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Browsing by Author "Ibeji, Jecinta Ugochukwu."

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    Bayesian spatio-temporal and joint modelling of malaria and anaemia among Nigerian children aged under five years, including estimation of the effects of risk factors = I-Bayesian spatio-temporal kanye nemodelingi ehlanganisa umalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ezinganeni zaseNigeria ezineminyaka engaphansi kwemihlanu kuhlanganisa nokulinganiselwa kwemithelela yezimo eziyingozi.
    (2023) Ibeji, Jecinta Ugochukwu.; Mwambi, Henry Godwell.; Iddrisu, Abdul-Karim.
    Childhood mortality and morbidity in Nigeria have been linked to malaria and anaemia. This thesis focused on exploring the risk factors and the complexity of the relationship between malaria and anaemia in under 5 Nigerian children. Data from the 2010 and 2015 Nigeria Malaria Indicator Survey conducted by Demographic Health Survey were used. In 2010, the prevalence of malaria and anaemia was 48% and 72%, respectively, while in 2015, 27% and 68% were the respective prevalences of malaria and anaemia diseases. Machine learning-based exploratory classification methods were used to explain the relationship and patterns between the independent variables and the two dependent variables, namely malaria and anaemia. Decisions made by the public health body are centered on the administrative units (i.e., states) within the country. Therefore, the development of disease mapping and a brief overview of limiting assumptions and ways of tackling them was explained. Consequently, malaria and anaemia spatial variation for 2010 and 2015 was analyzed with the inclusion of their respective risk factors. A separate multivariate hierarchical Bayesian logistic model for each disease was adopted to investigate the spatial pattern of malaria and anaemia and adjust for the risk factors associated with each disease. Furthermore, a multilevel model analysis was applied to independently investigate the spatio-temporal distribution of malaria and anaemia. A joint model was further adopted to check for the relationship between malaria and anaemia and their common risk factors and relax the nonlinearity assumption. In the 2010 data, type of place of residence, mother’s highest educational level, source of drinking water, type of toilet facility, child’s sex, main floor material, and households that have electricity, radio, television, and water were significantly associated with malaria and anaemia. While in the 2015 data, the type of place of residence, source of drinking water, type of toilet facility, households with radio, main roof material, wealth index, child’s sex, and mother’s highest educational level had a significant relationship with malaria and anaemia. The results from this study can guide policymakers to tailor-make effective interventions to reduce or prevent malaria and anaemia diseases. This will help adequately distribute limited state health system resources, such as personnel, funds and facilities within the country. Iqoqa. Ukugula kanye nokushona kwezingane eNigeria kuyamene nomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Lolu cwaningo lugxile ekuhloleni izimo eziyingozi kanye nobunkimbinkimbi bobudlelwano obukhona phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ezinganeni zaseNigeria ezineminyaka engaphansi kwemihlanu. Kwasetshenziswa imininingo yesaveyi eyenziwa ye-Nigeria Malaria Indicator Survey ngo-2010 nango-2015 eyenziwa yi-Demographic Health Survey. Ngo-2010 ukusabalala komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, kwakungama-48% kanye nama-72% ngokulandelana, kanti ngo-2015 kwakungama-27% kanye nama-68% ngokulandelana ukusabalala kwezifo zomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Kwasetshenziswa izindlela zokulinganisa ngokuhlwaya ezisebenzisa imishini ukuchaza ubudlelwano phakathi kwamavariyebuli azimele kanye namavariyebuli amabili angazimele, okungumalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Izinqumo ezathathwa yisigungu sezempilo yomphakathi ziqondene namayunithi okuphatha (izifunda) ezweni. Ngakho-ke, kwachazwa indlela okuthuthukiswe ngayo ukuvezwa kwezindawo okusabalele kuzo izifo kanye nohlaka olufingqiwe lwezimo eziyizithiyo kanye nezindlela zokubhekana nazo. Ekugcineni kwahlaziywa izingakwehlukana lomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, ngokwezindawo ngo-2010 kanye no-2015. Kulokhu kwahlanganiswa nezimo eziyingozi zakho ngokulandelana kwazo. Kwaqokwa imodeli ehlukile yohlaziyongxube oluyi-hierarchical Bayesian logistic model kuleso naleso sifo ukuhlola iphethini yezindawo okutholakala kuzo umalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, bese ilungiselwa izimo eziyingozi ezihlobene nesifo ngasinye. Okunye futhi, kwasetshenziswa imodeli yohlaziyo enamazinga ayingxube ukuhlola ngendlela ezimele ukusabalala ngokwendawo nesikhathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Imodeli eyinhlanganisela yasetshenziselwa ukuhlunga ubudlelwano obukhona phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, kanye nezimo ezivamile eziyingozi, bese kuthanjiswa ukucabangela ukuthi kunobudlelwano obuqondile phakathi kwamavariyebuli azimele nalawo angazimele. Emininingweni yango-2010 uhlobo lwendawo yokuhlala, izinga lemfundo kamama, umthombo wamanzi okuphuza, uhlobo lwendlu yangasese, ubulili bengane, izinto okwakhiwe ngazo isiyilo, kanti nemindeni enogesi, iwayilense/umsakazo, umabonakude, kanye namanzi yayihlobene kakhulu nomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Kanti imininingo yango-2015 yaveza ukuthi ubudlelwano obukhulu phakathi komalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia, buhambisana nohlobo lwendawo yokuhlala, umthombo wamanzi okuphuza,uhlobo lwendlu yangasese, imindeni enomsakazo, izinto okwakhiwe ngazo uphahla, isigaba ngokwezomnotho, ubulili bengane, kanye nebanga lemfundo kamama. Imiphumela yalolu cwaningo ingaba usizo ukucaba indlela kulabo abasezikhundleni zokwenza izinqubomgomo ukuze bakwazi ukungenelela ngezindlela ezisebenza kahle ekunciphiseni noma ekunqandeni/ekuvikeleni izifo zomalaleveva kanye nesifo samaseli abomvu angenele egazini, i-anaemia. Lokhu kuzosiza ekusabalaliseni izinsiza ezifana nabasebenzi, izimali kanye nezikhungo zezinsiza ohlelweni lukahulumeni kwezempilo.
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    Count data modelling application.
    (2019) Ibeji, Jecinta Ugochukwu.; North, Delia Elizabeth.; Zewotir, Temesgen Tenaw.
    The rapid increase of total children ever born without a proportionate growth in the Nigerian economy has been a concern and making prediction with count data requires applying appropriate regression model.. As count data assumes discrete, non-negative values, a Poisson distribution is the ideal distribution to describe this data, but it is deficient due to equality of variance and mean. This deficiency results in under/over-dispersion and the estimation of the standard errors will be biased rendering the test statistics incorrect. This study aimed to model count data with the application of total children ever born using a Negative Binomial and Generalized Poisson regression The Nigeria Demographic and Health Survey 2013 data of women within the age of 15-49 years were used and three models applied to investigate the factors affecting the number of children ever born. A predictive count modelling was also carried out based on the performance evaluation metrics (root mean square error, mean absolute error, R-squared and mean square error). In the inferential modeling, Generalized Poisson Model was found to be superior with age of household head (𝑃<.0001), age of respondent at the time of first birth (𝑃<.0001), urban-rural status (𝑃<.0001), and religion (𝑃<.0001) being significantly associated with total children ever born. In the predictive modeling, all the three models showed almost identical performance evaluation metrics but Poisson regression was chosen as the best because it is the simplest model. In conclusion, early marriage, religious belief and unawareness of women who dwell in rural areas should be checked to control total children ever born in Nigeria.