IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY (IJASMT )

E- ISSN 2489-009X
P- ISSN 2695-1908
VOL. 9 NO. 2 2023
DOI: https://doi.org/10.56201/ijasmt.v9.no2.2023.pg1.20

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Application of VEC Model in Modeling Hypertension and Comorbidities Related Data

Lekara-Bayo Ifeoma Better, Etuk, E. H & Wegbom, A. I.

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Abstract

The study investigated multivariate analysis of hypertension and its comorbidities related data in River’s state of Nigeria. The objectives of the study include to; estimate the interaction existing among systolic, diastolic and BMI and determine the direction of causality, significance of the causality and hence summaries the causal chambers among systolic, diastolic and BMI. The data for the study was sourced from the patients’ files who visited and were admitted at the University of Port Harcourt Teaching Hospital spanning from 6 th January, 2017 to 28 th January, 2021, while the software used for data analysis is Eview version twelve. The study adopted an ex -post- facto design. The study used the Vector Error Correction Model (VECM) for the data analysis. The results of the estimation show that age of the patients and Body Mass Index are negatively correlated, diastolic and the age of the patients are positively but weakly correlated, systolic and the age of the patients are positively associated with a weak correlation. Also, diastolic and Body Mass Index has weak negative correlation. Body Mass Index and systolic negatively weak correlated and it was found that there exists strong positive correlation between systolic and diastolic. It was found that there is a co-integrating (long-run) relationship between hypertension and comorbidities related data. The results of the VECM model shows that the order of co-efficient of determinations (R 2 ) were 0.648, 0.795, 0.761 when the age of the patients, Body Mass index, Diastolic and systolic were considered as dependent variable. This implied that 64.8%, 79.5% and 76.1% variation in the dependent variables is explained by variations in their respective independent variables. The remaining 31.3%, 20.5 and 23.9 are variations expounded by other variables not included in the model. Also, the correction of the previous period's deviation from the long-run equilibrium in the subsequent period at an a

keywords:

Application, Model, Hypertension, Comorbidities, Data

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