Adenomon, M.O.; John, D.O. Modelling Hypertension and Risk Factors among Adults Using Ordinal Logistics Regression Model. Preprints2020, 2020010291. https://doi.org/10.20944/preprints202001.0291.v1
Adenomon, M.O., & John, D.O. (2020). Modelling Hypertension and Risk Factors among Adults Using Ordinal Logistics Regression Model. Preprints. https://doi.org/10.20944/preprints202001.0291.v1
Adenomon, M.O. and Daniel Owoicholofu John. 2020 "Modelling Hypertension and Risk Factors among Adults Using Ordinal Logistics Regression Model" Preprints. https://doi.org/10.20944/preprints202001.0291.v1
There is high prevalence of hypertension and is rapidly increasing around the world, despite the intervention programme implemented, this study aimed at estimating the prevalence rate, test of association between hypertension and risk factors and model hypertension rate. Data used was obtained from the health record of Federal Medical Centre, Keffi from January 2016 – January 2019. Ordinal logistic regression model was used; Model Fitting Information, Goodness-of-Fit, Pseudo R-Square and Test of Parallel Lines are fitted to the data sets to test the accuracy and correctness of the model. The results indicated that the overall prevalence of hypertension rate is high at 36.4%, among the adult population, body mass index and gender are statistically significant, and Age is not significant in the study. Individuals that are overweight are more likely to be hypertensive compare to other weights. At age 40 – 49 years which have the highest rate of 26.5% and the odd ratio is 0.75 compared to others. One year increase in age 30 – 39, the cumulative odd of being hypertensive is 0.91 while other independent variables are held constant. The odd ratio of female being hypertensive is 0.85, therefore the females are more likely to be hypertensive with 54.4% compared to the males at 45.6% . There is no presence of multicolinearity among the variables and Logit models were formulated to calculate probabilities of the various possible outcomes.
Computer Science and Mathematics, Probability and Statistics
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