Hierarchical Logistic Regression Model for Multilevel Analysis on the Uptake of Health Insurance in Nouakchott, Mauritania

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2022 by IJPTT Journal
	Volume-12 Issue-2
	Year of Publication : 2022
	Authors : Tourad Cheikh Tourad, Antony Ngunyi, Herbert Imboga
	DOI : 10.14445/22492615/IJPTT-V12I2P401

How to Cite?

Tourad Cheikh Tourad, Antony Ngunyi, Herbert Imboga. "Hierarchical Logistic Regression Model for Multilevel Analysis on the Uptake of Health Insurance in Nouakchott, Mauritania" International Journal of P2P Network Trends and Technology, vol. 12, no. 2, pp. 1-12, 2022. Crossref, https://doi.org/10.14445/22492615/IJPTT-V12I2P401

Abstract

The availability of these complex statistical methods challenges public health researchers to articulate theories of the causes of health behaviour that bring together factors defined at different levels. This study seeks to discuss the hierarchical logistic regression model for multilevel analysis and test its application in analysing the uptake of health insurance in Mauratania. The specific objectives of this study are to develop the hierarchical logistic regression model, estimate the model parameters of the hierarchical logistic regression model, derive the maximum likelihood estimators of the parameters of the hierarchical logistic regression model and apply the estimation procedure for the uptake of health insurance data from Nouakchott, Mauritania. The study adopted an explanatory study design using secondary data obtained from National Health Insurance funds in Mauritania. The hierarchical logistic regression model for multilevel analysis was used in analysing the data. The analysed data is presented using the table. The obtained model can be used to predict the uptake.

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Keywords
Hierarchical logistic regression, Health insurance, Single model, Multilevel model, Maximum likelihood.