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<Journal>
<PublisherName>ajhe</PublisherName>
<JournalTitle>African Journal of Health Economics</JournalTitle>
<PISSN>C</PISSN>
<EISSN>o</EISSN>
<Volume-Issue>Volume 7 issue 2</Volume-Issue>
<PartNumber/>
<IssueTopic>Multidisciplinary</IssueTopic>
<IssueLanguage>English</IssueLanguage>
<Season>December 2018</Season>
<SpecialIssue>N</SpecialIssue>
<SupplementaryIssue>N</SupplementaryIssue>
<IssueOA>Y</IssueOA>
<PubDate>
<Year>-0001</Year>
<Month>11</Month>
<Day>30</Day>
</PubDate>
<ArticleType>Review & Research</ArticleType>
<ArticleTitle>AN APPLICATION OF HIERARCHICAL LOGISTIC MODELLING TO MATERNAL HEALTHCARE UTILIZATION IN NIGERIA</ArticleTitle>
<SubTitle/>
<ArticleLanguage>English</ArticleLanguage>
<ArticleOA>Y</ArticleOA>
<FirstPage>10</FirstPage>
<LastPage>19</LastPage>
<AuthorList>
<Author>
<FirstName>Akarawak Eno Emmanuella*</FirstName>
<LastName>Ogunniran</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>N</CorrespondingAuthor>
<ORCID/>
<FirstName>Ademola</FirstName>
<LastName>John</LastName>
<AuthorLanguage>English</AuthorLanguage>
<Affiliation/>
<CorrespondingAuthor>Y</CorrespondingAuthor>
<ORCID/>
</Author>
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<DOI>http://doi.org/10.35202/AJHE.2018.7200916 </DOI>
<Abstract>Background: With a maternal mortality rate of 576 deaths per 100,000 live births, Nigeria accounts for about 10% of all maternal deaths, globally, and has the second highest mortality rate in the world. This high mortality rate makes maternal health a huge public health problem in the country. This paper, therefore, aimed at investigating socio-demographic factors affecting the utilization of Maternal Health Care (MHC) in the context of hierarchical modelling.
Methods: Data were extracted from the Nigerian Demographic and Health Survey, 2013. The data have a hierarchical structure, with the 20,116 Ever-Married Women nested within their respective states of residence. Three different hierarchical logistic regression models were formulated to allow for comparison of outcomes between clusters.
Findings: A proportion of opposed odds ratio of 0.45 indicate that in 45% of pair-wise comparisons between the urban and rural residence, the odds of MHC utilization was higher at an urban residence than at a rural residence by 1.388 times. The Median Odds Ratio (MOR) for Model 2 indicated the odds of MHC utilization was less than 2.41 for a woman in a state at higher risk compared to a different woman in a state at lower risk. The intra-class correlation coefficient revealed a 40% (Model 1), 21% (Model 2) and 16% (Model 3) chances of utilizing MHC, explained by between-states differences, respectively.
Conclusion: In order to close the variation in healthcare delivery in Nigeria, there is a need for government to execute state-specific interventions that would allow fair distribution and utilization of MHC.</Abstract>
<AbstractLanguage>English</AbstractLanguage>
<Keywords>Maternal mortality, Maternal Health Care Services, hierarchical modelling, socio-demographic factors, healthcare utilization</Keywords>
<URLs>
<Abstract>https://ajhe.org.in/ubijournal-v1copy/journals/abstract.php?article_id=6284&title=AN APPLICATION OF HIERARCHICAL LOGISTIC MODELLING TO MATERNAL HEALTHCARE UTILIZATION IN NIGERIA</Abstract>
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<References>
<ReferencesarticleTitle>References</ReferencesarticleTitle>
<ReferencesfirstPage>16</ReferencesfirstPage>
<ReferenceslastPage>19</ReferenceslastPage>
<References>Nigerian Demographic Health Survey: Individual Recode Documentation. 2013
WHO. Trends in Maternal Mortality: 1990-2010. 2012. Available from: https://www.who.int.
Shrestha G, Shrestha G. Mathematical modeling of health service data using multiple logistic regression. BRAC University Journal. 2011; 8(1and;2): 47-54.
Adewara JA, Ogunniran AJ, Onyeka-Ubaka JN. Modeling of maternal healthcare services using multinomial logistic regression. University of Lagos Journal of Medicine, Science and Technology. 2014; 2(1 and; 2): 41-55. Available from: http://196.45.48.93/index.php/ujmst/article/view/97.
Tsawe M, Moto A, Netshivhera T, Ralesego L, Nyathi C. Factors influencing the use of maternal healthcare services and childhood immunization in Swaziland. International Journal for Equity in Health. 2015; 14:32.
Raudenbush SW, Bryk AS. Hierarchical linear models: applications and data analysis methods. Sage Publications: Thousand Oaks; 2002.
Garson GD. Fundamentals of Hierarchical Linear and Multilevel Modeling. Sage Publications Inc. USA; 2013.
Morselli D. and Sommet N. Keep calm and learn multilevel logistic modeling: a simplified three-step procedure using Stata, R, Mplus, and SPSS. International Review of Social Psychology. 2017;30 (1): 203-218.
Snijders T. and Bosker R. Multilevel analysis: an introduction to basic and advanced multilevel analysis, second edition. Sage, 2012.
Khan Md. HR, Shaw JEH. Multilevel logistic regression analysis applied to binary contraceptive prevalence data. Journal of Data Science. 2011;9: 93-110.
Neuhaus JM, Kalbfleisch JD, Hauck WW. A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Review. 1991;59(1): 25-35.
Austin PC, Merlo JA. Tutorial in biostatistics: intermediate and advanced topics in multilevel logistic regression analysis. Statistics in Medicine. 2017; DOI: 10.1002/sim.7336.
Goldstein H, Browne W, Rasbash J. Partitioning variation in generalized linear multilevel models. Understanding Statistics.2002; 1: 223-232.
Evans M, Hastings N, Peacock B. Statistical Distributions. John Wiley and Sons: New York; 1993.
Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. American Journal of Epidemiology. 2005;161(1): 81-88.
Ononokpono DN and Odimegwu, CO. Determinants of maternal health care utilization in Nigeria: A multilevel approach. Pan African Medical Journal. 2014; 17(1): 2.
Mekonnen ZA, Lerebo WT, Gebrehiwot TG, and Abadura SA. Multilevel analysis of individual and community-level factors associated with institutional delivery in Ethiopia. BMC Research Notes. 2015; 8:376.
Johnson FA, Frempong- Ainguah F, and Padmadas SS. Two-decades of maternal care fee exemption policies in Ghana: have they benefited the poor? Health Policy and Planning. 2016; 31(1): 46-55.
Chama-Chiliba CM and Koch SF. Utilization of focused antenatal care in Zambia: examining individual and community-level factors using a multilevel analysis. Health Policy and Planning. 2015; 30: 78-87.</References>
</References>
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