing HIV infection

Abstract

Many studies have addressed the factors associated with HIV in the Indian population. Some of these studies have used sampling weights for the risk estimation of factors associated with HIV, but few studies have adjusted for the multilevel structure of survey data. The National Family Health Survey 3 collected data across India between 2005 and 2006. 38,715 females and 66,212 males with complete information were analyzed. To account for the correlations within clusters, a three-level model...
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Publisher
Taylor & Francis
Copyright
© 2019 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2019.1671096
Publisher site
See Article on Publisher Site

Abstract

Many studies have addressed the factors associated with HIV in the Indian population. Some of these studies have used sampling weights for the risk estimation of factors associated with HIV, but few studies have adjusted for the multilevel structure of survey data. The National Family Health Survey 3 collected data across India between 2005 and 2006. 38,715 females and 66,212 males with complete information were analyzed. To account for the correlations within clusters, a three-level model was employed. Bivariate and multivariable mixed effect logistic regression analysis were performed to identify factors associated with HIV. Intracluster correlation coefficients were used to assess the relatedness of each pair of variables within clusters. Variables pertaining to no knowledge of contraceptive methods, age at first marriage, wealth index and noncoverage of PSUs by Anganwadis were significant risk factors for HIV when the multileveled model was used for analysis. This study has identified the risk profile for HIV infection using an appropriate modeling strategy and has highlighted the consequences of ignoring the structure of the data. It offers a methodological guide towards an applied approach to the identification of future risk and the need to customize intervention to address HIV infection in the Indian population.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 1, 2019

Keywords: HIV infection; logistic regression; multilevel modeling; multistage sampling

References